Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Sepehr Sadighi  -  Catalytic Reaction Engineering Department, Catalysis and Nanotechnology Division, Research Institute of Petroleum Industry (RIPI), West Blvd., Azadi Sports Complex, P.O. Box 14665-137, Tehran, Iran, Islamic Republic of
*Reza Seif Mohaddecy  -  Catalytic Reaction Engineering Department, Catalysis and Nanotechnology Division, Research Institute of Petroleum Industry (RIPI), West Blvd., Azadi Sports Complex, P.O. Box 14665-137, Tehran, Iran, Islamic Republic of
Ali Norouzian  -  Chemical Engineering Faculty, Islamic Azad University of Mahshahr, P.O. Box 63519, Khouzestan,, Iran, Islamic Republic of
Received: 27 Jul 2014; Published: 11 Jun 2015.
Open Access
Citation Format:
Cover Image

In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU) is presented. This model is also capable of predicting research octane number (RON) and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days), fifty-eight points are selected for building and training the DANN (60%), nineteen data points for testing (20%), and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating) data with AAD% (average absolute deviation) of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB) of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively. © 2015 BCREC UNDIP. All rights reserved.

Received: 27th July 2014; Revised: 31st May 2015; Accepted: 31th May 2015

How to Cite: Sadighi, S., Mohaddecy, R.S., Norouzian, A. (2015). Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique. Bulletin of Chemical Reaction Engineering & Catalysis, 10(2): 210-220. (doi:10.9767/bcrec.10.2.7171.210-220)

Permalink/DOI: http://dx.doi.org/10.9767/bcrec.10.2.7171.210-220


Keywords: Reforming; Heavy naphtha; Artificial neural network; Deactivation; Life model

Article Metrics:

  1. David, S.J., Pujado, P.P. (2006). Handbook of Petroleum Processing, Springer
  2. Leprine, P. (2001). Conversion Processes, Editions Technip, Paris
  3. Arce-Medina, E., Paz-Paredes, J.A. (2009). Artificial neural network modeling techniques applied to the hydrosulfurization process, Math. Comput. Model., 49: 207-214
  4. Wei, W., Bennett, C.A., Tanaka, R., Hou, G., Klein, M.T. (2008). Detailed kinetic models for catalytic reforming, Fuel Process. Technol., 89: 344-349
  5. Fazeli, A., Fatemi, S., Mahdavian, M., Ghaee, A. (2009). Mathematical Modeling of an Industrial Naphtha Reformer with Three Adiabatic Reactors in Series, Iran J. Chem. Chem. Eng., 28(3): 97-102
  6. Arani, H., Shirvani, M., Safdarian, K., Dorostkar, E. (2009). Lumping procedure for a kinetic model of catalytic naphtha reforming, Braz. J. Chem. Eng., 26: 723-732
  7. Serra, J.M., Corma, A., Argente, E., Valero, S., Botti, S. (2003). Neural networks for modelling of kinetic reaction data applicable to catalyst scale up and process control and optimisation in the frame of combinatorial catalysis, Appl. Catal. A: Gen. 254: 133-145
  8. Perazzini, H.F., Freire, B., Freire, J.T. (2013). Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Net- work Models, Chem. Eng. Technol., 36: 1-10
  9. Bellos, G.D., Kallinikos, L.E., Gounaris, C.E., Papayannakos, N.G. (2005). Modeling the performance of industrial HDS reactors using a hybrid neural network approach, Chem. Eng. Process., 44: 505-515
  10. Bhutani, N., Rangaiah, G.P., Ray, A.K. (2006). First-Principles, Data-Based, and Hybrid Modeling and Optimization of an Industrial Hydrocracking Unit, Ind. Eng. Chem. Res., 45: 7807-7816
  11. Wang, W., Zhang, Q., Ding, L., Zheng, Y. (2010). Simulation of Hydrosulfurization Using Artificial Neural Network, Can. J. Chem. Eng., 88: 801-807
  12. Sadighi, S., Ahmad, A., Irandoukht, A. (2010). Modeling a Pilot Fixed-bed Hydrocracking Reactor via a Kinetic Base and Neuro-Fuzzy Method, J. Chem. Eng. Jpn., 43: 174-185
  13. Sadighi, S., Zahedi, G. (2010). Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor, Bull. Chem. React. Eng. Catal., 8(2): 125-136. http://dx.doi.org/10.9767/bcrec.8.2.4722.125-136" target="_blank" >[CrossRef]
  14. Niaei, A., Towfighi, J., Khataee, A.R., Ro- stamizadeh, K. (2007). The Use of ANN and the Mathematical Model for Prediction of the Main Product Yields in the Thermal Cracking of Naphtha, Petrol. Sci. Technol., 25: 967-982
  15. Belohlav, Z., Zamostny, P., Herink, T., Eckert, E., Vanek, T. (2005). A Novel Ap- proach for the Prediction of Hydrocarbon Thermal Cracking Product Yields from the Substitute Feedstock Composition, Chem. Eng. Technol., 28: 1166-1176
  16. Zahedi, G., Lohiy, A., Karami, Z. (2009). A Neural Network Approach for Identification and Modeling of Delayed Coking Plant, Int. J. Chem. React. Eng., 7: 1-25
  17. Sadighi, S., Zahedi, S., Hayati, R., Bayat, M. (2013). Studying Catalyst Activity in an Isomerization Plant to Upgrade the Octane Number of Gasoline by Using a Hybrid Artificial Neural Network Model, Ener. Technol., 1: 743-750
  18. Istadi, I., Amin, N.A.S. (2006). Hybrid Artificial Neural Network-Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor, Ind. Eng. Chem. Res. Catal., 45: 6655-6664
  19. Istadi, I., Amin, N.A.S. (2007). Catalytic-Dielectric Barrier Discharge Plasma Reactor for Methane and Carbon Dioxide Conversion, Bull. Chem. React. Eng. Catal., 2: 37-44 (DOI: 10.9767/bcrec.2.2-3.8.37-44)
  20. Istadi, I., Amin, N.A.S. (2007). Modelling and optimization of catalytic dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network-genetic algorithm technique, Chem. Eng. Sci., 62: 6568-6581
  21. Mjalli, F.S., Al-Mfargi, A. (2009). Neural Network–Based Heat and Mass Transfer Coefficients for the Hybrid Modeling of Fluidized Reactors, Chem. Eng. Commun., 197: 318-342
  22. Manamalli, D., Kanagasabapathy, P., Dhivya, K. (2006). Expert Optimal Control of Catalytic Reformer Using ANN, Chem. Eng. Commun., 193: 729-742
  23. Zahedi, G., Mohammadzadeh, S., Moradi, M. (2008). Enhancing Gasoline Production in an Industrial Catalytic-Reforming Unit Using Artificial Neural Networks, Energ. Fuel., 22: 2671-2677
  24. Alves, R.M.B, Menten, F., Maejima, W.S., Guardani, R., Nascimento, C.A. (2008). A study on naphtha catalytic reforming reactor simulation and analysis, 18th European Symposium on Computer Aided Process Engineering, Bertrand Braunschweig and Xavier Jou- lia (Editors)
  25. Sadighi, S., Mohaddecy, R.S. (2013). Predictive Modeling for an Industrial Naphtha Reforming Plant Using Artificial Neural Net- work with Recurrent Layers. International Journal of Technology, 2: 1-11
  26. Weifeng, H., Hongye, S.U., Yongyou, H.U., Jian, C.H.U. (2006). Modeling, simulation and optimization of whole industrial catalytic naphtha reforming process on Aspen plus platform, Chinese J. Chem. Eng., 14: 584-591
  27. Chaturvedi, D.V. (2010). Modeling and simulation of systems using MATLAB and Simu- link, CRC Press, Taylor & Francis Group, New York
  28. Lewandowski, J., Lemcoff, N.O., Palosaari, S. (1998). Use of Neural Networks in the Simu- lation and Optimization of Pressure Swing Adsorption Processes, Chem. Eng. Technol., 21: 593-597
  29. Hagan, M.T., Demuth, H.B., Beale, M. (1995). Neural Network Design, PWS Publishing Company, Boston
  30. Haykin, S., Hamilton, O. (1998). Neural Networks, 2nd ed., Prentice Hall International, Inc., Upper Saddle River
  31. Przystalka, P. (2008). Model-Based Fault Detection and Isolation Using Locally Recurrent Neural Networks, In proceeding of: Artificial Intelligence and Soft Computing. ICAISC, 9th International Conference, Poland: 123-134
  32. Sadighi, S., Ahmad, A. (2013). An Optimisation Approach for Increasing the Profit of a Commercial VGO Hydrocracking Process, Can. J. Chem. Eng., 91: 1077–1091
  33. Rahimpour, M.R. (2006). Operability of an Industrial Catalytic Naphtha Reformer in the Presence of Catalyst Deactivation, Chem. Eng. Technol., 29: 616-624
  34. Kravtsov, A.V., Ivanchina, E.D., Averin, S.N., Fedorov, A.A., Krupenya, L.V., Poluboyartsev, D.S. (2004). Activity and Stability of Platinum Reforming Catalysts, Chem. Tech. Fuels Oils, 40: 176-180

Last update: 2021-03-01 19:31:09

No citation recorded.

Last update: 2021-03-01 19:31:11

  1. Modeling a commercial vacuum gas oil hydrocracking plant using adaptive-neuro fuzzy inference system (ANFIS)

    Sadighi S.. Petroleum and Coal, 59 (2), 2017.
  2. Intensification of flow blending technology in the production of motor fuels by the method of mathematical modelling

    Ivanchina E.. Chemical Engineering and Processing: Process Intensification, 122 , 2017. doi: 10.1016/j.cep.2017.07.015
  3. Operation parameter optimization of complex distillation system based on kriging surrogate

    Liu S.. Petroleum Processing and Petrochemicals, 51 (1), 2020.
  4. Catalytic Reforming Reactor Section Optimization Based on A Mathematical Model Accounting the Reaction Volume Changes

    Gubaydullin I.. 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019, 2019. doi: 10.1109/OPCS.2019.8880251
  5. Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit

    Dias T.. Computers and Chemical Engineering, 127 , 2020. doi: 10.1016/j.compchemeng.2020.106912
  6. Modeling, optimization and experimental studies of supported nano-bimetallic catalyst for simultaneous total conversion of toluene and cyclohexane in air using a hybrid intelligent algorithm

    Zabihi M.. RSC Advances, 8 (31), 2018. doi: 10.1039/c8ra01504j
  7. Bulletin of chemical reaction engineering & catalysis, 10 (2), 2015, VI

    Istadi I.. Bulletin of Chemical Reaction Engineering & Catalysis, 10 (2), 2015. doi: 10.9767/bcrec.10.2.8770.iv-vi
  8. Comparison between adaptive-neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) for predicting activity and selectivity of a laboratory scale isomerization plant

    Sadighi S.. Petroleum and Coal, 59 (6), 2017.
  9. Multi-Criterion Optimization of a Catalytic Reforming Reactor Unit Using a Genetic Algorithm

    Zainullin R.Z.. Catalysis in Industry, 12 (2), 2020. doi: 10.1134/S2070050420020129
  10. Evaluating the ability of R for modeling a commercial scale VGO hydrocracking plant using artificial neural network (ANN)

    Sadighi S.. Petroleum and Coal, 60 (3), 2018.
  11. Modeling and optimization of methanol steam reforming reaction over Cu/ZnO/Al2O3–ZrO2 catalyst using a hybrid artificial neural network

    Mobarake M.. Indian Journal of Chemical Technology, 26 (2), 2019.