Abstract
Applications of anaerobic ammonium oxidation (anammox) processes for treating municipal wastewater have promising benefits in reducing carbon emissions and have been heavily investigated recently. However, the practical operation of mainstream anammox processes still suffers from unclear external regulation strategies. Therefore, this study utilized the H2O automated machine learning (AutoML) algorithm and interpretable analysis to capture the internal relationships existing in the big dataset collected from anammox-based studies for treating municipal wastewater. The eXtreme Gradient Boosting and gradient boosting machine models automatically generated by the AutoML algorithm provided the most accurate prediction results (R2 = 0.814–0.993). Moreover, the optimal models showed good generalization ability (R2 = 0.725–0.945) for unseen data collected from this study. The appropriate ranges of operation conditions and influent characteristics response to the high removal efficiency of nitrogen pollutants and high nitrogen removal rate through the anammox reaction pathway were revealed by the one-dimensional, and two-dimensional partial dependence plots. This work would advance the understanding of how to improve the practical operation of mainstream anammox-based nitrogen removal processes for treating municipal wastewater.
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Acknowledgements
We thank the National Natural Science Foundation of China (52400032), Natural Science Foundation of Jiangsu Province (BK2041534), China Postdoctoral Science Foundation (2024M750744 and 2025T180352), Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB687) for supporting this work.
Funding
We thank the National Natural Science Foundation of China (52400032), Natural Science Foundation of Jiangsu Province (BK2041534), China Postdoctoral Science Foundation (2024M750744 and 2025T180352), Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB687) for supporting this work.
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Lou, JX., Cao, JS. & Xu, RZ. Elucidating response effects of anammox-based nitrogen removal processes for municipal wastewater using big data analysis and automated machine learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-57033-z
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DOI: https://doi.org/10.1038/s41598-026-57033-z
Keywords
- Mainstream anammox
- Automated machine learning
- Interpretable analysis
- Nitrogen removal efficiency
- Meta-analysis
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