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Aiming at the problem of safe mining of coal seam under the condition of hard roof, taking Xiagou coal mine as the background, the expression of coal seam supporting force in isolated island working face mining is derived through the analysis of the "umbrella arch" overlying rock structure mechanism in isolated island working face mining. Combined with the mine geological conditions, it is concluded that the supporting force near the inclined gob of ZF2822 isolated island working face is more than 1.26 times that near the strike gob. Based on the numerical simulation analysis, it is known that the stress and elastic energy of the working face near the inclined gob are obviously greater than that towards the gob, and the peak stress on the side of the transport gateway after the first square and the second square is 1.41 times and 1.55 times that of the return air gateway, respectively. Strata discrimination determines that the key stratum is medium-grained sandstone 22.32 m above the coal seam. The reasonable blasting pressure relief angle is determined according to the field situation. Pressure relief control makes the "high energy-high frequency" feature of mining face change into "low energy-low frequency". After pressure relief, the daily average microseismic energy of working face mining is reduced to below 104 J, which is 27.77% and 39.74% lower than that in March and April before pressure relief. The changes of width and height of transport gateway after pressure relief are 22.73% and 24.75% of those before pressure relief, respectively. The research results provide a scientific basis for the safe mining of isolated island working face under the condition of hard roof. Highlights: The "umbrella arch" overlying strata structure of isolated island working face mining is put forward, and the expression of supporting force of working face mining is deduced. The corresponding relationship between the side close to the gob and the side close to the gob in the isolated island working face is mastered theoretically. The variation law of peak value difference of elastic energy of solid coal side in transportation gateway and return gateway of irregular island mining face is mastered. The asymmetric pressure relief control strategy of roadway surrounding rock in isolated island working face mining is formed. [ABSTRACT FROM AUTHOR]

The conventional modes of transporting coal frequently result in substantial fiscal challenges and environmental deterioration. Slurry-based hydraulic transportation of coal through pipelines has proven to be a feasible and efficient method, offering economic benefits, reliability and minimal ecological damage. Due to the limited and rapidly depleting reserves of high-rank coal, the scientific community is compelled to utilize high ash coal (HAC) to meet the growing demands of the population. The HAC slurry is characterized by poor slurryability, stability and low calorific value. This research presents a straightforward solution to enhance the slurryability and stability of Indian HAC while improving its calorific value. Anthracite and anionic additive sodium dodecyl sulfate (SDS) were added in Indian HAC to prepare a high solid loading blended slurry with improved stability. The rheological characteristics, stability and zeta potential of prepared slurry samples were assessed and analyzed to determine the optimum anthracite proportion and SDS concentration. The optimum anthracite proportion and SDS concentration for low apparent viscosity and maximum stability were 30% and 0.6%, respectively. The inclusion of anthracite and SDS at optimal proportion yielded a slurry with 66% solid concentration, indicating a surge of 14% in the maximum possible solid loading. [ABSTRACT FROM AUTHOR]

The connecting buckles of underground transportation belts are a critical component. These components are susceptible to damage from material impact and pulley wear-induced damage, which can compromise industrial safety and transportation efficiency. While the importance of detecting defects in connecting buckles is self-evident, effective methods to solve this problem are currently lacking. To fill this gap, this study proposed a defect detection method using an enhanced version of the YOLOv7 model. The process incorporated a target detection head, an attention mechanism, and a loss function to enhance the model's generalization ability and robustness under complex working environments and diverse defect types. Through experimental validation using a self-constructed U38 buckle dataset, the method achieved an 83.2% detection rate for various defect types, with critical defects identified with an accuracy of up to 98.1%. The enhanced YOLOv7_m model exhibited strong performance in defect detection, providing valuable technical support for improving the safety and efficiency of coal mine transportation. This research provides a valuable reference for further optimizing the diagnosis of connection buckle defects based on deep learning. [ABSTRACT FROM AUTHOR]

This research examines the coal transportation system at Site Port S, a PT BCX coal mining concession. The site serves as a transit terminal for coal hauled from Site B using trucks, where the coal is dumped via two inloading conveyors. Currently, the daily haul truck capacity is 50,000 tons, while the conveyors handle 54,000 tons daily. However, when one of the conveyors is under maintenance or experiencing technical issues, production opportunities are lost, jeopardizing the achievement of future coal production targets, especially as peak production is expected over the next three years. To address this, the research aims to assess the feasibility of building a new inloading conveyor to serve as a backup system. The research uses capital expenditure (CAPEX) calculations, including previous inloading conveyor costs and inflation rates, as well as operating expenses (OPEX) and cost per ton to operate the plant. A discounted cash flow (DCF) analysis was conducted to determine the net present value (NPV) of building or renting a new conveyor unit. With a weighted average cost of capital (WACC) of 11.9%, the NPV results show a positive $4.8 million for building a new unit, compared to a negative $22.3 million for renting. The results indicate that building a new conveyor unit is significantly more profitable than renting. Additionally, owning the unit provides flexibility, as it could potentially be used at other sites in the future. The findings have important implications for optimizing coal transportation systems and ensuring long-term production efficiency. [ABSTRACT FROM AUTHOR]

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During coal storage and transportation, spontaneous combustion occurs occasionally. Heat pipes, as new fire prevention technology, have been applied and explored in the prevention and control of spontaneous combustion in coal yards. This paper combines the mechanism of spontaneous combustion in coal yards and the advantages and disadvantages of traditional prevention methods to explain the advantages of heat pipe technology, summarizes and analyzes factors affecting the heat transfer efficiency of gravity heat pipes, such as working medium, arrangement, heat pipe structure, and external environment, and looks forward to the development trend of gravity heat pipes in coal yards. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inspection and maintenance activities are effective ways to reveal and restore the health conditions of many industrial systems, respectively. Most extant works on inspection and maintenance optimization problems have assumed that systems operate under a time-invariant demand. Such a simplified assumption is oftentimes violated by a changeable market environment, seasonal factors, and even unexpected emergencies. In this article, with the aim of minimizing the expected total cost associated with inspections, maintenance, and unsupplied demand, a dynamic inspection and maintenance scheduling model is proposed for Multi-State Systems (MSSs) under a time-varying demand. Non-periodic inspections are performed on the components of an MSS and imperfect maintenance actions are dynamically scheduled based on the inspection results. By introducing the concept of decision epochs, the resulting inspection and maintenance scheduling problem is formulated as a Markov Decision Process (MDP). The Deep Reinforcement Learning (DRL) method with a Proximal Policy Optimization (PPO) algorithm is customized to cope with the "curse of dimensionality" of the resulting sequential decision problem. As an extra input feature for the agent, the category of decision epochs is formulated to improve the effectiveness of the customized DRL method. A six-component MSS, along with a multi-state coal transportation system, is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The layout of methane sensors in the working face cannot meet the needs for monitoring methane concentrations within confined spaces, and it is challenging to determine the precise locations for manual inspections. Therefore, the working face is firstly divided into different areas and grids. Then combined with the characteristics of methane emissions and the measured data on site, the boundary conditions of simulation experiments are set up and the research is carried out on the diffusion law of methane in the confined space of the working face under different conditions. The experimental results show that methane emission intensity from coal walls affects its distribution. As emission intensity rises, methane nearer the coal wall decreases, while methane further away increases. Among coal mining points, point 2 shows the widest methane diffusion range. Rising wind speeds decrease methane diffusion from the coal wall, increasing vertical diffusion distance. Methane from the coal wall shifts to the air inlet, while methane from the mining point diffuses increasingly to the downwind side. The location of the maximum methane concentration generated from falling coal and its transportation process is only related to the location of the coal mining point. The key areas for methane monitoring in confined spaces of the working face should be the overlapping locations of the vertical‐3 and vertical‐4 areas and the horizontal‐1 and horizontal‐3 areas. The key areas for manual inspection should be the overlapping locations of the vertical‐2 and vertical‐3 areas and the horizontal‐1 area. [ABSTRACT FROM AUTHOR]

Considering the shortcomings of the current monitoring system for tunnel anchor support systems, a tunnel anchor monitoring system based on LSTM-ARIMA prediction is proposed in this paper to prevent the deformation and collapse accidents that may occur in the underground mine tunnels during the backfilling process, which combines the Internet of Things and a neural network deep learning algorithm to achieve the real-time monitoring and prediction of the tunnel anchor pressure. To improve the prediction accuracy, a time series analysis algorithm is used in the prediction model of this system. In particular, an LSTM-ARIMA model is constructed to predict the tunnel anchor pressure by combining the Long Short-Term Memory (LSTM) model and the Autoregressive Integrated Moving Average (ARIMA) model. And a dynamic weighted combination method is designed based on model prediction confidence to acquire the optimal weight coefficients. This combined model enables the monitoring system to predict the anchor pressure more accurately, thereby preventing possible tunnel deformation and collapse accidents in advance. Finally, the overall system is verified using the anchor pressure dataset obtained from the 21,404 section of the Hulusu Coal Mine transportation tunnel in real-world engineering, whose results show that the pressure value predicted using the combined model is basically the same as the actual value on site, and the system has high real-time performance and stability, proving the effectiveness and reliability of the system. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China's coal industry has long faced challenges due to imbalance between supply and demand divisions and the discrepancies between supply and demand prices. Research on optimizing coal transfer amid fluctuating coal prices is crucial for enhancing coal resource allocation, ensuring a balanced and stable supply of coal resources, and promoting the sustainable development of the coal industry. This paper focuses on optimizing coal transfer schemes within a comprehensive transportation system, considering fixed transportation routes and opening times of railroad transportation and waterway transportation, with road transportation serving as a supplementary network. A three-dimensional spatio-temporal network model is established to represent the spatial and temporal paths and transportation modes of coal logistics. The study uses the coal transportation network from Inner Mongolia and Shaanxi to East China as a case study, analyzing factors such as coal price fluctuation, transportation rates, and the ratio of self-produced to purchased coal at the production end via sensitivity analysis. The findings indicate that the proposed model can effectively guide transportation practices under the integrated production, transportation, and sales framework in the energy industry. Additionally, it shows that enterprises can achieve higher profits and maintain revenue and supply stability despite coal price fluctuations, aligning with the coal industry's transformation and development in the current context. [ABSTRACT FROM AUTHOR]

Railway transportation is one of the main modes of long-distance coal transportation, and it inevitably causes environmental pollution during the transportation process. In order to improve the environment along the railway and increase the utilization rate of coal resources, this paper proposes a detection algorithm for the scattered coal solidification layer during transportation based on the YOLOv8 model and designs an intelligent recognition model suitable for coal solidification layer detection devices by combining fluorescence detection methods. Through experimental analysis, we found that the model meets the requirements of practical detection and can play a crucial role in environmental protection, with high practical application value. [ABSTRACT FROM AUTHOR]

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS. [ABSTRACT FROM AUTHOR]

Scraper conveyor is the most important transportation equipment in the comprehensive mining equipment, and the chain drive system is its core subsystem, its dynamic characteristics will significantly affect the efficiency of coal transportation in the comprehensive mining face. In this paper, the dynamic characteristics of chain drive system when impacted by falling coal are investigated by means of test. The impact test bench of scraper conveyor was set up to analyze the effects of chain speed, impact height and impact load mass on the dynamic characteristics of the chain drive system of scraper conveyor under the working conditions of unloaded and loaded. The results show that the longitudinal vibration of the scraper conveyor is most obvious when it is impacted by the falling coal, and the chain speed, impact height and impact load mass of the scraper conveyor all play an excitation role on the vibration of the chain drive system, and the vibration of the chain ring is the most intense in the chain drive system, and the loaded coal pile conveyed on the scraper conveyor plays an inhibiting role on the vibration of the chain drive system. This study can help to identify the location where the scraper conveyor fails first in the impact condition, so as to provide a basis for its structural design and improvement, which is of great significance for the stable operation and structural optimization of the scraper conveyor. [ABSTRACT FROM AUTHOR]

Coal slurry is a semi-solid material formed by the water content of pulverized coal, which is a product of the coal production and transportation process and has viscous and wet characteristics, and is easy to cause blockage in the process of mechanized mining and transportation. To study the resistance law of the probe rod pressed into the quasi-static viscous particle medium at a uniform speed, coal dust and deionized water were mixed into different water content of coal slurry, and the resistance of the probe rod under pressure in different water content of coal slurry was measured. It is found that when the cohesion of coal slurry is small, the press-in process tends to be stable, and the intrusion resistance shows a linear-slowly increasing-suddenly increasing trend as the depth of pressure increases. [ABSTRACT FROM AUTHOR]

As the key equipment of fully mechanized coal mining face, scraper conveyor undertakes the main task of coal transportation. The movement and mechanical behavior of coal have an important impact on the design and failure analysis of scraper conveyor. However, the working environment of the scraper conveyor is complex and harsh, and it is difficult to carry out effective research and experiments. The interaction effect between the underground scraper conveyor and the coal cannot be analyzed in the real test, based on the discrete element method and multi-body dynamics, the rigid-discrete coupling simulation model of the scraper conveyor was constructed, and the simulation model under normal working conditions was verified from the perspective of bulk materials and machinery. The simulation models were set based on different working inclination angles, different strike inclination angles and different traffic conditions, respectively, and the rigid-discrete coupling effect of the scraper conveyor under variable working conditions was studied. The results show that the rigid-discrete coupling model of scraper conveyor based on discrete element method and multi-body dynamics can effectively simulate the transport conditions of scraper conveyor. Under normal working conditions, the coal movement presents a heap-like intermittent distribution, and the coal accumulation on the front side of the scraper is more than that on the rear side of the scraper. With the increase of the working inclination, the highest point of coal accumulation gradually moved towards the rear scraper, the average force on the deck plate gradually increased, and the average force on the rear scraper gradually decreased. With the increase of strike inclination, the highest point gradually shifts to the inclined side, resulting in uneven distribution of coal materials in the line pan, making the stress of coal materials on the inclined side greater than that on the other side, and the stress of the chain fluctuates, and the synchronization of the two chains is poor. With the increase of the transportation volume, the average force of the deck plate and the scraper gradually increases. There is an effective conveying area in the scraper conveyor conveying process. The coal material speed in the effective conveying area is basically the same as the chain speed. The coal material speed near the chute wall is gradually lower. The wear of the deck plate is related to the coal particle - deck plate - scraper (chain) contact and when they form an effective three-body contact, there is a significant upward trend in the force and wear on the deck plate. The research results provide guidance for the subsequent simulation model construction of scraper conveyor wear characteristics and fault analysis under different working conditions, and provide a reference for the analysis of r rigid-discrete coupling effect of scraper conveyor under different working conditions.

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China's coal transportation model. To address this issue, this paper first establishes a static topological structure of China's coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China's coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage. [ABSTRACT FROM AUTHOR]

The present study aims to identify the pH values in preparation of coal water slurry (CWS) that were evaluated. Rheological characteristics and stability of coal water slurry (CWS) were observed with two types of impellers at pH varying from 6.99 to 7.24. The results showed that the concentration of coal water slurry, rheological characteristics and stability of CWS for propeller and rushton impellers. The parameters that are most effected on the objectives are impeller speed (N) and ratio of clearance height with diameter of tank (C/T). The third parameter, ratio of slurry height with tank diameter (Z/T), is not effecting on output. The pH values of concentrated coal water slurry are favorable to transport to coal gasifier. For propeller impeller, the pH values vary from 6.99 to 7.15; for Rushton impeller pH values vary from 7.12 to 7.24 comparatively Rushton impeller having more base which is most favorable and obtain good CWS concentration. The influence of coal particle size is also investigated on pH values for optimal parameters of equipment. [ABSTRACT FROM AUTHOR]

Railway transportation system is one of the most important components of the integrated transportation system. A functional and efficient railway transportation system can be conducive to sustainable development of society and economy, thus highlighting the importance of studies on railway transportation efficiency. Therefore, this research proposes the cross‐efficiency evaluation method based on the relative entropy evaluation method (CERE) to evaluate railway transportation efficiency in China. Firstly, the research constructs an evaluation indicators system for railway transportation efficiency from the perspective of input‐output. Secondly, the paper obtains cross‐efficiency matrix by using the cross‐efficiency evaluation method (CE). Then, the study takes the obtained the cross‐efficiency matrix as the decision matrix of multiple criteria decision‐making (MCDM) problem, and applies the relative entropy evaluation method to evaluate the efficiency of railway transportation. Finally, the empirical analysis of the efficiency evaluation results for 18 railway transportation companies shows that, in 2019, Taiyuan Railway Transportation Company has the highest efficiency score, owing probably to a greater coal railway transportation demand and an advanced railway heavy haul transportation technology; railway transportation companies in the Eastern China typically have the highest efficiency scores, owing probably to a greater regional railway transportation demand. [ABSTRACT FROM AUTHOR]

In this study, we focus on the analysis of factors influencing the siting decision of coal emergency reserve centers. Specifically, we first draw on the quality function deployment theory in marketing to logically integrate the ideas of this study. On this basis, we adopted an interdisciplinary fuzzy decision-making method, namely the G1-entropy method, to quantitatively evaluate the research of this paper. Thereafter, we constructed a three-level index system based on the characteristics of the coal emergency reserve site selection, and used the G1-entropy value method to calculate the weights of the indicators in the coal emergency reserve center siting decision index system and obtain the results. Our research findings have found that the three key indicators of coal conventional reserve, emergency coal transportation methods, and emergency response time play a crucial role in the decision-making of coal emergency reserve center location. Therefore, we propose specific countermeasures and suggestions for these three key indicators. Our study can provide support for the government to better select the location of emergency coal reserves, better improve the national energy layout, and provide support for relevant decision makers on how to better reserve coal. The location of the emergency coal reserve center can better play the role of strategic reserve to stabilize the market function, effectively respond to the impact of various events on the energy market, and can make corresponding suggestions to the construction of the national energy security reserve system. [ABSTRACT FROM AUTHOR]

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China’s coal industry has long faced challenges due to imbalance between supply and demand divisions and the discrepancies between supply and demand prices. Research on optimizing coal transfer amid fluctuating coal prices is crucial for enhancing coal resource allocation, ensuring a balanced and stable supply of coal resources, and promoting the sustainable development of the coal industry. This paper focuses on optimizing coal transfer schemes within a comprehensive transportation system, considering fixed transportation routes and opening times of railroad transportation and waterway transportation, with road transportation serving as a supplementary network. A three-dimensional spatio-temporal network model is established to represent the spatial and temporal paths and transportation modes of coal logistics. The study uses the coal transportation network from Inner Mongolia and Shaanxi to East China as a case study, analyzing factors such as coal price fluctuation, transportation rates, and the ratio of self-produced to purchased coal at the production end via sensitivity analysis. The findings indicate that the proposed model can effectively guide transportation practices under the integrated production, transportation, and sales framework in the energy industry. Additionally, it shows that enterprises can achieve higher profits and maintain revenue and supply stability despite coal price fluctuations, aligning with the coal industry’s transformation and development in the current context.

As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system's safe and efficient operation. This paper introduces a new 'hardware-friendly' method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to "hardware friendly" algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor. [ABSTRACT FROM AUTHOR]

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine's intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms. [ABSTRACT FROM AUTHOR]

The roller is an important part of the belt conveyor used in coal transportation. Due to the harsh environment of coal mines, the rollers are in a state of high load and high friction for a long time, which causes wear failure and has a serious impact on the reliability and safety of the equipment. In order to prepare roller material with excellent bearing performance and friction performance, CF/PUE composites were prepared by pouring method with polyurethane as the matrix and carbon fiber as reinforcement. Due to the low surface activity of unmodified carbon fibers and poor bonding performance with the matrix, MoS2 was generated on the surface of carbon fiber by the in situ generation method in this paper. It was found that the mechanical properties of MoS2/CF/PUE composites were better when the CF content was 0.3 wt%. The Shore hardness reached 92.2 HA, which is 10% higher than pure polyurethane. The tensile strength was 38.44 MPa, which is 53% higher than pure polyurethane. The elongation at break was 850%, which is 16% higher than pure polyurethane. The maximum compressive stress was 2.32 MPa, which is 42% higher than pure polyurethane. The friction coefficient was much lower than that of pure PUE composites, the friction coefficient was 0.284, which is 59% lower than pure polyurethane. [ABSTRACT FROM AUTHOR]

As one of the important transportation tools for coal transportation, the operating efficiency of belt conveyor is directly related to the production efficiency of coal. However, belt conveyor failures often occur due to belt deviation. Generally speaking, the transportation distance of belt conveyors is relatively long, and the transportation environment is relatively harsh. The use of manual monitoring of the operation status of belt conveyors is inefficient and increases labor costs. The existing belt deviation detection system can play a good role in some conventional environments. However, under complex working conditions such as heavy dust, insufficient light, and camera deviation, existing detection systems will be affected. Therefore, this paper proposes a belt deviation detection system based on deep learning under complex working conditions. The characteristics of the belt and idler are extracted through the convolutional neural network, so as to position the belt and idler. With the characteristic points on the belt and idler as input, a mathematical model of belt deviation is established to calculate the degree of belt deviation, and alarms according to different degrees of deviation. The system has been tested in the actual field, and has a certain accuracy and real-time, which largely solves the problem of belt deviation detection under complex working conditions, reduces the fault frequency of belt conveyor, and thus improves the working efficiency of belt conveyor. [ABSTRACT FROM AUTHOR]

As one of the most important coal-producing provinces of China, Shanxi Province has been concerned about soil potentially toxic elements (PTEs) contamination in recent years. The study aimed to determine the status and sources of PTEs contamination and evaluate the quality of the soil ecology. This study investigated the degree of 13 PTEs contamination. The sources and contributions of PTEs were traced by the absolute principal component score followed by a multiple linear regression model (APCS–MLR). And the status of the soil ecosystem was verified by evaluating the soil nematode community around the coal mining areas in Jinzhong. The results showed that the mean PTEs concentration of 5 trace elements were higher than the background values of Shanxi, and safe to considerable was indicated by the pollution and ecological risk values. Soil Hg was the most contaminated element, followed by Cd. The distribution of PTEs was determined by coal mining activities (44.72%) followed by agricultural practice (32.37%) and coal transportation (21.37%). The nematode genera Acrobeloides (4.01%), Aphelenchus (20.30%), Meloidogyne (11.95%) and Aporcelaimus (2.74%) could be regarded as bioindicators of soil PTEs contamination by their tolerance. Concentrations of soil Cr, Mn, Ti and Cd showed remarkable influences on the total nematode abundance, maturity index, enrichment index, structural index, Shannon–Wiener diversity index and Pielou index of soil nematode. It is an appropriate method to evaluate the status of soil PTEs contamination combining the response of a single nematode genus and the nematode community evaluation index. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most selective maintenance research strategies ignore the comprehensive evaluation of numerous following missions in favor of focusing just on the reliability of the next mission. In the same circumstance, however, assessing simply impending missions differs from considering the overall system's maintenance planning outcomes. In this study, a decision-making model for selective maintenance is developed by evaluating the total system reliability over a finite planning horizon. The purpose is to calculate the maintenance activities for each system component during each break and the best number of maintenance interruptions for the planning horizon to achieve maximum system reliability. Consequently, the selective maintenance problem is formulated as a max–min optimization model. Also, a hybrid imperfect maintenance model is used to formulate the component improvement after maintenance. Finally, simple case illustrations of maintaining the production system in coal transportation are given based on the assumed data. [ABSTRACT FROM AUTHOR]

The functional group structures of coal molecules are one of the most important factors affecting spontaneous combustion. However, it is difficult to determine the exact effects of such structures. Extraction technology is able to modify the functional groups in coal as a means of inhibiting spontaneous combustion reactions. The present work extracted coal from the Caojiatan mine in northern Shaanxi, China, with various solvents. The extraction effectiveness of these solvents was found to decrease in the order of dioctyl sulfosuccinate (AOT) > water > n-hexane > cyclohexane + AOT + ethanol > cyclohexane > ethanol > methanol. With the exception of the AOT, the concentration of functional groups in the extracted coal was decreased compared with that in a control specimen extracted using only water. Ethanol, n-hexane, and methanol provided the optimal extraction efficiencies in terms of capturing coal molecules with aromatic structures, aliphatic structures, and oxygen-containing groups, respectively. The results of this work are expected to assist in future research concerning the extraction of coal molecules with specific functional groups. This work also suggests new approaches to the active prevention and control of spontaneous combustion during the mining, storage, and transportation of coal. [ABSTRACT FROM AUTHOR]

This study clarifies the formation mechanism of attentional bias toward safety (ABS) of coal mine transportation drivers to reduce related accidents. It proposes a theoretical model of the formation mechanism of ABS based on the Stressor-Emotion Model and the following trait theory. We collected 360 questionnaires on Chinese coal transport drivers using multi-point data to explore the psychological cognitive mechanism and boundary conditions of role stress and abusive supervision affecting coal transport drivers' ABS, and found them to have a significant negative impact on their ABS. Both factors affect ABS through the mediating effect of emotional exhaustion. Additionally, positive followership trait regulates the positive effects of role stress and abusive supervision, and the mediating effect of emotional exhaustion. [ABSTRACT FROM AUTHOR]

Selective maintenance is an important condition-based maintenance strategy for multi-component systems, where optimal maintenance actions are identified to maximize the success likelihood of subsequent missions. Most of the existing works on selective maintenance assumed that after each mission, the components' states can be precisely known without additional efforts. In engineering scenarios, the states of the components in a system need to be revealed by inspections that are usually inaccurate. Inspection activities also consume the limited resources shared with maintenance activities. We, thus, put forth a novel decision framework for selective maintenance of partially observable systems with which maintenance and inspection activities will be scheduled in a holistic and interactively sequential manner. As the components' states are partially observable and the remaining resources are fully observable, we formulate a finite-horizon Mixed Observability Markov Decision Process (MOMDP) model to support the optimization. In the MOMDP model, both maintenance and inspection actions can be interactively and sequentially planned based on the distributions of components' states and the remaining resources. To improve the solution efficiency of the MOMDP model, we customize a Deep Value Network (DVN) algorithm in which the maximum mission success probability is approximated. A five-component system and a real-world multi-state coal transportation system are used to demonstrate the effectiveness of the proposed method. It is shown that the probability of the system successfully completing the next mission can be significantly increased by taking inspections into account. The results also demonstrate the computational efficiency of the customized DVN algorithm. [ABSTRACT FROM AUTHOR]

A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particle swarm optimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coal hauling, loading, and transportation activities impacted the emergence of coal dust which is harmful to health. Coal dust exposed from coal unloading stations and coal waterway transportation is escaped attention. This study aimed to determine the characteristics of coal dust, the influence of climate parameters on the spread of coal dust, and its impact on the health of children under five in the exposed area. Coal dust characteristics and concentrations of PM PM2.5 and PM10 were analyzed from ten points spread across three mining companies (A, B, and C). The effect of climate parameters on PM2.5 and PM10 was tested statistically. The results of the chemical analysis revealed that coal dust was dominated by the high content of Si, Al, S, and Fe. The concentration of PM2.5 and PM10 is affected by wind speed. PM2.5 and PM10 can exceed the annual threshold value, which has caused a high incidence of respiratory problems in two sub-districts, namely Makrayu and Gandus.

The analysis of the particle size of suspended solids in snow samples from various areas near the coal-mining enterprise “Razrez Arshanovsky” LLC in the Republic of Khakassia. Snow sampling sites of the snow cover were located in the area of the road along which coal is transported, and at a distance of 0,5 km from the village of Arshanovo in accordance with the wind rose. Particle size analysis was performed using a LaskaTD device. It was found that in the samples under study, the main proportion of suspended particles, regardless of the point of study, had sizes from 5 to 20 microns, which numerically amounted from 63.8 to 81,3 % of the total weight of suspended substances. The site located in the western direction at a distance of 0,5 km from the Arshanovo had such low values of suspended solids that it was not possible to determine them using the Laska-TD device. The average particle size, depending on the point of study, had the following values (μm): № 1 - 11,2 ± 0,4; № 2 - 11 ± 1,6; № 3 - 9,3 ± 0,2; № 4 - 7,3 ± 0,9; № 5 - 13,5 ± 0,3; № 6 - 11,5 ± 0,2; № 8 - 11,6 ± 0,3. The obtained results of the study allow to conclude that there is a large amount of fine dust in the snow cover, which can accumulate in the environment and negatively affect its biological component, including humans.

The world's largest coal export operation is located in New South Wales, Australia. The state has more than 87% of the coal transportation done through railways, and one of the strategies to increase throughput is the use of sophisticated computational techniques for rail traffic optimisation. The current state of the art shows a lack of practical applications, thus making scalability, decentralisation and real-world commitment three key research directions. Towards that, this research presents a simulation-based machine learning approach for the railway traffic management problem, in the context of the Hunter Valley Coal Chain (HVCC). We modelled trains, load points and terminals as autonomous intelligent agents that interact, learn and act independently—thus constituting a multi-agent system (MAS). The MAS is implemented on top of a rail network simulation model currently in use at the HVCC. The model is adapted as a decentralised partially-observed Markov decision process environment that allows multi-agent learning via a genetic algorithm. We present experiments with scenarios based on the actual rail network data, which show that the MAS outperforms the heuristic approach embedded in the HVCC simulation tool by up to 81% (in terms of the schedule's total dwell time). Further to those experiments, a comparison analysis evaluates the relevance of specific state features (e.g. track length, train conflicts, etc.). Finally, an important outcome was that the agents have learned to overcome very complex traffic situations that appear in train scheduling operations and that sometimes result in unnecessarily long dwell times. This type of high level learning represents a significant step forward in the use of complex computational techniques for rail transportation problems. [ABSTRACT FROM AUTHOR]

WC55-Y hydraulic support transporters allow an efficient transport of support equipment in fully mechanized mining faces. However, the diesel particulate matter (DPM) emitted by these transporters seriously pollutes the air environment along mine roadways, endangering the health of coal mine workers. In this paper, we simulated the diffusion dispersion of DPM during the functioning of a WC55-Y hydraulic support transporter (emitting high amounts of exhaust pollutants) by computational fluid dynamics, identifying high DPM concentration zones. While the transporter was driven along a coal auxiliary transportation roadway, the diffusion–dispersion characteristics of DPM changed: DPM reached a long horizontal diffusion distance and a high concentration. We found that to avoid the inhalation of DPM and reduce its potential harm, coal mine workers should keep a distance of at least 21.27 m from the hydraulic support transporter while the vehicle runs along the roadway. Moreover, according to our simulation, the operators responsible for disassembling the hydraulic support transporter should wear protective equipment with good filterability while unloading it. Overall, the findings of this study can be applied to outline new work practice guidelines and design new optimum auxiliary ventilation for reducing underground miner exposure to DPM. [ABSTRACT FROM AUTHOR]