Predictive Analytics for Wastewater Treatment Optimization using ML

Rapid urbanization and enhanced discharge rules mean that decision-makers need accurate real-time help with the wastewater treatment operation. Traditional systems based on reactive control, set points with fixed values and offline feedback from the lab result in over energy consumption, over chemical addition and poor compliance to regulations due to changes of influent quality. A predictive monitoring system is developed whereby feature engineering and composite models are optimized to predict the future effluent quality of a water treatment plant, as well as enable real-time operational adjustments. The proposed hybrid system consists of ensemble learning to provide robustness, deep temporal networks to cover delayed biological dynamics and an optimization layer used for translating predictions into actionable recommendations. Results and analysis show significant improvement over previous works with prediction accuracy greater than 98%, RMSE reduction of almost 40%, energy saving of 21%, chemical saving of 17% and compliance rate closer to 99%. These results validate the applicability in resolving predictive, data driven intelligence as a novel approach for decision-making towards sustainable wastewater treatment management.