Objectives

This project builds on the pioneering work of Idriss Sinapan, which led to the development of deep learning-based oxygen bubble detection and recognition tools in PEMWE electrolyzers. This work enabled, for the first time, a multi-class analysis (bubbly, slug, and stagnated) of bubble dynamics at the anode, using a transparent PEMWE cell coupled with a high-resolution video acquisition system on the SysPacRevers test bench.

The H2-DurabilitAI project continues and expands upon this work through four specific objectives:

1. Large-scale data acquisition and establishment of an experimental database

The objective is to generate a rich, multi-condition database on the transparent PEMWE. Several tasks are carried out:

• Recommissioning and calibration of the SysPacRevers test bench;
• High-resolution video acquisition synchronized with operating signals (current density, water flow rate, temperature, pressure, channel and porous medium topologies);
• Structuring, organization, and open sharing of data via a NAS system.

2. Development of an artificial intelligence pipeline for bubble analysis

The objective is to create an advanced AI tool dedicated to the detailed analysis of two-phase phenomena. Several tasks are carried out:

• Development of a multi-class bubble detection/segmentation model (bubbly, slug, stagnated);
• Generation of coverage non-uniformity heat maps;
• Implementation of bubble tracking to compute dynamic indicators such as residence time and mean evacuation time;
• Post-processing and performance evaluation (mAP, IoU, etc.).

3. Analysis of results and proposal of new topologies and optimizations

The objective is to leverage AI-derived indicators to improve PEMWE performance and durability. Several tasks are carried out:

• Comparative analysis of bubble regimes according to operating conditions and topologies;
• Proposal of component modifications (channels, porous transport layer, MEA assembly);
• Validation in partnership with Fraunhofer ISE.

4. Dissemination, outreach, and preparation of future projects

The objective is to ensure the dissemination of results and the strengthening of local expertise. Tasks include:

• Publication of results in a Category A scientific journal;
• Public release of the AI model and database on GitHub;
• Preparation of analysis reports and recommendations;
• Prefiguration of a Horizon Europe consortium.