Abstract
We present the dynamic optimization of an emulsion copolymerization process described by a deterministic kinetic ordinary differential equation model including a stochastic Monte Carlo submodel, describing the growth of polymer chains within particles. For the considered semi-batch operation, the time dependent input trajectories for monomer and initiator flow rates are optimized along with the isothermal reactor temperature. We use the surrogate-model-based optimizer MATSuMoTo for the optimization to avoid the need to compute derivatives of the stochastic model. Radial basis functions with linear polynomial tails are selected as surrogate functions which are updated during optimization by the newly evaluated points. A relevant application problem formulation together with results for two case studies are presented. Qualitatively similar input trajectories are obtained for different optimization runs due to the stochastic process and the limited number of iterations. All solutions reduce the batch time significantly.
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