Prediction of billet temperature field in reheating furnace for hot strip rolling based on general physics-informed neural network

In the process of hot strip rolling, the temperature rise curve of billet in reheating furnace has a significant impact on the quality of products. Due to the bad conditions in the furnace, the actual temperature of the billet is difficult to be directly monitored, so it is necessary to establish a real-time temperature prediction model for the billet heating process. The traditional mechanism model based on partial differential equation (PDE) is usually difficult to meet the needs of real-time prediction due to its high computational complexity; The neural network model has the characteristics of low accuracy and relying on a large number of training labels based on the actual temperature of billet, so it has not been well applied in practice. Aprediction model of hot rolling billet temperature based on generaliZed physics-informed (GPINN) was proposed. Firstly, the position of the billet in the reheating furnace was tracked according to the action signal of the walking beam, and the temperature of the whole fur- nace was predicted by cubic spline interpolation; secondly, the PDE describing billet heating was solved by PINN, and combined with the“ branch net-trunk net ”structure of deep operator network (DeepoNe t ), the different initial temperatures and specific time and space positions of billet were co- ded respectively, which effectively realiZes the real-time prediction of billet temperature in the heating process with different initial temperatures; Finally, the effectiveness of the method was verified by the case study of a real steel plant. Compared with the traditional neural network method and mechanism - based method, GPINN integrates the advantages of physical information and neural network, better captures the heat conduction characteristics in the process of billet heating and improves the interpret- ability and prediction accuracy of the model.