Machine Learning for Optimisation of Continuous Chemical Processes
Lead Research Organisation:
University of Leeds
Department Name: Sch of Chemistry
Abstract
This proposal is to develop an instrument that will use machine learning and flow chemistry to automatically screen a defined chemistry experimental space whilst also scanning a range of orthogonal UPLC methods in order to find all potential process related impurities. Machine learning can then be used to self-optimise the separation between all identified peaks. The use of a small scale flow reactor will enable us to achieve this with a minimal amount of starting material (target 2 to 5g) and access a wider parameter space (super ambient conditions) due to the higher pressures accessible in such systems.
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/T517860/1 | 30/09/2020 | 29/09/2025 | |||
2446912 | Studentship | EP/T517860/1 | 30/09/2020 | 31/03/2024 | Thomas Dixon |