School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea.
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea.
Water Res. 2021 Jun 1;197:117070. doi: 10.1016/j.watres.2021.117070. Epub 2021 Mar 20.
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/h for permeate flux and <10 µm 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.
控制膜过滤系统中的膜污染对于确保高过滤性能至关重要。对膜污染的预测可以促使采取初步措施来缓解膜污染的发展。因此，我们建立了一个长短期记忆（LSTM）模型来研究过滤性能和污染增长的变化。为了进行数据采集，我们首先进行了实验室规模的膜污染实验，以确定纳滤（NF）系统中天然有机物（NOM）的不同污染机制。考虑了四种 NOM 作为模型污染物：腐殖酸、牛血清白蛋白、海藻酸钠和鞣酸。此外，通过光学相干断层扫描（OCT）实时获取 2D 图像，以定量测量在膜上形成的滤饼层。随后，使用实验数据训练 LSTM 模型，将渗透通量和污染层厚度作为输出变量进行预测。该模型在训练和验证步骤中的渗透通量的均方根误差<1 L/m/h，污染层厚度的均方根误差<10 µm。在这项研究中，我们证明了深度学习可用于模拟 NOM 对 NF 系统的影响，并且也可应用于模拟其他膜过程。
