Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet, M.; Cojocaru, Corneliu

doi:10.1016/j.desal.2012.06.023

Cited by 95 publications

(24 citation statements)

References 24 publications

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“…In our study, the temperatures of the membrane module and the temperature polarization in SGMD have been explored by mathematical modeling (Boukhriss et al 2016;Khayet et al 2002). The model developed for the optimization of SGMD processes (Khayet et al 2012;Khayet and Cojocaru 2013) is necessary to further explore mass and heat transfer phenomena in this technology. This study aims to provide a new understanding of mass transfer phenomena in SGMD.…”

Section: Introductionmentioning

confidence: 99%

The design of a unit sweeping gas membrane distillation: experimental study on a membrane and operating parameters

et al. 2020

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The paper presents the focuses of application in membrane processes for the desalination of brackish water or salt water using the sweeping gas membrane distillation (SGMD); at first, it is the least used configuration of MD, and the design of this system is arduous and very expensive, and it is used to treat solutions containing non-volatile compounds, such us salts which is totally rejected, and high purity of water was recuperated. Vapor transfer is an important thermally induced phenomenon in the membrane-by-membrane (MD) evaporation and condensation. Our SGMD distillation system was simulated using MATLAB programmers on heat and mass transfer aspects. In our research, we found that heat and mass transfer in the SGMD is determined by the evaporation temperature of the gas and the sweep rate. The operational parameter (fluid velocity) is influenced across the layer on both sides of the membrane, because it is the source of the transfer of heat and mass in many membrane processes.

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Section: Introductionmentioning

confidence: 99%

The design of a unit sweeping gas membrane distillation: experimental study on a membrane and operating parameters

et al. 2020

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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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“…The optimization should be done based on the performance index (the oil rejection factor times the permeate flux) as done by Khayet and Cojocaru [31]. Therefore, the constructed GP models have been used to optimize the gas sparging assisted MF process.…”

Section: Process Optimizationmentioning

confidence: 99%

Genetic programming for modeling and optimization of gas sparging assisted microfiltration of oil-in-water emulsion

Tashvigh

Ashtiani

Fouladitajar

2015

Desalination and Water Treatment

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A B S T R A C TGenetic programming (GP) is an orderly method based on natural evolution rules for getting computers to regularly solve a problem. In the present study, GP is presented as a novel approach for modeling the gas sparging assisted microfiltration of oil-in-water emulsion process. The effects of gas flow rate (Q G ), oil concentration (C oil ), transmembrane pressure (TMP), and liquid flow rate (Q L ) on the permeate flux and oil rejection were studied and the GP models were developed to predict the membrane performance. C oil and TMP showed significant effects on both permeate flux and rejection. An interaction between C oil and TMP was detected, at low C oil and high TMP, in which the permeate flux increased considerably. It was found that Q L has a low effect on permeate flux, but its impact on rejection was significant. Increasing Q L from 0.5 to 2.75 L/min led to a considerable increment in rejection; however, further increase in the liquid flow rate decreased the oil rejection. On the contrary, Q G showed a small effect on oil rejection, but its effect on permeate flux was notable. To determine the optimum conditions, the performance index was maximized using the developed genetic algorithm. Under the obtained optimal conditions, maximum permeate flux and rejection (%) were 121.6 (Lm 2 /h) and 93.0%, respectively.

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Artificial neural network model for desalination by sweeping gas membrane distillation

Cited by 95 publications

References 24 publications

The design of a unit sweeping gas membrane distillation: experimental study on a membrane and operating parameters

The design of a unit sweeping gas membrane distillation: experimental study on a membrane and operating parameters

Condensation studies in membrane evaporation and sweeping gas membrane distillation

Genetic programming for modeling and optimization of gas sparging assisted microfiltration of oil-in-water emulsion

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