Machine learning for modeling N₂O emissions from wastewater treatment plants: Aligning model performance, complexity,...

Area of Science:

• Environmental Engineering
• Data Science
• Wastewater Treatment

Background:

• Nitrous oxide (N2O) emissions constitute a significant portion (up to 80%) of a wastewater treatment plant's (WWTP) carbon footprint.
• Mechanistic models struggle to accurately capture N2O emission dynamics due to complex pathways.
• Data-driven methods offer potential for N2O prediction, but a comprehensive approach for WWTPs is lacking.

Purpose of the Study:

• To develop and validate a comprehensive machine learning approach for online process modeling of N2O emissions in WWTPs.
• To prioritize model accuracy alongside complexity, computational speed, and interpretability for practical operator insights.
• To reduce data acquisition costs and computational burden through effective feature selection.

Main Methods:

• Utilized a long-term N2O emission dataset from a full-scale WWTP.
• Implemented and compared various machine learning algorithms including k-Nearest Neighbors (kNN), decision trees, ensemble learning (AdaBoost), and deep neural networks (DNN).
• Applied a parametric multivariate outlier removal method and feature selection to optimize models.

Main Results:

• Achieved high prediction accuracy with best models: AdaBoost (R² = 0.94), DNN (R² = 0.90), and kNN (R² = 0.88).
• Feature selection reduced the number of features by 40%, decreasing costs and computational load without compromising accuracy.
• Model interpretability was assessed by comparing feature importance with process knowledge.

Conclusions:

• The developed comprehensive machine learning approach effectively models N2O emissions in WWTPs.
• The approach provides operators with interpretable insights for informed N2O mitigation strategies.
• This data-driven methodology enhances the potential for full-scale application in reducing WWTP environmental impact.