Information and process systems engineering

We have developed new algorithms that combine fundamental process knowledge, mechanistic process models and data for more reliable and robust detection of process anomalies, as well as diagnosis of the root cause of such faults. As well as the theoretical work, we are in the process of applying these methods to predictive maintenance of critical equipment in the process industry and healthcare services.

In addition, we develop advanced methods for model analysis under uncertainty with applications in the design of experiments, model reduction and model predictive control.

Manufacturing and industrial systems are currently undergoing a radical change, termed Industry 4.0, where the chemical industry is implementing ever more advanced sensing techniques, enabled by the 'internet of things'. These new cyber-physical systems are revolutionising process industries and enable industry to respond to market requirements in real-time and provide visibility across production and value chains.

We develop custom algorithms and approaches to utilise this data through combining data-driven and model-driven approaches to offer real-time solutions to industrial partners.

We combine years of industrial and academic experience to create higher efficiency systems from retrofitting of existing processes, through to creating more tightly integrated or intensified processes.

By combining well-established heat and mass integration formulations using classical process system engineering approaches of mathematical optimisation, together with rigorous process models, incorporated into the flowsheets through surrogate modelling, we apply whole systems approaches to create next-generation processes for more sustainable and profitable industries.

We use state-of-the-art modelling and techno-economic analysis to model, understand and optimise large-scale systems using mathematical optimisation tools. Combining these techniques with industry-specific data, economic models and supply chains, we simulate and propose new, radical solutions to large-scale problems in energy, water, and health.

In particular, our group is a world-leader in bio-energy modelling, combining new experimental insights, with regional energy and supply chain models to determine future energy landscapes that are more sustainable and efficient. Our work on energy planning modelling optimisation allows policymakers and industrial partners to model and explore potential technologies and their technoeconomic and social impacts before implementation.

We have developed new models for a wide range of applications, from traditional chemical engineering applications such as chemical reactors, separation units, and flowsheets, to complex biological systems.

We work with experimentalists and industrial partners to develop new models to optimise, and scale-up new processes and explain and predict results using first-principles theory and data-driven techniques. In particular, our multiscale model of skin penetration is at the forefront of this area in the world, with applications in topical and transdermal drug delivery, skin exposure to personal care, cosmetic and occupational products, as well as supporting design of skin measurement instrument.

Combining years of experience in circular economy and optimisation modelling, our team uses mathematics to represent large-scale systems holistically to understand optimal integrated design and decision-making.

Using these developed frameworks, the team is able to suggest pathways to sustainable, efficient and profitable new integrated industrial systems and infrastructure for applications in energy, food, water, and consumer products.