Application of density functional theory and machine learning in the prediction of efficient catalysts for the oxidative coupling of methane with reduced CO2 production at low temperature
type: CreativeWork
description: The oxidative coupling of methane (OCM) remains a promising means for the production of ethylene. Though high temperatures of 900 oC and above lead to increased conversion of methane, at higher temperatures, the OCM reaction tends to favour a competing reaction that leads to the production of COx product. This thesis focuses on the generation of catalyst component electronic properties via density functional theory (DFT) and the analysis via machine learning (ML) techniques of the combination of the DFT-computed electronic properties and high-throughput experimental data comprising 12708 data points. The DFT data includes the catalyst components' bandgap, Fermi energy and magnetic moment. Variations of a dataset comprising experimental conditions, experiment performance and electronic catalyst properties were used to build a dataset for data modelling and analysis as well as ML analysis for the prediction of CO2 yield, C2H4 yield C2H6 yield, and CH4 conversion. With the aid of multi-linear regression models, Rh, Pt, Ru and Ir were found to be new catalyst promoters that enhance catalyst performance in OCM, particularly with improved methane conversion. A minimum of 58 new bimetallic combinations and 2784 unique catalytic materials with minimum CH4 conversion at 700 oC of 38.5% were identified and proposed as effective catalysts for OCM reaction, an improvement on the 36% CH4 conversion limit from previous studies. The Rh-Li3VO4/SBA15 and Ru-BaZrO3/SBA15 have been proposed as efficient catalysts for the OCM reaction with a predicted C2y of 30% and 29%, respectively. In comparing predictive model using a dataset containing a combination of catalyst electronic properties, deep neural networks (DNN) configured as deep feed-forward networks with back-propagation, along with random forest regression (RFR), support vector regression (SVR) and extreme gradient boost regression (XGBR), were compared on the basis on their mean-absolute-error, mean-squared-error and coefficien…
abstract: The oxidative coupling of methane (OCM) remains a promising means for the production of ethylene. Though high temperatures of 900 oC and above lead to increased conversion of methane, at higher temperatures, the OCM reaction tends to favour a competing reaction that leads to the production of COx product. This thesis focuses on the generation of catalyst component electronic properties via density functional theory (DFT) and the analysis via machine learning (ML) techniques of the combination of the DFT-computed electronic properties and high-throughput experimental data comprising 12708 data points. The DFT data includes the catalyst components' bandgap, Fermi energy and magnetic moment. Variations of a dataset comprising experimental conditions, experiment performance and electronic catalyst properties were used to build a dataset for data modelling and analysis as well as ML analysis for the prediction of CO2 yield, C2H4 yield C2H6 yield, and CH4 conversion. With the aid of multi-linear regression models, Rh, Pt, Ru and Ir were found to be new catalyst promoters that enhance catalyst performance in OCM, particularly with improved methane conversion. A minimum of 58 new bimetallic combinations and 2784 unique catalytic materials with minimum CH4 conversion at 700 oC of 38.5% were identified and proposed as effective catalysts for OCM reaction, an improvement on the 36% CH4 conversion limit from previous studies. The Rh-Li3VO4/SBA15 and Ru-BaZrO3/SBA15 have been proposed as efficient catalysts for the OCM reaction with a predicted C2y of 30% and 29%, respectively. In comparing predictive model using a dataset containing a combination of catalyst electronic properties, deep neural networks (DNN) configured as deep feed-forward networks with back-propagation, along with random forest regression (RFR), support vector regression (SVR) and extreme gradient boost regression (XGBR), were compared on the basis on their mean-absolute-error, mean-squared-error and coefficien…
author: Ugwu, Lord Ikechukwu
language: en
date published: 2024-03
date modified: 2026-07-24T04:03:47Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/16416

PropertyValue
property ID: DOI
value: 10.82465/4950