Full Text:   <2564>

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CLC number: TP273; TP183

On-line Access: 2018-09-04

Received: 2016-07-06

Revision Accepted: 2016-09-19

Crosschecked: 2018-07-08

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Zai-sheng Pan


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.7 P.834-846


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

Author(s):  Zai-sheng Pan, Xuan-hao Zhou, Peng Chen

Affiliation(s):  Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   panzs@zju.edu.cn

Key Words:  Neural network, Hot-dip galvanizing line (HDGL), Coating weight control

Zai-sheng Pan, Xuan-hao Zhou, Peng Chen. Development and application of a neural network based coating weight control system for a hot-dip galvanizing line[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 834-846.

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T1 - Development and application of a neural network based coating weight control system for a hot-dip galvanizing line
A1 - Zai-sheng Pan
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A1 - Peng Chen
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DOI - 10.1631/FITEE.1601397

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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