Yazar "Kumral, M." seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Bed blending design incorporating multiple regression modelling and genetic algorithms
    (Southern African Inst Mining Metallurgy, 2006) Kumral, M.
    The efficiency of an ore-processing unit depends on the consistency of the characteristics of raw material entering the plant. When the mined ore is highly variable in quality, the only way to ensure consistency is to homogenize the ore prior to feeding to the processing plant. The homogenization can generally be achieved by the bed blending operation. Given that the stockpiling and reclamation processes are very expensive, it is necessary to design the process in such a way as to minimize variabilities of specified properties of raw material. in this paper, for alternative stacking types, optimal stockpile geometry is found in three stages: First, stockpile input is simulated by sequential Gaussian simulation, and then the variance reduction ratios (VRR) as a criterion of stockpile efficiency are calculated for various stockpile geometry scenarios by a stockpile simulator written in FORTRAN. Second, multiple regression analysis is performed to model the VRR by the use of stockpile length, the number of layers and stacker speed as the independent variables. Finally, the model is an optimization problem. Decision variables are the stockpile length, the number of layers, stacker speed and stacking type. The genetic algorithms (GA) are used to minimize the VRR. The approach was demonstrated on data from an iron orebody. The problem was to reduce fluctuations of iron, silica, alumina and lime contents in the stockpile output. The results showed that the approach could be used for the bed blending design efficiently.
  • Küçük Resim Yok
    Öğe
    Incorporating geo-metallurgical information into mine production scheduling
    (Palgrave Macmillan Ltd, 2011) Kumral, M.
    Economic characterization ofmining parcels depends upon geo-metallurgical properties, which vary throughout orebody. Mine production scheduling should aim to obtain maximum utility from orebody in such a way as to ensure mine-mill reconciliation. As heterogeneity of geo-metallurgical variables increases, the scheduling will be a very complicated task. Geo-metallurgical and financial data used in the mine production scheduling are based on simulation and/or estimation generated from sparse drilling and unknown future events. Therefore, the scheduling process involves a significant degree of uncertainty. In order to deal with the uncertainty stemmed from geo-metallurgical and financial variables, two approaches are recommended in this paper. Firstly, mine production scheduling is formulated as a problem of stochastic programming with recourse. The extraction periods of mining blocks are treated as the first-stage variables and the block destinations represents a recourse vector. It is observed that the solution is implicitly robust. Secondly, the scheduling is expressed as a maximin problem to extract more uniform metal quantity in periods to coincide with mill requirements instead of maximization of net present value because the blending constraint in the traditional approach forces more uniform production. In the case where there is correlation between grade and geo-metallurgical variables, this model generates reasonably good results. Journal of the Operational Research Society (2011) 62, 60-68. doi:10.1057/jors.2009.174 Published online 10 February 2010
  • Küçük Resim Yok
    Öğe
    Reconstruction time of a mine through reliability analysis and genetic algorithms
    (Southern African Inst Mining Metallurgy, 2009) Kumral, M.
    A mining system consists of many sub-systems such as drilling, blasting, loading, hauling, ventilation, hoisting and supporting. During mining operation, these sub-systems may experience various problems that stop the operation because of possible environmental, equipment and safety issues. in order to ensure delivery contracts in the required quality and safe mining medium, the operation should be, at least, performed in the specified reliability level of the system. if the system reliability decreases below the specified level, there will be safety and financial losses for the mining company. Therefore, the mine should be maintained by a reconstruction procedure to guarantee the operation continuity. Given that each sub-system has a different reliability function and maintenance cost, the determination of reconstruction time will be a complicated decision making problem. in this paper, the determination of reconstruction time is formulated as a nonlinear optimization problem and solved by genetic algorithms (GA). A case study was conducted to demonstrate the performance of the approach for an underground operation. The results showed that the approach could be used to determine the best action time.