废水
产量(工程)
中国
污水处理
环境科学
废物管理
农业工程
机器学习
环境工程
计算机科学
工程类
地理
考古
材料科学
冶金
作者
Y. Hu,Renke Wei,Kun Yu,Zhouyi Liu,Qi Zhou,Meng Zhang,Chenchen Wang,Lujing Zhang,Gang Liu,Shen Qu
标识
DOI:10.1016/j.resconrec.2024.107467
摘要
Sludge management remains a challenge for municipal wastewater treatment plants (MWWTPs). In this study, we use machine learning models to predict sludge yield and employ interpretable methods to highlight the driving factors. We analyze over 27,000 data entries of monthly plant-level operational details to predict the sludge yield for 177 MWWTPs in 11 cities throughout China. Evaluated by multiple statistical indicators including Coefficient of Determination (R2), Mean Absolute Error (MAE), Normalized Mean Absolute Error (NMAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE), the machine learning model's performance proves superior to empirical estimation. Interpretative analysis reveals that pollutant removal quantities exert a more substantial influence on sludge yield than influent pollutant concentrations. The sludge yield becomes increasingly sensitive to wastewater quality when effluent discharge standards rise. The integration of interpretable machine learning models expands the research scope to a more holistic perspective, catalyzing interdisciplinary collaboration and novel insights.
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