Gas hydrate technology is a promising approach for carbon capture. However, due to the multi-physics and multi-scale complexity of the process, this technology is not sufficiently understood for real-life scale applications. In particular, further fundamental studies of the hydrate formation mechanisms and rate are needed to achieve relevant insights into the process design and intensification. High-fidelity numerical models are crucial to capture and explain the dominant physicochemical mechanisms involved in the process. This paper presents a new variation of the shrinking core model (SCM) that can capture the practically observed features of the carbon dioxide (CO2) hydration process, including the nucleation phase behavior and induction time, which have not been exploited previously. Accordingly, the most significant contribution of the current work to the literature is the proposal and demonstration of an efficient and rapid predictive tool for the CO2 hydrate nucleation process. Moreover, a model-based estimation of the induction time, as a critical parameter in CO2 hydrate rate estimation and control, is presented. Additionally, the temperature history profile over the nucleation and growth phases is simulated and compared against experimental data from the literature. The proposed model offers an in-depth and rationale analysis tool compared to the primary forms of the SCM and other models in which the nucleation stage has been compromised for the sake of mathematical modeling and numerical solution simplicity. The proposed concept is generic enough to be used for CH4 hydration process too.
Bibliographical note© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
- CO capture
- Gas hydrate
- Induction time
- Shrinking core model
Dashti, H., Thomas, D., & Amiri, A. (2019). Modeling of hydrate-based CO2 capture with nucleation stage and induction time prediction capability. Journal of Cleaner Production, 231, 805-816. https://doi.org/10.1016/j.jclepro.2019.05.240