Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming (MILP) optimization framework. The MILP formulation maximizes the Net Present Value (NPV) of the reserve when extracted with (i) open pit mining, (ii) underground mining, and (iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.

Strategic mining options optimization: Open pit mining, underground mining or both

An optimal layout or three-dimensional spatial distribution of stopes guarantees the maximum profitability over life span of an underground mining operation. Thus, stope optimization is one of the key areas in underground mine planning practice. However, the computational complexity in developing an optimal stope layout has been a reason for limited availability of the algorithms offering solution to this problem. This article shares a new and efficient heuristic algorithm that considers a three-dimensional ore body model as an input, maximizes the economic value, and satisfies the physical mining and geotechnical constraints for generating an optimal stope layout. An implementation at a copper deposit demonstrates the applicability and robustness of the algorithm. A parallel processing based modification improving the performance of the original algorithm in terms of enormous computational time saving is also presented.

Designing an optimal stope layout for underground mining based on a heuristic algorithm

mining by open pit and underground methods and transition from open pit to underground mining are among the most important challenges in mining engineering, and have been recently considered in many research investigations. Different researchers have attempted to solve these problems and presented solutions based on empirical, heuristic, and mathematical programming methods. In these works, the transition problem has been examined in three modes: (1) optimizing transition from open pit to underground mining, (2) determining the point, depth, or limit of transition from open pit to underground operation, and (3) determining the time of transition. Bakhtavar, Shahriar, and Mirhasani (2012) reviewed the solutions proposed for the transition problem, a summary of which is given in Table I, together with the most recent solutions. It can be seen from Table I that few of the solutions have an empirical basis, ultimately leading to only an estimated response. The main weaknesses of the empirical solutions are ignoring the time value of money, production planning, and uncertainties. Most of the solutions for the transition problem have a heuristic basis and follow a similar process by use of the cash flow solution introduced by Nilsson (1982). They make an economic comparison among different options, including open pit and underground mining. Some other heuristic solutions, such as the algorithms proposed by Bakhtavar and Shahriar (2007) and Shahriar (2007) and Bakhtavar, Shahriar, and Oraee (2008a, 2008b) are based on an economic comparison of open pit and underground mining methods at different levels of an ore deposit. The main drawback of the heuristic solutions is their complete dependence on the optimization algorithms of surface and underground mining. For this reason, they cannot solve the transition problem independently. Another deficiency of the heuristic solutions, excluding the research presented by Opoku and Musingwini (2013), is failure to consider uncertainty. This deficiency leads to the small difference between their responses and the reality. The working steps of the solution presented by Opoku and Musingwini (2013) are mostly similar to the solution by Visser and Ding (2007), except that Opoku and Musingwini emphasized uncertainties during geological simulation (in kriging) and prepared production planning and economic models employing conventional mining software. Among the solutions for the transition problem, studies by Bakhtavar, Shahriar, and Mirhasani (2012), Newman, Yano, and Rubio (2013), Chung, Topal, and Ghosh (2016), and MacNeil and Dimitrakopoulos (2017) have a mathematical basis and are more stable than others. These methods can be considered a foundation and then developed or modified to achieve an optimum solution to the transition problem, similar to the development of the optimization models of final pit limits and production planning. A stochastic mathematical model for determination of transition time in the non-simultaneous case of surface and underground mining by E. Bakhtavar*, J. Abdollahisharif†, and A. Aminzadeh*

/pdf/a-stochastic-mathematical-model-for-determination-of-3aai0deocy.pdf

A stochastic mathematical model for determination of transition time in the non-simultaneous case of surface and underground mining

The decision to exploit a mineral deposit that can be extracted by open pit and/or underground mining involves consideration of the following options: (a) independent open pit extraction; (b) indep...

A mixed integer linear programming framework for optimising the extraction strategy of open pit – underground mining options and transitions

Use of an artificial crown pillar in transition from open pit to underground mining

Criteria or transition indicators to guide the complex decision problem of open pit to underground (OP-UG) transition are currently not well defined and documented as most mines rely on context-dependent experiences of their respective project feasibility teams. This problem is further compounded by the fact that transition indicators such as net present value (NPV), stripping ratio and commodity price are dynamic over time. Therefore, traditional deterministic approaches to solving the OP-UG transition problem cannot fully address the practicalities that mining companies face due to the variability of the transition indicators. This paper therefore reviews the OP-UG transition decision problem from a stochastic perspective. Transition indicators identified from an extensive literature review were used to benchmark four case study mines against one which had recently made the OP-UG transition, subsequently leading to the development of a generic OP-UG transition model for gold mines. The model indicates t...

Stochastic modelling of the open pit to underground transition interface for gold mines

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Stochastic modelling of the open pit to underground transition interface for gold mines