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Energy-Optimal Siting of Decentralized Water Recycling Systems
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    Energy and Climate

    Energy-Optimal Siting of Decentralized Water Recycling Systems
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    • Yang Liu
      Yang Liu
      Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, United States
      More by Yang Liu
    • Alison Sim
      Alison Sim
      Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, United States
      More by Alison Sim
    • Meagan S. Mauter*
      Meagan S. Mauter
      Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, United States
      *Email: [email protected]
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2021, 55, 22, 15343–15350
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    https://doi.org/10.1021/acs.est.1c04708
    Published October 29, 2021
    Copyright © 2021 American Chemical Society

    Abstract

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    Decentralized water recycling systems (DWRS) have emerged as a viable option for incrementally augmenting water supply in water-stressed regions, but DWRS are generally more energy-intensive than traditional centralized water treatment systems. When DWRS are deployed incrementally in small batches, the marginal energy intensity (MEI) of water supply quantifies the location-specific energy footprint of centralized water supply and serves as a robust metric measuring the energy implications of replacing centralized supply with DWRS supply. This research develops and applies a MEI-based decision framework that identifies the energy-optimal siting of DWRS to minimize the overall system operational energy consumption given a target fraction of water demand to be met by newly deployed DWRS. In a small benchmark water supply system where the energy intensity of the intended DWRS is 5.3% higher than the current system average energy intensity of centralized supply, we demonstrate that the optimal siting of DWRS to offset 10% of the system-wide water demand reduces the overall system energy consumption by 0.77%. In contrast, the naive and worst-case siting of the same DWRS increases the energy consumption of the overall system by 0.65 and 2.0%, respectively. The proposed MEI-based decision framework is particularly valuable for application in large multi-source systems, where an optimization-based approach is computationally intractable. This study highlights the importance of accounting for both distribution and treatment energy intensity when evaluating new water sources and demonstrates the viability of DWRS as an energy-efficient tool for augmenting water supply.

    Copyright © 2021 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.1c04708.

    • Modifications to the “c-town” network; configuration of the greywater recycling unit; summary of main assumptions in the case study; relationship between MEI and elevation; hourly MEI values of individual consumers in the baseline scenario; disaggregation of the total energy consumption under the naive strategy of GWR deployment; and deviation in MEI value after the deployment of DWRS (PDF)

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    This article is cited by 8 publications.

    1. Yue Li, Zhongming Lu, Xiangru Zhang, Qingfu Zhang, Huifu Xu, Jimmy C. H. Fung, Glen Daigger. Multiobjective Spatial Optimization Framework for Determining the Optimal Degree of Decentralization for Nonpotable Water Reuse in Existing Cities: A Case Study of Hong Kong. Environmental Science & Technology 2024, 58 (46) , 20424-20433. https://doi.org/10.1021/acs.est.4c04755
    2. Dezhen Kong, Yumeng Zhao, Runzhi Wang, Jiaxuan Li, Jinkuo Li, Jun Ma. Inorganic Electrified Membrane: From Basic Science to Performance Translation. ACS ES&T Engineering 2023, 3 (12) , 2123-2146. https://doi.org/10.1021/acsestengg.3c00173
    3. Yang Liu, Meagan S. Mauter. High-Resolution Carbon Accounting Framework for Urban Water Supply Systems. Environmental Science & Technology 2022, 56 (19) , 13920-13930. https://doi.org/10.1021/acs.est.2c04127
    4. Yiseul Hong, Jooyoung Park, Yoonhee Ha. Trade-offs between water security and energy use: Lifecycle energy of water supply options in Paju, Korea. Journal of Cleaner Production 2023, 423 , 138601. https://doi.org/10.1016/j.jclepro.2023.138601
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    6. María J. López-Serrano, Fida Hussain Lakho, Stijn W. H. Van Hulle, Ana Batlles-delaFuente. Life cycle cost assessment and economic analysis of a decentralized wastewater treatment to achieve water sustainability within the framework of circular economy. Oeconomia Copernicana 2023, 14 (1) , 103-133. https://doi.org/10.24136/oc.2023.003
    7. Natalia Klanovicz, Thamarys Scapini, Aline Frumi Camargo, Larissa Pinheiro de Souza, Juliana Mendonça Silva de Jesus, Priscila Hasse Palharim, Helen Treichel, Antonio Carlos Silva Costa Teixeira. Removal of pharmaceuticals by on-site and decentralized wastewater treatment systems. 2023, 185-224. https://doi.org/10.1016/B978-0-323-95684-0.00013-0
    8. Yue Li, Weiwei Mo, Sybil Derrible, Zhongming Lu. Integration of multi-objective spatial optimization and data-driven interpretation to direct the city-wide sustainable promotion of building-based decentralized water technologies. Water Research 2022, 222 , 118880. https://doi.org/10.1016/j.watres.2022.118880

    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2021, 55, 22, 15343–15350
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.est.1c04708
    Published October 29, 2021
    Copyright © 2021 American Chemical Society

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