Self-optimizing process parameter screening in flow

Lead Research Organisation: Imperial College London
Department Name: Dept of Chemistry

Abstract

Once a step specific synthetic strategy has been qualified, a major challenge is to find an optimized set of concentrations and processing conditions. To this end, it is important to screen different conditions in an efficient and reproducible way, and to collect analytical data. This project will take advantage of the ultra-high throughput capability of flow systems. It will involve setting up a flexible flow reactor with bespoke in-line and at-line analytical equipment. It will benefit from the infrastructure that is available at Imperial's ROAR facility. The data captured will be subjected to machine learning algorithms that are to be developed in a separate project. The objective is to develop an intelligent system that that automatically decides on the next set of conditions based on its learnings from correlating a set of reaction parameters to predict and control the reaction outcome.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023232/1 31/03/2019 29/09/2027
2278642 Studentship EP/S023232/1 30/09/2019 29/09/2023 Linden Schrecker