Data Driven Performance Prediction in Steel Making

被引：14

作者：

Boto, Fernando ^{[1
]}

Murua, Maialen ^{[1
]}

Gutierrez, Teresa ^{[2
]}

Casado, Sara ^{[2
]}

Carrillo, Ana ^{[2
]}

Arteaga, Asier ^{[3
]}

机构：

[1] TECNALIA, Basque Res & Technol Alliance BRTA, Mikeletegi Pasealekua 7, Donostia San Sebastian 20009, Spain

[2] TECNALIA, Basque Res & Technol Alliance BRTA, Astondo Bidea,Edificio 700, Derio 48160, Spain

[3] Sidenor I D, Barrio Ugarte S-N, Basauri 48970, Spain

来源：

METALS | 2022年 / 12卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

steel making; ensemble learning; feature selection; random forest; optimization;

D O I：

10.3390/met12020172

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：19