eprintid: 16955
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/69/55
datestamp: 2023-12-19 03:23:26
lastmod: 2023-12-19 03:23:26
status_changed: 2023-12-19 03:07:11
type: article
metadata_visibility: show
creators_name: Lim, J.Y.
creators_name: Loy, A.C.M.
creators_name: Alhazmi, H.
creators_name: Fui, B.C.L.
creators_name: Cheah, K.W.
creators_name: Taylor, M.J.
creators_name: Kyriakou, G.
creators_name: Yoo, C.K.
title: Machine learning�assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses
ispublished: pub
keywords: Activation energy; Catalysts; Catalytic reforming; Decision making; Density functional theory; Economic and social effects; Hydrocarbons; Internet protocols; Ligands; Multiobjective optimization; Platinum compounds; Reaction kinetics, Catalytic dry reforming; CO2 utilization; Density-functional-theory; Dry reforming; Hydrogen productionmachine learningreaction mechanism network; Optimum reaction; Production-machines; Reaction mechanism; Reaction pathways; Reforming process, Carbon dioxide
note: cited By 6
abstract: The catalytic dry reforming (DR) process is a clean approach to transform CO2 into H2 and CO-rich synthetic gas that can be used for various energy applications such as Fischer�Tropsch fuels production. A novel framework is proposed to determine the optimum reaction configurations and reaction pathways for DR of C1-C4 hydrocarbons via a reaction mechanism generator (RMG). With the aid of machine learning, the variation of thermodynamic and microkinetic parameters based on different reaction temperatures, pressures, CH4/CO2 ratios and catalytic surface, Pt(111), and Ni(111), were successfully elucidated. As a result, a promising multicriteria decision-making process, TOPSIS, was employed to identify the optimum reaction configuration with the trade-off between H2 yield and CO2 reduction. Notably, the optimum conditions for the DR of C1 and C2 hydrocarbons were 800°C at 3 atm on Pt(111); whereas C3 and C4 hydrocarbons found favor at 800°C and 2 atm on Ni(111) to attain the highest H2 yield and CO2 conversion. Based on the RMG-Cat (first-principle microkinetic database), the energy profile of the most selective reaction pathway network for the DR of CH4 on Pt(111) at 3 atm and 800°C was deducted. The activation energy (Ea) for C-H bond dissociation via dehydrogenation on the Pt(111) was found to be 0.60 eV, lower than that reported previously for Ni(111), Cu(111), and Co(111) surfaces. The most endothermic reaction of the CH4 reforming process was found to be C3H3* + H2O* � OH* + C3H4 (218.74 kJ/mol). © 2021 John Wiley & Sons Ltd.
date: 2022
publisher: John Wiley and Sons Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121343142&doi=10.1002%2fer.7565&partnerID=40&md5=3f5c18eade92684cbacba91e122362fc
id_number: 10.1002/er.7565
full_text_status: none
publication: International Journal of Energy Research
volume: 46
number: 5
pagerange: 6277-6291
refereed: TRUE
issn: 0363907X
citation:   Lim, J.Y. and Loy, A.C.M. and Alhazmi, H. and Fui, B.C.L. and Cheah, K.W. and Taylor, M.J. and Kyriakou, G. and Yoo, C.K.  (2022) Machine learning�assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses.  International Journal of Energy Research, 46 (5).  pp. 6277-6291.  ISSN 0363907X