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In situ Image Processing and Data Binning Strategy for Particle Engineering Applications
Author(s) -
El Arnaout Toufic,
Cullen Patrick J.
Publication year - 2020
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201900311
Subject(s) - automation , particle (ecology) , computer science , macro , workflow , tracking (education) , range (aeronautics) , image processing , in situ , particle size , biological system , process engineering , image (mathematics) , computer vision , artificial intelligence , materials science , engineering , chemistry , mechanical engineering , chemical engineering , psychology , pedagogy , oceanography , organic chemistry , database , composite material , biology , programming language , geology
Crystallization control can be improved through real‐time monitoring technologies. Here, a workflow is demonstrated on rapid batch cooling crystallization of L ‐glutamic acid. First, in situ images were generated using video microscopy sensors and analyzed, by employing a single, rapid macro code to extract particle data descriptors. A binning procedure (over time) was performed, where every data point represented the counts of particles within a specific size or shape range per 100 images. This binning method was found more informative in tracking of the populations compared to whole image averages or individual particle datapoints. This study provides a step‐by‐step guide towards improving mechanistic modeling, control via feedback, automation, and continuous manufacturing for Industry 4.0.