Copper Concentrate Sampling, control over final product.

Patricio Valenzuela Fuenzalida

Codelco-Chile Div. Andina/ Los Andes Chile/ Jefe Unidad Muestreo_Pesaje, 795692098,

hvale003@codelco.cl

Vicente Esparza González

Minerals Sampling/Automation & Electrical Engineering/ FLSmidth, Chile, Business Manager,

+56-9-8249 2466, vicente.esparza@flsmidth.com

ABSTRACT

The representativity of the final product which comes out of its facilities is key for Andina division

of Codelco Chile, also a great challenge to project a sampling system that delivers certain unusual

characteristics due to space conditions, change on the final product’s transport, speed of the

conveyer belt, and reduced distances. This is why the staff of Codelco Andina’s Div. along with

FLSmidth’s have joined efforts to design a revolutionary DSS (Dual Sampling System), with the

great advantage of offering facilities on load cycles over train containers with the final product.

Regardless of the circuit where it is being loaded, this will always be sampled by one single

primary cutter; then the sample is reduced on a secondary common phase. We must point out that

the weighing tables are linked to the equipment’s control center in order not to exceed the

maximum weight each container can bear. The equipment’s philosophy is based in control links

and common primary circuit positioning sensors for both load circuits.

INTRODUCTION

Andina Division of Codelco Chile is the corporation’s second most important copper producer.

The challenge of the superintendence of processes belonging to the plants’ management is to

control effectively the final product of copper starting from concentrates collected in the plant’s

filter and warehouse.

Our project was based on the replacement of the current materials’ movement system by an

automatic system which allows to quantify the concentrate’s weight and grade before its dispatch

by railroad to the port of Ventanas. In order to achieve this change; belts 14 and 15 were replaced.

We also installed a weight-meter for each conveyer belt an upon its unloads as well as a dual

sampling system, which means, a single system which enables to operate the sampling

automatically when operating one of the two conveyor belts.

Since the copper concentrate is the commercial product of Codelco’s Andina Division, the top value

for fundamental error is 1%, the maximum weight for each container is 9 tons and in a filling time

below 50 s.

FLOWSHEET

Concentrated Flow

(Copper)

(930 ton/hr)

Primary sample

Secondary

Sample

Final Sample

(30 Kg/cutting)

(16 Kg/Final lot

Sample)

Concentrated to

Train Wagon

(loading less than 50''

per container)

Productive process of Codelco’s Andina Division

Primary Crushing Don Luis

A6-A7 Belt

SAG Grind Feeding Hopper

SAG Grinding Unitary

Grinding

North Primary Crushing

South Primary Crushing

Secondary Crushing

Fine Material Belt 4F

Belt 5

Pannel III

Thick Hoppers

Tertiary Crushing Quaternary Crushing

Conventional Grinding

Collective Flotation

Selective Flotation

Molybdenum Concentrate Copper Concentrate Ovejería Dam

Los Leones Dam

The results were satisfactory and the methodology’s development is to be explained next.

THEORETICAL BASE

The heterogeneity is presented in several sampling errors. Depending on the type of sampling there

are errors created by any of the systems. We can distinguish errors created from:

Systematic sampling:

Q

jZs

Sy

SyCE

)(2

)(

Stratified random sampling:

Completely random sampling:

The term Z (j) is the error generator term, and each type of sampling has its own calculation

formula.

The term Q is the number of increments employed to obtain the samples systematically.

The utilized formulas were derived by G. Matheron to estimate the variances and the extent of the

error of continuous selection.

The calculations are based on the exposed by Francis Pitard, in his book “Pierre Gy`s Sampling

Theory and sampling Practice” Francis Pitard.

For the case of Systematic Sampling, which is applied in the Dual Sampling System Planta Filtros

Saladillo Codelco Div. Andina, The Error Generaror Function Z (j) is calculated:

Where W is the first Integral of the Variogram obtained with a systematic interval.

Where W` is the second integral of the Variogram obtained according to the indicated above.

Q

jZs

Ra

RaCE

)(2

)(

WWjZ jjSy

,

)2/(2)(

DESCRIPTION OF THE ACTIVITIES

For this particular equipment, the following particular activities were performed:

2 sample collection campaigns, each campaign was obtained continuously, which means,

each one of the 60 samples Dual equipment with cuts of the primary on each downloading

position were taken at programmed times every one minute during the shift (the campaign

consists of the extraction of 60 samples. The time of each increase is every 60 seconds).

The collection was done in plastic bags to facilitate their transportation and further repairing.

The utilized sampling interval was of 60'' for the short time needed to fill the containers with

copper concentrates, which are sent to port.

The same procedure was used for each one of the collected samples:

1. Disgregation and weight record on the AND certified scale, model GF12K duly

callibrated (Photo 1).

Photo 1

2. Hebro rotary divider, model 30V with 6 mats (Photo 3) and subsample collection.

Photo 2 Photo 3

3. One of the subsamples was randomly selected and integrally reduced to 100%-100#

Tyler in an Essa® LM2 Pulverising Mill, (Photo 4).

Photo 4

4. Each one of the pulverised subsamples was completely sent to chemical analysis for

Volumetric Total Cu.

With the results of the analysis for Total Cu a variogram was confectioned via EMPV

(Effective Management Process Variability).

RESULTS

Once the chemical analyses and the physical determinations are obtained, the data was processed

with the EMPV (Effective Management Process Variability) Statistic Software.

This software allows to perform an analysis and calculation of different statistic parameters such as;

variograms, variance in the origin V(0), relative variance for each interval of time j and other

statistics parameters, used in the construction of the Maximum Acceptable Total Errors Curve.

In the annex, you can find the detailed results of both evaluation campaigns of the cutter’s

performance.

The data display as well as its respective Variograms are indicated next.

Variograms

Cº1 Mass Campaign

0

1.1e-005

2.2e-005

3.3e-005

4.4e-005

5.5e-005

0 6 12 18 24 30

X-axis

Vario

V_trend

V_trend_j

V(0)

V_cycle_j

Vario = Rel | Mean = 2.84E+01 | V(0) = 2.18E-05 | V[process j=1] = 2.14E-06

Begin = 1 | Std = 6.46E-03 | V(1) = 2.39E-05 | V[trend j=1] = 1.07E-06

End = 60 | Var = 4.18E-05 | V_sill = 5.27E-05 | V[Resid] = 1.52E-06

Rel Variogram: C1 Registro dual

Cº2 Mass Campaign

Copper Ore Grade Variograms

Both variograms behave similarly, where data variability comes almost exclusively from the pip

effect measurements. The value is not high and in neither of both cases does it surpass the 1% value

of recommended copper ore grade error.

We can see that the average ore grade of the concentrate varies during the time used for the

measurements.

In campaign Nº 1, the average of the 60 determinations was 28,4% Cu.

In campaign Nº 2, the average of the 60 determinations was 28,2% Cu.

This situation is also seen on relative Variograms, which are calculated to have independence from

the chemical composition variation. This way, the calculated error can be expressed as a percentage.

Determination of Maximum Acceptable Total Error

A graph was elaborated from both campaigns to determine the total error according to the number

of increases.

0

2.8e-005

5.6e-005

8.4e-005

0.000112

0.00014

0 5 10 15 20

X-axis

Vario

V_trend

V_trend_j

V(0)

V_cycle_j

Vario = Rel | Mean = 2.82E+01 | V(0) = 5.21E-05 | V[process j=1] = 2.73E-06

Begin = 1 | Std = 9.87E-03 | V(1) = 5.48E-05 | V[trend j=1] = 1.37E-06

End = 60 | Var = 9.74E-05 | V_sill = 1.28E-04 | V[Resid] = 3.47E-06

Rel Variogram: Registro Dual C2Dual Record C2

Determination of Maximum Total Error Acceptable

Minutes Stratified random Systematic Error

1 0,4777% 0,000021752 2,1752E-05 0,466%

2 0,6846% 4,4424E-05 0,667%

3 0,8503% 6,83052E-05 6,8305E-05 0,826%

4 0,9916% 9,4046E-05 0,970%

5 1,1156% 0,000120908 1,2091E-04 1,100%

6 1,2277% 1,4960E-04 1,223%

7 1,3312% 0,000175241 1,7524E-04 1,324%

8 1,4293% 2,0092E-04 1,417%

9 1,5222% 2,2603E-04 1,503%

10 1,6084% 2,5199E-04 1,587%

11 1,6885% 2,7876E-04 1,670%

12 1,7651% 3,0588E-04 1,749%

13 1,8397% 3,3381E-04 1,827%

14 1,9124% 3,6190E-04 1,902%

15 1,9831% 3,9388E-04 1,985%

16 2,0521% 4,2656E-04 2,065%

17 2,1208% 4,5206E-04 2,126%

18 2,1901% 4,7650E-04 2,183%

19 2,2593% 4,9288E-04 2,220%

20 2,3280% 5,0796E-04 2,254%

From the table we have calculated the variograms and the sampling frequency (calculating

stratified and systematic random sampling trend). In both cases, taking a top 1% error, there is

enough room to modify if so desired, the frequency to longer times.

In order to avoid losing changes, which could happen in the process, it is always recommended to

set a sampling frequency of about 15 minutes, but since in this case we have a continuous charge it

is not modified.

In the performed calculation, in both variograms, the frequency to obtain a higher error than 1% is

over an hour. Therefore, in the 20-minute frequency, these increments, which correspond to the

cutter’s normal operation, the total error is 0.18% quite far from the maximum acceptable of 1%.

CONCLUSIONS

The DUAL cutter operates with a nominal flow of 930 TM/h.

For the cutter’s normal operation, we take an increase every 30''.

In the experiences we took increments approximately every 60'', which was the minimum through

the equipment’s electronic controls.

According to calculations, if we took an increment every 60'', for each lot of 720 TM we would use 8

increments, according to the 930 TM/h flow.

With 8 increments per each lot, the error related to the equipment reaches relative 0,18%.

With this data, the cutter largely complies with the established error restrictions.(1% error

maximum)

The improvement achieved depends on having clear and accurate information for the product’s

departure from the division, along with the assessment of humidity that is later checked when

entering the port. It is a help for the independent balance that the mass be calculated on port for

having a static weighing system, but the product’s quality can be clearly determined upon arrival.

The Metallurgic Balance of the Andina Division can be calculated by extending the boundaries to

the port of Ventanas. It is particularly important since the product sent to port can be clearly

represented, to close ore grades with a probabilistic equipment x- to further compare with the

international sales departures in order not to lose product traceability.

TABLES

DUAL1 MASS RECORD

Id Campaign Total weight

(g)

Cu Grade

(%)

1 Dual 1 1637,5 27,72

2 Dual 1 1731,3 28,24

3 Dual 1 1267,9 28,30

4 Dual 1 1683,1 28,37

5 Dual 1 1603,2 28,22

6 Dual 1 1657,3 28,16

7 Dual 1 1514,6 28,33

8 Dual 1 1163,6 27,98

9 Dual 1 1774,9 28,16

10 Dual 1 1607,0 28,26

11 Dual 1 1585,8 28,24

12 Dual 1 1288,9 28,19

13 Dual 1 1291,0 28,27

14 Dual 1 1795,1 28,14

15 Dual 1 1870,0 28,30

16 Dual 1 1335,1 28,14

17 Dual 1 1435,1 28,34

18 Dual 1 1885,9 28,60

19 Dual 1 1748,5 28,26

20 Dual 1 1609,7 28,33

21 Dual 1 1259,4 28,32

22 Dual 1 1271,6 28,19

23 Dual 1 1706,4 28,20

24 Dual 1 1150,2 28,33

25 Dual 1 1388,0 28,14

26 Dual 1 1480,4 27,94

27 Dual 1 1871,1 28,51

28 Dual 1 1329,5 28,54

29 Dual 1 1701,9 28,53

30 Dual 1 1334,8 28,40

31 Dual 1 1259,2 28,46

32 Dual 1 1328,2 28,20

33 Dual 1 1173,6 28,61

34 Dual 1 1550,1 28,24

35 Dual 1 1264,5 28,30

36 Dual 1 1161,2 28,16

37 Dual 1 1402,8 28,65

38 Dual 1 1546,8 28,50

Id Campaign Total weight

(g)

Cu Grade

(%)

39 Dual 1 1387,2 28,30

40 Dual 1 1962,5 28,45

41 Dual 1 1696,6 28,42

42 Dual 1 1739,3 28,51

43 Dual 1 1199,1 28,59

44 Dual 1 1250,3 28,49

45 Dual 1 1840,5 28,29

46 Dual 1 1498,9 28,52

47 Dual 1 1440,5 28,33

48 Dual 1 1422,0 28,52

49 Dual 1 1507,8 28,53

50 Dual 1 1150,2 28,33

51 Dual 1 1721,8 28,43

52 Dual 1 1962,6 28,55

53 Dual 1 1264,7 28,59

54 Dual 1 1871,6 28,53

55 Dual 1 1669,1 28,50

56 Dual 1 1303,7 28,44

57 Dual 1 1614,7 28,52

58 Dual 1 1121,1 28,59

59 Dual 1 1836,1 28,51

60 Dual 1 1706,1 28,46

Average 1513,9 28,4

DUAL2 MASS RECORD

Id Campign Total weight

(g)

Cu Grade

(%)

1 Dual 2 1189,8 28,40

2 Dual 2 1566,7 28,35

3 Dual 2 1713,2 28,01

4 Dual 2 1691,4 28,20

5 Dual 2 1284,8 28,35

6 Dual 2 1231,6 28,20

7 Dual 2 1266,2 28,07

8 Dual 2 1680,4 28,54

9 Dual 2 1263,4 28,13

10 Dual 2 1077,4 28,20

11 Dual 2 1498,2 28,30

12 Dual 2 1355,1 28,10

13 Dual 2 1280,1 28,14

14 Dual 2 1475,6 27,91

15 Dual 2 1251,0 28,12

16 Dual 2 1566,8 28,08

17 Dual 2 1398,9 28,54

18 Dual 2 1524,5 28,14

19 Dual 2 1379,0 28,41

20 Dual 2 1484,0 28,68

21 Dual 2 1968,2 28,21

22 Dual 2 1704,8 28,53

23 Dual 2 1364,1 28,32

24 Dual 2 1687,4 27,93

25 Dual 2 1961,9 28,35

26 Dual 2 1504,1 28,02

27 Dual 2 1400,1 28,70

28 Dual 2 1657,1 28,24

29 Dual 2 1785,8 28,32

30 Dual 2 1206,3 28,18

31 Dual 2 1752,3 28,28

32 Dual 2 1345,9 28,07

33 Dual 2 1617,5 28,48

34 Dual 2 1494,6 28,53

35 Dual 2 1148,4 28,60

36 Dual 2 1469,3 28,32

37 Dual 2 1602,2 28,09

38 Dual 2 1670,8 28,42

39 Dual 2 1200,0 28,98

40 Dual 2 1528,7 28,23

41 Dual 2 1426,6 28,18

42 Dual 2 1267,9 28,02

Id Campign Total weight

(g)

Cu Grade

(%)

43 Dual 2 1724,5 27,88

44 Dual 2 1820,0 28,22

45 Dual 2 1807,5 28,14

46 Dual 2 1839,8 28,00

47 Dual 2 1630,2 28,03

48 Dual 2 1834,7 27,94

49 Dual 2 1711,4 28,08

50 Dual 2 1238,4 28,13

51 Dual 2 1672,7 28,19

52 Dual 2 1250,1 27,98

53 Dual 2 1950,2 28,05

54 Dual 2 1546,4 27,94

55 Dual 2 1250,7 27,84

56 Dual 2 1278,1 27,68

57 Dual 2 1456.3 27,73

58 Dual 2 1983,3 27,21

59 Dual 2 1491,6 27,85

60 Dual 2 1374,2 27,91

Average 1514,3 28,2

REFERENCES

G. Matheron, “The regionalized variables, theory and its applications”, Center of Mathematical

Morphology. Ecole Nationale Superieure des Mines de Paris. Textbook#5, pages 66, 72 y 73.

Francis Pitard “ Pierre Gy`s Sampling theory and Sampling Practice, CRC press 1993, Second

Edition.

APPENDIX

Photo # 5, Hopper of conveyor # 14 (left side) and hopper of conveyor # 15 (right side)

Photo # 6, Loading process to stacker #15

Photo # 7, Primary Cutter, bottom dump design