The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.225250
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.
The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.225141
This study established a three-dimensional transient gas-liquid two-phase flow model based on a 150-tonne converter to investigate the influence of the number of clogged bottom-blowing elements on the stirring efficiency of the molten pool. The numerical simulation results were validated against actual converter operating conditions. The findings revealed that the primary reason for deteriorated flow characteristics under multiple clogged tuyeres was the overall reduction in stirring energy input from the bottom-blowing gas. Specifically, when the number of clogged tuyeres reached three, the numerically simulated mixing time increased from 150.6 s to 219.3 s, a significant increase of 45.62%. This numerical result was in good agreement with water model experiments, indicating that prompt furnace bottom maintenance and tuyere replacement should be considered under such circumstances. At the same bottom-blowing intensity, the effective stirring area of a single inner-ring tuyere was 0.919 m2, while that of a single outer-ring tuyere was 1.651 m2. The combined effective area achieved through the synergy of inner and outer ring tuyeres was 2.940 m2, which was 14.4% greater than the sum of their individual areas. Clogging disrupted this synergistic stirring effect. A single clogged tuyere had a negligible impact on the distribution of dead zones. However, when tuyeres in both the inner and outer rings were clogged, dead zones became more numerous and concentrated. With 3 and 4 clogged tuyeres, the dead zone volume reached 3.703 and 5.946 m3, accounting for 17.31% and 27.79% of the total molten pool volume, respectively. An industrial plant trial conducted based on the numerical simulation scheme showed that key performance indicators deteriorated as the number of clogged tuyeres increased. With three clogged tuyeres, the average end-point oxygen content reached 0.0669wt%, which was 22.1% higher than that under non-clogged conditions. Concurrently, the total iron content in the slag reached 19.44%, a 24.5% increase compared to the non-clogged baseline.