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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In these years, plant control systems are being highly automated. But the control performances change with the passage of time, so it is necessary to tune them. 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 the oil refining controller, there are thousands of the control loops in the plant to keep the product quality desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between the control loops, it is difficult to estimate the plant dynamic model accurately. Neuro emulator is employed to model the plant characteristics. Combining neuro emulator and RNN model, 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.

Original languageEnglish
Title of host publicationSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOIs
Publication statusPublished - 2008
Event2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto, Japan
Duration: Sep 5 2007Sep 7 2007

Other

Other2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
CountryJapan
CityKumamoto
Period9/5/079/7/07

Fingerprint

Refining
Tuning
Three term control systems
Neural networks
Control systems
Controllers
Chemical plants
Dynamic models
Oils
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Mechanical Engineering

Cite this

Abe, Y., Konishi, M., Imai, J., Hasagawa, R., Watanabe, M., & Kamijo, H. (2008). PID gain tuning method for oil refining controller based on neural networks. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007 [4427750] https://doi.org/10.1109/ICICIC.2007.454

PID gain tuning method for oil refining controller based on neural networks. / Abe, Yoshihiro; Konishi, Masami; Imai, Jun; Hasagawa, Ryuusaku; Watanabe, Masanori; Kamijo, Hiroaki.

Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008. 4427750.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abe, Y, Konishi, M, Imai, J, Hasagawa, R, Watanabe, M & Kamijo, H 2008, PID gain tuning method for oil refining controller based on neural networks. in Second International Conference on Innovative Computing, Information and Control, ICICIC 2007., 4427750, 2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007, Kumamoto, Japan, 9/5/07. https://doi.org/10.1109/ICICIC.2007.454
Abe Y, Konishi M, Imai J, Hasagawa R, Watanabe M, Kamijo H. PID gain tuning method for oil refining controller based on neural networks. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008. 4427750 https://doi.org/10.1109/ICICIC.2007.454
Abe, Yoshihiro ; Konishi, Masami ; Imai, Jun ; Hasagawa, Ryuusaku ; Watanabe, Masanori ; Kamijo, Hiroaki. / PID gain tuning method for oil refining controller based on neural networks. Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008.
@inproceedings{edcc0ba446a9439fb41563da44b48502,
title = "PID gain tuning method for oil refining controller based on neural networks",
abstract = "In these years, plant control systems are being highly automated. But the control performances change with the passage of time, so it is necessary to tune them. 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 the oil refining controller, there are thousands of the control loops in the plant to keep the product quality desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between the control loops, it is difficult to estimate the plant dynamic model accurately. Neuro emulator is employed to model the plant characteristics. Combining neuro emulator and RNN model, 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.",
author = "Yoshihiro Abe and Masami Konishi and Jun Imai and Ryuusaku Hasagawa and Masanori Watanabe and Hiroaki Kamijo",
year = "2008",
doi = "10.1109/ICICIC.2007.454",
language = "English",
isbn = "0769528821",
booktitle = "Second International Conference on Innovative Computing, Information and Control, ICICIC 2007",

}

TY - GEN

T1 - PID gain tuning method for oil refining controller based on neural networks

AU - Abe, Yoshihiro

AU - Konishi, Masami

AU - Imai, Jun

AU - Hasagawa, Ryuusaku

AU - Watanabe, Masanori

AU - Kamijo, Hiroaki

PY - 2008

Y1 - 2008

N2 - In these years, plant control systems are being highly automated. But the control performances change with the passage of time, so it is necessary to tune them. 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 the oil refining controller, there are thousands of the control loops in the plant to keep the product quality desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between the control loops, it is difficult to estimate the plant dynamic model accurately. Neuro emulator is employed to model the plant characteristics. Combining neuro emulator and RNN model, 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.

AB - In these years, plant control systems are being highly automated. But the control performances change with the passage of time, so it is necessary to tune them. 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 the oil refining controller, there are thousands of the control loops in the plant to keep the product quality desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between the control loops, it is difficult to estimate the plant dynamic model accurately. Neuro emulator is employed to model the plant characteristics. Combining neuro emulator and RNN model, 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.

UR - http://www.scopus.com/inward/record.url?scp=39049132187&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=39049132187&partnerID=8YFLogxK

U2 - 10.1109/ICICIC.2007.454

DO - 10.1109/ICICIC.2007.454

M3 - Conference contribution

AN - SCOPUS:39049132187

SN - 0769528821

SN - 9780769528823

BT - Second International Conference on Innovative Computing, Information and Control, ICICIC 2007

ER -