Data analysis of CO2 hydrogenation catalysts for hydrocarbon production

Abstract

Literature data of CO2 hydrogenation catalysts for hydrocarbon production via Fischer-Tropsch synthesis (FTS) were extracted from literature, collected into a SQLite database, and analyzed using data science and machine learning techniques. A challenge was to include performance data obtained by different groups which vary in the applied reaction conditions but vary also in the set of performance indicators that are reported. The thermodynamic ratio was used for analyzing reverse water gas shift reaction as the first stage of CO2 hydrogenation. A tailored Anderson-Schulz-Flory distribution modified by two additional parameters was suggested based on the data and applied for describing hydrocarbon production. The data analysis indicated the existence of direct CO2 conversion to hydrocarbons via the FT mechanism for some catalysts. It was shown that the chain growth probability as the key parameter of catalyst selectivity slightly depends on reaction condition (temperature, pressure, H2:CO2 ratio) and is mainly defined by catalyst composition, method of catalyst preparation, nature of active sites and pretreatment conditions. With respect to the catalyst composition, it was found that doping catalysts by alkali metals like K or Na is the most effective measure to improve catalyst performance leading to increased chain growth probability, increased olefins content and a decrease of undesired CH4 formation.