Inverse parameter identifications and forward strip temperature simulations of the continuous annealing line with physics-informed neural network and operation big data

Abstract

The continuous annealing line is a key equipment in industrial metal heat treatment. It is a large-thermal-inertia cascade thermal system, and has minute-level time hysteresis between adjustable control parameters and outlet strip temperature, which is challenging for real operation. Large strip temperature deviation from targets will greatly damage the mechanical properties and the surface coating quality of the strip. Traditional methods such as energy balance method, computational fluid dynamics and end-to-end data-driven model are hard to solve the problem due to complex parameter settings, large computing costs and low interpretability, respectively. Here, a novel way seamlessly coupling physical model and data based on physics-informed neural network is used to solve the problem. The mathematical-physical model of the continuous annealing line is developed and a physics-informed neural network model is built to solve inverse problem to identify the heat transfer coefficient of the continuous annealing line. The strip temperature is numerically simulated both with physics-informed neural network model and computational fluid dynamics model. The simulated results are compared with the measured data. The simulated outlet strip temperatures of the two models agree well with the measured data and the accuracy of the identified heat transfer coefficient is verified, where the computational fluid dynamics model has higher accuracy. The physics-informed neural network models developed here will benefit intelligent feedforward cascade control of the continuous annealing line and improve the strip quality in real productions.