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Proceedings Article

Multi-variable PID neural network decoupling algorithm in scrap copper smelting process control

[+] Author Affiliations
Yingdao Li, Hongwei Guan, Weihong Zhong, Zhihuan Song, Xiushui Ma

Zhejiang Univ. (China)

Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973I (May 26, 2011); doi:10.1117/12.888559
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From Conference Volume 7997

  • Fourth International Seminar on Modern Cutting and Measurement Engineering
  • Jiezhi Xin; Lianqing Zhu; Zhongyu Wang
  • Beijing, China | December 10, 2010

abstract

In order to eliminate the coupling between the loops for control in the system of scrap copper smelting, we propose the methods to built the dynamic models of inverter-fan-furnace pressure loop and natural gas and combustion air flow-air fuel ratio-furnace temperature loop based on data-driven, established the thought of multi-variable control model with the amount of scrap copper, gas flow and speed of fan as input, temperature and pressure of furnace as output, then use the method of PID neural network to decouple. Simulation results show that the control system be with the features of fast response, small overshoot and without static error, and also multi-variable PID neural network adjusts the connection weights based on the effect produced by the changes of object parameters, achieve the decoupling of the coupling variables effectively; as with reference to the PID control requirements, making the whole system be simple and standard.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Yingdao Li ; Hongwei Guan ; Weihong Zhong ; Zhihuan Song and Xiushui Ma
"Multi-variable PID neural network decoupling algorithm in scrap copper smelting process control", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973I (May 26, 2011); doi:10.1117/12.888559; http://dx.doi.org/10.1117/12.888559


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