Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1002/pen.25702 http://hdl.handle.net/11449/210275 |
Resumo: | 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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Repositório Institucional da UNESP |
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Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerizationartificial neural networkdifferential calorimetric analysismolar masspolyurethaneThe 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Caxias Do Sul UCS, Postgrad Program Mat Sci & Engn PGMAT, Caxias Do Sul, RS, BrazilFed Univ Latin Amer Integrat UNILA, Foz Do Iguacu, Parana, BrazilSao Paulo State Univ Unesp, Sch Engn, Dept Mat & Technol, Guaratingueta, BrazilFed Inst Educ Sci & Technol Rio Grande Sul IFRS, Postgrad Program Technol & Mat Engn PPG TEM, Campus Feliz, Porto Alegre, RS, BrazilFed Univ Rio Grande Sul UFRGS, Dept Mat Engn DEMAT, Porto Alegre, RS, BrazilSao Paulo State Univ Unesp, Sch Engn, Dept Mat & Technol, Guaratingueta, BrazilCAPES: 001Wiley-BlackwellUniv Caxias Do Sul UCSFed Univ Latin Amer Integrat UNILAUniversidade Estadual Paulista (Unesp)Fed Inst Educ Sci & Technol Rio Grande Sul IFRSFed Univ Rio Grande Sul UFRGSDall Agnol, LucasOrnaghi, Heitor LuizMonticeli, Francisco [UNESP]Dias, Fernanda Trindade GonzalezBianchi, Otavio2021-06-25T15:03:24Z2021-06-25T15:03:24Z2021-04-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9http://dx.doi.org/10.1002/pen.25702Polymer Engineering And Science. Hoboken: Wiley, 9 p., 2021.0032-3888http://hdl.handle.net/11449/21027510.1002/pen.25702WOS:000644475700001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPolymer Engineering And Scienceinfo:eu-repo/semantics/openAccess2024-07-02T15:04:15Zoai:repositorio.unesp.br:11449/210275Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:21:33.012092Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
title |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
spellingShingle |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization Dall Agnol, Lucas artificial neural network differential calorimetric analysis molar mass polyurethane |
title_short |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
title_full |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
title_fullStr |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
title_full_unstemmed |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
title_sort |
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization |
author |
Dall Agnol, Lucas |
author_facet |
Dall Agnol, Lucas Ornaghi, Heitor Luiz Monticeli, Francisco [UNESP] Dias, Fernanda Trindade Gonzalez Bianchi, Otavio |
author_role |
author |
author2 |
Ornaghi, Heitor Luiz Monticeli, Francisco [UNESP] Dias, Fernanda Trindade Gonzalez Bianchi, Otavio |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Univ Caxias Do Sul UCS Fed Univ Latin Amer Integrat UNILA Universidade Estadual Paulista (Unesp) Fed Inst Educ Sci & Technol Rio Grande Sul IFRS Fed Univ Rio Grande Sul UFRGS |
dc.contributor.author.fl_str_mv |
Dall Agnol, Lucas Ornaghi, Heitor Luiz Monticeli, Francisco [UNESP] Dias, Fernanda Trindade Gonzalez Bianchi, Otavio |
dc.subject.por.fl_str_mv |
artificial neural network differential calorimetric analysis molar mass polyurethane |
topic |
artificial neural network differential calorimetric analysis molar mass polyurethane |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T15:03:24Z 2021-06-25T15:03:24Z 2021-04-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1002/pen.25702 Polymer Engineering And Science. Hoboken: Wiley, 9 p., 2021. 0032-3888 http://hdl.handle.net/11449/210275 10.1002/pen.25702 WOS:000644475700001 |
url |
http://dx.doi.org/10.1002/pen.25702 http://hdl.handle.net/11449/210275 |
identifier_str_mv |
Polymer Engineering And Science. Hoboken: Wiley, 9 p., 2021. 0032-3888 10.1002/pen.25702 WOS:000644475700001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Polymer Engineering And Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9 |
dc.publisher.none.fl_str_mv |
Wiley-Blackwell |
publisher.none.fl_str_mv |
Wiley-Blackwell |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129419789729792 |