Welcome to the Cordiner Group page!
Here we look to drive continual improvement in both process efficiency and process safety by employing cutting edge modelling technologies (mechanistic and data driven) to gain an in-depth understanding of the physical processes and properties at play in the real world.
As Industry evolves, our approach to tackling engineering problems must evolve too. New and innovative solutions are required to counter the multitude of new problems arising as a result of the digital era.
The work of this group is predominantly focused on developing novel modelling techniques, including both mechanistic, kinetic and data-driven models, for a range of purposes including:
Prediction and modelling of complex physical properties and processes
mRNA vaccine production and scale-up,
prediction of stable formulation design
decision making modelling for selection of decarbonisation strategies for industry
optimising energy and carbon credit purchasing.
Scale up and technoeconomic costing.
Particle processes, including fluidization and crystallisation.
Predictive Maintenance and root cause analysis using knowledge informed machine learning. This has been spun-out in our company Kausalyze.com
Process Safety and resilience
Hydrogen and Net Zero Systems modelling and process safety. https://ukhyres.co.uk/
Resilience and national security risk register
Systems thinking
Materials demand for net zero, https://nepc.raeng.org.uk/critical-materials
We're passionate about driving real change and strive to stay on the cutting edge by working on the interface between academia and industry. If you would like to hear more about how we're breaking down barriers between Industry and academia, please listen to Prof Joan Cordiner talking to Nature - Breaking down the barriers that curtail industry collaborations and career moves.
Listen to Prof Joan Cordiner talk about the advances in the Department of Chemical Engineering across 4 areas - Sustainability, Bioprocessing, Energy and Process Systems engineering (52:33 Onwards).