Advances of artificial intelligence applications to low-carbon metallurgy of iron and steel

Abstract Global carbon neutrality targets and rapid digitalization are reshaping steel production, accelerating a transition toward low-carbon and data-driven operations. Artificial intelligence provides a pathway beyond experience-based practice and purely mechanistic metallurgy by leveraging large, heterogeneous industrial datasets. This work synthesizes applications across the steelmaking production chain, with emphasis on sensing and soft sensing, process modeling, and process optimization and control. It further aligns these methods with decarbonization pathways, including energy and process efficiency, feedstock and energy substitution such as hydrogen-assisted routes and scrap-based electric arc furnace production, and carbon capture, utilization, and storage. Evidence from research and industrial practice suggests improvements in product quality and yield, alongside reductions in energy intensity and emissions. Future progress will depend on stronger data infrastructure and governance, effective translation from research to plant deployment, and robust physics-informed and hybrid modeling to support generalization across processes and operating regimes.