Digital twin of novel CO2 reduction and utilisation system

To combat climate change with the aim of limiting global warming to the target of 1.5 C, it requires carbon dioxide removal from the atmosphere of some 100–1000 GtCO2 over the 21st century. The bulk chemical sector is known as a ‘hard to decarbonise’ sector with a large amount of carbon emission coming from the use of fossil feedstock and intensive energy consumption. The decarbonisation requires the substitution of fossil feedstock with sustainable resources, such as recycled waste.

In this programme, you will join a dynamic team with years of experience in modelling, machine learning, multi-objective optimisation and techno-economic analysis. We will develop a system-level digital twin, which performs digital and data-centric modelling at system level as a tool to address both the intermittent emissions of CO2 and availability of renewable sources, i.e., solar and wind, that facilitates the optimal allocation of resources, enables improved design and operation of emerging chemical/electrochemical CO2 converting technologies.

Open to any UK or international candidates.

By the start of the PhD programme, applicants must have been awarded or to have nearly completed a master’s degree in any of the following or other related disciplines:

• Chemical engineering
• Physics
• Mechanical engineering
• Energy engineering
• Environmental engineering
• Computer science.

Applicants with track records or experience in modelling fuel cells and electrolysers, machine-learning, surrogate modelling, or techno-economic analysis are highly desirable.