Incremental multi-subreservoirs echo state network control for uncertain aeration process.

Area of Science:

• Environmental Engineering
• Control Systems Engineering
• Artificial Intelligence

Background:

• Controlling dissolved oxygen (DO) in wastewater treatment processes (WWTP) is challenging due to nonlinear dynamics and unknown disturbances.
• Existing methods struggle with the inherent uncertainties of aeration processes.

Purpose of the Study:

• To propose an advanced controller for robust dissolved oxygen control in WWTP.
• To enhance adaptability and reduce computational complexity in aeration control.

Main Methods:

• Utilizing an echo state network (ESN) as a system state approximator and a disturbance observer for unmeasurable disturbances.
• Incorporating an error-driven subreservoir increment mechanism for improved uncertainty approximation.
• Applying the minimum learning parameter (MLP) algorithm to optimize weight updates and reduce computational load.
• Employing Lyapunov stability theory to prove the stability of the closed-loop system.

Main Results:

• The proposed incremental multi-subreservoirs echo state network (IMSESN) controller demonstrated superior tracking accuracy compared to existing methods.
• The controller exhibited significant computational efficiency, validated through simulations on the benchmark simulation model no. 1 (BSM1).
• The system showed robustness under diverse weather conditions, indicating reliable performance in real-world scenarios.

Conclusions:

• The IMSESN controller offers an effective solution for precise dissolved oxygen control in WWTP.
• The developed method balances high control accuracy with reduced computational demands.
• This approach provides a promising advancement for optimizing wastewater treatment operations.