Neural network based PID gain tuning of chemical plant controller

Yoshihiro Abe, Masami Konishi, Jun Imai, Ryusaku Hasegawa, Masamori Watanabe, Hiroaki Kamijo

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)940-947+11
Journalieej transactions on industry applications
Issue number7
Publication statusPublished - Dec 1 2008


  • Flow rate controller
  • Gain tuning
  • Neuro emulator
  • Oil refining plant
  • PID controller
  • RNN model

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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