Methodology of developing mathematical-ANN hybrid model based mill setup model for hot strip rolling

Abstract

Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of the material being rolled using mathematical models derived from fundamental principles of heat transfer and plastic deformation. However, such mill setup models often compute inaccurate schedules leading to quality issues and operational problems. This paper describes a novel technique of developing a hybrid model by integrating mathematical models with artificial neural network (ANN) model. The trained hybrid models use a multivariable optimization algorithm to calculate the thermal, reduction and speed schedules during hot strip rolling. More than six hundred coils were successfully rolled in an industrial hot strip mill using the mill setup model developed under the present work. It is found that the mill setup model developed using the hybrid models is more accurate and faster than the mill setup models that use conventional mathematical models.