StaR

Water electrolysers from the assembly line as a central component of the energy transition

Green hydrogen is a basic building block of the energy transition. However, it is currently only rarely competitive. One reason for this is the expensive equipment - the electrolysers - required to produce green hydrogen from renewable energies. The StaR research project has therefore developed a new design for the electrolysis stacks. This enables automated series production, which can drastically reduce manufacturing costs. The task of the project is to develop the production concepts for this vision and to support the planning process with a digital twin.

Factory planning with artificial intelligence

When planning the factory for the cost-effective production of electrolysers on a gigawatt scale, scalable concepts for production and logistics are developed with the help of artificial intelligence and implemented together with the industrial partners. A special feature here is that planning is not carried out from construction stage to construction stage. Instead, the final target configuration of the factory is defined first. From this target state, both the preceding construction stages and the conversion phases between the construction stages are planned. This ensures rapid growth and minimises the planning risk. In order to map these dynamics transparently, a new planning methodology is being developed, which is supported by the innovative data model of the digital factory twin.

Digital Twin

The project is developing a digital twin that serves as a central exchange platform between the project partners. All information converges in a shared database, where it is processed and made available to the respective departments in a transparent format. This enables fast and always up-to-date communication, which also reduces the workload for each individual. Planning errors due to missing or outdated information are thus prevented. At the same time, this also enables complete documentation of the planning progress and provides the basis for detailed what-if analyses.