Open Pit Research Articles

Assessment of knowledge, attitude, and practice of solid waste management system among households of Darge sub-city, east Wollega Zone, Oromia, Western Ethiopia

Background: Inappropriate solid waste management produced in the course of human activities has serious problems for the environment that adversely affect the health of the human population. Thus, towns and cities in developing countries like Ethiopia face environmental health and aesthetic problems resulting from improper management of solid waste disposal. Uncontrolled waste management may also contribute to the transmission of communicable diseases from different systems as well as encourage fly breading, which has nuisance effects in addition to disease transmission. Objective: To assess knowledge, attitude, and practice of solid waste management systems in households in Darge sub-city, East Wollega Zone, Oromia, Ethiopia. Methods: A community-based cross-sectional study was conducted using systematic random sampling techniques from February 1st to February 15th, 2022. Data was collected from 278 households using a structured interviewer-administered questionnaire. After data collection, data analysis was performed using SPSS computer software version 22.0. Results: The study showed that 67.3% had good knowledge, 65.86% had a positive attitude, and 63.3% had good practice in solid waste management (SWM). Among the households, 12.6% and 42.1% dispose of solid waste in open fields and open disposal pits, respectively. The perceived reason for improper solid waste disposal was a lack of awareness (55.4%) and a shortage of a place for solid waste disposal (30.6%). Conclusion: The knowledge, attitude, and practice of households in the study area need improvement according to standards for towns and sub-cities. Increasing knowledge, changing attitudes, and improving practice in SWM using the existing structure are recommended.

Multidisciplinary Collaborative Design Optimization of Electric Shovel Working Devices

The development of the open-pit mining industry has set higher performance standards for mining electric shovels. Addressing issues such as low efficiency, high energy consumption, and high failure rates in working mining electric shovel devices, this paper comprehensively considers bulk mechanics, structural mechanics, and dynamics to conduct a multidisciplinary, collaborative design optimization for electric shovels by introducing the BLISCO method, which is based on an approximated model, into the structural-optimization design process of working electric shovel devices, aiming to enhance the overall performance of electric shovels. Firstly, a dynamic model of an electric shovel is established to analyze the hoist force and crowd force during the excavation process, and an accurate load input for the dynamic analysis is provided through the bulk material mechanics model. Additionally, to ensure that the stiffness of the boom meets the requirements, the maximum stress at the most critical position of the optimized boom is considered. Subsequently, the design variables are screened through experimental design, and an approximate model is established. Focusing on the hoist force, crowd force, maximum stress at the critical position of the boom, and the angle between the dipper arm and the wire rope, a mathematical model is constructed and optimized using a two-level integrated system co-optimization framework based on an approximate model (BLISCO-AM), followed by a simulation. Finally, a test bench for the electric shovel working device is constructed to compare pre- and post-optimization performance. Experimental results show that through the optimized design, the hoist force and crowd force required in a single excavation process are reduced by 6% and 8.48%, respectively, and the maximum angle between the wire rope and the dipper arm is increased by 4%, significantly improving excavation efficiency while ensuring the safety and reliability of the equipment.

Transformer-based berm detection for automated bulldozer safety in edge dumping

Ensuring the safety of automated bulldozers during edge dumping operations in open-pit mines requires accurate detection of the berm's edge. However, precisely detecting berm edges in open-pit mining scenes is challenging due to the presence of similar texture structures. To address this issue, this paper created a dataset for berm edge detection that covers multiple scenes and proposed the edge detection model with transformer (MEDTER). This model, based on a transformer with a two-stage detection structure, aims to restore thin edges. Additionally, the deep multilevel aggregation decoder enhances the information flow in the transformer to improve the sensitivity to detailed information. The BCE-Dice loss functions effectively handle the problem of pixel imbalance. Results demonstrate that the proposed model achieves significant improvements compared to others. Furthermore, the model exhibits excellent detection performance despite variations in scenes and detection distances, suggesting its practical value in ensuring the safety of automated bulldozers during edge dumping.

Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment

Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment

CORRECTION OF HEALTH DEVIATIONS CAUSED BY UNFAVORABLE ENVIRONMENTAL CONDITIONS USING NON-PHARMACOLOGICAL MEANS

Lack of atmospheric oxygen (exogenous hypoxia) in residents of high mountain areas leads to functional disturbances of blood circulation, blood composition, activity of tissue respiration enzymes and resulting other biochemical abnormalities, causing a number of diseases. And the open mines operating in these regions create an unfavorable ecological environment, contributing to the deepening of functional disorders of the body among the population. Adolescents in this region are also at risk for the development of pathological processes. These risks can be reduced, and diseases can be prevented using non-drug means, using herbs with antihypoxic effects and special breathing exercises.

Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex

Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex

Penilaian Tingkat Kekeritisan Lahan Pertambangan dan Beberapa Pengguanaan Lahan Lainnya di Kalimantan Selatan

Critical land is land that is no longer productive from an agricultural perspective, because management and use do not consider land management requirements and soil conservation principles. This research aims to assess the level of criticality of land based on the level of erosion that occurs at various coal mining land locations in several districts in South Kalimantan. This research is non-experimental research using exploratory descriptive methods. The data collected is secondary data obtained through official publications from the Environmental Service and related agencies from several environmental documents of coal mining companies that have received approval from the authorized Agency. The secondary data used is the data needed to calculate the amount of erosion which is used as a basis for evaluating land criticality. These data include: soil texture, permeability, bulk density, rainfall, slope and slope length, management factors, land cover vegetation factors and soil organic C. Erosion estimation uses the Universal Soil Loss Equation (USLE) method. The results of erosion calculations using USLE show that in open mine land in Banjar Regency it is 233.9 tons ha-1 year-1, while in Tanah Laut Regency is 173.1 tons ha-1 year-1 and in Kotabaru Regency is 275.1 tons ha-1 year-1. The level of criticality of land in the mining area is at a critical and very critical level compared to natural land and cultivated land due to differences in erosion values ​​and also the thickness of the soil solum studied.

Identification of sliding surface and classification of landslide warning based on the integration of surface and deep displacement under normal distribution theory

Advanced identification of the potential sliding surface of a slope and accurate early warning are crucial prerequisites for effective management of landslides and timely and prevention of catastrophic accidents. This study analyzes the statistical characteristics of landslide displacement evolution. Based on the normal distribution theory, random variables of displacement velocity and acceleration with random errors are introduced into the analysis of surface displacement information, and random variables of relative displacement with random errors are introduced into the analysis of deep displacement information. When the random variables do not follow the normal distribution, the warning time can be obtained. Therefore, an advanced landslide classification warning method is established. The analysis results showed that analysis results from the April 30 landslide project at an open pit mine indicate that the earliest warning time for landslide initiation is 2020/2/19, while the earliest warnings for acceleration occur on 2020/4/15 and the fast acceleration on 2020/4/25. These three-level warning times align with reality, and the inferred slip surface position corresponds to the actual weak layer range. The primary power source driving landslide originates from behind the sliding body which subsequently pushes rock mass along weak layers near the south wing, north wing, and front in succession. Research findings can enhance landslide warning accuracy, facilitate advance identification of sliding surface, provide scientific basis for open-pit slope engineering design, as well as mitigate casualties and property losses.

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye

Effects of uranium mining on the rhizospheric bacterial communities of three local plants on the Qinghai-Tibet Plateau.

In this study, we used 16S high-throughput sequencing to investigate the effects of uranium mining on the rhizospheric bacterial communities and functions of three local plant species, namely, Artemisia frigida, Acorus tatarionwii Schott., and Salix oritrepha Schneid. The results showed that uranium mining significantly reduced the diversity of rhizospheric bacteria in the three local plant species, including the Shannon index and Simpson index (P < 0.05). Interestingly, we found that Sphingomonas and Pseudotrichobacter were enriched in the rhizosphere soil of the three local plants from uranium mining areas, indicating their important ecological role. The three plants were enriched in various dominant rhizospheric bacterial populations in the uranium mining area, including Vicinamidobacteriaceae, Nocardioides, and Gaiella, which may be related to the unique microecological environment of the plant rhizosphere. The rhizospheric bacterial community of A. tatarionwii plants from tailings and open-pit mines also showed a certain degree of differentiation, indicating that uranium mining is the main factor driving the differentiation of plant rhizosphere soil communities on the plateau. Functional prediction revealed that rhizospheric bacteria from different plants have developed different functions to cope with stress caused by uranium mining activities, including enhancing the translational antagonist Rof, the translation initiation factor 2B subunit, etc. This study explores for the first time the impact of plateau uranium mining activities on the rhizosphere microecology of local plants, promoting the establishment of effective soil microecological health monitoring indicators, and providing a reference for further soil pollution remediation in plateau uranium mining areas.

Integrated analysis of ammonium origins in a Serbian anoxic alluvial aquifer: Insight from physicochemical, isotopic, microbiological data

The significance of examining groundwater quality is highlighted by the fact that 75% of the European Union population relies on groundwater for their water supply. In the Republic of Serbia, over 50% of the groundwater used for public water supply comes from alluvial aquifers, with 80–90% of this water originating from river water infiltration. This study investigates the origin of increased ammonium concentrations (up to 4.7 mgN/l) in the Danube alluvial aquifer near Kovin-Dubovac area, Serbia, a potential future regional water supply source surrounded by intensive agricultural production and an open coal mine. Comprehensive research involved statistical processing of physicochemical data (13 parameters from 33 sampling sites), isotopic analyses (δ15N–NH4 – 12 samples; δ34S–SO4, δ18O–SO4 in 5 samples), and microbiological tests (denitrifying, sulphate-reducing, iron-related, and heterotrophic aerobic bacteria in 15 samples). Factor analysis revealed significant positive loadings (>0.5) among indicators of autochthonous organic matter origin (Fe2+, Fetot, and TOC), geological matrix components (Na, H2S, Cl), and groundwater state parameters (pH, Eh, and NH4+). This multifaceted approach and the spatial concentration gradients of parameters associated within extracted principal components revealed the predominance of two NH4+ sources. The riparian zone is characterized by sediment organic matter mineralization, increased iron content, and natural ammonium origin (δ15N–NH4+ from +4.82‰ to +6.93‰) accompanied by conditions suitable for DNRA and sulphate reduction. Approaching to the hinterland lower iron and total organic matter content, accompanied by increased redox values revealed the signature of fertilizers application (δ15N–NH4+ −0.84‰ and −0.33‰), associated with denitrification influence. This multifaceted approach reduces ambiguity, providing a clearer interpretation of results when discerning the origin of nitrogen and aquifer potential for nitrogen loss or conservation in a reducing groundwater environment.

Exploring the restoration stability of abandoned open-pit mines by vegetation resilience indicator based on the LandTrendr algorithm

Open-pit mining causes serious damage to the ground surface and vegetation. Remote sensing technology helps assess and monitor the effectiveness of ecological restoration in abandoned open-pit mines, offering vital information for exploring the ecosystem recovery process. However, previous studies have mostly focused on changes in characteristics and lack quantitative characterization of restoration stability and its dynamics. In this study, we applied the vegetation resilience indicator based on the LandTrendr algorithm, which can combine vegetation disturbance and recovery characteristics for abandoned open-pit mines. Eighteen abandoned open-pit mines around Dongting Lake in China were taken as the research areas. Google Earth Engine and all available Landsat remote sensing images for June-September 1984–2022 were utilized. The effectiveness of mine restoration was determined according to the vegetation resilience indicator and recovery magnitude. The results showed that eighteen abandoned open-pit mines had a range of vegetation resilience between 5.06 and 9.45, indicating that the overall open-pit mines around Dongting Lake were well-restored. The recovery curves of the abandoned open-pit mines had an “inverted S-shape” and “S-shape,” indicating that the overall recovery characteristics of the different types of abandoned open-pit mines before and after the implementation of ecological restoration measures differed. Mine type and restoration measures affect the restoration stability of open-pit mines. The average values of vegetation resilience of the three types of mines were 7.73, 6.91, and 5.79, respectively, indicating that the resilience of the abandoned open-pit mines with serious damage was lower, and at the same time, the restoration was more dependent on the degree of input of artificial restoration. This study provides a reference for the selection of measures and the determination of restoration costs in the future ecological restoration of abandoned open-pit mines.

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models.

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Open-Pit Mining Area Extraction Using Multispectral Remote Sensing Images: A Deep Learning Extraction Method Based on Transformer

In the era of remote sensing big data, the intelligent interpretation of remote sensing images is a key technology for mining the value of remote sensing big data and promoting a number of major applications, mainly including land cover classification and extraction. Among these, the rapid extraction of open-pit mining areas plays a vital role in current practices for refined mineral resources development and management and ecological–environmental protection in China. However, existing methods are not accurate enough for classification, not fine enough for boundary extraction, and poor in terms of multi-scale adaptation. To address these issues, we propose a new semantic segmentation model based on Transformer, which is called Segmentation for Mine—SegMine—and consists of a Vision Transformer-based encoder and a lightweight attention mask decoder. The experimental results show that SegMine enhances the network’s ability to obtain local spatial detail information and improves the problem of disappearing small-scale object features and insufficient information expression. It also better preserves the boundary details of open-pit mining areas. Using the metrics of mIoU, precision, recall, and dice, experimental areas were selected for comparative analysis, and the results show that the new method is significantly better than six other existing major Transformer variants.

A Systems Engineering Approach to Decarbonizing Mining: Analyzing Electrification and CO2 Emission Reduction Scenarios for Copper Mining Haulage Systems

Due to climate change risks, the public, regulators, and investors require solid actions to minimize the greenhouse gas (GHG) emissions of mineral extraction and metals production. The mining sector considers alternatives to reduce its carbon footprint by transforming the business and adopting new technologies into operations. Given the capital intensity, technical characteristics, and business structure involved, a shift in the mining industry necessitates impartial insights into the trade-offs and risks. Considering the low-carbon transition trade-offs and risks in mining, this study presents the application of system dynamics modeling (SDM) in mining projects to analyze the impact of decarbonization alternatives with respect to carbon footprint and costs. A system dynamics model of an open-pit copper mine is developed to quantify greenhouse gas (GHG) emissions, as well as capital and operational costs, during the project life cycle. The change in GHG emissions in the business-as-usual scenario with diesel equipment haulage versus the alternative scenario with electric overland conveyor haulage is compared concerning GHG emissions and associated costs. The results unequivocally demonstrated that electrifying material mobility offers significant decarbonization in open-pit mining if the on-site electricity has a low emission factor. The findings also indicate that the substantial cost difference between electrification and diesel alternatives is another major obstacle to implementing electrification in an open-pit copper mine. This research proves that implementing SDM in the mining industry can offer impartial insights into decision-making and enable a thorough evaluation of options using quantitative criteria. It effectively assesses and communicates the trade-offs and risks of transitioning to low-carbon alternatives because it analyzes project variables quantitatively and holistically and is easy to run.

Semi-autonomous sensor fusion-based strategy for unmanned aerial atmospheric surveillance system in open field environments

&lt;p&gt;This paper explores the convergence of semi-autonomous systems and sensor fusion for monitoring hazardous atmospheric substances in open and semi-confined environments such as open-pit mines. As the heart of this system, a universal and flexible device leveraging wireless technologies to collect and analyze large volumes of data, designed by the authors, is proposed. The article outlines the key components of the platform, emphasizing its potential for increasing personnel safety levels as stipulated by the international public exposure guidelines. In modern mines, it becomes crucial to monitor elevated concentrations of nitrogen and carbon oxides, as well as other pollutants after blasting and the toxic gas emissions produced by the heavy transportation equipment such as mining trucks and excavators. In this paper, industrial-grade electrochemical sensors are compared with price-affordable but less precise microelectrochemical ones. The performed experiments for lowering computational requirements show promising results. The integration of an ultrasonic anemometer enhances the system’s capabilities, contributing to a comprehensive understanding of atmospheric conditions. In parallel, the research discusses the role of computer vision in autonomous control systems, with a focus on the architecture and processing pipeline of the state-of-the-art system-on-module Kria by AMD. Advocating the potential of adaptive computing for enhanced efficiency in dynamic environments, the paper underlines the importance of the integrated approach in developing a semi-autonomous atmospheric surveillance system and highlights the impact of adaptive computing in dynamic scenarios. The main part of the measuring equipment and methods has been experimented with in real conditions in open-pit mines in Bulgaria. This is an initial phase of ongoing research aimed at serving as a foundation for future improvements and the elaboration of a fully autonomous prototype for hazardous substances evaluation in the atmosphere of open-pit mines.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Study on preparation and properties of antifreeze compound road dust suppressant

Open-pit mine pavement dust dries and breaks easily. As such, a composite pavement dust suppressant with good wettability, moisturizing, coagulation, and antifreezing properties in winter was investigated. Monomer screening and orthogonal experiments were conducted, using evaporation rate, permeability rate, viscosity, and freezing point as evaluation indexes. Consequently, a dust suppressant solution is a mixture of glycerol (GLY), sodium dodecylbenzene sulfonate (SDBS), polyacrylamide (PAM), compound propylene glycol (PG), and potassium acetate (PA). The characteristics of the dust suppressant and its interaction mechanism with road dust were measured and analyzed. The results showed that the optimal ratio of the antifreeze-type composite dust suppressant is 3%GLY, 0.30%SDBS, 0.07% PAM, and 50%PG + 10%PA; the contact angle is 27.62°, which can effectively wet coal dust. Moreover, it easily forms hydrogen bonds with water molecules to release free -OH, which increases the oxygen-containing functional groups in the dust. The maximum viscosity is 25.4 mPa·s, and the hydrophobic groups adsorbed on the surface of the dust can condense and agglomerate the dust to form large particles, and effectively inhibit the occurrence of dust. It freezes at − 34.2 ℃, resists a temperature of − 30 ℃ without freezing, and improves dust suppression efficiency and antifreezing effect in cold areas.

Study of Slope Stability of the Mining Wall in an Open-Pit Coal Mine by the Paste Cut-and-Backfill Method

According to this study’s findings, slope stability problems in open-pit coal mines can be avoided, and mine wall collapse can be effectively mitigated by the use of cut-and-backfill mining techniques. The main research results are as follows: (1) The stope and waste rock’s geotechnical, physical, and mechanical characteristics were gathered and examined; the geotechnical and mechanical characteristics found in this study largely satisfy the criteria for slope stability analysis. (2) Cemented paste backfill (CPB) materials were made of mine waste rock and fly ash at a desired ratio, mixed with cement as a bond material, and were tested in the laboratory, using a combination of cement percentages of 6%, 8%, and 10% for the cement content and 25%, 30%, 35%, and 40% for the fly ash content, to determine the ideal mix for artificial ground support in underground mines, taking into account both economic and performance factors. (3) By using this model, the changes in CPB strength were investigated under various factors influencing the cement ratio, and limit equilibrium modeling was used with the FLAC-Slope 8.1 program with different cement paste backfill ratio to calculate the factor of safety for each cement percentage after 1 day, 3 days, 7 days, and 28 days of curing time (CT) to obtain the optimum compressive strength and shear straight of cemented paste backfill with high paste fill shear strength on the slope. (4) The research results are of great significance for the safety of important facilities in open-pit mines and provide a basis for the design and safety implementation of open-pit slope engineering.

Safety improvement in open-pit mines with challenging mining conditions through upgrading avalanche prevention measures

One of the most common problems mining companies face in developing open-cut deposits in complicated geological and climatic conditions is avalanches during the spring-winter work period. This phenomenon is accompanied by a rapid displacement of snow masses, which can lead to catastrophic consequences. In this regard, the article aims to develop a modernized set of measures for avalanche protection. Prevention of avalanches of catastrophic scale, leading to blockages, road closures, and accidents, and the introduction of measures to reduce accidents and injuries in quarries and dumps, is a time-consuming task that requires serious approaches to its solution. The main research method is analyzing the applied anti-avalanche measures for active impact on snow cover and considering the possibility of using safer methods for active impact on avalanches at a particular mining enterprise. This approach allows for the evaluation of the advantages and disadvantages of the avalanche measures used and for choosing a set of measures to ensure more effective avalanche protection. The authors analyzed the existing shortcomings in the regulatory documents related to the provision of avalanche protection and anti-avalanche measures. It was found that they are not able to effectively ensure the development of internal local documents of enterprises in the field of anti-avalanche measures at the present stage. The resulting study showed that a comprehensive solution to the problem is needed through the introduction of several anti-avalanche devices to reduce the likelihood of avalanches of catastrophic scale. For the first time, the authors attempted to justify the possibility of using a modernized set of measures for the preliminary descent of snow avalanches to solve the problem of avalanche protection in the conditions of a real mountain object. The results obtained can be used to reduce the level of occupational injuries by reducing or eliminating accidents associated with unforeseen avalanches during the operation of an existing quarry.

Comprehensive geospatial mapping and monitoring of an eastern coalfield in India

Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.