Membrane technologies for solar-desalination plants

Author(s):  
G. Caputo ◽  
A. Giaconia
Membranes ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 305
Author(s):  
Magda Kárászová ◽  
Mahdi Bourassi ◽  
Jana Gaálová

Membrane technologies are nowadays widely used; especially various types of filtration or reverse osmosis in households, desalination plants, pharmaceutical applications etc. Facing water pollution, they are also applied to eliminate emerging contaminants from water. Incomplete knowledge directs the composition of membranes towards more and more dense materials known for their higher selectivity compared to porous constituents. This paper evaluates advantages and disadvantages of well-known membrane materials that separate on the basis of particle size, usually exposed to a large amount of water, versus dense hydrophobic membranes with target transport of emerging contaminants through a selective barrier. In addition, the authors present several membrane processes employing the second type of membrane.


Author(s):  
Ali Bagheri ◽  
Nadia Esfandiari ◽  
Bizhan Honarvar ◽  
Amin Azdarpour

Abstract This study investigated a novel method for increasing desalinated water mass in solar desalination plants. For this purpose, solar panels and a cylindrical parabolic collector (CPC) were used to raise basin water temperature. The effect of different components of basin solar still on freshwater mass was also investigated. The aluminum basin has been associated with maximum water desalination among the different materials constituting a basin. The effects of different colors (e.g. black, brown, and red) on the basin, as well as different water depths (5, 10, and 15 mm), were also explored. The highest amount of freshwater in the black aluminum basin at a 5-mm water depth was 2.97 kg/day. ANN modeling was employed to validate the experimental data, indicating good compliance of experimental data with ANN prediction. According to the results of the simulation with varying numbers of neurons (n = 2–25), the highest and lowest agreement between experimental data and ANN prediction data were related to 24 and 10 neurons, respectively. Under optimum conditions, R2 and %AAD error were 0.993 and 2.654, respectively.


2010 ◽  
Vol 1 (08) ◽  
pp. 704-713
Author(s):  
D. Buschert ◽  
B. Bitzer
Keyword(s):  

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