Machine learning–assisted <scp> CO <sub>2</sub> </scp> utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

نویسندگان

چکیده

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 proposed determine the optimum reaction configurations pathways DR of C1-C4 hydrocarbons via mechanism generator (RMG). With aid machine learning, variation thermodynamic microkinetic parameters based on different temperatures, pressures, CH4/CO2 ratios surface, Pt(111), Ni(111), were successfully elucidated. As result, promising multicriteria decision-making process, TOPSIS, was employed identify configuration with trade-off between yield reduction. Notably, conditions C1 C2 800°C at 3 atm Pt(111); whereas C3 C4 found favor 2 Ni(111) attain highest conversion. Based RMG-Cat (first-principle database), profile most selective pathway network CH4 Pt(111) deducted. activation (Ea) C?H bond dissociation dehydrogenation 0.60 eV, lower than reported previously Cu(111), Co(111) surfaces. endothermic C3H3* + H2O* ? OH* C3H4 (218.74 kJ/mol).

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ژورنال

عنوان ژورنال: International Journal of Energy Research

سال: 2021

ISSN: ['0363-907X', '1099-114X']

DOI: https://doi.org/10.1002/er.7565