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

Document Type: Research Paper


Research Institute of petroleum Industry



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.



Zahedi Abghari S., Alizadehdakhel A., Mohaddecy R. S., and Alsairafi A. A., “Experimental and Modeling Study of a Catalytic Reforming Unit,” Journal of Taiwan Institute of Chemical Engineers, 2014, 45, 1411-1420.

Hayati R., Zahedi Abghari S., Sadighi S., and Bayat M., “Development of a Rule to Maximize the Research Octane Number (RON) of the Isomerization Production from Light Naphtha,”, Korean Journal of Chemical Engineering, 2015, 32(4) 629-635.

Zahedi Abghari S., Shokri S., Baloochi B., Marvast M. A., and et al ., “Analysis of Sulfur Removal in Gasoil Hydrodesulfurization Process by Application of Response Surface Methodology,” Korean Journal of Chemical Engineering, 2011, 28(1) 93-98.

Zahedi Abghari S., Towfighi Darian J., Karimzadeh R., and Omidkhah M. R., “Determination of Yield Distribution in Olefin Production by Thermal Cracking of Atmospheric Gasoil,” Korean Journal of Chemical Engineering, 2008, 25(4) 681-692.

Heydari M, Ebrahim H. A, and Dabir B., “Modeling of an Lndustrial Riser in the Fluid Catalytic Cracking,” American Journal of Applied Sciences, 2010, 7(2), 221-226.

Elamurugan P. and Dinesh Kumar D., “Modeling and Control of Fluid Catalytic Cracking Unit in Petroleum Refinery,” International Journal of Computing, Communication, and Information System, 2010, 2(1), 56-59.

Mythily M., Manamalli D., and Nandhini R. R., “Dynamic Modeling and Improvement in the Tuning of PI Controllers for Fluidized Catalytic Cracking Unit,” WSEAS Transactions on System and Control, 2015, 10297-10306.

Affum H. A., Adu P. S., Dagadu C. P. K., Addo M. and et al., “Modeling Conversion in a Fluid Catalytic Cracking Regenerator in Petroleum Refining,” Research Journal of Applied Sciences, Engineering, and Technology, 2011, 3(6), 533-539.

Dagde K. K. and Puyate Y. T., “Modelling and Simulation of Industrial FCC unit: Analysis Based on Five-lump Kinetic Scheme for Gas-oil Cracking,” International Journal of Engineering Research and Applications, 2012, 2(5), 698-714.

Dagde K. K. and Puyate Y. T ., “Modeling Catalyst Regeneration in an Industrial FCC Unit,” American Journal of Scientific and Industrial Research, 2013, 4(3), 294-305.

Negrão C. O. and Baldessar F., “Simulation of Fluid Catalytic Cracking Risers—a Six Lump Model,” In the 11th Brazilian congress of thermal sciences and engineering, Brazilian Society of Mechanical Sciences and Engineering, Curitiba, Brazil, 2006, 5-8.

Ansari S. H., Bin Rasheed T. A., Mustafa I., and Naveed S., “Optimization of Fluid Catalytic Cracker for Refining of Sybcrude oil for Production of High Quality Gasoline,” International Journal of Innovative Science Engineering and Technology, 2014, 1(4), 506-511.

Zahedi Abghari S. and Sadi M., “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,” Journal of Taiwan Institute of Chemical Engineers, 2013, 44(3), 365-376.

Li S. and Li F., “Prediction of Cracking Gas Compressor Performance and Its Application in process optimization,” Chinese Journal of Chemical Engineering , 2012, 20(6), 1089-1093.

Khazraee S. M. and Jahanmiri A. H., “Composition Estimation of Reactive Bach Distillation by Using Adaptive Neuro-Fuzzy Infeerence System,” Chinese Journal of Chemical Engineering, 2010, 18(4), 703-710.

Safan M. M., Abdelsafan Mahmoud M., Mohamad S. E., Sabry F. S., and et al., “An Adaptive Neuro-fuzzy Sliding Mode Controller for MIMO System with Disturbance,” Chinese Journal of Chemical Engineering, 2017, 25(4), 463-476.

LUO J., LIN W., CAI X., and LI J., “Optimization of Fermentation Media for Enhancing Nitrate Oxidizing Activity by Artificial Neural Network coupling Genetic Algorithm,” Chinese Journal of Chemical Engineering, 2012, 20(5), 950-957.

Hadi N., Niaei A., Nabavi S. R., Alizadeh R., and et al., “An Intelligent Approach to Design and Optimization of M-Mn/H-ZSM-5 (M:Ce, Cr, Fe, Ni) Catalyst in Conversion of Methanol to Propylene,” Taiwan Institute of Chemical Engineers, 2016, 59173-59185.

Jiang B., Zhang F., Sun Y., Zhou X. and et al., “Modeling and Optimization for Curing of Polymer Flooding Using an Artificial Neural Network and a Genetic Algorithm,” Taiwan Institute of Chemical Engineers, 2014, 45(5), 2217-2224.

Mohammadzadeh A., Ramezani M, and Ghaedi A. M., “Synthesis and Characterization of Fe2O3-ZnO-ZnFe2O4/Carbon Nano Composite and its Application to Removal of Bromophenol Blue Dye Using Ultrasonic Assisted Method; Optimization by Response Surface Methodology and Genetic Algorithm,” Taiwan Institute of Chemical Engineers, 2016, 59, 275-284.

Raja M. A. Z., Shah F. H., Khan A. A., and Khan N. A., “Design of Bio-inspired Computational Intelligence Technique for Solving Steady Thin Film Flow of Johnson-Segalman Fluid Fluid on Vertical Cylinder for Drainage Problems,” Taiwan Institute of Chemical Engineers, 2016, 60, 59-75.

Ronda A., Martin-Lara M. A., Almendros A. l., Perez A., and et al., “Comparison of Two Models for the Biosorption of Pb(II) Using Untreated and Chemically Treated Olive Stone; Experimental Design Methodologies and Adaptive Neural Fuzzy Inference System (ANFIS),” Taiwan Institute of Chemical Engineers, 2015, 5445-5456.

WANG J., XUE Y., YU T., and ZHAO L., “Simultaneous Hybrid Modeling of a Nosiheptide Fermentation Process Using Particle Swarm Optimization,” Chinese Journal of Chemical Engineering, 2010, 18(5), 787-794.

Saghatoleslami N., Mousavi M., and Sargolzaei J., “A Neuro-Fuzzy Model for a Dynamic Prediction of Milk Ultrafiltration Flux and Resistance,” Iranian Journal of Chemistry and Chemical Engineering, 2007, 26(2), 53-61.

Zeydan M., “The Comparison of Artificial Intelligence and Traditional Approaches in FCCU Modeling,” International Journal of Industrial Engineering, 2008, 15(1) ,1-15

Bispo V. D. S., Sandra E., Silva R. L., and Meleiro L. A. C, “Modeling, Optimization and Control of A FCC Unit Using Neural Networks and Evolutionary Method,” ENGEVISTA, 2014, 16(1), 70.

Michalopoulos J., Papadokonstadokis S., Arampatzis G., and Lygeros A., “Modeling of an Industrial Fluid Catalytic Cracking Unit Using Neural Network”, Trans. I Chem E., 2001, 79, 137-142.

Vasseghian Y. and Ahmadi M., “Artificial Intelligent Modeling and Optimization of an Industrial Hydrocracker Plant,” Journal of Chemical and Petroleum Engineering, 2014, 48(2), 125-137.

Wang Z., Yang B., Chen C., Yuan J., and et al., “Modeling and Optimization for the Secondary Reaction of FCC Gasoline Based on the Fuzzy Neural Network and Genetic Algorithm,” Chemical Engineering and Processing, 2007, 46, 175-180.

Zahran M., Ammar M. E., Ismail M. M., and Moustafa Hassan M. A., “Fluid Catalytic Cracking Unit Control and Adaptive Neuro Fuzzy Inference System: Comparative Study,” Proceeding International Computer Engineering Conference, 2017, 172-177.

Kasat R. B. and Gupta S. K., “Multi-objective Optimization of an Industrial Fluidized-bed Catalytic Cracking Unit (FCCU) Using Genetic Algorithm (GA) with the Jumping Genes Operator,” Computers and Chemical Engineering, 2003, 27(12), 175-180

Chen C., Yang B., Yuan J., Wang Z., and et al., “Establishment and Solution of Eight-lump Kinetic Model for FCC Gasoline Secondary Reaction Using Particle Swarm Optimization,” Fuel Journal, 2007, 86(15), 2325-2332.

Yang X. S., “Nature-Inspired Metaheuristic Algorithm 2nd (ed.)”, Luniver Press, 2010, 1-137.

Froment G. F. and Bischoff Kenneth B., “Chemical Reactor Analysis and design,” John Wiley and Sons, New York, 1979.