Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm

Reverse Osmosis (RO) can be considered as one of the most widely used technologies used to abate the existence of highly toxic compounds from wastewater. In this paper, a multilayer artificial neural network (MLANN) with Genetic Algorithm (GA) have been considered to build a comprehensive mathematical model, which can be used to predict the performance of an individual RO process in term of chlorophenol removal from wastewater. The MLANN model has been validated against 70 observational experimental datasets collected from the open literature. The MLANN model predictions have outperformed the predictions of several structures developed for the same chlorophenol removal using RO process based on performance in terms of coefficient of correlation, coefficient determination (R-2) and average error (AVE). In this respect, two structures (4-2-2-1) and (4-8-8-1) were also used to study the effect of a number of neurons in the hidden layers based on the difference between the measured and ANN predicted values. The model responses clearly confirm the successfulness of estimating the chlorophenol rejection for network structure 4-8-8-1 based on a wide range of the control variables. This also represents a high consistency between the ANN model predictions and the experimental data.