Model Reduction in Emulsion Polymerization Using Hybrid First-Principles/Artificial Neural Network Models

✍ Scribed by Alicia d'Anjou; F. Javier Torrealdea; José R. Leiza; José M. Asua; Gurutze Arzamendi

• Publisher: John Wiley and Sons
• Year: 2003
• Tongue: English
• Weight: 356 KB
• Volume: 12
• Category: Article
• ISSN: 1022-1344
• DOI: 10.1002/mats.200390007

✦ Synopsis

Abstract

A first‐principles mathematical model for emulsion polymerization was reduced by using a hybrid mathematical model composed by artificial neural networks (ANN) and material balances. The goal was to have an accurate model that may be integrated fast enough to be used for online optimization purposes. In the reduced model the polymerization rate and the instantaneous weight‐average molecular weight were calculated by means of artificial neural networks. These ANNs were incorporated to first‐principles material balances. The accuracy of the reduced model under a wide range of conditions was assessed. Savings in computer time were achieved by using the reduced model, which makes it suitable for online optimization purposes.

Effect of the temperature on the cumulative weight‐average molecular weight: first principles mathematical model (—); $\overline {{\rm M}_{{\rm w},{\rm inst}} }$(ANN2) and hybrid model predictions: (▵) 50 °C, (▪) 60 °C^(training)^, (▿) 70 °C^(validation)^, (•) 80 °C, (○) 90 °C.

imageEffect of the temperature on the cumulative weight‐average molecular weight: first principles mathematical model (—); $\overline {{\rm M}_{{\rm w},{\rm inst}} }$(ANN2) and hybrid model predictions: (▵) 50 °C, (▪) 60 °C^(training)^, (▿) 70 °C^(validation)^, (•) 80 °C, (○) 90 °C.