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Neural network‐based PID gain tuning of chemical plant controller
Author(s) -
Abe Yoshihiro,
Konishi Masami,
Imai Jun,
Hasegawa Ryusaku,
Watanabe Masanori,
Kamijo Hiroaki
Publication year - 2010
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20973
Subject(s) - pid controller , emulation , chemical plant , control engineering , control system , control theory (sociology) , artificial neural network , controller (irrigation) , process control , engineering , computer science , process (computing) , control (management) , temperature control , artificial intelligence , agronomy , electrical engineering , environmental engineering , economics , biology , economic growth , operating system
Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28–36, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20973