H2 PROD MAT PROJECT
New materials and methods applied to hydrogen production
H2 PROD MAT works on the development of new electrocatalytic materials specifically designed for alkaline electrolysis (AWE) and anion exchange membrane (AEMWE) technologies.
- From: 01/01/2025.
- To: 31/12/2027.
Navarre has shown a firm commitment to the development of green hydrogen through its Green Hydrogen Agenda and the S4 smart specialisation strategy, which has led to several initiatives based on alkaline electrolysis.
Although this technology is the most widely available on the market, it has several limitations and areas for improvement in terms of efficiency and commercial viability on a large scale.
One of the main ways of optimising this technology is to develop new electrocatalytic materials for the anode and cathode that are more effective, less costly and have a lower environmental impact. Improvements in these materials would represent a significant advance in the efficiency and sustainability of green hydrogen production.
The H2 PROD MAT project will build on a number of cross-cutting technologies to develop new electrocatalytic materials specifically designed for alkaline electrolysis (AWE) and anion exchange membrane (AEMWE) technologies, which will contribute to improving the efficiency and reducing the cost and environmental impact of green hydrogen production.
Objectives
This will be done through actions aimed at increasing the efficiency, durability and safety of the operation, as well as reducing the energy consumption, costs and environmental impact of the process.
To this end, a multidisciplinary approach will be applied, focusing on the formulation of new materials, supported by advanced manufacturing, computational simulation, artificial intelligence tools and life cycle analysis.
NAITEC's role in H2 PROD MAT
NAITEC’s tasks include:
- Development of electrocatalytic materials for AWE technology.
- Manufacturing of AWE monocells.
- 2D/3D printing of critical AWE/AEMWE elements.
- Manufacture of H2 optical sensors. Integration in single cells and testing.
- CFD modelling.
- Selection of continuous learning algorithms: UNIT networks, generative adversarial networks (GAN) and convolutional neural networks (CNN), among others. Training and testing from UPNA images.
Partners
Funding
