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.

The hydrate-based carbon dioxide (CO2) capture (HBCC) process has been widely studied for CO2 separation and sequestration. This paper aims to conduct a model-based investigation of the kinetics of the HBCC process. A variation of the shrinking core model (SCM) was developed for the analysis of this heterogeneous system under varying boundary conditions. The results revealed that while CO2 diffusion through the hydrate layer is the dominant controlling mechanism, for a realistic scenario in which a time-dependent bulk gas concentration exists, the model results would better match the experimental data if the effects of the reaction rate were incorporated into the diffusion-based model. Sensitivity analysis showed that increasing the diffusivity through the hydrate layer significantly decreases the full conversion time of the water. Moreover, the effect of temperature change was investigated, and it was found that lower temperatures slow the hydrate growth rate. The model was demonstrated to be a computationally effective and time-efficient predictive tool that does not require high-speed computers for large-scale (reactor) applications.