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

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. Morpholo...

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Autores principales:	Abhinandan Kumar Singh, Evangelos Tsotsas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	agglomeration morphology Monte Carlo tunable aggregation model polydisperse primary particles Technology T
Acceso en línea:	https://doaj.org/article/d1feeeae4545491c9636b4433f082fbc
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id	oai:doaj.org-article:d1feeeae4545491c9636b4433f082fbc
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spelling	oai:doaj.org-article:d1feeeae4545491c9636b4433f082fbc2021-11-11T15:59:22ZA Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration10.3390/en142172211996-1073https://doaj.org/article/d1feeeae4545491c9636b4433f082fbc2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7221https://doaj.org/toc/1996-1073Agglomeration 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.Abhinandan Kumar SinghEvangelos TsotsasMDPI AGarticleagglomerationmorphologyMonte Carlotunable aggregation modelpolydisperse primary particlesTechnologyTENEnergies, Vol 14, Iss 7221, p 7221 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	agglomeration morphology Monte Carlo tunable aggregation model polydisperse primary particles Technology T
spellingShingle	agglomeration morphology Monte Carlo tunable aggregation model polydisperse primary particles Technology T Abhinandan Kumar Singh Evangelos Tsotsas A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
description	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.
format	article
author	Abhinandan Kumar Singh Evangelos Tsotsas
author_facet	Abhinandan Kumar Singh Evangelos Tsotsas
author_sort	Abhinandan Kumar Singh
title	A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
title_short	A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
title_full	A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
title_fullStr	A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
title_full_unstemmed	A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration
title_sort	fast and improved tunable aggregation model for stochastic simulation of spray fluidized bed agglomeration
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d1feeeae4545491c9636b4433f082fbc
work_keys_str_mv	AT abhinandankumarsingh afastandimprovedtunableaggregationmodelforstochasticsimulationofsprayfluidizedbedagglomeration AT evangelostsotsas afastandimprovedtunableaggregationmodelforstochasticsimulationofsprayfluidizedbedagglomeration AT abhinandankumarsingh fastandimprovedtunableaggregationmodelforstochasticsimulationofsprayfluidizedbedagglomeration AT evangelostsotsas fastandimprovedtunableaggregationmodelforstochasticsimulationofsprayfluidizedbedagglomeration
_version_	1718432441621807104

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

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