The integration of modeling, simulation and optimization provides powerful tools for supporting advanced decision making in the competitive market. However, when applying the tools to polymerization processing, the challenging task is to accommodate the predictability of the mathematical model and the capability of model-based optimization due to its inherent complexities. In the present study, three model approaches are proposed, i.e. a data based model, a kinetic model and a multiscale model, whereby the developed models are implemented into the syndiotactic polymerization of styrene. The data based model was developed based on the correlation model from experimentally obtained data, where the classical linear or nonlinear models can be applied to correlate the variation in any set of data using experimental design. The kinetic model includes the polymerization kinetics scheme, polymerization rate analysis and polymer molecular weight distribution. The multiscale model is an integrated framework which consists of the coupling between the single particle growth model at mesoscale and the mixing phenomenon model at macroscale with the kinetic model at microscale, where by both part
