Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ces.2008.09.007
Title: In situ particle size estimation for crystallization processes by multivariate image analysis
Authors: Sarkar, D.
Doan, X.-T.
Ying, Z.
Srinivasan, R. 
Keywords: Imaging
Multivariate statistics
Particle size
Particulate processes
Segmentation
Signature curve
Issue Date: Jan-2009
Citation: Sarkar, D., Doan, X.-T., Ying, Z., Srinivasan, R. (2009-01). In situ particle size estimation for crystallization processes by multivariate image analysis. Chemical Engineering Science 64 (1) : 9-19. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ces.2008.09.007
Abstract: Crystal size estimation from in situ images has received attention recently as a means to estimate product properties in real-time. In this paper, an automated image analysis strategy that combines classical image analysis techniques with multivariate statistics has been developed for online analysis of in situ images from crystallization process. The strategy introduces a novel image segmentation step based on information extracted from multivariate statistical models. Experimental results for batch cooling crystallization of monosodium glutamate show that the strategy effectively extracts crystal size and shape information from in situ images. The robustness and efficiency of the method has been established by comparing its performance with those obtained by manual analysis of the images. The method yields reasonably good estimates of particle length and is also fast enough to provide online measurements for the purpose of online optimization and control of a typical crystallization process. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Chemical Engineering Science
URI: http://scholarbank.nus.edu.sg/handle/10635/64083
ISSN: 00092509
DOI: 10.1016/j.ces.2008.09.007
Appears in Collections:Staff Publications

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