| Predictive Modeling and Feature Importance Analysis for Aeration Energy Optimization in Extended Aeration Wastewater Treatment Plants: Case Study Eyvan Wastewater Treatment Plant |
| کد مقاله : 1132-IWWA |
| نویسندگان |
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فاطمه هوشمند *1، زهرا معافی2، سارا هوشمند3 1دانشگاه ملی مهارت، دانشگده علوم مهندسی 2Department of Engineering Sciences, Technical and Vocational University(TVU),Tehran, Iran. 3Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada |
| چکیده مقاله |
| The high energy demand of wastewater treatment plants (WWTPs), particularly for aeration, poses significant operational and economic challenges. This study presents a comprehensive machine learning approach for predicting energy consumption in aeration basins of extended aeration wastewater treatment plants. Using 10 years of daily operational data, we developed Random Forest and XGBoost models to identify key parameters influencing energy usage. The analysis revealed that MLSS (Mixed Liquor Suspended Solids) is the most significant predictor, accounting for 39.2% of energy consumption variation, followed by COD (27.7%) and inflow rate (13.7%). The models achieved impressive prediction accuracy with R² scores of 0.65 (Random Forest) and 0.68 (XGBoost), demonstrating the potential for substantial energy optimization through data-driven operational adjustments. This study concludes that data-driven models coupled with feature importance analysis provide a powerful framework for plant operators to identify key leverage points for targeted energy optimization, leading to more cost-effective and sustainable wastewater treatment operations at the Eyvan plant and similar facilities. |
| کلیدواژه ها |
| sewage treatment; energy efficiency; ecological balance; biodiversity; sustainability. |
| وضعیت: پذیرفته شده برای ارائه شفاهی |
