PID gain tuning method for oil refining controller based on neural networks

Yoshihiro Abe, Masami Konishi, Jun Imai, Ryuusaku Hasagawa, Masanori Watanabe, Hiroaki Kamijo

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


In these years, plant control systems are highly automated. It is unavoidable that control performances change with the passage of time, so it is necessary to tune control parameters. In this study, PID control system for oil refining chemical plant process is treated. In the oil refining facilities, there are thousands of control loops in the plant to keep the product qualities at desired values and to secure the safety of the plant operation. According to the ambiguity of the interferences between control loops, it is difficult to estimate the state of plant dynamics accurately. Neuro emulator is employed to estimate the plant characteristics. As for the method of PID gain tuning, a recurrent type neural network (RNN) is employed. Combining neuro emulator and RNN model, an auto tuning system of PID control gains has been constructed. Through numerical experiments using actual plant data, the effect of the proposed method was ascertained. Further, operator's guidance system for PID gain tuning was developed.

Original languageEnglish
Pages (from-to)2649-2662
Number of pages14
JournalInternational Journal of Innovative Computing, Information and Control
Issue number10
Publication statusPublished - Oct 1 2008


  • Flow rate controller
  • Neural network emulator (neuro emulator)
  • Oil refining facility
  • PID control
  • Recurrent neural network (RNN)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics


Dive into the research topics of 'PID gain tuning method for oil refining controller based on neural networks'. Together they form a unique fingerprint.

Cite this