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 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.04.2022
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhou, Mengchu Liu, Shixin Guo, Xiwang Ji, Yingjun Qi, Liang Zhao, Ziyan |
| Author_xml | – sequence: 1 givenname: Yingjun surname: Ji fullname: Ji, Yingjun email: jalonso@stumail.neu.edu.cn organization: State Key Laboratory of Synthetical Automation for Process Industries, and College of Information Science and Engineering, Northeastern University, Shenyang 110819, China – sequence: 2 givenname: Shixin surname: Liu fullname: Liu, Shixin email: sxliu@mail.neu.edu.cn organization: State Key Laboratory of Synthetical Automation for Process Industries, and College of Information Science and Engineering, Northeastern University, Shenyang 110819, China – sequence: 3 givenname: Mengchu surname: Zhou fullname: Zhou, Mengchu email: zhou@njit.edu organization: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA – sequence: 4 givenname: Ziyan surname: Zhao fullname: Zhao, Ziyan email: zhaoziyan@ise.neu.edu.cn organization: State Key Laboratory of Synthetical Automation for Process Industries, and College of Information Science and Engineering, Northeastern University, Shenyang 110819, China – sequence: 5 givenname: Xiwang surname: Guo fullname: Guo, Xiwang email: x.w.guo@163.com organization: College of Computer and Communication Engineering, Liaoning Shihua University, Fushun 113001, China – sequence: 6 givenname: Liang surname: Qi fullname: Qi, Liang email: qiliangsdkd@163.com organization: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China |
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| Keywords | Width deviation prediction Hot-rolled strip Feature construction Generalized linear regression Hierarchical clustering Genetic algorithm |
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| SubjectTerms | Feature construction Generalized linear regression Genetic algorithm Hierarchical clustering Hot-rolled strip Width deviation prediction |
| Title | A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems |
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