Understanding Ore Heterogeneity: In-situ and in Broken Ore

Sampling Australia 2013 - May 14 - 16

1

Understanding Ore Heterogeneity:

In-situ and in Broken Ore Dr Geoff Lyman

Materials Sampling & ConsultingSouthport, Queensland


2

Overview

• What is heterogeneity?• How do we quantify heterogeneity?• How does it impact exploration?• How does it impact mining and

beneficiation?• How does it impact analytical procedures?

• How to control the impact• Tools to evaluate heterogeneity


3

Heterogeneity

• Heterogeneity must be defined with respect to a particular analyte – a mineral phase or an elemental concentration

• In a broken ore:– particles vary in size, composition and density– it is the extent of variation in composition from

one particle to the next that controls the extent of heterogeneity

– we call this type of heterogeneity intrinsic heterogeneity


4

Intrinsic Heterogeneity

Size 1 Size 2 Size 3

Size 4 Size 5 Size 6The extent of liberation of the target phase increases as particle size

decreasesand so does the heterogeneity

pure ‘other’

pure ‘targe

t’


5


• Quantification of particulate heterogeneity– In statistics, we measure variability using the

variance of a probability distribution

2var x p x x x dx x x


6


• Quantification of particulate heterogeneity– within a size fraction, we measure intrinsic

heterogeneity much the same way, except we include particle density

1 2

0

L

L

a aIH y a aa

2

1

CN

j Lj j

Lj

a aIH y

a


7


• Quantification of particulate heterogeneity– For a sample as a whole, the intrinsic

heterogeneity is characterised uniquely by a ‘sampling constant’,

– The sampling constant has units of mass (grams)

SK

12

1 1

S

S C

N

S i i iiN N

ij Li i ij ij

Li j

K x v IH

a ax v y

a

22

varS S

SL

a KMa


8


• Quantification of particulate heterogeneity

1

2

1

S C

ij Lij ij

Li

N N

Si j

iKa

va

yxa

mass fraction in the size fraction

average volume of a particle in

the size fraction

IH of the material in the size fraction

The particle size effect is very important: it is usually the top 2 or 3 size fractions that control the value of the sampling constant

Potential problem: dilution of ore by waste


9


• Quantification of particulate heterogeneity– The value of the sampling constant will vary

from one analyte to the next– mineral phases present in low concentration

generally have larger sampling constants• nuggetty gold is a clear example of this

– the mineral phase with the highest sampling constant will usually control sampling protocol design, unless degraded precision for that phase can be tolerated


10


• Gy’s Simplified Formula– The simplified formula

is

• the critical factor is the determination of the liberation factor l.

• The plot at the right shows that this can be a complex issue and that simplifications can misleading

395SK cfg d

data of Pedro Carrasco (Codelco)


11

Heterogeneity

• Sampling a process stream or ROM ore– When we sample a process stream or use an

on-line analyser on the stream, we see deviations from the average grade for the analyte


12

Distributional Heterogeneity

• Sampling a process stream or ROM ore– The type of heterogeneity exhibited is called

distributional heterogeneity as it is a variation in time or position along the conveyor belt

– To statistically characterise the variation, we use a variogram or a covariance function

– With the variogram, we can calculate how often we need to sample the process stream in order to control the sampling variance due to distributional heterogeneity


13

Distributional Heterogeneity• Sampling a process stream or ROM ore– The shorter the variogram range, the shorter

the time between increments to control the variance


14

Heterogeneity

• Sampling a process stream or ROM ore– When sampling a particulate stream, the total

variance is the sum of the distributional heterogeneity variance and all intrinsic heterogeneity variances from the primary sample right down to the selection of the analytical aliquot plus the analysis variance

1 2

var var var var var var LaliquotS DH IH IH IH analysisa


15

In-situ Heterogeneity

• In-situ Heterogeneity (in the ore body)– The total picture of variability is captured in the

variogram and variability is a combination of distributional heterogeneity and intrinsic heterogeneity

Sill

Nugget

Range


16


• The nugget variance has two parts– The variance of sample preparation and

analysis when the core is split and crushed for assaying• if the preparation protocol is inappropriate, this

variance can be high and dominate the nugget variance

– The ‘geological’ component of nugget variance• this variance can be conceptualised as the variance

between assays of the 2 splits of the core, after subtraction of the preparation and analysis variance

• this variance depends on the texture of the ore and the size of the core sample

Nugget


17


• The sill – nugget variance– This difference is the spatially regionalised

variance – It is relevant to how densely the ore body

should be sampled to define block grades with an acceptable block estimation variance

– If the nugget variance is high, it makes capture of the regionalisation difficult and makes estimation of the variogram shape and range difficult

Sill - Nugget {


18


• To quantify in-situ heterogeneity, we can consider the following:– choose a standard block size– define an acceptable relative variance for the

block estimation variance,– choose a standard sample size (core diameter

and length)

– determine the total mass of standard samples per block that provides the target relative estimation variance

2 2 x


19

Exploration Impact

• In exploration, there are two objectives:– define the ore body structure/continuity– provide preliminary data for variography which

leads to estimates of the value of the resource• For an critical analyte showing a high nugget

value (for any reason), both objectives are compromised– There is motivation to establish a geological

nugget variance in each domain and reduce it to a reasonable value by choice of sample and core size

– Similarly, sample prep protocol can be optimised


20

Mining and Beneficiation Impact

• Failure to solve heterogeneity issues at exploration will lead to poor planning of infill drilling for the resource model and degrade the block model – this will affect the mine plan and definitions of ore

and waste– the design of the beneficiation plant and pre-plant

ore blending facilities may be adversely affected– project risk increases in a manner that may be

difficult to quantify


21

Impact on Analytical Procedures

• If the heterogeneity of the ore from each domain is well-understood, optimal sample prep protocols can be devised for each domain– sample prep variance can be minimised– the variance due to the intrinsic heterogeneity

of the analytical aliquot in conjunction with the analysis variance can be minimised

– optimal analytical procedures result• optimal aliquot mass• optimal analytical method


22

Controlling Heterogeneity

• For each domain– The intrinsic heterogeneity of the

ore is estimated at a series of material top sizes

– The impact of dilution of ore by waste is assessed

– The sample mass retained at each stage of sample preparation is determined to control the total preparation variance

– This work will support exploration as well

2 21 DH IH1SK

2SK

3SK

4SK

22 IH

23 IH

24 IH

5SK25 IH


23

Tools for Heterogeneity Evaluation

• Until recently, heterogeneity evaluation had to be done by analysis of many replicates– this is expensive and needs to be corrected for

preparation and analytical variance to get the correct value of at a given state of comminution

• It is now possible to use the Qemscan to determine heterogeneity for particle top sizes less than about 5 mm– three or four points on the curve of vs

can be determined first followed by other tests at larger sizes as necessary

SK

3SK d d


24

Tools for Heterogeneity Evaluation

• For low concentrations of analyte:– Qemscan ‘bright phase’ searches can be run– Preconcentration of heavy mineral phases can

be used as well• All the work should be guided by the

mineralogists and geologists

Mineralogy, Texture, Heterogeneity


25

Summary

• Intrinsic heterogeneity impacts all sampling

• Distributional heterogeneity impacts exploration work and sampling from process streams

• A full set of tools exists to evaluate heterogeneity in a quantitative manner– these are assisted by a careful evaluation of

ore textures and domains• Control of data accuracy reduces project

risk

Understanding Ore Heterogeneity: In-situ and in Broken Ore

Documents

Transcript of Understanding Ore Heterogeneity: In-situ and in Broken Ore