A Dynamic Data-driven Model for Predicting Strip Temperature in Continuous Annealing Line Heating Process

In the continuous annealing line heating process, it is hard to get an accurate predict result only by a steady state model as it is a complex, strongly time-delayed and confounding process. This study provides a method for building a dynamic model. First analyzes the mechanism of the annealing line to get the main parameters, and then use the data-driven modeling method to get a steady state model, finally combines with dynamic algorithm to establish a dynamic model. This modeling method improves the accuracy of predict result to guarantee the efficiency of enterprises.