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Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 25 October 2024 | Viewed by 7784

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Special Issue Editors

Dr. Yuanyuan Pu

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Guest Editor

School of Resource and Safety Engineering, Chongqing University, Chongqing 400044, China.
Interests: rock signaling and coal-rock dynamic disaster; big data and data-driven methods in mines
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Dr. Sitao Zhu

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Guest Editor

School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: rock dynamics; microseismic monitoring; rockburst and mine earthquake disaster prevention
Special Issues, Collections and Topics in MDPI journals

Dr. Xinglong Zhao

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Guest Editor

State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining & Technology, Xuzhou 221116, China
Interests: rock mechanics; hydraulic fracturing; stress disturbance; fracture propagation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The coal mining process involves extensive movements of rock and coal masses. Such activities lead to significant alterations in geostress and tectonic stress, paving the way for various mining-induced dynamic disasters, including bursts of rock/coal, roof collapses, and gas outbursts. These incidents pose severe threats to the safety of mining operations. Consequently, various mining safety monitoring techniques, such as microseismic and electromagnetic monitoring, have been developed to oversee changes in the state of coal and surrounding rocks. These methods produce a vast array of data in diverse structures and formats. The effective processing, analysis, and utilization of these data are vital for enhancing mining safety by predicting and preventing dynamic disasters. Traditional data processing and analysis techniques, however, struggle with the complexity and nonlinear relationships inherent in monitoring data. In contrast, the recent surge in intelligent operations across society and everyday life has led to an abundance of data generation. Advances in data storage, transmission, and processing technologies (e.g., the advent of distributed file systems like HDFS, and the development of sophisticated machine learning models) have elevated data to a crucial resource for scientific research. Data-driven approaches, recognized as the fourth scientific paradigm—supplementing the traditional triad of experimentation, theory, and computation—hold significant promise. They are particularly valuable when conventional methods fail to resolve complex issues, allowing for insights to be gleaned directly from the data itself.

This Special Issue aims to develop security intelligent monitoring and big data utilization theories and technologies in the coal mining process. The topics of interest for this Special Issue include, but are not limited to, the following:

Novel field monitoring theories and engineering applications in mining;
Monitoring system optimization and improvement;
Monitoring data processing and analysis;
Prediction of mining disasters based on data-driven methods.

Dr. Yuanyuan Pu
Dr. Sitao Zhu
Dr. Xinglong Zhao
Guest Editors

Manuscript Submission Information

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Keywords

intelligent monitoring
data processing and analysis
monitroing system optimization
microseismic monitoring
big data technology

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Published Papers (10 papers)

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Research

13 pages, 13458 KiB

Open AccessArticle

Influence Factors on the Energy Regulation Law of a Coal Seam after Hydraulic Slotting

by Shanshan Liu, Chuanru Yao, Deying Gao and Xinyuan Wang

Processes 2024, 12(10), 2062; https://doi.org/10.3390/pr12102062 - 24 Sep 2024

Viewed by 383

Abstract

Hydraulic slotting technology is an effective pressure relief method for coal seams with high stress and burst risks. Based on FLAC3D and field applications, the stress and energy evolution of coal under different slotting radiuses, slotting spacings, and slotting ranges are studied. The results show that the pressure relief effect of slotting is mainly affected by the spacing and radius of the slotting. When the cutting radius increases from 0.5 m to 1.5 m, the average stress in the cutting range decreases from 10 MPa to 7.1 MPa, and the average energy decreases from 155.7 kJ/m³ to 117.1 kJ/m³. When the slotting spacing decreases from 3 m to 1 m, the stress release increases from 62% to 72%, and the energy release increases from 77.8% to 80.3%. The difference in the slotting area only affects the transfer distance of the peak point. In the field application, the microseismic frequency near the test area after hydraulic slotting is reduced from 32 times to 19 times, and the total microseismic energy is reduced from 2.67 × 10⁴ J to 1.02 × 10³ J, which can effectively realize the high stress transfer of the roadway. It can be seen that the hydraulic slotting technology can strongly relieve the pressure at fixed points in the high stress concentration area of the coal seam. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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16 pages, 5633 KiB

Open AccessArticle

Surrounding Rock Control Technology of Thick Hard Roof and Hard Coal Seam Roadway under Tectonic Stress

by Zhongzong Cao, Honglin Liu, Chengfang Shan, Hongzhi Wang and Haitong Kang

Processes 2024, 12(9), 1973; https://doi.org/10.3390/pr12091973 - 13 Sep 2024

Viewed by 381

Abstract

In the process of roadway excavation in thick and hard coal seams with a hard roof, the instantaneous release of a large amount of elastic energy accumulated in coal and rock mass causes disasters. Especially under the action of tectonic stress, dynamic disasters of roadway-surrounding rock are extremely strong. Therefore, this paper takes the 110,505 roadway of the Yushuling Coal Mine as the engineering background. Aiming at the serious deformation of roadway-surrounding rock and the problem of strong mine pressure, the deformation mechanism of roadway-surrounding rock is studied by means of theoretical analysis, indoor experimentation, numerical simulation and field testing, and the surrounding rock control technology is proposed. Firstly, the results show that the stress field type of the Yushuling Coal Mine is a

σ_{H v}

type, the azimuth angle of the maximum horizontal principal stress is concentrated in 110.30°~114.12°, the dip angle is −33.04°~−3.43°, and the maximum horizontal principal stress is 1.94~2.76 times of the minimum horizontal principal stress. Secondly, the brittleness index of No. 5 is 0.62; the failure energy release of the surrounding rock compressive energy floor rock sample is up to 150,000 mv * ms. The more the cumulative number of rock samples, the greater the strength, and the more severe the damage. Thirdly, with the increase in tectonic stress, the stress of roadway-surrounding rock is asymmetrically distributed, and the plastic zone develops along the tendency. The maximum range of the plastic zone expands from 4.18 m to 10.19 m. Lastly, according to the deformation characteristics of roadway-surrounding rock, left side > roof > right side > floor, the surrounding rock control technology of ‘asymmetric anchor net cable support + borehole pressure relief’ is proposed, which realizes the effective control of roadway-surrounding rock deformation. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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20 pages, 7227 KiB

Open AccessArticle

The Changing of Micromechanical Properties of Coal after Water Immersion: The Insight from Nanoindentation Test

by Wei Xiong, Qing Ye, Yuling Tan, Zhenzhen Jia and Guanglei Cui

Processes 2024, 12(8), 1636; https://doi.org/10.3390/pr12081636 - 3 Aug 2024

Viewed by 706

Abstract

The application of the hydrodynamic method has enhanced the extraction of coal bed methane (CBM). In this method, fracturing fluid rapidly penetrates the coal reservoir, altering its intrinsic pore structure and microscopic mechanical properties. These changes impact the properties of the coal reservoir and CBM depletion. It is, therefore, crucial to explore how these micro-characteristics evolve following water invasion. In this context, using nanoindentation tests, the microscopic characteristics of three coal samples were measured under dry conditions and at water saturations corresponding to 44% and 75% relative humidity. The influence of water immersion on the pore structure was also assessed using mercury injection experiments. Moreover, cluster analysis was used to categorize the extensive measured data into three sub-components: fractures (large pores), inertinite, and vitrinite, to investigate the impact of water saturation on microscopic properties. The findings indicate that cluster analysis is well-suited to these data, showing excellent agreement with porosity and maceral tests. The relationship between the elastic modulus and hardness of dry and wet coal samples varies across the sub-components. There is a notable dependency in the case of vitrinite, whereas water content tends to reduce this dependency. It is also found that water content negatively affects elastic modulus and hardness and reduces the anisotropy ratio. The mechanical properties of inertinite are highly responsive to water immersion, whereas vitrinite exhibits lesser sensitivity. The softening mechanisms of coal when immersed in water, such as calcite phase dissolution, swelling stress fracturing, and weakening of macerals, are identified. This study offers new perspectives on the impact of moisture on the alteration of micromechanical properties in coal. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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21 pages, 13107 KiB

Open AccessArticle

Mechanism and Prevention of Rock Burst in a Wide Coal Pillar under the Superposition of Dynamic and Static Loads

by Bangyou Jiang, Yanan Xu, Wenshuai Li, Shitan Gu and Mingjun Ding

Processes 2024, 12(8), 1634; https://doi.org/10.3390/pr12081634 - 3 Aug 2024

Viewed by 772

Abstract

To address the frequent occurrence of rock burst disasters in areas with wide coal pillars during mining in the western mining area of China, the wide coal pillar area of the Tingnan coal mine in Shanxi Province was used as the research background. Theoretical analysis, numerical simulation, and field tests were used to establish the mechanical criterion and the energy criterion for the dynamic instability of wide coal pillars. The process and mechanism of wide coal pillar dynamic instability under dynamic and static load disturbances were revealed, and a wide coal pillar rock burst prevention and control scheme was proposed. The results indicated that when the load above a coal pillar reached the stress failure index and the energy failure index was met, the coal pillar reached the critical conditions for rock burst. With increasing static load, the stress, energy, and range of the plastic zone all showed increasing trends on both sides of the coal pillar. Under a given dynamic load, the stress and plastic zone range of the coal pillar significantly increased compared to those without a dynamic load. Under a given static load, the greater the dynamic load, the more likely the coal pillar was to undergo dynamic instability. The evolution of coal pillar dynamic instability was divided into three stages: energy accumulation, local instability, and dynamic instability. When the critical stress and energy conditions for coal pillar dynamic instability are exceeded, rock burst will occur. To reduce the static and dynamic loads of coal pillars, a rock burst prevention and control scheme of energy release and load reduction was proposed and applied onsite. The monitoring results showed that this control plan effectively reduced the stress of the coal pillar and the dynamic load generated by the fracture of the overlying rock layer, indicating safe mining in this area of wide coal pillars. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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17 pages, 11091 KiB

Open AccessArticle

Research on Wave Velocity Disparity Characteristics between Impact and Outburst Coal Seams and Tomography of Hazardous Zones

by Zhixin Fang, Qiang Liu, Liming Qiu, Zhanbiao Yang, Zhaohui Cao, Guifeng Wang, Zehua Niu and Yingjie Zhao

Processes 2024, 12(8), 1558; https://doi.org/10.3390/pr12081558 - 25 Jul 2024

Viewed by 455

Abstract

To investigate the variations in wave velocity fields between impact and outburst coal seams, we analyzed the fluctuations in wave velocity under loading conditions for both coal types. A comprehensive methodology was developed to correct coal wave velocities in response to stress and gas presence, which was then applied to field assessments of hazardous regions. Our findings reveal significant differences in wave velocity alterations between impact and outburst coal seams during loading-induced failure. Gas pressure exhibits a negative correlation with wave velocity in outburst coal (correlation coefficient R² = 0.86), whereas wave velocity in impact coal demonstrates a positive correlation with stress (R² = 0.63). A robust methodology for correcting coal wave velocities in response to stress and gas presence was established to enable more accurate measurement of wave velocity changes. In field applications, seismic wave computed tomography identified stress anomalies that closelycorresponded with geological structures and mining operations, effectively pinpointing hazardous zones. The abnormal wave velocity coefficient ranges for outburst coal seams and impact coal seams are −0.6 to 0.25 and −0.35 to 0.16, respectively, which correspond well with the field stress distribution. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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19 pages, 12450 KiB

Open AccessArticle

Study on the Application of Finite Difference in Geological Mine Fault Groups: A Case Study

by Jianbo Yuan, Chao Wang, Zhigang Liu, Jingchao Lyu, Yajun Lu, Wuchao You and Jiazheng Yan

Processes 2024, 12(6), 1162; https://doi.org/10.3390/pr12061162 - 5 Jun 2024

Viewed by 663

Abstract

Fault structures can cause a bad mining environment and increase the stress of surrounding coal pillar faults. The study investigates the stress evolution characteristics within fault structure groups and their surrounding coal pillars and explores the extent to which these fault structure groups influence the stress distribution in coal pillars. Based on three-dimensional modeling technology, a transparent geological model of the geological environment of fault structure groups was constructed and finite difference software was used to generate a numerical simulation model. Two survey lines and four survey points were arranged to analyze the stress distribution of a coal pillar fault. The results show that the fault structure groups have obvious stress barrier effects. There is a 35 m stress reduction zone in the hanging wall of the fault and a 30 m stress increase zone in the footwall of the fault. Both FL-1 and FL-3 faults have a stress barrier effect in the hanging wall. The obvious stress increases in the footwall of the fault are 37.7 MPa and 33.5 MPa, respectively. The stress of the FL-2 fault as a whole appears to be a more obvious superposition at the end of mining, and the peak stress reaches 41.5 MPa. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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13 pages, 8192 KiB

Open AccessArticle

Classification of Microseismic Signals Using Machine Learning

by Ziyang Chen, Yi Cui, Yuanyuan Pu, Yichao Rui, Jie Chen, Deren Mengli and Bin Yu

Processes 2024, 12(6), 1135; https://doi.org/10.3390/pr12061135 - 31 May 2024

Viewed by 520

Abstract

The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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23 pages, 5192 KiB

Open AccessArticle

Method and Validation of Coal Mine Gas Concentration Prediction by Integrating PSO Algorithm and LSTM Network

by Guangyu Yang, Quanjie Zhu, Dacang Wang, Yu Feng, Xuexi Chen and Qingsong Li

Processes 2024, 12(5), 898; https://doi.org/10.3390/pr12050898 - 28 Apr 2024

Cited by 4 | Viewed by 902

Abstract

Gas concentration monitoring is an effective method for predicting gas disasters in mines. In response to the shortcomings of low efficiency and accuracy in conventional gas concentration prediction, a new method for gas concentration prediction based on Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) is proposed. First, the principle of the PSO-LSTM fusion model is analyzed, and the PSO-LSTM gas concentration analysis and prediction model is constructed. Second, the gas concentration data are normalized and preprocessed. The PSO algorithm is utilized to optimize the training set of the LSTM model, facilitating the selection of the training data set for the LSTM model. Finally, the MAE, RMSE, and coefficient of determination R² evaluation indicators are proposed to verify and analyze the prediction results. Gas concentration prediction comparison and verification research was conducted using gas concentration data measured in a mine as the sample data. The experimental results show that: (1) The maximum RMSE predicted using the PSO-LSTM model is 0.0029, and the minimum RMSE is 0.0010 when the sample size changes. This verifies the reliability of the prediction effect of the PSO-LSTM model. (2) The predictive performance of all models ranks as follows: PSO-LSTM > SVR-LSTM > LSTM > PSO-GRU. Comparative analysis with the LSTM model demonstrates that the PSO-LSTM model is more effective in predicting gas concentration, further confirming the superiority of this model in gas concentration prediction. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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16 pages, 2616 KiB

Open AccessArticle

Improving Computer Vision-Based Wildfire Smoke Detection by Combining SE-ResNet with SVM

by Xin Wang, Jinxin Wang, Linlin Chen and Yinan Zhang

Processes 2024, 12(4), 747; https://doi.org/10.3390/pr12040747 - 7 Apr 2024

Cited by 1 | Viewed by 1367

Abstract

Wildfire is one of the most critical natural disasters that poses a serious threat to human lives as well as ecosystems. One issue hindering a high accuracy of computer vision-based wildfire detection is the potential for water mists and clouds to be marked as wildfire smoke due to the similar appearance in images, leading to an unacceptable high false alarm rate in real-world wildfire early warning cases. This paper proposes a novel hybrid wildfire smoke detection approach by combining the multi-layer ResNet architecture with SVM to extract the smoke image dynamic and static characteristics, respectively. The ResNet model is improved via the SE attention mechanism and fully convolutional network as SE-ResNet. A fusion decision procedure is proposed for wildfire early warning. The proposed detection method was tested on open datasets and achieved an accuracy of 98.99%. The comparisons with AlexNet, VGG-16, GoogleNet, SE-ResNet-50 and SVM further illustrate the improvements. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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14 pages, 11041 KiB

Open AccessArticle

The Distribution Law of Ground Stress Field in Yingcheng Coal Mine Based on Rhino Surface Modeling

by Zhi Tang, Zhiwei Wu, Dunwei Jia and Jinguo Lv

Processes 2024, 12(4), 668; https://doi.org/10.3390/pr12040668 - 27 Mar 2024

Cited by 1 | Viewed by 778

Abstract

The distribution law of the ground stress field is of great significance in guiding the design of coal mine roadway alignment, determining the parameters of roadway support, and preventing and controlling the impact of ground pressure in coal mines. A geostress inversion method combining Rhino surface modeling and FLAC^3D 6.0 numerical simulation software is proposed. Based on the geological data of the coal mine and the results of on-site measurements, a three-dimensional geological model of Yingcheng Coal Mine is established for the geostress inversion, and the distribution law of the geostress field in Yingcheng Coal Mine is obtained. Research shows the following: (1) The horizontal maximum principal stress values of the Yingcheng Mine are between 33.9 and 35.3 MPa, the horizontal minimum principal stress values are between 23.6 and 25.4 MPa, and the direction of the horizontal maximum principal stress is roughly in the southwest to west direction; (2) the three-way principal stress magnitude relationship is

σ_{H} > σ_{v} > σ_{h}

, indicating that the horizontal stress dominates in the study area, which belongs to the slip-type stress state; (3) The maximum principal stress of No. 3 coal seam is 33.1–34.8 MPa, the middle principal stress is 27.5–29.2 MPa, and the minimum principal stress is 17.3–22.9 MPa. Due to the influence of topography and burial depth, there is a phenomenon of stress concentration in some areas. By comparing the inversion values with the measured values, the accuracy of the geostress inversion is high, and the initial geostress inversion method based on Rhino surface modeling accurately inverts the geostress distribution pattern of the Yingcheng coal mine. Full article

(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)

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