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A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Karan, D 
Chen, G 
Jose, N 
McDaid, P 

Abstract

jats:pAn automated flow chemistry platform was designed to collect data for a lithium-halogen exchange reaction. The data was used to train a Bayesian multi-objective optimization algorithm to optimize the process parameters and build process knowledge.</jats:p>

Description

Acknowledgements: This work was supported by Pfizer within the Pharma Innovation Programme in Singapore (PIPS) project. The project was hosted by Cambridge Centre for Advanced Research and Innovation in Singapore (CARES) whose lab is supported by the National Research Foundation Project C4T within CREATE Campus. J. Bai acknowledges financial support provided by CSC Cambridge International Scholarship from Cambridge Trust and China Scholarship Council.

Keywords

4014 Manufacturing Engineering, 40 Engineering, 34 Chemical Sciences

Journal Title

Reaction Chemistry and Engineering

Conference Name

Journal ISSN

2058-9883
2058-9883

Volume Title

Publisher

Royal Society of Chemistry (RSC)
Sponsorship
Pfizer (Unassigned)
Cambridge Trust (Unassigned)
China Scholarship Council (Unassigned)