Machine learning based prediction of waste activated sludge generation for optimization of WWTP operational...

Area of Science:

• Environmental Engineering
• Water Treatment Technologies
• Data Science in Environmental Management

Background:

• Accurate prediction of waste activated sludge (WAS) is crucial for efficient wastewater treatment plant (WWTP) operations and cost reduction.
• Existing methods often lack the precision to account for complex interactions between process parameters and sludge generation.

Purpose of the Study:

• To develop and evaluate machine learning models for predicting WAS generation.
• To create an integrated pipeline for optimizing sludge reduction strategies under effluent constraints.

Main Methods:

• Utilized Random Forest, XGBoost, and LightGBM models with hyperparameter optimization and a sliding-window moving average.
• Developed an integrated prediction-optimization pipeline coupling WAS prediction with NSGA-II for optimal Solids Retention Time (SRT) determination.
• Employed SHAP analysis to identify key predictive variables.

Main Results:

• The XGBoost-Exp1 model achieved the highest predictive accuracy (R² = 0.911).
• COD (Chemical Oxygen Demand) at the effluent and influent were identified as the most significant factors influencing WAS generation.
• The NSGA-II algorithm generated a Pareto frontier for SRT set-points, balancing WAS reduction and effluent quality.

Conclusions:

• The proposed machine learning strategy offers high predictive accuracy for WAS generation.
• The integrated pipeline enables data-driven optimization of WWTP operations for enhanced efficiency and compliance.
• The model is suitable for real-time predictive systems and informs predictive maintenance strategies.