Intelligent Chemical Systems Engineering Research Group

We combine physical models with machine learning to predict chemical and biological process behaviour. Our work investigates how hybrid models can be developed with limited data, adapted between systems and scales, and used as a foundation for process digital twins.

We develop methods that discover mathematical relationships and candidate physical explanations from data. By combining symbolic regression, engineering knowledge and experimental design, we investigate how interpretable models can support scientific understanding and process development.

We turn industrial measurements and operating histories into models of product quality, batch behaviour and equipment condition. Our work includes soft sensors, interpretable quality models, reconstruction of batch operations and predictive maintenance using limited labelled data.

We connect predictive models with decisions about process operation. Our research investigates flexible operating regions, optimisation under uncertainty and intelligent control strategies for batch processes and biological production systems.

We connect biological behaviour with reactor conditions and production performance. Our work brings together cell metabolism, fermentation, mixing and light-dependent growth to investigate bioprocess design, prediction and scale-up.

We develop AI methods to generate molecules with targeted properties and assess their practical accessibility. Our work spans surfactant design and cost-aware molecular screening, connecting candidate generation with the challenges of making or purchasing promising compounds.