IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i19p10860-d646913.html
   My bibliography  Save this article

Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water

Author

Listed:
  • Kawtar Rahaoui

    (Energy Conservation and Renewable Energy Group, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia)

  • Hamid Khayyam

    (Energy Conservation and Renewable Energy Group, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia)

  • Quoc Linh Ve

    (Energy Conservation and Renewable Energy Group, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
    Faculty of Engineering and Food Technology, Hue University of Agriculture and Forestry, Thành phố Huế 530000, Vietnam)

  • Aliakbar Akbarzadeh

    (Energy Conservation and Renewable Energy Group, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia)

  • Abhijit Date

    (Energy Conservation and Renewable Energy Group, School of Engineering, RMIT University, Bundoora, VIC 3083, Australia)

Abstract

A sustainable circular economy involves designing and promoting products with the least environmental impact. This research presents an experimental performance investigation of direct contact membrane distillation with feed approaching supersaturation salinity, which can be useful for the sustainable management of reverse osmosis reject water. Traditionally, reject water from the reverse osmosis systems is discharged in the sea or in the source water body. The reinjection of high salinity reject water into the sea has the potential to put the local sea environment at risk. This paper presents a design of a solar membrane distillation system that can achieve close to zero liquid discharge. The theoretical and experimental analysis on the performance of the lab scale close to zero liquid discharge system that produces supersaturated brine is studied. The lab-based experiments were conducted at boundary conditions, which were close to the real-world conditions where feed water temperatures ranged between 40 °C and 85 °C and the permeate water temperatures ranged between 5 °C and 20 °C. The feed water was supplied at salinity between 70,000 ppm to 110,000 ppm, similar to reject from reverse osmosis. The experimental results show that the maximum flux of 17.03 kg/m 2 ·h was achieved at a feed temperature of 80 °C, a feed salinity of 10,000 ppm, a permeate temperature of 5 °C and at constant feed and a permeate flow rate of 4 L/min. Whereas for the same conditions, the theoretical mass flux was 18.23 kg/m 2 ·h. Crystal formation was observed in the feed tank as the feed water volume reduced and the salinity increased, reaching close to 308,000 ppm TDS. At this condition, the mass flux approached close to zero due to crystallisation on the membrane surface. This study provides advice on the practical limitations for the use of membrane distillation to achieve close to zero liquid discharge.

Suggested Citation

  • Kawtar Rahaoui & Hamid Khayyam & Quoc Linh Ve & Aliakbar Akbarzadeh & Abhijit Date, 2021. "Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10860-:d:646913
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/19/10860/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/19/10860/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khayyam, Hamid & Naebe, Minoo & Milani, Abbas S. & Fakhrhoseini, Seyed Mousa & Date, Abhijit & Shabani, Bahman & Atkiss, Steve & Ramakrishna, Seeram & Fox, Bronwyn & Jazar, Reza N., 2021. "Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning," Energy, Elsevier, vol. 225(C).
    2. Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ekaterina Sokolova & Khashayar Sadeghi & Seyed Hadi Ghazaie & Dario Barsi & Francesca Satta & Pietro Zunino, 2022. "Feasibility of Hybrid Desalination Plants Coupled with Small Gas Turbine CHP Systems," Energies, MDPI, vol. 15(10), pages 1-13, May.

    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. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    2. Zain Anwar Ali & Mahreen Zain & M. Salman Pathan & Peter Mooney, 2024. "Contributions of artificial intelligence for circular economy transition leading toward sustainability: an explorative study in agriculture and food industries of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19131-19175, August.
    3. Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    4. Zeinab Farshadfar & Tomasz Mucha & Kari Tanskanen, 2024. "Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    5. Ziad Al-Saadi & Duong Phan Van & Ali Moradi Amani & Mojgan Fayyazi & Samaneh Sadat Sajjadi & Dinh Ba Pham & Reza Jazar & Hamid Khayyam, 2022. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    6. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    7. Shi, Yao & Lin, Runze & Wu, Xialai & Zhang, Zhiming & Sun, Pei & Xie, Lei & Su, Hongye, 2022. "Dual-mode fast DMC algorithm for the control of ORC based waste heat recovery system," Energy, Elsevier, vol. 244(PA).
    8. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    9. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    10. Juraj Šebo & Miriam Šebová & Iztok Palčič, 2021. "Implementation of Circular Economy Technologies: An Empirical Study of Slovak and Slovenian Manufacturing Companies," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    11. Elahi, Ehsan & Khalid, Zainab, 2022. "Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms," Applied Energy, Elsevier, vol. 326(C).
    12. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.
    13. Kyle Pender & Konstantinos Bacharoudis & Filippo Romoli & Peter Greaves & Jonathan Fuller, 2024. "Feasibility of Natural Fibre Usage for Wind Turbine Blade Components: A Structural and Environmental Assessment," Sustainability, MDPI, vol. 16(13), pages 1-31, June.
    14. Daniel Silva & Ricardo Rocha & Filipe Ribeiro & Helena Monteiro, 2024. "Environmental Impact of an Innovative Aeronautic Carbon Composite Manufactured via Heated Vacuum-Assisted Resin Transfer Molding," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    15. Kristia Kristia & Mohammad Fazle Rabbi, 2023. "Exploring the Synergy of Renewable Energy in the Circular Economy Framework: A Bibliometric Study," Sustainability, MDPI, vol. 15(17), pages 1-27, September.
    16. Rohit Agrawal & Vishal A. Wankhede & Anil Kumar & Sunil Luthra & Abhijit Majumdar & Yigit Kazancoglu, 2022. "An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling," Operations Management Research, Springer, vol. 15(3), pages 609-626, December.

    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:13:y:2021:i:19:p:10860-:d:646913. 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.