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ANALYTICAL METHODS
Название On the use of duplicate testing to estimate random errors
DOI 10.17580/or.2019.06.07
Автор Kozin V. Z., Komlev A. S., Stupakova E. V.
Информация об авторе

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

Реферат

Random errors when testing ores and processing products at concentration plants are established experimentally. The method for determining such errors is based on duplicate testing, when the estimate of the root mean square value of the random error is calculated by the range of parallel analyzes. The practical use of the range of duplicate mass fraction values in assessing random sampling errors for a small number of duplicate analyzes under GOST 14180-80 leads to significant random and probable systematic errors. The main cause of duplicate sampling errors is the low probability of large range occurrences, and therefore, for the actual number of duplicate analyzes (ten according to standard), the experimentally obtained variance estimates are underestimated by 20 % and are additionally characterized by relative random errors in the range from +70 to –40 %. Duplicate testing with a large number of ranges used in the calculation (400–800) leads to correct estimates of the testing errors. The recommendations of testing standards and the methods for establishing the sampling errors at enterprises must be changed either to increase the number of ranges used by one or two orders of magnitude or to replace duplicate testing by direct establishment of variances using the existing theoretical formulas.

Ключевые слова Random error, systematic error, duplicate testing, range, variance, standards, error estimation
Библиографический список

1. GOST 14180-80. Non-ferrous metal ores and concentrates. Sampling and sample preparation methods for chemical analysis and moisture determination.
2. GOST 11.004-74. Applied statistics. Rules for determining estimates and confidence bounds for normal distribution parameters.
3. GOST R ISO 5725-1, 2, 3, 4, 5, 6-2002. Accuracy (correctness and precision) of measurement methods and results.
4. Karpenko N. V. Testing and quality control of ore dressing products. Мoscow: Nedra, 1987. 216 p.
5. ISO 9001:2008. Quality management systems. Requirements.
6. Lishchuk V., Lambery P., Lund C. Evaluation of sampling in geometallurgical programs through synthetic deposit model. XXVIII International mineral processing congress. Quebec City, Canada, 2016. Paper ID 378.
7. Glazatov А. N., Tsemekhman L. Sh., Spitsyn N. K., Kazakov А. М., Novikov М. N., Sokolov S. V. The OAO Kolskaya Mine-and-Mill Concentrating Plant classification section overflow sampling methods improvement. Obogashchenie Rud. 2010. No. 3. pp. 35–38.
8. Kozin V. Z., Komlev A. S. Random sampling error experimental determination at processing plants. Obogashchenie Rud. 2017. No. 2. pp. 44–48. DOI: 10.17580/or.2017.02.08.
9. Komlev A. S. Features of the content and application of the requirements of the state standard for methods of sampling and sample preparation of non-ferrous metal ores and concentrates. Scientific basis and practice of processing ores and industrial raw materials: Materials of the XXII International scientific and technical conference. Ekaterinburg: Fort Dialog-Iset', 2017. pp. 111–123.
10. GOST 8.531-2002 GSI. Standard samples of the composition of monolithic and dispersed materials. Methods of homogeneity estimation.
11. GOST 8.532-2002 GSI. Standard samples of the composition of substances and materials. Interlaboratory metrological certification. Content and procedure of works.
12. Pitard F. Correct sampling systems and statistical tools for metallurgical prosesses. XXVII International mineral processing congress. Santiago, Chile, 2014. Chap. 15. p. 1.
13. Kahn H., Antoniassi J., Shimizu V., Uliana D. X-ray diffraction cluster analysis and automated mineralogy of Sossego copper ore, Brazil. XXVII International mineral processing congress. Santiago, Chile, 2014. Chap. 14. pp. 229–239.
14. Usherov A. I., Ishmet'ev E. N., Lyapin A. G., Yamshchikov A. V., Tsygalov A. M. Continuous monitoring of the chemical composition of sulfide copper-zinc ore. Zavodskaya Laboratoriya. 2014. Vol. 80, No. 4. pp. 69–73.
15. Brochot S. Sampling for metallurgical test: how the test results can be used to estimate their confidence level. XXVIII International mineral processing congress. Quebec City, Canada, 2016. Paper ID 438.
16. Davaasambuu D., Erdenetsogt D. Optimization of flotation processes of copper-molybdenum ores based on on-line analysis of mineral composition. Scientific basis and practice of processing ores and industrial raw materials: Materials of the XX International scientific and technical conference. Ekaterinburg: Fort Dialog-Iset', 2015. pp. 52–56.
17. Glazatov A. N., Tsemekhman L. Sh. Development of raw material and product sampling methods with determination of content of non-ferrous and precious metals at concentration and metallurgical enterprises. Part 1. Tsvetnye Metally. 2015. No. 10. pp. 54–59. DOI: 10.17580/tsm.2015.10.09.
18. Bondarenko A. V., Zakharov P. A., Shevelev E. S. Automatic pulp assay system for mining and processing industry. Gornyi Zhurnal. 2016. No. 11. pp. 75–79. DOI: 10.17580/gzh.2016.11.14.
19. Petrov S. V., Bederova L. L., Borozdin А. P. Upon the method of reliable determination of noble metals grades in samples with high occurrence of native metals. Obogashchenie Rud. 2015. No. 4. pp. 44–48. DOI: 10.17580/or.2015.04.08.

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