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Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble

Bowen Li¹, Li Liu², Zikang Xu¹

¹College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; MOE Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, Nankai University, Tianjin, 300350, China.

Environmental Research

|December 19, 2024

Summary

Optimizing carbon source addition in wastewater treatment plants (WWTPs) using machine learning ensemble models significantly reduces operational costs and surplus sludge yield. This approach enhances efficiency and sustainability in wastewater management.

Keywords:

Carbon source Machine learning Model interpretation Surplus sludge yield Wastewater treatment plant Weighted average ensemble

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Area of Science:

Environmental Engineering
Machine Learning Applications
Wastewater Treatment Technology

Background:

Wastewater treatment plants (WWTPs) face challenges in optimizing operational costs and managing surplus sludge.
Carbon source addition is a critical factor influencing both cost and sludge yield.
Machine learning (ML) offers potential for complex pattern recognition in WWTPs but its application in optimizing carbon source and sludge yield is underdeveloped.

Purpose of the Study:

To develop and evaluate an ensemble machine learning model for optimizing carbon source addition in WWTPs.
To further utilize the developed model to control and reduce surplus sludge yield.
To create a practical, accessible tool for real-world WWTP application.

Main Methods:

A weighted average ensemble strategy was employed to combine multiple diverse basic machine learning models.
Two ensemble models were developed: Model-1 for carbon source addition optimization and Model-2 for surplus sludge yield control.
Feature selection was performed to identify optimal input subsets for model simplification and efficiency.

Main Results:

The ensemble models significantly outperformed individual models, achieving high accuracy (R² of 0.98 for Model-1, 0.93 for Model-2).
Optimized models with reduced features maintained high precision (R² of 0.97 for Model-1, 0.95 for Model-2).
Deployment in a web application demonstrated practical benefits, including 47.25% savings in carbon source and 15.89% reduction in surplus sludge yield.

Conclusions:

Ensemble machine learning provides a powerful and accurate approach for optimizing carbon source addition in WWTPs.
This method offers significant potential for reducing surplus sludge yield and improving operational efficiency.
The developed, deployable models offer a practical solution for real-world wastewater treatment management.