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D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

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  • Shu-zhi Gao
  • Jing Yang
  • Jie-sheng Wang

Abstract

PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

Suggested Citation

  • Shu-zhi Gao & Jing Yang & Jie-sheng Wang, 2014. "D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, April.
    • Handle: RePEc:hin:jnlmpe:681259
    DOI: 10.1155/2014/681259
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    Cited by:

    1. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.

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