Machine learning for chemical manufacture

Lead Research Organisation: University of Leeds
Department Name: Chemical and Process Engineering


This exciting project combines the expertise of IBM in the development of algorithms for optimisation and the use of automated model generation and discrimination by researchers at UCL with the experimental automation expertise within the Institute of Process Research and Development at Leeds and the use of advanced hydrothermal reactors developed at the University of Nottingham. This research capability will be used to develop new algorithms for machine learning based generation of chemical process design knowledge and coupling these algorithms to a cyber platform for automated experimentation. The combined cyber-physical system will be validated via in-depth case studies related to current pharmaceutical manufacturing challenges.

This project aims to develop an Industry 4.0 approach revolutionising the transfer from laboratory to production using advanced data-rich and cognitive computing technologies. We will develop new algorithms based on Bayesian
Optimisation and evolving Kinetic Motifs that merge data analysis and the generation of further experiments. Cloud based machine learning services (hubs) will generate experiment setpoints delivered through the cloud to automated laboratory platforms (LabBots). A key novelty is that the analysis services can receive and analyse results, and post further experiments to the LabBots, thus generating a data generation - data analysis closed-loop. This enables the application of machine learning to chemical development: the system will continuously learn, increasing in confidence and knowledge over time, from previous iterations.


- Develop optimisation algorithms that combine global and local search methodologies for rapid optimisation and definition of operating space
- Develop simulation tools for evaluation of optimisation algorithms on a variety of chemical kinetic profiles
- Perform case studies on real chemical systems demonstrating enhancements via benchmarking against traditional approaches
- Develop optimisation routines for non-reactive processes such as liquid-liquid separation and chromatography

This studentship will work alongside PDRAs from the Cognitive Chemical Manufacture grant:


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513258/1 30/09/2018 29/09/2023
2119004 Studentship EP/R513258/1 30/09/2018 30/03/2022 Jamie Manson