On flow regime transition in trickle bed: Development of a novel deep‐learning‐assisted image analysis method-Reference-Cited by-同舟云学术

On flow regime transition in trickle bed: Development of a novel deep‐learning‐assisted image analysis method

Published:2019-10-28 Issue:2 Volume:66 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J

Author:

Wang Chao¹,Li Shaoshuo¹,Yang Yao¹^ORCID,Huang Zhengliang¹^ORCID,Sun Jingyuan¹^ORCID,Wang Jingdai²^ORCID,Yang Yongrong²,Liao Zuwei¹^ORCID,Jiang Binbo¹

Affiliation:

1. College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology Zhejiang University Hangzhou China

2. College of Chemical and Biological Engineering, State Key Laboratory of Chemical Engineering Zhejiang University Hangzhou China

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16833

Reference50 articles.

1. Process intensification in trickle-bed reactors

2. Characterising gas behaviour during gas–liquid co-current up-flow in packed beds using magnetic resonance imaging

3. Hydrodynamic modeling on the external liquid-solid wetting efficiency in a trickling flow reactor

4. Effect of bed characteristics on local liquid spreading in a trickle bed

5. Hydrodynamics in a pilot‐scale cocurrent trickle‐bed reactor at low gas velocities

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