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.
Highlights
Low-pressure membranes, namely microfiltration (MF) and ultrafiltration (UF) membranes, are employed extensively in drinking and wastewater treatment systems [1], due to the better water quality compared to conventional treatment with high removal rates of particulate matter, turbidity, and microorganisms [2,3]
In the flow of increasing popularity of deep neural networks (DNNs), this study aimed, for the first time, at expanding the use of an auxiliary classifier generative adversarial network (ACGAN) for the description of surface and internal fouling caused by particle deposition on the structures of porous membranes
The ACGAN model was run for the maximum of 80,000 iterations, and the generated images were checked every 2560 iterations
Summary
Low-pressure membranes, namely microfiltration (MF) and ultrafiltration (UF) membranes, are employed extensively in drinking and wastewater treatment systems [1], due to the better water quality compared to conventional treatment with high removal rates of particulate matter, turbidity, and microorganisms [2,3]. Modeling of the membrane fouling processes helps better understand the factors influencing the membrane fouling process [7]. It contributes to design enhancement of membranes’ structures and materials [8] and allows online monitoring, for a simultaneous control of the status of the membranes and fouling-induced changes in performance [9]
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