Multi-Objective Optimization of an Industrial Isoprene Production Unit by Using Genetic Algorithm Approach

摘要

The present work deals with the multi-objective optimization of an industrial Isoprene production unit by using Genetic Algorithm (GA). The chemical process consists basically of a dimerization reactor and a separation column train. The GA-search was chosen as an optimization tool because of their successful application in many industrial optimization problems (Alves et al., 2004; Laquerbe et al., 2001; Pibouleau et al., 1999). Then, the aim of this paper is to present and discuss the applicability of a GA as an alternative procedure for a multi-objective optimization of an industrial process that may be difficult to handle by classical methods. In this case the optimization of the entire plant involves 21 variables to be optimized. So, in order to decrease the dimensionality of the problem, the global model was divided into three sections and each one was optimized separately, but sequentially, by using the optimal conditions from previous optimization section procedure. For this, a multi-objective genetic algorithm (MOGA) based on a Pareto sort (PS) procedure was implemented to manage this specific problem.