Artificial Neural Network Model for Membrane Desalination: A Predictive and Optimization Study

Abstract

Desalination is a sustainable method to solve global water scarcity. A Response Surface Methodology (RSM) approach is widely applied to optimize the desalination performance, but further investigations with additional inputs are restricted. An Artificial neuron network (ANN) method is proposed to reconstruct the parameters and demonstrate multivariate analysis. Graphene oxide (GO) content, Polyhedral Oligomeric Silsesquioxane (POSS) content, operating pressure, and salinity were combined as input parameters for a four-dimensional regression analysis to predict the three responses: contact angle, salt rejection, and permeation flux. Average coefficient of determination (R2) values ranged between 0.918 and 0.959. A mathematical equation was derived to find global max and min values. Three objective functions and three-dimensional diagrams were applied to optimize effective cost conditions. It served as the database for the membranologists to decide the amount of GO to be used to fabricate membranes by considering the effects of operating conditions such as salinity and pressure to achieve the desired salt rejection, permeation flux, contact angle, and cost. The finding suggested that a membrane with 0.0063 wt% of GO, operated at 14.2 atm for a 5501 ppm salt solution, is the preferred optimal condition to achieve high salt rejection and permeation flux simultaneously.