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 ...
Read More
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.
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 ...
Read More
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.
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 ...
Read More
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.
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 ...
Read More
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.
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 ...
Read More
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%.
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.
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 ...
Read More
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.
Exploitation
Ali Rezaei; Ebrahim Ghasemi; Ali Farhadian; Sina Ghavami
Abstract
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified ...
Read More
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified through six indices, including equivalent quartz content (EQC), rock abrasivity index (RAI), Schimazek abrasivity factor (F), Cerchar abrasivity index (CAI), building stone abrasivity index (BSAI), and the Taber wear index (Iw). Bi-variate regression analysis was applied to develop the predictive equations for relationship between TC and abrasivity indices. The investigations demonstrated that there is a direct relationship between TC and all abrasivity indices. Furthermore, TC has moderate to high relationship with abrasivity indices. After developing the equations, their accuracy was evaluated by performance criteria including determination coefficient (R2), the normalized root mean square error (NRMSE), the variance account for (VAF), and the performance index (PI). The strongest relationship was found between TC and RAI (with R2, VAF, NRMSE, and PI value of 0.850, 0.074, 85.386, and 1.630, respectively), while the weakest relationship was observed between TC and F (with R2, NRMSE, VAF, and PI value of 0.491, 0.532, 47.605, and 0.435, respectively). This research demonstrates importance of the textural characteristics of stones, especially TC as a reliable index, on the abrasivity properties of granite building stones. Thus, the equations developed herein can be practically used for estimating the stone abrasivity in building stone quarrying and processing projects.
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi; Seyyed Ali Hashemi
Abstract
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with ...
Read More
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications.
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 ...
Read More
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.
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 ...
Read More
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%.
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 ...
Read More
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.
Exploitation
Hemant Agrawal; SIDDHARTHA ROY; Chitranjan Prasad Singh
Abstract
Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, ...
Read More
Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, the Rihand Dam, and local settlements near the Khadia Opencast coal mine. This study aims to analyze the effect of blast hole diameter on peak particle velocity (PPV) to improve vibration control. Experimental investigations were conducted by executing multiple blasts using hole diameters of 159 mm, 269 mm, and 311 mm across different benches of the Khadia mine, with PPV values recorded at various scaled distances. The observed relationship between PPV and hole diameter was further validated through explicit dynamic modeling of the mine’s geology and blast conditions using ANSYS-Autodyn software. The results presents some exclusive observation that with same charge per delay, for smaller distances i.e. for less than 90 m the values of PPV is always higher in large diameter hole blasting while for distance above 500 m the PPV values are higher in smaller diameter holes blasting. The results provide a unique insight for optimizing blast parameters to minimize ground vibrations while maintaining production efficiency.
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 ...
Read More
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.
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 ...
Read More
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.
Exploitation
Yaşar Ağan; Türker Hüdaverdi
Abstract
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration ...
Read More
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration is defined peak particle velocity (ppv) in the ground. All data used to estimation were obtained by observing real blasting operations. After the measuring of the peak particle velocity, models of the prediction were created using independent site parameters. Most of the data is used to train the model, while remaining part is used for testing. The models were created using independent blasting parameters proportionally. Thus, more parameters are included in the models without complicating the models. A thorough validation process was conducted utilizing a diverse set of nine error criteria. Artificial intelligence models have been found to outperform traditional methods in predicting ground vibration. The mean absolute error values were found to be 1.42, 1.54, and 1.78 for ANFIS, GPR, and SVM, respectively. A similar situation is observed for other error criteria as well. ANFIS appears to be the most effective model for predicting ground vibration.
Exploitation
Pouya Nobahar; Yashar Pourrahimian; Roohollah Shirani Faradonbeh; Fereydoun Mollaei Koshki
Abstract
Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents ...
Read More
Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents a novel Deep Learning (DL)-based model to recognize the types of kaolinite samples. For this purpose, a dataset containing the images of drilled cores and their types determined from conventional chemical and physical analyses was used. Eight Convolutional Neural Network (CNN) topologies based on individual features were developed, named A, B, C, D, E, F, G, and H. Six of the eight proposed CNN topologies described above had accuracy below 80%, whereas two of them, model A and H, had higher accuracy than other topologies. Due to their similarity in results, both of them analyzed deeply. Model A was more efficient, with 90% accuracy, than model B, with 84% accuracy. Furthermore, the class detection performance of model A was further evaluated using different indices, including precision, recall, and F1-score, which resulted in values of 92%, 92%, and 90%, respectively, which are acceptable accuracies to identify the type of samples when using this approach on six different types of kaolinite.
Exploitation
Somaye Khajevand; Mojtaba Rezakhah; Masoud Monjezi; Fabián Alejandro Manríquez León
Abstract
Efficient loading and hauling systems, with trucks and shovels as the primary transportation machinery, are essential for optimizing mining operations. This study introduces a simulation-based approach to enhance the utilization of the hauling system in an Abbasbad copper mine in Iran. A dynamic truck ...
Read More
Efficient loading and hauling systems, with trucks and shovels as the primary transportation machinery, are essential for optimizing mining operations. This study introduces a simulation-based approach to enhance the utilization of the hauling system in an Abbasbad copper mine in Iran. A dynamic truck allocation model is proposed to overcome the limitations of fixed allocation methods. In this approach, trucks are assigned to loading equipment based on the real-time throughput data, prioritizing equipment experiencing the highest production delays. The simulation results demonstrate that this flexible allocation model improves productivity, achieving a 13% increase in waste material handling compared to the fixed allocation scenario. These findings indicate that the proposed framework to significantly improve the efficiency and productivity of haulage systems in mining operations.
Exploitation
Moslem Jahantigh; Hamidreza Ramazi
Abstract
Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. ...
Read More
Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. Examining and evaluating drilling data in the Lar copper mine in Sistan and Baluchistan province located in the southeast of Iran requires the use of these methods. Therefore, the main goal of the article is the clustering of the drilling data in the mentioned mine and its zoning of the geochemical data. To achieve this goal, 3500 samples taken from drilling cores have been used. Elemental analysis for six elements has been done using the ICP-Ms method. Pattern recognition methods including SOM and K-MEANS have been used to evaluate the relation between these elements. The silhouette method has been used to determine and evaluate the number of clusters. Using this method, 4 clusters have been considered for the mentioned data. According to this method, it was found that the accuracy of clustering is higher in the SOM method. By considering the 4 clusters, 4 zones were identified using clustering methods. By comparing the results of the two methods and using the graphical method, it was determined that the SOM method has a better performance for clustering geochemical data in the studied area. Based on that, zones 2 and 4 were recognized as high-grade zones in this area.
Exploitation
Sruti Narwal; Debasis Deb; Sreenivasa Rao Islavath; Gopinath Samanta
Abstract
A novel underground mining method is proposed to extract friable chromite ore bodies in weak and weathered limonitic host rock below an open-pit mine. The conventional underground methods do not instil confidence since GSI (Geological Strength Index) of ore bodies and host rock lies below 35. Series ...
Read More
A novel underground mining method is proposed to extract friable chromite ore bodies in weak and weathered limonitic host rock below an open-pit mine. The conventional underground methods do not instil confidence since GSI (Geological Strength Index) of ore bodies and host rock lies below 35. Series of dimensions of transverse stopes along the strike are suggested based on a detailed analysis of multiple mining and backfilling operations by simulating 36 three-dimensional numerical models. For each operation or sequence, a strength-based “Mining Sequence Factor (MSF)” is devised that helps quantifying its equivalent strength compared to in-situ conditions. This factor along with the average equivalent plastic strain (AEPS) developed on the pillars as obtained from numerical models is used to determine the safe operations with desired yearly production target. The paper provides an in-depth analysis of this method and suggests minimum pillar dimensions of 40 m, whether in-situ or backfilled. The paper, in addition, lays the design of underground drives and their support system as per NGI (Norwegian Geotechnical Institute) guidelines and 3D numerical studies, the performance of which is analysed considering distribution of stress and equivalent plastic strain.
Exploitation
Javad Lotfi Godarzi; Ahmad Reza Sayadi; Amin Mousavi; Micah Nehring
Abstract
The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced ...
Read More
The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced by the production rate and product price. A conventional approach optimizes each variable individually, and neglects the trade-off between production rate and cut-off grade, leading to a sub-optimal solution. This work aimed to address the simultaneous optimization of the production rate and cut-off grade and provided a novel solution for this problem. In this context, a non-linear mathematical model was developed. The Particle Swarm Optimization (PSO) algorithm was used due to the model's non-linear nature and the continuous decision variables. Implementing the model for a typical copper mine showed that the suggested model resulted in a concurrent optimization of production rate and cut-off grade. The maximum NPV of 1.153 billion dollars occurred at a production rate of 15.66 Mt/y, and a cut-off grade of 0.64%. Additionally, a sensitivity analysis was conducted for key factors such as product price, discount rate, and maximum capital cost.
Exploitation
Assefa Hailesilasie Wolearegay; Yowhas Birhanu Amare; Asmelash Abay Hagos; Kassa Amare Mesfin; Hagos Abraha; Bereket Gebresilassie; Nageswara Rao Cheepurupalli; Yewuhalashet Fissha
Abstract
The Dichinama area in northern Ethiopia is a potential source of dimension stone, but the quality of the marble has been a major challenge for mining operations. This research aims to evaluate the quality of dimension stone by conducting a comprehensive study involving geological mapping, geotechnical ...
Read More
The Dichinama area in northern Ethiopia is a potential source of dimension stone, but the quality of the marble has been a major challenge for mining operations. This research aims to evaluate the quality of dimension stone by conducting a comprehensive study involving geological mapping, geotechnical testing, and geochemical analysis. The study collected nine rock samples from three active mining sites in the Dichinama area, analyzing properties such as density, water absorption, compressive strength, flexural strength, and abrasion resistance. Additionally, ten samples were collected for geochemical analysis, focusing on parameters like calcite, CaO values, LOI, SiO2 content, and other oxide concentrations. The geotechnical tests revealed that the properties of the marble in the Dichinama area were mainly calcite, with compressive strength values ranging from 29.6 to 74.5 MPa, flexural strength from 7 to 52.5 MPa, abrasion resistance from 8.3 to 17.2, density from 2257 to 2562 kg/m3, and water absorption from 0.12 to 0.93. However, most of these parameters fell below the minimum ASTM standards for marble dimension stone. The results suggest that these inferior characteristics negatively affect the recovery and quality of the dimension stone.
Exploitation
Yehia Z. Darwish; Abdelrahem Khalefa Embaby; Samir Selim; Darwish El Kholy; Hani Sharafeldin; Hussin Farag
Abstract
The younger granites of Gabal Gattar area, Northern Eastern Desert of Egypt, host hydrothermal uranium mineralization at the northern segment of Gattar batholith and along its contacts with the oldest Hammamat sediments. The host rocks display many features of hydrothermal overprint results in changing ...
Read More
The younger granites of Gabal Gattar area, Northern Eastern Desert of Egypt, host hydrothermal uranium mineralization at the northern segment of Gattar batholith and along its contacts with the oldest Hammamat sediments. The host rocks display many features of hydrothermal overprint results in changing their basic engineering characteristics as a function of variations of the degree of alteration. Progression from less altered to altered and mineralized rocks as the result of the alteration processes was assessed by the chemical index of alteration (CIA). The CIA numerical values were calculated by the molecular proportion of Al to the cations Ca, Na, and K. The studied rocks were divided into five grades according to degree of alteration and strength properties including: fresh (AG-I), slightly altered (AG-II), moderately altered (AG-III), highly altered (AG-IV) and very highly altered (AG-V). The strength properties of the studied rock units correlated well with the alteration grades assigned to them. That is, as the grade increased from AG-I to AG-V, abrasion resistance and crushability index increased, whereas compressive strength, slake durability and impact strength decreased.
Exploitation
Sri Chandrahas; Bhanwar Singh Choudhary; MS Venkataramayya; Yewuhalashet Fissha; Blessing Olamide Taiwo
Abstract
To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An ...
Read More
To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An idea on joints density will give an idea on deciding with column charging in-terms of decking-stemming and firing patterns. The goal of the research is to develop a hybrid algorithm model to predict joints width and joint angle. In order to achieve the task, advanced softwares, machine learning models and a field data tests were used in this study.
Exploitation
Sahil Kumar; Abhishek Sharma; Kanwarpreet Singh
Abstract
This study investigates the application of the Rapid Mass Movement Simulation (RAMMS) tool in assessing and mitigating various types of landslides. The research encompasses comprehensive field visits to diverse landslide-prone areas, capturing detailed photographic evidence to document pre- and post-landslide ...
Read More
This study investigates the application of the Rapid Mass Movement Simulation (RAMMS) tool in assessing and mitigating various types of landslides. The research encompasses comprehensive field visits to diverse landslide-prone areas, capturing detailed photographic evidence to document pre- and post-landslide conditions. Utilizing the field data, RAMMS simulations were conducted to model the dynamics of different landslide scenarios, including rockfalls, debris flows, and avalanches. The simulations provided insights into the potential impact zones, flow velocities, and deposition patterns of landslides under varying environmental conditions. The results highlight the efficacy of RAMMS in predicting landslide behavior and guiding mitigation strategies. By comparing the simulation outputs with field observations, we validated the accuracy of RAMMS models, demonstrating their utility in real-world applications. Furthermore, the study identifies key factors influencing landslide susceptibility and proposes targeted mitigation measures to enhance community flexibility. This research underscores the importance of integrating advanced simulation tools like RAMMS with empirical field data to develop strong landslide risk management frameworks.
