When: Thursday 5th of August, 1pm AEST
Where: This seminar will be presented online via Zoom, RSVP here.
Speaker: Dr Mehala Balamurali
Title: Machine Learning Applications for Improved Ore Extraction and Tracking Uncertainty
Accuracy of grade control determines the success of mine that directly influences the profitability of the overall mine operation. This begins with the quality of the mining program that controls how the ore is collected, stored, and assayed geological data. This is then followed by the accurate prediction of grade variable of interest. These forcasted grades then determine the destination for waste and ore. Once the locations are determined, the material is moved using diggers and haulage trucks. In this seminar Mehala will talk about some of the methods used for increasing the efficiency of mining operations for maximizing ore extraction, interpreting the deposit, characterise the spatial uncertainty of grades through ore control block models and how this information can be utilised throughout the material movement pipeline.
Mehala completed her PhD at the University of New South Wales (UNSW) in 2012, where she investigated the important aspects of HIV infection from the dynamics of immune escape using mathematical models and computer simulation. Following her PhD, she continued Surveillance Evaluation and Research Program (SEPPH) work at the Kirby Institute UNSW to explore HIV/STI epidemic trajectories in Aboriginal populations. Mehala joined Rio Tinto Centre for Mine Automation (RTCMA) in 2013 as a post-doctoral research associate and was promoted to a research fellow in 2019. In RTCMA, Mehala has worked across different themes in multiple projects and is currently working with Perception and Technology (PT) theme. Before moving to Australia, Mehala held a permanent lecturing position at the University of Colombo School of Computing, Sri Lanka (2004-2008). Mehala’s research is interdisciplinary. It seeks a better understanding of decision making in general, currently with specific applications in industrial automation in the mining sector. Her research interest is finding solutions for discipline-specific problems by applying novel methodologies and using data to derive insight. Mehala is also interested in teaching, student supervision and mentoring.