机器学习在炼钢过程建模中的应用现状

IF 5.6 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Runhao Zhang, Jian Yang
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引用次数: 1

摘要

随着炼钢行业自动化和信息化的发展,人类的大脑逐渐无法应对炼钢过程中产生的越来越多的数据。机器学习技术为处理大量数据提供了一种超越生产经验和冶金原理的新方法。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,涉及铁水预处理、一次炼钢、二次精炼等方面。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的推理,分别占56%、14%和10%。炼钢厂收集的数据经常出错。因此,数据处理,尤其是数据清理,对机器学习模型的性能至关重要。变量重要度检测可用于优化工艺参数,指导生产。机器学习用于铁水预处理建模,主要用于终点S含量预测。在初级炼钢中,元素组成终点和工艺参数的预测问题得到了广泛的研究。机器学习主要用于二次精炼建模,主要用于钢包炉、鲁尔施塔尔-贺利氏、真空脱气、氩氧脱碳和真空氧脱碳过程。通过数据平台的建设、研究成果向实际炼钢过程的产业转化、提高机器学习模型的通用性等方面的努力,可以实现机器学习在炼钢过程建模中的进一步发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of the art in applications of machine learning in steelmaking process modeling

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.

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来源期刊
CiteScore
9.30
自引率
16.70%
发文量
205
审稿时长
2 months
期刊介绍: International Journal of Minerals, Metallurgy and Materials (Formerly known as Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material) provides an international medium for the publication of theoretical and experimental studies related to the fields of Minerals, Metallurgy and Materials. Papers dealing with minerals processing, mining, mine safety, environmental pollution and protection of mines, process metallurgy, metallurgical physical chemistry, structure and physical properties of materials, corrosion and resistance of materials, are viewed as suitable for publication.
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