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Mining
Special Issues
Mine Automation and New Technologies
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Mine Automation and New Technologies

A special issue of Mining (ISSN 2673-6489).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 18579

Special Issue Editors

Dr. Roohollah Shirani Faradonbeh

E-Mail Website
Guest Editor
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia
Interests: machine learning (ML) algorithms; artificial intelligence (AI); mine automation; rock mechanics; deep mining; static and dynamic response of rocks; rockburst; blasting operation
Dr. Robert Solomon

E-Mail Website
Guest Editor
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia
Interests: mining productivity; energy transition; data analysis; simulation modelling
Dr. Phillip Stothard

E-Mail Website
Guest Editor
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia
Interests: advanced mixed reality simulation; mine automation; mine simulation

Special Issue Information

Dear Colleagues,

Digitalisation and higher levels of automation will significantly affect mining efficiency and change the role of people working within the mining industry and elsewhere. Mine automation plays a critical role in eliminating or reducing workers’ exposure to hazardous areas in surface and underground mines. It has the potential to improve safety, reduce carbon emissions, and ensure mine sustainability. In this regard, during the last decade, significant advancements in adapting state-of-the-art communication systems, robots, sensor technology, and remotely controlled systems to the mining industry have been made.

On the other hand, due to the world's rapid economic development and depletion of conventional mineral resources, an emerging trend towards unconventional/alternative mining methods, such as space mining, deep-sea mining, brine mining, urban mining, in situ leaching, and deep underground mining, has appeared in the mining industry and academic environment globally. 

This Special Issue welcomes state-of-the-art contributions to mine automation and alternative mining methods within the scope of the following topics:

  1. Artificial intelligence— application of novel and robust machine learning algorithms for solving high-complex non-linear problems;
  2. Communication systems—wired, wireless, and hybrid communication systems, IoT, ICT, etc.;
  3. Automation—autonomous and remotely controlled mining systems, unmanned aerial vehicles (UAVs), robots, etc.;
  4. Sensor technology—collision avoidance systems, hazard monitoring systems, tracking systems, etc.;
  5. Simulation technologies—virtual reality (VR), augmented reality (AR), mixed reality (MR), metaverse, etc.;
  6. Mine Electrification—carbon emission reduction techniques, battery electric vehicles (BEVs), battery charging technologies, mine sustainability, etc.;
  7. Future/alternative mining methods—deep underground mining, deep-sea mining, brine mining, in situ leaching, urban mining, space mining, etc.

Dr. Roohollah Shirani Faradonbeh
Dr. Robert Solomon
Dr. Phillip Stothard
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. Mining is an international peer-reviewed open access quarterly 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 1000 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

  • future mining methods
  • automation
  • sustainability
  • electrification
  • artificial intelligence
  • simulation
  • sensors

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

17 pages, 1665 KiB  
Article
Enhancing Comminution Process Modeling in Mineral Processing: A Conjoint Analysis Approach for Implementing Neural Networks with Limited Data
by Carlos Moraga, César A. Astudillo, Rodrigo Estay and Alicia Maranek
Mining 2024, 4(4), 966-982; https://doi.org/10.3390/mining4040054 - 21 Nov 2024
Viewed by 551
Abstract
Mineral processing is a crucial stage in the mining process, involving comminution and concentration stages. Comminution is modeled using various ore variables and operational parameters, representing a complex system. An alternative to simplifying the complexity of these stages is adopting machine learning (ML) [...] Read more.
Mineral processing is a crucial stage in the mining process, involving comminution and concentration stages. Comminution is modeled using various ore variables and operational parameters, representing a complex system. An alternative to simplifying the complexity of these stages is adopting machine learning (ML) techniques; however, ML often requires a substantial amount of data for effective training and validation. The conjoint analysis methodology was used to develop a procedure for discretizing input variables and reducing the data needed for training neural networks, requiring only 77 different scenarios. Using the results from a comminution plant simulator built in Matlab Simulink, neural networks were trained to predict the key output parameters, such as the water consumption, energy consumption, operational parameters, and particle size generated by the plant. The predictive capability of the neural networks was excellent, achieving R2 > 0.99 in all cases. The networks were tested with a new set of scenarios to assess their response to values not categorized in the discretization process, achieving R2 > 0.98. However, the prediction capability was lost for out-of-range input variables. This approach is attractive for developing easy-to-implement ML tools capable of representing complex systems without needing large amounts of input data, thereby simplifying the modeling process in mineral processing. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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13 pages, 1633 KiB  
Article
Autonomous and Operator-Assisted Electric Rope Shovel Performance Study
by Ali Yaghini, Robert Hall and Derek Apel
Mining 2022, 2(4), 699-711; https://doi.org/10.3390/mining2040038 - 10 Nov 2022
Cited by 2 | Viewed by 3504
Abstract
Automation has been changing the mining industry for the past two decades. Material handling is a critical task in a mining operation, and truck-shovel handling systems are the primary method for surface mining. Mines have deployed autonomous trucks, and their positive impact on [...] Read more.
Automation has been changing the mining industry for the past two decades. Material handling is a critical task in a mining operation, and truck-shovel handling systems are the primary method for surface mining. Mines have deployed autonomous trucks, and their positive impact on both production and safety has been reported. This paper aims to study the extent to which autonomous and operator-assisted loading units could improve different aspects of a mining operation. Four different levels of automation ranging from operator-assisted swing and return to fully autonomous for a shovel were considered. A discrete event simulation model was developed and verified using detailed data from a shovel monitoring system. Later, the developed model was deployed to assess how each of the proposed technologies could improve productivity and efficiency. Results show that up to a 41% increase in production can be achieved. Both mining companies and equipment manufacturers can use the methodology and results of this study for future decision-making and product development. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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14 pages, 3077 KiB  
Article
Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine
by Pouya Nobahar, Yashar Pourrahimian and Fereidoun Mollaei Koshki
Mining 2022, 2(3), 528-541; https://doi.org/10.3390/mining2030028 - 24 Aug 2022
Cited by 10 | Viewed by 3636
Abstract
This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best [...] Read more.
This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. The performance of this method was studied at the Zenouz kaolin mine to optimize the type of loader and the number of trucks used to supply the processing plant’s ore demands. Accordingly, five years’ data, such as dates, weather conditions, number of trucks, routes, loader types, and daily hauled ore, were collected, adapted, and processed to train the following five practical algorithms: linear regression, decision tree, K-nearest neighbour, random forest, and gradient boosting algorithm. By comparing the results of the algorithms, the gradient boosting decision tree algorithm was determined to be the best fit and predicted test data values with 85% accuracy. Subsequently, 11,322 data were imported into the machine as various scenarios and daily hauled minerals as output results were predicted for each working zone individually. Finally, the data which had the minimum variation from the selected required scheduled value, and its related data concerning loader type and the number of demanded trucks, were indicated for each day of the working year. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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11 pages, 4875 KiB  
Article
Fragmentation Size Distribution Measurement by GNSS-Aided Photogrammetry at Real Mine Site
by Hisatoshi Toriya, Zedrick Paul L. Tungol, Hajime Ikeda, Narihiro Owada, Hyong Doo Jang, Tsuyoshi Adachi, Itaru Kitahara and Youhei Kawamura
Mining 2022, 2(3), 438-448; https://doi.org/10.3390/mining2030023 - 24 Jun 2022
Cited by 6 | Viewed by 2713
Abstract
In mining operations that employ explosives and mineral processing, one of the important factors for efficient and low-cost operation is the fragmentation size distribution of rock after it has been blasted. Automatic scaling is a critical component of fragmentation size distribution measurement as [...] Read more.
In mining operations that employ explosives and mineral processing, one of the important factors for efficient and low-cost operation is the fragmentation size distribution of rock after it has been blasted. Automatic scaling is a critical component of fragmentation size distribution measurement as it will directly determine the accuracy of the size estimation. In this study, we propose a method to create a system for creating a scaled 3D CG model, without the use of ground truth data such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using positional data such as GNSS (Global Navigation Satellite System)-aided photogrammetry. We confirmed the validation of the method through an experimental evaluation of actual muckpiles. The results showed evidence of improving the scaling aspect of 3D fragmentation measurement systems without using GCPs or manual scales, specifically in surface mines where GNSS data are available. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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18 pages, 4569 KiB  
Article
GIS-Based Subsurface Analysis and 3D Geological Modeling as a Tool for Combined Conventional Mining and In-Situ Coal Conversion: The Case of Kardia Lignite Mine, Western Greece
by Pavlos Krassakis, Konstantina Pyrgaki, Vasiliki Gemeni, Christos Roumpos, Georgios Louloudis and Nikolaos Koukouzas
Mining 2022, 2(2), 297-314; https://doi.org/10.3390/mining2020016 - 10 May 2022
Cited by 10 | Viewed by 5966
Abstract
The development of three-dimensional geological models has proven to be critical for conceptualizing complex subsurface environments. This is crucial for mining areas due to their various hazards and unstable conditions. Furthermore, three-dimensional (3D) models can be the initial step for the development of [...] Read more.
The development of three-dimensional geological models has proven to be critical for conceptualizing complex subsurface environments. This is crucial for mining areas due to their various hazards and unstable conditions. Furthermore, three-dimensional (3D) models can be the initial step for the development of numerical models in order to support critical decisions and sustainable mining planning. This paper illustrates the results and the development phases of a 3D geological model within the boundaries of the Kardia lignite deposit in western Macedonia, Greece. It also highlights the usefulness of a Geographic Information System (GIS) methodology in the subsurface geological and hydrogeological analysis regarding the Underground Coal Gasification (UCG) methodology. In addition, the work focuses on the integrated geospatial framework that was developed to support the Coal-to-Liquids Supply Chain (CLSC) integration in unfavorable geological settings. A 3D subsurface geological model of the study area was developed to identify a suitable area for in situ coal conversion and UCG considering criteria related to specific coal thickness and depth. In this context, the suggested integrated geomodelling workflow can positively contribute to the implementation of conventional and innovative mining, saving time and reducing the cost to improve the quality of information needed to support decisions related to UCG implementation. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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