当前位置：首页 > 过刊浏览->2025年46卷第10期
机器学习在炼钢工业的深度应用：机遇与挑战
Deep Applications of Machine Learning in the Steelmaking Industry: Opportunities and Challenges
浏览(41) 下载(1)
- DOI:
- 作者:
- 宗男夫 1，韩永德 2， 杨军 1，王子铮 3，荆涛 4，Jean-ChristopheGEBELI
ZONG Nanfu1,HAN Yongde2,YANG Jun1,WANG Zizheng3,JING Tao4,Jean-Christophe GEBELIN5
- 作者单位:
- 1. 本钢集团有限公司技术中心数智化研究所，辽宁 本溪 117000；2. 本钢集团有限公司，辽宁 本溪 117000；3. 本钢集团 有限公司 板材炼钢厂，辽宁 本溪 117000；4. 清华大学材料学院先进成形制造教育部重点实验室，北京 100084；5. 英国 莱斯特大学，数字化研发中心，英国 莱斯特郡 LE1 7RH
1.Technology Center of Ben Gang Group Corporation, Digital Intelligence Research Institute, Benxi 117000,China; 2. Ben Gang Group Corporation, Benxi 117000,China; 3. Ben Gang Group Corporation, Plate Steelmaking Plant, Benxi 117000, China; 4. Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, School of Materials, Tsinghua University, Beijing 100084,China; 5. School of Engineering, University of Leicester, Leicester LE1 7RH, United Kingdom
- 关键词:
- 机器学习；短流程炼钢；质量预测；自主决策；基础模型
machine learning; short-process steelmaking; quality prediction; autonomous decision; foundation models
- 摘要:
- 随着人工智能的快速发展，机器学习算法在炼钢工业中的应用成为研究热点。 本文系统探讨智能模型在短流程炼钢过程复杂工业场景的深度应用挑战与机遇，重点分析其在电炉、精炼和连铸等核心环节的应用现状。 结合钢铁流程的典型场景，阐述机器学习在工艺优化、异常诊断与自主决策等方面的思考。 针对炼钢环境下的实时性、可靠性需求，提出机器学习在智能制造体系中的研究方向，包括多模态感知、因果推理与数字孪生等前沿技术。 最后，探讨机器学习在短流程炼钢工业深度应用面临的挑战、潜在的解决方案和未来应用展望。With the rapid development of artificial intelligence, the application of machine learning algorithms in the steelmaking industry has become a research hotspot. This paper systematically explores the challenges and opportunities of intelligent models in complex industrial scenarios of short-process steelmaking, with a focus on analysing their current applications in key stages such as electric arc furnaces, refining, and continuous casting. An examination of typical scenarios in the steelmaking process elaborates on the role of machine learning in process optimization, anomaly detection, and autonomous decision-making. In response to the real-time and reliability demands of the steelmaking environment, this study proposes research directions for machine learning within intelligent manufacturing systems, including cutting-edge technologies such as multimodal sensing, causal reasoning, and digital twins. Finally, this study explores the challenges, potential solutions, and future application prospects of machine learning in deep integration with the short-process steelmaking industry.