Estimating error in measurements of mineral grain size distribution

Evans, C. L. and Napier-Munn, T. J. (2013) Estimating error in measurements of mineral grain size distribution. Minerals Engineering, 52 198-203. doi:10.1016/j.mineng.2013.09.005


Author Evans, C. L.
Napier-Munn, T. J.
Title Estimating error in measurements of mineral grain size distribution
Journal name Minerals Engineering   Check publisher's open access policy
ISSN 0892-6875
1872-9444
Publication date 2013
Sub-type Article (original research)
DOI 10.1016/j.mineng.2013.09.005
Open Access Status
Volume 52
Start page 198
End page 203
Total pages 6
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Collection year 2014
Language eng
Abstract When ore characteristics such as mineral grain size distributions are quantified using measurements on particulate samples there is an error associated with the measured values. The magnitude of this error is a function of the grade of mineral of interest, the texture of the ore and the number of ore particles measured in the analysis. In practice the desire to minimise the error due to sampling by increasing the number of particles measured must be balanced against the increase in time and cost of analysing this increased number of particles. A statistical method based on bootstrap resampling has been developed to estimate the error in measurements of textural characteristics which are quantified by automated mineralogy systems. An application of the method to estimate the error in measurements of mineral grain size distribution is presented; however, the method can equally be applied to estimate the error in other textural characteristics, for example mineral association. By estimating how the error in the characteristic of interest reduces as particle sample size increases, the bootstrap resampling approach assists mineralogists to identify how many particles must be analysed to achieve the desired variance in the measured value. Examples from a copper porphyry ore are presented to illustrate the practical applications of this methodology in quantitative mineralogy programmes.
Keyword Mineralogy
Ore characteristics
Statistics
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online: 2 October 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Julius Kruttschnitt Mineral Research Centre Publications
Official 2014 Collection
Sustainable Minerals Institute Publications
 
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Created: Tue, 14 Jan 2014, 12:57:50 EST by Karen Holtham on behalf of Julius Kruttschnitt Mineral Research Centre