IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v228y2021ics0360544221008124.html

Fouling propensity in reverse electrodialysis operated with hypersaline brine

Author

Listed:
  • Santoro, Sergio
  • Tufa, Ramato Ashu
  • Avci, Ahmet Halil
  • Fontananova, Enrica
  • Di Profio, Gianluca
  • Curcio, Efrem

Abstract

The impact of fouling on the performance of Reverse Electrodialysis operated with highly concentrated brine is a poorly investigated area. In this work, the fouling propensity and stability of Ion Exchange Membranes (IEMs), developed by Fujifilm Manufacturing Europe BV (The Netherlands), is investigated under the condition of seawater and brine. The fouling propensity of the IEMs was depicted by the determination of the Gibbs energy barrier of based-on the Classical Nucleation along with the Theoretical modeling of heterogeneous nucleation as a function of electrochemical (contact angle, permittivity, charge density) and morphological (roughness) membrane properties validated by CaCO3 precipitation. Results indicate that Cation Exchange Membranes (CEM) are more susceptible to the scaling due to the reduced energy barrier of heterogeneous nucleation. FTIR-ATR analysis on six months-aged membranes samples indicated a partial modification in the chemical structure of Anion Exchange Membranes (AEM) induced by the organic fouling associated with humic substances. The tensile tests demonstrated substantial mechanical stability of IEMs. Lab-scale RED tests operated with artificial brine over 30 days showed a significant increase in pressure drop through feed channels due to significant colloidal fouling along with a 23% reduction of maximum gross power density with consequent decrease of net power density.

Suggested Citation

  • Santoro, Sergio & Tufa, Ramato Ashu & Avci, Ahmet Halil & Fontananova, Enrica & Di Profio, Gianluca & Curcio, Efrem, 2021. "Fouling propensity in reverse electrodialysis operated with hypersaline brine," Energy, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:energy:v:228:y:2021:i:c:s0360544221008124
    DOI: 10.1016/j.energy.2021.120563
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221008124
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.120563?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    2. Tufa, Ramato Ashu & Pawlowski, Sylwin & Veerman, Joost & Bouzek, Karel & Fontananova, Enrica & di Profio, Gianluca & Velizarov, Svetlozar & Goulão Crespo, João & Nijmeijer, Kitty & Curcio, Efrem, 2018. "Progress and prospects in reverse electrodialysis for salinity gradient energy conversion and storage," Applied Energy, Elsevier, vol. 225(C), pages 290-331.
    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. Qian, Jiaxin & Wu, Jiahui & Yao, Lei & Mahmut, Saniye & Zhang, Qiang, 2021. "Comprehensive performance evaluation of Wind-Solar-CCHP system based on emergy analysis and multi-objective decision method," Energy, Elsevier, vol. 230(C).
    2. Khayet, Mohamed & Aytaç, Ersin & Essalhi, Mohamed & Cipollina, Andrea & García-Fernández, Loreto & Contreras-Martínez, Jorge & García-Payo, Carmen & Ruiz-García, Alejandro & Figoli, Alberto, 2025. "Elucidating the dynamics of salinity gradient energy research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
    3. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    4. Rajpal, Sheetal & Lakhyani, Navin & Singh, Ayush Kumar & Kohli, Rishav & Kumar, Naveen, 2021. "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    5. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    6. Jia, Wenlong & Yang, Fan & Li, Changjun & Huang, Ting & Song, Shuoshuo, 2021. "A unified thermodynamic framework to compute the hydrate formation conditions of acidic gas/water/alcohol/electrolyte mixtures up to 186.2 MPa," Energy, Elsevier, vol. 230(C).
    7. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    8. Bingzi Jin & Xiaojie Xu, 2026. "Machine learning wholesale white wheat price index forecasts," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 277-305, February.
    9. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    10. Hu, Rongchun & Zhang, Dongxu & Gu, Xudong, 2022. "Reliability analysis of a class of stochastically excited nonlinear Markovian jump systems," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    11. Boulif, Nadia & Evers, Renate & Driegen, Jelle & Nawaz, Ayesha & Borneman, Zandrie & Nijmeijer, Kitty, 2025. "The acid-base flow battery: Tradeoffs between energy density, efficiency, and stability," Applied Energy, Elsevier, vol. 383(C).
    12. Mehrpooya, Mehdi & Ansarinasab, Hojat & Mousavi, Seyed Ali, 2021. "Life cycle assessment and exergoeconomic analysis of the multi-generation system based on fuel cell for methanol, power, and heat production," Renewable Energy, Elsevier, vol. 172(C), pages 1314-1332.
    13. Marquina, Jesús & Colinet, María José & Pablo-Romero, María del P., 2021. "The economic value of olive sector biomass for thermal and electrical uses in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    14. Feng, Ping & Li, Xiaoyang & Wang, Jinyu & Li, Jie & Wang, Huan & He, Lu, 2021. "The mixtures of bio-oil derived from different biomass and coal/char as biofuels: Combustion characteristics," Energy, Elsevier, vol. 224(C).
    15. Xu, Yuxin & Gao, Fei, 2024. "A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    16. Guo, Shu-Ling & Yang, Yong-Ge & Sun, Ya-Hui, 2021. "Stochastic response of an energy harvesting system with viscoelastic element under Gaussian white noise excitation," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    17. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    18. Wei Wang & Bin Ma & Xing Guo & Yong Chen & Yonghong Xu, 2024. "A Hybrid ARIMA-LSTM Model for Short-Term Vehicle Speed Prediction," Energies, MDPI, vol. 17(15), pages 1-18, July.
    19. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    20. Gu, Suqian & Xu, Zhiqiang & Ren, Yangguang & Tu, Yanan & Sun, Meijie & Liu, Xiangyang, 2021. "An approach for upgrading lignite to improve slurryability: Blending with direct coal liquefaction residue under microwave-assisted pyrolysis," Energy, Elsevier, vol. 222(C).

    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:eee:energy:v:228:y:2021:i:c:s0360544221008124. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.