Exploitation
Assefa Hailesilasie Wolearegay; Yowhas Birhanu Amare; Asmelash Abay Hagos; Kassa Amare Mesfin; Hagos Abraha; Bereket Gebresilassie; Yewuhalashet Fissha; nageswararao cheepurupalli
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 ...
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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
Abdelrahem Khalefa Embaby; Yehia Darwish; 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 ...
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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
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
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 ...
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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.
Exploitation
mohammad hossein jalalian; Raheb Bagherpour; mehrbod khoshouei; S. Najmedin Almasi
Abstract
Diamond wire cutting is a common method to extract dimension stones, which depends on various factors, including the mechanical and physical properties of the stone, cutting specifications, and operational characteristics. Specific energy, production rate, efficiency, and wear of diamond beads are some ...
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Diamond wire cutting is a common method to extract dimension stones, which depends on various factors, including the mechanical and physical properties of the stone, cutting specifications, and operational characteristics. Specific energy, production rate, efficiency, and wear of diamond beads are some of the criteria that influence economic and environmental optimization of diamond wire cutting operations. In this study, the specific energy of the diamond wire cutting process was measured for 11 samples of Granite stones. By analyzing the impact of parameters such as stone density, porosity, and cutting rate on energy consumption, a linear regression model was developed with a correlation coefficient (R2) of 0.944 to predict specific energy for different types of stones. Statistical analyses, including ANOVA, have confirmed that the model accurately predicts specific energy values. Data from three new stone samples were used to validate the model, and their predicted energy values were compared with actual values. The model presented achieved an R2 value of 0.827, demonstrating its high accuracy. The results indicate that energy consumption in dimension stone cutting operation can be accurately predicted and characterized indirectly using high precision stone properties and operational parameters. This method can accurately and indirectly monitor energy consumption and cutting machine performance during the dimension stone cutting operation and can be used to optimize economic and environmental aspects of this process.
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 ...
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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
Avula Rajashekar Yadav; Sreenivasa Rao Islavath; Srikanth Katkuri
Abstract
The installation gallery/set-up room of a longwall panel is driven for installation of the longwall face machineries to start the extraction of coal from the longwall panel. The width of the installation gallery is 8 to 9 m. This gallery needs to be stabilized till the face machineries to be deployed ...
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The installation gallery/set-up room of a longwall panel is driven for installation of the longwall face machineries to start the extraction of coal from the longwall panel. The width of the installation gallery is 8 to 9 m. This gallery needs to be stabilized till the face machineries to be deployed from the driving of the room as it required to stand more than 8 to 10 months and develop the high stress concentration, roof-to-floor convergence and yield zone in the roof and sides. Hence, in this study, a deep longwall mine of India is considered to analyze the behavior of set-up room. For this, a total of twelve 3D numerical models are developed and analyzed considering Mohr’s-Coulomb failure criterion. Three panels located at 417, 462, 528 m having three different widths (8, 10 and 12 m) of set-up rooms are examined. The width of the set-up room is taken based on the length of the shield support. The results in terms of vertical stress distribution, vertical displacement, roof-to-floor convergence, plastic strain and yield zone distribution are presented.
Exploitation
Soufi Amine; Zerradi Youssef; Soussi Mohamed; Ouadif Latifa; Bahi Anas
Abstract
The aim of this study is to thoroughly analyze the relaxation zone developing around sublevel stopes in underground mines and identify the main parameters controlling its extent. A numerical approach based on the finite element method, combined with the Hoek-Brown failure criterion, was implemented to ...
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The aim of this study is to thoroughly analyze the relaxation zone developing around sublevel stopes in underground mines and identify the main parameters controlling its extent. A numerical approach based on the finite element method, combined with the Hoek-Brown failure criterion, was implemented to simulate various geometric configurations, geological conditions, and in-situ stress states. A total of 425 simulations were carried out by varying depth, horizontal-to-vertical stress ratio (k), rock mass quality (RMR), foliation orientation and spacing, as well as the height, width, and inclination of the sublevels. The results enabled the development of robust predictive models using regression analysis techniques and artificial neural networks (ANNs) to estimate the extent of the relaxation zone as a function of the different input parameters. It was demonstrated that depth and the k ratio significantly influence the extent of the relaxation zone. Additionally, a decrease in rock mass quality leads to a substantial increase in this zone. Structural characteristics, such as foliation orientation and spacing, also play a decisive role. Finally, the geometric parameters of the excavations, notably the height, width, and inclination of the sublevels, directly impact stress redistribution and the extent of the relaxation zone. The overall ANN model, taking into account all these key parameters, exhibited high accuracy with a correlation coefficient of 0.97. These predictive models offer valuable tools for optimizing the design of underground mining operations, improving operational safety, and increasing productivity.
Exploitation
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina Araujo; Eduardo Manuel Noriega Vidal; Jose Nestor Mamani Quispe; Johnny Henrry Ccatamayo Barrios; Joe Alexis Gonzalez Vasquez; Solio Marino Arango Retamozo
Abstract
The primary objective of this research was to apply machine learning techniques to predict the production of an open pit mine in Peru. Four advanced techniques were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (RB). The methodology ...
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The primary objective of this research was to apply machine learning techniques to predict the production of an open pit mine in Peru. Four advanced techniques were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (RB). The methodology included the collection of 90 datasets over a three-month period, encompassing variables such as operational delays, operating hours, equipment utilization, the number of dump trucks used, and daily production. The data were allocated 70% for training and 30% for testing. The models were evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), and the Coefficient of Determination (R2). The results indicated that the Bayesian Regression model was the most effective in predicting production in the open pit mine. The RMSE, MAPE, VAF, and R2 for the models were 3686.60, 3581.82, 4576.61, and 3352.87; 12.65, 11.09, 15.31, and 11.90; 36.82, 40.72, 1.85, and 47.32; 0.37, 0.41, 0.41, and 0.47 for RF, XGBoost, KNN, and RB, respectively. This research highlights the efficacy of machine learning techniques in predicting mine production and recommends adjusting each model's parameters to further enhance outcomes, significantly contributing to strategic and operational management in the mining industry.
Exploitation
Meisam Saleki; Reza Khaloo Kakaie; Mohammad Ataei; Ali Nouri Qarahasanlou
Abstract
One of the most critical designs in open-pit mining is the ultimate pit limit (UPL). The UPL is frequently computed initially through profit-maximizing algorithms like the Lerchs-Grossman (LG). Then, in order to optimize net present value (NPV), production planning is executed for the blocks that ...
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One of the most critical designs in open-pit mining is the ultimate pit limit (UPL). The UPL is frequently computed initially through profit-maximizing algorithms like the Lerchs-Grossman (LG). Then, in order to optimize net present value (NPV), production planning is executed for the blocks that fall within the designated pit limit. This paper presents a mathematical model of the UPL with NPV maximization, enabling simultaneous determination of the UPL and long-term production planning. Model behavior is nonlinear. Thus, in order to achieve model linearization, the model has been partitioned into two linear sub-problems. The procedure facilitates the model solution and the strategy by decreasing the number of decision variables. Naturally, the model is NP-Hard. As a result, in order to address the issue, the Dynamic Pit Tracker (DPT) heuristic algorithm was devised, accepting economic block models as input. A comparison is made between the economic values and positional weights of blocks throughout the steps in order to identify the most appropriate block. The outcomes of the mathematical model, LG, and Latorre-Golosinski (LAGO) algorithms were assessed in relation to the DPT on a two-dimensional block model. Comparative analysis revealed that the UPLs generated by these algorithms are consistent in this instance. Utilizing the new algorithm to determine UPL for a 3D block model revealed that the final pit profit matched LG UPL by 97.95%.
Exploitation
Mehdi Rahmanpour; Golpari Norozi; Hassan Bakhshandeh Amnieh
Abstract
Drift-and-fill mining is a variation of cut-and-fill mining method. Drift-and-fill mining method refers to the excavation of several parallel drifts in ore. Excavation of a new drift could start when its adjacent drifts are backfilled or not excavated. The amount of ore material and its grade depends ...
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Drift-and-fill mining is a variation of cut-and-fill mining method. Drift-and-fill mining method refers to the excavation of several parallel drifts in ore. Excavation of a new drift could start when its adjacent drifts are backfilled or not excavated. The amount of ore material and its grade depends on the excavation sequence of drifts. As the number of drifts increases, one will need a model to optimize the drift excavation and backfilling sequence. This paper introduces a mathematical model to determine the optimal drift-and-fill sequence while the safety constraints, excavation, and backfilling capacities and their dependencies are satisfied. The model seeks to minimize the deviations from some predefined goals, and it handles the long-term and short-term constraints in separate and integrated scenarios. An application of the model is presented based on the data available from a lead/zinc underground mining project. There are 91 drifts in the selected level. Based on the monthly planning horizon, the integrated model leads to the slightest deviations in both the mining rate and average grade, and the deviation from the predetermined annual goals is negligible. For the case where long-term and short-term plans are determined separately, the deviation is approximately 10%.
Exploitation
Mohammad Sina Abdollahi; Mehdi Najafi; Alireza Yarahamdi Bafghi; Ramin Rafiee
Abstract
The stability analysis of chain pillars is crucial, especially as coal extraction rates increase, making it essential to reduce the size of these pillars. Therefore, a new method for estimating the load on chain pillars holds significant importance. This research introduces a novel solution for estimating ...
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The stability analysis of chain pillars is crucial, especially as coal extraction rates increase, making it essential to reduce the size of these pillars. Therefore, a new method for estimating the load on chain pillars holds significant importance. This research introduces a novel solution for estimating side abutment load and analyzing the stability of chain pillars using the dynamic mode of the Coulmann Graphical (CG) method. The solution is implemented using Visual Studio software and is named Coulmann Chain Pillar Stability Analysis (CCPSA). The CG method is widely recognized in civil engineering as a highly efficient technique for determining soil side abutment pressure in both static and dynamic conditions. This method involves calculating the top-rupture wedge of chain pillars using the CG method. The CCPSA software functions share significant similarities with those of the Analysis Longwall Pillar Stability (ALPS) method. However, the main point of departure between the proposed method and the ALPS empirical method lies in their respective approaches to calculating side abutment load on chain pillars and evaluating subsidence conditions. The effectiveness of this method has been validated using a database of chain pillars from various mines worldwide and has been compared with the ALPS method. The results of the comparison demonstrate that the CCPSA is highly effective in evaluating chain pillar stability. This underscores the potential of the CG method and CCPSA software in providing valuable insights for assessing and ensuring the stability of chain pillars in mining operations.
Exploitation
Elham Lotfi; Javad Gholamnejad; Mehdi Najafi; Mohammad Sadegh Zamani
Abstract
In the context of open pit mining operations, long-term production scheduling faces significant challenges due to inherent uncertainties, particularly in commodity prices. Traditional mathematical models often adopt a single-point estimation strategy for commodity price, leading to suboptimal mine plans ...
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In the context of open pit mining operations, long-term production scheduling faces significant challenges due to inherent uncertainties, particularly in commodity prices. Traditional mathematical models often adopt a single-point estimation strategy for commodity price, leading to suboptimal mine plans and missed production targets. The simultaneous effect of commodity price uncertainty on the cut-off grade and long-term production scheduling is less considered. This paper introduces a novel model for optimizing open pit mine long-term production scheduling under commodity price uncertainty considering a dynamic cut-off grade strategy, based on a two-stage Stochastic Production Programming (SPP) framework. The presented model seeks to identify optimal mining block sequences, maximizing total discounted cash flow while penalizing deviations from production targets. To illustrate the model's efficiency, it was implemented in a copper mine. First, the Geometric Brownian Motion (GBM) model is used to quantify the future commodity price. Then, both deterministic and SPP models were solved using GAMS software. The results showed that the practical NPV obtained from the SPP model is approximately 3% higher than the DPP model, while all constraints are satisfied.
Exploitation
Hossein Mirzaei Nasir Abad; Mehrnaz Mohtasham; Farshad Rahimzadeh-Nanekaran
Abstract
Transportation of materials is the most cost-intensive component in open-pit mining operations. The aim of the allocation models is to manage and optimize transportation activities, leading to reduced wasted time, and ultimately, increasing profitability while reducing operational costs. Given that the ...
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Transportation of materials is the most cost-intensive component in open-pit mining operations. The aim of the allocation models is to manage and optimize transportation activities, leading to reduced wasted time, and ultimately, increasing profitability while reducing operational costs. Given that the implementation of allocation models is one of the essential requirements in Iranian mining operations, this research work focuses on the transportation system in the Sungun copper mine, one of the largest mines in Iran, and highlights the challenges faced by the fixed allocation approach. The aim is to develop and implement a mathematical model to evaluate its performance, and suggest improvements. The allocation model attempts to optimize truck capacity utilization and maximize mining production. Implementing the model in the mine results in a 13.42% increase in total production compared to the conventional method, with a cost increase of 14.7%. The model shows the potential to meet operational and technical constraints to achieve optimal production. Overall, the developed model, with optimized management and improved fleet efficiency, outperforms the traditional haulage method in the mine.
Exploitation
Behnam Alipenhani; Mehran Jalilian; Abbas Majdi; Hassan Bakhshandeh Amnieh; Mohammad Hossein Khosravi
Abstract
The paper presents the effect of the dip of joints, joint spacing, and the undercutting method on the height of the caving in block caving. The obtained results show that among the three investigated parameters, respectively, the dip of joints, undercutting method, and joint spacing have the greatest ...
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The paper presents the effect of the dip of joints, joint spacing, and the undercutting method on the height of the caving in block caving. The obtained results show that among the three investigated parameters, respectively, the dip of joints, undercutting method, and joint spacing have the greatest effect on increasing the height of the caving zone. Comparing the data obtained from physical and numerical modeling shows a 97% match. Also, by increasing the joint spacing from 4 to 6 cm, 14%, from 6 to 8 cm, about 35%, and from 8 to 10, about 50%, the height of the caving zone has decreased. Regarding the dip of the joint, with the dip increasing from 30 to 45 degrees, about 3% of the caving height decreases. By increasing the dip of the joint from 45 to 60 degrees, the caving height has decreased by 42%. By increasing this value from 60 to 75 degrees, the caving height has increased by 50%. Also, changing the undercutting method from symmetric to advanced undercutting has increased the caving height by 40%. Additionally, three mathematical models have been proposed based on the shape of the caving zone in physical modeling.
Exploitation
Israel Mamani; Angelica Vivanco; Eslainer Avendaño
Abstract
In open-pit mining operations, loading and haulage activities account for a significant portion, typically between 50% and 60%, of the operational costs of the entire mining process. Tires, in turn, rank second in terms of operating costs for most mining companies. Therefore, understanding and preserving ...
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In open-pit mining operations, loading and haulage activities account for a significant portion, typically between 50% and 60%, of the operational costs of the entire mining process. Tires, in turn, rank second in terms of operating costs for most mining companies. Therefore, understanding and preserving the useful life of Off-The-Road (OTR) tires is a critical factor in ensuring the profitability of a mining project. This study focuses on a specific mine to analyze the causes of operational damage in the tires of Mining Trucks (MTs) and Front-End Loaders (FELs). It aims to identify the factors leading to the premature disposal of these tires, and propose solutions to increase their useful life. The study identifies four key aspects that influence the low performance of extraction equipment, namely operator experience, environmental condition, raw materials, and equipment condition. Additionally, the study reveals that overinflation pressure significantly contributes to the premature disposal of tires, accounting for 70.5% of MT tire damage and 52.5% of FEL tire damage (primarily affecting MT rear and FEL front tires). The use of tire chains is proposed as a solution, with the potential to decrease the unit cost per labor hour by 28% for at least 50% of the tires.
Exploitation
Blessing Olamide Taiwo; Oluwaseun Victor Famobuwa; Melodi Mbuyi Mata; Mohammed Sazid; Yewuhalashet Fissha; Victor Afolabi Jebutu; Adams Abiodun Akinlabi; Olaoluwa Bidemi Ogunyemi; Ozigi Abubakar
Abstract
The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was ...
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The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. In this study, the efficacy of granite fragmentation was assessed using the WipFrag software. The relationship between particle size distribution, blast design, blast efficiency, and uniformity index were analyzed using the WipFrag result. The optimum blast design was also identified and recommended for mine production. The result revealed that large burden distances result in bigger X50, X80, and Xmax fragmentation sizes. A burden distance of 2 m and a 2 m spacing were identified as the optimum burden and spacing. The finding revealed that blast mean size and 80% passing mesh size have a positive correlation. The result from this study indicated that the uniformity index has a positive correlation with blast efficiency and a negative correlation with maximum blast fragmentation size. The prediction accuracy of the developed models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). The error analysis revealed that the ANN model is suitable for predicting quarry-generated profit.
Exploitation
Shahrokh Khosravimanesh; Masoud Cheraghi Seifabad; Reza Mikaeil; Raheb Bagherpour
Abstract
Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved ...
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Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved by delivering a suitable cooling lubricant into the drilling environment. The present study examines the mechanical characteristics of the drilled rock, the physical qualities of the cooling lubricant employed, and the drilling rig operational parameters related to the drilling-specific energy (DSE). To this end, seven rock samples (granite, marble, and travertine) were drilled using water and five other fluids as the cooling lubricants. A total of 492 drilling experiments were conducted with a custom-designed and built laboratory-scale drilling rig on cuboid rock specimens. The univariate linear regression analysis of experimental results revealed a significant drop in DSE after using cooling lubricants instead of conventional cooling fluid (i.e. water). Under constant conditions in terms of mechanical properties of the rock, using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of water led to 34% and 43% DSE reductions in the granite samples, 48% and 54% in the marble samples, and 41% and 50% in the travertine samples, respectively. These variations in specific energy suggest that the drilling efficiency and performance can be augmented using properly selected cooling lubricants.
Exploitation
Hassanreza Ghasemitabar; Andisheh Alimoradi; Hamidreza Hemati Ahooi; Mahdi Fathi
Abstract
Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in ...
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Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in installation of drilling rigs, restrictions caused by previous mining operation etc. The advances in artificial intelligence can help to solve these problems. In this research, we used python as one of the most pervasive and the most powerful programming languages in the field of data analysis and artificial intelligence. In this method mean shift algorithms have been used to cluster data, random forest to estimate clusters, and gradient boosting to estimate iron grade. Finally, in the studied area of Choghart in Central Iran, more than 91% accuracy was achieved in detection of ore blocks. Also, the results of the neural network indicate the mean square error (MSE) and mean absolute error (MAE) in the training data, respectively equal to 0.001 and 0.029, in the test data is 0.002 and 0.03, and in the validation boreholes, we reached a maximum of 0.06 and 0.2.
Exploitation
Rym Khettabi; Issam Touil; Mohamed Kezzar; Mohamed R. Eid; Fatima.Z Derdour; Kamel Khounfais; Lakhdar Khochmane
Abstract
It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity ...
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It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity of the bit, RPM, cutting angle , and rock resistance on the penetration rate of the drilling tool. In this examination, three kinds of limestone rocks were considered. The planned assessments were carried out at three stages of the considered four input variables. The statistical analysis was realized using both RSM approach and analysis of variance (ANOVA). This analysis allowed us to develop the appropriate penetration model with a higher determination coefficient of 96.19%, which demonstrates the high correlation between the predicted and experimental data, and consequently, it can be concluded that the obtained model is highly suitable for the prediction of the penetration rate. Also from variance analysis, the results obtained show that rotational speed, RPM, and weight on the bit (WOB) parameters, as well as the nature of the rock, which is determined by the rock compressive resistance, having a significant effect on the penetration rate; however, the rake angle has little effect. Finally, the optimal parameters were determined to find the best possible penetration rate of the drilling tool.
Exploitation
Morteza Javadi; Ashkan Shahpasand; Shahrbanou Sayadi; Arash Shahpasand
Abstract
The stratified-sedimentary rock mass, as the typical host ground of coal mine tunnels, is characterized by highly non-isotropic deformation due to the very persistent discontinuity of bedding planes. This study evaluates the effect of tunnel location relative to the host ground strata on the excavation-induced ...
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The stratified-sedimentary rock mass, as the typical host ground of coal mine tunnels, is characterized by highly non-isotropic deformation due to the very persistent discontinuity of bedding planes. This study evaluates the effect of tunnel location relative to the host ground strata on the excavation-induced displacements around a coal mine tunnel driven along the inclined coal seam. To achieve this goal, a calibrated finite element method (FEM) numerical model based on field monitoring displacements was developed for the coal mine tunnel at a depth of 300 m. This calibrated numerical model was then utilized to investigate the effect of the horizontal location of the tunnel on the induced displacement field through sensitivity analysis. Finally, the sensitivity analysis results were compared in terms of displacement components around the tunnel. The results of this study demonstrate a reasonable level of accuracy (for practical demands) of the calibrated numerical model, with an average error of about 8% for maximum displacements at measured points. The numerical models show an asymmetric spatial distribution of displacements around the tunnel due to the anisotropy of the rock mass, especially in the case of inclined layers. The arrangement of weak-strength coal and intercalary stone layers relative to the excavation line of the tunnel plays a key role in this issue. The critical state of displacements (maximum displacement in sensitivity analysis) occurs where the intersection line of the coal-intercalary stone is tangent to the tunnel excavation line. Additionally, the excavation-induced displacement decreases as the distance between the coal-intercalary stone interface and the tunnel increases, with a distance of about 1.5 m suggested for practical applications.
Exploitation
Emad Ansari; Ramin Rafiee; Mohammad Ataei
Abstract
Due to longwall mining, a large space without any support is created, and the in-situ stress regimes change. The change of the in-situ stress regimes affects the roof and face of the adjacent panel. In other words, the strata behavior would be different from the intact condition during the previous panel ...
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Due to longwall mining, a large space without any support is created, and the in-situ stress regimes change. The change of the in-situ stress regimes affects the roof and face of the adjacent panel. In other words, the strata behavior would be different from the intact condition during the previous panel mining. In this study, two adjacent panels are simulated in the FLAC3D software to study the effect of panel extraction on its adjacent panel strata behavior during longwall mining. The available data of the Tabas Parvadeh Coal Mine panels is used for this purpose. According to the numerical modeling results, the length of the first roof’s weighting effect (FRWE) in the gob of the first and second panels is calculated, respectively, as 26 and 21 meters. In other words, the gob dimension in the second panel is reduced by about 19.2%, and the vertical displacement value is increased by about 18.5%. In addition, the chance of roof collapse and face spalling during the first-panel mining is more than the second-panel. It means that roof and face instability in the (FRWE) during the first-panel mining is confirmed, while in the second-panel extraction is just very likely.
Exploitation
Sonu Singh; Vijay Shankar; Joseph Tripura
Abstract
Assessing the groundwater potential (GWP) and protective capacity of aquifers is essential to provide solutions to challenges in aquifer exploration and conditions in hilly terrain regions. The study was conducted in the hilly terrain region of Hamirpur, Himachal Pradesh, India, to obtain one-dimensional ...
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Assessing the groundwater potential (GWP) and protective capacity of aquifers is essential to provide solutions to challenges in aquifer exploration and conditions in hilly terrain regions. The study was conducted in the hilly terrain region of Hamirpur, Himachal Pradesh, India, to obtain one-dimensional vertical electrical sounding (VES) data for groundwater exploration and evaluate the vulnerability of sublayers. Forty VES sites were used in the Schlumberger electrode configuration. The analysis of data resulted in stratified 2-5 different curves. According to the geoelectric sections, there are two to five layers of soil beneath the region i.e. Shale/clay (10-650 Ohm-m), fractured sandstone/gravel/sand (10.3-436 Ohm-m), clay mix gravel/clay mix sand/coarse-grained sandstones (1.06-355 Ohm-m), conglomerate/clay/hard sandstone (60.5-658.7 Ohm-m), sandstone/shale (90.8-125 Ohm-m) with aquifer resistivity (AR) in parenthesis. Aquifer resistivity (AR), longitudinal conductance (S), layer thickness (LT), and transverse resistivity (TR) distribution maps were generated using interpreted VES data for various sub-layers using ArcGIS 10.1. The geologic second and third sub-surface layers are generally porous and permeable. S values for underlying layers are generally less than unity, which indicates vulnerable zones with a significant risk of contamination. Based on the S values, the strata are divided into five categories as Poor (5.55%), weak (19.43%), moderate (19.45%), good (38.89%), and very good (16.68%). Areas with moderate to very good protection capacity are planned as zones with high GWP. The study results are useful in preliminary pollution control and assessment for sustainable groundwater management.
Exploitation
Babatunde Adebayo; Blessing Olamide Taiwo; BUSUYI THOMAS AFENI; Aderoju Oluwadolapo Raymond; Joshua Oluwaseyi Faluyi
Abstract
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. ...
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The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations.
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.
