Diagnosing activated sludge dysfunction: A full-scale wastewater treatment study

Area of Science:

• Environmental Engineering
• Applied Microbiology
• Artificial Intelligence

Background:

• Activated sludge systems often fail due to complex, multifactorial issues.
• Conventional diagnostics are limited, hindering effective intervention prioritization.
• Integrated approaches are needed to understand and address wastewater treatment plant dysfunctions.

Purpose of the Study:

• To develop and validate an integrated diagnostic framework for identifying and prioritizing causes of operational failures in activated sludge systems.
• To apply machine learning, microbial profiling, and multi-parameter assessment to a full-scale wastewater treatment plant.
• To provide an objective basis for prioritizing interventions to improve plant performance.

Main Methods:

• Utilized SHAP (SHapley Additive exPlanations) analysis on stacking ensemble machine learning models.
• Integrated microbial community profiling and multi-parameter data (COD, MLSS, SOUR, SRT, F/M ratio).
• Assessed microbial indicators like methylotroph enrichment and Sludge Biotic Index.

Main Results:

• Chemical oxygen demand (COD) removal was identified as the dominant predictive feature for performance dysfunction.
• Analysis indicated substrate limitation and over-aged biomass (high MLSS, low SOUR, high SRT, low F/M) as primary drivers.
• Microbial analysis revealed significant methylotroph enrichment and metazoan proliferation, consistent with aged sludge.

Conclusions:

• The developed AI-assisted framework offers a reproducible and transferable method for prioritizing wastewater treatment optimization.
• Proposed interventions include biomass reduction, methanol supplementation, and advanced respirometry monitoring.
• The framework effectively prioritizes interventions by identifying predictive associations from routine monitoring data.