Search Results (694)

Accurate characterization of ore grindability is essential for optimizing mill throughput, reducing energy consumption, and predicting mill performance under varying ore conditions. However, the standard Bond work index (BWI) test remains time-consuming, costly, and requires a large amount of sample. This study evaluates the effectiveness of several rapid, low-cost alternatives, Leeb rebound hardness (LRH), Cerchar abrasivity Index (CAI), portable X-ray fluorescence (pXRF), and hyperspectral imaging (HSI), as proxies for grindability in gold-bearing ores. Sixty-two hand-size rock samples collected from two adjacent Canadian open-pit mines were analyzed using these techniques and subsequently grouped into ten ore groups for BWI testing. LRH and CAI effectively differentiated moderate (<15 kWh/t) from hard (>15 kWh/t) grindability classes, while geochemical features and HSI-based mineralogical attributes also showed strong predictive capability. HSI, in particular, provided non-destructive, spatially continuous data that are advantageous for complex geology and large-scale operational deployment. A conceptual workflow integrating HSI with complementary field measurements is proposed to support comminution planning and optimization, enabling more responsive and timely decision-making. While BWI testing remains necessary for circuit design, the results highlight the value of combining rapid proxy measurements with advanced analytics to enhance geometallurgical modelling, reduce operational risk, and improve overall mine-to-mill performance. Full article

This study introduces a deterministic framework for optimising the path planning of autonomous drill rigs in open-pit mining operations. While prior research has primarily focused on automating drilling mechanics, this study addresses the essential but underexplored phase of tramming, defined as the rig’s non-productive movement between holes. The proposed approach integrates geometric pattern recognition and slope-based route alignment. It also incorporates practical maneuverability constraints to generate efficient, smooth, and safe paths. Unlike evolutionary algorithms, which suffer from variability and demand extensive computation, this method delivers fast and consistent results. These are well-suited to the dynamic conditions of real-world mining. Applied to a 1596-hole case study, the framework reduced tramming time by over 50%, shortening the total project duration by 8% compared with the actual project. The findings demonstrate its potential to improve both operational efficiency and commercial readiness for autonomous drilling systems. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

Globally, more than 60,000 abandoned open-pit mines have been identified. Most of these sites lack effective management or ecological restoration measures. As a result, they pose substantial environmental and socioeconomic challenges. Against this backdrop, the reuse of quarry wastelands has emerged as a critical strategy for improving resource efficiency and promoting sustainable development in mining regions. Current domestic research mainly concentrates on ecological restoration techniques for abandoned quarry sites. However, systematic methods for prioritizing and ranking alternative reuse models remain limited. This study investigated four quarry reuse models: agricultural production, ecological protection, recreation-based education, and new energy development. The analysis integrated site suitability (${\mathrm{U}}_1$) with residents’ demands (${\mathrm{U}}_2$). Four representative quarry sites in Jiawang District, Xuzhou City, were selected as case studies. Based on coupled matching analysis, a priority identification method for quarry site reuse models was developed. Results indicated divergent prioritization between site suitability and resident demand. Site suitability composite values ranged from 3.9548 to 6.3094. Qishan and Kanshan sites demonstrated high suitability for recreation-based education and agricultural production, while the Dongshan site showed the highest ecological protection suitability. Suitability for emerging energy applications was generally low across all sites. Resident demand composite values showed significant variation across the four models. Recreation-based education demand (${\mathrm{U}}_2$ ranging from 0.3273 to 0.3778) substantially exceeded the other three land use types, with residents generally harbouring a degree of reluctance towards new energy development models. After coupling these factors, the original site suitability rankings were restructured: Qishan and Dongshan were selected for the recreation-based education model; Kanshan for agricultural production; and Changshan for ecological protection. This study offers insights for the diversified utilization of abandoned quarries in rural areas and provides a reference for ecological restoration and transformative development in mining regions. Full article

Under rapid urbanization and ecological transformation, balancing authenticity preservation with adaptive reuse presents a major challenge for industrial heritage landscapes. This study investigates the Dagushan Iron Mine in Anshan, China’s first large-scale open-pit iron mine and once the deepest in Asia, which is currently undergoing ecological backfilling that threatens its core landscape morphology and spatial integrity. Using a mixed-method approach combining archival research, spatial documentation, qualitative interviews, and expert evaluation through the Analytic Hierarchy Process (AHP), we construct a cross-validated evidence chain to examine how evidence-based industrial landscape heritage values can inform low-intervention digital narrative strategies for off-site learning. This study contributes theoretically by reframing authenticity and integrity under ecological transition as the traceability and interpretability of landscape evidence, rather than material survival alone. Evaluation involving key stakeholders reveals a value hierarchy in which historical value ranks highest, followed by social and cultural values, while scientific–technological and ecological–environmental values occupy the mid-tier. Guided by these weights, we develop a four-layer value-to-narrative translation framework and an animation design pathway that supports curriculum-aligned learning for off-site students. This study establishes an operational link between evidence chain construction, value weighting, and digital storytelling translation, offering a transferable workflow for industrial heritage landscapes undergoing ecological restoration, including sites with World Heritage potential or status. Full article

Blasting operations associated with in-pit ramp construction in open-pit mines generate gaseous emissions originating from both explosive detonation and diesel-powered drilling and loading equipment. The research object of this study is the ramp construction process in an operating open-pit quarry, and the objective is to comparatively evaluate gaseous emissions across alternative blasting scenarios to support emission-aware operational decision-making. Five realistic blasting scenarios are assessed using a combined methodology that integrates laboratory fume index data for ANFO, emulsion explosives, and dynamite with diesel-emission estimates derived from non-road mobile machinery inventory factors. Laboratory detonation tests provide standardized upper-bound emission potentials for CO_x and NO_x, while drilling and loading emissions are quantified using a fuel-based inventory approach. The results show that the dominant contribution to total mass emissions arises from diesel combustion during drilling and loading, consistent with studies on real-world non-road mobile machinery inventory factors. Detonation fumes, although chemically concentrated and relevant for short-term exposure risk, represent a smaller share of the mass-based emission budget. Among the explosive types, bulk emulsions consistently exhibit lower toxic-gas emission indices than ANFO, attributable to their more uniform microstructure and a moderated reaction temperature. Dynamite demonstrates the lowest fume potential but is operationally less scalable for large open-pit patterns due to manual loading. Uncertainty analysis indicates that both laboratory-derived fume indices and diesel emission factors introduce systematic variability: laboratory tests tend to overestimate detonation fumes, while inventory-based diesel estimates may underestimate real-world NO_x and particulate emissions. Notwithstanding these limitations, the scenario-based framework developed here provides a robust basis for comparative evaluation of blasting strategies during ramp construction. The findings support increased use of emulsion explosives and emphasize the importance of moisture management, field-integrated gas monitoring, and improved characterization of diesel-equipment duty cycles. Full article

The recovery and comprehensive utilization of tailings resources can effectively mitigate or eliminate safety hazards in the upper zones of open-pit mines. To ensure the safe recovery of accumulated tailings and enhance resource utilization efficiency, this study establishes a two-dimensional model based on the Discrete Element Method (DEM) for the overall stability of tailings recovery, which is integrated with the existing slope and ore pillar models of the open-pit mine. Leveraging the mechanical parameters of tailings and waste rock obtained from laboratory tests, this study systematically investigates the effects of tailings recovery on the stability of existing slopes. Results show that due to differences in fracture characteristics and tailings reserves, complete tailings extraction causes no landslides in some sections, but large-scale or varying landslides occur on southern/northern flank slopes in specific sections at certain excavation depths or after full extraction. Targeted recovery recommendations are proposed: “segmented excavation with bench reservation” prevents overall landslides on southern flank slopes of landslide-prone sections; 35° slope cutting ensures stability of northern flank slopes in all sections. Further field verification considering rainfall and seismic loading factors is required for practical applications. Full article

Timely adjustment of belt conveyor speed according to the conveyed load is a key approach to achieving energy-efficient operation. Line laser-assisted vision has been widely adopted for load measurement, in which image processing techniques are employed to extract and analyze the outer contour of material piles highlighted by laser stripes. To address issues such as laser stripe thinning and breakpoint handling, the point-by-point interpolation method (PPIM) was previously proposed, enabling column-wise extraction of laser stripe pixels by incorporating the geometric characteristics of material accumulation, thereby improving real-time performance. However, its adaptability remains limited under complex pile geometries and strong reflective interference. In this paper, the pixel traversal strategy is further optimized to achieve efficient and robust extraction of the laser stripe centerline. By performing a single, non-global image traversal, laser stripe thinning, breakpoint identification, interpolation, continuity reconstruction, and cross-sectional area calculation are integrated into a unified processing framework. Experimental results demonstrate that the improved method achieves a 0.3% increase in measurement accuracy compared with the original PPIM, while maintaining excellent real-time performance with a processing speed of up to 94 frames per second (FPS). The proposed approach provides a more reliable load perception basis for intelligent speed regulation of belt conveyors, contributing to energy-efficient and stable operation. Full article

Uncertainty in short-term grade estimations can significantly affect destination policies and dig-limit definitions in open-pit mining. However, most dig-limit techniques still rely on deterministic methods and manual procedures. This study proposes a stochastic optimization model for the dig-limit definition problem that incorporates geological uncertainty through multiple grade scenarios and explicitly controls deviations from production targets. Two real case studies were evaluated to compare the stochastic formulation against deterministic and manual definitions. Results show that the stochastic model systematically improves economic performance, with profit increases of up to 2.3% over deterministic policies and up to 4.3% when compared against manual solutions. The stochastic solution also reduces deviations from metal and grade targets, producing more stable outcomes across scenarios. The model is computationally efficient, with solution times below 25 s for all case studies, which are compatible with practical short-term planning workflows. Overall, our findings demonstrate that incorporating grade variability into the dig-limit definition improves profitability and reliability in short-term mine planning horizons. Full article

Personnel and equipment target detection algorithms in open pit mines have significantly improved mining safety, production efficiency, and management optimization. However, achieving precise target localization in complex backgrounds, addressing mutual occlusion among multiple targets, and detecting large-scale and spatially extensive targets remain challenges for current target detection algorithms in open pit mining areas. To address these issues, this study proposes a novel target detection algorithm named RSLH-YOLO, specifically designed for personnel and equipment detection in complex open pit mining scenarios. Based on the YOLOv11 (You Only Look Once version 11) framework, the algorithm enhances the backbone network by introducing receptive field attention convolution and dilated convolution to expand the model’s receptive field and reduce information loss, thereby improving target localization capability in complex environments. Additionally, a bidirectional fusion mechanism between high-resolution and low-resolution features is adopted, along with a dedicated small-target detection layer, to strengthen multi-scale target recognition. Finally, a lightweight detection head is implemented to reduce model parameters and computational costs while improving occlusion handling, making the model more suitable for personnel and vehicle detection in mining environments. Experimental results demonstrate that RSLH-YOLO achieves a mAP (mean average precision) of 89.1%, surpassing the baseline model by 3.2 percentage points while maintaining detection efficiency. These findings indicate that the proposed model is applicable to open pit mining scenarios with limited computational resources, providing effective technical support for personnel and equipment detection in mining operations. Full article

After the completion of open-pit coal mining, land reclamation is implemented to restore the disturbed eco–hydrological system, for which accurate soil moisture characterization is essential. We evaluated the feasibility and performance of an Auto-Regressive Moving Average (ARMA)-based ground-penetrating radar (GPR) inversion scheme for estimating soil moisture in a reclaimed mine area. GPR data were acquired over a reconstructed three-layer soil profile in a reclaimed open-pit coal mine, and soil moisture content was independently determined using the oven-drying method on core samples. An ARMA model was used to describe the relationship between the GPR reflections and soil electromagnetic properties and to invert the vertical distribution of soil moisture. The ARMA-derived GPR estimates reproduced the measured moisture profile well within the depth interval of 1.4–3.0 m and revealed the clear vertical zonation of soil moisture associated with the engineered layering. Correlation coefficients between the ARMA-inverted GPR estimates and oven-drying measurements ranged from 0.64–0.78 for 0–1.4 m, 0.84–0.93 for 1.4–2.2 m, and 0.98–0.99 for 2.2–3.0 m, indicating that inversion accuracy improves systematically with depth. These results demonstrate that ARMA-based GPR inversion provides a reliable and non-destructive approach for quantifying soil moisture in reclaimed mine soils and offers practical support for monitoring and assessing the effectiveness of reclamation in open-pit coal mining areas. Full article

Blast-induced rock fragmentation plays a critical role in mining and civil engineering. One of the primary objectives of blasting operations is to achieve the desired rock fragmentation size, which is a key indicator of the quality of the blasting process. Predicting the mean fragmentation size (MFS) is crucial to avoid increased production costs, material loss, and ore dilution. This study integrates three tree-based regression techniques—gradient boosting regression (GBR), histogram-based gradient boosting machine (HGB), and extra trees (ET)—with two optimization algorithms, namely, grey wolf optimization (GWO) and particle swarm optimization (PSO), to predict the MFS. The performance of the resulting models was evaluated using four statistical measures: coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicate that the GWO-HGB model outperformed all other models, achieving R², RMSE, MAE, and MAPE values of 0.9402, 0.0251, 0.0185, and 0.0560, respectively, in the testing phase. Additionally, the Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), and neural network-based sensitivity analyses were applied to examine how input parameters influence model predictions. The analysis revealed that unconfined compressive strength (UCS) emerged as the most influential parameter affecting MFS prediction in the developed model. This study provides a novel hybrid intelligent model to predict MFS for optimized blasting operations in open-pit mines. Full article

In arid open-pit mines, rainfall-triggered slope instability presents significant risks, but quantitative thresholds are poorly defined due to limited integration of transient seepage and stability in low-permeability soils. This study fills this gap by using GeoStudio’s SEEP/W and SLOPE/W modules to simulate rainfall effects on a moderately steep-slope (51° average) limestone mine slope in Ningxia’s Kazimiao Mining Area (annual precipitation: 181.1 mm). The novelty lies in identifying a 12 h saturation window under intense rainfall (≥100 mm h⁻¹), during which pore water pressure stabilizes as soil reaches saturation, creating an “infiltration buffering effect” driven by arid soil properties (hydraulic conductivity: 2.12 × 10⁻⁴ cm s⁻¹). Results show that the factor of safety (FOS) drops sharply within 12 h (e.g., from 1.614 naturally to 1.010 at 200 mm h⁻¹) and then stabilizes, with FOS remaining >1.05 (basically stable) under rainfall intensities ≤ 50 mm h⁻¹, but drops into the less-stable range (1.00–1.05) at 100–200 mm h⁻¹, reaching marginal stability (FOS ≈ 0.98–1.02) after 24 h of extreme events, according to GB/T 32864-2016. Slope protection measures increase FOS (e.g., 2.518 naturally). These findings quantify higher instability thresholds in arid compared to humid regions, supporting regional guidelines and informing early-warning systems amid climate-related extremes. This framework enhances sustainable slope management for mines worldwide in arid–semi-arid zones. Full article

This paper proposes a novel fusion paradigm integrating information entropy with deep learning architectures to address the challenges of uncertainty, robustness, and subjectivity in intelligent open-pit mining management. Methodologically, we develop three integrated models: an IE-LSTM model for dynamic ore blending that quantifies system uncertainty to balance efficiency with robustness, an IE-CNN model that utilizes local image entropy to filter false alarms for robust visual detection, and an IE-DNN model employing entropy-weighted objective evaluation for equipment performance. These models form the computational core of an intelligent control system deployed at the Qidashan Iron Mine. Industrial validation conducted over a two-year period demonstrated that the framework led to a blending accuracy of 93.6%, a 73.7% reduction in visual detection false alarms, and objective equipment performance scoring. System-wide outcomes included a resource utilization rate of 98.64%, improvements in overall equipment effectiveness between 4.07% and 22.70%, and cumulative direct economic benefits exceeding 43 million RMB. This research establishes a systematic framework that transitions mine operations from experience-based to data-driven intelligent control, distinguishing itself by explicitly quantifying and managing uncertainty through entropy–deep learning fusion. This provides a replicable blueprint for the mining industry’s digital transformation. It effectively addresses the high-dimensional complexity and dynamic uncertainties inherent in open-pit mining environments. Full article