Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis-Reference-Cited by-同舟云学术

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

Published:2024-06 Issue:1 Volume:2 Page:100062
ISSN:2949-7477
Container-title:Artificial Intelligence Chemistry
language:en
Short-container-title:Artificial Intelligence Chemistry

Author:

Liu Yujun,Zhang Xiaolong,Li Luotong,Liu Xingchen,Lei Tingyu,Bai Jiawei,Guo Wenping,Zhou Yuwei,Liu Xingwu,Teng Botao,Wen Xiaodong

Funder

National Science Fund for Distinguished Young Scholars

Chinese Academy of Sciences Youth Innovation Promotion Association

Publisher

Elsevier BV

Reference74 articles.

1. Short history and present trends of Fischer-Tropsch synthesis.;Schulz;Appl. Catal. A,1999

2. Chapter 1 - introduction to Fischer-Tropsch technology;Steynberg,2004

3. Research trends in Fischer-Tropsch catalysis for coal to liquids technology.;Hensen;FEM,2016

4. The renaissance of iron-based Fischer-Tropsch synthesis: on the multifaceted catalyst deactivation behaviour.;de Smit;Chem. Soc. Rev.,2008

5. Fischer–tropsch synthesis: a review of the effect of Co conversion on methane selectivity.;Yang;Appl. Catal. A,2014

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