2013
DOI: 10.1016/j.ces.2013.05.018
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A priori prediction of aggregation efficiency and rate constant for fluidized bed melt granulation

Abstract: This paper presents a predictive aggregation rate model for spray fluidized bed melt granulation. The aggregation rate constant was derived from probability analysis of particle-droplet contact combined with time scale analysis of droplet solidification and granule-granule collision rates. The latter was obtained using the principles of Kinetic Theory of Granular Flow (KTGF). The predicted aggregation rate constants were validated by comparison with reported experimental data for a range of binder spray rate, … Show more

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Cited by 19 publications
(15 citation statements)
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References 12 publications
(14 reference statements)
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“…In the present study ϵ is assumed to be constant; however it could depend on the pre-collisional velocity and the particle properties. The relative velocity is calculated basing on the granular temperature Θ of the flow as in Chua et al (2013):…”
Section: Derivation Of An Estimative Model For Collision Efficiencymentioning
confidence: 99%
See 1 more Smart Citation
“…In the present study ϵ is assumed to be constant; however it could depend on the pre-collisional velocity and the particle properties. The relative velocity is calculated basing on the granular temperature Θ of the flow as in Chua et al (2013):…”
Section: Derivation Of An Estimative Model For Collision Efficiencymentioning
confidence: 99%
“…Agarwal (2002) used this relation to estimate the efficiency of the doublet formation. Thielmann et al (2008) and Chua et al (2013) considered the fractional coverage as well as the viscous effects of the bridge in their expression for collision efficiency:…”
Section: Introductionmentioning
confidence: 99%
“…Several kernels have been listed in literature each with their own set of advantages (a well‐characterized summary of the different kernels is presented by Cameron et al). Correlations for aggregation kernel ( β ), both empirical and theoretical, have been put forth over the years, which include both size‐independent and size‐dependent descriptions . A proper representation of the kernel prefactor term β 0 that is size independent will enable us to model the system more efficiently, as it is this term that incorporates the various system parameters such as operating conditions and formulation properties into account .…”
Section: Introductionmentioning
confidence: 99%
“…Correlations for aggregation kernel (β), both empirical and theoretical, have been put forth over the years, which include both sizeindependent and size-dependent descriptions. [20][21][22] A proper representation of the kernel prefactor term β 0 that is size independent will enable us to model the system more efficiently, as it is this term that incorporates the various system parameters such as operating conditions and formulation properties into account. 22 When API and wet granulation operating conditions are the same, it is assumed that the term β 0 does not vary and thus in most simulations the term β 0 is assumed a constant and determined using experimental data fitting.…”
Section: Introductionmentioning
confidence: 99%
“…Effect of spray rate, droplet size, and operating temperature was investigated on PSD of substrate particles in a fluidized bed granulator using PEG 1500 as a binder. The PSD was modelled using 1D PBM and the hydrodynamics of fluidized bed was predicted using Computational Fluid Dynamics (CFD) modelling [7,8]. The aggregation efficiency of CaCO 3 particles in a high-shear granulator was estimated using PBM and particle image velocimetry.…”
Section: Introductionmentioning
confidence: 99%