Ural State Mining University (Ekaterinburg, Russia).

Kozin V. Z., Head of Chair, Doctor of Engineering Sciences, Professor, gmf.dek@ursmu.ru
Komlev A. S., Senior Researcher, Candidate of Engineering Sciences, tails2002@inbox.ru

Irgiredmet (Irkutsk, Russia):
Stupakova E. V., Head of Department

This paper discuses random errors occurring in processing product sampling, preparation, and assays. Separate identification of variouserror components prevents comprehensive evaluations of test results and targeted modification of the testing technology aimed to reduce the overall error. Random errors are calculated using absolute RMS deviations, while relative random error calculations seem far more reasonable. This article provides formulas for such calculations. It is advisable to combine the initial data into a single table, the format for which is suggested in the article. Sampling point calculation uses certain information about the product tested, namely: the coefficient of variation per shift (batch), the sample preparation procedure, assay subsample data, and the relative error for the measurementtechnique applied. A procedure is shown for establishing the coefficients of variation and the measurement technique error at an operating processing plant using routine testing data. A copper-zinc ore processing circuit, typical for the processing plants of the Urals, is used as an example for calculating random errors in the sampling of the original crushed ore, concentrate, and tailings. It is shown that the sampling error is critical when testing ore to establish the mass fraction of copper and the assay error produces the largest impact when establishing the mass fraction of gold. When sampling tailings and concentrate to determine the mass fraction of copper, sampling and assay errors make approximately equal contributions; when sampling to establish the mass fraction of gold, sample preparation and assay errors make a significant contribution. A comprehensive analysis of the impact produced by each sampling operation allows achieving the desired result in minimizing the random error.

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