A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace

To predict and optimize the billet heating process in the reheating furnace for rolling mills, this article proposes a hybrid model that combines the data-driven model with traditional mechanism knowledge, abbreviated as HMDM. By examining the heat conduction mechanism, a billet temperature distribution equation is established. Then, the billet temperature distribution in each heating zone is calculated and spliced with the corresponding process parameters. The stacked-autoencoder is utilized to extract the features of process parameters, and the long short-term memory model is employed to predict the temperature. Finally, using the previous predictions, the parameters of the subsequent heating stage are optimized and adjusted during the heating process. The experimental results on the real steel plant verify the effectiveness of HMDM. For example, the temperature prediction error has been reduced to less than 4 $^{\circ }$ C, and the number of billets with abnormal tapping temperature has been decreased by 42.9%.