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    Machine learning–assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

    86782.pdf (1.343Mb)
    Access Status
    Open access
    Authors
    Lim, Juin Yau Lim
    Loy, Adrian Chun Minh
    Alhazmi, Hatem
    Chin, Bridgid
    Cheah, Kin Wai
    Taylor, Martin J
    Kyriakou, Georgios
    Yoo, Chang Kyoo
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lim, J.Y.L. and Loy, A.C.M. and Alhazmi, H. and Chin, B.L.F. and Cheah, K.W. and Taylor, M.J. and Kyriakou, G. et al. 2021. Machine learning–assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses. International Journal of Energy Research.
    Source Title
    International Journal of Energy Research
    DOI
    10.1002/er.7565
    ISSN
    0363-907X
    Faculty
    Curtin International
    School
    Curtin International
    Remarks

    This is the peer reviewed version of the following article: Lim, JY, Loy, ACM, Alhazmi, H, et al. Machine learning–assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses. Int J Energy Res. 2021; 1- 15, which has been published in final form at 10.1002/er.7565. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

    URI
    http://hdl.handle.net/20.500.11937/86958
    Collection
    • Curtin Research Publications
    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).

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