Our project has achieved significant progress in the science of combustion, leveraging data analytics, machine learning, and digital twin technology to pave the way for cleaner, more efficient industrial combustion systems. These advancements are crucial for the widespread adoption of renewable synthetic fuels (RSFs) like ammonia and hydrogen.
WP1 - We've conducted detailed experiments to understand how ammonia and hydrogen burn under specific conditions known as Moderate or Intense Low-oxygen Dilution (MILD). By identifying the ideal fuel mixtures, we've found ways to balance stable combustion and minimal emissions. This research will directly inform the design of improved burners and combustion strategies for industrial use, as well as contribute to the development of regulations that support the adoption of ammonia as a clean fuel.
WP2 - We've employed machine learning techniques to make combustion simulations faster and more accurate. By training neural networks with high-quality data from Direct Numerical Simulations (DNS), we've significantly enhanced our ability to model combustion processes. We've also validated that MILD combustion effectively reduces harmful emissions and developed efficient models that predict emissions without requiring vast computational resources.
WP3 - We've developed a new method called low-cost Singular Value Decomposition (lcSVD) to analyze combustion data in real time. This allows us to model complex combustion processes using live data streams. Additionally, advanced feature extraction techniques help us identify critical combustion patterns, enabling faster data processing without sacrificing accuracy.
WP4 - Our project has focused on creating more accurate closure models using neural networks to improve predictions for renewable synthetic fuels. These virtual models enable the optimisation of combustion processes, leading to significant reductions in emissions. These models will be invaluable tools for industries seeking to enhance efficiency and minimise environmental impact.
WP5 - We've successfully developed digital twin technology to predict emissions, temperature, and combustion behaviour in real-time. We've also improved sensor strategies, allowing for more accurate combustion measurements with fewer sensors. These real-time digital twins will enhance efficiency, safety, and sustainability in industrial combustion systems while lowering costs and reducing pollution.
