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

Abhinandan Kumar Singh,Evangelos Tsotsas

doi:10.3390/en14217221

Abstract

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.

Highlights

The comprehensive morphological characterization of agglomerates is becoming increasingly important in the chemical, food, and pharmaceutical industries, as well as in research
The sequential tunable algorithm developed in previous studies to generate monodisperse spray fluidized bed (SFB) agglomerates is improved and extended to polydisperse primary particles
Agglomeration in a spray fluidized bed (SFB) is a particle growth process that enhances the physical properties of small particles [2]

Summary

Introduction

The comprehensive morphological characterization of agglomerates is becoming increasingly important in the chemical, food, and pharmaceutical industries, as well as in research. Compact SFB agglomerates were observed in the experiments [29] These SFB agglomerates with a combination of large prefactor k, with large fractal dimension D f (cf Table A1) cannot be reconstructed using the original versions of the algorithms presented in the past. 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. The fractal tunable aggregation algorithm from Singh and Tsotsas [27] is improved to reconstruct SFB agglomerates comprising of polydisperse spherical primary particles. This aggregation algorithm is integrated into the MC simulation scheme to evaluate both, formation kinetics and morphology of SFB agglomerates produced under several operating parameters (binder concentration and inlet fluidized gas temperature)

Numerical Simulations

CVMC Model

Findings

Conclusions