Mine In Iran Research Articles

Geological characteristics of coal mines in the Zagros basin of Iran: Unveiling rock mechanical properties and time-dependent behavior

The Zagros Basin in southwestern Iran is a significant source of coal, with numerous coal mines operating in the region. Ensuring the stability of coal mines is crucial for safe and efficient mining operations. This study investigates the time-varying response of rocks and roof resistance in coal mines in the Zagros Mountains using a novel approach that combines numerical simulation, relaxation testing, and rock displacement studies. The results show that rocks exhibit significant time-dependent behavior, with changes in rock mechanical properties over time. A comprehensive viscoelastic-plastic model is developed to accurately describe the time-varying strain-softening response of rocks and simulate laboratory tests. The model integrates the Burgers and strain-softening models, simulating stress relaxation curves and rock displacement over time. The study reveals that the rock mass displays significant nonlinear behavior, with changes in rock mechanical properties over time. The findings of this study highlight the importance of considering the time-varying response of rocks and roof resistance in coal mine stability analysis. The results provide valuable insights into the time-dependent behavior of rock mass in coal mines in Iran, which can inform mining practices and mitigate potential hazards. Results in this study can contribute to developing strategies for improving roof stability and reducing the likelihood of roof collapses.

Site-specific design response spectra proposed for Sarcheshmeh Copper Mine in Iran and seismic hazard analysis

Site-specific design response spectra proposed for Sarcheshmeh Copper Mine in Iran and seismic hazard analysis

Assessment of drinking water quality and identifying pollution sources in a chromite mining region

Water sources near mining regions are often susceptible to contamination from toxic elements. This study employs machine learning (ML) techniques to evaluate drinking water quality and identify pollution sources near a chromite mine in Iran. Human health risks were assessed using both deterministic and probabilistic approaches. Findings revealed that concentrations of calcium (Ca), chromium (Cr), lithium (Li), magnesium (Mg), and sodium (Na) in the water samples exceeded international safety standards. The Unweighted Root Mean Square water quality index (RMS-WQI) and Weighted Quadratic Mean (WQM-WQI) categorized all water samples as 'Fair', with average scores of 67.95 and 67.19, respectively. Of the ML models tested, the Extra Trees (ET) algorithm emerged as the top predictor of WQI, with Mg and strontium (Sr) as key variables influencing the scores. Principal component analysis (PCA) identified three distinct clusters of water quality parameters, highlighting influences from both local geology and anthropogenic activities. The highest average hazard quotient (HQ) for Cr was 1.71 for children, 1.27 for adolescents, and 1.05 for adults. Monte Carlo simulation for health risk assessment indicated median hazard index (HI) of 4.48 for children, 3.58 for teenagers, and 2.98 for adults, all exceeding the acceptable threshold of 1. Total carcinogenic risk (TCR) exceeded the EPA's acceptable level for 99.38 % of children, 98.24 % of teenagers, and 100 % of adults, with arsenic (As) and Cr identified as the main contributors. The study highlights the need for urgent mitigation measures, recommending a 99 % reduction in concentrations of key contaminants to lower both carcinogenic and non-carcinogenic risks to acceptable levels.

Effect of gold mine activity on water quality and human health

ABSTRACT This study aimed to investigate the levels of heavy metals in surface and groundwater sources within the vicinity of a gold mine in Iran, as well as to evaluate the associated health and carcinogenic risks. A total of 45 water samples were collected from the mine effluent, the downstream of a river receiving the effluent of the mine, and downstream water resources. Subsequent to laboratory digestion of the samples, arsenic levels were determined using atomic fluorescence spectrophotometry, while other heavy metals were quantified using inductively coupled plasma mass spectrometry (ICP-MS). Data analysis was conducted utilizing IBM SPSS software (Version 22). The health risks and carcinogenic potentials associated with arsenic, lead, cadmium, chromium, cobalt, manganese, mercury, nickel, copper, and zinc were assessed for both pediatric and adult populations. The findings revealed that, in most instances, heavy metal concentrations exceeded the recommended standards for drinking water quality. Furthermore, the health and carcinogenic risks posed to both children and adults were found to surpass the maximum acceptable levels.

Unraveling time-dependent roof stability dynamics in Iran's coal mines through laboratory-based rock displacement testing

Roof stability is a critical concern in coal mines, as the potential for roof collapse poses a significant risk to miners' safety and productivity. Roof stability is heavily influenced by the time-dependent properties of the rock mass above the workings. This study uses rock displacement testing to examine the effect of time-dependent properties on roof stability and resistance reduction in coal mines in Iran. The study employed laboratory-based rock displacement tests on samples collected from coal mines in Iran, subjected to varying stress levels over time, to simulate the gradual deterioration of rock mass strength in underground mining conditions. The samples were monitored for displacement under these conditions to quantify the reduction in roof resistance over time and assess its effect on roof stability. The study found that areas with high stress at equilibrium gradually fail with time, and the stress transfers from the failure zone into deeper solid rock. The results demonstrate that varying viscous parameters can lead to different relaxation behaviors and stress distribution. Furthermore, incorporating strength degradation into numerical simulation can capture the failure under creep conditions and improve the accuracy of predicting time-dependent roof failure. This research aims to enhance safety measures and reduce the risk of collapses by investigating the time-dependent properties of roof stability through rock displacement testing in Iran's coal mines. The study's innovative approach uses numerical simulation based on the viscoelastic-plastic model to simulate the time-dependent behavior of the rock and incorporate strength degradation into the simulation. The results provide valuable insights into the time-dependent behavior of rock mass in coal mines in Iran and contribute to developing strategies for improving roof stability and lessening the chance of roof collapses. The instantaneous elastic strain was 4.35 × 10–4, and creep simulation was activated to run for a time equivalent to 2 × 106 s.

Calculation of coal cleats spacing for methane degassing by image processing

Coal bed methane is one of the clean energy sources in the world. Methane molecules are confined inside the pores of coals and when the gas drainage wells are drilled into the coal seams, due to the resulted pressure difference. Coal cleats are spread all over the coal seam as face and butt cleats and play an essential role in the methane gas drainage operation from coal mines. Calculating the coal cleats spacing makes great help in modeling the amount of emitted gas followed by calculating the spacing of gas suction wells. Furthermore, the time required for gas drainage will be determined. Computer-based image processing technique has been utilized to identify the cleats spacing. In this study, three-dimensional computed tomography scan images were prepared for coal samples from Tabas Coal Mine in Iran. Computer-based image processing technology was conducted to determine the coal cleats. Their spacing was then calculated. Based on the results obtained using image processing with computer, the average distance between face cleats was 20 to 30 mm and the distance between butt cleats was 15 to 25 mm. The results of this study are used to modeling the amount of methane gas in the coal cleats. Based on this modeling, the amount of released gas and the distance between the wells can be designed.

Human reliability analysis in maintenance and repair operations of mining trucks: A Bayesian network approach

Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness. Given the challenge of limited and uncertain data on human errors, this study aims to estimate the probability of human errors using Bayesian networks (BN) under uncertain parameters. Applying this approach to assess HR in the maintenance and repair operations of mining trucks at Golgohar Iron Ore Mine in Iran, the study identifies critical factors influencing error occurrence in a fuzzy environment. The results highlight key factors impacting human error and offer insights into estimating HR with minimal human intervention.

A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran

Rock fragmentation assessment is a critical aspect of evaluating blasting operations in the mining industry, offering insights for optimising procedures and subsequent mining processes. Traditional methods, such as sieving, while accurate, are costly and time-consuming. Digital image analysis methods have emerged as alternatives, yet they face challenges in accurately handling fragments of varying sizes. Recent advancements in computer vision, particularly deep learning techniques, offer promising solutions to automate and refine fragmentation analysis. This paper introduces a novel intelligent stereo vision approach utilising YOLOv8, an advanced deep learning model, for blast-induced fragmentation size distribution assessment at the Golgohar open-pit mine in Iran. By training YOLOv8 with an automated self-labelled dataset and evaluating its performance against real mine site data, this research aims to enhance the accuracy and efficiency of blast fragmentation analysis. The proposed method leverages stereo images and machine learning to provide precise measurements, leading to improved optimisation of blasting parameters and potential economic benefits compared to traditional methods. Through rigorous validation processes and case studies across diverse scenarios, the proposed approach demonstrates robustness and reliability in assessing blast fragmentation, offering a practical and efficient solution for the mining industry’s evolving needs.

Sustainable Cultural Preservation in Central Asia – Involving Local Communities as Caretakers of Archaeological Sites

ABSTRACT This article presents cultural conservation projects in Iran and Iraq, in each of which the social context of the archaeological site and the collection were also considered, in order to develop long-term conservation planning. The objective of one project is to improve the water supply for the local village population near an ancient salt mine in Iran in order to retain them as stewards of the archaeological site. Another project shows how digital media and different narrative perspectives are intended to appeal to different target groups in Iran and worldwide to raise awareness of the so-called salt mummies recovered from the ancient salt mine. International experts are also addressed via online formats to discuss the challenges of the ethical handling of human remains for conservation and exhibition. In Kurdistan (Iraq), young archaeology students are introduced to the topics of archaeological heritage management in order to recruit them as future caretakers. What all projects have in common is that relationships and cross-sector networks are being established to build a crisis-proof local community that takes care of its cultural heritage. In this way, projects can be implemented that are primarily carried out by local actors, thereby building up expertise that will endure. These projects are the initial attempts to link cultural preservation and social well-being and thus to make a contribution to sustainable development in line with the UN's 2030 Agenda for Sustainable Development.

Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques

Blast toe volume, pivotal in explosive engineering, underpins explosive energy efficient utilization, blast safety and mine production sustainability. While current research explores the use of artificial intelligence (AI) model to maximize toe volume prediction, gaps persist in understanding the application of ensemble learning algorithm techniques like hybrid and voting techniques in addressing toe volume problem. Bridging these gaps promises enhanced safety and optimization in blasting operations. This study performs AI model hybrid and voting to enhance toe volume prediction model robustness by leveraging diverse algorithms, mitigating biases, and optimizing accuracy. The study combines separate models, looks for ways that hybrid approaches can work together, and improves accuracy through group voting in order to give more complete information and more accurate predictions for estimating blast toe volume in different approaches. To develop the models, 457 blasting data was collected at the Anguran lead and zinc mine in Iran. The accuracy of the developed models was assessed using nine indices to compare their prediction performance. To understand the input relationship, multicollinearity, Spearman, Pearson, and Kendall correlation analyses show that there is a strong link between the size of the toe and the explosive charge per delay. Findings from the model analysis showed that the light gradient boosting machine (LightGBM) was the most accurate of the eight traditional models, with R2 values of 0.9004 for the training dataset and 0.8625 for the testing dataset. The Hybrid 6 model, which combines LightGBM and classification and regression trees (CART) algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 models, consisting of LightGBM, gradient boosting machine (GBM), decision tree (DT), ensemble tree (ET), random forest (RF), categorical boosting (CatBoost), CART, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) had the greatest R2 scores of 0.9876 and 0.9726 in both the training and testing stages. Using novel modelling tools to forecast blast toe volume in this study allows for resource extraction optimization, decreases environmental disturbance through mine toe smoothening, and improves safety, supporting sustainable mining practices and long-term sustainability.

ASSESSING AND IMPROVING COMPRESSED AIR NETWORK IN UNDERGROUND MINES (A CASE STUDY IN IRAN)

Compressed air is one of the most common methods of energy transfer in industrial and mining systems. Compressed air has numerous applications in underground mines to ensure safety (ventilation fans), carry out extraction operations (compressed air equipment and machines), and manage technical services like dewatering and the operating of spare equipment. Therefore, it is necessary to investigate the compressed air supply systems in mines to assess the sufficiency of compressed air, reduce energy consumption, and lower operating costs. Considering the significance of utilizing compressed air in underground mines, it is essential to verify the adequacy of the flow rate and permissible pressure of compressed air for all mine air consumers. This requires proper design and adjustment of the compressed air distribution network. In this paper, the compressed air network in the main compressor house of the Qaleh-Zari copper mine in Iran was investigated. To achieve this goal, the compressed air consumption in various mining operation conditions was investigated and discussed after surveying the airflow transmission lines. The pressure drops in each line were estimated as well. Regarding the results, the airflow consumption in regular mining operations is about 36 cubic meters per second. The drop in air pressure within the network lines is below the permissible limit, but the overall pressure drop is significant. In this approach, practical adjustments such as changing the diameter of the pipes, increasing the airflow pressure, and utilizing local compressors near consumers’ locations were proposed and investigated.

IRON ORE ALLOCATION MODEL TO FEEDSTOCK CONCENTRATE PLANTS FROM MULTIPLE SOURCES TO MULTIPLE DESTINATIONS CONSIDERING THE UNCERTAINTY OF GRADE, IN IRON ORE MINES: A CASE STUDY IN SANGAN MINE, IRAN

Operations research is a science that is used in various fields. This science is a set of quantitative techniques that help managers make decisions using scientific methods. This science is related to many axial decision-making issues of managers. For this reason, it is also called the science of management. This science is widely used in industries and mines. One of the most appropriate methods for solving research problems in operations is using linear programming in mining projects. This study was conducted in the Sangan iron ore mine in Iran. In this article, by using linear programming modeling and solving the transportation problem, the allocation of iron ore to feedstock concentrate plants from multiple sources to multiple destinations has been done considering the uncertainty of the grade. This paper is in the field of studying and identifying the quantitative and qualitative amount of extractable iron ore reserves in the mines, and on the other hand, the needs of concentrate plants concerning their feedstock supply by considering the uncertainty of iron ore grade in mines and deciding on their proper allocation. Mineral modeling and estimation of the amount of extractable minerals have been done using the block model. Uncertainty of iron ore grade in the block model is investigated using the sequential Gaussian simulation method. The amount of mining capacity and its sequence is planned considering the type of iron ore used in the plans during 5-year periods. In this article, the modeling of the problem has been done by considering the existing limitations in mines, such as the amount of supply, grade of iron ore, iron oxide and sulfur. On the other hand, the limitations in plants, such as demand, type and grade of iron ore, iron oxide and sulfur are considered in the modeling. In the end, the amount of iron ore tonnage sent from each mine to each plant during 5 years is presented by solving the problem.

Comparison of bioleaching of a sulfidic copper ore (chalcopyrite) in column percolators and in stirred-tank bioreactors including microbial community analysis

Chalcopyrite is the most abundant Cu-sulfide and economically the most important copper mineral in the world. It is known to be recalcitrant in hydrometallurgical processing and therefore chalcopyrite bioleaching has been thoroughly studied for improvement of processing. In this study, the microbial diversity in 22 samples from the Sarcheshmeh copper mine in Iran was investigated via 16S rRNA gene sequencing. In total, 1063 species were recognized after metagenomic analysis including the ferrous iron- and sulfur-oxidizing acidophilic genera Acidithiobacillus, Leptospirillum, Sulfobacillus and Ferroplasma. Mesophilic as well as moderately thermophilic acidophilic ferrous iron- and sulfur-oxidizing microorganisms were enriched from these samples and bioleaching was studied in shake flask experiments using a chalcopyrite-containing ore sample from the same mine. These enrichment cultures were further used as inoculum for bioleaching experiments in percolation columns for simulating heap bioleaching. Addition of 100 mM NaCl to the bioleaching medium was assessed to improve the dissolution rate of chalcopyrite. For comparison, bioleaching in stirred tank reactors with a defined microbial consortium was carried out as well. While just maximal 32% copper could be extracted in the flask bioleaching experiments, 73% and 76% of copper recovery was recorded after 30 and 10 days bioleaching in columns and bioreactors, respectively. Based on the results, both, the application of moderately thermophilic acidophilic bacteria in stirred tank bioreactors, and natural enrichment cultures of mesoacidophiles, with addition of 100 mM NaCl in column percolators with agglomerated ore allowed for a robust chalcopyrite dissolution and copper recovery from Sarcheshmeh copper ore via bioleaching.

Studies of the Anaerobic Leaching of Iron and Copper Containing Dump Material and Comparison to Chemical Leaching

AbstractThe objective of this study was to investigate the leaching of a non‐sulfide copper ore by a new bioleaching process, the Ferredox process. In the Ferredox process, Acidithiobacillus ferrooxidans reduces ferric ions and oxidizes sulfur at low pH under anaerobic conditions. In this study, a bacterial culture (mainly A. ferrooxidans) was initially grown aerobically. Bacterial dissolution of iron sludge under anaerobic conditions versus sulfuric acid was compared. An iron slurry sample from the West Rand Ditch near Hoyerswerda, Germany and from the Golestan Mine in Iran was investigated. Two shake flasks each containing an iron slurry and non‐sulfidic Fe/Cu ore were used. One shake flask each was subjected to an anaerobic process by A. ferrooxidans, while the other sample was leached with dilute sulfuric acid. In the chemical leaching, the amount of dissolved copper was 2.5 %, only slightly higher than in the bioleaching. In a subsequent anaerobic column leaching with A. ferrooxidans, 35 % of the contained copper was mobilized from 1366 g ore.

Fuzzy Approach for the Safety Risk Assessment in Dimension Stone Mining

Mining activities are liable to injuries and different types of diseases. The occurrence of an accident threatens safety in dimension stone mines. Therefore, the safety risk assessment in such mines is an important issue that needs special consideration. In this paper, the safety risk of incidents in dimension stone mines in Iran is evaluated using the fuzzy inference system. The fuzzy analytical hierarchy process is used to identify the importance degree of each incidence and then, the overall risk priority number is calculated based on the fuzzy inference process. The results of this study show that vehicle traffic and wire rupture are the most hazardous incidents.

SELECTION OF THE MOST PROPER UNDERGROUND MINING METHOD FOR KODAKAN GOLD MINE IN IRAN

The selection of mining methods is a challenging and complicated concept in mining engineering. It depends on various and different factors such as geotechnical, geological and economic properties and characteristics. Kodakan Gold Mine in Iran is currently mined using the open pit method. However, due to the special conditions of this mine and the increase in waste removal costs, it is inevitable to decide to select an underground mining method in the future. The purpose of this research is to select the most proper underground mining method for this mine. The shape, dip, and depth of the deposit, the thickness of the ore, grade distribution, recovery, skilled manpower, output per worker, and strength specifications of the ore, hanging-wall, and footwall are considered as the main decision attributes. Since there are different parameters in selecting the appropriate mining method using the multi-attribute decision making approach, therefore hybrid multi-attribute decision-making method was employed in this paper to enhance the strength of the decision model and eliminate the weaknesses of the classical methods. Regarding the results of this study, rock quality designation of the hanging-wall and deposit shape have the highest weight value in selecting the underground mining method. Moreover, the shrinkage mining method is proposed as the most appropriate method.

Evaluation of human error in workers of an Iranian copper mine during the COVID-19 pandemic using the CREAM.

The outbreak of COVID-19 has adversely affected both global economy and public health around the world. These effects have also been observed in many workplaces, including mines. This study aimed to examine the human error of copper miners during the pandemic. This descriptive-analytical, cross-sectional study was performed on 192 workers of a copper mine in Iran. For this, occupation tasks were firstly analyzed using the Hierarchical Task Analysis (HTA), and then the human error in different subunits was assessed using the basic Cognitive Reliability and Error Analysis Method (CREAM). The prevalence of COVID-19 among miners was determined by assessing positive PCR test records. The probability of human error in the operational subunits including mining, crushing, processing, and support subunits was estimated to be 0.0056, 0.056, 0.0315, and 0.0177, respectively. All three operational units were found to be in the scrambling control mode. The support unit was determined to be in the tactical control mode. Approximately 50% of all workers had been infected with COVID-19, with the highest prevalence in support units. The results suggest that during the COVID-19 pandemic, copper miners are at higher risk of human error induced by poor working conditions. Therefore, it is recommended to employ some management strategies such as promotion of safety, health monitoring, and adopting supportive measures to control occupational stresses and therefore the probability of human error in the mine's operational units.

Investigating human error in Iran’s copper mines using the CREAM based on human cognitive reliability analysis

Objectives Human error has been recognized as one of the top causes of most accidents in mines. This study aimed to identify and assess cognitive errors among copper miners using the cognitive reliability and error analysis method (CREAM). Methods. This cross-sectional study was carried out in one of the copper mines in Iran. First, all tasks were analyzed by hierarchical task analysis (HTA). Then cognitive errors were assessed using the CREAM. Results. With respect to the basic CREAM results in the operational units, including extraction, crushing and processing, human error probability (HEP) was obtained as 0.056, 0.0315 and 0.0177, respectively. Based on the results of the extended CREAM, the types of errors identified for all tasks in the three operational units were mainly associated with execution (53.4%), observation (40%), interpretation (5%) and planning (1.6%). Also, the cognitive errors were related to execution, monitoring, observation, communication, diagnosis, recording and planning, respectively. Conclusion. The results showed that human error is very high in the operational units of the mine. Therefore, immediate actions are needed to improve safety performance in the mine units, which determine the role of various factors in human errors and can provide the possibility of implementing more effective interventions.

An uncertainty hybrid model for risk assessment and prediction of blast-induced rock mass fragmentation

Blasting is an important mining operation that usually produce several damaging consequences. Adverse rock fragmentation due to bench blasting is one of them. Hence, analysis of risk level and accurate estimation of particle size distribution of fragment size are of interest. This research developed a new model to simultaneously predict and risk assessment of rock fragmentation using 64 collected data from blasting operated in the Zarshouran gold mine in Iran. In this regard, a newly rock engineering system (RES) is developed based on the reliability information of Z-number theory and causal-effect relationship of the fuzzy cognitive map (FCM). This approach is named the reliability rock engineering causality system (RRECS). To do this, 15 principal effective parameters on rock fragment size were considered in the RRECS modeling process. The uncertainty of the interaction matrix was reduced using Z-number concept. Besides, the weight of effective parameters updated based on the combination of the nonlinear Hebbian algorithm (NLH) and differential evolution algorithm (DE) in FCM. The RRECS performance was validated by statistical linear and non-linear models. The results show R2, RSME, BIAS, and Accuracy using the proposed RRECS model calculated to be 0.957, 1.956, 0.001, and 96.741 for training and 0.931, 0.996, 0.016, and 99.053 for testing parts, respectively. Therefore, RRECS has performed better than the exponential, power, logarithmic, polynomial, and linear models. Furthermore, the sensitivity analysis results revealed that the hole diameter and powder factor parameters have the highest and lowest sensitivity on fragmentation, respectively.

Residual lifetime estimation for the mining truck tires

Reliability analysis has a very important role in supporting the operation of a system in harsh working conditions such as mines. Haul trucks are considering as a main asset of an open pit mine. Dump trucks are comprised of different sub-systems and among them, tires have a significant effect on the haulage economy. This study focused on the remaining life estimation of the mining truck tires considering the environmental operating conditions at an open pit iron ore mine in Iran. To achieve this, the proportional hazard model was used to estimate the conditional reliability functions and then the mean residual life of tires at the various initial survival times. The results of this study show that the ambient temperature has a significant effect on the reliability performance. By increasing the initial life of tires from 500 to 1000 h, the mean residual life will be decreased by 21% and 30% in low (below 15°C) and high (above 15°C) ambient temperature levels. These results are helpful to keep the truck tire performance at a desirable level by considering the appropriate preventive maintenance intervals.