Parametric analysis of CO2 hydrogenation via Fischer-Tropsch synthesis: A review based on machine learning for quantitative assessment

This review focuses on the parametric impacts upon conversion and selectivity during CO2 hydrogenation via Fischer-Tropsch (FT) synthesis using iron-based catalyst to provide quantitative evaluation. Using all collected data from reported literatures as training dataset via artificial neural networks (ANNs) in TensorFlow, three categorized parameters (namely: operational, catalyst informatic and mass transfer) were deployed to assess their impacts upon conversions (CO2) and selectivity. The lump kinetic power expressions among literature reports were compared, and the best fit model is the one that was proposed by this work without arbitrarily assuming power values of individual partial pressure (CO and H2). More than five sets of binary parameters were systematically investigated to find out corresponding evolving patterns in conversion and selectivity. Aided by machine learning, tailoring product distributions based on specific selectivity or conversion for optimization purpose is practically achievable by deploying the predictions generated from ANNs in this work.