Advanced Process Prediction and Control Group

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' research profile

Dr Kristy Nell
View Kristy Nell's research profile

Dr Christian Zuluaga Bedoya
View Christian Zuluaga Bedoya's research 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.

See more on the Cyanide socio-technical learning lab