Mixed matrix membranes’ gas separation performance prediction using an analytical model

• Published in: Chemical engineering research & design Vol. 93; pp. 710 - 719
• Main Authors: Bakhtiari, Omid, Sadeghi, Nasrin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2015
• Subjects: Analytical modeling; Chemical engineering; Filler particles; Fillers; Flux; Gas separation; Mathematical analysis; Mathematical models; Membranes; Mixed matrix membranes; Permeability; Permeability prediction; Surface chemistry; Surface flux; Transaction processing; Analytical modeling; Gas separation; Surface flux; Filler particles; Permeability prediction; Mixed matrix membranes
• ISSN: 0263-8762; 1744-3563
• DOI: 10.1016/j.cherd.2014.06.013

•A special care was attributed to the surface flux of nonporous fillers.•An analytical approach is developed to predict MMMs’ permeabilities.•Comparing the developed approach by using predictive models with experimental data.•Higher prediction accuracies were obtained by using the developed approach. Mixed matrix membranes (MMMs), as a new membrane generation, have been an attractive subject of the worldwide academic and industrial studies accomplished by many researchers. Although many different models have been adapted and/or developed for MMMs’ permeability prediction, almost of them neglect the so-called impermeable filler particles’ permeabilities, e.g. Pd=0. In the current study, a new theoretical model is developed to predict MMMs performance considering a surface flux (or permeability) for the so-called impermeable filler particles. Due to the adsorption of the penetrants on the filler particles’ surface, there is surface flux on the filler particles. In order to develop a proper model, the ideal MMM structure is divided into three regions: polymer matrix, coating and filler surface regions. Model equations for each region was developed and then the current and the employed predictive models prediction results were compared with those of experimentally measured data. Very well agreement with the current model result with the experimental data was found (e.g. AAREs were reduced from 52.65 to 31.74%, from 51.12 to 28.22% and from 55.33 to 31.50% for the Maxwell, the Bruggeman and the Pal models, respectively in the current proposed model).