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The world leader in serving science

AN INNOVATIVE & PRACTICAL

APPROACH TO SAMPLING

OF SLURRIES FOR METTALURGICAL

ACCOUNTING

Bernardo Ossandn Araya

J ess Caro Romero

Thermo Fisher Scientific Chile

23th

November 2006

PROCEMIN 2006

IV Taller Procesamiento de MineralesSantiago, Chile

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Combining Two Industry Leaders

Even Stronger Partner for Customers

Industry transforming

Exciting growth opportunities

World-class capabilities

$2.6 to $9.5 Billion in sales

11,000 to 30,000 employees

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Thermos Analytical Technologies

enable our customers to make the worlda healthier, cleaner and safer place

Drug Discovery

Forensic Science

Environmental TestingQuality Control

Petrochemical

Minerals Mining

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Thermo FisheFull offer to the Mining and Mineral Processing Industry

Lab Software

Solutions

Density Gauges

Lab

Analyzers

Flow Meters

Slurry Analyzers

Conveyor

Sampling & Analysis

Systems

Thickener Interface

Gauge

Slurry Sampling

Systems

Dry Material

Analyzers

Lab Sample

Prep Equipment

Level Gauges

Belt Scales &

Accessories

Solution Analyzers

Product

Solutions

Enviromental

Analyzers

Ai r/Gas/Water

Portable Analyzers

Laboratory

Mine

Environmental

Concentrators

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Metallurgical Sampler

In the Mining operations the following type of samples are collected :

Small samples are required after each shift for material

balancing/accounting

Small samples are requiered intermittently for plant/process control

and for calibration of on-line analyser

Continuous samples are required to feed on-line analyser, particle size

or elemental analysers

Any sampling system being used for Metallurgical accounting must be

accurate & representative (1).

(1).- FF Pitard-Pierre Gys Sampling Theory and Sampling Practice.1993

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The Metallurgical sampling is a process where representative

samples are collected for chemical analysis in laboratory

These analysis results allow to prepare the metallurgical balance of

the plant

The mass conservation law says that metals present in the Feed must

be present in Concentrate and/or Tailings.

All mineral processing plant require metallurgical samplers in order

to determinate the process recovery.

FeedPROCESS

Final Concentrate

Tailings

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There are three types of sampling :

Non probabilistic sampling, where a given part of the lot have has zero

chance of being selected

Probabilistic sampling, where all parts of the lot have a chance to be

part of the sample, and

Equi-Probabilistic sampling, where all parts of the lot have an equal

chance to be part of the sample.

Sampling is correct (ie., unbiased) when, and only when, samplingsystem is equi-probabilistic.

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Traditional Mettalurgical Samplers

Primary SamplerSecondary sampler. Vezin model.

Traditional Samplers

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SamStat:Sampling Station

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SamStat Compliance with Sampling Theory

Each stage complieswith the samplingtheory and reducesthe flow rate by afactor of 20

SamSat uses severalstages to reduce the

slurry stream to a smalland easily manageableflow-rate

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The primary flow enters

the SamSat and getsdirected downwards bythe first baffle

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The bottom opening isquite small and the totalflow gets accelerated andenters the second section

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The high turbulence created

by the increased flow-ratehomogenises the slurry

The baffle of the secondsection directs the flow

upwards

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From the high turbulencearea the flow goes overthe weir and reaches the

sample cutters.

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The cutters are equallydistributed over the width of thesampler to have the same area

of influence and to minimizeGrouping and SegregationError

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Each cutter collects the entireheight of the stream to ensureequal vertical distribution of the

sampled amount

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SamStat Design

The last stage works the sameway as the previous one.

For bigger streams up to20.000m3 additional stagescan be added

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SamStat Design

FlowWeir

HeightTank Width

Baffle

OpeningWeir width

Slurry above

Weir

Head Drop

Across Baf fle

Slurry Height

DS

Slurry

Height in AS

Slurry

above

baffle

opening

Height

of Tank

Freeboard

%

Baffle

Height

Velocity

facing

cutters

Velocity

in DS

Q E D O W S HD H1 H2 H3 HT Fb Bh Vds

m3/s m m m m m m m m m m % m m/s m/s

Velocity

D

Fb Baffle Height Cutter

Slurry level

HD m/s

HT Slurry level

H1 S

Q H3 H2

Vds Weir

O E

Deareation Section Analisis Section

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SamStat Design Sampling Theory

Total Error = Sampling Error + Preparation Error + Analysis Error

Sampling with Mettalurgical quality must be Equi-probabilist icThis means all parts of a the lot have equal chance to be become part of the

sample.

Sampling is correct (ie., unbiased) when, and only when, sampling system

is equi-probabilistic

Total =Sampling2 +Preparation2 +Analysis2

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Errors in Slurry Sampling

Weighted Error (WE): results from non-uniformity of the density or

flow rate-rate of the sampled material

Intregation Error (IE1): results from the long-range distribution

heterogeneity of the sampled material.

Periodic Error(IE2): results from frequent periodic quality

fluctuations of the sampled material.

Fundamental Error (FE): results from the Constitution

Heterogeneity of the sampled material.

Grouping & Segregation Error (GE): results from small-scale

Distribution Heterogeneity of the sampled material.

Delimitation Error (DE): results from an incorrect shape of the

volume boundaries of the material taken across the stream.

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Errors in Slurry Sampling (cont.)

Extraction Error (EE): results from the design of the cutter blades,

the opening of the cutter and the general design of the cutter.

Preparation Error (PE1): results from accidental losses alongincrement transportation pipes.

Preparation Error (PE2): results from accidental contamination

along increment transportation pipes.

Preparation Error (PE3): results from unintentional mistakes.

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Sampling Theory

A10. Preparation Error (PE3)B9. Preparation Error (PE2)

A8. Preparation Error (PE1)

A7. Extraction Error (EE)

A6. Delimitation Error (DE)

B5. Grouping & Segregation Error (GE)

A4. Fundamental Error (FE)

A3. Periodic Error (IE2)A2. Integrated Error (IE1)

A1. Weighted Error (WE)

SamStat

Samplers

Sampling errors

A = Error are negligible; B = Error are small; C = Error may be large

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Sampling Theory

SamStat provide better primary sampling for feeding analysers or cross-cut sampler(2)

(2).- Sampling System for large flow slurry. FF Pitard-Report March 20th, 1999

A10. Preparation Error (PE3)B9. Preparation Error (PE2)

A8. Preparation Error (PE1)

A7. Extraction Error (EE)

A6. Delimitation Error (DE)

B5. Grouping & Segregation Error (GE)

A4. Fundamental Error (FE)

A3. Periodic Error (IE2)A2. Integrated Error (IE1)

A1. Weighted Error (WE)

SamStat

Samplers

Sampling errors

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ProcessStream Inlet

First Stageof Sampling

Controller

Third Stageof Sampling

Second Stage

of Sampling

ProcessStream Inlet

First Stageof Sampling

Controller

Third Stageof Sampling

Second Stage

of Sampling

Slurry Sampling (SamStat/Anstat)

Benefit :

Statistically Correct Sampling Essential for Metallurgical Accounting

Lowest Hydraulic Head Loss Cheaper to engineer into plants

Reliable with Lowest Maintenance Static Cutters have no moving parts so extremely reliable

Easy to inspect and clean

Simple Cutter Reeplacement On-Line

Mechanical Cross-Cut Sampler in Small Scale (400 mm) so reliable

No sample transport no additional pumps and no no blocked samples lines

In addition to Dedicated Analyser provides, integrated Sampling and On-Line Analysis

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AnStat de 4 Etapas1,400 - 23,000m3/hr

SamStat Sizes

AnStat de 2 Etapas

10 80 m3/hr

AnStat de 3 Etapas

80 1,400m3/hr

Th A S

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Thermo AnStatEscondida Phase IV Rougher Feed, 115,000 tpd

Th S St t

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Thermo SamStatMinera Los Pelambres . Final Tailings . 145.000 tpd

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