Neural network based modelling and prediction for the coating weight of HDGL process

Abstract

To deal with the difficulties of large time-varying delay, seriously nonlinear and strong disturbance in the hot-dip galvanizing line (HDGL) control, this paper proposes a novel ANN based modelling and prediction method for the coating weight, the proposed method is then applied to a real plant at Valin LY Steel Co., Loudi, China. The prediction model is built through data pre-processing, data clustering and model training, with the model deviation being corrected through bias-update. The comparisons between measured coating weight and model prediction of areal HDGL process show the effectiveness of the proposed method, and the rationality of the prediction model is further verified though gain analysis between major input and output of HDGL process.