A novel pressure control method for nonlinear shell-and-tube steam condenser system via electric eel foraging optimizer

. 2025 Mar 04 ; 15 (1) : 7550. [epub] 20250304

Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid40038463

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)

Odkazy

PubMed 40038463
PubMed Central PMC11880308
DOI 10.1038/s41598-025-92576-7
PII: 10.1038/s41598-025-92576-7
Knihovny.cz E-zdroje

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.

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