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Predictive analytics in the petrochemical industry

2020-08-04Artigo científico Acesso aberto
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dc.contributor.authorDias, Tiago
dc.contributor.authorOliveira, Rodolfo
dc.contributor.authorSaraiva, Pedro
dc.contributor.authorReis, Marco S.
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblPERGAMON-ELSEVIER SCIENCE LTD
dc.date.accessioned2020-06-03T00:55:43Z
dc.date.available2022-05-28T00:31:26Z
dc.date.embargoedUntil2022-05-25
dc.date.issued2020-08-04
dc.descriptionDias, 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
dc.description.abstractThe 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.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent15
dc.format.extent1791593
dc.identifier.doi10.1016/j.compchemeng.2020.106912
dc.identifier.issn0098-1354
dc.identifier.otherPURE: 18415725
dc.identifier.otherPURE UUID: c378f64e-be7c-4c6f-8be3-16b33d32e8f9
dc.identifier.otherScopus: 85085205165
dc.identifier.otherWOS: 000555543100014
dc.identifier.urihttp://hdl.handle.net/10362/98757
dc.identifier.urlhttps://www.scopus.com/pages/publications/85085205165
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000555543100014
dc.language.isoeng
dc.peerreviewedyes
dc.subjectBig Data
dc.subjectCatalytic reforming
dc.subjectPredictive data analytics
dc.subjectResearch Octane Number
dc.subjectSoft sensors
dc.subjectGeneral Chemical Engineering
dc.subjectComputer Science Applications
dc.titlePredictive analytics in the petrochemical industryen
dc.title.subtitleResearch Octane Number (RON) forecasting and analysis in an industrial catalytic reforming uniten
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage15
degois.publication.titleComputers and Chemical Engineering
degois.publication.volume139
dspace.entity.typePublication
rcaap.rightsopenAccess

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