Machine Learning and Data-Driven Modeling to Discover the Bed Expansion Ratio Correlation for Gas–Liquid–Solid Three-Phase Flows-Reference-Cited by-同舟云学术

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Machine Learning and Data-Driven Modeling to Discover the Bed Expansion Ratio Correlation for Gas–Liquid–Solid Three-Phase Flows

Published:2022-12-28 Issue:1 Volume:62 Page:789-800
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Xie Le¹^ORCID,Zhou Guangming¹,Wang Dongdong²,Wang Huaifa³,Jiang Chongwen¹^ORCID

Affiliation:

1. College of Chemistry and Chemical Engineering, Central South University, Changsha410083, Hunan, China

2. Civil Aviation University of China, Tianjin300300, China

3. College of Mining Engineering, Taiyuan University of Technology, Taiyuan030024, Shanxi, China

Funder

Natural Science Foundation of?Hunan Province

National Natural Science Foundation of China

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.2c03668

Reference67 articles.

1. (Gas)−Liquid−Solid Circulating Fluidized Bed Reactors: Characteristics and Applications

2. CFD analysis of hydrodynamic, heat transfer and reaction of three phase riser reactor

3. Applications of fluidized bed reactors in wastewater treatment – A review of the major design and operational parameters

4. Review of (gas)-liquid-solid circulating fluidized beds as biochemical and environmental reactors

5. Bubble size distributions in gas–liquid–solid systems and their influence on flotation separation in a bubble column

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