Identifying Catalyst Property Descriptors for CO2 Hydrogenation to Methanol Via Big-Data Analysis

Carbon dioxide (CO2) captureand valorization have greatpotential for mitigating emissions of this greenhouse gas and accordinglyfor preserving the environment for future generations. In this regard,hydrogenation of CO2 to methanol is highly attractive becausethis product is a valuable energy carrier and can also be used forproduction of various everyday commodities. Although many researchpapers on this topic have been published in the past decades, thereis still a lack of fundamentals relevant to control catalyst performance.Herein, we demonstrate how statistically validated Big-Data analysisof available literature data identified hidden descriptors that canbe applied for purposeful catalyst development and for identificationof optimal reaction conditions. In view of catalyst development, thekinds of structural promoters or supports for bulk or supported Cu-,In-, or Pd-based catalysts are the most important descriptors formethanol selectivity, with Ce and Zr being the most efficient promoters.The type and the parameters of the preparation methods as well asthe kind of active component precursors are also important in thisregard. To validate the conclusion about the structural promoter,a series of supported CuZn-containing catalysts were prepared. Thebest-performing CuZn/CeO2 catalyst outperformed the state-of-the-artCuZn-based catalysts tested at a total pressure of up to 30 bar usinga feed with the ratio of H-2/CO2 of 3. In additionto the catalyst composition and the preparation method, our analysissuggests that the most often used Cu-based catalysts lose their methanolselectivity due to the decomposition of this product to CO. Our controlexperiments with the developed CuZn-based catalysts proved that thisundesired reaction can be hindered when the catalyst support containsCe or through increasing H-2 partial pressure. This knowledgeis important for further catalyst development.