Fast versus turbulent fluidization of Geldart Group B particles

Abstract

Both fast and turbulent fluidized beds exhibit entrainment, but the differences in the flow phenomena are not well understood. This study targeted a comparative analysis of the cluster (or streamer), mass flux, and segregation datasets from these two fluidization regimes. The particle systems were narrow particle size distributions (PSDs), binary mixtures, or broad PSDs of Geldart Group B particles. Relative to the fast fluidized bed, the turbulent bed exhibited (i) higher cluster probability and frequency, but lower cluster duration; (ii) lower local mass flux; and (iii) similar segregation extents. Regarding clusters, the relative dominance of the variables on probability was similar for both regimes, but there was a difference for probability and frequency. For overall mass flux, particle-related properties were more dominant with the turbulent bed. As for segregation, the radial position was the most influential in the fast fluidized bed, but the least in the turbulent one.

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

“…203 In addition to bubble detection above, another increasingly popular trend is to detect particle and droplet characteristics by ML. 204,245 Li et al investigated the nonspherical biomass particles and spherical polyethylene particles in a lab-scale fluidized bed using PIV and PTV techniques. 205 The ML pixel-wise classification methodology was trained and used to acquire particle masks for PIV and PTV processing.…”

“…They presented a detailed prediction study of flow field parameters including the evaporated liquid vapor fraction, temperature, pressure, velocity, and spray penetration and Sauter mean diameters in the liquid phase with reasonable accuracy (ranging from 0.05 to 13.5%). Chew and co-workers 152,154,206,213,244,245 directed efforts toward a deep understanding and an enhanced prediction of fast gasparticle riser flow characteristics assisted by several common ML methods including ANN, RF, and self-organizing map (SOM).…”

“…To be easier and more flexible for users, some tools modules such as a fuzzy inference system have been thereby developed for bubble mask extraction with a friendly graphic user interface . In addition to bubble detection above, another increasingly popular trend is to detect particle and droplet characteristics by ML. , Li et al investigated the nonspherical biomass particles and spherical polyethylene particles in a lab-scale fluidized bed using PIV and PTV techniques . The ML pixel-wise classification methodology was trained and used to acquire particle masks for PIV and PTV processing.…”

“…They presented a detailed prediction study of flow field parameters including the evaporated liquid vapor fraction, temperature, pressure, velocity, and spray penetration and Sauter mean diameters in the liquid phase with reasonable accuracy (ranging from 0.05 to 13.5%). Chew and co-workers ,,,,, directed efforts toward a deep understanding and an enhanced prediction of fast gas-particle riser flow characteristics assisted by several common ML methods including ANN, RF, and self-organizing map (SOM). In their essential study on the determination of the relative dominance of the riser flow parameters, it was suggested that the radial position is of key importance to the local solids flux and elutriation, and the overall solids flux has the most dominant effect on the local solids hold-up.…”

Application of Machine Learning-Based Models to Understand and Predict Critical Flux of Oil-in-Water Emulsion in Crossflow Microfiltration

“…21,29,34 RF is also considered a "black box" model, as it does not need the governing equations of a particular phenomenon to derive its prediction output and can be used to do a classification or regression. The RF method provides a unique advantage in dealing with complex data sets and predicting the relative importance value of different parameters; 24,35 bagging or bootstrap aggregation method and categorized as the in-bag samples. Some other subsets are separated as out-ofbag data that are used during the validation step.…”