Exploitation
Shambhavi sinha; Anup Tripathi; Akhil Avchar; Mritunjay kumar
Abstract
Accurate assessment of rock mass quality in marble quarries remains challenging because conventional empirical classification systems are largely strength-dominated and insufficiently sensitive to discontinuity-controlled block instability. This study proposes a quarry-specific empirical framework, termed ...
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Accurate assessment of rock mass quality in marble quarries remains challenging because conventional empirical classification systems are largely strength-dominated and insufficiently sensitive to discontinuity-controlled block instability. This study proposes a quarry-specific empirical framework, termed the Marble Rock System (MRS), designed to explicitly capture structural, hydro-mechanical, and alteration-driven controls governing bench-scale stability in dimension stone marble quarries. The primary objective was to develop and validate an empirically grounded classification system using machine learning as an independent diagnostic tool rather than as a black-box predictor.A comprehensive geomechanical database comprising 85 quarry-scale records was developed from three active marble quarries in southern Rajasthan, India. Six physically interpretable parameters intact strength, weathering or serpentinization, joint frequency, joint surface condition, groundwater influence, and block stability were incorporated into the MRS framework. Supervised machine learning models, including artificial neural networks, support vector machines, and linear regression, were trained to predict independently derived factors of safety for validation. Model performance was evaluated using coefficient of determination, root mean square error, cross-validation, and classification metrics.Results show that MRS-based models achieved consistently higher predictive accuracy, improved class separability, and more stable generalization than models trained using conventional Rock Mass Rating inputs. Sensitivity analysis revealed that block stability and joint characteristics dominate stability prediction, while intact strength plays a secondary role. These findings confirm that marble quarry slope behaviour is primarily discontinuity-controlled. The proposed MRS provides a physically interpretable, empirically validated framework for quarry-scale stability assessment and offers a robust alternative to conventional classification systems for operational decision-making.
Exploitation
Sina Ghavami; Ebrahim Ghasemi; Mohammad Hossein Kadkhodaei; Ali Farhadian
Abstract
Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the ...
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Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the stone abrasivity. One of the most commonly used methods is Cerchar abrasivity index (CAI). This study mainly focuses on investigating the relationship between CAI with petrographic and physico-mechanical properties of granitic building stones. For this purpose, 14 different types of commercial granitic building stones, collected from different regions of Iran, were subjected to laboratory investigations and the effect of the petrographic and physico-mechanical properties of these stones on CAI was examined using simple and multiple regression analysis. Meaningful and reasonable relationships were observed. According to the obtained results, equivalent quartz content (EQC) of granitic building stones was found to be the most effective parameter on CAI. Using linear and nonlinear regression analysis, two empirical correlations for CAI prediction based on EQC were developed. The results showed that both linear and nonlinear correlations have high performance with determination coefficients (R2) of 0.876 and 0.882, respectively. These correlations can determine the CAI with acceptable error, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.135 and 0.105, respectively. Furthermore, the relationship between the diamond segment wear (SW) and CAI was investigated for the studied stones. The results showed that SW is directly related to the CAI, and there is a strong linear correlation between these two parameters with R2 of 0.787. The proposed correlation can be applied for fast prediction of cutting tool wear for practical applications in building stone processing plants with circular sawing machine, which can lead to enhanced cutting efficiency and productivity.
Exploitation
Mehrnaz Mohtasham
Abstract
In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. ...
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In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.
Exploitation
saeideh Qaedrahmat; Javad Gholamnejad; Ali dabagh
Abstract
The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, ...
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The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, and slope constraints. One of the key objectives of Short-Term Production Scheduling (STPS) is to ensure a stable and continuous supply of ore to the processing plant, while minimizing operating costs through measures such as reducing unnecessary equipment movements and variation in feed quality. However, one of the major obstacles to the operational feasibility of STPS is the limited working space available for equipment, as well as the excessive equipment movement between benches within each scheduling period. To tackle these challenges, this paper employs an Integer Goal Programming (IGP) with a new constraint that limits active benches per period, enhancing the practicality of production schedules. Unlike previous GP-based STPS models, it improves operational feasibility by ensuring extraction continuity and minimizing equipment movement. The model was tested on a copper deposit using GAMS software. The results show that by applying this new constraint, the average number of active benches per month was reduced from 14 to 10 )36% reduction) and the number of extraction periods per bench from 6 to 4 (33% reduction) without violating the existing constraints such as ore grade, tonnage, or slope. This approach improves equipment efficiency, reduces fuel consumption, reducing equipment relocation costs, promoting operational continuity of extraction and enhances operational feasibility in real conditions.
Exploitation
Mohammad Reza Rezaei; Majid Noorian-Bidgoli
Abstract
Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling ...
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Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.
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.
Exploitation
Hassan Bakhshandeh Amnieh; Ebrahim Arefmand; Abbas Majdi
Abstract
The Power deck blasting technique is widely used in open-pit and underground mines to optimize explosive energy for effective rock fragmentation while minimizing adverse effects. This study examines the influence of primer location and air column length on ground vibration and limestone rock mass damage ...
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The Power deck blasting technique is widely used in open-pit and underground mines to optimize explosive energy for effective rock fragmentation while minimizing adverse effects. This study examines the influence of primer location and air column length on ground vibration and limestone rock mass damage through field tests and numerical simulations at the Nardaghi limestone mine. A two-hole blast using the Power deck method was performed, with vibrations recorded by a three-axis seismograph. The maximum particle velocity reached 70.22 mm/s at 11 meters from the blast. Field inspections indicated that damage was limited to areas around the blast hole openings. Numerical results show that placing the primer at the bottom of the explosive column reduces ground vibration by 37% compared to middle or top positions, whereas the top primer location causes greater surface damage. Damage at the blast hole bottom was comparable across all primer locations. Fixing the primer at the bottom, the effect of air column length (0.4 to 2 meters) on vibration and damage was studied. Increasing the air column length up to 1.4 meters increased vibration, but longer lengths led to significant vibration reduction. Maximum rock mass damage occurred at an air column length of 0.6 meters, indicating optimal energy transfer. The results highlight the critical effects of primer position and air column length on blasting outcomes. The best primer placement is at the bottom of the explosive column, and the optimal air column length is 0.6 meters to balance vibration control and fragmentation efficiency.
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within ...
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Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within the blasthole. However, its performance under production-scale conditions and the reliability of numerical predictions remain insufficiently validated. This study presents an integrated field–numerical investigation of air-deck blasting at the Gol-e-Gohar Iron Ore Mine No. 1, Iran. Fragmentation characteristics were quantified using image-based particle size distribution (PSD) analysis of muck piles processed with Split-Desktop software. Characteristic fragmentation indices (D20, D50, and D80) were extracted to evaluate blast performance. Three-dimensional numerical simulations were performed using the LS-DYNA explicit finite element code to model stress-wave propagation, damage evolution, and fragmentation development for both conventional blasting and air-deck blasting configurations. Numerical models were calibrated using site-specific blasting geometry, explosive properties, and rock mass parameters derived from field measurements. The results show strong agreement between numerical predictions and field observations, with coefficients of determination exceeding 0.95 and RMSE values below 10%. Compared with conventional blasting, air-deck blasting produced finer and more uniform fragmentation, reducing D50 by approximately 10–15% and D80 by up to 18%. The improvement is primarily attributed to stress-wave reflection at the air gap and enhanced tensile crack propagation. The proposed field-validated numerical framework provides a practical tool for blast design optimization and demonstrates the potential of air-deck blasting to improve fragmentation efficiency in large-scale open-pit mining operations.
Exploitation
Moslem Jahantigh; Hamidreza Ramazi
Abstract
The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were ...
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The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were preprocessed and prepared. Then, these layers were transferred to an equal distance of zero and one using a fuzzy function. The weighting of exploration witness layers in the study area was done using three methods: AHP, random forest algorithm, and area prediction method. Then, the weighted layers were combined using the MABAC multivariate decision-making method and three mineral potential models were formed for the study area. The produced models were compared using the main characteristic curve diagram. It was found that weighting using a data-driven method has more desirable results than a knowledge-driven method. Also, weighting exploration layers by random forest algorithm has more desirable results than the area prediction method. The better results of the random forest algorithm are because in this algorithm, weighting is based on mineralized and non-mineralized points, while in the area prediction method, weighting is only based on outcrops in the study area. This method can be implemented with appropriate accuracy in areas with limited training data, along with knowledge-based methods for weighting exploratory evidential layers.
Exploitation
Feng Yang; Pengjie Li; Qiang Sun
Abstract
Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles ...
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Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles and that are extremely close to overlying pillars. The technical principles are explained. First, FLAC3D simulation software was used to investigate the effect of the spacing between the lower roadway and the section coal pillar, which determined the optimal roadway position. Then, a coupled FLAC-PFC method was employed to optimize the coal drawing process parameters. The optimal scheme was analyzed to characterize roof deformation, stress distribution, and hydraulic support loads. An engineering case study demonstrates that a spacing greater than 18 m minimizes the influence of concentrated stress, resulting in limited deformation and improved roadway stability. The study investigates coal drawing under different sequences, port widths, and methods. The optimal process was identified as downward drawing, with a 1.5 m coal drawing port width and a two-wheel sequential method. This process achieves a drawing rate of 85.62% and a gangue content of 4.61%. Analysis shows that during the pillar drawing process, the concentrated stress on the roof plate is significantly reduced, with a maximum stress decrease of 21.1 MPa, effectively alleviating stress concentration. The total force on the section hydraulic support in fully mechanized caving is 1.6×10⁴ kN, while the force in the fully mechanized mining section is 1.4×10⁴ kN.
Exploitation
Pintu Kumar Mandal; Niroj Kumar Mohalik; Manoj Kumar Mishra; Gautam Chandra Mondal
Abstract
The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, ...
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The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, spontaneous combustion, and mine fires. Among these challenges, many underground coal mines continue to operate after dewatering the coal seams for production purposes. Spontaneous combustion poses a significant risk in the dewatered coal seams of underground mines, impacting the safety of both the mines and the miners. This study aims to assess the risk of spontaneous combustion in a water-immersed coal seam of RCF by conducting proximate analysis, TGA/DSC, FTIR studies, and water analysis. One coal sample was obtained from the RV seam at the Kottadih coal mine in RCF and was immersed in tap water at a ratio of 1:10. The water-immersed coal samples were removed after 15, 30, and 90 days for sample preparation and other experimental investigations. The experimental results indicate that the water-immersed coal samples exhibit optimal moisture levels (4–8%), a higher volatile matter content (>30.0%) compared to fresh samples, and a gradual decrease in the ignition temperature of the water-immersed coal over time. There is an increase in concentrations of functional groups such as Ar-, -CHO, >C=O, and -C=C- due to the adsorption of dissolved organic compounds onto the coal surface. All analyses suggest that the rise in organic compounds contributes to the accelerated risk of spontaneous combustion.
Exploitation
Samia Chaoui; Adel Djellali; Benghazi Zied; Sarker Debojit
Abstract
This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical ...
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This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical characterization revealed low interstitial porosity, strong to very strong uniaxial compressive strengths ranging from 50.4 MPa to 129 MPa, and significant fracture-related secondary porosity. Rock Mass Rating (RMR89) and Geological Strength Index (GSI) values suggest fair to good rock quality. The mine design features square pillars inclined at 10°, with walls originally oriented perpendicular to the orebody dip, measuring 5 m in width and 3 m in height. The rooms, situated under a cover depth of 145.3 m, are 9 m wide. This configuration yielded an effective extraction rate of 87.24% and a safety factor of 1.63, indicating stable mining conditions. Phase 2D finite-element simulation confirmed these findings, showing a maximum displacement of 3.96 mm, surface subsidence of 0.57 mm, and a safety factor of 1.66, suggesting minimal environmental impact and long-term stability. Shear/compressive stress results from tributary area theory, aligning with numerical results and validating both approaches for inclined orebodies. Finally, the pillar walls, originally perpendicular to the orebody dip, were modified to be vertical relative to the horizontal plane, while maintaining the same pillar and room dimensions and cover depth. This adjustment improved stability by enhancing stress distribution and pillar core confinement, increasing the safety factor to 1.85.
Exploitation
Abbas Khajouei Sirjani; Ruqyah Heydari; Ramin Rafiee; Mohammad Amiri Hosseini
Abstract
In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and ...
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In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and disruption in drilling and charging operations in subsequent stages. The objective of this research is to predict and optimize back-break by combining statistical models with the Firefly Algorithm (FA). For this purpose, a database comprising data from 28 blasts in the waste rock section of Gol-e-Gohar Iron Ore Mine No. 1 was compiled. After data collection, the input parameters, including blast hole length, burden, spacing, Stemming, charge per delay, and Number of holes in the last row, were identified and utilized in the modeling process. To predict back-break, modeling was performed using multiple regression analysis. Among the developed models, the Polynomial statistical model with non-integer coefficients model with an adjusted coefficient of determination 0.885 was identified as the best-performing model and was subsequently used as the objective function in the Firefly Algorithm. The optimization process was then carried out using this algorithm. According to the findings of this research, the implementation of the current operational patterns in the mine along with the optimized proposed patterns resulted in a reduction of 4 meters in the average back-break, decreasing it from 7.5 meters in the waste rock section. The results demonstrate that the Firefly Algorithm is a highly effective and reliable tool for model optimization and a more accurate reduction of back-breaks. This approach has the potential to significantly enhance the efficiency of mining operations and reduce operational costs.
Exploitation
Heydar Bagloo; Mohsen Soleiman Dehkordi
Abstract
Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing ...
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Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing methods. However, evaluating the effectiveness of these optimization techniques, particularly in short-term mining activities under varying operational conditions, remains essential. Additionally, understanding how changes in operational conditions impact productivity is important for addressing production fluctuations in daily mining operations. To tackle these challenges, this study uniquely applies advanced machine learning techniques to short-term mining planning, resulting in the development of a real-time Productivity Evaluation Model (PEM) based on supervised learning methods for optimizing truck-shovel operations in open-pit mining. The model, developed and tested using data from a large-scale mining operation in Iran, demonstrated that the Decision Tree was the most effective, achieving an R² value of 0.96. This was closely followed by Random Forest and Gradient Boosting, both with R² values of 0.95. However, the choice of the most suitable learning method may vary depending on the specific dataset and context. The model determines the most appropriate learning method for each dataset within specific mining operations.
Exploitation
Ali Nemati vardin; Masoud Monjezi; Hasel Amini Khoshalan; Jafar Hamidi Khademi; Mojtaba Rezakhah
Abstract
Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt ...
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Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt hammer rebound hardness. To achieve this, a dataset comprising the drilling operations of 85 blastholes from the Sungun copper mine in Iran was prepared and analyzed using statistical and intelligent methods. Multivariate regression analysis and artificial neural networks developed in Python, utilizing optimization algorithms such as gradient descent, stochastic gradient descent, and adaptive moment estimation, were applied to predict the penetration rate of drill bits in this study. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) served as performance indicators to evaluate the methods employed. Among these, the adaptive moment estimation (Adam)-based model exhibited superior performance compared to alternative models, achieving values of R² = 0.96, MAE = 4.55, and RMSE = 4.30. Furthermore, the sensitivity analysis revealed that mining rock mass rating is the most influential factor on the rate of penetration, while thrust pressure has the least impact.
Exploitation
Mojtaba Rezakhah
Abstract
Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these ...
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Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these challenges by directly integrating critical geometallurgical parameters, specifically rock hardness (SPI index) and clay content, into the short-term production planning process. The simultaneous integration of these key geometallurgical feed quality attributes within an operational MILP model distinguishes this work from previous approaches and effectively bridges geological data analytics with operational decision-making, aligning economic objectives with enhanced metallurgical performance. Utilizing real operational data from the Sarcheshmeh Copper Mine, the framework was validated over a 186-day period. It achieved optimal production conditions on 137 days (73.6% of the duration), realizing a maximum Net Present Value (NPV) of $132,000. Key outcomes included a significant 21% reduction in concentrate grade variability and a 15% decrease in flotation reagent consumption, achieved through the simultaneous control of SPI and clay content. Advanced statistical methods were employed to identify critical relationships. While the model demonstrates scalability for porphyry copper mines globally, its successful implementation depends on careful parameter customization and alignment with existing infrastructure. This research work underscores the substantial value of data-driven, integrated optimization techniques in enhancing both profitability and process stability within mineral processing circuits.
Exploitation
Fatemeh Asadi Ooriad; Javad Gholamnejad; Ali Dabagh
Abstract
Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical ...
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Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical and operational constraints. Mathematical programming methods have proven suitable for optimizing mine production scheduling. Previous studies have addressed various aspects, including the timing of deployment and periodic relocation of in-pit crushers. Nevertheless, significant challenges remain in integrating the in-pit crusher problem with production planning. This paper introduces a new mixed-integer linear programming model for long-term open-pit mine production planning, incorporating constrained pit deepening to enforce predominantly lateral progression throughout the planning horizon. To achieve this, the number of active benches in each time period was reduced, thereby decreasing the need for equipment movement between working benches. Furthermore, with the horizontal progression of the pit, more workspace became available for deploying in-pit crushers, reducing equipment movement costs between benches and overall transportation costs, ultimately lowering the mine's operational expenses. Finally, the proposed model was implemented at the Miduk copper mine. The results demonstrated that the proposed model successfully achieved the expected objectives, resulting in a 52.45% improvement in reducing the number of active benches and regarding execution time reduction, the model showed a 53.32% improvement.
Exploitation
Tapan Dey; Gopinath Samanta
Abstract
Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of ...
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Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of XGBoost, Random Forests (RFs), Multi Layer Perceptron (MLP), and Gradient Boosting Regression (GBR) models to predict iron ore grades in an Indian mine. An ensemble model was also applied to obtain a more stable grade prediction in the deposit. Models were trained using 4,112 sample data, which have spatial coordinates (east, north, and altitude) and iron grades. The dataset was divided into two parts: 80% (3,289 samples) of the data was used for model training, and 20% (823 samples) was used for model testing. The performance of the models was assessed through the coefficient of determination (R²), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the XGBoost model performs better in the estimation process when compared with other methods, such as RFs, GBR, and MLP. The XGBoost model produced R² of 0.77, MSE of 2.87, and MAPE of 1.8%. The findings indicate that the XGBoost model is effective for predicting iron ore grades in this type of deposit. However, considering geological uncertainty, the application of an ensemble model may be beneficial for grade prediction in an iron deposit.
Exploitation
Moein Bahadori; Moahammad Amiri Hosseini; Iman Atighi
Abstract
As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate ...
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As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate vibration control strategies for protecting the onsite processing plant. A Blastmate III seismograph was employed to record 54 three-component data sets, including waveform data, maximum amplitude, and dominant frequencies. By superimposing waves, optimal delay times (ODT) for the blast holes were determined and the corresponding effects on wave frequencies were analyzed. An experimental blasting pattern was designed based on the derived ODT values, and the impact on ground vibration was examined. The results indicated a 10% reduction in vibration levels with the proposed delay times. Furthermore, considering the minimum distance of 111 meters from the processing plant to the final pit and adhering to the DIN safety standard, it is recommended that blast holes with a maximum diameter of 165mm be used to ensure a safety factor of 15%. For distances exceeding 187 meters, blast holes with a 250mm diameter are recommended to maintain production efficiency and a safety factor of 50%.
Exploitation
Alireza Afradi; Arash Ebrahimabadi
Abstract
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised ...
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Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised the input parameters such as Burden (m), spacing (m), powder factor (kg/m³), and stemming (m), with fragmentation (cm) as the output parameter. The analysis of these datasets was conducted using the Ant Lion Optimizer (ALO) and Crow Search Algorithm (CSA) methodologies. To assess the predictive models' accuracy, metrics including the coefficient of determination (R²), Variance Accounted For (VAF), and Root Mean Square Error (RMSE) were employed. The application of ALO and CSA to the database yielded results indicating that for ALO, R² = 0.99, RMSE = 0.005, and VAF (%) = 99.38, while for CSA, R² = 0.98, RMSE = 0.02, and VAF (%) = 98.11. Ultimately, the findings suggest that the predictive models yield satisfactory results, with ALO demonstrating a greater level of precision.
Exploitation
Mojtaba Dehghani Javazm; Mohammadreza Shayestehfar
Abstract
In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with ...
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In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with a block model constructed for the analysis. Four methods were employed: UC, LUC, DCSBG, and SGS. The correlation coefficients for UC, DCSBG, and SGS methods in the supergene zone, as well as the results from extraction drill holes (extraction blocks) at a cut-off grade of 0.15%, were 0.637, 0.527, and 0.556, and the correlation coefficient for calculating tonnage and the metal content using UC was 0.364 and 0.629, respectively. For the hypogene zone, the correlation coefficients for metal content at a cut-off grade of 0.15% were 0.778, 0.788, and 0.790 for UC, DCSBG, and SGS, and at a cut-off grade of 0.65%, they were 0.328, 0.431, and 0.458, respectively. By employing The LUC method in the supergene zone with a change in SMU and comparing the results obtained from the E-Type map, the performance of this method is higher across all cut-off grades. As the cut-off grade increases in the hypogene zone, the performance of the LUC method relative to simulation methods decreases. The LUC method can be used to observe the impact of the convergence of results obtained from this method with real data from low-grade to high-grade sections, highlighting the necessity of differentiating this zone into low and high-grade segments during the estimation process.
Exploitation
Arman Khosravi; Mohammad Ataei
Abstract
The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining ...
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The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining method selection, with a focus on their historical evolution, integration with fuzzy logic, artificial intelligence, and machine learning, as well as bibliometric trends and parameter analysis. The findings reveal a growing tendency toward hybrid and intelligent MCDM models that enhance decision accuracy and adaptability under uncertainty. A bibliometric analysis of key authors, countries, journals, and citation patterns highlights the global scope and scientific impact of research in this area. Furthermore, the study categorizes influencing parameters into intrinsic and extrinsic groups, identifying ore geometry, grade distribution, and rock mass properties as dominant intrinsic factors, while economic, environmental, and operational considerations represent significant extrinsic influences. This review emphasizes the vital role of MCDM techniques in optimizing mining operations, and advocates for further development of dynamic, data-driven models to meet the evolving challenges of modern mining.
Exploitation
Victor Patson Mutambo; Barnabas Mpaka; Pardon Sinkala; Matheus Ipinge
Abstract
This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region ...
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This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region is notable for its complex, folded, and mineralized formations. The deeper levels of the synclinorium are characterised by poor ground conditions, faults, and shear zones. Stress induced by mining activities worsens the situation. These factors have significantly impacted the stability of excavation. Fall-of-ground (FOG) incidents have exhibited a concerning increase over the past nine years. This trend necessitates a thorough investigation into the factors contributing to it. Our research employed empirical methods for rock mass classification, specifically utilising Barton’s Q system and Bieniawski and Scanline mapping of geological structures along the crosscut walls at a 1.50 m elevation. We conducted borehole logging and pull-out tests to evaluate the working and ultimate capacities of rock bolt anchors deployed in the excavations. Borehole cores were analysed for geological formations, colour, and grain size. The findings indicate that excavations in areas with mined-through rock and stone necessitate urgent and intensive roof support to stabilise the surrounding rock mass, thereby enhancing standing time. Additionally, we identified joint patterns, joint orientations, and the various stresses affecting the surrounding rock mass in the crosscuts. The above highlights the importance of geological data in the design of effective ground control and support mechanisms. Pull-out testing conducted at the 3360 level recorded a 28.6% failure rate in primary development despite very competent ground.
Exploitation
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Solio Marino Arango-Retamozo; Luis Alex Rios-LLaure; Jose Nestor Mamani-Quispe; Salomon Ortiz-Quintanilla
Abstract
This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were ...
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This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were normalized using Z-score scaling. Second, a Ridge regression model was trained to predict fuel consumption based on variables such as truck utilization, trips, road gradient, material type, haul distance, and operating hours. Finally, three operational strategies were simulated: Controlled Reduction (CRS), Balanced Efficiency (BES), and Maximum Utilization (MUS), to evaluate environmental, economic, and social impacts. The results indicated that the Ridge model achieved strong predictive performance in estimating fuel consumption (R2 = 0.83; MSE = 38.16). According to the simulated scenarios, environmentally, CRS reduced fuel consumption by 30% and CO2 emissions by 1,481.3 tons; BES achieved 7.99% savings and 394.9 tons less CO2. Economically, CRS saved USD 664,924.6 in fuel costs and BES USD 177,276.3. Socially, the carbon cost decreased by USD 11,406.1 (CRS) and USD 3,041.0 (BES). MUS increased emissions by 864.3 tons and fuel costs by USD 387,966.4. This research proposes a novel integration of machine learning and sustainability analysis applied to haul trucks in open-pit mining material transport. It also offers a replicable, data-driven framework for mining companies to reduce emissions, optimize costs, and align their operations with sustainability goals.
Exploitation
Taiwo Blessing Oamide; Adebayo Babatunde; Toluwase Daniel Olaiya
Abstract
This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest ...
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This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Regression (SVR), a conventional Artificial Neural Network (ANN), and two Imperialist Competitive Algorithm-optimized ANNs (ICA-ANNs). The models were trained using eight input parameters including uniaxial compressive strength (UCS), spacing (S), burden (B), sub-drill (SB), drill hole length (DHL), stiffness ratio (SR), maximum instantaneous charge (MIC), and powder factor (K) with blast toe volume (TV) as the target output. Input data were collected through a combination of field measurements and laboratory analyses. Among all the models evaluated, the ICA-ANN with an 8-7-1 architecture achieved the highest predictive accuracy. It outperformed AdaBoost by 9.17%, SVR by 7.20%, GPR by 5.56%, RF by 4.75%, a standard ANN (8-5-1) by 0.78%, and a standard ANN (8-7-1) by 0.28%, based on mean squared error (MSE) and coefficient of determination (R²) metrics. Furthermore, the ICA-ANN model was applied to optimize blast design parameters. The optimal values obtained were: spacing = 1.0 m, burden = 0.8 m, sub-drill = 0.6 m, MIC = 0.72 kg, and powder factor = 0.65 kg/m³. These optimized parameters reduced the blast toe volume by 20.05%, from 209.50 m³ to 154.87 m³. The results highlight the robustness and efficiency of the ICA-ANN model for blast design optimization. By improving fragmentation quality and minimizing residual toe volume, the approach offers a practical pathway for enhancing both productivity and cost-effectiveness in small-scale mining operations.
