MODELING AND OPTIMIZATION OF RISER TYPE INDUSTRIAL FLUID CATALYTIC CRACKING UNIT

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. in last sixty years, the mathematical modeling of FCC unit have matured in many ways but the real process whose hardware is ever-changing to meet the needs of petroleum refining. The process is characterized by complex interactions among feed quality, catalyst properties, unit hardware parameters and process conditions. The FCC unit comprises of three stages: a riser reactor, a catalyst stripper, and a regenerator (along with other accessories). From modeling point of view, the riser reactor is of prime importance amongst these stages. Detailed modeling of the riser is a challenging task for due to complex hydrodynamics and the fact that there are thousands of unknown hydrocarbons in the FCC feed but because of the involvement of different types of reactions taking place simultaneously. The traditional and global approach of cracking kinetics is lumping. Mathematical models dealing with riser kinetics can be categorized into two main types. In one category, the lumps are made on the basis of boiling range of feedstocks and corresponding products in the reaction system. This kind of model has an increasing trend in number of lumps of the cracked gas components. The other category is that in which the lumps are made on the basis of molecular structure characteristics of hydrocarbon group composition in reaction system. This category of models emphasizes on more detailed description of the feedstock. These models do not include chemical data such as type of reaction and reaction stoichiometry. In the present work, five lump kinetic schemes is considered, where gas oil cracks to give lighter fractions (like gasoline, LPG, dry gas) and coke. vi The integrated reactor-regenerator steady state model makes gross assumption about the hydrodynamics, this model equation solved by Runga Kutta method in MATLAB. Rate equations of all the five lumps are integrated along the riser length with a small step size using Runga Kutta method. There are nine kinetic parameters and one catalyst deactivation activity. The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GAs 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 are 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. The multi-objective optimization of industrial operations using genetic algorithm and its variants often requires inordinately large amounts of computational (CPU) time. In the present work, the multi objective the binary coded elitist non-dominated sorting genetics algorithm (NSGA-II) is adapted, and the new code, NSGA-II JG is used to obtain solution for the multi-objective optimization of an industrial fluidized bed catalytic cracking unit. This unit is associated with a complex model that is highly compute-intense. The CPU time required, for this problem is found to reduce fivefold, when NSGA-II JG is used, as compared to when NSGA-II is used. This adaptation can prove to be of considerable value for solving other compute intense problems in chemical engineering.