Intelligent and Sustainable Safe Coal Mining: AI-Assisted Disaster Mitigation, Carbon Sequestration, and Energy Utilization
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 108
Special Issue Editors
Interests: mining safety engineering; AI-assisted disaster prediction; safety and emergency management; machine learning in mining
Special Issues, Collections and Topics in MDPI journals
Interests: mining safety engineering; AI-assisted disaster prediction; safety and emergency management; machine learning in mining
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-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 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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