Modeling and optimization for the secondary reaction of FCC gasoline based on the fuzzy neural network and genetic algorithm

Abstract

The secondary reaction of fluidized-bed catalytically cracked gasoline (FCC gasoline) was investigated experimentally with a riser reactor using an improved Y zeolite catalyst in different operating conditions. The product distributions were predicted using a new black-box model based on fuzzy neural network (FNN) combined with genetic algorithm (GA) named FNN–GA method. In this new approach, the fuzzy neural network model is constructed for correlating the values of input, namely feedstock components, operating variables with output, namely the yields of upgraded gasoline and the olefin fraction in it. And then, the inputs of operating variables are optimized using genetic algorithm with a view to maximize yields of upgraded gasoline and the restricting of olefin in the product gasoline. This new FNN–GA modeling and optimization can be conducted completely from the experimental data wherein the complicated knowledge of the reaction mechanisms, kinetics, mass and heat transfer are not required. Using artificial neural network (ANN)–GA strategy, a set of optimized operation conditions leading to maximized yields upgraded gasoline with olefin restrict for different feedstock were obtained. The experimental results agreed well with the predicted ones and a significant improvement in the upgraded gasoline product were gained under the optimized operating conditions.