期刊
CHEMICAL ENGINEERING JOURNAL
卷 384, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2019.123340
关键词
Automated flow reactor; Environmental chemistry; Machine learning; Reaction engineering; Sustainable chemistry
资金
- EPSRC [EP/R513258/1, EP/N509681/1]
- University of Leeds
- AstraZeneca
- Royal Academy of Engineering
- EPSRC [EP/R032807/1, 1803783] Funding Source: UKRI
There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be efficiently optimised. Here, we report the combination of multi-objective optimisation based on machine learning methods (TSEMO algorithm) with self-optimising platforms for the optimisation of multi-step continuous reaction processes. This is demonstrated for a pharmaceutically relevant Sonogashira reaction. We demonstrate how optimum reaction conditions are re-evaluated with the changing downstream work-up specifications in the active learning process. Furthermore, a Claisen-Schmidt condensation reaction with subsequent liquid-liquid separation was optimised with respect to three-objectives. This approach provides the ability to simultaneously optimise multi-step processes with respect to multiple objectives, and thus has the potential to make substantial savings in time and resources.
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