CNN-Based Identification of Bubble Size and Number Depending on Operating Conditions in PEM Water Electrolyzer
Authors: Toyama Kohei , Kanemoto Ryo , Shiono Ryuhei , Omori Hiroto , Araki Takuto , Mitsushima Shigenori 
Published: December 2024

In proton exchange membrane water electrolyzer (PEMWE), one of the factors contributing to the decrease in electrolysis efficiency is the entrapment of oxygen bubbles within the reaction area, impeding material transport. The physical and chemical properties of porous transport layers (PTL) influence bubble diameter and detachment position, thereby affecting energy efficiency. To further enhance efficiency, it is necessary to elucidate the transport characteristics and mechanisms of oxygen bubbles. To observe and analysis bubble behavior in PEMWE cell, automated detection methods by using computer are required for quantitative evaluation of vast amounts of data. However, classical object detection methods such as the Hough transform could not detect bubbles in PEMWE cell. This is because bubble images obtained within PEMWE cell have heterogeneous backgrounds derived from the PTL, with irregular patterns and unclear bubble contours. Resent study using Convolutional Neural Network (CNN), a type of deep learning, has shown better detection performance for bubbles with images have a uniform background and clear bubble contours [1]. The effectiveness of resent study for bubble detection in PEMWE was insufficient.

In this study, a CNN-based method to automatically detect and track bubbles within PEMWE cells was developed [2]. This method includes an algorithm to automatically generate a large amount of annotation data required for training CNN. The transport characteristics of oxygen bubbles and electrolysis performance were quantitatively evaluated by analyzing videos of multiple operating conditions in which current density and anode supply water pressure were manipulated.

A YNU standard cell [3] was used for our experiments, as depicted in Figure 1(a). The schematic diagram of observed area is shown in Figure 1(b). The cell was set so that reaction surface is horizontal. Observation was conducted vertically on the PTL surface under various operating conditions. For example, Figure 1(c) shows a frame of the video cropped from the entire observed area under 0.5 A/cm2 current condition, while Figure 1(d) displays that under 3.0 A/cm2 current condition. Bubble detection for all frames of the video was conducted. Comparison of the oxygen bubble volume increase per second calculated from change of bubbles with the theoretical value calculated from Faraday's law showed good agreement.

Our method enables automatic, high precision and speed quantitative evaluations under various operating conditions, which were previously laborious and challenging to perform manually. The method and findings presented in this research can provide better understanding the transport characteristics of oxygen bubbles, thereby contributing to the future optimization of operating conditions and improving electrolysis performance.

[Acknowledgement]

This study is based on results obtained from a project, JPNP20003, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

[References]

[1] Poletaev I., et al., Int. J. Multiphase Flow, 126, 103194 (2020)

[2] Asaoka K. et al., Trans. JSME (in Japanese), 89(919), (2023)

[3] Nagasawa K. et al., Int. J. Hydrogen Energy, 46(74), 36619 (2021)

[Caption]

Fig.1 (a) Overview of PEMWE cell, (b) Schematic diagram of observed area, (c) Bubble detection result under the 0.5 A/cm2, (d) Bubble detection result under the 3.0 A/cm2.

Figure 1