With the integration of a digital twin, the project can generate virtual copies of its experimental configurations. This allows SUSHEAT to conduct detailed simulations and investigate multiple scenarios in a controlled environment, saving time and resources, while increasing productivity.
By leveraging the power of Artificial Intelligence (AI), the SUSHEAT control system becomes a guiding force, optimizing energy consumption and refining processes to achieve exceptional performance.
Most digital twins are built to provide a holistic understanding in real-time, while the AI systems are built to provide an analytical perspective over a highly optimised set of scenarios. SUSHEAT will try to connect the two approaches to be able to build a robust, intelligent system that is self optimizing and highly adaptable.
The AI-guided control system will be connected to all the hardware and thermal equipment and will learn from human experts on how to control and optimize the processes.
Once this system is trained into common practices, it will serve as an assistant into decision making for complex scenarios, optimization recommendations and learning.
The second SUSHEAT focus aims to push the boundaries of control, automation and data-driven decision-making by providing optimization scenarios that will increase the production flexibility and financial efficiency of high-temperature heat pumps and energy storage systems.
Traditional renewable energy systems currently work using lower temperatures where efficiency of processes and energy allow some room for errors and decision delays.
By working at extremely high temperatures the SUSHEAT system will also push CIT limits and scenarios to a new level where there is an increased demand for near real time decisions and increased energy efficiency.
SUSHEAT digital control hub leadership
Alex Butean (WIZ Research) and Juan Enríquez (Analisis-DSC), supported by other partners, are leading the work for the SUSHEAT CIT system.
