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Collect screen paperboard of mine power transformer and ordinary power transformer in practical lifetime cycle to make insulation test samples, and according to the standard of power, use test means such as viscosity tester, scanning electron microscopy to compare and analyze microstructures of insulation paper such as polymerization degree, surface morphology of mine power transformer and ordinary power transformer, as well as electrical properties such as partial discharge and electrical strength; at the same time, effect of microstructure changes on electrical performances during aging was discussed. The analysis results show that the insulation defects gradually form and expand in the insulation paper during the aging process; compared with the insulation paper of ordinary power transformers, the surface morphology changes of the insulation paper of mine power transformers are more obvious, and the formed defects are more significant.In the aging process, the polymerization degree of insulation paper of ordinary power transformer and mine power transformer presents a gradual decline trend, but the decline speed of polymerization degree of mine power transformer insulation paper is faster.The operating environment of mining area is the main reason that the aging of insulation paper of mine power transformer is more serious than that of ordinary power transformer.In the process of operation, the insulation paper of mine power transformer will produce more discharge quantity, more discharge times and wider discharge phase than ordinary power transformer.In the aging process, the electrical strength of insulation paper of both ordinary power transformer and mine power transformer decreases gradually, but the electrical strength of insulation paper of mine power transformer is lower than that of ordinary power transformer.The microstructural defects of insulation paper of mine power transformers are the direct causes of the more significant changes in their electrical properties.In the process of state monitoring and maintenance of power equipment, it is suggested that the insulation state monitoring of mine power transformers should be carried out strictly according to the power standards,electrical property parameters are more suitable for insulation status monitoring and fault diagnosis of mine power transformer.

An online intelligent examination system of coal mining enterprises was proposed based on analysis of actual examination requirements of enterprise employees. A Web examination system is built firstly according to professional characteristics of the employees, and the system is optimized functionality. Secondly, GAAA algorithm integrated with genetic algorithm and ant algorithm is improved. The improved GAAA algorithm firstly updates ant colony pheromone, then searches test database by a comprehensive search of genetic algorithm and parallel distribution of ant algorithm for optimizing group volume of the database. At last, the improved GAAA algorithm is applied in the system to achieve examination of employees at anytime they like and anywhere they are and auto-generating examination paper. The practical application shows that speed of auto-generating examination paper of the system is high, repetition rate is low, the algorithm is feasible, and the system can meet practical requirements of online examination effectively and improve online examination efficiency of coal mine enterprise.

The paper introduced structure and key technologies of mine-used intrinsically safe wireless through-the-earth communication system，and analyzed working principle of the system. The system uses low-frequency electromagnetic radiation technology, digital communication technology and intrinsically safe power supply technology, which has characteristics of smaller volume, lighter weight and easy operation. The test results of prototype show that the system has realized two-way through-the-earth communication for 50 m with communication rate of about 150 bit/s and bit error rate of no larger than 0.2 %, and has a certain practical value

Rock blasting technology is crucial for national economic development, particularly resource extraction and infrastructure construction. This paper explores the constitutive relationships in rock blasting, proposing three evolutionary stages: constitutive relationships 1.0, 2.0, and 3.0. These stages focus on contradictory relationships, energy balance, and minimum action theory, respectively. Constitutive relationship 1.0 centers on the interaction between rock and explosive impact loads, emphasizing the offense–defense dynamic. It analyzes the dynamic mechanical response of rock materials under explosive and impact loads, highlighting the conflict and balance between the explosive force and the rock resistance. This stage provides fundamental insights into the behavior of rock materials under high-stress conditions. Constitutive relationship 2.0 approaches the problem from an energy perspective. It treats rocks with significant anisotropy, joints, fractures, bedding, and cavities as complex structural materials. This stage studies the dynamic equilibrium between load input energy and the energy required to destroy the material. By understanding the relationship between structural strength, input energy, dissipated energy, and releasable strain energy, researchers can better predict the response of complex rock structures to explosive loads and improve blasting efficiency. Constitutive relationship 3.0 examines the propagation laws of stress waves under explosive loads and their relationship with medium damage effects. This stage focuses on optimizing energy propagation paths based on the minimum action theory, aiming to maximize the effectiveness of the explosive force while minimizing unwanted damage to the surrounding rock mass. These theories not only reveal the mechanical behavior of rocks under different load conditions but also provide a theoretical basis for optimizing blasting design and improving blasting outcomes. In addition to these theoretical advancements, the integration of artificial intelligence and big data technologies offers a new approach to managing and predicting rock material performance. This paper proposes the concept of rock material engineering genes, which involves establishing a rock gene library to systematically manage the physical and mechanical parameters of rocks. By constructing performance prediction models using advanced data analytics and machine learning algorithms, this approach enhances the accuracy of predicting how different rock types will respond to various engineering applications. Such a comprehensive database has significant implications for resource extraction, geological disaster prevention, and infrastructure construction. The rock material gene library is expected to play an increasingly important role in mineral resource development, geological disaster prevention, and infrastructure construction, thereby promoting the development and application of engineering technology. Its integration with traditional blasting techniques can lead to more efficient and safer methods of rock blasting, ultimately contributing to the advancement of engineering practices and economic development of regions dependent on these technologies. This holistic approach underscores the importance of continued research and technological innovation in rock blasting and material science.

Lightweight soil is a novel technological material that boasts characteristics such as low density, high strength, thermal insulation, vibration isolation, environmental friendliness, and cost-effectiveness. These features make it highly suitable for a wide range of applications in geotechnical engineering, including roadbed backfill, soft foundation treatment, and tunnel load reduction. The influence of vibration loads resulting from transportation, earthquakes, waves, and other factors on the mechanical properties of lightweight soil has garnered considerable attention in recent research. This paper expounds on the influence of factors on the dynamic deformation characteristics and dynamic strength properties of lightweight soil. These factors include the mix ratio (such as the content of lightweight materials, dosage of curing agent, and moisture content), stress state, vibration frequency, dry–wet alternation effect, and freeze–thaw cycle. Additionally, we summarise the calculation model for the dynamic shear modulus and damping ratio of lightweight soil. The findings reveal that the incorporation of curing agents, such as cement and fly ash, substantially improves the resistance of lightweight soil to dynamic loads. Additionally, the distinctive pore structure of lightweight soil markedly enhances its vibration isolation effect. As dynamic strain increases, there is a nonlinear decrease in the dynamic modulus of lightweight soil while the damping ratio increases nonlinearly. Adjusting the content of lightweight materials and the dosage of curing agents can markedly improve the seismic reduction effect of lightweight soil, thus granting it greater dynamic stability. The coupling effects of dry–wet cycles, freeze–thaw cycles, and dynamic loads may lead to a degradation in the dynamic performance of lightweight soil. To extend its service life in practical engineering applications, the implementation of a waterproof layer is recommended. Model tests and numerical simulations substantiated the commendable dynamic stability and durability of lightweight soil in real-world engineering scenarios. Finally, following a comprehensive literature review, this paper identifies potential research directions. The study of dynamic characteristics in lightweight soil is still in its infancy, with the dynamic properties of novel solid waste lightweight soil remaining largely unexplored. Further exploration is required to fully understand the response mechanisms, mechanical properties, and constitutive models of lightweight soil under the combined effects of complex environmental factors and dynamic loads. Additionally, there is a need for continued research into the design and construction methods of lightweight soil across various engineering settings. In conclusion, this paper serves as a valuable reference for investigating the dynamics of lightweight soil and its extensive application in geotechnical engineering.

Autonomous path planning represents a cornerstone technology for enabling planetary rovers to carry out exploration missions on extraterrestrial planets. This technology facilitates planetary rovers in navigating safely and efficiently toward their mission goal. However, the special conditions of extraterrestrial planetary environments pose significant challenges for autonomous path planning compared with those faced by ground-based mobile robots. These challenges include communication delays and bandwidth limitations between the planetary rover and the ground control station, limited light, unknown and unpredictable terrain, distinct environmental features, and the difficulty of accurately modeling interactions between the rover wheels and the ground. Therefore, addressing how to generate a safe, efficient, and reasonable traversable path for a planetary rover under conditions of incomplete environmental data and uncertain positioning is a focal point of current research. This paper reviews advancements in perception map construction and autonomous path planning for planetary rovers, examining both research progress and engineering applications. First, this paper summarizes advances in perception map construction methods that rely on binocular vision. This method uses stereo vision to generate parallax maps, which in turn help construct a digital elevation model of the planet’s surface. Stereo matching algorithms are typically used to acquire parallax maps. The discussion includes an analysis of two main types of methods: region-based matching algorithms and feature-based matching algorithms. Second, the paper classifies existing autonomous path-planning methods for planetary rovers into two categories: those based on cost evaluation and those leveraging machine learning. As a typical representative of cost-evaluation–based path planning methods, the optional arc-based autonomous path planning method is spotlighted for its algorithmic principles and iterative applications in ongoing planetary rover missions. Furthermore, the potential of A* heuristic search algorithm, rapidly-exploring random tree star (RRT*) algorithm, and fast marching method(FMM) algorithms for enhancing planetary rover path planning is explored in a categorical manner. The application of machine learning in autonomous path planning for planetary rovers is also reviewed. Such methods are classified into two groups: end-to-end path planning methods and auxiliary approaches leveraging machine learning. Ultimately, based on the above analysis of the key technologies of planetary rover autonomous path planning, the paper identifies and discusses future research directions for improving autonomous path planning methods for planetary rovers. These include enhancing perception capabilities, improving optional arc paths, reducing slippage, integrating multiple path planning methods, and strengthening the use of machine learning.

Coal constitutes a pivotal element of China’s energy consumption structure. Xinjiang, which boasts considerable coal deposits, is a crucial energy succession zone and strategic reserve area within China. The coal resources in Xinjiang, particularly the Zhundong variety, typically exhibit elevated concentrations of alkali metals and alkaline earth metals (AAEMs), which are typical high-alkali coal well-suited for prolonged utilisation. The large-scale mining and utilisation of coal resources in Xinjiang has resulted in significant challenges, particularly in the form of slagging, staining and corrosion of high-alkali coal during combustion. These issues have severely limited the large-scale and efficient utilisation of this coal resource. The objective of achieving 100% clean combustion of high-alkali coal represents a significant challenge in the field of high-alkali coal utilisation. However, the primary obstacle lies in the resolution of the ash issue inherent to the clean combustion process. In this paper, about 4401 literatures in the field of high-alkali coal research in the past 20 years were obtained by using CiteSpace software. These literatures were subjected to literature clustering analysis, keyword highlighting analysis and time-series analysis. In addition, the peak interval of the heat of research in different periods was systematically analysed. It is highlighted that the future research in this field should prioritise the investigations into the existence form and migration characteristics of AAEMs, the low-temperature eutectic formation mechanism, and the development of combustion-efficient utilisation regulation measures. Based on a representative high-alkali coal, a summary of the fundamental physicochemical properties was presented, with a particular focus on the impact of the alkaline component of coal ash on the melting temperature of coal ash and the existence form of AAEMs. This study investigates the research progress of two core aspects, namely, AAEMs and low-temperature eutectics, with the objective of exploring the migration characteristics of AAEMs and the generation mechanism of low-temperature eutectics. This is of great significance for the clean and efficient utilisation of coal with low carbon. Subsequently, the regulatory measures for the efficient utilisation of high-alkali coal combustion are categorised into three principal groups: the reduction of AAEMs content, the increase of acidic components, and the optimisation of reactor operation and structure. The principles and effects of the utilisation measures on the mitigation of slagging and staining of high-alkali coals are then analysed, and the shortcomings of the corresponding methods are identified. Furthermore, it is proposed that the coal dust purification-combustion efficient utilisation technology may prove an effective means of solving the problem of efficient utilisation of high-alkali coal combustion. Ultimately, the dual-carbon context underscores the necessity for thermal power units to enhance their variable load regulation capabilities, thereby ensuring a stable power output. This paper puts forward suggestions and proposes avenues for future research in order to achieve the efficient utilisation of high-alkali coal combustion. It is essential that these are combined with local diversified resource advantages and that an overall plan is formulated in order to accurately control the problems of slagging, staining and corrosion throughout the entire process. This will enable the clean, efficient and low-carbon utilisation of the resource to be achieved.

Overheating and the resultant tube failures of boiler high temperature heating surfaces such as waterwall is one of the sore major problems affecting the safe operation of coal-fired power generating units. Boiler tube overheating generally occurs at some localized areas of boiler heating surfaces. To alleviate tube overheating and avoid the resultant tube failures, it’s necessary to monitor the tube temperature distribution in real-time to make preventive boiler operation adjustment. Due to the limitation of measurement method and the huge time cost of numerical method, currently there are still lack of methods that can realize the real-time prediction of waterwall tube temperature distribution during boiler operation. Therefore, this paper integrated the coupled heat transfer model with neural network and established a typical database of a 350 MW supercritical boiler using the coupled model, which contains 220 different typical boiler operating cases generated by adjusting 46 key operating parameters. Then, 4 400 expansion cases were derived at extremely time cost by the quick expansion method proposed in this paper. The deep neural network model was then constructed with 46 boiler operating parameters and waterwall coordinates as the model input and the tube temperature at the corresponding locations as the output. The DNN model was trained based on the comprehensive database containing 220 typical cases and 4 400 expansion cases. The MSE of the DNN model on validation set was only 0.005 3, the AUC5 was 0.988 and the calculation time was within 0.1 s. It illustrates that the data-driven model realized the real-time prediction of the waterwall tube temperature distribution over the entire boiler operating conditions by means of generalizing the numerical simulation results of finite cases. In addition, a quick database expansion method was proposed to address the problem that the heat transfer deterioration is difficult to be accurately predicted due to the insufficient data of the heat deterioration, and the prediction accuracy of the heat deterioration problem was significantly improved. Both the accuracy and the response speed of the model meet the demand of coal-fired plants for a real-time monitoring of tube temperature distribution.

In recent years, the global photovoltaic industry has been booming, and the installed capacity of photovoltaic has climbed rapidly. The carbon emission of photovoltaic power generation is only 10% compared with that of traditional fossil fuel power generation. However, the service life of solar photovoltaic panels is only 20 to 25 years, and as the first batch of photovoltaic modules reach their life limit, the world will successively usher in the "retirement tide" of photovoltaic modules. To realize the harmless and green disposal of retired photovoltaic modules and recycle the secondary energy in photovoltaic modules is the key link of the sustainable development of photovoltaic industry. At present, the mainstream crystalline silicon photovoltaic modules are composed of photovoltaic glass, EVA (ethylene-vinyl acetate copolymer) film, crystalline silicon cell sheet and fluorine backplane. The complete photovoltaic glass and crystalline silicon cell have high economic value, so how to selectively remove EVA film and fluoride backplate is an important step in the recycling of retired photovoltaic modules. Based on this, this paper first introduces the structure and application of each part of crystalline silicon photovoltaic module. Then according to the physicochemical characteristics of each part, the advantages and disadvantages of various processes including thermal disposal, physical separation and chemical dissociation are analyzed in detail from the perspective of the integrity of photovoltaic module recovery. Existing studies have shown that the difficulties in the recovery of retired crystalline silicon photovoltaic modules are mainly in the following three aspects: the removal of EVA film with high efficiency, low pollution and low energy consumption, the harmless disposal of fluorine-containing substances in the backplane, and the removal of pyrolysis carbon residues on crystalline silicon cells. In view of the above difficulties, this paper outlooks the future development trend of recycling technology for decommissioned photovoltaic module, and puts forward some reasonable suggestions on the research and design of recycling technology for decommissioned photovoltaic module in China. This paper will provide an important technical guidance for the efficient green recycling of decommissioned photovoltaic modules.

Aiming at the promotion and application of 5G communication technology in coal mines, this paper analyzes the explosion prevention risk of radio frequency (RF) electromagnetic energy under the condition of far field radiation. In this paper, the equivalent model of the loop antenna and the theoretical calculation model of the spark power are established under the assumption that the characteristic impedance of the spark is matched with the internal impedance of the antenna. Secondly, for the 3.55 GHz operating frequency, a narrow bandwidth loop receiving antenna and a horn transmitting antenna by theoretical calculation are designed, and the S11 curve of the designed antenna by CST simulation software is verified. Finally, based on this, a simulation platform for transceiver antenna is built, which can be used to estimate the electric field strength and integrated voltage at both ends of the breakpoint of the loop antenna. The simulation results show that: when the electric discharge machining (EDM) is matched with the internal resistance and impedance of the antenna, the EDM power is maximum, and the impedance value of the EDM has no effect on the induced voltage of the whole antenna, and the power obtained by the EDM decreases with the increase of the distance between the transmitting and receiving antennas. The simulation and calculation show that when the transmitting antenna power is 100 W and the transmitting antenna gain is 5, the theoretical maximum spark power generated under the far-field radiation condition is not enough to ignite the gas. Therefore, the threshold power index in the standard may be too conservative and needs to be further verified.

Efficient coal mining is essential for ensuring China’s energy security, and the advancement toward intelligent coal mining represents a strategic move to foster high-quality development within the coal industry. The cornerstone of realizing intelligent coal mines lies in underground space positioning technology. Accurate positioning is crucial not only for safeguarding miner safety but also for optimizing mining operations, improving equipment utilization and minimizing operational costs. The unique challenges of underground mining, such as the absence of satellite navigation signals, restricted and confined operating environments, and harsh electromagnetic and lighting conditions, place new requirements on positioning techniques. This paper focuses on the localization issues specific to underground coal mines, offering a systematic overview of four fundamental localization techniques: range intersection, database matching, dead reckoning, and multisource information fusion. Range intersection determines location by utilizing distance measurements obtained through various methods. Database matching employs online measurements to match against characteristics stored in a database. Dead reckoning calculates the current location by integrating measurements over time. Lastly, multisource information fusion enhances positioning accuracy by combining data from multiple sources. The paper further reviews detailed implementation schemes of these four positioning technologies in coal mines, highlighting typical sensors or systems and summarizing current research developments. For range intersection methods, methods such as visible light, ultrasonic, wireless local area network, bluetooth, ultra-wideband, and infrared positioning are examined. Database matching positioning methods include geomagnetic, gravity, and radio fingerprint positioning systems. Dead reckoning techniques cover inertial navigation, pedestrian navigation, and odometers, while multisource information fusion focuses on integrated navigation and simultaneous localization and mapping. Moreover, the paper identifies key challenges confronting underground positioning systems in coal mines. These challenges encompass the increasing inaccuracies in absolute localization methods, difficulties in correcting dead reckoning localizations, performance declines in multisource fusion localization methods, and the absence of comprehensive standardization. Given the complex environment of coal mines, there is still much room for improving the accuracy and reliability of underground positioning. To address these challenges, this paper proposes several avenues for improvement. Enhancing the performance of the positioning systems might involve fostering collaboration across multiple systems, incorporating brain-inspired navigation to explore new localization solutions, and strengthening seamless and emergency localization capabilities. Furthermore, in today’s interconnected intelligent age, the privacy and security aspects of positioning systems warrant careful consideration.

To address the issues of image feature insensitivity, high requirement of expert experience, and low recognition accuracy of traditional machine vision methods in train wheelset bearing damage detection, this paper proposes an identification method based on the framework of convolutional and transformer fusion networks for identifying damage to train wheelset bearings. First, due to the complexity of train-bearing images, their category imbalance is more severe; an image preprocessing method called image enhancement category reorganization is used to improve the quality of the acquired image dataset and eliminate the effects of the imbalance dataset. Second, a convolutional neural network (CNN) has high model construction and training efficiency due to adopting a local sensing field and weight-sharing strategy, which can only sense local neighborhoods but has limited ability to capture global feature information. Transformer is a network model based on a self-attention mechanism. With strong parallel computing ability, it can learn the remote dependencies between image pixels in the global scope and has a more powerful global information extraction ability. However, the ability to mine the local features of the image is not sufficient. Therefore, this paper presents a VGG and transformer parallel fusion network that integrates the global contour features and local details of the image based on the fusion of convolution and self-attention. Furthermore, a multiscale dilation spatial pyramid convolution (MDSPC) module is constructed to fully mine the multiscale semantic features in the feature map using multiscale dilation convolution progressive fusion. The proposed method effectively solves the problem of feature information loss due to the mesh effect caused by the expansion convolution. Additionally, embedding coordinate attention (CA) after the MDSPC module can obtain remote dependencies and more precise positional relationships of feature images from two spatial directions, which can more accurately focus on specific regions in the feature map. Finally, experimental analyses were conducted using the NEU-DET image defect and self-constructed train wheelset bearing image datasets. The experimental results demonstrate that the proposed model has an accuracy of 99.44% and 98% for recognizing six types of defects and four types of images of wheelset bearings in NEU-DET data, respectively. The feature extraction capability of the proposed model was verified using model visualization methods. Compared with existing CNN models, ViT model with self-attention mechanism, and CNN-transformer fusion model, the proposed method shows significantly better evaluation metrics and accurately identifies different types of image samples without significantly increasing the model complexity.

As optimization problems grow increasingly complex, characterized by their intricate difficulty, larger-scale, and diverse constraints, swarm intelligence optimization algorithms have emerged as an effective solution for addressing these multifaceted challenges. Among these, the marine predators algorithm, a recent innovation in intelligent optimization algorithms, has demonstrated remarkable efficacy in solving optimization issues. However, its application to complex CEC test function sets and engineering constraint problems reveals several limitations, including limited adaptive ability, low optimization accuracy, and high local shackle probability. This paper proposes an enhanced version of the marine predators algorithm designed to overcome its inherent shortcomings. The enhancement begins with the integration of a learning automata guided teaching–learning search mechanism during the marine memory stage. This adjustment aims to strike a better balance between exploration and exploitation across different iteration periods. Subsequently, the introduction of a logarithmic spiral exploration mechanism phase strengthens the algorithm’s ability to conduct nuanced searchers around the optimal solution, thereby improving convergence accuracy. Finally, an improved adaptive relative reflection strategy is added at the end of each iteration to enhance the algorithm’s capability to escape local optima and reduce the risk of local shackling. The optimization performance of this refined algorithm is evaluated through parameter sensitivity analysis, determining the optimal parameter values. To validate its effectiveness, the improved algorithm undergoes testing against six benchmark algorithms, including the basic marine predators algorithm and its variants, as well as other improved algorithms and those recognized with awards in the CEC2017 test suite across 100 dimensions. The evaluation focuses on optimization accuracy, the Wilcoxon rank sum test, and boxplot analysis. The test results indicate that the improved algorithm proposed in this paper outperforms the other six benchmark algorithms in optimization precision, convergence rate, and solution stability, particularly when solving complex functions in high-dimensional (100 dimensions) spaces. Furthermore, the applicability and superior performance of the improved algorithm are demonstrated through comparative analysis with four established algorithms on challenging engineering design optimization problems. These include welded beam design, process synthesis, heat exchanger network design, and design optimization of industrial refrigeration systems. The findings unequivocally showcase the enhanced algorithm’s exceptional ability to solve various engineering constraint problems effectively.

In materials genetic engineering, data-driven machine learning techniques have garnered significant attention as a powerful new tool in the field of magnesium alloys. Traditional empirical trial-and-error methods and those based on density functional theory have struggled to keep pace with the continuous advancements in material science needs owing to high time costs and low efficiency. By relying on statistical methods instead of solving physical equations, machine learning can quickly predict material properties at a low cost, provided the connection between descriptors and target properties is identified. This capability can streamline the experimental process. Magnesium and its alloys show tremendous potential in aerospace, automotive, and other fields owing to their low density and high specific strength. However, their industrialization has been limited by several challenges, including the varied effects of different alloying elements, preparation and processing defects, deformation difficulties, and the common trade-off between strength and ductility. Machine learning can accelerate the discovery of novel magnesium alloys or processing parameters, and explore the relationships between their physicochemical characteristics and target properties. This paper comprehensively and systematically reviews the research progress of machine learning applications in magnesium alloys. It introduces the basic processes and various methods of machine learning, including data set collection, data preprocessing, model building, and performance evaluation. The classification of machine learning algorithms is summarized briefly. The paper then focuses on the research achievements of machine learning applied in many aspects, such as machining processes, microstructure, mechanical properties, corrosion resistance, hydrogen storage properties, intrinsic properties (reinforcement mechanism, anisotropy, etc.) and inverse design. Factors such as alloy compositions, test temperature and time, second phase, and Schmid factor can be considered as features and input into machine learning models for training. These models not only accelerate the design of novel high-performance magnesium alloys but also enhance the understanding of magnesium alloy mechanisms. Additionally, the paper analyzes some urgent issues in the research and application of machine learning in magnesium alloys. These include insufficient prediction of the chemical and physical properties of magnesium alloys, the nascent stage of predicting the design and service performance of magnesium alloy components, and the lack of high-quality data sets. Finally, the paper proposes future research directions and development trends in the application of machine learning in magnesium alloys.

This paper proposes a spatiotemporal coordinated control method for unmanned aerial vehicle (UAV) swarms based on predefined time convergence in response to the coordinated ground attack problem of UAV swarms in a three-dimensional battlefield environment. First, we analyze the UAV swarm’s ground attack task scenario and its relevant elements and establish models for the UAV swarm, ground-moving targets, and attack problems. Building upon problem modeling, this paper considers the spatiotemporal coordinated strategy for a UAV swarm and designs a spatiotemporal coordinated control law for a three-dimensional spatial UAV swarm ground attack. The proposed spatiotemporal coordinated control law can guide the UAV swarm to reach the desired attack position within a specified time, achieving a spatiotemporal coordinated attack on ground-moving targets. A theoretical analysis based on the Lyapunov function demonstrates that the proposed control law can asymptotically stabilize the attack position error variable under a predefined time condition. Simulation results show that the proposed coordinated control law can achieve spatiotemporal coordination of the UAV swarm in a ground-moving target turning motion, enabling the completion of the multiangle coordinated attack on ground-moving targets within a specified time. Currently, research on the coordinated ground attack of UAV swarms mostly focuses on stationary targets, making it difficult to apply to coordinated attack tasks on moving targets. Although the coordinated tracking of moving targets by UAV swarms has been reported in the literature, spatiotemporal coordination among UAVs has not been considered. Therefore, this paper focuses on the spatiotemporal coordinated control strategy of UAV swarms, with a background of coordinated ground attacks by UAV swarms. First, this paper conducts a detailed analysis of the UAV swarm’s coordinated ground attack task in terms of time and multiple elements to establish a comprehensive task profile. Subsequently, by focusing on the core elements of coordinated ground attack by UAV swarms, this paper models both UAV swarms and ground-moving targets. Based on the established models and coordinated control strategy, this paper considers spatiotemporal coordination constraints and designs acceleration commands for each UAV to guide them to reach the specified attack positions at predefined time points, enabling coordinated attacks on the ground targets simultaneously. The designed acceleration control commands serve as inputs to the UAV’s autopilot, further generating lower-level control commands, such as speed, flight path angle, and heading angle, thereby achieving stable flight control of the UAVs. The proposed coordinated control strategy endows the UAV swarm with the capability to achieve spatiotemporal synchronized coordinated attacks, thereby enhancing the operational effectiveness of UAV-coordinated ground attacks.

Recently, testing and using micro-unmanned vehicles, such as unmanned aerial vehicles (UAVs), in scenarios such as supply transportation, agricultural management, and military operations have become more common. It is no longer sufficient to control a single UAV to accomplish all missions. With the increasing complexities associated with operating and task requirements, an unmanned swarm requires a series of algorithms with higher efficiency, greater generalization ability, and better adaptability than the earlier algorithms. A combination of unmanned swarms with artificial intelligence is becoming a common solution to manage the above requirements. Deep reinforcement learning (DRL) is a machine learning method that combines deep learning (DL) and reinforcement learning (RL); therefore, this method has the advantages of DL and RL. Using an RL method, an agent can learn from the environment by trial and error and make decisions that autonomously obtain high scores. However, when the given environment is complex, the decision function of the agent may be too difficult to implement and then the agent cannot make the correct decision. The DL method has strong fitting ability. A suitable deep neural network can simulate any linear or nonlinear function. If the DL method is used to simulate the decision function in RL, the hybrid method can solve the problem that an agent cannot solve and make a correct decision in a complex environment. The combination of an unmanned swarm and a DRL method has been widely studied. This paper introduces the concept of DRL from the perspective of principles and characteristics. This paper analyzes several typical DRL algorithms, discusses the various control requirements of a UAV swarm, and then focuses on the achievements of combining DRL and a UAV swarm control. Finally, this paper presents viewpoints on the application prospects and challenges related to landing and transformation in the combination field. The concept of an unmanned swarm originated from the study of the behavior of biological groups. Several species of bees, ants, birds, fish, and other creatures exhibit complex group behaviors. These clusters comprise many independent individuals in accordance with certain aggregation rules to form a coordinated, orderly group movement mechanism. Similar to biological clusters, in the field of robotics or UAVs, unmanned swarm systems are crowded intelligent systems. These systems consist of multiple homogeneous or heterogeneous unmanned equipment to achieve mutual behavior coordination and jointly complete specific tasks through interactive feedback and incentive response of information. In practical applications, an unmanned swarm system needs to meet the requirements of an open environment, a changeable situation, limited resources, and real-time responses. This system needs to have multicore collaborative capabilities such as distributed collaborative perception, intelligent collaborative decision-making, and robust collaborative control. The distributed intelligent collaborative control method based on DRL can fully meet the control requirements of high intelligence and robustness of unmanned cluster systems.

The energy structure, characterized by abundant coal, limited oil, and gas shortages, contributes to China’s prevalent coal consumption. In the context of carbon peaking and carbon neutrality, coal combustion exacerbates environmental pollution, highlighting the urgent need to promote clean and efficient coal use. As coal gasification technology rapidly advances as a pivotal method for achieving cleaner and more efficient coal use, the challenge of effectively managing coal gasification solid waste becomes increasingly important. The substantial carbon content and complex distribution of carbon and inorganic minerals in coal gasification residue hinder its comprehensive use in building materials and mixed combustion processes. Consequently, the primary disposal methods for coal gasification slag remain stacking and landfilling, which waste land resources and pollute the environment. To realize sustainable and eco-friendly development within the coal chemical industry, addressing the efficient disposal of coal gasification slag is of paramount importance. An abundance of aluminum, silicon, and carbon residue resources endows coal gasification slag with immense potential for the production of high-value-added materials, garnering considerable attention from researchers. The inorganic mineral composition of coal gasification slag is rich in silicon oxide, alumina, and iron oxide components, which can be harnessed for synthesizing zeolite, mesoporous silica, and various micro/nano materials. The carbon residue within coal gasification slag exists in a flocculent porous form, exhibiting a certain degree of graphitization and possessing a large specific surface area, thereby facilitating the production of porous carbon materials. Moreover, a strategy of simultaneous use of carbon and ash can be leveraged to prepare carbon–silicon mesoporous and zeolite–carbon composite materials from coal gasification slag. Looking ahead, based on large-scale coal gasification slag consumption through bulk resource use, the high-value utilization path of coal gasification slag must be further explored to increase the added value of the industry. Building upon the understanding of the production process of coal gasification slag, this paper introduces the composition and micromorphology of coal gasification slag while systematically detailing the preparation methods for synthesizing mesoporous silica, porous carbon, carbon–silicon mesoporous composite materials, zeolite, and zeolite–carbon composite materials. Additionally, the paper delves into the applications of functional micro/nano materials derived from coal gasification slag across various domains, such as adsorbents, catalysts, filling materials, supercapacitors, and microwave-absorbing materials. Finally, the existing problems and future development trends of coal gasification slag resource usage are addressed, aiming to provide guidance for the comprehensive use of coal gasification slag.

During the flight of a flapping-wing flying robot, the unique flapping-wing propulsion mechanism causes periodic pitching and rolling motions of the body. In passing through different airspeeds and altitudes over different terrain, the wings cycle up-and-down through predefined stroke patterns to generate the aerodynamic force required for powered flight. However, this oscillatory flapping motion also causes the aircraft structure to pitch and roll periodically about its center of mass in an oscillatory manner. Consequently, substantial high-frequency jitter shakes aerial video footage captured by the onboard optical sensors. In particular, the rapid shaking that disrupts the image is synchronized with the characteristic wing beat rhythm. This jitter negatively affects the quality and usefulness of the acquired aerial video. Repeated up-and-down pitching and rolling displacements shake aerial footage, greatly reducing its clarity and stability. Without mitigation, the jitter severely affects imaging results, limiting the potential applications of such video. To solve this problem, this paper proposes an electronic image stabilization algorithm based on oriented fast and rotated brief (ORB) and sliding mean filtering for online debounce processing. First, because the jitter period of aerial images of a flapping-wing flying robot is consistent with the wing flapping period, we designed an estimation algorithm to estimate the wing flapping period based on image features. This algorithm enables us to more accurately capture the periodic characteristics of jitter and provides important parameters for subsequent image stabilization processing. Second, we proposed a motion filtering algorithm associated with the flapping period, which can adaptively and dynamically adjust filtering parameters adaptively according to different flight conditions. The algorithm proposed in this paper is advantageous because it can dynamically adjust parameters in real time based on the actual flight conditions of flapping-wing robots, thereby better adapting to different flight conditions and further improving the image stabilization effect. Third, to verify the feasibility and stability of the algorithm, this paper performed a flight experiment by mounting a visual imaging device on a flapping-wing flying robot. Experimental results show that the proposed algorithm shows better image stabilization effects than commonly used electronic image stabilization algorithms in flapping-wing flying robots. Finally, we summarized the advantages of the proposed algorithm and provided an outlook on the future research directions.

The western region is rich in coal seam resources, and the intensity of mining is high. Traditional mining methods can easily lead to the loss of water resources in aquifers, resulting in serious water shortage problems. Therefore, researching water conservation mining technology in shallow-buried strong water-rich areas is of great significance for ensuring water resource safety, maintaining ecological balance, and promoting economic development. By controlling the movement of rock strata to maintain the stability of aquifers, developing cost-effective and widely applicable water conservation mining technologies and materials, a green and efficient three-dimensional water conservation mining system for coal seams in shallow-buried strong water-rich areas can be formed, achieving dual goals of coal mining and water resource protection. This paper first analyzes the current situation and difficulties of water conservation mining, compares existing technologies from aspects of technological advancement and safety, and conducts in-depth research on materials, equipment, and processes, summarizing and analyzing the applicable conditions of different technologies. Then, taking the Jinjie coal mine as an example, based on the water conservation coal mining plan and the actual measured height of the water-conducting fracture zone, the mining field is divided into regions, proposing the principle of “regional classification management”. Innovatively, the paper proposes the technology of remaking the bottom water-blocking layer of the quaternary loose water-rich layer before mining, forming an effective isolation, achieving coordinated development of coal mining and water resource protection, and protecting natural topography and surface vegetation. At the same time, in response to the inefficiency of the current comprehensive mining frame back paste filling and the inability of the support to flexibly switch between filling and non-filling, a new type of filling and non-filling dual-use support is designed and proposed. It can be used as a filling support and can also be used for the disassembly of the rear support, installing the side protection plate of the protective beam, meeting the needs of ordinary support use, and improving the efficiency of equipment use. Finally, the paper summarizes the technology of grouting repair and transformation of the top of the water-conducting fracture zone after mining and the evaluation of grouting effects, and illustrates it with the application of the Jinjie coal mine. It has a wide guiding significance for similar conditions of coal seam mining in the Ordos Basin and provides important support for green mining in the coal industry.

As an important energy support for China”s economic development, coal will remain dominant in energy consumption for a period of time in the future. With the continuous development of related technical equipment, some mines in China have achieved a single-well single-side output of more than 10 million tons. Safe and efficient development of coal resources is an important direction for China”s future scientific and technological development. This paper relies on the national key research and development plan project “Key Technologies and Equipment for Efficient Fully Mechanized Mining of Super-high and Super-long Working Faces in Coal Mines”, and takes the efficient mining of super-high and super-long working faces as the core. The rock control theory, technology and fully mechanized mining equipment development status of super-high and super-long high-efficiency fully mechanized mining faces at home and abroad are introduced. This paper focuses on three major scientific issues: “three-dimensional dynamic fracture law of full overburden rock in super-large mining space and instability failure mechanism of super-high coal wall”, “structural transformation and stress regulation mechanism of strong overburden rock in super-large mining space”, “dynamic response and efficient intelligent collaborative operation mechanism of super-high and super-long fully mechanized mining equipment group under multiple dynamic loads”. Five technical breakthroughs are carried out, including “fracture and migration mechanism of full overburden rock in super large mining space and collaborative control theory of surrounding rock”, “fracturing and pressure relief technology and equipment in strong mine pressure area of super large mining space”, “intelligent mining complete equipment of super high and super long working face”, “intelligent collaborative control technology and equipment of equipment group of super high and super long working face”, “high efficiency fully mechanized mining engineering demonstration of super high and super long working face”. Practice shows that some ultra-high and ultra-long coal mining faces in China have formed a complete set of technology and equipment system, which provides a reliable guarantee for the safe, efficient and green mining of coal resources in thick and medium-thick coal seam mining areas with relatively simple geological conditions in China. The development direction of theoretical technology, equipment and control of super-high and super-long high-efficiency fully mechanized mining face in China is pointed out.

Based on the insufficient intelligent perception capability of coal mine heading face in China, this paper proposes a three-dimensional spatial mapping method of coal mine roadways based on multi-sensor fusion. This method utilizes 3D sensors such as LiDAR to capture and preview the environmental information of the coal mine roadways, and constructs a real-time autonomous mapping model to provide spatial perception information for large-scale coal mine heading equipment. By optimizing the Extended Kalman Filter algorithm based on the attitude perception theory and implementing the multi-sensor fusion technology, the method achieves fast updates of global maps and high-precision mapping in the geometrically degraded mine heading environment. The main contribution of this study lies in the fusion of multiple sensor data to construct a high-precision underground three-dimensional map and improve the intelligent perception capability and autonomous operation level in the mine heading environment. Firstly, some advanced LiDAR sensors are used to capture point cloud data within the mine heading face, extract point cloud features, and establish inter-frame feature mapping models to create a centimeter-level underground three-dimensional map. Meanwhile, by fusing data from LiDAR with inertial navigation using an iterated error state Kalman filter, the robustness of the system is improved, ensuring the accuracy and stability of the underground three-dimensional map. Additionally, this paper considers the influence of factors such as underground dust and water mist on the accuracy of three-dimensional spatial mapping of mine roadways. The Kalman filter gain is optimized to adjust the weight of LiDAR odometry, enhancing the environmental adaptability of three-dimensional spatial mapping under low dust concentration conditions. Experimental results show that comparing to the LeGO-LOAM and LINS, the proposed three-dimensional spatial mapping method outperforms in terms of trajectory estimation accuracy, point cloud quantity, and point cloud density in mapping the environment. The method presented in this paper can be utilized to achieve high-precision and fast-updating three-dimensional maps for autonomous mine heading equipment, thus improving the intelligence level of coal mine heading face and providing a data support for the intelligent perception and autonomous operation in the mine heading environment.

Copper and cobalt, both strategic metals, play an indispensable role in many fields, such as national infrastructure and defense science and technology. Copper, one of the earliest nonferrous metals discovered, extracted, and applied by humans, teams up with cobalt—a silver-white metal—that serves as a crucial raw material for superalloys, permanent magnets, and chemical industries. The production and consumption of these metals significantly affect the development of national infrastructure, essentially controlling the lifeblood of the national economy. The wide-ranging application of copper and cobalt necessitates an efficient extraction process for these resources. For instance, Congo, Africa, hosts complex copper–cobalt co-associated deposits where the copper–cobalt ratio can reach 3∶1, making it a significant resource. Given the increasing global demand for copper and cobalt, extracting these valuable elements from complex copper–cobalt co-associated ores is of paramount importance. This paper reviews the mineralogical characteristics of these complex resources, including oxidized ores and sulfide ores. It compares and discusses the characteristics of extraction processes such as roasting and leaching. Ultimately, it evaluates the prospects and challenges of extracting copper and cobalt from complex co-associated copper–cobalt deposits. The review reveals that copper–cobalt oxide ores are more compatible with pure wet process treatment, while sulfide ores are better suited for an enhanced leaching process combined with roasting and leaching. The thermodynamic characteristics and kinetic limiting factors of metal extraction from copper–cobalt ores are also compared in this review. In the extraction of the copper–cobalt oxide ore, the mature extraction process is reduction leaching, and different reducing agents show different leaching effects. It is worth noting that there are significant differences in the degree of weathering of copper oxide cobalt ores. Considering Africa’s relatively underdeveloped power facilities and the shortage of chemical reagents, biological leaching and heap leaching present alternative methods for copper oxide cobalt ores. For copper and cobalt sulfide ores, the promising approach for industrial application is a method that combines enrichment, roasting, and leaching. As the copper–cobalt sulfide ore is nearly the only cobalt occurrence mineral, strengthening the phase transformation of copper–cobalt sulfide ore is vital for cobalt extraction. This can be optimized from both thermodynamic and kinetic perspectives. The aim of this paper is to provide some references for the efficient and reasonable collaborative extraction and utilization of copper–cobalt resources.

Over the past two decades, language modeling (LM) has emerged as a primary methodology for language understanding and generation. This technology has become a cornerstone within the field of natural language processing (NLP). At its core, LM is designed to train models to predict the probability of the next word or token, thereby generating natural and fluent language. The advent of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers and GPT-3, marks a significant milestone in the evolution of LM. These LLMs have left a profound impact on the field of artificial intelligence (AI) while also paving the way for advancements in other domains. This progression underscores the power and efficacy of AI, illustrating how the landscape of AI research has been reshaped by the rapid advancement of LLMs. This paper provides a comprehensive review of the evolution of LLMs, focusing on the technical architecture, model scale, training methods, optimization techniques, and evaluation metrics. Language models have evolved significantly over time, starting from initial statistical language models, moving onto neural network-based models, and now embracing the era of advanced pre-trained language models. As the scale of these models has expanded, so has their performance in language understanding and generation. This has led to notable results across various sectors, including education, healthcare, finance, and industry. However, the application of LLMs also presents certain challenges, such as data quality, model generalization capabilities, and computational resources. This paper delves into these issues, providing an analysis of the strengths and limitations of LLMs. Furthermore, the rise of LLMs has sparked a series of ethical, privacy, and security concerns. For instance, LLMs may generate discriminatory, false, or misleading information, infringe on personal privacy, or even be exploited for malicious activities such as cyber-attacks. To tackle these issues, this paper explores relevant technical measures, such as model interpretability, privacy protection, and security assessments. Ultimately, the paper outlines potential future research trends of LLMs. With ongoing enhancements to model scale and efficiency, LLMs are expected to play an even greater role in multimodal processing and societal impact. For example, by integrating information from different modalities, such as images and sound, LLMs can better understand and generate language. Additionally, they can be employed for societal impact assessment, providing support for policy formulation and decision-making. By thoroughly analyzing the current state of research and potential future directions, this paper aims to offer researchers valuable insights and inspiration regarding LLMs, thereby fostering further advancement in the field.

The situation of coal as the stabilizer guarantee the energy safety in China cannot be changed in the short term, and it will continue to face the problems of coal resource protection and the low utilization rate of coal-based solid waste. Through innovative mining methods, improving the recovery rate of coal resources and the large-scale disposal of coal-based solid waste, a new path is being paved for the green and sustainable development of the coal industry. Based on the in-depth research on the development status of coal mining technology, a new roadway excavation method of “excavation-backfill-retention” is proposed. This paper systematically discusses the scientific problems and key technologies of the “excavation-backfill-retention” construction method from three aspects: rapid excavation, continuous and efficient backfilling and safe construction of large-section roadways. The research results show that ① aiming at the engineering problems such as the fast excavation of large section roadway and the stability control of surrounding rock of heading face, three scientific problems are summarized: the matching mechanism between coal and rock characteristics and cutting parameters of roadway boring machine, the spatial transfer mechanism of overburden load and the deformation mechanism of surrounding rock, the interaction relationship between anchor/cable roof and support mechanism. The technology system has been built, which is based on the real-time sensing of the geological environment in the excavation area, the intelligent cooperative operation of the multi-process of falling, loading and transporting coal, the aging control of surrounding rock of the drilling anchor support together with excavation, the anti-leakage and ventilation optimization of the large section roadway. ② The scientific problems of continuous and efficient backfilling are summarized from the two aspects of timely construction of large section roadway bearing system and its bearing performance regulation, including the co-bearing mechanism of background coal anchor/cable, the hydration consolidation mechanism of multi-component solid waste base backfill materials, and the space-time evolution law of physical and mechanical characteristics of backfill are clarified. A continuous and efficient backfill key technology system with the core of safe and efficient backfill space construction, backfill material performance control and backfill material flow-consolidation perception has been established. The breakthrough of relevant theories and technologies can provide a theoretical basis for raw material selection and ratio design of backfill materials, additive development/selection and working performance control, excavation rate and backfill step optimization design, consolidation monitoring of backfill body, etc. ③ Based on the key problems of roadway safety and surrounding rock easy to break, sheet and damage in the whole life cycle, the paper elaborates three scientific problems: the spatial and temporal distribution characteristics of stress field of roadway surrounding rock, the evolution mechanism of roadway deformation and damage, the engineering quality monitoring and stability regulation theory of roadway surrounding rock. The key technology system is formed with the intelligent stability warning of the rock surrounding the roadway and the deformation control of the rock surrounding the roadway as the core. It is of great significance to conduct the scientific research and technical demonstration of the “excavation-backfill-retention” construction method in coal mines, and to realize the synergy of large-scale solid waste disposal, efficient coal pillar section recovery and rapid roadway excavation, which can promote the green and low-carbon transformation and development of the coal industry.

In order to systematically understand the various types of water hazards and standardize the management of water prevention and control work in coal mines, the National Mine Safety Administration in June 2022 extended the “three zones” management methodology for old goaf water prevention and control to more types of water hazards, such as roof water, floor water, and structural water. However, in the process of dividing the “three zones” of water prevention and control in coal mines, it is revealed that the water damage causing factors and water damage management situation are not closely linked with the division standard, and that technically different divisions are not made for different coal seams and mining areas, and there are inconsistencies in the scope of the division area, and the standardization and timeliness of the division results. Accordingly, combined with the purpose of water hazard prevention and control “three zones” division, compared the similarities and differences between the new and old “three zones” division work, this paper clarifies the degree of hydrogeological conditions survey, water hazard analysis, water hazard management as elements of the division principles, and determines the technical path of the division of the “three zones”. Combining the characteristics of five common types of water hazards in coal mines, the paper puts forward the division method of different types of water hazards, that is: surface water hazard should be divided according to the distribution and development characteristics of surface water bodies in the mining area and the degree of influence of atmospheric precipitation, landslides and other geological disasters in mining. The roof water hazard should be divided according to whether there is a strong water-rich aquifer, a medium water-rich but hydrostatic reservoir-rich aquifer, a loose aquifer. The floor water hazard should be divided according to the relationship between the thickness of the floor water barrier and the depth of the floor mine pressure damage zone in the coal seam pressure mining area, as well as the size of the floor water inrush coefficient. The old goaf water hazard should be divided according to the distribution range and amount of water in the mine and the surrounding mines. The structural water hazards should be divided according to the water-richness, water content, and water source conduction of geological structures and bad geological bodies such as faults, trap columns and caves in the mining area. The key points and difficulties of the division work include the clear division of time and space scope, the comprehensive analysis of division standards, the clear management responsibilities for the “three zones”, and the dynamic transformation of division results. On this basis, typical coal mines with various types of water hazards and complex hydrogeological conditions are selected as case studies. Following the principle of division, according to the division standard, the mineable zone, the slow mining zone and no-mining zone are divided by the method of separating coal seams and mining areas, and detailed explanations are given. The significance of carrying out the division of the “three zones” for water hazard prevention and control is clarified, which can enhance the comprehensive detection level, strengthen the prediction ability, and identify the direction of water hazard management in order to guide and standardize the division of the “three zones” for water prevention and control in the coal mines of China.

With the promotion of the “carbon peaking and carbon neutrality” goals, the social’s concern and demand for the treatment of coal gasification slag is becoming more and more urgent. Based on previous research, this paper introduces the main sources and discharge methods of coal gasification slag, and compares the properties and characteristics of coarse and fine slag in terms of morphology, particle size composition, mineral phase, chemical composition, etc, which providing a theoretical basis for the realization of its “reduction, resourcefulness and harmlessness” utilization. According to the obvious differences in properties between coarse slag and fine slag in entrained-flow gasification, this paper explores the different resource treatment methods both of them, and analyzes the limitations of the current utilization methods. Coarse slag has excellent hydration activity and volcanic ash activity, and is widely used in mine backfilling, roadbed filling, cement and concrete aggregate, ceramic materials, wall materials and other low-value utilization fields, but there are problems such as the risk of heavy metal leaching and low economic benefits. Fine slag contains rich carbon residue, and gravity separation and flotation are mainly used for combustible recovery and ash separation. However, the high fine-grained content reduces the precision of gravity separation, and the complex carbon-ash combination morphology increases the difficulty of flotation recovery. At the same time, the pore structure of fine slag is developed, with a specific surface area as high as 258.29 m2/g and certain adsorption capacity. Fine slag has great application potential in fields such as soil improvement, catalyst carrier, chemical raw materials, ceramic materials, adsorption materials, etc. But the complex high-value treatment process and high cost make industrial applications more difficult. In order to address these problems, it is necessary to explore the way of combing large-scale elimination with high-value utilization based on the physical and chemical properties of coal gasification slag, and realize the classified and graded resource utilization of coal gasification slag. It is necessary to explore the treatment method of on-site solution of coal gasification slag, combined with field production practice, which benefits to reduce the transportation cost. As well as the induction of physical and chemical properties to improve the treatment in the process of its generation.

This article addresses the bathing problem in multi-machine collaborative operations, proposing a method based on improved self-organizing iterative clustering. This approach circumvents the issues of traditional manual parameter setting in the self-organizing iterative clustering algorithm that is often inconvenient and non-intuitive. The proposed method allows multiple machines to autonomously adjust the parameters involved in the clustering process, given a small number of intuitive hyperparameters. The ultimate goal is to iterate toward reasonable editing results. Initially, this article focuses on selecting feature vectors for the multi-machine collaborative confrontation situation. It applies standardization and principal component analysis to high-dimensional multi-machine situation information to confirm the new vector space. This space mainly encompasses position information in three dimensions and speed information. Subsequently, the paper introduces the concept of neighborhood density discrimination from density clustering. This improves the merging and splitting operations of the traditional self-organizing iterative clustering method. It optimizes and reduces the artificial parameters involved in these operations, enhancing the intelligent autonomy for batch clustering tasks. Before optimization, artificial parameters primarily include the number of expected clusters, minimum number of points within a class, number of iterations, upper limit of standard deviation that limits data distribution within a class, and an allowable shortest distance indicator between classes. Post optimization, the artificial parameters are limited to the expected cluster quantity, minimum number of points, and the number of iterations within a single classification. These optimized parameters are relatively intuitive, and the algorithm output does not strongly correlate with the input parameters. Ultimately, the paper selects algorithm evaluation indicators, including Dunn, Davies–Bouldin, silhouette coefficient, and Calinski–Harabasz. It uses these to evaluate the proposed algorithms ISODATA+ and K-MEANS+, along with the original ISODATA algorithm, against multiple artificially synthesized data sets (completely random data, Gaussian-generated data, and sin-type data) and real-world scenarios. The experimental results suggest that while KMEANS+ shows significant advantages owing to multiple manually set hyperparameters, it requires constant debugging when adjusting parameters, which increases the complexity of the task. Compared with the original self-organizing iterative algorithm ISODATA, statistical results show that the improved algorithm has equivalent capabilities to the original algorithm. This demonstrates that the ISODATA+ algorithm maintains good clustering capabilities even after removing some artificial parameters. The batching results from actual scenario tests further illustrate the effectiveness of the improved self-organizing iterative clustering algorithm in specific application scenarios, demonstrating its practicability for future real-world applications.

In a complex and unknown battlefield environment, unmanned vehicle clusters can perform more complex tasks than a single unmanned ground vehicle (UGV). The formation and obstacle avoidance of unmanned vehicle clusters is one of the research hotspots in the field of swarm intelligence. To reduce the deviation between the actual formation position and expected formation when unmanned vehicle clusters avoid obstacles in an unknown environment, this paper proposes a cooperative formation and obstacle avoidance control method for unmanned vehicle clusters based on an improved dynamic window approach (DWA). In addition to the azimuth evaluation, obstacle evaluation, and speed evaluation factors of the path evaluation function in DWA, the direction coordination and formation maintenance factors are added. We established a mathematical model of unmanned vehicle cooperative formation and calculated in real time the expected position, direction, and speed of each following vehicle according to the expected formation. The sum of the deviation between the real driving and expected directions of each following vehicle was taken as the direction cooperation factor, and the sum of the absolute deviation between the real and expected positions of each following vehicle was taken as the formation-keeping factor. When unmanned vehicles approach unknown obstacles, the adaptive collaborative adjustment of the relative position and speed is performed based on the improved DWA, which can improve the precision of the formation positions during the obstacle avoidance process. In this paper, a simulation is conducted using a scenario of three unmanned vehicles forming a triangular formation to avoid obstacles and three obstacles. The simulation results show that the control adjustment based on the improved dynamic window and variable weight optimization algorithm is more timely than the traditional formation control based on the artificial potential field. Additionally, the formation position deviation is relatively small when avoiding obstacles. The average deviation between the actual position of the vehicle formation and the desired position of the formation near the obstacle is used as the evaluation function. The coefficients of the path evaluation function of the improved DWA are optimized based on the variable weight pigeon-inspired optimization (PIO). It can improve the formation, maintaining accuracy in obstacle avoidance. In summary, the proposed improved algorithm can make the unmanned vehicle cluster adjust the driving speed and direction cooperatively in obstacle avoidance. Moreover, it can improve the stability and accuracy of the dynamic formation.

Collaborative encirclement of multiple unmanned ground vehicles (UGVs) is a focal challenge in the realm of multiagent collaborative tasks, representing a fundamental issue in complex undertakings such as multiagent collaborative search and interception. Although optimization algorithms have yielded rich research outcomes in collaborative encirclement, challenges persist, including poor real-time computational efficiency and weak robustness. Reinforcement learning theory holds considerable promise for addressing multiagent sequential decision problems. This paper delves into the study of the collaborative encirclement of multiple UGVs based on deep reinforcement learning theory, focusing on the following key aspects: establishing a kinematic model for UGVs to describe the collaborative encirclement task, detailing the collaborative encirclement process, developing strategies for target UGV escape, and addressing challenges arising from the increasing number of UGVs, which results in a complex environment and issues such as algorithmic instability, dimension explosion, and poor convergence. This paper introduces a collaborative encirclement algorithm based on the soft actor–critic (SAC) framework. To address issues related to poor collaboration and weak generalization among multiple UGVs, long short-term memory is incorporated into the network structure, serving as a memory function for UGVs. This tactic aids in capturing and using information from historical observation sequences, effectively processing time–series data, making more accurate decisions, promoting mutual collaboration among UGVs, and enhancing system stability. To tackle the issue of increased state space dimensions and low training efficiency during collaborative encirclement, an attention mechanism is introduced to calculate and select attention weights in the state space, focusing attention on key states relevant to the task. This strategy helps constrain state space dimensions, ensuring network stability, achieving stable and efficient collaboration among multiple UGVs, and improving algorithm training efficiency. To address the problem of sparse rewards in collaborative encirclement tasks, a mixed reward function is proposed that divides the reward function into individual and collaborative rewards. Individual rewards guide UGVs toward the target, incentivizing their motion behavior, whereas collaborative rewards motivate a group of UGVs to collectively accomplish the encirclement task. This approach further guides UGVs to obtain more frequent reward signals, ultimately enhancing the algorithm convergence speed. Simulation and experimental results demonstrate that the proposed method achieves faster convergence than SAC, with a 15.1% reduction in encirclement time and a 7.6% improvement in success rate. Finally, the improved algorithm developed in this paper is deployed on a UGV platform, and real-world experiments in typical encirclement scenarios validate its feasibility and effectiveness in embedded systems.

Reinforcement learning, a cornerstone in the expansive landscape of artificial intelligence, has asserted its dominance as the prevailing methodology in contemporary multiagent system decision-making because of its formidable efficacy. However, the path to the zenith of algorithmic excellence is fraught with challenges intrinsic to traditional multiagent reinforcement learning algorithms, such as dimensionality explosion, scarcity of training samples, and the labyrinthine nature of migration processes. In a concerted effort to surmount these formidable challenges and propel the evolution of algorithmic prowess, this paper unfurls its inquiry from the perspective of learning mechanisms and undertakes an exhaustive exploration of the symbiotic integration of learning mechanisms and reinforcement learning. At the inception of this scholarly expedition, we meticulously delineate the rudimentary principles underpinning multiagent algorithms, present a historical trajectory tracing their developmental evolution, and cast a discerning eye upon the salient challenges that have been formidable impediments in their trajectory. The ensuing narrative charts a course into the avant-garde realm of multiagent reinforcement learning methods anchored in learning mechanisms, a paradigmatic shift that emerges as an innovative frontier in the field. Among these learning mechanisms, meta-learning and transfer learning are empirically validated as useful instruments in hastening the learning trajectory of multiagent systems and simultaneously mitigating the intricate challenges posed by dimensionality explosion. This paper assumes the role of a sagacious guide through the labyrinthine landscape of multiagent reinforcement learning, focusing on the manifold applications of learning mechanisms across diverse domains. A comprehensive review delineates the impact of learning mechanisms in curriculum learning, evolutionary games, meta-learning, hierarchical learning, and transfer learning. The research outcomes within these thematic realms are methodically cataloged, with a discerning eye cast upon the limitations inherent in each methodology and erudite propositions for the trajectory of future improvements. The discourse pivots toward synthesizing advancements and accomplishments wrought by fusion algorithms in practical milieus. This paper meticulously examines the transformative impact of fusion algorithms in real-world applications, with a detailed exposition of their deployment in domains as diverse as traffic control and gaming. Simultaneously, an incisive analysis charting the future trajectory of fusion algorithms is conducted. This prediction encompasses exploring nascent theories, refining algorithmic efficacy, and expanding dissemination and application across a broader spectrum of domains. Through this scholarly odyssey, this paper provides an invaluable compass for navigating the uncharted waters of future research endeavors and the judicious deployment of multiagent reinforcement learning algorithms in pragmatic scenarios.

This paper introduces the current situation of intelligent development of shearer at home and abroad. The shearer is currently developing towards intelligent robotics and is in the primary stage of intelligence. This paper elaborates on the development process and trends of intelligent shearer. There are six scientific research projects that have played important roles in the intelligent development of shearer, including the UK remotely operated longwall face (ROLF) program, mine operating system (MINOS), NASA long wall automation cooperation project, Australian Australian coal industry’s research program (ACARP), Australian commonwealth scientific and industrial research organization (CSIRO) project, and EU new mechanisation and automation of longwall and drivage equipment (NEMAEQ). The development trend of shearer is to use continuously developing monitoring, sensing, and remote control technologies to replace manual intervention and operation, continuously improve the production efficiency and reliability of working face equipment, and develop towards informationization, integration, unmanned, and intelligent direction. This paper introduces the key technologies of intelligent perception, intelligent control, and intelligent communication of shearer. It points out that intelligent cutting technology based on coal rock recognition, intelligent drum speed regulation technology, precise positioning technology of shearer, and planning and mining technology based on three-dimensional geological models are key technologies that urgently need to be broken through. The human-machine relationship that cannot be ignored in the intelligent development process of shearer is discussed from two aspects: dust reduction and noise reduction.

Ultra-wide band (UWB) radar can penetrate non-magnetic media such as coal and rock to detect life information of personnel after collapse. Due to the complex mining environment, UWB radar loaded with vital sign signals is prone to interference from environmental noise and clutter signals. It is difficult to recognize human subject information. This paper introduces the principle of UWB radar life detection system and its application in mine rescue. This paper summarizes the current research status of UWB radar life information recognition from three aspects: UWB radar life information extraction, dynamic and static human object recognition, and life quantification. This paper points out the current issues with the application of UWB radar life detection technology in the field of mine rescue. ① There is limited research on filtering methods for non-stationary signals and environmental noise in underground collapse environments. ② The extraction and representation methods for posture, behavior, life status, and other information of moving (or micro moving) objects need to be improved. The human life information recognition model is not yet perfect and the feature correlation between models is low. ③ There is a lack of solutions to the 'overlapping' problem caused by multiple objects. This paper proposes the prospects for the research direction of UWB radar life information recognition for mine rescue. ① It is suggested to continuously optimize noise and clutter adaptive filtering methods for multiple types of mine disaster environments. ② It is suggested to construct a human life information recognition model suitable for the field of mine rescue. ③ It is suggested to further improve the quantification capability of multi-object after mine shelter. ④ It is suggested to conduct depth exploration of the method for determining the optimal detection frequency band for UWB radar.

Early detection and distinguish of mine fires can avoid or reduce casualties, property damage, and secondary accidents. There are no natural light sources such as sunlight, moonlight, starlight, and lightning underground, and the main factor affecting the recognition of mine fire images is the mine light source. Circularity can eliminate interference from circular light sources, but it is difficult to exclude interference from non-circular light sources. Rectangularity can eliminate interference from rectangular light sources, but it is difficult to exclude interference from non-rectangular light sources. In engineering practice, due to the different shooting angles of the camera, the image of the mine light source may deform and cannot present an ideal regular shape. It is difficult to eliminate the interference of the mine light source using circularity and rectangularity algorithms. It has been revealed that the area of the external connection graphic area of the flame image is significantly larger than the actual area of the image, and the external connection graphic area of the actual light source image of the mine, such as circular lamps, rectangular lamps, and square lamps, is approximately equal to the actual area of the light source image. In this study, a mine fire recognition and anti-interference method is proposed based on the internal concavity of image, the ratio of the target image area to the external connection graphic area of the image (i.e. internal concavity of image) is calculated, and the flames and mine light sources are distinguished based on the small concavity value in the flame image and the large concavity value in the mine light source image. The internal concavity method proposed in this paper is not affected by the distance from the camera to the detection target and the size of the image, the installation position and angle of the camera to capture the detection target, and the shape of the mine light source. It has strong adaptability and high recognition accuracy. The experiment shows that the internal concave recognition method calculates the maximum average difference between the mine interference light source and the flame image, with the smallest fluctuation and the best discrimination. It is also least affected by the camera’s shooting angle and distance, and has the strongest anti-interference ability, with an accuracy of 91.6%. The rectangular recognition method calculates the average difference between the mine interference light source and the flame image, with small fluctuations and good discrimination. It is less affected by the camera’s shooting angle and distance, and has average anti-interference ability, with an accuracy rate of 72.5%. The roundness recognition method calculates the minimum average difference between the mine interference light source and the flame image, with the highest fluctuation and the worst discrimination. It is less affected by the camera shooting distance and more affected by the camera shooting angle, and has the worst anti-interference ability, with an accuracy of 12.0%. Therefore, the internal concave recognition method proposed in this paper is superior to rectangular and circular degrees, with the best discrimination, minimal influence from camera shooting angle and distance, and the strongest anti-interference ability.

In order to further improve the intelligent control level of fully mechanized mining equipment in coal mines and achieve the normalized operation of intelligent mining face, this paper proposes the related concepts and system architecture of intelligent mining face driving control based on digital twin. The digital twin system of intelligent mining face is composed of physical equipment, virtual twin and virtual and real interaction model. The virtual model mapped with physical equipment is established by digital method to achieve the purpose of virtual and real interaction, intelligent decision, accurate control and dynamic evolution in fully mechanized mining equipment during operation. The paper expounds the normal operation requirements of intelligent mining face and related control difficulties of shearer, hydraulic support and scraper conveyor, and proposes the single-machine digital twin driving control architecture of smart mining face, including mechanism model, control model, twin data model and digital twin model synchronization and evolution. The method of digital twin virtual-real interaction is described, the Cyber-Physical System is used to guarantee the information interaction ability, and the knowledge model is used to solve the data congestion problem, which provides a real-time virtual-real interaction for the digital twin control system of intelligent mining face. A three-machine cooperative control method driven by the digital twin of intelligent mining face is proposed, including three-machine correlation, intelligent coal mining control, intelligent support control and intelligent transportation control. Finally, taking the practical application of digital twin in mines as an example, the intelligent control of digital twin is verified by establishing the relevant model and designing the relevant experiment scheme. By carrying out theoretical research on digital twin intelligent control architecture and mode, the paper aims to solve the current problems of low environmental perception, poor equipment prediction accuracy and high manual intervention intensity in fully mechanized mining face, realize an adaptive control and human-computer interaction of equipment under complex environmental conditions, and provide a reference for the intelligent construction of coal mines.

Surface mining is an important way of mining mineral resources. The state and local governments have put forward higher requirements for surface mining in China. Therefore, it is particularly urgent to take surface mining as a science to carry out systematic research, reveal the laws of mining process and phenomenon occurred, and make scientific explanations. First of all, the major technical achievements in the field of surface mining of solid mineral resources in China in the past 40 years were summarized from three aspects: the basic theory of surface mining, the development of key complete sets of equipment and blasting engineering technology. Then, the modern scientific metrology and information metrology technologies were used to conduct a multivariate dynamic analysis of 7128 papers and reports in the past 40 years, and a multi-dimensional quantitative description of the development and evolution process of the main research topics, including the time dimension, was carried out by the cluster analysis. It is found that the evolution of research topics is the result of the coupling effect of many factors, such as technological development, mining status, policy environment, etc. Based on the sorting of the existing major scientific and technological achievements and the objective literature measurement results, it is pointed out that the goal of the future surface mining science is to form a systematic and comprehensive safe, green, efficient and intelligent surface mining technology system, and the realization path is given, that is, to form a hot research topic of surface mining technology system and major equipment, and to strengthen the deep cross integration between the existing hot research and surface mining technology system and major equipment. It also provides some useful suggestions on how to jointly tackle major scientific and technological problems with the advantageous resources of various themes inside and outside the industry to achieve a substantial collaborative innovation among multiple themes. Finally, from the perspective of energy and ecology, the paper looks forward to the development direction of major basic theories and technologies of the future surface mining science from seven aspects: mining area planning theory, intelligent mining design principle, intelligent cluster scheduling theory, safe and efficient mining technology of coal mining in spontaneous combustion prone extra thick coal seams, high slope stability control technology, efficient belt conveyor transportation technology under complex working conditions, and intelligent surface mine system engineering. The development direction of the future surface mining equipment is prospected from five aspects: high efficiency mobile crushing equipment, modular semi-fixed crushing equipment, continuous mining process system equipment, large inclination transportation equipment and large dump machine. It is expected that the study can be used as a reference for scientists and engineers in the surface mining field.

Intelligent recognition technology for underground safety violations in coal mines has become the primary means of safety management and information acquisition. However, the accuracy of intelligent recognition is reduced by factors such as low illumination, point light sources, and high dust in the underground spatial environment. To overcome those problems, this paper proposes a multi-weight fusion image enhancement method, which achieves the fusion enhancement of image brightness and illumination balance. The monitoring image brightness is increased by using the Gamma algorithm. Based on the brightness enhancement, the HSV spatial transformation is carried out to keep the hue component and saturation component unchanged, the illumination component is extracted by using a multi-scale Gaussian function, and then the regions where the illumination component is too strong and too weak are adjusted by using an improved two-dimensional gamma function to achieve an illumination equalization. Combining the three weights of Laplacian contrast, luminance, and saturation and fusing the luminance-enhanced and illuminance-balanced images by Gaussian and Laplacian pyramids, the intelligent surveillance image enhancement is achieved. Through the experimental verification of the intelligent recognition of helmets in mine monitoring, the image enhancement method proposed in this paper and the MSR, MSRCP, MSRCR, and AMSRCR algorithms are evaluated in terms of image evaluation indexes such as standard deviation, peak signal-to-noise ratio, and information entropy, and the peak signal-to-noise ratio is improved by 32.44% on average compared with other algorithms, the standard deviation is improved by 115.38% on average compared with the original image, and the average improvement in standard deviation is 115.38% compared with the original image and 47.30% compared with other algorithms, and the accuracy of helmet recognition reaches 86.7%, an average improvement of 47.52% compared with other algorithms. The results show that the algorithm in this paper can effectively improve the contrast and clarity of mine images, reduce the halo phenomenon, and significantly improve the accuracy of target recognition in intelligent mine monitoring. It can lay a solid foundation for the intelligent identification of safety violations in underground coal mines.

The condition monitoring and fault diagnosis of the rolling bearings of the main fan in the mine are significant to the safety of coal mine production. The existing fault diagnosis methods of rolling bearing have the problems of insufficient training and accuracy when applied directly in actual working conditions. Moreover, the rolling bearings of the mine main fan are in normal operation for a long time, and the number of normal samples is much more than the faulty samples, so there is a sample imbalance problem. Therefore, this paper proposes a fault diagnosis method for rolling bearings of mine main fan based on transfer learning. The method takes the conventional rolling bearing data as the source domain data and the mine main fan rolling bearing data as the target domain data. Firstly, the one-dimensional vibration signal is converted into two-dimensional SDP images using the SDP method, and then the conventional rolling bearing fault diagnosis model is trained using sufficient source domain image samples. After training, the parameters of the diagnostic model are transferred to the mine main fan rolling bearing fault diagnosis model, and the lower layer network is locked and the higher layer network of the model is fine-tuned by the target domain image samples during the transfer process, and finally the mine main fan rolling bearing fault diagnosis model with optimized parameter weights is obtained. Meanwhile, in order to solve the sample imbalance problem, a weighted cross-entropy loss function is added to the model for training, so that the diagnosis model gives higher weights to the fault samples as a minority class and pays more attention to the fault samples in the diagnosis process, thus improving the diagnosis accuracy. In order to verify the effectiveness of the proposed method, this paper uses a conventional rolling bearing fault test bench and the rolling bearing data of the mine main fan fan in actual working conditions for experimental verification. The results show that the proposed method can accurately identify and classify the operating status of the mine main fan rolling bearings, and the accuracy rate is 99.28%.

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of 'sky', 'air', and 'ground' , as well as integrated collaborative monitoring method of 'sky-air-ground'. The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the 'sky-air-ground' is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.

In order to predict rib spalling depth in fully-mechanized mining faces, taking the coal wall as the research object, the coal wall is regarded as a tri-linear strain softening material. Then the direct roof and main roof are taken as macro roof, and the bearing mechanical model for the interaction between macro roof, coal seam, and support is established. Combined with the boundary conditions, the analytical solution of rib spalling depth in fully mechanized mining faces is derived in this paper. The analytical solution was used to investigate the situation of coal wall spalling in the 12401 working face of Shangwan Coal Mine, and the factors and laws influencing coal wall spalling were researched. The results show that: ① The analytical solution of rib spalling depth in the fully mechanized working face in this paper is reliable and reasonable, and can quantitatively investigate the effects of roof characteristics and support parameters. ② Increasing the initial support force can reduce rib spalling depth. Increasing the stiffness of the support can reduce rib spalling depth, however the effect is poor. The supporting strength of the support protective plate, and the rib spalling depth decreases approximately linearly. ③ With the thickness and tensile strength of the roof macroscopic increasing, the rib spalling depth increases. However its growth rate gradually slows down. The main reasons are that the periodic fracture length increases with increment of the thickness and tensile strength of the roof, which leads to increment of the angle of rotation and sinking towards the goaf. ④ The mining height has a significant effect on the rib spalling depth. The rib spalling depth linearly increases with the mining height. ⑤ With uniaxial compressive strength of coal increasing, the rib spalling depth decreases. With the interface friction coefficient between the roof and the coal increasing, the rib spalling depth decreases. ⑥ With the dynamic loading coefficient of the roof increasing, the rib spalling depth increases linearly.

Coal chemical industry is one of the economic pillars of China. Coal gasification slag and gasification wastewater are the wastes produced in the process of coal gasification, and the current treatment is too simple and causes great pollution to the environment. In this paper, microemulsion was used as the capture agent for flotation of coal gasification fine slag, and the physicochemical properties of residual charcoal were analysed by means of industrial analyser, FTIR, BET, SEM, XRD, Raman, XPS and other means of characterization, and the electrochemical properties of the residual charcoal were analysed by means of CV, EIS test, and the electrochemical properties and stability of the residual charcoal as a particle electrode in the three-dimensional electrochemical system were examined, and the feasibility of using it as a The feasibility of the residual carbon as a particle electrode was investigated, and the mechanism of ammonia nitrogen removal by the residual carbon particle electrode was proposed to be analysed. The results show that the flotation residual carbon has a high specific surface area, and the pore structure is dominated by mesopores. In addition, some of the carbon in the residual carbon exhibits a certain degree of electrical conductivity due to graphitization, which provides a basis for its electrochemical applications. In addition, the residual carbon particles electrocatalytic system had lower electrical resistance, which was conducive to accelerating the charge transfer rate. The three-dimensional electrocatalytic system of residual carbon particles had 19.82% higher ammonia nitrogen removal rate than the two-dimensional electrocatalytic system under the same conditions of ammonia nitrogen wastewater degradation, and it has certain catalytic ability.In the residual charcoal particle electrode cycling test, the residual charcoal particles were reused for five times, and the removal rate of ammonia nitrogen only decreased by 5.3%, and this result shows that the residual charcoal particle electrode has good stability. In this paper, a novel particle electrode material in a three-dimensional electrocatalytic system is developed, and through systematic characterisation and performance evaluation, it is demonstrated that the residual charcoal particle electrode of gasification slag has great potential in improving the efficiency of wastewater treatment. The development of residual charcoal particle electrode provides a new idea for the resourceful utilisation of gasification slag, and also provides a new method for wastewater degradation treatment technology.

Pulverized fuel ash (PFA) is a by-product from coal-fired power plants. The generation of PFA is huge and increases yearly. A large amount of untreated PFA is harmful to the environment, and it is also a waste of resources. Recycling PFA as construction materials would improve resource utilization efficiency, reduce environmental risks, conform to the demand for high-efficient and low-carbon utilization of solid waste and help to achieve the national strategy goals of carbon peak and carbon neutrality. The utilization efficiency of PFA as construction materials could be improved by optimizing production processes and developing new technologies, which would promote its large-scale and high-value utilization. In this paper, the general situation and physical and chemical properties of PFA are summarized. Various value-added utilization technologies of PFA as construction materials, including raw materials for cement production, supplementary cementitious materials, bricks and blocks, artificial aggregates, glass-ceramic materials, fire-resistant insulation materials, and new intelligent construction materials are comprehensively reviewed. This paper also introduces the research status of the utilization of PFA in construction materials, analyzes the applicability of different technologies, summarizes the latest technologies for preparing the PFA-based green construction materials and challenges faced in future research, provides detailed introductions to the mechanism of PFA application, and proposes thoughts on the problems existing in the utilization of PFA as construction materials. Finally, the further perspectives of the development of the PFA-derived low-carbon construction materials are highlighted. The study suggests that the “traditional large-scale consumption of PFA” and “high-value utilization of PFA” should be researched in parallel, and theoretical research and engineering practice should support each other, which would provide some scientific references for the sustainable management of PFA.

Improving the positioning accuracy of underground personnel can not only strengthen mine safety monitoring, but also increase the speed of rescue, thus ensuring the life safety of underground personnel to the maximum extent.This paper proposes a positioning model based on IPSO-LSTM for position prediction of underground moving targets in response to the problem of existing ranging algorithms which are affected by the on-site environment, resulting in insufficient positioning accuracy.This article uses LSTM to build a fingerprint positioning model.It collects distance information through the UWB wireless module to build a distance-position fingerprint relationship database, which is used to train the PSO-LSTM model.Then we use the trained model to predict target trajectories.We compared four improvement strategies on PSO including random initialization of population position by chaotic mapping, nonlinear inertia weight reduction and fitness function optimization.Experiments show that the improved PSO optimization algorithm in this paper exhibit fast convergence speed and good robustness.In order to verify the positioning effect of IPSO-LSTM, we compared the IPSO-LSTM model with the Chan algorithm, PSO-LSTM model, LSTM neural network, SSA-LSTM model and GWO-LSTM.The average positioning error is used as the evaluation index.The results show that the average positioning error of the IPSO-LSTM positioning model proposed in this study is 30mm, which is 76% higher than the traditional Chan algorithm, 49% higher than the LSTM, and 24% higher than the PSO-LSTM model.In order to reduce large local errors, we used median filtering to process input information, further improving positioning accuracy.This study offers references for improving the accuracy and stability of the existing underground moving target positioning system.

This paper summarizes the application, review, and award of research funding in the Mining and Metallurgical Engineering discipline in the topics of petroleum engineering, mining engineering, safety science and engineering, and metallurgy and material processing engineering by the Department of Engineering and Materials Science of the National Natural Science Foundation of China in 2023. Additionally, this paper introduces the future development strategies in this discipline and proposes the next steps for continuous improvements.

To address the challenges of low roof-contacted filling rates and poor roof-contacted filling effects in an iron mine stope in the Anhui province, a composite expansive agent is introduced into the last roof-contacted filling slurry. This approach allows for active roof-contacted filling and reduces filling costs simultaneously. This paper analyzes the physical and chemical properties of the test materials to assess the strength evolution law and determine the optimal ratio for expansive backfill. Herein, a response surface methodology (RSM) is employed for performing the strength ratio test of expansive backfill using Box−Behnken design (BBD) in Design-Expert software. Further, the influence rules of slurry mass fraction, cementitious powder mass fraction, and composite expansive agent mass fraction on the compressive strength of expansive backfill are studied. Subsequently, the paper establishes response surface regression models for analyzing the compressive strength of expansive backfill. The results show that the regression coefficients for 3 d, 7 d, and 28 d compressive strength regression equations are 0.9813, 0.9758, and 0.9857, respectively. These figures suggest that the regression models have a high degree of fitting. Further analysis of variance and comparison between measured and predicted values suggest the accuracy and reliability of these models. The results reveal that the compressive strength of expansive backfill is considerably affected not only by a single factor but also by the interaction between various factors. Among the single factors, cementitious powder mass fraction exerts the greatest influence, followed by composite expansive agent mass fraction, while slurry mass fraction has the least effect. The interaction between slurry mass fraction and cementitious powder mass fraction considerably affects the compressive strength during the early and middle stages. Meanwhile, the interaction between the cementitious powder mass fraction and composite expansive agent mass fraction substantially affects the later compressive strength. Based on these findings, this paper applies the goal programming method to optimize the ratio of expansive backfill, and the optimization models are established considering the compressive strength, expansion effect, and filling cost of backfill. The optimal ratio of expansive backfill is determined to be 69% slurry mass fraction, 10% cementitious powder mass fraction, and 3×10–4 composite expansive agent mass fraction, meeting the requirements of compressive strength and roof-contacted filling. Finally, the microstructure of expansive backfill is analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results reveal that the composite expansive agent allows the filling slurry to expand and deform during the plastic stage, thereby improving the roof-contacted filling rate. Meanwhile, the composite expansive agent can loosen the internal structure and increase porosity, thereby reducing the compressive strength of backfill.

With the extension of coal mining in China, fault water inrush has become one of major disasters threatening the safety of coal mine production. Based on the research results related to the mining-induced fault water inrush, this paper proposes a conceptual model of water inrush caused by the erosion synergy of mining-induced rock mass damage rupture and fractured rock mass (fault), derives the permeability evolution equation of the two media, and systematically constructs the cooperative disaster causing mechanism model between mining failure and particle erosion inside faults. The numerical simulation is conducted to study the deformation and failure of rock mass, the particle transport in faults and the evolution characteristics of seepage channel, and systematically explain the temporal and spatial evolution mechanism of seepage catastrophe caused by mining-induced fault inrush. The results show that: ① With the continuous advancement of working face, the damage field of mine floor rock mass is connected with the fault erosion fracture, forming a seepage path of aquifer-fault-mining fracture-working face, and with the increase of erosion time, it finally develops into several dominant water diversion channels, resulting in a sharp increase in water inflow at the working face and a lagging water inrush. ② With the increase of seepage time, the water inflow and fracture opening degree inside faults all show three stages: slow change, sudden increase and stable, and the erosion particle concentration shows a trend of first increasing and then decreasing. ③ Under the geological conditions of the mining area studied in this paper, in order to prevent the occurrence of fault water inrush, the methods such as advanced grouting or leaving water prevention coal pillars can be adopted, and the advance grouting time should be before the bottom plate fracture zone connects faults, if grouting is not applied, the width of the reasonable water prevention coal pillar should not be less than 20 m.

The formation of residual coal pillars and void areas after mining of the upper seam close to the coal seam will affect the layout of the lower seam roadway. In this paper, taking 231101 working face of Xinliu Mine as engineering background, the comprehensive research means of theoretical analysis and numerical simulation is used to study and analyze the stress evolution law of mining stress field after overlying coal seam mining, and conduct an in-depth investigation on the asymmetric deformation damage mechanism of lower coal roadway, so as to arrive at the reasonable location of lower coal roadway layout. The research results show that: ① the overlying coal seam mining changes the principal stress magnitude, principal stress ratio and stress deflection angle at the location of the roadway, and the change of stress vector is the main reason for the asymmetric damage of the roadway. ② At 11 m from the center of the coal pillar, the maximum principal stress of the surrounding rock of the roadway reaches 14.43 MPa, the principal stress ratio is 2.74, and the stress deflection angle is 52.33°. At this time, the roadway is in a poor stress environment, resulting in butterfly-shaped damage of the surrounding rock and the rotation of the butterfly leaf to the right side of the roof of the roadway, and the right side of the roof of the roadway is prone to roofing accidents. ③ In order to make the roadway in a relatively stable area, this paper divides the working area into four zones based on three main influencing factors, and suggests that the best layout area is H–IV after considering the economic and safety factors of the coal mine, with the main stress deflection angle in the range of 0°−38° or 45°−90°.

With the overconsumption of fossil carbon resources, resulting in a large amount of CO2 and other greenhouse gas emissions, caused by global climate change has become one of the major challenges facing all mankind. At the same time, the development and utilization of coal resources will produce a large amount of coal-based solid wastes containing calcium and magnesium, such as fly ash, desulfurization gypsum and other solid wastes. How to realize efficient CO2 capture and sequestration is a very challenging issue nowadays, as well as the large-scale comprehensive utilization of fly ash, coal gasification slag, and other large-scale heavy polluting industrial solid wastes is still in need of a breakthrough. Under the goal of “double carbon”, CO2 mineralization from coal-based solid waste is a potential strategy to effectively address global warming, and mineral carbonation of coal-based solid wastes containing calcium and magnesium has great prospects for carbon dioxide capture and sequestration (CCS) as well as for the resource-based disposal of solid waste. However, the industrial application bottleneck of CO2 mineralization in coal-based solid waste is still unable to break through. This paper reviews the current development of CO2 mineralization from fly ash (FA), desulfurization gypsum (FGDG), and coal gasification slag (CGS), with the aim of exploring the reasons for their technical limitations. Firstly, this paper briefly elaborates on the pathways for CO2 mineralization of coal based solid waste, revealing the reaction process and principles of CO2 mineralization of coal based solid waste. Secondly, the mineralization potential and process of typical coal-based solid waste were summarized and compared to clarify the mechanism of the regulation of the process parameters on the product properties during CO2 mineralization process. Finally, the performance of CO2 mineralization products from coal-based solid waste was summarized, and the feasibility of the mineralization process and its environmental impact were elucidated using a Life Cycle Assessment (LCA). This paper will provide optimization suggestions on the process technology of coal-based solid waste CO2 mineralization to promote the strategic goal of low-carbon transformation in the coal industry.

The accurate identification and positioning of the anchor position of the coal mine tunneling roadway is the key technology that the drilling anchor robot urgently needs to break through to achieve intelligent permanent support. An intelligent identification and positioning method for roadway anchor hole position based on visual image and laser point cloud fusion is proposed, which includes three steps: image target recognition, point cloud image feature fusion, and positioning coordinate extraction: ① In order to solve the problem of blurred image of anchor hole contour caused by environmental factors such as low illumination, water mist and dust in coal mines, the IA (Image-Adaptive)-SimAM-YOLOv7-tiny network was used to visually identify the position of the anchor hole to be anchored in the roadway, which can adaptively enhance the image brightness and contrast, recover the high-frequency information at the edge of the anchor hole, and make the model focus on the characteristics of the anchor hole, so as to improve the success rate of anchor hole detection; ② The Region of Interest (ROI) of image detection is generated by perspective projection relationship to generate a cone-shaped area of interest to obtain the corresponding target point cloud cluster; ③ The point cloud processing algorithm is used to extract the anchor hole boundary point cloud, obtain the central coordinates of the hole position and its normal vector, and judge the correctness of the anchor hole recognition by comparing the coordinate depth difference. In this paper, a drilling and positioning system for bolting trolley manipulator is built to verify the accuracy and accuracy of the algorithm's autonomous positioning, and the experimental results show that the mean average precision (mAP) of the IA-SimAM-YOLOv7-tiny model is 87.3%, which is 4.6% higher than that of the YOLOv7-tiny model. Compared with the single vision method, the fusion algorithm proposed in this paper has a positioning error of 3 mm, and the average recognition time of the system in the case of a single anchor hole is 0.77 s, compared with the single vision method, the fusion of laser and visual multi-source can not only reduce the influence of environment and small sample training on positioning performance, but also obtain the normal vector of the anchor hole position, which provides a basis for the manipulator to adjust the drilling posture to achieve accurate anchoring.

The dual energy X-ray transmission identification of coal gangue still faces challenges in thickness, hardening, afterglow, and fan-shaped effects, among which the parameters for 5-150 mm wide thickness coal gangue separation fluctuate significantly and the recognition rate is to be improved. Therefore, this paper proposes a multi-dimensional identification method of dual-energy X-ray transmission of coal gangue based on geometric feature constraints. This method distinguishes the thickness of coal gangue by two geometric features of the minimum circumscribed circle diameter and area of the target image, restricts the spatial distribution of X-ray transmission response characteristics, and then weakens the influence of defects from multiple dimensions. With a small amount of low-density coal and high-density gangue, the paper obtains X-ray transmission response characteristics, position characteristics, and geometric characteristics, and combine them with Relief-F feature selection to establish a strong feature combination. To test the recognition performance of multiple classifiers, medium Gaussian SVM is selected as the classification model for multi-dimensional methods. Taking strong feature combinations as input, the final decision model and classification of unknown coal gangue pixels are automatically created, and the separation parameter p-value is obtained through pixel transformation image processing method. The results show that there is a strong linear correlation between p-value and coal gangue density, and density can be used to select p-value to regulate sorting. The p-value shows a weak linear correlation with the thickness of coal gangue. Within a wide thickness range, the p-value has a small degree of dispersion and good separability, giving separation parameters a large adjustment space. The mass experimental verification results show that the p-value of the multi-dimensional method for pre discharge gangue separation parameter is 33.01%. Using this separation parameter to identify coal gangue with different densities and coal types, the overall recognition rate reaches 99.57%. The overall recognition rate of raw coal pre discharge gangue in the thickness range of 5-150 mm is 99.37%. Compared with H-L method and R-L method, the multi-dimensional method has higher recognition rate, and the p-value calculated for coal gangue with different thickness has higher accuracy and better consistency. It demonstrates the effectiveness of multi-dimensional recognition methods under geometric feature constraints and the advantages of separation parameter regulation, which is of design reference meaning for current dual energy X-ray coal gangue separation device recognition algorithms.

One-time full-thickness mining with ultra-high mining height has the characteristics of high mining efficiency and high resource recovery rate. In order to explore the further breakthrough of ultra-high mining height fully mechanized mining technology and analyze the feasibility of surrounding rock stability control of its working face, on the basis of investigating the development process of ultra-high fully mechanized mining, this paper summarizes the surrounding rock pressure behavior and coal wall fragmentation law of ultra-high mining height fully mechanized mining working face, and analyzes the overall technical characteristics and applicable performance of the existing ultra-high hydraulic support, the paper also introduces the basic situation of the hydraulic support in the 10 m super-large mining height working face. Through analysis, it is found that the apparent strength of the rock pressure in the working face is basically positively correlated with the mining height. The greater the mining height, the greater the support resistance of the working face, and the coal wall spalling is one of the main factors that hinder the efficient promotion of the super-large mining height mining. The stress of the coal wall can be effectively reduced by improving the initial support capacity of the hydraulic support in the working face; the hydraulic support increases with the mining height of the working face, the selection resistance increases, the support center distance is widened, and the large gradient transition mode can effectively improve the resource recovery rate of the end zone while providing reliable support; in order to adapt to the transportation of large volume mining equipment, the working resistance of the advanced support increases with the increase of the cross section of the trench in the super high mining face; the supporting hydraulic support of the working face is optimized and upgraded through design means, overall structure, manufacturing process and other aspects to improve reliability and adaptability; relative to the support of the working face with lower mining height, the mining with super mining height pays more attention to the safety technical requirements in the support process, and needs to improve the safety measures such as coal gangue protection before the frame, dust prevention and dust reduction between the frames, and safety maintenance and inspection at the later stage; it is considered that the surrounding rock stability control and its supporting equipment of the working face are the key points for the development and promotion of the super-large mining height fully mechanized mining technology. The full life development of the super-large mining height hydraulic support from the selection of support parameters, the design of support structure, the manufacture of hydraulic support, and the later maintenance and inspection is the basis for supporting the efficient promotion of the super-large mining height mining.