A hybrid neural-network/mathematical prediction model for tandem cold mill

Abstract

In tandem cold mill, a strip is flattened by stands of rolls to a desired thickness. At Pohang Iron and Steel Company (POSCO) in Pohang, Korea, precalculation determines the mill settings before a strip actually enters the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to impoove the accuracy of the roll force prediction. The network is fed not only the usual mathematical model's input but also a set of additional inputs such as the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 4,944 process data collected at POSCO from March 1995 through December 1995, then was tested on the unseen 1,586 data from February 1996 through April 1996. The combined model reduced the prediction error by 33.88% on average.