A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

More documents

Recommendations

Info

limestone and some other industrial minerals, this formula cannot be applied to evaluate the economic value of blocks. The reasons is that, the aim of mining the latter group of minerals (i.e. iron ore, coal, limestone), is to achieve a predetermined quality, and the price obtained for blended ore is constant and it does not depend on the quality of each individual block. In fact, there are various blocks with different qualities and ore-types which must be blended to produce a marketable product with regard to consumer’s targets. Ore-types are assigned on the basis of ore grade and contaminants. The solution to the problem of how to generate an optimal pit which contains an optimal mix of ores blocks is a combined operation of pit and blend optimizing. The aim of combined pit and blend optimizing is to produce a pit which yields different oretypes in such quantities that the sum of the profits of mining and blending operation is maximized. Mol and Gillies (1984) take the advantages of grade-tonnage curve to determine a cut-off grade in iron ore deposits, in accordance with the blend requirements. The main drawback of the approach is that it does not optimize the pit design. This technique may also lead to abandoning of some low grade blocks with low impurities, which might be included in the blended product. Srinivasan and Whittle (1996) developed a heuristic method to combine pit and blend optimization. Their method iteratively changes the price of each ore type until the quality of ore types mined is equal to the blending requirements. Osanloo and Rahmanpour (2012) developed an approach based on a combination of Binary integer programming (BIP) and network flow. In this method, those blocks that meet the blending requirements are selected using BIP. Then applying maximum flow technique, the final pit limit of the mine considering blending options is determined. This approach iteratively uses BIP to select the ore blocks that meet the blending requirements, which is quite time consuming. Therefore, there is a need for some fast algorithms in order to optimize the blending and pit design. This will enable the planner to compare any blending options to select the most suitable and profitable case. Section 2, briefly reviews the available methods for open pit design. Section 3, provides a general mathematical model for blend optimization. In section 4, the combined approach introduced by Osanloo and Rahmanpour (2012) will be described. Then a self-adapting differential evolutionary algorithm is developed for blend optimization. The proposed algorithm is applied to determine the final pit limit for Gol-E-Gohar iron ore mine which is discussed in section 5. Section 6 gives the final remarks of the paper. 2 <strong>PIT</strong> OPTIMIZATION The aim of pit optimizing is to designs a pit in which (1) pit slope constraints are obeyed, and (2) total value (profit) of the pit is maximized. Pit value is the summation of those block-values that are included in the final pit. BEVs are normally calculated via equation 1. After the calculation of BEVs and developing an economic block model, the ultimate pit limit of the mine can be determined. Various techniques are available to determine the ultimate pit limit. When the ultimate pit limit is determined it means that the value of ore blocks (income) can pay the costs of removing the overlaying waste block (costs). Inside the pit limit, mining operations is economic with the highest profit, and outside the pit limit, open pit mining is not likely to be profitable. Optimum pit design methods are divided into Heuristic and Rigorous methods (Kim, 1979). Heuristics are short cuts to solve the ultimate pit limits problem and heuristics such as moving cone technique are not likely to determine the optimum solution of the ultimate pit limit problem. On the other hand, these methods are easy to implement but it should be noted that they lack the rigorous mathematical proof. Pana (1965) introduced the moving (floating) cone technique. This technique creates a cone for 120
23 rd every ore block and searches for any cone with positive value. Korobov (1974) attempted to improve the Pana’s moving cone method (Kim, 1979). Lerchs and Grossman (1965) used the concept of dynamic programming and they introduced their well-known twodimensional algorithm in order to find an optimum pit limit (Lerchs and Grossman, 1965). Later, Johnson and Sharp (1971) used this idea in order to design a 3Dimensional pit. Koenigsberg (1982) used dynamic programming to find a 3Dimensional optimum pit limit. It was also Lerchs and Grossman (1965) who introduced the use of graph theory in optimum pit design (LG algorithm). They converted the block model of the mine into a graph and determine the ultimate pit limits by solving for the maximum closure of the graph. Zhao and Kim (1992) used this idea with some modifications and they claim that their method is simpler and faster than the LG algorithm. Tolwinski and Underwood (1998) used the concept of graph theory and mathematical programming and solve the dual form of the ultimate pit limit problem. The concept of linear programming in pit limit design problem was first used by Meyer (1969). He made so many hypotheses in his work which results in a non-optimal solution. Huttagosol and Cameron (1992) dealt with pit design as a transportation problem. Pit parameterization is another method introduced by Matheron (1976). In this method instead of directly searching for the pit limit, a parameterization function is determined combine the pit limit determination and production planning using genetic algorithm. Network flow and maximum flow algorithms are other methods for solving the ultimate pit limits. Johnson (1969), Picard (1976), Giannini (1991), Yegulalp et al (1992) and Hochbaum and Chen (2000) used the algorithms of maximum flow to determine the pit limit. Hochbaum and Chen (2002) have extended the recent improvements in network flow algorithms into the LG algorithm (Muir, 2004). Newman et al. (2010) provided a thorough review of pit design algorithms. The traditional open pit optimization algorithms are affected by the uncertainty in key input parameters, leading to suboptimal solutions and deviations from production plans. Whittle and Bozorgebrahimi (2004) developed the concept of hybrid pits to determine the ultimate pit limit in the presence of grade uncertainty. In this approach, conditional simulation and LG algorithm are combined, and using set theory, it leads to the creation of pit outlines with quantifiable degree of risk. The hybrid pits can be used as design guides to avoid the higher uncertainty in early stages of a mine development. Meagher et al. (2009) combined conditional simulation and maximum flow theory to determine the ultimate pit limit and push back design in the presence of grade and price uncertainty. Osanloo et al. (2009) take the advantage of real option to determine the ultimate pit design, in the presence of price uncertainty. The algorithms, developed in the presence of uncertainty are beyond the scope of this paper. In all the algorithms developed to determine the ultimate pit limit, it is assumed that, BEVs are calculated correctly. It should be noted that, the BEVs of the blocks blended to be marketable are the same. However, pit optimization techniques do not deal with the blending issues. 3 BLEND OPTIMIZATION Different objective functions can be defined and used for blend optimization, such as, (1) the total tonnage of blended ore is maximized, while satisfying grade constraints, and (2) the cost of producing a unit of blended product is minimized by determining the ratios in which ore-types should be used, given a unit cost for each ore-type, and satisfying grade constraints of final product. The mathematical model to maximize the total tonnage of single blended product is presented in equations 2-5. In this model it is assumed that there is just one final blended 121
Page 1 and 2:
Chapter - A OPEN PIT MINING
Page 3 and 4:
23 rd Electric Drive Mining Class
Page 5 and 6:
23 rd change out at defined interv
Page 7 and 8:
23 rd D f = Fuel Density A typical
Page 9 and 10:
23 rd Gold-Mining and Ornamental S
Page 11 and 12:
23 rd greywackes and conglomerates
Page 13 and 14:
23 rd petrographic assemblages are
Page 15 and 16:
23 rd two main pudding layers met
Page 17 and 18:
23 rd (mortars and mills), made wi
Page 19 and 20:
23 rd Associazione Lombarda di Arc
Page 21 and 22:
23 rd
Page 23 and 24:
23 rd
Page 25 and 26:
23 rd
Page 27:
23 rd
Page 30 and 31:
30
Page 32 and 33:
32
Page 34 and 35:
34
Page 36 and 37:
36
Page 38 and 39:
38
Page 41 and 42:
23 rd
Page 43 and 44:
23 rd
Page 45 and 46:
23 rd
Page 47 and 48:
23 rd
Page 49 and 50:
23 rd
Page 51 and 52:
23 rd
Page 53 and 54:
23 rd
Page 55 and 56:
23 rd
Page 57 and 58:
23 rd
Page 59 and 60:
23 rd
Page 61 and 62:
23 rd
Page 63 and 64:
23 rd
Page 65 and 66:
23 rd
Page 67 and 68:
23 rd
Page 69 and 70: 23 rd
Page 81: 23 rd
Page 84 and 85: analisys degradation and erosion ri
Page 86 and 87: 3 METHOLOGICAL APPROACH AND EQUIPME
Page 88 and 89: Figure 3. The UAV multirotor platfo
Page 90 and 91: Table 2. Final results of data proc
Page 101 and 102: 23 rd
Page 103 and 104: 23 rd
Page 105 and 106: 23 rd Ultimate Pit Limit Design on
Page 107 and 108: 23 rd Figure 1 shows the procedure
Page 109 and 110: 23 rd Figure 4. UPL of the sample
Page 111 and 112: 23 rd Efficient Real Time Stabilit
Page 113 and 114: 23 rd stripping ratio of 2.28:1 to
Page 115 and 116: 23 rd The broad area coverage make
Page 117 and 118: 23 rd 4 FINAL REMARKS Slope monito
Page 119: 23 rd A Genetic Algorithm for Pit
Page 123 and 124: 23 rd BS. The mathematical model o
Page 125 and 126: 23 rd producing the new vector x i
Page 127 and 128: . 23 rd Figure 6. Ultimate pit lim
Page 129 and 130: 23 rd Selecting the Suitable Haula
Page 131 and 132: 23 rd Environment Economy Society
Page 133 and 134: 23 rd shape and depth of deposit,
Page 135 and 136: 23 rd 6 THE HIERARCHICAL PREFERENC
Page 137 and 138: 23 rd ranking place Truck & Shovel
Page 139 and 140: 23 rd Noguchi H., Ogawa M., Ishii
Page 141 and 142: 23 rd An Approach to Determine the
Page 143 and 144: 23 rd material to the nearby dumps
Page 145 and 146: 23 rd network and allocating spoke
Page 147 and 148: 23 rd IPC unit, but this is not wi
Page 149 and 150: 23 rd REFERENCES Alumur, S. A., &
Page 151 and 152: 23 rd
Page 153 and 154: 153 23 rd
Page 155 and 156: 23 rd
Page 157 and 158: 23 rd
Page 159 and 160: 23 rd
Page 161 and 162: 23 rd
Page 163 and 164: 23 rd
Page 165 and 166: 23 rd In - Pit Crushing and Convey
Page 167 and 168: 23 rd According to Lopes, the bigg
Page 169 and 170: 23 rd trucks because the conveyor
Page 171 and 172:
23 rd Identification of Causal Rel
Page 173 and 174:
23 rd Table 1. Geotechnical charac
Page 175 and 176:
23 rd 3.7 Results and Discussion T
Page 177 and 178:
23 rd Using Satellite Images (ASTE
Page 179 and 180:
23 rd largely covered by Quaternar
Page 181 and 182:
23 rd this figure, the lithology u
Page 183 and 184:
23 rd Figure 6. ASTER satellite im
Page 185 and 186:
23 rd mineralization in the sanand
Page 187 and 188:
23 rd Gas Pollutants and Dust of T
Page 189 and 190:
23 rd Figure 2. Truck transport th
Page 191 and 192:
23 rd Table 4. Determined values o
Page 193 and 194:
23 rd 4 DISCUSSION The results of
Page 195 and 196:
23 rd Surface Mining Statistics in
Page 197 and 198:
23 rd Dinogetia (Garvan). The chal
Page 199 and 200:
23 rd Table 1. The surface mining
Page 201 and 202:
23 rd pits along the Crisu Negru,
Page 203 and 204:
23 rd Voda, Nisipari, Rasova, Sili
Page 205 and 206:
23 rd 5.2 Magmatic rocks exploitat
Page 207 and 208:
23 rd mountains) and north Dobroge
Page 209 and 210:
23 rd Ponor quarry), sulphur (Nego
Page 211 and 212:
23 rd
Page 213 and 214:
23 rd
Page 215 and 216:
23 rd
Page 217 and 218:
23 rd
Page 219 and 220:
23 rd
Page 221 and 222:
23 rd
Page 223 and 224:
23 rd
Page 225 and 226:
23 rd
Page 227 and 228:
23 rd
Page 229 and 230:
23 rd
Page 231 and 232:
23 rd
Page 233 and 234:
23 rd
Page 235 and 236:
23 rd
Page 237 and 238:
23 rd
Page 239 and 240:
23 rd
Page 241 and 242:
23 rd
show all

A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?