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Details of Grant 

EPSRC Reference: EP/X024237/1
Title: DigiScale: Digitally driven scale up of chemical processes
Principal Investigator: Chamberlain, Dr TW
Other Investigators:
Kapur, Professor N Bourne, Professor RA Warren, Dr N J
Besenhard, Dr M O
Researcher Co-Investigators:
Dr C Taylor
Project Partners:
AstraZeneca Pfizer UCB
Department: Sch of Chemistry
Organisation: University of Leeds
Scheme: Standard Research
Starts: 02 October 2023 Ends: 01 October 2026 Value (£): 970,893
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant Physical Organic Chemistry
Reactor Engineering
EPSRC Industrial Sector Classifications:
Manufacturing Chemicals
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Nov 2022 Digital Manufacturing Full Announced
Summary on Grant Application Form
Chemical synthesis underpins our society in general - whether in the production of medicines, household or personal care items or for heat and power. The process of discovering a new molecule and taking it through to production scale remains a challenge, despite centuries of experience. The conditions of small scale discovery can be very different to those at the scales required to bring the new chemical product to wider society. Reducing this time may mean bringing drugs to market earlier, or providing more environmentally friendly alternatives to existing products. A digital twin is a representation of a physical process within a computational framework, allowing virtual experimentation to be carried out to assess the best set of conditions under which to run processes. These conditions can be selected and weighted towards (for example) the most environmentally friendly process, or the cheapest - depending on the wider demands of society. A digital twin also allows this to be addressed dynamically.

This project develops the framework for building digital twins. As all chemical processes are different, and depend very much on the particular synthesis, we have to first learn something about our specific reaction. This will be carried out at the small scale under computer control, to narrow down the wide range of conditions (e.g. temperatures, concentrations, choice of catalysts used to speed up the reaction) for the process.

This information then informs our digital twin model. At larger scale conditions vary through the reactor - dealing with many thousands of litres of material is very different to dealing with the small quantities at the discovery scale. Now we have gradients of temperature, material is much more poorly mixed - these can all influence the reaction. So we take our knowledge of these variations, which includes running flow simulations, and map onto it the information from our small scale tests. We can select which reactors we want to use, and their conditions that they are run under and make decisions about the overall process - just as before, driving it through questions of sustainability, cost, material purity.

This gives us a framework that spans the physical development of material with a digital representation. We can explore a much wider parameter space than ever possible through experimentation, and assess the entire process digitally. This brings unprecedented agility to the manufacturing of products.

The project builds on the world-class research that exists in the UK in automated tools used for self-optimising reactor systems and has a range of industrial partners spanning pharmaceuticals; AstraZeneca, Pfizer and UCB Pharma, and reactor control software experts; Perceptive Engineering, ensuring the barriers to adoption by industry of the approach developed in this project are minimised.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.leeds.ac.uk