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

Area of Science:

• Control Systems Engineering
• Artificial Intelligence in Engineering
• Thermal Power Plant Operations

Background:

• Precise pressure control in shell-and-tube steam condensers is vital for thermal power plant efficiency.
• Traditional controllers (PI, PD, PID) and tuning methods (Ziegler-Nichols, Cohen-Coon) exhibit limitations with nonlinearities and disturbances, causing poor performance.
• Existing metaheuristic algorithms and advanced controllers (FOPID) show room for improvement in condenser pressure regulation.

Purpose of the Study:

• To propose a novel cascaded PI-PDN control strategy for enhanced steam condenser pressure control.
• To optimize the controller parameters using the electric eel foraging optimizer (EEFO), a bio-inspired algorithm.
• To validate the effectiveness of the EEFO-optimized controller against recent metaheuristic algorithms and documented studies.

Main Methods:

• Implementation of a cascaded PI-PDN control structure.
• Optimization of controller parameters using the electric eel foraging optimizer (EEFO).
• Comparative performance analysis against state-of-the-art metaheuristic algorithms (SMA, GEO, KMA, QIO) and documented control strategies (FOPID, PI).

Main Results:

• The EEFO-based controller demonstrated superior performance in regulating condenser pressure compared to other metaheuristic algorithms.
• Simulation results showed significant reductions: 22.7% in settling time, 78.7% in overshoot, and 81.2% in ITAE.
• The proposed controller achieved a three-orders-of-magnitude reduction in steady-state error, indicating high precision.

Conclusions:

• The EEFO-optimized cascaded PI-PDN controller offers faster convergence, enhanced robustness to disturbances, and precise tracking capabilities.
• This approach presents a highly effective solution for real-world thermal power plant applications.
• The study contributes to optimization-based control strategies and highlights the potential of bio-inspired algorithms in industrial control systems.