Model construction and application for effluent prediction in wastewater treatment plant: Data processing method...

Area of Science:

• Environmental Engineering
• Water Treatment Technologies
• Machine Learning Applications

Background:

• Activated sludge wastewater treatment is complex and sensitive to operational variables, leading to effluent instability.
• Stringent environmental regulations necessitate accurate effluent quality prediction for early warning systems.
• Existing models often lack the precision required for real-world wastewater treatment plant (WWTP) management.

Purpose of the Study:

• To develop and optimize machine learning models for predicting effluent quality in WWTPs.
• To evaluate the performance of nine different machine learning algorithms for Chemical Oxygen Demand (COD) prediction.
• To assess the applicability of optimized models for predicting Total Phosphorus (TP), Total Nitrogen (TN), and pH.

Main Methods:

• Employed nine machine learning algorithms to predict effluent COD.
• Optimized models using Hydraulic Retention Time (HRT), K-FOLD data processing, and parameters like dissolved oxygen (DO), sludge return ratio (SRR), and mixed liquid suspended solids (MLSS).
• Validated model performance using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R²).

Main Results:

• K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) showed superior prediction accuracy for COD (MAPE 7.34%, R² 0.92).
• Optimized models demonstrated high accuracy for TN (MAPE 7.43%, R² 0.93, GBDT), TP (MAPE 17.81%, R² 0.99, KNN), and pH (MAPE 0.53%, R² 0.99, KNN).
• Model optimization significantly improved prediction capabilities for various effluent quality parameters.

Conclusions:

• Machine learning, particularly KNN and GBDT with optimized parameters, offers a robust solution for predicting WWTP effluent quality.
• The study provides insights into optimizing modeling conditions for improved wastewater treatment prediction.
• Findings support practical WWTP operation, management, and energy-saving initiatives.