Original Research Paper
Exploration
Naresh Kumar Katariya; Bhanwar Singh Choudhary
Abstract
Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling ...
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Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling using Slide 6.0 software. Factors of safety (FOS) of various mining sections varied from 0.475 to 1.495, as per limit equilibrium analysis with Slide 6.0. This signifies the presence of possibly unstable slopes that require certain stabilisation measures to ensure operational safety. The research considers how significant environmental factors, like temperature, wind speed, rainfall, and soil moisture, influence slope stability in addition to the geotechnical analysis. Rainfall and soil moisture were found to have a high and statistically significant positive correlation (Pearson correlation = 0.706, p = 0.005), implying that an increase in rainfall results in increased soil moisture content, which in turn affects the behaviour of slopes. Also, a moderate degree of negative relationship between temperature and wind speed was revealed (partial correlation = -0.593, p = 0.042), meaning that smaller wind speeds are characteristically associated with increased temperatures. These findings highlight the importance of continuous monitoring of the environment in open-pit mine operations and the importance of considering environmental factors when assessing slope stability. The information collected in this study provides a solid foundation for developing valuable recommendations intended to enhance safety, better control slopes, and promote the long-term development of mining activities in the region.
Original Research Paper
Exploration
Ahmed Mahmoud Abdelhameed; Maher Abdelateef El Amawy; Ayman Mahrous; Mohamed El-Khouly; Adel Fathy
Abstract
Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for ...
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Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for targeting mineralization. However, achieving comprehensive lithological mapping remains a challenge, hindering systematic mineral exploration. This work explores the use of PRISMA hyperspectral data and the Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms to objectively map the Precambrian rock assemblages at the El Ineigi area in the Central Eastern Desert (CED) of Egypt. For this purpose, PRISMA data in HDF5 format were first pre-processed and subsequently transformed through principal component analysis (PCA). The processed spectral data were then combined with extensive fieldwork and previously existing geological maps and classified using SVM and ANN to achieve enhanced discrimination of the exposed rock units in the study area. Our results conclusively demonstrate the exceptional capability of PRISMA data for detailed lithological mapping. The SVM and ANN classification achieved remarkably high overall accuracy, successfully generating a robust geological map that clearly discriminates between various Neoproterozoic basement rock units in the El Ineigi area. Through the integration of diagnostic spectral signatures with field verification, we confidently identified all major mappable units, including metavolcanics, metagabbro-diorite complexes, younger granites, and Wadi deposits. The proposed integrated approach demonstrates superior performance compared to traditional mapping techniques, offering enhanced discrimination precision and operational efficiency. These findings strongly support the combined use of PRISMA hyperspectral data and MLAs for lithological mapping applications.
Original Research Paper
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation ...
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Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation of copper ore grades in a deposit located in Peru. A total of 5,654 composites, each 15 meters in length, were obtained from 185 diamond drill holes. The data were transformed to a uniform scale to allow for copula fitting. Dependence structures were modeled by lag distance, with the dependence parameter fitted using fifth-degree polynomials, and three-dimensional conditional estimation was implemented. Results indicate that ordinary kriging yielded RMSE = 0.161, MAE = 0.104, R2 = 0.692, and a correlation of 0.861. The Clayton copula slightly improved these metrics (RMSE = 0.154, MAE = 0.101, R2 = 0.717, R = 0.871), while the Gumbel copula captured higher local variability (RMSE = 0.161, MAE = 0.116, R2 = 0.692, R = 0.855). The Frank copula achieved the best performance with RMSE = 0.137, MAE = 0.090, R2 = 0.778, and R = 0.905. In conclusion, Archimedean copulas significantly enhance geostatistical estimation by better capturing spatial dependence, offering a robust alternative to classical geostatistical methods.
Case Study
Environment
Asep Nurohmat Majalis; Muhammad Razzaaq Al Giffari; R Arif Suryanegara; M Rifat Noor; Rachmat Ramadhan; Noviarso Wicaksono
Abstract
Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of ...
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Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of nickel mining is necessary. This research has conducted erosion and infiltration tests at various locations in pre-nickel mining zones to mitigate the environmental impact of nickel mining activity. Erosion tests were performed using a rainfall simulator with five nozzles on a 12.5 m² demo plot. Infiltration tests were conducted using a double-ring infiltrometer. The result shows that surface runoff coefficients for disposal, limonite, saprolite, and quarry zones were higher than those for vegetated zones such as grassland, pepper plantation, and forest. The saprolite zone released the highest sediment load, i.e., 484.3 kg ha-1 hour-1, followed by the limonite and the pepper plantation zone, with 243.6 kg ha-1 hour-1 and 185 kg ha-1 hour-1. The highest Cr(VI) concentration, 0.7 mg L-1, was released from the disposal zone, followed by the saprolite, limonite, and pepper plantation zones, with concentrations of 0.56, 0.06, and 0.06 mg L-1, respectively. The infiltration equation obtained from each zone shows that revegetation can significantly reduce runoff. Therefore, revegetation should be prioritized in addition to end-of-pipe treatment to mitigate the impact of nickel mining activities.
Review Paper
Exploration
Ukpata Austin Odo; Jude S Ejepu; Bernd Striewski
Abstract
The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), ...
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The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), remote sensing (RS), LiDAR, drone mapping, and positioning systems find applications in mineral exploration, mine planning, operational monitoring, and post-mining rehabilitation. Artificial intelligence (AI) and machine learning (ML) systems enhance the functional potential of these technologies through predictive modeling capabilities, which work in conjunction with real-time analytic functions. The research shows that these technologies enable better decision-making, performance optimisation, and environmental risk reduction. Modern mining relies entirely on these technologies because they support accurate resource assessment, optimise design operations, and help enforce safety standards and environmental codes. Adopting such technologies requires resolving implementation costs, addressing data integration issues, and acquiring the necessary technical expertise. The future development of mining technology should focus on enhancing the integration of geospatial information platforms, creating sustainable solutions for medium-sized mining operations at affordable prices, and developing predictive evaluation systems utilizing AI algorithms. The mining industry accomplishes safer operation methods through efficient technologies, enhancing sustainability.
Original Research Paper
Exploration
Joshua Chisambi; Leornard Kalindekafe; Kettie Magwaza; Ruth Mumba; Martin Kameza
Abstract
The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic ...
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The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic rocks remain poorly understood, limiting systematic exploration and resource development. We conducted integrated field mapping, structural analysis, petrographic examination, and geochemical sampling to characterize gold mineralization controls in the Nathenje prospect, central Malawi. Detailed structural measurements combined with stereographic analysis reveal three deformation phases, with gold mineralization predominantly associated with D₂ transpressional structures. Fire assay results demonstrate significant gold concentrations (0.15–5.0 g/t Au) in arsenopyrite-bearing quartz veins, with the highest grades systematically occurring at structural complexity zones. Petrographic analysis reveals native gold particles (5–50 μm) intimately associated with arsenopyrite along grain boundaries and within microfractures, indicating coupled precipitation processes. Critically, we identify a hierarchical structural control system operating from regional NE-SW trending shear zones to microscale sulphide boundaries, with fold hinges, dilutional jogs, and amphibolite-gneiss contacts yielding consistently higher gold grades (>3 g/t Au) than other structural settings. Our results establish the first comprehensive structural model for gold mineralization in central Malawi's metamorphic terrain and provide specific targeting criteria applicable to similar high-grade metamorphic environments throughout the East African Orogen.
Original Research Paper
Environment
Clement Kweku Arthur; Yao Yevenyo Ziggah; Victor Amoako Temeng
Abstract
Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble ...
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Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble machine learning framework for predicting blast-induced noise using data from an open-pit gold mine in Ghana. Four ensemble models namely: Extreme Gradient Boosting (XGBoost), Gradient Boosting, Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost), were developed and evaluated using a comprehensive dataset of 324 blasting events. Performances of the developed models were assessed using coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of the variation of the root mean squared error (CVRMSE), with XGBoost emerging as the best-performing model (R² = 1.0000, RMSE = 0.0005, MAE = 0.0004, MAPE = 0.0010, CVRMSE = 0.0013). To address the black-box nature of ensemble method, Shapley Additive exPlanations (SHAP) was employed, offering both global and local interpretability. SHAP analysis identified the distance from the blast site to the monitoring point as the most influential factor. This integrative approach not only enhances predictive accuracy but also improves model transparency, supporting sustainable mining practices aligned with United Nations Sustainable Development Goals (SDGs) 3 and 15.
Original Research Paper
Environment
Ritu Bala Garg; Gurpreet Singh
Abstract
This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and ...
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This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and in combination, a series of concrete mixes were prepared incorporating these wastes at varying proportions, and were tested for workability, compressive strength, and durability (water absorption and chloride ion penetration). Results indicate that blends of FA, CBA, and QD can effectively substitute up to 40% of cement and fine aggregates without compromising structural performance. The mixes containing 20% fly ash, 10% bottom ash, and 10% quarry dust exhibited superior compressive, split tensile, and flexural strength, and reduced water absorption and chloride ion penetration, demonstrating their potential in aggressive environments.
Original Research Paper
Exploitation
Tapan Dey; Gopinath Samanta
Abstract
Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of ...
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Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of XGBoost, Random Forests (RFs), Multi Layer Perceptron (MLP), and Gradient Boosting Regression (GBR) models to predict iron ore grades in an Indian mine. An ensemble model was also applied to obtain a more stable grade prediction in the deposit. Models were trained using 4,112 sample data, which have spatial coordinates (east, north, and altitude) and iron grades. The dataset was divided into two parts: 80% (3,289 samples) of the data was used for model training, and 20% (823 samples) was used for model testing. The performance of the models was assessed through the coefficient of determination (R²), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the XGBoost model performs better in the estimation process when compared with other methods, such as RFs, GBR, and MLP. The XGBoost model produced R² of 0.77, MSE of 2.87, and MAPE of 1.8%. The findings indicate that the XGBoost model is effective for predicting iron ore grades in this type of deposit. However, considering geological uncertainty, the application of an ensemble model may be beneficial for grade prediction in an iron deposit.
Original Research Paper
Environment
Aditi Nag
Abstract
The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining ...
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The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining landscape—through the application of two established competitiveness frameworks: Dwyer & Kim’s Integrated Destination Competitiveness model and Porter’s Diamond Model. Drawing from a robust dataset of 441 stakeholder responses and employing perceptual mapping, cluster analysis, and ANOVA, the study identifies key strengths in cultural identity and community engagement, contrasted by critical weaknesses in interpretive infrastructure, service integration, and institutional coordination. Comparative analysis with national (Kenapara, Raniganj) and international (Ruhr Valley, Wieliczka Salt Mine) case studies further underscores the structural and narrative gaps Dhori must address. The findings inform a phased strategy—short-, mid-, and long-term—accompanied by a data-driven Competitiveness Monitoring Toolkit grounded in nine thematic criteria. The study contributes an India-specific empirical model for post-mining tourism transitions, highlighting how dual-identity sites can achieve competitive positioning through integrated cultural, environmental, and economic strategies.
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.
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
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
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
Exploration
Seyedeh golaleh Hosseini; Kourosh shahriar; mohammadamin karbala
Abstract
Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage ...
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Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage systems at the Angouran lead–zinc mine. In the first phase, the hydro-mechanical behavior of the rock mass was simulated using UDEC software, demonstrating that increased normal stress reduces fracture aperture and permeability. The simulated pore pressure (4.5×10⁵ Pa) closely matched the field measurements (4.4×10⁵ Pa), with only a 2.2% deviation. In the second phase, a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP) and input from 32 domain experts identified Q4 (very high quality) and Q2 (medium quality) indicators as the most influential criteria. In the third phase, three machine learning models—linear regression, polynomial regression, and artificial neural networks (ANNs)—were trained on piezometric data to predict water discharge. The ANN model outperformed the other models, achieving an R² of 0.94 and RMSE of 0.18, effectively capturing the nonlinear dynamics of groundwater flow within the mine. The findings highlight that the integration of numerical modeling, expert-based decision analysis, and AI-driven prediction provides a robust and innovative approach for designing and managing mine dewatering systems, with potential applicability to other complex hydrogeological environments.
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
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
Mohammad Shekari Nejad; Mohammad Fatehi Marji; Manouchehr Sanei
Abstract
The slope geometry, rock mass quality, groundwater level, and geological features of the mine mainly influence the slope stability of an open-pit mine. In this study, the stability analysis of the open pit slope under the influence of various factors was studied. The analysis was conducted based on data ...
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The slope geometry, rock mass quality, groundwater level, and geological features of the mine mainly influence the slope stability of an open-pit mine. In this study, the stability analysis of the open pit slope under the influence of various factors was studied. The analysis was conducted based on data collected from the Golgohar iron ore mine in Sirjan. To build the numerical model, first, the geomechanical and hydrogeological parameters of the mine were determined using laboratory and field tests. Then, numerical models of slope stability were built based on the finite difference method using hydromechanical coupling analysis. The real characteristics in these models include lithology types, variations in geomechanical properties, groundwater level, and real slope geometry. Numerical models were built based on three different conditions, including a model in dry conditions, a model considering the groundwater level, and a model after the drainage process. The results show that the whole slope angle of the mine that has the highest safety factor is 36 degrees. In addition, the groundwater level reduces the safety factor of slope stability compared to dry conditions, and the drainage process can increase the safety factor of the mine wall. In all three conditions, the whole slope angle of 36 degrees has the highest safety factor. Therefore, it is suggested that the whole slope angle be considered to increase the safety factor and reduce the stripping ratio to increase the profitability of the open pit mine.
Review Paper
Exploitation
Arman Khosravi; Mohammad Ataei
Abstract
The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining ...
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The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining method selection, with a focus on their historical evolution, integration with fuzzy logic, artificial intelligence, and machine learning, as well as bibliometric trends and parameter analysis. The findings reveal a growing tendency toward hybrid and intelligent MCDM models that enhance decision accuracy and adaptability under uncertainty. A bibliometric analysis of key authors, countries, journals, and citation patterns highlights the global scope and scientific impact of research in this area. Furthermore, the study categorizes influencing parameters into intrinsic and extrinsic groups, identifying ore geometry, grade distribution, and rock mass properties as dominant intrinsic factors, while economic, environmental, and operational considerations represent significant extrinsic influences. This review emphasizes the vital role of MCDM techniques in optimizing mining operations, and advocates for further development of dynamic, data-driven models to meet the evolving challenges of modern mining.
Original Research Paper
Rock Mechanics
Swaraj Chowdhury; Rakesh Kumar; Ankit Kumar
Abstract
The present study examines the strength and permeability behavior of glass fibre-reinforced fly ash-bentonite (FaB) mixture to assess its potential as an alternate geo-material. The FaB mixture is produced by adding 20% bentonite with 80% fly ash and is further reinforced with glass fibre. The unconfined ...
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The present study examines the strength and permeability behavior of glass fibre-reinforced fly ash-bentonite (FaB) mixture to assess its potential as an alternate geo-material. The FaB mixture is produced by adding 20% bentonite with 80% fly ash and is further reinforced with glass fibre. The unconfined compressive strength (UCS) tests have been conducted at a strain rate of 0.625 mm/min by varying the curing period (0 to 60 days), relative moisture content (R.M.C– 80% to 120%) and fibre content (0% to 1.0%). The effect of fibre content on the coefficient of permeability (k) and compressibility behavior of the FaB mixture has been investigated through one-dimensional consolidation tests. The findings indicate that the UCS of the FaB mix samples improves with an increase in curing period and fibre content. At 100% R.M.C, the UCS increases from 48 kPa to 228 kPa for the unreinforced samples as the curing period increases from 0 to 60 days. At 90% R.M.C, both unreinforced and reinforced FaB mix samples have exhibited the highest UCS values considering all curing periods. With fibre content increasing from 0% to 1.0%, the UCS rises about 33% to 44% at 100% R.M.C. Fibre reinforcement also contributes to reduction of k and compressibility. Based on the experimental findings, a closed-form equation has been developed for the prediction of UCS of FaB mixture reinforced with and without glass fibre. Results confirm that glass fibre reinforcement improves the strength, permeability, and compressibility of the FaB mixture, establishing it as an alternate geo-material.
Original Research Paper
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares ...
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The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares the performance of four unsupervised clustering algorithms Euclidean K-Means, Riemannian K-Means, Gaussian Mixture Model (GMM), and Agglomerative Clustering applied to 927 samples from the Quiulacocha tailings deposit in Peru, using six major elements (Zn, Pb, Cu, Fe, Ag, Au) and spatial coordinates. All methods consistently identified three main geochemical domains. Cluster 1 was enriched in Cu and Au, Cluster 2 in Pb and Fe, and Cluster 3 in Zn, Ag, and Fe. Covariance-based methods (Riemannian K-Means and Agglomerative Clustering) outperformed others in internal validation (Silhouette scores up to 0.58) and consistency (Adjusted Rand Index = 1.00), offering more interpretable and geologically coherent partitions. CLR transformation reduced clustering performance, highlighting the importance of preserving raw geochemical variance for spatial segmentation. These findings demonstrate the effectiveness of multivariate clustering for unraveling compositional heterogeneity in tailings and delineating domains of potential economic value. The approach provides a quantitative framework for supporting reprocessing decisions, reducing risk, and guiding future research on mine waste valorization.
Original Research Paper
Environment
Tulika Gupta; Mahasakti Mahamaya; Shamshad Alam
Abstract
The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was ...
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The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was combined with fly ash and cement to develop controlled low-strength material (CLSM). Initially, the raw materials were examined for their physical, chemical, and mineralogical properties. Subsequently, 24 different CLSM mixtures were prepared by varying cement, fly ash, MO, and water-to-binder ratios. The fresh mixes were tested for flowability, bleeding, and fresh density, while the hardened properties, including density, unconfined compressive strength (UCS), and durability, were also evaluated. Results showed that all CLSM mixes were highly flowable, with flow diameters exceeding 150 mm, and some exhibited self-leveling behavior. The 28-day compressive strength ranged from 0.52 MPa to 4.28 MPa, with a few mixes being soft enough for manual excavation. Durability tests indicated that approximately 60% of the mass remained intact after eight wet-dry cycles, demonstrating good resistance to erosion. This study highlights the potential for utilizing mining waste in sustainable construction materials.
Original Research Paper
Environment
Andrieanto Nurrochman; Zaenal Zaenal; Noor Fauzi Isniarno; Delina Mutiara; Sofie Nur’aini; Hasyim Fadhilah; Elfida Moralista
Abstract
Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards ...
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Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations.
Original Research Paper
Environment
Elena Drobinina; Marina Kitaeva; Artem Mizev; Elizaveta Romanova
Abstract
The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure ...
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The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure has been designed within the Pivovarovo karst area (Vladimir Region, Russia). The research highlights the advantages of automated zoning along the construction route based on both sinkhole distribution and environmental conditions. A significant methodological contribution to the assessment of karst susceptibility is the development of a custom Python-based tool for the automated morphometric analysis of sinkholes, including diameter measurement and orientation assessment. This approach provides an effective solution for karst susceptibility assessment, because it enables the rapid processing of large datasets, producing high-quality results that can support engineering design decisions.
Original Research Paper
Rock Mechanics
Barkat Ullah; Raja Khurram Mahmood Khan
Abstract
Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning ...
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Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning (ML) models in forecasting the UCS of sandstone obtained from the Murree and Kamlial formations in the Muzaffarabad area, northwestern Himalayas, Pakistan. The ML models—namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regressor (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were developed to predict UCS (MPa) based on porosity (η), point load index (Is(50)), Schmidt hammer rebound value (Rn), and aggregate impact value (AIV) as input variables. A dataset containing 80 points was divided using a 70:30 split ratio for training and testing sets. K-fold cross-validation (with 5 to 10 folds) was employed to enhance the models' generalization ability. The performance of the models was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). Results revealed that the XGBoost model outperformed the other models, achieving a high R² value of 0.99 and low error values for MAE (0.789), MSE (1.168), and RMSE (1.080). The overall accuracy of the models can be ranked as follows: XGBoost > RF > ANN > ANFIS > SVR. This study provides a benchmark for predicting the UCS of sandstones and similar rocks where complex geology complicates the collection of intact samples.
Original Research Paper
Exploitation
Pintu Kumar Mandal; Niroj Kumar Mohalik; Manoj Kumar Mishra; Gautam Chandra Mondal
Abstract
The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, ...
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The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, spontaneous combustion, and mine fires. Among these challenges, many underground coal mines continue to operate after dewatering the coal seams for production purposes. Spontaneous combustion poses a significant risk in the dewatered coal seams of underground mines, impacting the safety of both the mines and the miners. This study aims to assess the risk of spontaneous combustion in a water-immersed coal seam of RCF by conducting proximate analysis, TGA/DSC, FTIR studies, and water analysis. One coal sample was obtained from the RV seam at the Kottadih coal mine in RCF and was immersed in tap water at a ratio of 1:10. The water-immersed coal samples were removed after 15, 30, and 90 days for sample preparation and other experimental investigations. The experimental results indicate that the water-immersed coal samples exhibit optimal moisture levels (4–8%), a higher volatile matter content (>30.0%) compared to fresh samples, and a gradual decrease in the ignition temperature of the water-immersed coal over time. There is an increase in concentrations of functional groups such as Ar-, -CHO, >C=O, and -C=C- due to the adsorption of dissolved organic compounds onto the coal surface. All analyses suggest that the rise in organic compounds contributes to the accelerated risk of spontaneous combustion.
Original Research Paper
Environment
Mohammad Hadi Salehzadeh; Hadi Farhadian; Saeed Yousefi; Mohammad Dehju
Abstract
This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent ...
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This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent statistical analyses and evaluations were conducted for the study areas. To distinguish the effects of mining from those of climate change, the results were compared with a reference area located within a natural resource block (baseline area), and the outcomes were thoroughly analyzed. The findings indicate that the combined impacts of mining and climate change have caused significant environmental degradation in the region. In particular, vegetation cover has experienced a sharp decline in recent years, while soil erosion has increased at a slower rate. Projections of mining impacts on vegetation and soil were made by calculating the average NDVI and NDSI indices for 2030 and 2050 in the studied areas. These projections suggest that NDVI is expected to decrease by 0.25 by 2030 and by 0.72 by 2050, indicating further vegetation loss in the coming decades. In contrast, analysis of the NDWI index reveals no clear trend in soil moisture changes over the study period. Given the climatic conditions of the selected areas, it is essential to monitor, manage, and mitigate environmental risk factors to prevent the expansion of drought into northern forests, highlighting the need for appropriate intervention measures.
Original Research Paper
Environment
Aditi Nag
Abstract
This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts ...
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This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts a mixed-method design that integrates perceptual surveys (n = 440) with multivariate tools—including Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Discriminant Function Analysis (DFA)—to decode stakeholder attitudes and assess spatially differentiated tourism potential. Eight experiential themes emerge from the PCA, encompassing infrastructure adequacy, site distinctiveness, safety perception, interpretive depth, and cultural resonance. While respondents recognize Barr’s strong geo-heritage value and visual appeal, persistent deficiencies in accessibility, safety management, and narrative infrastructure constrain its tourism readiness. Findings demonstrate the site’s potential to be repositioned through themed geo-trails, multi-sensory interpretive environments, and community-based tourism models. Segment-specific discriminant profiles reveal differing perceptual priorities across tourists, residents, and experts, underscoring the need for tailored branding strategies rooted in geological authenticity, memory landscapes, and living community heritage. Benchmarking against Rajasthan’s regional tourism motivations—adventure, authenticity, storytelling, and geotourism—further highlights the competitive niche Barr can occupy within state-level heritage circuits. The study proposes a scalable, data-driven framework that couples perceptual clustering with participatory planning, offering a replicable model for transforming abandoned extraction sites into culturally rich, economically resilient, and ecologically responsive heritage destinations.
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
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
Rock Mechanics
Amirreza Kavandi; Ramin Doostmohammadi
Abstract
So far, limited research has been conducted on the swelling behavior of Marlstone in the presence of cations. In this study, swelling pressure experiments were performed on rock samples obtained from the Marash Dam, located in northwest Iran. The specimens underwent wetting and drying cycles to achieve ...
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So far, limited research has been conducted on the swelling behavior of Marlstone in the presence of cations. In this study, swelling pressure experiments were performed on rock samples obtained from the Marash Dam, located in northwest Iran. The specimens underwent wetting and drying cycles to achieve an equilibrium condition before cation infiltration. Rock specimens were infiltrated with distilled water and with 1, 2, and 3 mol/L solutions of sodium chloride (NaCl) and calcium chloride (CaCl2). The findings suggest that as the concentration of the solutions rises, the swelling pressure of Marlstone diminishes. Furthermore, at the same concentrations, the swelling pressure of samples soaked in CaCl2 solutions was less than that of those treated with NaCl solutions. Additionally, Marlstone saturated with Ca2+ ions exhibited greater resistance to leaching compared to those saturated with Na+ ions. The findings of this research can be applied to control the swelling pressure of weak rocks in proximity to support systems.
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
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
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
Exploitation
Ali Nemati vardin; 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
Exploitation
Mojtaba Rezakhah
Abstract
Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these ...
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Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these challenges by directly integrating critical geometallurgical parameters, specifically rock hardness (SPI index) and clay content, into the short-term production planning process. The simultaneous integration of these key geometallurgical feed quality attributes within an operational MILP model distinguishes this work from previous approaches and effectively bridges geological data analytics with operational decision-making, aligning economic objectives with enhanced metallurgical performance. Utilizing real operational data from the Sarcheshmeh Copper Mine, the framework was validated over a 186-day period. It achieved optimal production conditions on 137 days (73.6% of the duration), realizing a maximum Net Present Value (NPV) of $132,000. Key outcomes included a significant 21% reduction in concentrate grade variability and a 15% decrease in flotation reagent consumption, achieved through the simultaneous control of SPI and clay content. Advanced statistical methods were employed to identify critical relationships. While the model demonstrates scalability for porphyry copper mines globally, its successful implementation depends on careful parameter customization and alignment with existing infrastructure. This research work underscores the substantial value of data-driven, integrated optimization techniques in enhancing both profitability and process stability within mineral processing circuits.
Original Research Paper
Exploitation
Fatemeh Asadi Ooriad; Javad Gholamnejad; Ali Dabagh
Abstract
Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical ...
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Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical and operational constraints. Mathematical programming methods have proven suitable for optimizing mine production scheduling. Previous studies have addressed various aspects, including the timing of deployment and periodic relocation of in-pit crushers. Nevertheless, significant challenges remain in integrating the in-pit crusher problem with production planning. This paper introduces a new mixed-integer linear programming model for long-term open-pit mine production planning, incorporating constrained pit deepening to enforce predominantly lateral progression throughout the planning horizon. To achieve this, the number of active benches in each time period was reduced, thereby decreasing the need for equipment movement between working benches. Furthermore, with the horizontal progression of the pit, more workspace became available for deploying in-pit crushers, reducing equipment movement costs between benches and overall transportation costs, ultimately lowering the mine's operational expenses. Finally, the proposed model was implemented at the Miduk copper mine. The results demonstrated that the proposed model successfully achieved the expected objectives, resulting in a 52.45% improvement in reducing the number of active benches and regarding execution time reduction, the model showed a 53.32% improvement.
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
Exploration
Mohammed A.Amir; Hamzah S. Amir; Mokhtar Farkash
Abstract
Permeability estimation is an essential phase in assessing the hydrocarbon potential within porous media and designing reservoir management methods. Recently, machine learning (ML) methodologies have gained prominence in the prediction of permeability. The initial stage in constructing highly reliable ...
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Permeability estimation is an essential phase in assessing the hydrocarbon potential within porous media and designing reservoir management methods. Recently, machine learning (ML) methodologies have gained prominence in the prediction of permeability. The initial stage in constructing highly reliable ML models is to identify the optimum combinations of input logs, as permeability is a highly sensitive parameter; this step is essential and can influence model accuracy. While feature engineering methods provide valuable insights in selecting suitable input logs, the effectiveness of these logs or their combinations remains underexplored, particularly in the context of high-heterogeneity reservoirs. The current study intends to save time by evaluating the effectiveness of twelve distinct models, each constructed using a Multi-Layer Perceptron (MLP), based on various combinations of input logs using data from the Nubian reservoir, Sirt Basin, Libya. The methodology involved several steps, including preprocessing, splitting, optimization, and validation. The findings demonstrate that single-input logs, mainly the Gamma-ray (GR), bulk density (RHOB), and sonic logs (DT), exhibited higher correlation coefficients compared to the multiple log combinations. The GR model attained the best R² of 0.994, indicating its sensitivity in capturing non-linear relationships. On the other hand, multi-log models achieved variable accuracy, resulting in increased learning complexity. The study highlights the efficiency of selecting the optimal combination of input logs, providing practical guidance for ML-based permeability prediction in heterogeneous reservoirs.
Original Research Paper
Exploration
Mohammadreza Agharezaei; Ardeshir Hezarkhani
Abstract
Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based ...
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Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based on the Fibonacci sequence for the first time. The Fibonacci features of datasets were clarified and the method was introduced and applied on a dataset of bore-hole samples of Hired gold deposit located in southern Khorasan province, Iran. The main result of this study is the successfully establishing of a Fibonacci-based procedure that leads to separate geochemical anomalies. The determined thresholds by this method were compared with U-statistics and Concentration-Volume fractal modeling. Evaluation of the results revealed high consistence between the outcomes of the methods. The U-Statistics threshold for background was 105 ppb for gold and the Fibonacci Transformation method’s threshold was 109 ppb. This new method specified 170 ppb for gold moderate anomaly and the C-V fractal determined 169 ppb which are almost the same. The performance of the new method was assessed by calculating misclassification errors. The average total misclassification error was 0.023 which is acceptable and quite reasonable since the methods are fundamentally different. As the other main results of this study, it is confirmed that one of the Fibonacci features which is defined as Fibonacci index (FI) fluctuates among element pairs in the same way that geochemical correlation does. The FI could be considered as a genetic-related factor in geochemical studies and ore evolution researches.
Original Research Paper
Exploitation
Sina Ghavami; Ebrahim Ghasemi; Mohammad Hossein Kadkhodaei; Ali Farhadian
Abstract
Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the ...
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Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the stone abrasivity. One of the most commonly used methods is Cerchar abrasivity index (CAI). This study mainly focuses on investigating the relationship between CAI with petrographic and physico-mechanical properties of granitic building stones. For this purpose, 14 different types of commercial granitic building stones, collected from different regions of Iran, were subjected to laboratory investigations and the effect of the petrographic and physico-mechanical properties of these stones on CAI was examined using simple and multiple regression analysis. Meaningful and reasonable relationships were observed. According to the obtained results, equivalent quartz content (EQC) of granitic building stones was found to be the most effective parameter on CAI. Using linear and nonlinear regression analysis, two empirical correlations for CAI prediction based on EQC were developed. The results showed that both linear and nonlinear correlations have high performance with determination coefficients (R2) of 0.876 and 0.882, respectively. These correlations can determine the CAI with acceptable error, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.135 and 0.105, respectively. Furthermore, the relationship between the diamond segment wear (SW) and CAI was investigated for the studied stones. The results showed that SW is directly related to the CAI, and there is a strong linear correlation between these two parameters with R2 of 0.787. The proposed correlation can be applied for fast prediction of cutting tool wear for practical applications in building stone processing plants with circular sawing machine, which can lead to enhanced cutting efficiency and productivity.
Original Research Paper
Exploration
Hamed Norouzi; Aliakbar Daya
Abstract
Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting ...
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Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.
Original Research Paper
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez; Kevin Daniel Rondo-Jalca
Abstract
The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical ...
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The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical criteria for delineating resource categories. To mitigate this limitation, an innovative methodology integrating clustering based on Riemannian geometry with machine learning techniques was developed for mineral resource classification. A database of 5,654 composited samples from 185 diamond drill holes in a copper deposit in central Peru was utilized to classify 318,443 blocks. Copper grades were estimated through Ordinary Kriging (RMSE = 0.102; MAE = 0.069), generating geostatistical variables kriging variance, average distance to samples, and number of samples that served as input features for the classification. Clustering was performed using both classical KMeans and Riemannian KMeans, followed by spatial smoothing via XGBoost and Random Forest algorithms. Absolute coordinates were incorporated to address spatial discontinuities in classification outputs. The combination of the Riemannian model with Random Forest produced the highest classification performance, with a Silhouette index of 0.26 and a Davies-Bouldin index of 0.72. The resulting metal content was estimated at 4.24 Mt of copper at 0.44% grade (measured), 6.49 Mt at 0.34% Cu (indicated), and 7.68 Mt at 0.32% Cu (inferred), demonstrating close alignment with QP estimates while exhibiting improved spatial coherence. In summary, the Riemannian-based approach outperformed classical KMeans and conventional classification methods, providing a more robust, objective, and globally consistent alternative.
Original Research Paper
Exploitation
Mehrnaz Mohtasham
Abstract
In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. ...
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In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.
Original Research Paper
Rock Mechanics
Navid Afrasiabi; Mehdi Noroozi; Ahmad Ramezanzadeh
Abstract
In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different ...
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In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different joint configurations (possible combination of dip angles of 30, 60, 90 degrees with joint spacings of 3, 5, 10, 15, 20 cm). Numerical modeling results reveal that in general, the joint spacing has a greater effect on rock cutting efficiency than joint orientation. If the joint spacing is less than 10 cm, the role of the joint angle is reduced and the distances between the joints control the efficiency. When the joints are close together and have a spacing of less than 10 cm, particularly 3 to 5 cm, the best cutting efficiency can be achieved for a joint angle of 90 degrees. The cutting coefficient is decreased by increasing the joint spacing and the maximum CC occurs at a joint spacing of 5 cm. For joint spacing more than 10 cm, the joints with a 90 degrees dip angle have the greatest impact on the specific energy and reduce cutting efficiency. The best disc cutter efficiency and the minimum required normal force is achieved when joint spacing is more than 10 cm and the angle between the joints and advance direction of the disc cutter is 60 degrees. In the tunnel excavation process, with increasing joint spacing, the TBM machine thrust is more important than its torque. The findings of this research provide a basis for predicting TBM efficiency through joint characteristics.
Original Research Paper
Exploitation
saeideh Qaedrahmat; Javad Gholamnejad; Ali dabagh
Abstract
The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, ...
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The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, and slope constraints. One of the key objectives of Short-Term Production Scheduling (STPS) is to ensure a stable and continuous supply of ore to the processing plant, while minimizing operating costs through measures such as reducing unnecessary equipment movements and variation in feed quality. However, one of the major obstacles to the operational feasibility of STPS is the limited working space available for equipment, as well as the excessive equipment movement between benches within each scheduling period. To tackle these challenges, this paper employs an Integer Goal Programming (IGP) with a new constraint that limits active benches per period, enhancing the practicality of production schedules. Unlike previous GP-based STPS models, it improves operational feasibility by ensuring extraction continuity and minimizing equipment movement. The model was tested on a copper deposit using GAMS software. The results show that by applying this new constraint, the average number of active benches per month was reduced from 14 to 10 )36% reduction) and the number of extraction periods per bench from 6 to 4 (33% reduction) without violating the existing constraints such as ore grade, tonnage, or slope. This approach improves equipment efficiency, reduces fuel consumption, reducing equipment relocation costs, promoting operational continuity of extraction and enhances operational feasibility in real conditions.
Original Research Paper
Exploitation
Mohammad Reza Rezaei; Majid Noorian-Bidgoli
Abstract
Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling ...
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Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.
Original Research Paper
Rock Mechanics
Shadman Mohammadi Bolbanabad; Masoud Monjezi; Vahab Sarfarazi
Abstract
The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under ...
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The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under a constant pressure of 12.5 MPa to investigate the influence of both the edge notch length and its position relative to the incident bar on the size distribution of fragmented iron ore. By analyzing the fragmentation distribution characteristics of specimens subjected to controlled laboratory impact loading, this study focuses on fundamental rock breakage mechanisms relevant to blasting operations in open-pit iron ore mines, where the fragmented material classified into three particle size categories: large, medium, and fine fragments. Based on this classification, the variation in the mass percentage of fragments with respect to notch length and its position relative to the incident bar was investigated. Ultimately, within the context of laboratory-scale fragmentation analysis, an effective range of notch lengths and positions relative to the incident bar was identified for achieving optimal fragmentation. The results revealed that a notch length between 0.2 and 0.4 and a notch position between L/2 and 2L/3 from the incident bar (where L equals sample length), produced the most favorable fragment size distribution. These findings can help link laboratory-scale fracture behavior to field-scale rock fragmentation considerations and contribute to a broader understanding of breakage processes in mining engineering.
Original Research Paper
Environment
Fatemeh Vesmoridi; Feridon Ghadimi
Abstract
A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces ...
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A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces geochemical evidence maps derived from stream sediment data. The study comprised a three-stage factor analysis of geochemical data collected from the Khomain Dehno region. The first factor included Zn, Pb, As, and Cd, accounting for 41.63% of the variance. The second factor comprised Mn, Mo, and Zr, explaining 21.86% of the variance, while the third factor consisted of Fe, Cu, and Ti, representing 7.79% of the variance. The cumulative variance explained by these three factors was 81%. Furthermore, a novel intelligent methodology, termed Relevant Vector Regression (RVR), enhanced with Cocoa Search (CS) and Harmony Search (HS) algorithms, is proposed for the prediction of the GMPI. The HS and CS algorithms were integrated with the RVR model to optimize its hyperparameters. In these models, Zn, Pb, As, and Cd served as input variables, while the GMPI was designated as the output variable. The performance of the predictive models was evaluated using Mean Squared Error (MSE) and the Coefficient of Determination (R²). The results indicated that the RVR model optimized with the HS algorithm exhibits superior performance, achieving an R² value of 0.99256 and an MSE of 0.0031455. These findings underscore the efficacy of the proposed approach for accurate GMPI estimation.
Original Research Paper
Exploitation
Samia Chaoui; Adel Djellali; Benghazi Zied; Sarker Debojit
Abstract
This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical ...
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This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical characterization revealed low interstitial porosity, strong to very strong uniaxial compressive strengths ranging from 50.4 MPa to 129 MPa, and significant fracture-related secondary porosity. Rock Mass Rating (RMR89) and Geological Strength Index (GSI) values suggest fair to good rock quality. The mine design features square pillars inclined at 10°, with walls originally oriented perpendicular to the orebody dip, measuring 5 m in width and 3 m in height. The rooms, situated under a cover depth of 145.3 m, are 9 m wide. This configuration yielded an effective extraction rate of 87.24% and a safety factor of 1.63, indicating stable mining conditions. Phase 2D finite-element simulation confirmed these findings, showing a maximum displacement of 3.96 mm, surface subsidence of 0.57 mm, and a safety factor of 1.66, suggesting minimal environmental impact and long-term stability. Shear/compressive stress results from tributary area theory, aligning with numerical results and validating both approaches for inclined orebodies. Finally, the pillar walls, originally perpendicular to the orebody dip, were modified to be vertical relative to the horizontal plane, while maintaining the same pillar and room dimensions and cover depth. This adjustment improved stability by enhancing stress distribution and pillar core confinement, increasing the safety factor to 1.85.
Review Paper
Rock Mechanics
Shadman Mohammadi Bolbanabad; vahab sarfarazi; Masoud Monjezi
Abstract
One of the critical steps in experimental research is the precise preparation of specimens. This study aims to develop and present a comprehensive methodology for preparing magnetite iron ore specimens containing non-persistent edge notches for dynamic testing, as well as iron ore and ice specimens for ...
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One of the critical steps in experimental research is the precise preparation of specimens. This study aims to develop and present a comprehensive methodology for preparing magnetite iron ore specimens containing non-persistent edge notches for dynamic testing, as well as iron ore and ice specimens for uniaxial and Brazilian tests. Core drilling was performed using diamond drills with diameters of 54 mm for uniaxial and Brazilian tests and 22 mm for dynamic Split Hopkinson Pressure Bar (SHPB) tests. Non-persistent edge notches with a thickness of 3 mm, controlled length, and inclination were created using a cutter, and their geometric quality was verified through meticulous inspection. For ice specimens, filling the notches with water in tubes matching the specimen diameter provided optimal uniformity and stability. Additionally, precise control of parameters such as core and drill parallelism, drilling speed, cooling water flow, and environmental conditions (dry, saturated, and frozen) preserved the structural integrity and quality of the specimens. The results demonstrated that systematically following these procedures, along with detailed documentation of geometric and environmental specimen features, enables the production of intact, standardized, and reproducible specimens, ensuring reliable and consistent examination of the mechanical response and fracture of magnetite iron ore under both dynamic and quasi-static conditions.
Original Research Paper
Environment
Sadegh Abedi; Mohamad Reza Karimi; Alireza Alinezhad
Abstract
Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular ...
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Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular focus on the roles of emerging technologies and environmental regulations. The core research question investigates how combining fuzzy decision-making methods with intelligent modeling can guide the mining sector toward NZE goals. A multi-stage mixed-methods approach was employed. Initially, key variables were identified using the fuzzy Delphi method and expert judgment. The hesitant fuzzy analytic hierarchy process (HFAHP) was then applied to prioritize and weigh the main factors. Subsequently, fuzzy DEMATEL and interpretive structural modeling (ISM) were utilized to uncover causal relationships and hierarchical dependencies among variables. Finally, the adaptive neuro-fuzzy inference system (ANFIS) simulated potential pathways for achieving sustainable mining. Findings highlight four critical variables—carbon pricing policies, investment costs, global metal prices, and technological innovation—as the most influential drivers. Moreover, ANFIS results indicate that strengthening these factors significantly increases the likelihood of achieving the NZE scenario. Overall, the proposed model serves as a practical decision-support tool for policymakers and mining stakeholders, aiding in policy design, investment strategy develop.
Original Research Paper
Rock Mechanics
Mostafa Rahimiyan; Mohammad Ataei; Reza Kakaie; Hossein Khosravi
Abstract
The identification of rock discontinuities is a critical factor in the field of mining and construction projects. Traditional methods for conducting this task is often difficult, time-consuming, poses risks to the human safety, and lead to incomplete evaluations. With introduction of unmanned aerial ...
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The identification of rock discontinuities is a critical factor in the field of mining and construction projects. Traditional methods for conducting this task is often difficult, time-consuming, poses risks to the human safety, and lead to incomplete evaluations. With introduction of unmanned aerial vehicles (UAV) has changed this process and has allowed to cover all the area in a short time without endangering employees. The aim of this paper is to employ deep learning using python programming language to develop and train a neural network based on the UNET++ architecture in order to identify rock surface discontinuities automatically by means of UAV-captured imagery. It is also addresses challenges associated with supervised learning, particularly overfitting, by implementing data augmentation techniques and reducing model parameters by approximately 6%. Consequently, the pixel-wise precision criterion improved significantly from 53.27% to 75.6%. Especially, this work stands out from other studies by focusing specifically on UAV imagery for geological assessments, employing a dual strategy to overcome overfitting, and demonstrating effective performance despite the limited training data. The result showed that the model is capable to identify rock discontinuities accurately and is a suitable method for the mining and construction industries.
Original Research Paper
Exploration
Alireza Sadoughi; Ali Aalianvari; Hamidreza Shahbazian
Abstract
The study investigated how time-dependent viscosity affects the penetration length of cement-based grouts prepared with saline and fresh water. An idealized horizontal fracture, represented by two smooth, parallel, and frictionless plates, was assumed. The grout viscosity, varying over time, was analyzed ...
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The study investigated how time-dependent viscosity affects the penetration length of cement-based grouts prepared with saline and fresh water. An idealized horizontal fracture, represented by two smooth, parallel, and frictionless plates, was assumed. The grout viscosity, varying over time, was analyzed to determine the maximum penetration length under a constant injection period. A fracture model was developed and meshed in Gambit, and the two-phase fluid behavior with time-dependent viscosity was simulated in ANSYS Fluent. One saline water function and two fresh water functions were examined. The saline grout was tested at 1475 and 1625 kg/m³, while the fresh water grouts were analyzed at 1475 kg/m³. The resulting penetration lengths were 1.384 m and 1.789 m for the fresh water grouts, and 0.789 m and 0.427 m for the saline grouts, respectively. The outcomes reveal that saline water grout penetrates less effectively than fresh water grout. Furthermore, the effect of density was found to be minor compared to viscosity variations, though differences between saline and fresh water systems were clearly evident. This study introduces a stable grout formulation without additives, contrasting with previous research that relied on additives and adjustments to the water-to-cement ratio, which led to grout instability over time. Utilizing CFD simulations, this research models a two-phase water-cement mixture with varying densities and viscosities, treated as a non-Newtonian fluid. Furthermore, the viscosity of the grout over time under hydraulic pressure is examined, providing valuable insights into grout behavior under subsurface conditions.
Original Research Paper
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Jorge Chira-Fernandez; Cesar De la cruz-Poma; Solio Marino Arango-Retamozo
Abstract
The sustained increase in mining waste, particularly in the form of tailings, poses a significant environmental and economic challenge, especially in contexts where these deposits retain residual metal content. This study assessed the gold potential of Tailings Deposit I at La Cienega (Peru) by integrating ...
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The sustained increase in mining waste, particularly in the form of tailings, poses a significant environmental and economic challenge, especially in contexts where these deposits retain residual metal content. This study assessed the gold potential of Tailings Deposit I at La Cienega (Peru) by integrating geostatistical estimation and machine learning models optimized through metaheuristic algorithms. The methodology involved geochemical characterization, three-dimensional estimation using Ordinary Kriging (OK) as a geostatistical method, and prediction of gold grades through three models: XGBoost optimized with Particle Swarm Optimization (XGB+PSO), Support Vector Regression with Genetic Algorithm (SVR+GA), and Random Forest optimized using Ant Colony Optimization (RF+ACO). Estimates were validated using Leave-One-Out cross-validation and performance metrics including RMSE, MAE, Bias, and correlation coefficient (R). The RF+ACO model achieved an RMSE of 0.32 ppm, MAE of 0.24 ppm, Bias of 0.006, and an R value of 0.56. Average predicted grades ranged from 1.14 to 1.33 ppm, with estimated gold contents between 981.00 and 1,147.12 ounces, while OK yielded 1,028.77 ounces at an average grade of 1.19 ppm. These findings suggest that properly optimized machine learning models can provide reasonable estimates of metal content in tailings, particularly in settings characterized by high spatial heterogeneity and limited geological continuity.
Original Research Paper
Mineral Processing
Amirmohammad Nasrollahzadeh bafti; Laya Shakib Mehr; Esmaeel Darezereshki; Mohsen Akhoundi parizi; Hossein pour Shahnazari
Abstract
Copper smelting slag (CSS) represents a significant secondary resource containing valuable metals such as copper and molybdenum. However, its complex mineralogy and the glassy nature of the slag limit the efficiency of conventional flotation processes and require high reagent consumption. In this study, ...
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Copper smelting slag (CSS) represents a significant secondary resource containing valuable metals such as copper and molybdenum. However, its complex mineralogy and the glassy nature of the slag limit the efficiency of conventional flotation processes and require high reagent consumption. In this study, a native halophilic bacterium, Halomonas lutescens, was investigated as an eco-friendly bio-reagent to improve the flotation performance of CSS. Laboratory-scale experiments were conducted under controlled conditions to determine the optimal bacterial dosage, evaluate reusability, and analyze kinetic behavior. The results demonstrated that adding 40 mL of bacterial suspension (conditioned for 5 min) significantly enhanced copper and molybdenum recoveries compared to chemical flotation. Based on previous research on the adhesion of halophilic bacteria, supportive FTIR, SEM–EDS, and adhesion schematic analyses indicate that hydroxyl, carboxyl, and amine groups in bacterial EPS can coordinate with Cu²⁺/Fe³⁺ surface sites, thereby enhancing mineral hydrophobicity and improving Cu–Mo recoveries. Total copper recovery increased from 58.98% to 71.11%, and molybdenum recovery rose markedly from 4.50% to 28.51%, while maintaining similar concentrate grades. Kinetic modeling revealed higher rate constants and better fitting with bacterial presence, confirming enhanced flotation kinetics. Moreover, bacteria remained viable and reusable over multiple flotation cycles, indicating strong potential for process sustainability. Overall, H. lutescens acts as a bio-frother and collector aid, enabling more efficient and environmentally friendly flotation of copper smelting slag.
Original Research Paper
Environment
Ramin Mohammadi pour; Hossein Ali Akhlaghi Amiri; Hamed Janani
Abstract
This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide ...
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This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide (C-St-g-PAM)—on real iron ore tailings from four industrial sources representing different mining regions of Iran: North-East, West, Central Plateau, and South. The flocculants, previously developed via a straightforward one-step synthesis method, were assessed in terms of settling velocity, supernatant clarity, and zeta potential of flocs under controlled conditions (solid contents: 0.5–4 wt%; dosage: 80 ppm). Experimental results revealed that dual-modified flocculants consistently outperformed other variants: A-St-g-PAM and C-St-g-PAM achieved the highest settling rates (up to 0.82 cm/s at 2 wt.% solids) and produced supernatant turbidity values below 15 NTU, compared to >80 NTU for native starch. Zeta potential measurements confirmed enhanced particle destabilization, with floc surface charges approaching −20 mV after treatment. Given their facile synthesis route, high efficiency, and biodegradability, these dual-functional flocculants emerge as promising candidates for large-scale industrial dewatering. The findings highlight their potential as environmentally friendly substitutes for conventional synthetic flocculants, particularly in water-scarce mining regions where efficient water recovery and sustainable tailings management are urgent priorities.
Original Research Paper
Environment
Jalil Hanifehnia; Akbar Esmaeilzadeh; Solat Atalou; Reza Mikaeil
Abstract
Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research ...
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Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research aims to predict flyrock distances (FR) at the Sungun Copper Mine through the application of artificial intelligence (AI) models in conjunction with statistical approaches. Initially, a linear multivariate regression (LMR) model was constructed to establish the correlation between blasting parameters and flyrock range. Subsequently, an artificial neural network based on a multilayer perceptron (ANN-MLP) was developed and further optimized using two advanced hybrid algorithms: the Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). These algorithms were employed to calibrate the neural network’s weights and biases using variables such as number of blast holes, hole spacing, burden, total charge, specific drilling, charge per hole, and specific charge. Results showed that the ANN‑MLP model outperformed the LMR model, with performance metrics of root mean square error (RMSE = 9.31 m), mean absolute error (MAE = 7.10 m), and coefficient of determination (R² = 0.81) during the test phase. However, optimization of the ANN model with ICA and ACO significantly improved prediction accuracy. Among the hybrid models, the ICA-ANN model performed best with RMSE = 5.66 m, MAE = 4.60 m, and R² = 0.89, showing a considerable improvement over the LMR and ANN-MLP models. Sensitivity analysis further highlighted total charge and number of holes as the most influential parameters affecting flyrock dispersion. Overall, the findings underscore the potential of hybrid AI frameworks in advancing predictive modeling for safer and more efficient blasting operations.
Original Research Paper
Exploration
Poorandokht Soltani; Amin Roshandel Kahoo; Hamid Hassanpour
Abstract
Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex ...
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Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex and subjective decision-making processes. Constructing an initial geological model that aligns with seismic observations is a critical first step, but it is inherently non-unique and heavily influenced by the interpreter’s experience and preferences. Among various subsurface structures, salt domes are of particular interest due to their unique physical characteristics and their critical role in hydrocarbon entrapment, drilling risk management, and subsurface storage applications. Their distinct seismic textures, compared to surrounding sediments, make them identifiable using seismic texture attributes. Nevertheless, the manual delineation of salt dome geobody is a time-consuming and potentially error-prone task, especially given the volume, redundancy, and complexity of the seismic attributes used. To overcome these challenges, we propose a novel unsupervised framework for automatically identifying salt dome geobody in 2D seismic sections. The method begins by extracting a diverse set of seismic texture attributes, including both conventional attributes and novel texture descriptors derived from advanced image analysis techniques. Following attribute extraction, a attribute selection phase using techniques such as Laplacian Score is employed to eliminate redundant, irrelevant, or highly correlated attributes, thereby enhancing model efficiency and interpretability. The reduced set of relevant attributes is then used as input for clustering algorithms based on metaheuristic optimization techniques. These algorithms aim to partition the seismic data into meaningful clusters that correspond to geological attributes, particularly salt domes. Validation against multiple expert interpretations demonstrates the robustness and high accuracy of the proposed method. Results emphasize the capability of unsupervised clustering approaches especially those guided by metaheuristic strategies—in reducing interpretation uncertainty and improving segmentation quality.
Original Research Paper
Mine Economic and Management
mahdi sanei; Mohammadreza Ameri
Abstract
The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework ...
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The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework underscores the necessity of formulating effective, ethically grounded strategies for their mitigation to ensure the responsible and equitable exploitation of mineral reserves. In modern industrial contexts, mining operations are increasingly confronted with a wide spectrum of hazards, ranging from physical and chemical risks to environmental, social, and health-related challenges, each of which poses substantial threats to human welfare, ecological integrity, and the sustainability of natural resources. Employing an analytical–descriptive methodology, this study systematically investigates these hazards and contextualizes them within established jurisprudential principles. Through this alignment, the article proposes comprehensive strategies—including targeted education, heightened awareness, expert consultation, continuous evaluation, and robust monitoring mechanisms —to reduce or eliminate mining-related risks throughout the processes of policy formulation, legislative development, and operational implementation. Adherence to these jurisprudentially informed measures not only minimizes potential harm to individuals and the environment but also ensures the provision of appropriate remedies and compensation in cases involving negligence or procedural lapses. Consequently, the study emphasizes that employers, mine proprietors, technical supervisors, mining personnel, and governmental authorities each bear distinct and critical responsibilities in the collective effort to mitigate and ultimately eliminate mining hazards.
Original Research Paper
Exploration
Hasan Feizi Anhar; Ali Imamalipour; Peyman Afzal
Abstract
Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for ...
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Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for decades, this study introduces an enhanced three-dimensional geochemical zoning model specifically tailored for the Sungun porphyry deposit, based on geochemical data obtained from 264 drill cores comprising a total of 33,368 rock samples. The model is constructed using ratios of factors derived from Staged Factor Analysis (SFA) of ore-related major (Cu and Mo) and minor (Cd, Mn, Pb, Zn, and Ag) elements, and further refined through fractal modeling for classification. Fractal modeling method (C-V) clearly shows four distinct populations and three breakpoints, which to supergene (0.9–1.4%), hypogene (0.6–0.9%), and oxidized zones (0.1–0.6%). The application of the method to the Sungun porphyry system reveals a strong spatial correlation between the zoning index, copper grade distribution, and alteration patterns. SFA effectively separates supra-ore and sub-ore elements, while fractal modeling improves the robustness of zoning classification. Integration of the developed 3D zoning index with copper grade models reveals a clear structural relationship among alteration, geochemical ratios, and copper distribution. The proposed approach enhances the resolution of porphyry deposit zoning, offering improved targeting accuracy and reduced risk in deep drilling exploration.
Original Research Paper
Environment
Tingze Li; Yu Wang; Genyuan Tan
Abstract
Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading ...
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Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading to limitations in adaptability. This study establishes a COMSOL-based multiphysics coupling model that integrates stress-permeability interactions, gas adsorption-desorption kinetics, and fracture-induced permeability evolution to evaluate the gas drainage performance of cross-measure boreholes in floor strata. Simulation results indicate that directional borehole spacing is the most influential factor: reducing the spacing from 25 m to 20 m significantly increases gas drainage efficiency by 31.4%, while extending the drainage duration from 90 days to 270 days expands the influence radius by more than 35%. In contrast, variations in borehole diameter (75-115 mm) and negative pressure (10-90 kPa) exert a negligible impact on gas pressure (with a variation of less than 5%), reflecting limited sensitivity. The optimal borehole location is determined to be at the lower boundary of the mining-induced fracture zone. A gradient layer analysis further confirms that the perforation depth should match the range of the plastic deformation zone (15-25 m). The proposed parametric optimization strategy provides a quantitative framework for directional drilling design, enabling the matching of borehole layout with the scale of fracture development. These findings contribute to enhancing the accuracy of gas control and the engineering adaptability of gas drainage systems under complex geological conditions.
Review Paper
Mine Economic and Management
Mohamad Reza ameri; Mohammad Mehdi Rajaei; Abuzar Faraji
Abstract
AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify ...
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AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify publication trends, geographic distribution, citation patterns, and key themes. Using the PRISMA protocol, titles and abstracts were screened, and relevant studies were selected for detailed review The results indicate a steady growth in research output over the past decade, reflecting the increasing importance of intelligent systems in addressing uncertainty and complexity in mining operations. Developed countries tend to prioritize AI-driven methods such as machine learning, neural networks, and hybrid systems. In contrast, developing countries place greater reliance on fuzzy logic approaches, particularly in contexts where reliable data are limited. This methodological divergence underscores uneven technological development and highlights the existing knowledge gap across regions. Three main research pillars are identified: safety (39%), operational efficiency (45%), and environmental sustainability (16%). Methodologically, fuzzy approaches dominate (48%), followed by AI (34%) and hybrid methods (18%). These findings confirm the global relevance of AI and FIS in mining risk assessment and emphasize the need for collaboration to close existing gaps.
Original Research Paper
Exploitation
feng yang; pengjie li; qiang Sun
Abstract
Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles ...
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Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles and that are extremely close to overlying pillars. The technical principles are explained. First, FLAC3D simulation software was used to investigate the effect of the spacing between the lower roadway and the section coal pillar, which determined the optimal roadway position. Then, a coupled FLAC-PFC method was employed to optimize the coal drawing process parameters. The optimal scheme was analyzed to characterize roof deformation, stress distribution, and hydraulic support loads. An engineering case study demonstrates that a spacing greater than 18 m minimizes the influence of concentrated stress, resulting in limited deformation and improved roadway stability. The study investigates coal drawing under different sequences, port widths, and methods. The optimal process was identified as downward drawing, with a 1.5 m coal drawing port width and a two-wheel sequential method. This process achieves a drawing rate of 85.62% and a gangue content of 4.61%. Analysis shows that during the pillar drawing process, the concentrated stress on the roof plate is significantly reduced, with a maximum stress decrease of 21.1 MPa, effectively alleviating stress concentration. The total force on the section hydraulic support in fully mechanized caving is 1.6×10⁴ kN, while the force in the fully mechanized mining section is 1.4×10⁴ kN.
Original Research Paper
Exploration
Omid Robatjazi; Alireza Arab-Amiri; Keyvan Khayer
Abstract
Accurate delineation of subsurface controlling structures within complex geological settings is critical for reliable targeting of hematite mineralization, yet remains challenging. Interpretations relying on a single geophysical dataset typically suffer from limited structural resolution and interpretation ...
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Accurate delineation of subsurface controlling structures within complex geological settings is critical for reliable targeting of hematite mineralization, yet remains challenging. Interpretations relying on a single geophysical dataset typically suffer from limited structural resolution and interpretation ambiguity. This study integrates magnetic and geoelectrical datasets to investigate subsurface structures controlling hematite mineralization in the Aqda area, Yazd Province, Iran. Magnetic data were processed using reduction to the IGRF and several enhancement filters, including vertical and horizontal derivatives, analytic signal, and the Centre for Exploration Targeting (CET) technique. The results revealed five major magnetic anomalies trending northeast–southwest and northwest–southeast, interpreted as fault‑controlled intrusive bodies. Two dominant structural trends identified by CET, NE–SW and NW–SE correspond to the magnetic lineaments and delineate zones of high mineral potential. To validate these structures, seven IP‑RS profiles were acquired and inverted using the smooth‑model approach in RES2DINV software. The integrated resistivity and chargeability sections confirmed the position of the inferred faults and highlighted zones of elevated chargeability consistent with hematite mineralization. The combination of both datasets improved the structural resolution and significantly reduced interpretation ambiguity. This integrated approach demonstrates that the magnetic and geoelectrical methods complement each other and provide an effective tool for delineating mineralized zones in complex geological environments.
Original Research Paper
Environment
Ali Najmeddin; Taha Salahjoo; Kimia Zendehdel
Abstract
Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment ...
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Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment of tailings from the Sungun porphyry copper mine in northwestern Iran. A comprehensive and multidisciplinary approach was employed, combining physicochemical, mineralogical and geochemical analyses with statistical methods. Chemical speciation was done by employing a modified procedure suggested by the BCR (European Community Bureau of Reference) which has also been used in numerous studies to assess the geochemical fractionation and mobility of elements. The main goal was to advance from total concentration analysis to a more precise, bioavailability-based risk evaluation utilizing the USEPA framework for both children and adults. Mineralogical investigation indicated a net acid-generating capability, with pyrite content (~4%) typically surpassing that of the principal neutralizing mineral, calcite (~2%). Geochemical analyses verified that the tailings exhibit significant enrichment in Cu and Mo, along with moderate enrichment of As and Co. Among the studied elements, the highest mobility factors belonged to Cu (81.49%), Pb (76.71%), Zn (71.65%) and Mo (59.27%), respectively. The non-carcinogenic hazard index (HI) for children was 2.04, exceeding the safety threshold of 1.0, with bioavailable vanadium recognized as the principal risk factor. These findings highlight that relying solely on total PTE concentrations can be misleading, reinforcing the need for speciation-based assessments to accurately characterize the environmental behavior and health risks of mine tailings.
Original Research Paper
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo
Abstract
Integrating entropy-based uncertainty analysis with machine learning offers a novel approach to improving lithological classification in mineral exploration. This study applies supervised algorithms to predict lithology from spatial and geochemical data collected at a gold deposit in northern Peru. The ...
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Integrating entropy-based uncertainty analysis with machine learning offers a novel approach to improving lithological classification in mineral exploration. This study applies supervised algorithms to predict lithology from spatial and geochemical data collected at a gold deposit in northern Peru. The dataset includes 2,129 composited samples from 140 drillholes, containing spatial coordinates (East, North, Elevation) and gold content (Au). Six classifiers were tested: Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Multilayer Perceptron. Stratified five-fold cross-validation was applied to a 70/30 train-test split. The best performance was achieved by ANN-MLP (94.5% accuracy) and XGBoost (93.9%), with F1-scores above 94%. In zones of low uncertainty, models reached up to 100% precision, while accuracy dropped to 71.9% in highly uncertain regions. Entropy-based uncertainty mapping highlighted areas of geological ambiguity, such as lithological boundaries or sparsely sampled zones. The Friedman test confirmed statistically significant differences among classifiers (p < 0.001). These findings demonstrate that combining machine learning with spatial uncertainty quantification enhances both predictive reliability and geological interpretability, offering a practical tool for guiding exploration and reducing risk in complex mineral systems.
Original Research Paper
Environment
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Juan Antonio Vega-Gonzalez; Moises Bartolome Guia-Pianto
Abstract
The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites ...
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The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites from 40 vertical drillholes. Three supervised learning models—Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were trained using spatial coordinates (X, Y, Z) as the sole input features. Model validation was performed using leave-one-out cross-validation (LOOCV), and results were benchmarked against ordinary kriging (OK). Among the models, RF delivered the highest predictive performance (mean error = 0.53 g/t, RMSE = 7.21 g/t, R = 0.82), outperforming OK (R = 0.63, RMSE = 10.47 g/t). Block model predictions indicated higher silver content from machine learning models: 1,532.86 t (RF), 1,542.16 t (XGBoost), and 1,492.09 t (KNN), compared to 1,463.73 t from OK. Additionally, XGBoost maintained superior grade-tonnage relationships under elevated cutoff thresholds, highlighting its potential to delineate high-grade subdomains within the deposit. These findings confirm the value of machine learning in resource estimation under conditions of low spatial continuity, such as tailings, where material mixing and irregular deposition patterns limit correlation across space.
Original Research Paper
Rock Mechanics
Milad Manafi; Hamed Molladovoodi; Hamid Chakeri
Abstract
Tunneling in urban areas is associated with various challenges that must be carefully evaluated during pre-construction studies. Among these challenges, tunnel excavation through fault zones is particularly critical and has been widely investigated. Previous studies have primarily focused on the displacement ...
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Tunneling in urban areas is associated with various challenges that must be carefully evaluated during pre-construction studies. Among these challenges, tunnel excavation through fault zones is particularly critical and has been widely investigated. Previous studies have primarily focused on the displacement of tunnel linings under different fault movement conditions. In the present study, the effects of three key parameters, ground movement magnitude, grout layer thickness, and fault plane angle, on the induced bending moments and normal forces were examined. The numerical results indicate that ground movement magnitude has the most significant influence on induced stresses, whereas grout layer thickness and fault plane angle exhibit comparable effects. The analyses further show that a 100% increase in ground movement leads to a 60.67% rise in the induced normal force. Increasing the grout layer thickness reduces the induced forces by 32.9%, while a larger fault plane angle decreases the normal force by 34.52%. The modeling outcomes also reveal that grout layer thickness is the most influential factor effecting the induced bending moments. These findings provide valuable insights for evaluating the structural capacity and potential failure of tunnel lining crossing fault zones.
