Original Research Paper
Mineral Processing
Kwang Sok Jong; Chang Il Kim; Song Chol Kim; Kum Chon Jang; Hyon Hui Jang
Abstract
In this study, the effects of various reagents-sodium carbonate and sodium hydroxide as pH regulators, calcium lignosulfonic acid as depressant, and combined sodium oleate and amide as collector on the flotation of apatite ore were investigated using flotation experiments, and adsorption mechanism of ...
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In this study, the effects of various reagents-sodium carbonate and sodium hydroxide as pH regulators, calcium lignosulfonic acid as depressant, and combined sodium oleate and amide as collector on the flotation of apatite ore were investigated using flotation experiments, and adsorption mechanism of collector on apatite surface were evaluated using quantum mechanical simulations. The flotation experiments showed that the addition of 4 kg/t sodium carbonate and 1.5 kg/t sodium hydroxide as pH regulators, 3 kg/t calcium lignosulfonic acid as depressant and 60 g/t combined sodium oleic acid and oleamide (acid number of collector; 105 mgKOH/g) as collector exhibited excellent collecting ability for apatite. From low-grade apatite ore with P2O5 7.05%, a concentrate with P2O5 31.42% was obtained with 81.08% recovery in rougher flotation. Compared with the simulation results for the interaction energy between apatite {001} surface and collectors, and the relative concentration of collector on apatite {001} surface, adsorption strength has following order; combined sodium oleic acid and oleamide > sodium oleic acid > oleamide. From the simulation results on the equilibrium configuration of the collector with the fluorapatite {001} surface in the liquid environment, it was revealed that the two atoms (N and H) of the oleamide can form a strong bidentate conformation, and O atom in the C-O group and that in -C=O group of oleic acid anion can bond with the Ca atom on the surface {001} to form monodentate conformation.
Original Research Paper
Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi; Meisam Saleki
Abstract
Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting ...
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Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data.
Original Research Paper
Exploitation
Gopinath Samanta; Tapan Dey; Suranjan Sinha
Abstract
The optimal layout of the stope (stope boundary) in an underground metal mine maximizes the profit of a deposit, subject to the geotechnical and operational mining constraints such as stope length, stope width, stope height. Various approaches have been introduced to address the stope boundary optimization ...
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The optimal layout of the stope (stope boundary) in an underground metal mine maximizes the profit of a deposit, subject to the geotechnical and operational mining constraints such as stope length, stope width, stope height. Various approaches have been introduced to address the stope boundary optimization problem, but due to the computational complexity and numerous practical constraints, the existing models offer partial solutions to the problem. In the present work, a mixed integer programming model has been developed by incorporating mining constraints in a three-dimensional framework. This model is developed based on profit maximization. The sensitivity analysis applied in a case study mine indicates that the model is efficient in assessing the upside potential and downside risk of profit under fluctuating metal prices and mining costs. Additionally, it can be applied at different stages of mine design to facilitate resource appraisal, selection of stoping methods, and comprehensive mine planning. In a practical application on a real orebody, it shows that the proposed model can generate upto 37.32% more profit compared to current stope design practice in the mines.
Original Research Paper
Exploitation
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina Araujo; Jose Nestor Mamani Quispe; Solio Marino Arango-Retamozo; Johnny Henrry Ccatamayo-Barrios; Joe Alexis Gonzalez-Vasquez; Teofilo Donaires-Flores; Maxgabriel Alexis Calla-Huayapa
Abstract
Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation ...
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Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation efficiency. This work aims to optimize fragmentation (X50) and drilling and blasting costs using hybrid machine learning models, an innovative approach that improves predictive accuracy and economic feasibility. Six models were developed: Artificial Neural Networks (ANNs), Decision Trees (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The dataset, comprising 100 blasts, was split into 70% for training and 30% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of 0.27, MAE of 0.21, and R2 of 0.92. The RF+GA model was most effective for cost prediction, with an RMSE of 414.58, MAE of 354.14, and R2 of 0.99. Optimization scenarios were implemented by reducing burden (4.3 m to 3.8 m) and spacing (5.0 m to 4.5 m), achieving a 5.7% reduction in X50 (17.6 cm to 16.6 cm) and a 9.5% cost decrease (63,000 USD to 57,000 USD per blast). Predictions for 30 future blasts using the RF + GA model estimated a total cost of 1.7 MUSD, averaging 55,180 USD per blast. These findings confirm the effectiveness of machine learning in cost optimization and improving blasting efficiency, presenting a robust data-driven approach to optimizing mining operations.
Original Research Paper
Environment
Ahmed Amara Konate; Djénébou Bourama Sangare; Baba Faradji N'diaye; N'dji dit Jack Dembele
Abstract
Artisanal gold mining (AGM) is one of Mali's most significant economic activities. This activity become a serious environmental concern because of the chemicals used for AGM. The artisanal gold processing method uses a significant amount of water. The operating waste, often toxic by chemical mercury, ...
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Artisanal gold mining (AGM) is one of Mali's most significant economic activities. This activity become a serious environmental concern because of the chemicals used for AGM. The artisanal gold processing method uses a significant amount of water. The operating waste, often toxic by chemical mercury, is discharged or drained into watercourses. This study describes the main actors and the different methods of exploration, extraction, and processing of gold by artisanal miners, as well as their perception of environmental impacts. The methodology adopted is based on surveys and observations conducted at the Lollè and Sinty sites. The results show that no mining legislative rules were applied. Still, non-standard customary rules for a very long time. AGM has a particular organizational chart that actors manage. This study shows that AGM can cause health and environmental problems, especially with the use of chemicals, contamination of water resources, land degradation, and destruction of fauna and flora. Analysis of water samples shows that the mercury concentration at Lollè exceeds the WHO standard, while most mercury concentrations at Sinty are below this standard. The geomorphology of the study area shows a watershed with an area of 88.40 km2 with four orders of the hydrographic network in Lollè and 404.02 km2 with five orders in Sinty. While the slopes range from very weak to strong, and the study areas are practically flat. This study will provide accurate information to policy-makers for implementing environmental management strategies in a manner that miners can understand and evaluate.
Original Research Paper
Exploitation
Patrick Adeniyi Adesida; Sunday Adex Adaramola
Abstract
This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters ...
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This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters (uniaxial compressive strength, porosity, specific gravity, and the mineral content of quartz, plagioclase, hornblende, and biotite) were analysed via a RES interaction matrix to derive a new Drillability Index capturing their combined influence. This analysis revealed that UCS and porosity are the most influential factors in the system. The resulting RES-based model correlates strongly with observed bit wear rates, achieving a high coefficient of determination (R² ≈ 0.93) and low prediction errors (RMSE = 2.79, MAE = 2.14). The MAPE (= 38%) indicates a marked improvement in accuracy over traditional regression methods. Integrating mechanical and mineralogical factors is a novel approach to drillability prediction, providing a more comprehensive account of rock characteristics than conventional models. Validation results show that the RES-derived Drillability Index reliably predicts field performance, offering practical value for optimising drilling operations and guiding geomechanical analysis. Additionally, the study proposes a drillability classification scheme to further support the field application of the findings.
Original Research Paper
Exploitation
Gebremariam Mesele; Miruts Hagos; Bheemalingeswara Konka; Tsegabrhan Gebreset; Misgan Molla; N Rao Cheepurupalli; Girmay Hailu; Negassi Debeb; Assefa Hailesilasie
Abstract
The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural ...
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The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural variability. This research focuses on the potash-bearing section of the salt sequence, which consists of several distinct layers including the marker bed, sylvinite member, upper carnallitite member, bischofitite member, lower carnallitite member, and kainitite member. Employing satellite imagery (Landsat Thematic Mapper), geological and structural mapping, borehole data, and seismic analysis, this study characterizes the sub-surface features of the evaporites and estimates their reserves. The RockWorks software facilitated the development of a subsurface stratigraphic map and a three-dimensional fence diagram for enhanced interpretation. Seismic data indicate that while the upper layers of the evaporite deposits are largely horizontal and undeformed, deeper layers exhibit considerable tectonic disturbance. Thickness variations were observed, with evaporite and alluvial deposits being thinner at the southeastern rim and thicker in the eastern concession center. The total potash reserve is estimated at approximately 2.96 billion tons, of which 877.76 million tons (29.60%) remain unexploited. Current borehole designs restrict the company's extraction capacity to 24.64%. This study recommends revising mining strategies, incorporating updated borehole designs and advanced geophysical methods to improve potash recovery and promote sustainable practices in the Dallol region.
Original Research Paper
Rock Mechanics
Mahdi Bajolvand; Ahmad Ramezanzadeh; Amin Hekmatnejad; Mohammad Mehrad; Shadfar Davoodi; Mohammad Teimuri; Mohammad Reza Hajsaeedi; Mahya Safari
Abstract
Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by ...
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Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by environmental and operational factors, this study aims to develop an Adaptive Bit Wear Rate Predictor (ABWRP) algorithm for estimating the BWR during drilling operations for new wells. The structure of this algorithm consists of a data transmitter, data processor, deep learning-based bit wear rate estimator, and a bit wear updating module. To develop a model for the BWR estimation module, data from two wells in an oil field in southwest Iran were collected and analyzed, including petrophysical data, drilling data, and bit wear and run records. Both studied wells were drilled using PDC bits with a diameter of 8.5 inches. After preprocessing the data, the key factors affecting the bit wear rate were identified using the Wrapper method, including depth, confined compressive strength, maximum horizontal stress, bit wear percentage, weight on bit, bit rotational speed, and pump flow rate. Subsequently, seven machine learning (ML) and deep learning (DL) algorithms were used to develop the bit wear rate estimation module within the ABWRP algorithm. Among them, the convolutional neural network (CNN) model demonstrated the best performance, with Root Mean Square Error (RMSE) values of 0.0011 and 0.0017 and R-square (R²) values of 0.96 and 0.92 for the training and testing datasets, respectively. Therefore, the CNN model was selected as the most efficient model among the evaluated models. Finally, a simulation-based experiment was designed to evaluate the performance of the ABWRP algorithm. In this experiment, unseen data from one of the studied wells were used as data from a newly drilled well. The results demonstrated that the ABWRP algorithm could estimate final bit wear with a 14% error. Thus, the algorithm developed in this study can play a significant role in the design and planning of new wells, particularly in optimizing drilling parameters while considering bit wear effects.
Original Research Paper
Rock Mechanics
Amin Jamshidi; Deniz Akbay
Abstract
Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, ...
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Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, including slope stability analysis, blast design, rock support systems, excavation and equipment selection, fracture propagation, and hydraulic fracturing and drilling. So far, no classification of tensile strength of rock for mining applications has been presented. In the present study, a new rock classification based on BTS for the various rocks was proposed. To achieve this purpose, by a reviewing previous studies, uniaxial compressive strength (UCS) and BTS of various rock classes, including igneous, sedimentary, and metamorphic were collected. For each rock class, the correlation equations between UCS and BTS were developed using simple regression analysis. Using data analyses, the rocks was categorized into to seven BTS classes. The findings revealed that igneous, sedimentary, and metamorphic rocks have a wide range of BTS values, and subsequent fall into the different BTS classes. The validity of BTS classification was verified using data of BTS and UCS of various rock classes published in the literature, and results showed that BTS can be as a suitable indicator for preliminary assessment of rock quality. This can lead to a better understand from the strength behavior of the rock under tensile stresses in site a mining activity, and therefore, a more accurate design of a mining project.
Review Paper
Environment
Subhash Chandra Devrath; Aditi Nag; Sanjeev Pareek
Abstract
This paper explores sustainable redevelopment strategies for post-mining regions by integrating urban voids and underground housing solutions. Mining landscapes, often characterized by degraded environments, socio-economic stagnation, and underutilized spaces present significant challenges and opportunities ...
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This paper explores sustainable redevelopment strategies for post-mining regions by integrating urban voids and underground housing solutions. Mining landscapes, often characterized by degraded environments, socio-economic stagnation, and underutilized spaces present significant challenges and opportunities for transformation. Urban voids such as abandoned pits, industrial complexes, and obsolete worker settlements can be repurposed into green infrastructure, public amenities, or residential spaces. Underground housing, leveraging the natural insulation of subsurface environments, offers energy-efficient solutions, while preserving surface land for ecological and communal uses. The research proposes a conceptual framework that combines the adaptive reuse of urban voids with innovative underground housing designs to enhance urban attractiveness, sustainability, and inclusivity. key indexing metrics, including environmental, socio-economic, and urban attractiveness indicators, are developed to evaluate the effectiveness of redevelopment efforts. Case studies from Germany, Belgium, France, and the USA illustrate these strategies' practical applications and transformative potential. The findings emphasize the importance of addressing socio-economic constraints, environmental remediation, and regulatory challenges through participatory planning, innovative governance, and public-private partnerships. The paper concludes by identifying areas for future research, including socio-cultural acceptance of underground housing, region-specific policy frameworks, and advanced remediation technologies. This study provides a comprehensive roadmap for transforming mining regions into vibrant, sustainable, resilient urban environments.
Original Research Paper
Exploration
V.S.S.A Naidu Badireddi; Vije durga raju Mullagiri; MVS sekhar Bezawada; Ambili V; K S N Reddy
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
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The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
Original Research Paper
Environment
Zakirah Raihani Ya’la; Triyani Dewi; Ali Husni; Tri Joko Santoso; Samliok Ndobe; Eka Rosyida; Maemunah Maemunah; Marhawati Mappatoba; Muhammad Saleh Nurdin
Abstract
This study was conducted to determine heavy metal concentrations in sediments, assess the level of contamination using a contamination index, and identify potential sources of heavy metal contamination using multivariate analysis. This study employed contamination indices to evaluate sediment pollution ...
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This study was conducted to determine heavy metal concentrations in sediments, assess the level of contamination using a contamination index, and identify potential sources of heavy metal contamination using multivariate analysis. This study employed contamination indices to evaluate sediment pollution levels. Heavy metal concentrations were analyzed statistically by determining the minimum, maximum, mean, and standard deviation (SD) values. According to the contamination factor (Cf), Cd showed very high contamination levels, whereas Sn, Ni, and Pb indicated moderate contamination. Hg, As, Cr, and Cu were classified as having low levels of contamination. The degree of contamination (Cdeg) ranged from low to high across the sampled sites, reflecting the varied levels of pollution severity. Multivariate statistical analyses, including Principal Component Analysis (PCA), Pearson correlation matrix, and Cluster Analysis (CA), were used to identify potential sources of heavy metal contamination. Cu, Sn, Ni, Hg, and Cr are attributed to natural geological processes, whereas Pb, Cd, and As are linked to anthropogenic activities, likely originating from the nickel mining industry. In conclusion, this study underscores the complex environmental impact of nickel mining in Morowali, highlighting the need for stringent environmental management practices to mitigate further degradation and safeguard the coastal ecosystems in Central Sulawesi.
Original Research Paper
Environment
Feridon Ghadimi; Amirhossein Solaimani
Abstract
Chogan region is located in the west of the Urmia-Dokhtar volcanic belt and northwest of the Markazi province in Komijan City. Copper mineralization has a vein type with a length of 260 meters and an average thickness of 4 meters. Mineralization was taken in a sheared silica vein. Eighty three samples ...
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Chogan region is located in the west of the Urmia-Dokhtar volcanic belt and northwest of the Markazi province in Komijan City. Copper mineralization has a vein type with a length of 260 meters and an average thickness of 4 meters. Mineralization was taken in a sheared silica vein. Eighty three samples were taken from the surface ground, in the trenches and it determined the concentration of 10 elements such as Fe, Al, Ca, Ba, S, Mn, As, Pb, Zn, and Cu. It was determined, that S, Ba, Mn, Fe, and Cu are secondary elements in the tuffs by the method of factor and cluster analysis. The constituent mineral such as barite and malachite are vein-shaped, but iron oxides such as hematite and goethite in the form of iron gossan. Geochemical, mineralogical, and geophysical (IP/RS) indices were investigated to separate copper oxide and copper sulfide zones. Sulfur and Ba were used in barite and excess S was chosen as sulfide index (Is). Chalcopyrite and metal factor were chosen as separating oxide and sulfide zones. By combining the geochemical and metal factor, it was approximated the apparent sulfide zone depth and confirmed with actual depth in borehole and error was less than 12%.
Original Research Paper
Exploration
Bardiya Sadraeifar; Maysam Abedi; Seyed Hossein Hosseini
Abstract
The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical ...
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The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical surveys. This study aimed to target magnetite mineralization within the deposit using constrained susceptibility inversion. We began by simulating a multi-source synthetic model with three identical cubes at different depths to evaluate the sparse norm inversion approach. The method was then applied to the case study after the essential magnetic data corrections. To refine the interpretation of residual magnetic anomalies and gain insights into their source and depth, the analytic signal and upward continuation methods were employed. Inversion results across different cross-sections revealed two distinct, shallow, lens-shaped magnetite mineralizations with an average vertical extent of 60 meters. Notably, one magnetite body lies approximately 30 meters deeper due to the Dehshir-Baft fault influence. Low normalized mis-fit values confirmed the successful minimization of the objective function during inversion. Additionally, the reconstructed susceptibility models align well with the previous geological studies and borehole data, demonstrating the efficiency of the sparse norm inversion algorithm.
Original Research Paper
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
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The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
Original Research Paper
Exploration
Mobin Saremi; Abbas Maghsoudi; Reza Ghezelbash; mahyar yousefi; Ardeshir Hezarkhani
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were ...
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Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
Original Research Paper
Mineral Processing
Mohammad Jahani Chegeni; Sajad Kolahi; Asghar Azizi
Abstract
Consumed energy is the most important issue and concern in industrial ball mills, and includes a major part of the costs of mineral processing plants. By using suitable liners and the optimal lifter count, the energy of the mill is properly transferred to the balls. In Part 1 of this research work, five ...
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Consumed energy is the most important issue and concern in industrial ball mills, and includes a major part of the costs of mineral processing plants. By using suitable liners and the optimal lifter count, the energy of the mill is properly transferred to the balls. In Part 1 of this research work, five types of liners, i.e. Lorain, Osborn, Rib, cuboid, and Hi-lo, are examined. These liners all have separate lifters with the same volume. Their difference is in the width, height, and type of lifter profile. First, all types of liners are simulated with four lifters using the Discrete Element Method (DEM). Then the lifter count is increased four by four to fill the entire wall of the mill with lifters. Based on this, Lorain liner from 4 to 24 lifters, Osborn liner from 4 to 120 lifters, Rib liner from 4 to 40 lifters, and cuboid and Hi-lo liners from 4 to 64 lifters are simulated. For the first time, the kinetic (KE) and potential (PE) energies as well as the sum of these two energies (TE) of all the balls are calculated, and compared in the entire duration of the simulation from 0–13s for all the liner types and lifter counts mentioned above. Finally, by using data related to KE, PE, and TE for each type of liner, the optimal lifter count is obtained. Accordingly, 16 to 20 lifters are recommended for the Lorain liner, 64 to 76 lifters for the Osborn liner, 24 to 32 lifters for the Rib liner, 44 lifters for the cuboid liner, and 36 to 44 lifters for the Hi-lo liner.
Original Research Paper
Environment
Amirhossein Karimi; Amin Falamaki; Farid Soltani; Mehdi Homaee; Nader Shariatmadari
Abstract
Mining activities have led to the accumulation of large quantities of mineral tailings containing potentially hazardous metals, contaminating the surrounding soil. This study investigated the effectiveness of electrokinetic remediation combined with washing solvents for the decontamination of zinc and ...
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Mining activities have led to the accumulation of large quantities of mineral tailings containing potentially hazardous metals, contaminating the surrounding soil. This study investigated the effectiveness of electrokinetic remediation combined with washing solvents for the decontamination of zinc and lead from mine tailings. Samples were collected from various locations within the Angouran mine in Zanjan, Iran, and analyzed for total metal concentration using the standard ICP method. Electrokinetic tests were conducted using different washing solutions—hydrochloric acid, nitric acid, acetic acid, and sulfuric acid—each at a concentration of 0.1 M and mixed with soil in a 1:2 solution-to-solid ratio. A voltage of 1.5 V/cm was applied throughout the experiments. To mitigate heavy metal precipitation near the cathode, the same chemical solutions were used in the cathode chamber. The results demonstrated that distilled water resulted in the lowest removal efficiency for zinc (16%) and lead (11.5%), while hydrochloric acid showed the highest removal efficiencies of 64% for zinc and 45% for lead. These findings indicated that electrokinetic remediation, particularly when using hydrochloric acid as a complexing agent, was an effective method for removing significant quantities of zinc and lead from contaminated soil.
Original Research Paper
Exploitation
Hamid Saberi; Mohammad Golmohammadi; Mohammadali Zanjani; Yaghoub Saberi
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
Read More
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
Original Research Paper
Exploitation
Hadi Fattahi; Mohammad Amirabadifarahani; Hossein Ghaedi
Abstract
This study introduces an innovative application of the Power Deck method to optimize drilling and blasting operations in open-pit mining, with a focus on the Nizar cement factory in Qom, Iran. Unlike traditional blasting techniques, this method strategically utilizes a controlled air gap at the end of ...
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This study introduces an innovative application of the Power Deck method to optimize drilling and blasting operations in open-pit mining, with a focus on the Nizar cement factory in Qom, Iran. Unlike traditional blasting techniques, this method strategically utilizes a controlled air gap at the end of each blast hole to enhance explosive energy distribution, thereby reducing excessive drilling and minimizing explosive consumption. Through five blast phases, optimal hole diameters (76 mm and 90 mm) were implemented while maintaining a standardized 1-meter air gap, eliminating the need for additional drilling tests. The findings demonstrate a significant improvement in blasting efficiency, leading to a 12.5% reduction in specific charge and a 9% decrease in specific drilling compared to conventional methods. Post-blast fragmentation analysis, validated using the F50 index from Split-Desktop software, confirmed particle sizes ranging from 10 to 32 cm, aligning with predictions from the Kaz-Ram, Kaznetsov, and Swedifo models. Furthermore, the adoption of the Power Deck method resulted in a 1,448-ton increase in processed material over two months, minimizing crusher downtime due to oversized fragments. This study provides a novel, cost-effective approach to improving rock fragmentation, reducing blasting-related inefficiencies, and enhancing the overall economic performance of open-pit mining operations.
