Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis
arXiv:2511.05879v5 Announce Type: replace Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), but accurate prediction remains difficult because data are limited, transport physics are strongly coupled, and industrial operation requires reliable extrapolation beyond observed conditions. This study develops a hard-constraint physics-residual network (PR-Net) for hydrogen crossover prediction in PEMWE and compares it with a purely data-driven neural network (NN) and a soft-constraint physics-informed
The increasing global demand for green hydrogen production, coupled with the need for safer and more efficient PEMWE operations, is driving accelerated research into advanced predictive models.
Accurate and reliable prediction of hydrogen crossover is crucial for scaling up high-pressure PEMWE, offering significant advancements in both safety and efficiency for hydrogen production.
The application of hard-constraint physics-residual networks provides a more robust and extrapolatable method for managing critical safety parameters in industrial hydrogen electrolysis compared to prior models.
- · Green Hydrogen Producers
- · PEMWE Equipment Manufacturers
- · AI/ML for Industrial Applications
- · Traditional physics-based modeling without ML integration
- · Less efficient hydrogen production methods
Improved safety and efficiency in high-pressure PEMWE leads to lower costs for green hydrogen production.
Reduced production costs accelerate the adoption of green hydrogen in various industrial and energy sectors.
Widespread green hydrogen adoption contributes significantly to decarbonization efforts and energy independence in key industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG