Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive...

Area of Science:

• Environmental Engineering
• Water Treatment Technologies
• Applied Machine Learning

Background:

• Sewage sludge dewatering is hindered by the robust nature of extracellular polymeric substances (EPS).
• Advanced oxidation processes (AOPs) can disrupt EPS but require careful optimization for efficiency.
• Existing optimization methods for AOPs in sludge treatment are often complex and time-consuming.

Purpose of the Study:

• To develop predictive machine learning (ML) frameworks for optimizing AOP parameters in sewage sludge treatment.
• To identify key operational parameters influencing EPS disruption and sludge dewaterability.
• To provide mechanistic insights into the AOP-EPS interaction for improved sludge management strategies.

Main Methods:

• Integration of machine learning algorithms, including Bayesian-optimized XGBoost and AdaBoost, with experimental AOP data.
• Utilized SHAP (SHapley Additive exPlanations) analysis to interpret model predictions and identify influential parameters.
• Investigated synergistic effects of radical donor and catalyst concentrations on hydroxyl radical generation.

Main Results:

• The Bayesian-optimized XGBoost model achieved a high predictive accuracy (test R² = 0.87) for optimal AOP configurations.
• AdaBoost model (test R² = 0.81) provided insights, identifying radical donor dosage, catalyst loading, and pH as critical factors.
• Soluble EPS (S-EPS) significantly impacted dewaterability, while acidic conditions were found to enhance EPS disruption.

Conclusions:

• Data-driven ML approaches offer powerful tools for optimizing AOPs in sludge dewatering, improving efficiency.
• Understanding the dynamic transformation of EPS under varying operational conditions is crucial for sustainable wastewater treatment.
• The study advocates for adaptive control systems integrating ML for real-time sludge management optimization.