Predicting Rejection of Emerging Contaminants through RO Membrane Filtration based on ANN- QSAR Modeling Approach: Trends in Molecular Descriptors and Structures towards Rejections

Abstract

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 descriptors were chosen and their significance was computed. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. For the first time, an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. Moreover, the MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties.