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۱An Intelligent Approach to Estimate Pressure–Volume–Temperature Properties in the System of Methane– Tetrafluoromethane: Densities and Compressibility Factors
نویسنده(ها): ، ،
اطلاعات انتشار: ششمین کنگره بین المللی مهندسی شیمی، سال
تعداد صفحات: ۶
In this study, the ability of Artificial Neural Network or ANN based on back–propagation approach for predicting the densities and compressibility factor of gaseous binary mixtures of CH4–CF4 has been investigated. Some experimental data (1507 data points) of gas densities for pure CH4, pure CF4, and three mixtures (0.25, 0.50, and 0.75 mole fraction of methane) are used to find optimal network, for which a density range from 0.75 to 12.5 mole\lit were covered. Finally, a network included 10–5–1 neurons in its layer is selected. By using this number of neurons, admissible absolute average deviations (about 0.112593% and 0.121046% for training and testing steps, respectively) are provided. Then, a comparison of compressibility factors for a mixture containing 50% CH4 shows an acceptable deviation, about 0.023604%. These results show that there is an excellent agreement between experimental data and ANN predictions.<\div>

۲Enhanced flux and improved selectivity using two type of zeolite layer at feed and permeate side of membrane
اطلاعات انتشار: ششمین کنگره بین المللی مهندسی شیمی، سال
تعداد صفحات: ۵
Separation of CO2\CH4 were investigated using (SAPO–34) CHA and Deca–dodecasil3R (DDR) at 300K. in the context of zeolites membrane separation of CO2\CH4 mixtures ,CHA and DDR are better than MFI. It was found that applying two layer of zeolite in order first CHA and then DDR can improve the selectivity and enhanced the flux if we use them at feed and permeate side of the support. A sufficient model, based on the generalized Maxwell–Stefan equations, has been used to describe the transport mechanism in the membrane. The diffusivity of these species is strongly dependent on the loading that has been described by Reed and Ehrlich [1] model. Also we used molecular dynamic results [2].<\div>
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