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

被引：4
|
作者：
Pan, Zai-sheng [1 ]
Zhou, Xuan-hao [2 ]
Chen, Peng [2 ]
机构：
[1] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou 310027, Zhejiang, Peoples R China
[2] Zhejiang SUPCON Res Co Ltd, Hangzhou 310053, Zhejiang, Peoples R China
来源：
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING | 2018年 / 19卷 / 07期
关键词：
Neural network; Hot-dip galvanizing line (HDGL); Coating weight control; STEEL; STRIP; MODEL; JET;
D O I：
10.1631/FITEE.1601397
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：834 / 846
页数：13
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