Development and application of a neural network based coating weight control system for a hot-dip galvanizing line

Abstract

The hot-dip galvanizing line (HDGL) is a typical order-driven discrete-event process in steelmaking. It has some complicated dynamic characteristics such as a large time-varying delay, strong nonlinearity, and unmeasured disturbance, all of which lead to the difficulty of an online coating weight controller design. We propose a novel neural network based control system to solve these problems. The proposed method has been successfully applied to a real production line at VaLin LY Steel Co., Loudi, China. The industrial application results show the effectiveness and efficiency of the proposed method, including significant reductions in the variance of the coating weight and the transition time.

Keywords

Neural network / Hot-dip galvanizing line (HDGL) / Coating weight control

RIGHTS & PERMISSIONS

2018 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature