A Shape Prediction Model in Cold Strip Mill Integrating Principal Component Analysis and Neural Network

摘要

The efficient and reliable prediction of the strip shape in cold strip mill is a challenging problem, due to (a) too many different variables to be processed; (b) the strong intercorrelation and interaction among the process variables; (c) the time delay; (d) highly nonlinear behaviour. The conventional method to predict the strip shape in cold strip mill is difficult, so the artificial neural network with many complicated input variables was employed to simulate the complex system. To overcome the correlation effects among the process variables and the problem of dimensionality, principal component analysis (PCA) was introduced to the developed shape prediction model in cold strip mill. From the PCA, it was possible to decide the optimal dimension for the problem, to describe the dynamic behaviors of the strip shape. The calculated results are in good agreement with the measured values. The prediction model integrating principal component analysis and neural network has shown a good performance in terms of running speed and model accuracy, and it is suitable for efficient and reliable shape control in cold strip mill.