Machine Learning Driven Corrosion Modelling in Bio Feedstock Refining

About the Project

The global shift toward renewable bio based fuels is creating exciting new scientific and engineering challenges. Compared with traditional crude oil, bio feedstocks behave very differently during processing, sometimes causing much faster corrosion of refinery equipment. Understanding these behaviours is essential if society is to move confidently toward low carbon fuels. This PhD studentship offers the opportunity to contribute directly to this challenge by developing modern data driven tools that help predict and manage corrosion in next generation bio refineries.

The project brings together leading researchers from University of Leeds, Imperial College London, University College London, and the University of Illinois at Urbana–Champaign, supported by industrial scientists at bp. Regular engagement with bp and the experimental team in Illinois will provide unique insight into industrial corrosion challenges and support the development of adaptive, data driven sampling strategies to accelerate experimental progress. This environment will provide you with a unique perspective on how data, modelling, experiments, and industrial needs come together in an emerging area of sustainable technology. The research will also contribute to the creation of high throughput approaches for assessing the corrosivity of bio refinery environments.

Funding Notes

A highly competitive School of Mechanical Engineering Studentship, in support of the IMPACT-Bio Research Grant, providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year for 3.5 years. You will be responsible for paying the overtime fee in full in your writing up/overtime year (£340 in Session 2025/26), but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.