A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes

Abstract

The present paper illustrates the use of single neural networks (SNN) and bootstrap aggregated neural networks (BANN) for modeling the removal of organic compounds by nanofiltration and reverse osmosis. A set of 278 data points was used to build the SNN and BANN. Bootstrap aggregated neural networks are used to enhance the accuracy and robustness of neural network models built from a limited amount of training data. The training dataset is re-sampled using bootstrap re-sampling with replacement to form several sets of training data. For each set of training data, a neural network model is developed. The individual neural networks are then combined together to form a bootstrap aggregated neural network. Experimental removals were compared against calculated removals and excellent R correlation coefficients were found (0.9890, 0.9836, and 0.9841) for the training, test, and total dataset, respectively. The performance of the models (INN, BANN, and SNN) is shown that models built from BANN are more accurate and robust than those built from individual neural networks (INN) single neural networks (SNN).