Production Scheduling and Boundary Optimization in Block Caving Mines under Geologic and Material Flow Uncertainty

• Author / Creator

  Noriega, Roberto

• Block caving methods have become desirable underground massive mining techniques as close to the surface, high/grade deposits are exhausted, and current open-pit mines reach their final mining limits. Block caving mining is the only method that can rival the economies and production capacity of open-pit exploitation, offering low operating costs as well as reduced environmental impacts in comparison. Some of the disadvantages, however, are high capital cost requirements, long development times required, and the operational challenge associated with caving mining practices. Block caving mining is based on the undercutting of the rock mass, inducing fragmentation on the overlying mass and extract it as it flows through a developed opening called drawpoints. The flow component poses a major operational challenge as the potential dilution and mixing introduce a large source of uncertainty in the economic forecasts for planning purposes. Moreover, geological uncertainty in relation with the grade and rock type estimation for the generation of numerical deposit models is also an issue.
  This research presents a stochastic optimization model incorporates explicitly geological and material flow uncertainty to generate an optimal life of mine schedule for block caving mines at a block model scale. The optimization framework works over two steps: it initially aggregates the individual blocks into production units based on desired drawpoint spacing, representing the draw columns, and mining units based on the minimum draw rate, representing the slices that are commonly used in block caving mine planning. The mining units then become the basic scheduling unit for the stochastic integer programming scheduling model. Uncertainty is characterized by the development of multiple numerical deposit simulations. Geological simulations are developed using geostatistical simulation techniques, with sequential indicator simulation for rock types and sequential Gaussian simulation for grades. Material flow uncertainty is integrated by the concept of a cone of movement. As each mining unit is extracted, it leaves a void that can be filled by any fraction of the material on its surroundings based on the flow properties of the broken rock mass. A cone, based on potential horizontal displacement and vertical slip angle of the broken rock mass is used to generate grade and tonnage mixing scenarios for each mining unit. The cone is placed at the bottom of each mining unit, and a random sample of the blocks from the deposit model that are contained within it, “filling” the mining unit, is used to update its grade and tonnage. This allows for scenarios where each mining unit material could potentially be part of fractions of adjacent units as well as waste blocks at the orebody accounting for dilution.
  The stochastic mathematical model takes as an input the set of geological and material flow simulations to generate a single best schedule that maximizes the expected economic value from the uncertainty sources, while minimizes the deviations incurred in production and average grade targets due to the variability between the potential scenarios. The operational constraints considered in the model include mining capacity targets, average production grade, minimum and maximum heights of draw, minimum and maximum vertical draw rates, undercut development rate, maximum adjacent relative height of draw, mining precedence both horizontal and vertical, and mineral reserves.
  The model was tested in a case study, for which a set of 20 geology simulations were obtained. A deterministic, stochastic with only geological uncertainty and stochastic with both geological and material flow uncertainty schedules were generated for comparison and evaluation purposes. Moreover, the deterministic mine sequence was evaluated over the uncertainty scenarios to quantify the impact of the uncertainty in the economics of the project. The models were used at different undercut elevations, to identify the most profitable one. The deterministic case yields the best undercut at 635m while both stochastic cases find it at 605m, a significant difference. The deterministic schedule, when evaluated over the geological and geological and flow scenarios can lead to an expected NPV 8% to 13% lower than those reported. Although the stochastic schedules generate an expected NPV that is 3% to 11% lower than the reported NPV for the deterministic case, it is a more reliable estimate. Also, larger footprints are obtained through the stochastic schedule which could potentially unlock more value as more information is obtained.

• Subjects / Keywords

  • material flow uncertainty
  • Block caving
  • geological uncertainty
  • production scheduling

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-md58-5r55

• License

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