Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts-Reference-Cited by-同舟云学术

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Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts

Published:2023-11-20 Issue:48 Volume:62 Page:20658-20666
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Motaev Kirill¹,Molokeev Maxim¹^ORCID,Sultanov Bulat²,Kharitontsev Vladimir¹,Matigorov Alexey¹,Palianov Mikhail¹,Azarapin Nikita¹³,Elyshev Andrey¹²

Affiliation:

1. Laboratory of Theory and Optimization of Chemical and Technological Processes, University of Tyumen, 625003 Tyumen, Russia

2. Laboratory of Digital Catalysis of Centre for Nature-Inspired Engineering, University of Tyumen, 625003 Tyumen, Russia

3. Institute of Chemistry, University of Tyumen, 625003 Tyumen, Russia

Funder

Tyumen region

University of Tyumen

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.3c03147

Reference57 articles.

1. Study of innovative technologies in the energy industry: nontraditional and renewable energy sources

2. Economically attractive technologies of deep conversion of associated petroleum gas

3. Utilization of associated petroleum gas via small-scale power generation

4. Recent advances in Wacker oxidation: from conventional to modern variants and applications

5. Effect of Zeolite Topology and Reactor Configuration on the Direct Conversion of CO2 to Light Olefins and Aromatics

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1. Toward accelerated discovery of solid catalysts using extrapolative machine learning approach;Chemistry Letters;2024-07-31

2. Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy;Journal of Hazardous Materials;2024-06

3. Parametric analysis of CO2 hydrogenation via Fischer-Tropsch synthesis: A review based on machine learning for quantitative assessment;International Journal of Hydrogen Energy;2024-03

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