Enhancing Gasoline Production in an Industrial Catalytic-Reforming

Energy & Fuels 2008, 22, 2671–2677

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Enhancing Gasoline Production in an Industrial Catalytic-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-reforming unit (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 vapor pressure (RVP), and research octane number (RON) of gasoline. In this case, 90 data sets were collected from Tabriz 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 is 1.07%. Using ANN model, a set of optimized operation conditions leading to a maximized volume flow rate of produced gasoline were obtained. Applying optimal conditions, the gasoline production yield will increase from 80 to 82.38%.

1. Introduction The catalytic reforming process in the refineries converts virgin naphtha cuts of low octane number into gasolines with high octane number, where the total amount of aromatic hydrocarbons and branched paraffins is increased. The most common measure of the octane number is the research octane number (RON). By definition, iso-octane (2,2,4-trimethyl pentane) is given an octane number of 100 and n-heptane is given an octane number of 0.1–3 Catalytic reforming of straight run naphthas is a very important process for octane improvement and production of aromatic feedstocks for petrochemical industries. Hydrogen and lighter 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 and 520 °C, total pressures between 10 and 35 atm, and a molar hydrogen/hydrocarbon ratio between 3 and 8. A large number of reactions occur in catalytic reforming, such as dehydrogenation and dehydroisomerization of naphthenes to aromatics, dehydrogenation of paraffins to olefins, dehydrocyclization of paraffins and olefins to aromatics, isomerization or hydroisomerization to isoparaffins, isomerization of alkylcyclopentanes, and substituted aromatics and hydrocracking of paraffins and naphthenes to lower hydrocarbons. The major reactions in the first reactor are endothermic and very fast, such as dehydrogenation of naphthenes. As the feedstock passes through * To whom correspondence should be addressed. Fax: +98-831-4274542. E-mail: [email protected]. † 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 catalyst circulation and regeneration system. Comput. Chem. Eng. 1997, 21, 1105– 1110. (3) Taskar, U.; Riggs, J. B. Modeling and optimization of a semiregenerative catalytic naphtha reformer. AIChE J. 1997, 43 (3), 740–753.

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 gasoline and, next, because of the new legislation of benzene and aromatic contents in commercial gasoline. In this case, refiners have tried to reduced the amount of aromatics in gasoline; however, it adversely affects the reformate octane.4–6 For these reasons, developing an appropriate kinetic model that is capable of predicting the detailed reformate composition to use it, in combination with a catalytic reforming reactor model, for simulation and optimization purposes is important. One of the drawbacks of catalytic-reforming unit (CRU) modeling is the difficulty of obtaining a rigorous mechanistic model of the process, which accounts for several important operating 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. Accurate simulation of this unit using traditional modeling techniques because of various elements, such as reactors, furnace, and separator in CRU, is impossible. Common simulators, such as Aspen and Pro II, fail to provide fast response to sudden change of plant inputs. Therefore, optimization routines that need fast responding and an accurate model of the unit provide fruitless optimization results. During the last 15 years, neural networks (NNs) have been the focus of much attention, largely because of their wide range of applicability and ability that they handle complex and highly nonlinear problems. NNs were successfully applied to problems from various areas including the business, medical, and industrial fields.7 Process modeling is an area where NNs of varying configurations and structures have been considered as alternative (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.

10.1021/ef800025e CCC: $40.75  2008 American Chemical Society Published on Web 05/28/2008

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Zahedi et al.

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

modeling techniques, particularly in cases where reliable mechanistic models cannot be obtained; however, artificial neural network (ANN) methods use unit data to develop models.8,9 ANN models have been developed to determine RON of gasoline blends produced in a Greek refinery. The developed ANN models use the volumetric content of seven most commonly used fractions of feed as input variables and predict RON of gasoline.10 An ANN and genetic algorithm strategy was proposed to optimize the subsequent process of conventional fluidized-bed catalytically cracked (FCC), namely, secondary reaction to obtain clean gasoline with low olefins. The ANN model contains seven inputs, including three process operating and four parameters of feed, as input variables and two outputs, yield of upgraded gasoline and olefins fraction.11 There were not any works on ANN modeling of CRU based on our literature survey. Our proposed model that has 16 input and 11 output parameters 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, the best ANN configuration considering various training algorithms is found. Then, the ability of the best network in estimation of unseen data is examined. Finally, considering gasoline production as the objective function, the optimum feed temperature, reactor pressure, and hydrogen/feed ratio were found.

products from catalytic reforming and cracking and isomerization units are the most commonly used feeds for gasoline production. The catalytic-reforming (naphtha-reforming) process converts low-octane gasoline-blending components to highoctane components for use in high-performance gasoline fuels. Platforming is a catalytic-reforming process that is accomplished in Tabriz refinery using a bimetallic (platinum-rhenium) catalyst. Catalysts based on platinum supported on alumina are nowadays modified with a second metal, such as rhenium.12 Figure 1 represents a specified process flow diagram of Tabriz refinery CRU. The CRU feed contains a little H2S, aromatic, olefin, paraffin, and naphthen components. Feed is combined with created hydrogen, H2O, and ethane dichloride (EDC) and is heated in a heater. The rate of water injection can regulate the hydrogen humidify level. Water improves reactions, and EDC improves cyclization and isomerization reactions. The feed then enters a fixed bed reactor. After reactions, products are directed to the hydrogen separator and debutanizer column. The debutanizer column separates high octane fuel motor (gasoline) and LPG based on component boiling points. The platformer is used to convert relatively low-value, lowoctane naphtha into highly aromatic and high-octane motor fuels of increased value. Hydrogen gas in the feed avoids coking contamination of the catalyst. The upgraded product in the platformer is a high-grade motor fuel.12

2. CRU of Tabriz Refinery

3. NN Modeling

Gasoline is the key profit generator for the petroleum refining industry. The revenue of the gasoline production dominates in the overall refinery economics, because modern refineries try to convert 70% of the crude oil into gasoline. Gasoline is produced by blending different fuel streams coming from various production processes. Atmospheric straight cuts together with

Although the concept of ANN analysis was discovered 50 years ago, it is only in the last 2 decades that ANN softwares have been developed to handle practical problems. ANNs can be employed in tasks involving incomplete data sets, fuzzy or incomplete information, and highly complex and ill-conditioned problems. NNs are mathematical models designed to mimic certain aspects of neurological functioning of the brain. ANN is 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 key information patterns within a multi-information domain. In addition, ANNs are tolerant to noisy data. ANNs differ from the traditional modeling approaches in that they are trained to learn solutions rather than being programmed to model a specific problem in the normal way. They are usually used to address

(7) Demuth, H.; Beale, M. User’s Guide: Neural Network Toolbox for Use with Matlab; The Mathworks, Inc.: Natick, MA, 2007. (8) Nascimento, C. A. O.; Giudici, R.; Guardani, R. Neural network based 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 number prediction for gasoline blends. Fuel Process. Technol. 2006, 87, 505–509. (11) Wang, Z.; Yang, B. Modeling and optimization for the secondary reaction of FCC gasoline based on the fuzzy neural network and genetic algorithm. Chem. Eng. Process. 2007, 46, 175–180.

(12) UOP manual book, Tabriz Refinery, catalytic reforming data, 2006.

ANN Models for Simulation of Industrial CRU

Energy & Fuels, Vol. 22, No. 4, 2008 2673

feed-forward topology, for which I represents the number of neurons in the input layer, including the bias term, J represents the number of neurons in the hidden layer, and K represents the number of neurons in the output layer. An ANN consists of many interconnected processing nodes known as neurons that act as microprocessors. Each neuron receives a weighted set of inputs and produces an output. A neuron evaluates weighted sum of the inputs given by

( ) I

n)

∑w x

+b

ij i

(1)

i)1

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

problems that are intractable or cumbersome to solve with traditional methods. ANNs are able to deal with nonlinear problems and, once trained, can perform predictions at very high speed. ANNs have been used in many engineering applications, such as in control systems, classification, and modeling complex processes. The advantages of ANN compared to classical methods are speed, simplicity, and capacity to learn from examples. Their ability to learn by experimental data makes ANNs very flexible and powerful than any other parametric approaches. Therefore, neural networks have become very popular for solving regression and classification problems in many fields.13–15 In the past decade, some works about the use of ANN in energy systems have been published.16–22 Figure 2 represents the schematic of a typical ANN. A typical network consists of an input layer, at least one hidden layer, and an output layer. The most widely employed networks have one hidden layer only.13 For a feed-forward ANN, the information propagates in only the forward direction. In this case, each node within a given layer is connected to all of the nodes of the previous layer. The node sums up the weighted inputs and a bias and passes the result through a linear or nonlinear function.15 The setting of the number of neurons in the three layers, the input, the hidden, and the output ones, determine the multilayer (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 Logic and Genetic Algorithms; Prentice-Hall of India Pvt. Ltd.: New Delhi, India, 2006. (15) Haykin, S.; Hamilton, O. Neural Networks, 2nd ed.; Prentice Hall International, Inc.: Upper Saddle River, NJ, 1998. (16) Kalogirou, S. A. Applications of artificial neural networks in energy systems: A review. Energy ConVers. Manage. 1999, 40, 1073–1087. (17) Kalogirou, S. A. Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks. Appl. Energy 2000, 66, 63–74. (18) Kalogirou, S. A. Optimization of solar systems using neural networks and genetic algorithms. Appl. Energy 2004, 77 (4), 383–405. (19) Kalogirou, S. A.; Bojic, M. Artificial neural networks for the prediction of the energy consumption of a passive-solar building. Energy 2000, 25, 479–491. (20) Zahedi, G.; Fgaier, H.; Jahanmiri, A.; Al-Enezi, G. Artificial neural network dentification and evaluation of hydrotreater plant. Pet. Sci. Technol. 2006, 24, 1447–1456. (21) Zahedi, G.; Jahanmiri, A.; Rahimpor, M. R. A neural network approach 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 the unsteady state simulation and analysis of a packed bed reactor for CO2 hydrogenation to methanol. Chem. Eng. J. 2005, 115, 113–120.

where I is the number of elements in the input vector xi, wij and wjk are the interconnection weights, and b is the “bias” for the neuron.23 Note that neuron output only depends upon information that is locally available at the neuron, either stored internally or arrived via the weighted coefficients. The neuron output is a calculated summation of weighted inputs with a bias through an “activation function”. This activation function computes its output as below

[( ∑ ) I

hidden layer ) HLj ) f

]

bias1

wijxi +

i)1

and

[(

output layer ) f

I

∑ HL w

j jk

i)1

) ]

+ bias2 (2)

Generally, NNs are trained by adjusting the weighting coefficients to reach from a particular input to a specific target using a suitable learning method until the network output approaches the target. The error between the output of the network and the target, i.e., the desired output, is minimized by optimal selection of the weights and biases. The training process is ceased when the error falls below a determined value or the maximum number of epochs is exceeded. There are different learning algorithms that can be applied to train a NN. The most popular algorithm is the back-propagation algorithm, which is a gradient descent algorithm. It is very difficult to know which training algorithm will be suitable for a given problem, and the best one is usually chosen by trial and error. An ANN with a back-propagation algorithm learns by changing the connection weights, and these changes are stored as knowledge. ANN is trained by presenting it with a set of known inputs and outputs. It learns the patterns of these inputs and outputs by manipulating the weights. The weights are adjusted until the optimization criterion is minimized. The most widely used criterion is the mean square error (MSE) N

MSE )

∑

1 (P - Pi,simulated)2 N i)1 i,measurement

(3)

where N is the total number of output values used for training and P refers to the output values.7 4. Input and Output Data To build an ANN model, CRU data were collected from Tabriz refinery in Iran. In data selection, component analyses were carried out, and to ensure that they represent normal operating ranges, off data were deleted from the data list. Finally, 90 data sets were obtained. The variables of the model and their operating ranges are summarized in Table 1. Among 90 data (23) Haykin, S. Neural Networks: A ComprehensiVe Foundation; MacMillan: New York, 1994.


Zahedi et al.

Table 1. ANN Input Variables and Their Range quantity

value

heavy naphtha flow rate-feed (m3/h) hydrogen flow rate (m3/h) hydrogen humidify (mol %) feed temperature (°C) H2S (ppm) H2/HC (mol/mol) EDC (ppm/feed flow rate) H2O (ppm/feed flow rate) feed specific gravity reactors pressure (kg/cm2) reactor product separator pressure (kg/cm2) debutanizer reflux ratio (m3/h) debutanizer pressure (kg/cm2) debutanizer feed temperature (°C) top debutanizer column temperature (°C) bottom debutanizer column temperature (°C)

85-89 71 063.2-76 529.6 10-51 496-499 0-1 3.7-4.52 0.6-1.2 0-2.36 0.7410-0.7505 29-31.5 21-22.2 3.5-4.4 17-17.5 132-154 53-71 202-218

sets, 62 were used for training the ANN and the remaining 28 data sets were used for accuracy checks of the best obtained networks.12 The inputs to the network were the operating pressure, feed volume flow rate, feed temperature, feed specific gravity, hydrogen/hydrocarbon ratio, hydrogen humidity (mol %), H2O, EDC, H2S, reactor product separator pressure, volume flow rate of hydrogen, feed, bottom and top temperature of debutanizer column, debutanizer column pressure, and reflux ratio, and the outputs were the volume flow rate of hydrogen, gasoline and liquid petroleum gas, (LPG.G-LPG.L), outlet temperature for four reactors, gasoline specific gravity, Reid vapor pressure (RVP) of gasoline (at 38 °C), and RON.

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

5. Simulation of CRU Using ANN and Results As mentioned earlier, the NN used in this study has a feedforward structure trained using the back-propagation and radial basis function (RBF) method. The optimum number of hidden layers and nodes within each layer are problem-specific, and there is not a procedure to know this quantity in advance. For this reason, a trial and error approach (multiple runs) was followed to find best network architecture. These included one and two hidden layers and 40-120 nodes per each hidden layer. The activation function used in the hidden nodes is the sigmoid function7 1 (4) 1 + e-x where x is the sum of the weighted inputs to the neuron and f(x) represents the output of the node. As for the output layer nodes, a simple linear activation function was employed, f(x) ) x. For the RBF network, the activation function used in the hidden nodes is the radial basis transfer function7 f(x) )

f(x) ) e-x (5) Inputs of a network should be selected carefully if the best results are expected to be achieved. The input variables should reflect the underlying physics of the process to be analyzed. Various architectures of multilayer perceptron (MLP), RBF, and back propagation (BP) are used to predict measurement CRU outlet. BP networks with biases, a sigmoid layer, and a linear layer are capable of approximating any function with a finite number of discontinuities. Each type of input and output data were scaled by dividing to a maximum amount of that variable for scaling purposes. Each ANN has been trained with 2/3 of the data set, and 1/3 of samples have been used for testing the 2

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

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

∑| N

error )

|

measurement - sim 100 N i)1 measurement i

(6)

In this part of study, the objective is to find the optimal performance ANN model for CRU. Radial basis networks may require more neurons than standard feed-forward back-propagation networks, but often they can be designed in faster than feed-forward networks. They have good performance when many training vectors are available, and they are robust to noisy data. There are 16 input vectors and 7 output vectors. The task was to find the optimum number of nodes in the hidden layer that provides a good estimate of the outputs. The criterion for selection was MSE between net output and test data. The first network was for training the volume flow rate of gasoline, outlet temperature of reactors, gasoline specific gravity, and RON. The optimum number of hidden nodes was found to be 62 (Figure 3), and MSE in the test step was 1.08 × 10-26. The spread was selected 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 of best RBF network for the flow rate of gasoline, outlet temperature of reactors, product specific gravity, and RON are illustrated in Figures 5–11. In the second step for training the volume flow rate of hydrogen, liquid petroleum gas (LPG.G-LPG.L), and RVP for



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

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

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



gasoline, a feed-forward network, back-propagation architecture with a conjugate-gradient training algorithm was adopted. The task was to find the optimum number of nodes in the hidden layer that provide good estimates of the outputs. The criterion for 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 flow rate of hydrogen, liquid petroleum gas (LPG.G-LPG.L), and RVP are illustrated in Figures 13–16. Table 2 represents model outputs and percent of error between best ANN predictions and unseen plant data. The average error


for estimation was 1.07%, which is a very small and unreachable error in engineering applications. 6. Optimization of CRU To Increase Gasoline Production Because accurate and fast-responding ANN models were developed to simulate CRU, the optimization of the plant can be applied using these models. To implement the optimization routine, optimization variables should be selected noting applicability in process and major effect on objective function. From a kinetic point of view, an increase in the temperature and a decrease in the hydrogen/hydrocarbon molar ratio have positive impact on the reforming interactions, especially an increase rate of cyclization and hydrocracking. An increase in temperature causes a fall in the volume flow rate of gasoline


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

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

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

production and a decrease in pressure leads in a small rise in volume flow rate of gasoline production. A low hydrogen/ hydrocarbon molar ratio moves the chemical equilibrium to excessive coke production.8,9 Feed compositions are also affecting kinetics, but with regard to process operation, only pressure, temperature, and hydrogen/feed ratios can be changed in the real plant. The objective function for optimization was set to gasoline production. In this case, the best RBF network can be used for optimization. The optimization of the process condition of every unit for increasing output and decreasing energy consuming is a very important factor that is directly relate to the economical aspect. Using the CRU model effect of operating parameters of the platforming unit, i.e., temperature, pressure, and mole hydrogen/

Zahedi et al.

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

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

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

hydrocarbon ratio, on the gasoline production was studied. Table 3 shows optimization variables and their applicable range of change in the real plant.24 Figures 17–19 illustrate the effect of temperature, pressure, and mole hydrogen/hydrocarbon ratio on the rate of gasoline production. As indicated in Figure 17, the feed temperature first has a positive effect on gasoline production and, despite kinetic predictions,8,9 after T ) 499 °C, an increasing temperature decreases gasoline production. The same phenomenon was observed for effects of reactor pressures (Figure 18). The optimum pressure was found to be 29.8 kg/cm2. Increasing the (24) Al-Shayji, K. A.; Al-Wadyei, S.; Elkamel, A. Modeling and optimization of a multistage flash desalination process. Eng. Optim. 2005, 37 (6), 591–607.



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

percent error

T1 (°C) T2 (°C) T3 (°C) T4 (°C) H2 (m3/h) LPG.G (m3/h) LPG.L (m3/h) gasoline (m3/h) SG RVP (at 38 °C) RON average error

0.0940 0.1120 0.0352 0.0368 1.6510 2.3891 3.0257 0.2878 0.0861 0.8971 0.1672 1.07

Table 3. Optimization Constraints parameters temperature (°C) pressure (kg/cm2) hydrogen/hydrocarbon ratio (mol/mol)

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

limit change 480-540 28-31.9 3.1-7.1

H2/HC ratio first decreases gasoline production until point 3.5. Between 3.5 and 4.3 (optimum ratio), increasing the H2/HC ratio has a positive effect on the gasoline production rate. The interesting phenomenon is that increasing the H2/HC ratio more than 5.5 does not affect gasoline production. The network predicts that the optimum state in the operating conditions of the platforming unit to increase the rate of gasoline production is as followings: temperature of feeding, 499 °C; pressure of CRU reactors, 29.8 kg/cm2; and hydrogen/hydrocarbon ratio, 4.3 mol/mol. In these conditions, gasoline production will be 70 m3/h, which is equal to an 82.38 gasoline yield.

Table 4. Effect of Enhancing the Gasoline Volume Flow Rate on the RON and Specific Gravity volume flow rate of gasoline (m3/h) RON specific gravity

67

70

percent of variation

92.4 0.77

91.5 0.766

1 0.52

Table 4 illustrates the results of the prediction of the network about the effect of gasoline volume flow rate increase on the RON and specific gravity of gasoline production. The table indicates that, by increasing the gasoline volumertric flow rate from 67 to 70 (4.48% increase), RON and specific gravity values decrease 1 and 0.52%, respectively. This decrease is because of a little increase of noncyclic hydrocarbons production. 7. Conclusion In this work, both BP and RBF neural network models were developed for CRU simulation. The models were trained on the basis of measured plant data. The RBF model predicts the reactor outlet temperature, volumetric flow rate of gasoline, RON, and product specific gravity, and BP predicts the volume flow rate of hydrogen, liquid petroleum gas (gas-liquid), and product RVP. The prediction error of the networks is 1.07%. The difference between model predictions and validation data was very small, which confirmed the ability of ANN to accurately predict unseen data. Finally, obtained networks were applied to predict plant optimal operating conditions. Acknowledgment. The authors acknowledge the Iranian National Refinery Company-Tabriz Refinery for its financial support.

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

Nomenclature CRU ) catalytic-reforming unit MSE ) mean square error mea ) measurement data sim ) simulated data N ) total number of output, number of data error ) mean percent error EDC ) ethane dichloride MLP ) multilayer perceptron BP ) backpropagation RBF ) radial basis function SG ) specific gravity T1 ) outlet temperature of reactor number 1 T2 ) outlet temperature of reactor number 2 T3 ) outlet temperature of reactor number 3 T4 ) outlet temperature of reactor number 4 LPG.G ) liquid petroleum gas (gas) LPG.L ) liquid petroleum gas (liquid)

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

EF800025E

Enhancing Gasoline Production in an Industrial Catalytic-Reforming

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