Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration

Abstract

Mathematical models have been developed to obtain a better understanding of membrane fouling mechanisms. However, those models could not simulate the membrane fouling behaviors accurately because of the large number of fitting parameters related to feed water quality and flow pattern in a membrane filtration system. In this study, we developed a deep neural network (DNN) to model membrane fouling during nanofiltration (NF) and reverse osmosis (RO) filtration using in-situ fouling image data from optical coherence tomography (OCT). The performance of the DNN model was compared with that of existing mathematical models. In total, 13,708 high-resolution fouling layer images were used to develop the DNN model and validate the model performance. The DNN model was trained to simulate both organic fouling growth and flux decline, and it reproduced two- or three-dimensional images of the organic fouling growth. The DNN model demonstrated better predictive performance than the existing mathematical models. It achieved an R2 value of 0.99 and RMSE of 2.82 μm for the fouling growth simulation and R2 of 0.99 and RMSE of 0.30 Lm−2h−1 for the flux decline simulation. Therefore, the data-driven approach is an alternative way to model the membrane fouling and flux decline processes under high-pressure filtrations.