Search Results (415)

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

This study investigates the impact of two comminution technologies—Vertical Shaft Impactors (VSI) and High-Pressure Grinding Rolls (HPGR)—on gold flotation performance, using ore samples from the Ballarat Gold Mine, Australia. The motivation stems from the growing need to improve energy efficiency and flotation recovery in mineral processing, particularly under increasing economic and environmental constraints. Despite the widespread use of HPGR and VSI in the industry, limited comparative studies have explored their effects on downstream flotation behavior. Laboratory-scale experiments were conducted across particle size fractions (300–600 µm) using two collector types—Potassium Amyl Xanthate (PAX) and DSP002 (a proprietary dithiophosphate collector) to assess differences in flotation recovery, concentrate grade, and specific energy consumption. The results reveal that HPGR produces more fines and micro-cracks, enhancing liberation but also increasing gangue entrainment and energy demand. Conversely, VSI produces coarser, cubical particles with fewer slimes, achieving higher flotation grades and recoveries at lower energy input. VSI at 600 µm demonstrated the highest flotation efficiency (4241) with only 9.79 kWh/t energy input. These findings support the development of hybrid or tailored comminution strategies for improved flotation selectivity and sustainable processing. Full article

In 2022, the Russia–Ukraine conflict has severely impacted the EU’s energy supply chain, and the EU’s natural gas import pattern has begun to reconstruct, and exploring the spatiotemporal differentiation of EU natural gas trade and its driving factors is the basis for improving the resilience of its supply chain and ensuring the stable supply of energy resources. This paper summarizes the law of the change of its import volume by using the complex network method, constructs a multi-dimensional index system such as demand, economy, and security, and uses the geographic detector model to mine the driving factors affecting the spatiotemporal evolution of natural gas imports in EU countries and propose different sustainable development paths. The results show that from 2000 to 2023, Europe’s natural gas imports generally show an upward trend, and the import structure has undergone great changes, from pipeline gas dominance to LNG diversification. After the conflict between Russia and Ukraine, the number of import source countries has increased, the market network has become looser, France has become the core hub of the EU natural gas market, the importance of Russia has declined rapidly, and the status of countries in the United States, North Africa, and the Middle East has increased rapidly; natural gas consumption is the leading factor in the spatiotemporal differentiation of EU natural gas imports, and the influence of import distance and geopolitical risk is gradually expanding, and the proportion of energy consumption is significantly higher than that of other factors in the interaction with other factors. Combined with the driving factors, three different evolutionary directions of natural gas imports in EU countries are identified, and energy security paths such as improving supply chain control capabilities, ensuring export stability, and using location advantages to become hub nodes are proposed for different development trends. Full article

Optimization of energy consumption in urban infrastructures is essential to achieve sustainability and reduce environmental impacts. In particular, accurate regression-based energy forecasting of the energy consumption in various sectors plays a key role in informed decision-making, efficiency improvements, and resource allocation. This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. By evaluating performance of several regression algorithms using various metrics, this study identifies the most effective method for analyzing sectoral energy consumption. The results show that the Regression Tree Ensemble algorithm outperforms other techniques, achieving an accuracy of 97.00%, followed by Random Forest Regression (96.20%) and Gradient Boosted Regression Trees (95.50%). These results underline the potential of machine learning models to foster intelligent energy management and promote sustainable energy practices in smart cities. Full article

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property. Full article

As mine mining extends to greater depths, the challenge of heat damage in high-temperature and high-humidity deep mines has emerged as a significant obstacle to the safe mining of deep mines. This paper reviews the causes of mine heat damage, evaluates heat damage mechanisms, and explores deep mine cooling technologies. Traditional deep mine cooling technologies employ mechanical refrigeration to cool air. While these technologies can mitigate heat damage, they are associated with issues including high energy consumption, insufficient dehumidification, and significant cold loss. To address the high energy consumption and fully utilize geothermal resources, heat pump technology and combined cooling, heating, and power technology are employed to recover waste heat from deep mines, thereby achieving efficient mine cooling and energy utilization. To enhance the effectiveness of air dehumidification, the integration of deep dehumidification with mine cooling technology addresses the high humidity ratio in mine working faces. To enhance the refrigeration capacity of the system, liquid-phase-change refrigeration technology is employed to boost the refrigeration capacity. For the future development of deep mine cooling technology, this paper identifies four key directions: the integration of diverse technologies, collaboration cooling and geothermal mining, deep dehumidification and cooling, and intelligent control. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

The water–energy–land–food (WELF) nexus is an established framework that allows for a more holistic, systemic and integrated analysis of resources and territorial planning. The main objective of this study was to apply the WELF nexus approach to assess the environmental impacts from coal mining. Data on the water resource, electricity sector, food production and land occupation in the coal region of the Urussanga River basin (Brazil) were described and compared with the area without the coal industry (Canoas/Pelotas basin, Brazil). Indicators evaluating reliability, robustness, equilibrium and diversity (Shannon index-H) were used to evaluate the impacts of mining on the WELF system. The results indicate that coal provides socioeconomic development in the region; however, it has several negative environmental effects. WELF indicators showed that the Urussanga basin has less robustness in the subsystem of water consumption per capita (0.19), installed electrical capacity (0.01) and agricultural production per capita (0.22) compared to Canoas/Pelotas at 0.73, 1.0 and 1.0, respectively. The basin also presented lower diversity in the water consumption sector (H = 0.81) and in the variety of agricultural products (H = 1.58) compared to Canoas/Pelotas (H = 1.0; H = 1.69, respectively). It was concluded that coal mining can affect the WELF system globally, revealing the need to propose alternatives to prevent and mitigate its effects. Full article

Rock-breaking cutters are critical components in tunneling, mining, and drilling operations, where efficiency, durability, and energy consumption are paramount. Traditional cutter designs and empirical process optimization methods often fail to address the dynamic interaction between heterogeneous rock masses and tool structures, leading to premature wear, high specific energy, and suboptimal performance. Topology optimization, as an advanced computational design method, offers transformative potential for lightweight, high-strength cutter structures and adaptive cutting process control. This review systematically examines recent advancements in topology-optimized cutter design and its integration with rock-cutting mechanics. The structural innovations in cutter geometry and materials are analyzed, emphasizing solutions for stress distribution, wear/fatigue resistance, and dynamic load adaptation. The numerical methods for modeling rock–tool interactions are introduced, including discrete element method (DEM) simulations, smoothed particle hydrodynamics (SPH) methods, and machine learning (ML)-enhanced predictive models. The cutting process optimization strategies that leverage topology optimization to balance objectives such as energy efficiency, chip formation control, and tool lifespan are evaluated. Full article

This article is devoted to the study of the possibilities for improvAzing the quality of energy management systems adopted at open-pit mining enterprises in the Russian Federation. The main idea of the work is to apply stochastic boundary value analysis methods using the production function for individual and integral estimates of the performance of energy-consuming objects when performing various types of technological work. It is shown that mining enterprises are experiencing problems in the field of rational energy consumption due to the lack of strictly formalized ways to determine the frontiers of the efficiency value of the parameter of specific energy consumption (SEC). A justification is given for the need to apply stochastic frontier analysis (SFA) methods and use the Cobb–Douglas production function to account for the nonlinearity and stochasticity of the operating conditions of energy-consuming mining objects. The results of a statistical analysis of the data on the operation of EKG-10 excavators at operating enterprises in Siberia are presented, as well as an assessment of their energy efficiency using the adopted approach based on planning the target value of SEC. The results of computational experiments on constructing an energy efficiency model using the SFA/Cobb–Douglas function for various data segmentation options are presented. Computational experiments have been conducted to compare variants based on the Cobb–Douglas production function and translog function with semi-normal and exponential distribution forms for the same data set. A comparative assessment is given of the approaches to the complex analysis of activities adopted at enterprises and proposed in this study, characterizing potential hidden energy losses in the range from 4.53% to 20.73%. Full article

Waste valorization involves reusing and recycling waste materials to create useful products such as materials, chemicals, fuels, or energy. The primary goal is the transition to a circular economy model while minimizing the impacts of hazardous waste. Adopting such policies appears to be a one-way path due to the continuous increase in the consumption of raw materials. According to recent projections, by 2050, 180 billion tonnes of materials will be consumed annually. Since natural resources cannot meet these requirements, new sources must be explored. Waste can serve as an alternative source and cover at least part of the needs that arise. In this work, good practices regarding waste valorization are presented. The case studies examined include the waste/by-products of ultrabasic rocks resulting in chromite and magnesite mining, as well as the tannery sludge produced after the corresponding wastewater treatment. Full article

This study introduces CO₂ laser surface vitrification as an innovative method for managing copper mining tailings, offering a sustainable solution to critical challenges in mineral processing. This technique transforms tailings into a stable and impermeable layer, immobilizing hazardous metals contained within them. By achieving vitrification at the surface level and operating at temperatures around 1200 °C, the process significantly reduces energy consumption compared to traditional vitrification methods, making it suitable for large-scale applications in remote mining sites. Detailed geochemical and mechanical analyses confirmed the formation of a dense vitreous matrix with high hardness (7.19–7.48 GPa) and reduced permeability, ensuring compliance with stringent environmental regulations. However, the brittle nature of the vitrified layer underscores the need for further research to enhance mechanical resilience. This work positions CO₂ laser vitrification as a transformative approach for integrating energy-efficient technologies into mineral processing, addressing key environmental concerns while advancing the sustainable management of mining waste. Full article

Efficient identification and removal of low-grade minerals during graphite ore processing is essential for improving product quality, optimizing resource recovery, and promoting sustainable production. To address the limitations of traditional sorting methods and performance bottlenecks in edge devices, this paper proposes a lightweight instance segmentation model, GS-YOLO-seg, for rapid identification and intelligent sorting of low-grade graphite ore in industrial production lines. The model first reduces network depth by adjusting the depth factor. Subsequently, the backbone network adopts the lightweight and efficient GSConv to perform downsampling, while a novel C3k2-Faster architecture is proposed to improve the effectiveness of feature extraction. Finally, the Segment-Efficient segmentation head is optimized to reduce redundant computations, further lowering the model load. On a self-constructed graphite ore image dataset, GS-YOLO-seg achieved comparable segmentation performance to the baseline YOLO11n-seg, while achieving a 30% reduction in FLOPs, 59% fewer parameters, 56% smaller model size, and 8% higher FPS. This method enhances the intelligence of the sorting process, preventing low-grade ores from entering subsequent stages, thus reducing resource waste, energy consumption, and carbon emissions, providing crucial technical support and feasible deployment pathways for building intelligent, green, and sustainable mining systems. Full article

As coal’s share in primary energy consumption wanes, the annual increase in abandoned coal mines presents escalating safety and environmental concerns. This paper delves into cutting-edge models and attributes of integrating pumped storage hydropower systems with subterranean reservoirs and advanced wastewater treatment facilities within these decommissioned mines. By utilizing the expansive underground voids left by coal extraction, this method aims to achieve multifaceted objectives: efficient energy storage and generation, reclamation of mine water, and treatment of urban sewage. This research enhances the development and deployment of pumped storage technology in the context of abandoned mines, demonstrating its potential for fostering sustainable energy solutions and optimizing urban infrastructure. This study not only facilitates the progressive transformation and modernization of energy cities but also provides crucial insights for future advances in ecological mining practices, energy efficiency, emission mitigation, and green development strategies in the mining industry. Full article