首页|Cranfield University Reports Findings in Machine Learning (High-Throughput Scree ning of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Lea rning and Microkinetic Modeling)
Cranfield University Reports Findings in Machine Learning (High-Throughput Scree ning of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Lea rning and Microkinetic Modeling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Bedfordshire,United Kin gdom,by NewsRx journalists,research stated,"The catalytic activity of bimetal lic catalysts for the steam methane reforming (SMR) reaction was extensively stu died previously. However,the performance of these materials in the presence of sulfur-containing species is yet to be investigated." The news correspondents obtained a quote from the research from Cranfield Univer sity,"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 an d 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 values of 0.74,0.71,and 0.70,respec tively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally,the microkinetic model,together with the atomic adsorpt ion energies predicted by the Ensemble model,were used to screen over 500 bimet allic materials."
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