Modeling the Presence of Humic Acid in Ultrafiltration of Xenobiotic Compounds: Elman Recurrent Neural Network

Predicting the rejection of pesticides in ultrafiltration (UF) processes in the presence of common components of dissolved natural organic matter would be taken into consideration as a principle for surface water treatment. This paper presents the application of the Elman Recurrent Neural Network (ERNN) model, which has been trained with previously‐obtained experimental data so as to predict the rejection of a class of xenobiotic compounds (nitrophenols (NPs)) dynamically, in the absence and in the presence of humic acid at neutral and acidic conditions. For each trained network, the training function, number of neurons in the hidden and output layers, number of epochs, train and test MSE (mean square error) and MRE (mean relative error) were compared to find the best ERNN. The trained MRE and test MSE for all NPs at the neutral condition was, respectively, less than 1.03 % (4.9 % at acidic condition) and 2.4 % (2.01 % at acidic condition), which showed high network reliability. Dynamically modeling pesticides ultrafiltration may be considered as a basis for surface water treatment. The Elman Recurrent Neural Network can be the most suitable and powerful approach for representing arbitrary nonlinear dynamical systems. Such models could be used to screen membranes prior to conducting expensive large‐scale tests.