فهرست مطالب

International Journal of Mining & Geo-Engineering
Volume:56 Issue: 4, Autumn 2022

  • تاریخ انتشار: 1401/11/09
  • تعداد عناوین: 12
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  • Davood Namaei Roudi, Ali Behnamfard * Pages 301-307
    The variables including ore hardness based on the SAG power index (SPI), particle size of mill product (P80), trunnion pressure of the mill free head (p) and working time period of mill liner (H) were considered as variables for development of an adequate model for the prediction of autogenous (AG) mill power consumption in Sangan iron ore processing plant. The one-parameter models (SPI as variable) showed no adequate precision for the prediction of Sangan AG mill power consumption. Two-parameter models (SPI and P80 as variables), proposed by Starkey and Dobby, showed no adequate precision for the Sangan AG mill power consumption. Nonetheless, by exerting an adjustment factor in the model (0.604513 which obtained by what-if analysis using Solver Add-Ins program), the model precision increased significantly (an error of 7.11%). Finally, a four-parameter model in which the Sangan AG mill power consumption is predicated as a function of SPI, P80, p, and H was developed. Hence, initially the relationship between the mill power consumption and each of the variables was obtained and then the four-parameter model was developed by summation of these four equations and applying a similar coefficient of 0.25 for all of them. This model was modified through finding the best coefficients by what-if analysis using solver Add-Ins program through minimizing the ARE error function. The error function for the training and testing data sets was determined to be 2.93% and 2.39%, respectively.
    Keywords: Autogenous mill, Power consumption, modelling, What-if analysis
  • Marzieh Hosseini Nasab *, Mohammad Noaparast, Hadi Abdollahi Pages 309-313
    Laterites are the main resources of oxidized nickel in the world. Nickel and cobalt are embedded in limonite laterites within the goethite structure. Therefore, the removal of iron ions will lead to the simultaneous precipitation of iron, nickel, and cobalt. In our previous study investigating atmospheric leaching of laterite ore using sulfuric acid with the addition of NaCl to the solution, we determined the optimal parameters to minimize the co-dissolution of iron. Based on the determined optimum conditions, a PLS was prepared. In the current study, the effect of pH on iron precipitation from the PLS was investigated using sodium hydroxide as a neutralizing agent. Results indicated that a pH=4 can result in the highest removal of iron from the leaching solution (around 90%) while minimizing the loss of nickel and cobalt. The SEM analysis revealed ferrihydrite as the most important mineral in the final precipitation obtained at pH=4. The results of this study can be used for benchmarking more efficient methods for iron removal from the solution and improving the dissolution kinetics of nickel and cobalt.
    Keywords: Laterite, nickel, Cobalt, Leaching, Precipitation
  • Ali Behnamfard *, Mohammad Rivaz Pages 315-322
    An adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the dissolved oxygen concentration (DOC) as a function of the solution temperature (0-40oC), salinity based on conductivity (0-59000 µS/cm), and atmospheric pressure (600-795 mmHg). The data set was randomly divided into two parts, training and testing sets. 80% of the data points (80% = 11556 datasets) were utilized for training the model and the remainder data points (20% =2889 datasets) were utilized for its testing. Several indices of performance such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of correlation (R) were used for checking the accuracy of data modeling. ANFIS models for the prediction of DOC were constructed with various types of membership functions (MFs). The model with the generalized bell MF had the best performance among all of the given models. The results indicate that ANFIS is a powerful tool for the accurate prediction of DOC in the gold cyanidation tanks.
    Keywords: Dissolved oxygen concentration, Cyanidation process, Data modeling, Adaptive neuro-fuzzy inference system
  • Fariba Heydarzadeh Sohi, Abbas Akhavan Sepahi, Fereshteh Rashchi *, Mohammad Kargar, Seyed Abdolhamid Angaji Pages 323-330
    This research has focused on isolating and identifying different thermoacidophilic bacteria from a Sarcheshmeh low-grade copper ore and evaluating their ability of copper bioleaching from the mineral tailing. After the isolation of the bacteria, molecular identification was carried out based on the 16S rRNA gene sequences and drawing the phylogenetic tree. Then, the effect of the pH, pulp density, and composition of the media on the copper bioleaching was determined using the identified bacteria. The isolated strain (Strain SCM1) belonged to Delftia acidovorans with a 95.73% of identity. The optimal condition for the copper bioleaching was reported in a medium consisting of sulfur (10 g/L), glucose (10 g/L), yeast extract (2 g/L), and mineral tailing (5% wt/vol) at the pH of 2.00 at 50°C. Under this condition, the highest amount of copper (83%) was bioleached. It proves that the lately isolated strain can be effectively employed in the copper bioleaching process.
    Keywords: Bioleaching, Copper, Mineral tailings, Thermoacidophilic bacteria, 16S rRNA
  • Elhmam Ghadiri Sufi, Saeed Soltani Mohammadi *, Hadi Mokhtari Pages 331-337

    Exploratory drilling is one of the most important and costly stages of mineral exploration procedures, so the continuation of mining activities depends on the gathered data during this stage. Due to the importance of cost and time-saving in the performance of mineral exploration projects, the effective parameters for reducing the cost and time of drilling activities should be investigated and optimized. Road construction and the sequence of the drilling boreholes by drilling rigs are among these parameters. The main objectives of this research were to optimize the overall road construction cost and the difference in length drilled by each drilling rig. The problem has been modeled as a Multi-Objective Multiple Traveling Salesman Problem (MOmTSP) and solved by the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used to find the optimal solution among the solutions obtained by the NSGA-II.

    Keywords: Exploratory drilling, Multi-Objective Multiple Traveling Salesman Problem, Non-dominated Sorting Genetic Algorithm-II, Optimization, Technique for Order Preference by Similarity to Ideal Solution
  • Kiana Damavandi, Maysam Abedi *, GholamHossain Norouzi, Masoud Mojarab Pages 339-347

    Landslide, as a geohazard issue, causes enormous threats to human lives and properties. In order to characterize the subsurface prone to the landslide which is occurred in the Tehran-North freeway, Iran, a comprehensive study focused on geological field observations, and a geoelectrical survey as a cost-effective and fast, non-invasive geophysical measurement was conducted in the area. As a result of road construction, problems in this region have increased. The Vertical Electrical Sounding (VES) investigation in the landslide area has been carried out by the Schlumberger array for data acquisition, implementing eight survey profiles varying in length between 60 and 130 m. Based on the electrical resistivity models through a smoothness-constrained least-square inversion methodology, the landslide structure (i.e., depth of the mobilized material and potential sliding surface) is better defined. The inferred lithological units, accompanied by stratigraphical data from a borehole and geological investigations for the prone landslide region, consisted of a discontinuous slip surface, having a wide range of resistivity, observed to be characterized by tuff with silt. Electrical resistivity values above 150 Ωm indicate a basement of weathered marlstone and sand. Values between 15 and 150 Ωm illustrate a shale-content layer with outcrops in the area that is the reason for movement. The sliding surface is at a depth of about 12 m. The method used in this study is a good candidate to investigate the risk of landslides in this region and can be applied to other landslide areas where borehole exploration is inefficient and expensive due to local complications.

    Keywords: Vertical Electrical Sounding (VES), Geophysics, inversion, Landslide
  • Behnam Alipenhani, Hassan Bakhshandeh Amnieh *, Abbas Majdi Pages 349-359
    Incorrect estimation of undercut dimensions in the block caving method can lead to the cessation of caving operations and loss of a large portion of deposits. Numerical modeling is one of the methods for determining the minimum caving span. Numerical and physical modeling methods are useful for an accurate understanding of caving operations. Accordingly, this research focused on investigating the performance of physical and numerical modeling in determining the effects of depth and joint orientation on the minimum required caving span for the initiation and propagation of caving. The physical model was made with 1.5*1.5 square meter dimensions and consisted of travertine blocks with 4*4 square centimeter dimensions. In addition, joints were modeled with dips of 0, 90, 45, 135, 30, and 120 degrees. The physical model could simulate ground stress conditions to great depths and show the behavior of the jointed rock mass in a two-dimensional space. Further, by capturing this behavior, it was possible to compare its result with UDEC software. The results demonstrated that the number of falling blocks and the height of the caving increased by increasing the dip. Furthermore, the formation of arches due to high horizontal stress stops the caving, which will occur again with the increasing span. Although the horizontal stresses and geometrical properties of the joints affect the shape of the caving area, its shape largely follows the dip and orientation of the rock mass joints. Poor draw control causes caved ore columns, which can lead to the formation of a stable arc. Finally, the height of the caved back increases in each span by increasing the depth while decreasing the dip of the joints.
    Keywords: Block caving, Caving Span, Physical Modeling, Numerical Modeling, UDEC
  • Amirreza Nasimifar, Javad Mehrabani * Pages 361-382
    Vanadium is a strategic metal and its compounds are widely used in industry. Vanadium pentoxide (V2O5) is one of the important compounds of vanadium, which is mainly extracted from titanomagnetite, phosphate rocks, uranium-vanadium deposits, oil residues, and spent catalysts. The main steps of vanadium extraction from its sources include salt roasting, leaching, purification, and precipitation of vanadium compounds. In the hydrometallurgical method, first, the vanadium is converted to a water-soluble salt by roasting, and then the hot water is used to leach out the salt-roasted product and the leach liquor is purified by chemical precipitation, solvent extraction, or ion exchange processes to remove impurities. Then, a red cake precipitates from an aqueous solution by adjusting the conditions. To provide high pure vanadium pentoxide, it is necessary to treat the filtered red cake in an ammonia solution. So, ammonium metavanadate (AMV) is precipitated, calcined, and flaked to vanadium pentoxide. In the pyrometallurgical method, vanadium-containing concentrate is smelted, and by forming titanium-containing slag and molten pig iron, oxygen is blown into pig iron in a converter or shaking ladles, and vanadium is oxidized to produce vanadium-rich slag. In the next step, the slag is roasted and treated by the hydrometallurgical process. In this paper, the industrial processes and novel developed methods are reviewed for the extraction of vanadium pentoxide.
    Keywords: Vanadium pentoxide, extraction, Roasting, Leaching, process
  • Masood Lashkari Ahangarani, Saeed Mojeddifar *, Mohsen Hemmati Chegeni Pages 383-390
    A probabilistic neural network (PNN) is a feed-forward neural network using a smoothing parameter. We used the PNN algorithm based on single and multi-smoothing parameters for multi-dimensional data classification. Using multi-smoothing parameters, we implemented an improved probabilistic neural network (PNN) to estimate the porosity distribution of a gas reservoir in the North Sea. Comparing the results of implementing smoothing parameters obtained from model-based optimization and particle swarm optimization (PSO) indicated the efficiency of PNN in characterizing the gas. Also, results showed that while the PSO algorithm was able to specify smoothing parameters with more precision, about 9%, it was very time-consuming. Finally, multi PNN based on PSO was applied to estimate the porosity distribution of the F3 reservoir. The results validated the main fracture or gas chimney of the F3 reservoir with higher porosity. Also, gas-bearing layers were highlighted by energy and similarity attributes.
    Keywords: probabilistic neural network, Smoothing Parameter, model-based optimization, Particle Swarm Optimization
  • Mohanad AL-Farhan, Behrooz Oskooi *, Maysam Abedi, Vahid Ebrahim Zadeh Ardestani, Amar AL-Khalidy Pages 391-400
    Potential field geophysical measurements were conducted in the west of Kifl region in central Iraq to image a plausible oil-trapping reservoir. Ground-based magnetometry and gravimetry surveys were conducted to investigate this region by covering an area of 16  24 km by designing a regular grid spacing of 250 m. After preprocessing potential field data, different filters were utilized to separate the residuals from the regional anomalies. The complicated tectonic setting of the studied area was imaged by recognition of the fault system through simulation of the magnetic and gravity anomalies, which facilitates the configuration display of the oil-trapping mechanism. The geometry of a fault system was derived from parametric inversion of gravity data. The magnetic anomalies were extended with the trends of NS, NW, and NE and reached a maximum value of 55 nT. However, the gravity anomalies appeared with the same extensions and values ranging from -3.3 to 1.5 mGal. The intense magnetic susceptibility amount of the reservoir rocks is arising from chemical processes and iron-oxide ion replacements, accompanied by the migration and accumulation of hydrocarbon. Incorporating the results from the Euler’s depth estimation, parametric data modeling along with logging data assisted simultaneous modeling of the magnetic and gravity data. The 2D geological model of the subsurface layers at the Kifl area presents a graben-horst fault system within a thick sequence of sediment. Geological characteristics extracted from geophysical data modeling provided insightful information on the nature and essence of the hydrocarbon reservoirs in the Kifl area. It has formed through tectonic deformation and tension over the Arabian plate during the Permian – Paleocene cycle. Hence, it can be concluded that the aforementioned fault system has divided the hydrocarbon reservoirs.
    Keywords: magnetic, Gravity, inversion, Kifl area, Oil trapping
  • Hassan Moomivand, Hasel Amini Khoshalan *, Jamshid Shakeri, Hassan Vandyousefi Pages 401-411
    The fragment size of blasted rocks considerably affects the mining costs and production efficiency. The larger amount of blasthole diameter (ϕh) indicates the larger blasting pattern parameters, such as spacing (S), burden (B), stemming (St), charge length (Le), bench height (K), and the larger the fragment size.  In this study, the influence of blasthole diameter, blastability index (BI), and powder factor (q) on the fragment size were investigated. First, the relation between each of X20, X50, and X80 with BI, ϕh, and q as the main critical parameters were analyzed by Table curve v.5.0 software to find better input variables with linear and nonlinear forms. Then, the results were analyzed by multivariable linear regression (MLR) procedure using SPSS v.25 software and gene expression programming (GEP) algorithm for prepared datasets of four open-pit mines in Iran. Relations between each of X20, X50, and X80 with the combination of adjusted BI, ϕh, and q were obtained by MLR procedure with good correlations of determination (R2) and less root mean square error (RMSE) values of (0.811, 1.4 cm), (0.874, 2.5 cm) and (0.832, 5.4 cm) respectively. Moreover, new models were developed to predict X20, X50, and X80 by the GEP algorithm with better correlations of R2 and RMSE values (0.860, 1.3 cm), (0.913, 2.49 cm), and (0.885, 5.6 cm) respectively and good agreement with actual field results. The developed GEP models can be used as new relations to estimate the fragment sizes of blasted rocks.
    Keywords: rock fragmentation, Blasting, open pit mines, Multivariable linear regression, Gene Expression Programming
  • Nader Moussaei, MohammadHossein Khosravi *, Mohammad Farouq Hossaini Pages 413-422

    The distribution of earth pressure surrounding a tunnel is one of the most critical factors in designing tunnel support systems. In this study, a physical modeling setup has been designed and constructed to simulate the excavation procedure of a full-face circular tunnel. Silica sand was used with four different densities and three different cover-to-tunnel diameter ratios. The full-face excavation was simulated with a variation of tunneling-induced volume loss. The variations of earth pressure around the tunnel were measured by means of a series of miniature soil pressure cells. Particle Image Velocimetry (PIV), as a non-destructive image processing technique, was used to monitor the deformation of the soil surrounding the tunnel. The results obtained from both pressure cells and PIV showed that soil arching developed around the tunnel. As tunnel convergence increased, a loosened zone appeared above the tunnel, surrounded by a stress arch. It was discovered that there is a direct relationship between the height of the loosened zone and the depth of the tunnel. A linear equation has been established for the estimation of the height of the loosened zone, which has a direct influence on the design of the support system.

    Keywords: Tunneling, Physical model, Soil arching, Particle Image Velocimetry (PIV)