Development and application of a neural network based coating weight control system for a hot-dip galvanizing line
- 24 Downloads
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 wordsNeural network Hot-dip galvanizing line (HDGL) Coating weight control
CLC numberTP273 TP183
Unable to display preview. Download preview PDF.
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.
- Adams J, Miles LB, Parker DJ, et al., 1996. Coating mass control on No. 2 galvanizing line at LTV steel’s Indiana Harbor works. Iron Steel Eng, 73(1):123–131.Google Scholar
- Fei J, Zhang Y, Wang JS, et al., 2016. Development and application of coating thickness control system for cold rolling continuous galvanizing line. Iron Steel, 51(5): 57–61 (in Chinese). https://doi.org/10.13228/j.boyuan.issn0449-749x.20150388 Google Scholar
- Sanz–García A, Fernández–Ceniceros J, Fernández–Martínez R, et al., 2012. Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace. Iron Steel, 41(2):87–98. https://doi.org/10.1179/1743281212Y.0000000094 CrossRefGoogle Scholar
- Warwick K, Rees D, 1988. Industrial Digital Control Systems. IET, London, England.Google Scholar
- Yu W, Li XO, 2008. Optimization of crude oil blending with neural networks and bias–update scheme. Eng Intell Syst, 16(1):28–37.Google Scholar
- Zhang Y, Shao FQ, Wang JS, et al., 2011. Adaptive control of coating weight for continuous hot–dip galvanizing. J Northeast Univ (Nat Sci), 32(11):1525–1528 (in Chinese).Google Scholar