Simulated Learning Model for Mineable Reserves Evaluation in Surface Mining Projects

• Author / Creator

  Cuba-Espinoza, Miguel A

• The amount of information available for characterizing the geology of a deposit increases over time due to the continuous acquisition of data during mining. Throughout the lifetime of a mining project, the block model and the mining sequence are periodically updated to account for this new data. The acquisition of additional data increases the accuracy of the block model and clarifies the optimal mining sequence. There has been extensive research on mine planning, but current techniques do not consider the decrease in uncertainty as additional information becomes available. Conventional paradigms assume either 1) the kriged model is correct and uncertainty due to multiple realizations does not change the mining sequence, or 2) the mining sequence is unrealistically adapted to each realization.

  A new paradigm is proposed for evaluating minable reserves of surface mining projects. This new paradigm accounts for the effects of the continuous acquisition of additional information during the mining of the deposit. In the implementation, multiple scenarios characterizing the dynamic nature of mining and data collection are generated. Each scenario accounts for how the mine may develop over time as new information is acquired. This provides a more realistic framework for evaluating mineable reserves with an appropriate level of uncertainty.

  The new paradigm can be used to evaluate infill drilling. The acquisition of additional information increases the revenue of the mining sequence as the block model becomes progressively more accurate. However, this increment in the revenue comes at the cost of implementing the infill program. In the new paradigm, the infill drilling strategies are evaluated in terms of their contribution to profit, difference between increment in revenue and cost of infill drilling.

  The design of the mining sequence of the long-term plan may be problematic as each scenario has its own version of the mining sequence. To overcome this problem, the mining sequences of the scenarios are condensed into a few representative mining sequences by implementing customized clustering techniques. These few representative mining sequences can be used to design the mining sequence of the long-term plan along with contingency plans.

• Subjects / Keywords

  • Infill drilling
  • Simulated learning model
  • Mineable reserve

• Graduation date

  Fall 2014

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3CZ32B87

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.