Critical evaluation of image processing approaches for real-time crystal size measurements

Monitoring and control of particulate processes is quite challenging and has evoked recent interest in the use of image-based approaches to estimate product quality (e.g. size, shape) in real-time and in situ. Crystal size estimation from video images, especially for high aspect-ratio systems, has received much attention. In spite of the increased research activity in this area, there is little or no work that demonstrates and quantifies the success of the image analysis (IA) techniques to any reasonable degree. This is important because, although image analysis techniques are well developed, the quality of images from inline sensors is variable and often poor, leading to incorrect estimation of the process state. The present paper, to our knowledge, the first large-scale size estimation study with Lasentec's in-process video imaging system, PVM, seeks to fill this void by focusing on one key step in IA viz. segmentation. Using manual segmentation of particles as an independent measure of the particle size, we have devised metrics to compare the accuracy of automated segmentation during IA. These metrics provide a quantitative measure of the quality of results. Based on these metrics, a sensitivity study of IA parameters has also been performed and “optimal” parameter settings identified. A Monosodium Glutamate seeded cooling crystallization process is used to illustrate that, with proper settings, IA can be used to accurately track the size within ∼8% error.