Adaptive Background Correction of Crystal Image Datasets: Towards Automated Process Control
- PDF / 1,813,250 Bytes
- 20 Pages / 439.37 x 666.142 pts Page_size
- 92 Downloads / 119 Views
Adaptive Background Correction of Crystal Image Datasets: Towards Automated Process Control Luke Kiernan1 · Ian Jones1 · Lauri Kurki2,3 · Patrick J. Cullen4 · Toufic El Arnaout5,6 Received: 21 June 2020 / Revised: 3 August 2020 / Accepted: 12 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Improving the data descriptor calculation of crystal’s physical properties requires sophisticated imaging techniques and algorithms. It has been possible to construct 2D population balance models benefiting from characteristic measurements of both crystal’s length and width, compared to the single representative sizes used in 1D models. Our aim is to ameliorate the procedure of determining shape (and not only size) factors, in an automated fashion and directly from the process, for implementation in future models. Here, approaches suitable for real-time applications were employed including engineered imaging sensors and adaptive algorithms. We described the latter in detail for varying 2D image datasets. Their basic concept is similar. Each is applicable to an entire dataset, thus demonstrating efficacy for a variety of particle environments. While the challenge of particle segmentation for higher concentrations was not scrutinized here, this approach reduced processing time, steps and supervision, for the benefit of certain applications requiring process monitoring and automation.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1122 0-020-00310-6) contains supplementary material, which is available to authorized users. * Toufic El Arnaout [email protected] 1
Innopharma Technology, Sandyford, Dublin, Ireland
2
Timegate Instruments, 90590 Oulu, Finland
3
VTT Technical Research Center of Finland, 90570 Oulu, Finland
4
School of Chemical and Biomolecular Engineering, The University of Sydney, Darlington, NSW 2008, Australia
5
School of Food Science and Environmental Health, TU Dublin ‑ City Campus, Technological University Dublin, Dublin, Ireland
6
Kappa Crystals Ltd, Dublin, Ireland
13
Vol.:(0123456789)
48
Page 2 of 20
Sensing and Imaging
(2020) 21:48
Graphic Abstract
Keywords Crystallization imaging · Adaptive background correction · Particle engineering · Analytical technology
1 Introduction 1.1 Crystallization Modelling Several spectroscopic, laser and imaging methods now permit access to information in real-time and directly from the reaction (in situ) due to developments in Process Analytical Technology (PAT) [1, 2]. This information may be crystal size distributions (CSD) and particle size-shape distribution (PSSD), and many physico-chemical properties, found to influence population balance equations (PBEs) and models (PBMs) [3]. 2D models supported by size and shape information, mainly possible using imaging, have large advantages over basic 1D models and less assumptions. One of the main approaches employed to measure 1D information has been based on the chord length, represented by t
Data Loading...
