Search Results (464)

The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on the Python PyQt5 library was developed for Maping Phosphate Mine to improve ventilation efficiency, lower dust concentration at the working face, and enhance safety by addressing uneven air volume distribution. The implementation of an integrated system, comprising a 3D ventilation network model, remote control capabilities, and smart algorithms, has successfully realized zonal planning and on-demand ventilation in the mine’s underground workings. To adapt to the fluctuating air demand at the tunneling face, a remote intelligent control scheme for louvered dampers was implemented. This dynamic demand-based strategy achieves precise distribution of air volume throughout the ventilation network. The research results demonstrate that the system effectively addresses the uneven distribution of air volume, thereby improving the overall ventilation environment and reducing the risk of ventilation-related accidents. The system serves dual purposes: it provides an intelligent ventilation control mechanism and integrates seamlessly with the key subsystems for underground safety production. This synergy is instrumental in advancing the mine’s digitalization and intelligent transformation initiatives. Field test results indicate that the system achieved a 30% reduction in energy consumption and a 70% decrease in dust concentration at the working face, respectively. Full article

This perspective paper aims to set the stage for current development in the field of energy consumption and environmental impacts in two major digital industries: cryptocurrency mining and artificial intelligence (AI). To better understand current developments, this paper uses a comparative analytical framework of life-cycle assessment principles and high-resolution grid modeling to explore the energy impacts from academic and industry data. On the one hand, while both sectors convert energy into digital value, they operate according to completely different logics, in the sense that cryptocurrencies rely on specialized hardware (application-specific integrated circuits) and seek cheap energy, where they can function as “virtual batteries” for the network, quickly shutting down at peak times, with increasing hardware efficiency. On the other hand, AI is a much more rigid emerging energy consumer, in the sense that it needs high-quality, uninterrupted energy and advanced infrastructure for high-performance Graphics Processing Units (GPUs). The training and inference stages generate massive consumption, difficult to quantify, and AI data centers put great pressure on the electricity grid. In this sense, the transition from mining to AI is limited due to differences in infrastructure, with the only reusable advantage being access to electrical capacity. Regarding competition between the two industries, this dynamic can fragment the energy grid, as AI tends to monopolize quality energy, and how states will manage this imbalance will influence the energy and digital security of the next decade. Full article

Optimizing the blast energy distribution is crucial for enhancing rock fragmentation, minimizing overexcavation, and boosting profitability in mining operations. This study introduces a theoretical model to predict the blasting Energy Factor ${(\mathrm{F}}_{\mathrm{e}})$ in mining tunnels, based on the Cracking Energy ${(\mathrm{E}}_{\mathrm{g}})$ of the rock mass, derived from the deformation energy of brittle materials (Young’s modulus) and adjusted by the Rock Mass Rating (RMR). The model was validated using 42 blasting datasets from horizontal galleries at El Teniente mine, Chile. Data included geometric parameters (tunnel sections, drilling length, diameter, number of holes, meters drilled), explosive type and consumption, and geomechanical properties, particularly the RMR. Results show that as rock mass quality improves (higher RMR), both ${\mathrm{F}}_{\mathrm{e}}$ and $\%{\mathrm{E}}_{\mathrm{g}}$ increase, more competent rock masses require higher input energy to initiate and propagate cracks, and a greater portion of that energy is effectively utilized for crack formation. For instance, rock masses with an RMR of 66 exhibited an average ${\mathrm{F}}_{\mathrm{e}}$ of 7.62 MJ/m³ and $\%{\mathrm{E}}_{\mathrm{g}}$ of 4.8%, while those with an RMR of 75 showed higher values (${\mathrm{F}}_{\mathrm{e}}$ = 8.47 MJ/m³, $\%{\mathrm{E}}_{\mathrm{g}}$ = 6.4%). This confirms that less fractured rock masses require higher ${\mathrm{F}}_{\mathrm{e}}$ and $\%{\mathrm{E}}_{\mathrm{g}}$ for effective fragmentation. Lithology also plays a significant role in energy consumption. Diorite displayed the highest ${\mathrm{F}}_{\mathrm{e}}$ (8.34 MJ/m³) and higher efficiency ($\%{\mathrm{E}}_{\mathrm{g}}$ = 7.0%), whereas andesite showed lower ${\mathrm{F}}_{\mathrm{e}}$ (7.61 MJ/m³) and lower crack propagation efficiency ($\%{\mathrm{E}}_{\mathrm{g}}$ = 3.7%). Unlike traditional ${\mathrm{F}}_{\mathrm{e}}$ prediction methods, which rely solely on explosive data and excavation volume, this model integrates RMR, enabling more precise energy allocation and fostering sustainable mining practices. This approach enhances decision-making in blast design, offering a more robust framework for optimizing energy use in mining operations. Full article

With the deepening of mineral resource exploitation, conical picks are subjected to severe wear under high-stress and high-friction conditions, which has become a critical factor governing rock-breaking efficiency. To address this issue, this study systematically investigates the mechanism by which wear-induced geometric evolution of conical pick tips influences rock-breaking efficiency through controlled indentation tests. Three conical picks with varying wear degrees, characterized by different tip cone angles, were tested to quantify the peak indentation force, specific energy, indentation crater area, and indentation hardness index of rock specimens. The results show that progressive pick wear leads to tip blunting and an increase in cone angle, resulting in monotonic increases in peak indentation force, specific energy, indentation crater area, and indentation hardness index as functions of pick tip geometry. The experimental observations are interpreted using the cavity expansion model based on the Mohr–Coulomb yield criterion, following the Detournay–Huang theoretical framework. Wear-induced changes in pick tip geometry promote the expansion of the plastic zone and increase stress field complexity within the rock during indentation, thereby reducing rock-breaking efficiency. All reported trends are derived from repeated indentation tests and presented as mean values, demonstrating consistent and statistically reliable behavior. Based on these findings, optimizing pick tip geometry and improving wear resistance are identified as effective strategies to minimize energy consumption and enhance rock-breaking efficiency in deep hard-rock mining. This study provides a mechanistic understanding of how conical pick wear degrades rock-breaking efficiency through geometric control of plastic zone evolution, offering both theoretical insight and experimental evidence beyond previous material-focused studies. Full article

Heavy earth-moving machinery is essential for construction, mining, and infrastructure development, but its traditional hydraulic systems, powered by diesel engines, are major contributors to energy losses and inefficiencies. Hydraulic circuits typically account for significant parasitic losses due to throttling, leakage, and low energy recovery, resulting in high fuel consumption and emissions. Recent innovations are transforming hydraulic technology to improve energy efficiency and sustainability. This review highlights advancements such as electro-hydraulic actuators, independent metering systems, and digital hydraulics, which enable precise flow control and minimize throttling losses. The integration of energy recovery systems, including hydraulic accumulators and hybrid architectures, further enhances efficiency by capturing and reusing energy during braking and lowering operations. Additionally, the adoption of smart sensors, predictive analytics, and advanced control algorithms enables real-time optimization of hydraulic performance, reducing idle losses and improving overall system responsiveness. Emerging trends such as fluid power electrification, compact high-pressure components, and the use of eco-friendly hydraulic fluids are also discussed. By synthesizing current research and industrial practices, this paper provides insights into the challenges, opportunities, and future prospects for achieving substantial energy efficiency gains through next-generation hydraulic technologies in heavy earth-moving equipment. Full article

This article presents a comprehensive analysis of the primary electrical power distribution system in hybrid-electric aircraft, with particular emphasis on its environmental performance assessed through Life Cycle Assessment (LCA). High-resolution Life Cycle Inventory (LCI) data were developed in collaboration with industry partners and refined to reflect current production standards. The results indicate that printed circuit boards (PCBs), magnets, precious metals (gold and silver), and copper are the primary contributors to environmental impact, with PCBs alone accounting for over 50% of material-related emissions. Although precious metals constitute only 0.014% of the product’s mass, they account for nearly 9% of total emissions due to the energy-intensive nature of their mining and refining processes. Additionally, manufacturing stages involving thermal treatments—such as surface coating of iron cores at 850 °C for 14 h—significantly increase energy consumption and associated emissions. The study concludes with recommendations for reducing the carbon footprint of future aircraft power systems through improved material efficiency, process optimization, and supply chain sustainability. Full article

Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited ($R^2=0.0255$) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques. Full article

In cold climates, mine air conditioning systems are essential for preventing liners and shaft components from freezing. Traditionally, fossil fuel burners are used to heat intake air, resulting in high energy consumption and significant greenhouse gas emissions. As part of efforts to reduce both environmental impacts and energy use, mining companies are increasingly adopting innovative solutions, such as heat recovery systems. These systems offer a promising approach to significantly reduce energy demand for underground mine heating. This study evaluates several heat recovery technologies including exhaust air, water, hybrid exhaust air–water, diesel exhaust, jacket water, and hybrid diesel exhaust–jacket-water systems, through numerical modeling. Two case studies are presented: a grid-connected mine in British Columbia with moderately cold conditions, and an off-grid mine in the Northwest Territories, which experiences Arctic climate extremes. Results show that heat recovery can reduce heating costs by up to 89% in British Columbia and as much as 90% in the Northwest Territories, depending on the system applied. The findings also demonstrate substantial associated carbon emission reductions. Furthermore, a comprehensive feasibility analysis was carried out to evaluate the thermodynamic performance, financial savings, and carbon emission reductions of these systems across various mining operations, offering a preliminary assessment of their potential for mining settings. Full article

The mining industry places high priority on occupational safety, process quality and operational efficiency. Roadheaders are widely deployed in coal mines to support fully automated excavation, reducing workers’ physical strain and improving overall safety. This article examines an automatic control system for a roadheader cutting head designed to increase mining efficiency, reduce energy consumption and maintain stable performance under varying coal and rock conditions. The system integrates advanced control algorithms with geological strength index (GSI) analysis and asynchronous motor control strategies. GSI-based adaptive speed control conserves energy and increases cutting efficiency compared to manual control. By reducing dynamic load fluctuations, transitions between different cutting zones become smoother, which decreases equipment wear. The proposed control system incorporates speed feedback loops that use a proportional–integral (PI) controller with field-oriented control (FOC), as well as super-twisted sliding mode control (STSMC) with FOC. FOC with STSMC improves roadheader productivity by applying advanced control strategies, adaptive speed regulation and precise geological strength analysis. It is also better able to handle disturbances and sudden loads thanks to STSMC’s nonlinear control robustness. The result is safer, more efficient, and more cost-effective mining that can be implemented across a wide range of underground mining scenarios. Full article

Main shaft bearings are among the critical rotating components of subsea drilling rigs, and their health status directly affects the efficiency and reliability of the drilling system. However, in the high-pressure liquid environment of the deep sea, with intense noise, the vibration signals of the bearings attenuate rapidly. As a result, fault-related features have a low signal-to-noise ratio (SNR), which poses a challenge for bearing health monitoring. In recent years, Deep Neural Network (DNN)-based fault diagnosis methods for subsea drilling rig bearings have become a research hotspot in the field due to their strong potential for deep fault feature mining. Nevertheless, their reliance on high-power-consumption computational resources restricts their widespread application in subsea monitoring scenarios. To address the above issues, this paper proposes a fault diagnosis method for the main-spindle bearings of subsea drilling rigs that combines population coding with an adaptive-threshold k-winner-take-all (k-WTA) mechanism. The method exploits the noise robustness of population coding and the sparse activation induced by the adaptive k-WTA mechanism, achieving a noise-robust and energy-efficient fault diagnosis scheme for the main-spindle bearings of subsea drilling rigs. The experimental results confirm the effectiveness of the proposed method. In accuracy and generalization experiments on the CWRU benchmark dataset, the proposed method achieves good diagnostic accuracy that is not inferior to other SOTA methods, indicating relatively strong generalization and robustness. On the Paderborn real-bearing benchmark dataset, the results highlight the importance of selecting features that are adapted to specific operating conditions. Additionally, in the noise robustness and energy efficiency experiments, the proposed method shows advantages in both noise resistance and energy efficiency. Full article

Optimizing the start-extraction time for coalbed methane (CBM) wells in mining areas remains challenging. This is due to the limited understanding of mining-induced mechanical changes and fluid migration in protected seams, which restricts the development of clean fossil energy. To address this, a geological-engineering model is constructed to investigate the mining-induced zonal evolution of stress, strain, permeability, and gas migration in protected seams, with the goal of optimizing the start-extraction time. The results show that gas production is controlled by the mechanical properties and gas pressure of protected seams near the well. Initially, these seams experience prolonged elastic strain. Plastic compressive strain develops at close-distance protected seams only when the coalface advances to within 5 m of them. Subsequently, rapid stress relief and complex stress directions lead to continuous plastic shear and expansion strains. As the distance from the mining seam increases, the plastic strains delay and diminish, reverting to elastic strain. These transitions collectively characterize the dynamic development of five distinct permeability regimes. Within permeability-reduced zones, an enhanced gas pressure gradient mitigates production declines. As the start-extraction time is progressively delayed, post-initiation gas production manifests in four phases: gradual decline, slow rebound, rapid increase, and surge. The optimal start-extraction time aligns with the rapid increase phase, when the coalface reaches the well, shortening extraction by at least 5.75 days and reducing electricity consumption by more than 2.07 × 10⁴ kWh in the study area. This research provides practical solutions for methane emission reduction and sustainable CBM development in mining areas. Full article

The large-scale accumulation of high-sulfur lead–zinc tailings poses serious environmental and safety challenges, while the increasing shortage of traditional mineral admixtures such as fly ash and slag highlights the urgent need for sustainable alternatives. This study aims to develop a high-performance mineral admixture using lead–zinc tailings characterized by high SO₃ content and low pozzolanic activity. The effects of four activation routes—mechanical grinding, wet magnetic separation, wet magnetic separation–mechanical grinding, and mechanical grinding–high-reactivity mineral admixture synergistic modification—were systematically compared in terms of tailings fineness, SO₃ reduction, and activity index. The results indicate that single mechanical grinding can achieve the fineness requirement of Grade II admixtures specified in GB/T 1596–2017 (45 μm residue ≤ 30%), but the 28-day strength activity index only reached 58.64%, and the SO₃ content remained above the standard limit. Wet magnetic separation effectively reduced the SO₃ content to below 3.5%, and the combined process yielded a product with an activity index of up to 74.51%. Further improvement was achieved through a “mechanical grinding–high-reactivity mineral admixture synergistic modification” process, incorporating fly ash (FA), ground granulated blast furnace slag (GGBS), and silica fume (SF). Among these, SF exhibited the most pronounced synergistic effect. The optimal mixture, composed of 85.19% ground tailings and 14.81% SF, achieved the highest 28-day activity index of 76.35%. This process enables full utilization of tailings while maintaining a simplified flow, lower energy consumption, and superior product performance. The findings provide a feasible and efficient technological route for the high-value utilization of high-sulfur tailings and contribute to promoting green mining and sustainable resource development. Full article

An eco-friendly flotation process is of great significance to the green and sustainable development of the mining industry. The purpose of this study is to improve the traditional flotation process. Novel reagents, alkyl ether amine (Alkyl carbon chains with a length of 12 are simply referred to as DOEA) as collector and carboxymethyl starch (CMS) as depressant, were used for flotation uAlkyl ether aminender lower temperature, which did not need to heat the tonnage of pulp and reduced the energy consumption. The micro-flotation tests were carried out with three main minerals (quartz, hematite and magnetite) contained in Qidashan (Anshan, China) iron ore at room temperature in winter (18 °C). The bench-scale tests were carried out with flotation feed (mixture of strong magnetic concentrate and weak magnetic concentrate) from the Qidashan flotation workshop at room temperature (18 °C). And the industrial tests were carried out in the flotation workshop of Qidashan Concentrator of Anshan Iron and Steel Co., Ltd. The temperature of the pulp was 17.5~19.7 °C. The results of micro-flotation tests showed that the floatability of the three minerals under the DOEA system decreased in the following order: quartz > hematite > magnetite. The addition of CMS increased the floatability difference between quartz and ferric oxide minerals. DOEA and CMS could effectively separate quartz and ferric oxide minerals at room temperature in winter. The feasibility of the application of DOEA and CMS in Qidashan iron ore was verified by bench-scale tests, and the pulp circulation process was simulated by locked-cycle tests. The results of bench-scale tests showed that under the conditions of CMS dosage 200 g/t, DOEA dosage 150 g/t, and pulp temperature 18 °C, the iron grade of flotation concentrate was 66.54% and iron recovery was 78.37%. The industrial test results showed that the modified flotation process could continuously output qualified iron concentrate without heating the pulp. Compared with the on-site flotation process, it was found that the modified flotation process could save USD 6,460,100 per year. This technology could significantly reduce the energy consumption of iron ore reverse flotation, reduce the carbon emissions generated by heating tons of pulp, and achieve cleaner production. Full article

Shaft sinking in soft, water-rich strata frequently suffers from low cutting efficiency, cycle-time mismatches between excavation and muck removal, and weak system-level coordination. To elucidate the synergistic mechanisms governing excavation–muck removal interactions and to realize end-to-end performance gains, we investigate the East Ventilation Shaft of the Xinjie Taigemiao mining district as a representative artificial ground freezing (AGF) project. First, drawing on the mechanics of frozen ground and field monitoring, we establish a relationship model linking advance rate, drum rotational speed, cutting depth, and muck production, thereby clarifying why lower rotational speeds, moderate cutting depths, and rational traction reduce energy consumption and mitigate disturbances to the frozen wall. Next, for muck handling, we build a full-process discrete element method (DEM) model, integrate design-of-experiments with response-surface optimization to identify key factors, calibrate contact models, and select collection geometries. The results show that a graded-angle collecting structure improves pile concentration and discharge compliance; combined with a tiered chain-bucket–vertical belt–twin-skip configuration, it delivers matched cycle times and stable “gather–convey–hoist” operation. Finally, two-stage full-scale tests jointly validate excavation and muck removal, demonstrating that the proposed synergy model and optimized parameters sustain continuous, efficient performance across operating conditions. The study provides a reusable mechanistic framework and parameterization blueprint for AGF shaft design and construction. Full article

Mining activities in the study area have led to the formation of irregular depressions where rainwater accumulates, creating acidic mine ponds. The water in these ponds becomes contaminated through contact with mine wastes and bottom sediments, leading to the dispersion of toxic metals and metalloids into the surrounding environment and food chain. This study investigates electrokinetic remediation (EKR) of highly contaminated acidic mine pond sediments and evaluates the role of citric acid (CA) as a biodegradable and environmentally friendly chelating agent. The sediment was highly acidic (pH 3.35) and contained elevated concentrations of Al, Fe, Mn, and As. Laboratory-scale EKR experiments were conducted for 27 days under a constant potential gradient of 1 V/cm, using 0.1 M CA as the electrolyte. The results obtained from this study were compared with those obtained using deionised water (DIW) as the electrolyte. The results demonstrated that CA significantly enhanced metal mobility, leading to higher removal efficiencies for Al (82.4%), As (51.1%), Mn (32.9%), and Fe (29.5%) compared to DIW. The pH near the cathode remained more balanced, and metal precipitation was minimised. Furthermore, total energy consumption decreased by about 53% (from 551 to 262 kWh/m³), indicating improved process efficiency. These results reveal that CA-assisted EKR can be an effective and sustainable method for the remediation of highly acidic mine pond sediments. Full article