Crystallization is a critical operation in pharmaceutical, fine chemical, petrochemical, food and semiconductor industries. In crystallization, particle size and shape are important specifications of product quality that need to be well controlled. In pharmaceutical crystallization process, the dissolution rate, bioavailability and therapeutic effects of the drugs crystallizing out depends significantly on the particle size and shape. A narrow particle size distribution with specific particle shape is indicative of good product quality.

Real-time monitoring and control of the crystallization process is important to ensure that the desired final product quality is achieved. Traditionally, the control of crystallization processes has relied extensively on empirical experience. Complex chemistries, non-availability of detailed models, and the lack of in-situ sensors to directly measure product quality have been the main reasons for this state of affairs. Although technologies for offline particle size and shape measurements such as microscopy have been available and widely used, it is but recently that in-line measurements are becoming possible. Technologies such as Focused beam reflectance measurement (FBRM) and Particle vision and measurement, PVM (Wilkinson et al., 2000) from Lasentec / Mettler-Toledo, are widely used in manufacturing units to monitor particle size distribution and shape variation.

The development of image-based sensors has evoked recent interest in the use of image-analysis based approaches to estimate crystal size and shape in real-time and in situ (Zhou et al., 2006; Larsen et al., 2006 & 2007; Wang et al., 2008). In spite of the increased research activity in this area, there is little or no work that demonstrates the success of the image analysis (IA) techniques to any reasonable degree. While 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 lack of alternate approaches to compare the results has prevented large-scale validation. The primary objective of the present paper is to fill this void by addressing a key step in IA viz. segmentation. Also, segmentation involves several steps such as image selection, image enhancement, edge detection and morphology operation. Any IA algorithm involves the use of a series of user-specified parameters in each of the above steps. Our second objective is to estimate the sensitivity of the results to these parameters.

Our solution strategy uses manual segmentation of particles as an independent measure of the CSD. To quantify the similarities between the manual and automated segmentations, we have devised metrics which compare the particle sizes from the two segmentation techniques. These metrics provide a quantitative approach to estimate the quality of results as well as form the basis to suggest “optimal” parameter settings for the IA technique. Mono-sodium Glutamate (MSG) seeded cooling crystallization process is used to illustrate that, with proper settings, IA can be used to accurately track the CSD in real-time. As additional evidence, we also compare the results from IA to those from alternate sensors such as FBRM. Our results show that, with proper settings, the CSD estimates from IA can be obtained within ~10% error.

References

Larsen, P.A., Rawlings, J.B. & Ferrier, N.J., (2006), An algorithm for analyzing noisy, in situ images of high-aspect-ratio crystals to monitor particle size distribution. Chemical Engineering Science 61, 5236-5248.

Larsen, P.A., Rawlings, J.B. & Ferrier, N.J., (2007), Model-based object recognition to measure crystal size and shape distributions from in situ video images. Chemical Engineering Science 62, 1430-1441.

Ma, C. Y., Wang, X. Z. & Roberts, K. J., (2007), Multi-dimensional population balance modeling of the growth of rod-like L-glutamic acid crystals using growth rates estimated from in-process imaging. Advanced Powder Technol., Vol. 18, No. 6, pp. 702-723.

Wang, X. Z., Roberts, K. J. & Ma, C., (2008), Crystal growth measurement using 2D and 3D imaging and the perspectives for shape control. Chemical Engineering Science 63 (2008) 1173-1184.

Wilkinson, M. J., Jennings, K. H. & Hardy, M., (2000), Non-invasive video imaging for interrogating pharmaceutical crystallization. Microscopy and Microanalysis, Vol (6) (Supplement), 996-997.

Zhou, Y., Doan, X. T. & Srinivasan, R., (2006), Real-time imaging and product quality characterization for control of particulate processes. Joint 16th ESCAPE and 9th PSE: Computer-Aided Chemical Engineering 21A, W. Marquardt and C. Pantelides (Eds), 775-780. Elsevier, Garmish-Partenkirchen.