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Equipment and Operations Automation in Mining: A Review.

Authors :
Long, Michael
Schafrik, Steven
Kolapo, Peter
Agioutantis, Zach
Sottile, Joseph
Source :
Machines; Oct2024, Vol. 12 Issue 10, p713, 17p
Publication Year :
2024

Abstract

The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this critical industry. A key focus of this paper is the impact of automation on the safety and efficiency of mining operations. Highlighting the successful implementation of Automated Haul Truck Systems (AHSs) in surface mining. Additionally, this paper explores the development of automation in underground mining and its challenges, particularly limitations in communication and localization, which hinder the development and deployment of fully autonomous systems. It also provides an exploration of the challenges associated with widespread automation adoption in mining, including high initial investment costs, concerns about job displacement, and the need for specialized skills and training. Looking toward future advancements in enabling technologies will be critical for furthering automation in mining. Machine learning and AI will play an increasingly critical role in intelligent automation, enabling autonomous systems to adapt to dynamic environments, optimize processes, and make informed decisions. This paper provides a look into human–robot collaboration in the future of mining. As the industry transitions toward greater automation, it is essential to consider the evolving roles of human workers to foster a collaborative work environment. This involves prioritizing human safety, providing adequate training, and addressing concerns about job displacement to ensure a smooth transition toward a more automated future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20751702
Volume :
12
Issue :
10
Database :
Complementary Index
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
180524216
Full Text :
https://doi.org/10.3390/machines12100713