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Coal energy has made a significant contribution to the development of human industrialization, but coal energy is faced with the major problem of high carbon emissions. Modern coal-based energy is a new path to crack the contradiction between coal energy and carbon. It can realize independent and controllable energy based on China’s resource endowment, enhance the autonomy of China’s oil and gas, support the clean transformation of electricity and the development of the renewable energy industry, and is an important innovative direction for guaranteeing energy security and realizing the dual-carbon goal. Based on the concept and technical system of modern coal-based energy, this paper systematically introduces the connotation and product lineage of modern coal-based energy. By analyzing and discussing the five core technology modules, 19 technical units and 61 key technologies of modern coal-based energy, including the new technologies such as coal-based gas, coal-based oil, coal-based hydrogen, coal-based electricity production, and dynamic carbon neutrality in mining area, ten revolutionary technologies for the development of low carbonization coal-based energy are proposed: ① Intelligent underground coal gasification technology based on self-moving plasma gasification machine which has the functions of automatic combustion, propulsion, sealing, monitoring and controlling; ② The coupling of underground coal gasification and indirect liquefaction can form a low-cost, low-emission, short-process coal-to-oil route (UCG-ICL); ③ In situ retorting chemical mining technology of oil-rich coal using solar heating or hot dry rock heating;④ In the deep coal underground gasification hydrogen production path (UCG-H2) based on in-situ carbon capture, utilization and storage (CCUS), recycled CO2 is used as a gasification agent to improve the production of effective components of coal gas, and captured CO2 is stored in the underground gasification cavity to form a zero-emission UCG-H2 technical route. ⑤ nanometer hydrogen-rich coal water slurry hydrogen-mixed power generation technology to achieve low-carbon coal-based power production at low temperature and low pressure, and significantly reduce carbon dioxide emissions of coal power; ⑥ The Low carbon power generation system concept of underground coal gasification coupled with IGCC (UCG+IGCC) reduces the coal-to-gas process of IGCC and significantly reduces CO2 emissions. ⑦ Pulverized coal deflagration linear drive power generation technology structure, the theoretical work energy is more than 15 times of coal combustion; ⑧ Based on the CO2 hydrogenation synthesis “green methanol” route of modern coal-based energy recycling, methanol is synthesized from coal-based “blue hydrogen” produced by low-carbon UCG and captured CO2; ⑨ The CO2 in situ storage technology system generated by modern coal-based energy can help achieve zero-carbon coal-based energy production; ⑩ Dynamic carbon neutral coal-based energy system architecture in mining area, coupling intelligent supply protection, green development, clean transformation, low-carbon utilization and clean emission in a closed-loop system in mining area to achieve dynamic carbon neutrality in mining area.

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.

As one of the important forms of equipment for underground auxiliary transportation in coal mines, monorail cranes have the advantages of large carrying capacity and strong climbing ability, and unmanned driving is its inevitable development direction in the context of intelligent construction of coal mines. In order to ensure the safe driving of the unmanned monorail crane in the underground roadway, it is particularly important for the reliable detection of track joints and key targets. Firstly, the mine image dataset was enhanced to improve its diversity. A YOLOv5 algorithm with improved channel attention mechanism ECA_s and EIOU regression loss function was proposed, and the improved YOLOv5 algorithm was experimentally analyzed, and the results showed that the recognition accuracy was increased by 9.1% and mAP by 3.6% by using the improved YOLOv5 algorithm. Secondly, a machine vision-based single-track crane track seam detection method was established, and the track seam distance was calculated according to the calibration coefficient by using image preprocessing, histogram information statistics, morphological processing and other technologies, and the results showed that the detection error of a seam image processed by the track seam detection algorithm based on machine vision was only 0.3 mm. Finally, the unmanned target detection test of the monorail crane is carried out, and the results show that the average accuracy of the improved YOLOv5 target detection algorithm reaches 90.3%, which has a higher detection accuracy. The average detection error of the seam image processed by the monorail crane rail seam detection algorithm is 0.73 mm, and the maximum detection error is not more than 1.1 mm, which has higher detection accuracy. In order to ensure the safe passage and reliability of unmanned monorail cranes in coal mines, a pragmatic and feasible detection scheme and an accurate and reliable detection algorithm are provided.

In recent years, coal mine robots have become a research hotspot in the field of modern coal machine equipment, and the main coal flow transportation system of most coal mines has basically realized continuity, mechanization and automation, which also puts forward higher requirements for safety monitoring and inspection efficiency in the main transportation roadway, and accurate target detection is a necessary guarantee for intelligent safety monitoring in coal mines, but the existing object detection algorithm is applied to complex and harsh coal mine underground roadway environment, and there is a problem of low target detection accuracy. Aiming at the special working condition detection requirements of low lighting and chaotic environment in the downhole, the target data set in the underground roadway environment was produced, and the dataset annotation was completed and multi-dimensional analysis was carried out. A PT target detection algorithm based on the fusion of pyramid structure and attention mechanism is proposed, and the attention mechanism module is used to replace the convolution module in the pyramid structure, which improves the extraction ability of global features while controlling the amount of feature calculation, realizes the extraction effect of the fusion of local features and global features of the target, and improves the expression ability of the features of the target area of interest in the image. Finally, for the application scenario of underground inspection robot in coal mine, the proposed PT algorithm is compared with the traditional Faster R-CNN and YOLOv4 algorithms. Compared with the mainstream Faster R-CNN and YOLOv4 target detection networks, the PT algorithm has better comprehensive recognition capabilities, and the accuracy of identifying coal mine personnel is increased by 2.90% and 4.30%, the accuracy of identifying underground obstacles is increased by 0.20% and 4.80%, and the accuracy of identifying mine cracks is increased by 4.40% and 8.60%, respectively. The accuracy rate of identifying downhole equipment was improved by 3.00% and 8.70%, respectively. Therefore, the PT target detection algorithm can better adapt to the underground environment, and the target detection algorithm can obtain higher accuracy and detection speed than other algorithms, which can provide theoretical basis and technical support for the construction of underground roadway security control system.

Abstract The identification technology for coal and coal-measure rock is required across multiple stages of coal exploration, mining, separation, and tailings management. However, the construction of identification models necessitates substantial data support. To this end, we have established a near-infrared spectral dataset for coal and coal-measure rock, which includes the reflectance spectra of 24 different types of coal and coal-measure rock. For each type of sample, 11 sub-samples of different granularities were created, and reflectance spectra were collected from sub-samples at five different detection azimuths, 18 different detection zeniths, and under eight different light source zenith conditions. The quality and usability of the dataset were verified using quantitative regression and classification machine learning algorithms. Primarily, this dataset is used to train artificial intelligence-based models for identifying coal and coal-measure rock. Still, it can also be utilized for regression studies using the industrial analysis results contained within the dataset.

Abstract Autonomous mining is promising to address several current issues in the mining sector, such as low productivity, safety concerns, and labor shortages. Although partial automation has been achieved in some mining operations, fully autonomous mining remains challenging due to its complexity and scalability in field environments. Here we propose an autonomous mining framework based on the parallel intelligence methodology, employing self-evolving digital twins to model and guide mining processes in the real world. Our framework features a virtual mining subsystem that learns from simulating real-world scenarios and generates new ones, allowing for low-cost training and testing of the integrated autonomous mining system. Through initial validation and extensive testing, particularly in open-pit mining scenarios, our framework has demonstrated stable and efficient autonomous operations. We’ve since deployed it across more than 30 mines, resulting in the extraction of over 30 million tons of minerals. This implementation effectively eliminates the exposure of human operators to hazardous conditions while ensuring 24-hour uninterrupted operation.

Abstract This study explores the detonation characteristics and compositional changes of pulverized coal, focusing on its use in Rotary Detonation Wave (RDW) technologies. While pulverized coal has shown high fuel efficiency in RDW settings, transitioning from theory to practical detonation engineering presents substantial scientific and technical hurdles. A key issue is the reprocessing of detonation byproducts for in-situ coal mine gob filling, a topic that has received little attention. Utilizing advanced methods like X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR), this paper investigates the micro-morphology, composition, and aromatic structures of gas–solid products pre and post-detonation at the Tashan Coal Mine's 2305 working face. Results indicate that coal dust from the underground mining face has enhanced detonation characteristics, with the addition of coal powder fuel extending the gas detonation limits. This benefits economic aspects by reducing reliance on gas fuel and lowering detonation fuel costs. The highest recorded detonation wave velocity was 2450 m/s, 14.8% greater than that of coal dust from external sources, suggesting more effective energy release and pressure gain. Furthermore, the study links detonation combustion intensity to coal's aromatic properties, noting a post-detonation aromaticity index (I) of 0.4941. This indicates an improvement in the aromatic structure under high-temperature conditions, vital for coal's reactivity and energy efficiency in RDW applications. This research not only deepens the understanding of coal dust combustion mechanisms but also advances clean coal utilization and deep coal fluidization mining, addressing significant RDW technological challenges.

Coal has made important contributions to the industrialization process of human society, and has played a key supporting role in ensuring China’s energy security and stable economic development. Since the mid-19th century, the coal-based fuel of coal, kerosene, gas and coal electricity has gradually entered the era of large-scale use. However, the traditional use of coal produces high carbon emissions, which has become a major problem in energy transition. Facing the development of ecological civilization society, the clean and efficient use of coal is imminent, and the innovation of modern coal-based energy technology is imperative. This paper proposes the definition and connotation of modern coal-based energy technology system, which is defined as the production of low-carbon energy products such as coal-based gas, coal-based oil, coal-based hydrogen and coal-based electricity, and the in-situ absorption of carbon dioxide in mining areas to form a clean and low-carbon energy production system with carbon neutrality. Its technical connotation includes coal-based gas, coal-based oil, coal-based hydrogen, coal-based electricity and dynamic carbon neutrality in mining areas, with a total of 5 technical modules, 19 technical units and 61 key technologies. The modern coal-based energy technology system will play an important role of “one main body, three supports and one breakthrough” in the future new energy system. Coal-based electricity provides the main body of electricity system stability. Coal-based oil supports the China’s oil safety. Coal-based gas supports the China’s gas safety. Coal-based hydrogen plays a supporting role in the development of hydrogen energy in China. The major breakthrough is the dynamic carbon neutral technology of coal-based energy. The paper supposes that when the ratio of non-fossil energy and low-carbon coal-based energy is maintained in the range of 2.5−3.0, coal-based energy, oil and gas and wind, water, nuclear energy can be reasonably integrated to form a new quality energy system with strong complementarity, high reliability and low carbon emissions. Based on the major breakthrough and practical application of modern coal-based energy technology, it is predicted that China’s low-carbon coal-based energy production will reach 1.02 billion −1.46 billion tons of standard coal equivalent (tce) in 2060, equivalent to 18%−24% of the national energy demand, and carbon dioxide emissions will be controlled below 500 million tons. Compared with the current energy transformation planning path based on “de coal” planning, the share of coal in our energy system can increase from 10% to more than 20%, and carbon dioxide emissions will reduce by 50%. At that time, the modern coal-based energy system has great technological innovation significance for China’s energy security and carbon dioxide emission reduction, and helps China to become a country with independent energy supply.

The shallow coal in China is gradually being exhausted, and mining resources from deep parts of the Earth has become an inevitable trend and national demand. Faced with the difficulty of deep coal mining beyond 2000 meters, an autonomous walking mechanism suitable for fluidized mining equipment was designed, and the focus was on the problem of multi-cylinder synchronous control in the hydraulic propulsion system of this mechanism. Firstly, based on the principle of fluidized mining technology and equipment composition, a resistance increasing step type autonomous walking mechanism was designed, which can achieve segmented autonomous walking of multiple compartments such as excavation, transformation, and output. Secondly, in response to the requirements of multi-cylinder synchronous control in hydraulic propulsion systems, four control strategies, namely master-slave control, adjacent cross-coupling control, deviation coupling control, and mean coupling control, as well as the advantages and disadvantages of proportional-integral-derivative control(PID) algorithm and automatic disturbance rejection controller (ADRC), were analyzed and compared. Control performance simulation tests were conducted under three working conditions: uniform load, sudden load, and time-varying load. Once again, the radar chart evaluation method was used to comprehensively evaluate the synchronization control performance under different control strategies, and the mean coupled control method based on ADRC was selected as the optimal synchronization control strategy. Finally, an autonomous walking mechanism test bench was developed and multiple hydraulic cylinder synchronization control experiments were conducted. The test results showed that when using the mean coupled synchronization control strategy based on ADRC, the maximum synchronization error of the four hydraulic cylinders under different working conditions can be maintained within ± 5 mm, and they have excellent robustness. It can meet the requirements of multi-cylinder synchronous propulsion for the autonomous walking mechanism of fluidized mining equipment.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital–model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

The shearer is an important component of the smart mining workface, and its effective control is a key aspect that guarantees safe and high-efficient production in coal mines. To address the issue of autonomous height adjustment during the shearer’s cutting process, a self-adaptive speed control method driven by digital twin technology is proposed. A digital twin-based control architecture for the shearer is first established, which consists of physical and the corresponding virtual entities, as well as reality–virtual interaction between them. Based on the mathematic model formulated for height adjustment system of the shearer, an adaptive fuzzy sliding mode controller (AFSMC) with the displacement estimation is designed for the virtual entity, with the purpose of guiding the operation of the corresponding physical entity. Simulation experiments on MATLAB compares the control performance among the proposed method and four comparative ones, including PID controller, integral sliding mode controller (ISMC), feedback linearization controller (FLC), and fuzzy sliding mode controller (FSMC). The experimental results confirm the effectiveness of the proposed AFSMC. More specifically, its steady-state error is 0.024, the maximum absolute control input is 8.43, and the settling time is 1.74 s. This also proves that the digital twin-based control method enables the precise adaptive height adjustment of the shearer, providing potential reference for the intelligent development of a smart mining workface.

The long-term precision of shearer independent positioning device is an important research content of intelligent mining. The current shearer positioning accuracy is hard to meet the demands of automatic mining, and we established a fusion positioning model based on IMU/UWB, and proposed autonomous correction to compensate the drift error of inertial navigation device for the coal mine working face by using the fusion results of IMU/UWB to correct the position and posture of inertial positioning system. It can compensate the deviation of IMU in real time, and realize long-term high-precision autonomous cycle cutting of the shearer. To reduce the influence of uncertain measurement noise on the positioning results, we use variational Bayesian unscented Kalman filter (VB-UKF) technique to smooth the positioning results in order to further improve the fusion positioning accuracy. To verify the positioning accuracy of the mining face end, fusion positioning experiments are conducted in Taiyuan Heavy Machinery Co., Ltd on the ground and Shandong Energy Group coal mine working face in the underground, respectively. The experimental results demonstrate that, the accuracy of IMU/UWB fusion positioning is higher than that of the single UWB solution. After smoothing via the VB-UKF algorithm, the error in the x-axis and z-axis orientation of the IMU/UWB fusion localization for underground experiments decreased from 0.0102 m and 0.194 m to 0.082 m and 0.158 m, respectively, and the corresponding average accuracy improved by 19.6% and 18.6%. The error of the three pose angles is less than 2°, because the error extended Kalman filter compensates and modifies the IMU bias in the fusion localization model, which can effectively suppress the drift of the IMU posture angle. The underground field experiment proves the effectiveness of the positioning method of coal mining face end, which provides better reference and experience for the further application in underground coalmine by using IMU/UWB fusion positioning.

Abstract The effect of contact load and relative displacement on tribo-corrosion interaction of parallel steel wires of main cable in the suspension bridge was investigated in this study. A self-made tribo-corrosion test bench was employed to conduct tribo-corrosion tests of parallel steel wires in 3.5% (wt%) NaCl solution and deionized water under different contact loads and different relative displacements. The friction coefficient and wear coefficient of wires were presented. Electrochemical corrosion behavior (Tafel polarization curves, Nyquist diagram, and equivalent circuit diagram) was characterized by electrochemical analyzer. Wear morphology was observed by scanning electron microscope. Wear volume loss and corrosion-wear interaction were quantitatively demonstrated by high-precision weighing balance. The results show that the electrochemical corrosion ability of the steel wires increases with the increase of the contact load or relative displacement. The increased contact load or relative displacement increases the volume loss of corrosion-wear and pure wear, but decreases the wear coefficient. The wear mechanisms in 3.5% NaCl solution are adhesive wear, abrasive wear, and corrosive wear as compared to adhesive wear and abrasive wear in deionized water under different contact loads. The wear mechanisms of parallel steel wires are slightly different under different relative displacements. But the main wear mechanisms are similar to that under different contact loads. The interaction effects of corrosion and wear produced by the contact load and relative displacement are all the synergistic effects.

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.

Abstract Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing, this article proposed a convolution neural network (CNN) coal and rock identification method based on hyperspectral data. First, coal and rock spectrum data were collected by a near-infrared spectrometer, and then four methods were used to filter 120 sets of collected data: first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing. The coal and rock reflectance spectrum data were pre-processed to enhance the intensity of spectral reflectance and absorption characteristics, as well as effectively remove the spectral curve noise generated by instrument performance and environmental factors. A CNN model was constructed, and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations (i.e., the learning rate, the number of feature extraction layers, and the dropout rate) to generate the best CNN classifier for the hyperspectral data for rock recognition. The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%. Verification of the advantages and effectiveness of the method were proposed in this article.

The main focus of this work is the design and development of a three-dimensional force sensor for the cutting pick of a coal mining shearer’s simulated drum. This sensor is capable of simultaneously measuring the magnitude of force along three directions of the cutting pick during the cutting sample process. The three-dimensional force sensor is built based on the strain theory of material mechanics, and reasonable structural design is implemented to improve its sensitivity and reduce inter-axis coupling errors. The strain distribution of the sensor is analyzed using finite element analysis software, and the distribution of the strain gauges is determined based on the analysis results. In addition, a calibration test system is designed for the sensor, and the sensitivity, linearity, and inter-axis coupling errors of the sensor are calibrated and tested using loading experiments in three mutually perpendicular directions. Modal simulation analysis and actual cutting pick testing of the coal mining machine’s simulated drum are conducted to study the dynamic characteristics and functionality of the sensor in practical applications. The experimental results depict sensitivities of 0.748 mV/V, 2.367 mV/V, and 2.83 mV/V for the newly developed sensor, respectively. Furthermore, the cross-sensitivity error was lower than 5.02%. These findings validate that the sensor’s structure satisfies the measurement requirements for pick-cutting forces.

Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based on multiple hierarchical clustering was proposed. Morphology similarity and the coplanarity measurement method were defined to evaluate the similarity of clusters. The coal seam floor series in the face-advancing direction were clustered according to the morphology similarity and coplanarity, respectively. Taking the morphology-based and coplanarity-based cluster centers as generating lines and stretching angle, respectively, the coal seam floor was reconstructed. The reconstructed floor can be regarded as the cutting path. The coal seam geological model of the 18,201 longwall face was analyzed with the proposed cutting path planning method. Comparing the reconstructed floor and original floor, the amounts of coal left and cut gangue were 1999 m3 and 1856 m3, respectively, for the segmental floor. For the case of whole floor, the amounts of coal left and cut gangue were 5642 m3 and 5463 m3, respectively. The coal loss rates only were 0.57% and 0.87% for the segmental and whole coal seam, respectively.

Efficient, safe, and environmentally friendly methods for the low-carbon utilization of coal are required to exploit this resource as a source of cleaner energy. Tar-rich coal can produce a high volume of oil and is abundant in western China. Currently, tar-rich coal is mostly combusted for power generation, which results in wastage of valuable oil resources and serious environmental pollution. Researchers have focused on achieving zero or near-zero carbon emissions while alleviating damage and pollution to geological formations when mining coal resources. In particular, the use of an intelligent unmanned automatic mining machine (UAMM) integrating microwave pyrolysis technology for the in situ mining and conversion of coal into oil and gas has been proposed. However, to date, studies on the microwave pyrolysis of tar-rich coal for the efficient extraction of oil are lacking. In this study, we outline the state-of-the-art developments in the microwave treatments of coal. The principles and advantages of microwave heating compared with traditional heating methods are discussed. Further, current research and applications of microwave-assisted coal processing are reviewed. We also discuss the challenges and future developments in the field of the microwave-based pyrolysis of coal. This report provides a reference for the application of microwave technology to in situ mining, pyrolysis, and conversion of tar-rich coal into liquid energy resources based on a UAMM.

Accurate positioning of the shearer remains a challenge for automation of the longwall coal mining process. In this paper, the popular Ultra-wideband (UWB) positioning system that has attracted considerable attention is adopted to obtain the target node location. Unfortunately, localization accuracy is still unsatisfactory and unreliable in mixed line of sight (LOS) and non-line of sight (NLOS) scenarios. To ameliorate localization accuracy of UWB for complicate underground environment where the positioning scenarios suffered from frequently switching among LOS, NLOS, and mixed LOS-NLOS condition, the novel positioning algorithm GMM-IMM-EKF was proposed. Gaussian mixed model (GMM) was employed to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters (VBAKFs) under the structure of interacting multiple model (IMM) was utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively. Then, the position of the target node was determined by exploiting extended Kalman filter (EKF) based on the outcome of IMM-VBAKF. The proposed approach was assessed by exploiting UWB P440 modules. Comparative experimental verification demonstrated that GMM-IMM-EKF strategy outperformed other positioning approaches, which can effectively reduce the adverse effect of NLOS errors and achieve higher positioning accuracy in underground environment with LOS/NLOS/LOS-NLOS transition conditions.

The trackless rubber tyred vehicle (TRTV) is an important transporting equipment of underground coal mine auxiliary transportation system in the world. Presently, most TRTVs are driven by flameproof diesel engine. However, few researches were focused on their dynamic performance in the past. In this paper, the characterizing parameters and influencing factors of the dynamic performance of a flameproof diesel engine driven TRTV were summarized and discussed firstly. Then the basic dynamic equations of the TRTV were established. Finally, the dynamic performances of the WCQ-3B type TRTV were simulated by ADVISOR. And some important dynamic performance parameters such as maximum speed, acceleration time and maximum gradability were discussed by simulation. What is more, the fuel economy and emission performance of the vehicle were also analysed. It is shown that the dynamic performance of WCQ-3B type TRTV can satisfy the design and usage requirements of underground coal mine.

Because of the high organic carbon concentration in carbonaceous shale, a large proportion of carbonaceous shales are often misclassified into coals using visible and near-infrared (VIS-NIR) reflectance spectroscopy in the field of coal-gangue identification of hyperspectral remote sensing of coal mine. In order to study spectral characterization of coal and carbonaceous shale, three bituminite samples and three carbonaceous shales were collected from a coal mine of China, and their spectral reflectance curves were obtained by a field spectrometer in the wavelength range of 350–2500 nm. Only one carbonaceous shale could be easily identified from the three bituminite samples according to obvious absorption valleys near 1400 nm, 1900 nm, and 2200 nm of its reflectance curve while the other two carbonaceous shales have similar reflectance curves to the three bituminite samples. The effect of carbon concentration on reflectance curve was simulated by the mixed powder of ultralow ash bituminite and clay in 0.5 mm grain size under various mixing ratios. It was found that absorption valleys near 1400 nm, 1900 nm, and 2200 nm of the mixed powder become not obvious when the bituminite content is more than 30%. In order to establish an effective identification method of coal and carbonaceous shale, 250 other samples collected from the same coal mine were divided into 150 training samples and 100 prediction samples. Principal component analysis (PCA) and Gauss radial basis kernel principal component analysis (GRB-KPCA) were employed to extract principal components (PCs) of continuum removed (CR) spectra of the training samples in eight selected wavelength regions which are related to the main mineral and organic compositions. Two support vector machine- (SVM-) based models PCA-SVM and GRB-KPCA-SVM were established. The results showed that the GRB-KPCA-SVM model had better identification accuracies of 94% and 92% for powder and nature block prediction samples, respectively.

Skin can be damaged by the environment easily. Skin cream is an effective and rapid way to moisten the skin by changing the skin surface properties. Rat skin and pig skin are common animal models for studies and were used as skin samples in this study. The nano- and macroscale friction and durability of damaged skin were measured and compared with those of virgin (intact/undamaged) skin. The effect of skin cream on friction and durability of damaged and virgin skin samples is discussed. The effects of velocity, normal load, relative humidity and number of cycles were studied. The nanoscale studies were performed by using atomic force microscope (AFM), and macroscale studies were performed by using a pin-on-disk (POD) reciprocating tribometer. It was found that damaged skin has different mechanical properties, surface roughness, contact angle, friction and durability compared to that of virgin skin. But similar changes occur after skin cream treatment. Rat and pig skin show similar trends in friction and durability.

Tactile perception is essential for humans to perceive the world, and it usually results in mechanical responses from the finger. In this study, a nonlinear, viscoelastic, and multilayered finite element model of the finger was developed. The relationship between the mechanical responses within the finger and tactile perception while the finger scanned different surface textures was studied. The results showed that the sensitivity of tactile perception is affected by the peak value of von Mises stress, which is itself determined by the shape and density of a given texture. The von Mises stress varies periodically with time, and this variation depends on the periodicity of the texture. Displacement signals around Pacinian corpuscles have periodic variation. The period of displacement decreases as the density of the texture increases. The spectral centroid increases as the spacing of the texture decreases. The related mechanisms are discussed in this article.

Several types of electromagnetic transducer for the middle ear implants (MEIs) have been developed as an alternative to conventional hearing aids for the rehabilitation of sensorineural hearing loss. Electromagnetic transducer type and design are thought to have a significant influence on their hearing compensation performance. To investigate these effects, a middle ear computational model was constructed based on a complete set of microcomputerized tomography section images of a human ear. Its validity was confirmed by comparing the model predicted motions with published experimental measurements. The result shows that the eardrum driving transducer (EDT) is superior to the floating mass transducer (FMT) in hearing compensation when the transducer mass is small but inferior to the FMT when the mass gets bigger. The incus body driving transducer (IBDT) is the most ineffective type of transducer for hearing compensation. Moreover, the masses of the EDT and the FMT decrease the transducer performance mainly at higher frequencies: the greater the transducer mass, the lower the displacement of the stapes excited by these transducers. On the other hand, the IBDT driving rod stiffness decreases transducer's performance severely at low frequencies and its adverse effect on transducer performance increases with the decrease of the stiffness of the IBDT driving rod.