Journal
POLYMER ENGINEERING AND SCIENCE
Volume 61, Issue 6, Pages 1810-1818Publisher
WILEY
DOI: 10.1002/pen.25702
Keywords
artificial neural network; differential calorimetric analysis; molar mass; polyurethane
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Funding
- Brazilian Agency Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, Brazil) [001]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Brazil)
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This study used an artificial neural network and surface response methodology models to predict the calorimetric behavior of certain polyurethane bulk polymerizations, achieving a high level of reliability in predicting reaction kinetics. The polymerization kinetics were found to be influenced by the association phenomena of -OH groups, and the applied methodology can be extended to other materials or properties of interest.
The molar mass of the polyurethanes (PUs)' reagents directly influences their thermal response, affecting both the polymerization process and the enthalpy and the degree of reaction. This study reports applying an artificial neural network (ANN), associated with surface response methodology (SRM) models, to predict the calorimetric behavior of certain PU's bulk polymerizations. A noncatalyzed reaction between an aliphatic hexamethylene diisocyanate (HDI) and a polycarbonate diol (PCD) with distinct molar masses (500, 1000, and 2000 g/mol) was proposed. A high level of reliability of the predicted calorimetric curves was obtained due to an excellent agreement between theoretical and modeled results, enabling creating a 3D surface response to predict the reaction kinetics. Also, it was possible to observe that the polymerization kinetics is affected by the -OH group's association phenomena. The applied methodology can be extended for other materials or properties of interest.
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