Modeling multi-component vapor sorption in a poly(dimethyl siloxane) membrane
| Autor: | Chen Luen Tsai, Su Yu Wu, Shingjiang Jessie Lue, Shao Fan Wang, Li Ding Wang |
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| Rok vydání: | 2008 |
| Předmět: |
Ternary numeral system
UNIQUAC Mechanical Engineering General Chemical Engineering Thermodynamics Sorption General Chemistry Flory–Huggins solution theory Isopentane chemistry.chemical_compound chemistry Organic chemistry Gravimetric analysis General Materials Science Binary system Ternary operation Water Science and Technology |
| Zdroj: | Desalination. 233:286-294 |
| ISSN: | 0011-9164 |
| Popis: | This study establishes a mathematical model that describes the vapor sorption level as a function of its activity in poly(dimethyl siloxane) (PDMS) membrane in multi-component systems. The investigated vapors included polar (methanol, water) and non-polar ( m -xylene, toluene, and isopentane) permeants. The sorption experiments were carried out using the gravimetric method. The sorption isotherms in the binary (polymer+vapor) systems were obtained from the equilibrium weights gains at various activity levels. The Flory-Huggins equation and the UNIQUAC model were used to fit the parameters based on the experimental results. For binary systems, the UNIQUAC model gave better prediction power, especially at high activity levels. The sorption concentrations for the ternary (polymer+2 vapors) systems were measured after the individual sorbed constituents were purged and analyzed using gas chromatography. The predicted sorption levels from multi-component models for the ternary systems were compared with the experimental results. The two models fit the experimental data satisfactorily for the ternary systems. In this modeling procedure, only the interaction paramaters (χ 12 for Flory-Huggins equation and τ 12 and τ 21 for UNIQUAC model) between any two components are needed to estimate the sorption levels for multi-component systems. No ternary interaction parameters is required. The modeling process is therefore simplified and the prediction power greatly enhanced. |
| Databáze: | OpenAIRE |
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