The current research status of heat transfer models for hot rolling strip

Post-rolling cooling is a critical step in the hot rolling production process, and it has a significant impact on the microstructure and mechanical properties of the final product. This paper first introduces three main modules of research on advanced post-rolling cooling system models. Secondly, it analyzes how to establish high-precision predictive models for post-rolling cooling systems and improve temperature control accuracy. The paper summarizes some strategies and solutions proposed by scholars to address identified issues. By utilizing advanced control theory and deep learning, a post-rolling control and cooling predictive model is developed to effectively mine production big data and provide accurate forecasts for the cooling system. In terms of improving temperature control accuracy, scholars have optimized existing self-learning models, resulting in a dual-model parallel system known as VSG+DNN. This system significantly enhances the stability and robustness of the post-rolling cooling system.