Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/125628
Citations
Scopus Web of ScienceĀ® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAddo Junior, E.en
dc.contributor.authorMetcalfe, A.en
dc.contributor.authorChanda, E.en
dc.contributor.authorSepulveda, E.en
dc.contributor.authorAdeli, A.en
dc.date.issued2019en
dc.identifier.citationProceedings of the Geometallurgy Conference 2018, as published in Journal of the Southern African Institute of Mining and Metallurgy, 2019 / vol.119, iss.4, pp.339-346en
dc.identifier.issn2411-9717en
dc.identifier.issn2225-6253en
dc.identifier.urihttp://hdl.handle.net/2440/125628-
dc.descriptionPublished: April 2019en
dc.description.abstractThe accurate modelling of geometalhirgical data can significantly improve decision-making and help optimize mining operations. This case study compares models for predicting copper recovery from three indirect test measurements that are typically available, to avoid the cost of direct measurement of recovery. Geometallurgical data from 930 drill core samples, with an average length of 19 m, from an orebody in South America have been analysed. The data includes copper recovery and the results of three other tests: Bond mill index test; resistance to abrasion and breakage index; and semi-autogenous grinding power index test. A genetic algorithm is used to impute missing data at some locations so as to make use of all 930 samples. The distribution of the variables is modelled with D-vine copula and predictions of copper recovery are compared with those from regressions fitted by ordinary least squares and generalized least squares. The D-vine copula model had the least mean absolute error.en
dc.description.statementofresponsibilityE. Addo Jr, A.V. Metcalfe, E.K. Chanda, E. Sepulveda, W. Assibey-Bonsu, and A. Adelien
dc.language.isoenen
dc.publisherSouth African Institute Mining and Metallurgyen
dc.rightsAll the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License (Attribution 4.0 International (CC BY 4.0))en
dc.source.urihttps://www.saimm.co.za/publications/journal-papersen
dc.subjectcopula; geometallurgy; modelling; regression; miningen
dc.titlePrediction of copper recovery from geometallurgical data using D-vine copulasen
dc.typeConference paperen
dc.identifier.rmid0030121261en
dc.contributor.conferenceGeometallurgy Conference (07 Aug 2018 - 09 Aug 2018 : Cape Town, South Africa)en
dc.identifier.doi10.17159/2411-9717/319/2019en
dc.identifier.pubid473683-
pubs.library.collectionCivil and Environmental Engineering publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidAddo Junior, E. [0000-0002-5046-8934]en
dc.identifier.orcidMetcalfe, A. [0000-0002-7680-3577]en
Appears in Collections:Civil and Environmental Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_125628.pdfPublished version609.69 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.