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The simulation models have been validated against real production data, but the real time predicitons from the digital twin have mostly been validated only towards an emulation of the factory. Next steps are to validate and demonstrate the DTs in the plant.

The Digital Twins run efficiently on modest hardware. Reduced order modelling techniques make sure that the simulation results are fast online, even if the underlying physics model requires heavy computations. The time consuming modelling can be done offline. If the plant was expanded or another plant was built, another set of DTs could be running with very small additional cost. The DT optimizes production and energy consumption simultaneously. The benefit will scale with the business.

The development of a digital twin that combines process insight with energy planning requires a unique, tailor made approach for different types of industrial processes. The process DT must be as unique as the plant itself is. However, the methodology applied to develop the process DT is replicable, and the competence built up at IFE during the project is available for new customers. In particular, the competence on monitoring systems, digitalization of quality assurance, predictions of extrusion process stability and quality, are transferable. The DTs that are directly focusing on the energy analysis and planning, including the DT for the borehole thermal energy storage, are directly transferable to other major energy consumers/producers.

The digital twins (DTs) optimize production efficiency and energy consumption simultaneously, reducing material waste, energy use, and emissions. This creates value for stakeholders while delivering clear environmental and societal benefits.

By enabling efficient and reliable manufacturing of solar thermal panels, the DTs support wider adoption of solar thermal technology. As deployment grows, sustainability benefits – lower fossil fuel use and reduced emissions from heat production – will accumulate and accelerate.