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, ...
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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
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 ...
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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
Kamel Menacer; Abderrazak Saadoun; Abdellah Hafsaoui; Mohamed Fredj; Abdelhak Tabet; Djamel Eddine BOUDJELLAL; Riadh Boukarm; Radouane Nakache
Abstract
Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming ...
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Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming material significantly impacts the degree of rock fragmentation during mining operations. Stemming refers to the material used to fill the space above explosives in a borehole, which helps confine the explosive energy and optimise rock fragmentation during detonation.This study aims to evaluate the stemming materials and their effect on the particle size distribution of blasted rocks at the Chouf Amar quarry in M'Sila, Algeria. The analyses performed in this study indicate that the blasting results obtained by the company reflect poor fragmentation quality, with a significant quantity of oversized fragments, making up 20–23% of the total pieces. To address this issue, a new operational blasting plan is proposed to enhance fragmentation quality. This plan employed three stemming materials: drill cuttings, 3/8 crushed aggregates, and sand. The test blasts were performed in a limestone quarry, and the results were evaluated using the highly reliable and widely respected image analysis software WipFrag 3.3. The results reveal that using crushed aggregates as stemming material significantly improves fragmentation quality, reducing the proportion of oversized fragments from an average of 23% (with sand stemming) to 2.6%.
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 ...
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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
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
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
Ali Nemati vardni; 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
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina Araujo; Jose Nestor Mamani Quispe; Solio Marino Arango-Retamozo; Johnny Henrry Ccatamayo-Barrios; Joe Alexis Gonzalez-Vasquez; Teofilo Donaires-Flores; Maxgabriel Alexis Calla-Huayapa
Abstract
Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation ...
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Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation efficiency. This work aims to optimize fragmentation (X50) and drilling and blasting costs using hybrid machine learning models, an innovative approach that improves predictive accuracy and economic feasibility. Six models were developed: Artificial Neural Networks (ANNs), Decision Trees (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The dataset, comprising 100 blasts, was split into 70% for training and 30% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of 0.27, MAE of 0.21, and R2 of 0.92. The RF+GA model was most effective for cost prediction, with an RMSE of 414.58, MAE of 354.14, and R2 of 0.99. Optimization scenarios were implemented by reducing burden (4.3 m to 3.8 m) and spacing (5.0 m to 4.5 m), achieving a 5.7% reduction in X50 (17.6 cm to 16.6 cm) and a 9.5% cost decrease (63,000 USD to 57,000 USD per blast). Predictions for 30 future blasts using the RF + GA model estimated a total cost of 1.7 MUSD, averaging 55,180 USD per blast. These findings confirm the effectiveness of machine learning in cost optimization and improving blasting efficiency, presenting a robust data-driven approach to optimizing mining operations.
Exploitation
Patrick Adeniyi Adesida; Sunday Adex Adaramola
Abstract
This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters ...
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This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters (uniaxial compressive strength, porosity, specific gravity, and the mineral content of quartz, plagioclase, hornblende, and biotite) were analysed via a RES interaction matrix to derive a new Drillability Index capturing their combined influence. This analysis revealed that UCS and porosity are the most influential factors in the system. The resulting RES-based model correlates strongly with observed bit wear rates, achieving a high coefficient of determination (R² ≈ 0.93) and low prediction errors (RMSE = 2.79, MAE = 2.14). The MAPE (= 38%) indicates a marked improvement in accuracy over traditional regression methods. Integrating mechanical and mineralogical factors is a novel approach to drillability prediction, providing a more comprehensive account of rock characteristics than conventional models. Validation results show that the RES-derived Drillability Index reliably predicts field performance, offering practical value for optimising drilling operations and guiding geomechanical analysis. Additionally, the study proposes a drillability classification scheme to further support the field application of the findings.
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
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The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
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 ...
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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
R. Shamsi; M. S. Amini; H. Dehghani; M. Bascompta; B. Jodeiri Shokri; Sh. Entezam
Abstract
This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were ...
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This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were collected from the mine. Then, using GEP, a series of intelligent equations were proposed to predict flyrock distance. The best GEP equation was selected based on some well-established statistical indices in the next stage. The coefficient of determination for training and testing datasets of the GEP equation were 0.890 and 0.798, respectively. The model obtained from the GEP method was then optimized using teaching– learning-based optimization algorithm (TLBO). Based on the results, the correlation coefficient of training and testing data increased to 91% and 89%, which increased the accuracy of the Equation. This new intelligent equation could forecast flyrock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues.
