Artificial neural network model for desalination by sweeping gas membrane distillation



Downloads per month over past year

Khayet Souhaimi, Mohamed and Cojocaru, C. (2013) Artificial neural network model for desalination by sweeping gas membrane distillation. Desalination, 308 . pp. 102-110. ISSN 0011-9164

[thumbnail of khayetNO09.pdf]

Official URL:


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 degrees 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 x 10(-3) kg/m(2).s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

Item Type:Article
Additional Information:

© 2012 Elsevier B.V. The author (C. Cojocaru) is grateful to the Spanish Ministry of Science and Innovation for supporting the research grant (project SB2009-0009). The authors also acknowledge the financial support from the University Complutense of Madrid, UCM-BSCH (projects GR58/08 and GR35/10-A, UCM group 910336)

Uncontrolled Keywords:Permeate Flux, Filtration, Simulation, Prediction, Optimization, Separation, Decline, RO
Subjects:Sciences > Physics > Thermodynamics
ID Code:25938
Deposited On:26 Jun 2014 11:51
Last Modified:14 Mar 2016 15:34

Origin of downloads

Repository Staff Only: item control page