Abstract
ABSTRACT The economic importance of cut-off grade (COG) is that it differentiates ore from waste. Therefore, when COG is optimised, the economic value of a mining operation can be maximised. COG is optimised by considering several factors that include economic, geological, and operational parameters. Since uncertainty is inherent in some of these parameters, this paper used grade–tonnage distributions to incorporate grade uncertainty and a machine learning approach for commodity price prediction to account for price volatility when determining COGs. The paper demonstrates how uncertainty in these parameters can be incorporated in developing a dynamic COG policy over the life of mine.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Mining, Reclamation and Environment
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
