Material and Performance Optimisation for Syngas Preparation Using Artificial Intelligence (AI)-Based Machine Learning (ML)

Abstract

Working towards a more sustainable future with zero emissions, the International Future Laboratory for Hydrogen Economy at the Technical University of Munich (TUM) exhibits concerted efforts across various hydrogen technologies. The current research focuses on pre-reforming processes for high-quality reversible solid oxide cell feedstock preparation. An AI-based machine learning model has been developed, trained, and deployed to predict and optimise the controlled utilisation of methane gas. Using a blend of design of experiments and a validated 3D computational fluid dynamics model, pre-reforming process data have been generated for various syngas mixtures. The results of this study indicate that it is possible to achieve a targeted methane utilisation rate of 20% while decreasing the amount of catalyst material by 11%. Furthermore, it was found that precise process parameters could be determined efficiently and with minimal resource consumption in order to achieve higher methane fuel utilisation rates of 25% and 30%. The machine learning model has been effectively employed to analyse and optimise the fuel outlet conditions of the pre-reforming process, contributing to a better understanding of high-quality syngas preparation and furthering sustainable research efforts for a safe reversible solid oxide cell (r-SOC) process.