Adsorption in porous materials is a promising technology for CO2 capture and storage. Particularly important applications are adsorption separation of streams associated with the fossil fuel power plants operation, as well as natural gas sweetening. High surface area activated carbons are a promising family of materials for these applications, especially in the high pressure regimes. As the streams under consideration are generally multi-component mixtures, development and optimization of adsorption processes for their separation would substantially benefit from predictive simulation models. In this project we combine experimental data and molecular simulations to systematically develop a model for a high surface area carbon material, taking activated carbon Maxsorb MSC-30 as a reference. Our study starts from the application of the well-established slit pore model, and then evolves through the development of a more realistic model, based on a random packing of small graphitic fragments. In the construction of the model, we introduce a number of constraints, such as the value of the accessible surface area, concentration of the surface groups and pore volume, to bring the properties of the model structure close to the reference porous material. Once a plausible model is developed, its properties are further tuned through comparison between simulated and experimental results for carbon dioxide and methane. The model is then validated by predictions for the same species at different conditions and by prediction of other species involved in the carbon capture processes. The model is applied to simulate the separations involved in pre and post combustion capture processes and sweetening of sour natural gas, using realistic conditions and compositions for the multicomponent mixtures. Finally, it is used to explore the effect of water in pre and post combustion separations.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:693635 |
| Date | January 2015 |
| Creators | Di Biase, Emanuela |
| Contributors | Sarkisov, Lev ; Duren, Tina |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/16155 |
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