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instrumentation of the structure, whilst effective, can be logistically expensive to implement for the entire network. To address these challenges, the project aims to develop a novel, population-based indirect
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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reducing odours from pomace and digestate. The project comprises seven work packages. As a leading partner, the University of Surrey will develop a system digital twin (SDT) to enhance overall sustainability
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