Continuing the series exploring the project’s innovations, we move from infrastructure scalability to one of HYIELD’s most powerful enablers: the digital twin. As the project approaches its demonstration phase, one of its most important innovations is not physical, but digital: an AI-driven representation of the entire plant.
Led by EURECAT, the digital twin is designed to mirror the full waste-to-hydrogen process in real time. It transforms continuous data streams into insight, prediction and control, enabling performance not just to be monitored, but actively understood and improved.
Building a living model of the plant
The digital twin starts with a clear goal: making better use of waste while maximizing hydrogen production. Reaching that goal means capturing the full complexity of the process, from fluctuating waste composition to changing reactor conditions, and bringing it into a single, structured digital environment.
To achieve this, HYIELD defines an architecture that governs how data is collected, processed, and displayed. The demonstration plant is equipped with a scalable monitoring and automation system that gathers real-time information across the process. Even when data is incomplete or disrupted, the system is designed to compensate, ensuring that the model remains consistent and usable.
The result is not just a monitoring interface, but a living representation of the plant, one that reflects both what is happening and why.
From real-time data to predictive intelligence
The digital twin integrates AI-based models that capture how variables interact across the system. Waste composition, heat input, and reactor conditions all shape syngas production and hydrogen output. Instead of reacting to these changes, the system learns to anticipate them.
This predictive capability is critical in a process defined by variability. Waste streams are inconsistent, and operating conditions shift constantly. Being able to forecast performance allows operators to adjust in advance, improving efficiency, stability, and overall output.
At the same time, the digital twin evaluates performance through both technical and economic lenses. It connects process indicators like efficiency, hydrogen yield, and syngas composition with operational and capital costs, bringing optimization and decision-making together in one place.
From demonstration to broader industrial impact
The digital twin is validated in parallel with the HYIELD demonstration plant under real industrial conditions. This validation process goes beyond performance verification, aiming to identify inefficiencies, optimise system behaviour, and ensure long-term reliability.
Once validated, the digital twin moves beyond a single use case. Its modular and interoperable design enables simulation of new industrial scenarios, including applications in steel and copper production. It also allows the evaluation of alternative heat sources, such as concentrated solar energy, within the system before any physical implementation.
As more data is collected and more scenarios are explored, the model continuously improves. It strengthens the link between design, operation and optimisation, reducing risk and increasing confidence in deployment.
The physical plant may remain fixed, but its digital counterpart continues to evolve, supporting a more flexible, efficient and scalable approach to hydrogen production.
The project is Co-founded by Clean Hydrogen Partnership and European Commission.
Writer: Oria Pardo Fernández
Editorial: Lucía Salinas and Aleix Fornieles Espinel
June, 2026