Optimising combined open pit and underground strategic plan

Abstract

This paper examines the case of a large open pit that is being planned at the site of an existing underground mine. Strategic planning work is undertaken to investigate the project value associated with an expansion of underground operations in conjunction with the proposed pit. Previous analysis by mine planners has determined that any depletion of the available open pit resource that changes the optimum pushback sequence will have a significant negative impact on overall project value. As a result, the main objective of this case study is to determine a strategic plan capable of yielding the optimum economic value for a combined undergroundopen pit operation. To investigate the viability of an underground expansion, a system to identify the stope resource that would add value to a combined operation if mined ahead of the pit is required. The main solution to this problem is the use of a resource model variable that defines the blocks which have greater discounted value if mined by stoping rather than by the open pit. From this potential underground resource, a series of mineable stope shells are generated at various cut-offs. This stope reserve data formed the input to an optimisation process used to optimise the underground mining plan, at a conceptual level, for various project configurations. Then, a dynamic programming mathematical program is used to evaluate the optimum value of a combined open pit and underground operation. The results generated in the case study presented herein provide a clear focus and direction for the next level of detailed mine design and planning.

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

“…The indicators can be broadly grouped into geologic, operational, geotechnical, economic, and mine management strategy on global business economic outlook [7,61]. These transition indicators are key to the identification and development of the set of constraints required for optimizing the OP-UG mining options and transitions planning problem [4,9,25].…”

“…These include workforce requirement, shape and size of the orebody, and geotechnical properties of the rock formation. Economic parameters such as discounting rate of expected cash flows and OPUG mining costs, and primary factors including the competence of mine management, characteristics of the geology of the orebody, stripping ratio, productivity rate, and capital cost requirements for the UG mining option will affect the decision for OP-UG transition as well [9,63].…”

“…In resource development planning, optimizing resource exploitation depends largely on the mining option used in the extraction. The term mining options optimization has been used by researchers and professionals to refer to the initiatives or choices undertaken in the extractive industry to expand, change, defer, abandon, or adopt strategies for a mining method(s) and sometimes investment opportunities; based on changing economics, technology, or market conditions [2][3][4][5][6][7][8][9][10]. For mineral deposits with orebodies that extend from near surface to several depths, such orebodies are amenable to different variations of either open pit mining, underground mining, or both.…”

“…Existing optimization algorithms used in attempting the mining options problem include the Lerchs-Grossman (LG) algorithm, Seymour algorithm, floating cone technique, network flows, dynamic programming, neural network, theory of graphs, and mathematical formulations [25]. Some authors have studied the surface-underground mining options and transitions optimization problem with available commercial software packages including Surpac Vision, Datamine's NPV Scheduler, Whittle Four-X, Geovia MineSched, integrated 3D CAD systems of Datamine, Vulcan, MineScape, MineSight, Isatis, XPAC, Mineable Reserve Optimizer (MRO), Blasor pit optimization tool, COMET cut-off grade and schedule optimizer, and Datamine Studio 3 [7,9,17,26,27]. The techniques used by these authors are not generic but mostly scenario based and often lead to localized optimization solutions.…”

“…Although Bakhtavar, Shahriar, and Oraee [3] employed a heuristic algorithm to compare economic block values computed for both open pit and underground mining on a depth flow basis to solve the SUMOTO problem, results from the heuristic algorithm do not offer a measure of optimality as it is the case in mathematical programming optimization. Notable authors that used mathematical programming to solve the mining transition problem limit their model to the determination of transition depth and block extraction sequence for the open pit and underground mining operations [2,4,8,9,[11][12][13][14][15][17][18][19]24,25,28,29]. Similarly, other authors have developed stochastic mathematical programming models to solve the surface-underground mining options and transitions optimization problem.…”