Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model.

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Abstract

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.
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