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dc.contributor.authorNiño-Adan, Iratxe
dc.contributor.authorLanda-Torres, Itziar
dc.contributor.authorManjarres, Diana
dc.contributor.authorPortillo, Eva
dc.contributor.authorOrbe, Lucía
dc.date.accessioned2021-06-18T07:13:58Z
dc.date.available2021-06-18T07:13:58Z
dc.date.issued2021-06-09
dc.identifier.citationNiño-Adan, Iratxe, Itziar Landa-Torres, Diana Manjarres, Eva Portillo, and Lucía Orbe. “Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column.” Sensors 21, no. 12 (June 9, 2021): 3991. doi:10.3390/s21123991.en
dc.identifier.urihttp://hdl.handle.net/11556/1149
dc.description.abstractRefineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-ofthe- art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.en
dc.description.sponsorshipThis research received no external funding.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleSoft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Columnen
dc.typearticleen
dc.identifier.doi10.3390/s21123991en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsPentanesen
dc.subject.keywordsClassificationen
dc.subject.keywordsAutoMLen
dc.subject.keywordsSoft-sensoren
dc.subject.keywordsNormalisationen
dc.subject.keywordsFeature weightingen
dc.identifier.essn1424-8220en
dc.issue.number12en
dc.journal.titleSensorsen
dc.page.initial3991en
dc.volume.number21en


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    Attribution 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution 4.0 International