Search

It is indicated that the protection of the natural environment, the rational use of natural resources, ensuring the ecological safety of human activities are an integral condition for the sustainable economic and social development of Ukraine. Mining, including open-pit mining, is an important source of economic development and economic independence of the state. However, open-pit mining is accompanied by a significant negative impact on the environment. Therefore, in the context of European integration, environmental safety in open pit mining as a component of environmental safety is of great importance for Ukraine's national security. Due to the lack of scientific developments and clear legislative regulation of the issue of environmental safety in open pit mining, it is necessary to study the concept and legal features of legal support for environmental safety in open pit mining. To this end, the article highlights various scientific approaches to the definition of the concept of environmental safety and leg al support, and provides a legal analysis of these concepts, on the basis of which the author derives the concept of environmental safety in open pit mining, as well as the concept and legal features of legal support for environmental safety in open pit mining. To this end, the article highlights various scientific approaches to the definition of the concept of environmental safety and legal support, and conducts a legal analysis of these concepts, on the basis of which the author derives the concept of environmental safety in open pit mining, as well as the concept and legal features of legal support for environmental safety in open pit mining. As a result of the study, it is established that the study of the concept of environmental safety in open-pit mining is relevant and has practical significance for determining the ways and means of ensuring it, taking into account all the specifics of open-pit mining. And the study of the concept and legal features of legal support for environmental safety in open pit mining provides an opportunity to work out the shortcomings and develop effective legal mechanisms to ensure it. Therefore, this research is important both for the theory of environmental safety law and environment al law in general, and in practice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is to predict the quantity of ANFO required for bench blasting in an open pit mine in Peru, through the application of advanced machine learning techniques. Six models were selected: Artificial Neural Networks (ANN-MLP), Random Forests (RF), Support Vector Machines for Regression (SVR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (BR), due to their ability to handle complex multidimensional data and their success in similar applications, such as rock fragmentation prediction. The methodology included the collection of data from 208 drill holes, which were divided into training (70%), validation (15%), and testing (15%) sets. The models were evaluated using RMSE, MSE, MAE, and R2. The KNN model showed the best performance, with an R2 of 0.84, RMSE of 2.37, MSE of 5.60, and MAE of 1.35, standing out in predictive accuracy. This study contributes to the accurate prediction of the ANFO quantity required for bench blasting in open-pit mining, providing a useful tool for improving explosives management based on the specific characteristics of the terrain and operational conditions. [ABSTRACT FROM AUTHOR]

The Tarcha coal deposit, located beneath the Omo trachyte flows in the Omo‐Gibe River basin of southern Ethiopia, is actively mined using open‐pit methods for energy resources. However, these mines face significant slope instability issues. This study aims to assess and enhance the stability of the selected open‐pit coal mine quarries through field and laboratory analyses, supported by the limit equilibrium method (LEM) and the finite element method (FEM). Fieldwork involved detailed measurements of slope geometry and orientation, in situ rock testing, and soil and rock sampling for laboratory analysis. Results indicated that the rock quality designation (RQD) of the rocks ranged from 22 to 57.5, indicating rock quality from very poor to fair. Schmidt hammer tests revealed uniaxial compressive strength (UCS) values between 14.79 and 29.31 MPa. Soil analysis classified the soils as sand with low to high plasticity (8.89% to 31.86%) and a liquid limit range of 21% to 62.63%. Stability analyses using LEM and FEM on two critical slopes (MS‐1 and MS‐2) under saturated, unloading, and loading conditions indicated instability. However, geometric adjustments, such as reducing slope angle, decreasing slope height, and implementing benching, were found to significantly improve slope stability. [ABSTRACT FROM AUTHOR]

The study of quantitative and qualitative indicators of the non-metallic mineral deposits mining based on recognized methods of economic and technological assessment to determine the damages and the development of technical–technological measures for the providing the mining industry of construction and bulk materials from the crisis in the post-war period. To assess the state of non-ore mineral deposits, the authors of the article used the following research methods: critical analysis of literary sources, statistical processing of data on the mines functioning and the probability of their manifestation in specific mining and geological conditions. Based on the selected UNIDO method, which is used for the evaluation of high-budget projects, the quantitative performance indicators of the selected quarries of non-ore raw materials have been established. Systematization and visualization of the obtained results were carried out with the help of Excel calculation tables from the available software product MS Office. The main problems associated with the activity of mining enterprises that develop deposits of non-ore useful minerals are highlighted. Qualitative and quantitative values of the work of the selected mines have been prepared according to European standards, which serve as initial data for assessing the economic attractiveness and the possibility of their restructuring in the post-war period. A forecast of the development of the industry is provided on the example of individual technological units of entrepreneurial activity for the nearest period. Dependencies of changes in production capacity indicators of mining enterprises developing non-ore mineral deposits have been established. Data on the spatial distribution of reserves of construction and backfill materials (gravel, sand, clay) were studied. Using generally recognized international methods of assessing the efficiency of enterprises, changes in the priority of mine development were determined depending on the needs of the Ukrainian economy and the destruction of territories caused by military actions, that appeared due to the aggression of the RF. The effectiveness of the methodology was proposed and confirmed, which allows determining the operative parameters of mines, which are mining non-ore mineral deposits. It gives the possibility to assess the economic attractiveness of individual mines and subsequently predict the possibility of their restructuring for the introduction of the innovative equipment and technologies in accordance with generally recognized international standards. [ABSTRACT FROM AUTHOR]

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations. [ABSTRACT FROM AUTHOR]

Human activities play a key role in landscape evolution. Mining is one of the most important of these since it changes large parts of the Earth's surface. In abandoned mines and quarries, pit lakes are quite common due to water table blocking. These lakes have remarkable ecological characteristics, but they are still poorly understood. Northwestern Spain has a long tradition in the use of slate for roofs. This exploitation, mostly open-pit, has caused a great transformation in the landscape including the generation of pit lakes. In this work, 27 pit lakes in abandoned slate quarries areas have been analysed using orthophotos of different years, UAV (unmanned aerial vehicle) images and field work. In each lake, the topographic context, geomorphological dynamics, morphometric parameters (including depth), pH, and ecological variables were measured. Other uses for these quarry pit lakes and their significance as a new geo-ecological environment are also discussed. The results show different types of pit lakes and, consequently, different potential uses in each case. Only six offer good conditions for recreational uses, whereas the rest (21) show slope instability or poor water quality. [ABSTRACT FROM AUTHOR]

Numerous open-pit mines are scattered within the southern mountainous areas of China. Due to the complex mountainous terrain and abundant rainfall, surface disturbances caused by open-pit mining activities pose a serious risk of landslides. To identify potential landslide hazards in mountainous open-pit mining areas in advance, this study proposes a quantitative method that utilizes ascending and descending orbit Interferometric Synthetic Aperture Radar (InSAR) technology to accurately identify landslide hazards. We select the Xiaolongtan coal mining area in Yunnan, China, as a case study. Small Baseline Subset InSAR (SBAS-InSAR) technology was employed to obtain the Line of Sight (LOS) deformation of ascending and descending orbits from November 2019 to November 2021. Following this, two-dimensional deformations were calculated based on the obtained LOS deformations. Multiple remote sensing data sources, including Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Landsat 8 Operational Land Imager (OLI), were utilized to extract potential landslide points based on vertical deformation. A combined subjective and objective weighting method was then used to assess the landslide hazard in the study area, and an information quantity model was constructed for landslide hazards in the mining area. Finally, based on high-resolution remote sensing images from the study period, potential landslide hazards were identified in the study area. The results reveal that the vertical deformation rate in the mining area ranges from − 231.73 to 81.42 mm/year, indicating significant subsidence and uplift tendencies. A total of 2353 potential landslide points were identified, primarily located near the slopes of two open-pit mines and in areas with low vegetation coverage. The Xiaolongtan and Buzhaoba open-pit mines, along with the surrounding regions in the study area, were identified to exhibit relatively high landslide hazards. Among the three coal mine waste dumps, the Beipingba waste dump presents a higher landslide hazard. This study provides a scientific basis and practical reference for identifying landslide hazards in mountainous open-pit mining areas. [ABSTRACT FROM AUTHOR]

Arctic miners face significant risks from diesel exhaust and dust exposure, potentially leading to adverse respiratory health. Employers must limit harmful exposures, using personal protective equipment (PPE) as a last line of defense. This study explored the association between reported respiratory exposure and symptoms, and PPE training and usage. Data from the MineHealth study (2012–2014) included a total of 453 Arctic open pit miners in Norway, Sweden, and Finland. Participants answered questions on exposure to dust and diesel exhaust, respiratory symptoms, and PPE use, in addition to age, gender, BMI, smoking, and self-rated health. Estimated exposure to dust was common, reported by 91%, 80%, and 82% and that of diesel exhaust by 84%, 43%, and 47% of workers in Sweden, Finland, and Norway, respectively. Reported dust exposure was significantly related to respiratory symptoms (OR 2.2, 95% CI 1.3–3.7), diesel exposure increased the occurrence of wheezing (OR 2.6, 95% CI 1.3–5.4). PPE use varied between the studied mines. Non-use was common and related to reduced visibility, wetness, skin irritation and fogging of the respiratory PPE. Future research should employ more precise exposure assessment, respiratory function as well as explore the reasons behind the non-compliance of PPE use. [ABSTRACT FROM AUTHOR]

The importance of strategic mine planning to ensure the long-term viability of mining projects has increased significantly because of the limited availability of high-grade ore deposits. Given its high-dimensional and combinatorial nature, developing a precise mathematical optimization technique to solve the entire problem remains challenging, particularly for real-size block models. The primary objective of this study was to propose a method that combines a nested pit strategy with integer programming (IP) models to overcome computational limitations by reducing the problem’s complexity, decreasing solution times, and providing insights into alternative production schedules for large-scale open-pit mines. The proposed algorithm strategically fixes the decision variables based on parametric analysis of the ultimate pit limit to simplify the IP model. The approach was applied to various block models from MineLib, and the results were compared with standard IP solutions and findings from related studies using alternative algorithms. Applying the proposed method demonstrated significant reductions in the solution time (up to 95%) and the ability to solve intractable models.

Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock.

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984–2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies' inspections or decision making. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To investigate the stability of jointed slopes under seismic action, a random joint fracture network model was established using the Monte Carlo method. The discrete element program 3DEC was utilized to conduct a numeri- cal simulation study on the stability of an open-pit mine slope under seismic action. The results indicate that under seismic action, the vibration intensity and displacement at the bottom of the slope are significantly stronger than those at the top. The joint fractures within the slope have a notable attenuating effect on the energy of seismic waves. The bottom of the slope primarily exhibits a shear failure mode, indicating the need to reinforce the seismic resistance of the slope's bottom structure during routine production. [ABSTRACT FROM AUTHOR]

Purpose: Over the past three decades, open-pit mining has been expanding in arid and semi-arid areas of China.Open-pit mining profoundly changes the soil environment and has a profound impact on the circulation of soil water in the aeration zone.Therefore, this research explores the impacts of open-pit coal mining on soil moisture processes in the semi-arid grasslands of Eastern Inner Mongolia Autonomous Region, China. Materials and methods: Soil samples were collected from depths of 0–500 cm at Shengli No. 1 open-pit mine's inner dump and a nearby natural grassland. These soil samples were analyzed for stable isotope characteristics ( δ 2 H , δ 18 O ) and moisture content. Collection of underground water samples inside and outside the mining area for conductivity analysis. Results and discussion: Soil evaporation loss in the mine's inner dump was significantly higher than in the grassland, with rates of 22.26% for δ 18 O and 6.61% for δ 2 H . The limiting depth of soil evaporation at the mine was found to be 260 cm, compared to 200 cm in the grassland. The increased underground water conductivity in the mine area was linked to heightened soil evaporation loss. Isotopic profiling of the soil indicated that the open-pit mining led to deeper preferential flow infiltration during heavy precipitation, reaching 280 cm in the mine area versus 220 cm in the grassland. Conclusions: The surface soil moisture content (SMC) increased due to mining activities intensified water-heat exchanges with the atmosphere, leading to more frequent and severe wet-dry cycles. This study provides a comprehensive understanding of open-pit mining's impact on SMC, evaporation, and infiltration in semi-arid areas, offering critical insights for ecological reclamation and sustainable mine construction. [ABSTRACT FROM AUTHOR]

The issue of energy efficiency in belt conveyors aligns with the current trends of energy saving in mining, driven by sustainable development goals and European legislation. The topic of energy-efficient conveyor transport faces the challenge of objectively assessing the energy efficiency of belt conveyors. The presented article demonstrates an analysis of the energy efficiency of belt conveyors in a lignite open-pit mine. As part of the research, the energy consumption of conveyors operating in a transport system was compared using the parameter of electrical energy consumption and the specific energy consumption (SEC) index, highlighting significant parameters affecting its interpretation. Based on the values of the modified lifting resistance SEC index, and the volume of transported material, observations were divided into groups reflecting energy efficiency classes using a k-means algorithm. The research shows that a proper assessment of the energy efficiency of a belt conveyor should consider the amount of transported mass concerning the maximum capacity, the conveyor’s design parameters, and the working environment characteristics. [ABSTRACT FROM AUTHOR]

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes. [ABSTRACT FROM AUTHOR]

Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock. [ABSTRACT FROM AUTHOR]

The expansion of the open-pit exploitation of mineral raw materials, and especially the energy resources of fossil fuels, makes open-pit coal mines spatially dominant objects of large mining basins. Exploitation activities are accompanied by negative ecological impacts on the environment, which requires the integral planning, revitalization, reclamation, and rehabilitation of the disturbed area for human use in the post-exploitation period. The post-exploitation remediation and rehabilitation of open-pit mining areas and disposal sites, i.e., space disturbed by mining activities and accompanying facilities, are complex synthetic multidisciplinary multiphase engineering project tasks. In this paper, a hybrid fuzzy MCDM model (Multiple-Criteria Decision-Making) was developed for the selection of a reclamation solution for the Tamnava-West Field open-pit mine. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define the weights of 12 criteria of different structures used in the evaluation of reclamation solutions. The Fuzzy ROV (Range of Value) method was applied to select the reclamation solution from a total of 11 solutions previously obtained using a process approach. The results of the hybrid IMF SWARA—Fuzzy ROV model show that forestry is the best solution for the Tamnava-West Field open-pit mine. After the results had been obtained, verification analyses of the proposed model were performed and the best stable proposed reclamation solution was determined. [ABSTRACT FROM AUTHOR]

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Xinjiang has huge reserves of coal resources and has the conditions and advantages to develop and construct large-scale open-pit coal mines. However, due to the climate characteristics of Xinjiang’s arid areas, open-pit mining has a wide range and high degree of impact on the ecological environment and damage. It is of great practical significance to carry out ecological restoration of open-pit mines in arid areas. Due to the loose materials, high porosity and permeability of the waste dump, the ecological restoration of the waste dump in arid areas is a serious challenge. In order to solve the shortage of water resources in the ecological restoration process of open-pit coal mines in the arid areas of western China, improve the ecological restoration effect of open-pit coal mines in the arid areas, based on the dual technical objectives of material strength and permeability, explore and reconstruct the material ratio of the impervious layer of the open-pit mine external dump, carry out relevant research with different material ratio schemes using indoor permeability test and uniaxial compressive strength. The main composition, uniaxial compressive strength and permeability of the materials with different proportions were determined by XRD diffraction test, uniaxial compression test and variable head permeability test, and the material proportioning parameters of the reconstructed aquiclude were studied. The test results show that the compressive strength of the specimen increases by 22% when the curing agent ratio increases by 2%, but the increasing trend starts to decrease when the curing agent ratio exceeds 8%. When the curing agent ratio increases from 8% to 10%, the compressive strength of the specimen increases by only 6%. With the increase of fly ash content, its permeability decreases gradually. Every 2% increase of fly ash content can reduce the permeability of 28%. The following rules are obtained: the influence of the content of curing agent on the compressive strength of the specimen is greater than that of the fly ash content, and the influence of the fly ash content on the permeability is higher. When the fly ash content is fixed, the curing agent content is positively correlated with the uniaxial compressive strength of the specimen. When the ratio of curing agent is fixed, the uniaxial compressive strength of fly ash increases with the increase of the ratio.

Ore production is usually affected by multiple influencing inputs at open-pit mines. Nevertheless, the complex nonlinear relationships between these inputs and ore production remain unclear. This becomes even more challenging when training data (e.g. truck haulage information and weather conditions) are massive. In machine learning (ML) algorithms, deep neural network (DNN) is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers. This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data. Before the prediction models were built, principal component analysis (PCA) was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables. To verify the superiority of DNN, three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models. The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer. The DNN model outperformed the extensively applied benchmark models in predicting ore production. This can provide engineers and researchers with an accurate method to forecast ore production, which helps make sound budgetary decisions and mine planning at open-pit mines.

ABSTRACTArctic miners face significant risks from diesel exhaust and dust exposure, potentially leading to adverse respiratory health. Employers must limit harmful exposures, using personal protective equipment (PPE) as a last line of defense. This study explored the association between reported respiratory exposure and symptoms, and PPE training and usage. Data from the MineHealth study (2012–2014) included a total of 453 Arctic open pit miners in Norway, Sweden, and Finland. Participants answered questions on exposure to dust and diesel exhaust, respiratory symptoms, and PPE use, in addition to age, gender, BMI, smoking, and self-rated health. Estimated exposure to dust was common, reported by 91%, 80%, and 82% and that of diesel exhaust by 84%, 43%, and 47% of workers in Sweden, Finland, and Norway, respectively. Reported dust exposure was significantly related to respiratory symptoms (OR 2.2, 95% CI 1.3–3.7), diesel exposure increased the occurrence of wheezing (OR 2.6, 95% CI 1.3–5.4). PPE use varied between the studied mines. Non-use was common and related to reduced visibility, wetness, skin irritation and fogging of the respiratory PPE. Future research should employ more precise exposure assessment, respiratory function as well as explore the reasons behind the non-compliance of PPE use.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing demand for primary mineral resources and the finite nature of mineral reserves necessitate their efficient utilization while minimizing losses. To extend mine life and increase economic reserves, transitioning from open-pit to underground mining has proven to be a viable strategy. Identifying this critical transition depth is pivotal, as it significantly impacts mining profitability. Therefore, in this paper we propose a method to determine the open-pit/underground transition depth based on an economic block model, accounting for the final pit limit and considering the inherent uncertainty in mineral prices. To achieve our research aim, a hypothetical grade model was constructed initially, and we used @Risk software to perform Monte Carlo simulation of copper prices based on historical data. By generating 200 different mineral price scenarios, we created 200 economic block models. Subsequently, nine mining sequences were designed, and seven distinct scenarios were established to ascertain the final open-pit limit and the depth of the transition point between mining methods, contingent on fluctuating mineral prices. Based on the outcomes derived from the seven scenarios, the depth at which the mining method transition from open-pit to underground is recommended to be 375 m from the surface, under the aforementioned uncertainties. This recommendation is based on the cumulative net present value of open-pit/underground mining, as illustrated in scenario number 7. Our findings provide valuable insights for mining companies seeking to optimize resource utilization and maximize profitability while navigating uncertain mineral price environments. [ABSTRACT FROM AUTHOR]

The fleet management system (FMS) plays a vital role in truck and shovel open-pit mining operations. The list of expectations from the FMS ranges from data recording to making operational decisions. This article proposes a nested fleet management system (N-FMS) for open-pit mining operations. The primary contribution of the proposed FMS is connecting the operation to the strategic plan by incorporating shovel allocation and plant feed optimization. Another contribution of the proposed N-FMS is that it simultaneously optimizes the utilization of the shovel fleet and truck fleet. The proposed FMS makes decisions using two nested multiple-objective mixed-integer linear goal programming models. Results of the implementation of the developed N-FMS in a metal mining case study show that compared to a locked-in operation, the mine experienced a 14.6% improvement in its required truck fleet capacity to meet the production target. [ABSTRACT FROM AUTHOR]

The sustainable development of mining processes requires a deep knowledge of the effectiveness of mining equipment and is quite complex to analyze due to the intrinsic characteristics of the mining industry. In this regard, its measurement and control can lead to appropriate management, improving the mining processes' efficiency, increasing safety levels, and reducing environmental impacts. This paper developed a new methodology to study the effectiveness of equipment in mining processes, researching the impacts of process management, process control, operating conditions, operational mining schedule, and maintenance programs on the effectiveness of drilling rig fleets using two actual coal mines located in the northwest of Spain and data collected over 10 years. Thus, a new method, called Overall Mining Equipment Effectiveness (OMEE), was developed, verifying its appropriateness and flexibility to analyze the effectiveness of mining equipment by examining the availability rate, utilization rate, and productivity index. [ABSTRACT FROM AUTHOR]

Noise causes many negative effects both in our daily life and working life, reduces our quality of life, and affects our mental health directly or indirectly. The most common consequence of noise exposure is especially permanent hearing loss called noise-induced hearing loss (NIHL). NIHL is very prevalent in almost every stage of the mining industry. Therefore, the assessment of noise levels of mining operations and the estimation of NIHLs of employees is an important issue to prevent and minimize them. This study is aimed to the modeling of NIHL prediction at a quarry located in Aksaray, Turkey. Initially, noise levels were measured with a sound level meter for employees working in different positions for the quarry, and daily exposure levels (Lex,8h) were determined. Audiometry tests were also performed on all employees and NIHLs were evaluated and determined by an audiometrist. According to the results, 5 employees had NIHL in this enterprise. A fuzzy inference system (FIS)-based NIHL estimating model implemented on fuzzy logic using the Sugeno inference mechanism was developed. The model predicts NIHLs for given occupation, age, experience, and Lex,8h parameters. To determine the accurate prediction ability of the model, field noise measurements and audiometry test results data were used. The obtained results indicated that the model has accurate a prediction ability with a 94% success rate. This study proposes a method with high predictive ability using fuzzy sets theory, and will be a guide for the top management in considering the damage effects of noise in enterprises.

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations.

In order to improve the prediction accuracy of peak particle velocity during open-pit blasting operations, which is insufficient when using traditional empirical formulas and single bionic algorithms, the Grey Wolf Algorithm (GWO) is introduced to optimize 2 hyperparameters, the number and depth of decision trees in the Random Forest Algorithm (RF). This successfully constructs the GWO - RF blasting vibration velocity prediction model. By combining 69 sets of blasting monitoring data from a blasting project, input parameters such as blasthole distance, maximum interval charge, total charge, millisecond delay, number of blastholes, hole spacing, depth, and role spacing are used to compare the prediction of peak vibration velocity using the GWO - RF model and the RF model. The results show that the GWO-RF combined algorithm can consider more practical factors affecting blasting vibration velocity and improves the error rate by 37.83 percentage points compared to the RF; the prediction accuracy of blasting vibration velocity using the GWO-RF combined algorithm reaches 97. 72%. This indicates that the GWO successfully optimizes the 2 hyperparameters of decisiontrees in the RF model and demonstrates that the GWO - RF combined algorithm can be used for accurate prediction of blasting vibration velocity in open-pit mining. [ABSTRACT FROM AUTHOR]

Copyright of Eurasian Physical Technical Journal is the property of E.A. Buketov Karaganda University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effective slope stabilization measure in deep open-pit mines not only ensures the safety of employees and equipment, but also makes sound economic sense. Based on field investigations of a landslide at the Lishan Iron Mine, south China, an innovative and effective method for slope remediation is proposed. An in situ foundation embankment was first created by excavating an anti-slip foundation trench, then the slope was stabilized by building a restraining embankment against the slope. The in situ foundation embankment had a dense structure which provided good resistance both to slip and to overturn. The restraining embankment was constructed out of mining waste rock and reduced the potential for slope failure by diminishing the potential free area of the slope and inhibiting the development of fractures in the rock mass forming the slope. In view of the structural characteristics of the in situ foundation embankment, an analytical solution for the safety factor of the embankment slope is deduced using theoretical analysis and numerical simulation for various combinations of geometric parameters. The results show that the safety factor of the embankment decreases with an increase of step height and step slope angle and increases with an increase of platform width and that step slope angle is the key parameter which affects the safety factor and both the maximum displacement and maximum plastic strain. The reliability of the proposed method is validated using field monitoring results from the Lishan Iron Mine after the embankment construction. This mine has now been safely running without landslides for two hydrological years after the stabilization works were constructed. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human activities play a key role in landscape evolution. Mining is one of the most important of these since it changes large parts of the Earth’s surface. In abandoned mines and quarries, pit lakes are quite common due to water table blocking. These lakes have remarkable ecological characteristics, but they are still poorly understood. Northwestern Spain has a long tradition in the use of slate for roofs. This exploitation, mostly open-pit, has caused a great transformation in the landscape including the generation of pit lakes. In this work, 27 pit lakes in abandoned slate quarries areas have been analysed using orthophotos of different years, UAV (unmanned aerial vehicle) images and field work. In each lake, the topographic context, geomorphological dynamics, morphometric parameters (including depth), pH, and ecological variables were measured. Other uses for these quarry pit lakes and their significance as a new geo-ecological environment are also discussed. The results show different types of pit lakes and, consequently, different potential uses in each case. Only six offer good conditions for recreational uses, whereas the rest (21) show slope instability or poor water quality.

While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984–2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies’ inspections or decision making.

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enterprises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 × 105 m2, and the indirect impact area on vegetation was 7.371 × 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foundation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.

AbstractThis work deals with the short-term planning problem of a work shift for open-pit mines. The problem involves ore and waste fronts, shovels, heterogeneous truck fleets, and discharge points. The allocation of trucks is dynamic to allow multiple routes to be assigned to each truck. The problem consists of deciding which fronts must be mined and establishing the number of trucks, their routes, and the amount of material transported by them to each discharge point, satisfying a stripping ratio at the desired level. The objectives are to minimize the deviations from the targets for production, chemical grade, and particle size range of each control parameter at each plant and reduce the number of trucks needed for the process. To solve the problem, we developed a mixed-integer linear goal programming model and tested it using real data from an iron ore mine. The results showed that the proposed approach supports decision-makers in the sizing and allocation of truck fleets and in meeting the production and control parameter targets required by the ore processing plants according to the daily scenario, such as low availability of shovels and trucks, flexibility in ore quality, and need for increased production.

Copyright of Journal of Engineering & Architectural Faculty of Eskisehir Osmangazi University / Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi is the property of Eskisehir Osmangazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the application of end slope shearer mining technology to recover a large amount of residual coal, the determination of reasonable width of supporting coal pillar is a key factor whether it can be safely and efficiently popularization and application, especially considering the influence of blasting vibration on the stability of supporting coal pillar. Based on the southern end slope at the open-pit coal mine of Pingshuo, field vibration test, theoretical analysis and numerical calculation were used to study the web pillar stability in open-pit highwall mining and its parameter design under the action of triangular load and blasting vibration on the side slope. Based on the theory of limit balance and the mutation theory, the stress distribution at the coal pillar was analyzed, combined with Mohr-Coulomb failure criterion. Besides, the ultimate strength function expression of coal pillar under the influence of mining height, mining width, load stress of overlying strata, cohesion and internal friction angle of coal pillar is established. The calculation formula of the maximum allowable plastic zone width and rational width of web pillar under different safety reserve factor conditions are established. The three-dimensional simple harmonic vibration response model of the supported coal pillar was established, and the blasting parameters such as the amount of single shot, elevation difference and horizontal distance of the blast center were studied on the response of the maximum instantaneous dynamic stress of the coal pillar, which revealed the influence mechanism of the blasting dynamic load effect on the width and stability of the plastic zone of the supported coal pillar and proposed the design method of the parameters of the supported coal pillar under the blasting dynamic load. The results show that the blasting vibration has a greater influence on the stability of coal pillar, and the instantaneous maximum dynamic stress response of coal pillar under the blasting dynamic load is positively correlated with the amount of single shot, and negatively correlated with the elevation difference and horizontal distance. With the increase of the maximum instantaneous dynamic stress response of coal pillar, the width of plastic zone of coal pillar increases proportionally, and the safety factor of coal pillar decays in an approximately linear pattern. The width of coal pillar under dynamic-static load is determined to be 5 m, and its reasonableness is verified by engineering practice.

The sustainable development of mining processes requires a deep knowledge of the effectiveness of mining equipment and is quite complex to analyze due to the intrinsic characteristics of the mining industry. In this regard, its measurement and control can lead to appropriate management, improving the mining processes’ efficiency, increasing safety levels, and reducing environmental impacts. This paper developed a new methodology to study the effectiveness of equipment in mining processes, researching the impacts of process management, process control, operating conditions, operational mining schedule, and maintenance programs on the effectiveness of drilling rig fleets using two actual coal mines located in the northwest of Spain and data collected over 10 years. Thus, a new method, called Overall Mining Equipment Effectiveness (OMEE), was developed, verifying its appropriateness and flexibility to analyze the effectiveness of mining equipment by examining the availability rate, utilization rate, and productivity index.

Ecological restoration in open-pit coal mines faces significant challenges, particularly regarding vegetation restoration in dumping sites, which often experience unstable vegetation status after restoration. Hence, monitoring the vegetation restoration process and evaluating restoration effect in dumping sites are crucial. In this study, the restoration process in dumping sites was rebuilt based on the Fractional Vegetation Cover (FVC), and the restoration effect was evaluated with reference to the FVC before destruction. Considering the topographic factors in dumping sites, the relative contributions of climate change and human activities was quantified. The future restoration potential of dumping sites was explored using the constructed FVC Restoration Potential Index (FRPI). The results show that: (1) This study can accurately identify the actions and characteristics of vegetation destruction and restoration of open-pit coal mines’ dumping sites based on FVC time-series curves. (2) The vegetation restoration effect in dumping sites can be accurately evaluated based on the constructed “difference-value” indicator. Additionally, the FRPI enabled the assessment of dumping sites’ future FVC restoration potential, providing valuable guidance for future restoration projects. (3) The correlation between topographic factors and FVC in dumping sites was slightly weak, with climate change and human activities serving as the main driving forces. Climate change was key in outer dumping sites, whereas human activities had a greater influence in Inner dumping site. This study provided a valuable approach for assessing the restoration effect, potential, and attribution, facilitating the optimization of future restoration projects.

The residual open pit left in the wake of open-pit mining poses significant safety hazards, with backfilling being an effective strategy to wholly eliminate these risks. The stability of the slope following wet backfilling, however, should not be overlooked. This paper examines the impact of the seepage field conditions and backfill height on the stability of open-pit slopes using a case study of cemented backfill in a specific open pit in Anhui Province. Moreover, it utilizes onsite research, Slide simulations, and similar simulation tests. The study findings suggest that as the height of the tailing solidification backfill increases, the safety factor of open-pit slopes gradually elevates. When the backfill height exceeds 10 m, all profiles of the studied open-air slope fulfill the stability prerequisites. Furthermore, when the solidification backfill reaches 20 m, all profiles of the studied open-pit slope satisfy the stability requirements. The research outcomes offer a methodology for mining corporations to avert slope instability and destruction, thereby providing effective safeguards for the extraction of scarce resources in mines. [ABSTRACT FROM AUTHOR]

Real-time truck dispatching is an important function of the open-pit mine transportation system. However, most of the existing methods are not comprehensive enough to consider the optimisation of both full truck hauling and empty truck travelling stages. Further, most of the real-time dispatching criteria are fixed, which cannot meet the variety of production requirements. To address the above problems, we consider both full and empty truck dispatching, and different from previous studies, we develop a real-time dispatching model with two parts for heterogeneous fleets in open-pit mines: a full truck dispatching model for truck-finished loading at the loading place, and an empty truck dispatching model for truck-finished dumping at the dumping place. Specifically, the proposed model has three goals for minimisation: (a) the waiting time of the trucks, (b) the deviation from the planned path flow rate, and (c) the transportation cost. Furthermore, the weights of the three sub-objectives can be changed to meet the production requirements for different real scenarios. Simulation results show that the proposed model can achieve a higher production by at least 14% and decrease the cost by at least 6% and has better adaptability to different production requirements. [ABSTRACT FROM AUTHOR]

In the mining process of open-pit stope, geological conditions are gradually revealed, so a single slope design and construction parameter can not adapt to the complex engineering geological conditions. Therefore, it is necessary to carry out engineering geological zoning, and reasonable optimization of slope parameters. Taking a large openpit mine as the research object, the exposed strata were divided, and at the same time, the site structural plane investigation and analysis were carried out to establish a complete geological model. According to the different influencing factors, the stope was divided into engineering geological zones, and the typical section was selected to analyze the failure mode on the basis of each geological zone. The rock soil strength parameters were determined by laboratory test and engineering analogy method. According to the overall stability of slope and the local stability of step slope, back analysis for the strength parameters of alluvial layer, limestone, limestone fracture zone and porphyry fracture zone respectively was carried out, and the recommended value of rock soil strength parameters of open-pit stope slope was given. [ABSTRACT FROM AUTHOR]

The issue of blast-induced ground vibration poses a significant environmental challenge in open-pit mines, necessitating precise prediction and control measures. While artificial intelligence and machine learning models hold promise in addressing this concern, their accuracy remains a notable issue due to constrained input variables, dataset size, and potential environmental impact. To mitigate these challenges, data enrichment emerges as a potential solution to enhance the efficacy of machine learning models, not only in blast-induced ground vibration prediction but also across various domains within the mining industry. This study explores the viability of utilizing machine learning for data enrichment, with the objective of generating an augmented dataset that offers enhanced insights based on existing data points for the prediction of blast-induced ground vibration. Leveraging the support vector machine (SVM), we uncover intrinsic relationships among input variables and subsequently integrate them as supplementary inputs. The enriched dataset is then harnessed to construct multiple machine learning models, including k-nearest neighbors (KNN), classification and regression trees (CART), and random forest (RF), all designed to predict blast-induced ground vibration. Comparative analysis between the enriched models and their original counterparts, established on the initial dataset, provides a foundation for extracting insights into optimizing the performance of machine learning models not only in the context of predicting blast-induced ground vibration but also in addressing broader challenges within the mining industry [ABSTRACT FROM AUTHOR]

The article considers the possibilities of using automated and information systems with digital images to solve practical problems related to the extraction and processing the natural stone. Computerized processing and image analysis of the surfaces of natural stone and industrial products made from it allows measuring the characteristics of the color, texture of these surfaces, as well as the geometric dimensions and angles of the products along the outer contour. The obtained results will be useful for determining the mechanical characteristics of natural stone in areas of deposits that are mined or planned to be mined, for the study of physical and mechanical characteristics of rocks due to their crystalline structure, for quality control of the front surface and geometric dimensions of industrial products from natural stone. An example of experimental research and evaluation of the composition and structure of samples of gabro rock is given. The main directions of using automated and information systems with digital images to obtain measurement information about the characteristics of natural stone samples and products in the mining and geological field are presented and systematized. This makes it possible to increase the efficiency of mining operations for the open-pit mining of natural stone deposits, the accuracy of determining the characteristics of samples and products, and to increase the quality and competitiveness of industrial products of enterprises engaged in the extraction and processing block natural stone.