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Data-driven reduced order modelling for real-time food processing applications


   School of Chemical Engineering

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  Dr Estefania Lopez-Quiroga  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Food security is among the top UN Sustainable Development Goals to be achieved by 2030. The challenge for the food sector is not small: to sustainably and efficiently produce safe and healthier foods that feed a forecasted global population of 8.5 billion people by 2030 while meeting Net-Zero goals. Over the last decade, the food industry, which is the UK’s largest single manufacturing sector, has undertaken important transformations (e.g. fuel switching, investment in new energy-efficient equipment, and low carbon technologies) to meet long-term reduction goals on energy and water demand. However, additional efforts and different approaches to the design of food products and manufacturing processes are required to deliver further energy reductions and meet UK’s sustainability goals (reduce UK greenhouse gas emissions to net zero by 2050). One of those approaches, and the core of this PhD project, is based on the virtualisation of the food manufacturing sector. Virtualisation is a powerful tool for optimisation, process design, and innovation but its potential benefits have not been fully exploited by food manufacturers yet.

In this context, the main aim of this project would be to explore different data-driven reduced order modelling methods and compare their performance in a range of food processing case studies. The project will focus on energy-demanding and/or water-demanding food unit operations and processes, like freezing/crystallisation, freeze-drying and cleaning-in-place. Currently, there is a gap in the field for predictive capabilities that are suitable for real-time applications (e.g., control, optimisation), and the development of data-driven reduced order models will address this need. Since the project objectives require the design and implementation of suitable simulation and visualisation tools, the candidate should have at least a strong upper second-class (2.1) degree in Chemical Engineering/Computer Science/Maths.


Funding Notes

Full funding is available to support a PhD research student for 3.5 years (including university tuition fees and a scholarship of £17,668 per annum tax-free) starting early 2023. The candidate should be a UK citizen/EU with settled status.

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