Diagnostic Study of MBR Membrane Fouling based on CA-ResNet18

Abstract

In this paper, we propose a diagnostic model for addressing difficult-to-diagnose factors that influence membrane fouling, using a residual neural network (ResNet) optimized with a coordinate attention mechanism. After pre-processing fouling data from the membrane bioreactor using Principal Component Analysis (PCA) to derive nine categories of fouling factors, we determined the residual neural network structure and optimized it using the Coordinate Attention Mechanism (CA) to enhance feature extraction, improve diagnosis accuracy, and establish a stable and reliable diagnostic model for membrane scaling. Through experimental verification, the ResNet with the addition of the CA attention mechanism outperformed the ResNet with other attention mechanisms and the traditional ResNet in terms of prediction accuracy and convergence speed, achieving the research goal of accurately diagnosing the causes of membrane scaling.