کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1282079 | 1497543 | 2013 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Gas permeation through H2-selective mixed matrix membranes: Experimental and neural network modeling Gas permeation through H2-selective mixed matrix membranes: Experimental and neural network modeling](/preview/png/1282079.png)
Gas permeability through synthesized polydimethylsiloxane (PDMS)/zeolite 4A mixed matrix membranes (MMMs) were investigated with the aid of artificial neural network (ANN) approach. Kinetic diameter and critical temperature of permeating components (e.g. H2, CH4, CO2 and C3H8), zeolite content and upstream pressure as input variables and gas permeability as output were inspected. Collected data of the experimental operation was used to ANN training and optimum numbers of hidden layers and neurons were obtained by trial-error method. The selected ANN architecture (4:10:1) was used to predict gas permeability for different inputs in the domain of training data. Based on the results, the predicted values demonstrate an excellent agreement with the experimental data, with high correlation (R2 = 0.9944) and less error (RMSE = 1.33E−4). Furthermore, using sensitivity analysis, kinetic diameter and critical temperature were found as the most significant effective variables on gas permeability. As a result, ANN can be recommended for the modeling of gas transport through MMMs.
► Novel PDMS/zeolite A MMMs were synthesized.
► Homogeneous distribution of zeolite nanoparticles was observed.
► ANN of MMMs is rare until 2012 and the present work seems to be the first.
► The relative importance of operational conditions was determined.
Journal: International Journal of Hydrogen Energy - Volume 38, Issue 2, 24 January 2013, Pages 1128–1135