Deep learning analysis of plasma emissions: A potential system for monitoring methane and hydrogen in the pyrolysis processes

The estimation of methane and hydrogen production as output from a pyrolysis reaction is paramount to monitor the process and optimize its parameters. In this study, we propose a novel experimental approach for monitoring methane pyrolysis reactions aimed at hydrogen production by quantifying methane and hydrogen output from the system. While we appreciate the complexity of molecular outputs from methane hydrolysis process, our primary approach is a simplified model considering detection of hydrogen and methane only which involves three steps: continuous gas sampling, feeding of the sample into an argon plasma, and employing deep learning model to estimate of the methane and hydrogen concentration from the plasma spectral emission. While our model exhibits promising performance, there is still significant room for improvement in accuracy, especially regarding hydrogen quantification in the presence of methane and other hydrogen bearing molecules. These findings present exciting prospects, and we will discuss future steps necessary to advance this concept, which is currently in its early stages of development.