Review Paper
Environment
Saahil Hembrom; Neeta Kumari
Abstract
Mining activities adversely affect the groundwater quality. Human health also subsequently gets affected because of many environmental and ecological risks due to mobilization of contaminants and alteration of hydrogeochemical processes. This review assesses the hydrogeochemical characteristics and groundwater ...
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Mining activities adversely affect the groundwater quality. Human health also subsequently gets affected because of many environmental and ecological risks due to mobilization of contaminants and alteration of hydrogeochemical processes. This review assesses the hydrogeochemical characteristics and groundwater quality in mining areas emphasizing the crucial processes like rock-water interaction, acid mine drainage formation, and heavy metal contamination. These processes impact the end uses of groundwater quality like drinking, irrigation and industrial uses. To understand the causes of contamination and the availability and suitability of the water, groundwater investigation is required such as assessment of physicochemical parameters and hydrogeochemical faces. By using isotopic techniques and integration of spatial and temporal changes with remote sensing and GIS application, pollution load can be evaluated on water resources. A bibliographic analysis highlights the current research progress in mining sector, focusing on global and regional studies and their impact on water resources. Contamination from heavy metals like arsenic, chromium, cadmium, and other toxic elements has posed serious illnesses to human health and the surrounding ecosystem. The review also highlights the research gaps and prospects for improving groundwater resources through appropriate mitigation strategies like sustainable mining practices and water treatment technologies.
Original Research Paper
Exploration
Balbir Nagal; Ajay Krishna Prabhakar; Mahesh Pal
Abstract
This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density ...
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This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density (LD), elevation, rainfall, and topographic wetness index (TWI), were generated using datasets from SRTM, Sentinel-2, food and agriculture organization (FAO), and the India meteorological department (IMD) and weighted through the AHP. These layers were integrated using weighted overlay analysis (WOA) to generate the final GWP map. The GWP map was validated against field groundwater level (GWL) data from 646 wells recorded in 2018 by the central ground water board (CGWB), resulting in an accuracy of 77.55 percent. This confirmed the reliability of the geographic information system (GIS) and AHP technique. The study reveals that moderate GWP zones dominate (43.71%) the region, followed by high (33.24%) and very high (11.96%) zones, whereas low and very low GWP zones cover 7.59% and 3.51% of the area, respectively. The findings indicate that Haryana’s groundwater distribution is largely stable, with minor variation observed between 2018 and 2023. This shows stable aquifer behaviour and relatively unchanged recharge and extraction patterns over the five-year period. The outcomes of this study are valuable for strategic groundwater management, especially in arid and semiarid regions of Haryana state.
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.
Case Study
Exploitation
Victor Patson Mutambo; Barnabas Mpaka; Pardon Sinkala; Matheus Ipinge
Abstract
This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region ...
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This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region is notable for its complex, folded, and mineralized formations. The deeper levels of the synclinorium are characterised by poor ground conditions, faults, and shear zones. Stress induced by mining activities worsens the situation. These factors have significantly impacted the stability of excavation. Fall-of-ground (FOG) incidents have exhibited a concerning increase over the past nine years. This trend necessitates a thorough investigation into the factors contributing to it. Our research employed empirical methods for rock mass classification, specifically utilising Barton’s Q system and Bieniawski and Scanline mapping of geological structures along the crosscut walls at a 1.50 m elevation. We conducted borehole logging and pull-out tests to evaluate the working and ultimate capacities of rock bolt anchors deployed in the excavations. Borehole cores were analysed for geological formations, colour, and grain size. The findings indicate that excavations in areas with mined-through rock and stone necessitate urgent and intensive roof support to stabilise the surrounding rock mass, thereby enhancing standing time. Additionally, we identified joint patterns, joint orientations, and the various stresses affecting the surrounding rock mass in the crosscuts. The above highlights the importance of geological data in the design of effective ground control and support mechanisms. Pull-out testing conducted at the 3360 level recorded a 28.6% failure rate in primary development despite very competent ground.
Original Research Paper
Mineral Processing
Salih Aydogan; Mohamed Taha Osman Abdelraheem; Babiker Alkloosi; Mustafa Boyrazli
Abstract
This article describes the kinetics of utilizing ammonium nitrate to dissolve pure metallic silver in hydrogen peroxide solution (H2O2). Using pure metallic silver allows for precise leaching kinetics research by removing interference from impurities and facilitating accurate interpretation of rate-controlling ...
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This article describes the kinetics of utilizing ammonium nitrate to dissolve pure metallic silver in hydrogen peroxide solution (H2O2). Using pure metallic silver allows for precise leaching kinetics research by removing interference from impurities and facilitating accurate interpretation of rate-controlling mechanisms. The impact of temperature, rotation speed, H2O2 concentration, and ammonium nitrate concentration were all examined. The results show a favorable relationship between the rate of silver (Ag) dissolution and the rotation speed. Additionally, a low concentration of ammonium nitrate (between 0.003 and 0.20 M) has advantageous effects on Ag dissolution. The dissolution rate was significantly impacted by H2O2 concentrations between 0.08 and 0.15 M, because this range of H₂O₂ concentration required to provide sufficient oxidative potential for significant silver solubility. However, this effect is less pronounced in the 0.20–0.50 M range. 20 - 50 °C range of temperatures are advantageous since H2O2 is stable in this range. It was calculated that the activation energy was 25.66 kJ/mol.
Original Research Paper
Environment
Nanang Suparman; Muhammad Andi Septiadi; Yuflih Rizkia Timoty; Faizal Pikri
Abstract
This study aims to analyse the regulatory hierarchy and its implications within the regional autonomy regime in the context of bauxite mining management in Indonesia, with a focus on Tanjungpinang City. Although decentralization grants local governments the authority to manage natural resources, overlapping ...
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This study aims to analyse the regulatory hierarchy and its implications within the regional autonomy regime in the context of bauxite mining management in Indonesia, with a focus on Tanjungpinang City. Although decentralization grants local governments the authority to manage natural resources, overlapping regulations between central and regional authorities have resulted in governance conflicts, weak enforcement, and substantial environmental degradation. Utilizing a mixed-method approach informed by Kagan’s regulatory model, this research integrates field-based environmental assessments including bauxite sediment sampling and post-mining water quality analysis with a normative analysis of mining regulations and governance practices. The findings reveal a dominance of procedural legal frameworks over substantive environmental accountability. Regional autonomy laws tend to prioritize investor interests, often at the expense of community welfare and environmental restoration. Additionally, inadequate local oversight has allowed the continued export of unprocessed bauxite, exacerbating environmental harm. This study contributes new insights by exposing the structural misalignment between regulatory authority and environmental responsibility under Indonesia’s current autonomy regime. It underscores the urgent need for regulatory reform that clarifies lines of authority, mandates in-country bauxite processing prior to export, and enforces post-mining reclamation obligations at the regional level. These recommendations aim to support policymakers in designing enforceable and context-sensitive reforms for sustainable bauxite mining governance.
Original Research Paper
Exploitation
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Solio Marino Arango-Retamozo; Luis Alex Rios-LLaure; Jose Nestor Mamani-Quispe; Salomon Ortiz-Quintanilla
Abstract
This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were ...
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This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were normalized using Z-score scaling. Second, a Ridge regression model was trained to predict fuel consumption based on variables such as truck utilization, trips, road gradient, material type, haul distance, and operating hours. Finally, three operational strategies were simulated: Controlled Reduction (CRS), Balanced Efficiency (BES), and Maximum Utilization (MUS), to evaluate environmental, economic, and social impacts. The results indicated that the Ridge model achieved strong predictive performance in estimating fuel consumption (R2 = 0.83; MSE = 38.16). According to the simulated scenarios, environmentally, CRS reduced fuel consumption by 30% and CO2 emissions by 1,481.3 tons; BES achieved 7.99% savings and 394.9 tons less CO2. Economically, CRS saved USD 664,924.6 in fuel costs and BES USD 177,276.3. Socially, the carbon cost decreased by USD 11,406.1 (CRS) and USD 3,041.0 (BES). MUS increased emissions by 864.3 tons and fuel costs by USD 387,966.4. This research proposes a novel integration of machine learning and sustainability analysis applied to haul trucks in open-pit mining material transport. It also offers a replicable, data-driven framework for mining companies to reduce emissions, optimize costs, and align their operations with sustainability goals.
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
Taiwo Blessing Oamide; Adebayo Babatunde; Toluwase Daniel Olaiya
Abstract
This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest ...
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This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Regression (SVR), a conventional Artificial Neural Network (ANN), and two Imperialist Competitive Algorithm-optimized ANNs (ICA-ANNs). The models were trained using eight input parameters including uniaxial compressive strength (UCS), spacing (S), burden (B), sub-drill (SB), drill hole length (DHL), stiffness ratio (SR), maximum instantaneous charge (MIC), and powder factor (K) with blast toe volume (TV) as the target output. Input data were collected through a combination of field measurements and laboratory analyses. Among all the models evaluated, the ICA-ANN with an 8-7-1 architecture achieved the highest predictive accuracy. It outperformed AdaBoost by 9.17%, SVR by 7.20%, GPR by 5.56%, RF by 4.75%, a standard ANN (8-5-1) by 0.78%, and a standard ANN (8-7-1) by 0.28%, based on mean squared error (MSE) and coefficient of determination (R²) metrics. Furthermore, the ICA-ANN model was applied to optimize blast design parameters. The optimal values obtained were: spacing = 1.0 m, burden = 0.8 m, sub-drill = 0.6 m, MIC = 0.72 kg, and powder factor = 0.65 kg/m³. These optimized parameters reduced the blast toe volume by 20.05%, from 209.50 m³ to 154.87 m³. The results highlight the robustness and efficiency of the ICA-ANN model for blast design optimization. By improving fragmentation quality and minimizing residual toe volume, the approach offers a practical pathway for enhancing both productivity and cost-effectiveness in small-scale mining operations.
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.
Case Study
Environment
Saeed Omori; Arezoo Abedi; Kumars Seifpanahi-Shabani; Hamid Abbasdokht; Mohammad Ghafoori; Mohammad Abasian; Antony Van Der Ent
Abstract
This study evaluated the efficiency of the native hyperaccumulator Odontarrhena inflata in extracting nickel (Ni) from ultramafic soils in the Robat-Sefid region of northeastern Iran and assessed the feasibility of applying agromining under controlled conditions. A six-month greenhouse experiment was ...
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This study evaluated the efficiency of the native hyperaccumulator Odontarrhena inflata in extracting nickel (Ni) from ultramafic soils in the Robat-Sefid region of northeastern Iran and assessed the feasibility of applying agromining under controlled conditions. A six-month greenhouse experiment was conducted using homogenized serpentine soil with a total Ni concentration of 1,460 mg/kg. By the end of the cultivation period, the aerial parts of the plant yielded 122 g of dry biomass containing 2,195 mg/kg of Ni. The calculated bioconcentration factor (BCF = 1.5) and translocation factor (TF = 3.53) confirmed effective Ni uptake and translocation from roots to shoots. The biomass was pyrolyzed at 550 °C to produce ash, which underwent cross-washing and sulfuric acid (H₂SO₄) leaching. This leaching process achieved a Ni extraction efficiency of 78.9%, and the overall Ni recovery from soil to biomass ash was estimated at 3.53%. Elemental analyses showed substantial reduction of Magnesium (Mg) and Iron (Fe) in the final crystalline product; however, Calcium (Ca) and Sodium (Na) remained at appreciable levels, indicating that further recrystallization or purification steps are necessary to achieve industrial-grade ANSH (ammonium nickel sulfate hexahydrate). Compared with other Ni hyperaccumulators, O. inflata exhibited lower shoot Ni levels than Odontarrhena chalcidica and Alyssum murale, but the combination of its strong ecological adaptability, elevated TF, and native occurrence collectively designates it as a sustainable and promising candidate for agromining applications in nickel-rich soils of Iran.
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
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
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
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
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi
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
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
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
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
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.
