IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1686-d324557.html

Environmental and Economic Water Management in Shale Gas Extraction

Author

Listed:
  • José A. Caballero

    (Institute of Chemical Process Engineering, University of Alicante, PO 99, E-03080 Alicante, Spain)

  • Juan A. Labarta

    (Institute of Chemical Process Engineering, University of Alicante, PO 99, E-03080 Alicante, Spain)

  • Natalia Quirante

    (Institute of Chemical Process Engineering, University of Alicante, PO 99, E-03080 Alicante, Spain)

  • Alba Carrero-Parreño

    (Institute of Chemical Process Engineering, University of Alicante, PO 99, E-03080 Alicante, Spain)

  • Ignacio E. Grossmann

    (Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

Abstract

This paper introduces a comprehensive study of the Life Cycle Impact Assessment (LCIA) of water management in shale gas exploitation. First, we present a comprehensive study of wastewater treatment in the shale gas extraction, including the most common technologies for the pretreatment and three different desalination technologies of recent interest: Single and Multiple-Effect Evaporation with Mechanical Vapor Recompression and Membrane Distillation. The analysis has been carried out through a generic Life Cycle Assessment (LCA) and the ReCiPe metric (at midpoint and endpoint levels), considering a wide range of environmental impacts. The results show that among these technologies Multiple-Effect Evaporation with Mechanical Vapor Recompression (MEE-MVR) is the most suitable technology for the wastewater treatment in shale gas extraction, taking into account its reduced environmental impact, the high water recovery compared to other alternatives as well as the lower cost of this technology. We also use a comprehensive water management model that includes previous results that takes the form of a new Mixed-Integer Linear Programming (MILP) bi-criterion optimization model to address the profit maximization and the minimization Life Cycle Impact Assessment (LCIA), based on its results we discuss the main tradeoffs between optimal operation from the economic and environmental points of view.

Suggested Citation

  • José A. Caballero & Juan A. Labarta & Natalia Quirante & Alba Carrero-Parreño & Ignacio E. Grossmann, 2020. "Environmental and Economic Water Management in Shale Gas Extraction," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1686-:d:324557
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1686/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    2. Stamford, Laurence & Azapagic, Adisa, 2014. "Life cycle environmental impacts of UK shale gas," Applied Energy, Elsevier, vol. 134(C), pages 506-518.
    3. Hammond, Geoffrey P. & O’Grady, Áine, 2017. "Indicative energy technology assessment of UK shale gas extraction," Applied Energy, Elsevier, vol. 185(P2), pages 1907-1918.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Yi-Ming & Kang, Jia-Ning & Yu, Bi-Ying & Liao, Hua & Du, Yun-Fei, 2017. "A dynamic forward-citation full path model for technology monitoring: An empirical study from shale gas industry," Applied Energy, Elsevier, vol. 205(C), pages 769-780.
    2. Panos Xidonas & Haris Doukas & George Mavrotas & Olena Pechak, 2016. "Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model," Annals of Operations Research, Springer, vol. 247(2), pages 395-413, December.
    3. Satya Tamby & Daniel Vanderpooten, 2021. "Enumeration of the Nondominated Set of Multiobjective Discrete Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 72-85, January.
    4. H. Khorshidian & M. Akbarpour Shirazi & S. M. T. Fatemi Ghomi, 2019. "An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 163-184, January.
    5. Yang, Xue & Chen, Zeqin & Liu, Xiaoqiang & Xue, Zhiyu & Yue, Fen & Wen, Junjie & Li, Meijun & Xue, Ying, 2022. "Correction of gas adsorption capacity in quartz nanoslit and its application in recovering shale gas resources by CO2 injection: A molecular simulation," Energy, Elsevier, vol. 240(C).
    6. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    8. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    9. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    10. De Silva, P.N.K. & Simons, S.J.R. & Stevens, P., 2016. "Economic impact analysis of natural gas development and the policy implications," Energy Policy, Elsevier, vol. 88(C), pages 639-651.
    11. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    12. Sahar Moazzeni & Sobhan Mostafayi Darmian & Lars Magnus Hvattum, 2023. "Multiple criteria decision making and robust optimization to design a development plan for small and medium-sized enterprises in the east of Iran," Operational Research, Springer, vol. 23(1), pages 1-32, March.
    13. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.
    14. Raj, Ratan & Ghandehariun, Samane & Kumar, Amit & Linwei, Ma, 2016. "A well-to-wire life cycle assessment of Canadian shale gas for electricity generation in China," Energy, Elsevier, vol. 111(C), pages 642-652.
    15. S. Razavyan, 2016. "A Method for Generating a Well-Distributed Pareto Set in Multiple Objective Mixed Integer Linear Programs Based on the Decision Maker’s Initial Aspiration Level," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(04), pages 1-23, August.
    16. Zou, Youqin & Yang, Changbing & Wu, Daishe & Yan, Chun & Zeng, Masun & Lan, Yingying & Dai, Zhenxue, 2016. "Probabilistic assessment of shale gas production and water demand at Xiuwu Basin in China," Applied Energy, Elsevier, vol. 180(C), pages 185-195.
    17. Liu, Wen & Ramirez, Andrea, 2017. "State of the art review of the environmental assessment and risks of underground geo-energy resources exploitation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 628-644.
    18. Zhong, Jia & Yu, T. Edward & Clark, Christopher D. & English, Burton C. & Larson, James A. & Cheng, Chu-Lin, 2018. "Effect of land use change for bioenergy production on feedstock cost and water quality," Applied Energy, Elsevier, vol. 210(C), pages 580-590.
    19. Farajiamiri, Mina & Meyer, Jörn-Christian & Walther, Grit, 2023. "Multi-objective optimization of renewable fuel supply chains regarding cost, land use, and water use," Applied Energy, Elsevier, vol. 349(C).
    20. Majid Askarifard & Hamidreza Abbasianjahromi & Mehran Sepehri & Ehsanollah Zeighami, 2021. "A robust multi-objective optimization model for project scheduling considering risk and sustainable development criteria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11494-11524, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1686-:d:324557. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.