Improving mining functionality using an algorithm based on soap bubbles

Using an algorithm based on the physical properties of soap bubbles, researchers at the University of Melbourne have developed software to solve operational issues that have not been implemented in traditional approaches to open pit mine design.

The outcome

Miners will now be able to optimise the value of ore they are able to extract from open-cut mines, by designing pits in nested clusters that provide connectivity and minimum bench width needed for equipment access, using software developed by University of Melbourne researchers through their start-up company, Thinking Mine Design.

The changes to the way open-cut mines are designed have the potential to support values of between 5 and 15 per cent greater than previous pit construction allowed, depending on the complexity of the design.

The need

Large-scale mining of primary resources presents a range of complex challenges, and companies have been investing heavily in finding ways to make mining more cost-effective, and efficient.

Open-pit mining is the most common method used to excavate ore deposits that are relatively close to the surface of the earth. For decades, the industry has been trying to find a way of incorporating geometrical constraints of the shapes of the pits into the design process, in order to maximise value and satisfy operational needs.

Traditionally, open-pit mines have been built through a series of nested pits, known as pushbacks, leading to the easily recognisable terraced pits found in mines throughout the world.

Developing the solution

Now, new software developed by mathematician Professor Hyam Rubinstein and mining engineer Dr Juan L. Yarmuch can be used to address the problems that have faced the industry for many years.

The ‘Bubble Pit’ software uses the geometric properties of masses of soap bubbles to find the optimal design shape for the nested pits.

Key ideas for this project were developed during Dr Yarmuch’s University of Melbourne PhD, which was co-supervised by Professor Rubinstein.

After receiving a grant through AMIRA Global, the Bubble Pit software was completed in July 2021. The software development was supported by Australian’s Newcrest Mining and the world’s largest gold mining company, Newmont and is being trialled in their mining operations.

Next steps

The team is now looking at two additional software phases exploring how the bubble model can address scheduling and haulage. Newmont and Newcrest have agreed to sponsor the further development based on the success of the Bubble Pit software.

Since the upfront investment in establishing large mines is so significant, it’s important to maximise the efficiency of scheduling and hauling material, both of which are ‘hot topics’ in the mining industry. The bubble platform developed by Thinking Mine Design can help make these processes more effective.

Thinking Mine Design has an experienced technology manager David Niall to assist with business strategy.

Funding

AMIRA Global with sponsors Newcrest and Newmont

Patents

Mine Planning method and system. Inventors: J.H. Rubinstein, J.L. Yarmuch. Patent numbers:CA3100082A1, PE20201442A1, US20210208305A1, CL2020003055A1, BR112020024085A2.

Mine planning method and system

The background Intellectual Property (IP) is owned by the University of Melbourne through a patent and Thinking Mine Design has an exclusive agreement with the University of Melbourne for commercialisation of the IP.

Publications

J. L. Yarmuch, M. Brazil, J. H. Rubinstein, D. A. Thomas, Optimum Ramp Design in open pit mines, Computers and Operations Research, 115, (2020)  https://doi.org/10.1016/j.cor.2019.06.013

J. L. Yarmuch, M. Brazil, J. H. Rubinstein, D. A. Thomas, A mathematical model for mineable pushback designs, International Journal of Mining, Reclamation and Environment, (2021), 1-17  https://doi.org/10.1080/17480930.2021.1885582

J. L. Yarmuch, M. Brazil, J. H. Rubinstein, D. A. Thomas, A model for open-pit pushback design with operational constraints, Optimisation and Engineering, Nov. 2021 DOI https://doi.org/10.1007/s11081-021-09699-9

People

Professor Hyam Rubinstein, School of Mathematics and Statistics, University of Melbourne

Dr Juan L. Yarmuch, Assistant Professor, Department of Mining Engineering, Universidad de Chile