Modeling and simulation of VMD desalination process by ANN

• An ANN model was constructed to describe the performance of VMD desalination process by STATISTICA.
• The neural network approach was found to be capable of predicting accurately the unseen data.
• The amount of available experimental samples used for ANN modeling is very limited.
• The optimal VMD operating conditions can be obtained based on the ANN model.

In this work, an artificial neural network (ANN) model based on the experimental data was developed to study the performance of vacuum membrane distillation (VMD) desalination process under different operating parameters such as the feed inlet temperature, the vacuum pressure, the feed flow rate and the feed salt concentration. The proposed model was found to be capable of predicting accurately the unseen data of the VMD desalination process. The correlation coefficient of the overall agreement between the ANN predictions and experimental data was found to be more than 0.994. The calculation value of the coefficient of variation (CV) was 0.02622, and there was coincident overlap between the target and the output data from the 3D generalization diagrams. The optimal operating conditions of the VMD process can be obtained from the performance analysis of the ANN model with a maximum permeate flux and an acceptable CV value based on the experiment.