Ocean salinity is a renewable energy source that has not been recognized and could provide an opportunity to capture significant amount of clean energy when it mixes with river water. One of the processes emerging as a sustainable method for capturing energy from seawater is reverse electrodialysis (RED), which generates power via the transport of the positive and negative ions in the water through selective ion exchange membranes (IEMs). RED power generation is relatively close to commercialization, but its application is often limited by system power efficiency in natural water conditions. Although various types of salt ions exist in environmental saline water, most efforts have been focused on sodium chloride as a single ionic source in the water and the effects of other common multivalent ions (e.g., magnesium and sulfate) on power generation remain unexplored. Moreover, the commercial feasibility of RED is highly challenged by the absence of specialized RED membranes. Currently available IEMs are not optimized for RED power conversion systems, but successful operation is highly dependent on the membranes used. Major advances in manufacturing of proper IEMs will be a critical pathway to accelerate large-scale energy conversion by RED.
Therefore, this study aimed at advancing our understanding of the RED power system for efficient and stable salinity gradient energy generation. Specifically, it is comprised of three parts. First, a mathematical model is developed for three different monovalent and multivalent ion combinations to determine the effect of different ionic compositions of the feed solution on the power density. Efforts are further made to optimize the RED system with respect to improving power density by investigating the sensitivity of key response parameters such as flow rate ratios and intermembrane distance ratios. Second, novel organic-inorganic nanocomposite cation exchange membranes (CEMs) are synthesized for RED application by introducing functionalized inorganic materials into an organic polymer matrix. The effect of inorganic particle filler loading within the organic polymer matrix on physico- and electrochemical performance is investigated. The results revealed that the increase of functionalized nanoparticle loading controls the effective ion transport in the membrane structure and there exists an optimum amount of nanoparticles (i.e., charged groups), which performs the best in selectively exchanging counter-ions, while excluding co-ionic species. Third, the membrane structure modification is demonstrated to enhance ion transport while maintaining large surface-charged functional groups in the polymer matrix. We have synthesized custom nanocomposite CEMs to tailor porous membrane structures of various thicknesses, aging (evaporation) time, and inorganic nanoparticle loadings. We have further tailored the membrane structure by incorporating different inorganic particle filler sizes. These engineered design approaches are found to be highly effective in obtaining desired physico- and electrochemical properties, which allowed higher ionic current flow throughout the system. Furthermore, for the first time we showed the successful application of tailor-made nanocomposite CEMs in a RED stack and achieved superb power density, which exceeds the power output obtained with the commercially available membranes.
In summary, this dissertation has advanced our understanding of salinity gradient energy generation using RED technique. Specifically, computational modeling and simulation study investigates the development and optimization approaches of the RED process for practical application of RED using natural water conditions. Furthermore, the RED membranes developed in this dissertation focuses on fabrication, characterization, and optimization of cation exchange membranes. Overall, the results of this study are anticipated to benefit the future optimization of energy-capturing mechanisms in RED and provide the better pathway for the sustainable salinity gradient power generation.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53530 |
| Date | 08 June 2015 |
| Creators | Hong, Jin Gi |
| Contributors | Chen, Yongsheng |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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