TY - JOUR
T1 - Adaptive Background Correction of Crystal Image Datasets
T2 - Towards Automated Process Control
AU - Kiernan, Luke
AU - Jones, Ian
AU - Kurki, Lauri
AU - Cullen, Patrick J.
AU - El Arnaout, Toufic
N1 - Funding Information:
The research leading to these results has received funding from the European Community’s Seventh Framework Program (FP7-SME-2013) under the CRYSTAL-VIS project, Grant Agreement Number 605814, and from Science Foundation Ireland (SFI) through a Technology Innovation Development Award (TIDA). The authors would like to thank the PAT group (TU Dublin, Ireland), VTT (Oulu, Finland), and Topchem (Ireland).
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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
AB - 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
KW - Adaptive background correction
KW - Analytical technology
KW - Crystallization imaging
KW - Particle engineering
UR - http://www.scopus.com/inward/record.url?scp=85091995312&partnerID=8YFLogxK
U2 - 10.1007/s11220-020-00310-6
DO - 10.1007/s11220-020-00310-6
M3 - Article
AN - SCOPUS:85091995312
SN - 1557-2064
VL - 21
JO - Sensing and Imaging
JF - Sensing and Imaging
IS - 1
M1 - 48
ER -