Characteristics of China's coal mine methane emission sources at national and provincial levels.

Methane (CH4) is the second most abundant greenhouse gas. China is the largest CH4 emitter in the world, with coal mine methane (CMM) being one of the main anthropogenic contributions. Thus, there is an urgent need for comprehensive estimates and strategies for reducing CMM emissions in China. However, the development of effective strategies is currently challenged by a lack of information on temporal variations in the contributions of different CMM sources and the absence of provincial spatial analysis. Here, considering five sources and utilization, we build a comprehensive inventory of China's CMM emissions from 1980 to 2022 and quantify the contributions of individual sources to the overall CMM emissions at the national and provincial levels. Our results highlight a significant shift in the source contributions of CMM emissions, with the largest contributor, underground mining, decreasing from 89% in 1980 to 69% in 2022. Underground abandoned coal mines, which were ignored or underestimated in past inventories, have become the second source of CMM emissions since 1999. From 2011 to 2022, we identified Shanxi, Guizhou, and Shaanxi as the three largest CMM-emitting provinces, while the Emissions Database for Global Atmospheric Research (EDGAR) v8 overestimated emissions from Inner Mongolia, ranking it third. Notably, we observed a substantial decrease (exceeding 1 Mt) in CMM emissions in Sichuan, Henan, Liaoning, and Hunan between 2011 and 2022, which was not captured by EDGAR v8. To develop targeted CMM emission reduction strategies at the provincial level, we classified 31 provinces into four groups based on their CMM emission structures. In 2022, the number of provinces with CMM emissions mainly from abandoned coal mines has exceeded that of provinces with mainly underground mines, which requires attention. This study reveals the characteristics of the source of CMM emissions in China and provides emission reduction directions for four groups of provinces.

Study on methane degradation by microbial agents based on chelating wetting agent carriers.

Due to the low permeability characteristics of the deep gas-containing coal seam, the conventional prevention and control measures that cannot solve the problems of gas outbursts are unsatisfactory for the prevention and control of the coal and gas outbursts disaster. Therefore, in this study, a strain of methane-oxidizing bacteria M07 with high-pressure resistance, strong resistance, and high methane degradation rate was selected from coal mines. The growth and degradation abilities of M07 in chelating wetting agent solutions to assess its adaptability and find the optimal agent-to-M07 ratio. It provides a new method for integrating the reduction of impact tendency and gas pressure in deep coal mines. The experimental results show that M07 is a Gram-positive bacterium of the genus Bacillus, which has strong resistance and adaptability to high-pressure water injection. By degrading 70 mol of methane, M07 produces 1 mol of carbon dioxide, which can reduce gas pressure and reduce the risk of gas outbursts in coal mines. As the experiment proves, the best effect was achieved when the M07 concentration of the chelating wetting agent was 0.05%. The methane-oxidizing bacteria based on the chelating wetting agent as carriers prove a new prevention and control method for the integrated prevention and control of coal and gas outbursts in coal mines and also provide a new idea for microbial application in coal mine disaster control.

Microbial community structure in an uranium-rich acid mine drainage site: implication for the biogeochemical release of uranium.

The generation of acid mine drainage (AMD) characterized by high acidity and elevated levels of toxic metals primarily results from the oxidation and dissolution of sulfide minerals facilitated by microbial catalysis. Although there has been significant research on microbial diversity and community composition in AMD, as well as the relationship between microbes and heavy metals, there remains a gap in understanding the microbial community structure in uranium-enriched AMD sites. In this paper, water samples with varying levels of uranium pollution were collected from an abandoned stone coal mine in Jiangxi Province, China during summer and winter, respectively. Geochemical and high-throughput sequencing analyses were conducted to characterize spatiotemporal variations in bacterial diversity and community composition along pollution groups. The results indicated that uranium was predominantly concentrated in the AMD of new pits with strong acid production capacity, reaching a peak concentration of 9,370 µg/L. This was accompanied by elevated acidity and concentrations of iron and total phosphorus, which were identified as significant drivers shaping the composition of bacterial communities, rather than fluctuations in seasonal conditions. In an extremely polluted environment (pH < 3), bacterial diversity was lowest, with a predominant presence of acidophilic iron-oxidizing bacteria (such as Ferrovum), and a portion of acidophilic heterotrophic bacteria synergistically coexisting. As pollution levels decreased, the microbial community gradually evolved to cohabitation of various pH-neutral heterotrophic species, ultimately reverting back to background level. The pH was the dominant factor determining biogeochemical release of uranium in AMD. Acidophilic and uranium-tolerant bacteria, including Ferrovum, Leptospirillum, Acidiphilium, and Metallibacterium, were identified as playing key roles in this process through mechanisms such as enhancing acid production rate and facilitating organic matter biodegradation.

A combined bibliometric and sustainable approach of phytostabilization towards eco-restoration of coal mine overburden dumps.

Extraction of coal through opencast mining leads to the buildup of heaps of overburden (OB) material, which poses a significant risk to production safety and environmental stability. A systematic bibliometric analysis to identify research trends and gaps, and evaluate the impact of studies and authors in the field related to coal OB phytostabilization was conducted. Key issues associated with coal extraction include land degradation, surface and groundwater contamination, slope instability, erosion and biodiversity loss. Handling coal OB material intensifies such issues, initiating additional environmental and physical challenges. The conventional approach such as topsoiling for OB restoration fails to restore essential soil properties crucial for sustainable vegetation cover. Phytostabilization approach involves establishing a self-sustaining plant cover over OB dump surfaces emerges as a viable strategy for OB restoration. This method enhanced by the supplement of organic amendments boosts the restoration of OB dumps by improving rhizosphere properties conducive to plant growth and contaminant uptake. Criteria essential for plant selection in phytostabilization are critically evaluated. Native plant species adapted to local climatic and ecological conditions are identified as key agents in stabilizing contaminants, reducing soil erosion, and enhancing ecosystem functions. Applicable case studies of successful phytostabilization of coal mines using native plants, offering practical recommendations for species selection in coal mine reclamation projects are provided. This review contributes to sustainable approaches for mitigating the environmental consequences of coal mining and facilitates the ecological recovery of degraded landscapes.

Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines - Insights from SEM-EDS imaging.

The increasing prevalence of coal mine dust-related lung diseases in coal miners calls for urgent and meticulous scrutiny of airborne respirable coal mine dust (RCMD), specifically focusing on particles at the nano-level. This necessity is driven by expanding research, including the insights revealed in this paper, that establish the presence and significantly increased toxicity of nano-sized coal dust particles in contrast to their larger counterparts. This study presents an incontrovertible visual proof of these tiny particulates in samples collected from underground mines, utilizing advanced techniques such as scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The intricate elemental composition of nano-sized coal dust identified through EDS analysis reveals the presence of elements such as silica and iron, which are known to contribute to lung pathologies when inhaled over prolonged periods. The outcomes of the statistical analyses reveal significant relationships between particle size and elemental composition, highlighting that smaller particles tend to have higher carbon content, while larger particles exhibit increased concentrations of elements like silica and aluminum. These analyses underscore the complex interactions within nano-sized coal dust, providing critical insights into their behavior, transport, and health impacts. The nano-sized coal dust could invade the alveoli, carrying these toxic elements from where they are impossible to exhale. The revelation of nano-sized coal dust's existence and the associated health hazards necessitate their incorporation into the regulatory framework governing the coal mining industry. This study lays the groundwork for heightened protective measures for miners, urging the invention of state-of-the-art sampling instruments, comprehensive physicochemical profiling of RCMD nanoparticles, and the pursuit of groundbreaking remedies to neutralize their toxic impact. These findings advocate for a paradigm shift in how the coal mining industry views and handles particulate matter, proposing a re-evaluation of occupational health standards and a call to action for protecting coal miners worldwide.

Study on preparation and properties of antifreeze compound road dust suppressant.

Open-pit mine pavement dust dries and breaks easily. As such, a composite pavement dust suppressant with good wettability, moisturizing, coagulation, and antifreezing properties in winter was investigated. Monomer screening and orthogonal experiments were conducted, using evaporation rate, permeability rate, viscosity, and freezing point as evaluation indexes. Consequently, a dust suppressant solution is a mixture of glycerol (GLY), sodium dodecylbenzene sulfonate (SDBS), polyacrylamide (PAM), compound propylene glycol (PG), and potassium acetate (PA). The characteristics of the dust suppressant and its interaction mechanism with road dust were measured and analyzed. The results showed that the optimal ratio of the antifreeze-type composite dust suppressant is 3%GLY, 0.30%SDBS, 0.07% PAM, and 50%PG + 10%PA; the contact angle is 27.62°, which can effectively wet coal dust. Moreover, it easily forms hydrogen bonds with water molecules to release free -OH, which increases the oxygen-containing functional groups in the dust. The maximum viscosity is 25.4 mPa·s, and the hydrophobic groups adsorbed on the surface of the dust can condense and agglomerate the dust to form large particles, and effectively inhibit the occurrence of dust. It freezes at - 34.2 â„ƒ, resists a temperature of - 30 â„ƒ without freezing, and improves dust suppression efficiency and antifreezing effect in cold areas.

Evaluation of PVC and PTFE filters for direct-on-filter crystalline silica quantification by FTIR.

Direct-on-Filter (DoF) analysis of respirable crystalline silica (RCS) by Fourier Transform Infrared (FTIR) spectroscopy is a useful tool for assessing exposure risks. With the RCS exposure limits becoming lower, it is important to characterize and reduce measurement uncertainties. This study systematically evaluated two filter types (i.e., polyvinyl chloride [PVC] and polytetrafluoroethylene [PTFE]) for RCS measurements by DoF FTIR spectroscopy, including the filter-to-filter and day-to-day variability of blank filter FTIR reference spectra, particle deposition patterns, filtration efficiencies, and pressure drops. For PVC filters sampled at a flow rate of 2.5 L/min for 8 h, the RCS limit of detection (LOD) was 7.4 µg/m3 when a designated laboratory reference filter was used to correct the absorption by the filter media. When the spectrum of the pre-sample filter (blank filter before dust sampling) was used for correction, the LOD could be up to 5.9 µg/m3. The PVC absorption increased linearly with reference filter mass, providing a means to correct the absorption differences between the pre-sample and reference filters. For PTFE, the LODs were 12 and 1.2 µg/m3 when a designated laboratory blank or the pre-sample filter spectrum was used for blank correction, respectively, indicating that using the pre-sample blank spectrum will reduce RCS quantification uncertainty. Both filter types exhibited a consistent radially symmetric deposition pattern when particles were collected using 3-piece cassettes, indicating that RCS can be quantified from a single measurement at the filter center. The most penetrating aerodynamic diameters were around 0.1 µm with filtration efficiencies ≥ 98.8% across the measured particle size range with low-pressure drops (0.2-0.3 kPa) at a flow rate of 2.5 L/min. This study concludes that either the PVC or the PTFE filters are suitable for RCS analysis by DoF FTIR, but proper methods are needed to account for the variability of blank absorption among different filters.

Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents.

Effective emergency responses are crucial for preventing coal mine accidents and mitigating injuries. This paper aims to investigate the characteristics of emergency psychophysiological reactions to coal mine accidents and to explore the potential of key indicators for identifying emergency behavioral patterns. Initially, virtual reality technology facilitated a simulation experiment for emergency escape during coal mine accidents. Subsequently, the characteristics of emergency reactions were analyzed through correlation analysis, hypothesis testing, and analysis of variance. The significant changes in physiological indicators were then taken as input features and fed into the three classifiers of machine learning algorithms. These classifications ultimately led to the identification of behavioral patterns, including agility, defensiveness, panic, and rigidity, that individuals may exhibit during a coal mine accident emergency. The study results revealed an intricate relationship between the mental activities induced by accident stimuli and the resulting physiological changes and behavioral performances. During the virtual reality simulation of a coal mine accident, subjects were observed to experience significant physiological changes in electrodermal activity, heart rate variability, electromyogram, respiration, and skin temperature. The random forest classification model, based on SCR + RANGE + IBI + SDNN + LF/HF, outperformed all other models, achieving accuracies of up to 92%. These findings hold promising implications for early warning systems targeting abnormal psychophysiological and behavioral reactions to emergency accidents, potentially serving as a life-saving measure in perilous situations and fostering the sustainable growth of the coal mining industry.

Coupled CFD-FEM analysis of the damage causes of the retention bunker: a case study at hard coal mine.

Underground coal storage bunkers serve as crucial infrastructural components in the coal mining industry, providing secure and accessible locations for the storage of mined coal. The interaction between stored coal and underground water in coal storage bunkers indeed poses significant challenges due to the unpredictable nature of the resulting coal-water mixture. This phenomenon is particularly prevalent in coal mines operating under water hazards, where groundwater infiltration into storage areas can lead to the formation of coal-water mixtures, altering the physical properties of the stored coal. The interaction between coal and water can result in the formation of coal-water mixtures (hydromixture), which exhibit complex rheological properties. These mixtures may vary in viscosity, density, and particle size distribution, making their behavior difficult to predict. Underground water may exert hydrostatic pressure on the stored coal, influencing its mechanical behavior and compaction properties. Changes in pressure can result in coal compaction or expansion, affecting bunker stability and the integrity of surrounding rock strata. The main goal of the paper was to determine the values of pressure field variations exerted by the flowing hydromixture within underground coal storage bunkers. This objective reflects a critical aspect of understanding the dynamic behavior of coal-water mixtures (hydromixture) under varying conditions, particularly in environments where water hazards pose significant challenges to storage and operational stability. The paper utilized computational fluid dynamics (CFD) methods to examine the changes in pressure within underground coal storage bunkers induced by the flow of coal-water mixtures. The examination of damage to an underground coal storage bunker due to stress distribution was conducted using the finite element method (FEM). This computational technique is widely utilized in engineering and structural analysis to model complex systems and predict the behavior of materials under various loading conditions The results of the CFD numerical simulation were compared with the mathematical models.

Vegetation resilience assessment and its climatic driving factors: Evidence from surface coal mines in northern China.

Vegetation resilience is a key concept for understanding ecosystem responses to disturbances and is essential for maintaining ecosystem sustainability. However, assessing vegetation resilience remains challenging, especially for areas with significant disturbances and ecological restoration, such as surface coal mine ecosystems. Vegetation resilience assessment requires a combination of disturbance magnitude, recovery magnitude, and recovery time. In this study, we propose a vegetation resilience assessment method by integrating disturbance magnitude, recovery magnitude and recovery time. Forty-six surface coal mines in northern China were analysed as the study areas. A geographical detector model was used to explore the influence of climatic factors on vegetation resilience. The results indicated that the vegetation resilience curves included three shapes, inverted U-shaped, S-shaped, and monotonically decreasing, and the different disturbance-recovery relationships of the curves indicated that natural and social factors jointly changed the ecological restoration process. The vegetation resilience of the 46 surface coal mines varies widely, ranging from 0.87 to 7.22, showing a spatial decreasing trend from east to west. The explanatory power of different climatic factors on vegetation resilience by indirectly affecting hydrothermal conditions varies, with the effect of atmospheric pressure being the most significant and the superposition of the two climatic factors enhancing the effect on vegetation resilience. This study enriches the understanding of vegetation resilience assessment and provides important information to guide the differentiation of ecological restoration and resource development of surface coal mines in different regions.

Resting diffusing capacity and severity of radiographic disease predict gas exchange abnormalities with exercise in former US coal miners.

BACKGROUND: The US Department of Labor (DOL) does not fund diffusing capacity (DLCO) or metabolic measurements from cardiopulmonary exercise testing (CPET) for coal miners' disability evaluations. Although exercise arterial blood gas testing is covered, many miners are unable to perform maximal tests, and sampling at peak exercise can be challenging. We explored the relationship between resting DLCO, radiographic disease severity, and CPET abnormalities in former US coal miners. METHODS: We analyzed data from miners evaluated between 2005 and 2015. Multivariable linear and logistic regression analyses were used to examine relationships between percent predicted (pp) forced expiratory volume in 1 s (FEV1pp), DLCOpp, VO2maxpp, A-a oxygen gradient (A-a)pp, dead space fraction (Vd/Vt), disabling oxygen tension (PO2), and radiographic findings of pneumoconiosis. RESULTS: Data from 2015 male coal miners was analyzed. Mean tenure was 28 years (SD 8.6). Thirty-twopercent had an abnormal A-a gradient (>150 pp), 20% had elevated Vd/Vt (>0.33), and 34% a VO2max < 60 pp. DLCOpp strongly predicted a disabling PO2, with an odds ratio (OR) of 2.33 [2.09-2.60], compared to 1.18 [1.08-1.29] for FEV1. Each increase in subcategory of small opacity (simple) pneumoconiosis increased the odds of a disabling PO2 by 42% [1.29-1.57], controlling for age, body mass index, pack-years of tobacco smoke exposure, and years of coal mine employment. CONCLUSIONS: DLCO is the best resting pulmonary function test predictor of CPET abnormalities. Radiographic severity of pneumoconiosis was also associated with CPET abnormalities. These findings support funding DLCO testing for impairment and suggest the term "small opacity" should replace "simple" pneumoconiosis to reflect significant associations with impairment.

Hydrochemical characteristics of abandoned coal mines derived acid mine drainage in a typical karst basin (Wuma river basin, Guizhou China).

The hydrochemical characteristics of acid mine drainage (AMD) were investigated in Wuma River Basin, China. AMD was sampled from nine closed coal mine (CCM) sites to study the temporal and spatial evolution of pH, dissolved oxygen (DO), electrical conductivity (ED), total hardness (THR), total dissolved salt (TDS), and trace elements. The surface water (river) and groundwater surrounding mine sites were sampled to evaluate the potential pollution derived from AMDs. The TDS content of AMD was higher than that of surface water and groundwater. The dominant factors influencing TDS were the pH, temperature, and wet or dry season (which played a role in controlling microbial activity), HCO3 - balance, and REDOX during the evolutionary process. The hydrochemical type of AMD was dependent on the evolutionary stage. From observations, most AMDs were in the form of the SO4 2--Ca2+•Mg2+ type that was characterized by a low pH, low [HCO3 -], high [SO4 2-], and high [Fe]. In addition, the AMD samples were undergoing stage I and II processes, in which SO4 2- and trace elements were generated. The surface water and groundwater were primarily classified as the HCO3 --Ca2+•Mg2+ type, which accounted for their self-cleaning capacity, as indicated by the high [HCO3 -]. The surface water and groundwater could be affected by the surrounding AMD depending on the geographical location. The surface water and groundwater sites that were located downstream of subsurface and surface runoff were obviously affected by AMD. After being polluted by AMD, surface water and groundwater contained higher levels of trace elements and emerged as the HCO3 -•SO4 2--Ca2+•Mg2+ type.

[Neural network analysis of mechanization's impact on coal miner's occupational health].

In order to clarify the transmission mechanism of the impact of mechanization on the occupational health of miners and to provide empirical evidence for the development of new quality productivity in the coal industry that balances health and efficiency. In August 2022, we selected a typical coal mine, constructed a comprehensive evaluation index of miners' occupational health through a questionnaire survey based on the fully connected neural network model. A Bayesian model was used to verify the influence of mechanization level on miners' occupational health. We found that: the predicted probability of occupational diseases could be used as a comprehensive indicator of the level of occupational health, providing a basis for early intervention and prevention of occupational diseases. Mechanization could directly promote the improvement of miners' occupational health level, and also indirectly affect occupational health level by influencing hazards level and work intensity. The indirect effect of mechanization on work intensity was positive, and the indirect effect of mechanization on hazards level was positive. Presented the "inverted U-shaped" process in the mechanization breakthrough semi-mechanized level would realize the economies of scale of health protection, its impact on the prevention and control of occupational hazards would turn from negative to positive.

Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces.

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.

Multi-condition simulation analysis of a sliding hydraulic support system based on elastoplastic mechanics.

In response to the challenges of supporting fractured and weak surrounding rock in deep coal mines in the Huainan region of China, a self-moving hydraulic support system for roof support was designed and developed. This innovative solution addresses the difficulties encountered in providing continuous support to roof structures. Based on the theory of elastoplastic mechanics, a numerical analysis model was established to calculate the mechanical parameters such as the displacement, stress, and strain of hydraulic supports during the stepping process under multiple operating conditions. The results of the numerical simulation were compared and verified with those from an actual working site. The results show that the maximum equivalent stress is 245.33 MPa for operating condition 1, 246.82 MPa for operating condition 2, and 245.27 MPa for operating condition 3. The maximum stress values under the three working conditions do not exceed the yield strength of the material, satisfying the requirements for normal bracket support operations. These research findings can establish a theoretical framework for the comprehensive assessment of the reliability and stability of hydraulic supports and the optimization of construction processes.

Applying Nanofiltration to Decrease Energy Consumption and Sensitivity toward Feed Composition Fluctuations in Salt Production.

The only currently active industrial-scale plant that uses coal mine brines, located in Czerwionka-Leszczyny, uses ZOD (Zaklad Odsalania Debiensko, the name of the plant's former owner) technology, based on mechanical vapor compression evaporators. The plant produces evaporated salt that meets the specifications for edible salt; however, the technology is highly energy-consuming. The presented work focuses on the modeling of ZOD technology if applied to the water treatment of the 'Ziemowit-650' coal mine. Using the results of bench-scale investigation of brine nanofiltration and a mathematical model of ZOD technology based on Czerwionka-Leszczyny performance, the energy consumption per ton of produced salt was estimated for two cases: (1) ZOD technology treating the 'Ziemowit-650' brine and (2) ZOD technology treating the permeate of nanofiltration (NF) working on the 'Ziemowit-650' brine. The sensitivity of the system was investigated in the range of -10% to + 10% of Cl-, SO42-, Mg2+, and Ca2+ concentration, assuming that the sodium concentration also changes to meet the electroneutrality requirement. The results show that nanofiltration pretreatment not only decreases energy consumption but it also makes salt production less sensitive to fluctuations in feed water composition.

Optimizing coal mine planning and design for sustainable development in the context of mass exploitation of coal deposits.

Sustainable mining practices is a concept that embeds the principles of sustainable development into the whole mine life-cycle, from exploration, extraction and processing through to mine closure. The optimization of coal mine planning and the developing a standardized design for its sustainable development is very challenging and requires more effort. The present research attempts to address the conditions of sustainability and necessary measures for sustainable development, thereby providing appropriate solutions for each stage of mining operation besides expressing the necessity of sustainable development integration at different stages of mining life cycle (MLC). The approach of systems engineering is essential to assist the sustainability goals which are integrated with the expected results. Hence a method depending more on systems engineering principles and optimization can be incorporated to attain better results. Several socio-environmental factors associated with sustainability depends on the geographic condition and few mining engineering considerations such as mine location, topography, coal seam characteristics and so on. These systems engineering approach can be further enhanced by incorporating tools like Geographic Information System (GIS), which provides more accuracy and precision of the geographic conditions of the site identified for the coal mining plan. In order to begin this way of approach towards the sustainability development and mining planning, the appropriate optimization parameters should be identified. The outcome of these optimization parameters can be also achieved by optimizing coal mining system models.

A framework for selecting and assessing soil quality indicators for sustainable soil management in waste dumps.

The primary objective of this study was to develop soil quality indexes (SQIs) to reveal the changes in SQ during the restoration of vegetation in the reclaimed waste dumps of the Hequ open-pit coal mine. The study built an SQI evaluation model for waste dumps based on the soil management assessment framework. The total data set (TDS) consisted of nine physicochemical property indicators. The selection of the minimum data set (MDS) involved the utilization of principal component analysis (PCA) and Norm values. The SQ was comprehensively evaluated for nine indicators, taking into account the non-linear membership function and the improved Nemerow index. The findings suggested a notable disparity in the SQ between the reclaimed area and the unreclaimed area, yet the overall SQ fell short. In the TDS index system, the organic matter has the highest weight and a greater contribution to the soil quality of the waste dumps. In the MDS indicator system, the weights of organic matter and total nitrogen are both 0.5. According to Nemerow index method, the average SQIN of 5 plots is calculated to be 0.4352 ± 0.194. The average value obtained from TDS is 0.581 ± 0.236, and the average value obtained from MDS is 0.602 ± 0.351. The weighted additive method was employed to compute three SQIs, all of which yielded satisfactory outcomes. And the above evaluation methods indicate that the overall soil quality level of the waste dumps is at a moderate level. The sequence of SQ in various waste dumps was as follows: No.4lower > No.1 > No.2 > No.3 > No.4upper. Specifically, the non-linear membership function indicated that pH, available nitrogen (AN), available phosphorus (AP), surface moisture content (SMC), and bulk density (BD) were crucial in limiting SQIs in total waste dumps. The crucial limiting SQIs in unreclaimed areas were total phosphorus (TP) and total nitrogen (TN). This analysis demonstrates its efficacy in formulating strategies for the SQ evaluation and targeted soil reclamation plans of waste dumps.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Design and study of mine silo drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy.

Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2-7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section-water level-rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.