Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids--颗粒学报PARTICUOLOGY

Zhang, Y., Liu, J. J., Zhang, L., De Anda, J. C., & Wang, X. Z. (2016). Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids. Particuology, 24, 61-68. https://doi.org/10.1016/j.partic.2014.12.012

Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids

Yang Zhang ^{a b}, Jing J. Liu ^a, Lei Zhang ^b, Jorge Calderon De Anda ^c, Xue Z. Wang ^{a c *}

a School of Chemistry and Chemical Engineering, South China University of Technology, 381 Wushan Road, Guangzhou 510641, China
b School of Bioscience and Bioengineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Panyu District, Guangzhou 510006, China
c Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK

10.1016/j.partic.2014.12.012

Volume 24, February 2016, Pages 61-68

Received 3 October 2014, Revised 21 November 2014, Accepted 4 December 2014, Available online 6 June 2015, Version of Record 21 January 2016.

E-mail: xuezhongwang@scut.edu.cn; x.z.wang@leeds.ac.uk

Highlights

• Automated microscopic imaging instrument was applied to measure polymorphic particle shape.

• Shape descriptors of physical meanings and based on Fourier transform and PCA were examined.

• A new method for calculating shape descriptors was proposed.

• The new shape descriptors proved to be able to effectively identify batch-to-batch variations.

Abstract

It is known that size alone, which is often defined as the volume-equivalent diameter, is not sufficient to characterize many particulate products. The shape of crystalline products can be as important as size in many applications. Traditionally, particulate shape is often defined by several simple descriptors such as the maximum length and the aspect ratio. Although these descriptors are intuitive, they result in a loss of information about the original shape. This paper presents a method to use principal component analysis to derive simple latent shape descriptors from microscope images of particulate products made in batch processes, and the use of these descriptors to identify batch-to-batch variations. Data from batch runs of both a laboratory crystalliser and an industrial crystallisation reactor are analysed using the described approach. Qualitative and quantitative comparisons with the use of traditional shape descriptors that have physical meanings and Fourier shape descriptors are also made.

Graphical abstract

Keywords

Batch-to-batch variation; Classification; Principal components analysis; Shape descriptors

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