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Novel Technologies in Intelligent Coal Mining
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Novel Technologies in Intelligent Coal Mining

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 November 2025 | Viewed by 4577

Special Issue Editors

Prof. Dr. Xingliang Xu

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Guest Editor
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent coal mining; intelligent excavation; intelligent perception; roadway support; mining equipment
Dr. Suchuan Tian

E-Mail
Guest Editor
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent excavation; intelligent percep-tion; disaster warning; roadway support
Dr. Zhengxiang He

E-Mail
Guest Editor
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent perception; disaster warning; safety management; unmanned mining
Dr. Xiaoran Wang

E-Mail Website
Guest Editor
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent perception; disaster warning; safety management; rock fracture mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coal resources are one of the most crucial energy sources for human society and an essential foundation for global economic development. In recent years, coal mining technology has developed rapidly amid concerns over safety, efficiency, green mining and other issues, and the need for novel mining technologies has become increasingly urgent. Traditional mining theories have been continuously developed to form more cutting-edge mining theories, and cutting-edge technologies such as the Internet of Things, big data, artificial intelligence, 5G, edge computing, and virtual reality have also considerably advanced the level of mining technology for coal resources. Therefore, the development of coal mining should focus not only on the development and innovation of traditional technologies such as road support and rock control but also on the development and application of novel technologies such as intelligent mining and big data.

This Special Issue aims to include innovative research achievements pertaining to different aspects of the technologies used in intelligent coal mining. Key areas include, but are not limited to, the following:

  1. Novel theories on the construction of intelligent mines;
  2. Novel techniques for the dynamic reconstruction of a transparent geological model;
  3. Novel methods of coal mining and fast roadway excavation;
  4. Novel equipment and novel systems for intelligent coal mining;
  5. Novel techniques for the intelligent perception of mining environments and surrounding rock states;
  6. Novel methods and materials for mine roadway support and reinforcement;
  7. Novel ideas for the intelligent identification and early warning of coal and rock disasters;
  8. Novel systems for intelligent coal mining safety management.
  9. Related case presentations.

Prof. Dr. Xingliang Xu
Dr. Suchuan Tian
Dr. Zhengxiang He
Dr. Xiaoran Wang
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 coal mining
  • intelligent excavation
  • intelligent perception
  • disaster warning
  • roadway support
  • safety management
  • mining equipment

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Published Papers (7 papers)

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Research

19 pages, 12566 KiB  
Article
Spatial and Temporal Distribution Pattern of Pre-Mining Grouting-Induced Microseismicity and Prediction of Water Inrush
by Ermeng Zhang, Qifeng Jia, Zhaoxing Liu, Zhenhua Li and Yu Fei
Appl. Sci. 2025, 15(9), 4925; https://doi.org/10.3390/app15094925 - 29 Apr 2025
Viewed by 179
Abstract
Pre-mining grouting is an effective means to prevent mine water inrush, while the microseismicity information induced by pre-mining grouting is often ignored. This paper proposes a novel method to predict the danger of mine floor water inrush based on pre-mining grouting-induced microseismicity (PMGIM). [...] Read more.
Pre-mining grouting is an effective means to prevent mine water inrush, while the microseismicity information induced by pre-mining grouting is often ignored. This paper proposes a novel method to predict the danger of mine floor water inrush based on pre-mining grouting-induced microseismicity (PMGIM). The mechanical mechanism and characteristics of PMGIM are explored through mechanical analysis and numerical simulation. Taking 182602 working face in Wutongzhuang coal mine as a case study, the temporal and spatial distribution law of PMGIM is analyzed, and the connection between the grouting process and microseismic energy is established. Based on the PMGIM information, Moran’s index is used for the prediction of water inrush possibility, and the validity of the method is verified by electric monitoring. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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18 pages, 2147 KiB  
Article
The Impact of Task Interruptions on the Unsafe Behavior of Coal Mine Tunneling Machine Operators: The Moderating Role of Fatigue
by Guangtong Shao, Shuicheng Tian, Fangyuan Tian, Lei Chen and Yifan Zhao
Appl. Sci. 2025, 15(5), 2764; https://doi.org/10.3390/app15052764 - 4 Mar 2025
Viewed by 628
Abstract
Task interruptions and fatigue in high-risk environments such as coal mining significantly affect the safety behavior of coal mine tunneling machine operators. Understanding the cognitive mechanisms underlying unsafe behaviors is crucial for improving workplace safety. This study, based on the executive control and [...] Read more.
Task interruptions and fatigue in high-risk environments such as coal mining significantly affect the safety behavior of coal mine tunneling machine operators. Understanding the cognitive mechanisms underlying unsafe behaviors is crucial for improving workplace safety. This study, based on the executive control and resource allocation theory in the ACT-R cognitive architecture, investigates the impact of task interruptions under fatigue on unsafe behavior and cognitive neural mechanisms in these operators. A dual-perspective analysis of behavioral performance and event-related potentials (ERPs) was employed. The behavioral analysis revealed that fatigue exacerbates the negative effects of task interruptions. Under non-fatigued conditions, individuals compensated for interruptions with longer reaction times, maintaining accuracy without significant decline. However, under fatigue, task interruptions notably reduced accuracy, especially during recovery trials. ERP analysis showed that fatigue impaired cognitive and neural mechanisms that are critical for task performance. Following interruptions, an increase in P200 amplitude and prolonged latency indicated reduced task switching efficiency. Under fatigue, a decline in frontal P300 amplitude over time reflected weakened executive control, while an increase in central P300 amplitude suggested compensatory control mechanisms’ efforts. However, these compensatory control mechanisms were insufficient to counteract the negative impact of fatigue. In conclusion, fatigue-induced impairments in attention shifting, response inhibition, and the imbalance between facilitation and inhibition further exacerbated performance declines after task interruptions. Although compensatory control mechanisms attempted to mitigate these effects through resource reallocation, they were unable to fully counteract fatigue’s negative impact. This study underscores the moderating role of fatigue in the relationship between task interruptions and unsafe behaviors, and highlights the limitations of brain compensatory control mechanisms. These findings offer valuable theoretical insights and practical guidance for optimizing workflow and task design for tunneling machine operators in coal mining operations. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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25 pages, 6184 KiB  
Article
Study on the Optimization of Coal Pillar Width in Goaf-Side Roadway Under the Synergistic Effect of Mining and Seepage
by Shuai Yan, Shuihua Liu, Xiangdong Wang, Jianbiao Bai and Yonghong Guo
Appl. Sci. 2025, 15(5), 2397; https://doi.org/10.3390/app15052397 - 24 Feb 2025
Viewed by 359
Abstract
In coal mine roadways excavated along the goaf with water accumulation, the roadway is subjected to the combined effects of water infiltration and multiple stresses from excavation activities, leading to significant deformation and challenges in determining the appropriate coal pillar width. This study, [...] Read more.
In coal mine roadways excavated along the goaf with water accumulation, the roadway is subjected to the combined effects of water infiltration and multiple stresses from excavation activities, leading to significant deformation and challenges in determining the appropriate coal pillar width. This study, based on the Jianxin Coal Mine 4301 tailgate, utilizes the advanced three-dimensional numerical calculation software FLAC3D 6.0 to develop a comprehensive seepage flow model. By analyzing the distribution of key roadway surrounding rock properties, such as deviatoric stress, plastic zone, and dissipated energy, the influence of coal pillar width on roadway deformation and failure characteristics is systematically investigated. The findings provide novel insights into the roadway stability control under complex geological conditions. Specifically, the results reveal that: (1) When the coal pillar width is less than 9 m, stress concentration zones are observed, fully connected by plastic zones and dissipated energy. For widths exceeding 9 m, the influence of the goaf diminishes, leading to a stress reduction zone within the coal pillar and a shift in dissipated energy density distribution from a penetrating shape to an independent double-core shape. The plastic zones on both the goaf and roadway sides become independent, indicating a transition from an unstable to a stable coal pillar state. (2) Using the Analytic Hierarchy Process (AHP), a zoning control system for the roadway surrounding rock is established, dividing the roadway into three regions: normal support, reinforced support, and special support. Industrial experiments corroborate the simulation results, and on-site monitoring demonstrates that the control measures significantly improve roadway stability. This study presents an innovative approach to the design and control of coal pillars in water-affected mine roadways, offering valuable contributions to both the scientific understanding and practical application of mining engineering in similar geological settings. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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19 pages, 8273 KiB  
Article
Fine Identification of Landslide Acceleration Phase Using Time Logarithm Prediction Method Based on Arc Synthetic Aperture Radar Monitoring Data
by Chong Li, Liguan Wang, Jiaheng Wang and Jun Zhang
Appl. Sci. 2025, 15(4), 2147; https://doi.org/10.3390/app15042147 - 18 Feb 2025
Viewed by 388
Abstract
In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. [...] Read more.
In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. The key findings include the following: (1) This strategy utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the starting point of accelerated deformation. (2) Coupled with coordinate transformation, the time logarithm prediction method was constructed, unifying the units of measurement and resolving convergence issues in data fitting. (3) Empirical research conducted at the Kambove open-pit mine in the Democratic Republic of the Congo demonstrates that this method successfully predicts landslide times four hours in advance, with an error margin of only 0.18 h. This innovation offers robust technical support for slope landslide prevention and control in open-pit mines, enhancing safety standards and mitigating disaster losses. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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21 pages, 19981 KiB  
Article
Research on Image Segmentation and Defogging Technique of Coal Gangue Under the Influence of Dust Gradient
by Zhenghan Qin, Judong Jing, Libao Li, Yong Yuan, Yong Li and Bo Li
Appl. Sci. 2025, 15(4), 1947; https://doi.org/10.3390/app15041947 - 13 Feb 2025
Viewed by 536
Abstract
To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real [...] Read more.
To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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18 pages, 11358 KiB  
Article
A Method and Engineering Practice for a Fully Mechanized Caving Coalface to Rapidly Pass Through a Large Fault
by Wei Zhang, Feili Yang, Jingyu Chang, Shengxun Zhao, Bin Xu and Jinyong Xiang
Appl. Sci. 2025, 15(2), 731; https://doi.org/10.3390/app15020731 - 13 Jan 2025
Viewed by 596
Abstract
One of the technical problems that must be solved in coal mine production is when the coalface rapidly crosses the fault. Based on the occurrence characteristics of the F6 fault (maximum throw: 13.5 m) in the #3up1101 fully mechanized caving [...] Read more.
One of the technical problems that must be solved in coal mine production is when the coalface rapidly crosses the fault. Based on the occurrence characteristics of the F6 fault (maximum throw: 13.5 m) in the #3up1101 fully mechanized caving coalface at Gaozhuang Coal Mine, two different solutions allowing the coalface to pass through this fault were proposed, and the solution of pre-driven roadways with rock pillars was optimally determined. The main implementation steps of the method include designing the layout parameters of the pre-driven roadways, determining the width for rock pillars between the adjacent pre-driven roadways, construction of pre-driven roadways by smooth wall blasting, and controlling the surrounding rock deformation of the pre-driven roadways. The results of engineering practice show that it took only 23 days for this coalface to pass through fault F6, about one month shorter than the time required by traditional methods (e.g., proactively taking a detour). Moreover, this method helped achieve stable coal production (an 8.5 × 104 t increase), prevented much gangue from mixing with coal, reduced wear and tear on the mining equipment, and enhanced safety. The economic benefits delivered totaled about CNY 71.1 million. Therefore, this method can ensure continuous, safe, and efficient mining at the coalface, alleviating the tight situation of mine production succession. The results of this study can provide a good reference to help coalfaces rapidly move across faults under similar geological conditions in other mines. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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26 pages, 24328 KiB  
Article
Response Characteristics of Anchored Surrounding Rock in Roadways Under the Influence of Vibrational Waves
by Hongsheng Wang, Siyuan Wei, Guang’an Zhu, Yuxin Yuan and Weibin Guo
Appl. Sci. 2024, 14(23), 11266; https://doi.org/10.3390/app142311266 - 3 Dec 2024
Viewed by 721
Abstract
The vibration waves generated by pressure fluctuations can substantially impair and jeopardize the structural integrity of roadway anchorage within adjacent rock formations, thereby presenting a significant risk to the safety and operational efficiency of mining activities. In order to address this issue and [...] Read more.
The vibration waves generated by pressure fluctuations can substantially impair and jeopardize the structural integrity of roadway anchorage within adjacent rock formations, thereby presenting a significant risk to the safety and operational efficiency of mining activities. In order to address this issue and elucidate the response characteristics of roadway-anchored surrounding rock subjected to P-wave and S-wave influences, this study employs a roadway that is experiencing actual impact instability within a mine situated in Xinjiang as the engineering context. The synchrosqueezing wavelet transform, enhanced by a Butterworth filter, is utilized to isolate and filter seismic wave data, thereby facilitating the extraction of time-frequency signals corresponding to both P-waves and S-waves. Subsequently, a dynamic numerical model is developed to simulate the propagation of these vibration waves. An analysis of the dynamic behavior and response characteristics of P-waves and S-waves is performed, focusing on their interaction with roadway anchoring within the surrounding rock at various stages of propagation. The results indicate that weak rock and plastic zones can absorb vibrational waves, with S-waves exhibiting a stronger absorption effect than P-waves. S-waves contribute to increased stress and displacement in the surrounding rock, leading to the accumulation of elastic energy and an expansion of the plastic zone. The rapid fluctuations in the axial force of bolts along the roadway, caused by S-waves, can result in instability within the roadway. The research findings possess considerable reference value and practical applicability for the design of anti-scour support systems in roadways. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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