Multiobjective Optimization of an Emulsion Copolymerization Process

A multiobjective optimization procedure based on evolutionary algorithm has been developed to determine the optimal control policies for a fed-batch emulsion copolymerization reactor, particularly for styrene and butyl acrylate in the presence of n-C12 mercaptan as chain transfer agent (CTA). The process model was elaborated and validated experimentally in order to predict the global monomer conversion, the number and weight average molecular weights, the particle size distribution and the residual monomers. The process objectives are to produce core-shell particles with specific end-use properties and high productivity. This has been achieved by the maximization of the conversion at the end of the process and the minimization of the error between the glass transition temperature and a designed profile subject to a set of operational constraints. The nondominated Pareto solutions obtained were ranked according to the decision maker preferences using multiple attribute utility theory (MAUT). The selected solution gives the best set of the decision variables to be implemented to the real system.