Production Forecasting to Support Mining Operations


Production forecasting is an important tool used to optimise mining operations through fact-based decision making. These techniques are equally applicable to the forecasting of both precious metals and bulk commodity production output. The forecast is based on a model of the process or supply chain. This model is simulated into the future using inputs such as the mine production, plant capacity, and scheduled downtime. Parameters can be varied between simulation runs to qualify the impacts of feed uncertainty, and unplanned outages. Once production output and risk have been quantified, the business can make informed decisions and manage threats to the production output. This session explores how MATLAB and Simulink are used to develop a complete production forecasting application.

This session will:

  • Create a model of a production supply chain including the processing stages. This will leverage the graphical block-based modelling environment such as Simulink and SimEvents for continuous and discrete event simulation capabilities. A library of unit process models including a stockpile, ore handling plant, and vehicle loading will be developed.
  • Implement an optimal blending strategy leveraging constraint-based optimisation algorithms to build the feed piles for the ore-handling plant.
  • Simulate the model to estimate production over the life-of-mine. Production risk will be quantified using Monte Carlo simulation.
  • Perform global optimisation of the process by balancing the trade-off between product quality, time to market, and capital investments. The trade-offs are evaluated using the Net Present Value which is calculated from the life of mine simulations.
  • Deploy the tool as a web application.

A model of an Iron Ore bulk commodity supply chain will be used to demonstrate the concepts.

Go to Mining Seminar Series Overview page



MATLAB and Simulink are used to develop a complete production forecasting application.

About the Presenter

Samuel Oliver is a consultant engineer from MathWorks who specializes in helping organizations improve their operations and logistics through statistical analysis, along with predictive modelling and simulation. Solutions provided typically include the integration of modern IT technologies to support data analytics, data science, big data, cloud, with more traditional OT or process industry production technologies. He has gained extensive experience over the last 7 years working on a broad range of problems with customers in Iron-Ore, Copper and Gold mining across the value chain. Prior to joining MathWorks, Sam work in the automotive industry developing advanced dynamic control systems. He received an M.Eng.Sc. in mechatronics, a B.Eng. in mechanical and manufacturing, and a B.Sc. in computer science from the University of Melbourne, Australia.

Emmanuel is an application engineer at MathWorks who first joined the company as a training engineer. He taught several MATLAB, Simulink and SImscape courses as well as specialized topics such as machine learning, statistics, optimization, image processing and parallel computing. Prior to joining MathWorks, he was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a PhD in Mechanical Engineering from Virginia Tech. He also worked as a Systems / Controls Engineer at Cummins Engine Company and as a research assistant in several research institutions in California and Virginia.


This live webinar has ended. You can now view the on-demand webinar.

See on-demand webinars