Model Reduction in Emulsion Polymerization Using Hybrid First Principles/Artificial Neural Networks Models, 2

Summary: A “series” hybrid model based on material balances and artificial neural networks to predict the evolution of weight average molecular weight, $\overline M _{\rm w}$ , in semicontinuous emulsion polymerization with long chain branching kinetics is presented. The core of the model is composed by two artificial neural networks (ANNs) that calculate polymerization rate, R p , and instantaneous weight‐average molecular weight, $\overline M _{{\rm winst}}$ from reactor process variables. The subsequent integration of the material balances allowed to obtain the time evolution of conversion and $\overline M _{\rm w}$ , along the polymerization process. The accuracy of the proposed model under a wide range of conditions was assessed. The low computer‐time load makes the hybrid model suitable for optimization strategies.Effect of the monomer feed rate on $\overline M _{{\rm winst}}$ .