Environment
Asghar Azadehranjbar; Shahrzad khoramnejadian; Saeidreza Asemi Zavareh; Alireza Pendashteh
Abstract
Mining and minerals extraction and purification are critical in today’s world. However, these processes may have negative consequences on the environment. Xanthates which are essential in the floatation process are found to be significant polluting chemicals. In this manuscript, the effect of different ...
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Mining and minerals extraction and purification are critical in today’s world. However, these processes may have negative consequences on the environment. Xanthates which are essential in the floatation process are found to be significant polluting chemicals. In this manuscript, the effect of different parameters on the recovery of lead from Nakhlak lead mine was investigated considering the impact of used chemicals on the surrounding environment including air, soil and native plant species. The reason for this investigation was to achieve the optimal conditions for the minimum consumption of xanthates and other chemicals. The optimal recovery was obtained in the presence of xanthate (1 kg/t) and sodium silicate (0.4 kg/t). In addition, MIBC showed to be more efficient in the floatation process. Furthermore, it was observed that higher xanthate contents are required for the floatation of large particles. Therefore, smaller particles of feed can decrease xanthate consumption. A particle size of 100 µm showed the best floatation recovery with the least xanthate requirement.
Exploration
Hossein Mahdiyanfar; Mirmahdi Seyedrahimi-Niaraq
Abstract
In this investigation, the hybrid approach of wavelet transforms and fractal method named Wavelet-Fractal model has been utilized for geochemical contamination mapping as a novel application. For this purpose, the distribution maps of pollutant elements were transformed to the position-scale domain using ...
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In this investigation, the hybrid approach of wavelet transforms and fractal method named Wavelet-Fractal model has been utilized for geochemical contamination mapping as a novel application. For this purpose, the distribution maps of pollutant elements were transformed to the position-scale domain using two-dimensional discrete wavelet transformation (2DDWT). The Symlet2 and Haar mother wavelets were applied for two-dimensional signal analysis of elemental concentrations of As, Pb, and Zn based on soil samples taken from the Irankuh mining district, Central Iran. The Symlet2 and Haar wavelet coefficients of approximate and detail components were obtained at one level frequency decomposition using 2DDWT. The wavelet coefficients of approximate component (WCAC) were modeled using a fractal method for delineating the geochemical contamination populations of toxic elements. Based on the results of wavelet-fractal models, the As, pb, and Zn were classified into three and four populations. Two areas contaminated with metals have been found in the district. These areas are within the limit of mining operations and its surroundings. The wavelet-fractal proposed model has been able to separate environmental areas contaminated with toxic metals accurately. Anomalously intense pollution has spread to one kilometer outside the mining operation limit. This dispersion in the case of Pb and Zn elements is well seen in the geochemical map prepared with the Haar class.
Exploration
Hossein Mahdiyanfar; Mirmahdi Seyedrahimi-Niaraq
Abstract
The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized ...
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The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as the contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for the contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population, which has not been affected or is less affected by these activities. Five geo-chemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geo-chemical populations were achieved using geo-chemical filtering and two-dimensional inverse Fourier transformation. The geo-chemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geo-chemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations.
F. Razavi Rad; F. Mohammad Torab; A. Abdollahzadeh
Abstract
Considering the importance of Cd and U as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the Eshtehard region in Iran by means of a developed artificial neural network (ANN) model. The forward selection (FS) method is used to select ...
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Considering the importance of Cd and U as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the Eshtehard region in Iran by means of a developed artificial neural network (ANN) model. The forward selection (FS) method is used to select the input variables and develop hybrid models by ANN. From 45 input candidates, 13 and 14 variables are selected using the FS method for Cadmium and Uranium, respectively. Considering the correlation coefficient (R2) values, both the ANN and FS-ANN models are acceptable for estimation of the Cd and U concentrations. However, the FS-ANN model is superior because the R2 values for estimation of Cd and U by the FS-AAN model is higher than those for estimation of these elements by the ANN model. It is also shown that the FS-ANN model is preferred in estimating the Cd and U population due to reduction in the calculation time as a consequence of having less input variables.
