Issue 3, 2024

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

Abstract

Discovering the optimum process parameters of ultra-fast reactions, such as lithium–halogen exchange reactions, is typically achieved by time and resource inefficient methods including one factor at a time optimization (OFAT) or classical factorial design of experiments (DoE). Herein, we demonstrate the development of a machine learning workflow coupled with a flow chemistry platform for the optimization of the reaction conditions of a lithium–halogen exchange reaction. Flow chemistry platform allowed us to precisely control the process parameters (temperature, residence time and stoichiometry) and enabled robust and reliable data collection to train a machine learning algorithm. A Bayesian multi-objective optimization algorithm TSEMO (Thompson sampling efficient multi-objective optimization) was used to optimize the process parameters and to build process knowledge for different optimization campaigns with different mixing intensifications (capillary reactor vs. microchip reactor). The algorithm successfully identified a set of optimal conditions corresponding the trade-off between yield and impurity in different optimization campaigns. Furthermore, the optimization results and Gaussian process (GP) surrogate models within TSEMO were further analyzed to infer the operating regime of the system for different mixing intensifications (mixing controlled vs. reaction-controlled regime). The machine learning workflow has proven to be robust and data efficient, revealing rich information about the reaction studied compared to single-objective, OFAT and DoE approaches.

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

Supplementary files

Article information

Article type
Paper
Submitted
13 out 2023
Accepted
22 nov 2023
First published
04 dez 2023
This article is Open Access
Creative Commons BY license

React. Chem. Eng., 2024,9, 619-629

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

D. Karan, G. Chen, N. Jose, J. Bai, P. McDaid and A. A. Lapkin, React. Chem. Eng., 2024, 9, 619 DOI: 10.1039/D3RE00539A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements