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Updated: May 17, 2026

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Published on: May 2, 2025

Multivariate control based on recurrent wavelet neural network for wastewater treatment process.

Yu Fang¹, Yin Su¹, You Li¹

¹College of Mechanical Engineering, Jiaxing University, Jiaxing, P. R. China.

|May 15, 2026

Summary

This study introduces a novel self-organizing recurrent wavelet neural network controller (ISRWNN) to precisely manage wastewater treatment processes (WWTP). The ISRWNN effectively addresses the coupled dynamics of dissolved oxygen and nitrate nitrogen for improved control outcomes.

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Last Updated: May 17, 2026

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Published on: May 2, 2025

Area of Science:

Environmental Engineering
Biochemical Engineering
Control Systems

Background:

Wastewater treatment processes (WWTP) are complex, coupled, and dynamic systems.
Achieving precise control in WWTP is challenging due to intricate biochemical reactions.
Existing multi-controller approaches often fail to address the coupling between key parameters like dissolved oxygen and nitrate nitrogen.

Purpose of the Study:

To propose an innovative control strategy for enhancing the precision of wastewater treatment processes.
To develop a self-organizing recurrent wavelet neural network controller (ISRWNN) with a joint input mechanism.
To address the coupled dynamics of dissolved oxygen (DO) and nitrate nitrogen (NO) in WWTP control.

Main Methods:

Establishment of a joint input mechanism considering both DO and NO errors to manage parameter coupling.
Development of a self-organization algorithm for automatic controller structure adjustment to adapt to WWTP dynamicity.
Analysis of the ISRWNN stability using the Lyapunov stability theorem.

Main Results:

The proposed ISRWNN effectively handles the coupled dynamics of DO and NO in wastewater treatment.
The self-organization algorithm successfully adapts the controller structure to the dynamic nature of WWTP.
Experimental validation demonstrates the capability of ISRWNN to achieve good control results.

Conclusions:

The ISRWNN offers a robust and adaptive solution for precise control of wastewater treatment processes.
The joint input mechanism is crucial for managing the coupled DO and NO parameters.
The proposed method shows significant potential for improving the efficiency and stability of WWTP.