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- Online First 2024, Volume 50, Issue 9 Cover Catalogue Previous Issue
Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
2024, 50(9): 1-12.
doi: 10.13272/j.issn.1671-251x.18213
Abstract: The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.
2024, 50(9): 13-19, 27.
doi: 10.13272/j.issn.1671-251x.2024070055
Abstract: The type, surface texture, metamorphic degree, and developmental stage of coal and gangue significantly influence their emissivity. Accurate settings for emissivity parameters are essential for infrared temperature measurements and the identification of coal and gangue in infrared images. This study proposed a method for measuring the emissivity of coal and gangue based on the matching method. The approach integrated surface thermocouples with infrared thermography to assess emissivity. Samples were uniformly heated in a closed electric furnace, and once the temperature stabilized, a surface thermocouple measured the actual temperature of a selected area (denoted as t1). Concurrently, the infrared thermography system measured the temperature of the same area (denoted as t2). The emissivity of the infrared thermography system was calibrated until t2 equaled t1. At this point, the calculated emissivity reflected the true emissivity of the coal and gangue at that temperature. The experimental results indicated that: ① Under isothermal conditions, greater surface roughness of coal and gangue correlated with higher emissivity values, suggesting that surface roughness is a fundamental factor restricting the emissivity of these materials. ② The emissivity of four different types of coal and gangue decreased with increasing temperature, following a power function, with the fitting function's correlation coefficient (R2) exceeding 0.98, thereby confirming the feasibility of the matching method for measuring emissivity. ③ The inverse method revealed that the error rates between the measured and theoretical values under varying temperature conditions were all below 3%, validating the accuracy of the measured emissivity of coal and gangue.
2024, 50(9): 20-27.
doi: 10.13272/j.issn.1671-251x.18211
Abstract: This paper analyzes the current status and existing issues in the control technology of surrounding rock and intelligent top coal caving technology for thick and ultra-thick coal seams in fully mechanized caving faces. The study focuses on five aspects: efficient support of roadway surrounding rock, advanced support of working faces, the caving behavior of hard ultra-thick top coal, hydraulic support position monitoring, and intelligent top coal caving. To tackle the technical challenges and engineering demands for safe, efficient, and intelligent caving mining, research was conducted on surrounding rock control technology and intelligent coal caving technology. A mechanical model for cantilever beams of hard ultra-thick top coal was developed, and key technologies to enhance caving characteristics and extraction rate of top coal were created, facilitating large-height caving mining of hard ultra-thick coal seams. A modular advanced hydraulic support with a rotating self-resetting device was developed, allowing the hydraulic support's beam to automatically rotate based on the inclination angle of the roadway roof, significantly improving its adaptability to the roof and floor of roadway. The idea of replacing traditional bolt-mesh support with hydraulic supports for roadway support was proposed, offering high support efficiency, low cost, and savings on advanced support. A monitoring device and algorithm for the support posture of fully mechanized caving hydraulic supports based on the stroke of the jacks of columns and tail beams were developed, enhancing calculation efficiency and accuracy. An intelligent coal drawing control method integrating transparent geological models, coal volume monitoring devices, and coal and gangue identification devices was proposed, effectively addressing the challenges of intelligent coal drawing from ultra-thick top coal with multi-gangue layers. The paper concludes that trends in intelligent fully mechanized caving mining technology and equipment include intelligent geological assurance technology, precise measurement and intelligent sensing via machine vision, adaptive control technology for fully mechanized caving mining equipment, and digital twin technology.
2024, 50(9): 28-40.
doi: 10.13272/j.issn.1671-251x.18212
Abstract: Precise control of the coal caving mechanism is a crucial foundation for realizing intelligent and unmanned top coal caving mining. The spatial relationship between the coal caving mechanism and the rear scraper conveyor, as well as the top coal influence of the hydraulic support's posture on this spatial relationship, is key to constructing a control model for the caving support. Using the ZF17000/27.5/42D low-position top coal caving support as the research object, this study explained the spatial relationship between the coal caving mechanism and the rear scraper conveyor under different pitch angles of the support's roof and base. Based on the control logic for the opening degree of the hydraulic support's coal caving mechanism, a support posture sensing system was established, and a method for kinematic analysis of the hydraulic support's coal caving mechanism was proposed. A kinematic model for the end of the low-position hydraulic support's coal caving mechanism based on the D-H matrix was developed, and a calculation model for the opening degree of the hydraulic support's coal caving mechanism was constructed. The average calculation error was only 1.71%, meeting the accuracy requirements for field applications. A closed-loop control method for the coal caving mechanism based on posture feedback was proposed, and the coal caving decision model developed from the opening degree calculation model was applied in the field. Application results showed that during automatic coal caving, the mean square deviation of the average caving time for each support was only 0.13 minutes, with an overall caving efficiency improvement of 20%-43.9% compared to manual caving. The top coal recovery rate reached 89%, and the load on the rear scraper conveyor was more balanced, with an overload rate of only 0.73%.
2024, 50(9): 41-46, 107.
doi: 10.13272/j.issn.1671-251x.2024030041
Abstract: Current research on intelligent fully mechanized coal caving mining primarily focuses on perception, with limited studies on the intelligence of the coal drawing process. Existing automatic coal drawing control technologies face issues such as insufficient adaptability, low efficiency, and difficulty in quality control. To enhance the intelligence and operational efficiency of the coal drawing process, a planning coal drawing control system based on a process engine was designed. This system consisted of a coal drawing management unit and a window decision-making unit. The planning coal drawing management unit employed an asynchronous progressive scheduling strategy, flexible switching technology, and a process editing engine to achieve automated sequential coal drawing with weak correlation to the mining machine's position and online process editing. By associating with the load of the rear scraper conveyor, the system dynamically adjusted process starts and stops, ensuring safe operation of the scraper conveyor. The window decision-making unit utilized a PID control algorithm to dynamically adjust the tail beam angle, implementing feedback control of the coal drawing window. A genetic algorithm optimized a BP neural network to make intelligent decision about the size of the coal drawing window to adapt to varying operating conditions and improve coal drawing quality. Field application results indicated that the asynchronous progressive scheduling strategy and flexible switching technology enhanced the efficiency of automatic operation, eliminating the need for manual intervention. The number of automated operations per shift increased by 33.3%. The system's associated rear scraper conveyor load, pump station, and other equipment could dynamically adjust process starts and stops, resulting in a 61.1% decrease in the average stopping frequency of the rear scraper conveyor per shift, ensuring operational safety. The process editing engine accommodated various applications, substantially reducing adjustment time. The overlap of rear and front actions shortened the average operation time by 9.3%, increasing extraction efficiency. The correlation control of the tilt angle and intelligent decision-making for the planning coal release window improved daily calorific value by 10.3%, enhancing coal drawing quality.
2024, 50(9): 47-58.
doi: 10.13272/j.issn.1671-251x.2024060003
Abstract: Currently, in the numerical simulation research on the release laws of top coal during fully mechanized mining, complex coupling algorithms are required to address the continuity-discontinuity issues of top coal movement and ensure precise interaction of coal-rock interface information. However, the conveying process of scraper conveyors is typically neglected in these simulations. To address this problem, a meshless numerical computation model was constructed based on smoothed particle hydrodynamics (SPH). The discrete equations of SPH, derived from the control equations of continuous medium mechanics, were established. An elastic-plastic soil constitutive model along with the Drucker-Prager yield criterion were introduced to achieve dynamic simulation of the caving, movement, and release processes of the top coal. Considering the actual coal release and conveying processes in the mining area, a scraper conveyor model was constructed to simulate the release of top coal and the conveying of bottom coal along the working face, obtaining the variations in coal-rock interface and coal flow velocity at different scraper conveyor operating speeds (0-1.5 m/s). The simulation results indicated that the elastic-plastic soil constitutive model effectively simulated the flow behavior of particles. By setting parameters such as friction angle and elastic modulus, the issue of uncertain parameters commonly found in traditional discrete element models was avoided. After stabilization of the coal flow velocity, the stress distribution of the top coal near the coal drawing outlet exhibited a "double peak" pattern. The operating speed of the scraper conveyor significantly impacted the coal drawing time, while its effect on the coal-rock interface at termination and the shape of the released body was minimal. When multiple supports released coal simultaneously, the conveying capacity of the scraper conveyor needed to be considered, as interference in bottom coal transportation between different supports could lead to blockage effects at the release port. The "gangue closing" rule resulted in variations in the amount of coal drawing at different coal drawing outlets, with the standard deviation of top coal drawing amount from 40 coal drawing outlets (7.52 m²) being greater than that of automatic coal drawing (1.93 m²).
Detection method for gangue mixed ratio in fully mechanized caving faces based on the GSL-YOLO model
2024, 50(9): 59-65, 137.
doi: 10.13272/j.issn.1671-251x.2024080011
Abstract: Aiming to address the issues with current gangue mixed ratio detection methods in fully mechanized caving face based on deep learning, such as low accuracy of coal gangue identification, poor segmentation performance, large model parameters and computation load, and the inability to achieve real-time detection of gangue mixed ratio under complex conditions such as low lighting, high dust, and coal and angue stacking, the paper proposed a gangue mixed ratio detection method based on the GSL-YOLO model. The GSL-YOLO model introduced the following improvements to the YOLOv8-seg model: the incorporation of a global attention mechanism (GAM) in the backbone network to enhance feature extraction by reducing information dispersion and amplifying global interaction representation; the use of a spatial pyramid pooling with efficient local aggregation network (SPPELAN) module to improve detection performance for targets of varying sizes; and the adoption of a lightweight asymmetric dual-head (LADH) to reduce training difficulty while increasing inference speed. Additionally, a gangue mixed ratio calculation method based on category segmentation masks was proposed, which calculated the ratio of the pixel area of gangue to the total pixel area in the segmentation mask of coal flow images, serving as the instantaneous gangue mixed ratio. Experimental results showed that: ① The GSL-YOLO model achieved an mAP@0.5∶0.95 of 96.1%, which was 0.8% higher than the YOLOv8-seg model. ② The GSL-YOLO model had 2.9×106 parameters, 11.4×109 floating-point operations, and a model weight of 6.0 MiB, representing reductions of 12.1%, 5.8%, and 11.8% respectively compared to the YOLOv8-seg model, achieving model lightweighting. ③ The GSL-YOLO model achieved a frame rate of 12 frames per second on the test set, essentially meeting the requirements for real-time detection. ④ Compared with the YOLO series models, the GSL-YOLO model had the best segmentation effect, the highest detection accuracy, fewer parameters and computation load, and the best overall performance. ⑤ Based on three frames of images captured from the coal flow on the rear scraper conveyor of the fully mechanized caving face, the instantaneous gangue mixed ratio was calculated, and the results showed that the proposed method successfully realized real-time calculation of the gangue mixed ratio in fully mechanized caving face.
2024, 50(9): 66-74.
doi: 10.13272/j.issn.1671-251x.2024070058
Abstract: Traditional studies on the dynamic characteristics of coal flow during the top coal caving process, based on image detection technology, have primarily focused on specific-stage image analysis, lacking a comprehensive analysis of dynamic characteristics across all stages. Existing research has rarely integrated the changes in the loose zone of the overlying strata with coal and gangue separation and coal flow characteristics during top coal caving, resulting in a lack of systematic and holistic analysis of the entire coal caving process. In response to these issues, this study systematically investigated coal flow dynamics, coal and gangue separation effectiveness, and the subsidence of the loose zone in the overlying strata during the top coal caving. First, this paper proposed a dynamic analysis method for the top coal caving process based on a dual optical flow network. The results indicated that the coal caving speed was not affected by the caving method and pattern, and that average detection accuracy increased with the number of caving openings, exhibiting a notably linear increase during the periodic caving stage. The release rate of top coal showed a positive correlation with average detection accuracy, validating the effectiveness of the method in the top coal caving process monitoring. Second, OpenCV technology was used to conduct experimental analysis on the subsidence area of the loose zone in the overlying strata. Results demonstrated that the subsidence area grew sharply during the initial caving stage and gradually stabilized over time. The dynamic changes in the subsidence area effectively indicated the progression of top coal release, enabling transparent monitoring of release process. Finally, based on data from weighing experiments, the relationships among caving amount, release rate, and gangue content were analyzed. Results showed that the amount of pure coal release was the highest in the initial caving stage and stabilized in the periodic caving stage, while gangue content decreased as the number of caving openings increased. These findings further reveal the influence of caving methods on coal and gangue separation and the release rate of top coal.
