Theory and experiments on sweeping gas membrane distillation

Condensation studies in membrane evaporation and sweeping gas membrane distillation

“…permeate side) can be expressed as (9) Similarly, when using air as the sweeping gas, (10) In reality, the vapor partial pressure, humidity ratio and permeate flux associated with the temperature along the membrane surface are not homogenous. In modelling, however, these parameters are often supposed to change homogenously along the membrane based on a series of assumptions [18,19].…”

“…K n <0.01), molecule-molecule collisions become more significant and the mass transfer resistance is caused by the stagnant air trapped within the membrane pore. In this case, water vapor flux can be described by molecule diffusion [4,10,18]: (18) with (19) where D M is the molecular diffusion coefficient, P air-lm is the logarithmic mean pressure of air, D is the water vapor diffusion coefficient and P is the total pressure inside the membrane pore.…”

“…8. Khayet et al ignored the effect of liquid flow rate on mass transfer in SGMD possibly because of the much thinner boundary layer on the liquid side at higher liquid rates [18,19].…”

“…Work on SGMD was first reported in 1987 and the effects of operational parameters on evaporation efficiency were studied [17]. After more than a decade, Khayet et al conducted a series of experimental and modelling studies to further explore the influences of operational parameters on the process performance in SGMD [18][19][20][21]. In their work, the temperature profile along the membrane module and the temperature polarization in SGMD were explored by mathematical modelling [19,20].…”

“…After more than a decade, Khayet et al conducted a series of experimental and modelling studies to further explore the influences of operational parameters on the process performance in SGMD [18][19][20][21]. In their work, the temperature profile along the membrane module and the temperature polarization in SGMD were explored by mathematical modelling [19,20]. Recently, both response surface model and artificial neural network have been developed for the prediction and optimization of SGMD processes [22,23].…”