机器学习在炼钢过程建模中的应用现状

张润灏; 杨健

doi:10.1007/s12613-023-2646-1

Volume 30 Issue 11

Nov. 2023

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文章导航 > 矿物冶金与材料学报（英文） > 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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特约综述

机器学习在炼钢过程建模中的应用现状

张润灏,
杨健

上海大学材料科学与工程学院，省部共建高品质特殊钢冶金与制备国家重点实验室，上海200444，中国

通讯作者:
杨健 E-mail: yang_jian@t.shu.edu.cn

文章亮点

(1) 系统地阐述了机器学习在处理炼钢任务时所使用的算法与数据处理方法。

(2) 系统地回顾了21世纪以来机器学习在炼钢过程建模中的应用。

(3) 总结并展望了机器学习技术在炼钢环节未来的发展应用方向。

中文摘要

随着钢铁工业自动化和信息化的发展，炼钢过程中产生了越来越多的数据。相比于传统的基于生产经验和冶金机理的方式，机器学习技术作为一种新的方法可以用于处理大数据，机器学习在炼钢过程中的应用已成为近年来的研究热点。本文概述了机器学习在炼钢过程建模中的应用，主要包括铁水预处理、一次炼钢、二次精炼等方面。在炼钢过程建模中，最常用的三种机器学习算法是人工神经网络、支持向量机和基于案例推理，分别占56%、14%和10%。炼钢厂收集到的工业大数据经常出现各种异常与错误。因此，数据的处理，尤其是数据清洗，对机器学习模型的性能表现至关重要。变量重要性也能用于优化工艺参数和指导工业生产。机器学习应用于铁水预处理建模，主要用于终点硫含量的预测。在一次炼钢中，包含了平炉、电炉和转炉炼钢，机器学习主要应用于元素终点和工艺参数的预测。对于二次精炼建模，机器学习在LF、RH、VD、AOD、VOD工艺均有应用。机器学习在炼钢过程建模中的进一步发展可以通过数据平台建设，研究成果向炼钢过程产业转化，以及提高机器学习模型的通用性等方面的努力来实现。

关键词:

Invited Review

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

Runhao Zhang and
Jian Yang

+ Author Affiliations

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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