Advances in online monitoring and data collection present an opportunity to enhance the efficiency, sustainability and profitability of engineering processes. However, despite the promise of “Big Data” and the impact it has had on other sectors, its application in the chemical and minerals processing industries has not yet reached its full potential.
The mathematical modelling and machine learning group aims to address this gap by combining fundamental knowledge with statistical techniques. Our focus is on the use of theoretical process models as well as data-driven machine learning algorithms (often in combination) to improve the operation, monitoring and control of chemical plants. Applications include fault detection and diagnosis, causality analysis, reinforcement learning, and hybrid modelling, and we work in fields ranging from minerals processing and petroleum refining to environmental engineering.
The researchers listed below all form part of this group. Follow the link to their individual profiles to find out more about their research interests and activities.
Distinguished Professor | Extractive metallurgy, metal recycling & machine learning
Senior Lecturer | Thermodynamic modelling, thermophysical property measurement & machine learning
Associate Professor | Process modelling & monitoring