Long-term mine plans are based on forecast future metal prices. By the time the development is put in place, the forecasts may have been proved wrong and the production plan might not meet the company's financial objectives. At that point, the common reaction to this situation is to create a new revised long-term plan and spend more capital, only to find out at a later time that the metal prices have changed again. This results in an inefficient use of capital with low returns to the investors. The objective of this thesis is to develop a methodology to determine the cut-off grade and production rate of a narrow-vein underground mine such that the long-term strategic plan is robust. As a requirement to do so, it is necessary to have a good understanding of the resources, revenues, capital and operating costs as a function of the design parameters. Also, the operational limits of the mine must be determined so that the solution is practical. Afterwards, annual metal prices are randomly generated with a Monte Carlo process on stochastic metal price models, and the combination of production rate and cut-off grade yielding the highest net present value is identified and recorded. This process is repeated many times, and the probabilities of the solutions occurring at any given design combination are calculated. The results are plotted on a bubble graph, where the size of a bubble is directly proportional to the probability a solution occurs at that point. Finally, the combination with the largest bubble is the solution, as this point has the highest probability of yielding the highest net present value in most circumstances. The model was first tested on an actual gold-copper orebody where very detailed resource and cost information was available. The methodology was applied with success and the solution reflected the important impact of the copper milling and roasting process on revenues. Other tests were then done on a hypothetical gold orebody and the results showed a great degree of sensitivity to the average grade of the deposit. / Thesis (Ph.D, Mining Engineering) -- Queen's University, 2008-04-25 12:42:24.623

Mining operations exploit mineral deposits, processing a portion of the extracted material to produce salable products. The concentration of valuable commodities within these deposits, or the grade, is heterogeneous. Not all material has sufficiently high grades to economically justify processing. Cut-off grade is the lowest grade at which material is considered ore and is processed to create a concentrated commodity product. The choice of cut-off grade at a mining project can be varied over time and dramatically impacts both the operation of the mine and the economics of the project. The majority of literature and the accepted industry practices focus on optimizing cut-off grade under known commodity prices. However, most mining operations sell their products into highly competitive global markets, which exhibit volatile commodity prices. Making planning decisions assuming that a given commodity price prediction is accurate can lead to sub-optimal cut-off grade strategies and inaccurate valuations. Some academic investigations have been conducted to optimize cut-off grade under stochastic or uncertain price conditions. These works made large simplifications in order to facilitate the computation of a solution. These simplifications mean that detailed mine planning data cannot be used and the complexities involved in many real world projects cannot be considered. A new method for optimizing cut-off grade under stochastic or uncertain prices is outlined and demonstrated. The model presented makes use of theory from the field of Real Options and is designed to incorporate real mine planning data. The model introduces two key innovations. The first is the method in which it handles the cut-off grade determination. The second innovation is the use of a stochastic price model of the entire futures curve and not simply a stocastic spot price model. The model is applied to two cases. The first uses public data from a National Instrument 43-101 report. The second case uses highly detailed, confidential data, provided by a mining company from one of their operating mines. / Thesis (Master, Mining Engineering) -- Queen's University, 2012-04-30 22:36:51.257