WU Lingling, ZHANG Xinmin, LIU Xiaojie, JIANG Qingchao, SONG Zhihuan
XWith the continued development of intelligent steel manufacturing, green and low-carbon transition, and industrial big data platforms, steel production is shifting from experience-driven operation and isolated automation toward intelligence driven by data perception, knowledge constraints, and closed-loop optimization. However, steel processes are characterized by long process routes, numerous variables, strong coupling, evident time delays, and delayed quality feedback, making multi-source heterogeneous data difficult to use directly for reliable modeling and on-site decision-making. From a knowledge-data dual-wheel perspective, this paper reviews recent progress in big data-based intelligent modeling, deployment, and applications in the steel industry. It analyzes the governance and fusion of time-series, image, text, material genealogy, and multi-source heterogeneous data; summarizes the role of industrial knowledge graphs in semantic alignment, knowledge-based reasoning, diagnostic interpretation, and quality traceability; and discusses the applicability and limitations of key techniques, including time-series prediction, visual inspection, industrial natural language processing, multimodal fusion, federated learning, continual learning, and cloud-edge collaboration. On this basis, typical applications such as real-time detection and diagnosis, key quality indicator prediction, process scheduling optimization, and full-process quality traceability are reviewed. Future directions are further discussed, including knowledge-enhanced modeling, trustworthy deployment, industrial foundation models, digital-twin closed loops, and green low-carbon decision-making. This review provides a reference for the evolution of steel industry intelligence from local model applications toward full-process trustworthy closed-loop systems.
