Using an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibers

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dc.contributor.authorMonticeli, Francisco M. [UNESP]
dc.contributor.authorNeves, Roberta Motta
dc.contributor.authorOrnaghi Júnior, Heitor Luiz
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionFederal University of Rio Grande do Sul (UFRGS)
dc.contributor.institutionFederal University for Latim American Integration (UNILA)
dc.date.accessioned2021-06-25T10:20:28Z
dc.date.available2021-06-25T10:20:28Z
dc.date.issued2021-03-01
dc.description.abstractAbstract: Vegetal fibers are prominent reinforcements for polymer composite materials, considering their properties and application possibilities. In particular, thermal degradation behavior is crucial for determining an application subjected to a temperature range. Methods to predict properties are a trend in materials science and have the main advantage of saving cost and time. For this reason, in the present study, an artificial neural network (ANN) approach was used to predict the thermal degradation curves. The heating rate of 10 °C·min− 1 was carried out to train the network with 12 hidden layers and optimal training dataset of 60. Other heating rates were simulated and showed an excellent agreement with the experimental data. The coefficient of determination was R2 > 0.99 for all sources of biomass, exhibiting appropriate predictive fit with error following the sequence: ramie (1.15 %) < kenaf (1.33 %) < curaua (1.83 %) < jute (1.97 %). In conclusion, ANNs can learn from their data and optimize processing, formulations, predict properties, and other input data combinations. The predictive curves present high reliability with the experimental fit allowing the prediction of the mass loss for different temperatures versus the heating rate set. Graphic abstract: [Figure not available: see fulltext.].en
dc.description.affiliationDepartment of Materials and Technology School of Engineering São Paulo State University (Unesp)
dc.description.affiliationGraduate Program in Mining Metallurgical and Materials Engineering Federal University of Rio Grande do Sul (UFRGS)
dc.description.affiliationFederal University for Latim American Integration (UNILA)
dc.description.affiliationUnespDepartment of Materials and Technology School of Engineering São Paulo State University (Unesp)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.format.extent1961-1971
dc.identifierhttp://dx.doi.org/10.1007/s10570-021-03684-2
dc.identifier.citationCellulose, v. 28, n. 4, p. 1961-1971, 2021.
dc.identifier.doi10.1007/s10570-021-03684-2
dc.identifier.issn1572-882X
dc.identifier.issn0969-0239
dc.identifier.scopus2-s2.0-85099377822
dc.identifier.urihttp://hdl.handle.net/11449/205740
dc.language.isoeng
dc.relation.ispartofCellulose
dc.sourceScopus
dc.subjectArtificial neural network
dc.subjectCellulose
dc.subjectSimulation
dc.subjectThermal analysis
dc.subjectWood
dc.titleUsing an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibersen
dc.typeArtigo
unesp.author.orcid0000-0002-0814-8160[1]
unesp.author.orcid0000-0002-7017-0852[2]
unesp.author.orcid0000-0002-0005-9534[3]

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