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Zeolites are crystalline oxides with uniform, molecular-pore diameters of 3-14Å. Significant developments since 1950 made production of synthetic zeolites with high purity and controlled chemical composition possible. In powder-form, zeolites are major role-players in high-tech, industrial catalysis, adsorption, and ion exchange applications. Understanding properties of thin-film zeolites has been a focus of recent research. The ability to fine-tune desired macroscopic properties by controlled alteration at the molecular level is paramount. The relationships between macroscopic and molecular-level properties are established by experimental research. Because generating macroscopic, experimental data in a controlled laboratory can be prohibitively costly and time-consuming, reliable numerical simulations, which remove such difficulties, are an attractive alternative. Using a Configurational Biased Monte Carlo (CBMC) approach in grand canonical ensemble, numerical models for pure component and multicomponent adsorption processes were developed. Theoretical models such as ideal (IAST) and real adsorbed solution theory (RAST) to predict mixture adsorption in nanopores were used for comparison. Activity coefficients used in RAST calculations were determined from the Wilson, spreading pressure and COSMO-RS models. Investigative testing of the method on known materials, represented by all-silica zeolites such as MFI (channel type) and DDR (cage type), proved successful in replicating experimental data on adsorption of light hydrocarbons - alkanes, such as methane, ethane, propane and butane. Additionally, adsorption of binary and ternary mixtures was simulated. The given numerical approach developed can be a powerful, cost and time saving tool to predict process characteristics for different molecular-structure configurations. The approach used here for simulating adsorption properties of nanopore materials including process characteristics, may have great potential for other properties of interest. |
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Adsorption
Zeolites
Binary data
Molecules
Monte Carlo methods
Chemical species
Methane
