东北大学学报(自然科学版) ›› 2025, Vol. 46 ›› Issue (7): 113-130.DOI: 10.12068/j.issn.1005-3026.2025.20250070

• 绿色冶金 • 上一篇    

数字化高炉炼铁技术研发与应用研究进展

储满生1,2, 王国栋3, 唐珏2,4(), 石泉2   

  1. 1.东北大学 低碳钢铁前沿技术教育部工程研究中心,辽宁 沈阳 110819
    2.东北大学 冶金学院,辽宁 沈阳 110819
    3.东北大学 数字钢铁全国重点实验室,辽宁 沈阳 110819
    4.东北大学 辽宁省低碳钢铁;前沿技术工程研究中心,辽宁 沈阳 110819
  • 收稿日期:2025-06-18 出版日期:2025-07-15 发布日期:2025-09-24
  • 通讯作者: 唐珏
  • 作者简介:储满生(1973—),男,安徽岳西人,东北大学教授,博士生导师
    王国栋(1942—),男,辽宁大连人,东北大学教授,博士生导师,中国工程院院士
  • 基金资助:
    国家自然科学基金资助项目(52404343);国家自然科学基金资助项目(52274326);中央高校基本科研业务费专项资金资助项目(N2425031);中央高校基本科研业务费专项资金资助项目(N25BJD007);中国博士后科学基金资助项目(2024M760370);辽宁省科技计划联合项目(重点研发项目)(2023JH2/101800058)

Research Progress on Development and Application of Digital Blast Furnace Ironmaking Technology

Man-sheng CHU1,2, Guo-dong WANG3, Jue TANG2,4(), Quan SHI2   

  1. 1.Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking,Ministry of Education,Northeastern University,Shenyang 110819,China
    2.School of Metallurgy,Northeastern University,Shenyang 110819,China
    3.State Key Laboratory of Digital Steel,Northeastern University,Shenyang 110819,China
    4.Liaoning Low-Carbon Steelmaking Technology Engineering Research Center,Northeastern University,Shenyang 110819,China.
  • Received:2025-06-18 Online:2025-07-15 Published:2025-09-24
  • Contact: Jue TANG

摘要:

在数字信息时代的推动下,高炉数字化转型拉开序幕.钢铁企业(简称钢企)应用智能闭环控制、数字孪生和AI预测模型,构建智慧操炉、炉况评价与质量优化等智能系统.数字化高炉研究成果主要集中在变量预测、状态诊断、炉况优化,这些领域由传统方式分别向复杂优化建模、多维综合评价和多目标协同优化方向发展.但目前预测模型需强化在线自更新与数据机理融合,评价体系需注重多维度精细化诊断,炉况优化需以低风险、低成本、多目标耦合为核心突破单指标局限.以高炉现场需求为出发点,开发高炉信息物理系统,将数据、机理和经验合理匹配调用,形成数据治理-规则挖掘-智能预测-综合评价-多目标优化-决策反馈一体化技术是数字化高炉炼铁的未来发展重点之一.

关键词: 高炉, 数字化技术, 智能预测, 状态诊断, 炉况优化

Abstract:

With the advancement of the digital information era, the digital transformation of blast furnaces has begun. Steel enterprises have applied intelligent closed-loop control, digital twins, and AI-based predictive models to develop intelligent systems for smart blast furnace operation, blast furnace condition assessment, and quality optimization. Research on digital blast furnaces primarily focuses on variable prediction, state diagnosis, and blast furnace condition optimization, with these domains evolving from traditional approaches toward complex optimization modeling, multidimensional comprehensive evaluation, and multi-objective collaborative optimization, respectively. However, current predictive models require enhanced online self-updating and integration of data and mechanisms; evaluation systems need to emphasize multidimensional and fine-grained diagnostics, and blast furnace condition optimization has to overcome single-indicator limitations by focusing on low-risk, low-cost, and multi-objective coupled strategies. According to the actual needs of the blast furnace site, a physical system of blast furnace information was developed, where data, mechanisms, and experience were reasonably matched and called upon to form an integrated technology encompassing data governance, rule mining, intelligent prediction, comprehensive evaluation, multi-objective optimization, and decision feedback, which was identified as one of the key directions for future development of digital blast furnace ironmaking.

Key words: blast furnace, digital technology, intelligent prediction, state diagnosis, blast furnace condition optimization

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