Deep learning model for simulating influence of natural organic matter in nanofiltration

Controlling membrane fouling in a membrane filtration system is critical to ensure high filtration performance. A forecast of membrane fouling could enable preliminary actions to relieve the development of membrane fouling. Therefore, we established a long short-term memory (LSTM) model to investigate the variations in filtration performance and fouling growth. For data acquisition, we first conducted lab scale membrane fouling experiments to identify the diverse fouling mechanisms of natural organic matter (NOM) in nanofiltration (NF) systems. Four types of NOMs were considered as model foulants: humic acid, bovine-serum-albumin, sodium alginate, and tannic acid. In addition, real-time 2D images were acquired via optical coherence tomography (OCT) to quantify the cake layer formed on the membrane. Subsequently, experimental data were used to train the LSTM model to predict permeate flux and fouling layer thickness as output variables. The model performance exhibited root mean square errors of < 1 L/m(2)/h for permeate flux and < 10 mu m for fouling layer thickness in both the training and validation steps. In this study, we demonstrated that deep learning can be used to simulate the influence of NOMs on the NF system and also be applied to simulate other membrane processes. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study established an LSTM model to investigate membrane fouling control in membrane filtration systems. By training the model with experimental data, it effectively predicts permeate flux and fouling layer thickness, demonstrating the application of deep learning in simulating the influence of NOMs on NF systems.