Machine learning-based optimization of enhanced nitrogen removal in a full-scale urban wastewater treatment plant with ecological combination ponds
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View abstract on PubMed
Summary
This summary is machine-generated.An interpretable machine learning model optimizes wastewater treatment plants (WWTPs) with ecological combination ponds (ECPs) for enhanced total nitrogen (TN) removal. This approach reduces energy use and chemical oxygen demand (COD) dosage, cutting carbon emissions.
Area Of Science
- Environmental Engineering
- Water Treatment Technologies
- Machine Learning Applications
Background
- Urban wastewater treatment plants (WWTPs) using ecological combination ponds (ECPs) face challenges in adjusting operations to variable influent quality, leading to inefficient treatment and non-compliance with total nitrogen (TN) standards.
- Over-aeration and excessive external carbon source (e.g., chemical oxygen demand - COD) dosing are common issues in meeting stringent discharge limits.
- Developing dynamic operational models is crucial for optimizing TN removal in ECP-based WWTPs.
Purpose Of The Study
- To establish an interpretable machine learning model for predicting and optimizing effluent TN concentration in ECP-based WWTPs.
- To identify optimal operating parameters for enhanced TN removal while minimizing energy consumption and COD dosage.
- To develop a user-friendly interface for real-time prediction and coordinated control of WWTP operations.
Main Methods
- Collected three years of operational data from a full-scale urban WWTP utilizing ECPs.
- Implemented an interpretable machine learning approach, specifically the XGBoost model, for TN prediction and optimization.
- Utilized Shapley additive explanation (SHAP) analysis and partial dependence plots to understand parameter influences and identify optimal settings.
Main Results
- The XGBoost model achieved high accuracy, with R-squared values of 0.997 (training) and 0.911 (testing).
- Optimal operating parameters were identified, leading to a 17.50% annual reduction in effluent TN concentration and a 33.29% reduction in COD dosage.
- Simultaneous reductions in energy consumption and external carbon source usage were achieved, contributing to significant carbon emission reductions.
Conclusions
- The developed interpretable machine learning model provides an effective solution for enhanced TN removal in ECP-based WWTPs facing variable influent conditions.
- The model facilitates meeting stringent TN discharge standards while simultaneously reducing operational costs (energy, COD) and environmental impact (carbon emissions).
- This approach offers a scalable and sustainable strategy for optimizing urban wastewater treatment in small towns.
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