Exploitation
Sruti Narwal; Debasis Deb; Sreenivasa Rao Islavath; Gopinath Samanta
Abstract
A novel underground mining method is proposed to extract friable chromite ore bodies in weak and weathered limonitic host rock below an open-pit mine. The conventional underground methods do not instil confidence since GSI (Geological Strength Index) of ore bodies and host rock lies below 35. Series ...
Read More
A novel underground mining method is proposed to extract friable chromite ore bodies in weak and weathered limonitic host rock below an open-pit mine. The conventional underground methods do not instil confidence since GSI (Geological Strength Index) of ore bodies and host rock lies below 35. Series of dimensions of transverse stopes along the strike are suggested based on a detailed analysis of multiple mining and backfilling operations by simulating 36 three-dimensional numerical models. For each operation or sequence, a strength-based “Mining Sequence Factor (MSF)” is devised that helps quantifying its equivalent strength compared to in-situ conditions. This factor along with the average equivalent plastic strain (AEPS) developed on the pillars as obtained from numerical models is used to determine the safe operations with desired yearly production target. The paper provides an in-depth analysis of this method and suggests minimum pillar dimensions of 40 m, whether in-situ or backfilled. The paper, in addition, lays the design of underground drives and their support system as per NGI (Norwegian Geotechnical Institute) guidelines and 3D numerical studies, the performance of which is analysed considering distribution of stress and equivalent plastic strain.
Exploitation
Javad Lotfi Godarzi; Ahmad Reza Sayadi; Amin Mousavi; Micah Nehring
Abstract
The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced ...
Read More
The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced by the production rate and product price. A conventional approach optimizes each variable individually, and neglects the trade-off between production rate and cut-off grade, leading to a sub-optimal solution. This work aimed to address the simultaneous optimization of the production rate and cut-off grade and provided a novel solution for this problem. In this context, a non-linear mathematical model was developed. The Particle Swarm Optimization (PSO) algorithm was used due to the model's non-linear nature and the continuous decision variables. Implementing the model for a typical copper mine showed that the suggested model resulted in a concurrent optimization of production rate and cut-off grade. The maximum NPV of 1.153 billion dollars occurred at a production rate of 15.66 Mt/y, and a cut-off grade of 0.64%. Additionally, a sensitivity analysis was conducted for key factors such as product price, discount rate, and maximum capital cost.
Exploitation
Pouya Nobahar; Yashar Pourrahimian; Roohollah Shirani Faradonbeh; Fereydoun Mollaei Koshki
Abstract
Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents ...
Read More
Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents a novel Deep Learning (DL)-based model to recognize the types of kaolinite samples. For this purpose, a dataset containing the images of drilled cores and their types determined from conventional chemical and physical analyses was used. Eight Convolutional Neural Network (CNN) topologies based on individual features were developed, named A, B, C, D, E, F, G, and H. Six of the eight proposed CNN topologies described above had accuracy below 80%, whereas two of them, model A and H, had higher accuracy than other topologies. Due to their similarity in results, both of them analyzed deeply. Model A was more efficient, with 90% accuracy, than model B, with 84% accuracy. Furthermore, the class detection performance of model A was further evaluated using different indices, including precision, recall, and F1-score, which resulted in values of 92%, 92%, and 90%, respectively, which are acceptable accuracies to identify the type of samples when using this approach on six different types of kaolinite.
Exploitation
Moslem Jahantigh; Hamidreza Ramazi
Abstract
Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. ...
Read More
Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. Examining and evaluating drilling data in the Lar copper mine in Sistan and Baluchistan province located in the southeast of Iran requires the use of these methods. Therefore, the main goal of the article is the clustering of the drilling data in the mentioned mine and its zoning of the geochemical data. To achieve this goal, 3500 samples taken from drilling cores have been used. Elemental analysis for six elements has been done using the ICP-Ms method. Pattern recognition methods including SOM and K-MEANS have been used to evaluate the relation between these elements. The silhouette method has been used to determine and evaluate the number of clusters. Using this method, 4 clusters have been considered for the mentioned data. According to this method, it was found that the accuracy of clustering is higher in the SOM method. By considering the 4 clusters, 4 zones were identified using clustering methods. By comparing the results of the two methods and using the graphical method, it was determined that the SOM method has a better performance for clustering geochemical data in the studied area. Based on that, zones 2 and 4 were recognized as high-grade zones in this area.
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
Read More
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
Exploitation
Mojtaba Rezakhah; somaye khajevand; Masoud Monjezi; Fabián Alejandro Manríquez León
Abstract
Efficient loading and hauling systems, with trucks and shovels as the primary transportation machinery, are essential for optimizing mining operations. This study introduces a simulation-based approach to enhance the utilization of the hauling system in an Abbasbad copper mine in Iran. A dynamic truck ...
Read More
Efficient loading and hauling systems, with trucks and shovels as the primary transportation machinery, are essential for optimizing mining operations. This study introduces a simulation-based approach to enhance the utilization of the hauling system in an Abbasbad copper mine in Iran. A dynamic truck allocation model is proposed to overcome the limitations of fixed allocation methods. In this approach, trucks are assigned to loading equipment based on the real-time throughput data, prioritizing equipment experiencing the highest production delays. The simulation results demonstrate that this flexible allocation model improves productivity, achieving a 13% increase in waste material handling compared to the fixed allocation scenario. These findings indicate that the proposed framework to significantly improve the efficiency and productivity of haulage systems in mining operations.
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi; Seyyed Ali Hashemi
Abstract
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with ...
Read More
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications.
Exploitation
Alireza Afradi; Arash Ebrahimabadi
Abstract
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised ...
Read More
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised the input parameters such as Burden (m), spacing (m), powder factor (kg/m³), and stemming (m), with fragmentation (cm) as the output parameter. The analysis of these datasets was conducted using the Ant Lion Optimizer (ALO) and Crow Search Algorithm (CSA) methodologies. To assess the predictive models' accuracy, metrics including the coefficient of determination (R²), Variance Accounted For (VAF), and Root Mean Square Error (RMSE) were employed. The application of ALO and CSA to the database yielded results indicating that for ALO, R² = 0.99, RMSE = 0.005, and VAF (%) = 99.38, while for CSA, R² = 0.98, RMSE = 0.02, and VAF (%) = 98.11. Ultimately, the findings suggest that the predictive models yield satisfactory results, with ALO demonstrating a greater level of precision.
Exploitation
Assefa Hailesilasie Wolearegay; Yowhas Birhanu Amare; Asmelash Abay Hagos; Kassa Amare Mesfin; Hagos Abraha; Bereket Gebresilassie; Nageswara Rao Cheepurupalli; Yewuhalashet Fissha
Abstract
The Dichinama area in northern Ethiopia is a potential source of dimension stone, but the quality of the marble has been a major challenge for mining operations. This research aims to evaluate the quality of dimension stone by conducting a comprehensive study involving geological mapping, geotechnical ...
Read More
The Dichinama area in northern Ethiopia is a potential source of dimension stone, but the quality of the marble has been a major challenge for mining operations. This research aims to evaluate the quality of dimension stone by conducting a comprehensive study involving geological mapping, geotechnical testing, and geochemical analysis. The study collected nine rock samples from three active mining sites in the Dichinama area, analyzing properties such as density, water absorption, compressive strength, flexural strength, and abrasion resistance. Additionally, ten samples were collected for geochemical analysis, focusing on parameters like calcite, CaO values, LOI, SiO2 content, and other oxide concentrations. The geotechnical tests revealed that the properties of the marble in the Dichinama area were mainly calcite, with compressive strength values ranging from 29.6 to 74.5 MPa, flexural strength from 7 to 52.5 MPa, abrasion resistance from 8.3 to 17.2, density from 2257 to 2562 kg/m3, and water absorption from 0.12 to 0.93. However, most of these parameters fell below the minimum ASTM standards for marble dimension stone. The results suggest that these inferior characteristics negatively affect the recovery and quality of the dimension stone.
Exploitation
Yehia Z. Darwish; Abdelrahem Khalefa Embaby; Samir Selim; Darwish El Kholy; Hani Sharafeldin; Hussin Farag
Abstract
The younger granites of Gabal Gattar area, Northern Eastern Desert of Egypt, host hydrothermal uranium mineralization at the northern segment of Gattar batholith and along its contacts with the oldest Hammamat sediments. The host rocks display many features of hydrothermal overprint results in changing ...
Read More
The younger granites of Gabal Gattar area, Northern Eastern Desert of Egypt, host hydrothermal uranium mineralization at the northern segment of Gattar batholith and along its contacts with the oldest Hammamat sediments. The host rocks display many features of hydrothermal overprint results in changing their basic engineering characteristics as a function of variations of the degree of alteration. Progression from less altered to altered and mineralized rocks as the result of the alteration processes was assessed by the chemical index of alteration (CIA). The CIA numerical values were calculated by the molecular proportion of Al to the cations Ca, Na, and K. The studied rocks were divided into five grades according to degree of alteration and strength properties including: fresh (AG-I), slightly altered (AG-II), moderately altered (AG-III), highly altered (AG-IV) and very highly altered (AG-V). The strength properties of the studied rock units correlated well with the alteration grades assigned to them. That is, as the grade increased from AG-I to AG-V, abrasion resistance and crushability index increased, whereas compressive strength, slake durability and impact strength decreased.
Exploitation
Sri Chandrahas; Bhanwar Singh Choudhary; MS Venkataramayya; Yewuhalashet Fissha; Blessing Olamide Taiwo
Abstract
To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An ...
Read More
To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An idea on joints density will give an idea on deciding with column charging in-terms of decking-stemming and firing patterns. The goal of the research is to develop a hybrid algorithm model to predict joints width and joint angle. In order to achieve the task, advanced softwares, machine learning models and a field data tests were used in this study.
Exploitation
Sahil Kumar; Abhishek Sharma; Kanwarpreet Singh
Abstract
This study investigates the application of the Rapid Mass Movement Simulation (RAMMS) tool in assessing and mitigating various types of landslides. The research encompasses comprehensive field visits to diverse landslide-prone areas, capturing detailed photographic evidence to document pre- and post-landslide ...
Read More
This study investigates the application of the Rapid Mass Movement Simulation (RAMMS) tool in assessing and mitigating various types of landslides. The research encompasses comprehensive field visits to diverse landslide-prone areas, capturing detailed photographic evidence to document pre- and post-landslide conditions. Utilizing the field data, RAMMS simulations were conducted to model the dynamics of different landslide scenarios, including rockfalls, debris flows, and avalanches. The simulations provided insights into the potential impact zones, flow velocities, and deposition patterns of landslides under varying environmental conditions. The results highlight the efficacy of RAMMS in predicting landslide behavior and guiding mitigation strategies. By comparing the simulation outputs with field observations, we validated the accuracy of RAMMS models, demonstrating their utility in real-world applications. Furthermore, the study identifies key factors influencing landslide susceptibility and proposes targeted mitigation measures to enhance community flexibility. This research underscores the importance of integrating advanced simulation tools like RAMMS with empirical field data to develop strong landslide risk management frameworks.
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 ...
Read More
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
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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 ...
Read More
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.
