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Minerals
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Application of Emerging Technology in Mining Operations
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Application of Emerging Technology in Mining Operations

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6962

Special Issue Editors

Prof. Dr. Bekir Genc

E-Mail Website
Guest Editor
School of Mining Engineering, University of the Witwatersrand, Johannesburg, Johannesburg 2000, South Africa
Interests: coal combustion; energy; mine planning and software
Special Issues, Collections and Topics in MDPI journals
Dr. Samson Bada

E-Mail Website
Guest Editor
DSI-NRF SARChI Clean Coal Technology Research Group, School of Chemical and Metallurgical Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Private Bag X3, Wits 2050, Johannesburg 2006, South Africa
Interests: renewable energy technologies; biomass conversion; clean energy; coal
Dr. Moshood Onifade

E-Mail Website
Guest Editor
Department of Civil and Mining Engineering, University of Namibia, Ongwediva P.O. Box 3624, Namibia
Interests: coal-related research; sustainable mining practices

Special Issue Information

Dear Colleagues,

The most recent technological developments in the mining industry point to a significant change in favour of sustainability. A fully modern, safe, and productive mine that meets the rising demand for extracted resources, while also exceeding consumer expectations and contributing to global sustainability programs, is now more possible than ever thanks to digital technology. Rapid technological change is being implemented in the mining industry in order to reinvent itself. Therefore, mines in the future would not resemble those we see today at all. High-end technology has the ability to open up new avenues for increasing productivity at this pivotal time for this massive industry. Plasma, robotics, and the Internet of Things are making mining a safer and more productive industry. Robotic technology has a lot of potential for the mining industry, despite its very limited use in the world's existing mining operations. Artificial intelligence-powered robots are capable of a variety of tasks, including drilling, blasting, loading, carrying, fastening mine roofs, ore sampling, and rescuing trapped miners. Numerous academic institutions and technological firms have created autonomous load haul dump (LHD) vehicles employing robotic technology. A potential technique is the use of robots in rescue operations. By developing new techniques for sustaining mine safety and productivity, the Internet of Things, an emerging network technology built on the fusion of wireless technologies, micro-electromechanical systems (MEMS), and the Internet, has the potential to completely change the mining industry.

This Special Issue is organized into the following sections:

  1. Internet of Things (IoT);
  2. Mine digitization and automation;
  3. Spatial data visualisation;
  4. 3D imaging technologies for mineral exploration;
  5. Application of artificial intelligence in the mineral industry.

This Special Issue aims to see how technology plays a critical role in the mining industry to improve the efficiency of its processes, to reduce costs, and meet the increasing social and environmental concerns among communities and authorities.

Prof. Dr. Bekir Genc
Dr. Samson Bada
Dr. Moshood Onifade
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. 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

  • information technology
  • digital mining
  • Internet of Thing (IoT)
  • machine learning/ artificial intelligence
  • sustainable development goals
  • mine automation, robotics
  • Geographical information system (GIS)

Published Papers (4 papers)

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Research

18 pages, 5446 KiB  
Article
Application of Artificial Neural Network for the Prediction of Copper Ore Grade
by Ntshiri Batlile Tsae, Tsuyoshi Adachi and Youhei Kawamura
Minerals 2023, 13(5), 658; https://doi.org/10.3390/min13050658 - 10 May 2023
Cited by 1 | Viewed by 2246
Abstract
Precise prediction of ore grade is essential in feasibility studies, mine planning, open-pit and underground optimization, and ore grade control. Conventional methods, such as geometric and geostatistical methods, are the most popular techniques for mineral resource estimation but fail to capture the complexity [...] Read more.
Precise prediction of ore grade is essential in feasibility studies, mine planning, open-pit and underground optimization, and ore grade control. Conventional methods, such as geometric and geostatistical methods, are the most popular techniques for mineral resource estimation but fail to capture the complexity of orebodies. Due to this limitation, grades are incorrectly estimated, leading to inaccurate mine plans and costly financial decisions. Here, we propose an ore grade prediction method using an artificial neural network (ANN). We collected 14,294 datasets from the Jaguar mine in Western Australia. The proposed model was developed by incorporating lithology, alteration, eastings, northwards, altitude, dip, and azimuth to predict the grade, and the performance evaluation metrics were measured based on the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), correlation coefficient, R, and coefficient of determination (R2). The proposed ANN model outperformed classic machine learning methods with R2, R, MAE, MSE, and RMSE of 0.584, 0.765, 0.0018, 0.0016, and 0.041, respectively. The Shapley technique was used to evaluate the feature importance of the input variables for the grade prediction. Lithology demonstrated the highest influence on ore prediction, whereas eastings had the least impact on output. The proposed approach is promising for ore model prediction. Full article
(This article belongs to the Special Issue Application of Emerging Technology in Mining Operations)
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22 pages, 13679 KiB  
Article
Distribution Law of Mine Ground Pressure via a Microseismic Sensor System
by Zilong Zhou, Yinghua Huang and Congcong Zhao
Minerals 2023, 13(5), 649; https://doi.org/10.3390/min13050649 - 8 May 2023
Cited by 1 | Viewed by 1362
Abstract
The particularity of the occurrence conditions of the ore body in Xianglushan Tungsten Mine determines the mining form of the ore body and the particularity of the ground pressure distribution after mining. A large number of mined-out areas, supporting pillars, and natural and [...] Read more.
The particularity of the occurrence conditions of the ore body in Xianglushan Tungsten Mine determines the mining form of the ore body and the particularity of the ground pressure distribution after mining. A large number of mined-out areas, supporting pillars, and natural and human factors have formed a comprehensive disaster environment. This can lead to frequent disasters, great harm, serious economic losses, and the necessity of severe environmental protection operations in the mine. This study aims to establish a microseismic monitoring system according to the actual needs of the site and to reveal the law of ground pressure manifestation by analyzing the distribution characteristics of microseismic events; to analyze the occurrence stability of the goaf; further verify it laterally; and finally, demonstrate the feasibility and effectiveness of the microseismic monitoring sensor system. In view of the current ground pressure problem in Xiangxuoshan tungsten mine, the stress change characteristics during dynamic mining and filling were obtained through comparative analysis of different perspectives such as surface change, energy release, and mining loudness, and key areas were identified to improve the reliability of underground ground pressure monitoring. The results show that the process of deposit destabilization caused by ore body mining can be further analyzed by microseismic monitoring, and the combination of surface settlement, mining intensity, and energy release can verify the accuracy of stress distribution and ground pressure transfer. In turn, the general reliability of underground ground pressure hazard warning is empirically improved. Full article
(This article belongs to the Special Issue Application of Emerging Technology in Mining Operations)
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21 pages, 7886 KiB  
Article
Instability Mechanism, Pressure Relief, and Long Anchorage Control Countermeasures for Surrounding Rock of Strong Mining Roadway at Large Mining Height Working Face
by Deyu Qian, Hexi Jiao, Jinping Deng, Jingxuan Yang, Mingzhi Jiao, Guihong Xian, Chenshi Yu, Yingli Zhu, Jiale Liu, Sen Huang and Binyong Li
Minerals 2023, 13(3), 391; https://doi.org/10.3390/min13030391 - 10 Mar 2023
Cited by 1 | Viewed by 1133
Abstract
Double-roadway tunneling could mitigate the contradiction between mining production needs and tunneling speed, which is pivotal to the sustainable development of underground mines. However, it is very difficult to control the stability of a mining roadway on an adjacent working face suffering from [...] Read more.
Double-roadway tunneling could mitigate the contradiction between mining production needs and tunneling speed, which is pivotal to the sustainable development of underground mines. However, it is very difficult to control the stability of a mining roadway on an adjacent working face suffering from strong mining disturbance due to double-roadway tunneling, especially at a large mining height working face. In order to control the stability of the return air roadway (RAR) 23205 of a strong mining roadway at working face 23205 in the Zhuanlongwan Coal Mine in Inner Mongolia, we carried out field monitoring, theoretical analysis, numerical simulations, and engineering practice to identify the main factors influencing the deformations and the stress distribution law of the surrounding rock in order to propose countermeasures for strong mining roadways. The results show the factors influencing the large deformation of strong mining roadways include large mining height, repeated mining, stress concentration due to the large coal pillar, and a small thickness of the anchorage layer in the roof. The stress peak in the central coal pillar caused by the first and second mining is 23.19 MPa and 27.49 MPa, respectively, and the stress concentration coefficients are 4.538 and 5.379, respectively. Countermeasures (pressure relief via large-diameter boreholes in the large coal pillar and long anchorage for roof reinforcement) were created to control the stability of a strong mining roadway, i.e., RAR 23205. Field measurements indicated that deformations in RAR 23205 could be efficiently controlled. The maximum deformation of the surrounding rock was 50 mm, which meets the safety and efficient production requirements of the coal mine. In addition, new roadway layout optimization and control countermeasures are put forward to control the stability of mining roadways. Full article
(This article belongs to the Special Issue Application of Emerging Technology in Mining Operations)
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15 pages, 4675 KiB  
Article
Automatic Cutting Speed Control System of Boom-Type Roadheader Based on Geological Strength Index
by Zheng Dong, Xuhui Zhang, Wenjuan Yang, Mengyu Lei, Chao Zhang, Jicheng Wan and Lei Han
Minerals 2022, 12(12), 1582; https://doi.org/10.3390/min12121582 - 9 Dec 2022
Cited by 2 | Viewed by 1324
Abstract
The boom-type roadheader is the foremost mining equipment in coal mines. At present, the automatic cutting technology is still immature for adjusting cutting speed automatically in accordance with rock strength, resulting in energy dissipation. In this study, we put forward a method with [...] Read more.
The boom-type roadheader is the foremost mining equipment in coal mines. At present, the automatic cutting technology is still immature for adjusting cutting speed automatically in accordance with rock strength, resulting in energy dissipation. In this study, we put forward a method with respect to detecting the geological strength index of coal seam profile through visual inspection, as well as characterize the geological strength index and control the cutting head for adjusting speed automatically based on inspecting fracture features on coal rock’s surface, aiming at achieving energy conservation control of boom-type roadheader. The image processing algorithm is adopted for detecting joint characteristics of palisades fracture, and a quantitative model of the geological strength index is established. The fractal dimension is used to obtain the distribution of geological strength indicators of a coal seam, and the heading machine’s cutting head is controlled for adjusting speed automatically. A vision control platform of boom-type roadheader is built in the laboratory to perform ground simulation experiments. According to experimental results, the difference between the geological strength index of the coal seam detected through visual inspection and the set value in the geological strength index chart is up to 3.5%, and the results are basically consistent, so the quantification of geological strength index can be performed rapidly and effectively. The average energy consumption of boom-type roadheader decreases by 5.4% after adopting self-adaptation control, realizing energy conservation and consumption reduction as well as intelligent control of coal mine machinery. Full article
(This article belongs to the Special Issue Application of Emerging Technology in Mining Operations)
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