Mineralogy and Petrology

, Volume 113, Issue 4, pp 463–475 | Cite as

Optimizing the sampling protocols for aluminum ores—a new approach

  • Daniel Armelim BortoletoEmail author
  • Ana Carolina Chieregati
  • Raiza Cavalcante de Oliveira
Original Paper


Controlling the consistency of aluminium ore concentrate is difficult when there is no proper sampling protocol in place. A proper sampling strategy is necessary to obtain the data for effective quality assurance and quality control. Inputs from several disciplines, including statistics, geostatistics, geology, and sampling, are necessary to establish realistic goals for assessing the accuracy and precision levels regarding the main constituents of aluminum ore, namely available alumina and reactive silica. Selecting an appropriate sampling protocol requires the estimation of inherent heterogeneities and the application of the theory of sampling (Gy 1976) to minimize all sources of bias. The international sampling standards (AS 2806.1 2003; AS 2806.3 2001; AS 2806.4 2004; AS 2806.5 2003; AS 2806.6 2003 and AS 2806.7 2004) describe tests to evaluate precision and accuracy in order to improve data reliability; however, these tests can still be improved upon. This study outlines a proposal for optimizing the sampling protocols used when mining aluminum ore; it employs current standard procedures, Pierre Gy’s Theory of Sampling (Gy 1979), as well as general geostatistical concepts (David 1977).


Aluminum ore Sampling protocol Constitutional heterogeneity Geostatistics International standards 



The authors thank the Department of Mining and Petroleum Engineering, University of São Paulo/SP, for providing the resources needed to conduct this research.


  1. Albuquerque OR (1922) Reconhecimentos geológicos no Valle do Amazonas: Brasil Serviço Geológico Mineral. Boletim 3:74–83Google Scholar
  2. AS 2806.1 (2003) Aluminium ores – sampling. Part 1: sampling procedures. Standards Australia, Sydney/NSW, 34 ppGoogle Scholar
  3. AS 2806.3 (2001) Aluminium ores – sampling. Part 3: preparation of samples. Standards Australia, Sydney/NSW, 26 ppGoogle Scholar
  4. AS 2806.4 (2004) Aluminium ores – sampling. Part 4: determination of heterogeneity of constitution. Standards Australia, Sydney/NSW, 8 ppGoogle Scholar
  5. AS 2806.5 (2003) Aluminium ores – sampling. Part 5: methods for checking the precision of sampling. Standards Australia, Sydney/NSW, 18 ppGoogle Scholar
  6. AS 2806.6 (2003) Aluminium ores – sampling. Part 6: methods for checking the bias of sampling. Standards Australia, Sydney/NSW, 8 ppGoogle Scholar
  7. AS 2806.7 (2004) Aluminium ores – sampling. Part 7: determination of quality variation. Standards Australia, Sydney/NSW, 10 ppGoogle Scholar
  8. Behrang K, Hooman A, Clayton D (2014) A linear programming model for long-term mine planning in the presence of grade uncertainly and stockpile. Int J Min Sci Technol 24:451–450CrossRefGoogle Scholar
  9. Carrasco P, Jarra E (2004) The economic impact of correct sampling and analysis practices in the copper mining industry. Chemom Intell Lab Syst 74:209–213CrossRefGoogle Scholar
  10. Carvalho A, Boulangé B, Melfi AJ, Lucas Y (1997) Brazilian Bauxites. USP/FAPESP/ORSTROM, São Paulo/SP, 331 ppGoogle Scholar
  11. Chieregati AC (2009) Reconciliação pró-ativa em empreendimentos mineiros. Tese de doutorado, Universidade de São Paulo, São Paulo/BR, 174 ppGoogle Scholar
  12. Costa Neto PLO (2002) Estatística, 2nd edn. Editora Blücher, São Paulo, 280 ppGoogle Scholar
  13. David M (1977) Geostatistical ore reserve estimation. Elsevier Scientific Publishing Company, Amsterdam/NL. Developments in Geomathematics, 364 ppGoogle Scholar
  14. Dennen WH, Norton HA (1977) Geology and geochemistry of bauxite deposits in the lower Amazon Basin. Econ Geol 72:82–89CrossRefGoogle Scholar
  15. Francisco BHR, Loewenstein P (1968) Léxico estratigráfico da região norte do Brasil: Museu Paraense Emílio Goeldi, Belém, Pará, Brasil. Publicação Avulsa 9:17–18Google Scholar
  16. François-Bongarçon D (2004) Theory of sampling and geostatistics: an intimate link. Chemom Intell Lab Syst 74:143–148CrossRefGoogle Scholar
  17. Gy PM (1976) The sampling of particulate materials – a general theory. Int J Miner Process 3:289–312CrossRefGoogle Scholar
  18. Gy PM (1979) Sampling of particulate materials, theory and practice. Elsevier Scientific Publishing Company, Amsterdam/NL. Developments in Geomathematics (4):450Google Scholar
  19. Gy PM (1995) Introduction to the theory of sampling I. Heterogeneity of a population of uncorrelated units. Trends Anal Chem 14(2):67–76CrossRefGoogle Scholar
  20. Gy PM (1998) Sampling for analytical purposes. Wiley, Chichester, 153 ppGoogle Scholar
  21. Koyama IK, Chieregati AC, Eston SM (2010) Teste de heterogeneidade como método de otimização de protocolos de amostragem. Brasil Mineral, n.299, Brazil, pp 63–68Google Scholar
  22. Petersen L, Dahl CK, Esbensen KH (2004) Representative mass reduction in sampling – a critical survey of techniques and hardware. Chemom Intell Lab Syst 74:95–114CrossRefGoogle Scholar
  23. Pitard FF (1993) Pierre Gy’s sampling theory and sampling practice: heterogeneity, sampling correctness, and statistical process control, 2nd edn. CRC Press, Boca Raton, 488 ppGoogle Scholar
  24. Pitard FF (2009) Pierre Gy’s theory of sampling and C.O. Ingamells’ poisson process approach. PhD Thesis, Aalborg University, Aalborg, 309 ppGoogle Scholar
  25. Pitard FF (2015) Theory of sampling as applied to process control. Short Course handout, 7th World Conference on Sampling and Blending, 69 ppGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mining and Petroleum EngineeringUniversity of São PauloSão Paulo cityBrazil
  2. 2.Technology InstituteFederal University of ParáBelém cityBrazil

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