Journal of Mining and Environement
Volume:10 Issue: 3, Summer 2019

Predicting the wear performance of circular diamond saw in the process of sawing hard dimensional stone is an important step in reducing production costs in the stone sawing industry. In the present research work, the effective parameters on circular diamond saw wear are defined, and then the weight of each parameter is determined through adopting a fuzzy rock engineering system (Fuzzy RES) based on defining an accurate Gaussian pattern in fuzzy logic with analogous weighting. After this step, genetic algorithm (GA) is used to determine the levels of the four major variables and the amounts of the saw wear (output parameter) in the classification operation based on the fixed, dissimilar, and logarithmic spanning methods. Finally, a mathematical relationship is suggested for evaluation of the accuracy of the proposed models. The main contribution of our method is the novelty of combination of these methods in fuzzy RES. Before this work, all Fuzzy RESs only use simple membership functions and uniform spanning. Using GA for spanning and normal distribution as membership function based upon our latest work is the first work in fuzzy RES. To verify the selected proposed model, rock mechanics tests are conducted on nine hard stone samples, and the diamond saw wear is measured and compared with the proposed model. According to the results obtained, the proposed model exhibits acceptable capabilities in predicting the circular diamond saw wear.

This paper presents a quantitative modeling for delineating alteration zones in the hypogene zone of the Miduk porphyry copper deposit (SE Iran) based on the core drilling data. The main goal of this work was to apply the Ordinary Kriging (OK), Artificial Neural Networks (ANNs), and Concentration-Volume (C-V) fractal modelings on Cu grades to separate different alteration zones. Anisotropy was investigated and modeled based on calculating the experimental semi-variograms of Cu value, and then the main variography directions were identified and evaluated. The block model of Cu grade was generated using the kriging and ANN modelings followed by log-log plots of the C-V fractal modeling to determine the Cu threshold values used in delineating the alteration zones. Based on the correlation between the geological models and the results derived via C-V fractal modeling, Cu values less than 0.479% resulting from kriging modeling had more overlapped voxels with the phyllic alteration zone by an overall accuracy (OA) of 0.83. The spatial correlation between the potassic alteration zone in a 3D geological model and the high concentration zones in the C-V fractal model showed that Cu values between 0.479% and 1.023%, resulting from kriging modeling, had the best overall accuracy (0.78). Finally, based on the correlation between classes in the binary geological and fractal models of the hypogene zone, this research work showed that kriging modeling could delineate the phyllic (with lower grades) and potassic (with higher grades) alteration zones more effectively compared with ANNs.

In general, underground spaces are associated with high risks because of their high uncertainty in geotechnical environments. Since most accidents and incidents in these structures are often associated with uncertainty, the development of risk analysis and management methods and prevention of accidents are essential. A deeper recognition of the factors affecting the implementation process can pave the way for this purpose. Risk rating of projects is a key part of the risk assessment stage in the risk management process of each project. Various multi-criteria decision-making methods, as quantitative approaches, are used to allow them to be used in the risk rating issue of each project. In this work, a new model is provided for risk management of Mashhad Urban Railway Line 3 using the game theory and multi-criteria decision-making methods. Based on the answers of the specialists and experts to the prepared questionnaires, various risk groups identified using the TOPSIS and AHP multi-criteria decision-making methods are ranked. Accordingly, the group of economic risks, as the most important risk and social risk group, is ranked as the least significant in both methods. In the following, the appropriate response to the main risks of the ratings is proposed based on the modeling of the game theory, and ranked in terms of importance. Also the worst risk scenario in the project is identified, and the appropriate responses for this state are also expressed in order of importance. The results obtained indicate that the risk of financing problems is the most significant risk, and other risks are ranked in terms of importance in the next ranks. Additionally, the use of new financing methods at times of credit scarcity and project financial problems is also considered as the most important response to the risk in this project.

Upscaling based on the bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with bandwidth. In a smooth variability region, a large number of cells will be merged, and vice versa, they will remain fine with severe variability. Bandwidth variation can be effective in upscaling results. Therefore, determining the optimal bandwidth in this method is essential. For each bandwidth, the upscaled model has a number of upscaled blocks and an upscaling error. Obviously, higher thresholds or bandwidths cause a lower number of upscaled blocks and a higher sum of squares error (SSE). On the other hand, using the smallest bandwidth, the upscaled model will remain in a fine scale, and there will be practically no upscaling. In this work, different approaches are used to determine the optimal bandwidth or threshold for upscaling. Investigation of SSE changes, the intersection of two charts, namely SSE and the number of upscaled block charts, and the changes of SSE values versus bandwidths, are among these approaches. In this particular case, if the goal of upscaling is to minimize the upscaling error, the intersection method will obtain a better result. Conversely, if the purpose of upscaling is computational cost reduction, the SSE variation approach will be more appropriate for the threshold setting.

Knowledge of the airflow distribution inside a Tunnel Boring Machine (TBM) can create a safe working environment for workers and machinery. The airflow quality and the related mass flow rate in the ventilation system should be sufficient to dilute gases and remove dust inside the tunnel. In this work, airflow distribution in the single shield TBM tunnel was studied using computational fluid dynamics. The finite volume-based finite element method was used in the simulation based on the 3D complex geometry of TBM. In order to validate the numerical results, the air velocity inside the Chamshir tunnel was measured experimentally at different sections. With a length of 7050 m and a final diameter of 4.6 m, the Chamshir water transport tunnel is located in the south of Iran. The results obtained show that there is not enough airflow in 59.6% of the TBM space in the current working conditions. In other words, there are many dead zones from the control cabin to the end of gantry 6 in the backup system. Several applicable scenarios were studied to remove the dead zone area and optimize the airflow velocity by employing high capacity jet fan in the ventilation system. The results show that the dead zone volume can be decreased by about 5.21% by increasing the airflow rate of the jet fan.

This paper presents an innovative solution for estimating the proximate parameters of coal beds from the well-logs. To implement the solution, the C# programming language was used. The data from four exploratory boreholes was used in a case study to express the method and determine its accuracy. Then two boreholes were selected as the reference, namely the boreholes with available well-logging results and the proximate analysis data. The values of three well-logs were selected to be implemented in a system of equations that was solved, and the effect of each well-log on the estimated values of the proximate parameter was expressed as a coefficient called the effect factor. The coefficients were incorporated in an empirical relationship between the parameter and the three well-logs. To calculate the coefficients used for the most accurate estimation, a total of 22960 systems of equations were defined and solved for every three logs. As there was the possibility of 560 combinations for selecting three logs from all the available 16 logs, the three equation-three variable systems were solved more than 12 million times. The programming methods were utilized to achieve the final results. The results of each system were tested for deviation of the estimated values of volatile matter, ash, and moisture, and the coefficients of the lowest deviation were accepted to be applied in the relation. Implementing this method for estimating the volatile matter resulted in an average deviation of 10.5%. The corresponding estimated values of the ash and moisture contents were 22% and 14%, respectively.

This researchdescribe wastewater pre-treatment that contaminated with Methylene Blue dye (MB) and Ni(II) ion by Athelia Bombacina fungus dead biomass (ABFDB). Researches finding on ABFDB characterization by SEM, XRD, CHNS and FT-IR analysis show that ABFDB can be used as efficient sorbent, because ABFDB cellular wall consist of Chitin, β-Glucan and Cellulose biopolymers, generally. Results show that removal of MB and Ni(II) ion by ABFDB sorbent is more than 86.41 and 66.2%, respectively. So, after parameters investigation of MB and Ni(II) ion sorption process by ABFDB, the response surface method was employed for optimization and study the interaction of operational parameters on the sorption of pollutants. This low-cost and natural environmental friendly biosorbent can be utilized for pretreatment process in the first step of wastewater treatment project.

Due to the nature of the geological and mining activities, different input parameters in the grade estimation and mineral resource evaluation are always tainted with uncertainties. It is possible to investigate the uncertainties related to the measurements and parameters of the variogram model using the fuzzy kriging method instead of the kriging method. The fuzzy kriging theory has already been the subject of relatively various research studies but the main weak point in such studies is that the results of the fuzzy estimations are not used in decision-making and planning. A very common, but key, tool of decision-making for mining engineers is the tonnage-average grade models. Under conditions where measurements or/and variogram model parameters are tainted with uncertainties, the tonnage-average grade model will be uncertain as well. Therefore, it is necessary to use the fuzzy tonnage-grade model instead of the crisp ones, and the next analysis steps and decision-makings are done accordingly. In this paper, the computational principles of the fuzzy tonnage-average grade curve and a case study regarding its usage are presented.

Kriging is an advanced geostatistical procedure that generates an estimated surface or 3D model from a scattered set of points. This method can be used for estimating resources using a grid of sampled boreholes. However, conventional ordinary kriging (OK) is unable to take locally varying anisotropy (LVA) into account. A numerical approach has been presented that generates an LVA field by calculating the anisotropy parameters (direction and magnitude) in each cell of the estimation grid. After converting the shortest anisotropic distances to Euclidean distances in the grid, they can be used in variography and kriging equations (LVAOK). The ant colony optimization (ACO) algorithm is a nature-inspired metaheuristic method that is applied to extract image features. A program has been developed based on the application of ACO algorithm, in which the ants choose their paths based on the LVA parameters and act as a moving average window on a primary interpolated grid. If the initial parameters of the ACO algorithm are properly set, the ants would be able to simulate the mineralization paths along continuities. In this research work, Choghart iron ore deposit with 2,447 composite borehole samples was studied with LVA-kriging and ACO algorithm. The outputs were cross-validated with the 111,131 blast hole samples and the Jenson-Shannon (JS) criterion. The obtained results show that the ACO algorithm outperforms both LVAOK and OK (with a correlation coefficient value of 0.65 and a JS value of 0.025). Setting the parameters by trial-and-error is the main problem of the ACO algorithm.

Delineation of oxide and sulfide zones in mineral deposits, especially in gold deposits, is one of the most essential steps in an exploration project that has been traditionally carried out using the drilling results. Since in most mineral exploration projects there is a limited drilling dataset, application of geophysical data can reduce the error in delineation of the sulfide and oxide zones. For this purpose, we produced a 3D model of Induced Polarization (IP) data using the ordinary kriging technique. Then the modelling results were compared with the drilling data. The results obtained showed that the 3D geophysical models would properly delineate the sulfide and oxides zones. This work presents a new application of the IP results for separation of these zones. In addition, the conducted variography in this work suggests reducing the profile spacing of dipole-dipole IP arrays down to 25 m. This would properly enrich the integration of geophysical and geological results in the modelling of gold deposits.

Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.

The aim of this work was to delineate the prospects of podiform-type chromite by staged factor analysis and geochemical mineralization prospectivity index in Balvard area, SE Iran. The stream sediment data and fault density were used as the exploration features for prospectivity modeling in the studied area. In this regard, two continuous fuzzified evidence layers were generated and integrated using fuzzy operator. Then fractal modeling was used for defuzzification of the prospectivity model obtained. Furthermore, the prediction-area plot was used for evaluation of the predictive ability of the generated target areas. The results obtained showed that using the prospectivity model, 82% of mineral occurrences was predicted in 18% of the studied area. In addition, the target areas were correlated with the geological particulars including ultrabasic and serpentinization rocks, the host rocks of the podiform-type chromite deposit type.

Low-intensity magnetic separators are widely used in the research works and the industry. Advancement in the magnetic separation techniques has led to an expansion in the application of this method in different fields such as enrichment of magnetic mineral, wastewater treatment, and medicine transfer in the human body. In the mineral processing industry, the main application of wet magnetic separation is via drum separators. The design of this separator is based on drum rotation inside a tank media, where a permanent magnet placed inside the drum as an angle form produces a magnetic field. In the present work, the magnetic variables involved (magnetic flux density, intensity of magnetic field, and gradient of magnetic field intensity) were simulated in the drum wet low-intensity magnetic separator using the finite element method and a COMSOL Multiphysics simulator; these variables were further validated through the measured data. A comparison between the simulation and laboratory measurements (of the magnetic field) showed that the mean value of the simulation error in 94 points in 2 sections was equal to 9.6%. Furthermore, the maximum simulation error in the middle of the magnets, as the most important part of the magnetic field distribution in the process of magnetic separation, was in the 6th direction and equal to 7.8%. Therefore, the performed simulation can be applied as a first step to design and construct more advanced magnetics separators.

The Sangan processing plant consists of four consecutive low-intensity magnetic separation steps with the same magnetic field intensity of 1300 Gauss for upgradation of iron ore. Hence, the iron ore minerals with lower magnetic susceptibility or interlocked with gangue minerals have no opportunity for upgradation, and proceed to the tailing dam. Flotation is a powerful technique for upgradation of these materials, and it is the focus of this research work. A sample of 43.09% Fe and 12.1% FeO was taken from the tailings of second step of magnetic separation. The ore minerals of the sample were determined to be magnetite and hematite. A concentrate of 67% Fe and mass recovery of 50% was produced through the Davis tube test. A reverse flotation route was selected for upgradation of the sample. Fatty acid-based anionic collectors with trade names Alke and Dirol were used in the flotation experiments. The design of experiments was done by resolution IV fractional factorial design with nine factors at two levels per factor. A resolution IV design allows discrimination of all main effects and two-factor interactions. A concentrate of 53.92% Fe at a mass recovery of 60% was obtained at optimum flotation conditions of solid content 20%, pH 12, collector concentration of 1 kg/t, starch as depressant at a concentration of 5 kg/t, Alke/Dirol collector mass ratio of 30/70, conditioning time of 10 min., and concentration of Ca2+ as activator 1 kg/t. In this research work, the concept of natural depression of iron minerals in the reverse flotation was introduced and evaluated.

Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing techniques in the analysis of stream sediment geochemical datasets and ASTER satellite images. The processing methods have led to identification of eight new prospective areas. These methods are aimed at providing univariate geochemical maps. The stream sediment geochemical mapping for Cu and Mo was performed by the sample catchment basin approach. The results derived from this approach have been mapped in four classes associated with the first quartile, third quartile and threshold values obtained from Median Absolute Deviation method. False-colour composite and band ratio techniques were adopted as two well-known methods for the processing of an ASTER scene spanning the study area. Eight new targets for possible mineralization have been resulted from geochemical data analyses. Image processing techniques on the ASTER multispectral data have also revealed widespread hydrothermal alterations associated with the known porphyry copper deposits and the new prospects.

In this work, the effects of temperature, acid concentration, and mechanical activation on dissolution of ilmenite were studied using the statistical design of experiment technique. Mechanical activation was carried out using a planetary ball mill in dry mode, and the resulting structural changes were characterized by the particle size analysis, specific surface area measurements, and X-ray diffraction method. The results obtained indicated that intensive milling led to a significant decrease in the ilmenite particle size and that after 20 minutes, particles tended to agglomerate. However, after 90 minutes, the BET specific surface area increased to 9.36 m2/g. In addition to surface changes, mechanical activation led to intense changes and disorders in the crystal structure of ilmenite as amorphization degree increased to 94.30% and the volume weighted crystallite size and lattice strain changed from 346 nm and 0.13% to 14 nm and 1.44%, respectively. The results of the dissolution tests in the form of experimental design indicated that a suitable model could fit the experimental data in 95% confidence level. The coefficient factors for acid concentration, mechanical activation, and temperature were 3.75%, 33.04%, and 9%, respectively. Mechanical activation had the highest effect on titanium extraction in comparison to the other factors involved. Also in addition to its dominant effect on ilmenite dissolution, it also weakened the temperature effect. However, the results of the kinetic tests proved that mechanical activation led to promotion of the temperature effect on increasing the dissolution reaction rate in the initial stages. Finally, a dissolution yield of more than 98% was achieved through 90 minutes of activation at 95° C and 55 wt.% acid concentration.

Recently, the non-destructive methods have become of interest to the scientists in various fields. One of these method is Ground Penetration Radar (GPR), which can provide a valuable information from underground structures in a friendly environment and cost-effective way. To increase the signal-to-noise (S/N) ratio of the GPR data, multi-fold acquisition is performed, and the Common-Mid-Points (CMPs) are acquired. Compared to the traditional CMP method, which is applied to a CMP, the Common-Reflection-Surface (CRS) method is introduced for seismic data processing considering the neighboring CMPs. In addition, instead of a point on the reflector, CRS assumes that the reflector is part of a circle. With these two characteristics, CRS produces a stack section with a high S/N ratio. The Common-Diffraction-Surface (CDS) method, which is a simplified version of CRS, enhances the diffractors related to the underground anomalies like pipeline, flume, and caves. We apply the CDS stack method on a multi-fold GPR data and compare it to the CRS results. These results show that the CDS method can provide a high S/N ratio stack section compared to the traditional CMP method.

The influence of microwave treatment on the surface roughness, hydrophobicity, and chemical composition of galena was studied. The pure galena specimens and purified galena concentrate were used in this work. A conventional multi-modal oven (with a frequency of 2.45 GHz and a maximum power of 900 W) was used to conduct the experiments. The results obtained from the atomic-force microscopy analysis showed that the surface roughness of galena decreased after the microwave radiation. The results also showed that the surface hydrophobicity of galena increased with increase in the duration of the microwave radiation, which was in good agreement with the micro-flotation mass recovery results. The increased surface hydrophobicity may be attributed to the decreased surface roughness by microwave radiation or formation of sulfur on the surface. The results of the SEM/EDS analyses indicated that after microwave radiation, the amount of S increased, whereas Pb decreased on the surface of galena, indicating that the average atomic number of the galena surface changed due to microwave treatment.

In the present work, we investigated and optimized the digestion efficiency, A/S (Al2O3/SiO2 in red mud), and N/S (Na2O/SiO2 in red mud) of mixed bauxite in Iran Alumina Company using the Bayer process. Digestion experiments were carried out in an induction rotary autoclave on a mix of Jajarm, Yazd, Tash, and Shirin Cheshmeh bauxites. A 4-factor 3-level response surface methodology was applied for the design and analysis of the experiment with the optimization of Na2O concentration, digestion temperature, residence time, and amount of lime addition. Towquadratics and one linear model were derived for the prediction of digestion efficiency, and A/S and N/S responses. The results obtained showed that the optimum amounts for Na2O concentration, temperature, amount of lime addition and residence time were 180 g/L, 275°C, 7.73%, and 50 minutes, respectively, in which the digestion efficiency, A/S, and N/S reached 72.05%, 1.169, and 0.27, respectively. Validation experiment showed that the digestion efficiency, A/S, and N/S were 72.24%, 1.162, and 0.28% respectively, which meant a 2% increase in digestion efficiency and a 0.09 and 0.02 decrease in A/S and N/S, respectively, compared to the current operating condition.

According to the classification of the nickel laterite, this paper describes mineralogy test is to reveal where valuable elements are located in the ore, in which mineralogical form. The purpose of the sieving test was to study if some specific particle size contains most of the valuable metals. Based on its chemical composition nickel laterite is classified as a limonite type and the nickel and cobalt content was 0.7 and 0.04%, respectively. Nickel is predominantly associated with hematite and goethite. Based on the mineralogical analysis of the ore, it is observed that remarkable part of nickel is located in hematite. Therefore, nickel cannot be released from hematite lattice. The nickel content in the laterite was 0.7% and the cobalt content 0.04%. The chemical composition of laterite equals with the occurrence of 38.9% iron oxides, 26.9% carbonates, 26.9% quartz, 4.8% chromite, 2.7% magnetite and 1.9% other minerals. EDS line profile analyses were completed on hematite/goethite ooids and there was a slight correlation in the quantities between iron and nickel in each individual ooid. However, iron and nickel do not always show a positive correlation. Nickel grade could be enriched from 0.7 wt.-percent to 0.91 wt.-percent; however nickel recovery was only 45%.