Circle geometric constraint model for open-pit mine ore-matching and its applications

Abstract

The circle geometric constraint model (CGCM) was put forward for resolving the open-pit mine ore-matching problems (OMOMP). By adopting the approaches of graph theory, block model of blasted piles was abstracted into a set of nodes and directed edges, which were connected together with other nodes in the range of circle constraints, to describe the mining sequence. Also, the constructing method of CGCM was introduced in detail. The algorithm of CGCM has been realized in the DIMINE system, and applied to a short-term (5 d) program calculation for ore-matching of a cement limestone mine in Hebei Province, China. The applications show that CGCM can well describe the mining sequence of ore blocks and its mining geometric constraints in the process of mining blasted piles. This model, which is applicable for resolving OMOMP under complicated geometric constraints with accurate results, provides effective ways to solve the problems of open-pit ore-matching.

Keywords

open-pit mine / block model / blasted-pile mining optimization / linear programming / mixed integer programming (MIP)

References

[1] WangQin.Mining science [M], 2007BeijingMetallurgy Industry Press344-394

[2] ShuHang.Ore quality balance and control [M], 1994BeijingMetallurgical Industry Press39-53

[3] YuanH.-yu.. United dynamic optimization study on the rational beneficiation feed grade [J]. Journal of University of Science and Technology Beijing, 2002, 24(3): 239-242

[4] XU Tie-jun, Yang Peng. Application of improved Genetic algorithm in mine ore blending [C]// The 3rd International Symposium on Modern Mining & Safety Technology Proceedings. Fuxin, 2008: 174–180.

[5] HuQ.-huai.. A mathematical model study on ore blending in mine production under restrictions [J]. Mining Research and Development, 2002, 22(6): 1-6

[6] JanaC., ChattopadhyayR. N.. Block level energy planning for domestic lighting-A multi-objective fuzzy linear programming approach [J]. Energy, 2004, 29: 1819-1829

[7] ArenasM., BilbaoA., RodriguezM. V.. Solving a multi-objective possibility problem through compromise programming [J]. European Journal of Operational Research, 2005, 164: 748-758

[8] GaoJ.-w., LiuB.-ding.. Fuzzy multilevel programming with a hybrid intelligent algorithm [J]. Computers & Mathematics with Applications, 2005, 49(9/10): 1539-1545

[9] ChenL. H., TsaiF. C.. Fuzzy goal programming with different importance and priorities [J]. European Journal of Operational Research, 2001, 133: 548-556

[10] LiuB.-d., LiuY.-kui.. Expected value of fuzzy variable and fuzzy expected value models [J]. IEEE Trans Fuzzy Syst, 2002, 10(4): 445-454

[11] WangBin.Study on optimization for ore blending system of limestone mine in hills [D], 2005WuhanWuhan University of Science and Technology49-58

[12] XuJ.-j., LiJun.Multi-objective decision theory and methodology [M], 2005BeijingTsinghua University Press86-99

[13] ChenB.-lin.Optimization theory and algorithm [M], 20052nd Ed.BeijingTsinghua University Press432-450

[14] PangB.-jun.Linear programming and stochastic programming [M], 2009ZhengzhouZhengzhou University Press1-16

[15] BiL., WangL.-g., ChenJ.-hong.. Fast triangulation algorithm for polygon regions and implementation [J]. Application Research of Computers, 2008, 25(10): 3030-3033

[16] HuangJ.-x., WangL.-g., BiLin.. The improved geometrical constraint model of open-pit mine boundary optimum and its application [J]. Journal of Chongqing University, 2010, 33(12): 78-83