The application of sampling theory in bauxite protocols

Bortoleto, Daniel Armelim; Chieregati, Ana Carolina; Pereira, Antônio Henrique Rietra; Oliveira, Raiza Cavalcante

doi:10.1590/s0370-44672014000200014

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…A number of publications discuss the relative pros and cons of the heterogeneity test work [13][14][15][18][19][20][21][22][23][24][25][26] using the simplified method for IH L estimation described by Pitard (Chapter 11) [3]. It is not the authors' intention to detail or judge the theoretical fundamentals of this approach.…”

Section: Calculation Of Ih Lmentioning

confidence: 99%

An Automated Approach to the Heterogeneity Test for Sampling Protocol Optimization

Prado,

Chieregati,

Dominy

2024

Minerals

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The fundamental sampling error is one of the sampling errors defined by Pierre Gy’s Theory of Sampling and is related to the constitution heterogeneity of the mineralisation. Even if a sampling procedure is considered ideal or perfect, this error will still exist and, therefore, cannot be eliminated. A key input into Gy’s fundamental sampling error equation is intrinsic heterogeneity. The intrinsic heterogeneity of a fragmented lot can be estimated by the “calibrated” formula of Gy, which can be written as a function of the sampling constants K and α. These constants can be calibrated by the standard heterogeneity test, originally developed by Pierre Gy and Francis Pitard. This method is based on the selection of rock fragments, individually and randomly, in an equiprobabilistic way from a lot of particulate material, aiming to estimate the intrinsic heterogeneity of the lot. This test, in addition to demanding time and space, can be influenced by human biases, and is difficult to quantify or measure. Aiming to simplify the test execution and eliminate the variance generated by human biases, a prototype called the intrinsic heterogeneity tester was developed as an automated alternative for heterogeneity testing. This prototype selects fragments from a falling stream, one by one, by means of a predefined laser count. To evaluate the prototype, a study was carried out, using painted chickpeas to simulate mineralisation grades and, sequentially, processing the same lot in the intrinsic heterogeneity tester prototype several times. The statistical and mineral content analysis, and comparisons between the intrinsic heterogeneity tester and the standard heterogeneity test sampling constants and constitution heterogeneities were undertaken. As a result, the authors conclude that the intrinsic heterogeneity tester prototype can be used as an alternative to the manual selection of individual fragments and for estimating the intrinsic heterogeneity of particulate material lots to support sampling protocol optimization.

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Section: Calculation Of Ih Lmentioning

confidence: 99%

An Automated Approach to the Heterogeneity Test for Sampling Protocol Optimization

Prado,

Chieregati,

Dominy

2024

Minerals

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“…Research describes the experimental procedures for the calibration sampling parameters i.e. segregation-free analysis, heterogeneity test, and sampling tree experiments [17]. The calibration is necessary to reduce the sampling error caused by minimum representative sample masses.…”

Section: Composite Sampling Applicationsmentioning

confidence: 99%

Upgraded Analytical Protocols in Bauxite Refining Industry Using Composite Sampling Approach to Minimize Laboratory Analysis Load

AlDossari¹

2023

MSA

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The laboratories in the bauxite processing industry are always under a heavy workload of sample collection, analysis, and compilation of the results. After size reduction from grinding mills, the samples of bauxite are collected after intervals of 3 to 4 hours. Large bauxite processing industries producing 1 million tons of pure aluminium can have three grinding mills. Thus, the total number of samples to be tested in one day reaches a figure of 18 to 24. The sample of bauxite ore coming from the grinding mill is tested for its particle size and composition. For testing the composition, the bauxite ore sample is first prepared by fusing it with X-ray flux. Then the sample is sent for X-ray fluorescence analysis. Afterwards, the crucibles are washed in ultrasonic baths to be used for the next testing. The whole procedure takes about 2 -3 hours. With a large number of samples reaching the laboratory, the chances of error in composition analysis increase. In this study, we have used a composite sampling methodology to reduce the number of samples reaching the laboratory without compromising their validity. The results of the average composition of fifteen samples were measured against composite samples. The mean of difference was calculated. The standard deviation and paired t-test values were evaluated against predetermined critical values obtained using a two-tailed test. It was found from the results that paired test-t values were much lower than the critical values thus validating the composition attained through composite sampling. The composite sampling approach not only reduced the number of samples but also the chemicals used in the laboratory. The objective of improved analytical protocol to reduce the number of samples reaching the laboratory was successfully achieved without compromising the quality of analytical results.How to cite this paper: AlDossari, F.M.

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“…The typical lithological profile of the deposit 2, from top to bottom, is described as in Bortoleto et al (2014):…”

Section: N > 10 Qpmentioning

confidence: 99%

“…• -38.1 mm + 25.4 mm; • -25.4 mm + 12.7 mm; • -12.7 mm + 6.3 mm; • -6.3 mm + 1.2 mm. The four size fractions were used for the bauxite samples of Barro Alto and Juruti (Bortoleto et al, 2014) and the last three for the aluminum ore from Poços de Caldas (Alves et al, 2020).…”

Section: Deposit 2 State Of Parámentioning

confidence: 99%

Comparison between K and alpha sampling constants with the Pierre Gy’s factors for bauxite

Marques

Chieregati

2023

REM, Int. Eng. J.

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Sampling is an important tool for gathering information about mineral deposits, helping to estimate the ore grade, and has great relevance at any stage of a mining project, to ensure reliable results and consequently, to project a better mining planning. There are several errors to which the sampling is exposed and among them we can mention the fundamental sampling error (FSE). This error cannot become zero as it is related to the constitutional heterogeneity of the ore, so it represents the minimum sampling error. This study applies Pierre Gy's theory of sampling (TOS) to estimate the values of K and α sampling constants at different deposits of bauxite in Brazil and compare them with the calculated factors, then calculate the relative standard deviation of the fundamental sampling error s(FSE) based on the results of the heterogeneity test (HT) in each deposit. The deposits are located in: Barro Alto -State of Goiás, Juruti -State of Pará, and Poços de Caldas -State of Minas Gerais. The weathering process is the main reason of the alteration of the physical and chemical factors diversifying bauxites in Brazil and worldwide. However, when sampling is carried out correctly, the Gy's formula becomes a powerful tool for improving mining processes as long as the factors are estimated correctly for each ore.

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The application of sampling theory in bauxite protocols

Cited by 5 publications

References 1 publication

An Automated Approach to the Heterogeneity Test for Sampling Protocol Optimization

An Automated Approach to the Heterogeneity Test for Sampling Protocol Optimization

Upgraded Analytical Protocols in Bauxite Refining Industry Using Composite Sampling Approach to Minimize Laboratory Analysis Load

Comparison between K and alpha sampling constants with the Pierre Gy’s factors for bauxite

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