Artificial Intelligent Modeling and Optimizing of an Industrial Hydrocracker Plant

Document Type : Original Paper


Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran


The main objective of this study is the modelling and optimization of an industrial Hydrocracker Unit (HU) by means of Adaptive Neuro Fuzzy Inference System (ANFIS) model. In this case, some data were collected from an industrial hydrocracker plant. Inputs of an ANFIS include flow rate of fresh feed and recycle hydrogen, temperature of reactors, mole percentage of H2 and H2S, feed flow rate and temperature of debutanizer, pressure of debutanizer receiver, top and bottom temperature and pressure of fractionator column. The network was employed to calculate the flow rate of gas oil, kerosene, Light Naphtha (LN), and Heavy Naphtha (HN).Unseen data points were used to check generalization capability of the best network. There were good adjustment between network estimations and unseen data. Finally optimization was performed to maximize the production volume percent of gas oil, kerosene, HN and LN and to identify the sets of optimum operating parameters in order to maximize yields of mentioned product. Optimum conditions were found as feed flow rate of 90.9 m3/h, reactor temperature of 378.4 °C, hydrogen flow rate of 54.31 MSCM/h and LN (feed vol.%) of 9.34.


[1] Maples, R.E. (2000). Petroleum Refinery Process Economics, second edition.
[2] William, L.C. (2005). Standard Handbook of Petroleum and Natural Gas Engineering, second edition, Gulf Professional Publishing, USA.
[3] Bhutani, N., Rangaiah, G.P. and Ray, A.K. (2006b). "First-principles, data-Based, and hybrid modeling and optimization of an industrial hydrocracking unit." Ind. Eng. Chem. Res., Vol. 45, No. 23, pp. 7807–7816.
[4] Aguiar, H.C. and Filho, R.M. (2001)."Neural network and hybrid model: a discussion about different modeling techniques to predict pulping degree with industrial data." Chem. Eng. Sci., Vol. 56, No. 2, pp. 565–570.
[5] Al-Enzi, G. and Elkamel, A. (2000). "Predicting the effect of feedstock on product yields and properties of the FCC process." Petrol. Sci. Technol., Vol. 18, No. 3-4, pp. 407–428.
[6] Bellos, G.D., Kallinikos, L.A., Gounaris, C.E. and Papayannakos, N.G. (2005). "Modeling of the performance of industrial HDS reactors using a hybrid neural network approach." Chem. Eng. Process., Vol. 44, No. 5, pp. 505–515.
[7] Bollas, G.M., Papadokonstantakis, S., Michalopoulos, J., Arampatzis, G., Lappas, A.A., Vasalos, I.A. and Lygeros, A. (2003). "Using hybrid neural networks in scaling up an FCCmodel froma pilot plant to an industrial unit." Chem. Eng. Process., Vol. 42, No. 8-9, pp. 697–713.
[8] Bollas, G.M., Papadokonstantakis, S., Michalopoulos, J., Arampatzis, G., Lappas, A.A., Vasalos, I.A. and Lygeros, A. (2004). "A computer-aided tool for the simulation and optimization of the combined HDS-FCC processes." Chem. Eng. Res. Des., Vol. 82, No. 7, pp. 881–894. Artificial Intelligent Modeling ….. 137
[9] Falla, F.S., Larini, C., Le Roux, G.A.C., Quina, F.H., Moro, L.F.L. and Nascimento, C.A.O. (2006). "Characterization of crude petroleum by NIR." J. Petrol. Sci. Eng., Vol. 51, No. 1-2, pp. 127–137.
[10] Shirvani,Y., Zahedi, G. and Bashiri, M. (2010). "Estimation of Sour Natural Gas Water Content." J. Pet. Sci. Eng., Vol. 73, No. 1-2, pp. 156–160.
[11] Zahedi, G., Fgaier, H., Jahanmiri, A. and Al-Enezi, G. (2006). "Artificial neural network identification and evaluation of hydrotreater plant." Petrol. Sci. Technol., Vol. 24, No. 12, pp. 1447–1456.
[12] Zahedi, G., Parvizian, F. and Rahimi, M.R. (2010). "An expert model for estimation of distillation sieve tray efficiency based on artificial neural network approach." J. Appl. Sci., Vol. 10, No. 12, pp. 1076–1082.
[13] Aminian, J. and Shahhosseini, S. (2008). "Evaluation of ANN modeling for prediction of crude oil fouling behavior." Appl. Therm. Eng., Vol. 28, No. 7, pp. 668–674.
[14] Motlaghi Jalali, F. and Nili Ahmadabadi, M. (2008). "An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework." Expert Syst. Appl., Vol. 35, No. 4, pp. 1540–1545.
[15 Lotfi Zadeh, A. (1965). Fuzzy sets. Information Control, 8th edition, pp. 338-353.
[16] Jang, J. S. R., Sun, C. T. and Mizutanim, E. (1997). Neuro-fuzzy and soft computing, Prentice-Hall, Inc.
[17] Zhou, Y., Li, S. and Jin, R. (2002). "A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control." Fuzzy Sets Syst., Vol. 132, No. 2, pp. 201-216.
[18] Aliyari Shoorehdeli, M., Teshnehlab, M., Khaki Sedigh, A. and Ahmadieh Khanesar, M. (2009). "Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods." Appl. Soft Compt., Vol. 9, No. 2, pp. 833-850.
[19] Marafi, A. Kam, E. and Stanislaus, A. (2008). "A Kinetic Study on Non-Catalytic Reactions in Hydroprocessing Boscan Crude Oil." Fuel, Vol. 87, No. 10-11, pp. 2131–2140.