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Processes
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Security Intelligent Monitoring and Big Data Utilization in Coal Mining...
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Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process

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 1789

Special Issue Editors

Dr. Yuanyuan Pu

E-Mail Website
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
Dr. Sitao Zhu

E-Mail Website
Guest Editor
School of Civil and Resource a Engineering, University of Science and Technology Beijing, Beijing, China
Interests: rock dynamics; microseismic monitoring; rockburst and mine earthquake disaster prevention
Dr. Xinglong Zhao

E-Mail Website
Guest Editor
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining & Technology, Xuzhou, 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (3 papers)

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Research

23 pages, 5192 KiB  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/pr12050898 - 28 Apr 2024
Viewed by 338
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 [...] Read more.
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  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/pr12040747 - 7 Apr 2024
Viewed by 575
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 [...] Read more.
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  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/pr12040668 - 27 Mar 2024
Viewed by 497
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 [...] Read more.
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 FLAC3D 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 σH > σv > σh, 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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