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工业大数据赋能下的炼铁系统智能制造综述:历程梳理、应用现状及未来展望

Intelligent manufacturing in ironmaking front-end processes empowered by industrial big data: a review of historical evolution, current applications, and future perspectives

  • 摘要: 炼铁系统涵盖原料场、烧结、球团、焦化及高炉冶炼工序,是钢铁生产能耗与碳排放最集中的环节,其智能化程度直接影响全流程综合竞争力与绿色低碳发展进程。本文以工业大数据赋能为主线,梳理近50年来炼铁系统数智化技术演进历程,划分为自动化启蒙、数字化转型、智能化升级与全工序协同突破4个阶段,提炼出控制层级跃升、建模范式转变与决策模式演进3条发展主线。在此基础上,从原料场、烧结、球团、焦化、高炉5大工序出发,深入剖析工业大数据与人工智能技术在关键工艺场景中的应用现状,揭示数据驱动与机理模型在不同场景下的融合机制。通过对标国内外主流技术发展路线,指出当前研究在数据-机理-场景三元融合深度与跨工序协同广度方面的不足。立足行业发展趋势,从工业大数据技术、大模型研究技术、智能制造技术及绿色低碳融合4个方向预判后续技术创新路径。本文可为炼铁系统智能制造的理论研究与工程实践提供参考,助力钢铁行业向高端化、智能化、绿色化发展。

     

    Abstract: The ironmaking front-end processes, encompassing the raw material yard, sintering, pelletizing, coking, and blast furnace ironmaking, constitute the most energy-intensive and carbon-emitting segment of steel manufacturing. Consequently, the level of intelligence achieved within these processes directly determines the overall competitiveness and sustainability of the entire production chain. Taking industrial big data empowerment as the central narrative, this paper systematically reviews the nearly fifty-year evolution of digital and intelligent technologies in ironmaking front-end processes. This evolution is delineated into four distinct stages: automation enlightenment, digital transformation, intelligent upgrading, and whole-process collaborative breakthrough. From this historical analysis, three overarching developmental trajectories are distilled: the hierarchical advancement of control architecture, the paradigm shift in modeling approaches, and the progressive evolution of decision-making modes. Building upon this foundation, the paper provides an in-depth examination of the current applications of industrial big data and artificial intelligence technologies across the five core unit operations, elucidating the fusion mechanisms between data-driven methods and mechanistic models under diverse operational scenarios. Through a systematic comparison of domestic and international technological pathways, the paper identifies existing limitations regarding the depth of data-mechanism-scenario integration and the breadth of cross-process coordination. Looking forward, future innovation directions are envisioned from four critical dimensions: industrial big data technologies, research on large foundation models, smart manufacturing technologies, and the synergistic integration of green and low-carbon strategies. This review aims to provide a systematic reference for both theoretical research and engineering practice in the intelligent manufacturing of ironmaking front-end processes, thereby supporting the high-end, intelligent, and green transformation of the steel industry.

     

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