Managing solid loss rates from a fluidized bed is a critical aspect of design and operation, particularly in catalytic processes where annualized losses can add up to millions of dollars in a suboptimal system.1 Effective sizing of the cyclones and diplegs associated with the return of entrained solids is crucial but relies on having a reliable estimate of particle size and loading in the disengagement zone/freeboard above the bed. Conventionally, particles in a fluidized bed are assumed to behave independently, once suspended, but particle clustering has long been recognized as a phenomenon with significant potential to affect entrainment behavior.2-⁴ A better understanding of the impact of particle clustering, and of the factors that affect it, is becoming increasingly important to support the growing use of computational fluid dynamics (CFD) to tackle fluidized bed challenges.

Fluid catalytic cracking (FCC), the conversion of high-boiling, high-molecular weight hydrocarbon fractions to gasoline, olefin gases and other valuable products, is one of the most commercially important applications of fluidized bed technology. Currently around 350 FCC units are operating worldwide with a total processing capacity of more than 14.7 million barrels per day; typical catalyst circulation rates are in excess of 50,000 metric tons of catalyst per day. ⁵ Controlling fluidization behavior and managing solids losses in both the FCC and the associated catalyst regenerator, is crucial for beneficial economic performance. 

In this study, the dynamic flow, shear and bulk properties of FCC catalysts with different fines content were characterized using an FT4 Powder Rheometer from Freeman Technology with the aim of developing insight into particle clustering behavior. A key goal was to use dynamic powder testing to investigate how fines influence the characteristics of a powder bed and fluidization performance, to elucidate the potential impact of particle clustering.

Understanding fluidized bed processes

In a fluidized bed, particles are suspended in an upward flowing stream of fluid — a gas, vapor or liquid — to form a highly mobile mixture. Particles vary significantly in terms of their response to fluidization, behavior notably classified by the Geldart groupings, which are used extensively in fluidized bed design.⁶ This classification of fluidization behavior divides powders into four groups — A through D — which differ primarily in terms of particle size and density. Most FCC catalysts fall into group A, which includes particles with a size range of 20 to 100 µm and a density less than 1.4 g/cc.⁶, ⁷ A typical particle size distribution for FCC catalysts ranges from 10 to 150 µm while density tends to be less than 1g/cc (0.80 to 0.96 g/cc) .⁵,, ⁹ Group A powder beds typically exhibit significant expansion when fluidized, reducing substantially in bulk density, before transitioning to a steady bubbling bed state.2, 

Figure 1. Bulk property data for the three catalyst samples shows that the Medium Fines sample is more permeable and less compressible than would be expected from the remaining data, suggesting it results in a very efficiently packed powder bed. All graphics courtesy of Freeman Technology


Solids losses are a result of the entrainment of powder from the fluidized bed, which is directly influenced by gas velocity, and by particle properties such as size, shape and surface morphology. Particle clustering has the potential to reduce the rate of entrainment at a given gas velocity, by effectively increasing particle size, and is a behavior particularly associated with Group A powders, a finding attributed to the fact that powders in this group contain relatively high levels of fines.⁴, 10 Solids losses can also be influenced by particle attrition, the breakdown of primary catalyst particles by collisions and/or the shear force associated with fluidization. Higher levels of fines in the fresh catalyst, or as a result of attrition, might be expected to increase entrainment, but in fact the opposite has been observed, an effect similarly attributed to the propensity of finer particles to form clusters. Clusters tend to consist of several smaller particles adhering to just one or two larger particles but are also prone to break down above a certain critical size.3, 11, 13 These multiple, competing mechanisms that influence particle characteristics within the bed may help to explain the variability seen in predictions of entrainment rate,1, 1⁵ variability that directly impacts the optimization of solids management equipment. 

Particle clusters have been observed in the freeboard of fluidized beds containing FCC catalysts but the electrostatic and physical mechanisms that drive their formation are not yet fully resolved. There is evidence that cluster formation is influenced by the level of fines in the bed⁴, 1, 1⁶ but it is still unclear:

  • Where clusters form. Solids concentration in the freeboard is low, suggesting that clusters are more likely to form in the bed itself.
  • If clusters form in the bed, then whether their formation is limited to the region around and below bubbles, in which case clustering would just affect entrainment rates, or whether the process occurs throughout the bed, thereby also impacting its hydrodynamics.
  • How fines affect cluster formation and associated fluidization behavior.

Figure 2. Cohesion and specific energy data for the three FCC samples suggest that in the Medium Fines sample the level of fines may be sufficient to have a smoothing effect on the particle surface that lubricates particle-particle interactions.


Historically, fluidized bed processes have largely been developed and scaled up using empirical models developed from experimental studies, which inherently account for particle clustering. Increasingly though, experimental work is giving way to the application of CFD models that have the potential to be more cost-effective for scale-up and optimization studies. A better understanding of particle clustering is essential for the development of CFD models that are robustly representative of process behavior. The application of powder characterization methods that can quantify properties that directly relate to fluidization behavior has the potential to provide insight into particle clustering that can directly support these efforts.

Dynamic powder testing: A tool for the investigation of fluidization behavior

Dynamic powder testing quantifies the flow properties of a powder from measurements of the torque and force acting on a helical blade as it rotates along a prescribed path through a powder sample. Basic flowability energy (BFE) is a primary dynamic flow property, generated during a downward traverse of the blade that exerts a compressive action on the powder, forcing it down against the base of the sample vessel. Conversely, specific energy (SE) is measured using an upward traverse of the blade and more closely reflects the flow properties of a powder when it is flowing under gravity, in an unconfined environment. The repeatability of dynamic flow measurements is enhanced by the use of a conditioning cycle, which gently displaces the powder bed prior to measurement, removing the packing history and any operator-induced variability. High repeatability and the inherent sensitivity of the technique combine to make dynamic flow data highly differentiating.

Equally importantly, dynamic powder flow testing enables tailoring of the test environment to simulate a specific process, including fluidization. The introduction of a controllable flow of air through the base of the sample vessel during BFE measurement enables the characterization of aerated powders up to the point of fluidization. Aerated energy (AE), the flow energy of a powder at a specific air velocity, quantifies how resistance to flow changes with the addition of air. This capability makes dynamic powder testing uniquely valuable for investigating fluidized bed behavior.  

Figure 3. BFE and Consolidation Index data indicate a clear trend with fines level that is consistent with clustering increasing with increases in fines content.

Experimental study: Investigating the impact of fines on FCC catalyst behavior

The dynamic flow, bulk and shear properties of three samples of FCC catalyst powder were measured using an FT4 Powder Rheometer. The three samples differed in terms of fines concentration, percent of material less than 44 µm, and were designated Low Fines (3 percent fines), Medium Fines (12 percent fines) and High Fines (100 percent fines). All tests were carried out in accordance with the standard protocols for the instrument, in duplicate.1, 1

Fine particles have a high specific surface area, relative to coarser analogs, and are associated with correspondingly higher inter-particular forces and inefficient packing behavior; powders with fine particles have a tendency to trap entrained air and exhibit low bulk density. The High Fines sample exemplifies this behavior. It is far less permeable than the other two samples (see Figure 1 — left), higher inter-particular forces creating greater resistance to the passage of air, and exhibits the highest compressibility (see Figure 1 – right), the application of a compaction stress breaking down the weak structure within the bed, forcing out entrained air and significantly increasing bulk density.  

The permeability of the Low and Medium Fines samples is closely similar, with the Low Fines sample exhibiting just marginally higher permeability. Compressibility data helps to provide a rationale for these results. The Medium Fines sample exhibits the lowest compressibility suggesting optimal packing within the uncompacted powder sample. This unexpected trend in compressibility is mirrored by the Cohesion and SE data (see Figure 2). The attachment of fine particles on to a coarser host, as in the formation of clusters, can have various effects depending on the characteristics of the particles involved. At certain concentrations, for example, fines can act as a flow aid/lubricant, coating particles and “smoothing” the surface texture, thereby reducing friction and the strength of cohesive bonds. The shear data suggests that the concentration of fines in the Medium Fines sample may be more optimal from this perspective than in the Low Fines sample, enabling the formation of a more efficiently packed bed that resists compaction, presents little resistance to air and has relatively low cohesion. 

In the BFE data, a clear trend is evident with the level of fines in the sample (see Figure 3 — left). With higher levels of fines the energy required to move the sample reduces, a trend consistent with a steadily increasing level of clustering. A rationale for this observation is that increasing levels of clusters in the bed are associated with increased air entrainment, an effect that dampens/absorbs the force associated with movement of the blade through the powder bed, thereby reducing BFE. This hypothesis is supported by the Consolidation Index data (see Figure 3 — right), which shows that when the samples are subjected to vibrational consolidation (tapping), there is a clear trend in the change in flow energy. These data suggest that tapping breaks down the weak clusters and therefore has a more pronounced effect on samples in which clustering is higher.


Figure 4. Above the minimum fluidization velocity, AE increases with increasing fines content and does not appear to be markedly affected by bubbling, suggesting that clusters originate within the bed rather than at the bubble interface.


The AE data for the three samples (see Figure 4) reveals the minimum fluidization velocity associated with each, which is 0.002 m/s (2 mm/s) for the High Fines sample and 0.004 m/s (4 mm/s) for the Medium and Low Fines samples. These values are consistent with experimental measurements based on ASTM 7743.1⁹ With Group A powders, the onset of fluidization and bubbling are not necessarily coincidental, with bubbling typically establishing at a higher superficial gas velocity. Once bubbling has established, further increases in gas velocity are not associated with further changes in flow behavior. At air flow rates above the minimum fluidization velocity, there is little difference in the behavior of the three materials although AE is slightly but consistently higher for the High Fines sample. By separating the particles, aeration reduces the effect of particle shape and surface roughness on particle-particle interaction, so this observation is consistent with the size or number of clusters increasing with fines level. However, this hypothesis cannot be securely drawn without some measurement of actual cluster level within the bed. 

In the absence of a marked change in behavior with the onset of bubbling, which occurs at velocities above the minimum fluidization velocity, it seems likely that clusters form within the bed rather than at the bubble interface. This conclusion can also be drawn from the potential effect of clustering behavior on trends observed in BFE and bulk properties. 


Managing solids losses is a critical aspect of fluidized bed operation that relies on having a robust understanding of particle attrition and clustering within the fluidized bed. In this study, dynamic powder flow testing has been applied to three FCC catalysts to determine whether the resulting data could provide insight into particle clustering. Correlations observed between the dynamic powder flow properties of BFE and AE and the level of fines suggest that the technique may be helpful in providing parameters that can be used to more effectively account for particle clustering in CFD models thereby enhancing their value in tackling fluidized bed challenges.  


  1. Cocco, R., et al., Small-Scale Particle Interactions Are Having Significant Effects on Global Fluidized Bed Behavior. KONA Powder and Particle Journal, 2017. 34: p. 155-167.
  2. Geldart, D. and A.C.Y. Wong, Entrainment of particles from fluidized beds of bed of fine powder. AIChE Symp. Series, 1987. 255(83): p. 1.
  3. Baeyens, J., D. Geldart, and S.Y. Wu, Elutriation of fines from gas fluidized beds of Geldart A-type powders — effect of adding superfines. Powder Technology, 1992. 71(1): p. 71-80.
  4. Cocco, R., et al., Particle clusters in and above fluidized beds. Powder Technology, 2010. 203(1): p. 3-11.
  5. Gary, J.H. and G.E. Handwerk, Petroleum Refining. 2001: Taylor & Francis.
  6. Geldart, D., The expansion of bubbling fluidised beds. Powder Technology, 1968. 1(6): p. 355-368.
  7. Geldart, D., et al. The effect of the size of the fines on the fludization behaviour of equlibrium cracking catalyst. in Fluidization VI: Proceedings of the International Conference on Fluidization. 1989. Engineering Foundation, 1989.
  8. Sadeghbeigi, R., Fluid Catalytic Cracking Handbook: An Expert Guide to the Practical Operation, Design, and Optimization of FCC Units. 2012: Butterworth-Heinemann.
  9. Vogt, E.T.C. and B.M. Weckhuysen, Fluid catalytic cracking: recent developments on the grand old lady of zeolite catalysis. Chemical Society Reviews, 2015. 44(20): p. 7342-7370.
  10. Kaye, B. and R.P. Boardman. Cluster Formation in Dilute Suspnsion. in Symposium on the Interaction Between Fluids and Particles. 1962. London: Institution of Chemical Engineers.
  11. Subbarao, D., A model for cluster size in risers. Powder Technology, 2010. 199(1): p. 48-54.
  12. Fortes, A.F., D.D. Joseph, and T.S. Lundgren, Nonlinear mechanics of fluidization of beds of spherical particles. Journal of Fluid Mechanics, 2006. 177: p. 467-483.
  13. Jayaweera, K.O.L.F., B.J. Mason, and G.W. Slack, The behaviour of clusters of spheres falling in a viscous fluid Part 1. Experiment. Journal of Fluid Mechanics, 2006. 20(1): p. 121-128.
  14. Chew, J.W., et al., Review of entrainment correlations in gas–solid fluidization. Chemical Engineering Journal, 2015. 260: p. 152-171.
  15. Yang, N., et al., Simulation of Heterogeneous Structure in a Circulating Fluidized-Bed Riser by Combining the Two-Fluid Model with the EMMS Approach. Industrial & Engineering Chemistry Research, 2004. 43(18): p. 5548-5561.
  16. Hays, R., et al. Small Particles Cluster Formation in Fluidized Beds and its Effect on Entrainment. in Circulating Fluidized Bed IX. 2008. Hamburg, Germany: TuTech Innovation GmbH.
  17. Freeman, R., Measuring the flow properties of consolidated, conditioned and aerated powders — A comparative study using a powder rheometer and a rotational shear cell. Powder Technology, 2007. 174(1–2): p. 25-33.
  18. Freeman, T., Characterizing Powder Flow, in Chemical Engineering. 2011, Access Intelligence: Online. p. 68-72.

ASTM International, ASTM D7743-12, Standard Test Method for Measuring the Minimum Fluidization Velocities of Free Flowing Powders, in D18.24 Characterization and Handling of Powders and Bulk Solids. 2008, ASTM International: West Conshohocken, PA.


Jamie Clayton is operations director at powder characterization company Freeman Technology, and is based at the company’s headquarters in Tewkesbury, U.K. He graduated from the University of Sheffield with a degree in control engineering and is responsible for all daily activities of the company, including overall management of the administration, production, R&D, sales and customer support teams. Clayton also works with the company’s clients to provide application-based support. He may be reached at



Dr. Ray Cocco has been with Particulate Solid Research Inc. (PSRI) for 12 years and is currently president and CEO. Before PSRI, he spent 17 years with The Dow Chemical Company where he led research and development efforts in numerous particle technology platforms. Cocco was instrumental in bring in computational fluid dynamics, Six Sigma and Design for Six Sigma methodologies into Dow’s research environment. Today, he is a board member of the University of Florida Chemical Engineering Advisory Board, Auburn University Chemical Engineering Advisory Board, University College at London’s CNIE. He has 55 publications, three book chapters, several patents, and holds a Bachelor of Science degree in chemical engineering (University of Florida) and a doctorate degree (Auburn University).