Case Study
Exploitation
Rahul Shende; Omkar Navagire; Himanshu Kumar Jangir; Srinivasan V.; Anirban Mandal; Anjan Patel
Abstract
Due to its intricate challenges, Black cotton (BC) soil is dumped separately in mining areas, and this study focuses on a BC soil dump at an open-cast mine site. This soil, characterized by cohesion of 26-40 kPa and internal friction angle of 13°-17°, exhibits significant expansion and contraction ...
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Due to its intricate challenges, Black cotton (BC) soil is dumped separately in mining areas, and this study focuses on a BC soil dump at an open-cast mine site. This soil, characterized by cohesion of 26-40 kPa and internal friction angle of 13°-17°, exhibits significant expansion and contraction with moisture fluctuations, swelling during wet periods, and shrinking during dry spells, posing considerable challenges in mining areas. The Ministry of Environment, Forest and Climate Change (MoEFCC) has suggested constructing a 15-20m wall to protect the village on the periphery of the BC soil dump of 36m height. This study aimed to identify sustainable and economical alternative feasible remedial solutions. Field testing, including borehole investigation, was conducted to determine the stratigraphy beneath the dump. Numerical analysis using SLOPE/W software was performed for slope optimization and to evaluate remedial measures such as stone pitching and rockfill trench. The study shows that the dump can be stabilized using the design modification and possible cost-effective measures. Based on field observations, dump material testing, and numerical analysis, alternative remedial measures were proposed and implemented. The study also includes a cost-benefit analysis of the recommended remedial measures.
Original Research Paper
Exploration
Shoopala Uugulu; Nazlene Poulton; Akaha Tse; Martin Harris; Taiwo Bolaji
Abstract
The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater ...
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The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater for their domestic, irrigation and other uses, there is a need to thoroughly investigate groundwater quality in the area to ascertain the extent of the contamination. This study characterises groundwater quality using a comprehensive quality assessment approach. On-site parameters reveal that pH ranges between 6.82-7.8, electrical conductivity ranges between 678 - 2270 μS/cm, and dissolved oxygen ranges between 1.4 -5.77 mg/L. With the exception of two samples, the onsite parameters indicate that water is of excellent quality according to the Namibian guidelines. The stable isotopic composition ranges from -7.26 to -5.82‰ and -45.1 to -35.9‰ for δ18O and δ2H, respectively. The groundwater plots on and above the Global Meteoric Water Line, and the best-fit line is characterised by a slope of 4.9, implying the evaporation effect. Hydrochemical analyses indicate bicarbonate and chloride as dominant anions, while calcium and sodium are dominant cations, indicating groundwater dissolving halite and mixing with water from a recharge zone. The Heavy Metal Pollution Index suggested that the water samples are free from heavy metal pollution. The Heavy Metal Evaluation Index clustered around 3, implying that heavy metals moderately affect groundwater. The groundwater quality is suitable for irrigation purposes. The findings offer valuable insights into the area's hydrochemistry and highlight potential environmental risks; hence, groundwater monitoring is recommended.
Original Research Paper
Exploitation
Hemant Agrawal; SIDDHARTHA ROY; Chitranjan Prasad Singh
Abstract
Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, ...
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Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, the Rihand Dam, and local settlements near the Khadia Opencast coal mine. This study aims to analyze the effect of blast hole diameter on peak particle velocity (PPV) to improve vibration control. Experimental investigations were conducted by executing multiple blasts using hole diameters of 159 mm, 269 mm, and 311 mm across different benches of the Khadia mine, with PPV values recorded at various scaled distances. The observed relationship between PPV and hole diameter was further validated through explicit dynamic modeling of the mine’s geology and blast conditions using ANSYS-Autodyn software. The results presents some exclusive observation that with same charge per delay, for smaller distances i.e. for less than 90 m the values of PPV is always higher in large diameter hole blasting while for distance above 500 m the PPV values are higher in smaller diameter holes blasting. The results provide a unique insight for optimizing blast parameters to minimize ground vibrations while maintaining production efficiency.
Case Study
Environment
Aditi Nag; Anurag Singh Rathore
Abstract
This research is focused on analyzing the possibilities and challenges of developing tourism in mining heritage cities (MHCs) within conflict areas. These cities simultaneously have vibrant historical and cultural resources and tourism possibilities in the context of security threats and infrastructural ...
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This research is focused on analyzing the possibilities and challenges of developing tourism in mining heritage cities (MHCs) within conflict areas. These cities simultaneously have vibrant historical and cultural resources and tourism possibilities in the context of security threats and infrastructural inadequacy, which usually characterize conflict areas. The study aims to find ways of boosting tourism competitiveness for such areas with a specific interest in formulating sustainable tourist management policies that foster community involvement and cultural heritage protection. The case study analyzes different conflict areas, representing the best practices and the most effective way of exploiting heritage in mining and luring tourist attractions based on the authentic experience. The results exhibit how tourism can serve as an agent towards economic recovery and social empowerment and acts towards peacebuilding in conflict-affected areas. This study furnishes pragmatic recommendations for legislators, the tourism sector, and community members to favor a more robust and inclusive tourism model that benefits the local community and cultural heritage conservation. Finally, the paper underlines the need to understand the complexity of tourism in conflict areas, using some invisible resources for renewal and growth.
Original Research Paper
Rock Mechanics
Manthri Rakesh; Ashish Kumar Dash; Sunny murmu
Abstract
India's growing energy demand has intensified the need for efficient and safe coal extraction methods, particularly in underground mining, where mechanized depillaring using Continuous Miner (CM) technology has gained prominence. This study explores the critical role of Cut-Out Distance (COD) in optimizing ...
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India's growing energy demand has intensified the need for efficient and safe coal extraction methods, particularly in underground mining, where mechanized depillaring using Continuous Miner (CM) technology has gained prominence. This study explores the critical role of Cut-Out Distance (COD) in optimizing production and ensuring safety during mechanized depillaring operations. COD, defined as the stable drivage length that can be cut without support, directly impacts productivity, roof stability, and operational safety. Despite its importance, there are no standardized guidelines for determining COD in Indian coal mines, leading to trial-and-error practices that compromise efficiency and safety. This paper reviews global and domestic practices, highlighting the inadequacies in existing methods for COD estimation. It identifies key factors influencing COD, including Rock Mass Rating (RMR), roof elasticity, geological conditions, and machinery capabilities. The work also examines case studies of strata control failures in Indian coal mines, highlighting the consequences of improper strata assessment in mines. The research work advocates for the development of standardized guidelines tailored to Indian mining conditions by integrating numerical simulations and machine learning tools for precise COD estimation. A flow chart of methodology for the development of guidelines is proposed; the findings aim to enhance safety, reduce accidents, and improve productivity, paving the way for sustainable growth in India's underground coal mining sector.
Original Research Paper
Exploitation
Yaşar Ağan; Türker Hüdaverdi
Abstract
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration ...
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The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration is defined peak particle velocity (ppv) in the ground. All data used to estimation were obtained by observing real blasting operations. After the measuring of the peak particle velocity, models of the prediction were created using independent site parameters. Most of the data is used to train the model, while remaining part is used for testing. The models were created using independent blasting parameters proportionally. Thus, more parameters are included in the models without complicating the models. A thorough validation process was conducted utilizing a diverse set of nine error criteria. Artificial intelligence models have been found to outperform traditional methods in predicting ground vibration. The mean absolute error values were found to be 1.42, 1.54, and 1.78 for ANFIS, GPR, and SVM, respectively. A similar situation is observed for other error criteria as well. ANFIS appears to be the most effective model for predicting ground vibration.
Original Research Paper
Exploitation
Kamel Menacer; Abderrazak Saadoun; Abdellah Hafsaoui; Mohamed Fredj; Abdelhak Tabet; Djamel Eddine BOUDJELLAL; Riadh Boukarm; Radouane Nakache
Abstract
Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming ...
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Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming material significantly impacts the degree of rock fragmentation during mining operations. Stemming refers to the material used to fill the space above explosives in a borehole, which helps confine the explosive energy and optimise rock fragmentation during detonation.This study aims to evaluate the stemming materials and their effect on the particle size distribution of blasted rocks at the Chouf Amar quarry in M'Sila, Algeria. The analyses performed in this study indicate that the blasting results obtained by the company reflect poor fragmentation quality, with a significant quantity of oversized fragments, making up 20–23% of the total pieces. To address this issue, a new operational blasting plan is proposed to enhance fragmentation quality. This plan employed three stemming materials: drill cuttings, 3/8 crushed aggregates, and sand. The test blasts were performed in a limestone quarry, and the results were evaluated using the highly reliable and widely respected image analysis software WipFrag 3.3. The results reveal that using crushed aggregates as stemming material significantly improves fragmentation quality, reducing the proportion of oversized fragments from an average of 23% (with sand stemming) to 2.6%.
Original Research Paper
Exploration
Hassanreza Ghasemi Tabar; Sajjad Talesh Hosseini; Andisheh Alimoradi; mahdi fathi; Maryam Sahafzadeh
Abstract
Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for ...
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Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for simulating “identical twins” of borehole copper grade values using geophysical attributes derived from the geoelectrical method in the Kahang porphyry copper deposit, central Iran. By treating the simulated values as digital twins of actual borehole grades, we employed four machine learning algorithms—Linear Regression (LR), Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM)—to model the complex relationships between geophysical inputs and copper grades. After data preprocessing with Principal Component Analysis (PCA), a refined dataset was used to train, test, and validate each model. The results demonstrate that GB yielded the highest predictive accuracy, generating grade estimates closely aligned with actual values. This identical twin modeling approach highlights the potential of machine learning to enhance early-stage mineral exploration by reducing dependence on costly drilling.
Original Research Paper
Exploration
Mahbubeh Arabzadeh Bani asadi; Habib Ghasemi; Mehdi Rezaei-Kahkhaei; Lambrini Papadopoulou
Abstract
The lower Jurassic (180 ± 1.5 Ma) Gowd-e-Howz granitoid stock, as a part of the Sanandaj-Sirjan Metamorphic-Magmatic Zone (SSMMZ), SE Iran, intruded in the Upper Paleozoic metamorphic and Triassic igneous-sedimentary rocks. It consists of three main rock units including diorite, granodiorite and ...
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The lower Jurassic (180 ± 1.5 Ma) Gowd-e-Howz granitoid stock, as a part of the Sanandaj-Sirjan Metamorphic-Magmatic Zone (SSMMZ), SE Iran, intruded in the Upper Paleozoic metamorphic and Triassic igneous-sedimentary rocks. It consists of three main rock units including diorite, granodiorite and granite/alkali feldspar granite, which accompanied by minor amounts of gabbro. The stock is predominantly composed of medium to coarse-grained granular granitoids consisting of clinopyroxene, amphibole, biotite, plagioclase, alkali feldspar and quartz. Clinopyroxenes exhibit calcic compositions, ranging from diopside to augite and salite, while amphiboles are primarily calcic with hornblende as the dominant phase. Feldspar display compositional ranges from orthoclase and oligoclase to labradorite. Mineralogical and geochemical evidence indicates this I-type calc-alkaline granitic magma produced in an active continental margin arc setting with potential for Cu-Au mineralization. Geothermobarometry estimations based on clinopyroxene (T= 800 to 1300°C and P= ~12 to 4.5 kbar), amphiboles (T= 742 to 769°C and P=4.5- 2 kbar) and biotite (T = 589 to 875°C and P= 0.45- 2.27 kbar), offer three different magma chamber levels for magma storaging and plumbing at the lower (~45 Km), middle (~16 Km) and upper (~5 Km) continental crust in an active continental arc setting in the Late Triassic-Early Jurassic in the southern part of the SSMMZ, SE Iran.
Original Research Paper
Rock Mechanics
Mahan Amirkhani; Mojtaba Bahaaddini; Alireza Kargar; Amin Hekmatnejad
Abstract
The stability of tunnels and underground openings in jointed rock masses is significantly influenced by the development an Excavation Damage Zone (EDZ), where discontinuities alter stress distribution and the fractured propagation zone. In previous studies on EDZ, rock mass is commonly considered as ...
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The stability of tunnels and underground openings in jointed rock masses is significantly influenced by the development an Excavation Damage Zone (EDZ), where discontinuities alter stress distribution and the fractured propagation zone. In previous studies on EDZ, rock mass is commonly considered as a continuum medium, while the joint system can dictate the size of EDZ. This study aims to investigate the EDZ around a tunnel excavated in a jointed rock mass using the Discrete Fracture Network (DFN) and Discrete Element Method (DEM). Three DFN models with different fracture intensities of 0.5, 1.0, and 1.5 m2/m3 were simulated to explore the progressive failure mechanisms and damage evolution around a tunnel. The DFN models were then imported into the DEM code. The area of the plastic zone was considered a representative measure of the EDZ. The influence of joint mechanical properties, including cohesion, friction angle, normal, and shear stiffnesses, was investigated. A dimensionless sensitivity analysis was conducted to evaluate and compare the influence of each parameter. The results show that the joint friction angle is the most influential parameter in all fracture intensities. These insights provide a more precise understanding of joint behaviour and its impact on tunnel stability in different geological settings.
Original Research Paper
Exploration
Mohammad Ebdali; Ardeshir Hezarkhani; Adel Shirazy; Amin Beiranvand Pour
Abstract
This research endeavor concentrates on minerals exploration within the context of a hydrothermal polymetallic vein deposit environment. Stream sediment sampling was executed to acquire geochemical signatures pertinent to mineralization zones. The mineralization nature is classified as epithermal, predominantly ...
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This research endeavor concentrates on minerals exploration within the context of a hydrothermal polymetallic vein deposit environment. Stream sediment sampling was executed to acquire geochemical signatures pertinent to mineralization zones. The mineralization nature is classified as epithermal, predominantly involving polymetallic sulfides. The geochemical analyses yielded multi-element concentration maps, facilitating the identification of anomalies and the establishment of zoning. Although recent developments underscore the efficacy of machine learning, notably deep learning techniques, in the detection of geochemical anomalies, the majority of preceding studies were predicated on univariate statistical methodologies. To address this constraint, a multivariate approach was implemented, incorporating spatial characteristics such as shape, overlap, and zoning within anomalies and halos. Considering the limited availability of validated mineralized samples, unsupervised and semi-supervised methodologies—most notably Generative Adversarial Networks (GANs)—were employed. GANs were trained using multi-element geochemical maps, applying transfer learning to mitigate the challenges posed by restricted deposit data, thereby facilitating the delineation of prospective exploration zones. Quantitative analyses have indicated that the approach utilizing GANs attained an accuracy exceeding 92% alongside a minimal cross-entropy loss of approximately 0.07, thereby surpassing conventional methodologies in detecting of weak anomalies. The model effectively corroborated previously recognized anomalies while simultaneously detecting new prospective mineralization areas, thereby augmenting exploration opportunities. This investigation illustrates that GANs enable a more thorough utilization of geochemical datasets, integrating a wide range of variables and intricate spatial characteristics. Although GANs demonstrate superior proficiency in modeling weak anomalies, conventional techniques continue to be effective for more pronounced anomalies. The integration of both methodologies may enhance the efficiency of mineral exploration endeavors. In summary, the results emphasize the promise of GANs and sophisticated machine learning frameworks in enhancing anomaly detection and expanding mineral exploration within hydrothermal polymetallic systems.
Case Study
Exploration
reza Shahnavehsi; Farnusch Hajizadeh
Abstract
The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose ...
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The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose a novel kind of clustering for data mining in the gravity field to reach the presenting connection among all elements in the same time. For this research work, 867 gravity surveying points were collected in the southern part of Iran (near diapir of Larestan) with a range of absolute gravity from 978579.672 to 978981.186. In this paper, clustering by self-organizing- maps, by utilizing scatter plot matrix is utilized for detecting the relation between the easting, northing, elevation, and absolute gravity simultaneously. In the proposed method, the relations between arrays, two by two, are defined, and like matrix, each raw and column has different i and j values, which represent elements of the studied area, instead of number; for example, array A23 is data division between i = 2 or raw two (in our case northing) and j = 3 or column, three (in our case elevation). In this algorithm, firstly, by using self-organizing maps, clustering is done, and this processing is generated to all arrays by scatter plot matrix, and in all arrays, three clusters are proposed; the result of this clustering shows that in arrays A12, A13, A14, A21, A23, A24, A31, A32, A41, A42, clustering is performed perfectly, and the relationship between the parameters of the studied area near Larestan salt, diaper, can be useful in notifying the properties of this salt diapir.
Original Research Paper
Rock Mechanics
Sina Alizadeh; Mohammad Reza Ghassemi; Mehran Arian; Ali Solgi; Zahra Maleki; Reza Mikaeil
Abstract
One of the most significant risks for investors in the dimension stone industry is the presence of natural discontinuities in the rock mass, which affect the quality of the extracted stone blocks. These discontinuities not only reduce extraction efficiency but also hinder the optimal utilization of the ...
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One of the most significant risks for investors in the dimension stone industry is the presence of natural discontinuities in the rock mass, which affect the quality of the extracted stone blocks. These discontinuities not only reduce extraction efficiency but also hinder the optimal utilization of the quarry. Therefore, it is essential to identify and analyze discontinuities in the rock before initiating any extraction activities and to assess the optimization of the extraction direction in dimension stone quarries. This study examines the key characteristics of discontinuities and joint sets, including their coordinates, strike, dip, spacing and aperture, in the Melika marble dimension stone quarry in Kerman. The collected data are then analyzed using 3DEC software to construct a quarry block model. Additionally, the azimuth rotation of different joint sets is investigated in four categories. The results obtained from the modeling indicate that, to achieve maximum blocking, the current extraction direction should be shifted 70° westward. This adjustment increases the number of blocks to 14,550, the average block volume to 5.5 m³, and the total volume of extracted stone to 79,918.9 m³. These changes are projected to generate approximately $3,180,000 in revenue for the quarry. The study highlights a practical optimization strategy that can significantly enhance the efficiency and profitability of dimension stone quarries by improving extraction direction based on discontinuity analysis.
Original Research Paper
Exploration
Hamid Reza Baghzendani; Hamid Aghajani; Gholam Hossein Karami
Abstract
Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous ...
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Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous period and is affected by tectonic changes and faulting caused by movements related to the Zagros folding. In this work, geophysical methods of microgravity, electrical resistivity, and induced polarization have been utilized to identify the extent of karst development in the limestone units. The minimum residual gravity values are associated with karstification. The field dataset comprised two electrical profiles with the dipole- dipole and pole-dipole arrays. The resistivity and gravity data were inverted using a 2D algorithm based on the least square’s technique with a smoothing constraint. According to the processing and 3D modelling of gravity data; not only cavity-shaped voids and spacious cavity chambers were identified but also sub-structures and micro-karstification in carbonate rocks were detected. The most significant finding from the field survey is the detection of low gravimetric values, indicating relatively large holes and chambers that were previously unknown and inaccessible from ground level. These findings are consistent with known collapse and sediment infill features, as seen in surface sinkholes, cavities, and karstification systems. Geophysical surveys and field surveys show that the holes and karsts in the area are related to tectonic phenomena and faulting and are conduits for transporting water to the Ravansar spring.
Original Research Paper
Rock Mechanics
Farhad Mollaei; Ali Moradzadeh; Reza Mohebian
Abstract
The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including ...
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The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and CNN + LSTM (CL) by well log and core test data of one Iranian hydrocarbon field. As only 12 UCS and 6 FR core tests of single well in this field were available, they were firstly calculated, and then generalized to other depths using two newly derived equations and relevant logs. Next, the effective input logs' data for predicting these parameters have been selected by an auto-encoder DL method, and finally, the values of UCS and φ angle were predicted by the MLP, LSTM, CNN, and CL networks. The efficiency of these four prediction models was then evaluated using a blind dataset, and a range of statistical measures applied to training, testing, and blind datasets. Results show that all four models achieve satisfactory prediction accuracy. However, the CL model outperformed the others, yielding the lowest RMSE of 1.0052 and the highest R² of 0.9983 for UCS prediction, along with an RMSE of 0.0201 and R² of 0.9917 for φ angle prediction on the blind dataset. These findings highlight the high accuracy of deep learning algorithms, particularly the CL algorithm, which demonstrates superior precision compared to the MLP method.
Original Research Paper
Exploration
Zohre Hoseinzade; Mohammad Hassan Bazoobandi
Abstract
Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information ...
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Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information is available about the area of interest. Therefore, employing methods that can aid in the exploration process under such conditions and with limited data is highly valuable. In this study, the Deep-Embedded Self-Organizing Map (DE-SOM), an unsupervised deep learning approach, was used to detect geochemical anomalies. The research focused on identifying multivariate geochemical anomalies in the Moalleman region. After detecting the region's geochemical anomalies, the effectiveness of the algorithm was assessed alongside two other types of SOM algorithms. For this purpose, the prediction area plot was utilized, with the intersection points for DE-SOM, Batch SOM, and SOM were determined to be 0.75, 0.67, and 0.65, respectively. The multivariate geochemical anomaly in the Moalleman area shows a good correlation with known mineral occurrences and the andesite and dacite units. Based on this, it can be stated that the DE-SOM method is a useful tool for identifying anomalies and patterns associated with mineralization.
Original Research Paper
Rock Mechanics
Shahla Miri Darmarani; Erfan Khoshzaher; Hamid Chakeri
Abstract
Shotcrete is used as a component of the support system in tunnels, and one of the methods to enhance its mechanical properties is by incorporating fibers. Fibers can significantly improve the mechanical properties of shotcrete, including compressive and tensile strength. This leads to savings in time, ...
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Shotcrete is used as a component of the support system in tunnels, and one of the methods to enhance its mechanical properties is by incorporating fibers. Fibers can significantly improve the mechanical properties of shotcrete, including compressive and tensile strength. This leads to savings in time, cost, and post-installation maintenance. In recent years, due to the environmental pollution caused by the production of synthetic fibers, there has been increasing interest in using recycled materials, mainly recycled steel fibers from worn tires. The present study is a laboratory-based research program investigating the feasibility of using recycled fibers to improve the mechanical properties of shotcrete. In this study, recycled steel fibers from worn tires and shaves of basalt stone were used to create laboratory samples. The laboratory samples included cubic (10×10 cm) and cylindrical (15×30 cm) specimens with five different mix designs: ordinary shotcrete, shotcrete containing 0.5%, 1%, 1.5%, and 2% recycled fibers. These fibers were categorized into three length groups: coarse, mixed, and fine. The laboratory tests included compressive and tensile (Brazilian) strength tests at 3-day intervals. The results of the laboratory studies indicated that recycled fibers from worn tires could significantly enhance the mechanical properties of shotcrete, with a two-fold increase in compressive strength observed when the fiber content was increased by 2%. Moreover, the inclusion of basalt stone shaves not only improved the compressive strength of the samples but also had a substantial effect on enhancing the tensile strength.
Original Research Paper
Rock Mechanics
Amin Maleki; Hamid Chakeri; Hadi Shakeri; Erfan Khoshzaher; Mohammad Darbor
Abstract
Today, due to technological advancements and increasing demand, various types of Tunnel Boring Machines (TBM) are extensively used for tunneling in both soil and rock. The mechanical excavation method has become attractive in tunnel excavation and underground spaces due to its high safety, rapid progress ...
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Today, due to technological advancements and increasing demand, various types of Tunnel Boring Machines (TBM) are extensively used for tunneling in both soil and rock. The mechanical excavation method has become attractive in tunnel excavation and underground spaces due to its high safety, rapid progress rate, low human labor requirement, and mechanization capability. The high capital costs of mechanical excavation make it essential to conduct laboratory tests, such as linear cutting tests on rocks, before selecting the machine type and adjusting the cutter head blade. The main objective of this study is to investigate the impact of rock mechanical properties on the cutting tool wear using a newly developed small-scale Linear Cutting Machine (LCM). To achieve this, laboratory linear cutting tests on rocks were conducted after constructing the small-scale linear cutting machine. To evaluate the rock cuttability and analyze the performance of disc cutters, 5 rock samples were used at three different penetration depths of 1, 1.5, and 2 mm. The results showed that the wear values of the cutting discs increased with penetration depth in all rock types, with the highest wear observed in basalt. Additionally, Brazilian tensile strength exhibited the highest correlation with cutting disc wear parameters. Furthermore, these studies indicated that determining the mineralogical and physical characteristics of rocks, such as texture, crystal size, and porosity, alongside their mechanical properties, is crucial for predicting rock wear.
Original Research Paper
Environment
Reyhaneh Khashtabeh; Morteza Akbari; Ava Heidari; Ali Asghar Najafpour; Rokhsareh Khashtabeh
Abstract
The Heavy Metal (HM) contamination in surface soils poses significant environmental and health concerns near the mining operations. This study examined the concentrations and health risks of the five HMs lead (Pb), nickel (Ni), copper (Cu), arsenic (As), and iron (Fe) in soils surrounding the Sangan ...
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The Heavy Metal (HM) contamination in surface soils poses significant environmental and health concerns near the mining operations. This study examined the concentrations and health risks of the five HMs lead (Pb), nickel (Ni), copper (Cu), arsenic (As), and iron (Fe) in soils surrounding the Sangan iron ore mines in eastern Iran. Sixty soil samples were collected at depths of 0-20 cm from sites adjacent to the mining area and one control site. The HM concentrations were compared to the global shale values. Soil contamination was quantified using the geo-accumulation index (Igeo). Health risks to the local residents were assessed using the US Environmental Protection Agency's Human Health Risk Evaluation Index. The analysis revealed that the lead concentrations near the mine exceeded the global shale standards, while the arsenic levels remained marginally below permissible limits established by global soil standards. The Igeo values indicated low to moderate the contamination levels for both Pb and As in the mining-adjacent areas. The risk assessment results showed that non-carcinogenic risk indices were within acceptable limits for both children and adults. However, arsenic posed a significant carcinogenic risk to adults through two exposure pathways: ingestion (3.36E-04) and dermal absorption (1.36E-04). These findings highlight the importance of implementing regular monitoring protocols for potentially hazardous elements in the mining region to prevent and mitigate pollution-related health risks.
Original Research Paper
Exploration
Peyman Afzal; Sina Samadi; Mehran Arian; Ali Solgi; Zahra Maleki; Mohammad Seraj
Abstract
An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The ...
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An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The mud loss was 3D estimated by ordinary kriging method. Then, fractal number-size, concentration-volume, and concentration-distance to fault models were applied for permeability zone classification. The concentration-distance to fault fractal model shows three permeability zones, and the concentration-volume fractal modeling represents eight zones with an index multifractal behavior. Moreover, the number-size fractal analysis presented that a multifractal behavior with five societies. The correlation between the results obtained by these fractal methods reveals that the obtained zones have a proper overlap together. High value permeability zones based on the concentration-distance to fault and concentration-volume fractal models are began from 501 Barrel Per Day (BPD) mud loss, and 630 BPD obtained by the N-S modeling. Fractal modeling indicates that the permeability zones occur in the SW, NW and southern parts of the Gachsaran oilfield which can be the fractured section of the Asmari reservoir rock. Main faults from this oilfield are correlated with the permeability zones derived via fractal modeling.
Original Research Paper
Mineral Processing
Chol Ung Ryom; Kwang Hyok Pak; Il Chol Sin; Kwang Chol So
Abstract
Shaking table and flotation are often used in scheelite (CaWO4) beneficiation, and usually they are applied in sequence. In this paper, analysis of mineral movement have been investigated in shaking table in which pulp was conditioned with xanthate as a collector and fed, heavy scheelite was concentrated, ...
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Shaking table and flotation are often used in scheelite (CaWO4) beneficiation, and usually they are applied in sequence. In this paper, analysis of mineral movement have been investigated in shaking table in which pulp was conditioned with xanthate as a collector and fed, heavy scheelite was concentrated, while heavy pyrite removed directly on the deck by the action of collector. Artificially mixed mineral with 1% scheelite and 2% pyrite was used in CFD simulations and experiments. Through CFD simulations, it was found that pyrite particles, which were hydrophobic by collector, were attached to the water-air interface and subjected to upward buoyancy, which increased the density difference between scheelite and pyrite particles and enabled the separation of both minerals in the shaking table. The experiment results showed that the concentrate grade in conventional table concentration was 23.5% WO3, the separation efficiency was 77.89%, while the concentrate grade of scheelite in the table concentration of xanthate presence was 65.0% WO3 and the separation efficiency was 80.88%. The combination of flotation in table with collector addition not only eliminated the flotation to remove pyrite after table but also resulted in a lower rate of scheelite loss.
Original Research Paper
Mineral Processing
Mostafa Maleki Moghaddam; Hosein Najmaddaini; Saeid Zare; Masoud Rezaei; Mohammad Ali Motamedineya; Gholamreza Biniaz
Abstract
Abstract
The structural characteristics of mill liners, such as lifter shape and mill speed, significantly influence the grinding process. At the Sarcheshmeh slag flotation plant, the 6×6 meters SAG mill was initially equipped with 48 rows of liners, designed in a Hi-Lo configuration for the first ...
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Abstract
The structural characteristics of mill liners, such as lifter shape and mill speed, significantly influence the grinding process. At the Sarcheshmeh slag flotation plant, the 6×6 meters SAG mill was initially equipped with 48 rows of liners, designed in a Hi-Lo configuration for the first half and a Lo-Lo configuration for the second. Throughout the mill shell liner's 1700-hour operational period, monitoring identified 30 failures. Investigations revealed that defects in the liner design and improper charge motion were the main causes. This study proposes modifications and standardization of the shell liner design, tailored to the specific circuit conditions, to enhance performance and reliability. The redesign included several key changes: 1) Reducing the number of rows: The number of liner rows was decreased from 48 to 32. 2) Adjusting lifter angle: The lifter angle was increased from 23 to 30o to optimize performance. 3) Eliminating Hi-Lo design liners: The Hi-Lo design liners were changed to Hi-Hi, and 4) Reducing liner variety: The variety of liners was streamlined from 5 types to 2. The installation of the proposed liners optimized the charge trajectory for grinding, resulting in higher liner's lifetime. It extended the liner life by 30% and eliminated liner failures, reducing them from 30 to zero. The wear rate for the proposed design was 0.05 mm/hour, while the original design had a wear rate of 0.11 mm/hour. This difference corresponds to a factor of 2.3 times improvement.
Original Research Paper
Mineral Processing
Sajad Kolahi; Mohammad Jahani Chegeni; Asghar Azizi
Abstract
In Part 2 of this research work, five types of liners, i.e. wave, step, step@, ship-lap, and ship-lap@, are examined. These liners all have similar connected lifters with different volumes. Their difference is in the width, height, and type of the lifter profile. All the five liner types, from 8 to 64 ...
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In Part 2 of this research work, five types of liners, i.e. wave, step, step@, ship-lap, and ship-lap@, are examined. These liners all have similar connected lifters with different volumes. Their difference is in the width, height, and type of the lifter profile. All the five liner types, from 8 to 64 lifters, are simulated using the Discrete Element Method (DEM). In this research work, for the first time, data from the sum of the kinetic and potential energies of individual balls (79,553 particles) are used to find the appropriate range for the number of lifters. In other words, the kinetic and potential energies of all particles within the system (inside the ball mill) are the basis for determining the appropriate number of lifters. The results suggest that for the wave liner, the appropriate range of the number of lifters is between 8 and 16, for the step, step@, and ship-lap liners; it is between 12 and 20, and for the ship-lap@ liner, it is between 8 and 20. In fact, using the data on the kinetic and potential energies of the balls inside the mill, it is possible to determine the appropriate range of the number of lifters, which is done for the first time in this study. In general, it is suggested that the data on the kinetic and potential energies of the balls can be used to determine the number of mill lifters, and unlike what has been done. So far, by other researchers, the number of mill lifters should not be determined solely by using its diameter or the dimensions of the lifters. Also the effect of mill-rotation direction on the values of kinetic and potential energies in step and ship-lap liners is investigated. It is shown that the step@ and ship-lap@ liners transfer more energy to the balls than the step and ship-lap liners, and have a suitable direction of rotation.
Original Research Paper
Mineral Processing
Meysam Nikfarjam; Ardeshir Hezarkhani; Farhad Azizafshari; Hamidreza Golchin
Abstract
Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting ...
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Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting process properties, including semi-autogenous power index (SPI), recovery (Re), and concentration grade. To overcome the additivity limitations of geostatistical methods, we utilized machine learning algorithms for enhanced predictive modeling, aiming to improve decision-making and optimize mining operations in geometallurgy. The research incorporates crucial data inputs such as sample coordinates, grades, lithology, mineralization zones, and alteration to assess the accuracy and reliability of different machine learning regression methods. The Relative Standard Deviation (RSD) is highlighted as a significant metric for comparing the accuracy of predicted process properties. Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) further confirm the superiority of specific modeling methods in certain scenarios. The K-Nearest Neighbors (KNN) method exhibits superior accuracy, lower error metrics (RMSE and MAE), and a higher R2 for modeling the SPI test. For modeling Cu grade in concentrate, Support Vector Regression (SVR) proves to be effective and reliable, outperforming the Multilayer Perceptron (MLP) method. Despite MLP's high R2, its higher RSD suggests increased uncertainty and variability in the predictions. Therefore, SVR is considered more suitable for modeling Cu grade in concentrate. Findings optimize operations at Sungun Copper mine, improving decision-making, efficiency, and profitability.
Original Research Paper
Mine Economic and Management
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi
Abstract
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies ...
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Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
Original Research Paper
Exploration
Hamid Geranian; Mohammad Amir Alimi
Abstract
This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided ...
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This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided into three and fifteen rock units. The classification accuracy of the resulting model is 90.97 and 84.85% for the three-class training and testing data, and 94.76 and 63.92% for the fifteen-class training and testing data, respectively. These models are then applied to the Sentinel-2 satellite images' data of the Birjand-IV region to prepare two preliminary geological maps. The Birjand-IV region's three-class geology map reveals that igneous rocks are present in the northern and southern regions, while sedimentary rocks occupy the middle section and metamorphic rocks are found within the region's igneous masses. Similarly, the fifteen-class geology map of Birjand-IV indicates that andesite, dacite, intermediate tuff rock units, and metamorphic rocks characterize the northern region. Conversely, the southern part of the region is mainly composed of ophiolite, flysch sediments, basaltic and ultra-basic volcanic rocks, and limestone and shale interlayers. Field studies in three areas confirm the accuracy of the preliminary geology maps.
Original Research Paper
Exploitation
Ali Rezaei; Ebrahim Ghasemi; Ali Farhadian; Sina Ghavami
Abstract
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified ...
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In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified through six indices, including equivalent quartz content (EQC), rock abrasivity index (RAI), Schimazek abrasivity factor (F), Cerchar abrasivity index (CAI), building stone abrasivity index (BSAI), and the Taber wear index (Iw). Bi-variate regression analysis was applied to develop the predictive equations for relationship between TC and abrasivity indices. The investigations demonstrated that there is a direct relationship between TC and all abrasivity indices. Furthermore, TC has moderate to high relationship with abrasivity indices. After developing the equations, their accuracy was evaluated by performance criteria including determination coefficient (R2), the normalized root mean square error (NRMSE), the variance account for (VAF), and the performance index (PI). The strongest relationship was found between TC and RAI (with R2, VAF, NRMSE, and PI value of 0.850, 0.074, 85.386, and 1.630, respectively), while the weakest relationship was observed between TC and F (with R2, NRMSE, VAF, and PI value of 0.491, 0.532, 47.605, and 0.435, respectively). This research demonstrates importance of the textural characteristics of stones, especially TC as a reliable index, on the abrasivity properties of granite building stones. Thus, the equations developed herein can be practically used for estimating the stone abrasivity in building stone quarrying and processing projects.
Original Research Paper
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi; Seyyed Ali Hashemi
Abstract
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with ...
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Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications.
Original Research Paper
Rock Mechanics
Aram Ardalanzdeh; Seyed Davoud Mohammadi; Vahab Sarfarazi; Hossein Shahbazi
Abstract
Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from ...
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Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from the Alvand granitic batholith of Hamadan, and the petrological and textural indexes of the rocks were determined. There are four types of rock textures, ranging from coarse-grained to fine-grained. The texture coefficients (TC) for the four types of rocks (G1 to G4) were 1.709, 1.730, 1.774, and 1.697, respectively. The brittleness index (B1) for the four types of rocks (G1 to G4) were 9.13, 11.01, 12.07, and 10.65, respectively. After that, using a diamond drill, one hole was created in each rock core specimen, and at the end of drilling, a fracture pit was separated from the bottom of each hole in the specimen. The results show that as the mineral size decreases, the fracture pit depth also decreases, and in porphyry texture, the fracture pit depth is between the fracture pit depths of coarse-grained and medium-grained rocks. As the texture coefficient (TC) and brittleness of the rock specimens increase, the fracture pit depth decreases, and in porphyry texture, the fracture pit depth remains between the fracture pit depths of coarse-grained and medium-grained rocks. Finally, the results from laboratory tests indicate that creating holes using a drill to study the effect of the holes on rock behavior can cause damage to the rocks.
Original Research Paper
Exploration
Ahmadreza Erfan; Saeed Soltani Mohammad; Maliheh Abbaszadeh
Abstract
Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful ...
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Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful tools that robust techniques that integrate multiple predictive models to improve performance beyond that of any individual learner. This study proposes a novel ensemble method for estimating ore grades by localizing the base learner weights in ensemble method. Ordinary kriging, inverse distance weighting, k-nearest neighbors, support vector regression, and artificial neural networks have been used as the base learners of the algorithm. In ML base learners, coordinates (easting, northing and elevation) of samples have been defined as input nodes and grade has been defined as target. The proposed method has been validated for predicting the copper grade (Cu%) in Darehzar porphyry deposit. The performance of proposed method has been by individual base learners and famous ensemble methods. This comparison shows that performance of proposed method is better than other ones. The findings highlight the necessity of adapting ensemble methods to address spatial variability in geological data, thereby establishing a robust framework for ore grade estimation.
Original Research Paper
Environment
Amirmahmood Razavian; Alireza Arab Amiri; Abolghasem Kamkar Rouhani; Meysam Davoodabadi Farahani
Abstract
Mining activities cause environmental pollution. Satellite remote sensing is considered an effective strategy for monitoring pollution, as other direct methods of testing soil pollution levels are often costly and face accessibility challenges in certain areas. Unlike optical sensors, radar systems can ...
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Mining activities cause environmental pollution. Satellite remote sensing is considered an effective strategy for monitoring pollution, as other direct methods of testing soil pollution levels are often costly and face accessibility challenges in certain areas. Unlike optical sensors, radar systems can capture data in all weather conditions and operate around the clock. However, radar systems do not display details and borders of zones and lack multispectral data collection capability. Consequently, combining various characteristics of optical images and radar data offers a comprehensive approach to monitoring pollution. Given these pros and cons, a combination of optical and radar images from the Sentinel satellite was employed in this study to identify surface and physical pollution areas caused by mining activities. The proposed method is a combination of Curvelet Transform, Simple Linear Iterative Clustering, Principle Components Analysis, and integration of radar and optical results using a statistical based clustering scheme, which allows the detection of contaminated zones. This research benefits from several innovative strategies, such as the separate processing and integration of optical and radar images, the simultaneous application of the curvelet transform and principle component analysis, and the utilization of two distinct clustering methods. Finally, the results obtained from radar and optical images of the Damghan region in Semnan province, Iran, on a 1 to 100.000 scale showed the proposed methodology can segment the contaminated zone caused by the eastern Alborz coal preparation plant through soil pollution modelling.
Original Research Paper
Exploration
Mojtaba Bazargani Golshan; Mehran Arian; Peyman Afzal; Lili Daneshvar Saein; Mohsen Aleali
Abstract
The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four ...
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The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four different geochemical groups were obtained for REEs and lithium in the mining area of North Kochakali coal deposit. The comparison of the threshold values and the models obtained based on the Concentration-Area and Concentration-Number fractal models indicate that the Concentration-Area Fractal model has performed better in determining different geochemical groups and separating anomalies from the background for REEs and lithium in North Kochakali coal deposit. Based on the fractal models in the mining area, the southeastern and western parts have the highest concentrations of REEs and the northeastern parts have the highest concentrations of lithium. These parts should be considered in mining operations due to their higher economic value. The locations of the REEs anomalies are consistent with the location of right-lateral faults with a normal component, since these faults are young and have operated after the formation of coal seams, so the mineralization of REEs in North Kochakali coal deposit is epigenetic.
Original Research Paper
Environment
farhad samimi namin; Zahra S Tarasi; Keyvan Habibi kilak
Abstract
Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, ...
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Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, and economic analyses, using the Analytic Hierarchy Process (AHP) for environmental assessments. Tests on density, water absorption, and strength showed that stones containing effluents were superior to other products. Increasing effluent percentages did not significantly affect density but improved water absorption and strength. Artificial stones containing 40% effluent demonstrated the greatest resistance and the least water absorption. This formulation achieves compressive strengths of 36.07 MPa, flexural strengths of 15.09 MPa, and tensile strengths of 1.89 MPa. Furthermore, it possesses a dry density of 2.33 gr/cm³, and a water absorption rate of 3.82%. Additionally, stones with effluent demonstrated better resistance to corrosion acid. The research methodology employed in the environmental analysis involved the application of the Analytic Hierarchy Process (AHP). Findings from environmental studies indicated that the volume of waste emerged as the most significant criterion with 27.3% weight when evaluating the selection of construction products that are environmentally compatible. Furthermore, research in environmental studies indicates that artificial stone is at least 10% more preferred than natural stone, 48% more preferred than tile, and 63% more preferred than brick. The analysis within the economic section demonstrated that the production of artificial stone incorporating waste, which achieved an internal rate of return of 138%, was more cost-effective than comparable products.
Original Research Paper
Exploration
Shirin Jahanmirir; Ali Aalianvari; Hossein Ebrahimpour-Komleh
Abstract
This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous ...
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This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous geological conditions, and while machine learning approaches have offered improvements, they often require significant computational resources. The HMS algorithm, inspired by human cognitive processes, employs memory recall, adaptive clustering, and strategic selection to efficiently refine solutions. Our results demonstrate that HMS significantly outperforms established algorithms in predicting groundwater seepage, achieving an R² value of 0.9988, a Mean Squared Error (MSE) of 0.0002, and a Root Mean Squared Error (RMSE) of 0.0137. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of 0.9951 with much higher MSE and RMSE, and other methods, like Genetic Programming and ANN-PSO, show higher error values. The HMS algorithm also showed a Variance Accounted for (VAF) of 99.88% and a Mean Absolute Error (MAE) of 0.0041, further validating its high predictive accuracy. This study highlights the HMS algorithm’s superior performance and computational efficiency for optimizing groundwater seepage predictions, positioning it as a powerful tool for geotechnical engineering applications, including real-time monitoring.
Case Study
Exploitation
Mojtaba Dehghani Javazm; Mohammadreza Shayestehfar
Abstract
In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with ...
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In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with a block model constructed for the analysis. Four methods were employed: UC, LUC, DCSBG, and SGS. The correlation coefficients for UC, DCSBG, and SGS methods in the supergene zone, as well as the results from extraction drill holes (extraction blocks) at a cut-off grade of 0.15%, were 0.637, 0.527, and 0.556, and the correlation coefficient for calculating tonnage and the metal content using UC was 0.364 and 0.629, respectively. For the hypogene zone, the correlation coefficients for metal content at a cut-off grade of 0.15% were 0.778, 0.788, and 0.790 for UC, DCSBG, and SGS, and at a cut-off grade of 0.65%, they were 0.328, 0.431, and 0.458, respectively. By employing The LUC method in the supergene zone with a change in SMU and comparing the results obtained from the E-Type map, the performance of this method is higher across all cut-off grades. As the cut-off grade increases in the hypogene zone, the performance of the LUC method relative to simulation methods decreases. The LUC method can be used to observe the impact of the convergence of results obtained from this method with real data from low-grade to high-grade sections, highlighting the necessity of differentiating this zone into low and high-grade segments during the estimation process.
Original Research Paper
Exploitation
Moein Bahadori; Moahammad Amiri Hosseini; Iman Atighi
Abstract
As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate ...
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As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate vibration control strategies for protecting the onsite processing plant. A Blastmate III seismograph was employed to record 54 three-component data sets, including waveform data, maximum amplitude, and dominant frequencies. By superimposing waves, optimal delay times (ODT) for the blast holes were determined and the corresponding effects on wave frequencies were analyzed. An experimental blasting pattern was designed based on the derived ODT values, and the impact on ground vibration was examined. The results indicated a 10% reduction in vibration levels with the proposed delay times. Furthermore, considering the minimum distance of 111 meters from the processing plant to the final pit and adhering to the DIN safety standard, it is recommended that blast holes with a maximum diameter of 165mm be used to ensure a safety factor of 15%. For distances exceeding 187 meters, blast holes with a 250mm diameter are recommended to maintain production efficiency and a safety factor of 50%.
Original Research Paper
Rock Mechanics
Mohammad Amin HajiMohammadi; Mojtaba Bahaaddini; Mohammad Hossein Khosravi; Hassan Vandyoosefi
Abstract
Discontinuities are known as a primary factor in instability of tunnels and underground excavations. To prevent potential damage and overbreak by underground advancement, it is essential to provide a model, which considers both the geometrical and mechanical characteristics of discontinuities. Discrete ...
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Discontinuities are known as a primary factor in instability of tunnels and underground excavations. To prevent potential damage and overbreak by underground advancement, it is essential to provide a model, which considers both the geometrical and mechanical characteristics of discontinuities. Discrete Fracture Network (DFN) is a conceptual model to represent and analyse the complex system of discontinuities within the rock mass. Combined DFN with analytical or numerical methods can be employed as a scientific tool to analyse generated rock blocks, and their stabilities under different loading conditions. This paper aims to investigate the created overbreak by tunnel advancement in the Alborz tunnel located in the Tehran-North freeway in Iran. First, the geometrical characteristics of discontinuities were surveyed by tunnel advancement in 200 meters. Four major joint sets and a bedding plane were identified and their statistical characteristics were measured. The DFN model was generated and its validity was investigated through a comparison against field data. The average volume of generated blocks in the studied area was measured 0.22 m3. The stability of generated blocks around the opening was kinematically evaluated. The volume of formed blocks around tunnel in the DFN model prone to instability due to static or dynamic loads was estimated 2605 m3 while the measured overbreak in field was 2735 m3. The depth of overbreak in DFN model showed a good agreement with field measurements. The results show that DFN model combined with kinematic stability analysis can provide a scientific tool to investigate geological overbreak in underground excavations.
Original Research Paper
Exploration
Shaghayegh Esmaeilzadeh; Ali Moradzadeh; omid Asghari; Reza Mohebian
Abstract
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings ...
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Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.
Original Research Paper
Exploitation
Alireza Afradi; Arash Ebrahimabadi
Abstract
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised ...
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Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised the input parameters such as Burden (m), spacing (m), powder factor (kg/m³), and stemming (m), with fragmentation (cm) as the output parameter. The analysis of these datasets was conducted using the Ant Lion Optimizer (ALO) and Crow Search Algorithm (CSA) methodologies. To assess the predictive models' accuracy, metrics including the coefficient of determination (R²), Variance Accounted For (VAF), and Root Mean Square Error (RMSE) were employed. The application of ALO and CSA to the database yielded results indicating that for ALO, R² = 0.99, RMSE = 0.005, and VAF (%) = 99.38, while for CSA, R² = 0.98, RMSE = 0.02, and VAF (%) = 98.11. Ultimately, the findings suggest that the predictive models yield satisfactory results, with ALO demonstrating a greater level of precision.
Original Research Paper
Environment
Hosein Esmaeili; Mohammad Ali Afshar Kazemi; Reza Radfar; Nazanin Pilevari
Abstract
This study introduces a Hybrid Markov–Bayesian Framework for predicting and managing accident risks in high-risk industries, with a specific focus on the mining sector. The framework integrates Markov models to analyze dynamic risk transitions and Bayesian networks to infer causal relationships ...
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This study introduces a Hybrid Markov–Bayesian Framework for predicting and managing accident risks in high-risk industries, with a specific focus on the mining sector. The framework integrates Markov models to analyze dynamic risk transitions and Bayesian networks to infer causal relationships among key human and environmental factors. Drawing from a comprehensive dataset of mining operations, the framework evaluates variables such as age, experience, task type, and injury characteristics to predict and control accident risks. The results highlight the model's high performance, achieving an accuracy of 87%, precision of 85%, and an F1-score of 0.84. This innovative approach enables real-time safety interventions and proactive risk management strategies. The findings underscore the framework's potential to improve workplace safety and serve as a scalable tool for accident prevention in other high-risk industries. Future research will focus on enhancing the framework’s adaptability and incorporating additional contextual variables for broader applicability.
Original Research Paper
Environment
Feridon Ghadimi; Abolfazl Shafaei; Abdolmotaleb Hajati
Abstract
This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations ...
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This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations of 25% (ranging from 2–32% and peaking at 55% in localized southwestern areas). The playa’s surface is primarily clay-covered (94%) and interbedded with evaporitic facies including Gypsum, Halite, and carbonate minerals. Seasonal water inflows of 200–800 l/s from a wastewater treatment plant, together with 3.5 m-deep extraction pits and gravitational drainage, have resulted in stagnant ponds over 25% of the southern lake area and an annual reduction in surface area of 5–10%. Stratigraphic analysis further indicates pure Glauberite layers (0.5–1 m thick) at depths of 1,653–1,656 m, in contrast with thicker impure Glauberite-Mirabilite sequences (up to 9 m) present between 1,649–1,659 m. To mitigate these challenges, an integrated engineering approach is proposed that includes pumping seepage brine (with a moisture content of 40%) to solar evaporation pools, employing continuous dual-pump slurry systems for tailings management, and implementing hydraulic balancing through retaining walls and winter brine reserves—measures that enhance extraction efficiency by 30–42% in high-concentration zones. These adaptive mining practices, incorporating in-situ brine leaching and advanced wastewater treatment, are designed to meet 70% of Iran’s annual sodium sulfate demand from an 8 km² operational area while reducing environmental degradation.
Original Research Paper
Mineral Processing
Fatemeh Kazemi; Ali akbar Abdollahzadeh
Abstract
This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests ...
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This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests were performed on each sample under various operating conditions. Process mineralogy studies were conducted to determine the mineralogy modal of the feed and product of each test. An Artificial Neural Network (ANN) model was used to make a model that related the grade and recovery of magnetite in the product to the mineralogy modal of the tailing piles. The magnetite grade and association index of feed, the magnetic intensity, and the water flow rate were the inputs to this network. The grade and magnetite recovery correlation coefficients were 0.954 and 0.86, respectively. The grade of magnetite in the feed emerged as a limiting factor on the grade and recovery of magnetite in concentrate. An increase of one unit in magnetite grade in the feed resulted in a 1.68 decrease in the recovery. The association index changes with the coefficients of -0.173 cause the changes in predicted magnetite recovery in the concentrate.
Original Research Paper
Environment
Masoud Monjezi; Safa Moezinia; Jafar Khademi Hamidi; Mojtaba Rezakhah; Vahid Amini; Amir Batarbiat
Abstract
Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy ...
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Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy Process (FAHP). These options are restoring the mine to its original state, planting trees, building a wind farm, creating a recreational area, setting up pastures, farming, building a solar power plant, and creating a tourist attraction. A panel of twelve experts evaluated these alternatives according to ten key criteria: air temperature intensity, number of sunny days, soil conditions, distance from residential areas, topographic irregularity, vegetation density, average wind speed, local animal species, site access, and the size and shape of the mined area. The results indicate that the construction of a solar power plant is identified as the most suitable rehabilitation option for the Zarshuran Gold Mine, considering the region’s climatic conditions (particularly the high number of sunny days per year) and its potential for clean energy generation and revenue creation. This study emphasizes the importance of considering environmental, social, and technical criteria in the decision-making process for mine rehabilitation and provides a framework for selecting sustainable rehabilitation methods in similar mining contexts.
Original Research Paper
Exploitation
Abbas Khajouei Sirjani; Ruqyah Heydari; Ramin Rafiee; Mohammad Amiri Hosseini
Abstract
In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and ...
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In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and disruption in drilling and charging operations in subsequent stages. The objective of this research is to predict and optimize back-break by combining statistical models with the Firefly Algorithm (FA). For this purpose, a database comprising data from 28 blasts in the waste rock section of Gol-e-Gohar Iron Ore Mine No. 1 was compiled. After data collection, the input parameters, including blast hole length, burden, spacing, Stemming, charge per delay, and Number of holes in the last row, were identified and utilized in the modeling process. To predict back-break, modeling was performed using multiple regression analysis. Among the developed models, the Polynomial statistical model with non-integer coefficients model with an adjusted coefficient of determination 0.885 was identified as the best-performing model and was subsequently used as the objective function in the Firefly Algorithm. The optimization process was then carried out using this algorithm. According to the findings of this research, the implementation of the current operational patterns in the mine along with the optimized proposed patterns resulted in a reduction of 4 meters in the average back-break, decreasing it from 7.5 meters in the waste rock section. The results demonstrate that the Firefly Algorithm is a highly effective and reliable tool for model optimization and a more accurate reduction of back-breaks. This approach has the potential to significantly enhance the efficiency of mining operations and reduce operational costs.
Original Research Paper
Exploitation
Heydar Bagloo; Mohsen Soleiman Dehkordi
Abstract
Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing ...
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Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing methods. However, evaluating the effectiveness of these optimization techniques, particularly in short-term mining activities under varying operational conditions, remains essential. Additionally, understanding how changes in operational conditions impact productivity is important for addressing production fluctuations in daily mining operations. To tackle these challenges, this study uniquely applies advanced machine learning techniques to short-term mining planning, resulting in the development of a real-time Productivity Evaluation Model (PEM) based on supervised learning methods for optimizing truck-shovel operations in open-pit mining. The model, developed and tested using data from a large-scale mining operation in Iran, demonstrated that the Decision Tree was the most effective, achieving an R² value of 0.96. This was closely followed by Random Forest and Gradient Boosting, both with R² values of 0.95. However, the choice of the most suitable learning method may vary depending on the specific dataset and context. The model determines the most appropriate learning method for each dataset within specific mining operations.
Original Research Paper
Rock Mechanics
Saeed Mahdavi; Mohammad Mohammadi; Raheb Bagherpour
Abstract
EPB machines have been the most applicable for tunneling in urban areas over the last decades. To increase soil consistency, reduce machine torque, and stabilize the tunnel face in EPB tunneling, foam injection is essential. The shear strength of the soil in the EPB chamber affects the machine torque. ...
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EPB machines have been the most applicable for tunneling in urban areas over the last decades. To increase soil consistency, reduce machine torque, and stabilize the tunnel face in EPB tunneling, foam injection is essential. The shear strength of the soil in the EPB chamber affects the machine torque. Therefore, in this research, the effects of soil water content, clay percentage, foam injection ratio, and soil granular size on the shear strength are investigated. The Isfahan subway line 2 in Iran was selected as a case study. Based on the results of the vane shear test, the shear strength of soil first increases rapidly and then gradually with an increase in soil particle size, and particle size is the most significant parameter that controls the shear strength of soil samples. The result of the analysis also indicates that increasing FIR up to 40% can lead to a 44% reduction in soil shear strength and, as a result, a decrease in excavation power. Increasing the clay percentage from 20 to 40 percent reduces the soil shear strength by up to 36 percent. The lowest shear strength of soil is achieved when the water content is 5 percent. By increasing the FIR from 10 to 20 percent, the shear strength of samples decreases rapidly and remains constant when the FIR rises up to 40 percent.
Original Research Paper
Environment
Ali Rasouli; Akbar Esmaeilzadeh; Reza Mikaeil; Solat Atalou
Abstract
Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel ...
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Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel clustering approach integrating the Harmony Search (HS) and Particle Swarm Optimization (PSO) algorithms to classify joint sets in the Sungun copper mine. Initially, joint characteristics were classified using the Fuzzy C-Means (FCM) method, with the elbow method selecting a four-class clustering solution. To optimize clustering, FCM was combined with HS and PSO, and joint data were assessed using Davies-Bouldin, Calinski–Harabasz, and Silhouette indices. The results demonstrated that the hybrid FCM-PSO method outperformed alternatives, achieving scores of 0.80, 347.48, and 0.57, respectively, indicating superior clustering performance and stability. In contrast, the FCM-HS method performed worse than FCM alone, ranking third overall. The findings confirm that FCM-PSO effectively classifies joint sets, providing reliable insights into rock mass behavior in the Sungun mine. Considering the features and advantages of the FCM-PSO method, it is concluded that the proposed approach has significant potential for effective joint classification in mining engineering. This improved clustering approach enhances geological analysis, supporting safer and more efficient mining operations.
Original Research Paper
Rock Mechanics
Mohammad Reza Zeerak; Mohammad Fatehi Marji; Manouchehr Sanei; Mehdi Najafi; Abolfazl Abdollahipour
Abstract
The Extended Finite Element Method (XFEM) is a leading computational approach for studying crack growth in rocks, as it can effectively model complex crack paths and discontinuities without the need for re-meshing. In this context, XFEM is particularly well-suited for simulating the development of hydraulic ...
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The Extended Finite Element Method (XFEM) is a leading computational approach for studying crack growth in rocks, as it can effectively model complex crack paths and discontinuities without the need for re-meshing. In this context, XFEM is particularly well-suited for simulating the development of hydraulic fractures. XFEM is employed to investigate crack initiation, propagation, and aperture size in rock formations, with validation using a Boundary Element Method (BEM)-based approach. Three scenarios are analyzed for crack orientation and interaction in: single cracks at and crack displacement behavior at and multiple cracks at and . Displacement in the vertical direction (U2) and stress distribution around the crack tip in the S22 direction are examined to understand fracture mechanics parameters. The findings highlight that crack at higher angles, such as , exhibit more straightforward propagation, while those at or beyond often require additional stress to continue growing. The comparison between XFEM and BEM results confirms the reliability of the numerical approach, demonstrating strong agreement in predicting fracture behavior in rock materials. The results provide deeper insights into fracture evolution, stress intensity factors, and fracture toughness in geological media. These simulations advance computational fracture mechanics, contributing to optimizing hydraulic fracturing techniques for improved efficiency and safety in subsurface formations. This study is limited to 2D geometries and isotropic materials, potentially missing 3D heterogeneous subsurface complexities. Future work could explore 3D models, anisotropy, and fluid pressure/thermal effects to improve crack growth predictions.
Original Research Paper
Rock Mechanics
Hossein Azad; Hamid Chakeri; Hadi Shakeri
Abstract
Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates ...
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Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates the actual ground settlement caused by over 10 kilometers of tunnelling along Tabriz Metro Line 2, with a particular focus on the materials and positions of the tunnelling machine. The results show that 55-60% of the total settlements occur behind the shield of the tunnelling machine, which is consistent with Thewes’ (2009) diagram. The surrounding soil was categorized, and using data from settlement pins, the actual Volume Loss (VL) was analyzed across three geological sections consisting of sandy, clayey, and mixed materials. The findings reveal that volume loss in sandy materials is greater than in clayey and mixed soils, at approximately 1.02%. Additionally, the volume loss in mixed soils was calculated to be 0.82%, while in clay soils, it was 0.53%. To assess the impact of different materials on surface settlement, numerical modeling was carried out using Plaxis 3D software. The numerical results, considering volume losses of 1.05% for sandy materials, 0.8% for mixed materials, and 0.5% for clay materials, closely matched the actual settlement data.
Original Research Paper
Mineral Processing
Mohammad Reza Vashadi Arani; Seyed Mohammad Razavian
Abstract
The use of lithium-ion batteries has increased significantly in recent years due to their high energy density and the presence of valuable materials such as cobalt and nickel, making them an important source for secondary material recovery. However, recycling these batteries presents substantial safety ...
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The use of lithium-ion batteries has increased significantly in recent years due to their high energy density and the presence of valuable materials such as cobalt and nickel, making them an important source for secondary material recovery. However, recycling these batteries presents substantial safety risks, primarily from fire and explosion hazards caused by unwanted short circuits and high voltage components. These risks are especially pronounced during mechanical preparation, crushing, storage, and transportation, where damaged or improperly handled batteries can ignite or explode. To mitigate these hazards, rapid and controlled discharge of batteries before recycling is critical. Discharging using salt solutions is recognized as a simple, fast, and cost-effective method to reduce residual charge and minimize the risk of fire during subsequent handling. In this research, four different types of natural salts at various concentrations were tested, prioritizing the use of accessible, low-cost, and impure salts over pure laboratory-grade salts to enhance scalability and economic feasibility. Initial experiments involved direct immersion of batteries in salt solutions at concentrations of 10%, 15%, and 20% by weight. Among the complementary processes evaluated, the use of a high-speed magnetic stirrer, iron powder, and ultrasonic operations (ultrasonic bath and probe) were found to further reduce discharge time and help achieve target voltages more quickly. Notably, ultrasonic agitation at 28 kHz was particularly effective, significantly accelerating the discharge process and enabling the batteries to reach lower voltage thresholds such as 0.5 volts in a shorter time.
Original Research Paper
Exploitation
Hasan Ghasemzadeh; Hassan Madani; Farhang Sereshki; Sajjad Afraei
Abstract
One of the most prevalent risks in coal mines is spontaneous combustion (spon com) of coal, which is a major source of coal loss in these environments. Therefore, to avoid coal loss and preventing the potential risks, a criterion for predicting the spon com of coal is essential. The main purpose of this ...
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One of the most prevalent risks in coal mines is spontaneous combustion (spon com) of coal, which is a major source of coal loss in these environments. Therefore, to avoid coal loss and preventing the potential risks, a criterion for predicting the spon com of coal is essential. The main purpose of this work is to present a new model for predicting the spon com of coal potential using a decision tree technique, known as the Spon com of coal decision Tree (SCCDT). In this research work, after identifying the effectiveness of each parameter on the spon com of coal, several parameters were examined, including characteristics such as moisture, ash, pyrite, volatile matter, fixed carbon, mineralogy, and petrography. Subsequently, the primary phases of applying the decision tree model were analyzed, and the probability of the spon com of coal potential was determined based on intrinsic parameters. Finally, the mentioned parameters were categorized, and an appropriate model for classifying the spon com of coal potential was developed. In the SCCDT model, the spon com of coal potential was divided into three classes: low, medium, and high. The model was then applied to Parvadeh I to IV coal mines in Tabas. A comparison of the study's findings showed relatively good agreement with the SCCDT model. Using the proposed model can help to predict the spon com hazard and prevent the various life-threatening/mortal and financial risks.
Original Research Paper
Mineral Processing
Hossein Hamedani; Arash Sobouti; Mohammad Baqaeifar; Bahram Rezai; Fatemeh Sadat Hoseinian
Abstract
In this work, a representative sample was initially prepared from exploratory drilling cores, followed by identification and characterization studies based on XRD analysis; the sample consists primarily of quartz, kaolinite, muscovite-illite, calcite, potassium, feldspar, albite, dolomite, siderite, ...
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In this work, a representative sample was initially prepared from exploratory drilling cores, followed by identification and characterization studies based on XRD analysis; the sample consists primarily of quartz, kaolinite, muscovite-illite, calcite, potassium, feldspar, albite, dolomite, siderite, and chalcopyrite. Optical and scanning electron microscopy studies revealed that the sulfide minerals in the sample include chalcopyrite, chalcocite, and pyrite, with the most significant copper minerals primarily comprising chalcopyrite, chalcocite, and malachite. No free gold was observed, and gold mainly exists as a substitute within the structure of sulfide minerals. AAS analysis results indicated that the copper grade in the sample is 0.99%. To investigate the flotation of copper minerals, influential parameters such as pH, collector concentration, frother concentration, sodium sulfide concentration, and the effect of particle size were examined. The results demonstrated that under optimal conditions (pH = 11, collector concentration of 100 g/t Potassium Amyl Xanthate (PAX), 100 g/t Sodium Isopropyl Xanthate (SIPAX), 60 g/t frother methyl isobutyl carbinol (MIBC), 1000 g/t Na2S at a particle size of d80= 75μ), the total copper grade and recovery following two stages cleaner flotation were achieved at 21.2% and 60.2%, respectively.
Original Research Paper
Mineral Processing
Mehrshad Asghari; Mohammad Noaparast; Mohammad Jahani Chegeni
Abstract
Because roller screens are connected to the pelletizing discs on one side and the green iron ore induration furnaces on the other side in pelletizing plants, they play a crucial role in the plant's productivity and steel production process. Consequently, an optimal performance and structural design are ...
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Because roller screens are connected to the pelletizing discs on one side and the green iron ore induration furnaces on the other side in pelletizing plants, they play a crucial role in the plant's productivity and steel production process. Consequently, an optimal performance and structural design are essential in this context. A significant issue with roller screens during the classification of green pellets is the deformation of the rolls caused by the force exerted by the pellets during operation. This deformation disrupts the uniformity of the gap between the rolls, thereby reducing the efficiency of the screen, and the overall performance of the circuit, as well. Despite the importance of this issue, no studies have been conducted to investigate the force exerted by the pellets during classification on the screen or the subsequent mechanical behavior of the rolls. Therefore, this study employs the discrete element method–finite element method (DEM-FEM) coupling simulation technique to examine, for the first time, the mechanical behavior of rolls and to optimize their structural design. The results indicated that decreasing the roll diameter from 80 mm to 30 mm led to 1088 times increase in the average total deformation of the rolls. Furthermore, increasing the thickness of the polyurethane liner from 3 mm to 14 mm caused the average total deformation to rise by 54 times.
Original Research Paper
Mineral Processing
Arefeh Zahab Nazoori; Bahram Rezai; Aliakbar Abdolahzadeh
Abstract
Assessing frother performance through various indices is crucial to understanding how their molecular structure affects functionality, as well as evaluating their effectiveness in floating both fine and coarse particles. This study investigates for the first time the frothing behavior and froth stability ...
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Assessing frother performance through various indices is crucial to understanding how their molecular structure affects functionality, as well as evaluating their effectiveness in floating both fine and coarse particles. This study investigates for the first time the frothing behavior and froth stability of Polyethylene Glycol 300 (PEG300), Dipropylene Glycol (DPG), and Tetraethylene Glycol (TEG) and compares them with conventional frothers such as Dow Froth-250 (DF-250). To evaluate frother performance, air flow rate and frother concentration were selected as the main operational variables influencing froth formation and stability index. Initially, the frothing behavior of the reagents was predicted using the HLB-MW diagram, and then the frothing power of the desired frothers was examined using the dynamic frothability and dynamic froth stability indices. The results revealed that PEG300 exhibited the highest dynamic frothing index (13000 s.dm3/mol) and high froth stability, which is suitable for the flotation of coarse particles. In contrast, DPG showed the lowest frothing power and froth stability, with a dynamic frothing index of 2500 s.dm3/mol. TEG, with an intermediate frothing index of 5000 s.dm3/mol, demonstrated moderate performance in both froth production and stability. DF-250, with an exceptionally high frothing index, outperformed all the other agents, providing both superior froth generation and stability. Froth stability was assessed using dynamic froth stability indices and dynamic frothing capability, providing meaningful insights into frother performance. The results also showed that both air flow rate and frother concentration had a significant impact on frothing index and stability, with higher concentrations generally enhancing froth stability, particularly for PEG300 and DF-250.
Case Study
Mineral Processing
Dorna Pirouzan; Reza Parvareh; Ziaeddin Pourkarimi; Mehdi Rahimi; Javad Moosavi; Hossein Habibi
Abstract
In our country, a massive volume of slag is generated annually from steel production facilities, amounting to about 20 percent of the total steel produced. This slag is an important and valuable source for extracting vanadium, with 67 percent of the world's vanadium production sourced from slag. Iran ...
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In our country, a massive volume of slag is generated annually from steel production facilities, amounting to about 20 percent of the total steel produced. This slag is an important and valuable source for extracting vanadium, with 67 percent of the world's vanadium production sourced from slag. Iran ranks among the top five countries that possess this vital metal; however, vanadium extraction from slag has not been carried out to date. Moreover, due to the unstable quality of the slag, its utilization in other industries has not been feasible. To prevent the environmentally harmful effects of accumulating slag and the inability to utilize it in various industries, it is essential to implement an economic solution for recovering the components present in steel-making slag. In the present project, after sampling from the stored slag deposits at Mobarakeh Steel Company, comprehensive laboratory and pilot-scale studies were conducted on the representative samples. Through processes involving roasting with sodium carbonate, acid leaching with 2 M sulfuric acid, iron cementation, solvent extraction using DEHPA, stripping, and scrubbing, we successfully extracted pentoxide vanadium with high purity suitable for producing ferrovanadium.
Original Research Paper
Mineral Processing
Raheleh Hazrati; Shahram Rostami; Sadegh Marahem
Abstract
The components of low-grade bauxite were 28.4% silica, 34.9% alumina, 16.1% iron oxide as ferric oxide and 11.26% loss on ignition. Due to the high silica content of this type of bauxite, it couldn’t be processed by Bayer method. Therefore, a sintering method with limestone and sodium carbonate ...
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The components of low-grade bauxite were 28.4% silica, 34.9% alumina, 16.1% iron oxide as ferric oxide and 11.26% loss on ignition. Due to the high silica content of this type of bauxite, it couldn’t be processed by Bayer method. Therefore, a sintering method with limestone and sodium carbonate was used for selective extraction of alumina. Experimental design was performed by surface response method (RSM) using central composite design. Selected parameters were temperature, soaking time, mole ratio of sodium oxide to alumina, mole ratio of calcium oxide to silica. The maximum amount of extraction of alumina from low-grade Jajarm bauxite by sintering method was 74.2%, which was obtained in the optimal values of the parameters as follows: A temperature of 1157°C, a soaking time of 35 minutes, a mole ratio of alkaline oxide (K2O + Na2O) of 1.25 and a mole ratio of calcium oxide to silica of 1.99. In 31 run experiments, the mixture of materials powder was transferred to an alumina crucible and heated in a muffle furnace at temperatures and soaking times determined by the experimental design. The sintered material was pulverized. The resulting powder was leached by 150 mL of a boiling alkaline solution (20 g/L NaOH + 20g/L Na2CO3) for 30 minutes at a stirring speed of 300rpm. Extracted aluminum from the leaching stage was analyzed by atomic absorption spectrometry.
Original Research Paper
Exploration
Satyajeet Parida; Abhishek Kumar Tripathi; Tarek Salem Abdennaji; Yewuhalashet Fissha
Abstract
Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques ...
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Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques offer a robust alternative for accurate and rapid predictions. This study employs seven coal quality parameters. Total Moisture (TM), Ash (ASH), Volatile Matter (VM), Hydrogen (H), Carbon (C), Nitrogen (N), and Sulphur (S), as independent variables to develop predictive models for GCV. Four conventional regression techniques, namely Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), along with two robust regression models Random Sample Consensus (RANSAC) and Huber Regressor (HR) are explored. The dataset comprises coal samples from five Asia-Pacific countries: China, Indonesia, Korea, the Philippines, and Thailand. Comparative performance analysis reveals that the robust regression models significantly outperform the conventional ML techniques. The RANSAC and Huber Regressor models achieve superior prediction accuracy with R² values of 0.9941 and 0.9952, respectively. These findings highlight the potential of robust regression approaches for reliable GCV estimation, facilitating efficient coal quality assessment in large-scale applications.
Original Research Paper
Mineral Processing
Chaimae LOUDARI; Moha Cherkaoui; Imad El Harraki; Rachid Bennani; Mohamed El Adnani; EL Hassan Abdelwahed; Intissar Benzakour; François Bourzeix; Karim Baina
Abstract
Energy efficiency and product quality control are critical concerns in grinding mill operations, particularly within the innovative context of Mine 4.0. This study introduces a novel Genetic Algorithm (GA)-based optimization framework specifically developed to address these challenges. Given the mining ...
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Energy efficiency and product quality control are critical concerns in grinding mill operations, particularly within the innovative context of Mine 4.0. This study introduces a novel Genetic Algorithm (GA)-based optimization framework specifically developed to address these challenges. Given the mining industry’s significant energy consumption, especially in grinding processes, the proposed approach optimizes key parameters such as feed composition, water flow rates, and power consumption levels, while maintaining sieve refusal near the target threshold of 20%. Using real operational data from a Moroccan plant, the GA achieved a Mean Absolute Error (MAE) of 0.47, outperforming Simulated Annealing (SA) and Particle Swarm Optimization (PSO), which yielded MAEs of 1.14 and 0.74, respectively. The GA also demonstrated superior convergence stability and robustness, as evidenced by lower variability in predicted power consumption. These results validate the effectiveness of the GA framework in navigating nonlinear, high-dimensional parameter spaces and improving energy efficiency while ensuring product quality consistency. Ultimately, this research confirms the potential of metaheuristic optimization in enhancing grinding mill efficiency and supports the broader shift towards intelligent and sustainable mining operations under the Mine 4.0 paradigm.
Short Communication
Rock Mechanics
Sonu Saran; Prudhvi Raju Gadepaka; Ashok Jaiswal
Abstract
The stability of underground coal galleries is critically influenced by time-dependent deformation behavior of surrounding rock masses, particularly in deep mining environments where long-term stress redistribution can lead to delayed failure. In continuous miner-based mining systems, determining an ...
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The stability of underground coal galleries is critically influenced by time-dependent deformation behavior of surrounding rock masses, particularly in deep mining environments where long-term stress redistribution can lead to delayed failure. In continuous miner-based mining systems, determining an appropriate cut-out distance is essential to ensure productivity and safety, especially for weak rock mass. This study proposes a novel numerical–statistical framework for the optimal design of cut-out distance (COD) in room-and-pillar coal mining using continuous miners. A time-dependent viscoelastic-viscoplastic constitutive model was implemented in FLAC3D to simulate roof deformation across varying geo-mining conditions, including gallery widths (5 & 6 m), depths (100 to 400 m), and COD values (4 to 12 m). The Coal Roof Index (CRI), a composite geotechnical classification parameter, was incorporated to evaluate roof integrity. Results from the numerical simulations were used to develop two empirical models, COD₁ for depths ≤ 200 m and COD₂ for depths > 200 m, via multivariate nonlinear regression. The models demonstrated high predictive accuracy, with R² values of 0.95 and 0.90, respectively. The results reveal a strong correlation between the cut-out distance and various influencing parameters, i.e., width, depth, and CRI classification. Statistical validation through t-tests and ANOVA confirms the significance and reliability of the proposed model. Both proposed models have been validated by two field cases of the Indian coal mine. Critical CRI thresholds were quantified for safe CODs, offering actionable insights for field implementation. The proposed design approach provides a robust framework for improving the safety and sustainability of underground coal mine development, particularly under weak roof conditions.
Original Research Paper
Exploitation
Ali Nemati vardni; Masoud Monjezi; Hasel Amini Khoshalan; Jafar Hamidi Khademi; Mojtaba Rezakhah
Abstract
Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt ...
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Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt hammer rebound hardness. To achieve this, a dataset comprising the drilling operations of 85 blastholes from the Sungun copper mine in Iran was prepared and analyzed using statistical and intelligent methods. Multivariate regression analysis and artificial neural networks developed in Python, utilizing optimization algorithms such as gradient descent, stochastic gradient descent, and adaptive moment estimation, were applied to predict the penetration rate of drill bits in this study. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) served as performance indicators to evaluate the methods employed. Among these, the adaptive moment estimation (Adam)-based model exhibited superior performance compared to alternative models, achieving values of R² = 0.96, MAE = 4.55, and RMSE = 4.30. Furthermore, the sensitivity analysis revealed that mining rock mass rating is the most influential factor on the rate of penetration, while thrust pressure has the least impact.
Original Research Paper
Mineral Processing
Hossna Darabi; Faraz Soltani
Abstract
The main characteristic of mechanical flotation cells is to have an impeller, which is responsible for creating particle suspension, gas dispersion, and producing turbulence necessary to create effective bubble-particle interactions. For this purpose, in this paper, the conditions for complete gas dispersion ...
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The main characteristic of mechanical flotation cells is to have an impeller, which is responsible for creating particle suspension, gas dispersion, and producing turbulence necessary to create effective bubble-particle interactions. For this purpose, in this paper, the conditions for complete gas dispersion in a Denver laboratory flotation cell have been investigated. Then, the critical impeller speed has been investigated for quartz particles with different size fractions. The effect of complete dispersion of introduced gas and critical impeller speed on the flotation rate constant (k) of particles was investigated. The results showed that k was the minimum value at an impeller speed of 700 rpm in the superficial gas velocity of 0.041- 0.125 cm/s for all size fractions. The impeller speed of 700 rpm was sufficient to keep -106µm quartz particles suspended, but at all superficial gas velocities, the minimum impeller speed required for complete gas dispersion was 850 rpm. Therefore, it can be stated that the reason for the low k value at a stirring speed of 700 rpm is the incomplete distribution of bubbles and particles (+106µm), resulting in a reduced probability of air bubbles colliding with solid particles. By increasing the impeller speed to values greater than 700 rpm, the k value increased, which is due to the complete distribution of particles and air bubbles in the flotation cell (increased probability of bubble-particle collision). Therefore, it is necessary to provide suitable operating conditions for the complete dispersion of air bubbles and also to keep solid particles suspended.
Original Research Paper
Environment
Aditi Nag
Abstract
India's mining heritage sites (MHSs) represent underdeveloped tourist avenues for culture conservation and community upliftment. This study undertakes a dual-site comparison depending on a mixed-methods approach combining perception surveys of visitors, satellite image analysis, and statistical techniques ...
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India's mining heritage sites (MHSs) represent underdeveloped tourist avenues for culture conservation and community upliftment. This study undertakes a dual-site comparison depending on a mixed-methods approach combining perception surveys of visitors, satellite image analysis, and statistical techniques involving t-tests, chi-square analysis, and hierarchical clustering, for Dhori Mines (Jharkhand) and Barr Conglomerate (Rajasthan). Results starkly reveal contrasts: while Barr confirms ecological recovery and community integration, Dhori suffers due to infrastructure and interpretive constraints. Other strategies include AI-powered heritage interpretation and visitor segmentation to improve site competitiveness. It emerges from the findings that data-oriented landscape and tourism planning coupled with local participation can sustain and promote post-mining landscapes effectively.
