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 wo...

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Vydáno v:Information sciences Ročník 589; s. 360 - 375
Hlavní autoři: Ji, Yingjun, Liu, Shixin, Zhou, Mengchu, Zhao, Ziyan, Guo, Xiwang, Qi, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.04.2022
Témata:
Feature construction
Generalized linear regression
Genetic algorithm
Hierarchical clustering
Hot-rolled strip
Width deviation prediction
Width deviation prediction
Hot-rolled strip
Feature construction
Generalized linear regression
Hierarchical clustering
Genetic algorithm
ISSN:0020-0255, 1872-6291
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Shrnutí: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.12.063