Coal Mine Environment Research Articles

Portable Intelligent Electromagnetic Flowmeter Controlled by Magnetic Induction Intensity

In this paper, we propose a portable intrinsically safe, intelligent electromagnetic flowmeter for the application of coal mines and provide a detailed description of its working principle. In terms of hardware circuit design, several important modules, including electromagnetic flow rate sensor, excitation circuit, core control circuit, signal conditioning circuit, and air traffic control detection circuit, were analyzed and designed in sequence. It adopts a high-performance, low-power MCU STM32407ZET6 and high-precision chips, ensuring measurement accuracy and reducing power consumption. The excitation method using low-frequency rectangular waves dynamically adjusts the sampling interval time, further prolonging the battery life. Finally, the performance of the electromagnetic flowmeter was measured, and the experimental data showed that the measured flow rate was basically linear with the output voltage. The relative indication error of the electromagnetic flowmeter was within 0.3%, and the repeatability fluctuated within the range of 0.03%. The flowmeter has good measurement accuracy, which is suitable for long-term use in coal mining environments.

Ventilation characteristics of double-forced air pipe ventilation in comprehensive mechanized excavation face and determination of its optimal parameters

ABSTRACT This study proposes a ventilation and dust control approach using double-forced air pipes at the tunnel excavation face, aiming to optimize the dust control and reduction measures for underground coal mining environment. We designed a physical model in proportion to the 11,139 excavation face in the Zhangji Coal Mine of China Huaihe Energy Holding Group Coal Company. To begin with, CFD numerical simulation was employed to unveil the basic laws of the double-forced air pipe ventilation approach, including airflow characteristics and dust concentrations. The orthogonal experimental method was then adopted to analyze the influence mechanism of installation location and airflow ratio of the double pipes on the dust density around the driver’s operating space. Results suggest that the double-forced air pipe ventilation can efficiently improve the airflow velocity at the excavation face, reaching 5.54 m/s. With the minimum dust density around the driver’s operating space as the optimal parameter standard and combined with the R value, the three factors in the orthogonal experiment can be sorted as: the distance between the forced ventilation pipe at the driver’s side and the excavation face (R = 206.91) > the distance between the forced ventilation pipe at the return air side and the excavation face (R = 171.72) > the airflow ratio of the two pipes (R = 77.52). The optimal combination is that the distance between the ventilation pipe at the driver’s side and the excavation face is 5 m, the distance between the ventilation pipe at the return air side and the excavation face is 5 m, and the airflow ratio of the two pipes is 6:4. In such a case, the dust density around the driver’s operating space is 48.21 mg/m3, achieving an ideal dust control effect.

Mechanical properties of cemented backfill under different unloading rates after cured at different temperatures

In the process of deep filling mining in coal mine, due to the influence of mining and other factors, the confining pressure on both sides of cemented backfill decreases. It is necessary to master the mechanical characteristics of cemented backfill under unloading conditions in different temperature environments to ensure the stability of cemented backfill. To simulate the unloading process of cemented backfill in the deep highland environment of coal mine, triaxial unloading confining pressure tests of cemented coal gangue- fly ash backfill at different unloading rates after curing at different temperatures (20 ℃, 35 ℃ and 50 ℃) were carried out by the Rock Triaxial Testing System, and the mechanical characteristics of cemented backfill unloading were systematically studied. The findings of the study show that, for the same unloading rate, the peak strength of cemented backfill specimen reduces initially before increasing with curing temperature rises. After curing at the same temperature, the peak strength of cemented backfill specimen varies exponentially with an increase in unloading rate. The deformation modulus and volume strain at different unloading rates satisfy an exponential function relationship, while the generalized Poisson's ratio and volume strain satisfy a quadratic function relationship. Under unloading conditions, there was no large fracture or fragmentation of cemented backfill, only cracks appeared on surface. At the same unloading rate, cemented backfill specimen cured at 35 ℃ and 50 ℃ exhibits significantly more shear cracks after failure compared to the material cured at 20 ℃. As the unloading rate increases, the proportion of shear cracks generated during unloading failure of cemented backfill specimen cured at 35 ℃ and 50 ℃ gradually increases. In addition, the theoretical curve of the four-stage cemented backfill unloading constitutive model established in sections is essentially compatible with experimental curve, confirming the theoretical model's reasonableness. The study's findings provide a theoretical basis for evaluating stability of cemented backfill after unloading in deep high temperature (35–50 ℃) environments.

Research on Coal Volume Detection and Energy-Saving Optimization Intelligent Control Method of Belt Conveyor Based on Laser and Binocular Visual Fusion

Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang’s calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system’s performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt’s operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.

Design and Study of LoRa-based IIoT Network for Underground Coal Mine Environment

Design and Study of LoRa-based IIoT Network for Underground Coal Mine Environment

Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR

CO gas is not only lethal but also a significant forecasting indicator for the spontaneous combustion of coal mines. It is imperative that monitoring modules for CO gas that work well in the coal mine environment are available. A feasible solution is the detection of CO by using monitoring modules based on tunable diode laser absorption spectroscopy (TDLAS) over a mid-infrared waveband near 4.6 μm. However, in most cases, the mid-infrared TDLAS-based CO monitoring module tends to introduce severe interference fringe noise into the TDLAS spectral backgrounds which is difficult to filter out using traditional spectral filtering methods, reducing the detection performance of the module. In order to filter out the noise and improve the stability of the module in complex coal mine environments, this work proposed an algorithm based on support vector regression (SVR) to extract the TDLAS spectral backgrounds. Spectral analysis indicates that the TDLAS spectral background can be predicted over the entire scanning spectrum range by using this algorithm, and the noise in the spectral background can be effectively filtered out when calculating the absorbance spectrum based on the Lambert–Beer law. Compared to extracting spectral backgrounds using the traditional least square polynomial fit, the obtained correlation coefficients between regression models of spectral backgrounds and corresponding training point datasets were increased from below 0.998 to above 0.999. The peak-to-peak value of the obtained N2 absorbance spectrum was suppressed below 0.022 from nearly 0.045. The signal-to-noise ratio of the obtained 25 ppm CO absorbance spectrum was increased to 13.35 from 6.95. A CO monitoring module polluted by dust was used to conduct experiments to further test the SVR-based algorithm. The experiment results showed that after programming the SVR-based algorithm to the module, the estimated limit of detection of the module was reduced to 5.46 ppm from 29.08 ppm, and all the absolute measuring errors of the standard CO gases with different low concentrations were reduced to less than 4 ppm from a majority of the errors of more than 10 ppm, compared to least square polynomial fit. The CO monitoring module could still maintain the performance of high-precision quantitative detection when using the SVR-based algorithm even if it had been polluted severely. So, the CO monitoring module has good adaptability to harsh field environments, and its operation stability can be effectively improved by using the algorithm proposed in this work.

Effect of a Simulated Coal Mine Environment on Polyurethane Grouting Material and a Proposed Polyurethane Strengthening Method.

When it comes to grouting in coal mines, polyurethane (PU) is often utilized. However, it is of vital importance to consistently improve the mineral PU, considering the significant amount of environmental deterioration to which it is prone. Laboratory experiments were used to model various coal mine conditions. Additionally, a workable technique for PU strengthening using ultrasonic waves was proposed. Compression tests and scanning electron microscopy (SEM) were used to describe the PU-gangue material's induration characteristics. The results showed that ultrasound has a positive impact on PU's mechanical strength. The final strength of the PU was significantly impacted by the size of the coal gangue particles, the amount of dust, and the amount of water. The induration made of gangue and PU with the same mass but differing particle sizes was noticeably different in its compressive strength. The strengthening mechanism showed that the average size of the rigid foam after the ultrasound treatment was smaller, and the 'honeycomb'-structured space in the inner section was more compact, resulting in the rigid PU foam having a higher compressive strength after ultrasound treatment. Furthermore, the dust content and water content of coal mines need to be controlled within a specific range to ensure the effective use of PU grouting materials.

Repurposing End-of-Life Coal Mines with Business Models Based on Renewable Energy and Circular Economy Technologies

This paper presents a methodology to select the most exciting business models based on renewable energy and circular economy technologies within end-of-life coal mines to help develop a renewable-based energy sector, promote sustainable local economic growth, and maximise the number of green and quality jobs. To achieve this goal, first, a structural analysis was developed to select the technical variables that better identify this complex system. Second, a morphological analysis allowed the construction of the scenario space. Third, a multicriteria assessment was developed to achieve this goal, based on the previously assessed relevant scenarios, considering the European Green Deal policies, technical variables that characterise end-of-life coal mine environments, technology readiness level, the European taxonomy, synergistic potentials, contributions to the circular economy, and sector coupling. Finally, result indicators were selected to analyse the alternative options derived from the justification approach, considering the targets set by the European Green Deal and related taxonomy and the regional policy indicators for the Just Transition Fund. The results show that eco-industrial parks with virtual power plants represent the most appropriate business model choice, according to the scoring given to the different aspects. They may be complemented by a hydrogen production plant, provided that specific economic subventions are obtained to achieve balanced financial results.

Energy evolution mechanism during rockburst development in structures of surrounding rocks of deep rockburst-prone roadways in coal mines

Influenced by the deep high-stress environment, geological structures, and mining disturbance in coal mines, the frequency of rockburst disasters in roadways is increasing. This research analyzed energy evolution characteristics during rockburst development in the elastic bearing zone and energy conversion in the plastic failure zone. The critical energy criteria for structural instability of roadway surrounding rocks were deduced. Numerical software was also applied to simulate the energy evolution during rockburst development in surrounding rocks of rockburst-prone roadways under conditions of different mining depths and coal pillar widths. The occurrence mechanism of rockburst deep in coal mines was analyzed from the perspective of energy in structures of deep roadway surrounding rock in coal mines. The research results show that the critical energy criteria are closely related to the elastic strain energy stored in deep roadway surrounding rocks and the energy absorbed by support systems. The impact energy in roadways is directly proportional to the square of the stress concentration factor k. Moreover, as the mining depth increases, the location of the peak point of maximum energy density gradually shifts to coal ahead of the working face. The larger the mining depth is, the more significantly the energy density is influenced by advanced abutment pressure of the working face and the wider the affected area is. With the increment of the coal pillar width, the distance from the peak point of energy density to the roadway boundary enlarges abruptly at first and then slowly, and the critical coal pillar width for gentle change in the distance is 30 m. Changes in the peak elastic energy density in coal pillars with the coal pillar width can be divided into four stages: the slow increase stage, abrupt increase stage, abrupt decrease stage, and slow decrease stage. The elastic energy density is distributed asymmetrically in deep roadway surrounding rocks in coal mines. Under the action of structures of roadway surrounding rocks, energy evolution in these structures differs greatly during rockburst development under conditions of different coal pillar widths. This research provides an important theoretical basis for the support of rockburst-prone roadways during deep coal mining.

Study on the suppression characteristics and mechanism of ABC powder on pulverized coal explosion based on the analysis of thermal decomposition characteristics and reaction kinetics

This study investigates the inhibitory characteristics and mechanisms of ABC powder on coal powder explosion using a combination of experimental, theoretical, and numerical methods. In a 20 L spherical explosion experimental system, coal powder was mixed with ABC powder at various mass concentrations. The microstructure and major functional groups of the explosion products were characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The proportions of the major functional groups were determined through peak fitting analysis. The research findings demonstrate that the introduction of 60 wt% ABC powder into the coal sample led to a significant decrease in the maximum explosion pressure, from 0.683 MPa to 0.454 MPa, indicating a remarkable reduction of 33.38%. Moreover, the inclusion of 70 wt% ABC powder exhibited complete suppression of coal dust explosions. Thermal decomposition characteristics of the mixed system were investigated using a thermogravimetric analyzer, and the Coats-Redfern integral method was employed to analyze the pyrolysis experimental data. The results suggest that the optimal reaction kinetics models for the combustion stage of coal powder and the mixed system are F1 and F2, respectively. Adding ABC powder increases the activation energy by 27.35% and slows down the pyrolysis rate by 37.97%. Furthermore, numerical simulations using CHEMKIN software were conducted to study the inhibitory effect of ABC powder on coal powder explosion. The results demonstrate that the addition of ABC powder significantly reduces the content of O·, H·, and OH·radicals, which are responsible for sustaining explosive chain reactions within the explosion system. By incorporating the findings of this investigation, the present study offers a comprehensive explication of the intricate micro-level mechanisms that underlie the inhibitory properties exerted by ABC powder on coal dust explosions. These results contribute novel scientific and theoretical foundations, yielding profound insights into the prevention and mitigation of calamitous coal dust explosion incidents within coal mining environments.

Position measurement technology of boom-type roadheader based on binocular vision

Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

A Roadheader Positioning Method Based on Multi-Sensor Fusion

In coal mines, accurate positioning is vital for roadheader equipment. However, most roadheaders use a standalone strapdown inertial navigation system (SINS) which faces challenges like error accumulation, drift, initial alignment needs, temperature sensitivity, and the demand for high-quality sensors. In this paper, a roadheader Visual–Inertial Odometry (VIO) system is proposed, combining SINS and stereo visual odometry to adjust to coal mine environments. Given the inherently dimly lit conditions of coal mines, our system includes an image-enhancement module to preprocess images, aiding in feature matching for stereo visual odometry. Additionally, a Kalman filter merges the positional data from SINS and stereo visual odometry. When tested against three other methods on the KITTI and EuRoC datasets, our approach showed notable precision on the EBZ160M-2 Roadheader, with attitude errors less than 0.2751° and position discrepancies within 0.0328 m, proving its advantages over SINS.

Near-infrared wide-range dual-gas sensor system for simultaneous detection of methane and carbon monoxide in coal mine environment

To effectively monitor gas explosion and coal spontaneous combustion in coal mine, an intrinsically safe and explosion-proof dual-gas sensor system was developed for methane (CH4) and carbon monoxide (CO) measurement. A distributed feedback (DFB) laser was used to scan two adjacent absorption lines (CH4: 4284.51 cm−1, CO: 4285.01 cm−1). Two measurement ranges of 0–500 parts per million by volume (ppmv) and 0–300,000 ppmv for CH4 and 0–100 ppmv and 0–4000 ppmv for CO were realized using a ∼25 cm single-pass gas cell (SPGC) and a ∼25 m multi-pass gas cell (MPGC), respectively. A gas pre-treatment module was used to remove moisture and aerosols from gas samples. A temperature compensation algorithm was adopted to deal with ambient temperature change. Based on the Allan deviation analysis, the limit of detection (LoD) of CH4 is 0.19 ppmv and the LoD of CO is 0.12 ppmv, respectively, at an averaging time of 0.5 s. A continuous two-day CH4 measurement was performed to demonstrate the long-term stability and environmental monitoring capability. The sensor system was successfully deployed in coal mine gas analysis at Longjiapu, Changchun City, China.

A Multimodal Robust Simultaneous Localization and Mapping Approach Driven by Geodesic Coordinates for Coal Mine Mobile Robots

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.

Research on coal gangue recognition method based on XBS-YOLOv5s

Aiming at the problems of misdetection, omission and low recognition accuracy of coal gangue recognition due to the harsh environmental factors such as low illumination, motion blur and large quantities of coal gangue mixing in coal mines, a coal gangue recognition method based on XBS-YOLOv5s is proposed. Simulate the actual underground production environment to build a machine vision platform, construct a coal gangue image data set, and provide a test environment for various target detection algorithms. In this paper, we construct a real-time detection model of coal gangue in the complex environment of coal mine by fusing SimAM parameter-free attention mechanism, BiFPN feature fusion network and XIoU loss function in YOLOv5s, so as to improve the model’s ability of extracting, fusing and localizing key features of the target. The experimental results show that the recognition accuracy of XBS-YOLOv5s algorithm for coal gangue in the complex environment of low illumination, motion blur and large quantities of coal gangue mixed are effectively improved. Its mean average precision reaches 96%, which is 4.3% higher than the original YOLOv5s algorithm, meanwhile, compared with other YOLO series algorithms, it has the best comprehensive detection performance, which can provide technical support for intelligent and efficient sorting of coal gangue.

High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China

High-resolution seismic image is critically important for mining minerals. In this study, we collected seismic data from a local dense seismic array consisting of 154 stations around the Anyuan mining area and its adjacent region of Pingxiang City, Jiangxi Province in South China, and applied the ambient noise tomography (ANT) method to image the shear wave velocity structure in the study area. Shallow crustal velocities at depths less than 3.3 km were determined by direct inversion of Rayleigh wave group velocity dispersion curves at the period range of 0.5–5.0 s. Overall, the S-wave velocity structure has a tight correlation with surface geological and tectonic features in the study area. The shear wave velocity structure in the shallow crust of the Anyuan Mine and its adjacent areas displayed distinct low-velocity anomalies, which can be attributed to the depression of sedimentary structures and coal mining activities in the Pingxiang-Leping region. The zones surrounding the Anyuan fault (AYF) and Wangkeng fault (WKF) zones exhibited low-velocity anomalies from the ground surface to ~ 3.3 km underground. And the low-velocity anomalies at depths less than 1.2 km could be related to the sedimentary environment of coal mine and the coal mining activities, while the low-velocity anomalies at depths below 1.2 km are caused by the presence of fracture medium, oil and gas in the fault zone. The shear wave velocity changes sharply across the AYF, and the characteristics of the velocity change interface indicate that the AYF is inclined toward the northwest, with its extension reaching depths of approximately 3 km underground.Graphical

Study on the transport and deposition of micron-sized particles with different densities in the coal mine environment in miners

To address the health problems caused by the long-term intrusion of high-concentration dust generated in coal mining operations into the human respiratory tract, this study reconstructed a 3D digital model of the upper airway based on fine industrial CT scanning images, and established the micron particle migration calculation model by Euler-Lagrange method. The transport and deposition mechanisms of dust with different densities under different inspiratory intensities were simulated and analyzed. The simulated results were compared with the results of dust exposure experiments in the upper airway combined with 3D printing technology, and the relative errors obtained were in the range of 2.9% to 6.3%. The results showed that the airflow volume peaked in the nasopharynx, oropharynx, epiglottis, and larynx, leading to the airflow separation and formation of circulation that tend to cause dust deposition in these regions. As the dust density and inspiratory airflow volume increased, the deposition fraction of small dust particles (<1μm) in the upper airway slightly changed, and most of them escaped to the lung. For 7.07 μm dust particles, as the dust density increased from 1210 kg/m3 to 2750 kg/m3, the escape rate decreased from 31.43% to 0.48%, and this trend can be seen in the fitting function of escape rate and inspiratory airflow volume Y7.07μm=(80–1.37ν + 0.0064ν2) × 100%. When the dust particle diameter exceeded 20 μm, the impact of dust density was weakened by the large size of the dust, and the escape rate was close to 0% when the inspiratory airflow volume was 50–110 L/min. This study can provide theoretical support for the research and development of respiratory dust sensors and targeted dust prevention technologies.

In Situ Formation of MoS2 on the Surface of CF to Improve the Tribological Properties of PUE.

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

Study on PM diffusion and distribution of trackless rubber-tyred vehicle under different driving conditions in underground coal mining environment.

Particulate matter (PM) is one of the most harmful exhaust pollutants to human health. In this study, the PM diffusion and distribution emitted by trackless rubber-tyred vehicle under different driving conditions in coal mine were analyzed with numerical simulations and field measurements. The results show that when the vehicle velocity was constant, the PM concentration of the trackless rubber-tyred vehicle decreased with increasing distance from the exhaust pipe orifice. In addition, the proportion of PM with a concentration below 10mg/m3 was the highest owing to the influences of diffusion and airflow dilution. However, when the diffusion distance is less than 3m, the PM concentration far exceeds the occupational exposure limit (10mg/m3). In this case, underground personnel should stay away from the area near and along the exhaust pipe as far as possible. With increasing vehicle velocity, the PM concentration gradient at a diffusion distance of 0-6m showed the most significant slope. Besides, the concentration fluctuation of PM was the largest and relatively high when the diffusion distance was 5-15m. Therefore, the area 15m from the exhaust gas pipe opening of the trackless rubber-tyred vehicle should be controlled. In addition, the relative errors between the measured and numerical simulation results were mostly less than 10%, which proved that the numerical simulation results were reliable.

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment.

Coal is an important resource that is closely related to people's lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.