With the global deployment of renewable energy generation at record rates, clean energy is steadily becoming competitive with its fossil-fuel counterparts. However, further expansion is limited by the inherent intermittency of renewable energy sources (solar, wind, wave, etc.), which typically do not match with daily and seasonal variations of global (and local) energy demand. Thermochemical energy storage (TCES) has demonstrated strong potential in being a technological pathway to provide on-demand process heat and handle the intrinsic variations in renewable energy generation and energy demand. TCES works on the premise of excess renewable heat driving an endothermic reduction reaction, in which thermal energy is converted to chemical potential energy. The reversed exothermic oxidation reaction is subsequently triggered (on-demand) to recover thermal energy which can be used as process heat.
While the benefits of TCES have been demonstrated experimentally at the lab-scale, accurate numerical modeling of TCES reactors is key for future development, optimization, and implementation of large industrial-scale energy storage systems. This dissertation focuses on the development of continuum-scale models to accurately simulate and predict performance of high temperature (up to 1500 °C) moving-bed reactors for TCES. The efficacy of present volume- averaging approaches is briefly reviewed, with the major focus of the work on the development of multi-dimensional multi-physics models of increasing complexity for moving-bed TCES reduction and oxidation reactors.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6936 |
| Date | 08 August 2023 |
| Creators | Korba, David |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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