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<p>The viscosity of refining slags plays a critical role in metallurgical processes. However, obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments, often relying on empirical models with limited predictive capabilities. This study focuses on the influence of optical basicity on viscosity in CaO–Al<sub>2</sub>O<sub>3</sub>-based refining slags, leveraging machine learning to address data scarcity and improve prediction accuracy. An automated framework for algorithm integration, parameter tuning, and evaluation ranking framework (Auto-APE) is employed to develop customized data-driven models for various slag systems, including CaO–Al<sub>2</sub>O<sub>3</sub>–SiO<sub>2</sub>, CaO–Al<sub>2</sub>O<sub>3</sub>–CaF<sub>2</sub>, CaO–Al<sub>2</sub>O<sub>3</sub>–SiO<sub>2</sub>–MgO, and CaO–Al<sub>2</sub>O<sub>3</sub>–SiO<sub>2</sub>–MgO–CaF<sub>2</sub>. By incorporating optical basicity as a key feature, the models achieve an average validation error of 8.0% to 15.1%, significantly outperforming traditional empirical models. Additionally, symbolic regression is introduced to rapidly construct domain-specific features, such as optical basicity-like descriptors, offering a potential breakthrough in performance prediction for small datasets. This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity, providing a robust machine learning-based approach for optimizing refining slag properties.</p>

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Viscosity prediction of refining slag based on machine learning with domain knowledge

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Jianhua Chen,
  • Yijie Feng,
  • Yixin Zhang,
  • Jun Luan,
  • Xionggang Lu,
  • Zhigang Yu,
  • Kuochih Chou

摘要

The viscosity of refining slags plays a critical role in metallurgical processes. However, obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments, often relying on empirical models with limited predictive capabilities. This study focuses on the influence of optical basicity on viscosity in CaO–Al2O3-based refining slags, leveraging machine learning to address data scarcity and improve prediction accuracy. An automated framework for algorithm integration, parameter tuning, and evaluation ranking framework (Auto-APE) is employed to develop customized data-driven models for various slag systems, including CaO–Al2O3–SiO2, CaO–Al2O3–CaF2, CaO–Al2O3–SiO2–MgO, and CaO–Al2O3–SiO2–MgO–CaF2. By incorporating optical basicity as a key feature, the models achieve an average validation error of 8.0% to 15.1%, significantly outperforming traditional empirical models. Additionally, symbolic regression is introduced to rapidly construct domain-specific features, such as optical basicity-like descriptors, offering a potential breakthrough in performance prediction for small datasets. This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity, providing a robust machine learning-based approach for optimizing refining slag properties.