Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology.

Area of Science:

• Environmental Engineering
• Data Science
• Biochemical Engineering

Background:

• Wastewater treatment plants (WWTPs) exhibit complex, variable biochemical processes challenging accurate prediction.
• Predicting wastewater quality parameters is essential for efficient plant operation and environmental compliance.

Purpose of the Study:

• To develop and evaluate Machine Learning (ML) models for predicting wastewater quality parameters.
• To assess the impact of operational changes and data quality on predictive model performance in both simulated and real-world WWTPs.

Main Methods:

• Applied Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) models to datasets from a simulated (WEST WWTP) and a real-world (AMBEV WWTP) facility.
• Evaluated model performance in continuous data scenarios and analyzed feature importance using Partial Dependence Plots (PDP) and Permutation Importance (PI).

Main Results:

• MLP achieved an R² of 0.72 for Total Nitrogen (TN) prediction in the WEST WWTP.
• RF demonstrated better adaptation to real-world AMBEV WWTP data, despite observed discrepancies.
• Influent nitrogen parameters showed a strong correlation with prediction outcomes in the simulated scenario.

Conclusions:

• High-quality data collection and operational information are vital for robust predictive modeling in WWTPs.
• ML models offer a promising approach for wastewater quality prediction, advancing operational efficiency and environmental management.
• Further research should focus on refining models to account for operational dynamics and data variability.