Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by...Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value （NPV）, are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% （135 M$ to 151 M$）. A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine.展开更多
A methodology applicable at any phase of a surface mining project for evaluating its current technical and economic feasibility is presented.It requires the typically available quantitative data on the ore-body,with i...A methodology applicable at any phase of a surface mining project for evaluating its current technical and economic feasibility is presented.It requires the typically available quantitative data on the ore-body,with its three-dimensional block model developed upon accurate interpolations.Thus it allows estimations of exploitable reserves in function of various cut-off grades,such as the average grade of mineable ore,the tonnages of ore and waste rock,stripping ratios and proft estimates for different production levels.If cost evaluations of essential mine operations are available(such as ore mining,waste removal,ore concentration,transportation,indirect project costs and expected concentrate selling prices),the methodology will provide clear indications on the economic feasibility of mining,including the best available options at any moment.Simple expressions are developed on the basis of a proft mathematical function and an application example is presented with data available from an existing iron ore deposit.展开更多
文摘Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value （NPV）, are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% （135 M$ to 151 M$）. A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine.
文摘A methodology applicable at any phase of a surface mining project for evaluating its current technical and economic feasibility is presented.It requires the typically available quantitative data on the ore-body,with its three-dimensional block model developed upon accurate interpolations.Thus it allows estimations of exploitable reserves in function of various cut-off grades,such as the average grade of mineable ore,the tonnages of ore and waste rock,stripping ratios and proft estimates for different production levels.If cost evaluations of essential mine operations are available(such as ore mining,waste removal,ore concentration,transportation,indirect project costs and expected concentrate selling prices),the methodology will provide clear indications on the economic feasibility of mining,including the best available options at any moment.Simple expressions are developed on the basis of a proft mathematical function and an application example is presented with data available from an existing iron ore deposit.
