Open Pit Mining Operations Research Articles

Truck Transportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

Granular characterisation of coal spoil dump using unmanned aerial vehicle data to enhance stability analysis

Open pit mining operations generate significant spoil dumps that need to be characterised for stability to identify potentially unstable slopes. However, the current subjective practice for spoil characterisation often involves tedious and risky field work. To this end, this study demonstrated the use of periodically acquired unmanned aerial vehicle (UAV)-based images over a coal mine spoil dump in New South Wales, Australia. A granular approach that captures the variability of each truck offload pile on a dump was adopted through morphology-based segmentation and ensemble algorithm-based classification which consolidates predictions from multiple classifiers. Overall accuracy of over 90% in the material characterisation based on the classification framework was achieved. The two-dimensional classification outcome was then transformed into three-dimensional (3D) block models using a point-based interpolation approach for stability analysis. The factor of safety derived from the granular approach offered improved assessment of failure risk compared to the conventional approaches, which treat the entire dump as a uniform category. This rapid classification and assessment method proposed in this study will help reduce the uncertainty associated with the variability of spoil dumps in slope stability assessments, thereby enhancing the safety and efficiency of mining operations.

Block method of mining quarry fields as a tool to reduce environmental impact in conditions of intensification of production

Relevance. The intensification of open-pit mining in the coal basins of the Russian Federation is accompanied by an increase in negative impact on the environment. Directly in Kuzbass, the environment is exposed to the impact of the coal industry, especially when conducting open-pit mining using deep mining systems, with external dumping, which is accompanied by the withdrawal of a large amount of undisturbed land. The block method of mining quarry fields, well known in mining, has been rarely implemented in existing and planned quarry fields. The general line of its application is the lack of a unified approach to the design of block parameters, including environmental and economic assessment of the impact of this technological solution on the environment. Aim. Reduce environmental payments of mining enterprises with justified parameters of blocks for the development of quarry fields of promising inclined and steeply dipping coal deposits. Methods. Systematic generalization of the actual situation of mining operations in open-pit mines using the block method of mining quarry fields, the method of options and technical and economic modeling using spreadsheets. Results and conclusions. The authors have proposed the ecological and economic criteria for assessing the application of the block method of quarry field development. This allows us to study the patterns of change in environmental indicators. Using the example of the Krasnobrodskoe open-pit mine, the costs of environmental payments for the use of land resources, emissions and dusting are calculated for two options different from the design documentation. Based on the calculation results, the authors proved the efficiency of applying the block procedure for developing promising coal deposits, allowing us to reduce the costs for environmental impact by 22.5 and 63%, respectively, when changing the length of the initial block along the bottom from 2.2 to 1.4 km. From which it follows that the combined ecological and economic indicators allow us to fully assess the feasibility of applying the method for calculating block parameters, which is generally reflected in reduction in the costs for negative impact on the environment.

An analytical approach for evaluation of conveyor exit strategies in open-pit mines

Transportation is the most expensive task in deep open-pit mining operations. An In-Pit Crushing and Conveying (IPCC) system, as an alternative to the conventional shovel-truck system, requires precise planning to avoid wasting high capital expenditures on trucks. The decision to fit a conveyor system into the pit design is challenging and needs to be addressed in the literature. Further optimisation of the IPCC system requires choosing an appropriate conveyor exit strategy. Conveyor exit schemes include a Dedicated Ramp Slot (DRS), an Existing Ramping System, an inclined tunnel and High-Angle Conveyors (HAC). This paper provides an analytical approach to compare the cost of different conveyor exit strategies in open-pit mines. The results indicate that for a depth up to 100 m, the Present Value of Cost (PVC) for the DRS is the least. For the cases deeper than 300 m, the HAC has the least PVC. Sensitivity analysis showed that for the depths between 100 and 300 m, a more accurate and detailed definition of parameters is needed. Besides, in most cases, the HAC provides the lowest payback period and the highest internal rate of return among the alternatives.

Research on Comprehensive Evaluation Indicators and Methods of World-Class Open-Pit Coal Mines

Since the 20th century, with the rapid development of the global economy, human demand for fossil energy such as coal has been increasing. In the face of fierce competition, the state and various ministries and commissions have put forward the goals and requirements of building a world-class enterprise, and how to become a world-class open-pit coal mine and establish the corresponding evaluation standards has become an urgent scientific problem to be solved. According to the characteristics of open-pit coal mine production and operation, from the perspective of benchmarking world class, the key factors affecting the construction of world-class open-pit coal mines are extracted by using the fuzzy DEMATEL method, and the comprehensive evaluation index system, evaluation standards, and methods of open-pit coal mines are established, which provides benchmarks and guidelines for the construction of world-class open-pit coal mines and improves the construction level of world-class open-pit coal mines.

Optimizing Mean Fragment Size Prediction in Rock Blasting: A Synergistic Approach Combining Clustering, Hyperparameter Tuning, and Data Augmentation

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R2 of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data.

Management Solutions and Stabilization of a Pre-Existing Concealed Goaf Underneath an Open-Pit Slope

Pre-existing concealed goafs underneath open-pit slopes (PCO-goafs) pose a serious threat to the stability of open-pit slopes (OP-slopes), which is a common problem worldwide. In this paper, the variable weight-target approaching method, equilibrium beam theory, Pratt’s arch theory, and numerical simulation are used to analyze the management solutions and stability of five PCO-goaf groups in the Nannihu molybdenum mine located in Luoyang City, Henan Province, China. The five PCO-goaf groups, numbered 1#, 2#, 3#, 4#, and 5#, are divided into four hazard classes, ranging from extremely poor to good stability. The stability of 1#, 2#, and 4# is poor and must be managed by filling, and the design strength of backfill is 1.2 MPa; caving is used to treat 3# and 5#, and the safe thickness of the overlying roof is calculated to be 10.5–41 m. After treatment, the safety coefficient of the slope is greater than 1.2, indicating that the slope is stable. This study provides insight and guidance for the safe operation of open-pit mines threatened by the existence of PCO-goafs.

Forecasting open-pit slope runout distances

Geotechnical engineers are routinely tasked with advising suitable stand-off distances below high-risk sections of slopes in open-pit mines that are identified to have potential to deform or collapse. Accurate prediction of failed material runout can mean the difference between continuous safe mining and unwanted high-potential incidents that result in loss of production, equipment damage, injury, or loss of life. This paper updates previous empirical relationships presented by the authors for estimating the volume and runout distance of excavated slope failures, in an open-pit mine operation, using slope geometry as the primary predictor. Cases are sourced from varying slope geometries (fall heights up to 385 m, slope angles up to 80°) and a range of commodities (iron ore, coal, nickel, gold, copper, boron, and limestone), excavated in sedimentary, banded-sedimentary, epithermal, and copper-porphyry deposits, across all six inhabited continents. Analysis of these cases identified positive correlations between slope height and runout distance, and slope height and failed material volume. In general, negative correlations were identified between Fahrböschung angle and slope height, and Fahrböschung angle and failed material volume; however, significant scatter is observed in these datasets. A definitive relationship could not be derived comparing Fahrböschung angle with failed material runout. Slope angle was also found to be a poor indicator of runout. Of the parameters analysed, slope height (i.e., fall height) was found to be the simplest and best predictor of runout distance. This paper presents new charts for predicting failed material runout distance for rock slopes. Relationships are defined for structurally and rock-mass driven slope failure mechanisms at average, 75%, and 95% prediction intervals.

Particulate matter emissions from open pit mines; measurement methodologies, instruments, and research undertaken

Particulate matter is one of the primary pollutants in open pit mining operations. Measurements must be taken to control particulate matter created during open pit mining activities and to compare them to the regulatory limits. Numerous studies have been undertaken to estimate particulate matter emissions produced by open pit mining. It was discovered that the research were largely conducted on coal mines (69.4%), with little study done in other mining types. Research studies on particulate matter estimation took into consideration mostly machine characteristics (loader bucket volume, truck capacity, number of truck wheels etc.) and atmospheric conditions (air temperature, wind speed, relative humidity etc.). This study emphasizes on particulate matter measurement methods along with other measuring parameters and equipment for particulate matter estimation (TSP, PM10, PM4, PM2.5, and PM1).

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.

Оценка нагрузок приводов рабочего оборудования карьерного экскаватора (часть 1)

One of the major tasks in carrying out open-pit mining operations is timely and high quality maintenance and repair of mining equipment, in particular, mechanical shovels, which secures a required availability level. The most loaded are the lift and pressure drives of the shovel's working attachments, which significantly increases the risk of failure of their components. An adequate assessment of the loads acting on these drives through analyzing the changes in currents and voltages of their motors makes it possible to adjust the time between maintenance. In the course of the study, the representative points of the following three lumped working processes were obtained within the working cycle of 26.75 s: excavating; platform rotation with simultaneous movement of the shovel from the upper point of the path to the unloading point; reverse movement of the platform rotation mechanism with the returning and its positioning of the working attachment to the initial position. Changes in forces acting on the ropes of the lifting winch of the shovel were determined based on the evaluation of the obtained layout of the working attachment positions for the EKG-18R mechanical shovel and taking into account the driving and motion resistance forces within the working cycle. Analysis of the obtained force values acting on the ropes of the lifting winch at the three linear excavation paths with the inclination angles of 60, 70 and 80° has shown that the lowest forces in the lifting winch rope occur at the closest path with the angle of 60°. The received data will allow not only to select a rational path and the angle of bench mining, but also to adjust the time between maintenance according to the actual operating time of the lifting, pressure and movement drives.

A novel framework for integrating environmental costs and carbon pricing in open-pit mine plans: Towards sustainable and green mining

As the worldwide need for mineral resources continues to grow, there is an increasing emphasis on mitigation of the environmental consequences of the mining sector, necessitating a shift towards sustainable and green mining practices. This paradigm requires the meticulous integration of environmental considerations throughout all stages of planning and operation in open-pit mining. Therefore, this research introduces an innovative integrated framework for long-term mine planning, strategically combining economic and environmental considerations in open-pit mining operations. The methodology integrates carbon pricing into the decision-making process, addressing potential financial risks and introducing two distinct scenarios: one focused on minimizing environmental costs and the other on maximizing adjusted Net Present Value (NPV). The application of this integrated framework to an open-pit iron mine in the Middle East region yielded insightful results. The integrated framework's impact on block sequencing, greenhouse gas emissions, and financial outcomes was assessed, highlighting the divergent strategies employed in the two scenarios. The minimization scenario demonstrated it was feasible to attain 90.54% of the global warming impact and 90.48% of environmental costs while achieving 96.25% of the adjusted NPV compared to the maximization scenario. Sensitivity analyses further underscored the adaptability of the framework to varying carbon pricing scenarios. This research validates a sustainable approach to mine planning, emphasizing a balance between economic returns and ecological responsibility, and provides valuable insights for decision-makers in the mining industry.

Optimizing open-pit coal mining operations: Leveraging meteorological conditions for dust removal and diffusion

Dust pollution from Chinese open-pit coal mines (OPCMs) threatens the coexistence of resource development and environmental protection. This research introduces a new approach to designing OPCMs based on meteorological indicators for dust removal and diffusion. It analyzes the production, distribution, and dust emission features of large-scale OPCMs in China. The factors affecting dust dispersion and atmospheric pollution characteristics were also examined. The findings reveal a surge in the number and output of OPCMs, intensifying the conflict between resource development and environmental protection. Notably, over 80% of OPCMs are in arid and semi-arid regions, exacerbating the challenge. Microclimate effects, including circulation and inversion effects, further amplify dust pollution. Regional and seasonal dust pollution patterns were identified, with the southern region experiencing the highest pollution levels, followed by the northern and central regions. Seasonally, dust pollution exhibits the following pattern: winter > autumn > spring > summer. An alarming decline in atmospheric self-cleaning capacity over the past two decades underscores the pressing challenges ahead for dust control. The increase in air stagnation days/events highlights the urgency for effective dust prevention and control measures. This research suggests considering meteorological elements in OPCM design for dust control. Optimizing mining operations based on weather forecasts enables the utilization of natural conditions for effective dust prevention and control. The results provide insights for dust prevention and control in open-pit mines to foster green and climate-smart mining.

Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era

Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning.

Strategic planning of open pit mining operations using the Micromine Beyond Optimiser

Modern software products allow to determine the quantity and quality of ore at any point of the deposit and, accordingly, to identify the optimal pit shell using specialized algorithms. Such algorithms make it possible to create the final open pit shell even with a broken bottom for several neighboring ore bodies. Currently, optimization solutions in the mining industry require the determination of the maximum present value of profit or the minimum stated costs. It allows to make strategic planning decisions. Strategic planning of open-pit mining operations in the development of mineral deposits in Ukraine can be implemented using the Micromine Beyond Pit Optimizer. The authors considered the parameters that affect the boundaries of the pit field and the optimization criteria.

A stacked deep multi-kernel learning framework for blast induced flyrock prediction

Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24.

DENSITY MONITORING OF BULK EMULSION EXPLOSIVES IN CONFINED BLASTHOLES

Parameter’s control of explosives used in open pit mining operations have become more relevant over the years, bringing a better understanding of detonation performance. Giving into consideration that bulk emulsion density reflects on blasting results due to its importance in product sensitivity and velocity of detonation (VOD), blast designs are conducted assuming that cup density measurements are reliable and explosives gassing agents will perform as per specifications. Nevertheless, confinement conditions in boreholes are often neglected on predictive modelling. This paper describes the assembly of a device equipped with pressure sensors and its utilization in confined boreholes, obtaining in situ monitoring of density variation in explosive columns. The results enabled a comparison between bulk emulsion density variation when acting in different environments, providing detailed insights into how chemically sensitized explosives behave into different circumstances, giving the opportunity to re-evaluate blast design.

2020–2022年环渤海地区8 m分辨率露天采矿区空间分布数据集

With the increase of mining activities in China, the ecological environment around mines is facing unprecedented pressure, leading to a series of resource, ecological and environmental problems, such as air pollution, soil erosion, solid waste pollution, landslides and debris flows. To precisely obtain the spatial distribution data of open-pit mining, scholars and regulatory bodies are actively seeking timely and efficient methods to monitor open-pit mining areas by using the remote sensing, an innovative technology, for this purpose. In this research, we adopted high-resolution optical remote sensing image GF-6 with a resolution of 8m to obtain the open-pit mining dataset in the Bohai Rim through rule-based feature extraction, time-series image discrimination and visual interpretation methods, covering over 8000 open-pit mining areas. The dataset exhibits a high level of accuracy, with an overall accuracy of 95.0%, a producer accuracy of 98.0% and a user accuracy of 92.4%. It effectively reflects the spatial dynamics in the distribution of open-pit mining areas within the Bohai Rim region and reveals the influence of economic growth and policy-making on these changes. Thus, the dataset can offer valuable data support for managing open-pit mining operations and the development of related policies.

Комплексная оценка геоэкологических рисков при ведении открытых и подземных горных работ

Introduction. Geo-ecological risk is the probability of adverse events leading to a violation of the stability of the existing ecological system or the natural and technical geo-system “rock mass – technology – mining”, formed as a result of mining activities. The main elements of geo-ecological risk in mining are dangerous geological (landslides, rock collapses, suffusion-erosion processes, karst, etc.) and hydrogeological processes. The purpose of the research. Development of a method for integrated assessment and management of geo-ecological risks in open-pit and underground mining operations. Methodology. The paper calculates the geo-ecological risk in relation to each technological process. At the same time, the ranking of the possibility of a negative process in mining activities is determined on the basis of an expert approach, taking into account the specifics of mining and technological operations. Statistical data and expert assessments formed the basis for determining the quantitative values of geo-ecological risks in mining operations. Results and discussion. The obtained quantitative values of geo-ecological risks indicate that a management system is needed. The developed geo-ecological risk management system makes it possible to conduct a comprehensive assessment of them in the conditions of mining and mining and construction production and, based on forecast calculations, determine the distribution zones of the most dangerous processes in the “rock mass – technology – mining” system. Conclusion. Research has been conducted on the formation of a comprehensive assessment of geo-ecological risks, as well as their forecasting during open-pit and underground mining operations. It is determined that the geo-ecological risk in open-pit mining consists of the risk of alienation and degradation of soil, the risk of pollutants into the atmospheric air, the risk of pollution and depletion of the water basin, and in underground mining – from the risk of atmospheric pollution of the working area, the risk of changes in the groundwater regime and the risk of geo-mechanical transformations of the rock mass.

Optimization of cycle time for loading and hauling trucks in open-pit mining

Purpose. The purpose of the paper is to provide open-pit mining operations with practical strategies and insights to optimize truck loading and hauling cycles, ultimately leading to enhanced productivity and economic advantages. Methods. The objectives are to minimize loading time, optimize the haul road network, enhance truck performance, and optimize dumping and return time. By diligently implementing these methods and achieving these objectives, open-pit mining operations can significantly reduce the truck cycle times, resulting in increased productivity, lower costs and improved profitability. Findings. In this case, the total loading time of the excavator and shovel is determined to be 3.98 and 2.92 minutes, respectively, while the hauling time for total loading of the open-pit floor depends mainly on the average distance and speed of 239 m and 10.1 km/hour, which results in 1.53 minutes. Originality. As a result, the total cycle time for open-pit mining is 19.765 minutes, resulting from the total loading time, hauling time for total loading, total dumping time, and total return time for empty transport of 4.265, 8.46, 0.86 and 6.18 minutes, respectively. Practical implications. By combining theoretical analysis with practical insights and site-specific considerations, the paper aims to provide a comprehensive and applicable framework for optimizing truck cycle time in open-pit mining, resulting in improved efficiency and profitability of mining operations.