Journal of Mining and Environement
Volume:8 Issue: 4, Autumn 2017

In this work, the parameters affecting the recovery of copper from the low-grade sulfide minerals of Sarcheshmeh Copper Mine were studied. A low-grade sulfide ore was used with a copper grade of 0.25%, which was about 28% of the mineral oxide, and the sulfide minerals made up the rest. Much more sulfide minerals were found to be pyrite and most of the gangue minerals were quartz, anorthite, biotite, and muscovite. In order to investigate, simultaneously, the solids (10 to 20%) and acidity (1.5 to 2.5) and shaking (110 to 150 rpm), the separation of bacteria from Sarcheshmeh Copper Mine was carried out. After adjustment of the sample, bio-leaching tests were performed in accordance with the pattern defined by the software DX7 in shaking flasks, and the Cu recovery was modeled and optimized using the response surface methodology. The influential parameters were comprehensively studied. The central composite design methodology was used as the design matrix to predict the optimal level of these parameters. Then the model equation was optimized. The results obtained showed that increasing solids (from 10 to 20%) was bad for bacteria. The highest copper recovery was equivalent to 69.91%, obtained after 21 days at 35 degrees using the Acidi Thiobacillus Ferrooxidans bacteria and a K9 medium with a pulp density of 10% and pH 1.5.

The new copper processing plant of the Sarcheshmeh copper complex consists of two parallel circuits. After a primary crushing, the ore is sent to a SAG mill, and the product is further ground in a ball mill. The overflow of the hydrocyclones is fed to a flotation circuit that contains 8 rougher tank cells (RCS130), 3 cleaner cells (RCS50), 5 scavenger cells (RCS50), and a flotation column (as recleaner). The circuit was initially designed to process a feed containing 0.8% Cu but due to a change in the ore type, the feed grade decreased to 0.6% Cu. This resulted in a reduction in the final concentrate grade and the recovery from 28% and 85.5% to 24% and 84.4%, respectively. Based on the original design, the copper and silica recovery in the cleaner cells should be 69% and 55%, respectively, but these values increased to 85% and 75% due to a higher retention time. The rather high silica recovery was found to be the main source of the lower final concentrate grade. In order to reduce the retention time of particles in the cleaner cell from 13.7 to 6.9 min, the rougher concentrates of two parallel circuits were fed to only one cleaner-scavenger and regrind circuit. This modification increased the cleaner and final concentrate grade from 15.1% and 24.5% to 17% and 26%, respectively. The overall outcome of the circuit modification was evaluated to be a 10% reduction in the energy consumption without any loss in the overall copper recovery.

Vertical roller mills (VRMs) are well-established grinding equipment for various tasks in the coal and cement industry. There are few studies on simulation of VRMs. In this research work, application of perfect mixing model for simulation of a VRM in a cement grinding plant was investigated. Two sampling surveys were carried out on the VRM circuit. The samples and data from the first survey were used for the experimental determination of the breakage function and model calibration. The breakage distribution function of the material was determined by the compressed bed breakage test in a piston-die cell device. The model parameters were back-calculated using the feed and product size distribution data and the breakage distribution function. The model parameters obtained were used for simulation of the second survey and validation of the model. The simulation results showed that the simulated product size distribution curves fitted the measured product curves quite well.

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).

Partition curves are widely used to determine the spiral separator efficiency. In this work, the partition curves were used in order to investigate the particle transportation to concentrate and tailing streams. Simulation of fine particle removal using the size-by-size partition curves showed that the recovery of gangue particles to concentrate can decrease 8.7%. It also showed that the recovery of valuable particles would increase by 6.5% and reaches 90%. Therefore, pilot-scale tests were conducted to verify the simulations. After removal of fine particles from the feed of spiral separator and treating the removed materials with high-intensity magnetic separator, total mass recovery, iron recovery, and iron grade increased from 71%, 85%, and 54% to 80%, 91%, and 56%, respectively.

In this paper, first the limitations of the ray-based method and the one-way wave-field extrapolation migration (WEM) in imaging steeply dipping structures are discussed by some examples. Then a new method of the reverse time migration (RTM), used in imaging such complex structures is presented. The proposed method uses a new wave-field extrapolator called the Leapfrog-Rapid Expansion Method (L-REM) for wave-field extrapolation. This improved method also includes a new imaging condition based on Poynting vector for wave-field separation and calculating the reflection angles. Afterwards, the results obtained for the application of the new RTM method are compared with those obtained by the harmonic-source method as a delay shot or plane wave RTM. Finally, the efficiency of these imaging methods is tested using the BP 2004 2D seismic dataset. The results obtained indicate the superiority of the presented RTM method in imaging such steep dip structures in comparison with the other imaging procedures.

The leaching kinetics of a low-grade zinc oxide ore in different acid media was investigated with respect to the experimental variables including acid concentration, temperature, liquid to solid (L/S) ratio, and stirring speed. The results obtained showed that the leaching reagent concentration and the reaction temperature exerted significant effects on the extraction of zinc, whereas the L/S ratio and stirring speed exhibited a relatively moderate effect on the leaching rate. The maximum leaching rate with inorganic acids was obtained to be 90.76%, while the maximum zinc recovery with citric acid was determined to be 88.68%. It was found that the zinc leaching process followed the kinetic law of the shrinking core model. It was distinguished that the dissolution rate was controlled by diffusion through the fluid film in the HNO3 medium with the activation energy of 4.38 kJ/mol, whereas when dissolution was performed in the presence of HCl, H2SO4, and citric acid, an intermediate process (i.e. a physico-chemical process) was the rate-controlling step.

The Zarshuran Carlin-like gold deposit is located at the Takab Metallogenic belt in the northern part of the Sanandaj-Sirjan zone, NW Iran. The high-grade ore bodies are mainly hosted by black shale and cream to gray massive limestone along the NNE-trending extensional fault/fracture zones. The aim of this investigation was to determine and separate the gold mineralized stages based on the surface litho-geochemical Au, Hg, and As data using the Concentration-Area (C-A) fractal model and stepwise factor analysis in the Zarshuran gold deposit. Three mineralized stages were determined by the C-A fractal modeling and factor analysis, which were correlated with the mineralized stages from geological studies. The main stage of Au mineralization was higher than 1.995 ppm, which was correlated with the main sulfidation stage, whereas the As and Hg highly intense anomalies (higher than 6409 and 19 ppm, respectively) were associated with the quartz-sulfide veins and veinlets. The results obtained by the C-A fractal model and stepwise factor analysis showed that the main gold mineralized stage occurred in the southern part of the Zarshuran deposit, which was correlated with the geological particulars.

Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration. In this work, five integration methods were compared consisting of fuzzy, continuous fuzzy, index overlay, AHP, and fuzzy AHP. For this purpose, geological maps, geochemical samples, and geophysics data were collected, and a spatial database was constructed. ETM images were used to extract the hydroxyl and iron-oxide alterations, and to identify the linear and fault structures and prospective zones in regional scale; ASTER images were used to extract SiO2 index, kaolinite, chlorite, and propylitic alterations in a district scale. All the geological, geochemical, and geophysical data was integrated for MPM by different analysis. The values were determined by expert knowledge or logistic functions. Based upon this analysis, three main exploration targets were recognized in the Feyz-Abad district. Based on field observation, MPM was proved to be valid. The prediction result is accurate, and can provide directions for future prospecting. Among all the methods evaluated in this work, which tend to generate relatively similar results, the continuous fuzzy model seems to be the best fit in the studied area because it is bias-free and can be used to generate reliable target areas.

Temperature has a significant role in many actions performed on rocks. An example would be the effect of temperature on rocks in the burial of nuclear waste, geothermal energy extraction, deep oil well drilling, and fires in tunnels. In addition, due to diurnal/nocturnal as well as seasonal temperature variations, rocks undergo a process of heating and cooling. In the present work, the effect of temperature as well as heating and cooling cycles on the rock properties was studied. The utilized samples included tuff, andesite, and sandstone. In addition to natural samples, concrete was also studied in this research work. The aim of this work was to evaluate the effect of temperature on the tensile strength of rocks and the velocity of longitudinal waves in a single heating and cooling cycle of samples as well as evaluating the effect of the number of heating and cooling cycles on the tensile strength of rocks and the velocity of longitudinal waves. In order to investigate the effect of temperature on the tensile strength of rocks as well as the velocity of longitudinal waves in a single heating and cooling cycle, the samples were heated in a furnace. After cooling the samples, the Brazilian and the sound velocity tests were carried out on them. These tests were conducted at the three temperatures of 100, 200, and 300 °C. In order to examine the effect of the number of heating and cooling cycles on the tensile strength and the velocity of longitudinal waves, the samples were heated up to the temperature of 100 °C and then cooled down in order to reach the room temperature. In this case, the work was conducted in the three modes of 5, 10, and 15 cycles. The test results showed that the velocity of longitudinal waves and the tensile strength of samples decreased but their porosity increased. Reduction in the tensile strength varied in different rocks so that the greatest and lowest reduction in the tensile strength was observed in concrete and andesite, respectively.

A series of physical modeling tests were conducted by means of a beam type geotechnical centrifuge machine in order to investigate the drainage impact on the slope failure mechanism under centrifugal acceleration. Meanwhile, the phenomenon of stress redistribution in undercut slopes and the formation of arching effect were studied. For this purpose, a poorly graded sandy soil (Silica sand No. 6) as well as a relatively well-graded sandy soil (Edosaki sand) were used as the modeling materials. The humid modeling material was compacted on a low friction oblique rigid plate simulating the potential slippage plane. The process of undercutting was conducted, while the earth pressure redistribution inside the model was recorded by means of a miniature set of pressure cells. The results obtained showed completely different failure mechanisms for the two different modeling soils. By undercutting the slope, the earth pressure redistributed and the arch action was formed in a slope model made from a well-graded soil leading to a clear arch-shaped failure. However, in using the poorly graded soil, the water was drained out during centrifuge g-up, the modeling material properties changed, and an avalanche failure was observed. Therefore, in selecting a humid compacted soil as the centrifugal modeling material, a well-graded soil is recommended.

In underground excavation, where the road-headers are employed, a precise prediction of the road-header performance has a vital role in the economy of the project. In this paper, a new model is developed for prediction of the road-header performance using the non-linear multivariate regression analysis. This model is able to estimate the instantaneous cutting rate (ICR) of roadheader based on rock properties such as Brazilian tensile strength (BTS), rock mass cuttability index (RMCI), and alpha angle (α: is the angle between the tunnel axis and the planes of weakness). In order to construct and test the proposed model, a database including 62 cutting cases is used in the Tabas coal mine No. 1 in Iran. Various statistical performance indices were employed to evaluate the model efficiency. The results obtained indicate that the proposed non-linear regression model can be efficiently used to predict the road-header cutting performance. Furthermore, the prediction capacity of this model is better than the empirical models developed previously. Finally, it should be noted that the developed model is site-specific, and it can be used for preliminary estimation of ICR in future phases of Tabas coal mine No. 1. The outcome of this model can be helpful in adjustment of time-scheduling of the project.

Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial neural network, was used to recognize the mineral potential area in Korit 1:100000 sheet, located 15 km south of Tabas, South Khorasan Province (East of Iran). For this purpose, 470 geochemical stream sediment samples were collected from the study area and analyzed for 36 elements. In order to achieve the goal, in the first step, the robust factor analysis on compositional data was applied to reduce the data dimension and to limit the multivariate analysis by selecting the main components of mineralization. In this procedure, the third factor (out of 6) consisting of Cu, Pb, Zn, Sn, and Sb, related to the metallogenic properties, was considered as the input set in CRBM. In continuation, the CRBM structure with the best efficiency after trying different parameters was stabilized. High-identified error values or anomalies were exteracted using two different thresholds (ASC and ASE) after training with the whole data and reconstructing it by CRBM. The anomalies were then mapped. These indicated the promissing areas. The field studies and existing mining indices confirmly demonestrated the results obtained by CRBM.

Liner design is becoming an increasingly more important tool for the AG/SAG mill performance optimization. The Gol-E-Gohar iron ore concentration plant uses three 9 m × 2.05 m autogenous mills (AG) in parallel in a dry operation. Due to large variations in feed characteristics and inadequate blending, the performance of AG mills has been lower than the target value. In order to increase the circuit throughput while maintaining the desired product size, based upon physical and numerical simulations, it was proposed to convert the AG mills to SAG mills. Simulation of the charge trajectory indicated that increasing the liner lifter face angle from 7 to 30° could provide an appropriate charge trajectory in the SAG mode. Installation of the new liners and conversion of AG mill No. 2 to SAG mill, by adding 5% (v/v) balls, resulted in an overall increase of 31% in throughput (from 419 to 548 t/h). Measurement of the wear profiles of shell liners indicated that the wear along the liner length was not uniform. In order to arrive at a uniform wear profile, a new liner design was proposed. Installing the second liner design in AG mill No. 1 and converting it to SAG mill increased the mill throughput by 18% (from 413 to 489 t/h), while the liner life showed a 7% increase. Measurement of the wear profiles of the second liner set indicate that the maximum wear occurs in the centre of the mill. A new liner design was then designed by increasing the width of the lifter top from 12.5 to 15 cm and increasing the lifter height from 16 to 26 cm to enhance the liner life.

Achieving minimum cost and time in reservoir drilling requires evaluating the effects of the drilling parameters on the penetration rate and constructing a drilling rate estimator model. Several drilling rate models have been presented using the drilling parameters. Among these, the Bourgoyne and Young (BY) model is widely utilized in order to estimate the penetration rate. This model relates several drilling parameters to the penetration rate. It possesses eight unknown constants. Bourgoyne and Young have suggested the multiple regression analysis method in order to define these constants. Using multiple regressions leads to physically meaningless and out of range constants. In this work, the Cuckoo Optimization Algorithm (COA) is utilized to determine the BY model coefficients. To achieve this goal, the corresponding data for two wells are collected from one of the oilfields located in SW of Iran. The BY model constants are determined individually for two formations in one of the wells. Then the determined constants are used to estimate the drilling rate of penetration in the other well having the same formations. To compare the results obtained for COA, first, the two mathematical methods including progressive stochastic and multiple regressions were implemented. Comparison between these methods indicated that COA yields more accurate and reliable results with respect to the others. In the following, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as meta-heuristic algorithms were applied on the field data in order to determine BY model’s coefficients. Comparison between these methods showed that the COA has fast convergence rate and estimation error less than others.