An effluent risk informed closed-loop framework for early warning of influent anomalies using COD soft sensing

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technology
• Artificial Intelligence in Environmental Science

Background:

• Sudden shock loads in wastewater influent disrupt biological treatment and cause effluent quality issues in integrated facilities.
• Early warning systems are crucial for proactive intervention, minimizing environmental and operational risks in wastewater treatment plants (WWTPs).

Purpose of the Study:

• To develop a COD-centric closed-loop early warning framework for integrated wastewater treatment plants (IWTPs).
• To integrate machine learning-based soft-sensing with multi-step effluent prediction for anomaly detection.
• To evaluate serially coupled (SCA) and jointly coupled (JCA) architectures for enhanced early warning capabilities.

Main Methods:

• Development of a COD-centric closed-loop early warning framework integrating soft-sensing and effluent prediction modules.
• Evaluation of two coupling architectures: Serial Coupled Architecture (SCA) and Jointly Coupled Architecture (JCA).
• Application of SHAP analysis for feature importance interpretation and ablation studies for validation.

Main Results:

• The proposed framework achieved 95.0% accuracy and 87.2% anomaly detection precision for 12-hour ahead warnings in a full-scale IWTP.
• The Jointly Coupled Architecture (JCA) demonstrated superior performance compared to the Serial Coupled Architecture (SCA).
• Backward inference from predicted effluent compliance risk effectively identified upstream disturbances before limit violations.

Conclusions:

• The developed framework provides a novel, real-time, and cost-effective solution for linking effluent-risk forecasting with influent-anomaly diagnosis.
• This approach enhances operational resilience and proactive management in integrated wastewater treatment plants.
• The interpretable nature of the framework aids in understanding key process variables for improved early warning responsiveness.