Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/125628
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Type: Conference paper
Title: Prediction of copper recovery from geometallurgical data using D-vine copulas
Author: Addo Junior, E.
Metcalfe, A.
Chanda, E.
Sepulveda, E.
Adeli, A.
Citation: Proceedings of the Geometallurgy Conference 2018, as published in Journal of the Southern African Institute of Mining and Metallurgy, 2019 / vol.119, iss.4, pp.339-346
Publisher: South African Institute Mining and Metallurgy
Issue Date: 2019
ISSN: 2411-9717
2225-6253
Conference Name: Geometallurgy Conference (07 Aug 2018 - 09 Aug 2018 : Cape Town, South Africa)
Statement of
Responsibility: 
E. Addo Jr, A.V. Metcalfe, E.K. Chanda, E. Sepulveda, W. Assibey-Bonsu, and A. Adeli
Abstract: The 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.
Keywords: copula; geometallurgy; modelling; regression; mining
Description: Published: April 2019
Rights: All 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))
RMID: 0030121261
DOI: 10.17159/2411-9717/319/2019
Published version: https://www.saimm.co.za/publications/journal-papers
Appears in Collections:Civil and Environmental Engineering publications

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