Online Monitoring of Membrane Fouling based on EIT and Deep Learning

Membrane separation has been demonstrated to be the most efficient and effective method for water treatment and desalination. However, membrane filtration modules inevitably become contaminated during the water treatment process, which reduces their filtration efficiency. Electrical impedance tomography (EIT)is an effective method for online monitoring of membrane fouling. However, the accuracy of foulant distribution needs to be improved due to the low resolution of EIT images. In this paper, an intelligent monitoring system based on EIT and deep learning is designed to generate the conductivity distribution of the membrane surface and track and monitor the dynamic changes of the membrane pollution in real-time. A deep learning architecture for EIT image reconstruction, namely TransUNet+Root-Net, is proposed. TransUNet combines the global perception ability of the Transformer with the local feature extraction advantages of UNet. To address the cross-domain generalization challenge encountered when training with simulation data and validating with experimental data, this paper designs an unsupervised dual-domain mapping network (Root-Net) to map simulation data into a form resembling experimental data. By using the large amount of simulation data labels mapped by Root-net to train TransUNet, the model's ability to represent the dynamic distribution of real membrane fouling is significantly improved. The results indicate 2.72% error for the TransUNet+Root-Net method. This approach more accurately characterizes the location and shape of the fouling, enabling real-time monitoring of its spatial distribution and providing insights into the evolution of membrane fouling.