Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning
Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning
About this item
Full title
Author / Creator
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
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Scope and Contents
Contents
Purpose
Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, identify key predictive features, and arrive at formulations with improved flow properties.
Metho...
Alternative Titles
Full title
Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_12055667
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_12055667
Other Identifiers
ISSN
0724-8741,1573-904X
E-ISSN
1573-904X
DOI
10.1007/s11095-025-03855-x
