Eliminating Barriers for the Implementation of Automation in the Mining Industry

Details

• Journal Article:
  Mining, Metallurgy & Exploration
• Personal Author:
  Adewuyi, Sefiu O. ; Luxbacher, Kray D. ; Kanagy, David ; Miller, Ben ; Miller, Hugh ; Moats, Michael ; Savit, Mark
• Description:
  The integration of new technologies into U.S. mining operations is necessary for achieving continuous improvement in worker health and safety, as well as improving operational efficiency, but many have questioned why uptake of autonomy in the U.S. mining industry has been slow compared to other nations. Despite extensive research on mining automation, there remains a critical gap in understanding and systematically identifying the barriers to its adoption within the U.S. mining industry and globally. To quantify these barriers, a workshop model of data collection was employed. A total of 708 invitations were extended to professionals to participate in nine different workshops between 2022 and 2024, representing 480 companies, with a total of 146 participants. In addition, online virtual meetings were held with leadership of the U.S. Mine Safety and Health Administration (MSHA), National Institute of Occupational Safety and Health (NIOSH), Society of Mining, Metallurgy and Exploration (SME) and academia, with a total of about 139 participants. Participants categorize major barriers to mining automation into regulation, economics, technology readiness, corporate willingness, and social license. The weighted average of the ranks of these barriers indicates that economics, technology readiness, and regulation are the three most significant barriers to mining automation, contributing 37.9%, 17.4%, and 16.6%, respectively. While the first two significant barriers may be handled by the private sector, removing the regulatory barrier requires government collaboration. The review of the regulation reveals that there are approximately 98 regulations identified as presenting potential barriers to automation or innovation in the U.S. mining industry. Description provided by NIOSH
• Subjects:
  • Artificial Intelligence
  • Mining
  • Occupational Health
  • Safety
• Keywords:
  • Mining industry
  • Automation
  • Sensors
  • Artificial intelligence
  • Emerging technology
  • Regulations
  • Mine workers
  • Mining equipment
  • Worker health
  • Worker safety
  More +
• ISSN:
  2524-3462
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Arizona ; Colorado ; Denver Region OSHA ; Kansas City Region OSHA ; Missouri ; San Francisco Region OSHA
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  22 pdf pages
• Volume:
  43
• Issue:
  3
• NIOSHTIC Number:
  nn:20071728
• Citation:
  Min Metall Explor 2026 Jun; 43(3):3081-3102
• Contact Point Address:
  Sefiu O. Adewuyi, School of Mining Engineering and Mineral Resources, The University of Arizona, Tucson, AZ, 85721, USA
• Email:
  sadewuyi@arizona.edu
• Federal Fiscal Year:
  2026
• Performing Organization:
  Society For Mining, Metallurgy, And Exploration
• Peer Reviewed:
  True
• Start Date:
  2022/09/30
• End Date:
  2024/12/31
• Download URL:
  https://stacks.cdc.gov/view/cdc/259953/cdc_259953_DS1.pdf
• File Type:
  [PDF - 2.07 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:e46c7c479f207baf75ed256eeca328fcba96722b63a25674b242aeefd165667f70824e8e484934a7233226e70f37263d4f6b564e25b9c0cc8241ed5b6eaf0783