Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations

Jinze Li; Zexuan Du; Junyan Liu; Linji Xu; Li-Ping He; Li Gu; Hong Cheng; Qiang He

doi:10.1016/j.scitotenv.2024.171033

Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations

Sci Total Environ. 2024 Apr 10:920:171033. doi: 10.1016/j.scitotenv.2024.171033. Epub 2024 Feb 17.

Authors

Jinze Li¹, Zexuan Du¹, Junyan Liu², Linji Xu¹, Li-Ping He², Li Gu³, Hong Cheng¹, Qiang He¹

Affiliations

¹ Key laboratory of the Three Gorges Reservoir Region's Eco-environments, Ministry of Education, Institute of Environment and Ecology, Chongqing University, 174 Shapingba Road, Chongqing 400045, PR China.
² Key Laboratory of Water Environment Evolution and Pollution Control in the Three Gorges Reservoir, Chongqing Three Georges University, Chongqing 404100, China.
³ Key laboratory of the Three Gorges Reservoir Region's Eco-environments, Ministry of Education, Institute of Environment and Ecology, Chongqing University, 174 Shapingba Road, Chongqing 400045, PR China. Electronic address: guli@cqu.edu.cn.

PMID: 38369164
DOI: 10.1016/j.scitotenv.2024.171033

Abstract

Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers. Identifying the factors influencing energy consumption is crucial for enhancing the energy efficiency of WWTPs. To address this, the unit energy consumption (UEC) of WWTPs was predicted using machine learning models. In order to accurately evaluate WWTPs' energy utilization efficiency, a comprehensive energy evaluation indicator, UEC (kWh/kg TOD_removed) was utilized in this study. Among the prediction models, the eXtreme Gradient Boosting (XGBoost) achieves the highest prediction accuracy. SHapley Additive exPlanations (SHAP) was adopted as the model explanation system, and the results revealed that UEC was negatively affected by TN concentration, which was the most influential factor. The stoichiometry-based model calculation result indicates that the nitrification consumes average 77 % of the overall oxygen demand. SHAP analysis illustrated that the UEC of main technologies decreases with increasing influential factors. Partial dependence plot (PDP) compared average UEC of these technologies and SBR consumed the least amount of energy. The research also indicated that low influent TN concentration is the main problem in China. Consequently, it is imperative to exert efforts in ensuring the influent TN concentration while simultaneously making appropriate adjustments to the treatment process. This study provides valuable implications and methods for retrofitting and upgrading WWTPs.

Keywords: Energy consumption; Machine learning; Main wastewater treatment technologies; SHapley additive exPlanations (SHAP); Total oxygen demand (TOD).