Journal of Mining and Environement
Volume:8 Issue: 3, Summer 2017

Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure that uses the principle of cross-over genetic algorithms as the global optimization technique.
The model perturbation towards the objective function is performed recurring to direct sequential simulation and co-simulation. This new algorithm was applied to a synthetic dataset with and without noise. The results obtained for the inverted impedance were satisfactory in both cases. In addition, a real dataset was chosen to be applied by the algorithm. Good results were achieved regarding the real dataset. For the purpose of validation, blind well tests were done for both the synthetic and real datasets. The results obtained showed that the algorithm was able to produce inverted impedance that fairly matched the well logs. Furthermore, an uncertainty analysis was performed for both the synthetic and real datasets. The results obtained indicate that the variance of acoustic impedance is increased in areas far from the well location.

It is well-known that entrainment of particles into the froth is a key factor in the selectivity and performance of the flotation process, especially for fine particle recovery. Since flotation is a continuous process, in this work, the effects of operating parameters on the entrainment of ash materials in a sample coal flotation is investigated from a time-sequence viewpoint. The effects of the pulp solid content, collector concentration, frother concentration, impeller speed, and particle size on the entrainment factor and water recovery at different flotation times are evaluated using a D-optimal response surface experimental design. The experimental work carried out shows that some parameters, especially particle size and pulp density, can yield completely different responses from those reported in the literature. The observed unusual behaviours can be attributed to the entrainment mechanisms and verified by the experimental results. It is also shown that the dominant entrainment mechanism can be varied by time. In addition, the statistical analyses of the experimental design show that the effects of some parameters change during time from the initial to the final stages of the flotation process. The results obtained indicate that the particle size and pulp density are the most important parameters influencing the entrainment rate and water recovery. The effects of the collector and frother concentrations are less on the entrainment and water recovery. In addition, the interaction between the solid percentage and particle size is the only significant mixed effect.

Truck and shovel operations comprise approximately 60% of the total operating costs in open pit mines. In order to increase productivity and reduce the cost of mining operations, it is essential to manage the equipment used with high efficiency. In this work, the chance-constrained goal programing (CCGP) model presented by Michalakopoulos and Panagiotou is developed to determine an optimal truck allocation plan in open pit mines and reduce the waiting times of trucks and shovels. The developed goal programming (GP) model is established considering four desired goals: “maximizing shovel production”, “minimizing deviations in head grade”, “minimizing deviations in tonnage feed to the processing plants from the desired feed” and “minimizing truck operating costs”. To employ the developed model, a software is prepared in Visual Studio with C# programming language. In this computer program, the CPLEX optimizer software is incorporated for solving the developed goal programing model. The case study of Sungun copper mine is also considered to evaluate the presented GP model and prepared software. The results obtained indicate that the developed model increases the mine production above 20.6% with respect to the traditional truck allocation plan, while meeting the desired grade and the stripping ratio constraints.

The soil samples were collected from 170 sampling stations in an arid area in Shahrood and Damghan, characterized by prevalence of mining activity. The levels of Co, Pb, Ni, Cs, Cu, Mn, Sr, V, Zn, Cr, and Tl were recorded in each sampling location. A new method known as min/max autocorrelation factor (MAF) was applied for the first time in the environmental research works to de-correlate these elements before their geo-statistical simulation. The high cross-correlation among some elements, while poor spatial correlation among the others, could have made spectral decomposition of MAFs unstable, resulting in some negative eigenvalues, so it was decided to reduce the dimensionality of the original variables by Principal Component Analysis (PCA). The resultant 6 heavy metals (Cr, Mn, Cu, V, Ni, and Co) were converted to their respective MAFs followed by their geo-statistical simulation using Sequential Gaussian Simulation (SGS) independently. Examination of the cross-variograms of MAFs indicated that the resultant factors had been rigorously de-correlated, especially at zero lag and around ∆ lag distance. Several validation checks including reproduction of variograms in data and normal score space, close matching between distribution of MAFs versus simulated realizations, and reproduction of descriptive statistics and data histograms all confirmed that the data values had been honored by this applied method. The results obtained indicated that this method could reproduce the data values as well as the spatial continuity of heavy metals (e.g. semi-variograms) successfully. In addition, this technique is simpler and more computationally efficient than its equivalent sequential Gaussian co-simulation as fitting a linear model of co-regionalization (LMC) is not required in the data-driven MAF method.

Multiplicity of the effective factors in drilling reflects the complexity of the interaction between rock mass and drilling bit, which is followed by the dependence of parameters and non-linear relationships between them. Rock mass or, in other words, the formation intended for drilling, as the drilling environment, plays a very essential role in the drilling speed, depreciation of drilling bit, machines, and overall drilling costs. Therefore, understanding the drilling environment and the characteristics of the in-situ rock mass contributes a lot to the selection of the machines. In this work, a 1D geo-mechanical model of different studied wells is built by collecting the geological data, well logs, drilling data, core data, and pressure measurements of the formation fluid pressure in various wells. Having the drilling parameters of each part of the formation, its specific energy is calculated. The specific energy index can be used for predicting the amount of energy consumed for drilling. In order to find the relationship between the drilling specific energy (DSE) and its effective parameters, the multivariate regression model is used. Modeling DSE is done using the multivariate regression, which contains the parameters rock characteristics, well logs, and a combination of these two features. 70% and 30% of the data are, respectively, selected as the training and test for validation. After analyzing the model, the correlation coefficients obtained for the training and test data were, respectively, found to be 0.79 and 0.83. The parameters uniaxial compressive strength (UCS), internal friction angle, and fluid flow are among the most important factors found to affect DSE.

The Ramand region is a part of the magmatic belt in Urmieh-Dokhtar structural zone in Iran, located in the SW of BuinÜZahra. This area mainly consists of felsic extrusions such as rhyolites and rhyodacites. Argillic alterations with occurrences of mineralized silica veins are abundant in most of the volcanic units. In this research work, we used the GIS facilities for modeling the Ramand geo-spatial databases according to the Fuzzy logic algorithms. The main phase of mineralization occurred in the altered regions and is located near the cross cut fault systems. Therefore, the main criteria for integration were the geological, structural, geophysical, and remotely sensed (Landsat7, ETM) layers. Also we used a contoured aeromagnetic map for revealing and weighting lineaments. By the Fuzzy techniques applied, all the evidential themes were integrated to prognosis of ore mineralization potentials based on γ = 0.75. As a result, the hydrothermal alterations and their relevant post-magmatic mineralization were introduced in the south and eastern parts of the Ramand region by the fuzzification procedures. Our highlighted recommendation for more exploration activities is focused on the geophysical land surveys (electric and magnetic fields), and the geochemical sampling from mineralized regions in the depth and outcrops of alterations.

The present work was planned to evaluate the phytoextraction of metal mine tailings, Sarcheshmeh copper mine, SE of Iran, by the endemic plant species Medicago sativa L. (Alfalfa). In this pot experiment, we investigated the effects of seven amendments on the growth of alfalfa and uptaking metals from the mine tailings and stream sediment of tailing dam surface. The mean metal concentrations in both the tailing and stream sediment increased in the order of Hg

Discrimination of the blind and dispersed mineralization deposits is a challenging problem in geochemical exploration. The frequency domain (FD) of the surface geochemical data can solve this important issue. This new exploratory information can be achieved using the interpretation of FD of geochemical data, which is impossible in spatial domain. In this research work, FD of the surface geochemical data is analyzed to decompose the complex geochemical patterns related to the mineral deposits. In order to identify the dispersed mineralization zone in the Chichakloo Pb–Zn deposit, a newly developed approach is proposed based on the coupling of two-dimensional Fourier transform (2DFT) and principal component analysis (PCA). The surface geochemical data is transferred to FD using 2DFT, and two low-pass filters are designed and performed on FD. Then the PCA method is employed on these frequency bands (FBs) separately. This proposed scenario desirably illustrates the relationship between the low frequencies in the surface geochemical distribution map (GDM) and the deep deposits. The informations obtained from the detailed exploration and the exploration drillings such as boreholes confirm the results obtained from this method. This new combined approach is a valuable data-processing tool and pattern-recognition technique in geochemical explorations. This approach is quite inexpensive compared to the traditional exploration methods.

In the environment, two main sources of heavy metals are natural backgrounds derived from parent rocks and anthropogenic contamination including mineral industrial wastes, tailing damps of sulfide mines, agrochemicals, and other outputs of industrial activities and factories. In this work, the physico-chemical aspects of the magnetic Nano- mineral surfaces are studied in contrast to acid mine drainage using the multi- -analytical techniques XRF, XRD, BET, SEM, TEM, FT-IR, and AFM before and after adsorption of toxic elements. According to the results obtained, the FT-IR analysis presents a suitable curve, showing that the adsorption site of the sorption is filled with Ni(II) and Cd(II) ions. The results obtained show that the adsorption reaction is due to the high removal of the toxic elements from acid mine drainages.

In order to analyze the slope stability in open-pit mines, the structural parameters of rock mass such as persistence and spatial orientation of discontinuities are characterized through field surveys, which involve spending high costs and times as well as posing high risks of rock toppling and rock fall. In the present work, a new application of terrestrial digital photogrammetry is introduced for characterizing the rock mass structural parameters through preparing photogrammetry images from open-pit walls and building stereomodels. The data extracted from processing the stereo-model generations using photogrammetry images with different focal distances are highly consistent with the data collected through field surveys. However, it must be noted that the weather conditions, natural lighting angle, and applied observation scale may considerably affect the results obtained from stereomodel processing. Nevertheless, by taking into account the parameters such as time, cost, and full access to the required data, this new method can effectively be used in the estimation of rock mass structural parameters for analysis of steep slopes in open pits.

The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS.

In order to catch up with reality, all the macro-decisions related to long-term mining production planning must be made simultaneously and under uncertain conditions of determinant parameters. By taking advantage of the chance-constrained programming, this paper presents a stochastic model to create an optimal strategy for producing bimetallic deposit open-pit mines under certain and uncertain conditions. The uncertainties of grade, price per product, and capacities of the various stages in the process of production of the final product were considered. The results of solving the deterministic and stochastic models showed that the stochastic model had a greater compatibility and performance than the other ones.

The longwall mining method is one of the most applied methods in extracting low-inclined to high-inclined coal seams. Selection of the most suitable extraction equipment is very important in the economical, safety, and productivity aspects of mining operations. There are a lot of parameters affecting the selection of an extraction equipment in mechanized longwall mining in steeply inclined coal seams. The important criteria involved are the geometric properties of coal seam (dip, thickness, and uniformity of coal seam), geological and hydraulic conditions (faults, fractures, joints, and underground water), and geomechanical properties of coal seam and surrounding rocks. Extraction of inclined coal seams with gradients greater than 40 degree is different from low-inclined seams, and requires a special equipment. Therefore, the influence of the above-mentioned parameters must be considered simultaneously in the selection of extraction equipment for steeply inclined seams. This paper presents an application of the Fuzzy Analytical Hierarchy Process (FAHP) method in order to select a suitable extraction equipment in the Hamkar coal mine. In the proposed FAHP model, fifteen main criteria are considered, as follow: dip of coal seam, thickness of coal seam, seam uniformity, expansion of coal seam, faults, fractures and joints, underground waters, hangingwall strength, footwall strength, coal strength, in-situ stress, equipment salvage, dilution, system flexibility, and operational costs. Among the 6 considered longwall extraction equipment system alternatives, the findings show that the most suitable extraction equipment system is shearer on footwall and a support system using hydraulic props and the transport of coal with the force of gravity.

Inversion of magnetic data is an important step towards interpretation of the practical data. Smooth inversion is a common technique for the inversion of data. Physical bound constraint can improve the solution to the magnetic inverse problem. However, how to introduce the bound constraint into the inversion procedure is important. Imposing bound constraint makes the magnetic data inversion a non-linear inverse problem. In this work, a new algorithm is developed for the 3D inversion of magnetic data, which uses an efficient penalization function for imposing the bound constraint and Gauss Newton method to achieve the solution. An adaptive regularization method is used in order to choose the regularization parameter in this inversion approach. The inversion results of synthetic data show that the new method can produce models that adequately match the real location and shape of the synthetic bodies. The test carried out on the field data from Mt. Milligan copper-gold porphyry deposit shows that the new inversion approach can produce the magnetic susceptibility models consistent with the true structures.

The life cycle cost of a system is influenced by its maintainability. Maintainability is a design parameter, whose operational conditions can affect it significantly. Hence, the effects of these operational conditions should be quantified early in the design phase. The proportional repair model (PRM), which is developed based on the proportional hazard model (PHM), can be used to analyze maintainability considering the effects of the operational conditions. In PRM, the effects of the operational conditions are considered to be time-independent. However, this assumption may not be valid for some cases. The aim of this paper is to present an approach for prediction of the maintainability performance of the mining facilities considering the time-dependent influencing factors. The stratified Cox regression method (SCRM) is used to determine maintainability in the presence of time-dependent covariates for fleet vehicles operating in Sungun Copper Mine, Iran.