Coal Mine Production Research Articles

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

Reduction of production delay cost in underground coal mines: An FMEA-based method study approach

Coal is the backbone of the Indian economy, essential for electricity, steel, cement, and other vital items. Although India aims to be carbon emission-free by 2070, it must rely on coal for a few more decades. In the fiscal year 2021–22, India produced 96 percent of its coal from open-cast (OC) mines, whereas 70 percent of India's total coal reserve is in deep undergrounds. Despite underground (UG) coal mining being eco-friendly and yielding richer coal, its production cost is nearly four times higher than OC mining. Therefore, further research on UG mines is necessary to address these challenges. This study analyzed an underground coal mine production system in India and identified production delays as a major cost driver. Causes of delays were categorized by their association with machine, material, and man. An innovative technique, the Machine Criticality Score (MCS), has been developed to quantify production delay costs and guide management in mitigating them. Additional methods like failure mode and effects analysis (FMEA), standard operating procedure (SOP), inventory management tools, and method study were also used in this study to reduce production delay costs.

Spatial evolution characteristics of the mechanical properties of gangue-cemented paste backfill with high-water-content materials: an experimental investigation

ABSTRACT The mechanical properties of cemented paste backfill (CPB) directly determine the effect of backfilling in coal mining. However, the current studies on the mechanical properties of CPB are all based on standard cylinder or cube specimens, which cannot accurately characterise the distribution of mechanical properties in different spatial positions of CPB in situ. In addition, high-water-content material (HWM) has developed rapidly as a new binder in recent years. Therefore, in this paper, the uniaxial compressive strength (UCS) and elastic modulus of CPB with gangue as the aggregate and HWM as the binder (GHW-CPB) were investigated under different gangue particle sizes. The following conclusions were drawn: (1) The spatial evolution and distribution characteristics of mechanical properties of GHW-CPB are analysed. (2) The spatial evolution regression models of mechanical properties of GHW-CPB are established. (3) We improved the GHW-CPB spatial evolution regression models for mechanical properties due to their limitations and confirmed its validity and rationality. Through the research results of this paper, the failure position of the CPB in the goaf can be accurately predicted, so that manual interventions can be carried out in the vulnerable areas in advance to reduce the risk of coal mining production.

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

Influence of roof confined water drainage on stress distribution in coal seam

Coal and gas outbursts, along with rock bursts, are common dynamic disasters in coal mining, threatening the safety and green production of coal mines. The distribution of coal seam stress is an external factor contributing to coal mine power disasters. The drainage of confined water in coal seam roof is the primary factor of coal seam stress changes. Using theoretical analysis and numerical simulations, the influence of roof confined water on coal seam stress by looking at the law of stress and stress conservation transfer in the drainage layer is investigated. Our results show that: (1) after drainage, there is an increase in the local stress within the coal seam, with a stress concentration factor of 1.35. The influence range of theoretically calculated stress is consistent with the measured results. (2) the maximum value of drainage boundary stress correlated positively with the angle of the water-rich abnormal area, with a linear fitting of R2 = 0.98. These findings hold significant theoretical and practical implications for preventing and controlling dynamic disasters during the mining of water-rich coal seams.

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Patterns of spatiotemporal variations in the hydrochemistry and controlling factors of bedrock aquifers in the northern region of the Linhuan mining area

Systematically studying the hydrochemical evolution of bedrock groundwater in mining areas during mining process is crucial for effective groundwater resource management and coal mine production. The spatiotemporal characteristics and hydrochemical evolution patterns of the Permian fractured sandstone aquifer (PA) and the Carboniferous Taiyuan Formation limestone aquifer (CTA), both of which are directly associated with coal mining in the northern Linhuan mining area, China, were investigated using multivariate statistical analyses, hydrochemical graphical methods, ion ratio analysis, and a conceptual model. 72 groundwater samples, collected before and after mining, were classified into four groups by hierarchical cluster analysis (HCA). Principal component analysis (PCA) and ion ratio analysis indicated that water-rock interactions involve mineral dissolution (carbonates, gypsum, dolomite, silicates), cation exchange, and common ion effects. Hydrochemical evolution is influenced by bedrock paleotopography, aquifer hydraulic conductivity, and mining drainage. Paletopographic differences significantly influence water-rock interactions and spatial variability in hydrochemistry, with ion concentrations in groundwater increasing as paleotopographic elevation decreases. The pattern of hydraulic conductivity reflects the control exerted by variations in aquifer characteristics on mineral dissolution, leading to minor changes in hydrochemical characteristics. Mining activities disrupt the aquifer's reducing environment, resulting in a significant increase in groundwater SO42− concentration. These findings provide insights and a solid theoretical foundation for studying the hydrochemical variations patterns of groundwater and these control mechanisms in the hidden coal fields of North China.

Application and Research of Mine Safety Management Strategy Based on the BP Network Model

Coal mine safety production is undoubtedly the cornerstone of the healthy, table, and sustainable development of China’s coal industry, and it also profoundly affects the effective implementation of the national energy strategy. In this context, exploring the application of network models in coal mine safety management strategies not only has significant practical significance, but also has profound development value. By integrating these factors, we can have a more comprehensive understanding of the causes of coal mine safety accidents and provide a scientific basis for formulating effective preventive measures. By collecting and analyzing various data in real-time during the coal mine production process, we can promptly detect abnormal situations, predict potential safety risks, and take corresponding measures for intervention and prevention. This dynamic monitoring and early warning mechanism can not only improve the safety of coal mine production, but also enhance the efficiency and efficiency of coal mine production. In the analysis process, we particularly focused on the advantages of Back Propagation (BP) neural network models in mine safety evaluation. BP neural network models can handle uncertainty and fuzzy information and have strong learning and adaptive capabilities. By constructing a safety evaluation model based on BP neural networks, we can more accurately evaluate the safety risks in the coal mine production process, providing strong support for formulating more scientific and reasonable safety management strategies.

Stress Evolution and Rock Burst Prevention in Triangle Coal Pillars under the Influence of Penetrating Faults: A Case Study

The occurrence of rock bursts due to penetrating faults are frequent in China, thereby limiting the safe production of coal mines. Based on the engineering background of a 501 working face in a TB coal mine, this paper investigates stress and energy evolution during the excavation of this working face due to multiple penetrating faults. Utilizing both theoretical analysis and numerical simulations, this study reveals the rock burst mechanism within the triangular coal pillar influenced by the penetrating faults. Based on the evolution of stress within the triangular coal pillar, a stress index has been devised to categorize both the rock burst danger regions and the levels of rock burst risks associated with the triangular coal pillar. Furthermore, targeted stress relief measures are proposed for various energy accumulation areas within the triangular coal pillar. The results demonstrate that: (1) the superimposed tectonic stress resulting from the T6 and T5 penetrating faults exhibits asymmetric distribution and has an influence range of about 90 m in the triangular coal pillar, reaching a peak value of 11.21 MPa at a distance of 13 m from the fault plane; (2) affected by the barrier effect of penetrating faults, the abutment stress of the working face is concentrated in the triangular coal pillar, and the magnitude of the abutment stress is positively and negatively correlated with the fault plane barrier effect and the width of the triangular coal pillar, respectively; (3) the exponential increase in abutment stress and tectonic stress as the width of the triangular coal pillar decreases leads to a high concentration of static stress, which induces pillar burst under the disturbance of dynamic stress from fault activation; (4) the numerical simulation shows that when the working face is 150 m away from the fault, the static stress and accumulated energy in the triangle coal pillar begins to rise, reaching the peak at 50 m away from the fault, which is consistent with the theoretical analysis; (5) the constructed stress index indicates that the triangular coal pillar exhibits moderate rock burst risks when its width is between 73 to 200 m, and exhibits high rock burst risks when the width is within 0 to 73 m. The energy accumulation pattern of the triangular coal pillar reveals that separate stress relief measures should be implemented within the ranges of 50 to 150 m and 0 to 50 m, respectively, in order to enhance the effectiveness of stress relief. Blasting stress relief measures for the roof and coal are proposed, and the effectiveness of these measures is subsequently verified.

Spatio-temporal differentiation characteristics of coal mine safety levels in China based on spatial spillover effects

In order to solve the problem of "potpourri" of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

Study on the evolution of fractures in overlying strata during repeated mining of coal seams at extremely close distances

In particular, the secondary development of overlying strata fractures can easily lead to the upper goaf, resulting in gas and water gathered in the goaf entering the working face of the lower coal seam through the overlying strata fractures, threatening the safety of coal mine production. Security risks may arise. To further understand the caving and evolution law of overlying strata during repeated mining in extremely close distance coal seam down mining, 9# coal and 10# coal in the Nanyaotou Coal Industry were used as the engineering background. The caving characteristics and fracture evolution law of overlying strata during single and repeated mining were analyzed through similar material simulation tests. Based on fractal geometry theory, the relationship between the advancing distance of the working face and the fractal dimension of the overlying strata fracture is established to reflect the changing trend of fracture development. The calculation formula is derived from the tensile rate of rock strata to predict the development height of water-conducting fractures. The results show that the overlying strata failure structure is mainly a “hinged structure” and a “step structure,” which respectively promotes and inhibits the development of overlying strata fractures. Repeated mining causes mining-induced fractures in the lower coal seam to pass through the goaf of the upper coal seam and develop more vigorously in the upper coal seam, and the fractal dimension can effectively reflect the development of overlying strata fractures. The height of the water-conducting fracture zone increases in four stages: incubation, gradual increase, further gradual increase, and stability, eventually stopping development under the influence of the key layer (thick mudstone) bearing the load above. The development height of water-conducting fractures predicted by on-site water injection measurement is similar to that predicted by simulation experiments and theoretical calculations, verifying the feasibility of predicting the development height of water-conducting fractures through simulation tests and theoretical analysis. This study provides a reference for coal seam mining under similar conditions.

Research on Comprehensive Evaluation Indicators and Methods of World-Class Open-Pit Coal Mines

Since the 20th century, with the rapid development of the global economy, human demand for fossil energy such as coal has been increasing. In the face of fierce competition, the state and various ministries and commissions have put forward the goals and requirements of building a world-class enterprise, and how to become a world-class open-pit coal mine and establish the corresponding evaluation standards has become an urgent scientific problem to be solved. According to the characteristics of open-pit coal mine production and operation, from the perspective of benchmarking world class, the key factors affecting the construction of world-class open-pit coal mines are extracted by using the fuzzy DEMATEL method, and the comprehensive evaluation index system, evaluation standards, and methods of open-pit coal mines are established, which provides benchmarks and guidelines for the construction of world-class open-pit coal mines and improves the construction level of world-class open-pit coal mines.

The Effect of CO on the Exothermic Characteristics of Low-Temperature Oxidation in Coal

ABSTRACT In recent years, abnormally high concentrations of CO gas have often been detected in goaf areas of coal mines in China, which have posed a serious threat to safe production in coal mines and the life and safety of underground personnel. The effect of CO on the exothermic characteristics of low-temperature oxidation in coal was studied by analyzing the exothermic characteristics, heat release, oxidation stages, and activation energy of coal at different CO concentrations using a C600 microcalorimeter. The experimental results indicated that CO promoted the coal oxidation at low temperature, but inhibited it after reaching 144°C. When the CO concentration is 30%, the exothermic characteristics of coal in low-temperature oxidation adsorption stage were significantly affected, with the heat flow value decreased by 27.0% and the initial exothermic temperature point moved forward by 3.9 K. In the later stage of the oxidation reaction, the heat release of coal was linearly negatively correlated with CO concentration. The higher the concentration, the lower the heat release. These findings contribute to understanding the combustion mechanism of coal in high-concentration CO atmosphere and provide valuable insights for designing and optimizing coal utilization technologies.

Influence of imidazole-based ionic liquid surfactants on the wetting effect of long-flame coals

In order to study the wetting effect of C12MImBF4, C12MImBr and C12MImCl on long-flame coal, and their mechanism of action on coal-water bonding, physical experiments, molecular dynamics simulation and DFT theoretical calculation were combined in this work. It is confirmed that the imidazole ionic liquid surfactant optimizes the wetting effect by forming hydrogen bonds, reducing the surface tension of water, and increasing the number of free hydroxyl groups in coal. After the wettability test, the concentration of 4 g/L of the three ionic liquid surfactants had the best wetting effect on long-flame coal, and the contact angle at this concentration was reduced by more than 40 % compared with water within 0.2 s after dripping on the coal. Due to the difference in the anionic head group of the three surfactants, there are differences in the number and strength of hydrogen bonds formed between the three surfactants and water molecules, the results of wettability C12MImBF4 > C12MImCl > C12MImBr were presented. Combined with these characteristics of imidazole ionic liquids on long-flame coal, the reasonable application in the wet dust removal process of actual coal mine production can optimize the efficiency of droplet capture of coal particles, so as to effectively improve the dust removal capacity.

Real-Time Prediction of Oil-Type Gas Emissions in Coal-Oil-Gas Coexistence Mines.

Oil-type gas disasters are a frequently occurring concern in coal-oil-gas coexistence mines. In order to actively predict the volume of oil-type gas emissions from floor rocks, this study presents an investigative methodology to forecast the geological conditions of floor rocks in front of the roadway face by utilizing the Direct Current (DC) method. The assessment of electrical resistance in rock formations, which is widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect evaluation of fracture development. As a real-time geological detection index for floor rocks that are 100 m ahead of the roadway face, it enhances predictive capabilities. Moreover, when combined with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to predict the oil-type gas emissions from the floor rocks in the excavation roadway area. The results show consistency with field monitoring outcomes, confirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions in front of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Moreover, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be taken to reduce risks and uphold safety standards.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

A non-contact solid particle moisture sensor based on near-infrared spectroscopy technology

The detection of moisture content in solid particles has important applications in fields such as biological research, coal mine production, and medicine. This article develops a non-contact solid particle moisture sensor (NcSPMS). NcSPMS uses the multi-light source detection method based on the principle of diffuse reflection absorption to detect the moisture content of solid particles with different particle sizes, distributions and colors. Firstly, the stacked LED light source structure (SLED-LSS) was designed to achieve illumination of LED light source for measurement (M-LED) and the LED light source for reference (R-LED) at the same position and exit angle, eliminating the influence of irregular particle size and distribution on diffuse reflected light intensity. Secondly, the stacked LED array light source structure (SLEDA-LSS) was designed to improve the detection light intensity and enable NcSPMS to detect the moisture content of solid particles of different colors. Thirdly, modulated light technology is used for the transmission of moisture content information. Two modulation frequencies were set to modulate the measurement light source and reference light source, and different intensities of square wave currents were determined to drive the M-LED and R-LED to improve detection accuracy. Fourthly, the FFT-KF data processing method was designed to effectively extract the information of diffuse reflection measurement light intensity. Finally, the function expression of moisture content was obtained through cubic fitting, and the detection of moisture content by NcSPMS was achieved. In order to verify the effectiveness of NcSPMS, the NcSPMS control system was designed. The experimental results show that compared with SLED-LSS, SLEDA-LSS improves measurement accuracy; the different current pairs driving M-LED and R-LED affect the measurement accuracy of NcSPMS; NcSPMS can detect the moisture content of white, yellow, green, red, and black sand particles, with the detection range of 0.10–11.00 %. The relative uncertainty obtained by applying NcSPMS to the detection of nickel ore particles is 4.37 %.

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Abstract Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.

Study on Variation Law of Hazardous Areas in Goaf Based on Coal Oxidation Kinetics Theory

ABSTRACT Regarding various problems on safety production in coal mines, residual coal in goaf’s spontaneous combustion is extensive, determining its hazardous areas is critical to extinguish or prevent fires in goaf. Taking production practices at the 010803-working face of Wang Wa Coal Mine as background, a combined approach involving on-site experiments, laboratory tests, and numerical simulations is adopted to investigate “three-zone” spontaneous combustion as well as the distribution patterns of temperature fields in shallowly buried coal seams. The mathematical and physical models of spontaneous combustion in goaf are constructed. Upon oxidation of coal at low temperatures, experimental data about the intensity of release of heat and the rate of consumption of oxygen inform the construction of a kinetic model for coal oxidation in goaf. Utilizing field-measured data on air leakage, a distribution model for permeability within the working face’s goaf is constructed. To study combustion “three-zone” and temperature field distribution patterns in goaf under various air supply volumes, as well as to quantitatively analyze the relationship between the width and area of oxidation zones, the area of high-temperature areas, and the distribution of high-temperature sites in the goaf, with relation to the working face’s supply volumes of air. The results show that the maximum simulated air leakage in the goaf of the working face is 148.03 m3/min, with a relative error of just 1.4% compared to the measured value. Furthermore, validation confirms that the simulated oxygen concentration variation pattern closely aligns with the measured oxygen concentration. As the airflow rate at the working face increases, the oxidation zone width at the intake side gradually grows. While on the return side, it gradually decreases, resulting in a maximum width difference of 22.6 m. The goaf area’s concentration of oxygen exhibits an “S” shape distribution pattern. The overall temperature change range within the high-temperature zone of the goaf is 4.5 K, gradually migrating toward the deeper areas. As the air supply rate rises, oxidation zone area gradually decreases, exhibiting an overall sinusoidal distribution. Concurrently, the high-temperature zone area progressively expands, conforming to a Boltzmann function distribution. When the Air supply rate is 800 m3/min to 1120 m3/min, the areas of the two are linearly correlated with the air supply volumes. Under varying air supply volumes, the position of the high-temperature point within the goaf changes in both strike and dip directions, and the relative distance with the intake air corner follows an exponential function in both strike and dip directions. Through comprehensive analysis, it is concluded that the actual air supply volume on-site should ideally range between 800 m3/min and 1120 m3/min. This range effectively prevents and controls the hazardous area of spontaneous combustion in the goaf and optimizes the ventilation scheme.

A Digital Twin-Based Adaptive Height Control for a Shearer

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.