抄録
Surface mine planning techniques are reasonably sequential and consist of geological modeling, deposit ranking, pit design and scheduling. In all deposits a pre-requisite is a sound geological model. Ranking uses such a model to find the economic pit limits and a broad mining path. The mine is then designed and scheduled, normally will involve a number of iterations to resolve issues such as blending of ore to a specification and smoothing ore and waste mining within a reasonably stable fleet capacity. In some cases these iterations are unsuccessful because the original ranking was flawed.
Dynamic commodity prices and increasing competition have reduced the technical staffing levels in many companies while increasing the requirement for faster mineral evaluations. Two recent developments in coal mine evaluation are presented. The first is a pit optimization method based on a variable block size to honor thin coal seam geology. The second is a scheduler using expert systems. Both techniques allow faster and more accurate coal project evaluations.
Z grid pit optimization is used in open pit mine planning to generate a pit, which is break-even at its limits. For a block model, coal values and mine costs; the optimum pit is the maximum revenue pit. A slightly smaller pit will leave revenue on the pit walls, while a slightly bigger pit is unprofitable at its limit. Pit optimization is also used to generate nested pits, which are optimum pits for incremental coal values. The smallest pit is the most profitable and to maximize NPV, and mitigate risk, should be mined first. Mining through the nested pits provides an initial schedule path
Life of mine (LOM) scheduling considers the planning, not as separate periods, but by simultaneously examining multiple periods on a project life basis. These LOM planning systems use mixed integer linear programming techniques to optimise the mine schedule, including blending. By considering multiple periods future profits and capital spending can be discounted and thus a LOM plan maximizes project NPV.
