XGB-SEGA coupled energy saving method for wastewater treatment plants

Abstract

Abstract To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4 +–N loading rate, etc., and the energy consumption could be reduced by 13–27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

Keywords

• Energy saving
• Extreme gradient boosting (XGB)
• Genetic algorithm optimization
• Carbon neutrality
• Wastewater treatment