2020-11-27T00:28:34Z
http://jme.shahroodut.ac.ir/?_action=export&rf=summon&issue=39
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Investigation on selective rhenium leaching from molybdenite roasting flue dusts
Ali
Entezari
Mohammad
Karamoozian
M
Eskandari Nasab
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%.
Leaching
flue dust
molybdenum
rhenium
selectivity
2013
10
01
77
82
http://jme.shahroodut.ac.ir/article_143_9e8c07d5716a4161f4626c2ab0b61c04.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data
Saeed
Mojeddifar
Hojatollah
Ranjbar
Hossain
Nezamabadipour
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.
Mineral exploration
remote sensing
image classification
ANFIS
Hydrothermal Alteration
2013
10
01
83
96
http://jme.shahroodut.ac.ir/article_163_afc5e5735a9cd7f0c86cb32b8f0017b2.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Estimation of 3D density distribution of chromites deposit using gravity data
Ali
Nejati Kalateh
Amin
Roshandel kahoo
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.
density distribution
positivity constrain
chromites deposit
Inversion
Gravity Data
2013
10
01
97
104
http://jme.shahroodut.ac.ir/article_170_d29535975ec7ebe624c7efdda1a7022c.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
A developed approach based on grinding time to determine ore comminution properties
Negar
Saeidi
Dariush
Azizi
Mohammad
Noaparast
Soheila
Aslani
R
Ramadi
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.
iron ore
chadormalu
Work Index
comminution properties
selection function
Energy Efficiency
2013
10
01
105
112
http://jme.shahroodut.ac.ir/article_180_631f3a5271ac5d2fa7e31b00d4d40ef7.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Metal extraction competence of plants on waste dumps of magnesite mine, Salem District, South India
N
Mathiyazhagan
Natarajan
D
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.
Mine Tailings
Trace Elements
Agriculture plants
Phytoremediation
2013
10
01
113
124
http://jme.shahroodut.ac.ir/article_199_2c72a237e09efe81d39b2bad1b086910.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Predicting peak particle velocity by artificial neural networks and multivariate regression analysis - Sarcheshmeh copper mine, Kerman, Iran
Hassan
Bakhsandeh Amnieh
Alireza
Mohammadi
M
Mozdianfard
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.
Peak particle velocity
Artificial Neural Networks
Multivariate regression analysis
Blast operations
2013
10
01
125
132
http://jme.shahroodut.ac.ir/article_209_48bbfa72ced45d02c474267e78dfe46c.pdf
Journal of Mining and Environment
2251-8592
2251-8592
2013
4
2
Approximate resistivity and susceptibility mapping from airborne electromagnetic and magnetic data, a case study for a geologically plausible porphyry copper unit in Iran
Maysam
Abedi
Gholam-Hossain
Norouzi
Nader
Fathianpour
Ali
Gholami
This paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. The study area located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. Based on previous studies, which assume a homogenous half-space earth model, two approximate methods involving the Siemon and the Mundry approaches are used in this study to generate a resistivity-depth image of underground geologically plausible porphyry unit derived from airborne electromagnetic data. The 3D visualization of the 1D inverted resistivity models along all flight lines provides a resistive geological unit which corresponds to the desired porphyry andesite. To reduce uncertainty arising from single geophysical model, i.e., the resistivity model acquired from the frequency domain electromagnetic data, a fast implementable approach for 3D inversion of magnetic data called the Lanczos bidiagonalization method is also applied to the large scale airborne magnetic data in order to construct a 3D distribution model of magnetic susceptibility, by which the obtained model consequently confirms the extension of an arc-shaped porphyry andesite at depth. The susceptible-resistive porphyry andesite model provided by integrated geophysical data indicates a thicker structure than what is shown on the geological map while extends down at depth. As a result, considering simultaneous interpretation of airborne magnetic and frequency domain electromagnetic data certainly yield lower uncertainty in the modeling of andesite unit as a potential source of copper occurrences.
Electromagnetic and Magnetic data
Approximate inversion
Electrical resistivity
Magnetic susceptibility
Porphyry copper unit
2013
10
01
133
146
http://jme.shahroodut.ac.ir/article_266_75fc9ebcac74e12095abe774941a5b6d.pdf