Theory and Technology of Intelligent Mining in Coal Mines
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".
Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 512
Special Issue Editor
Interests: theory and technology of intelligent mining; adaptive control of equipment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Intelligent mining is a new stage in the development of coal comprehensive mining technology, which is an inevitable requirement for the technological revolution and upgrading development of the coal industry and is one of the core systems of intelligent mines. In the face of the new round of energy science and technology revolution and industrial change, for the new coal mine safety development requirements, intelligent mining is the way forward to achieve the safe and efficient production of China's coal mines.
Intelligent mining is based on the concept of modern coal mine intelligence, the Internet of Things, cloud computing, big data, artificial intelligence, automatic control, mobile Internet, robotized equipment and the deep integration of other modern coal mining technology, the formation of mining processes’ comprehensive perception, real-time interconnection, analysis and decision making, independent learning, dynamic prediction, and collaborative control. Intelligent mining works with fully automated and intelligent control functions, highly reliable hydraulic support, coal mining machinery, scraper conveyor, transfer machine, crusher, and the electromechanical integration of other sets of equipment for the development of unmanned and efficient safe mining.
The scope of this Special Issue is to gather original experimental, theoretical, and numerical research articles that contribute toward a deeper understanding of recent advances in the field of "Theory and Technology of Intelligent Mining in Coal Mines". Potential topics include, but are not limited to:
(1) Theory and technology of 3D geological modeling visualization;
(2) Intelligent sensing technology of mining environment under complex geological conditions;
(3) Theory and technology of adaptive control of working face equipment;
(4) Theory and technology of intelligent coal release control with multi-source information fusion;
(5) "5G"/"Internet+" technology for coal mines.
Prof. Dr. Yong Yuan
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 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 mining
- intelligent perception
- intelligent decision
- intelligent adaptive control
- 3D geological modeling
- coal mine 5G
- neural networks
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