Unveiling optimal operating conditions for an epoxy polymerization process using multi-objective evolutionary computation

Abstract

The optimization of the epoxy polymerization process involves a number of conflicting objectives and more than twenty decision parameters. In this paper, the problem is treated truly as a multi-objective optimization problem and near-Pareto-optimal solutions corresponding to two and three objectives are found using the elitist non-dominated sorting GA or NSGA-II. Objectives, such as the number average molecular weight, polydispersity index and reaction time, are considered. The first two objectives are related to the properties of a polymer, whereas the third objective is related to productivity of the polymerization process. The decision variables are discrete addition quantities of various reactants e.g. the amount of addition for bisphenol-A (a monomer), sodium hydroxide and epichlorohydrin at different time steps, whereas the satisfaction of all species balance equations is treated as constraints. This study brings out a salient aspect of using an evolutionary approach to multi-objective problem solving. Important and useful patterns of addition of reactants are unveiled for different optimal trade-off solutions. The systematic approach of multi-stage optimization adopted here for finding optimal operating conditions for the epoxy polymerization process should further such studies on other chemical process and real-world optimization problems.