IRANIAN JOURNAL OF MINING ENGINEERING (IRJME)
Year:2020 | Volume:14 | Issue:45 |Papers Count:9

Rock weathering affects its durability and strength in mountainous areas during the time. Evaluating durability and strength variations of rocks under freezing and thawing (F-T) cycles is a key factor for stability analysis of bedrocks in engineering projects such as tunnel, mine and etc. In this study, the influence of F-T cycles on second durability index, fifth durability index, dry density and schist rock volume has been evaluated. For this purpose, 13 boreholes have been drilled in western wall of Angouran mine and required samples have been prepared. In overall, 75 F-T cycles have been conducted upon the samples and laboratory tests including durability index of second and fifth cycles, dry density and measurement of samples volume have been carried out in 0, 7, 15, 40 and 75 cycles of F-T. On this basis, new equations presented and suggested to calculate durability index and dry density of samples in different F-T cycles. The obtained results showed that durability index of second and fifth cycles and dry density both decreased exponentially. Also, volume of samples has been increased by increasing the number of F-T cycles. In addition, mineralogical characteristics of schist rock have been studied by optical microscope and XRD analysis. Results of these analyses showed that Chlorite, Biotite, Calcite and Quartz are main constructive minerals of the schist rock. Finally, intact and weathered schist samples (after 75 cycles of F-T) are studied by an electronic microscope to validate the results and evaluate the effect of weathering on the microstructure of specimen’ s texture. Obtained results indicated that the texture of samples was dense before weathering in which spacing of the schistosity beddings was very low. However, after 75 cycles of F-T, the discontinuity spacing was increased and new cracks were created in the body of samples perpendicular to the longitudinal axis.

Due to the relation of mineralization zones with grade variability in porphyry copper deposits, the preparation of the three-dimensional model of these zones is one of the pre-estimation steps in evaluation this type of deposits. The quality of this model has a significant impact on the quality of the grade estimates, the proper design of long-term extraction and ultimately reducing the problems between the mine and the processing plant. The usual way to prepare this model is to use a constrained modeling technique, which is a complex and time consuming process. One of the possible solutions for the preparation of these models is the use of unconstrained methods, such as intelligent methods. This paper attempts to study the performance of artificial neural network and support vector machine in the separation of mineralization zones (including leached, hypogene and supergene zones) in Miduk copper deposit. The northing co-ordinate, easting co-ordinate and height of the samples are used as input variables, and the observed mineralization zones in them are used as the output variable. Investigating the results of these intelligent algorithms in the separation of geological zones shows that the support vector machine classifier has a better performance than the artificial neural network. The better performance of the support vector machine method is shown by 1) the higher accuracy of this method in the training and testing stages and 2) the comparison between the block model with the grade control observations.

The most effective way of maximizing the throughput is to maintain the highest possible crusher power draw which has been practised in various plants. A process audit at the Sarcheshmeh copper complex tertiary crushing circuit indicated that the crushing efficiency at this stage was lower than the target value. To initiate the standradzation process, the power data were transferred to the control room and the ultrasound level sensor was used to monitor the level of the material in the crushing chamber. The high variation in the power draw and some basic problems justified the use of process standardization through process inspections. The first problem was non-uniform feeding of crushers which was alleviated by changing the feed chute design. The uniform distribution of the feed increased the life time of the fixed bowl by twofold and reduced the standard deviation of the power draw measurements from 8. 5 to 3. 2 kW. The lower feed tonnage compared to the design value (531 t/h) was the second challenge which was resolved by increasing the height of the feeder adjustment plate, removing the blocked part of the feeder and increasing the feeders speed. The resulting increase in the feed rate from 350 to 540 t/h reduced the operating time of the crushing circuit by 18%. Another difficulty was not taking full advantage of the control loops which was made operational by a series of corrective actions which decreased the number of crusher shutdowns from 10 times to 1. The coarse particle entrance to the under screen product was the fourth problem. In order to minimize this problem, the large gap between the screen frames and the screen cloth was eliminated and screen ear rings were installed. By implementing these modifications, the amount of product (percentage of passing 12. 7 mm) increased from 65% to 80%.

There are various methods for pore pressure prediction in hydrocarbon (HC) reservoirs which have their advantages and disadvantages. Herein seismic velocity was used to evaluate the pore pressures in one of the Iranian HC reservoirs along with its creation mechanisms. Seismic velocity was derived by seismic multi-attributes analysis method, to study interval velocities. The overburden pressure cube was estimated from the density cube. Thereafter effective pressures were obtained with Terzaghi equation at well locations by using well pressure tests (RFT) and overburden pressures. The velocity-density cross-plots represent that disequilibrium compaction is the main mechanism of pore pressure creation. Thus, an initial relationship was obtained by fitting of the effective pressure and seismic velocity data and it was then used for determining the calibration coefficients required for conversion of seismic velocity cube to the effective pressure cube. Finally, the effective pressure cube was converted to pore pressure cube by Terzaghi equation. The obtained results with high consistency, high correlation coefficient and low mean squares error indicate that pore pressures could be perfectly predicted by multi-seismic attributes method in carbonate formation before drilling. Moreover, comparative studies of the pore pressure cube estimated from the proposed model indicates that the pore pressure model obtained by multi-attribute analysis is precise enough to separate the reservoir layers from its upper and lower sections. This predicted pore pressure can be used to evaluate the drilling risk and also to estimate the required mud weights.

The research project in this study is a part of a 36 kilometer tunnel with 6. 32 m in diameter for conveyance of water from the western of Zagros to the Uromia Lake that is under construction through hard rock by using of two tunnel boring machines. Lithology of the tunnel alignment including of granite, granodiorite, hornfels, schist, limestone, dolomite, shale, slate and phyllite. The wear of cutting tools is one of the main challenges of the studied tunnel, which impact significant cost and serious delays on schedule. Wear can be defined as the continuous and unwanted loss and removal of material from the surface of a solid object, as e result of mechanical actions, such as contact and relative motion between two objects. Abrasive wear and impact wear are one of the most important type of wear occurs on the TBM disc cutters. In this paper field actual cutter wear data belong to 9. 5 Km of the bored tunnel, geotechnical properties and laboratory information are used to study the wear phenomena. Field data show that abnormal wear of disc cutters with 51 percent is more than normal wear. Also the study of disc cutter consumption rate in relation the geological characteristics and TBM parameters show that the Cerchar abrasivity index (CAI) and uniaxial compressive strength (UCS) are in agreement with disc cutter consumption rate. The obtained results indicate that thrust, torque and penetration rate have a relatively good relation with disc cutter consumption rate. It was not found any relation between rock mass quality index, geological strength index and disc cutter wear rate. In general the relative cutter life in the studied tunnel is in accordance with the relation proposed after Bruland (1998).

In this paper, four data mining algorithms, namely, kernel density estimation, local outlier factor, OPTICS-OF and SVDD are used to determine multivariate outlier data. So, stream sediment geochemical data, in 1: 100000 Roum sheet, with 902⨉ 41 matrix dimensions have been utilized. Replacing censored data, converting the data set to an open number system and finally standardizing them are used as pre-processing methods. Results show that in error sample detection approach, 10 samples that have the highest outlier probability, and are present in equal numbers in the four mentioned algorithms, can be considered for more study as replicate sampling. In non-normal sample detection approach, form 150 selected samples, 74. 5% of samples are detected as outliers in the four mentioned algorithms, and 16. 1 and 9. 4 percent are recognized as the outlier data in one and two of the aforementioned algorithms, respectively. Determining of replicate sampling, calculating location and scatter matrices in multivariate robust statistics after eliminating non-normal samples and geochemical anomaly detection are suggested as the applications of these algorithms.

Objectives: Provision of safety for miners is one of the important mining requirements. Hazards and accidents in mines should be eliminated, controlled or minimized. The most common cause of mineral events is the human error factor. In this research, human-caused accidents in Tabas coal mine have been investigated by Sherpa method. Methods: In SHERPA, which is a systematic way of predicting and reduction the human error, the most important human errors that cause accidents are categorized by location or type of activity. The results are presented in the form of job charts. Results: Out of the 37 identified errors, the highest number of errors was 18 (48. 44%) and the functional type, 13 errors were related to Checking (35. 13%), 5 errors were the type of communication (51. 13%) and 2 errors related to the selection (4. 5%). Then, AHP prioritization was applied and appropriate control strategies were proposed to reduce the risk of errors based on priorities. Human error reduction alternatives include upgrading equipment, education, instructions, and organization. Conclusion: The results of the study showed that upgrading equipment is the most suitable alternative for reducing human error. This upgrade was considered for the whole mine, and it has been shown that in the absence of resource and cost constraints or time limitation, one can examine the effect of alternatives on each of the main activities for better results, the effects of alternatives on each of the main activities could be examined for better results. In this case, the above method is better able to perform, because instead of upgrading equipment for all sectors, upgrading each section is done according to the more effective alternatives.

Kivi region is located in Ardabil province of Iran. This research is on kivi geochemical sheet (on scale 1: 100000) which is investigated by geological survey & mineral explorations of Iran (GSI) using stream sediment analyzes. This region consists of sedimentary, igneous and metamorphic rock units. The oldest existing sedimentary unit, the pre-Cretaceous rocks and the newest, is related to Quaternary and the present. Due to the ability of metal mineralization, especially the copper element in this region, it is important to study it carefully. Accordingly, finding information about the relation and behavior of the elements of gold, silver and molybdenum to the copper element in this region is important. The purpose of this study is to determine the behavior of geochemical halos in region. In this study with the aim of geochemical behavior investigating the mentioned elements K-means method was used. This method is based on clustering methods that minimize the total Euclidean intervals of each sample from the center of the groups to which it is assigned. In this research, the clustering quality function ( p(k) ) and the desirability of sample in the desired cluster ( S (i) ) were usedto determine the optimum numberof clusters. Then, taking into account clusters centers and results, equations were provided to predict the amount of copper with a special look at the method. After elemental behavioral studies, an artificial neural network test using general regression and backward propagation of errors was conducted to estimate the amount of copper. The accuracy value (R) of the estimation in the experimental data in the artificial neural network of general regression and backward propagation of errors was 0. 77 and 0. 74, respectively. Finally, it was determined that the general regression artificial neural network method has an advantage in The optimal estimation of copper element in the studyarea.