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

✍ Scribed by Gurutze Arzamendi; Alicia d'Anjou; Manuel Graña; José R. Leiza; José M. Asua

• Publisher: John Wiley and Sons
• Year: 2005
• Tongue: English
• Weight: 167 KB
• Volume: 14
• Category: Article
• ISSN: 1022-1344
• DOI: 10.1002/mats.200400064

✦ Synopsis

Abstract

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 $\overline M _{{\rm winst}}$.

imageEffect of the monomer feed rate on $\overline M _{{\rm winst}}$.