Artificial neural network model for desalination by sweeping gas membrane distillation

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/m2.s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

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► Sweeping gas membrane distillation is modeled and optimized by artificial neural network.
► The input parameters are the feed temperature, the feed flow rate and the air flow rate.
► The response is the sweeping gas membrane distillation performance index.
► Study of interaction between Sweeping Gas Membrane Distillation parameters.
► The optimal response determined by Monte Carlo simulation was confirmed experimentally.