Modeling and Analysis of Particle Deposition Processes on PVDF Membranes Using SEM Images and Image Generation by Auxiliary Classifier Generative Adversarial Networks

Abstract

Due to highly complex membrane structures, previous research on membrane modeling employed extensively simplified structures to save computational expense, which resulted in deviation from the real processes of membrane fouling. To overcome those shortcomings of the previous models, this study aimed to provide an alternative method of modeling membrane fouling in water filtration, using auxiliary classifier generative adversarial networks (ACGAN). Scanning electron microscope (SEM) images of 0.45 µm polyvinylidene difluoride (PVDF) flat sheet membranes were taken as inputs to ACGAN, before and after the filtration of feed waters containing 0.5 µm diameter particles at varied concentrations. The images generated with the ACGAN model successfully reconstructed the real images of particles deposited on the membranes, as verified by human validation and particle counting of the real and generated images. This indicated that the ACGAN model developed in this research successfully built a model architecture that represents the complex structure of the real PVDF membrane. The image analysis through particle counting and density-based spatial clustering of application with noise (DBSCAN) revealed that both real and generated membranes had an uneven deposition of particles, which was caused by the complex structures of the membranes and by different particle concentrations. These results indicated the importance and effectiveness of modeling intact membranes, without simplifying the structure using such models as the ACGAN model presented in this paper.

Keywords

• deep learning
• generative adversarial network
• membrane filtration modeling
• particle deposition
• PVDF membrane