A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems

Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel production systems. This paper considers a width deviation prediction problem and proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to obtain a prediction model. Existing work mainly focuses on high prediction accuracy, while ignoring interpretability. This work aims to build a prediction model that can make a good trade-off between two industry-required criteria, i.e., prediction accuracy and interpretability. It first collects some process variables in a hot rolling process and includes them as well as some constructed variables in a feature pool. Then we propose MGH to find representative variables from it and build a prediction model. MGH results from the integration of hierarchical clustering, genetic algorithm, and generalized linear regression. In detail, hierarchical clustering is applied to divide variables into clusters. Genetic algorithm and generalized linear regression are innovatively combined to select a representative variable from each cluster and develop a prediction model. The computational experiments conducted on both industrial and public datasets show that the proposed method can effectively balance prediction accuracy and interpretability of its resulting model. It has better overall performance than the compared state-of-the-art models. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to predict width deviation in steel production systems. The MGH method aims to find a balance between prediction accuracy and interpretability in the resulting model by combining hierarchical clustering, genetic algorithm, and generalized linear regression. Experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed method in achieving a good trade-off between prediction accuracy and interpretability. The MGH method outperforms compared state-of-the-art models.