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Intelligent and Sustainable Safe Coal Mining: AI-Assisted Disaster Mitigation, Carbon...
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Intelligent and Sustainable Safe Coal Mining: AI-Assisted Disaster Mitigation, Carbon Sequestration, and Energy Utilization

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1220

Editors

Dr. Feng Du

E-Mail Website
Guest Editor
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: mining safety engineering; AI-assisted disaster prediction; safety and emergency management; machine learning in mining
Special Issues, Collections and Topics in MDPI journals
Dr. Yongbo Cai

E-Mail
Guest Editor Assistant
China Coal Research Institute, Beijing 100083, China
Interests: mining safety engineering; AI-assisted disaster prediction; safety and emergency management; machine learning in mining
Prof. Dr. Qiming Huang

E-Mail Website
Guest Editor
College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: mining safety engineering; prevention and control of coal rock dynamic disasters; coal dust prevention and control; theory of gas flow in coal

Special Issue Information

Dear Colleagues,

Coal mining continues to play a vital role in global energy supply, especially in regions where coal remains essential for energy security. Under the constraints of carbon neutrality and sustainable development goals, coal mines are increasingly required to improve safety performance while reducing emissions, enhancing energy efficiency, and promoting intelligent operation. Achieving safe, efficient, and low-carbon coal mining under realistic engineering conditions has therefore become a critical challenge. Recent advances in artificial intelligence, sensing technologies, and data-driven methods have provided new tools for coal mine safety engineering. AI-assisted approaches have demonstrated significant potential in disaster monitoring, prediction, and early warning for coal and gas dynamic disasters, rock bursts, and other mining hazards. Meanwhile, coal mines offer practical opportunities for carbon sequestration and energy utilization, including CO2 storage in coal seams, CO2–CH4 interaction and enhanced coalbed methane recovery, as well as the safe utilization of mine gas and associated energy resources. This Special Issue aims to collect high-quality research focusing on engineering-oriented and practically feasible solutions for sustainable and safe coal mining. Emphasis is placed on AI-assisted disaster mitigation, coal-mine-scale carbon sequestration, energy utilization, and process safety, supported by experimental studies, numerical modeling, field measurements, or engineering applications. The Special Issue seeks to promote the integration of intelligent technologies with traditional mechanism-based approaches, contributing to measurable improvements in safety, energy efficiency, and emission reduction in coal mining systems.

Topics include, but are not limited to:

  • Intelligent prediction and early warning of dynamic disasters in coal mines
  • Intelligent monitoring, sensing, and data-driven analysis technologies for coal mine safety
  • Mechanisms and control of coal and gas dynamic disasters
  • Gas migration, seepage behavior, and multi-field coupling processes in coal seams
  • AI-assisted modeling and simulation of coal mine safety-related processes
  • Carbon sequestration in coal mines, including CO2 storage in coal seams and CO2–CH4 interaction
  • Safety assessment and risk control of carbon sequestration processes in coal mining environments
  • Energy utilization and emission reduction technologies in coal mines, including coal mine gas utilization
  • Intelligent optimization of coal mine ventilation systems for safety and energy efficiency
  • Coordinated control of energy efficiency improvement and carbon emission reduction in coal mining processes
  • Intelligent control and optimization of coal mine gas drainage and extraction systems
  • Integration of intelligent technologies with coal mine safety engineering and process safety
  • Experimental investigations, numerical modeling, and field applications related to intelligent and sustainable coal mining 

Dr. Feng Du
Guest Editor

Dr. Yongbo Cai
Guest Editor Assistant

Prof. Dr. Qiming Huang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 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
  • AI-assisted disaster prediction
  • coal and gas dynamic disasters
  • carbon sequestration in coal seams
  • gas migration and seepage in coal
  • energy utilization in coal mines
  • machine learning in mining

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Published Papers (2 papers)

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Research

19 pages, 9746 KB  
Article
Research on the Characteristics of Mismatch Horizontal Deformation in Primary Coal-Rock Combination
by Qiang Fu, Taotao Du, Yongbo Cai, Chao Xu, Zihan Qin, Ruda Sun and Ruibing Yan
Processes 2026, 14(13), 2100; https://doi.org/10.3390/pr14132100 - 28 Jun 2026
Cited by 1 | Viewed by 361
Abstract
The essence of coal-rock dynamic disasters is the instantaneous instability that occurs when the coal-rock combination system reaches its strength limit. The differing mechanical properties of coal and rock lead to mismatching deformation within the combination, thereby altering the mechanical characteristics of the [...] Read more.
The essence of coal-rock dynamic disasters is the instantaneous instability that occurs when the coal-rock combination system reaches its strength limit. The differing mechanical properties of coal and rock lead to mismatching deformation within the combination, thereby altering the mechanical characteristics of the coal-rock body under combination structures. Consequently, studying the mismatching horizontal deformation of coal-rock combinations is significant for the prevention and control of coal-rock dynamic disasters. To investigate the mismatching horizontal deformation characteristics of primary coal-rock combinations, this study conducted uniaxial and conventional triaxial loading experiments on primary coal-rock combinations to obtain their strength and horizontal deformation characteristics. Subsequently, numerical simulations were performed to analyze the factors influencing the horizontal deformation of primary coal-rock combinations, ultimately leading to the construction of a horizontal deformation distribution model for primary coal-rock combinations. The research results indicate that due to the influence of the coal-rock interface, the primary coal-rock combinations exhibit continuous and smooth mismatching horizontal deformation under loading conditions, with the magnitude of deformation ordered as coal > interface > rock. The influence of the interface on the horizontal deformation of the coal-rock body does not suddenly vanish; rather, it gradually diminishes with increasing distance from the interface, becoming negligible at approximately 48% of the model’s lateral dimensions. A horizontal deformation mismatch distribution model for primary coal-rock combinations was established based on the Sigmoid function, and the key parameter b in the model was obtained through fitting with numerical simulation results. The reliability of the model was subsequently validated through experimental results. The findings of this study provide a basis for further research on the mechanical properties and deformation characteristics of coal-rock combinations. Full article
(This article belongs to the Special Issue Intelligent and Sustainable Safe Coal Mining: AI-Assisted Disaster Mitigation, Carbon Sequestration, and Energy Utilization)
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20 pages, 5609 KB  
Article
Enhanced YOLO11n for UAV-Based Surface Crack Detection in Mining Subsidence Areas
by Mo Wang, Nan Zhao, Chuangchuang Liu, Wanxiang Rao and Zhijun Zhang
Processes 2026, 14(12), 1988; https://doi.org/10.3390/pr14121988 - 18 Jun 2026
Viewed by 458
Abstract
Mining-subsidence-induced surface cracks pose substantial risks to ecological systems, infrastructure stability, and mining safety. Their thin, elongated, discontinuous, and low-contrast characteristics make accurate detection from unmanned aerial vehicle (UAV) imagery challenging, particularly under complex environmental conditions. This study proposes an enhanced YOLO11n framework [...] Read more.
Mining-subsidence-induced surface cracks pose substantial risks to ecological systems, infrastructure stability, and mining safety. Their thin, elongated, discontinuous, and low-contrast characteristics make accurate detection from unmanned aerial vehicle (UAV) imagery challenging, particularly under complex environmental conditions. This study proposes an enhanced YOLO11n framework for detecting surface cracks in mining subsidence areas. Switchable Atrous Convolution (SAConv) was incorporated to strengthen multi-scale feature extraction, while Cascaded Group Attention (CGA) was introduced to suppress background interference and improve feature discrimination, and Shape-IoU loss was adopted to enhance the localization of slender crack targets. The model was evaluated using 5000 annotated UAV images collected in the Zhungeer mining area. It achieved a precision of 85.6%, a recall of 77.9%, an mAP@0.5 of 84.3%, and an F1-score of 81.6%. Compared with the baseline YOLO11n, precision, recall, and mAP@0.5 increased by 1.4, 4.6, and 3.2 percentage points, respectively. Cross-dataset evaluation on the public Crack500 dataset further demonstrated improved robustness under domain variation. These results indicate that the proposed framework improves the detection and localization of slender and discontinuous cracks in complex mining environments, supporting its application in UAV-based geological hazard monitoring. Full article
(This article belongs to the Special Issue Intelligent and Sustainable Safe Coal Mining: AI-Assisted Disaster Mitigation, Carbon Sequestration, and Energy Utilization)
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