Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids
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...
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Veröffentlicht in: | Particuology 2016-02, Vol.24 (1), p.61-68 |
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creator | Zhang, Yang Liu, Jing J. Zhang, Lei De Anda, Jorge Calderon Wang, Xue Z. |
description | 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 nhwical meanings and Fourier shape descriptors are also made. |
doi_str_mv | 10.1016/j.partic.2014.12.012 |
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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. 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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 nhwical meanings and Fourier shape descriptors are also made.</description><subject>Batch-to-batch variation</subject><subject>Classification</subject><subject>Crystal structure</subject><subject>Crystallization</subject><subject>Fourier analysis</subject><subject>Microscopes</subject><subject>Microscopy</subject><subject>Principal component analysis</subject><subject>Principal components analysis</subject><subject>Reactors</subject><subject>Shape descriptors</subject><subject>产品设计</subject><subject>工业通用技术</subject><subject>粉末技术</subject><subject>颗粒学</subject><issn>1674-2001</issn><issn>2210-4291</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqFkM1u1TAQhS0EEpe2b9CFxYpNwvgnTrJBQhUFpEqwgLXlTCa9vkri1HaQ-hI8c3Obii2rkUbnfDPnMHYtoBQgzMdTubiYPZYShC6FLEHIV-wgpYBCy1a8Zgdhal1IAPGWvUvpBGCkkerA_v58do7E09EtxPHoosNM0SeXfZi5m3uOo0vJDx731Zr8fM_dmsPkMvV88hhDwrA8Pqt3UE8Jo19yiIn7mXcu45FPbl6HDb9G4mHg-9fruFF4CqPv0yV7M7gx0dXLvGC_b7_8uvlW3P34-v3m812BupG5aOvKtb0yDSit2soNTY0DGuEQKwBJrmuqTjRA2oAeTN-1NfQGOqwQpJKkLtiHnbvE8LBSynbyCWkc3UxhTVbUrZK6rbXapHqXnkOmSINdop9cfLQC7Ll-e7J7EHuu3wppt_o326fdRluMP56iTehpRup9JMy2D_5_gPcvd49hvn_YKv932JjtPV2JRj0Be5ufgQ</recordid><startdate>20160201</startdate><enddate>20160201</enddate><creator>Zhang, Yang</creator><creator>Liu, Jing J.</creator><creator>Zhang, Lei</creator><creator>De Anda, Jorge Calderon</creator><creator>Wang, Xue Z.</creator><general>Elsevier B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7TB</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>KR7</scope></search><sort><creationdate>20160201</creationdate><title>Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids</title><author>Zhang, Yang ; Liu, Jing J. ; Zhang, Lei ; De Anda, Jorge Calderon ; Wang, Xue Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c482t-975a9d368034395af87cfc61acc5002eab85b180e4604f6db970d60bc5c0232e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Batch-to-batch variation</topic><topic>Classification</topic><topic>Crystal structure</topic><topic>Crystallization</topic><topic>Fourier analysis</topic><topic>Microscopes</topic><topic>Microscopy</topic><topic>Principal component analysis</topic><topic>Principal components analysis</topic><topic>Reactors</topic><topic>Shape descriptors</topic><topic>产品设计</topic><topic>工业通用技术</topic><topic>粉末技术</topic><topic>颗粒学</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Yang</creatorcontrib><creatorcontrib>Liu, Jing J.</creatorcontrib><creatorcontrib>Zhang, Lei</creatorcontrib><creatorcontrib>De Anda, Jorge Calderon</creatorcontrib><creatorcontrib>Wang, Xue Z.</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Particuology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Yang</au><au>Liu, Jing J.</au><au>Zhang, Lei</au><au>De Anda, Jorge Calderon</au><au>Wang, Xue Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids</atitle><jtitle>Particuology</jtitle><addtitle>China Particuology</addtitle><date>2016-02-01</date><risdate>2016</risdate><volume>24</volume><issue>1</issue><spage>61</spage><epage>68</epage><pages>61-68</pages><issn>1674-2001</issn><eissn>2210-4291</eissn><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. 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subjects | Batch-to-batch variation Classification Crystal structure Crystallization Fourier analysis Microscopes Microscopy Principal component analysis Principal components analysis Reactors Shape descriptors 产品设计 工业通用技术 粉末技术 颗粒学 |
title | Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids |
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