| Catalytic methane decomposition (CMD) is a promising process for the simultaneous production of high-purity turquoise hydrogen and valuable solid carbon without direct carbon dioxide emissions. In particular, the CMD process utilizing carbon-based catalysts under contactless induction heating has been extensively investigated due to its operational advantages, especially the strong reaction-catalyst synergy. Despite its significant potential, optimizing this reaction remains challenging because methane conversion is highly dependent on several non-linear and interdependent factors, including catalyst characteristics, temperature, and gas flow rate. Consequently, this study proposes a time-series forecasting model to predict methane conversion dynamically during the reaction. Given the complex interdependencies governing the CMD process, multidimensional inputs are required, comprising endogenous, exogenous, and static variables. Therefore, a Transformer-based architecture was employed and specifically tailored for conversion forecasting. The proposed model was trained and evaluated using empirical data obtained from laboratory-scale experiments. The results demonstrate the robust potential of machine learning to predict methane conversion accurately, which is essential for real-time reaction monitoring and performance optimization. Furthermore, the model exhibits a highly promising capability to generalize and predict conversion rates when a previously unseen catalyst is introduced to the system. |
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