Exploration
Moslem Jahantigh; Hamid Reza Ramazi
Abstract
The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential ...
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The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential layers include airborne magnetic data, airborne radiometric data (potassium and thorium), lineament density map, cu geochemistry signature, and multi-variate geochemistry signature (PC1). High magnetic anomalies, lineament structures, and alteration zones (K/Th) were derived from airborne geophysics data. Geochemistry signatures (Cu and PC1) were derived from stream sediment data. The principal Component Analysis (PCA) as an unsupervised machine learning method and five evidential layers were used to produce a porphyry prospectivity model. As a result of this combination, mineral prospectivity model was produced. Then a plot of cumulative percent of the studied area versus pca prospectivity value was used to discrete high potential areas. Then to evaluate the ability of this MPM, the location of known cu indications was used. The results confirm an acceptable outcome for porphyry prospectivity modeling. Based on this model high-potential areas are located in south southwestern and eastern parts of the studied area.
Hassan Bakhsandeh Amnieh; Alireza Mohammadi; M Mozdianfard
Abstract
Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting ...
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Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting can be achieved once peak particle velocity (PPV) is predicted. In this paper, the values of PPV have been predicted and compared using the artificial neural network (ANN), multivariate regression analysis (MVRA) and empirical relations. The necessary data were gathered from 11 blast operations in Sarcheshmeh copper mine, Kerman. The neural network input parameters include distance from blast point, maximum charge weight per delay, spacing, stemming and the number of drill-hole rows in each blasting operation. The network is of the multi-layer perception (MLP) type with 24 sets of training data including 2 hidden layers, 1 output layer with the network architecture of {5-11-12-1}, and Sigmoid tangent and linear transfer functions. To insure the training accuracy, the network was tested by 6 data sets; the determination coefficient and the average relative error were found to be 0.977 and 8.85%, respectively, showing the MLP network’s high capability and precision in estimating the values of the PPV. To predict these values, MVRA and empirical relations were analyzed. The results have revealed that these relations have low capability in estimating the PPV parameter.
Mineral Processing
M. B. Fathi; B. Rezai; E. K. Alamdari; R. D. Alorro
Abstract
The effects of the functional groups and structures of two different resins, weak base/macroporous and strong base/gel type, Purolite A170 and Dowex 21K on the adsorption properties of Re(VII) ions were investigated experimentally and described by the isotherm, kinetic, and thermodynamic modeling. In ...
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The effects of the functional groups and structures of two different resins, weak base/macroporous and strong base/gel type, Purolite A170 and Dowex 21K on the adsorption properties of Re(VII) ions were investigated experimentally and described by the isotherm, kinetic, and thermodynamic modeling. In this regard, four widely used adsorption isotherm models including Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich (D-R) were subjected to the sorption data in order to describe the reactions involved. Evaluating the correlation coefficients showed that the Freundlich and D-R isotherm models provided the best fit. The Langmuir isotherm capacities (qm) indicated that the perrhenate ion (ReO4-) adsorption was higher for the weak base/macroporous type resin rather than the others (166.67 mg/g and 142.86 mg/g, respectively). Moreover, the results of the EDX studies were in agreement with the previous results. Furthermore, the adsorption kinetics was demonstrated through fitting the data into different mechanisms, among which the pseudo-second-order mechanism was found to be successful for both resins; however, in the case of Dowex 21K, the rate of perrhenate ion uptake was more rapid than that for Purolite A170. Evaluation of the thermodynamic parameters also showed that the reaction mechanism was different for each case and that the adsorption of rhenium on Dowex 21K became more feasible with increase in temperature due to negative values for ΔH.
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
Mohammad Hossein Jalalian; Raheb Bagherpour; Mehrbod Khoshouei; S. Najmedin Almasi
Abstract
Diamond wire cutting is a common method to extract dimension stones, which depends on various factors, including the mechanical and physical properties of the stone, cutting specifications, and operational characteristics. Specific energy, production rate, efficiency, and wear of diamond beads are some ...
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Diamond wire cutting is a common method to extract dimension stones, which depends on various factors, including the mechanical and physical properties of the stone, cutting specifications, and operational characteristics. Specific energy, production rate, efficiency, and wear of diamond beads are some of the criteria that influence economic and environmental optimization of diamond wire cutting operations. In this study, the specific energy of the diamond wire cutting process was measured for 11 samples of Granite stones. By analyzing the impact of parameters such as stone density, porosity, and cutting rate on energy consumption, a linear regression model was developed with a correlation coefficient (R2) of 0.944 to predict specific energy for different types of stones. Statistical analyses, including ANOVA, have confirmed that the model accurately predicts specific energy values. Data from three new stone samples were used to validate the model, and their predicted energy values were compared with actual values. The model presented achieved an R2 value of 0.827, demonstrating its high accuracy. The results indicate that energy consumption in dimension stone cutting operation can be accurately predicted and characterized indirectly using high precision stone properties and operational parameters. This method can accurately and indirectly monitor energy consumption and cutting machine performance during the dimension stone cutting operation and can be used to optimize economic and environmental aspects of this process.
Mine Economic and Management
Hadi Fattahi; Hossein Ghaedi
Abstract
The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach ...
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The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.
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.
Ali Nouri Qarahasanlou; Abbas Barabadi; Meisam Saleki
Abstract
Implementing maintenance protocols for industrial machinery is essential since a well-thought-out plan may support and improve machinery dependability, production quality, and safety precautions. Implementing a maintenance plan that considers the equipment's actual functional behavior and the effects ...
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Implementing maintenance protocols for industrial machinery is essential since a well-thought-out plan may support and improve machinery dependability, production quality, and safety precautions. Implementing a maintenance plan that considers the equipment's actual functional behavior and the effects of failures will be easier and more practical. Engineers must consider environmental conditions when studying in hostile environments such as mine. The major goal of this study is to create a mining equipment maintenance program that is as effective as possible while incorporating risk and performance indicators and taking environmental factors into account. The study uses the “reliability-centered maintenance” method, which combines the reliability operating index and risk. The Cox model also includes the risk factors associated with environmental conditions in the reliability analysis. The proposed approach was implemented in a 5-758 Komatsu dump-truck case study at the Sungun copper mine in Iran. The reliability-centered maintenance approach is implemented for dump-truck in three scenarios based on risk factors: 1- baseline, 2- First semi-annual, cheap maintenance, and 3- second semi-annual, expensive maintenance. All failure modes are low-risk, making corrective maintenance appropriate. In Scenario 1, electrical-electrical, electrical-start, mechanical, and pneumatic-related failures are low-risk, making corrective maintenance suitable. In Scenario 2, corrective maintenance is recommended for pneumatic-related failure. In Scenario 3, the fuel-related failure has a high criticality number and failure intensity, indicating a high-risk situation. Time-based preventive maintenance is the most appropriate strategy for this scenario.
Environment
Hamid Sarkheil; Shahram Alghasi; Ali Sadeghy Nejad
Abstract
Environmental degradation, particularly in marine ecosystems, has become a critical issue, due to industrial activities. Offshore areas are significantly impacted by the deep sea mining operations, leading to pollution and ecological imbalances. The existing environmental risk assessment models often ...
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Environmental degradation, particularly in marine ecosystems, has become a critical issue, due to industrial activities. Offshore areas are significantly impacted by the deep sea mining operations, leading to pollution and ecological imbalances. The existing environmental risk assessment models often fail to integrate the qualitative and quantitative data effectively, highlighting a significant research work gap. This work aims to address this gap by developing a comprehensive framework using the Bayesian Networks (BN), and the NETICA software to evaluate the risks associated with the installation of three-legged deep sea mining structures. The major goals are to systematically identify and prioritize the risks, and to develop effective mitigation strategies. The novelty of this work lies in its innovative use of the Bayesian modeling to combine the expert knowledge with the empirical data, providing a detailed categorization of risks into the low, medium, and high levels. The output parameters focus on the severity, likelihood, and detectability of risks. The results indicate that 40% of the habitat destruction risks are low, 46% fall within the ALARP region, and 14% are high, while the species destruction risks are 31% low, 50% ALARP, and 19% high. These findings guide the targeted mitigation measures to ensure effective protection of the offshore marine environment. Also the work concludes with a set of recommendations aimed at mitigating identified risks, and minimizing the environmental impacts. These include the implementation of advanced monitoring technologies, adoption of best management practices, and enforcement of stricter regulatory frameworks.
Exploitation
Hassanreza Ghasemitabar; Andisheh Alimoradi; Hamidreza Hemati Ahooi; Mahdi Fathi
Abstract
Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in ...
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Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in installation of drilling rigs, restrictions caused by previous mining operation etc. The advances in artificial intelligence can help to solve these problems. In this research, we used python as one of the most pervasive and the most powerful programming languages in the field of data analysis and artificial intelligence. In this method mean shift algorithms have been used to cluster data, random forest to estimate clusters, and gradient boosting to estimate iron grade. Finally, in the studied area of Choghart in Central Iran, more than 91% accuracy was achieved in detection of ore blocks. Also, the results of the neural network indicate the mean square error (MSE) and mean absolute error (MAE) in the training data, respectively equal to 0.001 and 0.029, in the test data is 0.002 and 0.03, and in the validation boreholes, we reached a maximum of 0.06 and 0.2.
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
Aghil Haghdadi; Sima Mohammadnejad
Abstract
The presence of copper bearing minerals in cyanidation of gold ores may lead to several challenges in the CIP/CIL circuits. Many solutions have been proposed to address these problems, one being the use of glycine in the cyanidation process. Here, the experimental as well as molecular modelling studies ...
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The presence of copper bearing minerals in cyanidation of gold ores may lead to several challenges in the CIP/CIL circuits. Many solutions have been proposed to address these problems, one being the use of glycine in the cyanidation process. Here, the experimental as well as molecular modelling studies using Density Functional Theory (DFT) have been conducted to investigate the glycine role in cyanidation of copper bearing gold ores. The results show that in the presence of glycine in the solution containing copper-cyanide species and in very low or zero free cyanide content, the dissolution rate of gold is significantly improved (3.02 vs. 0.23 ppm), while no improvement is observed in copper free or cyanide enriched solutions. Molecular modeling has been performed to interpret the laboratory results as well as to identify the mechanisms. The modeling results demonstrate that in cyanide deficient solutions, cyanide complex of copper complexes (E = -319 kCal.mol-1) is replaced by glycine, and the free cyanide produced results in higher gold cyanidation as well as lower copper cyanide formation.
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.
