Structured Zeolite Adsorbents for PSA Applications

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Structured Zeolite Adsorbents for PSA Applications ( structured-zeolite-adsorbents-psa-applications )

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a CO2 analyzer (IR 1507 fast response CO2 infrared transducer). The response of the analyzer is less than 1 s to reach 95% of its final value and the dispersion by axial diffusion in the line is small (Peclet number: 36) and hence it was assumed that dispersion in the line is mainly caused by the mass transfer kinetics [58], which justified the use of the analytical solution by Klinkenberg [37, 38] to model the data. The goal of the experiments was to estimate the mass transfer coefficient k and from that derive the effective diffusivity De in the zeolite adsorbents. It was assumed that all the channels in the monolith samples were identical and that the velocity in all channels were the same [33-36]. To model the data, the experimental breakthrough curves were shifted by the delay-time stemming from the volume of tubing and valves, and simulation was performed on the resulting curves. The analytical solution for the breakthrough front by Klinkenberg was used [37, 38]: c c 12 e r f c ( 18 18 ) 0 kKz1 = v = k(t – z/v), E q . ( 4 ) Eq.(5) Eq.(6) where c/c0 is the dimensionless concentration, k (s-1) the mass transfer coefficient, K the dimensionless Henry constant, z (m) the column length (m), t (s) the time, v (m/s) the superficial velocity, the void volume, defined as the ratio between the square of the channel width and the square of the sum of channel width, zeolite layer thickness, and wall thickness, and the dimensionless bed length and time, respectively. The Klinkenberg solution is valid for an isothermal plug flow systems in which a single trace component with linear adsorption isotherm is adsorbed. The dimensionless Henry constant K (estimated from CO2 adsorption isotherm recorded at 20o C, not shown here) is equal to 32.1 or 55.7, and the void volume is 0.84 for samples C5*1h20min or G9. The mass transfer coefficient k in the model was fitted to experimental data by minimizing the sum S of squared residuals: 70

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