Journal of Petroleum Science and Technology

Journal of Petroleum Science and Technology

Development of A Procedure to Improve The Efficiency and Yields of The Gasoline Production Process Through Pseudo-Dynamic Kinetic Modeling and Optimization

Document Type : Research Paper

Author
Sorood Abghari
Division of Refinery Process Technology Development, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
10.22078/jpst.2020.3901.1620
Abstract
Different sets of experiments were designed and conducted in a pilot plant to determine the effect of different operating conditions on the process efficiency and deactivation of the catalyst in the naphtha catalytic reforming process. Based on the experimental results, a kinetic model was developed and adapted for the purposes of prediction. The developed model represents the impacts of all input variables – including operating conditions, physical properties of feedstock, and chloride injection – on research octane number (RON), the yields of reformate l, and outlet reactor temperatures. Finally, by applying the model and an optimization algorithm, a pseudo-dynamic optimization of the process was carried out to minimize energy usage and improve the process efficiency by determining an optimum operating plan that also maximizes the yields of the average octane number and the liquid product. Also, by minimizing the increasing rate of weight average bed temperature of the reactors, the average energy consumption throughout a definite period was minimized. Finally, it is also estimated that by applying this method, the average conversion and average RON could be improved by 6% and 2.5 units respectively.
Keywords
Gasoline Production Process
Deactivation of the Catalyst
Chloride Injection
Kinetic Model
Pseudo-Dynamic Optimization
  1. Xiaofang Liu, RiuWang, (2018) Upgrading of carbohydrates to the biofuel candidate 5-Ethoxymethyl furfural (EMF); International journal of chemical engineering, 1-10. ##
  2. Fan W, Liu Y, Wang K (2016) Detailed experimental study on the performance of monoethanolamine, diethanolamine, and diethylenetriamine at absorption/regeneration conditions, Journal of Cleaner Production, 125, 296-308. ##
  3. Wei M, Yang M, Feng Q, Du W, He W, Zhang W (2017) Dynamic modeling and economic model predictive control with production mode switching for and Industrial catalytic naphtha reforming process, Industrial & Engineering Chemistry Research 56, 31: 8961-8971. ##
  4. Rangaish GP, Sharma S (2017) Advances in Process System Engineering–Vol.6; differential evolution in chemical engineering development and application, World Scientific, 84-85. ##
  5. Mohaddecy SR, Sadighi S, Zahedi S, Bonyad H (2006) Reactor modeling and simulation of catalytic reforming process, Petroleum and Coal Journal 48, 28-35. ##
  6. Belinskaya N, Ivanchina E, Silko G (2014) Effect of feed composition changing at naphta catalytic reforming unit due to involvement of gasoline fraction obtained by diesel fuels hydro-dewaxing into the processing, Procedia Chemistry, 10, 267-270. ##
  7. Kim S, Urm J, Shik Kim D, Lee K, Min Lee J (2018) Heavy naphtha fractions 85-155oC recycling in the catalytic reforming industrial unit, Korean Journal of Chemical Engineering, 35, 2327-2335. ##
  8. Krane H G, Groh A B, Schulman B D, Sinfelt J H (1959)Reactions in catalytic reforming of naphtas, Fifth World Petroleum Congress, 111, 39-51, New York. ##
  9. Marin GB, Froment GF (1982) Reforming of C6 hydrocarbons on a Pt-Al2O3 catalyst, Chemical Engineering Science 37, 759-773.
  10. Padmavathi G (1997) Modeling and simulation of commercial catalytic naphtha reformers, Journal of Chemical Engineering, 75, 930-937. ##
  11. Smith R B, Kinetic analysis of naphtha reforming with platinum catalyst, Chem. Prog., 1959, 55, 76-80.
  12. Sadighi S, Mohaddecy R S, Abbasi A (2018) Modeling and optimizing a vacuum gasoil hydrocracking plant using an artificial neural network, IJ Tech, volume, 4(2), 99-109. ##
  13. Wei W, Craig A B, Tanaka R, Hou G, Klein MT (2008) Detailed kinetic models for catalytic reforming”, Fuel Processing Technology, 89,344-349. ##
  14. Zhorov Y M (1965) Mathematical description of platforming for optimization of a process, Kinetika i Kataliz, 6, 1092-1098. ##
  15. Zahedi Abghari S, Towfighi Darian J, Karimzadeh R, Omidkhah M R, (2008) Determination of Yield distribution of olefin production by thermal cracking of atmospheric gasoil, Korean Journal of Chemical Engineering, 25(4), 681-692. ##
  16. Zahedi.Abghari S, Sadi M (2013) Application of adaptive neuro-fuzzy inference system for the prediction of the yield distribution of the main products in the steam cracking of atmospheric gasoil, J Taiwan Inst Chem Eng, 44(3), 365-376. ##
  17. Hayati R, Zahedi Abghari S, Sadighi S, Bayat M (2015) Development of a rule to maximize the research octane number (RON) of the isomerization product from light naphtha, Korean Journal of Chemical Engineering,  32(4), 629-635. ##
  18. Kadijani J A, Narimani E, Kadijani H A (2016) Using response surface methodology to optimize ultra sound- assisted oxidative desulfurization, Korean Journal of Chemical  Engineering, 33, 1286-1295. ##
  19. Naderi H, Shokri S, AhmadPanah S (2018) Optimization of kinetic lumping model parameters to improve products quality in the hydrocracking process”, Braz. J.Chem.Eng., 35(2); 757-768. ##
  20. Hao Z, Qian X, Cen K, Fan J (2003) Optimizing pulverized coal combustion performance based on ANN and GA, Fuel Processing Technology., 85, 113-124. ##
  21. Wu C, Zhang J, Li W, Wang Y, Cao H (2006) Artificial neural network model to predict cold filter plugging point of blended diesel fuels, Fuel processing technology 87; 585-590. ##
  22. Pasadakis N, Gaganis V, Foteinopoulos C (2006) Octane number prediction for gasoline blends, Fuel Process. Technol., 87, 505-509. ##
  23. Lukec I, Sertic-Bionda K, Lukec D (2008) Prediction of sulphur content in the industrial hydrotreatment process, Fuel Process. Technol., 89,292-300.
  24. Quan- Shan L, Da-Zi L, Liu-Lin C (2015) Modeling and optimum operating conditions for FCCU using artificial neural network”, J. Cent. South Univ., 22, 1342-1349. ##
  25. Inal F (2006) Artificial neural network predictions of polycyclic aromatic hydrocarbon Formation in premixed n-heptane flames, Fuel Processing Technology, 87, 1031-1036. ##
  26. Murty B S N, Rao R N (2004) Global optimization for prediction of blend composition of gasolines of desired octane number and properties, Fuel Processing Technology, 85, 1595-1602. ##
  27. Zahedi G, Zadeh S M, Moradi G (2008) Enhancing gasoline production in an industrial catalytic reforming unit using artificial neural networks, Energ. Fuel, 22, 2671-2677. ##
  28. Marzbanrad J, Mashadi B, Afkar A, Pahlavani M (2016) Dynamic rupture and crushing of an extruded tube using artificial neural network (ANN) approximation method;  Journal of Central South University, 23, 869-879. ##
  29. Yang Z, Qiao W M, Liang X Y, (2017) Modeling and optimization of the pore structure of carbon aerogels using an artificial neural network, New Carbon Mater, 32(1), 77-85. ##
  30. Swang A, Rouge B (1989) Catalyst reforming process: US Patent, 4832821. ##
  31. Yaws C L (1999) Chemical Properties Hand Book, McGraw-Hill, New York. ##