State of the art in applications of machine learning in steelmaking process modeling

Runhao Zhang; Jian Yang

doi:10.1007/s12613-023-2646-1

Volume 30 Issue 11

Nov. 2023

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2023 > 30(11): 2055-2075

Runhao Zhangand Jian Yang, State of the art in applications of machine learning in steelmaking process modeling, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2055-2075. https://doi.org/10.1007/s12613-023-2646-1

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Runhao Zhangand Jian Yang, State of the art in applications of machine learning in steelmaking process modeling, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2055-2075. https://doi.org/10.1007/s12613-023-2646-1

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Invited Review

State of the art in applications of machine learning in steelmaking process modeling

Runhao Zhang and
Jian Yang

+ Author Affiliations

Corresponding author:
Jian Yang E-mail: yang_jian@t.shu.edu.cn
Received: 20 January 2023; Revised: 14 March 2023; Accepted: 7 April 2023; Available online: 8 April 2023

Abstract

Abstract

With the development of automation and informatization in the steelmaking industry, the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process. Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data. The application of machine learning in the steelmaking process has become a research hotspot in recent years. This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment, primary steelmaking, secondary refining, and some other aspects. The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network, support vector machine, and case-based reasoning, demonstrating proportions of 56%, 14%, and 10%, respectively. Collected data in the steelmaking plants are frequently faulty. Thus, data processing, especially data cleaning, is crucially important to the performance of machine learning models. The detection of variable importance can be used to optimize the process parameters and guide production. Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction. The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking. Machine learning is used in secondary refining modeling mainly for ladle furnaces, Ruhrstahl–Heraeus, vacuum degassing, argon oxygen decarburization, and vacuum oxygen decarburization processes. Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform, the industrial transformation of the research achievements to the practical steelmaking process, and the improvement of the universality of the machine learning models.
- machine learning;
- steelmaking process modeling;
- artificial neural network;
- support vector machine;
- case-based reasoning;
- data processing

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