Journal Article

Oxygen Bubble Dynamics in PEM Water Electrolyzers with a Deep-Learning-Based Approach

Kadjo, Jean-Jacques Amangoua, Benne, Michel, Lin-Kwong-Chon, Christophe, Damour, Cédric, Sinapan, Idriss

2023

Overview

Oxygen bubble accumulation on the anodic side of a polymer exchange membrane water electrolyzer (PEMWE) may cause a decrease in performance. To understand the behavior of these bubbles, a deep-learning-based bubble flow recognition tool dedicated to a PEMWE is developed. Combining the transparent side of a single PEMWE cell with a high-resolution high-speed camera allows us to acquire images of the two-phase flow in the channels. From these images, a deep learning vision system using a fine-tuned YOLO V7 model is applied to detect oxygen bubbles. The tool achieved a high mean average precision of 70%, confirmed the main observations in the literature, and provided exciting insights into the characteristics of two-phase flow regimes. In fact, increasing the water flow rate from 0.05 to 0.4 L/min decreases the bubble coverage (by around 32%) and the mean single-bubble area. In addition, increasing the current density from 0.3 to 1.4 A/cm2 leads to an increase in bubble coverage (by around 40%) and bubble amount.

• Publisher: MDPI AG
• Subject: Acoustics / Algorithms / Alternative energy sources / bubble flow recognition / Bubbles / Computer vision / Deep learning / deep learning algorithm / Design / Efficiency / Energy resources / Geometry / High speed cameras / high-resolution camera acquisition / Image acquisition / Methods / Neural networks / Oxygen / polymer exchange membrane water electrolysis / Renewable resources / Two phase flow / Vision systems / Water flow