This thesis deals with diffusion and flow of sub-critical hydrocarbons in activated carbon by using a differential permeation method. The hydrocarbons are selected according to the effect on environmental concerns and their unique characteristics such as polarity and affinity towards activated carbon. Although it has been known that transport processes in activated carbon consist of Knudsen diffusion, gaseous viscous flow, adsorbed phase diffusion (so called, surface diffusion) and condensate flow, there have been no rigorous models to describe the transport processes in activated carbon with a full range of pressures. In particular among the four processes, the mechanism of adsorbed phase diffusion in activated carbon is still far from complete understanding. Also due to the dispersion interactions between adsorbing molecules and the solid surface, one would expect that Knudsen diffusion is influenced by the dispersive forces. From intensive experimental observations with a great care over a full range of pressures, conventional methods (for example, direct estimation from inert gas experiments) to determine adsorbed phase diffusion are found to be inadequate for strongly adsorbing vapors in activated carbon. By incorporating the effect of adsorbate-adsorbent interactions into Knudsen diffusivity, the general behavior of adsorbed phase diffusion in terms of pressure (or surface loading) can be obtained, showing a significant role in transport at low pressures. For non-polar hydrocarbons such as benzene, carbon tetrachloride and n-hexane, a mathematical model, which accounts for the effects of adsorbate-adsorbent interactions and pore size distribution, is formulated and validated, resulting in a good agreement with experimental data. Moreover, the adsorption and dynamic behaviors of alcohol molecules (which are polar compounds) are investigated with an aim to compare their behaviors against those of non-polar compounds.
Identifer | oai:union.ndltd.org:ADTP/289831 |
Creators | BAE, Jun-Seok |
Source Sets | Australiasian Digital Theses Program |
Detected Language | English |
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