Stochastic Differential Equation in Chemical Engineering

Detalhes bibliográficos
Autor(a) principal: dos S. Vianna Jr., Ardson
Data de Publicação: 2021
Outros Autores: Oliveira, Christian Junior
Tipo de documento: Artigo
Idioma: por
Título da fonte: Vetor (Online)
Texto Completo: https://periodicos.furg.br/vetor/article/view/12971
Resumo: The generation process of a mathematical model should present as a result the simulation, which must represent the experimental data. Several phenomena in Nature show erratic fluctuations that are phenomenological.  An expressive example is a path that pollen develops through a river surface, the Brownian movement. Chemical Engineering encompasses several issues that are evaluated considering stochastic processes, such as process optimization and control, diffusion, and chemical kinetics. In the present work, fundamental concepts that are related to stochastic differential equations (SDE) are exposed, as well as some classic examples and one in Chemical Engineering. An open-source tool by Python was used, viewing to generate the sample paths, the Python open tool, specifically the PyPI sdeint algorithm.  
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spelling Stochastic Differential Equation in Chemical EngineeringEquações Diferenciais Estocásticas na Engenharia QuímicaSample pathsPythonModeling and SimulationTrajetórias amostraisPythonModelagem e SimulaçãoThe generation process of a mathematical model should present as a result the simulation, which must represent the experimental data. Several phenomena in Nature show erratic fluctuations that are phenomenological.  An expressive example is a path that pollen develops through a river surface, the Brownian movement. Chemical Engineering encompasses several issues that are evaluated considering stochastic processes, such as process optimization and control, diffusion, and chemical kinetics. In the present work, fundamental concepts that are related to stochastic differential equations (SDE) are exposed, as well as some classic examples and one in Chemical Engineering. An open-source tool by Python was used, viewing to generate the sample paths, the Python open tool, specifically the PyPI sdeint algorithm.  O processo de geração de um modelo matemático deve ter como resultado uma simulação que represente o conjunto de dados experimentais. Diversos fenômenos na Natureza apresentam flutuações erráticas que são fenomenológicas. Um exemplo significativo é a trajetória que desenvolve um pólen que se movimenta na superfície de um rio, o movimento browniano. A Engenharia Química engloba diversos itens que são avaliados por processos estocásticos como otimização e controle de processos, difusão e cinética de reações químicas. No presente trabalho são apresentados conceitos fundamentais relacionados com equações diferenciais estocásticas, alguns exemplos clássicos e um em engenharia química. Para gerar as trajetórias amostrais foi usada a ferramenta aberta Python, especificamente a biblioteca sdeint do PyPI.   Universidade Federal do Rio Grande2021-07-21info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.furg.br/vetor/article/view/1297110.14295/vetor.v30i2.12971VETOR - Journal of Exact Sciences and Engineering; Vol. 30 No. 2 (2020); 14-21VETOR - Revista de Ciências Exatas e Engenharias; v. 30 n. 2 (2020); 14-212358-34520102-7352reponame:Vetor (Online)instname:Universidade Federal do Rio Grande (FURG)instacron:FURGporhttps://periodicos.furg.br/vetor/article/view/12971/8891Copyright (c) 2021 VETOR - Revista de Ciências Exatas e Engenhariasinfo:eu-repo/semantics/openAccessdos S. Vianna Jr., ArdsonOliveira, Christian Junior2021-08-05T21:47:31Zoai:periodicos.furg.br:article/12971Revistahttps://periodicos.furg.br/vetorPUBhttps://periodicos.furg.br/vetor/oaigmplatt@furg.br2358-34520102-7352opendoar:2021-08-05T21:47:31Vetor (Online) - Universidade Federal do Rio Grande (FURG)false
dc.title.none.fl_str_mv Stochastic Differential Equation in Chemical Engineering
Equações Diferenciais Estocásticas na Engenharia Química
title Stochastic Differential Equation in Chemical Engineering
spellingShingle Stochastic Differential Equation in Chemical Engineering
dos S. Vianna Jr., Ardson
Sample paths
Python
Modeling and Simulation
Trajetórias amostrais
Python
Modelagem e Simulação
title_short Stochastic Differential Equation in Chemical Engineering
title_full Stochastic Differential Equation in Chemical Engineering
title_fullStr Stochastic Differential Equation in Chemical Engineering
title_full_unstemmed Stochastic Differential Equation in Chemical Engineering
title_sort Stochastic Differential Equation in Chemical Engineering
author dos S. Vianna Jr., Ardson
author_facet dos S. Vianna Jr., Ardson
Oliveira, Christian Junior
author_role author
author2 Oliveira, Christian Junior
author2_role author
dc.contributor.author.fl_str_mv dos S. Vianna Jr., Ardson
Oliveira, Christian Junior
dc.subject.por.fl_str_mv Sample paths
Python
Modeling and Simulation
Trajetórias amostrais
Python
Modelagem e Simulação
topic Sample paths
Python
Modeling and Simulation
Trajetórias amostrais
Python
Modelagem e Simulação
description The generation process of a mathematical model should present as a result the simulation, which must represent the experimental data. Several phenomena in Nature show erratic fluctuations that are phenomenological.  An expressive example is a path that pollen develops through a river surface, the Brownian movement. Chemical Engineering encompasses several issues that are evaluated considering stochastic processes, such as process optimization and control, diffusion, and chemical kinetics. In the present work, fundamental concepts that are related to stochastic differential equations (SDE) are exposed, as well as some classic examples and one in Chemical Engineering. An open-source tool by Python was used, viewing to generate the sample paths, the Python open tool, specifically the PyPI sdeint algorithm.  
publishDate 2021
dc.date.none.fl_str_mv 2021-07-21
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.furg.br/vetor/article/view/12971
10.14295/vetor.v30i2.12971
url https://periodicos.furg.br/vetor/article/view/12971
identifier_str_mv 10.14295/vetor.v30i2.12971
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.furg.br/vetor/article/view/12971/8891
dc.rights.driver.fl_str_mv Copyright (c) 2021 VETOR - Revista de Ciências Exatas e Engenharias
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 VETOR - Revista de Ciências Exatas e Engenharias
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio Grande
publisher.none.fl_str_mv Universidade Federal do Rio Grande
dc.source.none.fl_str_mv VETOR - Journal of Exact Sciences and Engineering; Vol. 30 No. 2 (2020); 14-21
VETOR - Revista de Ciências Exatas e Engenharias; v. 30 n. 2 (2020); 14-21
2358-3452
0102-7352
reponame:Vetor (Online)
instname:Universidade Federal do Rio Grande (FURG)
instacron:FURG
instname_str Universidade Federal do Rio Grande (FURG)
instacron_str FURG
institution FURG
reponame_str Vetor (Online)
collection Vetor (Online)
repository.name.fl_str_mv Vetor (Online) - Universidade Federal do Rio Grande (FURG)
repository.mail.fl_str_mv gmplatt@furg.br
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