Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/32473
Title: Development, evaluation and optimization of image based methods for monitoring crystallization processes
Authors: ZHOU YING
Keywords: Image analysis; In-process video measurement; Crystallization; Size distribution
Issue Date: 28-Jun-2011
Source: ZHOU YING (2011-06-28). Development, evaluation and optimization of image based methods for monitoring crystallization processes. ScholarBank@NUS Repository.
Abstract: 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 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. This thesis studies large-scale size estimation with Lasentec?s in-process video imaging system, PVM. It 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. 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. Any image processing algorithm involves a number of user-defined parameters and, typically, optimal values for these parameters are manually selected. Manual viii selection of optimal image processing parameters may become complex, time-consuming and infeasible when there are a large number of images and particularly if these images are of varying quality, as could happen in batch crystallization processes. This thesis combines two optimization approaches to systematically locate optimal sets of image processing parameters ? one approach is a model-based optimization approach used in conjunction with uniform experimental design; another approach is the sequential simplex optimization method. Our study shows that these two approaches or a combination of them can successfully locate the optimal sets of parameters and the image processing results obtained with these parameters are better than those obtained via manual tuning. Combination of these two approaches also helps to overcome the drawbacks of the two individual methods. Our work also demonstrates that the optimal sets of parameters obtained from one batch of process images can also be successfully applied to another batch of process images obtained from the same system. The in-process video microscopy (PVM) images that are acquired from Monosodium Glutamate (MSG) seeded cooling crystallization process are used to demonstrate the workability of the proposed approach.
URI: http://scholarbank.nus.edu.sg/handle/10635/32473
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