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

Food security 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 manufacture 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 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. Applications comprising a detailed CV, cover letter, the names and addresses of two referees (and any supporting transcripts, if available) should be sent by email to Dr E. Lopez-Quiroga: [email protected], who would also welcome informal enquiries. The successful applicant will be required subsequently to submit a standard application to the University.

Funding notes:

Full funding is available for UK/EU with settled status PHD candidates for 3.5 years (including university tuition fees and tax free stipend). Applications from self-funded candidates will be also considered.