Pankaj Rathore; Suresh Kumar Tiwari
Abstract
The main aim of this experimental analysis is to understand the effectiveness of ceramic waste (CW) in stabilizing the clayey soil. The effect of adding various CW percentages (5%, 10%, 15%, 20%, 25%, and 30%) on the geotechnical properties of clayey soil is evaluated by performing a series of laboratory ...
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The main aim of this experimental analysis is to understand the effectiveness of ceramic waste (CW) in stabilizing the clayey soil. The effect of adding various CW percentages (5%, 10%, 15%, 20%, 25%, and 30%) on the geotechnical properties of clayey soil is evaluated by performing a series of laboratory tests like the Atterberg’s limit test, compaction test, unconfined compressive strength (UCS) test, California bearing ratio (CBR) test, and swelling pressure test. Micro-structural analysis including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform-infrared (FT-IR) spectroscopy are carried out on untreated and treated clay-ceramic composites. The results obtained indicate that the incorporation of 30% ceramic waste in clay soil increase the maximum dry unit weight (γmax) from 17.20 kN/m3 (CL + 0% CW) to 18.25 kN/m3 (CL + 30% CW). The unconfined compressive strength of clayey soil increases with the addition of ceramic waste. A maximum UCS of 217 kPa is obtained with 25% ceramic content, beyond which it starts decreasing. Similarly, increasing trend in CBR results is observed with an increase in the ceramic waste content. The increment in CBR is approximately 152% (unsoaked condition) and 142% (soaked condition). At the same time, the addition of ceramic waste in clay soil reduces the Atterberg limits, optimum water content (ωopt), and swelling pressure. “It can be concluded from the experimental study that CW can be used as a sustainable alternative soil stabilizer.
H. Dehghani; A. Siami; P. Haghi
Abstract
One of the most important steps involved in mining operations is to select an appropriate extraction method for mine resources. After choosing the extraction method, it is usually impossible to replace it with another one because it may be so expensive that implementation of the entire project could ...
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One of the most important steps involved in mining operations is to select an appropriate extraction method for mine resources. After choosing the extraction method, it is usually impossible to replace it with another one because it may be so expensive that implementation of the entire project could be economically impossible. Choosing a mining method depends on the geological and geometrical characteristics of the mine. Due to the complexity of the process of choosing an appropriate mining method and the effect of the parameters involved on the results of this process, it is necessary to utilize the new decision-making methods that have the ability to consider the relationship between the existing parameters and the mining methods. Grey and TODIM (an acronym in Portuguese, i.e. Tomada de Decisão Interativa Multicritério) decision-making methods are among the existing ones, which in addition to the convenience, show high accuracy. The proposed models are presented to determine the best mining method in the Gol-e-gohar iron ore mine in Iran. The results obtained are compared with the methods used in the previous research works. Among the decision-making methods introduced, the open pit mining method is the most appropriate option and the square-set mining is the worst one.
Environment
B. Shokouh Saljoughi; A. Hezarkhani
Abstract
The Shahr-e-Babak district, as the studied area, is known for its large Cu resources. It is located in the southern side of the Central Iranian volcano–sedimentary complex in SE Iran. Shahr-e-Babak is currently facing a shortage of resources, and therefore, mineral exploration in the deeper and ...
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The Shahr-e-Babak district, as the studied area, is known for its large Cu resources. It is located in the southern side of the Central Iranian volcano–sedimentary complex in SE Iran. Shahr-e-Babak is currently facing a shortage of resources, and therefore, mineral exploration in the deeper and peripheral spaces has become a high priority in this area. This work aims to identify the geochemical anomalies associated with the Cu mineralization using the Spectrum–Area (S–A) multi-fractal and Wavelet Neural Network (WNN) methods. At first, the Factor Analysis (FA) is applied to integrate the multi-geochemical variables of a regional stream sediment dataset related to major mineralization elements in the studied area. Then the S–A model is applied to decompose the mixed geochemical patterns obtained from FA and compare with the results obtained from the WNN method. The S–A model, based on the distinct anisotropic scaling properties, reveals the local anomalies due to the consideration of the spatial characteristics of the geochemical variables. Most of the research works show that the capability (i.e. classification, pattern matching, optimization, and prediction) of an ANN considering its successful application is suitable for inheriting uncertainties and imperfections that are found in mining engineering problems. In this paper, an alternative method is presented for mineral prospecting based on the integration of wavelet theory and ANN or wavelet network. The results obtained for the WNN method are in a good agreement with the known deposits, indicating that the WNN method with Morlet transfer function consists of a highly complex ability to learn and track unknown/undefined complicated systems. The hybrid method of FA, S–A, and WNN employed in this work is useful to identify anomalies associated with the Cu mineralization for further exploration of mineral resources.
Exploration
N. Mahvash Mohammadi; A. Hezarkhani
Abstract
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...
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Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area.
Exploitation
S. Moosazadeh; H. Aghababaie; Seyed H. Hoseinie; B. Ghodrati
Abstract
Utilization is one of the main managerial factors that is applied for construction process analysis well. It directly affects the project duration and construction costs. Therefore, a utilization study in tunneling projects is essential. In this work, the utilization of an earth pressure balance Tunnel ...
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Utilization is one of the main managerial factors that is applied for construction process analysis well. It directly affects the project duration and construction costs. Therefore, a utilization study in tunneling projects is essential. In this work, the utilization of an earth pressure balance Tunnel Boring Machine (TBM) in Tabriz urban railway project was studied using the Monte Carlo simulation approach. For this purpose, the unit operation during one working shift such as boring time, ring building time, and locomotive travel time was recorded and saved in data base. In addition, the general down times such as TBM and back-up system maintenance, surface and tunnel logistic maintenance, cutting tools’ replacement, and locomotive delay times were recorded and considered in simulation. The results of this work show that the mean simulated project duration time of case study TBM is approximately 859 shifts and close to the real data with a difference of 0.92%. Finally, the average estimated utilization factor was found to be approximately 14%.
S. Shaffiee Haghshenas; R. Mikaeil; A. Esmaeilzadeh; N. Careddu; M. Ataei
Abstract
Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing ...
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Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing on the machine's energy-consuming is unavoidable. For this purpose, in the first step, laboratory studies under different operating conditions at different cutting depths and feed rates are performed on 12 soft and hard rock samples. In the continuation of the laboratory studies, the rock samples are transferred to the rock mechanics laboratory in order to determine the mechanical properties (uniaxial compressive strength and modulus of elasticity). The statistical studies are performed in the SPSS software in order to predict the electrical current consumption of the cutting machine according to the mechanical characteristics of the rock samples, cutting depth, and feed rate. The statistical models proposed in this work can be used with a high reliability in order to estimate the electrical current consumed in the cutting process.
Exploitation
Rym Khettabi; Issam Touil; Mohamed Kezzar; Mohamed R. Eid; Fatima.Z Derdour; Kamel Khounfais; Lakhdar Khochmane
Abstract
It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity ...
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It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity of the bit, RPM, cutting angle , and rock resistance on the penetration rate of the drilling tool. In this examination, three kinds of limestone rocks were considered. The planned assessments were carried out at three stages of the considered four input variables. The statistical analysis was realized using both RSM approach and analysis of variance (ANOVA). This analysis allowed us to develop the appropriate penetration model with a higher determination coefficient of 96.19%, which demonstrates the high correlation between the predicted and experimental data, and consequently, it can be concluded that the obtained model is highly suitable for the prediction of the penetration rate. Also from variance analysis, the results obtained show that rotational speed, RPM, and weight on the bit (WOB) parameters, as well as the nature of the rock, which is determined by the rock compressive resistance, having a significant effect on the penetration rate; however, the rake angle has little effect. Finally, the optimal parameters were determined to find the best possible penetration rate of the drilling tool.
Rock Mechanics
Ajay Sharma; Neha Shrivastava
Abstract
The present study aims to assess the utility of construction and demolition (C&D) waste, specifically recycled concrete aggregates (RCA) and recycled brick aggregates (RBA), as fill materials in highway embankments. The assessment of slope stability is crucial in determining the suitability of any ...
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The present study aims to assess the utility of construction and demolition (C&D) waste, specifically recycled concrete aggregates (RCA) and recycled brick aggregates (RBA), as fill materials in highway embankments. The assessment of slope stability is crucial in determining the suitability of any material for embankment fill. GeoStudio software is employed in this study for slope stability assessment of 12 models with LS, RCA, RBA, and their blends as embankment fill materials. The embankment configuration is designed to represent a six-lane highway (carriageway width = 13 m, adhering to IRC: 36 standards), featuring varying slope elevations (3 m, 6 m, and 9 m) and diverse horizontal to vertical slope ratios (H:V = 2:1, 1:1, 1:2, and 1:3). The Morgenstern-Price method is employed to analyze slope stability and determine factor of safety (FOS) values. The study highlights the impact of slope heights, slope ratios, and fill materials (RCA, RBA, LS, and their blends) on FOS values in embankment models. Incorporating RCA or RBA in LS significantly boosts embankment FOS, exceeding stability expectations beyond 45˚ slope angles, potentially reducing costs and required area in construction projects. The incorporation of RCA/RBA into LS increases the FOS values to a range of 1.38 to 5.91, indicating very stable slopes for highway embankments. Based on the findings, replacing LS with RCA or RBA in embankment fill can enhance environmental sustainability and economic efficiency. However, these slope stability results apply specifically to C&D waste with similar composition, grain size, geotechnical properties, and embankment conditions.
sima razmjouee; mahmood abdollahy; seyed mohammad javad koleini
Abstract
Using microflotation method, this study explored the collectorless flotation of Chalcocite and its dependence on the redox potential of pulp . Electrochemical studies were performed by cyclic voltammetry in specific potential ranges and at different pH values. The results show that significant ...
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Using microflotation method, this study explored the collectorless flotation of Chalcocite and its dependence on the redox potential of pulp . Electrochemical studies were performed by cyclic voltammetry in specific potential ranges and at different pH values. The results show that significant floatability of Chalcocite occurs in the specific reducing conditions. By increasing potentials, on the other hand, the floatability of Chalcocite is reduced. The effect of pH was also examined: At pH=4, the maximum recovery of 73%, was obtained at E= -222 mV (Eh= -17); and at pH=9, the maximum recovery of 71% was obtained at E= -501 mV (Eh= -296). On the basis of the results obtained, the possible mechanisms of collectorless flotation of Chalcocite in different conditions were discussed.
Anhay Soni; B. Mishra; Siddharth Singh
Abstract
Theoretical review of ‘mining pit lakes’ indicates that like natural lakes such lakes display a huge diversity. They are typically in a non-equilibrium state with respect to their surroundings. Hence, at the decommissioning phase of mining operation a detailed technical study are required ...
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Theoretical review of ‘mining pit lakes’ indicates that like natural lakes such lakes display a huge diversity. They are typically in a non-equilibrium state with respect to their surroundings. Hence, at the decommissioning phase of mining operation a detailed technical study are required on different aspects of such created water bodies considering its morphometry, geology, hydrology, water quality (geo-chemistry), rate of filling, and biology. Pit lakes has their value as resources for miscellaneous purposes e.g. recreation, fisheries, water supply, and wildlife habitat which is dependent mostly on their topography, location water use and safety characteristics. Internationally, pit lakes as self sustaining aquatic ecosystems have been developed in the past e.g. Alberata Pit Lake in Canada [1, 2] ; Sleeper pit lake [3] and Westfield pit lake, Scotland [4]. In Indian mining industry neither scientific studies nor case record of mining pit lake development are available because ‘closure plans’ are introduced recently. One such attempt in India at Kerendari coal mine in Jharkhand state is a laudable and new attempt which is at the stage of planning. In brief, since the opportunities for development of ‘mining pit lakes’ are immense and company owning it can nurture their ecological and commercial benefits appropriately, this review will be practically useful particularly in those countries which has less number of age old surface mines heading towards the decommissioning phase. The review done here can be practically utilized for evaluation, assessment, new project clearances and statuary compliance purposes.
ebrahim elahi; Reza Kakaie; amir yusefi
Abstract
Ultimate limits of an open pit, which define its size and shape at the end of the mine’s life, is the pit with the highest profit value. A number of algorithms such as floating or moving cone method, floating cone method II and the corrected forms of this method, the Korobov algorithm and the corrected ...
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Ultimate limits of an open pit, which define its size and shape at the end of the mine’s life, is the pit with the highest profit value. A number of algorithms such as floating or moving cone method, floating cone method II and the corrected forms of this method, the Korobov algorithm and the corrected form of this method, dynamic programming and the Lerchs and Grossmann algorithm based on graph theory have been developed to find out the optimum final pit limits. Each of these methods has special advantages and disadvantages. Among these methods, the floating cone method is the simplest and fastest technique to determine optimum ultimate pit limits to which variable slope angle can be easily applied. In contrast, this method fails to find out optimum final pit limits for all the cases. Therefore, other techniques such as floating cone method II and the corrected forms of this method have been developed to overcome this shortcoming. Nevertheless, these methods are not always able to yield the true optimum pit. To overcome this problem, in this paper a new algorithm called floating cone method III has been introduced to determine optimum ultimate pit limits. The results show that this method is able to produce good outcome.
M. R. Tavakoli Mohammadi; Seyed M. J. Koleini; M. Abdollahy
Abstract
Efforts to increase the mass transfer coefficient, enhance the contact area, and decrease the power input of contractors have given risen to the development of the pre-dispersed solvent extraction (PDSE) contactor and the devise of the new dissolved nitrogen PDSE (DNPDSE) contactors. The studies conducted ...
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Efforts to increase the mass transfer coefficient, enhance the contact area, and decrease the power input of contractors have given risen to the development of the pre-dispersed solvent extraction (PDSE) contactor and the devise of the new dissolved nitrogen PDSE (DNPDSE) contactors. The studies conducted after the design of the new contactor to determine the working conditions for its suitable performance (2.5-3.5 bar pressure, 0.1 L/min sparger flow rate, and 1.5 L of the aqueous phase) showed that for all the evaluated conditions (i.e. the pressure, polyaphron type, and dilution percentage), the recovery in the DNPDSE contactor was higher than that in the PDSE one. In addition, pictures of the performance modes of the two contactor indicated the presence of the organic phase in the form of colloidal gas aphrons (CGAs) in the DNPDSE contactor and of polyaphron aggregations in the PDSE one. This is a good reason for the increased copper recovery in the DNPDSE contactor. The best recovery for the extraction process in the DNPDSE contactor was achieved using the anionic polyaphron of sodium dodecylbenzene sulphonate (NaDBS) with five-fold dilution at 3.5 bar.
H. Fattahi
Abstract
Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability, and their performance is not an easy task. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was utilized to build ...
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Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability, and their performance is not an easy task. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was utilized to build an estimation model for the prediction of FOS. Three ANFIS models were implemented including grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering method (FCM). Several important parameters such as cohesion coefficient, internal angle of friction, slope height, slope angle, and unit weight of slope material were utilized as the input parameters, while FOS was used as the output parameter. A comparison was made between these three models, and the results obtained showed the superiority of the ANFIS-SCM model. Also performance of the ANFIS-SCM model was compared with multiple linear regression (MLR). The results obtained demonstrated the effectiveness of the ANFIS-SCM model.
Exploitation
J. Gholamnejad; A. Azimi; M.R. Teymouri
Abstract
Stockpiling and blending play a major role in maintaining the quantity and quality of the raw materials fed into processing plants, especially the cement, iron ore and steel making, and coal-fired power generation industries that usually require a much uniformed feed. Due to the variable nature of such ...
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Stockpiling and blending play a major role in maintaining the quantity and quality of the raw materials fed into processing plants, especially the cement, iron ore and steel making, and coal-fired power generation industries that usually require a much uniformed feed. Due to the variable nature of such materials, they even come from the same source and the produced ores or concentrates are seldom homogeneous enough to be directly fed to the processing plant ore furnaces. Processing plants in iron ore mines need uniform feed properties in terms of each variable (in this work, iron phosphorous ratio and Fe content in magnetite phase) grade of ore, and therefore, homogenization of iron ore from different benches of an open pit or ore dumps has become an essential part of modern mine scheduling. When ore dumps are considered as an ore source, the final grade of the material leaving the dump to the blending bed cannot be easily determined. This difficulty contributes to mixing the materials of different grades in a dump. In this work, the ore dump elements were treated as normally distributed random variables. Then a stochastic programming model was formulated in an iron ore mine in order to determine the optimum amount of ore dispatched from different bench levels in open pit and also four ore dumps to a windrow-type blending bed in order to provide a mixed material of homogenous composition. The chance-constrained programming technique was used to obtain the equivalent deterministic non-linear programming problem of the primary model. The resulting non-linear model was then solved using LINGO. The results obtained showed a better feed grade for the processing plant with a higher probability of grade blending constraint satisfaction.
Exploitation
H. Nikoogoftar Safa; A. Hezarkhani
Abstract
In this paper, we aim to present a quantitative modeling for delineating the alteration zones and lithological units in the hypogene zone of Masjed-Daghi Cu-Au porphyry deposit (NW Iran) based on the drill core data. The main goal of this work is to apply Ordinary Kriging (OK) and concentration-volume ...
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In this paper, we aim to present a quantitative modeling for delineating the alteration zones and lithological units in the hypogene zone of Masjed-Daghi Cu-Au porphyry deposit (NW Iran) based on the drill core data. The main goal of this work is to apply Ordinary Kriging (OK) and concentration-volume (C-V) fractal model based on Cu grades in order to separate the different alteration zones and lithological units. Initially, anisotropy was investigated and modeled based on calculating the experimental semi-variograms of the Cu values, and the main variography directions were identified and evaluated. Then a block model of the Cu grades was generated using the kriging, and the estimation obtained for OK was applied to the C-V fractal model. The C–V log–log plot based on the estimation method represents the various alteration and lithological zones via threshold values. The comparison and interpretation of the alteration zones and lithological units based on the C–V fractal modeling proved that the method was acceptable and capable of correctly delineating the alteration and lithological units. Regarding the correlation derived from log ratio matrix (used to compare the geological model with the C-V fractal results), it was observed that Cu values less than 0.4% were obtained for OK overlapped voxels with the phyllic alteration zone by an overall accuracy (OA) of 0.737. The spatial correlation between the potassic alteration zones resulting from a 3D geological modeling and the high concentration zones in the C-V fractal model based on OK indicated that the alteration zone contained Cu values greater than 0.4% with OA of 0.791. Also using this method, trustworthy results were obtained for the rock units.
F. Khorram; O. Asghari; H. Memarian; A. Hoseein Morshedy; X. M. Emery
Abstract
The key input parameters for mine planning and all subsequent mining activities is based on the block models. The block size should take into account for the geological heterogeneity and the grade variability across the deposit. Providing grade models of smaller blocks is more complex and costly than ...
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The key input parameters for mine planning and all subsequent mining activities is based on the block models. The block size should take into account for the geological heterogeneity and the grade variability across the deposit. Providing grade models of smaller blocks is more complex and costly than larger blocks, but larger sizes cannot represent areas with high spatial variability accurately. Hence, a unique block size is not an optimal solution for modeling a mine site. This paper presented a novel algorithm to create an adaptive block model with locally varying block sizes aiming to control dilution and ore loss in Sungun porphyry copper deposit of Iran with a complex geometry characterized by multiple dikes. Three grade block models with different block sizes and simulated by direct block simulation are the inputs of algorithm. The output is a merged block model, assigning the smaller blocks to the complex zones, such as ore-waste boundaries, and larger blocks to the continuous and homogeneous zones of the ore body. The presented algorithm is capable to provide an accurate spatial distribution model with a fewer number of blocks in comparison to the traditional block modeling concepts.
M. Sakizadeh; M. T. Sattari; H. Ghorbani
Abstract
The soil samples were collected from 170 sampling stations in an arid area in Shahrood and Damghan, characterized by prevalence of mining activity. The levels of Co, Pb, Ni, Cs, Cu, Mn, Sr, V, Zn, Cr, and Tl were recorded in each sampling location. A new method known as min/max autocorrelation factor ...
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The soil samples were collected from 170 sampling stations in an arid area in Shahrood and Damghan, characterized by prevalence of mining activity. The levels of Co, Pb, Ni, Cs, Cu, Mn, Sr, V, Zn, Cr, and Tl were recorded in each sampling location. A new method known as min/max autocorrelation factor (MAF) was applied for the first time in the environmental research works to de-correlate these elements before their geo-statistical simulation. The high cross-correlation among some elements, while poor spatial correlation among the others, could have made spectral decomposition of MAFs unstable, resulting in some negative eigenvalues, so it was decided to reduce the dimensionality of the original variables by Principal Component Analysis (PCA). The resultant 6 heavy metals (Cr, Mn, Cu, V, Ni, and Co) were converted to their respective MAFs followed by their geo-statistical simulation using Sequential Gaussian Simulation (SGS) independently. Examination of the cross-variograms of MAFs indicated that the resultant factors had been rigorously de-correlated, especially at zero lag and around ∆ lag distance. Several validation checks including reproduction of variograms in data and normal score space, close matching between distribution of MAFs versus simulated realizations, and reproduction of descriptive statistics and data histograms all confirmed that the data values had been honored by this applied method. The results obtained indicated that this method could reproduce the data values as well as the spatial continuity of heavy metals (e.g. semi-variograms) successfully. In addition, this technique is simpler and more computationally efficient than its equivalent sequential Gaussian co-simulation as fitting a linear model of co-regionalization (LMC) is not required in the data-driven MAF method.
Mineral Processing
S. Mohammadi; B. Rezai; A. A. Abdolahzadeh
Abstract
Geometallurgy tries to predict the instability the behavior of ores caused by variability in the geological settings, and to optimize the mineral value chain. Understanding the ore variability and subsequently the process response are considered to be the most important functions of an accurate geometallurgical ...
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Geometallurgy tries to predict the instability the behavior of ores caused by variability in the geological settings, and to optimize the mineral value chain. Understanding the ore variability and subsequently the process response are considered to be the most important functions of an accurate geometallurgical study. In this paper, the geometallurgical index is presented as a new tool to optimize the mining activities. Geometallurgical index is described as any geological feature that makes a footprint on the process performance of ores. In a comprehensive research work at the Sarcheshmeh porphyry copper mine, the geological features that affect the main process responses including the product grade and recovery and plant’s throughput are subjected to investigation. In the current report, the rock hardness variability in terms of semi-autogenous grinding power index (SPI) and its effects on the mill throughput and energy consumption are presented. Ninety samples are collected based on the geological features including lithology, hydrothermal alteration, and geological structures. The samples are mineralogically characterized using XRD, XRF, and electron and optical microscopy. The Starkey laboratory mill, commercialized by Minnovex, is used to perform the SPI comminution test. The SPI results show a wide range of hardness, varying from 12 to 473 minutes. The correlation between the SPI results and the geological features show that lithology is a key geological feature that defines the hardness variability. In addition, the hydrothermal alteration would be an effective parameter in the period that the plant is fed with a single lithology.
J. Shakeri; H. Amini Khoshalan; H. Dehghani; M. Bascompta; K. Onyelowe
Abstract
In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis ...
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In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.
Ankit Kumar; Ravi Kumar Sharma
Abstract
Granular pile anchor is a new technique that is commonly used to improve the pull-out resistance of expansive soil like soft clay, loose sand, and black cotton soil. Using the Abaqus software, this work presents a numerical investigation to estimate the pull-out capacity of granular pile anchor ...
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Granular pile anchor is a new technique that is commonly used to improve the pull-out resistance of expansive soil like soft clay, loose sand, and black cotton soil. Using the Abaqus software, this work presents a numerical investigation to estimate the pull-out capacity of granular pile anchor in soft clay. By applying a specified displacement of 10% of D (pile diameter) on the granular pile anchor, the effects of length, diameter, angle of inclination (α), and number of GPA at varying spacing values on uplift capacity is examined. Additionally, L/D ratios of both individual and group piles are examined using various variables. The study uses expansive soil and GPA of unit weight 17 kN/m3 and 22 kN/m3, poisson’s ratio of 0.4 and 0.3, modulus of elasticity 4 MPa, and 11 MPa, respectively, for the estimation of uplift capacity. The cohesion value for the expansive clay is 25 kPa, and the angle of shearing resistance for GPA is 36˚. According to the numerical study, both for a single pile and for piles placed in a group, with increases in pile length and diameter, the granular pile anchor's pull-out capability improves. For a pile placed in group the value of the pull-out capacity shows optimum result when spacing (S) is 2.5D. Additionally, the uplift capacity of the granular pile anchor increases with an increase in angle inclination (α) from 0˚ to 10˚, and then decreases from 10˚ to 15˚. The efficiency of GPA is examined, which assists in the choice of the different granular pile anchor parameters.
Environment
Aditi Nag; Smriti Mishra
Abstract
This study examines the revitalization of mining ghost towns (MGTs) through heritage tourism, focusing on sustainability and heritage preservation. The study highlights the transformative potential of heritage tourism in revitalizing these towns, highlighting the economic resilience achieved through ...
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This study examines the revitalization of mining ghost towns (MGTs) through heritage tourism, focusing on sustainability and heritage preservation. The study highlights the transformative potential of heritage tourism in revitalizing these towns, highlighting the economic resilience achieved through diversified local economies and responsible tourism practices. Cultural preservation ensures the endurance of unique identities and cultural legacies, sparking community pride and cultural exchange. Sustainability measures extend beyond heritage preservation, promoting environmental stewardship and long-term ecological well-being. Community engagement, educational initiatives, and responsible tourism practices are crucial in sustaining the heritage of these towns. The implications extend beyond individual communities, offering a model for responsible and sustainable tourism practices with global relevance. The significance of revitalizing MGTs through heritage tourism lies in preserving history, empowering communities, and creating vibrant, sustainable destinations for generations.
Exploration
Mustafa Yasser Elgindy; Ahmed Zakaria Nooh; Ali Mostafa Wahba
Abstract
Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify ...
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Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify the kick after a large amount of influx has been recorded on the surface. So, we aim to recognize the kick before it enters the wellbore by detecting the abnormal formation pressure once the bit penetrates the rock. This paper proposes a new machine learning model as an alternative solution using field drilling parameters such as true vertical depth, porosity, bulk density, resistivity, rate of penetration, weight on bit, rotation per minute, torque, standpipe pressure, flow rate, flowline temperature, and gas level. The data-driven models were developed using three separate algorithms: K-Nearest Neighbor, Random Forest, and XG Boost. 6022 field data points were split for training, testing, and validation processes. On average, the model using the random forest algorithm showed the highest accuracy in forecasting the formation pressure, with root mean squared error values and coefficient of determination values of 12.868 and 0.9638, respectively. Streamlit Deployment tool was used to create a user interface program to facilitate the prediction process. The program was tested using 196 field data points and recorded a high accuracy of 95%.
A. Salimi; M. Ziaii; A. Amiri; M. Hosseinjani Zadeh
Abstract
Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed ...
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Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).
B. A. Mert
Abstract
This paper presents the procedures used for determining and defining the tonnage and grade of the coalfields of Kangal basin from the developed GIS-aided block model. In this work, firstly, all the lithological logs of drill holes and chemical analysis data of core in the basin were analyzed with the ...
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This paper presents the procedures used for determining and defining the tonnage and grade of the coalfields of Kangal basin from the developed GIS-aided block model. In this work, firstly, all the lithological logs of drill holes and chemical analysis data of core in the basin were analyzed with the help of geostatistics, and then the digital raster maps of each one of the attributes such as the thickness, calorific value (LCV), ash content (AC%), moisture content (MC%), and surface maps of lignite seams were mapped in GIS environment. In the second stage, quantities of the overburden and resources with different categories were calculated on the basis of field-based quality and volume queries with the help of the digital maps on GIS platform. As a result, it was estimated that the Kalburçayırı field had a tonnage of 116 Mt of lignite with an LCV of 1308 kcal/kg, the Hamal field had a tonnage of 30 Mt of lignite with an LCV of 987 kcal/kg, and the Etyemez field had a tonnage of 48 Mt of lignite with an LCV of 1282 kcal/kg. Also it was estimated that almost 24,278,151 tons of lignite in the Hamal and Etyemez fields had a quality of less than 950 kcal/kg that could be directly fired without the blending process in the power plant. As a consequence, the Hamal and Etyemez fields should go into production as soon as possible and be fired in the power plant after being mixed with the lignite in the Kalburcayırı field so that they can be redounded to economy.
M. R. Azad; A. Kamkar Rouhani; B. Tokhmechi; M. Arashi
Abstract
Upscaling based on the bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with bandwidth. In a smooth variability region, a large number of cells will ...
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Upscaling based on the bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with bandwidth. In a smooth variability region, a large number of cells will be merged, and vice versa, they will remain fine with severe variability. Bandwidth variation can be effective in upscaling results. Therefore, determining the optimal bandwidth in this method is essential. For each bandwidth, the upscaled model has a number of upscaled blocks and an upscaling error. Obviously, higher thresholds or bandwidths cause a lower number of upscaled blocks and a higher sum of squares error (SSE). On the other hand, using the smallest bandwidth, the upscaled model will remain in a fine scale, and there will be practically no upscaling. In this work, different approaches are used to determine the optimal bandwidth or threshold for upscaling. Investigation of SSE changes, the intersection of two charts, namely SSE and the number of upscaled block charts, and the changes of SSE values versus bandwidths, are among these approaches. In this particular case, if the goal of upscaling is to minimize the upscaling error, the intersection method will obtain a better result. Conversely, if the purpose of upscaling is computational cost reduction, the SSE variation approach will be more appropriate for the threshold setting.
