A generalized cryo-adsorber model and 2-D refueling results

Our earlier work [Int J Hyd Energy 2010; 35: 3598–3609], describes a 3-D model for the cryo-adsorber and the 1-D results for the isobaric refueling period using quasi-static adsorption approximation. Herein the isobaric constraint is relaxed by solving the Darcy equation for computing the pressure drop in the bed, the quasi-static approximation is relaxed by introducing the Langmuir adsorption kinetics, and the 2-D refueling results are compared with the 1-D quasi-static isobaric refueling results. In spite of the significant differences in formulation, the two results compare well with each other. The 2-D refueling results show that the pressure transients equilibrate quickly and a nearly steady pressure profile gets established in the bed. This observation justifies the isobaric approximation used earlier. The Langmuir kinetics used here has a desorption rate constant which could vary depending on the diffusional resistance at adsorbent particle level. A sensitivity analysis of this parameter shows that the refueling rate varies negligibly while this parameter is varied over many orders of magnitude. This observation shows that as long as the pellets are small enough, refueling is controlled by macroscopic processes like simultaneous cooling/adsorption in the bed and the movement of the adsorption front out of the bed, rather than the molecular processes like sorption at an adsorbent site or diffusion through the adsorbent lattice. This observation justifies the quasi-static approximation used earlier. The above two approximations offer significant computational advantage to the design and optimization of cryo-adsorber beds with complex geometry.

► Two-dimensional model for a cryo-adsorption hydrogen storage tank refueling.
► Pressure transients equilibrate quickly, justifying an isobaric approximation.
► Simultaneous cooling & adsorption control, rather than sorption or diffusion.
► Hence, quasi-static approximations are justified.
► These approximations simplify modeling and optimization of complex bed geometry.