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

Wenjun Liu1, Yang He1, Yaorong Shu2

  • 1Hubei Key Laboratory of Multi-media Pollution Cooperation Control in Yangtze Basin, School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Key Laboratory of Water and Wastewater Treatment (HUST), MOHURD, Huazhong University of Science and Technology, Wuhan, 430074, China.

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|April 12, 2026
PubMed
Summary

Sudden shock loads in wastewater treatment plants are detected early using a new framework. This system predicts effluent quality, enabling proactive interventions to prevent operational issues and environmental risks.

Keywords:
COD soft sensingEffluent risk predictionIntegrated wastewater treatment plantsInterpretable model architectureMachine learningTimely early warning

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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.