Enhancing Gasoline Production in an IndustrialCatalytic-Reforming Unit Using Artificial Neural Networks

Gholamreza Zahedi,*,† Sasan Mohammadzadeh,† and Gholamreza Moradi‡

Simulation and AI Research Center, and Department of Chemical Engineering, Razi UniVersity,Kermanshah 55157, Iran

ReceiVed January 11, 2008. ReVised Manuscript ReceiVed March 31, 2008

In this paper, two artificial neural network (ANN) models for simulation of an industrial catalytic-reformingunit (CRU), platforming unit, are presented. The proposed models predict the volume flow rate of hydrogen,gasoline, and liquid petroleum gas (LPG), outlet temperature of reactors, gasoline specific gravity, Reid vaporpressure (RVP), and research octane number (RON) of gasoline. In this case, 90 data sets were collected fromTabriz Refinery CRU. A total of 70% of these data sets were used to build and train suitable ANN architecture.Various training algorithms and network architectures were examined, and finally, suitable network were found.Results show excellent ANN capability to predict the unseen plant data. Prediction error of the networks is1.07%. Using ANN model, a set of optimized operation conditions leading to a maximized volume flow rateof produced gasoline were obtained. Applying optimal conditions, the gasoline production yield will increasefrom 80 to 82.38%.

1. Introduction

The catalytic reforming process in the refineries convertsvirgin naphtha cuts of low octane number into gasolines withhigh octane number, where the total amount of aromatichydrocarbons and branched paraffins is increased. The mostcommon measure of the octane number is the research octanenumber (RON). By definition, iso-octane (2,2,4-trimethyl pen-tane) is given an octane number of 100 and n-heptane is givenan octane number of 0.1–3

Catalytic reforming of straight run naphthas is a veryimportant process for octane improvement and production ofaromatic feedstocks for petrochemical industries. Hydrogen andlighter hydrocarbons are produced as side products. Generally,the reforming is carried out in three or four fixed bed reactors,which operate adiabatically at temperatures between 450 and520 °C, total pressures between 10 and 35 atm, and a molarhydrogen/hydrocarbon ratio between 3 and 8. A large numberof reactions occur in catalytic reforming, such as dehydroge-nation and dehydroisomerization of naphthenes to aromatics,dehydrogenation of paraffins to olefins, dehydrocyclization ofparaffins and olefins to aromatics, isomerization or hydroi-somerization to isoparaffins, isomerization of alkylcyclopen-tanes, and substituted aromatics and hydrocracking of paraffinsand naphthenes to lower hydrocarbons. The major reactions inthe first reactor are endothermic and very fast, such asdehydrogenation of naphthenes. As the feedstock passes through

the reactors, the reactions become less endothermic. Recently,there has been a renewed interest in the reforming process, first,because reformate is a major source of aromatics in gasolineand, next, because of the new legislation of benzene andaromatic contents in commercial gasoline. In this case, refinershave tried to reduced the amount of aromatics in gasoline;however, it adversely affects the reformate octane.4–6 For thesereasons, developing an appropriate kinetic model that is capableof predicting the detailed reformate composition to use it, incombination with a catalytic reforming reactor model, forsimulation and optimization purposes is important.

One of the drawbacks of catalytic-reforming unit (CRU)modeling is the difficulty of obtaining a rigorous mechanisticmodel of the process, which accounts for several importantoperating factors, such as feed temperature, feed mole fraction,molar hydrogen/hydrocarbon ratio, and pressure of reactors.Another drawback is that the catalyst deactivation, mass transfer,and cocking mechanisms are not well-understood. Accuratesimulation of this unit using traditional modeling techniquesbecause of various elements, such as reactors, furnace, andseparator in CRU, is impossible. Common simulators, such asAspen and Pro II, fail to provide fast response to sudden changeof plant inputs. Therefore, optimization routines that need fastresponding and an accurate model of the unit provide fruitlessoptimization results.

During the last 15 years, neural networks (NNs) have beenthe focus of much attention, largely because of their wide rangeof applicability and ability that they handle complex and highlynonlinear problems. NNs were successfully applied to problemsfrom various areas including the business, medical, and industrialfields.7 Process modeling is an area where NNs of varyingconfigurations and structures have been considered as alternative

* To whom correspondence should be addressed. Fax: +98-831-4274542.E-mail: grzahedi@razi.ac.ir.

† Simulation and AI Research Center.‡ Department of Chemical Engineering.(1) Bommannan, D.; Srivastava, R. D.; Saraf, D. N. Modeling of catalytic

naphtha reformers. Can. J. Chem. Eng. 1989, 67, 405–411.(2) Lee, J. W.; Ko, Y. C.; Jung, Y. K.; Lee, K. S.; Yoon, E. S. A

modeling and simulation study on a naphtha reforming unit with catalystcirculation and regeneration system. Comput. Chem. Eng. 1997, 21, 1105–1110.

(3) Taskar, U.; Riggs, J. B. Modeling and optimization of a semiregen-erative catalytic naphtha reformer. AIChE J. 1997, 43 (3), 740–753.

(4) Unzelman, G. H. Oil Gas J. 1990, 88 (15), 43.(5) Maples, R. E. Petroleum Refinery Process Economics, 2nd ed.;

Pennwell Books: Tulsa, OK, 2000.(6) Ancheyta-Juarez, J.; Villafuerte-Macıas, E. Kinetic modeling of

naphtha catalytic reforming reactions. Energy Fuels 2000, 14, 1032–1037.

Energy & Fuels 2008, 22, 2671–2677 2671

10.1021/ef800025e CCC: $40.75 2008 American Chemical SocietyPublished on Web 05/28/2008

modeling techniques, particularly in cases where reliablemechanistic models cannot be obtained; however, artificialneural network (ANN) methods use unit data to developmodels.8,9

ANN models have been developed to determine RON ofgasoline blends produced in a Greek refinery. The developedANN models use the volumetric content of seven mostcommonly used fractions of feed as input variables and predictRON of gasoline.10 An ANN and genetic algorithm strategywas proposed to optimize the subsequent process of conventionalfluidized-bed catalytically cracked (FCC), namely, secondaryreaction to obtain clean gasoline with low olefins. The ANNmodel contains seven inputs, including three process operatingand four parameters of feed, as input variables and two outputs,yield of upgraded gasoline and olefins fraction.11 There werenot any works on ANN modeling of CRU based on our literaturesurvey. Our proposed model that has 16 input and 11 outputparameters provides detailed description of CRU.

In this paper, first, Tabriz refinery CRU is described. Next,an outline of ANN concept is depicted. In the next step, thebest ANN configuration considering various training algorithmsis found. Then, the ability of the best network in estimation ofunseen data is examined. Finally, considering gasoline produc-tion as the objective function, the optimum feed temperature,reactor pressure, and hydrogen/feed ratio were found.

2. CRU of Tabriz Refinery

Gasoline is the key profit generator for the petroleum refiningindustry. The revenue of the gasoline production dominates inthe overall refinery economics, because modern refineries tryto convert 70% of the crude oil into gasoline. Gasoline isproduced by blending different fuel streams coming from variousproduction processes. Atmospheric straight cuts together with

products from catalytic reforming and cracking and isomeriza-tion units are the most commonly used feeds for gasolineproduction. The catalytic-reforming (naphtha-reforming) processconverts low-octane gasoline-blending components to high-octane components for use in high-performance gasoline fuels.Platforming is a catalytic-reforming process that is accomplishedin Tabriz refinery using a bimetallic (platinum-rhenium)catalyst. Catalysts based on platinum supported on alumina arenowadays modified with a second metal, such as rhenium.12

Figure 1 represents a specified process flow diagram of Tabrizrefinery CRU. The CRU feed contains a little H2S, aromatic,olefin, paraffin, and naphthen components. Feed is combinedwith created hydrogen, H2O, and ethane dichloride (EDC) andis heated in a heater. The rate of water injection can regulatethe hydrogen humidify level. Water improves reactions, andEDC improves cyclization and isomerization reactions.

The feed then enters a fixed bed reactor. After reactions,products are directed to the hydrogen separator and debutanizercolumn. The debutanizer column separates high octane fuelmotor (gasoline) and LPG based on component boiling points.The platformer is used to convert relatively low-value, low-octane naphtha into highly aromatic and high-octane motor fuelsof increased value. Hydrogen gas in the feed avoids cokingcontamination of the catalyst. The upgraded product in theplatformer is a high-grade motor fuel.12

3. NN Modeling

Although the concept of ANN analysis was discovered 50years ago, it is only in the last 2 decades that ANN softwareshave been developed to handle practical problems. ANNs canbe employed in tasks involving incomplete data sets, fuzzy orincomplete information, and highly complex and ill-conditionedproblems. NNs are mathematical models designed to mimiccertain aspects of neurological functioning of the brain. ANNis a parallel structure consisting of nonlinear processing elements(neurons or nodes) interconnected by fixed or variable weights.The nodes are grouped into layers. ANNs are able to learn keyinformation patterns within a multi-information domain. Inaddition, ANNs are tolerant to noisy data. ANNs differ fromthe traditional modeling approaches in that they are trained tolearn solutions rather than being programmed to model a specificproblem in the normal way. They are usually used to address

(7) Demuth, H.; Beale, M. User’s Guide: Neural Network Toolbox forUse with Matlab; The Mathworks, Inc.: Natick, MA, 2007.

(8) Nascimento, C. A. O.; Giudici, R.; Guardani, R. Neural networkbased approach for optimization of industrial chemical processes. Comput.Chem. Eng. 2000, 24 (9-10), 2303–2314.

(9) Willis, M. J.; Montague, G. A.; Di Massimo, C.; Tham, M. T.;Morris, A. J. Artificial neural networks in process estimation and control.Automatica 1992, 28 (6), 1181–1187.

(10) Pasadakis, N.; Gaganis, V.; Foteinopoulos, C. Octane numberprediction for gasoline blends. Fuel Process. Technol. 2006, 87, 505–509.

(11) Wang, Z.; Yang, B. Modeling and optimization for the secondaryreaction of FCC gasoline based on the fuzzy neural network and geneticalgorithm. Chem. Eng. Process. 2007, 46, 175–180. (12) UOP manual book, Tabriz Refinery, catalytic reforming data, 2006.

Figure 1. Process flow diagram of Tabriz refinery CRU (platforming unit).

2672 Energy & Fuels, Vol. 22, No. 4, 2008 Zahedi et al.

problems that are intractable or cumbersome to solve withtraditional methods. ANNs are able to deal with nonlinearproblems and, once trained, can perform predictions at very highspeed. ANNs have been used in many engineering applications,such as in control systems, classification, and modeling complexprocesses. The advantages of ANN compared to classicalmethods are speed, simplicity, and capacity to learn fromexamples. Their ability to learn by experimental data makesANNs very flexible and powerful than any other parametricapproaches. Therefore, neural networks have become verypopular for solving regression and classification problems inmany fields.13–15 In the past decade, some works about the useof ANN in energy systems have been published.16–22

Figure 2 represents the schematic of a typical ANN. A typicalnetwork consists of an input layer, at least one hidden layer,and an output layer. The most widely employed networks haveone hidden layer only.13 For a feed-forward ANN, the informa-tion propagates in only the forward direction. In this case, eachnode within a given layer is connected to all of the nodes ofthe previous layer. The node sums up the weighted inputs anda bias and passes the result through a linear or nonlinearfunction.15

The setting of the number of neurons in the three layers, theinput, the hidden, and the output ones, determine the multilayer

feed-forward topology, for which I represents the number ofneurons in the input layer, including the bias term, J representsthe number of neurons in the hidden layer, and K representsthe number of neurons in the output layer.

An ANN consists of many interconnected processing nodesknown as neurons that act as microprocessors. Each neuronreceives a weighted set of inputs and produces an output. Aneuron evaluates weighted sum of the inputs given by

n) (∑i)1

I

wijxi)+ b (1)

where I is the number of elements in the input vector xi, wij

and wjk are the interconnection weights, and b is the “bias” forthe neuron.23 Note that neuron output only depends uponinformation that is locally available at the neuron, either storedinternally or arrived via the weighted coefficients. The neuronoutput is a calculated summation of weighted inputs with a biasthrough an “activation function”. This activation functioncomputes its output as below

hidden layer)HLj ) f[(∑i)1

I

wijxi)+bias1] and output layer) f[(∑

i)1

I

HLjwjk)+ bias2] (2)

Generally, NNs are trained by adjusting the weighting coef-ficients to reach from a particular input to a specific target usinga suitable learning method until the network output approachesthe target. The error between the output of the network and thetarget, i.e., the desired output, is minimized by optimal selectionof the weights and biases. The training process is ceased whenthe error falls below a determined value or the maximum numberof epochs is exceeded. There are different learning algorithmsthat can be applied to train a NN. The most popular algorithmis the back-propagation algorithm, which is a gradient descentalgorithm. It is very difficult to know which training algorithmwill be suitable for a given problem, and the best one is usuallychosen by trial and error. An ANN with a back-propagationalgorithm learns by changing the connection weights, and thesechanges are stored as knowledge.

ANN is trained by presenting it with a set of known inputsand outputs. It learns the patterns of these inputs and outputsby manipulating the weights. The weights are adjusted until theoptimization criterion is minimized. The most widely usedcriterion is the mean square error (MSE)

MSE) 1N∑

i)1

N

(Pi,measurement -Pi,simulated)2 (3)

where N is the total number of output values used for trainingand P refers to the output values.7

4. Input and Output Data

To build an ANN model, CRU data were collected fromTabriz refinery in Iran. In data selection, component analyseswere carried out, and to ensure that they represent normaloperating ranges, off data were deleted from the data list. Finally,90 data sets were obtained. The variables of the model and theiroperating ranges are summarized in Table 1. Among 90 data

(13) Hagan, M. T.; Demuth, H. B.; Beale, M. Neural Network Design;PWS Publishing Company: Boston, MA, 1995.

(14) Rajasekaran, S.; Vijayalakshmi, G. A. Neural Network, Fuzzy Logicand Genetic Algorithms; Prentice-Hall of India Pvt. Ltd.: New Delhi, India,2006.

(15) Haykin, S.; Hamilton, O. Neural Networks, 2nd ed.; Prentice HallInternational, Inc.: Upper Saddle River, NJ, 1998.

(16) Kalogirou, S. A. Applications of artificial neural networks in energysystems: A review. Energy ConVers. Manage. 1999, 40, 1073–1087.

(17) Kalogirou, S. A. Long-term performance prediction of forcedcirculation solar domestic water heating systems using artificial neuralnetworks. Appl. Energy 2000, 66, 63–74.

(18) Kalogirou, S. A. Optimization of solar systems using neuralnetworks and genetic algorithms. Appl. Energy 2004, 77 (4), 383–405.

(19) Kalogirou, S. A.; Bojic, M. Artificial neural networks for theprediction of the energy consumption of a passive-solar building. Energy2000, 25, 479–491.

(20) Zahedi, G.; Fgaier, H.; Jahanmiri, A.; Al-Enezi, G. Artificial neuralnetwork dentification and evaluation of hydrotreater plant. Pet. Sci. Technol.2006, 24, 1447–1456.

(21) Zahedi, G.; Jahanmiri, A.; Rahimpor, M. R. A neural networkapproach for prediction of the CuO-ZnO-Al2O3 catalyst deactivation. Int.J. Chem. Reactor Eng. 2005, 3, A8.

(22) Zahedi, G.; Elkamel, A.; Lohi, A.; Jahanmiri, A.; Rahimpor, M. R.Hybrid artificial neural networksFirst principle model formulation for theunsteady state simulation and analysis of a packed bed reactor for CO2hydrogenation to methanol. Chem. Eng. J. 2005, 115, 113–120.

(23) Haykin, S. Neural Networks: A ComprehensiVe Foundation;MacMillan: New York, 1994.

Figure 2. Schematic representation of the multilayer feed-forward ANNfor the present study.

ANN Models for Simulation of Industrial CRU Energy & Fuels, Vol. 22, No. 4, 2008 2673

sets, 62 were used for training the ANN and the remaining 28data sets were used for accuracy checks of the best obtainednetworks.12

The inputs to the network were the operating pressure, feedvolume flow rate, feed temperature, feed specific gravity,hydrogen/hydrocarbon ratio, hydrogen humidity (mol %), H2O,EDC, H2S, reactor product separator pressure, volume flow rateof hydrogen, feed, bottom and top temperature of debutanizercolumn, debutanizer column pressure, and reflux ratio, and theoutputs were the volume flow rate of hydrogen, gasoline andliquid petroleum gas, (LPG.G-LPG.L), outlet temperature forfour reactors, gasoline specific gravity, Reid vapor pressure(RVP) of gasoline (at 38 °C), and RON.

5. Simulation of CRU Using ANN and Results

As mentioned earlier, the NN used in this study has a feed-forward structure trained using the back-propagation and radialbasis function (RBF) method. The optimum number of hiddenlayers and nodes within each layer are problem-specific, andthere is not a procedure to know this quantity in advance. Forthis reason, a trial and error approach (multiple runs) wasfollowed to find best network architecture. These included oneand two hidden layers and 40-120 nodes per each hidden layer.The activation function used in the hidden nodes is the sigmoidfunction7

f(x)) 1

1+ e-x(4)

where x is the sum of the weighted inputs to the neuron andf(x) represents the output of the node. As for the output layernodes, a simple linear activation function was employed, f(x)) x.

For the RBF network, the activation function used in thehidden nodes is the radial basis transfer function7

f(x)) e-x2(5)

Inputs of a network should be selected carefully if the bestresults are expected to be achieved. The input variables shouldreflect the underlying physics of the process to be analyzed.Various architectures of multilayer perceptron (MLP), RBF, andback propagation (BP) are used to predict measurement CRUoutlet. BP networks with biases, a sigmoid layer, and a linearlayer are capable of approximating any function with a finitenumber of discontinuities. Each type of input and output datawere scaled by dividing to a maximum amount of that variablefor scaling purposes. Each ANN has been trained with 2/3 ofthe data set, and 1/3 of samples have been used for testing the

predictions of ANN. The relative percent of errors was used inthe generalization section as indicated below

error) 100N ∑

i)1

N |measurement- simmeasurement |i (6)

In this part of study, the objective is to find the optimalperformance ANN model for CRU. Radial basis networks mayrequire more neurons than standard feed-forward back-propaga-tion networks, but often they can be designed in faster thanfeed-forward networks. They have good performance whenmany training vectors are available, and they are robust to noisydata. There are 16 input vectors and 7 output vectors. The taskwas to find the optimum number of nodes in the hidden layerthat provides a good estimate of the outputs. The criterion forselection was MSE between net output and test data. The firstnetwork was for training the volume flow rate of gasoline, outlettemperature of reactors, gasoline specific gravity, and RON. Theoptimum number of hidden nodes was found to be 62 (Figure3), and MSE in the test step was 1.08 × 10-26. The spread wasselected to be 0.075, and MSE in the test step was 5.56 × 10-6.The results are illustrated in Figures 3 and 4. The results ofbest RBF network for the flow rate of gasoline, outlet temper-ature of reactors, product specific gravity, and RON areillustrated in Figures 5–11.

In the second step for training the volume flow rate ofhydrogen, liquid petroleum gas (LPG.G-LPG.L), and RVP for

Table 1. ANN Input Variables and Their Range

quantity value

heavy naphtha flow rate-feed (m3/h) 85-89hydrogen flow rate (m3/h) 71 063.2-76 529.6hydrogen humidify (mol %) 10-51feed temperature (°C) 496-499H2S (ppm) 0-1H2/HC (mol/mol) 3.7-4.52EDC (ppm/feed flow rate) 0.6-1.2H2O (ppm/feed flow rate) 0-2.36feed specific gravity 0.7410-0.7505reactors pressure (kg/cm2) 29-31.5reactor product separator pressure (kg/cm2) 21-22.2debutanizer reflux ratio (m3/h) 3.5-4.4debutanizer pressure (kg/cm2) 17-17.5debutanizer feed temperature (°C) 132-154top debutanizer column temperature (°C) 53-71bottom debutanizer column temperature (°C) 202-218

Figure 3. Performance of the RBF network based on the number ofhidden neurons.

Figure 4. Performance of the RBF network based on the spread for 62neurons in the hidden layer.

2674 Energy & Fuels, Vol. 22, No. 4, 2008 Zahedi et al.

gasoline, a feed-forward network, back-propagation architecturewith a conjugate-gradient training algorithm was adopted. Thetask was to find the optimum number of nodes in the hiddenlayer that provide good estimates of the outputs. The criterionfor selection was MSE between net output and training data.The optimum number of hidden nodes was found to be 40(Figure 12). MSE in the test step obtained was 7.1452 × 10-7.Generalization results for best obtained BP network for the flowrate of hydrogen, liquid petroleum gas (LPG.G-LPG.L), andRVP are illustrated in Figures 13–16.

Table 2 represents model outputs and percent of error betweenbest ANN predictions and unseen plant data. The average error

for estimation was 1.07%, which is a very small and unreachableerror in engineering applications.

6. Optimization of CRU To Increase Gasoline Production

Because accurate and fast-responding ANN models weredeveloped to simulate CRU, the optimization of the plant canbe applied using these models. To implement the optimizationroutine, optimization variables should be selected noting ap-plicability in process and major effect on objective function.From a kinetic point of view, an increase in the temperatureand a decrease in the hydrogen/hydrocarbon molar ratio havepositive impact on the reforming interactions, especially anincrease rate of cyclization and hydrocracking. An increase intemperature causes a fall in the volume flow rate of gasoline

Figure 5. Comparison of unseen measured and simulated product RONusing the best RBF model.

Figure 6. Comparison of measurement and simulated product specificgravity using the best RBF model.

Figure 7. Comparison of measurement and simulated temperature outlet(T1) using the best RBF model.

Figure 8. Comparison of measurement and simulated temperature outlet(T2) using the best RBF model.

Figure 9. Comparison of measurement and simulated temperature outlet(T4) using the best RBF model.

Figure 10. Comparison of measurement and simulated temperatureoutlet (T3) using the best RBF model.

ANN Models for Simulation of Industrial CRU Energy & Fuels, Vol. 22, No. 4, 2008 2675

production and a decrease in pressure leads in a small rise involume flow rate of gasoline production. A low hydrogen/hydrocarbon molar ratio moves the chemical equilibrium toexcessive coke production.8,9 Feed compositions are also af-fecting kinetics, but with regard to process operation, onlypressure, temperature, and hydrogen/feed ratios can be changedin the real plant. The objective function for optimization wasset to gasoline production. In this case, the best RBF networkcan be used for optimization.

The optimization of the process condition of every unit forincreasing output and decreasing energy consuming is a veryimportant factor that is directly relate to the economical aspect.Using the CRU model effect of operating parameters of theplatforming unit, i.e., temperature, pressure, and mole hydrogen/

hydrocarbon ratio, on the gasoline production was studied. Table3 shows optimization variables and their applicable range ofchange in the real plant.24

Figures 17–19 illustrate the effect of temperature, pressure,and mole hydrogen/hydrocarbon ratio on the rate of gasolineproduction.

As indicated in Figure 17, the feed temperature first has apositive effect on gasoline production and, despite kineticpredictions,8,9 after T ) 499 °C, an increasing temperaturedecreases gasoline production. The same phenomenon wasobserved for effects of reactor pressures (Figure 18). Theoptimum pressure was found to be 29.8 kg/cm2. Increasing the

(24) Al-Shayji, K. A.; Al-Wadyei, S.; Elkamel, A. Modeling andoptimization of a multistage flash desalination process. Eng. Optim. 2005,37 (6), 591–607.

Figure 11. Comparison of unseen measured data and simulated gasolineproduction using the best RBF model.

Figure 12. Performance of the best BP network based on the numberof hidden neurons.

Figure 13. Comparison of measurement and simulated gasoline vaporpressure using the best BP model.

Figure 14. Comparison of measurement and simulated hydrogenproduction using the best BP model.

Figure 15. Comparison of measurement and simulated LPG (gas) usingthe best BP model.

Figure 16. Generalization result for LPG (liquid) using the best BPmodel.

2676 Energy & Fuels, Vol. 22, No. 4, 2008 Zahedi et al.

H2/HC ratio first decreases gasoline production until point 3.5.Between 3.5 and 4.3 (optimum ratio), increasing the H2/HC ratiohas a positive effect on the gasoline production rate. Theinteresting phenomenon is that increasing the H2/HC ratio morethan 5.5 does not affect gasoline production.

The network predicts that the optimum state in the operatingconditions of the platforming unit to increase the rate of gasolineproduction is as followings: temperature of feeding, 499 °C;pressure of CRU reactors, 29.8 kg/cm2; and hydrogen/hydrocarbonratio, 4.3 mol/mol. In these conditions, gasoline production willbe 70 m3/h, which is equal to an 82.38 gasoline yield.

Table 4 illustrates the results of the prediction of the networkabout the effect of gasoline volume flow rate increase on theRON and specific gravity of gasoline production. The tableindicates that, by increasing the gasoline volumertric flow ratefrom 67 to 70 (4.48% increase), RON and specific gravity valuesdecrease 1 and 0.52%, respectively. This decrease is becauseof a little increase of noncyclic hydrocarbons production.

7. Conclusion

In this work, both BP and RBF neural network models weredeveloped for CRU simulation. The models were trained onthe basis of measured plant data. The RBF model predicts thereactor outlet temperature, volumetric flow rate of gasoline,RON, and product specific gravity, and BP predicts the volumeflow rate of hydrogen, liquid petroleum gas (gas-liquid), andproduct RVP. The prediction error of the networks is 1.07%.The difference between model predictions and validation datawas very small, which confirmed the ability of ANN toaccurately predict unseen data. Finally, obtained networks wereapplied to predict plant optimal operating conditions.

Acknowledgment. The authors acknowledge the Iranian NationalRefinery Company-Tabriz Refinery for its financial support.

NomenclatureCRU ) catalytic-reforming unitMSE ) mean square errormea ) measurement datasim ) simulated dataN ) total number of output, number of dataerror ) mean percent errorEDC ) ethane dichlorideMLP ) multilayer perceptronBP ) backpropagationRBF ) radial basis functionSG ) specific gravityT1 ) outlet temperature of reactor number 1T2 ) outlet temperature of reactor number 2T3 ) outlet temperature of reactor number 3T4 ) outlet temperature of reactor number 4LPG.G ) liquid petroleum gas (gas)LPG.L ) liquid petroleum gas (liquid)

EF800025E

Table 2. Comparison of Best ANN Models and Measurements(Plant Data)

output percent error

T1 (°C) 0.0940T2 (°C) 0.1120T3 (°C) 0.0352T4 (°C) 0.0368H2 (m3/h) 1.6510LPG.G (m3/h) 2.3891LPG.L (m3/h) 3.0257gasoline (m3/h) 0.2878SG 0.0861RVP (at 38 °C) 0.8971RON 0.1672average error 1.07

Table 3. Optimization Constraints

parameters limit change

temperature (°C) 480-540pressure (kg/cm2) 28-31.9hydrogen/hydrocarbon ratio (mol/mol) 3.1-7.1

Figure 17. Effect of the feed temperature on gasoline production.

Figure 18. Effect of the pressure of reactors on gasoline production.

Figure 19. Effect of the hydrogen/hydrocarbon ratio on the rate ofgasoline production.

Table 4. Effect of Enhancing the Gasoline Volume Flow Rateon the RON and Specific Gravity

volume flow rate of gasoline (m3/h)

67 70 percent of variation

RON 92.4 91.5 1specific gravity 0.77 0.766 0.52

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