A value adding approach to hard-rock underground mining operations: Balancing orebody orientation and mining direction through meta-heuristic optimization
Martha E. Villalba Matamoros , Mustafa Kumral
Journal of Central South University ›› 2020, Vol. 26 ›› Issue (11) : 3126 -3139.
Underground mines require complex construction activities including the shaft, levels, raises, winzes and ore passes. In an underground mine based on stoping method, orebody part(s) maximizing profit should be determined. This process is called stope layout optimization (SLO) and implemented under site-specific geotechnical, operational and economic constraints. For practical purpose, the design obtained by SLO shows consecutive stopes in one path, which assists in defining the mining direction of these stopes. However, this direction may not accommodate the spatial distribution of the ore grade: if the orebody orientation and mining direction differ, the value of the mining operation may decrease. This paper proposes an approach whereby paths in the SLO are defined as decision variables to avoid the cost of mining in the wrong direction. Furthermore, in the genetic-based formulation, which accounts for orebody uncertainty, a robust cluster average design process is proposed to improve SLO’s performance regarding metal content. A case study in narrow gold vein deposit shows that the profit of an underground mining operation could be underestimated by 25%–48% if the algorithm ignores stope layout orientation.
underground mine planning / orebody uncertainty / orebody orientation / mining direction / stope layout optimization
| [1] |
STADLER H. A framework for collaborative planning and state-of-the-art [M]// Supply Chain Planning. Springer, 2009: 1–26. |
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
RUSSELL S, NORVIG P. Artificial intelligence: A modern approach [M]. 3rd ed. Pearson Education, Inc., 1995. |
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
OVANIC J, YOUNG D S. Economic optimization of stope geometry using separable programming with special branch and bound techniques [C]// MITRI H S. The 3rd Canadian Conference on Computer Applications in the Mineral Industry. Montreal, 1995: 129–135. |
| [11] |
VOß S. Meta-heuristics: The state of the art [M]// NAREYEK A. Workshop on Local Search for Planning and Scheduling. Springer, 2000: 1–23. |
| [12] |
|
| [13] |
ALFORD C, HALL B. Stope optimisation tools for selection of optimum cut-off grade in underground mine design [C]// Project Evaluation Conference. Melbourne, 2009: 137–144. |
| [14] |
NIKBIN V, ATAEE-POUR M, SHAHRIAR K, POURRAHIMIAN Y. A 3D approximate hybrid algorithm for stope boundary optimization [J]. Computers & Operations Research, 2018. DOI: https://doi.org/10.1016/j.cor.2018.05.012. |
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
JOURNEL A G, HUIJBREGTS C J. Mining geostatistics [M]. Academic Press, 1978. |
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
JORC. Australaisian code for reporting of exploration results, mineral resources and ore reserves [M]. AusIMM and AIG. 2012: 44. |
| [31] |
MITCHELL M. An introduction to genetic algorithms [M]. Massachusetts Institute of Technology, 1999. |
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
/
| 〈 |
|
〉 |