Understanding catalytic biomass conversion through data mining

Abstract

Catalytic conversion of biomass is a key challenge that we chemists face in the twenty-first century. Worldwide, research is conducted into obtaining bulk chemicals, polymers and fuels. Our project centres on glucose valorisation via furfural derivatives using catalytic hydrogenation. We present here new results for a set of 48 bimetallic catalysts supported on silica, and demonstrate the application of data mining tools to identify major trends in the data. These results are combined with a full factorial data set for the hydrogenation of 5-ethoxymethylfurfural over alumina-supported transition metal catalysts. All the catalysts in the combined datasets were synthesized and tested for performance under identical conditions. This, combined with the fact that no combinations of metals were left out, enables the use of advanced data mining tools. The paper describes the data and highlights the relevant trends from a chemist's viewpoint. © The Author(s) 2010.