research article steady modeling for an ammonia synthesis
TRANSCRIPT
Research ArticleSteady Modeling for an Ammonia Synthesis Reactor Based ona Novel CDEAS-LS-SVM Model
Zhuoqian Liu1 Lingbo Zhang1 Wei Xu2 and Xingsheng Gu1
1 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education Shanghai 200237 China2 Shanghai Electric Group Co Ltd Central Academe Shanghai 200070 China
Correspondence should be addressed to Xingsheng Gu xsguecusteducn
Received 6 December 2013 Accepted 5 February 2014 Published 18 March 2014
Academic Editor Huaicheng Yan
Copyright copy 2014 Zhuoqian Liu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
A steady-state mathematical model is built in order to represent plant behavior under stationary operating conditions A novelmodeling using LS-SVR based on Cultural Differential Evolution with Ant Search is proposed LS-SVM is adopted to establish themodel of the net value of ammoniaThemodelingmethod has fast convergence speed and good global adaptability for identificationof the ammonia synthesis processThe LS-SVRmodel was established using the above-mentionedmethod Simulation results verifythe validity of the method
1 Introduction
Ammonia is one of the important chemicals that has innu-merable uses in a wide range of areas that is explosivematerials pharmaceuticals polymers acids and coolersparticularly in synthetic fertilizers It is produced worldwideon a large scale with capacities extending to about 159milliontons at 2010 Generally the average energy consumption ofammonia production per ton is 1900KG of standard coal inChina which is much higher than the advanced standard of1570KG around the world At the same time the haze andparticulate matter 25 has been serious exceeded in big citiesin China at recent years and one of the important reasons isthe emission of coal chemical factories Thus an economicpotential exists in energy consumption of the ammoniasynthesis as prices of energy rise and reduce the ammoniasynthesis pollution to protect the environment Ammoniasynthesis process has the characteristics of nonlinearitystrong coupling large time-delay and great inertia load andso forth Steady-state operation-optimization can be a reliabletechnique for output improvement and energy reductionwithout changing any devices
The optimization of ammonia synthesis process highlyrelies on the accurate system model To establish an appro-priate mathematical model of ammonia synthesis process is a
principal problem of operation optimization It has receivedconsiderable attention since last century Heterogeneoussimulation models imitating different types of ammonia syn-thesis reactors have been developed for design optimizationand control [1] Elnashaie et al [2] studied the optimizationof an ammonia synthesis reactor which has three adiabaticbedsThe optimal temperature profile was obtained using theorthogonal collocation method in the paper Pedemera et al[3] studied the steady state analysis and optimization of aradial-flow ammonia synthesis reactor
The above study indicated that both the productive capac-ity and the stability of the ammonia reactor are influenced bythe cold quench and the feed temperature significantly Babuand Angira [4] described the simulation and optimizationdesign of an auto-thermal ammonia synthesis reactor usingQuasi-Newton and NAG subroutine method The optimaltemperature trajectory along the reactor and optimal flowsthroughput 33 additional ammonia production Sadeghiand Kavianiboroujeni [1] evaluated the process behavior ofan industrial ammonia synthesis reactorby one-dimensionalmodel and two-dimensional model genetic algorithm (GA)was applied to optimize the reactor performance in varyingits quench flows FromThe above literatures we can find thatmost models are built based on thermodynamic kinetic andmass equilibria calculations It is very difficult to simulate the
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014 Article ID 168371 18 pageshttpdxdoiorg1011552014168371
2 Mathematical Problems in Engineering
specific internal mechanism because a lot of parameters areunknown in real industrial process
In order to achieve the required accuracy of the modelsome researches focus on the novel modeling methodscombining some heuristic methods such as ANN (ArtificialNeural Network) LS-SVM (Least Squares Support VectorMachine) with Evolutionary Algorithm for example geneticalgorithm ant colony optimization (ACO) particle swarmoptimization (PSO) differential evolution (DE) and so forthDE is one of themost popular algorithms for this problemandhas been applied in many fields Sacco and Hendersonb [5]introduced a variant of the differential evolution algorithmwith a new mutation operator based on a topographicalheuristic and used it to solve the nuclear reactor core designoptimization problem Rout et al [6] proposed a simple butpromising hybrid prediction model by suitably combiningan adaptive autoregressive moving average architecture anddifferential evolution for forecasting of exchange rates Ozcanet al [7] carried out the cost optimization of an air coolingsystem by using Lagrange multipliers method differentialevolution algorithm and particle swarm optimization forvarious temperatures andmass flow ratesThe results showedthat the method gives high accuracy results within a shorttime interval Zhang et al [8] proposed a hybrid differentialevolution algorithm for the job shop scheduling problemwithrandom processing times under the objective of minimizingthe expected total tardiness Arya and Choube [9] describeda methodology for allocating repair time and failure rates tosegments of a meshed distribution system using differentialevolution technique Xu et al [10] proposed a model ofammonia conversion rate by LS-SVM and a hybrid algorithmof PSO and DE is described to identify the hyper-parametersof LS-SVM
To describe the relationship between net value of ammo-nia in ammonia synthesis reactor and the key operationalparameters least squares support vectormachine is employedto build the structure of the relationship model in which anovel algorithm called CDEAS is proposed to identify theparametersThe experiment results showed that the proposedCDEAS-LS-SVM optimizing model is very effective of beingused to obtain the optimal operational parameters of ammo-nia synthesis converter
The remaining of the paper is organized as followsSection 2 describes the ammonia synthesis production pro-cess Section 3 proposes a novel Cultural Differential Evolu-tion with Ant Colony Search (CDEAS) algorithm Section 4constructs a model using LS-SVM based on the proposedCDEAS algorithm Section 5 presents the experiments andcomputational results and discussion Finally Section 6 sum-marizes the above results and presents several problemswhich remain to be solved
2 Ammonia Synthesis Production Process
A normal ammonia production flow chart includes thesynthesis gas production purification gas compression andammonia synthesis Ammonia synthesis loop is one of themost critical units in the entire process The system has
been realized by LuHua Inc a medium fertilizers factory ofYanKuang Group China
Figure 1 represents a flow sheet for the ammonia syn-thesis process The ammonia synthesis reactor is a one-axialflow and two-radial flow three-bed quench-type unit [11]Hydrogen-nitrogen mixture is reacted in the catalyst bedunder high temperature and pressure The temperature inthe reactor is sustained by the heat of reaction because thereaction is exothermic [1]The reaction of ammonia synthesisprocess contains
3
2H2+1
2N2999445999468 NH
3+Q (1)
The reaction is limited by the unfavorable position ofthe chemical equilibrium and by the low activity of thepromoted iron catalysts with high pressure and temperature[12] In general no more than 20 of the synthesis gas isconverted into ammonia per pass even at high pressure of30MPa [12] As the ammonia reaction is exothermic it isnecessary for removing the heat generated in the catalyst bedby the progress of the reaction to obtain a reasonable overallconversion rate as same as to protect the life of the catalyst[13] The mixture gas from the condenser is divided into twoparts Q1 and Q2 to go to the converter The first cold shotQ1 is recirculated to the annular space between the outershell reactor and catalyst bed from the top to the bottomto refrigerate the shell and remove the heat released by thereaction Then the gas Q1 from the bottom of reactor goesthrough the preheater and is heated by the counter-currentflowing reacted gas from waste heat boiler Q1 gas is dividedinto 4 cold quench gas (q1 q2 q3 and q4) and Q2 gas formixing with the gas between consecutive catalyst beds toquench the hot spots before entry to the subsequent catalystbeds The hot spot temperatures (TIRA705 TIRA712N andTIRA714) represent the highest reaction temperatures at eachstage of the catalyst bed
Figure 2 represents the ammonia synthesis unit Thereacted gas including N
2 H2 NH3 and inert gas after reactor
passes through the waste heat boiler Then it goes throughthe preheater and the water cooler to be further cooled Partof the ammonia is condensed and separated by ammoniaseparator I Inert gas from the ammonia synthesis loop areejected by purge gas from separator to prevent accumulationof inert gas in the system The fresh feed gas is producedby the Texaco coal gasification air separation section aprocess that converts the Coal Water Slurry into synthesisgas for ammonia The fresh gas consists of hydrogen andnitrogen in stoichiometric proportions of 3 1 approximatelyand mixes with small amounts of argon and methane Thefresh gas which passes compressor is compounded with therecycle gas which comes from the circulator and then themixture goes through oil separator and condenser Mixturegas is further cooled by liquid ammonia and goes throughammonia separator II to separate the partial liquid ammoniaand then it goes out with very few ammonia The liquidammonia from ammonia separator I and separator II flowsto the liquid ammonia jar Mixture is heated in ammoniacondenser to about 25∘C and flows to the reactor and thewhole cycle starts again
Mathematical Problems in Engineering 3
Ammoniareactor
CirculatorWaste heat
boiler Preheater Water cooler Ammonia
separator IOil
separator
Condenser
Ammonia cooler
Synthesis gas
Evaporator
Ammonia separator II
Hydrogen recovery unit
Ammonia recovery unit
TIRA705
TIR712N
TIRA714
AR701
AR701-4
FIR705 703
PI
725TI
Liquid ammonia
Compressor
Figure 1 Ammonia synthesis system
Preheater
Q2
Preheaterq1
q4
q3
q2
Waste heat boiler
Q1
Q 1
I radial bed
II radial bed
Inter-changer
Axial bedFIR704
703
702
FIR705
FIR
FIR
Figure 2 The ammonia synthesis unit
3 Proposed Cultural Differential Evolutionwith Ant Search Algorithm
31 Differential Evolution Algorithm Evolutionary Algo-rithms which are inspired by the evolution of species havebeen adopted to solve a wide range of optimization problemssuccessfully in different fields The primary advantage ofEvolutionaryAlgorithms is that they just require the objectivefunction values while properties such as differentiability andcontinuity are not necessary [14]
Differential evolution proposed by Storn and Price is afast and simple population based stochastic search technique[15] DE employs mutation crossover and selection opera-tions It focuses on differential vectors of individuals with thecharacteristics of simple structure and rapid convergenceThedetailed procedure of DE is presented below
(1) Initialization In a 119863-dimension space NP parametervectors so-called individuals cover the entire search spaceby uniformly randomizing the initial individuals within thesearch space constrained by the minimum and maximumparameter bounds119883min and119883max
1199090
119894119889= 1199090
min119889 + rand (0 1) (1199090
max119889 minus 1199090
min119889) 119895 = 1 2 119863
(2)
4 Mathematical Problems in Engineering
(2)MutationDE employs themutation operation to produceamutant vector 119906119905
119894119889called target vector corresponding to each
individual 119909119905119894119889after initialization In iteration 119905 the mutant
vector 119906119905119894119889
of individual 119909119905119894119889
can be generated according tocertain mutation strategies Equations (3)ndash(7) indicate themost frequent mutation strategies version respectively
DErand1 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889) (3)
DErand2 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)
+ 119865 (119883119905
1199034119894119889minus 119883119905
1199035119894119889)
(4)
DEbest1 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889) (5)
DEbest2 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
+ 119865 (119883119905
1199033119894119889minus 119883119905
1199034119894119889)
(6)
DErand-to-best1 119906119905
119894119889= 119883119905
119894119889+ 119865 (119883
119905
best119889 minus 119883119905
119894119889)
+ 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
(7)
where 1199031119894 1199032119894 1199033119894 1199034119894 and 1199035119894are mutually exclusive integersrandomly generated within the range [1NP] which shouldnot be 119894 119865 is the mutation factor for scaling the differencevector usually bounded in [0 2] 119883119905best is the best individualwith the best fitness value at generation 119905 in the population
(3) Crossover The individual 119883119905119894and mutant vector 119906119905
119894are
hybridized to compose the trial vector 119910119905119894after mutation
operation The binomial crossover is adopted by the DE inthe paper which is defined as
119910119905
119894119889= 119906119905
119894119889if rand le 119862
119877or 119894 = 119894rand
119883119905
119894119889otherwise
(8)
where rand is a random number between in 0 and 1 dis-tributed uniformly The crossover factor 119862
119877is a probability
rate within the range 0 and 1 which influences the tradeoffbetween the ability of exploration and exploitation 119894rand is aninteger chosen randomly in [1 119863] To ensure that the trialvector (119910119905
119894) differs from its corresponding individual (119883119905
119894) by
at least one dimension 119894 = 119894rand is recommended
(4) Selection When a newly generated trial vector exceedsits corresponding upper and lower bounds it is reinitializedwithin the presetting range uniformly and randomly Thenthe trial individual119910119905
119894is comparedwith the individual119883119905
119894 and
the one with better fitness is selected as the new individual inthe next iteration
119883119905+1
119894119889= 119910119905
119894119889if 119891 (119910119905
119894119889) le 119891 (119883
119905
119894119889)
119883119905
119894119889otherwise
(9)
(5) Termination All above three evolutionary operationscontinue until termination criterion is achieved such asthe evolution reaching the maximumminimum of functionevaluations
As an effective and powerful random optimizationmethod DE has been successfully used to solve real worldproblems in diverse fields both unconstrained and con-strained optimization problems
32 Cultural Differential Evolution with Ant Search As wementioned in Section 31 mutation factor 119865 mutation strate-gies and crossover factor 119862
119877have great influence on the bal-
ance ofDErsquos exploration and exploitation ability119865decides theamplification of differential variation 119862
119877is used to control
the possibility of the crossover operation mutation strategieshave great influence on the results of mutation operation Insome literatures 119865 119862
119877 and mutation strategies are defined in
advance or varied by some specific regulations But the factors119865119862119877 and strategies are very difficult to choose since the prior
knowledge is absent Therefore Ant Colony Search is usedto search the suitable combination of 119865 119862
119877 and mutation
strategies adaptively to accelerate the global search Someresearchers have found an inevitable relationship betweenthe parameters (119865 119862
119877 and mutation strategies) and the
optimization results of DE [16ndash18] However the approachesabove are not applying the most suitable 119865 119862
119877 and mutation
strategies simultaneouslyIn this paper based on the theory of Cultural Algorithm
and Ant Colony Optimization (ACO) an improved Cul-tural Differential Algorithm incorporation with Ant ColonySearch is presented In order to accelerate searching out theglobal solution the Ant Colony Search is used to searchthe optimal combination of 119865 and 119862
119877in subpopulation 1 as
well as mutation strategy in subpopulation 2 The frameworkof Cultural Differential Evolution with Ant Search is brieflydescribed in Figure 3
321 Population Space The population space is divided intotwo parts subpopulation 1 and subpopulation 2 The twosubpopulations contain equal number of the individuals
In subpopulation 1 the individual is set as ant at each gen-eration 119865 and 119862
119877are defined to be the values between [0 1]
119865 isin 01times119894 119894 = 1 2 10 and119862119877isin 01times119894 119894 = 1 2 10
Each of the ants chooses a combination of119865 and119862119877according
to the information which is calculated by the fitness functionof ants During search process the information gathered bythe ants is preserved in the pheromone trails 120591 By exchanginginformation according to pheromone the ants cooperatewitheach other to choose appropriate combination of 119865 and 119862
119877
Then ant colony renews the pheromone trails of all antsThen the pheromone trail 120591
119898119899is updated in the following
equation
120591119898119899(119905 + 1) = (1 minus 120588
1) 120591119898119899(119905) +
subpopulation1
sum
119894=1
Δ120591119894
119898119899(119905)
(10)
where 0 le 1205881lt 1 means the pheromone trail evaporation
rate 119899 = 1 2 10119898 = 1 2 1st parameter represents 119865 and
Mathematical Problems in Engineering 5
Subpopulation 1 Subpopulation 2
Belief space
Influencefunction
Acceptancefunction
Select Performancefunction
Ant Search of mutation strategy
Knowledge exchange
Population space
Ant Search ofF and CR
(situational knowledge and normative knowledge)
Figure 3 The framework of CDEAS algorithm
01 02 03 10
01 02 03 10
12059111 12059112 12059113 120591110
F
F
CR
CR
12059121 12059122 12059123 120591210
01
10
02
03
01
10
02
03
middot middot middot
middot middot middot
middot middot middot
middot middot middot
Figure 4 Relationship between pheromone and ant paths of 119865 119862119877
2nd parameter represents 119862119877 Δ120591119894119898119899(119905) is the quantity of the
pheromone trail of ant 119894
Δ120591119894
119898119899(119905)
=
1 if 119894 isin 119883119898119899
and fitness (119910119905119894) lt fitness (119909best119905)
05 if 119894 isin 119883119898119899
and fitness (119909best119905) lt fitness (119910119905119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(11)
where 119883119898119899
is the ant group that chooses 119899th value as theselection of119898th parameter119909best119905 denotes the best individualof ant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant chooses 119899th value of119898th parameter (119865 and 119862
119877) in Figure 4 is set by
119901119898119899(119905) =
120591119894
119898119899(119905)
sum119899120591119898119899(119905)
if rand1lt 119875119904
rand2
otherwise(12)
Figure 4 illustrates the relationship between pheromonematrix and ant path of 119865 and 119862
119877 where 119875
119904is a constant
which is defined as selection parameter and rand1and rand
2
are two random values which are uniformly distributed in[0 1] Selection of the values of 119865 and 119862
119877depends on the
pheromone of each path According to the performance ofall the individuals the individual is chosen by the mostappropriate combination of 119865 and 119862
119877in each generation
In subpopulation 2 the individual is set as ant at eachgeneration Mutation strategies which are listed at (3)ndash(7) are
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
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Stochastic AnalysisInternational Journal of
2 Mathematical Problems in Engineering
specific internal mechanism because a lot of parameters areunknown in real industrial process
In order to achieve the required accuracy of the modelsome researches focus on the novel modeling methodscombining some heuristic methods such as ANN (ArtificialNeural Network) LS-SVM (Least Squares Support VectorMachine) with Evolutionary Algorithm for example geneticalgorithm ant colony optimization (ACO) particle swarmoptimization (PSO) differential evolution (DE) and so forthDE is one of themost popular algorithms for this problemandhas been applied in many fields Sacco and Hendersonb [5]introduced a variant of the differential evolution algorithmwith a new mutation operator based on a topographicalheuristic and used it to solve the nuclear reactor core designoptimization problem Rout et al [6] proposed a simple butpromising hybrid prediction model by suitably combiningan adaptive autoregressive moving average architecture anddifferential evolution for forecasting of exchange rates Ozcanet al [7] carried out the cost optimization of an air coolingsystem by using Lagrange multipliers method differentialevolution algorithm and particle swarm optimization forvarious temperatures andmass flow ratesThe results showedthat the method gives high accuracy results within a shorttime interval Zhang et al [8] proposed a hybrid differentialevolution algorithm for the job shop scheduling problemwithrandom processing times under the objective of minimizingthe expected total tardiness Arya and Choube [9] describeda methodology for allocating repair time and failure rates tosegments of a meshed distribution system using differentialevolution technique Xu et al [10] proposed a model ofammonia conversion rate by LS-SVM and a hybrid algorithmof PSO and DE is described to identify the hyper-parametersof LS-SVM
To describe the relationship between net value of ammo-nia in ammonia synthesis reactor and the key operationalparameters least squares support vectormachine is employedto build the structure of the relationship model in which anovel algorithm called CDEAS is proposed to identify theparametersThe experiment results showed that the proposedCDEAS-LS-SVM optimizing model is very effective of beingused to obtain the optimal operational parameters of ammo-nia synthesis converter
The remaining of the paper is organized as followsSection 2 describes the ammonia synthesis production pro-cess Section 3 proposes a novel Cultural Differential Evolu-tion with Ant Colony Search (CDEAS) algorithm Section 4constructs a model using LS-SVM based on the proposedCDEAS algorithm Section 5 presents the experiments andcomputational results and discussion Finally Section 6 sum-marizes the above results and presents several problemswhich remain to be solved
2 Ammonia Synthesis Production Process
A normal ammonia production flow chart includes thesynthesis gas production purification gas compression andammonia synthesis Ammonia synthesis loop is one of themost critical units in the entire process The system has
been realized by LuHua Inc a medium fertilizers factory ofYanKuang Group China
Figure 1 represents a flow sheet for the ammonia syn-thesis process The ammonia synthesis reactor is a one-axialflow and two-radial flow three-bed quench-type unit [11]Hydrogen-nitrogen mixture is reacted in the catalyst bedunder high temperature and pressure The temperature inthe reactor is sustained by the heat of reaction because thereaction is exothermic [1]The reaction of ammonia synthesisprocess contains
3
2H2+1
2N2999445999468 NH
3+Q (1)
The reaction is limited by the unfavorable position ofthe chemical equilibrium and by the low activity of thepromoted iron catalysts with high pressure and temperature[12] In general no more than 20 of the synthesis gas isconverted into ammonia per pass even at high pressure of30MPa [12] As the ammonia reaction is exothermic it isnecessary for removing the heat generated in the catalyst bedby the progress of the reaction to obtain a reasonable overallconversion rate as same as to protect the life of the catalyst[13] The mixture gas from the condenser is divided into twoparts Q1 and Q2 to go to the converter The first cold shotQ1 is recirculated to the annular space between the outershell reactor and catalyst bed from the top to the bottomto refrigerate the shell and remove the heat released by thereaction Then the gas Q1 from the bottom of reactor goesthrough the preheater and is heated by the counter-currentflowing reacted gas from waste heat boiler Q1 gas is dividedinto 4 cold quench gas (q1 q2 q3 and q4) and Q2 gas formixing with the gas between consecutive catalyst beds toquench the hot spots before entry to the subsequent catalystbeds The hot spot temperatures (TIRA705 TIRA712N andTIRA714) represent the highest reaction temperatures at eachstage of the catalyst bed
Figure 2 represents the ammonia synthesis unit Thereacted gas including N
2 H2 NH3 and inert gas after reactor
passes through the waste heat boiler Then it goes throughthe preheater and the water cooler to be further cooled Partof the ammonia is condensed and separated by ammoniaseparator I Inert gas from the ammonia synthesis loop areejected by purge gas from separator to prevent accumulationof inert gas in the system The fresh feed gas is producedby the Texaco coal gasification air separation section aprocess that converts the Coal Water Slurry into synthesisgas for ammonia The fresh gas consists of hydrogen andnitrogen in stoichiometric proportions of 3 1 approximatelyand mixes with small amounts of argon and methane Thefresh gas which passes compressor is compounded with therecycle gas which comes from the circulator and then themixture goes through oil separator and condenser Mixturegas is further cooled by liquid ammonia and goes throughammonia separator II to separate the partial liquid ammoniaand then it goes out with very few ammonia The liquidammonia from ammonia separator I and separator II flowsto the liquid ammonia jar Mixture is heated in ammoniacondenser to about 25∘C and flows to the reactor and thewhole cycle starts again
Mathematical Problems in Engineering 3
Ammoniareactor
CirculatorWaste heat
boiler Preheater Water cooler Ammonia
separator IOil
separator
Condenser
Ammonia cooler
Synthesis gas
Evaporator
Ammonia separator II
Hydrogen recovery unit
Ammonia recovery unit
TIRA705
TIR712N
TIRA714
AR701
AR701-4
FIR705 703
PI
725TI
Liquid ammonia
Compressor
Figure 1 Ammonia synthesis system
Preheater
Q2
Preheaterq1
q4
q3
q2
Waste heat boiler
Q1
Q 1
I radial bed
II radial bed
Inter-changer
Axial bedFIR704
703
702
FIR705
FIR
FIR
Figure 2 The ammonia synthesis unit
3 Proposed Cultural Differential Evolutionwith Ant Search Algorithm
31 Differential Evolution Algorithm Evolutionary Algo-rithms which are inspired by the evolution of species havebeen adopted to solve a wide range of optimization problemssuccessfully in different fields The primary advantage ofEvolutionaryAlgorithms is that they just require the objectivefunction values while properties such as differentiability andcontinuity are not necessary [14]
Differential evolution proposed by Storn and Price is afast and simple population based stochastic search technique[15] DE employs mutation crossover and selection opera-tions It focuses on differential vectors of individuals with thecharacteristics of simple structure and rapid convergenceThedetailed procedure of DE is presented below
(1) Initialization In a 119863-dimension space NP parametervectors so-called individuals cover the entire search spaceby uniformly randomizing the initial individuals within thesearch space constrained by the minimum and maximumparameter bounds119883min and119883max
1199090
119894119889= 1199090
min119889 + rand (0 1) (1199090
max119889 minus 1199090
min119889) 119895 = 1 2 119863
(2)
4 Mathematical Problems in Engineering
(2)MutationDE employs themutation operation to produceamutant vector 119906119905
119894119889called target vector corresponding to each
individual 119909119905119894119889after initialization In iteration 119905 the mutant
vector 119906119905119894119889
of individual 119909119905119894119889
can be generated according tocertain mutation strategies Equations (3)ndash(7) indicate themost frequent mutation strategies version respectively
DErand1 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889) (3)
DErand2 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)
+ 119865 (119883119905
1199034119894119889minus 119883119905
1199035119894119889)
(4)
DEbest1 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889) (5)
DEbest2 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
+ 119865 (119883119905
1199033119894119889minus 119883119905
1199034119894119889)
(6)
DErand-to-best1 119906119905
119894119889= 119883119905
119894119889+ 119865 (119883
119905
best119889 minus 119883119905
119894119889)
+ 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
(7)
where 1199031119894 1199032119894 1199033119894 1199034119894 and 1199035119894are mutually exclusive integersrandomly generated within the range [1NP] which shouldnot be 119894 119865 is the mutation factor for scaling the differencevector usually bounded in [0 2] 119883119905best is the best individualwith the best fitness value at generation 119905 in the population
(3) Crossover The individual 119883119905119894and mutant vector 119906119905
119894are
hybridized to compose the trial vector 119910119905119894after mutation
operation The binomial crossover is adopted by the DE inthe paper which is defined as
119910119905
119894119889= 119906119905
119894119889if rand le 119862
119877or 119894 = 119894rand
119883119905
119894119889otherwise
(8)
where rand is a random number between in 0 and 1 dis-tributed uniformly The crossover factor 119862
119877is a probability
rate within the range 0 and 1 which influences the tradeoffbetween the ability of exploration and exploitation 119894rand is aninteger chosen randomly in [1 119863] To ensure that the trialvector (119910119905
119894) differs from its corresponding individual (119883119905
119894) by
at least one dimension 119894 = 119894rand is recommended
(4) Selection When a newly generated trial vector exceedsits corresponding upper and lower bounds it is reinitializedwithin the presetting range uniformly and randomly Thenthe trial individual119910119905
119894is comparedwith the individual119883119905
119894 and
the one with better fitness is selected as the new individual inthe next iteration
119883119905+1
119894119889= 119910119905
119894119889if 119891 (119910119905
119894119889) le 119891 (119883
119905
119894119889)
119883119905
119894119889otherwise
(9)
(5) Termination All above three evolutionary operationscontinue until termination criterion is achieved such asthe evolution reaching the maximumminimum of functionevaluations
As an effective and powerful random optimizationmethod DE has been successfully used to solve real worldproblems in diverse fields both unconstrained and con-strained optimization problems
32 Cultural Differential Evolution with Ant Search As wementioned in Section 31 mutation factor 119865 mutation strate-gies and crossover factor 119862
119877have great influence on the bal-
ance ofDErsquos exploration and exploitation ability119865decides theamplification of differential variation 119862
119877is used to control
the possibility of the crossover operation mutation strategieshave great influence on the results of mutation operation Insome literatures 119865 119862
119877 and mutation strategies are defined in
advance or varied by some specific regulations But the factors119865119862119877 and strategies are very difficult to choose since the prior
knowledge is absent Therefore Ant Colony Search is usedto search the suitable combination of 119865 119862
119877 and mutation
strategies adaptively to accelerate the global search Someresearchers have found an inevitable relationship betweenthe parameters (119865 119862
119877 and mutation strategies) and the
optimization results of DE [16ndash18] However the approachesabove are not applying the most suitable 119865 119862
119877 and mutation
strategies simultaneouslyIn this paper based on the theory of Cultural Algorithm
and Ant Colony Optimization (ACO) an improved Cul-tural Differential Algorithm incorporation with Ant ColonySearch is presented In order to accelerate searching out theglobal solution the Ant Colony Search is used to searchthe optimal combination of 119865 and 119862
119877in subpopulation 1 as
well as mutation strategy in subpopulation 2 The frameworkof Cultural Differential Evolution with Ant Search is brieflydescribed in Figure 3
321 Population Space The population space is divided intotwo parts subpopulation 1 and subpopulation 2 The twosubpopulations contain equal number of the individuals
In subpopulation 1 the individual is set as ant at each gen-eration 119865 and 119862
119877are defined to be the values between [0 1]
119865 isin 01times119894 119894 = 1 2 10 and119862119877isin 01times119894 119894 = 1 2 10
Each of the ants chooses a combination of119865 and119862119877according
to the information which is calculated by the fitness functionof ants During search process the information gathered bythe ants is preserved in the pheromone trails 120591 By exchanginginformation according to pheromone the ants cooperatewitheach other to choose appropriate combination of 119865 and 119862
119877
Then ant colony renews the pheromone trails of all antsThen the pheromone trail 120591
119898119899is updated in the following
equation
120591119898119899(119905 + 1) = (1 minus 120588
1) 120591119898119899(119905) +
subpopulation1
sum
119894=1
Δ120591119894
119898119899(119905)
(10)
where 0 le 1205881lt 1 means the pheromone trail evaporation
rate 119899 = 1 2 10119898 = 1 2 1st parameter represents 119865 and
Mathematical Problems in Engineering 5
Subpopulation 1 Subpopulation 2
Belief space
Influencefunction
Acceptancefunction
Select Performancefunction
Ant Search of mutation strategy
Knowledge exchange
Population space
Ant Search ofF and CR
(situational knowledge and normative knowledge)
Figure 3 The framework of CDEAS algorithm
01 02 03 10
01 02 03 10
12059111 12059112 12059113 120591110
F
F
CR
CR
12059121 12059122 12059123 120591210
01
10
02
03
01
10
02
03
middot middot middot
middot middot middot
middot middot middot
middot middot middot
Figure 4 Relationship between pheromone and ant paths of 119865 119862119877
2nd parameter represents 119862119877 Δ120591119894119898119899(119905) is the quantity of the
pheromone trail of ant 119894
Δ120591119894
119898119899(119905)
=
1 if 119894 isin 119883119898119899
and fitness (119910119905119894) lt fitness (119909best119905)
05 if 119894 isin 119883119898119899
and fitness (119909best119905) lt fitness (119910119905119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(11)
where 119883119898119899
is the ant group that chooses 119899th value as theselection of119898th parameter119909best119905 denotes the best individualof ant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant chooses 119899th value of119898th parameter (119865 and 119862
119877) in Figure 4 is set by
119901119898119899(119905) =
120591119894
119898119899(119905)
sum119899120591119898119899(119905)
if rand1lt 119875119904
rand2
otherwise(12)
Figure 4 illustrates the relationship between pheromonematrix and ant path of 119865 and 119862
119877 where 119875
119904is a constant
which is defined as selection parameter and rand1and rand
2
are two random values which are uniformly distributed in[0 1] Selection of the values of 119865 and 119862
119877depends on the
pheromone of each path According to the performance ofall the individuals the individual is chosen by the mostappropriate combination of 119865 and 119862
119877in each generation
In subpopulation 2 the individual is set as ant at eachgeneration Mutation strategies which are listed at (3)ndash(7) are
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Mathematical Problems in Engineering
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Differential EquationsInternational Journal of
Volume 2014
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Decision SciencesAdvances in
Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 3
Ammoniareactor
CirculatorWaste heat
boiler Preheater Water cooler Ammonia
separator IOil
separator
Condenser
Ammonia cooler
Synthesis gas
Evaporator
Ammonia separator II
Hydrogen recovery unit
Ammonia recovery unit
TIRA705
TIR712N
TIRA714
AR701
AR701-4
FIR705 703
PI
725TI
Liquid ammonia
Compressor
Figure 1 Ammonia synthesis system
Preheater
Q2
Preheaterq1
q4
q3
q2
Waste heat boiler
Q1
Q 1
I radial bed
II radial bed
Inter-changer
Axial bedFIR704
703
702
FIR705
FIR
FIR
Figure 2 The ammonia synthesis unit
3 Proposed Cultural Differential Evolutionwith Ant Search Algorithm
31 Differential Evolution Algorithm Evolutionary Algo-rithms which are inspired by the evolution of species havebeen adopted to solve a wide range of optimization problemssuccessfully in different fields The primary advantage ofEvolutionaryAlgorithms is that they just require the objectivefunction values while properties such as differentiability andcontinuity are not necessary [14]
Differential evolution proposed by Storn and Price is afast and simple population based stochastic search technique[15] DE employs mutation crossover and selection opera-tions It focuses on differential vectors of individuals with thecharacteristics of simple structure and rapid convergenceThedetailed procedure of DE is presented below
(1) Initialization In a 119863-dimension space NP parametervectors so-called individuals cover the entire search spaceby uniformly randomizing the initial individuals within thesearch space constrained by the minimum and maximumparameter bounds119883min and119883max
1199090
119894119889= 1199090
min119889 + rand (0 1) (1199090
max119889 minus 1199090
min119889) 119895 = 1 2 119863
(2)
4 Mathematical Problems in Engineering
(2)MutationDE employs themutation operation to produceamutant vector 119906119905
119894119889called target vector corresponding to each
individual 119909119905119894119889after initialization In iteration 119905 the mutant
vector 119906119905119894119889
of individual 119909119905119894119889
can be generated according tocertain mutation strategies Equations (3)ndash(7) indicate themost frequent mutation strategies version respectively
DErand1 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889) (3)
DErand2 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)
+ 119865 (119883119905
1199034119894119889minus 119883119905
1199035119894119889)
(4)
DEbest1 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889) (5)
DEbest2 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
+ 119865 (119883119905
1199033119894119889minus 119883119905
1199034119894119889)
(6)
DErand-to-best1 119906119905
119894119889= 119883119905
119894119889+ 119865 (119883
119905
best119889 minus 119883119905
119894119889)
+ 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
(7)
where 1199031119894 1199032119894 1199033119894 1199034119894 and 1199035119894are mutually exclusive integersrandomly generated within the range [1NP] which shouldnot be 119894 119865 is the mutation factor for scaling the differencevector usually bounded in [0 2] 119883119905best is the best individualwith the best fitness value at generation 119905 in the population
(3) Crossover The individual 119883119905119894and mutant vector 119906119905
119894are
hybridized to compose the trial vector 119910119905119894after mutation
operation The binomial crossover is adopted by the DE inthe paper which is defined as
119910119905
119894119889= 119906119905
119894119889if rand le 119862
119877or 119894 = 119894rand
119883119905
119894119889otherwise
(8)
where rand is a random number between in 0 and 1 dis-tributed uniformly The crossover factor 119862
119877is a probability
rate within the range 0 and 1 which influences the tradeoffbetween the ability of exploration and exploitation 119894rand is aninteger chosen randomly in [1 119863] To ensure that the trialvector (119910119905
119894) differs from its corresponding individual (119883119905
119894) by
at least one dimension 119894 = 119894rand is recommended
(4) Selection When a newly generated trial vector exceedsits corresponding upper and lower bounds it is reinitializedwithin the presetting range uniformly and randomly Thenthe trial individual119910119905
119894is comparedwith the individual119883119905
119894 and
the one with better fitness is selected as the new individual inthe next iteration
119883119905+1
119894119889= 119910119905
119894119889if 119891 (119910119905
119894119889) le 119891 (119883
119905
119894119889)
119883119905
119894119889otherwise
(9)
(5) Termination All above three evolutionary operationscontinue until termination criterion is achieved such asthe evolution reaching the maximumminimum of functionevaluations
As an effective and powerful random optimizationmethod DE has been successfully used to solve real worldproblems in diverse fields both unconstrained and con-strained optimization problems
32 Cultural Differential Evolution with Ant Search As wementioned in Section 31 mutation factor 119865 mutation strate-gies and crossover factor 119862
119877have great influence on the bal-
ance ofDErsquos exploration and exploitation ability119865decides theamplification of differential variation 119862
119877is used to control
the possibility of the crossover operation mutation strategieshave great influence on the results of mutation operation Insome literatures 119865 119862
119877 and mutation strategies are defined in
advance or varied by some specific regulations But the factors119865119862119877 and strategies are very difficult to choose since the prior
knowledge is absent Therefore Ant Colony Search is usedto search the suitable combination of 119865 119862
119877 and mutation
strategies adaptively to accelerate the global search Someresearchers have found an inevitable relationship betweenthe parameters (119865 119862
119877 and mutation strategies) and the
optimization results of DE [16ndash18] However the approachesabove are not applying the most suitable 119865 119862
119877 and mutation
strategies simultaneouslyIn this paper based on the theory of Cultural Algorithm
and Ant Colony Optimization (ACO) an improved Cul-tural Differential Algorithm incorporation with Ant ColonySearch is presented In order to accelerate searching out theglobal solution the Ant Colony Search is used to searchthe optimal combination of 119865 and 119862
119877in subpopulation 1 as
well as mutation strategy in subpopulation 2 The frameworkof Cultural Differential Evolution with Ant Search is brieflydescribed in Figure 3
321 Population Space The population space is divided intotwo parts subpopulation 1 and subpopulation 2 The twosubpopulations contain equal number of the individuals
In subpopulation 1 the individual is set as ant at each gen-eration 119865 and 119862
119877are defined to be the values between [0 1]
119865 isin 01times119894 119894 = 1 2 10 and119862119877isin 01times119894 119894 = 1 2 10
Each of the ants chooses a combination of119865 and119862119877according
to the information which is calculated by the fitness functionof ants During search process the information gathered bythe ants is preserved in the pheromone trails 120591 By exchanginginformation according to pheromone the ants cooperatewitheach other to choose appropriate combination of 119865 and 119862
119877
Then ant colony renews the pheromone trails of all antsThen the pheromone trail 120591
119898119899is updated in the following
equation
120591119898119899(119905 + 1) = (1 minus 120588
1) 120591119898119899(119905) +
subpopulation1
sum
119894=1
Δ120591119894
119898119899(119905)
(10)
where 0 le 1205881lt 1 means the pheromone trail evaporation
rate 119899 = 1 2 10119898 = 1 2 1st parameter represents 119865 and
Mathematical Problems in Engineering 5
Subpopulation 1 Subpopulation 2
Belief space
Influencefunction
Acceptancefunction
Select Performancefunction
Ant Search of mutation strategy
Knowledge exchange
Population space
Ant Search ofF and CR
(situational knowledge and normative knowledge)
Figure 3 The framework of CDEAS algorithm
01 02 03 10
01 02 03 10
12059111 12059112 12059113 120591110
F
F
CR
CR
12059121 12059122 12059123 120591210
01
10
02
03
01
10
02
03
middot middot middot
middot middot middot
middot middot middot
middot middot middot
Figure 4 Relationship between pheromone and ant paths of 119865 119862119877
2nd parameter represents 119862119877 Δ120591119894119898119899(119905) is the quantity of the
pheromone trail of ant 119894
Δ120591119894
119898119899(119905)
=
1 if 119894 isin 119883119898119899
and fitness (119910119905119894) lt fitness (119909best119905)
05 if 119894 isin 119883119898119899
and fitness (119909best119905) lt fitness (119910119905119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(11)
where 119883119898119899
is the ant group that chooses 119899th value as theselection of119898th parameter119909best119905 denotes the best individualof ant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant chooses 119899th value of119898th parameter (119865 and 119862
119877) in Figure 4 is set by
119901119898119899(119905) =
120591119894
119898119899(119905)
sum119899120591119898119899(119905)
if rand1lt 119875119904
rand2
otherwise(12)
Figure 4 illustrates the relationship between pheromonematrix and ant path of 119865 and 119862
119877 where 119875
119904is a constant
which is defined as selection parameter and rand1and rand
2
are two random values which are uniformly distributed in[0 1] Selection of the values of 119865 and 119862
119877depends on the
pheromone of each path According to the performance ofall the individuals the individual is chosen by the mostappropriate combination of 119865 and 119862
119877in each generation
In subpopulation 2 the individual is set as ant at eachgeneration Mutation strategies which are listed at (3)ndash(7) are
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
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4 Mathematical Problems in Engineering
(2)MutationDE employs themutation operation to produceamutant vector 119906119905
119894119889called target vector corresponding to each
individual 119909119905119894119889after initialization In iteration 119905 the mutant
vector 119906119905119894119889
of individual 119909119905119894119889
can be generated according tocertain mutation strategies Equations (3)ndash(7) indicate themost frequent mutation strategies version respectively
DErand1 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889) (3)
DErand2 119906119905
119894119889= 119883119905
1199031119894119889+ 119865 (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)
+ 119865 (119883119905
1199034119894119889minus 119883119905
1199035119894119889)
(4)
DEbest1 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889) (5)
DEbest2 119906119905
119894119889= 119883119905
best119889 + 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
+ 119865 (119883119905
1199033119894119889minus 119883119905
1199034119894119889)
(6)
DErand-to-best1 119906119905
119894119889= 119883119905
119894119889+ 119865 (119883
119905
best119889 minus 119883119905
119894119889)
+ 119865 (119883119905
1199031119894119889minus 119883119905
1199032119894119889)
(7)
where 1199031119894 1199032119894 1199033119894 1199034119894 and 1199035119894are mutually exclusive integersrandomly generated within the range [1NP] which shouldnot be 119894 119865 is the mutation factor for scaling the differencevector usually bounded in [0 2] 119883119905best is the best individualwith the best fitness value at generation 119905 in the population
(3) Crossover The individual 119883119905119894and mutant vector 119906119905
119894are
hybridized to compose the trial vector 119910119905119894after mutation
operation The binomial crossover is adopted by the DE inthe paper which is defined as
119910119905
119894119889= 119906119905
119894119889if rand le 119862
119877or 119894 = 119894rand
119883119905
119894119889otherwise
(8)
where rand is a random number between in 0 and 1 dis-tributed uniformly The crossover factor 119862
119877is a probability
rate within the range 0 and 1 which influences the tradeoffbetween the ability of exploration and exploitation 119894rand is aninteger chosen randomly in [1 119863] To ensure that the trialvector (119910119905
119894) differs from its corresponding individual (119883119905
119894) by
at least one dimension 119894 = 119894rand is recommended
(4) Selection When a newly generated trial vector exceedsits corresponding upper and lower bounds it is reinitializedwithin the presetting range uniformly and randomly Thenthe trial individual119910119905
119894is comparedwith the individual119883119905
119894 and
the one with better fitness is selected as the new individual inthe next iteration
119883119905+1
119894119889= 119910119905
119894119889if 119891 (119910119905
119894119889) le 119891 (119883
119905
119894119889)
119883119905
119894119889otherwise
(9)
(5) Termination All above three evolutionary operationscontinue until termination criterion is achieved such asthe evolution reaching the maximumminimum of functionevaluations
As an effective and powerful random optimizationmethod DE has been successfully used to solve real worldproblems in diverse fields both unconstrained and con-strained optimization problems
32 Cultural Differential Evolution with Ant Search As wementioned in Section 31 mutation factor 119865 mutation strate-gies and crossover factor 119862
119877have great influence on the bal-
ance ofDErsquos exploration and exploitation ability119865decides theamplification of differential variation 119862
119877is used to control
the possibility of the crossover operation mutation strategieshave great influence on the results of mutation operation Insome literatures 119865 119862
119877 and mutation strategies are defined in
advance or varied by some specific regulations But the factors119865119862119877 and strategies are very difficult to choose since the prior
knowledge is absent Therefore Ant Colony Search is usedto search the suitable combination of 119865 119862
119877 and mutation
strategies adaptively to accelerate the global search Someresearchers have found an inevitable relationship betweenthe parameters (119865 119862
119877 and mutation strategies) and the
optimization results of DE [16ndash18] However the approachesabove are not applying the most suitable 119865 119862
119877 and mutation
strategies simultaneouslyIn this paper based on the theory of Cultural Algorithm
and Ant Colony Optimization (ACO) an improved Cul-tural Differential Algorithm incorporation with Ant ColonySearch is presented In order to accelerate searching out theglobal solution the Ant Colony Search is used to searchthe optimal combination of 119865 and 119862
119877in subpopulation 1 as
well as mutation strategy in subpopulation 2 The frameworkof Cultural Differential Evolution with Ant Search is brieflydescribed in Figure 3
321 Population Space The population space is divided intotwo parts subpopulation 1 and subpopulation 2 The twosubpopulations contain equal number of the individuals
In subpopulation 1 the individual is set as ant at each gen-eration 119865 and 119862
119877are defined to be the values between [0 1]
119865 isin 01times119894 119894 = 1 2 10 and119862119877isin 01times119894 119894 = 1 2 10
Each of the ants chooses a combination of119865 and119862119877according
to the information which is calculated by the fitness functionof ants During search process the information gathered bythe ants is preserved in the pheromone trails 120591 By exchanginginformation according to pheromone the ants cooperatewitheach other to choose appropriate combination of 119865 and 119862
119877
Then ant colony renews the pheromone trails of all antsThen the pheromone trail 120591
119898119899is updated in the following
equation
120591119898119899(119905 + 1) = (1 minus 120588
1) 120591119898119899(119905) +
subpopulation1
sum
119894=1
Δ120591119894
119898119899(119905)
(10)
where 0 le 1205881lt 1 means the pheromone trail evaporation
rate 119899 = 1 2 10119898 = 1 2 1st parameter represents 119865 and
Mathematical Problems in Engineering 5
Subpopulation 1 Subpopulation 2
Belief space
Influencefunction
Acceptancefunction
Select Performancefunction
Ant Search of mutation strategy
Knowledge exchange
Population space
Ant Search ofF and CR
(situational knowledge and normative knowledge)
Figure 3 The framework of CDEAS algorithm
01 02 03 10
01 02 03 10
12059111 12059112 12059113 120591110
F
F
CR
CR
12059121 12059122 12059123 120591210
01
10
02
03
01
10
02
03
middot middot middot
middot middot middot
middot middot middot
middot middot middot
Figure 4 Relationship between pheromone and ant paths of 119865 119862119877
2nd parameter represents 119862119877 Δ120591119894119898119899(119905) is the quantity of the
pheromone trail of ant 119894
Δ120591119894
119898119899(119905)
=
1 if 119894 isin 119883119898119899
and fitness (119910119905119894) lt fitness (119909best119905)
05 if 119894 isin 119883119898119899
and fitness (119909best119905) lt fitness (119910119905119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(11)
where 119883119898119899
is the ant group that chooses 119899th value as theselection of119898th parameter119909best119905 denotes the best individualof ant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant chooses 119899th value of119898th parameter (119865 and 119862
119877) in Figure 4 is set by
119901119898119899(119905) =
120591119894
119898119899(119905)
sum119899120591119898119899(119905)
if rand1lt 119875119904
rand2
otherwise(12)
Figure 4 illustrates the relationship between pheromonematrix and ant path of 119865 and 119862
119877 where 119875
119904is a constant
which is defined as selection parameter and rand1and rand
2
are two random values which are uniformly distributed in[0 1] Selection of the values of 119865 and 119862
119877depends on the
pheromone of each path According to the performance ofall the individuals the individual is chosen by the mostappropriate combination of 119865 and 119862
119877in each generation
In subpopulation 2 the individual is set as ant at eachgeneration Mutation strategies which are listed at (3)ndash(7) are
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
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Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
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Mathematical PhysicsAdvances in
Complex AnalysisJournal of
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OptimizationJournal of
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International Journal of
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Operations ResearchAdvances in
Journal of
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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Algebra
Discrete Dynamics in Nature and Society
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Decision SciencesAdvances in
Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 5
Subpopulation 1 Subpopulation 2
Belief space
Influencefunction
Acceptancefunction
Select Performancefunction
Ant Search of mutation strategy
Knowledge exchange
Population space
Ant Search ofF and CR
(situational knowledge and normative knowledge)
Figure 3 The framework of CDEAS algorithm
01 02 03 10
01 02 03 10
12059111 12059112 12059113 120591110
F
F
CR
CR
12059121 12059122 12059123 120591210
01
10
02
03
01
10
02
03
middot middot middot
middot middot middot
middot middot middot
middot middot middot
Figure 4 Relationship between pheromone and ant paths of 119865 119862119877
2nd parameter represents 119862119877 Δ120591119894119898119899(119905) is the quantity of the
pheromone trail of ant 119894
Δ120591119894
119898119899(119905)
=
1 if 119894 isin 119883119898119899
and fitness (119910119905119894) lt fitness (119909best119905)
05 if 119894 isin 119883119898119899
and fitness (119909best119905) lt fitness (119910119905119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(11)
where 119883119898119899
is the ant group that chooses 119899th value as theselection of119898th parameter119909best119905 denotes the best individualof ant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant chooses 119899th value of119898th parameter (119865 and 119862
119877) in Figure 4 is set by
119901119898119899(119905) =
120591119894
119898119899(119905)
sum119899120591119898119899(119905)
if rand1lt 119875119904
rand2
otherwise(12)
Figure 4 illustrates the relationship between pheromonematrix and ant path of 119865 and 119862
119877 where 119875
119904is a constant
which is defined as selection parameter and rand1and rand
2
are two random values which are uniformly distributed in[0 1] Selection of the values of 119865 and 119862
119877depends on the
pheromone of each path According to the performance ofall the individuals the individual is chosen by the mostappropriate combination of 119865 and 119862
119877in each generation
In subpopulation 2 the individual is set as ant at eachgeneration Mutation strategies which are listed at (3)ndash(7) are
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
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Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
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Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
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Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
6 Mathematical Problems in Engineering
Mutation strategy
Mutation strategy
DErand1 DErand2 DEbest1 DEbest2 DErand-to-best1
02 04 06 08 1
1205761 1205762 1205763 1205764 1205765
04
06
02
10
Figure 5 Relationship between and ant paths of mutation strategy
defined to be of the values 02 04 06 08 10 respectivelyFor example 02 means the first mutation strategy equation(3) is selected Each of the ants chooses a mutation strategyaccording to the informationwhich is calculated by the fitnessfunction of ants During search process the informationgathered by the ants is preserved in the pheromone trails 120576By exchanging information according to pheromone the antscooperate with each other to choose appropriate mutationstrategy Then ant colony renews the pheromone trails of allants
Then the pheromone trail 120576 is updated in the followingequation
120576119896(119905 + 1) = (1 minus 120588
2) 120576119896(119905) +
subpopulation2
sum
119894=1
Δ120576119894
119896(119905) (13)
where 0 le 1205882lt 1 means the pheromone trail evaporation
rate and Δ120576119894119896(119905) is the quantity of the pheromone trail of ant 119894
120576119894
119896(119905)
=
1 if 119894 isin 119883119896and fitness (119910119905
119894) lt fitness (119909best119905)
05 if 119894 isin 119883119896and fitness (119909best119905) lt fitness (119910119905
119894)
and fitness (119910119905119894) lt fitness (119909119905
119894)
0 otherwise(14)
where 119883119896is the ant group that chooses 119896th value as the
selection of parameter 119909best119905 denotes the best individual ofant colony till 119905th generation
In order to prevent the ants from being limited to oneant path and improve the possibility of choosing other paths
considerably the probability of each ant choosing 119899th valueof119898th parameter (mutation strategies) is set by
119901119896(119905) =
120576119894
119896(119905)
sum119899120576119896(119905)
if rand3lt 1198751015840
119904
rand4
otherwise(15)
where1198751015840119904is a constant which is defined as selection parameter
and rand3and rand
4are two random values which are
uniformly distributed in [0 1] Selection of the values ofmutation strategies depends on the pheromone of each pathAccording to the performance of all the individuals theindividual is chosen by the most appropriate combination ofmutation strategies in each generation
Figure 5 illustrates the relationship between pheromonematrix and ant path of mutation strategies
322 Belief Space In our approach the belief space isdivided into two knowledge sources situational knowledgeand normative knowledge
Situational knowledge consists of the global best exem-plar 119864 which is found along the searching process andprovides guidance for individuals of population space Theupdate of the situational knowledge is done if the bestindividual found in the current populations space is betterthan 119864
The normative knowledge contains the intervals thatdecide the individuals of population space where to move 119897
119894
and 119906119894are the lower and upper bounds of the search range
in population space 119871119894and 119880
119894are the value of the fitness
function associated with that bound If the 119897119894and 119906
119894are
updated the 119871119894and 119880
119894must be updated too
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Differential EquationsInternational Journal of
Volume 2014
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 7
119897119894and 119906
119894are set by
119897119894= 119909119894min if 119909
119894min lt 119897119894 or 119891 (119909119894min) lt 119871 119894119897119894
otherwise
119906119894= 119909119894max if 119909
119894max gt 119906119894 or 119891 (119909119894max) gt 119880119894119906119894
otherwise
(16)
323 Acceptance Function Acceptance function controls theamount of good individuals which impact on the update ofbelief space [19] In this paper 30 of the individuals inthe belief space are replaced by the good ones in populationspace
324 Influence Function In the CDEAS situational knowl-edge and normative knowledge are involved to influence eachindividual in the population space and then population spaceis updated
The individuals in population space are updated in thefollowing equation
119909119905+1
119894119889=
119909119905
119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119906119894
119909119905
119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119883
119905
1199032119894119889minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889lt 119906119894
119883119905
1199031119894119889+10038161003816100381610038161003816119873 (05 03) lowast (119906
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889le 119864119895 119909119905
119894119889ge 119897119894
119883119905
1199031119894119889minus10038161003816100381610038161003816119873 (05 03) lowast (119897
119894minus 119883119905
1199033119894119889)10038161003816100381610038161003816times rand
if 119909119905119894119889gt 119864119895 119909119905
119894119889gt 119897119894
119909119905+1
119894119889=
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119883119905
1199031119894119889) times rand if 119909
119894119889gt 119897119894
119883119905
1199031119894119889minus 119865 lowast (119883
119905
1199031119894119889minus 119897119894) times rand if 119909
119894119889lt 119906119894
119883119905
1199031119894119889+ 119865 lowast (119906
119894minus 119897119894) times rand if 119897
119894lt 119909119894119889lt 119906119894
(17)where 119865 is a constant of 02
325 Knowledge Exchange After 119905 steps the 119865 and 119862119877
of subpopulation 2 are replaced by the suitable 119865 and 119862119877
calculated by subpopulation 1 and the mutation strategyof subpopulation 1 is displaced by the suitable mutationstrategy calculated by subpopulation 2 simultaneously Sothe 119865 and 119862
119877and mutation strategy are varying in the two
subpopulations to enable the individuals to converge globallyand fast
326 Procedure of CDEAS The procedure of CDEAS isproposed as follows
Step 1 Initialize the population spaces and the belief spacesthe population space is divided into subpopulation 1 andsubpopulation 2
Step 2 Evaluate each individualrsquos fitness
Step 3 To find the proper 119865 119862119877 and mutation strategy the
Ant Colony Search strategy is used in subpopulation 1 andsubpopulation 2 respectively
Step 4 According to acceptance function choose good indi-viduals from subpopulation 1 and subpopulation 2 and thenupdate the normative knowledge and situational knowledge
Step 5 Adopt the normative knowledge and situationalknowledge to influence each individual in population spacethrough the influence functions and generate two corre-sponding subpopulations
Step 6 Select individuals from subpopulation 1 and subpop-ulation 2 and update the belief spaces including the twoknowledge sources for the next generation
Step 7 If the algorithm reaches the given times exchangethe knowledge of 119865 119862
119877 and mutation strategy between
subpopulation 1 and subpopulation 2 otherwise go to Step 8
Step 8 If the stop criteria are achieved terminate the itera-tion otherwise go back to Step 2
33 Simulation Results of CDEAS The proposed CDEASalgorithm is compared with original DE algorithm To getthe average performance of the CDEAS algorithm 30 runson each problem instance were performed and the solutionquality was averagedThe parameters of CDEAS and originalDE algorithm are set as follows the maximum evolutiongeneration is 2000 the size of the population is 50 for originalDE algorithm 119865 = 03 and 119862
119877= 05 for CDEAS the size
of both two subpopulations is 25 the initial 119865 and 119862119877are
randomly selected in (0 1) and the initial mutation strategyis DErand1 the interval information exchanges between thetwo subpopulations 119905 is 50 generations the thresholds 119875
119904=
1198751015840
119904= 05 and 120588
1= 1205882= 01
To illustrate the effectiveness and performance of CDEASalgorithm for optimization problems a set of 18 representa-tive benchmark functions which were listed in the appendixwere employed to evaluate them in comparison with originalDE The test problems are heterogeneous nonlinear andnumerical benchmark functions and the global optimum for1198912 1198914 1198917 1198919 11989111 11989113 and 119891
15is shifted Functions 119891
1sim1198917are
unimodal and functions1198918sim11989118are multimodalThe detailed
principle of functions is presented in [11] The comparisonsresults of CDEAS and original DE algorithm are shown inTable 4 of the appendix The experimental results of originalDE and CDEAS algorithm on each function are listed inTable 1 Mean best worst std success rate time representthe mean minimum best minimum worst minimum thestandard deviation of minimum the success rate and theaverage computing time in 30 trials respectively
From simulation results of Table 1 we can obtain thatCDEAS reached the global optimum of 119891
2and 1198917in all trials
and the success rate reached 100 of functions 1198911 1198912 1198913 1198914
1198916 1198917 and 119891
18 For most of the test functions the success
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
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Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
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Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Mathematical PhysicsAdvances in
Complex AnalysisJournal of
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OptimizationJournal of
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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
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Operations ResearchAdvances in
Journal of
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Discrete Dynamics in Nature and Society
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Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
8 Mathematical Problems in Engineering
Table 1 The comparison results of the CDEAS algorithm and original DE algorithm
Original DE CDEASSphere function 119891
1
Best 11746 times 10minus65 50147 times 10minus79
Worst 10815 times 10minus23 93244 times 10minus75
Mean 36052 times 10minus25 16390 times 10minus75
Std 19746 times 10minus24 22315 times 10minus75
Success rate () 100 100Times (s) 18803 146017
Shifted sphere function 1198912
Best 0 0Worst 80779 times 10
minus28 0Mean 33658 times 10
minus29 0Std 15078 times 10
minus28 0Success rate () 100 100Times (s) 21788 181117
Schwefelrsquos Problem 12 1198913
Best 24386 times 10minus65 30368 times 10minus78
Worst 24820 times 10minus22 92902 times 10minus73
Mean 82736 times 10minus24 72341 times 10minus74
Std 45316 times 10minus23 20187 times 10minus73
Success rate () 100 100Times (s) 31647 241178
Shifted Schwefelrsquos Problem 12 1198914
Best 0 0Worst 56545 times 10
minus27 34331 times 10minus27
Mean 20868 times 10minus28 18848 times 10minus28
Std 10323 times 10minus27 79813 times 10minus28
Success rate () 100 100Times (s) 33956 277058
Rosenbrockrsquos function 1198915
Best 130060 52659Worst 1661159 1391358Mean 709399 394936Std 400052 312897Success rate () 8667 9667Times (s) 19594 167233
Schwefelrsquos Problem 12 with noise in fitness 1198916
Best 31344 times 10minus39 398838 times 10minus49
Worst 361389 times 10minus36 16124 times 10minus43
Mean 57744 times 10minus37 74656 times 10minus45
Std 95348 times 10minus37 29722 times 10minus44
Success rate () 100 100Times (s) 32141 242426
Shifted Schwefelrsquos Problem 12 with noise in fitness 1198917
Best 0 0Worst 0 0Mean 0 0
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
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Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Decision SciencesAdvances in
Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 9
Table 1 Continued
Original DE CDEASStd 0 0Success rate () 100 100Times (s) 33374 285638
Ackleyrsquos function 1198918
Best 71054 times 10minus15
35527 times 10minus15
Worst 48999 times 10minus7 13404Mean 16332 times 10minus8 01763Std 89457 times 10minus8 04068Success rate () 100 8333Times (s) 24820 209353
Shifted Ackleyrsquos function 1198919
Best 71054 times 10minus15 35527 times 10minus15
Worst 09313 09313Mean 00310 00620Std 01700 02362Success rate () 9667 9333Times (s) 27337 216841
Griewankrsquos function 11989110
Best 0 0Worst 00367 00270Mean 00020 00054Std 00074 00076Success rate () 90 5667Times (s) 2535 207793
Shifted Griewankrsquos function 11989111
Best 0 0Worst 00319 00343Mean 00056 00060Std 00089 00088Success rate () 80 7667Times (s) 27768 228541
Rastriginrsquos function 11989112
Best 81540 19899Worst 355878 129344Mean 203594 65003Std 63072 26612Success rate () 333 90Times (s) 27264 223237
Shifted Rastriginrsquos function 11989113
Best 59725 09949Worst 369923 67657Mean 194719 82581Std 89164 38680Success rate () 1667 7667Times (s) 29313 238838
Noncontiguous Rastriginrsquos function 11989114
Best 207617 39949Worst 299112 119899Mean 254556 81947
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
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Mathematical PhysicsAdvances in
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OptimizationJournal of
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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
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Operations ResearchAdvances in
Journal of
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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Discrete Dynamics in Nature and Society
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
10 Mathematical Problems in Engineering
Table 1 Continued
Original DE CDEASStd 29078 22473Success rate () 0 8667Times (s) 31663 255374
Shifted noncontiguous Rastriginrsquos function 11989115
Best 0 0Worst 16 6Mean 67666 15333Std 34509 18519Success rate () 40 9667Times (s) 33374 259430
Schwefelrsquos function 11989116
Best 1184387 2368770Worst 7106303 13620521Mean 35761725 6764166Std 14441244 3242317Success rate () 90 40Times (s) 25028 190009
Schwefelrsquos Problem 221 11989117
Best 01640 03254Worst 45102 47086Mean 11077 19849Std 08652 116418Success rate () 5333 2333Times (s) 23806 192505
Schwefelrsquos Problem 222 11989118
Best 12706 times 10minus35 85946 times 10minus45
Worst 16842 times 10minus34 18362 times 10minus42
Mean 61883 times 10minus35 26992 times 10minus43
Std 34937 times 10minus35 46257 times 10minus43
Success rate () 100 100Times (s) 26297 208573
rate of CDEAS is higher in comparison with original DEMoreover CDEAS gets very close to the global optimum insome other functions 119891
1 1198913 1198914 1198916 and 119891
18 It also presents
that the mean minimum best minimum worst minimumthe standard deviation of minimum and the success rate ofCDEAS algorithm are clearly better than the original DE forfunctions 119891
1 1198913 1198914 1198915 1198916 11989112 11989113 11989114 11989115 and 119891
18although
the computing time of CDEAS is longer than that of originalDE because of its complexity
The convergence figures of CDEAS comparing withoriginal DE for 18 instances are listed as Figure 6
From Figure 6 one can observe that the convergencespeed of CDEAS is faster than original DE for 119891
1 1198912 1198913 1198914
1198916 1198917 11989111 11989112 11989113 11989114 11989115 and 119891
18
All these comparisons of CDEAS with original DE algo-rithm have shown that CDEAS is a competitive algorithmto solve all the unimodal function problems and most ofthe multimodal function optimization problems listed aboveAs shown in the descriptions and all the illustrations beforeCDEAS is efficacious on those typical function optimizations
4 Model of Net Value of Ammonia UsingCDEAS-LS-SVM
41 Auxiliary Variables Selection of theModel There are someprocess variables which have the greatest influence on the netvalue of ammina such as system pressure recycle gas flowrate feed composition (HN ratio) ammonia and inert gascencetration in the gas of reactor inlet hot spot temperaturesand so forth The relations between the process variablesare coupling and the operational variables interact with eachother
The inlet ammonia concentration is an important processvariable which is beneficial to operation-optimization but thedevice of online catharometer is very expensive Accordingto the mechanism and soft sensor model a IIO-BP modelwas built to get the more accurate value of the inlet ammoniaconcentration [20]
Δ (NH3) = 119860NH3OUT minus 119860NH3IN (18)
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
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Decision SciencesAdvances in
Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 11
0 400 800 1200 1600 2000Evolution generation
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f1
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f2
0 400 800 1200 1600 2000Evolution generation
0
minus80
minus70
minus60
minus50
minus40
minus30
minus20
minus10
10
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f3
0 400 800 1200 1600 2000Evolution generation
10
log(fi
tnes
s val
ue)
minus30
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f4
123456789
1011
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f5
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus40
minus45
minus25
minus20
minus15
minus10
minus5
0
5
Convergence figure of originalDE and CDEAS for f6
10
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
log(fi
tnes
s val
ue)
minus30
minus35
minus25
minus20
minus15
minus10
minus5
0
5
Convergence fgure of originalDE and CDEAS for f7
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
2
log(fi
tnes
s val
ue)
Convergence fgure of originalDE and CDEAS for f8
Figure 6 Continued
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
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Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
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Operations ResearchAdvances in
Journal of
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Algebra
Discrete Dynamics in Nature and Society
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
12 Mathematical Problems in Engineering
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
081
12141618
222242628
0 400 800 1200 1600 2000Evolution generation
0
05
1
15
2
25
3
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
22242628
332343638
442
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
02
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2 024
minus16
minus14
minus12
minus10
minus8
minus6
minus4
minus2
0
1
2
3
minus3
minus2
minus1
Convergence figure of originalDE and CDEAS for f9
Convergence figure of originalDE and CDEAS for f10
Convergence figure of originalDE and CDEAS for f11
Convergence figure of originalDE and CDEAS for f12
Convergence figure of originalDE and CDEAS for f13
Convergence figure of originalDE and CDEAS for f14
Convergence figure of originalDE and CDEAS for f15
Convergence figure of originalDE and CDEAS for f16
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
log(ft
tnes
s val
ue)
Figure 6 Continued
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 13
0
05
1
15
2
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
Original DECDEAS
0 400 800 1200 1600 2000Evolution generation
minus50 minus05
minus40
minus30
minus20
minus10
0
10
log(fi
tnes
s val
ue)
log(fi
tnes
s val
ue)
Convergence figure of originalDE and CDEAS for f17
Convergence figure of originalDE and CDEAS for f18
Figure 6 Convergence figure of CDEAS comparing with original DE for 1198911sim11989118
Table 2 Auxiliary variables of model of net value of Ammonia
List Symbols Name Unit1 119877HN HN ratio 2 119860CH4 Methane concentration in recycled synthesis gas at the reactor inlet Mole ratio3 ANH3 Ammonia concentration in recycled synthesis gas at the reactor inlet Mole ratio4 119875
119878System pressure Mpa
5 119865119876
Recycle gas flow rate Nm3h6 119865
1198761Quench gas flows of axial layer Nm3h
7 1198651199022
Cold quench gas flows of 1st radial layers Nm3h8 119865
1199023Quench gas flows of 2nd radial layers Nm3h
9 1198651199024
Hot quench gas flows of 1st radial layers Nm3h10 119879
119860Hot-spot temperatures of axial bed ∘C
11 1198791198771 Hot-spot temperatures of radial bed I ∘C
12 1198791198772 Hot-spot temperatures of radial bed II ∘C
13 119879EO Outlet gas temperature of evaporator ∘C
From the analysis discussed above some important vari-ables have significant effects on the net value of ammoniaBy discussion with experienced engineers and taking intoconsideration a priori knowledge about the process thesystem pressure recycle gas flow rate the HN ratio hot-spottemperatures in the catalyst bed and ammonia and methaneconcentration in the recycle gas are identified as the keyauxiliary variables to model net value of ammonia which islisted in Table 2
42 Modeling the Net Value of Ammonia Using CDEAS-LS-SVM LS-SVM is an alternate formulation of SVMwhich is proposed by Suykens The e-insensitive loss func-tion is replaced by a squared loss function which con-structs the Lagrange function by solving the problem linearKarush Kuhn Tucker (KKT)
[0 119868
119879
119899
119868119899119870 + 120574
minus1119868] [1198870
119887] = [
0
119910] (19)
where 119868119899is a [119899 times 1] vector of ones 119879 is the transpose of a
matrix or vector 120574 is a weight vector 1198870means the model
offset and 119887 is regression vector119870 is Mercer kernel matrix which is defined as
119870 = (
11989611
sdot sdot sdot 1198961119899
d
1198961198991
sdot sdot sdot 119896119899119899
) (20)
where 119896119894119895is defined by kernel function
There are several kinds of kernel functions such ashyperbolic tangent polynomial and Gaussian radial basisfunction (RBF) which are commonly used Literatures haveproved that RBF kernel function has strong generalizationso in this study RBF kernel was used
119896119894119895= 119890minus|119909119894minus119909119895|
221205902
(21)
where 119909119894and 119909
119895indicated different training samples 120590 is the
kernel width parameter
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
14 Mathematical Problems in Engineering
Table 3 The comparisons of training error and testing error of LS-SVM
Method Type of error RElowast MAElowast MSElowast
BP-NN Training error 94422 times 10minus04 10544 times 10minus04 13970 times 10minus04
Testing error 0008085 89666 times 10minus04
0001188
LS-SVM Training error 0002231 24785 times 10minus04
41672 times 10minus04
Testing error 0005328 59038 times 10minus04
78169 times 10minus04
DE-LS-SVM Training error 0002739 304286 times 10minus04
408512 times 10minus04
Testing error 0005252 58241 times 10minus04
77032 times 10minus04
CDEAS-LS-SVM Training error 0002830 31415 times 10minus04
33131 times 10minus04
Testing error 0004661 51752 times 10minus04 68952 times 10minus04lowastRE relative error MAE mean absolute error MSE mean square error
As we can see from (19)sim(21) only two parameters(120574 120590) are needed for LS-SVM It makes LS-SVM problemcomputationally easier than SVR problem
Grid search is a commonly used method to select theparameters of LS-SVM but it is time-consuming and inef-ficient CDEAS algorithm has strong search capabilities andthe algorithm is simple and easy to implementTherefore thispaper proposes the CDEAS algorithm to calculate the bestparameters (120574 120590) of LS-SVM
5 Results and Discussion
Operational parameters such as 119860H2 119860CH4 and 119875119878 werecollected and acquired from plant DCS from the year 2011-2012 In addition data on the inlet ammonia concentrationof recycle gas 119860NH3 were simulated by mechanism and softsensor model [20]
The extreme values are eliminated from the data using the3120590 criterion After the smoothing and normalization eachdata group is divided into 2 parts 223 groups of trainingsamples which are used to train model while 90 groups oftesting samples which are valuing the generalization of themodel for identifying the parameters of the LS-SVM thekernel width parameter and the weight vector
BP-NN LS-SVM and DE-LS-SVM are also used tomodel the net value of ammonia respectively BP-NN isa 13-15-1 three-layer network with back-propagation algo-rithm LS-SVM gains the (120574 120590) with grid-search and cross-validation The parameter settings of CDEAS-LS-SVM arethe same as those in the benchmark tests Each model is run30 times and the best value is shown in Table 3 Descriptivestatistics of training results and testing results of modelinclude the relative error absolute error and mean squareerror The performance of the four models is compared asshown in Table 3 The training and testing results of fourmodels are illustrated in Figure 7
Despite the fact that the training error using BP-NN issmaller than that using CDEAS-LS-SVM which is becauseBP-NN is overfitting to the training data the mean squareerror (MSE) on training data using CDEAS-LS-SVM isreduced by 256 and 232 compared with LS-SVM andDE-LS-SVM respectively In comparison with the othermodels (BP-NN LS-SVM and DE-LS-SVM) testing errorusing CDEAS-LS-SVMmodel is reduced by 141 and 112
respectively The results indicate that the proposed CDEAS-LS-SVM model has a good tracking precision performanceand guides production better
6 Conclusion
In this paper an optimizing model which describes therelationship between net value of ammonia and key opera-tional parameters in ammonia synthesis has been proposedSome representative benchmark functions were employed toevaluate the performance of a novel algorithm CDEAS Theobtained results show that CDEAS algorithm is efficaciousfor solving most of the optimization problems comparisonswith original DE Least squares support vector machineis used to build the model while CDEAS algorithm isemployed to identify the parameters of LS-SVM The sim-ulation results indicated that CDEAS-LS-SVM is superiorto other models (BP-NN LS-SVM and DE-LS-SVM) andmeets the requirements of ammonia synthesis process TheCDEAS-LS-SVM optimizing model makes it a promisingcandidate for obtaining the optimal operational parametersof ammonia synthesis process and meets the maximumbenefit of ammonia synthesis production
Appendix
(1) Sphere function
1198911(119909) =
119863
sum
119894=1
1199092
119894
119900 = [0 0 0] the global optimum
(A1)
(2) Shifted sphere function
1198912(119909) =
119863
sum
119894=1
1199112
119894
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A2)
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 15
0 50 100 150 200 2500106010701080109
011011101120113011401150116
Sample number
Net
val
ue o
f am
mon
ia (
)
0 10 20 30 40 50 60 70 80 9001050106010701080109
01101110112011301140115
Sample number
Net
val
ue o
f am
mon
ia (
)
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
0 50 100 150 200 250Sample number
Net
val
ue o
f am
mon
ia (
)
0106010701080109
011011101120113011401150116
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Net
val
ue o
f am
mon
ia (
)
Sample number0 50 100 150 200 250
0106010701080109
011011101120113011401150116
Actual valuesTraining results of CDEAS-LS-SVM
Net
val
ue o
f am
mon
ia (
)
Sample number0 10 20 30 40 50 60 70 80 90
0107
0108
0109
011
0111
0112
0113
0114
0115
Actual valuesTraining results of CDEAS-LS-SVM
Figure 7 The analyzed results training results and testing results of BP-NN LS-SVM DE-LS-SVM and CDEAS-LS-SVM
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
16 Mathematical Problems in Engineering
Table 4 Global optimum search ranges and initialization ranges of the test functions
119891 Dimension Global optimum 997888119909 119891(997888119909) Search range Target
1198911
30
0 0 [minus100 100]119863
10minus5
1198912
120579 0 [minus100 100]119863
10minus5
1198913
0 0 [minus100 100]119863
10minus5
1198914
120579 0 [minus100 100]119863
10minus5
1198915
1 1 [minus100 100]119863 100
1198916
0 0 [minus32 32]119863
10minus5
1198917
120579 0 [minus32 32]119863
10minus5
1198918
0 0 [minus32 32]119863
10minus5
1198919
120579 0 [minus32 32]119863 01
11989110
0 0 [0 600]119863 0001
11989111
120579 0 [minus600 600]119863 001
11989112
0 0 [minus5 5]119863 10
11989113
120579 0 [minus5 5]119863 10
11989114
0 0 [minus5 5]119863 10
11989115
120579 0 [minus5 5]119863 5
11989116
4189829 0 [minus500 500]119863 500
11989117
0 0 [minus100 100]119863 1
11989118
0 0 [minus10 10]119863
10minus5
120579 is the shifted vector
(3) Schwefelrsquos Problem 12
1198913(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
119900 = [0 0 0] the global optimum
(A3)
(4) Shifted Schwefelrsquos Problem 12
1198914(119909) =
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A4)
(5) Rosenbrockrsquos function
1198915(119909) =
119863minus1
sum
119894=1
(100(1199092
119894minus 1199092
119894+1)2
+ (119909119894minus 1)2
)
119900 = [1 1 1] the global optimum
(A5)
(6) Schwefelrsquos Problem 12 with noise in fitness
1198916(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119909119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119900 = [0 0 0] the global optimum
(A6)
(7) Shifted Schwefelrsquos Problem 12 with noise in fitness
1198917(119909) = (
119863
sum
119894=1
(
119894
sum
119895=1
119911119894)
2
) lowast (1 + 04 |119873 (0 1)|)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A7)
(8) Ackleyrsquos function
1198918(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199092
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119909119894)) + 20 + 119890
119900 = [0 0 0] the global optimum
(A8)
(9) Shifted Ackleyrsquos function
1198919(119909) = minus 20 exp(minus02radic 1
119863
119863
sum
119894=1
1199112
119894)
minus exp( 1119863
119863
sum
119894=1
cos (2120587119911119894)) + 20 + 119890
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A9)
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 17
(10) Griewankrsquos function
11989110(119909) =
119863
sum
119894=1
1199092
119894
4000minus
119863
prod
119894=1
cos119909119894
radic119894
+ 1
119900 = [0 0 0] the global optimum
(A10)
(11) Shifted Griewankrsquos function
11989111(119909) =
119863
sum
119894=1
1199112
119894
4000minus
119863
prod
119894=1
cos119911119894
radic119894
+ 1
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A11)
(12) Rastriginrsquos function
11989112(119909) =
119863
sum
119894=1
(1199092
119894minus 10 cos (2120587119909
119894) + 10)
119900 = [0 0 0] the global optimum
(A12)
(13) Shifted Rastriginrsquos function
11989113(119909) =
119863
sum
119894=1
(1199112
119894minus 10 cos (2120587119911
119894) + 10)
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A13)
(14) Noncontiguous Rastriginrsquos function
11989114(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119909119894
10038161003816100381610038161199091198941003816100381610038161003816 lt1
2round (2119909
119894)
2
10038161003816100381610038161199091198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119900 = [0 0 0] the global optimum(A14)
(15) Shifted noncontiguous Rastriginrsquos function
11989115(119909) =
119863
sum
119894=1
(1199102
119894minus 10 cos (2120587119910
119894) + 10)
119910119894=
119911119894
10038161003816100381610038161199111198941003816100381610038161003816 lt1
2round (2119911
119894)
2
10038161003816100381610038161199111198941003816100381610038161003816 ge1
2
for 119894 = 1 2 119863
119911 = 119909 minus 120579
120579 = [1205791 1205792 120579
119863] the shifted global optimum
(A15)
(16) Schwefelrsquos function
11989116(119909) = 4189829 times 119863 minus
119863
sum
119894=1
119909119894sin (1003816100381610038161003816119909119894
1003816100381610038161003816
12
)
119900=[4189829 4189829 4189829] the global optimum(A16)
(17) Schwefelrsquos Problem 221
11989118(119909) = max 1003816100381610038161003816119909119894
1003816100381610038161003816 1 le 119894 le 119863
119900 = [0 0 0] the global optimum(A17)
(18) Schwefelrsquos Problem 222
11989117(119909) =
119863
sum
119894=1
10038161003816100381610038161199091198941003816100381610038161003816 +
119863
prod
119894=1
119909119894
119900 = [0 0 0] the global optimum
(A18)
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors are grateful to the anonymous reviewers forgiving us helpful suggestions This work is supported byNational Natural Science Foundation of China (Grant nos61174040 and 61104178) and Fundamental Research Funds forthe Central Universities Shanghai Commission of Scienceand Technology (Grant no 12JC1403400)
References
[1] M T Sadeghi and A Kavianiboroujeni ldquoThe optimizationof an ammonia synthesis reactor using genetic algorithmrdquoInternational Journal of Chemical Reactor Engineering vol 6 no1 article A113 2009
[2] S S E H Elnashaie A T Mahfouz and S S ElshishinildquoDigital simulation of an industrial ammonia reactorrdquoChemicalEngineering and Processing vol 23 no 3 pp 165ndash177 1988
[3] M N Pedemera D O Borio and N S Schbib ldquoSteady-Stateanalysis and optimization of a radial-flow ammonia synthesisreactorrdquo Computers amp Chemical Engineering vol 23 no 1 ppS783ndashS786 1999
[4] B V Babu and R Angira ldquoOptimal design of an auto-thermalammonia synthesis reactorrdquo Computers amp Chemical Engineer-ing vol 29 no 5 pp 1041ndash1045 2005
[5] W F Sacco and N Hendersonb ldquoDifferential evolution withtopographical mutation applied to nuclear reactor core designrdquoProgress in Nuclear Energy vol 70 pp 140ndash148 2014
[6] M Rout B Majhi R Majhi and G Panda ldquoForecasting ofcurrency exchange rates using an adaptive ARMA model withdifferential evolution based trainingrdquo Journal of King SaudUniversity vol 26 no 1 pp 7ndash18 2014
[7] H Ozcan K Ozdemir and H Ciloglu ldquoOptimum cost of anair cooling system by using differential evolution and particleswarm algorithmsrdquo Energy and Buildings vol 65 pp 93ndash1002013
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
18 Mathematical Problems in Engineering
[8] R Zhang S Song and C Wu ldquoA hybrid differential evolutionalgorithm for job shop scheduling problems with expected totaltardiness criterionrdquo Applied Soft Computing Journal vol 13 no3 pp 1448ndash1458 2013
[9] R Arya and S C Choube ldquoDifferential evolution based tech-nique for reliability design of meshed electrical distributionsystemsrdquo International Journal of Electrical Power amp EnergySystems vol 48 pp 10ndash20 2013
[10] W Xu L Zhang and X Gu ldquoSoft sensor for ammoniaconcentration at the ammonia converter outlet based on animproved particle swarm optimization and BP neural networkrdquoChemical Engineering Research and Design vol 89 no 10 pp2102ndash2109 2011
[11] M R Sawant K V Patwardhan A W Patwardhan V GGaikar and M Bhaskaran ldquoOptimization of primary enrich-ment section of mono-thermal ammonia-hydrogen chemicalexchange processrdquo Chemical Engineering Journal vol 142 no3 pp 285ndash300 2008
[12] Z Kirova-Yordanova ldquoExergy analysis of industrial ammoniasynthesisrdquo Energy vol 29 no 12ndash15 pp 2373ndash2384 2004
[13] B Mansson and B Andresen ldquoOptimal temperature profilefor an ammonia reactorrdquo Industrial amp Engineering ChemistryProcess Design and Development vol 25 no 1 pp 59ndash65 1986
[14] R Mallipeddi P N Suganthan Q K Pan andM F TasgetirenldquoDifferential evolution algorithm with ensemble of parametersand mutation strategiesrdquo Applied Soft Computing Journal vol11 no 2 pp 1679ndash1696 2011
[15] R Storn and K Price ldquoDifferential evolutionmdasha simple andefficient heuristic for global optimization over continuousspacesrdquo Journal of Global Optimization vol 11 no 4 pp 341ndash359 1997
[16] C Hu and X Yan ldquoA hybrid differential evolution algorithmintegrated with an ant system and its applicationrdquo Computers ampMathematics with Applications vol 62 no 1 pp 32ndash43 2011
[17] K Vaisakh and L R Srinivas ldquoGenetic evolving ant directionHDE for OPF with non-smooth cost functions and statisticalanalysisrdquo Expert Systems with Applications vol 38 no 3 pp2046ndash2062 2011
[18] R Wang J Zhang Y Zhang and X Wang ldquoAssessment ofhuman operator functional state using a novel differentialevolution optimization based adaptive fuzzymodelrdquoBiomedicalSignal Processing and Control vol 7 no 5 pp 490ndash498 2012
[19] W Xu L Zhang and X Gu ldquoA novel cultural algorithm and itsapplication to the constrained optimization in ammonia syn-thesisrdquo Communications in Computer and Information Sciencevol 98 no 2 pp 52ndash58 2010
[20] Z Liu and X Gu ldquoSoft-sensor modelling of inlet ammoniacontent of synthetic tower based on integrated intelligentoptimizationrdquo Huagong Xuebao vol 61 no 8 pp 2051ndash20552010
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of