Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant

Abstract

This study carried out machine-learning (ML) modeling using activated sludge microbiome data to predict the operational characteristics of biological unit processes (i.e., anaerobic, anoxic, and aerobic) in a full-scale municipal wastewater treatment plant. An ML application pipeline with optimization strategies (e.g., model selection, input data preprocessing, and hyperparameter tuning) could significantly improve prediction performance. Comparative analysis of the ML prediction performance suggested that linear models (support vector machine and logistic regression) had a high prediction performance (93% accuracy), comparable to that of non-linear models such as random forest. Feature importance analysis using the linear ML models identified the microbial taxa that were specifically associated with anoxic processes, many of which (e.g., Ferruginibacter) were found to have ecologically important genomic and phenotypic characteristics (e.g., for nitrate reduction). Time-series microbial community dynamics demonstrated that the taxa identified using ML were frequently occurring and dominating in the anoxic process over time, thus representing the core nitrate-reducing community. Despite the general dominance of the core community over time, the analysis further revealed successional seasonal patterns of distinct sub-groups, indicating differences in the functional contribution of sub-groups by season to the overall nitrate-reducing potential of the system. Overall, the results of this study suggest that ML modeling holds great promise for the predictive identification and understanding of key microbial players governing the functioning and stability of biological wastewater systems.

Keywords: Biological wastewater treatment; Denitrification; Machine learning; Nitrate reduction; Process prediction.