%0 Journal Article
%A Jiang, Shuxian
%A Wu, Kaiqiao
%A Francia, Victor
%A Ouyang, Yi
%A Coppens, Marc-Olivier
%D 2024
%F discovery:10192050
%I American Chemical Society (ACS)
%J Industrial & Engineering Chemistry Research
%T Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds
%U https://discovery.ucl.ac.uk/id/eprint/10192050/
%X 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.
%Z 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/