A new paradigm in activated sludge management: an interpretable framework integrating mechanistic dynamics with a...

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technologies
• Intelligent Systems

Background:

• Activated sludge systems are crucial for wastewater treatment but prone to operational instability due to static thresholds and limitations of purely data-driven models.
• Existing management approaches often result in false alarms and lack interpretability, hindering effective process control.

Purpose of the Study:

• To develop and validate a three-tiered intelligent management framework that combines knowledge-driven and data-driven approaches for stable activated sludge system operation.
• To improve the reliability of system diagnostics and provide interpretable insights into operational risks.

Main Methods:

• A knowledge-driven tier using a dynamic model with differential equations to establish a theoretical equilibrium point (SVIeq) for the Sludge Volume Index (SVI).
• Principal Component Analysis (PCA) for extracting key information from high-dimensional water quality, microorganism, and Extracellular Polymeric Substances (EPS) data.
• A data-driven proxy diagnostic tier employing a random forest classifier trained on risk states derived from SVI and SVIeq deviations.
• SHAP (Shapley Additive Explanations) technology for interpreting the diagnostic results and identifying key operational factors.

Main Results:

• The dynamic model successfully reproduced historical Sludge Volume Index (SVI) trends, establishing a robust 'dynamic theoretical equilibrium point' (SVIeq).
• The random forest proxy model achieved excellent classification performance, providing reliable early warnings for system deterioration.
• SHAP analysis offered precise, quantitative explanations for diagnostic outcomes, highlighting critical operational factors influencing system risk.

Conclusions:

• The integrated three-tiered framework effectively addresses the limitations of static thresholds and black-box models in activated sludge systems.
• This approach enhances operational stability by providing accurate early warnings and interpretable diagnostics, facilitating proactive management.
• The study demonstrates the value of combining mechanistic understanding with machine learning for advanced wastewater treatment process control.