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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, 2nd Edition

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2993

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
Special Issues, Collections and Topics in MDPI journals
Dr. Sitao Zhu

E-Mail Website
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

E-Mail Website
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,

This is the 2nd Edition of the Special Issue.

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.

The first edition: https://www.mdpi.com/journal/processes/special_issues/X6LHYER0J7

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
  • monitoring system optimization
  • microseismic monitoring
  • big data technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

19 pages, 4134 KiB  
Article
Dynamic Risk Assessment of Gas Accumulation During Coal and Gas Outburst Catastrophes Based on Analytic Hierarchy Process and Information Entropy
by Jingxiao Yu, Zongxiang Li, Dingding Yang and Yu Liu
Processes 2025, 13(5), 1305; https://doi.org/10.3390/pr13051305 - 25 Apr 2025
Viewed by 75
Abstract
Gas accumulation triggered by coal and gas outbursts is the core cause of secondary disasters in coal mines. This study focuses on the risk assessment of gas accumulation during disaster scenarios, proposing a multidimensional evaluation method integrating the analytic hierarchy process (AHP), information [...] Read more.
Gas accumulation triggered by coal and gas outbursts is the core cause of secondary disasters in coal mines. This study focuses on the risk assessment of gas accumulation during disaster scenarios, proposing a multidimensional evaluation method integrating the analytic hierarchy process (AHP), information entropy theory, kernel density estimation, and dynamic risk propagation modeling. A unified intelligent prevention system encompassing “monitoring–prediction–decision making” is established. Leveraging the TFIM3D simulation platform and case studies from the Qunli Coal Mine accident, this research reveals spatiotemporal evolution patterns of gas concentration and explosion risk thresholds. A ventilation optimization strategy based on risk classification is proposed. The results demonstrate that the dynamic risk index (DRI), derived from the coupling of the roadway air volume stability coefficient and gas concentration information entropy, can accurately identify high-risk zones. The findings provide theoretical foundations and practical pathways for dynamic risk management in ventilation systems during coal and gas outburst disasters. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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24 pages, 16405 KiB  
Article
Control Mechanism of Earthquake Disasters Induced by Hard–Thick Roofs’ Breakage via Ground Hydraulic Fracturing Technology
by Feilong Guo, Mingxian Peng, Xiangbin Meng, Yang Tai and Bin Yu
Processes 2025, 13(3), 919; https://doi.org/10.3390/pr13030919 - 20 Mar 2025
Viewed by 274
Abstract
To investigate the mechanism of ground hydraulic fracturing technology in preventing mine earthquakes induced by hard–thick roof (HTR) breakage in coal mines, this study established a Timoshenko beam model on a Winkler foundation incorporating the elastoplasticity and strain-softening behavior of coal–rock masses. The [...] Read more.
To investigate the mechanism of ground hydraulic fracturing technology in preventing mine earthquakes induced by hard–thick roof (HTR) breakage in coal mines, this study established a Timoshenko beam model on a Winkler foundation incorporating the elastoplasticity and strain-softening behavior of coal–rock masses. The following conclusions were drawn: (1) The periodic breaking step distance of a 15.8 m thick HTR on the 61,304 Workface of Tangjiahui coal mine was calculated as 23 m, with an impact load of 15,308 kN on the hydraulic support, differing from measured data by 4.5% and 4.8%, respectively. (2) During periodic breakage, both the bending moment and elastic deformation energy density of the HTR exhibit a unimodal distribution, peaking 1.0–6.5 m ahead of cantilever endpoint O, while their zero points are 40–41 m ahead, defining the breaking position and advanced influence area. (3) The PBSD has a cubic relationship with the peak values of bending moment and elastic deformation energy density, and the exponential relationship with the impact load on the hydraulic support is FZJ=5185.2e0.00431Lp. (4) Theoretical and measured comparisons indicate that reducing PBSD is an effective way to control impact load. The hard–thick roof ground hydraulic fracturing technology (HTRGFT) weakens HTR strength, shortens PBSD, effectively controls impact load, and helps prevent mine earthquakes. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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13 pages, 2693 KiB  
Article
Big Data Processing Application on the Identification Method of the Dominant Channel in Polymer Flooding
by Ziwu Zhou, Ao Xia, Rui Guo, Lin Chen, Fengshuo Kong, Xiaoliang Zhao and Qi Zhang
Processes 2025, 13(3), 630; https://doi.org/10.3390/pr13030630 - 23 Feb 2025
Viewed by 415
Abstract
Polymer flooding is a critical enhanced oil recovery technique; however, the development of polymer channeling along dominant channels during its later stages can adversely affect the process by increasing comprehensive water cut and dispersing remaining oil, thereby diminishing development benefits. This study aims [...] Read more.
Polymer flooding is a critical enhanced oil recovery technique; however, the development of polymer channeling along dominant channels during its later stages can adversely affect the process by increasing comprehensive water cut and dispersing remaining oil, thereby diminishing development benefits. This study aims to address this challenge by investigating the identification methods and distribution patterns of dominant channels in polymer flooding to inform and optimize the development strategy. Through a series of experiments, we analyzed how factors such as permeability, heterogeneity, reservoir thickness, and mineral composition influence the formation of dominant channels. We developed an identification method for dominant channels post-polymer flooding using a combination of reservoir engineering and mathematical analysis techniques. Our results highlight the significant role of rock and mineral composition, injection rate, and injection pressure in the formation of dominant channels. By integrating formation physical properties and production data from oil and water wells with the grey correlation method, we effectively identified dominant channels. This identification is crucial for guiding the development and adjustment of polymer flooding, enhancing oil recovery efficiency, and maximizing reservoir performance. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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11 pages, 4330 KiB  
Article
Drill Cuttings Test of Coal Under Different Stresses and Characteristics of Coal Particle Distribution During Borehole Collapse
by Yanchun Yin, Qingzhi Zhang, Lei Guo, Cunwen Wang, Shudong He and Dongdong Zhang
Processes 2025, 13(2), 499; https://doi.org/10.3390/pr13020499 - 11 Feb 2025
Viewed by 584
Abstract
The drill cuttings method is a commonly used method for evaluating coal burst risk in mines. In engineering applications, due to the development of fractures in coal seams, borehole collapse can easily occur during drilling, which leads to a greater quantity of drill [...] Read more.
The drill cuttings method is a commonly used method for evaluating coal burst risk in mines. In engineering applications, due to the development of fractures in coal seams, borehole collapse can easily occur during drilling, which leads to a greater quantity of drill cuttings. This in turn affects the accuracy of the evaluation results of coal burst risk. Through laboratory tests on drill cuttings from intact coal and fractured coal specimens, the impact of coal stress and diameter of the borehole on the quantity of drill cuttings and the occurrence of borehole collapse was studied. When there is no collapse, the quantity of drill cuttings increases in proportion to the diameter of the borehole and the coal stress and has a power function relationship with the diameter of the borehole and an exponential function relationship with the coal stress. When the collapse occurs, the failure characteristics of coal specimens mainly present two forms. One is the cylindrical collapse area, and the other is the conical collapse area. Compared to normal drilling, there are notable changes in the particle size of drill cuttings after borehole collapse, and the characteristic value of drill cuttings size D50 increases significantly after the collapse of the borehole, which can be used to determine whether the borehole collapse occurs. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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20 pages, 14504 KiB  
Article
Acoustic Emission/Mine Microseismic Sensor Network Optimization Based on Grid Loop Search and Particle Swarm Source Location
by Yiling Chen, Xueyi Shang, Yi Ren, Linghao Liu, Xiaoying Li, Yu Zhang, Xiao Wu, Zhuqing Li, Yang Tai, Yuanyuan Pu and Guanghua Xiang
Processes 2025, 13(2), 496; https://doi.org/10.3390/pr13020496 - 10 Feb 2025
Viewed by 719
Abstract
The layout of acoustic emission sensors plays a critical role in non-destructive structural testing. This study proposes a grid-based optimization method focused on multi-source location results, in contrast to traditional sensor layout optimization methods that construct a correlation matrix based on sensor layout [...] Read more.
The layout of acoustic emission sensors plays a critical role in non-destructive structural testing. This study proposes a grid-based optimization method focused on multi-source location results, in contrast to traditional sensor layout optimization methods that construct a correlation matrix based on sensor layout and one source location. Based on the seismic source travel time theory, the proposed method establishes a location objective function based on minimum travel time differences, which is solved through the particle swarm optimization (PSO). Furthermore, based on location accuracy across various configurations, the method systematically evaluates potential optimal sensor locations through grid search. Synthetic tests and laboratory pencil-lead break (PLB) experiments are conducted to compare the effectiveness of PSO, genetic algorithm (GA), and simulated annealing (SA), with the following conclusions. (1) In the synthetic tests, the proposed method achieved an average location error of 1.78 mm, outperforming that based on the traditional layout, GA and SA. (2) For different noise cases, the location accuracy separately improved by 24.89% (σ = 0.5 μs), 12.59% (σ = 2 μs), and 15.06% (σ = 5 μs) compared with the traditional layout. (3) For the PLB experiments, the optimized layout achieved an average location error of 9.37 mm, which improved the location accuracy by 59.15% compared with the traditional layout. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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19 pages, 8594 KiB  
Article
The Effect of Oxidation on Coal’s Molecular Structure and the Structure Model Construction of Oxidized Coal Molecular
by Dahu Li, Fangjia Yang, Zhao Cao, Ruoqi Li and Yiwen Hu
Processes 2025, 13(1), 187; https://doi.org/10.3390/pr13010187 - 10 Jan 2025
Viewed by 630
Abstract
Three kinds of 1/3 coking coals with different degrees of oxidation were used for this study in Inner Mongolia, China. Using analytical testing methods such as X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance carbon spectroscopy (13C-NMR) and Fourier transform infrared spectroscopy [...] Read more.
Three kinds of 1/3 coking coals with different degrees of oxidation were used for this study in Inner Mongolia, China. Using analytical testing methods such as X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance carbon spectroscopy (13C-NMR) and Fourier transform infrared spectroscopy (FT-IR), combined with computer-aided software such as Chemdraw, Materials Studio (2017), and MestRenova, three coal samples were characterized and analyzed. On this basis, the molecular formulas of three coal samples with different degrees of oxidation were constructed by optimizing the model energies: we used C228H165N3O21S4 for Suhaitu9# coal, C244H171N3O31S2 for Guoyu coal, and C225H177N3O33S2 for Lu9# coal. The results showed that, at the same coal rank, the oxidation degrees of S9, GY, and L9 coal samples were 21.10%, 48.30%, and 53.12%, respectively. As the oxidation degree increased, the proportion of oxygen-containing functional groups and nitrogen oxides in the coal macromolecular structure gradually increased. The bridge circumference ratios were 0.3786, 0.3351, and 0.2228, respectively, showing a gradual decrease. The average methylene chain lengths were 4.9569, 2.6843, and 1.9055, respectively, showing a gradual decrease. This indicates that the condensation degree of the compounds decreases with the increase in the degree of oxidation. These findings reflect the effect of oxidation on the modeling of coal’s macromolecular structure and lay a theoretical foundation for the further study of impact of the degree of oxidation on the physicochemical properties of coal. Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process, 2nd Edition)
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