Application of GA in Optimization of Modified Benzene Alkylation Process

Abstract

A genetic algorithm is used to optimize the modified benzene alkylation process. Based on the previous studies, the modified process increases ethylbenzene selectivity and decreases energy consumption at the same time. The inlet ethylene flow rate of each alkylation reactor is optimized in order to reduce the chance of transalkylation reactions but increase ethylbenzene selectivity. The byproduct trans-ethylbenzene concentration is used as the fitness variable in the optimization process to confine undesired reactions throughout the process. The obtained optimal values of ethylene flow rate for the adiabatic reactors are 3.50, 2.94, 2.58, and 0.36 m3/hr. The ethylbenzene selectivity has been increased by applying the optimized values indicating the current unit is not operating under optimal conditions. Temperature profile within the alkylation reactors and temperature and concentration profiles through the towers of the fraction unit under the optimized conditions of the modified process are also presented.