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    Ion exchange membrane systems: Modeling and optimization for salinity gradient energy generation

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    ZHANG-DISSERTATION-2018.pdf (4.695Mb)
    Date
    2018-05-17
    Author
    Zhang, Bopeng
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    Abstract
    Energy can be sustainably generated by harnessing natural salinity gradients in coastal environments. Power derived from the mixing of freshwater and seawater can be recovered as electrical energy by regulated ion transport in reverse electrodialysis (RED) systems. Cation exchange membranes and anion exchange membranes, known together as ion exchange membranes (IEMs), are crucial components to the energy generation efficiency in RED stacks. Considering the fundamental nature of electrochemical systems, it is conceivable that membrane functional properties, including ionic conductivity and permselectivity, have significant effects on RED energy performance. A better understanding of these determining factors is therefore critical to advance commercialization feasibility. This study focused on advancing the understanding of IEMs through modeling, simulation and experimental validation in addition to novel approaches for RED energy performance improvement. Specifically, conductivity gains were realized through implementation of ion exchange resin in low-concentration compartments. Mathematical modeling and experimental validation were leveraged to infer crucial factors in membrane conductivity and other physical property determinations. In addition, this framework was extended to illuminate the role of nanoparticle introduction during the synthesis process. Modeling and simulation results were successful in revealing the underlying dependencies of IEM characterization and improving the system energy performance. A majority of these theories and simulations are generalized – potentially yielding broad impacts to similar membrane-based systems and processes (e.g., electrodialysis).
    URI
    http://hdl.handle.net/1853/61613
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Civil and Environmental Engineering Theses and Dissertations [1755]

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