Open Pit Optimization for Short-term Forecasting Using Mining Simulator

Simulation modelling has long been used as a decision support tool in the mining industry. This is typically done to address issues on the strategic time horizon, with a heavy focus on experimentation and sensitivity analysis. These issues include mining equipment selection, pit optimization, design and operation of the mine-plant interface, testing the robustness of a mine plan and blending.

Mining simulators can be used to forecast production in the short term to test the quality of truck dispatch decisions (allocation of trucks to loaders) and evaluate the value of alternate scheduling rules. It can also be used to produce a forecast of the likelihood of achieving a shift target and allow operators to test what-if options to reduce the risk of production loss or reduce costs by putting excess equipment on standby. Being able to make these decisions with confidence helps to drive improvements in operations efficiency.

In this project, mining simulator was developed using the AnyLogic mine modeling software to perform mining productivity improvement and optimize the load and haul operations of an open-pit mine. The model evaluates the ability of a given set of mobile fleet to achieve a target mine schedule. It includes a discrete-event simulation of load and haul operations and is integrated with a dispatch scheduling algorithm that allocates trucks to shovels.

The model used as a mining simulator was applied to evaluate short-term production options for a single pit in a large iron ore mine. The production target, including individual targets for blending of ore sources over the course of the shift, was chosen to reflect a particular shift in 2017. The pit optimization model was configured to represent the road networks and equipment available during the shift, including 21 trucks and 8 loaders. The model was validated against actual data for the period to assess cycle-progress prediction error and bias.

Experiments were run to perform further mining productivity improvement and assess the impact of the production levels available within the shift. These included increasing or decreasing the number of trucks available as well as changes to the balking rules at the crusher. 30 replications of each simulation were run for representative statistics.

Increasing truck numbers led to increased likelihood of achieving the plan, albeit at lower productivity. Conversely, reducing truck numbers led to reduced likelihood, higher productivity and reduced queuing time. These and other results of pit optimization process were intuitive but could not previously be quantified by a dispatch operator during live production.

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