Transforming process prediction for effective control.
The Advanced Process Prediction and Control (APPCo) Group pioneers and implements cutting-edge solutions for real-time process prediction and optimisation. The group’s goal is to enhance the Advance Process Control (APC) platforms in the industry, elevating their performance and efficiency by deploying its real-time process prediction solutions.
Aims
The APPCo group aims to transform unit process modelling and simulation, moving on from the steady-state models previously developed at the Julius Kruttschnitt Mineral Research Centre, to advance and apply new techniques that make greater use of data generated on-site and sensor technologies in combination with advanced process control, computational analytics and modelling techniques.
Research themes
Research within the APPCo groups focuses on foundation themes:
- Integrated process prediction
- Advanced ore characterisation for mechanistic modelling
- Dynamic process modelling
- Resource utilisation and sustainability metrics
- Liberation modelling
- Advanced data analytics
- Instrumentation and soft sensors
Research lead
Professor Mohsen Yahyaei
View Mohsen Yahyaei's research profile
Key researchers
Dr Gordon Forbes
View Gordon Forbes' research profile
Dr Marko Hilden
View Marko Hilden's research profile
Tania Ledezma Torres
View Tania Ledezma Torres' profile
Dr Kristy Nell
View Kristy Nell's profile
New Economy Mineral Testing Technology
The New Economy Mineral Testing Technology project aims to commercialise a suite of small-scale continuous conventional and novel processing units, to allow mining companies to rapidly prototype various processing flowsheets for new economy minerals, mine tailing and battery minerals.
See more on the New Economy Mineral Testing Technology project
AMIRA P9Q
AMIRA P9Q is the latest in a long line of P9 projects focused on translating the previous P9 research projects to industry focussed process improvement tools. The project was a three-year initiative which delivered, validated multi-component equipment models that will run in the Integrated Extraction Simulator (IES).
Stress intensity in stirred mills
This is a study of innovative stirrer design for stirred milling technology. Its aim is to build a prototype that will improve the performance of the grinding zone through experimentation and investigation of stirrer designs.
Process Improvement Toolbox
The Process Improvement Toolbox project links fundamental applied research outcomes to site personnel and on-site usability. It focuses on training and engagement of metallurgical staff end-users of the previously developed Toolbox. Tools are coupled with detailed diagnostic theories sources from journals, research reports and conferences. Current implementation covers unit operations from crushing through to flotation.
Mining the Corporate Memory
This project in collaboration with Endellion Technology, exploits corporate memory for concentrator performance improvement.
Dynamic Generic Mill Model
This project focuses on developing a dynamic AG/SAG & ball mill model for model-informed Process Control.
See more on the Dynamic Generic Mill Model project
Anglo American Centre for Sustainable Comminution
This is a five-year partnership with Anglo American focussing on research output that delivers value to Anglo American operations.
See more on the Anglo American Centre for Sustainable Comminution
Cyanide socio-technical learning lab
The cyanide socio-technical learning lab is an example output from a project aimed at exploring the linkages between technical, environmental and social challenges facing mining projects, and is supported by the Complex Orebodies Program.
Contact us
Get in touch to learn more about our research group.
Professor Mohsen Yahyaei
Director, Julius Kruttschnitt Mineral Research Centre
+61 7 3346 5989 m.yahyaei@uq.edu.au
The Advanced Process Prediction and Control (APPCo) Group is a research group within the Julius Kruttschnitt Mineral Research Centre.