Structure sensitivity of ethanol steam reforming over the Rh catalyst: Reaction kinetics and deactivation mechanisms

Wu, Haoquan; Yang, Bo

doi:10.1016/j.apsusc.2022.156116

Cited by 8 publications

(2 citation statements)

References 62 publications

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“…26 By providing the reaction mechanism and energetics (which can be obtained by experimental testing or first-principle-based calculations), the catalytic activity of any given material can be predicted as a function of simple descriptors, such as atomic adsorption energies. 27 MKM has been successfully applied to various reforming processes for the evaluation of catalyst performance based on their composition, 28,29 structure, 30 and size. 31 High-throughput screening aided by machine learning (ML) has also been applied in the rapid scanning of large material databases to find materials that may perform well based on their physical and chemical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Throughput Screening of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Learning and Microkinetic Modeling

Wang,

Kasarapu,

Clough

2024

ACS Omega

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The catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R 2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge

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