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Öğe Application of chance-constrained programming based on multi-objective simulated annealing to solve a mineral blending problem(Taylor & Francis Ltd, 2003) Kumral, MThis paper addresses the optimal blending of different available ores in such a way that the total expected cost of buying ore is minimized while satisfying the quality specifications. The risk limitation criterion used consists of the simultaneous minimization of the variance of the total cost. The problem is solved by Chance-Constrained Programming (CCP) based on multi-objective simulated annealing. The technique is able to deal with the stochastic nature of the variables in the blending problem. In generic form the objectives are to minimize expected value and standard deviation of cost in such a way as to meet the blend requirements within the specified reliability level. The variability of each variable in each flow is quantified by semi-variograms. Each flow is simulated to reproduce the characteristics, or behaviour, of the phenomenon as observed in the available data. The expected value of each variable in each flow is calculated by averaging of the simulated values. The problem expressed in terms of CCP is transformed into an appropriate deterministic equivalent, which is a non-linear optimization problem. The new form of the problem is solved by multi-objective simulated annealing. A case study is carried out to demonstrate the technique. The problem is formulated in terms of iron, silica and alumina grades with four ore sources. Pros and cons of the technique are discussed.Öğe Genetic algorithms for optimization of a mine system under uncertainty(Taylor & Francis Ltd, 2004) Kumral, MThe production planning of a mine system associated with mining, processing and refining stages dictates to determine optimal system parameters such as optimal production rates, location of refining facility and the best reconstruction time of production rates. This paper proposes a combination of the chance constrained programming (CCP) and the genetic algorithms (GA) to find the optimal system parameters simultaneously. In generic form the problem is expressed as the maximization of net present value of future cash flows such that the capacity constraint and predefined specifications are satisfied. The blending requirements expressed in the CCP are transformed into deterministic equivalents. A new form of the problem is solved by the GA. The approach was demonstrated on extraction, processing and refining of four iron ore mines with varying reserves, ore qualities, geological and topographic conditions, four mineral processing units and one re. ning facility. The results showed that the proposed algorithm could be used to determine optimal production rates, the facility location and the best reconstruction time.Öğe Geostatistical simulation of a quarry(Kozan Ofset Malbaacilik San Vetic Ltd Sti, 2001) Kumral, MMine production scheduling requires quantification of the variability of the attributes of the mined product as delivered to the facility. Geostatistical simulation is a method of generating, on any specified scale, realisations of these attributes. Geostatistical simulation provides a set of values that can be used in mine production planning. In this paper, after the importance of geostatistical simulation is briefly reviewed, the sequential Gaussian simulation, which has been widely accepted for the simulation of in-situ mineral properties, is introduced in a case study.Öğe Optimal location of a mine facility by genetic algorithms(Maney Publishing, 2004) Kumral, MWhen raw material is extracted from multiple mines or faces and when the construction of a mine facility is being planned, the selection for the optimal location of the facility is an important consideration because of high transportation costs. This paper presents an approach for the selection of optimal location of a mining facility based on the genetic algorithms which is a directed random search technique. The technique, simple and easy to apply, is demonstrated for the case of the location of a coal washery plant, whose supply comes from five mines with varying reserves and coal qualities, topographical conditions and mine status. The result shows that the solution produced by the proposed algorithm can be used to find the optimal location of any mining facility.Öğe Predicting elastic properties of intact rocks from index tests using multiple regression modelling(Pergamon-Elsevier Science Ltd, 2005) Karakus, M; Kumral, M; Kilic, O[Abstract Not Available]Öğe Quadratic programming for the multivariable pre-homogenization and blending problem(South African Inst Mining Metallurgy, 2005) Kumral, MRaw 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.Öğe Reliability-based optimisation of a mine production system using genetic algorithms(Elsevier Sci Ltd, 2005) Kumral, MIn this research, a safety management system is planned for a mine operation. The objective is to develop a mine production design that will achieve the desired reliability and variance of reliability estimation of system while performing all sub-system functions at a minimum cost. The problem was formulated in an underground mine system containing five sub-systems with varying reliability estimates, estimation variances and feasibilities and solved by genetic algorithms (GA). (c) 2005 Elsevier Ltd. All rights reserved.Öğe A simulated annealing approach to mine production scheduling(Taylor & Francis Ltd, 2005) Kumral, M; Dowd, PAIncreasing global competition, quality standards, environmental awareness and decreasing ore prices impose new challenges to mineral industries. Therefore, the extraction of mineral resources requires careful design and scheduling. In this research, simulated annealing ( SA) is recommended to solve a mine production scheduling problem. First of all, in situ mineral characteristics of a deposit are simulated by sequential Gaussian simulation, and averaging the simulated characteristics within specified block volumes creates a three-dimensional block model. This model is used to determine optimal pit limits. A linear programming ( LP) scheme is used to identify all blocks that can be included in the blend without violating the content requirements. The Lerchs-Grosmann algorithm using the blocks identified by the LP program determines optimal pit limits. All blocks that lie outside of the optimal pit limit are removed from the system and the blocks within the optimal pit are submitted to the production scheduling algorithm. Production scheduling optimization is carried out in two stages: Lagrangean parameterization, resulting in an initial sub-optimal solution, and multi-objective SA, improving the sub-optimal schedule further. The approach is demonstrated on a Western Australian iron ore body.
