- Institution: Loughborough University
- Primary supervisor: Professor Valerie Pinfield
- Secondary supervisor: Professor Wen Feng Lin
- PhD student Alex O’Connell
Project summary
This project addresses challenges in the electrochemical production of hydrogen and associated products. The research adopts advanced modelling approaches, including physics-informed generative AI, machine learning techniques and multi-scale modelling, to develop and optimise electrolyser systems and the wider energy systems in which they operate.
The research focuses on optimising materials for electrolyser systems, considering degradation mechanisms, microstructure, catalyst design and catalyst distribution. It also explores AI-assisted optimisation of stack operation within renewable energy systems, taking account of fluctuating energy supply and energy costs.
In addition, the project examines the co-production of hydrogen and valorisable products, alongside the use of alternative feedstocks to support sustainability and circular economy approaches. The ultimate aim is to develop modelling and optimisation approaches that improve electrolyser systems and their integration within wider energy systems.
Meet the researcher
Hi, I’m Alex, I have a BEng in Automotive Engineering from Loughborough University and am now part of EnerHy’s second cohort, based in Loughborough’s Chemical Engineering Department.
My PhD focuses on applying AI and multi-scale modelling to the design and optimisation of electrochemical systems for hydrogen production.