A comprehensive assessment of energy efficiency of wastewater treatment plants: An efficiency analysis tree approach

被引:20
作者
Maziotis, Alexandros [1 ]
Sala-Garrido, Ramon [2 ]
Mocholi-Arce, Manuel [2 ]
Molinos-Senante, Maria [1 ,3 ,4 ]
机构
[1] Pontificia Univ Catolica Chile, Dept Ingn Hidraul & Ambiental, Avda Vicuna Mackenna, Santiago 4860, Chile
[2] Univ Valencia, Dept Math Econ, Avd Tarongers S-N, Valencia, Spain
[3] Univ Valladolid, Inst Sustainable Proc, C Dr Mergelina, S-N, Valladolid, Spain
[4] FONDAP, Ctr Desarrollo Urbano Sustentable ANID, 15110020 Ave Vicuna Mackenna, Santiago, Chile
关键词
Energy efficiency; Energy savings; Regression trees; Linear programming; Bootstrap regression; Wastewater treatment plants; PERFORMANCE; SUSTAINABILITY; CONSUMPTION; MODELS;
D O I
10.1016/j.scitotenv.2023.163539
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wastewater treatment plants (WWTPs) are energy intensive facilities. Controlling energy use in WWTPs could bring substantial benefits to people and environment. Understanding how energy efficient the wastewater treatment process is and what drives efficiency would allow treating wastewater in a more sustainable way. In this study, we employed the efficiency analysis trees approach, that combines machine learning and linear programming techniques, to estimate energy efficiency of wastewater treatment process. The findings indicated that considerable energy inefficiency among WWTPs in Chile existed. The mean energy efficiency was 0.287 suggesting that energy use should cut reduce by 71.3 % to treat the same volume of wastewater. This was equivalent to a reduction in energy use by 0.40 kWh/m3 on average. Moreover, only 4 out of 203 assessed WWTPs (1.97 %) were identified as energy efficient. It was also found that the age of treatment plant and type of secondary technology played an important role in explaining energy efficiency variations among WWTPs.
引用
收藏
页数:10
相关论文
共 48 条
  • [31] Comparative energy efficiency of wastewater treatment technologies: a synthetic index approach
    Molinos-Senante, Maria
    [J]. CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2018, 20 (08) : 1819 - 1834
  • [32] Assessing the sustainability of water companies: A synthetic indicator approach
    Molinos-Senante, Maria
    Cunha Marques, Rui
    Perez, Fatima
    Gomez, Trinidad
    Sala-Garrido, Ramon
    Caballero, Rafael
    [J]. ECOLOGICAL INDICATORS, 2016, 61 : 577 - 587
  • [33] Economic and environmental performance of wastewater treatment plants: Potential reductions in greenhouse gases emissions
    Molinos-Senante, Maria
    Hernandez-Sancho, Francesc
    Mocholi-Arce, Manuel
    Sala-Garrido, Ramon
    [J]. RESOURCE AND ENERGY ECONOMICS, 2014, 38 : 125 - 140
  • [34] Energy intensity of wastewater treatment plants and influencing factors in China
    Niu, Kunyu
    Wu, Jian
    Qi, Lu
    Niu, Qianxin
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 670 : 961 - 970
  • [35] A graphically based machine learning approach to predict secondary schools performance in Tunisia
    Rebai, Sonia
    Ben Yahia, Fatma
    Essid, Hedi
    [J]. SOCIO-ECONOMIC PLANNING SCIENCES, 2020, 70
  • [36] Environmental and economic profile of six typologies of wastewater treatment plants
    Rodriguez-Garcia, G.
    Molinos-Senante, M.
    Hospido, A.
    Hernandez-Sancho, F.
    Moreira, M. T.
    Feijoo, G.
    [J]. WATER RESEARCH, 2011, 45 (18) : 5997 - 6010
  • [37] A hundred years of activated sludge: time for a rethink
    Sheik, Abdul R.
    Muller, Emilie E. L.
    Wilmes, Paul
    [J]. FRONTIERS IN MICROBIOLOGY, 2014, 5
  • [38] Estimation and inference in two-stage, semi-parametric models of production processes
    Simar, Leopold
    Wilson, Paul W.
    [J]. JOURNAL OF ECONOMETRICS, 2007, 136 (01) : 31 - 64
  • [39] Machine learning for energy cost modelling in wastewater treatment plants
    Torregrossa, Dario
    Leopold, Ulrich
    Hernandez-Sancho, Francesc
    Hansen, Joachim
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2018, 223 : 1061 - 1067
  • [40] UNESCO, 2017, UN WORLD WAT DEV REP