Yazar "Kumral M." seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Concurrent optimization of mine block sequencing and cut-off grades(Korza Matbaacilik, 2011) Kumral M.Mine block sequencing, ore-waste discrimination and determination of production rates are main sub-problems of mine production scheduling. The objective is to maximize net present value under access and capacity constraints. The problems are currently solved in a sequential way: First, production rates and corresponding costs are initiated. Using this cost structure, a cut-off schedule is determined. Finally, the block sequencing is executed for marginal cut-off grades and pre-defined production rates. This process is repeated until maximum net present value (NPV) is found. However, these problems should be solved simultaneously instead of iterative procedure because production rates and cut-offs cannot be known without block sequencing. In this paper, ore-waste discrimination and block sequencing are optimized concurrently through mixed integer programming (MIP) model. This procedure also considers relationship between capacities and accessibility. To observe the performance of new model, a case study is implemented. An increase in NPV is observed.Öğe Determination of operation parameters of a mineral processing study incorporating multiple regression analysis and multiobjective genetic algorithms(Grup Matbaacilik, 2009) Birinci M.; Kumral M.; Sarikaya M.The design of a mineral processing plant dictates to determine optimal system parameters such as impeller speed, temperature, pH, process duration, solid % in pulp. The paper proposes a combination of multiple regression modelling and the multiobjective genetic algorithms (GA) to find the optimal system parameters simultaneously. In this research, impeller speed, process duration and solid % in pulp are considered as independent variables for the beneficiation of Al2O3 in pyrophyllite ore. Grade and electricity consumption are dependant variables. The multiple regression analysis is performed to relate dependant and independent variables to each other. The obtained models are used in optimisation procedure as objective functions. The first objective is to maximise the Al2O3 grade and the second one is to minimise electricity consumption. This is a multiobjective optimization problem and is solved by GA. The results showed that the proposed approach could be used to determine operation parameters in a mineral processing plant.Öğe A new production scheduling approach considering mill requirements(Korza Matbaacilik, 2011) Kumral M.Geo-metallurgical variables control the performances of mining and mineral processing operations. As the fluctuations of geo-metallurgical variables increases, mine production scheduling will be difficult procedure. In conventional approach, the objective is to maximize net present value (NPV) of project. This objective generates descending order of average grade mean. This imposes additional problems on recoveries and throughput in the mill. The problem may be, to some extent, controlled by blending constraint in traditional approach. The blending constraint keeps average grade within upper and lower limits. If there is a very narrow range between upper and lower limits, the average grade of production in each period will be similar. However, the inclusion of this constraint will substantially increase computational time. Therefore, a new model is proposed to tackle with the problem such that computational time is reduced. The idea behind this model is to extract more homogenous metal quantity in the periods instead of maximization of NPV. In new model, the objective function is expressed as a maximin (maximize the minimum) problem. In the case where there is correlation between grade and geo-metallurgical variables, this model generates reasonably good results.Öğe Quadratic programming for the multi-variable pre-homogenization and blending problem(2005) Kumral M.Raw material fed into a processing or refining plant is required to be uniform in composition for several reasons. When the mined ore is highly variable in quality the only way to ensure consistency is to homogenize the ore prior to the process. The problem is more complicated in the case of multiple ore sources. The idea behind the research is that theoretical blending ratios can serve not only to meet predefined specific criteria but also to reduce grade fluctuations of variables under consideration. In this paper, the problem is formulated as a quadratic programming problem, whose objective function is in quadratic form and constraints are linear. A case study was conducted on a data set from an iron orebody to demonstrate the technique. The objective was to minimize the blend variability in terms of each variable (in this study, iron, silica, alumina and lime) grade of ores extracted in three different production faces. The stockpile capacity, lower and upper limits of each variable satisfying operational requirements and non-negativity were constrained to the model. A modified simplex method developed by Wolfe was used for solving the blending and homogenization problem. The promising results can be used as a part of the stacking and reclaiming design. © The South African Institute of Mining and Metallurgy, 2005.Öğe Robustness in mine planning problem: Trade-off between optimality and feasibility(Korza Matbaacilik, 2011) Kumral M.Mine production scheduling under uncertainty is one of main challenges in mining ventures. Since the values of coefficients in the optimization procedure are obtained in medium of sparse data, sampling and assaying errors, and unknown future events, the implementations based on the deterministic models may lead to destructive consequences to the mining company. In this paper, a robust stochastic optimization (RSO) approach is used to deal with mine production scheduling in a manner that the solution is insensitive to changes in input data. The model was demonstrated on a case study. It was observed that RSO could be executed to solve mine production scheduling problem under an uncertain environment efficiently.
