Online detection of particle agglomeration during solution crystallization by microscopic double-view image analysis

Yan Huo, Tao Liu, Xue Z. Wang, Cai Y. Ma, Xiongwei Ni

Research output: Contribution to journalArticle

8 Citations (Scopus)
38 Downloads (Pure)

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. First, 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 pseudoagglomerates 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 pseudoagglomeration and counting the primary particles in these agglomerates by using the proposed method.
Original languageEnglish
Pages (from-to)11257–11269
Number of pages13
JournalIndustrial and Engineering Chemistry Research
Volume56
Issue number39
Early online date11 Sep 2017
DOIs
Publication statusPublished - 4 Oct 2017

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Online detection of particle agglomeration during solution crystallization by microscopic double-view image analysis'. Together they form a unique fingerprint.

  • Cite this