Journal of Mining and Environement
Volume:13 Issue: 2, Spring 2022

The Aynak copper deposit is the most important strata-bound copper reserve in Afghanistan. The main purpose of this work is the ore deposit boundary modification and reserve estimation of the Aynak central copper deposit using the geostatistical methods. The ordinary kiging (OK), indicator kriging (IK) and sequential indicator simulation (SIS) methods were used to modify the optimum ore deposit boundary and ore reserve estimation. Then the results, accuracy and efficiency of these three methods are compared. Before the ore reserve estimation, the pre-processing,  statistical and geostatistical analysis of the sampled data are performed. For a precise estimation process, it is necessary to modify the optimum ore body boundary as an estimation space. Therefore, the IK and SIS methods are applied to revise the conventional ore deposit boundary and estimation space. At the first stage, the ore body wireframe and solid model are obtained using the conventional cross-section method. The block model is created covering the mineralization space of the ore body, and firstly constrained by the conventional model (solid model). Consequently, the ore body model is adapted and bounded using the IK and SIS geostatistical methods. Finally, the log-kriging method that is basically unbiased and guarantees the minimum estimation error is used to estimate the Cu concentration in each block, and after back-transformation, the grade-tonnage curves are plotted. The total tonnage of the deposit is calculated based on different cut-off grades. Assuming the cut-off grade of 0.2% for Cu, the tonnage of ore reserve based on the conventional OK method, IK method, and SIS constrained ore body model are estimated as 453.4, 459.1, and 467.7 million tons with an average grade of 1.077%, 1.08%, and 1.05%, respectively. The proximity of the obtained reserve estimation results using different implemented methodologies is due to the low-grade variability and genetical regularity in the Aynak staratabound copper deposit and guarantees the accuracy of the results obtained in the ore reserve evaluation.

The significance of rock failure can be found from the fact that microfracture genesis and coalescence in the rock mass results in macroscale fractures. Rock may fail due to an increase in the local stress, natural fractures, weathering inducing micro-crack genesis, coalescence, and propagation. Therefore, a comprehensive understanding of the micro-scale failure mechanism of various weathering grade sandstones based on micro-level observation and microstructure-based simulation is essential. The microscale failure response of various weathering grade sandstones is studied under the wet and dry cycles. Each sample is tested for the micro-structure and micro-fracture characteristics using the image analysis. Furthermore, the micrographs obtained are also used to create the microstructure-based models, which are then simulated in the ANSYS software. The findings indicate that the moderately weathered sandstones indicate less weight reduction than the slightly weathered sandstone. The results obtained also demonstrate that the wet and dry cycles have little effect on the particle shape and size. However, variation in the particle shape and size implies that this is a result of the prevailing interaction of rock and water particle. The microscale simulation reveal that both UCS and BTS decrease from 37 MPa to 19 MPa and 9 MPa to 4 MPa as the density of the micro-structure increases. The results reveal that the primary fracture deviation from the loading axis increases with increasing density in the micro-structural micro-structures, although this effect reduces with further increasing density in the micro-structures.

Mining Method Selection (MMS) is the first and the most critical problem in mine design, and depends on some parameters such as the geo-technical and geological features and economic factors. The factors affecting MMS are determined by some mining experts, and the most suitable mining method is selected using the hesitant fuzzy group decision-making (HFGDM) and technique for order performance by similarity to the ideal solution (TOPSIS) method. These factors include the type of deposit, slope of deposit, thickness of orebody, depth below the surface, grade distribution, hanging wall Rock Mass Rating (RMR), footwall RMR, ore body RMR, recovery, capital cost, mining cost, annual productivity, and environmental impact. Firstly, we propose the group decision-making (GDM) method to determine the weights of multi-attributes based on the score function with the decision-makers’ weights, in which the n-dimensional hesitant fuzzy environment take the form of hesitant fuzzy sets (HFS). Then we calculate the weights of these factors using the HFGDM method. A simple case study is also presented in order to illustrate the competence of this method. Here, we compare the seven mining methods for an Apatite mine, and select the optimal mining method using the TOPSIS method. Finally, the sub-level stope mining method is selected as the most suitable method to this mine.

In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.

This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were collected from the mine. Then, using GEP, a series of intelligent equations were proposed to predict flyrock distance. The best GEP equation was selected based on some well-established statistical indices in the next stage. The coefficient of determination for training and testing datasets of the GEP equation were 0.890 and 0.798, respectively. The model obtained from the GEP method was then optimized using teaching– learning-based optimization algorithm (TLBO). Based on the results, the correlation coefficient of training and testing data increased to 91% and 89%, which increased the accuracy of the Equation. This new intelligent equation could forecast flyrock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues.

The interaction between an internal hole and two surrounded joints under a uniaxial compression are examined using the experimental and discrete element procedures. Inside the concrete sample, two notches and an internal hole are created. The joint angle change from 0° to 90° with an increment of 30°. The distances between the joint and the internal hole are 2 cm and 3 cm. Also the numerical models are provided. The joint angle change from  0° to 90° with an increment of 15°. The distances between the joint and the internal hole are 2 cm, 3 cm, and 4 cm. The compressive strength is 7.2 MPa. The rate of loading is 0.005 mm/s. The experiment indicates that the failure process is significantly dependent on the notch angle and the joint distance from the hole. The pattern of fracture and mechanism of failure of joints affect the shear strengths of the samples. The models with joint angles of 30° and 60° have a less compressive strength since the pure tensile failure occurs in these configurations. The model strength decreases with decrease in the join spacing. In fact, in the case that the joint spacing is 2 cm, the interaction between the hole and the neighboring joint is so strong. Consequently, the compressive strength is declined. In both approaches of the numerical simulation and experimental methods, the pattern and strength of failure are identical.

In this work, the mechanism of zinc hydroxide and ammine complexation in caustic and ammonia leaching is investigated by molecular modelling using the density functional theory method. The speciation of zinc complexes is defined based on the thermodynamic data and Pourbiax diagrams. The mechanism of Zn+2 complexation by hydroxide and ammine ligands is simulated by molecular modeling. The structure of reactants in the form of individual clusters is modelled using the density function theory. In order to compare the hydroxide and ammine species structures, the geometry studies are carried out as well. The ammoniacal salt effectiveness to improve the dissolution and stability of the ammine species is studied. The ligand single molecule interaction with a smithsonite molecule is done for a better understanding. Molecular modeling show that the zinc hydroxide species are more stable based on the higher reaction free energies. The reaction free energies decrease by adding the OH- and NH3 ions to the complexes from -30.12 kcal/mol to -16.943 kcal/mol, and -22.590 kcal/mol to 66.516 kcal/mol, respectively. The Zn-OH bonds are shorter than Zn-NH3, and the ammine species show more regular structures in comparison with the hydroxide structures. The change of free energies in the presence of ammoniacal salts indicate that the sulfate ions can significantly improve the dissolution of zinc oxide in ammonia. The smithsonite interaction with ammonia and hydroxide reveal that hydroxide ions lead to a higher interaction energy than ammonia (-36.396 vs. -28.238), which is consistent with the higher stability of hydroxide species. The results obtained well-explain the experimental results obtained before, and can be effectively used to optimize the alkaline leaching of zinc oxide ore.

Shear wave velocity (Vs) is considered as a key parameter in determination of the subsurface geomechanical properties in any hydrocarbon-bearing reservoir. During a well logging operation, the magnitude of Vs can be directly measured through the dipole shear sonic imager (DSI) logs. On a negative note, this method not only is limited to one dimensional (1D) interpretation, it also appears to be relatively costly. In this research work, the magnitude of Vs is calculated using one set of controversial petrophysical logs (compressional wave velocity) for an oil reservoir situated in the south part of Iran. To do this, initially, the pertinent empirical correlations between the compressional (Vp) and shear wave velocities are extracted for DSI logs. Then those empirical correlations are deployed in order to calculate the values of Vs within a series of thirty wells, in which their Vp values are already recorded. Afterwards, the Kriging estimator along with the Back Propagation Neural Network (BPNN) technique are utilized to calculate the values of Vs throughout the whole reservoir.  Eventually, the results obtained from the two aforementioned techniques are compared with each other. Comparing those results, it turns out that the Kriging estimation technique presents more accurate values of Vs than the BPNN technique. Hence, the supremacy of the Kriging estimation technique over the BPNN technique must be regarded to achieve a further reliable magnitude of Vs in the subjected oil field. This application can also be considered in any other oil field with similar geomechanical and geological circumstances.

In the recent years, according to the difficulty of accurately measuring parameters and demarcation of earth sciences, attempts have been made to simplify the natural events for better investigation using geo-modelling. Modeling with intelligent methods is one of the new methods that has been considered in this field in the recent years. In this work, the intelligent method of adaptive neural-fuzzy inference system (ANFIS) is used to predict the elements of lead and zinc located in the Guard Kooh area, north of Yazd province in Iran. Descriptive statistics of data and correlation matrices of studied elements are obtained using the SPSS software. After the data is standardized, imported to the MATLAB software, and the lead and zinc elements are predicted using the ANFIS-SCM method. In this method, 70% of the data (175 samples) are set as the training data, and the rest (75 samples) are set as the test data, which are randomly selected. Using the obtained results, it is found that the grade of the estimated elements in the studied area has a good accuracy and a high correlation with the grade of the analyzed elements. As a result, the ANFIS-SCM intelligent method is a useful and accurate method for estimating the lead and zinc elements.

In this work, the machine learning prediction models are used in order to evaluate the influence of rock macro-parameters (uniaxial compressive strength, tensile strength, and deformation modulus) on the rock fracture toughness related to the micro-parameters of rock. Four different types of machine learning methods, i.e. Multivariate Linear Regression (MLR), Multivariate Non-Linear Regression (MNLR), copula method, and Support Vector Regression (SVR) are used in this work. The fracture toughness of mode I and mode II (KIC and KIIC) is selected as the dependent variable, whereas the tensile strength, compressive strength, and elastic modulus are considered as the independent variables, respectively. The data is collected from the literature. The results obtained show that the SVR model predicts the values of KIC and KIIC with the determination coefficients (R2) of 0.73 and 0.77. The corresponding determination coefficient values of the MLR model and the MNLR model for KI and KII are R2 = 0.63, R2 = 0.72, and R2 = 0.62,0.75, respectively. The copula model predicts that the value of R2 for KI is 0.52, and for KII R2=0.69. K-fold cross-validation testing method performs for all these machine learning models. The cross-validation technique shows that SVR is the best-designed model for predicting the fracture toughness mode-I and mode-II.

Selective recovery of platinum group metals including Pt, Pd, and Rh from the spent automobile catalysts is investigated by functionalized magnetite nanoparticles as a novel adsorbent. Magnetite nanoparticles are synthesized by co-precipitation of ferrous and ferric salts with ammonium hydroxide, and then coated with a tetraethyl orthosilicate to form well-dispersed silica-coated magnetite nanoparticles. The silica-coated nanoparticles are then functionalized with three different types of organosilane ligands including monoamine (FeSiORA), ethylenediamine (FeSiORDA), and diphenylphosphino (FeSiORP). The effects of initial pH, amount of adsorbent, contact time, and chloride concentration in a multi-component leaching solution are examined in batch tests on [PdCl4]2-, [PtCl4]2-, [PtCl6]2-, and [RhCl6]3-. Among the different types of organosilane ligands examined, the FeSiORA nanoparticles and FeSiORDA, for selective sorption of PGM from the leaching solution, are unsuccessful. It is found that FeSiORPs can effectively adsorb Pt and Pd but exhibit no affinity towards Rh and base metal ions. Under the optimum conditions, the adsorption rates of Pt, Pd, and Rh are estimated 97.5%, 97.0%, and 15.0%, respectively.

Methane has been known as a safety risk for the coal mining activities. Accordingly, one can mitigate this risk, and hence, the level of hazard to which the mining workers are exposed, by predicting the possible exceedance of allowable methane dosage should be provided with a reliable information on the distribution of methane across the working face considering the uncertainties associated with the gas content of such deposits. In this work, the gas content uncertainty in a coal seam is first investigated using the geo-statistical simulation. Then a method is proposed in order to predict methane gas emission based on the Monte Carlo random simulation method. Next, the results obtained are introduced into a 3D Computational Fluid Dynamics (CFD) model to estimate the methane distribution considering the uncertainty associated with the gas content. Defined as zones where the methane concentration is so high that an explosion is much likely to occur, the elevated methane zones (EMZs) are delineated across the working faces. The results obtained show that UGC has an impact on the ventilation parameters and EMZs. The proposed method could be carried out in order to guide the ventilation design in improving safety.

Signal analysis approaches are a powerful and widely used tool in processing multi-spectral satellite images for detection of alteration zones. The main goal of this work is application of the spectrum-area fractal methodology based on the Landsat 8 OLI satellite images’ data for separation alteration zones for iron oxides at the Tarom region (NW Iran). These alteration zones, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NWDI) are detected using the band-ratio and band combination methods. Then the calculated values are categorized by Spectral Angle Mapper (SAM), k-means, and S-A fractal model. Considering a positive correlation of iron oxides alterations along with magnetite mineralization as an index of mineralization at the studied region, the promising areas are classified by a decision-making model using the TOPSIS method with an acceptable accuracy for presenting in the exploration models.

A coal waste sample loaded with Fe3O4 nanoparticles is employed as an efficient adsorbent to remove Cd from synthetic wastewater. The synthesized nanocomposite is characterized using the Fourier transform-infrared (FT-IR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The visual analysis of the microscopic image shows that the mean size of the magnetite nanoparticles is about 10 nm. The effects of the operating variables of the initial solution pH (3-11) and nanocomposite to pollutant ratio (7-233) are evaluated using the response surface methodology on cadmium adsorption. The process is also optimized using the quadratic prediction model based on the central composite design. The statistical analysis reveals that both factors play a significant role in Cd adsorption. The maximum Cd removal of 99.24% is obtained under optimal operating conditions at pH 11 and nanocomposite/cadmium ratio of 90 after 2 h of equilibrium contact time. A study of the adsorption kinetics indicates that the maximum removal could be attained in a short time of about 2 min following a first-order model. The isotherm investigations present that the Cd adsorption on the Fe3O4/coal waste nanocomposite has a linearly descending heat mechanism based on the Temkin isotherm model with the minor applicability parameters than the other isotherm models. The overall removal behaviour is attributed to a two-step mechanism including a rapid adsorption of cadmium ion onto the active sites at the surface of nanocomposite followed by a slow cadmium hydroxide precipitation within the pores over the nanocomposite surface.

The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). The main advantage of the DSS algorithm against the SGS algorithm is that in the DSS algorithm no Gaussian transformation of the original data is made. In this work, these two simulation algorithms are explained, and their applications to a 3D spatial dataset are deeply investigated. The dataset consists of the porosity values of 16 vertical wells extracted from an actual cube obtained by a seismic inversion process. One well data is excluded from the simulation process for the blind well test. Comparison between the histograms show that the histogram reproduction is slightly better for the SGS algorithm, although the population reproductions are the same for both SGS and DSS results. The DSS algorithm reproduce the mean of input data closer to the mean of well data compared to that of the SGS algorithm. Considering one realization from each simulation algorithm, the RMS error corresponding to all simulated cells against the real values is approximately equal for both algorithms. On the other hand, the error show a slightly less value when the mean of 100 realizations of the DSS result is considered.

Fracture toughness is an important concrete property that controls crack extension and concrete fracture. Concrete is the most widely used material in civil engineering containing the most conventional and cheapest materials. Accordingly, cracks and fractures may cause irreparable damages. To this end, fibre-reinforced concretes have been recently constructed in order to overcome the aforementioned weaknesses. Crack propagation and fracture toughness of various concrete specimens are analyzed by the straight notched Brazilian disc (SNBD) test. The specimens are conventional concrete lacking micro-silica and limestone powder, and those containing various volume percentages of fibers including the concrete specimens containing 0.35% individual polypropylene (PP) fibers, 0.35% individual glass fibers, concrete specimens containing 0.17% PP and 0.18% glass fibers, and concrete fibers containing 0.1% PP and 0.25% glass fibers. Micro-silica has replaced 10 wt% cement in all fiber-reinforced concrete specimens, and limestone has replaced 5 wt% cement. Crack extension from the pre-existing cracks in the specimens and mode I, mode II, and mixed-mode fracture toughness are calculated. The BD test is performed on the specimens at the crack inclination angles of 0°, 15°, 28.83°, 45°, 60°, 75°, and 90°. The experimental results show the initiation of wing cracks at angles less than 60° (0 < α < 60°) from the tip of the pre-existing cracks. The crack growth and propagation path approach the loading direction by continuing loading. However, the cracks are initiated at a distance of d from the crack tip at angles larger than 60°. The observed distance is larger in the fiber-less specimens than in the fiber-reinforced specimens. The concrete specimens reinforced by 0.17% PP and 0.18% glass hybrid fibers containing micro-silica and limestone powder showed the highest mode I, mode II, and mixed-mode fracture toughness compared to the other concrete specimens.

The present work aims at implementing Response Surface Methodology (RSM) in order to generate a statistical model for Minimum Required Caving Span (MRCS) and estimate both the individual and mutual effects of the rock mass parameters on rock mass cavability. The adequate required data is obtained from the result of numerical modeling. In this work, various arrays of numerical simulations (480 models) are carried out using the UDEC software in order to study the rock mass cavability thoroughly. The effect of each individual parameter and their mutual effect on MRCS are investigated by means of ANOVA. ANOVA indicates that all the chosen parameters (depth, dip of the joint, number of joints, angle of friction of the joint surface, and joint spacing) highly affect MRCS. In other words, the results of ANOVA are in high agreement with the results of the conventional sensitivity analysis. Moreover, a combination of joint spacing and joint inclination has the highest mutual effect on MRCS, and a combination of undercut depth and joint spacing has the lowest effect on MRCS.

The in-situ coal is converted to the synthetic gas in the process of underground coal gasification (UCG).  In order to increase the rate of in-situ coal combustion in the UCG process, the contact surfaces between the steam, heat, and coal fractures should be raised. Therefore, the number of secondary cracks should be increased by raising the heat and existing steam pressure during the process. This paper emphasises on the secondary crack growth mechanism of the pre-existing cracks in the coal samples under different loading conditions. Different geometric specifications such as the length of the pre-existing cracks (coal cleats) and their inclinations are considered. The numerical modeling results elucidate that the first crack growths are the wing cracks (also called the primary or tensile cracks) formed due to unbonding the tensile bonds between the particles in the assembly. Ultimately, these cracks may lead to the cleat coalescences. On the other hand, the secondary or shear cracks in the form of co-planar and oblique cracks may also be produced during the process of crack growth in the assembly. These cracks are formed due to the shear forces induced between the particles as the initial cleat length is increased and exceed the dimension of coal blocks. The cavity growth rate increases as the secondary cracks grow faster in the coal blocks. In order to achieve the optimum conditions, it is also observed that the best inclination angle of the initial coal cleat changes between 30 to 45 degrees with respect to the horizon for the coal samples with the elasto-brittle behavior.