Prediction of powder flow of pharmaceutical materials using machine learning
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Pereira-Diaz, Laura and Brown, Cameron J. and Florence, Alastair J. (2022) Prediction of powder flow of pharmaceutical materials using machine learning. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.
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Abstract
The lack of understanding of powder flow adds cost and time to the development of robust production routes and compromises manufacturing process performance in the pharmaceutical industry. In this work, implementing machine learning models enables rapid decision-making regarding manufacturing route selection, thus, minimizing the time and amount of material required. This work focuses on using ML models to predict powder flow behavior of pharmaceutical materials for routine, widely available materials.
ORCID iDs
Pereira-Diaz, Laura, Brown, Cameron J. ORCID: https://orcid.org/0000-0001-7091-1721 and Florence, Alastair J. ORCID: https://orcid.org/0000-0002-9706-8364;-
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Item type: Conference or Workshop Item(Poster) ID code: 83016 Dates: DateEvent16 May 2022PublishedSubjects: Medicine > Therapeutics. Pharmacology Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Strategic Research Themes > Advanced Manufacturing and Materials
Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)Depositing user: Pure Administrator Date deposited: 31 Oct 2022 15:44 Last modified: 27 Nov 2024 01:41 URI: https://strathprints.strath.ac.uk/id/eprint/83016
CORE (COnnecting REpositories)