Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
-
Mahmud Atarianshandiz
und
Akbar Shahsavand
Abstract
This paper applies an existing advanced model to improve key outputs in the continuous catalytic reforming (CCR) process for Persian Gulf Star Oil Company. Using tools like Aspen Custom Modeler and Aspen Plus, we focus on optimizing two main results: Research Octane Number (RON) and yield. A design of experiments was conducted to examine the effects of key input variables, including reactor temperatures and the hydrogen-to-hydrocarbon (H₂/HC) ratio, through 256 simulations. Various data fitting methods, including Response Surface Methodology (RSM), Radial Basis Function Network (RNLOOCV), and Artificial Neural Networks (ANN), were applied to describe process behavior. The Akaike Information Criterion (AIC)-optimized ANN model demonstrated the best performance, offering a balanced approach between accuracy and complexity. A sensitivity analysis revealed that increasing reactor temperatures improves RON but reduces yield due to enhanced cracking reactions. The H₂/HC ratio had a minimal impact on RON and yield, primarily serving to limit catalyst coke formation. Optimization using a genetic algorithm confirmed that optimal RON and yield can be achieved within specific temperature ranges. The results provide insights for enhancing CCR efficiency and refinery profitability.
-
Research ethics: Not applicable.
-
Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: All data generated or analyzed during this study are included in this published article.
References
1. Antos, GJ, Aitani, AM. Catalytic naphtha reforming, revised and expanded. Florida, USA: CRC Press; 2004.10.1201/9780203913505Suche in Google Scholar
2. Hongjun, Z, Mingliang, S, Huixin, W, Zeji, L, Hongbo, J. Modeling and simulation of moving bed reactor for catalytic naphtha reforming. Petrol Sci Technol 2010;28:667–76. https://doi.org/10.1080/10916460902804598.Suche in Google Scholar
3. Lapinski, MP. Catalytic reforming in petroleum processing. In: Handbook of Petroleum Processing. Germany: Springer; 2015:229–60 pp.10.1007/978-3-319-14529-7_1Suche in Google Scholar
4. Iranshahi, D, Rafiei, R, Jafari, M, Amiri, S, Karimi, M, Rahimpour, MR. Applying new kinetic and deactivation models in simulation of a novel thermally coupled reactor in continuous catalytic regenerative naphtha process. Chem Eng J 2013;229:153–76. https://doi.org/10.1016/j.cej.2013.05.052.Suche in Google Scholar
5. Donald, M. Catalytic reforming. Tulsa: OK: Penn Well Publishing Company; 1985.Suche in Google Scholar
6. Babaqi, BS, Takriff, MS, Kamarudin, SK, Othman, NTA. Mathematical modeling, simulation, and analysis for predicting improvement opportunities in the continuous catalytic regeneration reforming process. Chem Eng Res Des 2018;132:235–51. https://doi.org/10.1016/j.cherd.2018.01.025.Suche in Google Scholar
7. Iranshahi, D, Karimi, M, Amiri, S, Jafari, M, Rafiei, R, Rahimpour, MR. Modeling of naphtha reforming unit applying detailed description of kinetic in continuous catalytic regeneration process. Chem Eng Res Des 2014;92:1704–27. https://doi.org/10.1016/j.cherd.2013.12.012.Suche in Google Scholar
8. Rahimpour, MR, Jafari, M, Iranshahi, D. Progress in catalytic naphtha reforming process: a review. Appl Energy 2013;109:79–93. https://doi.org/10.1016/j.apenergy.2013.03.080.Suche in Google Scholar
9. Yusuf, AZ, Aderemi, BO, Patel, R, Mujtaba, IM. Study of industrial naphtha catalytic reforming reactions via modelling and simulation. Processes 2019;7:192. https://doi.org/10.3390/pr7040192.Suche in Google Scholar
10. Marin, GB, Froment, GF, Lerou, JJ, De Backer, W. Simulation of a catalytic naphtha reforming unit. Amsterdam, Netherlands: Elsevier; 1983:C117.1–7 pp. https://doi.org/10.1021/ef800771x.Suche in Google Scholar
11. Rodríguez, MA, Ancheyta, J. Detailed description of kinetic and reactor modeling for naphtha catalytic reforming. Fuel 2011;90:3492–508. https://doi.org/10.1016/j.fuel.2011.05.022.Suche in Google Scholar
12. Ancheyta-Juárez, J, Villafuerte-Macías, E. Kinetic modeling of naphtha catalytic reforming reactions. Energy & Fuel 2000;14:1032–7. https://doi.org/10.1021/ef0000274.Suche in Google Scholar
13. Marin, GB, Froment, GF. Reforming of C6 hydrocarbons on a Pt⋅Al2O3 catalyst. Chem Eng Sci 1982;37:759–73. https://doi.org/10.1016/0009-2509(82)85037-9.Suche in Google Scholar
14. Marin, GB, Froment, GF, Lerou, JJ, De Backer, W. Simulation of a catalytic naphtha reforming unit 1983:C117.1–7.Suche in Google Scholar
15. Smith, R. Kinetic analysis of naphtha reforming with platinum catalyst. Chem Eng Prog 1959;55:76–80.Suche in Google Scholar
16. Jiang, H, Sun, Y, Jiang, S, Li, Z, Tian, J. Reactor model of counter-current continuous catalyst-regenerative reforming process toward real time optimization. Energy & Fuel 2021;35:10770–85. https://doi.org/10.1021/acs.energyfuels.1c00812.Suche in Google Scholar
17. Shakor, ZM, AbdulRazak, AA, Sukkar, KA. A detailed reaction kinetic model of heavy naphtha reforming. Arabian J Sci Eng 2020;45:7361–70. https://doi.org/10.1007/s13369-020-04376-y.Suche in Google Scholar
18. Iranshahi, D, Bahmanpour, A, Paymooni, K, Rahimpour, M, Shariati, A. Simultaneous hydrogen and aromatics enhancement by obtaining optimum temperature profile and hydrogen removal in naphtha reforming process; a novel theoretical study. Int J Hydrogen Energy 2011;36:8316–26. https://doi.org/10.1016/j.ijhydene.2011.04.023.Suche in Google Scholar
19. Mohaddeci, S. Reactor modeling and simulation of catalytic reforming process. Petroleum & Coal 2006;48:28–35.Suche in Google Scholar
20. Rahimpour, MR, Iranshahi, D, Pourazadi, E, Bahmanpour, AM. Boosting the gasoline octane number in thermally coupled naphtha reforming heat exchanger reactor using de optimization technique. Fuel 2012;97:109–18. https://doi.org/10.1016/j.fuel.2012.01.015.Suche in Google Scholar
21. Talaghat, MR, Roosta, AA, Khosrozadeh, I. A novel study of upgrading catalytic reforming unit by improving catalyst regeneration process to enhance aromatic compounds, hydrogen production, and hydrogen purity. J Chem Petrol Eng 2017;51:81–94.Suche in Google Scholar
22. Askari, A. Simulation and modeling of catalytic reforming process. Petroleum & Coal 2012;54:76–84.Suche in Google Scholar
23. Boukezoula, T, Bencheikh, L, Belkhiat, D. A heterogeneous model for a cylindrical fixed bed axial flow reactors applied to a naphtha reforming process with a non-uniform catalyst distribution in the pellet. React Kinet Mech Catal 2020;131:335–51. https://doi.org/10.1007/s11144-020-01851-3.Suche in Google Scholar
24. Lid, T, Skogestad, S. Data reconciliation and optimal operation of a catalytic naphtha reformer. J Process Control 2008;18:320–31. https://doi.org/10.1016/j.jprocont.2007.09.002.Suche in Google Scholar
25. Otaraku, IJ, Egun, IL. Optimization of hydrogen production from Nigerian crude oil samples through continuous catalyst regeneration (CCR) reforming process using aspen hysys. Am J Appl Chem 2017;5:69–72. https://doi.org/10.11648/j.ajac.20170505.11.Suche in Google Scholar
26. Ramage, MP, Graziani, KR, Krambeck, F. 6 Development of mobil’s kinetic reforming model. Chem Eng Sci 1980;35:41–8. https://doi.org/10.1016/0009-2509(80)80068-6.Suche in Google Scholar
27. Froment, G. The kinetics of complex catalytic reactions. Chem Eng Sci 1987;42:1073–87. https://doi.org/10.1016/0009-2509(87)80057-x.Suche in Google Scholar
28. Taskar, U, Riggs, JB. Modeling and optimization of a semiregenerative catalytic naphtha reformer. AIChE J 1997;43:740–53. https://doi.org/10.1002/aic.690430319.Suche in Google Scholar
29. Vathi, GP, Chaudhuri, KK. Modelling and simulation of commercial catalytic naphtha reformers. Can J Chem Eng 1997;75:930–7. https://doi.org/10.1002/cjce.5450750513.Suche in Google Scholar
30. Taghavi, B, Fatemi, S. Modeling and application of response surface methodology in optimization of a commercial continuous catalytic reforming process. Chem Eng Commun 2014;201:171–90. https://doi.org/10.1080/00986445.2013.766601.Suche in Google Scholar
31. Mahdavian, M, Fatemi, S, Fazeli, A. Modeling and simulation of industrial continuous naphtha catalytic reformer accompanied with delumping the naphtha feed. Int J Chem React Eng 2010;8. https://doi.org/10.2202/1542-6580.1859.Suche in Google Scholar
32. Krane, H. 4. Reactions in catalytic reforming of naphthas. In: 5th World Petroleum Congress. New York, USA: OnePetro; 1959.Suche in Google Scholar
33. Ancheyta-Juarez, J, Villafuerte-Macías, E, Díaz-García, L, González-Arredondo, E. Modeling and simulation of four catalytic reactors in series for naphtha reforming. Energy Fuel 2001;15:887–93. https://doi.org/10.1021/ef000273f.Suche in Google Scholar
34. Elizalde, I, Ancheyta, J. Dynamic modeling and simulation of a naphtha catalytic reforming reactor. Appl Math Model 2015;39:764–75. https://doi.org/10.1016/j.apm.2014.07.013.Suche in Google Scholar
35. Hou, W. Lumped kinetics model and its on-line application to commercial catalytic naphtha reforming process. J Chem Industry and Eng-China 2006;57:1605.Suche in Google Scholar
36. Weifeng, H, Hongye, S, Shengjing, M, Jian, C. Multiobjective optimization of the industrial naphtha catalytic reforming process. Chin J Chem Eng 2007;15:75–80. https://doi.org/10.1016/s1004-9541(07)60036-6.Suche in Google Scholar
37. Stijepovic, V, Linke, P, Alnouri, S, Kijevcanin, M, Grujic, A, Stijepovic, M. Toward enhanced hydrogen production in a catalytic naphtha reforming process. Int J Hydrogen Energy 2012;37:11772–84. https://doi.org/10.1016/j.ijhydene.2012.05.103.Suche in Google Scholar
38. Fazeli, A. Mathematical modeling of an industrial naphtha reformer with three adiabatic reactors in series. Iran J Chem Chem Eng 2009;28:97–102.Suche in Google Scholar
39. Dong, X-J, He, YJ, Shen, JN, Ma, ZF. Multi-zone parallel-series plug flow reactor model with catalyst deactivation effect for continuous catalytic reforming process. Chem Eng Sci 2018;175:306–19. https://doi.org/10.1016/j.ces.2017.10.007.Suche in Google Scholar
40. Wang, L, Zhang, Q, Liang, C. 38-lumped kinetic model for reforming reaction and its application in continuous catalytic reforming. CIESC J 2012;63:1076–82.Suche in Google Scholar
41. Iranshahi, D, Jafari, M, Rafiei, R, Karimi, M, Amiri, S, Rahimpour, MR. Optimal design of a radial-flow membrane reactor as a novel configuration for continuous catalytic regenerative naphtha reforming process considering a detailed kinetic model. Int J Hydrogen Energy 2013;38:8384–99. https://doi.org/10.1016/j.ijhydene.2013.04.059.Suche in Google Scholar
42. Jafari, M, Rafiei, R, Amiri, S, Karimi, M, Iranshahi, D, Rahimpour, MR, et al.. Combining continuous catalytic regenerative naphtha reformer with thermally coupled concept for improving the process yield. Int J Hydrogen Energy 2013;38:10327–44. https://doi.org/10.1016/j.ijhydene.2013.06.039.Suche in Google Scholar
43. Saeedi, R, Iranshahi, D. Multi-objective optimization of thermally coupled reactor of CCR naphtha reforming in presence of SO2 oxidation to boost the gasoline octane number and hydrogen. Fuel 2017;206:580–92. https://doi.org/10.1016/j.fuel.2017.04.024.Suche in Google Scholar
44. Saeedi, R, Iranshahi, D. Hydrogen and aromatic production by means of a novel membrane integrated cross flow CCR naphtha reforming process. Int J Hydrogen Energy 2017;42:7957–73. https://doi.org/10.1016/j.ijhydene.2017.01.118.Suche in Google Scholar
45. Zagoruiko, AN, Belyi, AS, Smolikov, MD, Noskov, AS. Unsteady-state kinetic simulation of naphtha reforming and coke combustion processes in the fixed and moving catalyst beds. Catal Today 2014;220:168–77. https://doi.org/10.1016/j.cattod.2013.07.016.Suche in Google Scholar
46. Babaqi, BS, Takriff, MS, Othman, NTA, Kamarudin, SK. Yield and energy optimization of the continuous catalytic regeneration reforming process based particle swarm optimization. Energy 2020;206. https://doi.org/10.1016/j.energy.2020.118098.Suche in Google Scholar
47. Al-Shathr, A, Shakor, ZM, Majdi, HS, AbdulRazak, AA, Albayati, TM. Comparison between artificial neural network and rigorous mathematical model in simulation of industrial heavy naphtha reforming process. Catalysts 2021;11:1034. https://doi.org/10.3390/catal11091034.Suche in Google Scholar
48. Hu, Y, Su, H, Chu, J. Modeling, simulation and optimization of commercial naphtha catalytic reforming process. In: 42nd IEEE International Conference on Decision and Control (IEEE Cat. No. 03CH37475). Maui, USA: IEEE; 2003.Suche in Google Scholar
49. Mohaddecy, SRS, Sadighi, S, Bahmani, M. Optimization of catalyst distribution in the catalytic naphtha reformer of Tehran Refinery. Petroleum & Coal 2008;50:60–8.Suche in Google Scholar
50. Atarianshandiz, M, McAuley, KB, Shahsavand, A. Modeling and parameter tuning for continuous catalytic reforming of naphtha in an industrial reactor system. Processes 2023;11:2838. https://doi.org/10.3390/pr11102838.Suche in Google Scholar
51. Sotelo-Boyas, R, Froment, GF. Fundamental kinetic modeling of catalytic reforming. Ind Eng Chem Res 2009;48:1107–19. https://doi.org/10.1021/ie800607e.Suche in Google Scholar
52. Zagoruiko, AN, Belyi, AS, Smolikov, MD. Thermodynamically consistent kinetic model for the naphtha reforming process. Ind Eng Chem Res 2021;60:6627–38. https://doi.org/10.1021/acs.iecr.0c05653.Suche in Google Scholar
53. Ivanchina, E, Chuzlov, V, Ivashkina, E, Nazarova, G, Tyumentsev, A, Vymyatnin, Е. Modeling of motor gasoline components complex production. Catal Today 2021;378:211–18. https://doi.org/10.1016/j.cattod.2020.11.007.Suche in Google Scholar
54. Lee, S. Encyclopedia of chemical processing. Florida, USA: Taylor & Francis US; 2006, 1.Suche in Google Scholar
55. Moljord, K, Hellenes, HG, Hoff, A, Tanem, I, Grande, K, Holmen, A. Effect of reaction pressure on octane number and reformate and hydrogen yields in catalytic reforming. Ind Eng Chem Res 1996;35:99–105. https://doi.org/10.1021/ie940582r.Suche in Google Scholar
56. Loryuenyong, V, Rohing, S, Singhanam, P, Kamkang, H, Buasri, A. Artificial neural network and response surface methodology for predicting and maximizing biodiesel production from waste oil with KI/CaO/Al2O3 catalyst in a fixed bed reactor. ChemPlusChem 2024:e202400117. https://doi.org/10.1002/cplu.202400117.Suche in Google Scholar PubMed
57. Buasri, A, Sirikoom, P, Pattane, S, Buachum, O, Loryuenyong, V. Process optimization of biodiesel from used cooking oil in a microwave reactor: a case of machine learning and Box–Behnken design. ChemEngineering 2023;7:65. https://doi.org/10.3390/chemengineering7040065.Suche in Google Scholar
58. Kutner, MH. Applied linear statistical models. USA: McGraw-Hill; 2005.Suche in Google Scholar
59. Myers, RH, Montgomery, DC, Anderson-Cook, CM. Response surface methodology: process and product optimization using designed experiments. USA: John Wiley & Sons; 2016.Suche in Google Scholar
60. Montgomery, DC. Design and analysis of experiments. USA: John Wiley & Sons; 2017.Suche in Google Scholar
61. Draper, N. Applied regression analysis. USA: McGraw-Hill. Inc.; 1998.10.1002/9781118625590Suche in Google Scholar
62. Bishop, CM, Nasrabadi, NM. Pattern recognition and machine learning. Germany: Springer; 2006, 4.Suche in Google Scholar
63. Haykin, S. Neural networks and learning machines, 3/E. London, England: Pearson Education India; 2009.Suche in Google Scholar
64. Goodfellow, I. Deep learning. USA: MIT press; 2016.Suche in Google Scholar
65. Akaike, H. A new look at the statistical model identification. IEEE Trans Automat Control 1974;19:716–23. https://doi.org/10.1109/tac.1974.1100705.Suche in Google Scholar
66. Burnham, KP, Anderson, DR. Multimodel inference: understanding AIC and BIC in model selection. Socio Methods Res 2004;33:261–304. https://doi.org/10.1177/0049124104268644.Suche in Google Scholar
67. Leontaritis, IJ, Billings, SA. Input-output parametric models for non-linear systems part I: deterministic non-linear systems. Int J Control 1985;41:303–28. https://doi.org/10.1080/0020718508961129.Suche in Google Scholar
68. Zainullin, R, Zagoruiko, AN, Koledina, KF, Gubaidullin, IM, Faskhutdinova, RI. Multi-criterion optimization of a catalytic reforming reactor unit using a genetic algorithm. Catalysis in Industry 2020;12:133–40. https://doi.org/10.1134/s2070050420020129.Suche in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Cogeneration system’s energy performance improvement by using P-graph and advanced process control
- Numerical simulation of R134a evaporation in a cold water production system
- Implementing a radial basis function model to anticipate the outcomes of the gasification
- Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
- Evaluating the therapeutic potential of 4-hydroxyflavanes diastereomers derivatives against (MetAP2) for anti-cancer therapy: a molecular docking study
- Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach
- Natural Gas and hydrogen blending: a perspective on numerical modeling and CFD analysis for transient and steady-state scenarios
- Simulation and optimization of Venturi type bubble generator to improve cavitation
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Cogeneration system’s energy performance improvement by using P-graph and advanced process control
- Numerical simulation of R134a evaporation in a cold water production system
- Implementing a radial basis function model to anticipate the outcomes of the gasification
- Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
- Evaluating the therapeutic potential of 4-hydroxyflavanes diastereomers derivatives against (MetAP2) for anti-cancer therapy: a molecular docking study
- Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach
- Natural Gas and hydrogen blending: a perspective on numerical modeling and CFD analysis for transient and steady-state scenarios
- Simulation and optimization of Venturi type bubble generator to improve cavitation
