Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/93946
Type: Artigo de evento
Title: Artificial Neural Networks Associated To Calorimetry To Preview Polymer Composition Of High Solid Content Emulsion Copolymerizations
Author: Giordani D.S.
Dos Santos A.M.
Krahenbuhl M.A.
Lona L.M.F.
Abstract: Artificial Neural Networks (ANN) have demonstrated to be powerful tools to model non linear systems, such as high solid content latexes produced by emulsion polymerisation. This system has a great importance in the polymeric industry, essentially for environmental reasons, since they usually have water as continuous phase. In order to propose technical and economically feasible alternatives to control polymeric structure, this work is aimed to develop a new methodology based on artificial neural networks associated with calorimetry to preview polymeric structure. The designed artificial neural networks presented excellent results when tested with process condition variations as well as when they were submitted to test concerning to the variation on the proportion of monomers in the latex formulation. Hence, it was possible to conclude that artificial neural networks, associated to calorimetry, lead to an efficient method to preview the polymer composition in emulsion copolymerizations. © 2005 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/IJCNN.2005.1556249
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-33750138797&partnerID=40&md5=f130b7295300bfe65fd1f63f07b98808
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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