Monitoring fouling behavior for better understanding and control has recently gained increasing attention. However, there is no practical method for observing membrane fouling in real time, especially in the forward osmosis (FO) process. In this article, we used the optical coherence tomography (OCT) technique to conduct real-time monitoring of the membrane fouling layer in the FO process. Fouling tendency of the FO membrane was observed at four distinguished stages for 21 days using a regular membrane cleaning method. In this method, chemical cleaning, which extracts two to three times as much organic matter (OM) as physical cleaning, was used as an effective method. Real-time OCT image observations indicated that a thin, dense, and flat fouling layer was formed (initial stage). On the other hand, a fouling layer with a thick and rough surface was formed later (final stage). A deep learning convolutional neural network model was developed to predict membrane fouling characteristics based on a dataset of real-time fouling images. The model results show a very high correlation between the predicted data and the actual data. R² equals 0.90, 0.86, 0.92, and 0.90 for the thickness, porosity, roughness, and density of the fouling layer, respectively. As a promising approach, real-time monitoring of fouling layers on the surface of FO membranes and the prediction of fouling layer characteristics using deep learning models can characterize and control membrane fouling in FO and other membrane processes.

监视结垢行为以更好地理解和控制近来已引起越来越多的关注。但是，没有实时观察膜污染的实用方法，尤其是在正向渗透（FO）过程中。在本文中，我们使用光学相干断层扫描（OCT）技术对FO过程中的膜污染层进行了实时监控。使用常规膜清洁方法，在四个不同的阶段观察到FO膜的结垢趋势，持续了21天。在这种方法中，化学清洗是一种有效的方法，化学清洗所提取的有机物（OM）数量是物理清洗的2至3倍。实时OCT图像观察表明，形成了一个薄，致密且平坦的结垢层（初始阶段）。另一方面，稍后形成具有厚且粗糙表面的结垢层（最终阶段）。开发了深度学习卷积神经网络模型，以基于实时污损图像数据集预测膜污损特征。模型结果表明，预测数据与实际数据之间具有很高的相关性。[R对于结垢层的厚度，孔隙率，粗糙度和密度，^2分别等于0.90、0.86、0.92和0.90。作为一种有前途的方法，实时监控FO膜表面的结垢层并使用深度学习模型预测结垢层的特性可以表征和控制FO和其他膜工艺中的膜结垢。