Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds-Reference-Cited by-同舟云学术

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Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds

Published:2024-05-01 Issue:19 Volume:63 Page:8819-8832
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Jiang Shuxian¹^ORCID,Wu Kaiqiao¹²,Francia Victor³,Ouyang Yi⁴^ORCID,Coppens Marc-Olivier¹^ORCID

Affiliation:

1. Centre for Nature-Inspired Engineering and Department of Chemical Engineering, University College London, London WC1E 6BT, United Kingdom

2. Department of Chemical Engineering, Guangdong University of Technology, Guangzhou 510006, China

3. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom

4. Laboratory for Chemical Technology, Ghent University, Ghent 9052, Belgium

Funder

Engineering and Physical Sciences Research Council

Synfuels China Co.

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00631

Reference51 articles.

1. A review on pulsed flow in gas-solid fluidized beds and spouted beds: Recent work and future outlook

2. Experimental study on the pressure wave attenuation across gas-solid fluidized bed by single bubble injection

3. Bubble Splitting in a Pseudo-2D Gas-Solid Fluidized Bed for Geldart B-Type Particles

4. Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

5. Bubble size and bubble rise velocity estimation by means of electrical capacitance tomography within gas-solids fluidized beds

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