Artificial Neural Network based modeling of the vacuum membrane distillation process: Effects of operating parameters on membrane fouling

Abstract

Vacuum membrane distillation is a novel separation process that requires less energy and provides high selectivity. However, membrane fouling is a severe issue in the commercialization of this process. One of the methods to minimize membrane fouling is the use of appropriate operating conditions. In this study, an Artificial Neural Network (ANN) based approach is used to model the vacuum membrane distillation process and analyze the effects of operating parameters on membrane fouling. An ANN was developed through MATLAB's Deep Learning Toolbox. First, the number of nodes in the hidden layer was optimized. The minimum value of mean square error (0.58) was achieved with ten nodes. The model predictions were successfully validated with a correlation coefficient of more than 0.98. The trained ANN was then used to analyze the effects of operating conditions on flux and membrane fouling. High membrane fouling was observed at high feed temperature and vacuum tightness. Higher feed solute concentrations were also responsible for high membrane fouling. In the optimization study, high feed temperature and moderate to high vacuum tightness for lower solute concentration; and high feed temperature and low to moderate vacuum tightness for higher solute concentration were found optimum operating conditions to achieve maximum fluxes.