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dc.contributor.authorMurua, Maialen
dc.contributor.authorBoto, Fernando
dc.contributor.authorAnglada, Eva
dc.contributor.authorCabero, Jose Mari
dc.contributor.authorFernandez, Leixuri
dc.date.accessioned2021-08-23T09:42:34Z
dc.date.available2021-08-23T09:42:34Z
dc.date.issued2021
dc.identifier.citationMurua, Maialen, Fernando Boto, Eva Anglada, Jose Mari Cabero, and Leixuri Fernandez. “A Slag Prediction Model in an Electric Arc Furnace Process for Special Steel Production.” Procedia Manufacturing 54 (2021): 178–183. doi:10.1016/j.promfg.2021.07.027.en
dc.identifier.issn2351-9789en
dc.identifier.urihttp://hdl.handle.net/11556/1178
dc.description.abstractIn the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.en
dc.description.sponsorshipThe project leading to this research work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 820670.en
dc.language.isoengen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA slag prediction model in an electric arc furnace process for special steel productionen
dc.typeconferenceObjecten
dc.identifier.doi10.1016/j.promfg.2021.07.027en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/820670/EU/Innovative and efficient solution, based on modular, versatile, smart process units for energy and resource flexibility in highly energy intensive processes/CIRMETen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsSlag prediction modelen
dc.subject.keywordsSoft-sensorsen
dc.subject.keywordsElectric arc furnace processen
dc.subject.keywordsMachine Learningen
dc.journal.titleProcedia Manufacturingen
dc.page.final183en
dc.page.initial178en
dc.volume.number54en
dc.conference.title10th CIRP Sponsored Conference on Digital Enterprise Technologies (DET 2021) –Digital Technologies as Enablers of Industrial Competitiveness and Sustainability


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