Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Multioutput support vector regression (SVR) is implemented to simultaneously predict the selectivities and the CH₄ conversion against experimental conditions in methane oxidation catalysts. The predictions unveil the details of how each selectivity and CH₄ conversion behaves in each catalyst. In particular, the selectivity and the CH₄ conversion of Mn-Na₂WO₄/SiO₂, Ti-Na₂WO₄/SiO₂, Pd-Na₂WO₄/SiO₂, and Na₂WO ₄/SiO₂ are predicted, and the effects of Mn, Ti, and Pd are unveiled. In addition, the trade-off points of CO and C₂H₆ are identified for each catalyst, leading to maximization of the C₂H₆ yield. Thus the simultaneous prediction of the reaction trend with catalysts not only will help with the understanding of the catalyst activities but also will provide guidance for designing the experimental conditions.