P. Tahmasebizadeh; S. Javanshir
Abstract
In this work, zinc extraction from an industrial leach solution was investigated by saponified di(2-ethylhexyl)phosphoric acid (D2EHPA). The solution obtained was from a bioleaching process of a low-grade lead-zinc sulfide ore that contained 50 g/L of zinc and 6.3 g/L of iron. The selective and high ...
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In this work, zinc extraction from an industrial leach solution was investigated by saponified di(2-ethylhexyl)phosphoric acid (D2EHPA). The solution obtained was from a bioleaching process of a low-grade lead-zinc sulfide ore that contained 50 g/L of zinc and 6.3 g/L of iron. The selective and high Zn(II) extraction yield were obtained by modification of D2EHPA in a proposed two-step process. Firstly, a significant amount of iron (87%) was removed as sodium-jarosite via precipitation from the pregnant leaching solution (PLS) prior to zinc extraction, and secondly, the effective parameters involved in zinc extraction including the contact time, saponification degree, type of saponifier, stirring speed, pH, temperature, D2EHPA concentration, and phase ratio (A:O) were investigated. The results obtained showed that 98.4% of zinc could be extracted under the optimum conditions, i.e. 20% D2EHPA, 15% saponification degree, 650 rpm, pH 2, and an A:O ratio of 1:1 at the ambient temperature (25 ± 2 °C) during 90 s; it was 25% higher than using non-saponified D2EHPA under the same conditions. Moreover, while one theoretical step was required for the complete extraction of zinc by saponified D2EHPA, the required number of steps using D2EHPA was about three. Therefore, the advantages of the process would be two-fold: reducing the number of extraction stages and no need for neutralizing the raffinate in every extraction stage.
Rock Mechanics
M. Hosseini; A. R. Khodayari
Abstract
In an era of continued economic development around the globe, numerous rock-related projects including mining and gas/oil exploration are undertaken in regions with cold climates. Winters in the Iranian western and northwestern provinces are characterized by a high precipitation rate and a cold weather. ...
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In an era of continued economic development around the globe, numerous rock-related projects including mining and gas/oil exploration are undertaken in regions with cold climates. Winters in the Iranian western and northwestern provinces are characterized by a high precipitation rate and a cold weather. Under such conditions, rocks are exposed to long freezing periods and several freeze-thaw (F-T) cycles. It is thus necessary to examine the impact of these cycles on the physical and mechanical properties of rocks. Considering the abundant sandstone resources in Iran, in this work, we focused on the Lushan sandstone by investigating the effects of F-T cycles and freezing temperatures on the uniaxial and triaxial compressive strengths, cohesion, internal friction angle, and elastic modulus of the rocks. To study the impact of the number of F-T cycles on the strength of rocks, the specimens frozen at -16 °C were subjected to 1, 4, 8, 16, and 32 F-T cycles. Similar tests were also carried out on the specimens frozen at -24 °C. Furthermore, a number of tests were undertaken at the ambient temperature (25 °C) on specimens that did not undergo an F-T cycle. According to the results obtained, an increase in the number of F-T cycles and freezing temperatures reduced the uniaxial and triaxial compressive strengths, cohesion, internal friction angle, and elastic modulus due to the growth of the existing cracks and the nucleation of new cracks in the rock. Consequently, the effective porosity increased, whereas the dry specific gravity decreased with more F-T cycles and lower freezing temperatures.
H. Sarfaraz; A.R. Bahrami; R. Samani
Abstract
A common instability in the rock slopes is a toppling failure. If this type of slope failure occurs due to another kind of failure, it is considered as the secondary toppling failure. A type of secondary toppling failure is the slide-head-toppling failure. In this instability, the upper portion of the ...
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A common instability in the rock slopes is a toppling failure. If this type of slope failure occurs due to another kind of failure, it is considered as the secondary toppling failure. A type of secondary toppling failure is the slide-head-toppling failure. In this instability, the upper portion of the slope is toppled, and the pressure caused by the overturning of rock blocks leads to a semi-circular sliding in the soil mass at the slope toe. This instability is examined through the theoretical analysis and physical modelling. Firstly, the failure mechanism mentioned above is described. Next, the slide-head-toppling failure is studied through seven numerical simulations. The Phase2 and UDEC softwares, as the finite element and distinct element methods, respectively, are used in this work. Different kinds of slide-head-toppling failure are modelled such as the blocky, block-flexural, and flexural toppling failures. The numerical modelling results are compared with the existing physical tests and theoretical approaches. This comparison illustrates that the safety factor is underestimated due to the plane strain supposition in numerical modelling. However, the side-friction in the physical models has violated this assumption. The results obtained demonstrate that the distinct element method has an acceptable accuracy compared to the finite element method. Thus this numerical code can be used in order to examine the mentioned failure.
Mine Economic and Management
R. Bastami; A. Aghajani Bazzazi; H. Hamidian Shoormasti; K. Ahangari
Abstract
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone ...
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The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (GEP), linear multivariate regression (LMR), and non-linear multivariate regression (NLMR) models. In all models, the ANFO value, number of detonators, Emolite value, hole number, hole length, hole diameter, burden, spacing, stemming, sub-drilling, specific gravity of rock, hardness, and uniaxial compressive strength are used as the input parameters. The ANN model results in the test stage indicating a higher correlation coefficient (0.954) and a lower root mean square error (973) compared to the other models. In addition, it has a better conformity with the real blasting costs in comparison with the other models. Although the ANNs method is regarded as one of the intelligent and powerful techniques in parameter prediction, its most important fault is its inability to provide mathematical equations for engineering operations. In contrast, the GEP model exhibits a reliable output by presenting a mathematical equation for BC prediction with a correlation coefficient of 0.933 and a root mean square error of 1088. Based on the sensitivity analysis, the spacing and ANFO values have the maximum and minimum effects on the BC function, respectively. The number of detonators, Emolite value, hole number, specific gravity, hardness, and rock uniaxial compressive strength have a positive correlation with BC, while the ANFO value, hole length, hole diameter, burden, spacing, stemming, and sub-drilling have a negative correlation with BC.
Mahsa Khoshfarman Borji; Ahmad Reza Sayadi; Ehsan Nikbakhsh
Abstract
The iron and steel industry is one of the most resource-intensive and pollutant industries that creates the highest value across all mining and metal industries. While the recent studies provide recommendations to improve sustainable development in this industry, the complexity of the socio-environmental ...
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The iron and steel industry is one of the most resource-intensive and pollutant industries that creates the highest value across all mining and metal industries. While the recent studies provide recommendations to improve sustainable development in this industry, the complexity of the socio-environmental impacts of activities in this industry due to its multi-tier and multi-supply chain structures has given rise to the problem of sustainable steel supply chain network design. This work proposes a new multi-objective mixed-integer linear programming model to integrate sustainability factors with managerial and technical restrictions. The total economic profitability is maximized, while environmental pollution is minimized. There is also a focus on the social and environmental compliance. Accordingly, a novel sustainability assessment system is proposed. The managerial restrictions are also satisfied by maximizing the demand fulfillment priority using a new method. The augmented ε-constraint method is applied to tackle the mathematical problem under study. Finally, a real case study is used. The results obtained 35% and 41% reductions in CO2 and particulate matter emissions, respectively, while the total profit decreases up to 15%. A sensitivity analysis is also performed. In addition, several managerial insights are discussed based on the results.
Exploitation
M. Jahangiri; Seyed R. Ghavami Riabi; B. Tokhmechi
Abstract
Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. ...
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Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. In this work, multi-layer neural network was used to estimate the concentration of geochemical elements. From 1755 surface and boreholes data available, analyzed by ICP, 70% was used for training, and the rest for testing. The average accuracy of estimators for 22 geochemical elements when using all data was equal to 75%. Based on validation, the optimal number of clusters for the total data was identified. The Gustafson-Kessel (GK) clustering was used to design the estimator for the geochemical element concentrations in different clusters, and the clusters were selected for estimation. The results obtained show that using GK, the estimator's average accuracy increase up to 84%. The accuracy of the elementsZn, As, Pb, Mo, and Mn with low accuracies of 0.51, 0.62, 0.64, 0.65, and 0.68 based on all data were developed to 0.76, 0.86, 0.76, 0.80, and 0.71 with the clustered data, respectively. The mean square error using all the data was 0.079, while in the case of hybrid developed method, it decreased to 0.048. There were error reductions in Al from 0.022 to 0.012, in As, from 0.105 to 0.025, and from 0.115 to 0.046 for S.
M. Samadi; S. Torbati; S. Alipour
Abstract
Heavy metal(loid) contamination in the environment of mining areas has become an important problem. Cheshemeh-Konan is one of the main copper deposits in NW Iran that is currently abandoned. In the present work, the intensity of some metal(loid) pollutions in the soil of the mining area was assessed ...
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Heavy metal(loid) contamination in the environment of mining areas has become an important problem. Cheshemeh-Konan is one of the main copper deposits in NW Iran that is currently abandoned. In the present work, the intensity of some metal(loid) pollutions in the soil of the mining area was assessed using three reliable indices. In addition, the potential of Sonchus oleraceus L., as the dominant plant grown in the area, in the uptake of some metal(loid)s from the soil was evaluated. The plant and soil samples were collected from the mining area and analyzed by inductively coupled plasma-mass spectrometry (ICP-MS). The results obtained revealed that the soil of the studied mining area was considerably contaminated by As (CF = 3.1), Cr (CF = 3.8), and Ni (CF = 4.07). It was confirmed that S. oleraceus had a good ability to accumulate Cd (0.74 mg/kg), Mo (0.67 mg/kg), Sr (285.80 mg/kg), Sn (161.10 mg/kg), and Sc (30.35 mg/kg) when mean concentrations of these metals in the soil were 0.14, 0.12, 161.05, 1.94, and 17.9 mg/kg, respectively. The plant biological absorption coefficient for these 5 elements was more than 1. The correlations between the Mo and Sr contents in the soil and plant were significantly positive. According to the results obtained, the present work provides some geochemical findings about the substrate, and leads to the increasing information about the relationship between the element concentrations in the plants and different soils.
M. Azadi; M. Abedi; Gh. H. Norouzi Baghkameh
Abstract
< p>Attenuation of the signal received from sources causing anomalies and reduction of data resolution are the negative features of airborne measurements. Using a stable downward continuation method is a practical way to address these shortcomings. In this study, we investigated the efficiency ...
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< p>Attenuation of the signal received from sources causing anomalies and reduction of data resolution are the negative features of airborne measurements. Using a stable downward continuation method is a practical way to address these shortcomings. In this study, we investigated the efficiency of various stabilizers in achieving stable downward continued data. The purpose of this study is to select the most appropriate stabilizer(s) for this operation. We examined the various stabilizing functions by introducing them into the Tikhonov regularization problem. The results of research on synthetic airborne gravity and magnetic data showed that βL1 (the other definition of L1 norm) and SM (the smoothest model) stabilizers have the potential to be used in the stable implementation of the downward continuation method. These stabilizers performed better than the other in the three comparisons, including visual, quantitative (RMS error), and graphical comparisons. Also, by examining the airborne magnetic data related to the Esfordi district in Yazd province (Iran), it was found that in general the βL1 stabilizer is more suitable than the other stabilizing functions studied in this research.
M. Hosseini; H. Madani; K. Shahriar
Abstract
Stations are the main components of the subway systems. Despite the progress in the construction and maintenance, stations have always been exposed to the natural and man-made disasters. In such incidents, the station’s evacuation capability has a direct relation with a passenger's life. Various ...
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Stations are the main components of the subway systems. Despite the progress in the construction and maintenance, stations have always been exposed to the natural and man-made disasters. In such incidents, the station’s evacuation capability has a direct relation with a passenger's life. Various factors affect the station's evacuation capability. Investigation of these factors and evaluation of the station’s evacuation capability have important roles in protecting a passenger's life. For this purpose, the catastrophic events that lead to the evacuation of a station and the factors affecting the evacuation of the station are identified. Due to the difference in the catastrophic event probabilities at each station, the risk of catastrophic events is evaluated. Then the station score is calculated according to the value and weight of the evacuation factors and the wighted influence of the catastrophic events. Accordingly, the proposed model is implemented in the Tehran subway. Based on the results obtained, uncrowded stations, even though served by a small number of passengers, may also have a low evacuation capacity and lead to casualties in an emergency situation. This is due to the lack of emergency management and safety facilities. Also by assessing the risk of catastrophic events at stations and equipping stations on its basis, the degree of safety and evacuation capability can be improved more effectively. The sensitivity analysis of the evacuation factors show that the most effective way to increase the station’s evacuation capability is to improve its status in management factors. Using the proposed model to evaluate the station's evacuation capability is an appropriate method for identifying the stations that have a poor evacuation capability.
M.R. Shahbazi; M. Najafi; M. Fatehi Marji; A. Abdollahipour
Abstract
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 ...
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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.
Exploitation
J. Balaraju; M. Govinda Raj; C.H.S.N. Murthy
Abstract
Reliability estimation plays a significant role in the performance assessment of mining equipment, and aids in designing efficient and effective preventive maintenance strategies. Continuous and random/irregular occurrence of failures in a system could be the main cause for performance drop of machinery. ...
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Reliability estimation plays a significant role in the performance assessment of mining equipment, and aids in designing efficient and effective preventive maintenance strategies. Continuous and random/irregular occurrence of failures in a system could be the main cause for performance drop of machinery. The accomplishment of a projected level of production is possible only by an efficient operation of the equipment. In order to improve the equipment life, a critical analysis of failure/breakdown occurrences is required to be carried out, and appropriate remedial measures need to be designed and implemented to enhance reliability. This paper presents a reliability analysis of Load-Haul-Dumper (LHD) in an underground coal mine. The goodness-of-fit distribution of each LHD was made through the Cramer-Von-Mises statistic test. The parameters involved were estimated using both the maximum likelihood analytical estimation process and the graphical process. Further, an attempt was made to reduce the total cost of operation by estimating the reliability-based preventive maintenance time intervals.
Mineral Processing
K. Barani; M. Azghadi; M. R. Azadi; A. Karrech
Abstract
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) ...
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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.
H. Fattahi; M. Hasanipanah; N. Zandy Ilghani
Abstract
The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, ...
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The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, achieving a high-accurate method to estimate PWV is a very important task. This work investigates the use of different intelligent models such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), group method of data handling (GMDH), and gene expression programming (GEP) for the prediction of PWV. The proposed models are then evaluated using several error statistics, i.e. squared correlation coefficient (R2) and root mean squared error (RMSE). The values of R2 obtained from the CART, MARS, GMDH, and GEP models are 0.983, 0.999, 0.995, and 0.998, respectively. Furthermore, the CART, MARS, GMDH, and GEP models predict PWV with the RMSE values of 0.037, 0.007, 0.023, and 0.020, respectively. According to the aforementioned amounts, the models presented in this work predict PWV with a good performance. Nevertheless, the results obtained reveal that the MARS model yields a better prediction in comparison to the GEP, GMDH, and CART models. Accordingly, MARS can be offered as an accurate model for predicting the aims in other rock mechanics and geotechnical fields.
H. Fattahi
Abstract
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due ...
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The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.
Mineral Processing
M. Salehfard; M. Noaparast; Seyed Z. Shafaei; H. Abdollahi
Abstract
A lead-zinc carbonate ore sample containing 2.5% Pb and 9.39% Zn was used in this research work. The sample was prepared from the Darreh-Zanjir mine located in the Yazd province (Iran). Influences of the influential factors on flotation of smithsonite and cerussite were investigated. Among the different ...
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A lead-zinc carbonate ore sample containing 2.5% Pb and 9.39% Zn was used in this research work. The sample was prepared from the Darreh-Zanjir mine located in the Yazd province (Iran). Influences of the influential factors on flotation of smithsonite and cerussite were investigated. Among the different parameters involved, dosages of the dispersant, depressants, sulfidizing agent, and collectors de-sliming prior to lead or zinc flotation were essential for the effective recovery and grade of the Zn and Pb flotation concentrates. In addition, the anionic, cationic, and mixed (cationic/anionic) collectors were employed for flotation of smithsonite. The results of reverse and cumulative flotation of both Zn and Pb were relatively low in comparison with the direct process without depressant. Flotation of smithsonite using mixed collectors (Armac C+KAX) showed promising results. Also dosages of chemicals in the cleaning stage for the Zn and Pb concentrates were optimized, and finally, after the cleaner stage for both lead and zinc, a cerussite concentrate with Pb grade and recovery of 49.82% and 60.06%, respectively, and smithsonite concentrate with Zn grade and recovery of 35.47% and 68.56%, respectively, were obtained under the optimal conditions. Furthermore, kinetics of Zn and Pb oxide mineral flotations in the rougher and cleaner stages were studied, which showed that some kinetics models, especially the classical first-order model, could predict the flotation behaviour of the Zn and Pb oxide minerals.
Exploitation
A. Mozafari; A. H. Bangian Tabrizi; M. Taji; A. Parhizkar
Abstract
In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the ...
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In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the quantity of ores and wastes to be mined from each one of the selected blocks. These two methods use improved estimates of the orebody characteristics utilizing the blast hole data in addition to the usual borehole statistics to improve the prediction accuracy of the block level ore body characteristics. This work aims to make a mathematical model to figure out the ideal width and length of the blast block in order to curtail drilling and blasting expenses in open-pit mines. As a consequence, the effective blast block size is heeded so as to decrease the expenses of drilling and blasting. Furthermore, a complete set of actual principles is presented to specify the applicable size of the mineable block by means of the multi-criteria decision-making method of fuzzy logic. The aforementioned model is practiced to forecast the block size necessary for the purpose of production planning. Next, a mixed integer programming model is developed to blast planning in order to select the optimal size of the blast block by considering the mineable block. The proposed model is applied in the Chadormalu iron ore mine and the rationality of the model is demonstrated by the outcomes of dissimilar circumstances.
M. M. Nazempour; A. Majdi
Abstract
Prediction of the length of grout penetration and assessment of the groutability around the boreholes in the jointed rocks have a crucial effect on the planning and execution of grouting. Grout distribution in jointed rocks is a function of the geo-mechanical properties of rock mass, grout properties, ...
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Prediction of the length of grout penetration and assessment of the groutability around the boreholes in the jointed rocks have a crucial effect on the planning and execution of grouting. Grout distribution in jointed rocks is a function of the geo-mechanical properties of rock mass, grout properties, and grout operational performance. This paper describes an analytical model based on the Newton’s second law, with the assumption of disk-shape model for the joints in order to calculate the maximum length of grout penetration in the horizontal and angled joints. It is shown that the proposed formulas can predict the length of grout penetration in rock masses with numerous joint sets as well. In order to validate the proposed model, it is compared with the existing analytical and empirical criteria, showing a very good accordance with their calculated results. Finally, the proposed analytical model is used to design the grout planning of a water conveying tunnel that is subjected to a heavy inflow. The design results in a successful filling of the vacant space behind the segmental lining and sealing the tunnel to stop the inrush water. These show that the model proposed in this paper can be successfully applied in practice.
E. Pouresmaeili; A. Ebrahimabadi; H. Hamidian
Abstract
Sustainability assessment has received numerous attentions in the mining industry. Mining sustainability includes the environmental, economic, and social dimensions, and a sustainable development is achieved when all these dimensions improve in a balanced manner. Therefore, to measure the sustainability ...
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Sustainability assessment has received numerous attentions in the mining industry. Mining sustainability includes the environmental, economic, and social dimensions, and a sustainable development is achieved when all these dimensions improve in a balanced manner. Therefore, to measure the sustainability score of a mine, we require an approach that evaluates all these three dimensions of mining sustainability. Some frameworks have been developed to compute the sustainability score of mining activities; however, some of them are very complicated and the others do not cover all the environmental, economic, and social aspects of sustainability. In order to fill this gap, this work was designed to introduce a practical approach to determine the score of mining sustainability. In order to develop this approach, initially, 14 negative and positive influential macro factors in the sustainability of open-pit mines were identified. Then the important levels of the factors were estimated based on the comments and scores of some experts. Two checklists were constructed for the negative and positive factors. The sustainability score was computed using these checklists and the importance levels of the factors. The score range was between -100 and +100. In order to implement the proposed approach, the Angouran lead and zinc mine was selected. The sustainability score of the Angouran mine was +47.91, which indicated that the this mine had a sustainable condition. This score could increase through modification of some factors.
Sirvan Moradi; Seyed Davoud Mohammadi; Abbas Aghajani Bazzazi; Ali Aali Anvari; Ava Osmanpour
Abstract
Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since ...
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Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since several uncertain parameters are incorporated in the modelling process, distribution functions are employed to explain the parameters. However, due to the usual constrain of limited data, these functions cannot significantly explain the variation of those uncertain parameters. Support vector machine, one of the efficient techniques of artificial intelligence, provides the appropriate results in the classification and regression tasks. The principal aims of this research work are to integrate the simulation and artificial intelligence methods to manage the risk prediction of an economic system under uncertain conditions. The financial process of the Halichal mine in the Mazandaran province, Iran, is considered a case study to prove the performance of the support vector machine technique. The results show that integrating the simulation and support vector machine techniques can provide more realistic results, especially when including uncertain parameters. The correlation between the net present value obtained from the simulation and the net present value is about 0.96, which shows the capability of artificial intelligence methods and the simulation process. The root mean square error of the support vector machine prediction is about 0.322, which indicates a low error rate in the net present value estimation. The values of these errors prove that this method has a high accuracy and performance for predicting a net present value in the Halichal granite mine.
Sajjad Aghababaei; Hossein Jalalifar; Ali Hosseini; Farhad Chinaei; Mehdi Najafi
Abstract
In this work, two rock engineering system (RES)-based models are presented, the first model to predict the roof failure when a longwall face advances toward a pre-driven recovery room (PDRR) and the second model to select the type of recovery room method for longwall mining. For the first model, an international ...
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In this work, two rock engineering system (RES)-based models are presented, the first model to predict the roof failure when a longwall face advances toward a pre-driven recovery room (PDRR) and the second model to select the type of recovery room method for longwall mining. For the first model, an international database of 43 case histories from the pre-driven rooms including technical parameters and type of corresponding operation outcome of each case history is considered. In this regard, a vulnerability index (VI) that refers to the risk of roof failure is calculated for each case history and the VIs are compared with the type of the corresponding outcomes. The obtained results indicate that the calculated VIs have a good adaptation with the corresponding outcomes. This approach could be used to analyze the risk of failure in PDRR, and determine the critical VI that specifies the boundary between the hazard range and the safe range that leads to an accurate operational planning. In the following, a method called multi-options RES-based model (MORESM) is adopted for the selection of recovery room methods in longwall operation. By this model, selecting the optimum option from several options in terms of many effective parameters on the system is possible. Based on the evaluations, CRR, PDRR3, and PDRR2&3 are the suitable options for the case study. This model could introduce the suitable option based on geotechnical conditions but the final decision depends on the economic policy of the managing team.
Maysam Abedi; Gholam-Hossain Norouzi; Nader Fathianpour; Ali Gholami
Abstract
This paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. The study area located in Semnan province of Iran consists ...
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This paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. The study area located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. Based on previous studies, which assume a homogenous half-space earth model, two approximate methods involving the Siemon and the Mundry approaches are used in this study to generate a resistivity-depth image of underground geologically plausible porphyry unit derived from airborne electromagnetic data. The 3D visualization of the 1D inverted resistivity models along all flight lines provides a resistive geological unit which corresponds to the desired porphyry andesite. To reduce uncertainty arising from single geophysical model, i.e., the resistivity model acquired from the frequency domain electromagnetic data, a fast implementable approach for 3D inversion of magnetic data called the Lanczos bidiagonalization method is also applied to the large scale airborne magnetic data in order to construct a 3D distribution model of magnetic susceptibility, by which the obtained model consequently confirms the extension of an arc-shaped porphyry andesite at depth. The susceptible-resistive porphyry andesite model provided by integrated geophysical data indicates a thicker structure than what is shown on the geological map while extends down at depth. As a result, considering simultaneous interpretation of airborne magnetic and frequency domain electromagnetic data certainly yield lower uncertainty in the modeling of andesite unit as a potential source of copper occurrences.
Exploitation
B. Sohrabian; R. Mikaeil; R. Hasanpour; Y. Ozcelik
Abstract
The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be ...
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The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be estimated at unknown locations. Cokriging has been traditionally used in the estimation of spatially cross-correlated variables. However, it can face unsolvable matrices in its algorithm. An alternative to cokriging is to transform variables into spatially orthogonal factors, and then to apply kriging to them. Independent Component Analysis (ICA) is one of the methods that can be used to generate these factors. However, ICA is applicable to zero lag distance so that using methods with distance parameter in their algorithms would be advantageous. In this work, Minimum Spatial Cross-correlation (MSC) was applied to six mechanical properties of Cubuk andesite quarry located in Ankara, Turkey, in order to transform them into approximately orthogonal factors at several lag distances. The factors were estimated at 1544 (5 m × 5 m) regular grid points using the kriging method, and the results were back-transformed into the original data space. The efficiency of the MSC-kriging was compared with Independent Component kriging (IC-kriging) and cokriging through cross-validation test. All methods were unbiased but the MSC-kriging outperformed the IC-kriging and cokriging because of having the lowest mean errors and the highest correlation coefficients between the estimated and the observed values. The estimation results were used to determine the most profitable blocks and the optimum direction of extraction.
Exploitation
I. Masoumi; Gh.R. Kamali; O. Asghari
Abstract
Dilution can best be defined as the proportion of waste tonnage to the total weight of ore and waste in each block. Predicting the internal dilution based on geological boundaries of waste and ore in each block can help engineers to develop more reliable long-term planning designs in mining activities. ...
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Dilution can best be defined as the proportion of waste tonnage to the total weight of ore and waste in each block. Predicting the internal dilution based on geological boundaries of waste and ore in each block can help engineers to develop more reliable long-term planning designs in mining activities. This paper presents a method to calculate the geological internal dilution in each block and to correct the ultimate grade of each geological block according to the internal dilution values that have already been calculated for each one of them. In this regard, the input data is first indexed based on the lithological logs of drill holes. The occurrence probabilities of ore and waste in each block are calculated via 100 realizations using the sequential indicator simulation. Dilution is computed as the ratio of waste rock tonnage to the total tonnage of ore and waste. Furthermore, joint simulation of the continuous variables is performed for each mining block using the minimum/maximum auto-correlation factors. In the next step, for each block, the final grade variables including iron and iron oxide are computed by considering the calculated internal dilution. These analyses are applied to the Gohar Zamin iron ore deposit, and the actual internal dilution calculated based on the lithological logs of blast holes is compared with the same values obtained based on the proposed method in each block. The results obtained were found to be satisfactory.
Y. Asgari Nezhad; A. Moradzadeh
Abstract
One of the most essential factors involved in unconventional gas reserves for drilling and production is a suitable quality facies determination. The direct core and geochemical analyses are the most common methods used for studying this quality. Due to the lack of this data and the high cost, the researchers ...
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One of the most essential factors involved in unconventional gas reserves for drilling and production is a suitable quality facies determination. The direct core and geochemical analyses are the most common methods used for studying this quality. Due to the lack of this data and the high cost, the researchers have recently resorted to the indirect methods that use the common data of the reservoir (including petro-physical logs and seismic data). One of the major problems in using these methods is that the complexities of these reproducible repositories cannot be accurately modeled. In this work, the quality of facies in shale gas is zoned using the deep learning technique. The applied method is long short-term memory (LSTM) neural network. In this scheme, the features required for zoning are automatically extracted and used to model the reservoir complexities properly. The results of this work show that zoning is done with an appropriate accuracy (86%) using the LSTM neural network, while it is 78% for a conventional intelligent MLP network. This specifies the superior accuracy of the deep learning method.
J. Zadhesh; A. Majdi
Abstract
The mechanisms of deformation and failure of the structures in and on the jointed rock masses are often governed by the characteristics of the geometrical properties of joints. Since the joint geometry properties have a range of values, it is helpful to understand the distribution of these values in ...
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The mechanisms of deformation and failure of the structures in and on the jointed rock masses are often governed by the characteristics of the geometrical properties of joints. Since the joint geometry properties have a range of values, it is helpful to understand the distribution of these values in order to predict how the extreme values may be compared with the values obtained from a small sample. This work studies three datasets of joint systems (1652 joint data) from nine outcrops of igneous, sedimentary, and metamorphic rocks in order to determine the probability distribution function of the rock joint geometry properties. Consequently, the goodness-of-fit (GOF) tests are applied to obtain the data. According to these GOF tests, the Lognormal is the best probability distribution function representing the joint spacing, aperture, and trace length. The Cauchy is the best probability distribution function for the joint dip angle. It is found that the Cauchy distribution function is the best probability distribution function to represent the joint dip direction of igneous rocks, and the Burr distribution function is the best probability distribution function to define the joint dip direction of the sedimentary and metamorphic rocks.
