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

Abstract

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.