Membrane fouling diagnosis of membrane components based on multi-feature information fusion

Abstract

CBAM-MUL-CNN (convolutional block attention module - multiple - convolutional neural networks) model based on attention mechanism is proposed to solve the problem that the membrane fouling feature extraction capability of membrane bioreactor membrane component is insufficient, which resulted in the complex structure of the membrane fouling data, so that the efficient localization and classification of membrane fouling in membrane bioreactor could not be achieved. First, the time domain and frequency domain information about the fault data is used as the input of CNN (convolutional neural networks), and the features are extracted by convolution layer. Then, the input classifier is classified by splicing the time domain and frequency domain features using the full connection layer. BN (batch normalization) layer in the model can effectively prevent the disappearance of gradients, ReLU (rectified linear uint) layer can improve the non-linear model expression ability, CBAM (convolutional block attention module) can simplify the model complexity, improve the network features expression ability, and pooling layer can improve the model fault tolerance. The comparison results show that the model has excellent comprehensive performance in the membrane fouling diagnosis experiments of series tubular membrane devices and parallel hollow fiber membrane devices, and can effectively classify and locate all membrane fouling, making the treatment of water by membrane process improve the quality of effluent while reducing energy consumption, which provides a theoretical basis for actual production.