Journal of Mining and Environement
Volume:4 Issue: 2, Summer 2013

The possibility of selective leaching process was investigated during molybdenite flue dust leaching to recover its rhenium content. The results show that addition of alcohols to water makes the medium less favorable for molybdenum transfer into aqueous phase. On the other hand, addition of small amounts of alcohols (5-15%) makes a noticeable separation of rhenium over molybdenum, but by increasing the alcohol content recovery of both metals decreases. More than 90% of Re transferred into leach solution but the corresponding amount for Mo was only about 0.5%.

The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuzzy system in overcoming this problem. The proposed system is applied to the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA), which hosts many areas of porphyry and vein-type copper mineralization. A software program based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using the MATLAB ANFIS toolbox. The ANFIS program was used to classify Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data based on the spectral properties of altered and unaltered rocks. The ANFIS result was then compared with other classified images based on artificial neural networks (ANN) and the maximum likelihood classifier (MLC). The verification of the results, based on field and laboratory investigations, revealed that the ANFIS method produces a more accurate map of the distribution of alteration than that obtained using ANN or MLC.

We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the earth model into rectangular cells of constant andunidentified density. The number of cells is often greater than the number of observation points thus we have an underdetermined inverse problem. The densities are estimated by minimizing a cost function subject to fitting the observed data. The synthetic results show that the recovered model from the first strategy is characterized by broad density distribution around the true model, butthat of the second strategy is closer to true models.We carry out inversion of gravity data taken over chromite deposit located at Hormozgan providence of Iran for estimating of subsurface density distribution. The recovered model obtained from second strategy has appropriate agreement with previous study.

In this paper, iron ore sample from the Chadormalu was investigated to determine some comminution properties. Chadormalu deposit is one of the largest iron ore mine in Iran, which is located in Yazd province. The representative ore sample contained 57%Fe, 0.9%P and 0.17%S. The sample was crushed; afterward, it was ground in various grinding times according to the Bond Ball mill approach to specify the work index values. Based on different grinding times and the obtained results, a new work index equation was then simulated through which grinding time was considered as the main variable. The relationships between work index, the work input and P80 were then concluded. In addition, the results of tests were then used to estimate the selection function parameter. A new equation was applied to determine energy efficiency which could be implemented for energy consumption calculation. Two equations for EB and EB/Elimit were then obtained, where EB is the efficiency of comminution, and the ELimit is the maximum limiting energy efficiency for particle fracture under compressive loading. These equations could estimate the parameters of the iron ore would be precisely estimated. Indeed, by means of work index value; some crushing and grinding characteristics of the taken sample were assessed by which comminution circuit would be designed much better.

An ex-situ experiment to assess the metal extractive potential of fourteen agriculture plants (Vigna unguiculata, Gossypium hirsutum, Jatropha curcas, etc.) was conducted on Magnesite mines which had above permissible levels of Cadmium and Lead. There was no much difference in the total chlorophyll a and b, carbohydrate and protein contents in the plants grown in the mining soil and adjacent control area (farm soil). While considering the phytoextractive potential, among the 14 plants studied, V. ungiculata, O. sativa, S. bicolour, S. indium, R. communis, M. uniflorum, G. hirsutum and J. curcas contained considerable amount of heavy metals Cd and Pb other test plants. The experiment confirms that these plants have potential to accumulate the toxic trace elements from soil especially mining waste or dump. The subsequent confirmation studies on their metal tolerant index, metal transfer factor, translocation factor and MREI index values auger their potential phyto-extractive properties. The present study will pave way for in depth related studies in future.

Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting can be achieved once peak particle velocity (PPV) is predicted. In this paper, the values of PPV have been predicted and compared using the artificial neural network (ANN), multivariate regression analysis (MVRA) and empirical relations. The necessary data were gathered from 11 blast operations in Sarcheshmeh copper mine, Kerman. The neural network input parameters include distance from blast point, maximum charge weight per delay, spacing, stemming and the number of drill-hole rows in each blasting operation. The network is of the multi-layer perception (MLP) type with 24 sets of training data including 2 hidden layers, 1 output layer with the network architecture of {5-11-12-1}, and Sigmoid tangent and linear transfer functions. To insure the training accuracy, the network was tested by 6 data sets; the determination coefficient and the average relative error were found to be 0.977 and 8.85%, respectively, showing the MLP network’s high capability and precision in estimating the values of the PPV. To predict these values, MVRA and empirical relations were analyzed. The results have revealed that these relations have low capability in estimating the PPV parameter.

In this paper, after collecting the rock samples from eight mines and one high way slope, the tests for determination of dry density, Uniaxial Compressive Strength, tensile Strength (Brazilian Test), elastic modulus, Schmidt hammer rebound number have been done on samples. In addition, in order to calculating the mean size of rock grains, quartz content, hardness and abrasivity, a thin sections of each rock have been studied. Then, the rock samples have been drilled using actual pneumatic top hammer drilling machine with 3½ inches diameter cross type bit. The regression analyses showed that Brazilian tensile strength (R2=0.81), uniaxial compressive strength (R2=0.77) and Schmidt hammer rebound (R2=0.73) are the most effective parameters on drilling rate and have a partly good correlation with drilling rate.