ニューラルネットワークによる化学プラントにおけるPIDゲイン調整システム  [in Japanese] Neural Network Based PID Gain Tuning of Chemical Plant Controller  [in Japanese]

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Author(s)

Abstract

In these years, plant control systems are highly automated and applied to many industries. The control performances change with the passage of time, because of the deterioration of plant facilities. This is why human experts tune the control system to improve the total plant performances. In this study, PID control system for the oil refining chemical plant process is treated. In oil refining, there are thousands of the control loops in the plant to keep the product quality at the desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using neuro emulator and recurrent neural networks model (RNN model) for emulation and tuning parameters, PID gain tuning system of chemical plant controller is constructed. Through numerical experiments using actual plant data, effect of the proposed method was ascertained.

Journal

  • IEEJ Transactions on Industry Applications

    IEEJ Transactions on Industry Applications 128(7), 940-947, 2008-07-01

    The Institute of Electrical Engineers of Japan

References:  11

Cited by:  3

Codes

  • NII Article ID (NAID)
    10021134847
  • NII NACSIS-CAT ID (NCID)
    AN10012320
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    09136339
  • NDL Article ID
    9564514
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-1608
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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