S Foroughi; M Monjezi; M.R Khalesi
Abstract
Fluctuation of the grade of the ore feeding the processing plants is one of the most important factors in plant's recovery, so that plant efficiency increases as the fluctuations decrease. In order to reduce the grade variability, homogenization piles are largely used in mining industry. The mass and ...
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Fluctuation of the grade of the ore feeding the processing plants is one of the most important factors in plant's recovery, so that plant efficiency increases as the fluctuations decrease. In order to reduce the grade variability, homogenization piles are largely used in mining industry. The mass and the number of layers in the pile have an important role in the rate of grade variability reduction. The grade variability decreases as the mass and the number of layers increase, although this increase can lead to an increase in operation complexity and costs. In this research, a method based on geostatistical simulations was used to predict grade variability in the Anguran mine. In this regard, 25 equi-probable grade realizations are first generated using the Sequential Gaussian Simulation method by SGeMS Software, then the block model for each realization is built in DATAMINE Software. In the next stage, the blocks that would be sent to the pile are specified taking into account the production planning (mine schedule) obtained from NPV Software. Finally, grade variability was determined for various pile sizes and number of forming layers. Based on the obtained results, piles with a mass of 187 kt and 25 layers have the lowest variability.
A. Sayadi; M. Manjezi; H. Shahr Abadi
Abstract
Reserve evaluation is one of the most important parameters for mine designing and several methods have been developed in this regards. Among these methods, in addition to geostatistical methods, artificial methods such as Artificial Neural Networks (ANN) are suitable for reserve evaluation. In this research, ...
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Reserve evaluation is one of the most important parameters for mine designing and several methods have been developed in this regards. Among these methods, in addition to geostatistical methods, artificial methods such as Artificial Neural Networks (ANN) are suitable for reserve evaluation. In this research, geometrical and block model of ٍEsfordi phosphate mine are prepared and the reserve is estimated. The block model contains 100 thousand blocks with dimensions of 25×25×5 m. To estimate the grade of each block,both methods of geostatistical and ANN methods are used. For geostatistical estimation, normal kriging is applied. In ANN a perceptron multilayer network is used and for training of network LM method is considered. Based on geostatistical and ANN methods, the amount of estimated reserve is 16.5 Mt with an average grade of 11.44% and 17.5 Mt with an average grade of 11.83%, respectively, considering a cut-off grade of 6%. The results obtained from these two methods are identical to each other and difference is less than 6%. This estimation is a requisite for improving present design of the mine with an objective of selective mining up to sea level of +1720.
