Applications of Unmanned Aerial Vehicle and Artificial Intelligence Technologies in Mining from Exploration to Reclamation, Volume II

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 18 April 2024 | Viewed by 2231

Special Issue Editors

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,

Recently, much attention has focused on unmanned aerial vehicle (UAV) technology, to the extent that the term “drone industry” has been created. UAV technology is now widely used in many industries for purposes such as investigation of agricultural crops, observation of weather, relay broadcasting and communication, investigation of the extent of damage during disasters, recognition of traffic flow, and unmanned security. Artificial intelligence (AI) technology is also spreading across all industries so that innovative changes are taking place in the industrial field through the combination of AI and domain knowledge.

UAV and AI technologies have a significant impact on the mining industry. Applications of UAV and AI technologies have been reported in the mining industry for mineral exploration, mine planning and design, mine operation, mineral/metallurgical processing, mine health and safety management, and mine reclamation. This Special Issue (SI) encourages engineers, geologists, metallurgists, educators, students, and researchers to address recent applications of UAV and AI technologies in mining from exploration to reclamation. Original research contributions and reviews providing examples of the improvements brought by UAV and AI technologies 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

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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. Minerals 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

  • unmanned aerial vehicles in mining
  • unmanned aerial systems in mining
  • drone in mining
  • photogrammetry in mining
  • 3D modeling in mining
  • digital surface model in mining
  • artificial intelligence in mining
  • machine learning in mining
  • deep learning in mining
  • soft computing in mining

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Published Papers (2 papers)

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Research

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24 pages, 10349 KiB  
Article
Prediction of Ore Production in a Limestone Underground Mine by Combining Machine Learning and Discrete Event Simulation Techniques
Minerals 2023, 13(6), 830; https://doi.org/10.3390/min13060830 - 19 Jun 2023
Viewed by 948
Abstract
This study proposes a novel approach for enhancing the productivity of mining haulage systems by developing a hybrid model that combines machine learning (ML) and discrete event simulation (DES) techniques to predict ore production. This study utilized time data collected from a limestone [...] Read more.
This study proposes a novel approach for enhancing the productivity of mining haulage systems by developing a hybrid model that combines machine learning (ML) and discrete event simulation (DES) techniques to predict ore production. This study utilized time data collected from a limestone underground mine using tablet computers and Bluetooth beacons for 15 weeks. The collected data were used to train an ML model to predict truck cycle time, and the support vector regression with particle swarm optimization (PSO–SVM) model demonstrated the best performance. The PSO–SVM model accurately predicted cycle time with a mean absolute error (MAE) of 2.79 min, mean squared error (MSE) of 14.29 min2, root mean square error (RMSE) of 3.79 min, and coefficient of determination (R2) of 0.68. The output of the ML model was linked to the DES model to predict ore production for each truck, section, and time period. Verification of the DES model demonstrated its ability to accurately simulate the haulage system in the study area by comparing production logs with the simulation results. This study’s novel approach offers a new method for predicting ore production and determining the optimal equipment combination for each workplace, thus enhancing productivity in mining haulage systems. Full article
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Review

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23 pages, 6672 KiB  
Review
Mine Closure Surveillance and Feasibility of UAV–AI–MR Technology: A Review Study
Minerals 2024, 14(1), 110; https://doi.org/10.3390/min14010110 - 19 Jan 2024
Viewed by 702
Abstract
In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising [...] Read more.
In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising sustainable mining practices. This paper assesses the role of unmanned aerial vehicles (UAVs) in mine closure, exploring sensor technology, artificial intelligence (AI), and mixed reality (MR) applications. Prior research validates UAV efficacy in mining, introducing various deployable sensors. Some studies delve into AI’s use for UAV data analysis, but a comprehensive review integrating AI algorithms with MR methods for mine rehabilitation is lacking. The paper discusses data acquisition methods, repeatability, and barriers toward fully autonomous monitoring systems for mine closure projects. While UAVs prove adaptable with various sensors, constraints such as battery life and payload capacity impact effectiveness. Although UAVs hold potential for AI testing in mine closure studies, these applications have been overlooked. AI algorithms are pivotal for creating autonomous systems, reducing operator intervention. Moreover, MR’s significance in mine closure is evident, emphasising its application in the mining industry. Ultimately, a hybrid UAV–AI–MR technology is not only viable but essential for achieving successful mine closure and sustainable mining practices in the future. Full article
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