Advances in Steel Desulfurization: Thermodynamic, Kinetic, and Machine Learning Perspectives

Studies on desulfurization during the steelmaking process have been summarized. First, the detrimental effect of sulfur in the steel and desulfurization techniques was introduced. Desulfurization mechanisms influenced by desulfurization parameters and interfacial reactions were discussed, including sulfur capacity, sulfur partition ratio, and desulfurization index. Experimental and theoretical analyses were conducted to evaluate the effect of slag properties on desulfurization, specifically CaO/Al 2 O 3 , CaO/SiO 2 , oxidation potential, melting point, and viscosity. Quantitative correlations between slag composition and desulfurization parameters were further established. Subsequently, theoretical modeling for predicting desulfurization processes was systematically summarized, including kinetic models, numerical simulations, and machine learning. Finally, opportunities and challenges in steel desulfurization were proposed, which provided new insights for the development of advanced desulfurization. By clarifying these mechanisms and prediction models, this review highlighted the importance of integrating thermal modeling with machine learning to guide slag design and process optimization, thereby improving the efficiency, robustness, and sustainability of desulfurization in modern steelmaking.