Prediction and optimization of wastewater treatment process effluent chemical oxygen demand and energy consumption based on typical ensemble learning models

Abstract

Pollution integration and carbon reduction has become a primary focus in wastewater treatment processes. In this study, water quality and control indicators were used as input features and the dataset was extended using the moving average method. Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine algorithms were used to predict the effluent chemical oxygen demand (COD) and total energy consumption (TEC). The results indicated that the model prediction performance could be effectively improved when the data were amplified by two times and that the XGBoost model exhibited the best prediction performance for effluent COD and TEC. The Non-dominated Sorting Genetic Algorithm II model was employed for the multi-objective optimization of effluent COD and TEC, resulting in reductions of 15% and 18%, respectively. The ensemble learning model proposed in this study to achieve synergy between water quality improvement and energy saving is practical.