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.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1021/acs.iecr.4c00631 |
| Publisher version: | http://dx.doi.org/10.1021/acs.iecr.4c00631 |
| Language: | English |
| Additional information: | 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/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10192050 |
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