Open Access

ARTICLE

Optimization of Industrial Fluid Catalytic Cracking Unit having Five Lump Kinetic Scheme using Genetic Algorithm

Shishir Sinha¹, Praveen Ch.

1 Department of Chemical Engineering,Indian Institute of Technology Roorkee, Roorkee-247667, Email:sshishir@gmail.com.

Computer Modeling in Engineering & Sciences 2008, 32(2), 85-102. https://doi.org/10.3970/cmes.2008.032.085

Abstract

Fluid catalytic cracking (FCC) unit plays most important role in the economy of a modern refinery that it is use for value addition to the refinery products. Because of the importance of FCC unit in refining, considerable effort has been done on the modeling of this unit for better understanding and improved productivity. The process is characterized by complex interactions among feed quality, catalyst properties, unit hardware parameters and process conditions. \newline The traditional and global approach of cracking kinetics is lumping. In the present paper, five lump kinetic scheme is considered, where gas oil crack to give lighter fractions (like gasoline, LPG, dry gas) and coke. There are present nine kinetic parameters and one catalyst deactivation activity. The integrated reactor-regenerator steady state model makes gross assumption about the hydrodynamics, using Runga Kutta method. \newline The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solution applying the principle of survival of the fittest to produce better and better approximations to a solution. At each generation, a new set of approximations is created by the process of selecting individuals according to their level of fitness in the problem domain and breeding them together using operators borrowed from natural genetics. In the present work, the multi objective binary coded elitist non-dominated sorting genetics algorithm (NSGA-II) is studied, and for the new code, NSGA-II JG is used to obtain optimal solution. In the present paper, the optimal solutions are compare which obtained by NSGA-II JG and NSGA-II performance.

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