Multi-scale experimental investigation on the fluidization of Geldart B 13X zeolite particles: A comprehensive dataset for CFD validation

Mingming Ge*, Hang Zhou, Avinash Vaidheeswaran*, William Rogers, Dixia Fan*, Cheng Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The fluidization of zeolite 13X in a bubbling fluidized bed was experimentally investigated. The objective is to analyze the fluidization behavior of particles in a lab-scale system with a total of 1.5 million particles, enabling accurate characterization of uncertainties. Validation datasets were generated for the fluidization process in a bench-scale rectangular unit. A comprehensive characterization of the particle properties, including size, density, coefficient of friction, coefficient of restitution, and minimum fluidization velocity, is presented using advanced diagnostic tools. Corresponding fluidized bed measurements, such as pressure drop and bed expansion, are reported under three test settings with different flow rates. The tested particles have a Sauter mean diameter of 1,345 μm and a density of 2,054 kg/m3. It is classified in Geldart group B for its vigorous bubbling and good mixing behavior. With increasing the superficial inlet velocity from 0.347 to 0.740 m/s corresponding to 1.41–3.01 Umf, where Umf = 0.2458 m/s is the minimum fluidization velocity, the particles tend to transit from bubbling flow to slug/plug flow regimes. Using high-speed imaging, bubble sizes and velocities in the pseudo-two-dimensional (2D) fluidized bed could be obtained directly. The data acquisition rate is 1200 fps, far above the quantities of interest (QoI) dominant frequency (typically below 10 Hz). The QoI obtained from this study were statistics of interface height, differential pressure, and particle velocities using particle image velocimetry. The results confirm consistency given the use of randomization and repeat in the run order as well as multiple batches of bed material. This experimental dataset can serve as a benchmark for validating numerical models and as a blind study for the multiphase flow modeling community.
Original languageEnglish
JournalChemical Engineering Journal
StatePublished - 1 Sep 2023


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