Neural Network-Based Adaptive Tracking Control for Denitrification and Aeration Processes With Time Delays

Area of Science:

• Environmental Engineering
• Control Systems Engineering
• Artificial Intelligence

Background:

• Wastewater treatment processes (WWTPs) are crucial for environmental protection and water resource management.
• WWTPs exhibit complex dynamics, uncertainties, nonlinearities, and time delays, posing significant control challenges.
• Existing control strategies often struggle to maintain optimal performance under these conditions.

Purpose of the Study:

• To develop an adaptive neural controller for enhancing the performance of wastewater treatment processes.
• To address the challenges posed by complexities, uncertainties, nonlinearities, and time-varying delays in WWTPs.
• To ensure stable dissolved oxygen (DO) and nitrate concentrations within specified ranges.

Main Methods:

• Utilized radial basis function neural networks (RBF NNs) for identifying unknown dynamics within WWTPs.
• Established time-varying delayed models for denitrification and aeration processes based on mechanistic analysis.
• Applied Lyapunov-Krasovskii functional (LKF) to compensate for time-varying delays and Barrier Lyapunov function (BLF) to maintain DO and nitrate levels.
• Proved the closed-loop system stability using Lyapunov theorem.

Main Results:

• Successfully identified unknown dynamics in WWTPs using RBF NNs.
• Developed and validated time-varying delayed models for key treatment processes.
• Demonstrated the controller's ability to maintain DO and nitrate concentrations within desired ranges despite delays and disturbances.
• Verified the effectiveness and practicability of the proposed control method on the Benchmark Simulation Model 1 (BSM1).

Conclusions:

• The proposed adaptive neural controller offers a robust solution for controlling complex WWTPs with time-varying delays.
• The integration of RBF NNs, LKF, and BLF ensures stable and efficient wastewater treatment.
• The method provides a promising approach for improving water resource recycling and reducing environmental pollution.