Boto, FernandoMurua, MaialenGutierrez, TeresaCasado, SaraCarrillo, AnaArteaga, Asier2022-01-18Boto , F , Murua , M , Gutierrez , T , Casado , S , Carrillo , A & Arteaga , A 2022 , ' Data Driven Performance Prediction in Steel Making ' , Metals , vol. 12 , no. 2 , 172 , pp. 172 . https://doi.org/10.3390/met120201722075-4701researchoutputwizard: 11556/1295Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.This 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.15004886enginfo:eu-repo/semantics/openAccessjournal article10.3390/met12020172Steel makingEnsemble learningFeature selectionRandom forestOptimizationSteel makingEnsemble learningFeature selectionRandom forestOptimizationGeneral Materials ScienceMetals and AlloysProject IDinfo:eu-repo/grantAgreement/EC/H2020/723661/EU/Coordinating Optimisation of Complex Industrial Processes/COCOPinfo:eu-repo/grantAgreement/EC/H2020/723661/EU/Coordinating Optimisation of Complex Industrial Processes/COCOPFunding InfoThis 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.This 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.http://www.scopus.com/inward/record.url?scp=85122889197&partnerID=8YFLogxK