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<p>Continuous casting is a critical process in steel production, in which molten steel is solidified into cast products through continuous cooling. Solidification control significantly affects microstructural evolution, defect formation, and the mechanical performance of final products. To achieve such precise control, numerical analysis based on heat transfer and solidification models is widely used to calculate the temperature field in continuous casting. However, this approach struggles to strike a balance between computational accuracy and efficiency. In this study, molten steel flow and mold–shell interfacial heat transfer under various processes were investigated using numerical simulations. Based on simulation data, deep learning-based surrogate models were developed to predict molten steel velocity and interfacial heat flux. The surrogates were integrated into a finite-difference heat transfer and solidification model, forming a hybrid data-driven and mechanistic model for high-accuracy, real-time analysis of slab continuous casting. Experimental results showed that the proposed model reduced the absolute deviation of the surface temperature at the caster exit from 24 to 7&#xa0;°C, with the relative error decreasing from 2.9 to 0.8 %. Compared with conventional models, the proposed model can more accurately describe the complex heat transfer behavior without introducing significant computational cost, demonstrating great potential for real-time monitoring and quality control in slab continuous casting.</p>

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A Hybrid Data-Driven and Mechanistic Model for Heat Transfer and Solidification in Continuous Casting

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Jiyang Zhang,
  • Xinyu Ning,
  • Haijun Li,
  • Haoyi Chen,
  • Guodong Wang

摘要

Continuous casting is a critical process in steel production, in which molten steel is solidified into cast products through continuous cooling. Solidification control significantly affects microstructural evolution, defect formation, and the mechanical performance of final products. To achieve such precise control, numerical analysis based on heat transfer and solidification models is widely used to calculate the temperature field in continuous casting. However, this approach struggles to strike a balance between computational accuracy and efficiency. In this study, molten steel flow and mold–shell interfacial heat transfer under various processes were investigated using numerical simulations. Based on simulation data, deep learning-based surrogate models were developed to predict molten steel velocity and interfacial heat flux. The surrogates were integrated into a finite-difference heat transfer and solidification model, forming a hybrid data-driven and mechanistic model for high-accuracy, real-time analysis of slab continuous casting. Experimental results showed that the proposed model reduced the absolute deviation of the surface temperature at the caster exit from 24 to 7 °C, with the relative error decreasing from 2.9 to 0.8 %. Compared with conventional models, the proposed model can more accurately describe the complex heat transfer behavior without introducing significant computational cost, demonstrating great potential for real-time monitoring and quality control in slab continuous casting.