AI-assisted digital twin

AISOFHyST is seeking a research or technology partner with expertise in AI-assisted process engineering, dynamic modelling, optimisation and digital-twin development. The partner will model an integrated Power-to-X system comprising a high-temperature electrolyser and an metal-hydride storage tank with an intermediate thermochemical hydrogen compressor. The models should represent the relevant mass and energy flows, thermal behaviour, hydrogen pressure, component dynamics and operating constraints.

The partner will develop a digital twin capable of supporting system design, component sizing, heat integration and evaluation of alternative operating strategies. Physics-based models may be combined with data-driven and AI methods to optimise efficiency, minimise hydrogen-compression requirements, improve pressure and temperature matching between components, and support predictive control.

The digital twin will be validated using experimental data from individual components and the integrated pilot plant. Where feasible, it should communicate with the plant’s monitoring and control architecture to compare predicted and measured performance and assist operators in selecting safe and efficient operating conditions.

The partner will work closely with CIIAE and the rSOC, storage-tank and compressor providers. Expected contributions include modelling tools, optimisation algorithms, system-level simulations, a validated digital-twin environment and recommendations for pilot operation and future scale-up.