RESUMO
This study presents the development and industrial application of an integrated neural system in coating weight control for a modern hot dip coating line (HDCL) in a steel mill. The neural system consists of two multilayered feedforward neural networks and a neural adaptive controller. They perform coating weight real-time prediction, feedforward control (FFC), and adaptive feedback control (FBC), respectively. The production line analysis, neural system architecture, learning, associative memories, generalization and real-time applications are addressed in this paper. This integrated neural system has been successfully implemented and applied to an HDCL at Burns Harbor Division, Bethlehem Steel Co., Chesterton, IN. The industrial application results have shown significant improvements in reduction of coating weight transitional footage, variation of the error between the target and actual coating weight, and the coating material used. Some practical aspects for applying a neural system to industrial control are discussed as concluding remarks.