Huo, Y, Liu, T, Wang, XZ et al. (2 more authors) (2017) Online Detection of Particle Agglomeration during Solution Crystallization by Microscopic Double-View Image Analysis. Industrial & Engineering Chemistry Research, 56 (39). pp. 11257-11269. ISSN 0888-5885
Abstract
To detect the particle agglomeration degree for assessing crystal growth quality during a crystallization process, an in-situ image analysis method is proposed based on a microscopic double-view imaging system. Firstly, a fast image preprocessing approach is adopted for segmenting raw images taken simultaneously from two cameras installed at different angles, to reduce the influence from uneven illumination background and solution turbulence. By defining an index of the inner distance based curvature for different particle shapes, a preliminary sieving algorithm is then used to identify candidate agglomerates. By introducing two texture descriptors for pattern recognition, a feature matching algorithm is subsequently developed to recognize pseudo agglomerates in each pair of the double-view images. Finally, a fast algorithm is proposed to count the number of recognized particles in these agglomerates, besides the unagglomerated particles. Experimental results from the potassium dihydrogen phosphate (KDP) crystallization process demonstrate good accuracy for recognizing pseudo agglomeration and counting the primary particles in these agglomerates by using the proposed method.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Industrial and Engineering Chemistry Research, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.iecr.7b02439 |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Oct 2017 14:03 |
Last Modified: | 11 Sep 2018 00:38 |
Status: | Published |
Publisher: | American Chemical Society |
Identification Number: | 10.1021/acs.iecr.7b02439 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:122286 |