eprintid: 10192050
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/19/20/50
datestamp: 2024-05-10 11:23:10
lastmod: 2024-05-10 11:23:10
status_changed: 2024-05-10 11:23:10
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Jiang, Shuxian
creators_name: Wu, Kaiqiao
creators_name: Francia, Victor
creators_name: Ouyang, Yi
creators_name: Coppens, Marc-Olivier
title: Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F43
note: This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third-party material in this article are included in the Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/
abstract: This study introduces a machine learning (ML)-
assisted image segmentation method for automatic bubble
identification in gas−solid quasi-2D fluidized beds, offering
enhanced accuracy in bubble recognition. Binary images are
segmented by the ML method, and an in-house Lagrangian
tracking technique is developed to track bubble evolution. The MLassisted
segmentation method requires few training data, achieves
an accuracy of 98.75%, and allows for filtering out common sources
of uncertainty in hydrodynamics, such as varying illumination
conditions and out-of-focus regions, thus providing an efficient tool to study bubbling in a standard, consistent, and repeatable
manner. In this work, the ML-assisted methodology is tested in a particularly challenging case: structured oscillating fluidized beds,
where the spatial and time evolution of the bubble position, velocity, and shape are characteristics of the nucleation-propagationrupture
cycle. The new method is validated across various operational conditions and particle sizes, demonstrating versatility and
effectiveness. It shows the ability to capture challenging bubbling dynamics and subtle changes in velocity and size distributions
observed in beds of varying particle size. New characteristic features of oscillating beds are identified, including the effect of
frequency and particle size on the bubble morphology, aspect, and shape factors and their relationship with the stability of the flow,
quantified through the rate of coalescence and splitting events. This type of combination of classic analysis with the application of
the ML assisted techniques provides a powerful tool to improve standardization and address the reproducibility of hydrodynamic
studies, with the potential to be extended from gas−solid fluidization to other multiphase flow systems.
date: 2024-05-01
date_type: published
publisher: American Chemical Society (ACS)
official_url: http://dx.doi.org/10.1021/acs.iecr.4c00631
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2273738
doi: 10.1021/acs.iecr.4c00631
lyricists_name: Coppens, Marc-Olivier
lyricists_id: MCOPP36
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Industrial & Engineering Chemistry Research
citation:        Jiang, Shuxian;    Wu, Kaiqiao;    Francia, Victor;    Ouyang, Yi;    Coppens, Marc-Olivier;      (2024)    Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds.                   Industrial & Engineering Chemistry Research        10.1021/acs.iecr.4c00631 <https://doi.org/10.1021/acs.iecr.4c00631>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192050/1/jiang-et-al-2024-machine-learning-assisted-experimental-characterization-of-bubble-dynamics-in-gas-solid-fluidized-beds.pdf