Mining Safety: Challenges and Prevention of Mine Disasters

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 3572

Special Issue Editors


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Guest Editor
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: coal mine gas control and utilization; coal and gas outburst; igneous intrusions; mine safety and emergency management

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Guest Editor
College of Safety and Environmental Engineering (College of Safety and Emergency Management), Shandong University of Science and Technology, Qingdao 266590, China
Interests: coal mine ventilation; dust control; fire prevention and suppression; mine disaster prevention and control
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WASM Minerals Energy and Chemical Engineering, Faculty of Science and Engineering, Curtin University, Kalgoorlie, WA 6430, Australia
Interests: rock mechanics; coal seam gas; fluid mechanics; CO2 geo-sequestration; ECBM; mining engineering; permeability; gas diffusion in porous media; cemented past backfill; fly ash
Special Issues, Collections and Topics in MDPI journals
School of Emergency Management and Safety Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
Interests: coal mining safety; gas diffusion; ECBM; emergency management and science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Preventing mine disasters and improving mine safety performance are important issues for both scientists and enterprises.

The occurrence conditions of coal resources in underground coal mines are extremely complex. Common disasters in coal mines (coal and gas outbursts, gas explosions, roof falls, spontaneous combustion of coal, flood hazards, rock bursts, coal dust explosions, heat hazards, etc.) have emerged around the world. The risk of coal production still remains at a high level. In addition, with the increasing depth of coal mining, coal mechanical properties and the coal mining environment will change dramatically compared to that during shallow mining. The increasing geostress, gas pressure, and geothermal gradient lead to nonlinear characteristics of the coal and rock mass, showing rheological behaviors. Dynamic disasters will occur more easily and their mechanism will be more complex with the increased depth. Thus, the prevention and control of coal mine disasters become more difficult. Coal mine safety is facing new and more severe challenges.

This Special Issue will be devoted to exploring new perspectives on safety challenges and protective measures encountered in the current coal-mining process. It will focus on the hot issues, latest research results, and future directions of mine disaster prevention and control through numerical simulations, laboratory studies, field applications, and reviews, mainly in the areas of coal and gas outburst mechanisms, roof falls, spontaneous combustion of coal, and other dynamic hazards.

Prof. Dr. Jingyu Jiang
Prof. Dr. Wen Nie
Dr. Jia Lin
Dr. Wei Zhao
Guest Editors

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Keywords

  • coal gas hazards
  • roof fall
  • spontaneous combustion of coal
  • mine flood hazards

Published Papers (2 papers)

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Research

20 pages, 6427 KiB  
Article
A Hybrid Model for Predicting Low Oxygen in the Return Air Corner of Shallow Coal Seams Using Random Forests and Genetic Algorithm
by Kai Wang, Zibo Ai, Wei Zhao, Qiang Fu and Aitao Zhou
Appl. Sci. 2023, 13(4), 2538; https://doi.org/10.3390/app13042538 - 16 Feb 2023
Cited by 1 | Viewed by 1245
Abstract
In order to better solve the phenomenon of low oxygen in the corner of return airway caused by abnormal gas emission in goaf during shallow coal seam mining, by analyzing the source and reason of low oxygen phenomenon, a prediction model of oxygen [...] Read more.
In order to better solve the phenomenon of low oxygen in the corner of return airway caused by abnormal gas emission in goaf during shallow coal seam mining, by analyzing the source and reason of low oxygen phenomenon, a prediction model of oxygen concentration in the corner of return airway based on genetic algorithm (GA) and random forest (RF) technology was proposed. The training sample set was established by using the field data obtained from actual monitoring, including the oxygen concentration in the return airway corner, the periodic pressure step distance of the roof, the surface temperature and atmospheric pressure. GA was used to optimize the parameters in the RF model, including trees and leaves in the forest. The results showed that the model prediction error was minimum when the number of trees was 398 and the number of leaves was 1. In addition, GA was used to optimize the number of hidden neurons and the initial weight threshold of the back-propagation neural network (BPNN). In order to verify the superiority of the model, the GA optimized RF and BPNN model are compared with the conventional RF and BPNN model. Analyze the average absolute percentage error (MAPE), root mean square error (RMSE), and average absolute error (MAE) of the prediction data of each model. The results show that the optimized RF prediction model is better than other models in terms of prediction accuracy. Full article
(This article belongs to the Special Issue Mining Safety: Challenges and Prevention of Mine Disasters)
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15 pages, 15685 KiB  
Article
Experimental Study of the Self-Potential Response Characteristics of Anisotropic Bituminous Coal during Deformation and Fracturing
by Jun Zhang, Shengdong Liu, Cai Yang and Juanjuan Li
Appl. Sci. 2023, 13(2), 1095; https://doi.org/10.3390/app13021095 - 13 Jan 2023
Cited by 1 | Viewed by 1031
Abstract
The deformation and fracturing of coal rock is a crucial part of coal and rock dynamic disasters and is accompanied by variations in the electrical field of the rock. In this study, the self-potential characteristics of coal rock were measured to dynamically monitor [...] Read more.
The deformation and fracturing of coal rock is a crucial part of coal and rock dynamic disasters and is accompanied by variations in the electrical field of the rock. In this study, the self-potential characteristics of coal rock were measured to dynamically monitor the spatiotemporal evolution of coal rock deformation and fracturing. By using an MTS816 rock mechanics test system, an AE acoustic emission system and a self-developed SEMOS-LAB experimental system, synchronous measurements of the self-potential, stress and acoustic emission of anisotropic bituminous coal under uniaxial compression were obtained. The self-potential of anisotropic bituminous coal exhibited a good correspondence with the stress and acoustic emission counts during the damage and fracturing. As the stress gradually increased, the bedding-perpendicular coal samples exhibited a stronger linear relationship with the stress during initial loading than the bedding-parallel samples. The amplitude of the self-potential and stress of the bedding-perpendicular samples were higher than those of the bedding-parallel samples. Anisotropy is an important factor that affects the variation in the self-potential of a rock mass under loading. The results of this study can be applied to evaluate the stress state of coal by measuring its loading-induced electrical potential; thus, this work is important in the field for the monitoring and warning of coal and rock dynamic disasters. Full article
(This article belongs to the Special Issue Mining Safety: Challenges and Prevention of Mine Disasters)
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