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
Zaheer Ahmed; Muhammad Bilal; Zulfiqar Ali; Muhammad Badar Hayat; Kazutoshi HAGA; Atsushi Shibayama
Abstract
Low-grade copper–gold porphyry ores present significant processing challenges due to poor mineral dissemination and high gangue content. In this study, a representative ore sample from the Saindak Copper–Gold Project, Pakistan, with an average copper grade of 0.4 wt%, was investigated to ...
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Low-grade copper–gold porphyry ores present significant processing challenges due to poor mineral dissemination and high gangue content. In this study, a representative ore sample from the Saindak Copper–Gold Project, Pakistan, with an average copper grade of 0.4 wt%, was investigated to optimize flotation parameters for improved copper recovery. Mineralogical characterization using XRD, XRF, SEM–EDS, and ICP-OES confirmed chalcopyrite as the primary copper-bearing mineral finely disseminated within silicate gangue and iron sulfides. A series of batch flotation experiments were conducted by systematically varying particle size, slurry pH, collector dosage, and flotation time. The results showed that optimal conditions (particle size <75 μm, pH 12, and 1-min flotation time) enabled selective chalcopyrite recovery. Optimal flotation performance was achieved at pH 12 with a collector dosage of 5 g/t and a flotation time of 1 min, yielding a copper recovery of approximately 93% and a concentrate grade of 23 wt% under multistage flotation conditions. Flotation kinetics analysis indicated that the first-order kinetic model provided the best fit to the experimental data. The findings demonstrate that efficient copper upgrading from low-grade Saindak ore can be achieved with minimal collector consumption, offering a potentially cost-effective and sustainable processing strategy for similar porphyry copper deposits.
Case Study
Exploration
Mohammed Yehia Hassan Amer; said K Elsayed; Ibrahim Salahudin Mohamed
Abstract
Electric submersible pumps (ESPs) are among the most widely deployed artificial lift systems in the petroleum industry; however, their operation depends heavily on surface-supplied electrical power transmitted through long downhole cables, resulting in significant energy losses, high installation costs, ...
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Electric submersible pumps (ESPs) are among the most widely deployed artificial lift systems in the petroleum industry; however, their operation depends heavily on surface-supplied electrical power transmitted through long downhole cables, resulting in significant energy losses, high installation costs, reliability challenges, and increased carbon intensity. Simultaneously, large quantities of geothermal energy remain unused within deep hydrocarbon wells despite the substantial thermal gradients naturally available along the wellbore. This study introduces a novel multidisciplinary framework that transforms the well itself into an integrated energy-generation system by coupling geothermal heat recovery, thermoelectric power generation, advanced power electronics, and ESP technology. Unlike conventional approaches that treat geothermal energy and artificial lift as separate domains. The proposed framework combines a two-dimensional wellbore heat-transfer model, thermoelectric energy conversion, and silicon-carbide-based power conditioning with a complete ESP design methodology including inflow performance analysis, total dynamic head determination, pump selection, and stage optimization. A case study on Well AG-119X in Abu El Gharadig Field, Western Desert, Egypt, with a pump setting depth of 19,000 ft., demonstrates the technical feasibility of hydrocarbon–geothermal co-production and predicts a thermoelectric generation potential of approximately 350 kW under optimized conditions. Beyond demonstrating energy recovery, the study establishes a scalable roadmap for integrating geothermal resources directly into artificial lift infrastructure, reducing dependence on external power supply, improving overall system efficiency, and enabling more sustainable oil production. The proposed approach represents a step toward the convergence of petroleum engineering, geothermal energy, thermoelectric science, and power electronics, providing a foundation for next-generation self-powered ESP systems
Original Research Paper
Exploitation
Sherzod Vafo ugli Karimov; Eldor Ilkhom ugli Tuychiboev
Abstract
Near-wall waste dumping can reduce overburden haulage, but the shortest route is limited by slope stability. This study formulates a geomechanically allowable haul distance (GAHD) as a time-dependent lower bound in a discounted haulage optimization problem. The framework combines two-dimensional limit-equilibrium ...
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Near-wall waste dumping can reduce overburden haulage, but the shortest route is limited by slope stability. This study formulates a geomechanically allowable haul distance (GAHD) as a time-dependent lower bound in a discounted haulage optimization problem. The framework combines two-dimensional limit-equilibrium analysis, an effective affine fuel relation, an empirical cycle-time model, integer fleet sizing, an active-set finite-difference trajectory solution, and uncertainty propagation. The contribution is methodological and integrative - a geomechanically constrained planning layer added to haulage optimization - rather than a new mine-planning algorithm. The Kalmakyr copper mine (Almalyk MMC, Uzbekistan) is the primary application; a Muruntau wall section provides a second, structural application of the multi-tier dump concept. Across the four Kalmakyr walls, GAHD is 1.894-1.965 km, with a production-weighted value of 1.909 km. For a 2.0-km operating case, relative to 4.5 km, the model estimates fuel reduction from 86.0 to 51.2 L per trip, cycle-time reduction from 36.5 to 31.1 min, and fleet reduction from 60 to 51 trucks. The boundary advances by approximately 7.2 m yr−1, corresponding to a drift number of 0.02; a static constraint is therefore adequate for this seven-year case, and the moving-boundary formulation becomes relevant for faster-deepening or longer-horizon operations. A 50,000-draw Monte Carlo analysis gives a median net present value of 20.0 M USD and a 5th-95th percentile interval of 6.7-34.0 M USD. The findings support a reproducible decision-support workflow but remain conditional on two-dimensional stability analysis, aggregate fuel data, and site-specific calibration; independent operational validation is required before industrial implementation.
Review Paper
Mine Economic and Management
Eliezer Pantoja-Quiroz; Jairo Jhonatan Marquina-Araujo; Jose Mamani-Quispe; Jose Guartan; Marco Antonio Cotrina-Teatino; Felix Segundo Castillo-Vera
Abstract
This study evaluates the strategic positioning of Peru’s main formal coal mining companies by integrating firm-level competitiveness and investment attractiveness within a transparent multicriteria framework. Six representative title holders were selected using objective criteria related to productive ...
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This study evaluates the strategic positioning of Peru’s main formal coal mining companies by integrating firm-level competitiveness and investment attractiveness within a transparent multicriteria framework. Six representative title holders were selected using objective criteria related to productive relevance, operational continuity, and availability of verifiable information for 2020–2025. Competitiveness was assessed through productive capacity, mineral resource base, commercial positioning, and management and compliance, while investment attractiveness was evaluated through geological potential, logistical accessibility, market conditions, and the socio-institutional environment. Ordinal rubrics were converted to a 0–100 scale, weighted using the Analytic Hierarchy Process, and synthesized through composite indices and the General Electric-McKinsey (GE-McKinsey) matrix. The results reveal a fragmented formal coal industry dominated by small-scale producers with heterogeneous strategic profiles. Productive capacity is the main driver of firm competitiveness, whereas the socio-institutional environment and logistics are the main determinants of investment attractiveness. Obras Civiles and Mineras records the strongest overall strategic position, followed by Minera Santa Mercedes and Carbonífera San Benito. Sensitivity tests based on ±20% weight perturbations indicate stable rankings, supporting the robustness of the multicriteria assessment. The study contributes a traceable firm-level adaptation of the AHP–GE-McKinsey framework for fragmented small-scale extractive industries, combining coal-specific rubrics, nested weights, and robustness testing
Original Research Paper
Exploitation
Kusnawi Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
Abstract
Open-pit haulage must reduce fuel use without sacrificing material movement or operational safety. This study examined whether a telemetry-driven, task-specific Digital Twin surrogate could support vehicle-level eco-driving policies that improve fuel intensity while preserving haul-cycle productivity. ...
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Open-pit haulage must reduce fuel use without sacrificing material movement or operational safety. This study examined whether a telemetry-driven, task-specific Digital Twin surrogate could support vehicle-level eco-driving policies that improve fuel intensity while preserving haul-cycle productivity. The framework combined full haul-cycle reconstruction, an ensemble XGBoost fuel-rate surrogate, nonlinear state-transition models, equal-budget multi-seed Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), reward-sensitivity screening, a rule-based benchmark, and uncertainty- and out-of-distribution-aware evaluation. One-day telemetry from 71 Komatsu HD785-7 trucks contained 1,927,867 records and yielded 2,386 validated haul cycles. The fuel surrogate achieved a temporal-test MAE of 27.33 L/h, RMSE of 37.75 L/h, and R² of 0.6608. Across 26 complete independent test cycles, common-estimator rescoring showed that PPO reduced fuel intensity by 8.79% relative to the surrogate-human baseline (0.2854 versus 0.3163 L/t; p=0.0029), with a 4.91% mean throughput decrease that was not statistically significant. SAC reduced fuel intensity by 9.53% (0.2820 L/t; p=0.0032) and increased mean throughput by 3.16%, although the throughput change was not significant and loaded-route safety-envelope exceedance increased significantly. PPO and SAC did not differ significantly in fuel intensity. Policy-generated states also showed substantial out-of-distribution exposure. The framework therefore identified efficiency–productivity–safety trade-offs rather than universal algorithm superiority and should be interpreted as an offline policy-screening tool pending operator-in-the-loop and controlled field validation.
Original Research Paper
Rock Mechanics
AYOBAMI JOSHUA ADEGBOYE; KAYODE AUGUSTINE IDOWU
Abstract
Quarry blasting in Plateau State, Nigeria, typically follows generic, site-unspecific designs that overlook the marked geological contrast between the Precambrian granitic gneiss of Mista Ali and the Younger Jurassic granite of Mararaba’n Jama’a. This study provides the first quantitative, ...
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Quarry blasting in Plateau State, Nigeria, typically follows generic, site-unspecific designs that overlook the marked geological contrast between the Precambrian granitic gneiss of Mista Ali and the Younger Jurassic granite of Mararaba’n Jama’a. This study provides the first quantitative, ISRM-standardised comparison of intact-rock geomechanical properties, field-assessed joint density and post-blast fragmentation between these two formations. Uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), bulk density, specific gravity and brittleness index were determined for ten specimens per site (n = 20). Post-blast fragmentation from two monitored production blasts per site was characterised by WipFrag image analysis. The Younger granite had significantly higher mean UCS (190.4 vs 142.2 MPa) and BTS (15.4 vs 10.3 MPa; p < 0.001, Cohen’s d > 4.3), while field observation recorded predominantly sparse jointing at that site against dense jointing at the gneiss site. The gneiss site (powder factor 0.573 kg/m³) produced finer, more uniform fragmentation (X50 = 213 mm, n = 1.47) than the granite site (powder factor 0.495 kg/m³; X50 = 460 mm, n = 1.18), an association consistent with the combined influence of intact strength and joint density. UCS and BTS were near-perfectly correlated across the pooled dataset (r = 0.998). Because only two blasts were monitored per site, this is presented as a preliminary comparative case study rather than a statistically established causal relationship. The findings support trialling an increased powder factor (0.60–0.65 kg/m³) at the granite site and provide a mechanics-based geomechanical dataset currently scarce for African granite formations.
Original Research Paper
Rock Mechanics
Mohammad-Taghi Hamzaban; Alireza Chehreghan; Roozbeh Geraili Mikola
Abstract
In the companion Part I paper, an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework was developed for automated tunnel back analysis, coupling numerical simulation with evolutionary optimization to estimate geotechnical parameters from field monitoring data. Part II ...
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In the companion Part I paper, an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework was developed for automated tunnel back analysis, coupling numerical simulation with evolutionary optimization to estimate geotechnical parameters from field monitoring data. Part II demonstrates the application and validation of this framework through the back analysis of the Pardis Highway Tunnel, excavated within the Hezar Darreh conglomerate formation in northern Tehran, Iran. Three-point convergence measurements from 24 monitoring stations were processed to derive representative reference displacements for calibration. Thirty independent FDM–GA optimization runs were performed to estimate the ground parameters, yielding narrow calibrated ranges for cohesion (c), friction angle (φ), Young's modulus (E), and the coefficient of lateral earth pressure at rest (K0). A representative engineering parameter set was identified by integrating optimization results with inter-parameter correlations and geotechnical evidence from comparable tunnel projects. Unlike conventional tunnel back analysis studies that primarily emphasize parameter optimization, the proposed framework integrates optimization with geological interpretation to identify representative engineering parameter sets. It also systematically evaluates how stress relaxation assumptions influence parameter calibration and the engineering interpretation of calibrated solutions. Recalculated Ground Reaction Curve (GRC) and Longitudinal Displacement Profile (LDP) showed that relatively small variations in stress relaxation assumptions significantly affect predicted convergence and calibrated geotechnical parameters. The results demonstrate that reliable estimation of pre-support deformation and stress release is essential for realistic tunnel back analysis and confirm that the integrated FDM–GA framework provides a robust, repeatable tool for geotechnical parameter calibration in NATM tunnel engineering.
Case Study
Mineral Processing
Mojtaba Saeidi; Vahideh Shojaei; Hamid Khoshdast; Alireza Gholami
Abstract
Effective blast design is crucial for downstream comminution efficiency. However, modeling its impact on size-based production remains challenging due to complex, non-linear rock–fragmentation interactions. This study developed an integrated mine-to-mill framework using optimized Artificial Neural ...
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Effective blast design is crucial for downstream comminution efficiency. However, modeling its impact on size-based production remains challenging due to complex, non-linear rock–fragmentation interactions. This study developed an integrated mine-to-mill framework using optimized Artificial Neural Networks (ANNs) to predict total and size-fractioned production (0–40 mm, 40–80 mm) from blasting parameters at the Sar-Asiab Limestone Complex, Iran. The dataset comprises 55 real industrial observations after preprocessing. Two optimization algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), were employed to enhance ANN performance. The PSO-optimized ANN demonstrated significantly superior generalization, achieving a testing RMSE of 1341.8 (vs. 1549.5 for GA), an MAE of 1108.9 (vs. 1260.4), and an R² of 0.9487 (vs. 0.8937). Sensitivity-based feature importance analysis quantified the dominant predictors: explosive quantity (Emulite) contributed 18.4%, the number of boosters 14.9%, and key geometric parameters (mean hole depth, diameter, and spacing) collectively accounted for over 41%. In contrast, initiation system variables such as cortex length showed high linear correlation (r = –0.91) but minimal predictive importance (1.5%). These results underscore that explosive energy and its distribution, not just initiation configuration, are the primary drivers of size-based output. This data-driven framework provides a practical tool for aligning blast design with processing needs, directly supporting energy reduction and production stability in open-pit mining.
Original Research Paper
Rock Mechanics
Vahab Sarfarazi; Hadi Haeri; Jinwei Fu; Pejman Kalvandi; Mohammad Fatehi Marji; Alireza Shaker
Abstract
Determining the tensile strength of brittle, layered materials, including layered rocks and cracked concrete, is difficult when using commonly adopted indirect loading configurations that do not induce pure tensile conditions. Therefore, obtaining tensile strength through direct experimental quantification ...
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Determining the tensile strength of brittle, layered materials, including layered rocks and cracked concrete, is difficult when using commonly adopted indirect loading configurations that do not induce pure tensile conditions. Therefore, obtaining tensile strength through direct experimental quantification is crucial for such materials, particularly when examining crack onset and subsequent crack growth in the vicinity of crack tips. The present research proposes a novel direct tension-based testing framework designed to deliver more reliable strength characterization of layered quasi-brittle materials, while simultaneously enabling a detailed assessment of fracture initiation and propagation mechanisms. The testing apparatus consists of a steel beam with a load converter that serves as both the loading fixture and the load cell. A pre-holed layered sample is securely positioned at the center of the apparatus using a strong metal plate, while strain gauges on both sides capture critical data on the specimen's tensile behavior. Significantly, the apparatus can detect and quantify asymmetric failure within the specimen, offering a detailed understanding of crack formation and failure patterns. The design, calibration protocols, and empirical test results are thoroughly detailed and analyzed. Additionally, this study employs 3D PFC modeling to clarify the breakage mechanisms in various layer configurations, thereby enhancing the accuracy of tensile strength measurements. The numerical modeling results demonstrate that increasing the thickness of the hard layer leads to a nonlinear increase in the specimen's resistance. Furthermore, at a constant hard layer thickness, increasing the number of hard layers results in a decrease in the model's resistance.
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
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
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
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
Rock Mechanics
Ebrahim Ebrahimnezhad Sadigh; Kazem Badv
Abstract
Understanding the rheological behavior of soft marine and lacustrine sediments is crucial for the success of geotechnical and civil engineering projects. Coastal and offshore structures such as artificial islands, lake causeways, piers, and oil platforms directly interact with these sediments. Their ...
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Understanding the rheological behavior of soft marine and lacustrine sediments is crucial for the success of geotechnical and civil engineering projects. Coastal and offshore structures such as artificial islands, lake causeways, piers, and oil platforms directly interact with these sediments. Their safe and stable performance depends on accurate characterization of sediment behavior under complex loading conditions. This study investigates the rheological properties of soft sediments from Lake Urmia, Iran, through a combined experimental and numerical approach. Two key tests were performed: extrusion tests and unconfined compression tests. The extrusion tests were conducted on both undisturbed and remolded samples under various conditions, including different loading rates, moisture contents, and discharge orifice sizes. For the numerical simulation, the Bonded Particle Discrete Element Method (BPDEM) was employed, with the model's micro-parameters calibrated using experimental extrusion test data. The numerical results showed excellent agreement with experimental data: the force-displacement curve was replicated with less than 2% error. The calibrated model also successfully simulated the unconfined compression test, reproducing the stress-strain curve with less than 2% deviation from laboratory results. These findings demonstrate the accuracy of BPDEM in modeling soft sediment behavior. The results indicate that integrating laboratory methods with BPDEM modeling provides a powerful tool for analyzing soft sediments. This approach is particularly effective for Holocene and Late Pleistocene soft to ultra-soft sediments, offering reliable predictions of rheological and mechanical behavior in geotechnical applications.
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
Exploitation
Eslam Ghojoghi; Hamid Mansouri; Mohamad Ali Ebrahimi Farsangi; Esmat Rashedi
Abstract
Back break is an undesirable consequence of rock blasting, which causes explosive energy loss and reduces operation efficiency. Consequently, it is essential to forecast it to exercise control and avert the occurrence of operational cost losses. The objective of this scientific research is to utilize ...
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Back break is an undesirable consequence of rock blasting, which causes explosive energy loss and reduces operation efficiency. Consequently, it is essential to forecast it to exercise control and avert the occurrence of operational cost losses. The objective of this scientific research is to utilize Deep Neural Network, Extreme Gradient Boosting, and Lasso Regression in conjunction with Gravitational Search Algorithm to make predictions and minimization regarding the occurrence of blast-induced back break at Gol-e-Gohar 4 iron ore mine, Sirjan, Kerman, Iran. The constructed models comprise a set of nine input parameters, encompassing blasting design parameters, and rock geomechanical properties and produces back break as single output. The datasets used for training and evaluation consist of 266 blasting records extracted from Gol-e-Gohar 4 iron ore mine. The results obtained showed that the Deep Neural Network model with R2 of 0.81 and MSE of 0.70 has better performance over the Extreme Gradient Boosting and Lasso regression models to predict back break. Furthermore, the application of optimization algorithm resulted in optimized parameter values, which minimize back break.
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
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.
