A novel pressure control method for nonlinear shell-and-tube steam condenser system via electric eel foraging optimizer
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
CZ.10.03.01/00/22_003/0000048
European Union
CZ.10.03.01/00/22_003/0000048
European Union
CZ.10.03.01/00/22_003/0000048
European Union
TN02000025
National Centre for Energy II
TN02000025
National Centre for Energy II
TN02000025
National Centre for Energy II
101139527
ExPEDite (European Union's Horizon Mission Programme)
101139527
ExPEDite (European Union's Horizon Mission Programme)
101139527
ExPEDite (European Union's Horizon Mission Programme)
PubMed
40038463
PubMed Central
PMC11880308
DOI
10.1038/s41598-025-92576-7
PII: 10.1038/s41598-025-92576-7
Knihovny.cz E-zdroje
- Klíčová slova
- Cascaded PI-PDN controller, Electric eel foraging optimizer, Metaheuristics, Nonlinear system, Pressure control, Steam condenser,
- Publikační typ
- časopisecké články MeSH
Precise pressure control in shell-and-tube steam condensers is crucial for ensuring efficiency in thermal power plants. However, traditional controllers (PI, PD, PID) struggle with nonlinearities and external disturbances, while classical tuning methods (Ziegler-Nichols, and Cohen-Coon) fail to provide optimal parameter selection. These challenges lead to slow response, high overshoot, and poor steady-state performance. To address these limitations, this study proposes a cascaded PI-PDN control strategy optimized using the electric eel foraging optimizer (EEFO). EEFO, inspired by the prey-seeking behavior of electric eels, efficiently tunes controller parameters, ensuring improved stability and precision. A comparative analysis against recent metaheuristic algorithms (SMA, GEO, KMA, QIO) demonstrates superior performance of EEFO in regulating condenser pressure. Additionally, validation against documented studies (CSA-based FOPID, RIME-based FOPID, GWO-based PI, GA-based PI) highlights its advantages over existing methods. Simulation results confirm that EEFO reduces settling time by 22.7%, overshoot by 78.7%, steady-state error by three orders of magnitude, and ITAE by 81.2% compared to metaheuristic based methods. The EEFO-based controller achieves faster convergence, enhanced robustness to disturbances, and precise tracking, making it a highly effective solution for real-world applications. These findings contribute to optimization-based control strategies in thermal power plants and open pathways for further bio-inspired control innovations.
Applied Science Research Center Applied Science Private University Amman 11931 Jordan
College of Engineering University of Business and Technology Jeddah 21448 Saudi Arabia
Computer Science Department Al Al Bayt University Mafraq 25113 Jordan
Department of Computer Engineering Batman University Batman 72100 Turkey
Department of Electrical and Electronics Engineering Bursa Uludag University Bursa 16059 Turkey
Department of Electrical Engineering Graphic Era Dehradun 248002 India
ENET Centre CEET VSB Technical University of Ostrava Ostrava 708 00 Czech Republic
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