Development of a Virtual Sensor for Real-Time Prediction of Granule Flow Properties.
flowability
machine learning
monitoring
size and shape distribution
virtual sensor
Journal
ESCAPE. European Symposium on Computer Aided Process Engineering
Titre abrégé: ESCAPE
Pays: England
ID NLM: 101734507
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
15
2
2023
pubmed:
16
2
2023
medline:
16
2
2023
Statut:
ppublish
Résumé
We report progress of an ongoing work to develop a virtual sensor for flowability, which is a critical tool for enabling real time process monitoring in a granulation line. The sensor is based on camera imaging to measure the size and shape distribution of granules produced by wet granulation. Then, statistical methods were used to correlate them with flowability measurements such as ring shear tests, drained angle of repose, dynamic angle of repose, and tapped density. The virtual sensor addresses the issue with these flowability measurements, which are based on off-line characterization methods that can take hours to perform. With a virtual sensor based on real-time measurement methods, the prediction of granule flowability become faster, allowing for timely decisions regarding process control and the supply chain.
Identifiants
pubmed: 36790943
doi: 10.1016/b978-0-323-95879-0.50181-8
pmc: PMC9923503
mid: NIHMS1870576
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1081-1086Subventions
Organisme : FDA HHS
ID : U01 FD006487
Pays : United States
Références
Phys Rev Lett. 2003 Sep 5;91(10):104302
pubmed: 14525481