Recognizing overlapped particles during a crystallization process from in situ video images for measuring their size distributions

Ola Suleiman Ahmad (1, 2, 3, 4) , Johan Debayle (1, 2, 3, 4) , Nesrine Gherras (3, 5, 6) , Benoît Presles (3, 5, 6) , Jean-Charles Pinoli (1, 2, 3, 4) , Gilles Févotte (3, 5, 6, 7)

1 CIS-ENSMSE - Centre Ingénierie et Santé
2 DIM-ENSMSE - Département Imagerie et Morphologie
3 LPMG-EMSE - Laboratoire des Procédés en Milieux Granulaires
4 IFRESIS - Institut Fédératif de Recherche en Sciences et Ingénierie de la Santé
5 SPIN-ENSMSE - Centre Sciences des Processus Industriels et Naturels
6 GENERIC-ENSMSE - Département Géochimie, environnement, écoulement, réacteurs industriels et cristallisation
7 LAGEP - Laboratoire d'automatique et de génie des procédés

This paper presents a method to recognize polygonal-shaped particles (i.e. rectangles, regular/irregular prisms) among agglomerated crystals from in situ images during a crystallization process. The aim is to measure the particle size distributions (PSD), which are key measurements needed for the purification operations and the quality control of chemical products or drugs. The method is first based on detecting the geometric features of the particles identified by their salient corners. A clustering technique is then applied by grouping three correspondent salient corners belonging to the same polygon. The efficiency of the proposed method is tested on particles of Ammonium Oxalate during batch crystallization in pure water. Particle size distributions are calculated, and a quantitative comparison between automatic and manual sizing is performed.

Keywords: Salient Corner Detection; Grouping Method; Shape Recognition; Particle Size Distributions.; Particle Size Distributions

Domains: Chemical and Process Engineering

Ola Suleiman Ahmad, Johan Debayle, Nesrine Gherras, Benoît Presles, Jean-Charles Pinoli, et al.. Recognizing overlapped particles during a crystallization process from in situ video images for measuring their size distributions. Tenth International Conference on Quality Control by Artificial Vision QVAC 2011, Jun 2011, Saint Etienne, France. pp.8000-08, ⟨10.1117/12.890883⟩. ⟨hal-00614080⟩