Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High-Performance Catalysts

• Published in: ChemCatChem Vol. 3; no. 12; pp. 1935 - 1947
• Main Authors: Zavyalova, Ulyana, Holena, Martin, Schlögl, Robert, Baerns, Manfred
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 16.12.2011; WILEY‐VCH Verlag; Wiley Subscription Services, Inc
• Subjects: catalyst composition; catalyst development; heterogeneous catalysis; methane; oxidative coupling; Statistical analysis
• ISSN: 1867-3880; 1867-3899
• DOI: 10.1002/cctc.201100186

A database consisting of 1870 data sets on catalyst compositions and their performances in the oxidative coupling of methane was compiled. For this goal, about 1000 full‐text references from the last 30 years have been analyzed and about 420 of them, which contained all the necessary information, were selected for the data extraction. The accumulated data were subject to statistical analysis: analysis of variance, correlation analysis, and decision tree. On the basis of the results, 18 catalytic key elements were selected from originally 68 elements. All oxides of the selected elements, which positively affect the selectivity to C2 products, show strong basicity. Analysis of binary and ternary interactions between the selected key elements shows that high‐performance catalysts are mainly based on Mg and La oxides. Alkali (Cs, Na) and alkaline‐earth (Sr, Ba) metals used as dopants increase the selectivity of the host oxides, whereas dopants such as Mn, W, and the Cl anion have positive effects on the catalyst activity. The maximal C2 selectivities for the proposed catalyst compositions range from 72 to 82 %, and the respective C2 yields range from 16 to 26 %. The more the merrier: This paper discusses the contributions of significant combinations of elements to the mean values of selectivity and yield in the oxidative coupling of methane (OCM) reaction derived from the statistical analysis of about 1870 past catalytic data sets (see figure). The applied methodology allows the identification of the main prerequisites in catalyst compositions for advanced design of high‐performance OCM catalysts. Correlation coefficients, decision trees, and analysis of variance are most useful tools.