Valorization of Methane for Ethylene Production Through Oxidative Coupling: An Application of Density Functional Theory and Data Analytics in Catalyst Design for Improved Methane Conversion †

Lord Ugwu, Yasser Morgan, Hussameldin Ibrahim

Research output: Contribution to journalArticlepeer-review

Abstract

The combination of electronic and catalytic features, in conjunction with empirical investigation, provides enriched perspectives on the analysis of catalysts, thus propelling progress and design. This study employs computational methods to deduce electronic characteristics, including properties such as bandgap, Fermi energy, and magnetic moment, for known catalysts involved in the oxidative coupling of methane (OCM) reaction. Through the comparison of these attributes with existing experimental OCM data, the ability to forecast the effectiveness of catalysis and subsequent reaction results is achieved, spanning CH4, C2H4, C2H6, and CO2 production. Transition metals, including Pt, Rh, Ru, and Ir, turn out to be promising catalyst promoters of OCM reactions. This study identified 58 innovative blends of metallic oxides and 3480 new catalytic configurations specifically designed for methane conversion at a moderately low temperature of 700 °C, placing them as effective catalysts for the OCM reaction. These emerging catalysts are projected to result in a rise in methane conversion extending from ±38.5% to ±95%, presenting a significant increase from the upper limit methane conversion of 36% reported in previous investigations.

Original languageEnglish
Article number83
JournalEngineering Proceedings
Volume76
Issue number1
DOIs
StatePublished - 2024

Keywords

  • catalysis
  • data analysis
  • DFT
  • machine learning
  • methane
  • oxidative coupling
  • regression

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