Mine Automation and New Technologies, 2nd Edition

Special Issue Editor


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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
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Special Issue Information

Dear Colleagues,

The digitalisation of the mining industry, coupled with increasing levels of automation, is poised to significantly enhance operational efficiency and redefine the roles of personnel both within and beyond the mining sector. Automation plays a pivotal role in minimising or eliminating workers’ exposure to hazardous environments in both surface and underground mines. It also holds the potential to improve safety, reduce carbon emissions, and promote long-term sustainability in mining operations. Over the past decade, substantial progress has been made in integrating cutting-edge communication systems, robotics, sensor technologies, and remotely operated systems into mining practices.

Simultaneously, the rapid pace of global economic development and the depletion of conventional mineral resources have driven interest in unconventional and alternative mining methods. These include space mining, deep-sea mining, brine extraction, urban/technospheric mining, and in situ leaching—areas that are gaining traction in both industry and academia worldwide.

This Special Issue invites state-of-the-art contributions in the fields of mine automation and alternative mining methods, with a focus on the following topics:

  • Artificial Intelligence: the application of advanced and robust machine learning algorithms to address complex, non-linear mining challenges.
  • Communication Systems: wired, wireless, and hybrid systems, including IoT and ICT technologies.
  • Automation: autonomous and remotely operated mining systems, unmanned aerial vehicles (UAVs), robotics, and related technologies.
  • Sensor Technology: systems for collision avoidance, hazard monitoring, personnel and equipment tracking, and more.
  • Simulation Technologies: the use of virtual reality (VR), augmented reality (AR), mixed reality (MR), and metaverse platforms in mining.
  • Mine Electrification: techniques for reducing carbon emissions, the deployment of battery electric vehicles (BEVs), battery charging innovations, and strategies for sustainable mining.
  • Future and Alternative Mining Methods: explorations of deep underground mining, deep-sea mining, brine extraction, in situ leaching, urban mining, and space mining.

Dr. Roohollah Shirani Faradonbeh
Guest Editor

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Keywords

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

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 1895 KB  
Article
Modernization of Hoisting Operations Through the Design of an Automated Skip Loading System—Enhancing Efficiency and Sustainability
by Keane Baulen Size, Rejoice Moyo, Richard Masethe, Tawanda Zvarivadza and Moshood Onifade
Mining 2025, 5(4), 62; https://doi.org/10.3390/mining5040062 - 4 Oct 2025
Viewed by 270
Abstract
This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate [...] Read more.
This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies, 2nd Edition)
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21 pages, 2550 KB  
Article
Design and Implementation of an Edge Computing-Based Underground IoT Monitoring System
by Panting He, Yunsen Wang, Guiping Zheng and Hong Zhou
Mining 2025, 5(3), 54; https://doi.org/10.3390/mining5030054 - 9 Sep 2025
Viewed by 1068
Abstract
Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their [...] Read more.
Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their responsiveness during emergencies. To address these limitations, this study presents an underground Internet of Things (IoT) monitoring system based on edge computing. The system architecture is composed of three layers: a perception layer for real-time sensing, an edge gateway layer for local data processing and decision-making, and a cloud service layer for storage and analytics. By shifting computation closer to the data source, the system significantly reduces latency and enhances response efficiency. The system is tailored to actual mine-site conditions. It integrates pressure monitoring for artificial expandable pillars and roof subsidence detection in stopes. It has been successfully deployed in a field environment, and the data collected during commissioning demonstrate the system’s feasibility and reliability. Results indicate that the proposed system meets real-world demands for underground safety monitoring. It enables timely warnings and improves the overall automation level. This approach offers a practical and scalable solution for enhancing mine safety and provides a valuable reference for future smart mining systems. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies, 2nd Edition)
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