TOS forum, Issue 5, p. 137 (2015)

Evaluation of sampling error sources in a multiple cuttermetallurgical sampler

J. Loimia, P. Minkkinenb, C. von Alfthana, J. Lohilahtia, T. Korpelaa
aOutotec, Rauhalanpuisto 9, P.O. Box 1000, FIN-02231 Espoo, Finland, E-mail: [email protected]
bLappeenranta University of Technology PO Box 20, FIN-53851, Lappeenranta, Finland. E-mail: [email protected]

The head loss caused by metallurgical sampling for slurry streams can be significantly reduced by appropriate sampler design. When the process flow is sampled by vertical static cutters before an equal number of moving cutters, the installation requires less installation head space than other sampling arrangements and is easy to accommodate at suitable process locations. Low head loss reduces building costs for the processing plant and operational costs during the life time of the plant. The presence of a possible systematic bias in the particle size distribution or the chemical composition between the vertical static cutters caused by segregation in the metallurgical sampler can be estimated by a designed sampling campaign where sub-samples are cut from each of the moving cutter sample streams simultaneously. The sub-sample assay results can be evaluated by an F-test to reveal if there exists significant variance between the cutter assays. The Minimum Possible Error (MPE) caused by the sampling and analysis system can be estimated in another sampling campaign where spot samples are collected at equal intervals to perform a variographic experiment to characterise process heterogeneity and MPE by estimating the V(0) intercept. The V(0) is the variability of a single measurement and furthers an indication of the minimum sampling variance that can be expected in practice. MPE includes the Fundamental Sampling Error (FSE), the Grouping and Segregation Error (GSE), the Total Analysis Error (TAE) as well as preparation errors and the possible Incorrect Sampling Errors (ISE) perhaps not fully eliminated. In this paper we present an approach to evaluate the various sampling error sources and magnitudes in a multiple cutter metallurgical sampler.

Metrics

(since September 2017)

Downloads:

676