Effect of polydispersity on bubble characteristics of Geldart Group B particles

In order to enhance the understanding of the influences of bubble characteristics in bubbling fluidized beds of Gaussian and lognormal particle size distributions (PSDs) of Geldart Group B particles, machine learning tools were harnessed. The PSDs had the same Sauter-mean diameter and widths (i.e.,...

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Main Authors: Chew, Jia Wei, Cocco, Ray A.
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160433
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1604332022-07-22T02:46:12Z Effect of polydispersity on bubble characteristics of Geldart Group B particles Chew, Jia Wei Cocco, Ray A. School of Chemical and Biomedical Engineering Nanyang Environment and Water Research Institute Singapore Membrane Technology Center Engineering::Chemical engineering Bubbling Fluidized Bed Meso-Scale In order to enhance the understanding of the influences of bubble characteristics in bubbling fluidized beds of Gaussian and lognormal particle size distributions (PSDs) of Geldart Group B particles, machine learning tools were harnessed. The PSDs had the same Sauter-mean diameter and widths (i.e., ratio of standard deviation to Sauter-mean diameter) of between 10 and 30% and 10 – 70%, respectively. Self-organizing maps (SOMs) analysis of more than a thousand data rows each of Gaussian and lognormal PSD data indicate that bubble velocity, frequency, length, and probability are highly correlated. The optimal number of data assemblies that either the Gaussian or lognormal dataset can be divided into per the Calinski-Harabasz criterion was determined to be three, and it was found that the critical parameter that underlies the demarcation of the datasets was PSD width. This agrees with an earlier study on clusters, wherein the non-monodispersity of the particle systems was also responsible for the division of the dataset into distinct data assemblies. Furthermore, the number-based frequency of the particle species was better correlated with the bubbles than the mass-based one. The key highlight is the predominant influence of PSD width in demarcating the datasets into distinct data assemblies, which underscores the need to account for the polydispersity of particle systems and provides valuable insights towards model development. Ministry of Education (MOE) We acknowledge funding from the Singapore Ministry of Education Tier 1 Grant (2019-T1-002-065; RG100/19). 2022-07-22T02:46:12Z 2022-07-22T02:46:12Z 2021 Journal Article Chew, J. W. & Cocco, R. A. (2021). Effect of polydispersity on bubble characteristics of Geldart Group B particles. Chemical Engineering Journal, 420(Part 1), 129880-. https://dx.doi.org/10.1016/j.cej.2021.129880 1385-8947 https://hdl.handle.net/10356/160433 10.1016/j.cej.2021.129880 2-s2.0-85104641001 Part 1 420 129880 en 2019-T1-002-065 RG100/19 Chemical Engineering Journal © 2021 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Chemical engineering
Bubbling Fluidized Bed
Meso-Scale
spellingShingle Engineering::Chemical engineering
Bubbling Fluidized Bed
Meso-Scale
Chew, Jia Wei
Cocco, Ray A.
Effect of polydispersity on bubble characteristics of Geldart Group B particles
description In order to enhance the understanding of the influences of bubble characteristics in bubbling fluidized beds of Gaussian and lognormal particle size distributions (PSDs) of Geldart Group B particles, machine learning tools were harnessed. The PSDs had the same Sauter-mean diameter and widths (i.e., ratio of standard deviation to Sauter-mean diameter) of between 10 and 30% and 10 – 70%, respectively. Self-organizing maps (SOMs) analysis of more than a thousand data rows each of Gaussian and lognormal PSD data indicate that bubble velocity, frequency, length, and probability are highly correlated. The optimal number of data assemblies that either the Gaussian or lognormal dataset can be divided into per the Calinski-Harabasz criterion was determined to be three, and it was found that the critical parameter that underlies the demarcation of the datasets was PSD width. This agrees with an earlier study on clusters, wherein the non-monodispersity of the particle systems was also responsible for the division of the dataset into distinct data assemblies. Furthermore, the number-based frequency of the particle species was better correlated with the bubbles than the mass-based one. The key highlight is the predominant influence of PSD width in demarcating the datasets into distinct data assemblies, which underscores the need to account for the polydispersity of particle systems and provides valuable insights towards model development.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Chew, Jia Wei
Cocco, Ray A.
format Article
author Chew, Jia Wei
Cocco, Ray A.
author_sort Chew, Jia Wei
title Effect of polydispersity on bubble characteristics of Geldart Group B particles
title_short Effect of polydispersity on bubble characteristics of Geldart Group B particles
title_full Effect of polydispersity on bubble characteristics of Geldart Group B particles
title_fullStr Effect of polydispersity on bubble characteristics of Geldart Group B particles
title_full_unstemmed Effect of polydispersity on bubble characteristics of Geldart Group B particles
title_sort effect of polydispersity on bubble characteristics of geldart group b particles
publishDate 2022
url https://hdl.handle.net/10356/160433
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