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<p>In this study, a multiphase reaction kinetic model, refractory–slag–steel–inclusion–air (ReSSIA), was developed based on the Effective Equilibrium Reaction Zone framework and coupled with Factsage 8.1 <i>via</i> a custom program to investigate the evolution of molten steel composition and inclusions during ladle furnace (LF) refining of GCr15 bearing steel. The model explicitly accounts for reoxidation induced by slag eye formation under bottom-blown argon, capturing dynamic inclusion transformations along deoxidation–desulfurization trajectories while considering coupled chemical reactions. Results indicate that Al<sub>2</sub>O<sub>3</sub> inclusions dominate the early to mid-refining stage and are eliminated after about 30&#xa0;minutes, with removal time closely correlated to argon flow rate, whereas MgO·Al<sub>2</sub>O<sub>3</sub> spinel inclusions persist throughout the process. LF refining achieves an actual inclusion removal rate of 50&#xa0;pct. ReSSIA accurately predicts the temporal evolution of key elements (T[Al], T[S], T[O]) in representative-heat trials and reliably reproduces major element trends across over 300 industrial heats, with an average prediction accuracy of 89.1&#xa0;pct. This model provides a robust theoretical and computational framework for optimizing steel composition control and inclusion removal during LF refining.</p>

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Modelling of Multi-phase Reactions in Refractories-Slag-Molten Steel-Inclusions-Air Systems Based on Computational Thermodynamics: Kinetic Models for Predicting Inclusion Evolution and Industrial Data Validation

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Zhijie Guo,
  • Yanhui Sun,
  • Hongyu Wang,
  • Chao Chen

摘要

In this study, a multiphase reaction kinetic model, refractory–slag–steel–inclusion–air (ReSSIA), was developed based on the Effective Equilibrium Reaction Zone framework and coupled with Factsage 8.1 via a custom program to investigate the evolution of molten steel composition and inclusions during ladle furnace (LF) refining of GCr15 bearing steel. The model explicitly accounts for reoxidation induced by slag eye formation under bottom-blown argon, capturing dynamic inclusion transformations along deoxidation–desulfurization trajectories while considering coupled chemical reactions. Results indicate that Al2O3 inclusions dominate the early to mid-refining stage and are eliminated after about 30 minutes, with removal time closely correlated to argon flow rate, whereas MgO·Al2O3 spinel inclusions persist throughout the process. LF refining achieves an actual inclusion removal rate of 50 pct. ReSSIA accurately predicts the temporal evolution of key elements (T[Al], T[S], T[O]) in representative-heat trials and reliably reproduces major element trends across over 300 industrial heats, with an average prediction accuracy of 89.1 pct. This model provides a robust theoretical and computational framework for optimizing steel composition control and inclusion removal during LF refining.