首页|Chongqing University Reports Findings in Machine Learning (Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations)
Chongqing University Reports Findings in Machine Learning (Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
New research on Machine Learning is the subject of a report. According to news reporting originating from Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers.” Our news editors obtained a quote from the research from Chongqing University, “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) 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.”
ChongqingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTechnology
NETL
NSTL
