Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis

Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis

Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performanc...

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Bibliographic Details
Main Authors: Yujun Liu, Xiaolong Zhang, Luotong Li, Xingchen Liu, Tingyu Lei, Jiawei Bai, Wenping Guo, Yuwei Zhou, Xingwu Liu, Botao Teng, Xiaodong Wen
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Artificial Intelligence Chemistry
Subjects:
Artificial Intelligence
Fischer-Tropsch
Catalysis
Methane
Online Access:http://www.sciencedirect.com/science/article/pii/S2949747724000204

Internet

http://www.sciencedirect.com/science/article/pii/S2949747724000204

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