One way of increasing the efficiency of the polymer electrolyte membrane is to study two-phase flow effects in order to improve the evacuation of the product of the reaction, namely the water in fuel cells [1] and the dioxygen in the electrolyser [2]. The analysis of the water evacuation in PEMFC has been studied extensively [3], but the formation and the evacuation of dioxygen in PEMWE are not yet systematic. We propose to detect water bubbles through 3 actions in order to observe and identify dioxygen bubbles. Firstly, the image segmentation via morphological processing to create a sketchily data set. This approach has some limits, such as size or shape detection. Therefore, for a second stage a training of a shape regression convolutional neural network is used to estimate shape and complex forms [4]. And finally, the last action a validation of the neural model in online and real-time is provided. We hope that this work will help further studies, allowing to link shape, frequency, residence time, and velocity of bubbles of the different parameters used with electrolyser.
Figure 1: Detection and regression shape of dioxygen bubble through CNN
REFERENCES
1. Y. Ding, X.T. Bi, D.P. Wilkinson, Numerical investigation of the impact of two-phase flow maldistribution on PEM fuel cell performance, International Journal of Hydrogen Energy, Volume 39, Issue 1, 2014
2. F. Aubras, J. Deseure, J.-J.A. Kadjo, I. Dedigama, J. Majasan, B. Grondin-Perez, J.-P. Chabriat, D.J.L. Brett,Two-dimensional model of low-pressure PEM electrolyser: Two-phase flow regime, electrochemical modelling and experimental validation,International Journal of Hydrogen Energy,Volume 42, Issue 42,2017, Pages 26203-26216.
3. J.G. Carton, V. Lawlor, A.G. Olabi, C. Hochenauer, G. Zauner, Water droplet accumulation and motion in PEM (Proton Exchange Membrane) fuel cell mini-channels, Energy, Volume 39, Issue 1, 2012
4. Tim Haas, Christian Schubert, Moritz Eickhoff, Herbert Pfeifer, BubCNN: Bubble detection using Faster RCNN and shape regression network, Chemical Engineering Science, Volume 216, 2020.
Keywords: PEMWE, high-speed camera, bubble gas tracking, CNN, segmentation
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