@article{discovery10192050,
            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,
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            year = {2024},
           title = {Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas-Solid Fluidized Beds},
           month = {May},
         journal = {Industrial \& Engineering Chemistry Research},
       publisher = {American Chemical Society (ACS)},
          author = {Jiang, Shuxian and Wu, Kaiqiao and Francia, Victor and Ouyang, Yi and Coppens, Marc-Olivier},
             url = {http://dx.doi.org/10.1021/acs.iecr.4c00631},
        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.}
}