Stochastic Differential Equation in Chemical Engineering
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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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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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 |
_version_ |
1797041761783119872 |