Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/40951
Tipo: Artigo de Periódico
Título: Hopfield neural network-based algorithm applied to differential scanning calorimetry data for kinetic studies in polymorphic conversion
Autor(es): Bárbara Caroline Rodrigues de Araujo
Felipe Silva Carvalho
Maria Betânia de Freitas Marques
João Pedro Braga
Rita de Cássia de Oliveira Sebastião
Resumo: A 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.
Assunto: Algoritmo
Rede Neural Hopfield
Redes neurais artificiais
Calorimetria
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Instituição: UFMG
Departamento: FAR - DEPARTAMENTO DE ALIMENTOS
ICX - DEPARTAMENTO DE QUÍMICA
Tipo de Acesso: Acesso Aberto
Identificador DOI: 10.21577/0103-5053.20200024
URI: http://hdl.handle.net/1843/40951
Data do documento: Jul-2020
metadata.dc.url.externa: https://www.scielo.br/j/jbchs/a/Q4CmPZ9dzDc3CGKJGjk9kJx/?lang=en
metadata.dc.relation.ispartof: Journal of the Brazilian Chemical Society
Aparece nas coleções:Artigo de Periódico



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