development of advanced automation of expert systems for monitoring and control … ·...

DEVELOPMENT OF ADVANCEDAUTOMATION OF EXPERT

SYSTEMS FOR MONITORING ANDCONTROL THE AMOUNT OF

SUGAR CONTENT IN BAGASSEFOR THE APPLICATION OFBOILER IN SUGAR INDUSTRY

S.Balasubramanayam1, Ch. Jeevan Kumar2,P Sivaseshu3, A Harsha Vardhan Reddy4,

Gurram Narendra Santosh Kumar5, KSK Mallik6,Sk.Hasane Ahammad7

1,2,4Assistant Professor,Gurunanak Institutions Technical Campus,

Ibrahimpatnam,Hyderabad,3Assistant Professor,

Kandula Sreenivasa Reddy Memorial College,5,6,7Research Scholar,

Koneru Lakshmaiah Education Foundation,Vaddeswaram, Guntur, Andhra Pradesh, India

[email protected],[email protected],[email protected], [email protected],

[email protected], [email protected]

July 23, 2018

1

International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 9681-9701ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

9681

Abstract

Sensors measure the physical amount and changes overinto flag which can be seen by onlooker or by an instrument.The right sugar dampness level has turned into a basic seg-ment amid handling. Sugar can undoubtedly turn out to beeither excessively wet or dry, along these lines squanderingthe producers time and cash. Dampness Sensors measurethe water content. A dampness test is comprised of variousdampness sensors. The dampness sensor works when the ar-rangement of plates in the sensor grows they get wet. Withthe assistance of industry accomplices and 40 long stretchesof information, MoistTech has built up the IR-3000 sugardampness sensor that is in a perfect world suited to quan-tify both the sugar dampness and polymerization (POL)levels in the distinctive phases of the sugar producing pro-cess.And for the most part how the baggase is utilized foradvancement of more simply sugar process wet bagasse isused as a fuel in sugar industry around the globe. Theclamminess content on the bagasse is effort on the calorificregard (CV) of bagasse and evaporator efficiencies. Bagasseis the stringy issue that outstanding parts after sugarcanestalks are beat to isolate their juice. It is dry thick store leftafter the extraction of juice from sugar cane.] Bagasse isused as a bio fuel and in the make of crush and buildingmaterials. Bagasse can likewise be exceptionally helpful toproduce power. Dry bagasse is singed to deliver steam.

Key Words:Moisture Sensor, Sugar Industry, Sugar,Baggase

1 INTRODUCTION

India’s 527 working sugar factories pound around 240 million tonesof stick for every year and create 80 million tones of wet bagasse(half dampness), of which they devour around 70 million for meet-ing hostage prerequisites of intensity and steam. In this manner,power creation through cogeneration in sugar processes in India isan imperative road for providing minimal effort, non-regular power.By and by, India has around 206 cogeneration units with an aggre-gate introduced exportable limit of 3,123 MW (top season). Inaddition, India has a capability of creating 500 MW of intensity

2

International Journal of Pure and Applied Mathematics Special Issue

9682

through bagasse and with modernization of the new and existingsugar plants.

Figure 1 A general process of Bagasses by sugarcane

The steam is utilized to pivot turbines to deliver control. Foreach 10 tons of sugarcane pounded, a sugar processing plant createsalmost three tons of wet bagasse. Since bagasse is a result of theunadulterated sweetener industry, the amount of creation in everynation is in accordance with the amount of sugarcane delivered.The existence cycle of bagasse will be appeared in beneath figure.

3


9683

Figure 2 Life Cycle of Bagasse

The high dampness substance of the bagasse, regularly 40-50percent, is unfavorable to its utilization as a fuel. All in all, bagasseis put away before additionally handling. For the creation of power,it is put away under damp conditions, and the gentle exothermicprocess coming about because of the corruption of the lingeringsugars, dries the bagasse stack somewhat. For the creation of pa-per and mash, it is generally put away wet to help in the expulsionof short strands from the marrow, which prevents the papermak-ing procedure, and additionally to evacuate any outstanding sugar.Bagasse is frequently utilized as an essential fuel hotspot for sugarfactories. When it consumes in amount, it creates enough warm vi-tality to fulfill every one of the necessities of a run of the mill sugarprocess, with vitality to save. To this end, an auxiliary use for thiswaste item is in cogeneration, the utilization of a fuel source to giveboth warm vitality, utilized as a part of the plant, and power, whichis commonly sold to the shopper’s system.The lower calorific esteem (LCV) of bagasse in kJ/kg might be as-sessed utilizing the recipe:LCV = 18260 - 207.01 Moisture - 31.14 Brix - 182.60 Ash,where the dampness, Brix and fiery remains substance of the bagasseare communicated as a rate by mass. Likewise, the higher calorificesteem (HCV) of bagasse might be assessed utilizing: HCV = 19605- 196.05 Moisture - 31.14 Brix - 196.05 Ash.

4


9684

2 TECHNICAL DETAILS

Figure 3 Flow chart of the Methodology

Modelling of sugar content in bagasse processA scientific model is a depiction of a framework utilizing numer-

ical ideas and dialect. Scientific models are utilized as a part of thenormal sciences and designing controls, and also in the sociologies. A model may disclose a framework and to contemplate the im-pacts of various parts, and to make expectations about behavior[2].The official strategy for the Australian business for figuring bagassemass (mb) as exhibited by Bureau of Sugar Experiment Stations

5


9685

(1984) is:

mb =0.95Fc

100 −Mb −Bb

mc (1)

Where mc is stick mass, Fc is stick fiber content, Mb is bagassedampness substance and Bb is bagasse brix content. Albeit all mparameters are characterized as mass in this paper, they could sim-ilarly well be characterized as mass stream rate. Truth be told, con-dition 1 was characterized in Bureau of Sugar Experiment Stations(1984) as mass stream rate.The denominator in condition 1 speaksto the fiber substance of bagasse (Fb), as characterized by Method 5of Bureau of Sugar Experiment Stations (2001).The 0.95 factor rep-resents the loss of some fiber, most strikingly into mud. The benefitof this technique for computing bagasse creation is that it is basicand figured from routine plant estimations. The brix substance ofbagasse isn’t routinely estimated however can be promptly ascer-tained from the pol substance of bagasse and either a deliberate oraccepted an incentive for immaculateness of bagasse, as depictedby Method 5 of Bureau of Sugar Experiment Stations (2001).Bu-reau of Sugar Experiment Stations(1984) additionally exhibited anelective condition:

mb =Fc − Fj

mj

mc

100 −M − b−B − bmc (2)

where mj is the mass of blended juice and Fj is the suspendedsolids substance of blended juice. The upside of this strategy is thatit disposes of the exact 0.95 factor. The hindrance of this strategyis that the extra parameters are not all around characterized. Sus-pended solids in blended juice isn’t routinely estimated. Whileblended juice stream rate is regularly estimated by a stream meter,it is generally estimated after the blended juice tank where reusestreams, for example, filtrate are included. Wright (2003) exhibitedtwo minor departure from conditions 1 and 2 (characterized in hispaper as conditions 3 and 4) which somewhat beat the weaknessesrecorded previously. Muller et al. (1982) in like manner exhibiteda minor departure from condition 2 (characterized in their paperas condition 5). Wright (2003) exhibited an elective condition thatcontained adjustments for mud solids in juice and soil in cane[3].

6


9686

mb =1

Fb

[Fc −Fmff

Fmfm

Fjmbmj

mc

− (Fmff

Fmfm

+ 1)Dc(1 − mbd

mcd

)]mc (3)

Where Fmff is the fiber content in mud, Fmfm is the mud solidscontent in mud, Fjmb is the base mud solids content in blendedjuice, Dc is the earth content in stick and mbd/mcd is the extentof soil in stick that remaining parts with bagasse. Soil content instick is an extra estimation that isn’t routinely estimated.Calculating the Bagasse of sugar content: The mass of insolublesolids in stick (mcf) can be resolved from:

mcf =Fc

100mc (4)

Insoluble solids content (Fc) and mass (mc) of stick are bothroutine estimations made at Australian crude sugar plants for stickinstallment purposes.Condong and Broad water factories measurethe insoluble slag segment of stick (Fca) specifically thus the massof insoluble fiery remains in stick (mcfa) can be figured from:

mfca =Fca

100mc (5)

Calculating the bagasse terms: Condong and Broad water fac-tories don’t straightforwardly quantify the insoluble powder sub-stance of bagasse yet do gauge the aggregate fiery remains sub-stance of bagasse. To ascertain the mass of insoluble slag in bagasse(mbfa), the mass of solvent fiery remains in bagasse (mbba) oughtto be subtracted from the aggregate mass of powder (mba):

mbfa = mba −mbba (6)

The mass of aggregate fiery remains in bagasse can be figuredfrom:

mba =Ab

100mb (7)

Where Ab is the aggregate cinder content in bagasse and mb isthe mass of bagasse. The solvent fiery remains substance of bagasse

7


9687

isn’t routinely estimated. It is, nonetheless, a small amount of theaggregate pollutions in bagasse

mbba =Abi

100(mbb −mbp) (8)

Where Abi is the slag content in pollutions in bagasse, mbb isthe mass of brix in bagasse and mbp is the mass of pol in bagasse.Solvent contaminations in juice ordinarily comprise of moderatelymeasure up to extents of lessening sugars, cinder and other naturalissue. Thus, it is viewed as sensible to accept that the dissolvablefiery remains part of bagasse is 33% of the solvent polluting influ-ences segment of bagasse (Abi = 33%).The mass of pol and brix inbagasse can be computed from:

mbp =Pb

100mb (9)

mbb =Bb

100mb (10)

where Pb is the pol content in bagasse and Bb is the brix con-tent in bagasse. The mass of bagasse can be ascertained from:

mb =100

Fb

mbf (11)

Where Fb is the insoluble solids content in bagasse and mbfis the mass of insoluble solids in bagasse. Conditions 2to 11 canbe utilized to figure all terms in conditions 4 and 5 with the ex-ception of mbf and mxfa. Therefore, conditions 4 and 5 are twoconcurrent conditions with two questions thus can be understoodto figure these two amounts. The arrangement is:

mb =mcf −mcfa −mmf +mfa

1 − Ab−Abi100

(Bb−Pb)

Fb

(12)

In this condition, mcf, mcfa, mmfe, and mmfa are characterizedin conditions 6, 7, 8 and12 respectively. Now that mbf has beenresolved, mb can be ascertained from condition 12.Improvement of Mathematical model of Bagasse: The model for

8


9688

the age-solidifying behvaiour of these composites was acquired byspeaking to the hardness esteems by W, the reaction capacity canbe communicated by condition underneath:W= (AT, BP, At)Where AT= Ageing temperatureBP= weight % bagasse ash particleAt =Ageing timeThe model selected includes the effects of main variables first-orderand second-order in of all variableractionses. Hence the generalmodel is written as:W=β0 + β1AT + β2BP + β3At+ β4ATBP + β5ATAt+ β6BPAt+β7ATBPAt Where β0 is average response of W and β1, β2, β3,β4, β5, β6, β7 are coefficients associated with each variable AT,BP, At and interaction are calculated using the linear regressionmethod. Plan of Non-Conventional (FLC):The fuzzy rationale con-troller (FLC) displayed by Siler and Ying (1989) is examined hereand is turned out to be identical to a non-fuzzy, nonlinear, corre-sponding indispensable (PI) controller. Some trademark propertiesof this fuzzy rationale controller are then explored. The achievableexecution of such a particular fuzzy controller is analyzed and ob-served to be not really superior to that of the traditional, straight,non-fuzzy PI controller[4]. Different expanded outlines of the fun-damental FLC, incorporating the FLC with double control lawsand the three-piece FLC, are then exhibited to improve control ex-ecution. These expansions can give servo-control execution. Theseaugmentations can give servo-control execution better than thatof the essential FLC configuration, as showed by recreation comesabout. At long last a profoundly nonlinear balance process is pro-gressed to show the pertinence of the different FLCs to modernprocess control. Fuzzy controllers are nonlinear and it is more hardto set the controller picks up when contrasted with traditional con-trollers. It proposes an outline strategy for identical fuzzy PIDcontroller from the ordinary PID controller area over to single cir-cle speed control of DC engine. The thought is to begin with atuned ordinary PID controller and supplant it with an identicalstraight fuzzy controller. This is pertinent at whatever point a PIDcontroller is conceivable or effectively actualized. From the reen-acted consequences of Active VHDL, it is demonstrated that thefuzzy controller enhances the execution over ordinary controllers.Outline of Conventional (PID): Conventional PID controllers have

9


9689

been and still broadly utilized as a part of the compound business.PID controllers are basic, straightforward and execute in equipmentand programming, and don’t require a procedure display for instate-ment or activity. Be that as it may, visit calibrating of the PI con-troller for good execution or potentially soundness is fundamentalparticularly for exceedingly nonlinear as well as coupled procedures.In genuine application, tuning of a PID is typically a bulky ordeal.Numerous endeavors managing tuning of such controllers for bet-ter execution have been accounted for. These strategies can becharacterized under three classifications; response bend technique,persistent wavering strategy and recurrence space method[5]. Re-gardless of the variety between these techniques, they are altogetheractualized disconnected and connected for SISO control circles. Ingenuine practice, the vast majority of the concoction forms are mul-tivariable with solid cross-circle cooperation. For this situation, ifeach SISO circle in the process is tuned separately, the general pro-cedure execution may fall apart because of communication got byshutting all circles at the same time. In addition, the procedure maywork at various working conditions either intentionally because ofreview changeover or definitely because of extreme load changes.For this situation, a SISO circle tuned for a particular working con-dition may debase for another condition. Hence, controllers tunedutilizing the previously mentioned techniques may not really givedependability or great execution over extensive variety of workingconditions.Utilizations of Fuzzy Logic in Sugar Industries in Real Time: Thesugar business is India’s second biggest agro handling industry.Presently multi day the Sugar Industry anticipated that would actnaturally adequate in regard to the vitality. In past the sugar wasthe primary item and bagasse was considered as manufacturingplant waste and its transfer was the issue. With the cost of fuelexpanding step by step at that point need of enhancing heater pro-ductivity turns into an unobtrusive undertaking. By and by thebagasse is for the most part used to create power as a vitalitycogeneration. The present paper features the plan and advance-ment of Embedded Fuzzy Module for vitality productivity changeof bagasse heater for a sugar industrial facility proposed for cogen-eration framework. The multipurpose heater utilized in the SugarFactory creates items like Heat, Steam, and Gasses and so forth.

10


9690

The created steam by kettle generally going waste is utilized forpower age purposes. We propose an Embedded Fuzzy Module thatassists the usage of steam for Electric power age with the enhancedeffectiveness. Measure of air blown in, supply of biofuel, amountof water and steam outlet frame the control parameters of a kettle.These parameters are reliant of each other. B. Hemalathaet al pro-posed a dynamic conduct of the drum and water level of the kettlecontrolled by PID controller[1]. They have created scientific modeland concentrated different reactions of the framework N. Magasineret al presented a control parameter of the kettle and their impactutilizing lab see. Yonghong Huanget al proposed another progressedhereditary versatile control system for drum level direction. Theypresented the self-tuned parameters as a novel hereditary versatilecomponent utilizing MATLAB [7].Correlation of Performance and controller: A PID framework demon-strates poor execution when the procedure is non-direct. As a re-sult of the nonlinearity controllers in view of mathematic modelsare exceptionally hard to outline with customary control hypothe-sis. Anyway fuzzy control is a savvy control technique, which hasthe upside of brisk reaction and exact control impact. Moreover,it is anything but difficult to configuration control principles andneed not the mathematic model of controlled protest. Entirely,all mechanical procedures are nonlinear. Subordinate term of thePID causes substantial change in process yield regardless of whetherthere is a little clamor accessible at the information. Controllers useanother ways to deal with the controller plan in which learning of ascientific model of a procedure by and large isn’t required. Cases ofcapricious controller are a fuzzy controller and neuro or neuro-fuzzycontrollers. The idea of Fuzzy Logic (FL) was brought about byLotfi Zadeh, a teacher at the University of California at Berkley,and introduced as a method for handling information by permittinghalfway set participation instead of fresh set enrollment or non-enrollment. Educator Zadeh contemplated that individuals don’trequire exact, numerical data input, but then they are prepared todo profoundly versatile control. On the off chance that criticismcontrollers could be modified to acknowledge loud, uncertain infor-mation, they would be substantially more powerful and maybe lessdemanding to actualize. A fuzzy rationale framework (FLS) can allthe while handle numerical information and etymological learning.

11


9691

It is a nonlinear mapping of an info information vector into a scalaryield,Fuzzy Logic gives a straightforward method to touch base atan unmistakable conclusion in view of unclear, uncertain, loose,boisterous, or missing info data. Fuzzy rationale (FL) manages thevulnerability by connecting level of assurance in our response tointelligent questions[9]. Zadeh proposed how one can teach fuzzylearning into the frameworks and choices influenced in view of thatfuzzy information to can be on a par with people. A fuzzy set isportrayed by an enrollment work which appoints to each questiona review of participation extending in the vicinity of zero and one.The enrollment work is a bend that characterizes the participationestimation of every component of the universe of talk in the range 0to 1. The universe of talk is at times likewise alluded as informationspace or all inclusive set, which contains all the conceivable compo-nents of worry in every specific case. On the off chance that Y, is theuniverse of talk and its components are meant by y, at that pointa fuzzy set F in Y is characterized as an arrangement of requestedsets. F = x, F(x) — x I X Here F(x) = Membership Function (orMF) of x in F. Figure (iii) demonstrates a Gaussian ringer shapeenrollment work. A participation work bend can have a triangu-lar shape, trapezoidal shape, R-work shape, L-work shape, etc[12].FL is a model free approach as it fuses a straightforward approachfor tackling a control issue instead of endeavoring to demonstratea framework scientifically. The FL show depends on an adminis-trator’s experience instead of the specialized comprehension of theframework. Fuzzy Logic (FL) is a critical thinking control frame-work technique which can be executed in equipment, programming,or a blend of both. FL gives a basic method to touch base at a dis-tinct conclusion in view of unclear, vague, uncertain, uproarious, ormissing info data. FL is utilized as a part of control applicationssince it lessens the ideal opportunity for designing advancement andon account of profoundly complex frameworks, is the best way totake care of the issue. Without exact information profoundly versa-tile control can be accomplished by utilizing fuzzy rationale. FuzzyLogic gives a basic method to touch base at a distinct conclusionin light of unclear, questionable, loose, loud, or missing info data.The fuzzy control calculation depends on the administrator’s un-derstanding and field tests. The pith of fuzzy control calculations isa contingent explanation between a fuzzy information variable and

12


9692

a fuzzy yield variable. The advantages of fuzzy controllers could beoutlined as takes after:

1. Fuzzy controllers are more hearty than PID controllers sincethey can work with clamor and aggravations of various nature.

2. Developing a fuzzy controller is less expensive than buildingup a model-based controller.

3. Fuzzy controllers are adjustable, since it is less demanding tocomprehend and change their govern, which utilize a humanadministrator’s procedure, as well as are communicated incommon semantic terms.

4. It is anything but difficult to figure out how fuzzy controllerswork and how to plan and apply them to a solid applica-tion. It is additionally essential to take note of that a fuzzyrationale can be interfaced with a customary controller.

The sugar business throughout the years has been creating sug-arcane bagasse as a component of the sugar processing process.As of now this sugar process biomass is burned wastefully as amethods for their transfer to create steam and power, which byand large are just barely enough to supply the vitality required torun the factories, along these lines leaving almost no or no addi-tional vitality available to be purchased to get additional salarynotwithstanding deals income from sugar. In any case, the ongo-ing precariousness and vulnerabilities in the cost of sugar and theworldwide require a green and practical condition have requiredthe look for methods for influencing successful utilization of thisbiomass to supply sugar to process vitality requests, while deliver-ing additional vitality as power and other vitality items available tobe purchased and in the meantime contributing towards ecologicalmaintainability. The fundamental goal of this work was to createprocess models for the handling of sugar process biomass into vi-tality and vitality items. In view of this, biomass to vitality changeprocess (BMECP) models have been created for different processarrangements of two thermo substance forms. Burning and FastPyrolysis utilizing the Aspen Plus reproduction programming. Thepoint of process demonstrating was to using sugar stick bagasse

13


9693

as an info vitality source to supply the vitality necessities of twosugar process arrangements (proficient and less effective factories),while creating additional power and high esteemed vitality itemsavailable to be purchased. The point of process demonstrating wasto using sugar stick bagasse as an information vitality source tosupply the vitality prerequisites of two sugar process designs (pro-ficient and less effective plants), while creating additional power andhigh esteemed vitality items for sale[10]. The impacts of evaporatorworking weight and bagasse dampness content on the execution ofthe burning based BMECP models have likewise been researched.At long last, point by point monetary models have been createdutilizing the Aspen Process Economic Analyzer (Icarus) to evalu-ate the financial feasibility of the BMECP models and affectabilityinvestigation performed to consider the reaction of the BMECPmodels to varieties in monetary parameters. Specialized executioninvestigation demonstrates the burning based BMECP models per-form superior to the Pure Fast Pyrolysis and Partial Fast PyrolysisBMECP models with respect to steam and power creation, alongthese lines giving them higher electrical efficiencies. The power agerate has been appeared to increment with expanding evaporatorworking weight and diminishing bagasse dampness content whilesteam generation rate has been appeared to increment with dimin-ishing bagasse dampness content and diminishing heater workingweight. In spite of the lower electrical efficiencies of the quick py-rolysis based BMECP models, the investigation demonstrates thattheir general procedure efficiencies contrast exceptionally well andthose of the burning based BMECP models because of the creationof high vitality esteem pyrolysis items. In view of normal workingweight and half bagasse dampness content, the Pure Fast Pyrolysisand the Partial.The possibility of fuzzy rationale was first best in class by Dr.LotfiZadeh of the University of Califormia on 1965. Fuzzy rationale be-gins with and expands on an arrangement of client provided humandialect rules. The fuzzy frameworks change over these tenets totheir numerical counterparts. This streamlines the activity of theframework creator and the PC, and results in considerably moreexact portrayals of the way frameworks carry on in reality. Extraadvantages of fuzzy rationale incorporate its effortlessness and itsadaptability. Fuzzy rationale can deal with issues with loose and

14


9694

inadequate information, and it can show nonlinear elements of dis-cretionary multifaceted nature. You can make a fuzzy frameworkto coordinate any arrangement of information yield information.The Fuzzy Logic Toolbox makes this especially simple by providingversatile procedures, for example, versatile neuro-fuzzy derivationframeworks (ANFIS) and fuzzy subtractive grouping. Fuzzy ratio-nale models, called fuzzy derivation frameworks, comprise of vari-ous restrictive ”assuming at that point” rules. For the originatorwho comprehends the framework, these standards are anything butdifficult to compose, and the greatest number of guidelines as fun-damental can be provided to portray the framework enough (albeitregularly just a direct number of tenets are needed).The degree towhich any fuzzy explanation is genuine is indicated by an incentivein the vicinity of 0 and 1.Not just do the lead based approach andadaptable enrollment work conspire make fuzzy frameworks clear tomake, yet they additionally disentangle the outline of frameworksand guarantee that you can undoubtedly refresh and keep up theframework after some time.Presentation of Fuzzy Logic Controller: Mainly Fuzzy Logic Sys-tems contains 4 primary Parts. They are Fuzzifier, Inference En-gine, Rules and Defuzzifier. The square chart is clarified and ap-peared in beneath figure.

15


9695

Figure 4 Block Diagramof fuzzy control systems

The first step is to take the crisp inputs, x1 and y1 (projectfunding and project staffing), and determine the degree to whichthese inputs belong to each of the appropriate fuzzy sets. The sec-ond step is to take the fuzzified inputs, m(x=A1) = 0.5, m(x=A2)= 0.2, m(y=B1) = 0.1 and m(y=B 2) = 0.7, and apply them to theantecedents of the fuzzy rules. If a given fuzzy rule has multipleantecedents, the fuzzy operator (AND or OR) is used to obtain asingle number that represents the result of the antecedent evalua-tion. This number (the truth value) is then applied to the conse-quent membership function.mAE B(x) = max [mA(x), mB(x)]Similarly, in order to evaluate the conjunction of the rule antecedents,we apply the AND fuzzy operation intersection:mA B(x) = min [mA(x), mB(x)]PID controller needs more suggestion for chosen to calculate PIDparameters and Fuzzy controller uses trial and error method tocalculated Fuzzy parameters. The PID control needs constantlyadjustment in order to achieve better control performance. Fuzzyself-tuning parameters controller is automatically adjusted parame-ters according to the speed error and the rate of speed error-change,

16


9696

and the fuzzy logic response is compared with that of conventionalPID controller. The results show that the overshoot, settling timeand control performance has been improved greatly by using FuzzyLogic controller. When applied torque load 0.1 N-m at time t = 5sec. the speed reduced but speed again got its reference point whenused PID controller but in fuzzy controller the speed is not affectedand it remain constant, this is advantage of fuzzy controller so itcan say the fuzzy controller is the better than PID controller.Statement of Problem: On extension of study to state the problem,determination wet of bagasse sugar sucrose in imbibition processwhile processing of tandem mills. So to determine the amountof sugar juice in bagasse is a challenge issue for the applicationof boiler energy and the product of sugar improvement and thebagasse is the fibrous residue of the cane stalk left after crushingand extraction of the juice. It consists of fibres, water and relativelysmall quantities of soluble solids mostly sugar.Scope and objectives for the proposed research work: The majorScope of objective of the problem is to monitor and control theamount of sucrose in bagasse based on losses in sugar process.Therefore, the objective is to design and development of expertsystems which perform to minimize the losses of sugar in bagasseprocess with following objectives.To study and development of modeling of sugar process based oncontent of wets sucrose in bagasse.Design of expert system by using fuzzy logic controller based onnon-linear process of bagasse in sugar process based on determi-nation of content of the sucrose in bagasse by image digital imageprocessing.Methodology :The raw sugar canes crushed and the juice extractis filtered to obtain the creamy level .The remaining extract of thesolid content called bagasse is mixed with water under a potentialto maintain imbibition pressure so that The bagasse absorbs theimbibition water. Then it is filtered to obtain second level of thejuice extract. This process is repeated until all the sucrose contentis absorb from the bagasse. The block diagram of internal process-ing of sugar factory for electrical energy using bagasse is shown inbelow figure as follows.

17


9697

Figure 5 Block chart of inner handling of sugar processing plantfor electrical vitality utilizing bagasse

The normal creation of factory run bagasse is the accompany-ing:Fiber (counting ash):- 48.0 PercentMoisture:- 50.0 PercentDissolvable solids:- 2.0 PercentThe fiber comprises essentially of cellulose (27 percent), pentosans(30 percent), lignin (20 percent) and cinder (3 percent).The calorificesteem (CV) of bagasse is given by the recipe:Net CV = 18 309 - 31.1 S - 207.3 W - 196.1 A (communicated inkJ/kg) Bagasse is utilized for the age of steam and power requiredto work the sugar manufacturing plant. A regular industrial facilitycreating crude sugarcane require, per ton of stick, around 35 kWhand 450 kg of fumes steam. Much advance has been accomplishedof late and, with consistent activity of the container, crystallizersand axes and a productive dissipation station, a cutting edge crudesugar manufacturing plant would now be able to work with 30 kWhand 300 kg of fumes steam per ton of stick. Such a manufactur-ing plant can spare 50 percent of the bagasse it produces and thisbagasse can be utilized to create power for the matrix or spared as

18


9698

crude material for the generation of paper, board, furfural, etc.Thistask of bagasse vitality advancement was an intelligent expansion ofSugar Sector Action Plan where in Government and the private seg-ment took an interest in the restructuration procedure of the sugarbusiness given that improved utilization of side-effects, includingbagasse for power creation was a key target in the arrangement.Moreover, the dynamic cooperation of the private part with Gov-ernment in figuring all the arrangement measures combined withproper institutions counted achieved an enhanced business condi-tion for the sugar area.

References

[1] Ministry of Agriculture and Natural Resources(1991).Reportof the High Powered Committee on Bagasse Energy Develop-ment Programme.

[2] Control of Moisture Content in Bagasse by Using BagasseDryer by J.Sudhakar#1, P.Vijay.

[3] Deepchand, K.(2004).Sugar Cane Bagasse for Electricity Gen-eratio in Africa. ESI Africa, 2/2004/44.

[4] Sk.Hasane Ahammad,Yogesh Misra,Polaiah Bojja A ReviewOf Application Of Fuzzy Controller In Sugar Industry, Journalof Advanced Research in Dynamical and Control Systems ,p.35

[5] Deepchand,K.(2000).Cogeneration of Bagasse Energy in Mau-ritus Energy for Sustainable Development, V(I): 15 22.

[6] Paturao,J.M. (1982).By products of the cane sugar industry,an introduction to their industrial utilization. Amsterdam-Oxford-New York: Elsevier Scientific Publication Company.

[7] Anwar,S.I.(2005).Assessment of calorific value of jaggery andkhandsari bagasse and development of waste heat recovery sys-tem.Proceedings of the National Seminar on Sugarcane pro-duction technique for increasing Recovery of sugar in initialperiod of crushing season in sugar Lucknow, India, 209-211.

19


9699

[8] Manohar R.P.J.(1997).Industrial utilization of sugar and itsco-products. New Delhi, India: ISPCK Publishers and distrib-utors.

[9] Baboo,B.Anwar, S.I.; and Khare, A.K. (2002). Calculatingreduced juice Extraction from cane in dry crushing pro-cess.Proceedings of the India Science Congress, Lucknow, In-dia.

[10] Magasiner.N.(1996).Bagasse Fired boiler design with referenceCo-generationVol.98 No.1167 pp.100-104, 107-109 ref.5.

20


9700

development of advanced automation of expert systems for monitoring and control … ·...

Documents