Modeling and optimization of sweeping gas membrane distillation

Khayet, M.; Cojocaru, Corneliu; Baroudi, Alia

doi:10.1016/j.desal.2011.04.070

Cited by 87 publications

(35 citation statements)

References 20 publications

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“…As stated previously, the increase of T f,in leads to an increase ofŶ , whereas the influence of U w onŶ is insignificant. Similar results were obtained in our previous study dealing with modeling SGMD using response surface methodology (RSM) [27]. From the RSM model, it was also concluded that the effect of the liquid flow rate on the distillate flux was less significant.…”

Section: Resultssupporting

confidence: 76%

“…However, for higher U a values, the increase of U w does not changeŶ significantly. Similar interaction effects have been detected when RSM was used for SGMD modeling [27]. It was stated that this last effect was attributed to the formation of small air bubbles on the membrane surface contacting the liquid (i.e.…”

Section: Resultsmentioning

confidence: 58%

“…(Table 2), the performance index corresponding to the optimum operating condition is the highest one. In our recent published paper [27], using the same SGMD system, the obtained SGMD optimal condition by RSM model was a liquid circulation velocity of 0.16 m/s (i.e. 165 L/h liquid flow rate), an air circulation velocity of 2.11 m/s (i.e.…”

Section: Resultsmentioning

confidence: 99%

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Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69°C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493×10−3 kg/m 2 .s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

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Section: Resultssupporting

confidence: 76%

Section: Resultsmentioning

confidence: 58%

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Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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“…Indicative bulk and wall temperatures are also shown in the figures; of course, in all cases the sign of T w -T b is arbitrary. In the case of Membrane Distillation (MD), case (a) (one-side heat transfer) is usually realized in laboratory-scale experimental rigs [7,17,18] or small-scale, plate-and-frame, MD equipment [19,20] because it is simpler to build and allows an easier experimental characterization of temperatures and heat fluxes.…”

Section: One-side Against Two-side Heat / Mass Transfermentioning

confidence: 99%

Influence of the boundary conditions on heat and mass transfer in spacer-filled channels

Ciofalo

Cerva

Liberto

et al. 2017

J. Phys.: Conf. Ser.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Condensation studies in membrane evaporation and sweeping gas membrane distillation

Zhao

Feron

Xie

et al. 2014

Journal of Membrane Science

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Vapor transfer is an important phenomenon in various thermally driven membrane processes such as membrane distillation (MD), membrane evaporation and membrane condensation. In this study, we explore the mass transfer phenomena in sweeping gas membrane distillation (SGMD) by systematically investigating the effects of operational parameters on the process performance. It is found that mass transfer in SGMD is principally determined by both the evaporation temperature and the sweeping gas flow rate, and is significantly influenced by the operational parameters (i.e. fluid velocities) through multiple effects, including the boundary layer effect on both sides of the membrane, and the temperature polarization effect. We also prove that at low gas flow rates (i.e. insufficient gas vapor-holding capacity) the sweeping gas becomes saturated and water vapor forms droplets due to condensation on the gas side of the membrane. To the best of our knowledge, this is the first study reporting the interesting mass transfer phenomena in terms of vapor condensation and droplet re-evaporation in SGMD, which are of great significance for the heat and mass transfer in many thermally driven membrane processes using stripping gas.

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Modeling and optimization of sweeping gas membrane distillation

Cited by 87 publications

References 20 publications

Artificial neural network model for desalination by sweeping gas membrane distillation

Artificial neural network model for desalination by sweeping gas membrane distillation

Influence of the boundary conditions on heat and mass transfer in spacer-filled channels

Condensation studies in membrane evaporation and sweeping gas membrane distillation

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