TY  - Journal Article
AU  - Boto, Fernando
AU  - Murua, Maialen
AU  - Gutierrez, Teresa
AU  - Casado, Sara
AU  - Carrillo, Ana
AU  - Arteaga, Asier
TI  - Data Driven Performance Prediction in Steel Making
PY  - 2022
PB  - Multidisciplinary Digital Publishing Institute (MDPI)
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.
UR  - http://hdl.handle.net/11556/1295
DX  - 10.3390/met12020172
ER  -