Sensitivity Analysis and Development of a Set of Rules to Operate FCC Process by Application of a Hybrid Model of ANFIS and Firefly Algorithm

http://jpst.ripi.ir Journal of Petroleum Science and Technology 2019, 9(3), 10-26 © 2019 Research Institute of Petroleum Industry (RIPI) ABSTRACT Fluid catalytic cracking (FCC) process is a vital refinery process which majorly produces gasoline. In this research, a hybrid algorithm which was constituted of Adaptive Neuro-Fuzzy Inference System (ANFIS) and firefly optimization algorithm was developed to model the process and optimize the operating conditions. To conduct the research, industrial data of Abadan refinery FCC process were carefully gathered along a definite period. Then a model based on ANFIS was developed to investigate the effect of operating variables including reactor temperature, feed flow rate, temperature of top of main column, and the temperature of bottom of the debutanizer tower on quality and quantity of gasoline, LPG flow rate, and process conversion. Moreover, statistical parameters comprising R2, RMSE, and MRE approved the accuracy of the model. Eventually, validated ANFIS model and firefly algorithm as an evolutionary optimization algorithm were applied to optimize the operating conditions. Also, three different optimization cases including maximization of Research Octane Number (RON) , gasoline flow rate, and conversion were considered. In addition, to obtain maximum target output variables, inlet reactor temperature, temperature of top of main column, temperature of the bottom of debutanizer column, and feed flow rate should be respectively set at 523, 138, 183 °C and 40731 bbl/day. Also, the sensitivity analysis between the input and output variables was carried out to derive some effective rules of thumb to facilitate operation of the process under unsteady state conditions. Finally, the obtained result introduces a methodology to compensate for the negative effect of undesirable variation of some of the operating variables by manipulating the others.

[1]  Weijun Zhang,et al.  Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization , 2016 .

[2]  Chun Chen,et al.  Modeling and optimization for the secondary reaction of FCC gasoline based on the fuzzy neural network and genetic algorithm , 2007 .

[3]  G. Froment,et al.  Chemical Reactor Analysis and Design , 1979 .

[4]  Sabry F. Saraya,et al.  An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance , 2017 .

[5]  R. Alizadeh,et al.  An intelligent approach to design and optimization of M-Mn/H-ZSM-5 (M: Ce, Cr, Fe, Ni) catalysts in conversion of methanol to propylene , 2016 .

[6]  M. A. Addo,et al.  Modeling Conversion in a Fluid Catalytic Cracking Regenerator in Petroleum Refining , 2011 .

[7]  Mojtaba Ahmadi,et al.  Artificial Intelligent Modeling and Optimizing of an Industrial Hydrocracker Plant , 2014 .

[9]  Luiz Augusto da Cruz Meleiro,et al.  MODELING, OPTIMIZATION AND CONTROL OF A FCC UNIT USING NEURAL NETWORKS AND EVOLUTIONARY METHODS , 2013 .

[10]  Shahid Naveed,et al.  Optimization of Fluid Catalytic Cracker for Refining of Syncrude Oil for Production of High Quality Gasoline , 2014 .

[11]  Bin Jiang,et al.  Modeling and optimization for curing of polymer flooding using an artificial neural network and a genetic algorithm , 2014 .

[12]  罗剑飞,et al.  Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm* , 2012 .

[13]  Santosh K. Gupta,et al.  Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator , 2003, Comput. Chem. Eng..

[14]  J. Michalopoulos,et al.  Modelling of an Industrial Fluid Catalytic Cracking Unit Using Neural Networks , 2001 .

[15]  M. M. Ismail,et al.  Fluid catalytic cracking unit control using model predictive control and adaptive neuro fuzzy inference system: Comparative study , 2017, 2017 13th International Computer Engineering Conference (ICENCO).

[16]  Mithat Zeydan,et al.  The Comparison of Artificial Intelligence and Traditional Approaches In FCCU Modeling , 2008 .

[17]  K. Dagde,et al.  Modelling catalyst regeneration in an industrial FCC unit , 2013 .

[18]  M. Raja,et al.  Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems , 2016 .

[19]  Determination of yield distribution in olefin production by thermal cracking of atmospheric gasoil , 2008 .

[20]  S. M. Khazraee,et al.  Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System , 2010 .

[21]  Maryam Sadi,et al.  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 , 2013 .

[22]  Bolun Yang,et al.  Establishment and solution of eight-lump kinetic model for FCC gasoline secondary reaction using particle swarm optimization , 2007 .

[23]  N. Saghatoleslami,et al.  A Neuro-Fuzzy Model for a Dynamic Prediction of Milk Ultrafiltration Flux and Resistance , 2007 .

[24]  A. Alsairafi,et al.  Experimental and modeling study of a catalytic reforming unit , 2014 .

[25]  S. Sadighi,et al.  Development of a rule to maximize the research octane number (RON) of the isomerization product from light naphtha , 2015, Korean Journal of Chemical Engineering.

[26]  Y. Puyate,et al.  Modelling And Simulation Of Industrial FCC Unit: Analysis Based On Five-Lump Kinetic Scheme For Gas-Oil Cracking , 2012 .