Predictive analytics in the petrochemical industry

Detalhes bibliográficos
Autor(a) principal: Dias, Tiago
Data de Publicação: 2020
Outros Autores: Oliveira, Rodolfo, Saraiva, Pedro, Reis, Marco S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/98757
Resumo: Dias, T., Oliveira, R., Saraiva, P., & Reis, M. S. (2020). Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit. Computers and Chemical Engineering, 139, [106912]. https://doi.org/10.1016/j.compchemeng.2020.106912
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spelling Predictive analytics in the petrochemical industryResearch Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unitBig DataCatalytic reformingPredictive data analyticsResearch Octane NumberSoft sensorsChemical Engineering(all)Computer Science ApplicationsDias, T., Oliveira, R., Saraiva, P., & Reis, M. S. (2020). Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit. Computers and Chemical Engineering, 139, [106912]. https://doi.org/10.1016/j.compchemeng.2020.106912The Research Octane Number (RON) is a key parameter for specifying gasoline quality. It assesses the ability to resist engine knocking as the fuel burns in the combustion chamber. In this work we address the critical but complex problem of predicting RON using real process data in the context of a catalytic reforming process from a petrochemical refinery. We considered data collected from the process over an extended period of time (21 months). RON measurements are obtained offline, by laboratory analysis, with a significant delay and at much lower rates when compared to process measurements. The proposed workflow covers all the way from data collection, cleaning and pre-processing to data-driven modelling, analysis and validation for a real industrial refinery located in Portugal. The accuracy achieved with the best soft sensors open up perspectives for industrial applications and the results obtained also provide relevant information about the main RON variability sources.NOVA Information Management School (NOVA IMS)RUNDias, TiagoOliveira, RodolfoSaraiva, PedroReis, Marco S.2022-05-28T00:31:26Z2020-08-042020-08-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/98757eng0098-1354PURE: 18415725https://doi.org/10.1016/j.compchemeng.2020.106912info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:45:48Zoai:run.unl.pt:10362/98757Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:45:48Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predictive analytics in the petrochemical industry
Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit
title Predictive analytics in the petrochemical industry
spellingShingle Predictive analytics in the petrochemical industry
Dias, Tiago
Big Data
Catalytic reforming
Predictive data analytics
Research Octane Number
Soft sensors
Chemical Engineering(all)
Computer Science Applications
title_short Predictive analytics in the petrochemical industry
title_full Predictive analytics in the petrochemical industry
title_fullStr Predictive analytics in the petrochemical industry
title_full_unstemmed Predictive analytics in the petrochemical industry
title_sort Predictive analytics in the petrochemical industry
author Dias, Tiago
author_facet Dias, Tiago
Oliveira, Rodolfo
Saraiva, Pedro
Reis, Marco S.
author_role author
author2 Oliveira, Rodolfo
Saraiva, Pedro
Reis, Marco S.
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Dias, Tiago
Oliveira, Rodolfo
Saraiva, Pedro
Reis, Marco S.
dc.subject.por.fl_str_mv Big Data
Catalytic reforming
Predictive data analytics
Research Octane Number
Soft sensors
Chemical Engineering(all)
Computer Science Applications
topic Big Data
Catalytic reforming
Predictive data analytics
Research Octane Number
Soft sensors
Chemical Engineering(all)
Computer Science Applications
description Dias, T., Oliveira, R., Saraiva, P., & Reis, M. S. (2020). Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit. Computers and Chemical Engineering, 139, [106912]. https://doi.org/10.1016/j.compchemeng.2020.106912
publishDate 2020
dc.date.none.fl_str_mv 2020-08-04
2020-08-04T00:00:00Z
2022-05-28T00:31:26Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/98757
url http://hdl.handle.net/10362/98757
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0098-1354
PURE: 18415725
https://doi.org/10.1016/j.compchemeng.2020.106912
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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