Influence of polydispersity and breakage on stochastic simulations of spray fluidized bed agglomeration

Singh, Abhinandan Kumar; Tsotsas, Evangelos

doi:10.1016/j.ces.2021.117022

Cited by 12 publications

(21 citation statements)

References 49 publications

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“…Regardless of each agglomerate having a variable fractal dimension in the TSA model, generated agglomerates resembled the experimental agglomerates from [29]. This model was further adapted and extended in Singh and Tsotsas [27] to investigate the influence of polydisperse primary particles on SFB agglomeration. However, the fractal properties (D f and k) of agglomerates with a standard deviation of σ = 0.1R p (also corresponding to the experimental data) were not accurately retained.…”

Section: Introductionmentioning

confidence: 92%

“…The aggregation model is presented in detail in the following section. The breakage model was taken from [27].…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…The radius of gyration of a solid sphere (considered in the middle term of right-hand side of Equation ( 4)) is not equivalent to its physical radius as used in [10,23,24,27]. Equation ( 4) is the corrected correlation for generating any particle-cluster monodisperse aggregate.…”

Section: Fractal Tunable Aggregation Modelmentioning

confidence: 99%

“…A notable exception is an algorithm proposed by Filippov et al [23], which could preserve both fractal dimension and prefactor. This algorithm was further extended and adapted in various fields to investigate the morphology of fractal aggregates [10,[24][25][26][27][28]. Relatively compact SFB agglomerates were observed in the experiments [29].…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, the fractal tunable aggregation algorithm from Singh and Tsotsas [27] is improved to reconstruct SFB agglomerates comprising of polydisperse spherical primary particles. Morphological parameters of the agglomerates obtained by employing this tunable aggregation algorithm are validated by the experimental data.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

Singh

Tsotsas

2021

Energies

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Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.

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Section: Introductionmentioning

confidence: 92%

“…The aggregation model is presented in detail in the following section. The breakage model was taken from [27].…”

Section: Numerical Simulationsmentioning

confidence: 99%