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 logoORCID: https://orcid.org/0000-0002-2191-1319, Johnston, Blair ORCID logoORCID: https://orcid.org/0000-0001-9785-6822, Brown, Cameron ORCID logoORCID: https://orcid.org/0000-0001-7091-1721 and Florence, Alastair ORCID logoORCID: https://orcid.org/0000-0002-9706-8364;

Persistent Identifier

https://doi.org/10.17868/strath.00081234
CORE (COnnecting REpositories)