Artificial Neural Networks Associated to Calorimetric Measurements Used as a Method to Predict Polymer Composition of High Solid Content Emulsion Copolymerizations

• Published in: Macromolecular materials and engineering Vol. 290; no. 5; pp. 485 - 494
• Main Authors: Giordani, Domingos Sávio, Lona, Liliane M. F., McKenna, Timothy F., Krähenbühl, Maria A., dos Santos, Amilton Martins
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 23.05.2005; WILEY‐VCH Verlag
• Subjects: artificial neural networks; emulsion; latex; polymers
• ISSN: 1438-7492; 1439-2054
• DOI: 10.1002/mame.200500033

Inspired by biological systems, artificial neural networks (ANN) have demonstrated to be powerful tools to model non‐linear systems, such as high solid content latexes produced by emulsion polymerization which have a great importance in the polymeric industry, essentially for environmental reasons, since they usually have water as a continuous phase. The quality of the produced polymer is closely related to the structure of the polymeric chain. In order to propose technical and economically feasible alternatives to control a polymeric structure, this work is aimed to develop a new methodology based on ANN associated with calorimetry to predict the polymeric structure. The designed ANN presented excellent results when tested with process condition variations (such as temperature and reaction time) as well as when they were submitted to test concerning the variation on the proportion of monomers in the latex formulation. Hence, it was possible to conclude that ANN, associated to calorimetry, lead to an efficient method to predict the polymer composition in emulsion copolymerizations.