Research Progress and Intelligent Trend of Slag Foaming Prediction
Slag foaming is a critical phenomenon in electric arc furnace (EAF) steelmaking, enhancing thermal efficiency, suppressing metal splashing, and stabilizing the refining process. Accurate prediction and control of slag foaming are essential for green and efficient steelmaking. This review systematically examines research progress on slag foaming prediction, clarifying the applicability, advantages, and limitations of different predictive methods to support intelligent control of foamy slags. Following the framework of 'influencing factors-prediction methods-development trends', the study summarizes the coupling effects of multiple variables such as basicity, viscosity, surface tension, suspended particles, gas parameters, and temperature on foam formation and stability. It compares five major prediction approaches: empirical formulas, dimensionless modeling, thermodynamic calculations, computational fluid dynamics (CFD) simulations, and machine learning models, analyzing their core concepts, merits, and constraints. Results indicate that single models often struggle to balance real-time capability and accuracy, particularly under multi-variable coupling and complex operating conditions. Therefore, a hybrid prediction framework combining mechanism-based and data-driven models is proposed, emphasizing physical constraints, multi-scale coupling, and multi-source data fusion. This integrated approach is expected to advance slag foaming prediction from 'computable' to 'controllable and adjustable', offering methodological insights for the development of green and intelligent EAF steelmaking.