Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 161, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2022.107768
Keywords
Proton exchange membrane fuel cell; X-ray micro-computed tomography; Image segmentation; Deep learning; Convolutional neural network; Gas diffusion layer
Funding
- UNSW Research Infrastructure Scheme
- Australian Research Council [ARC DP170104550, DP170104557, LP170100233, LE20 0100209, FT170100224, LP200100255]
- internal UNSW grant [RIS RG193860]
- Australian Research Council [LP200100255] Funding Source: Australian Research Council
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This study demonstrates the benefits of using convolutional neural networks (CNNs) in accurately classifying different materials of proton exchange membrane fuel cells using X-ray micro-computed tomography. The study shows that a novel UResNet CNN can effectively segment the complete volume of the fuel cells with high accuracy. The CNN outperforms the manual segmentation, especially in separating carbon fibres and binder phase in the gas diffusion layer. Additionally, the CNN provides realistic permeability calculation results for the binder void space.
This study demonstrates the benefit of convolutional neural networks to accurately classify the different materials of proton exchange membrane fuel cells using X-ray micro-computed tomography. Nineteen 2D orthoslices from a 3D tomography dataset were segmented with high quality and used to train a novel UResNet convolutional neural network (CNN) to segment the complete volume. The results were compared with a 3D manual segmentation performed under time constraints. The CNN segmented all phases with equal or greater accuracy in comparison to the manual segmentation. In particular, the CNN excelled in separating the carbon fibres and binder phase in the gas diffusion layer, which is usually completely avoided due to difficulty. Further, permeability calculations were performed on the binder void space for both segmentations, with the CNN displaying realistic results. Therefore, CNNs have been shown to be a viable and valuable method in segmenting such fuel cells with increased efficiency and accuracy. (c) 2022 Elsevier Ltd. All rights reserved.
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