Explainable machine-learning predictions for catalysts in CO <sub>2</sub> -assisted propane oxidative dehydrogenation
Hongyu Liu, Kang-Yu Liu, Hairuo Zhu, Weiqing Guo, Yuming Li
Abstract
-nearest neighbors (KNN), support vector regression (SVR) and random forest regression (RF)and were used to predict the propylene space-time yield. Specifically, the RF method serves as a superior performing algorithm for propane conversion and propylene selectivity prediction, and SHapley Additive exPlanations (SHAP) based on the Shapley value performs fine model interpretation. Reaction conditions and chemical components show different impacts on catalytic performance. The work provides a valuable perspective for the machine learning in light alkane conversion, and helps us to design catalyst by catalytic performance hidden in the data of literatures.
Topics & Concepts
DehydrogenationPropaneCatalysisAlkanePerspective (graphical)Oxidative phosphorylationComputer scienceChemistryChemical engineeringProcess engineeringPhotochemistryMaterials scienceOrganic chemistryArtificial intelligenceEngineeringBiochemistryCatalysis and Oxidation ReactionsMachine Learning in Materials ScienceCatalytic Processes in Materials Science