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Recent Advances in Smart Mining Technology, Volume II

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3548

Special Issue Editors


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Guest Editor
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: smart mining; renewables in mining; space mining; AICBM (AI, IoT, cloud, big data, mobile) convergence; unmanned aerial vehicle; mine planning and design; open-pit mining operation; mine safety; geographic information systems; 3D geo-modeling; geostatistics; hydrological analysis; energy analysis and simulation; design of solar energy conversion systems; renewable energy systems
Special Issues, Collections and Topics in MDPI journals
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: smart mining; digital twins in mining; AICBM (AI, IoT, cloud, big data, mobile) conversion technologies; photovoltaic system; green mobility (e.g., solar-powered electric vehicles); geographic information systems (GIS); spatial analysis; mining simulation; mine safety system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution, starting from Industry 4.0 in Germany and the United States, has developed into the concept of an “integrated intelligent society” and is now spreading across all industries. As the core technologies of the Fourth Industrial Revolution, represented by artificial intelligence (AI), Internet of Things (IoT), cloud computing, big data, mobile and wearable devices, augmented/virtual/mixed reality, 3D printing, open source, self-driving, drones, robotics, etc., are fused with domain knowledge by sector, innovative changes are taking place in the industrial field, giving way to a productivity revolution.

This change is also having a significant impact on the mineral industry. The core technologies of the Fourth Industrial Revolution are being introduced and integrated throughout the entire cycle, including exploration, development, production, processing, and environmental restoration of mineral resources. The concept of “smart mining”, which combines traditional mining technology with information and communication technology (ICT), has become a representative keyword representing the Fourth Industrial Revolution of the mineral industry in the age of digital transformation.

This Special Issue (SI) aims to encourage researchers to address recent advances in smart mining technology for the mineral industry. Original research contributions and reviews providing examples of the improvements brought about by smart mining technology in all areas of the mineral sector can be included in this SI.

Prof. Dr. Yosoon Choi
Dr. Jieun Baek
Guest Editors

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. 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

  • artificial intelligence in mining
  • internet of things in mining
  • cloud computing in mining
  • big data analytics in mining
  • mobile and wearable devices in mining
  • augmented, virtual, and mixed realities in mining
  • 3D printing in mining
  • open-source hardware and software in mining
  • self-driving and robotics in mining

Published Papers (2 papers)

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Research

17 pages, 4252 KiB  
Article
Application of Interpretable Machine Learning for Production Feasibility Prediction of Gold Mine Project
by Kun Kang, Qishen Chen, Kun Wang, Yanfei Zhang, Dehui Zhang, Guodong Zheng, Jiayun Xing, Tao Long, Xin Ren, Chenghong Shang and Bojing Cui
Appl. Sci. 2023, 13(15), 8992; https://doi.org/10.3390/app13158992 - 5 Aug 2023
Cited by 1 | Viewed by 1030
Abstract
In the context of globalization in the mining industry, assessing the production feasibility of mining projects by smart technology is crucial for the improvement of mining development efficiency. However, evaluating the feasibility of such projects faces significant challenges due to incomplete data and [...] Read more.
In the context of globalization in the mining industry, assessing the production feasibility of mining projects by smart technology is crucial for the improvement of mining development efficiency. However, evaluating the feasibility of such projects faces significant challenges due to incomplete data and complex variables. In recent years, the development of big data technology has offered new possibilities for rapidly evaluating mining projects. This study conducts an intelligent evaluation of gold mines based on global mineral resources data to estimate whether a gold mine project can be put into production. A technical workflow is constructed, including data filling, evaluation model construction, and production feasibility evaluation. Based on the workflow, the missing data is filled in by the Miceforest imputation algorithm first. The evaluation model is established based on the Random Forest model to quantitatively predict the feasibility of the mining project being put into production, and important features of the model are extracted using Shapley Additive explanation(SHAP). This workflow may enhance the efficiency and accuracy of quantitative production feasibility evaluation for mining projects, with an accuracy rate increased from 93.80% to 95.99%. Results suggest that the features of estimated mine life and gold ore grade have the most significant impact on production feasibility. Full article
(This article belongs to the Special Issue Recent Advances in Smart Mining Technology, Volume II)
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19 pages, 5586 KiB  
Article
Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety
by Heonmoo Kim and Yosoon Choi
Appl. Sci. 2023, 13(6), 3717; https://doi.org/10.3390/app13063717 - 14 Mar 2023
Cited by 3 | Viewed by 2104
Abstract
To improve the working conditions in underground mines and eliminate the risk of human casualties, patrol robots that can operate autonomously are necessary. This study developed an autonomous patrol robot for underground mines and conducted field experiments at underground mine sites. The driving [...] Read more.
To improve the working conditions in underground mines and eliminate the risk of human casualties, patrol robots that can operate autonomously are necessary. This study developed an autonomous patrol robot for underground mines and conducted field experiments at underground mine sites. The driving robot estimated its own location and autonomously operated via encoders, IMUs, and LiDAR sensors; it measured hazards using gas sensors, dust particle sensors, and thermal imaging cameras. The developed autonomous driving robot can perform waypoint-based path planning. It can also automatically return to the starting point after driving along waypoints sequentially. In addition, the robot acquires the dust and gas concentration levels along with thermal images and then combines them with location data to create an environmental map. The results of the field experiment conducted in an underground limestone mine in Korea are as follows. The O2 concentration was maintained at a constant level of 15.7%; toxic gases such as H2S, CO, and LEL were not detected; and thermal imaging data showed that humans could be detected. The maximum dust concentration in the experimental area was measured to be about 0.01 mg/m3, and the dust concentration was highly distributed in the 25–35 m section on the environmental map. This study is expected to improve the safety of work by exploring areas that are dangerous for humans to access using autonomous patrol robots and to improve productivity by automating exploration tasks. Full article
(This article belongs to the Special Issue Recent Advances in Smart Mining Technology, Volume II)
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