Particle characterization with on-line imaging and neural network image analysis

We proposed a deep learning-based in situ microscopic image analysis system for detecting particles and performing size analysis in a high-density slurry, which shows great potential usage in the area of solution crystallization process. A cost-effective imaging system consisting of a flow-through cell and a 3D-printed microscopic probe was built for high quality image acquisition. The state-of-the-art deep learning model, Mask RCNN, was used to segment the overlapping particles and classify their categories with high accuracy. A comprehensive performance evaluation of the proposed system was conducted including extrapolation to unseen particle scale, detection in different solids concentration levels, and separation of two different types of particles. Compared with the previous studies, the solids concentration detection limit was improved by five times higher in terms of particle number per frame and three times higher regarding the particle pixel fill ratio (PFR). The categorized detections successfully classified the two different particles in a mixed suspension, and the individual particle size information was extracted, which showed high consistency with the particle information. What's more, a progressive labelling strategy was employed to improve the processing efficiency and accuracy, which would enable the transfer application in solution crystallization process for various crystal species. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.