The gold processing industry is experiencing change. As free-milling and oxide ores become depleted, more complex polymetallic and refractory ores are being processed, coupled with increasing pressure for stricter environmental compliance. Recent years have also seen a steady reduction in mineral processing and metallurgy graduates and a gradual loss of older operating experience. A contribution to documenting current and future best practice in gold ore processing seems timely.
The focus of this volume is on advances in current gold plant operation, from conception to closure; chapters also cover innovations at the bench and pilot-scale level that would be expected to find commercial application at some stage. Sufficient coverage is also given to the chemistry and engineering aspects. The general principle behind the structure of the volume is that of flowsheeting based on unit operations and applied to a mineralogical classification of gold ore types. From concept to closure, this book covers all unit operations, mineralogies and processes that are relevant to dealing with today's complex orebodies.
Practical experience is vital to the successful development, operation and closure of any operation. The 42 chapters have been contributed by a total of 66 authors and co-authors who are experts from countries spanning the globe, and representing exhaustive practical knowledge covering many disciplines relevant to gold processing.* Current best practice as elucidated by a select panel of experts in the field* Innovations at the bench and pilot-scale level that would be expected to find commercial application at some stage* Mineralogical-based approach to flowsheeting
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Developments in Mineral Processing, Vol. 15, Number Suppl (C), 2005
ISSN: 0167-4528
doi: 10.1016/S0167-4528(05)15001-7
Part I Project Development
I.1 Feasibility Study Management
Chapter 1
Sampling procedures
R.J. Holmes,
CSIRO Minerals, Kenmore, Australia
1 Introduction
It is still surprising that sampling is often not given the attention it deserves and the task is delegated to personnel who do not fully appreciate the significance and importance of sampling. Yet correct sampling and sample processing practices are critical to accurate delineation of ore resources as well as the evaluation and control of subsequent processing operations from mining through to delivery of the final product (FranƧois-BongarƧon and Gy, 2002; Holmes, 2002, 2004). Cost is usually the driving force for this phenomenon, and this can result in major flaws in sampling procedures as well as selection of inappropriate sampling equipment. However, it is false economy, because there is no point in using the latest analytical methods and equipment and spending considerable time and effort estimating resources or attempting to reconcile production figures if the samples presented for analysis are not representative in the first place. If sampling is not carried out correctly, the entire measurement chain is corrupted at the outset and no amount of reanalysis will fix the problem.
2 Sampling Basics
Samples are taken from a range of locations, including percussion and diamond drill holes, blast holes, relevant feed and product streams, trucks, trains and stockpiles. The fundamental rule for correct sampling and sample processing is that all parts of the material being sampled must have an equal probability of being collected and becoming part of the final sample for analysis (Gy, 1982, b; Pitard, 1993), otherwise bias is easily introduced and the sample is not representative. Examples of poor sampling practices include loss of drill cuttings prior to sampling, taking a grab sample from percussion drill chips or the side of a stockpile, or taking a dip sample from a slurry tank. Percussion drill chips are bound to be segregated, so a grab sample will not be representative of all particle sizes. Similarly, coarse particles tend to rill down the side of stockpiles, so samples taken from the side of a stockpile will not be representative of all particle sizes and will not provide information on the material in the main body of the stockpile. Hence, such samples cannot possibly be representative of the whole stockpile; at best samples taken from the side of a stockpile may provide an indication of what was last stacked on the outside of the stockpile. In addition, segregation is typical of particles suspended in a slurry, with a greater proportion of the coarser and denser particles occurring near the bottom of the tank. Hence, it is impossible to take a representative sample of a slurry by taking a dip sample at a fixed depth using a ladle.
2.1 Importance of minimizing bias
The presence of bias is a major problem in sampling, because it does not average out over time. In contrast to poor precision, which can be improved by calculating the average of replicate sampling, sample preparation and analysis, no amount of replicate sampling and analysis will eliminate bias once it is present. The final result is just a more precisely incorrect analytical value. Consequently, minimizing or preferably eliminating biases is in many respects more important than improving precision. Sources of bias that can be eliminated at the outset by correct design of sampling and sample processing systems include sample spillage, sample contamination and incorrect extraction of increments, such as by taking a grab sample. Sources of bias that can be minimized but not completely eliminated include change in moisture content, size degradation and dust loss. Samples for determination of moisture content require special attention and must be extracted as quickly as possible with minimum handling and placed in impervious airtight containers or bags. They should then be returned to the laboratory without delay for weighing and moisture determination and not stored for long periods near the sampling point, particularly when the ambient temperature is high.
2.2 Overall precision
Once bias has been addressed, precision can be assessed by determining the overall variance of the final analysis
by breaking up the sampling (and sample processing) variance into its components for each sampling stage, i.e.,
(Gy, 1982a), as follows:
(1)
where
is the sampling variance for Stage 1,
the sampling variance for Stage i,
the sampling variance for Stage uā1, the second last stage and
the analysis variance, which includes selection of the final test portion.
This approach is particularly useful for designing, optimizing and assessing sampling schemes, because the contribution of each stage to the overall variance can be examined; it already appears in a number of ISO (International Standards Organization) sampling Standards (e.g., ISO 12743, 1998). Once the relative magnitudes of the variance components are known, efforts to reduce the overall variance can be focused in the right area.
3 Components of Sampling Error
A better understanding of the sources of sampling errors and how to eliminate sampling bias and minimize variance is obtained by splitting the total sampling error TEi for a given sampling Stage i into a number of independent components as follows (Gy, 1982a; Pitard, 1993):
(2)
where FEi is the fundamental error, GEi the grouping and segregation error, QE2i the long-range quality fluctuation error, QE3i the periodic quality fluctuation error, WEi the weighting error, DEi the increment delimitation error, EEi the increment extraction error and PEi the preparation error.
This equation is strictly applicable to sampling from moving streams, so not all of the above components of error apply to every sampling situation. For example, the long-range and period quality fluctuation and weighting errors do not apply to percussion and blast-hole sampling, but the fundamental error, increment delimitation, increment extraction and preparation errors do apply. The last three components require special attention, because they can result in the introduction of sampling bias. Fortunately, these biases can be eliminated by the use of correct sampling equipment and procedures. The others are largely random errors that can never be completely eliminated, but they can be reduced to acceptable levels by careful design of the sampling system.
3.1 Preparation error
Preparation errors in the sampling context are non-selective operations without change of mass, such as crushing, grinding, mixing, sample transfer, drying, etc. Typical errors include sample contamination, sample loss (e.g., due to sample spillage), moisture loss and operator mistakes, such as mixing up sample labels. These errors can be eliminated using correct sampling equipment and practices. For example, sample cutters should be covered if necessary to prevent entry of extraneous material in the parked position and moisture samples should be prepared and weighed quickly to avoid change in moisture content. Care also needs to be taken to ensure that the finer particles are not lost in crushers or mills or during sample division due to excessive air flow from dust extraction systems installed in sample preparation laboratories.
3.2 Delimitation and extraction errors
Increment delimitation errors arise from incorrect geometrical definition of the increment to be taken, i.e., not taking a complete column when sampling an ore deposit in situ or incorrect cutter geometry when sampling from a moving stream. Increment extraction errors occur when increments are not fully extracted, i.e., sample material is lost. For example:
ā¢ When sampling an ore deposit using percussion drilling, the drill hole must have parallel sides (correct delimitation) and all the cuttings must be transported to the surface for subsequent sample division (correct extraction).
ā¢ When sampling an ore deposit using diamond drilling, the drill hole must have parallel sides (correct delimitation) and all of the core must be recovered (correct extraction).
ā¢ When sampling a blast hole cone, a complete sector of the cone of cuttings must be selected (correct delimitation) and all the cuttings in the sector collected (correct extraction).
ā¢ When sampling concentrate in a truck using a spear sampler, the spear must be pushed down to the bottom of the truck (correct delimitation) and the complete increment extracted without any loss of concentrate from the spear (correct extraction...
