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Unveiling Optimal Operating Conditions for an Epoxy Polymerization Process Using Multi-objective Evolutionary Computation
Kalyanmoy Deb1, Kishalay Mitra2, Rinku Dewri3, and Saptarshi Majumdar4
1Mechanical Engineering Department, Indian Institute of Technology Kanpur, Kanpur-208016
deb@iitk.ac.in
http://www.iitk.ac.in/kangal/deb.htm
2Manufacturing Practice, Tata Consultancy Services, 54B Hadapsar Industrial Estate, Pune-411013, India
kmitra@pune.tcs.co.in
3Department of Mathematics, Indian Institute of Technology Kharagpur, Kharagpur-721302
rinku@webteam.iitkgp.ernet.in
4Tata Research Development and Design Centre, 54B Hadapsar Industrial Estate, Pune 411013, India
smajumdar@pune.tcs.co.in
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.
Keywords: Multi-objective optimization, genetic algorithms, real-world optimization, Pareto-optimal solutions, chemical engineering process optimization.
LNCS 3103, p. 920 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004