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

  • Zai-sheng Pan
  • Xuan-hao Zhou
  • Peng Chen
Article
  • 6 Downloads

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.

Key words

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

CLC number

TP273 TP183 

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Notes

Acknowledgements

This research was partially based on the work done by Prof. Yongzai Lu with Zhejiang University, and the control system was further improved and implemented under his guidance. The authors would like to express their thanks to Prof. Lu for his great help.

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Copyright information

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

Authors and Affiliations

  1. 1.Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina
  2. 2.Zhejiang SUPCON Research Co., Ltd.HangzhouChina

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