Using machine learning to predict residence time distributions in Coiled Flow Inverter (CFI) reactors
Barrera, Maria Cecilia and Josifovic, Aleksander and Robertson, John and Johnston, Blair and Brown, Cameron and Florence, Alastair (2022) Using machine learning to predict residence time distributions in Coiled Flow Inverter (CFI) reactors. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.
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Abstract
INTRODUCTION - A CFI reactor consist of a inner tube wrapped around a circular frame. This geometry leads to the formation of secondary flow patterns (Dean vortices) known to improve radial mixing. - Better radial mixing results in a tighter residence time distribution (). Many processes, such as virus inactivation, require a tight to avoid unwanted transformations or product damage. - The tightness of the can be evaluated using the relative width (), the ratio between the minimum and maximum residence times. An value of 1 corresponds to an ideal plug flow reactor.
ORCID iDs
Barrera, Maria Cecilia, Josifovic, Aleksander, Robertson, John ORCID: https://orcid.org/0000-0002-2191-1319, Johnston, Blair ORCID: https://orcid.org/0000-0001-9785-6822, Brown, Cameron ORCID: https://orcid.org/0000-0001-7091-1721 and Florence, Alastair ORCID: https://orcid.org/0000-0002-9706-8364;Persistent Identifier
https://doi.org/10.17868/strath.00081234-
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Item type: Conference or Workshop Item(Poster) ID code: 81234 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: 22 Jun 2022 11:40 Last modified: 27 Nov 2024 01:41 URI: https://strathprints.strath.ac.uk/id/eprint/81234