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March 19, 2026Metals0 citationsOpen Access

A Kinetic Model for the Quantitative Estimation of Carryover Slag During BOF Tapping Using Computational Thermodynamics

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PCPuhong ChengCBChristian BernhardDKDaniel Kavić

Key Points

  • To develop a model for estimating the amount of carryover slag during basic oxygen furnace tapping.
  • Developed a kinetic carryover slag estimation model using the effective equilibrium reaction zone method.
  • Determined carryover slag amounts by adjusting the carryover slag coefficient until results matched industrial measurements.
  • Validated the model with four industrial heats under varying deoxidation conditions.
  • Model effectively predicts carryover slag under complete and incomplete deoxidation conditions.
  • Increasing the carryover slag coefficient from 2 to 4 kg per tonne of steel leads to an increase of 9.3 ppm in phosphorus reversion.
  • Higher amounts of carryover slag cause increased refractory wear due to more readily reducible components.

Abstract

Carryover slag (COS) entrained from the basic oxygen furnace (BOF) during tapping is highly oxidizing and affects secondary steelmaking by increasing deoxidizer consumption, refractory wear, P reversion, and decreasing steel cleanliness. A kinetic COS amount estimation model was developed by using the effective equilibrium reaction zone (EERZ) method. The amount of COS was determined by iteratively adjusting the carryover slag coefficient (CSC) until predicted steel and slag compositions approached industrial measurements. Validation with four industrial heats confirmed that the model effectively predicts COS under both complete and incomplete deoxidation conditions. Further simulation results show that increasing the CSC from 2 to 4 kg per tonne of steel leads to 9.3 ppm P reversion. The calculations also confirmed that larger COS amounts accelerate refractory wear due to the higher input of readily reducible components, particularly FeO and MnO.

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Cite This Study

Cheng et al. (2026) studied this question.

synapsesocial.com/papers/69bb9313496e729e62980dc8https://doi.org/10.3390/met16030334
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