RT Journal Article
T1 Data Driven Performance Prediction in Steel Making
A1 Boto, Fernando
A1 Murua, Maialen
A1 Gutierrez, Teresa
A1 Casado, Sara
A1 Carrillo, Ana
A1 Arteaga, Asier
AB 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.
SN 2075-4701
YR 2022
FD 2022-01-18
LA eng
NO Boto , 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/met12020172
NO Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
DS TECNALIA Publications
RD 1 sept 2024