Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/54912
Type: Artigo de periódico
Title: Artificial neural networks associated to calorimetric measurements used as a method to predict polymer composition of high solid content emulsion copolymerizations
Author: Giordani, DS
Lona, LMF
McKenna, TF
Krahenbuhl, MA
dos Santos, AM
Abstract: 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.
Subject: artificial neural networks
emulsion
latex
polymers
Country: Alemanha
Editor: Wiley-v C H Verlag Gmbh
Rights: fechado
Identifier DOI: 10.1002/mame.200500033
Date Issue: 2005
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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