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

The catalytic dry reforming (DR) process is a clean approach to transform CO2 into H-2 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 C-1-C-4 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 H-2 yield and CO2 reduction. Notably, the optimum conditions for the DR of C-1 and C-2 hydrocarbons were 800 degrees C at 3 atm on Pt(111); whereas C-3 and C-4 hydrocarbons found favor at 800 degrees C and 2 atm on Ni(111) to attain the highest H-2 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 degrees C was deducted. The activation energy (E-a) 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).