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Model Reduction in Emulsion Polymerization Using Hybrid First Principles/Artificial Neural Networks Models, 2
Author(s) -
Arzamendi Gurutze,
d'Anjou Alicia,
Graña Manuel,
Leiza José R.,
Asua José M.
Publication year - 2005
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.200400064
Subject(s) - polymerization , monomer , emulsion polymerization , branching (polymer chemistry) , materials science , artificial neural network , thermodynamics , biological system , polymer chemistry , mathematics , chemistry , physics , computer science , polymer , composite material , artificial intelligence , biology
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 $\overline M _{{\rm winst}}$ .