High-temperature pressure swing adsorption cycle design for sorption

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High-temperature pressure swing adsorption cycle design for sorption ( high-temperature-pressure-swing-adsorption-cycle-design-sorp )

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1 0.98 0.96 0.94 0.92 0.9 0.88 0.86 0.84 0.82 0.8 101 100 Carbon capture ratio CO2 product dry purity LDF prefactor Figure 10: Cycle performance versus kLDF pre-factor for SEWGS cycle Case 1 441 rity is only slightly affected by the value of kLDF. The separation between CO2 and 442 impurities during the rinse and subsequent pressure equalisations becomes slightly 443 more disperse and consequently less effective. But the profiles were fairly sharp 444 and the separation remains good. The capture ratio, in contrast, is very sensitive 445 towards the value of kLDF. Figure 11 shows the effect of the pre-factor on the CO2 446 loading profiles in the column at the end of the adsorption and purge steps. Firstly, 447 during the purge, the regeneration of the column becomes less effective, especially 448 near the top of the column (12.2 m). More CO2 remains at the top of the column 449 that will increase the CO2 slip to the H2 product stream. Secondly, during the feed 450 step, the lower value of kLDF causes the CO2 profile in the column to become more 451 disperse as well. Again, the consequence is a higher CO2 slip to the H2 product. 452 An improved intraparticle mass transfer coefficient will thus lead to a lower purge 453 steam consumption. 454 3.2. Parameter study 455 Starting from the cases outlined above, a series of 62 simulations was done 456 for 200–750 s cycle time, S/Crinse 0.001–0.5, and S/Cpurge 0.001–2. Each of the 457 conditions yields a cyclic steady state with carbon capture ratio and CO2 purity 458 values. The cycles had a carbon capture ratio in the range of 85–100%, and a 459 CO2 purity in the range of 62–100%, causing the productivity to be in the range of 460 1.5-1.7 mol kg−1 hr−1 and a cyclic working capacity of 0.32 mol kg−1. The total 461 steam consumption ( S/Crinse+ S/Cpurge) was varied between 0.4 and 43. Using 462 generalised linear regression (Matlab 8.1), two quadratic equations are fitted to 463 − log(1 − CCR) and − log(1 − CP) from the simulation results. The trends described 464 by the equations are depicted in Figure 12. 22 Carbon capture ratio [] CO2 product dry purity []

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