Machine learning for chemical manufacture

Summary

It is an unceasing challenge to reduce the time scale for development of new chemical products to the point of reliable manufacture and entrance into the market place. These processes however, are complex with process outcome being affected by a vast number of chemical and physical parameters; e.g. temperature, pressure, reagent stoichiometry, pH, heat and mass transfer affect quality and scalability making the definition of a chemical process at manufacturing scale a very challenging task.

This project aims to develop an Industry 4.0 approach revolutionizing 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 analyze 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. Note: Position may be filled before close date and recruitment closed.

The earliest start date fro this project will be 1 October 2020.

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