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dc.contributor.authorBoto, Fernando
dc.contributor.authorMurua, Maialen
dc.contributor.authorGutierrez, Teresa
dc.contributor.authorCasado, Sara
dc.contributor.authorCarrillo, Ana
dc.contributor.authorArteaga, Asier
dc.date.accessioned2022-03-21T00:20:43Z
dc.date.available2022-03-21T00:20:43Z
dc.date.issued2022-01-18
dc.identifier.citationBoto, Fernando, Maialen Murua, Teresa Gutierrez, Sara Casado, Ana Carrillo, and Asier Arteaga. “Data Driven Performance Prediction in Steel Making.” Metals 12, no. 2 (January 18, 2022): 172. doi:10.3390/met12020172.en
dc.identifier.urihttp://hdl.handle.net/11556/1295
dc.description.abstractThis work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process.en
dc.description.sponsorshipThis research is supported by the European Union’s Horizon 2020 Research and Innovation Framework Programme [grant agreement No 723661; COCOP; http://www.cocop-spire.eu (accessed on 6 January 2022)]. The authors want to acknowledge the work of the whole COCOP consortium.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.titleData Driven Performance Prediction in Steel Makingen
dc.typearticleen
dc.identifier.doi10.3390/met12020172en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/723661/EU/Coordinating Optimisation of Complex Industrial Processes/COCOPen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsSteel makingen
dc.subject.keywordsEnsemble learningen
dc.subject.keywordsFeature selectionen
dc.subject.keywordsRandom foresten
dc.subject.keywordsOptimizationen
dc.identifier.essn2075-4701en
dc.issue.number2en
dc.journal.titleMetalsen
dc.page.initial172en
dc.volume.number12en


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