Zhang, Y, Liu, JJ, Zhang, L et al. (2 more authors) (2016) Particle Shape Characterisation and Classification using Automated Microscopy and Shape Descriptors in Batch Manufacture of Particulate Solids. Particuology, 24. pp. 61-68. ISSN 1674-2001
Abstract
It is known that size alone, as often defined as the volume equivalent diameter, is not sufficient for characterizing many particulate products. The shape of crystalline products can be as important as size in many applications. Traditionally particulate shape is often defined by some simple descriptors such as the maximum length and aspect ratio. Although these descriptors are intuitive, they result in loss of some information of the original shape. This paper presents a method to use principal component analysis (PCA) to derive simple latent shape descriptors from microscope images of particulate products made in batch processes, and the use of the descriptors for identification of batch to batch variations. Data from batch runs of both a laboratory crystalliser and an industrial crystallisation reactor are analysed using the approach. Qualitative and quantitative comparison with the use of traditional shape descriptors that have physical meanings and Fourier shape descriptors is also made.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. This is an author produced version of a paper published in Particuology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | batch to batch variation; classification; principal components analysis; shape descriptors |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) > Institute for Particle Science and Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Oct 2015 13:17 |
Last Modified: | 13 Jun 2016 01:31 |
Published Version: | http://dx.doi.org/10.1016/j.partic.2014.12.012 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.partic.2014.12.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82622 |