Search Results (3,632)

Mine waste remains a persistent challenge for the minerals industry, posing significant environmental concerns if not properly managed. The 1996 Marcopper Mining Disaster in Marinduque, Philippines, left a legacy of mine tailings that continue to threaten local ecosystems and communities. This study investigates the valorization and stabilization of Marcopper river sediments laden with mine tailings using a combined geopolymerization and cement hydration approach. Hybrid mortar samples were prepared with 7.5%, 15%, 22.5%, and 30% mine tailings by weight, utilizing potassium hydroxide (KOH) as an alkaline activator at concentrations of 1 M and 3 M, combined with Ordinary Portland Cement (OPC). The mechanical properties of the hybrid geopolymer cement mortars were assessed via unconfined compression tests, and their crystalline structure, phase composition, surface morphology, and chemical bonding were also analyzed. Static leaching tests were performed to evaluate heavy metal mobility in the geopolymer matrix. The compression tests yielded strength values ranging from 24.22 MPa to 53.99 MPa, meeting ASTM C150 strength requirements. In addition, leaching tests confirmed the effective encapsulation and immobilization of heavy metals, demonstrating the potential of this method for mitigating the environmental risks associated with mine tailings. Full article

As a key sustainable green-mining technology, ultra-high-water backfill mining is widely used to control surface subsidence and sustain extraction of constrained coal seams. Focusing on the Hengjian coal mine in the Handan mining area, this study uses physical modeling and industrial tests to clarify surface subsidence under different filling rates and identify the rock layers that hydraulic supports must control at various equivalent mining heights. A method is proposed to improve the filling rate by optimizing the thickness of the hydraulic support canopy through topological analysis. Results show that, compared with a filling rate of 85%, a 90% filling rate reduces subsidence of the basic roof, key layer, and surface by 51%, 57%, and 63%, respectively, while the industrial practice results have verified that the filling rate can significantly control surface subsidence. The equivalent mining height thresholds for instability of the immediate roof and high basic roof at the 2515 working face are 0.44 m and 1.26 m. Reducing the trailing beam thickness by 10 cm can theoretically raise the filling rate of the 2515 working face by about 2%, offering guidance for similar mines. Full article

Workers’ exposure to silica dust is a global occupational and public health concern and is particularly prevalent in Southern Africa, mainly because of inadequate dust control measures. It is worsened by the high prevalence of HIV/AIDS, which exacerbates tuberculosis and other occupational lung diseases. The prevalence of silicosis in the region ranges from 9 to 51%; however, silica dust exposure levels and controls, especially in the informal mining sector, particularly in artisanal small-scale mines (ASMs), leave much to be desired. This is important because silicosis is incurable and can only be eliminated by preventing worker exposure. Additionally, several studies have indicated inadequate occupational health and safety policies, weak inspection systems, inadequate monitoring and control technologies, and inadequate occupational health and hygiene skills. Furthermore, there is a near-absence of silica dust analysis laboratories in southern Africa, except in South Africa. This protocol aims to systematically evaluate the effectiveness of respirable dust and respirable crystalline silica dust exposure evaluation and control methodology for the mining industry. The study will entail testing the effectiveness of current dust control measures for controlling microscale particles using various exposure dose metrics, such as mass, number, and lung surface area concentrations. This will be achieved using a portable Fourier transform infrared spectroscope (FTIR) (Nanozen Industries Inc., Burnaby, BC, Canada), the Nanozen DustCount, which measures both the mass and particle size distribution. The surface area concentration will be analysed by inputting the particle size distribution (PSD) results into the Multiple-Path Particle Dosimetry Model (MPPD) to estimate the retained and cleared doses. The MPPD will help us understand the sub-micron dust deposition and the reduction rate using the controls. To the best of our knowledge, the proposed approach has never been used elsewhere or in our settings. The proposed approach will reduce dependence on highly skilled individuals, reduce the turnaround sampling and analysis time, and provide a reference for regional harmonised occupational exposure limit (OEL) guidelines as a guiding document on how to meet occupational health, safety and environment (OHSE) requirements in ASM settings. Therefore, the outcome of this study will influence policy reforms and protect hundreds of thousands of employees currently working without any form of exposure prevention or protection. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

This paper explores the evolution of royalty payments from the extraction of reserved mineral resources in the Czech Republic between 1992 and 2025, with a particular focus on their allocation for the reclamation of environmentally affected areas. It presents the legislative framework governing these payments, including Acts No. 44/1988 Coll., No. 61/1988 Coll., and No. 280/2009 Coll., as well as Government Regulation No. 354/2023 Coll., which collectively define the obligations of mining companies regarding royalty payments. The study addresses the adjustment of royalty rates in response to current economic conditions to ensure sustainable financing of environmental projects. It also emphasizes the importance of continuous evaluation of the regulatory system to maintain a balance between the economic capacity of extractive industries and the protection of the environment. Full article

The aim of this paper is to analyze the potential of circular economy in the context of increasing the supply of raw materials for modern economy with particular focus on the role of science and business. The article presents an approach consistent with the concept of sustainable development and fitting in with the implementation of four Sustainable Development Goals: Industry, Innovation, and Infrastructure (SDG 9), Responsible Consumption and Production (SDG 2), Climate Action (SDG 13), and Life on Land (SDG 15). An innovative approach to raw material supply sources is also presented. In addition, the potential of urban mining e-waste in meeting the demand for critical metals is emphasized. The paper presents barriers and challenges for using the potential of raw materials deposited in spoil heaps and landfills or in tailings ponds, with emphasis on the role of modern technologies in increasing the competitiveness of Polish industry. The necessity of a systemic approach to the topic of the circular economy was also emphasized, particularly regarding secondary raw materials as essential for securing critical resources. Full article

The ball mill is a critical size reduction equipment in industries such as mining and metallurgy. However, the sustainable reliability modeling of the entire system is challenging due to its complex service conditions. This paper presents a systematic framework for the reliability analysis of ball mills based on Stress–Strength Interference Theory (SSIT). Based on a reliability block diagram (RBD), this study establishes a system-level reliability model for the ball mill. Within this framework, the cylinder model is developed using the energy conservation principle between impact energy and strain energy; the gear model comprehensively considers both contact and bending fatigue failure modes; and the bolt model is constructed through mechanical analysis in conjunction with Hooke’s law. In the case study, a laboratory-scale mill (Φ5.5 × 2.6 m shell, effective grinding chamber: 5.3 m inner diameter × 2.376 m length) operating at 14 RPM under dry grinding conditions is analyzed. The reliability of individual components and the entire system is computed using Monte Carlo simulation. The results indicate that the overall system reliability increases when one of the following three conditions is met: the surface hardness of the gear is higher and the tangential force is lower; the impact velocity on the cylinder is lower and the impacted area is larger; or the tensile force on the bolt is reduced. Full article

Because of its strong bearing capacity and large size, a thick and hard roof is the main source of strong ground pressure in a stope, and its breaking and migration mechanism and effective control are very important for realizing safe and efficient mining in coal mines. In this paper, by constructing a numerical model that fully considers the actual occurrence conditions of such a roof, the control law of the occurrence conditions of a thick and hard roof on its fracture law and strata behavior is systematically studied, and the control mechanism of the movement and hydraulic fracturing of this kind of roof is revealed. The results show that (1) the fracture process of a thick hard roof is characterized by three stages—crack initiation, extension, and overall instability—and the “pressure arch” structure formed by the overlying huge hard rock stratum is the fundamental force source leading to strong ground pressure; (2) the roof thickness and horizon significantly control the stress distribution and fracture behavior of coal and rock mass, and the peak stress of coal and rock mass is positively correlated with the roof thickness, but negatively correlated with its horizon; (3) with the increase in roof thickness, the dominant fracture mechanism changes from tension type to tension–shear composite type, which leads to a significant increase in fracture step. Hydraulic fracturing technology can effectively cut off the “pressure arch” structure and optimize the stress field of surrounding rock. After fracturing, the first weighting step and weighting strength are reduced by 36% and 38.1%, respectively. An industrial test shows that a fracturing treatment realizes timely and orderly roof caving and achieves the controllable weakening and safe promotion of the thick and hard roof. This study provides a solid theoretical basis and a successful engineering practice model for roof disaster prevention and control under similar geological conditions. 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

While multi-view visualization systems are widely used for mining vehicle teleoperation, they often impose high cognitive load and restrict operator attention. To explore a more efficient alternative, this study evaluated a merged-view interface that integrates multiple camera perspectives into a single coherent display. In a controlled experiment, 35 participants navigated a teleoperated robot along a 50 m lab-scale path representative of an underground mine under both multi-view and merged-view conditions. Task performance and eye-tracking data—including completion time, path adherence, and speed-limit violations—were collected for comparison. The merged-view system enabled 6% faster completion times, 21% higher path adherence, and 28% fewer speed-limit violations. Eye-tracking metrics indicated more efficient and distributed attention: blink rate decreased by 29%, fixation duration shortened by 18%, saccade amplitude increased by 11%, and normalized gaze-transition entropy rose by 14%, reflecting broader and more adaptive scanning. NASA-TLX scores further showed a 27% reduction in perceived workload. Regression-based sensitivity analysis revealed that gaze entropy was the strongest predictor of efficiency in the multi-view condition, while fixation duration dominated under merged-view visualization. For path adherence, blink rate was most influential in the multi-view setup, whereas fixation duration became key in merged-view operation. Overall, the results indicated that merged-view visualization improved visual attention distribution and reduced cognitive tunneling indicators in a controlled laboratory teleoperation task, offering early-stage, interface-level insights motivated by mining-relevant teleoperation challenges. Full article

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents. Full article

Accurate detection of oil and gas (O&G) facilities in high-resolution remote sensing imagery is critical for infrastructure surveillance and sustainable resource management, yet conventional detectors struggle with severe class imbalance, extreme scale variation, and arbitrary orientation. In this work, we propose OGF Oriented R-CNN (Oil and Gas Facility Detection Oriented Region-based Convolutional Neural Network), an enhanced oriented detection model derived from Oriented R-CNN that integrates three improvements: (1) O&G Loss Function, (2) Class-Aware Hard Example Mining (CAHEM) module, and (3) Feature Pyramid Network with Feature Enhancement Attention (FPNFEA). Working in synergy, they resolve the coupled challenges more effectively than any standalone fix and do so without relying on rigid one-to-one matching between modules and individual issues. Evaluated on the O&G facility dataset comprising 3039 high-resolution images annotated with rotated bounding boxes across three classes (well sites: 3006, industrial and mining lands: 692, drilling: 244), OGF Oriented R-CNN achieves a mean average precision (mAP) of 82.9%, outperforming seven state-of-the-art (SOTA) models by margins of up to 27.6 percentage points (pp) and delivering a cumulative gain of +10.5 pp over Oriented R-CNN. Full article

Soil heavy metal contamination poses significant risks to ecological safety and human health, particularly in rapidly industrializing cities. Effectively identifying pollution sources is crucial for risk management and remediation. GIS coupled with data mining techniques, provide a powerful tool for quantifying and visualizing these sources. This study investigates the concentration, spatial distribution, and sources of heavy metals in urban soils of Bengbu City, an industrial and transportation hub in eastern China. A total of 139 surface soil samples from the urban core were analyzed for nine heavy metals. Using integrated GIS and PCA-APCS-MLR data mining techniques, we systematically determined their contamination characteristics and apportioned sources. The results identified widespread Hg enrichment, with concentrations exceeding background levels at all sampling sites, and a Cd exceedance rate of 28.06%, leading to a moderate ecological risk level overall. Spatial patterns revealed significant heterogeneity. Quantitative source apportionment identified four primary sources: industrial source (37.1%), which was the dominant origin of Cr, Cu, and Ni, primarily associated with precision manufacturing and metallurgical activities; mixed source (26.7%) governing the distribution of Mn, As, and Hg, mainly from coal combustion and the natural geological background; traffic source (22.3%) significantly contributing to Pb and Zn; and a specific cadmium source (13.9%) potentially originating from non-ferrous metal smelting, electroplating, and agricultural activities. These findings provide a critical scientific basis for targeted pollution control and sustainable land-use management in analogous industrial cities. 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

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article