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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/40951
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dc.creatorBárbara Caroline Rodrigues de Araujopt_BR
dc.creatorFelipe Silva Carvalhopt_BR
dc.creatorMaria Betânia de Freitas Marquespt_BR
dc.creatorJoão Pedro Bragapt_BR
dc.creatorRita de Cássia de Oliveira Sebastiãopt_BR
dc.date.accessioned2022-04-09T00:24:06Z-
dc.date.available2022-04-09T00:24:06Z-
dc.date.issued2020-07-
dc.citation.volume31pt_BR
dc.citation.issue7pt_BR
dc.citation.spage1392pt_BR
dc.citation.epage1400pt_BR
dc.identifier.doi10.21577/0103-5053.20200024pt_BR
dc.identifier.issn0103-5053pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/40951-
dc.description.resumoA general kinetic equation to simulate differential scanning calorimetry (DSC) data was employed along this work. Random noises are used to generate a thousand data, which are considered to evaluate the performance of Levenberg-Marquardt (LM) and a Hopfield neural network (HNN) based algorithm in the fitting process. The HNN-based algorithm showed better results for two different initial conditions: exact and approximated values. After this statistical analysis, DSC experimental data at three heating rates for losartan potassium, an antihypertensive drug, was adjusted by the HNN method using different initial conditions to obtain the activation energy and frequency factor. Additionally, it was possible to recover the parameters for the kinetic model with accuracy, showing that the conversion is described by a complex process, once these values do not correspond to any ideal models described in the literature.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOSpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE QUÍMICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofJournal of the Brazilian Chemical Societypt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectKinetic studypt_BR
dc.subjectNeural networkpt_BR
dc.subjectThermal analysispt_BR
dc.subjectDSCpt_BR
dc.subject.otherAlgoritmopt_BR
dc.subject.otherRede Neural Hopfieldpt_BR
dc.subject.otherRedes neurais artificiaispt_BR
dc.subject.otherCalorimetriapt_BR
dc.titleHopfield neural network-based algorithm applied to differential scanning calorimetry data for kinetic studies in polymorphic conversionpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.scielo.br/j/jbchs/a/Q4CmPZ9dzDc3CGKJGjk9kJx/?lang=enpt_BR
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