Konttijärvi Battery Mineral geometallurgical case study

Konttijärvi Battery Mineral geometallurgical case study

Simon Michaux, Alan Butcher, Quentin Dehaine

30/09/2020

1

Summary• Background of task

• Experimental plan

• Sample starting mineralogy

• Sorting results

• Magnetic separation results

• Flotation results

• Gravity results

• Data patterns

• SAP Konttijärvi (10 orientation samples)o Economic minerals in order of importance Palladium (2g/t), Pt (0.5g/t), Cu (0.16%), Ni

(0.08%), Au (0.1g/t), Co, Ag, Rhodium

o PGE most valuable

o Orginally a leaching plant but is now a flotation plant (client believes)

o Cu flotation first and Ni flotation on Cu tails

Select 5-10 Orientation samples

OrientationSample 1

OrientationSample 2

OrientationSample 3

OrientationSample 4

OrientationSample 5

OrientationSample 7

OrientationSample 8

Sample Preparation

Flotation(5kg)

GravitySeparation

(5kg)

MagneticSeparation

(5kg)

Leaching(5kg)

Sorting(5kg)

Characterization(5kg)

Orientation Sample φ(25kg)

-12mm

-3.35mm -3.35mm

-3.35mm

-3.35mm

-3.35mm

Each Orientation End Member Sample

Hyperspectral Imaging

Chemical Assay

Geophysics

Lithology Min

era

l Sig

na

ture

C

ha

ract

eri

sati

on

Intact Core Texture

FeedSample

A (flow)

ai (components)

B (flow)

bi (components)

C (flow)

ci (components)

Product Samples

SeparationProcess

Gravity Separation

AutomatedMineralogy

XRDChemical

Assay

FeedSample

A (flow)

ai (components)

B (flow)

bi (components)

C (flow)

ci (components)

Product Samples

SeparationProcess

Leaching Testwork

FeedSample

A (flow)

ai (components)

B (flow)

bi (components)

C (flow)

ci (components)

Product Samples

SeparationProcess

Batch Flotation

AutomatedMineralogy

XRDChemical

AssayAutomatedMineralogy

XRD Chemical Assay

Pro

cess

Be

ha

vio

ur

Ch

ara

cte

risa

tio

n

Meso - Micro Texture

Crushed Ore

XRDChemical

AssayAutomatedMineralogy

Company Knowledge & Data

SampleSelection

Digital Image

Min

era

log

ica

lsi

gn

atu

res

tha

tco

ntr

ol p

roce

ssb

eh

avi

or

Conclusion for each Orientation Sample

• Which process path is more effective in the

recovery of each target metal?

• Which process path is most effective in

recovery of the 2-3 most valuable metals?

• What is the mineralogical signature that

controls that process path?

LeachSLA

FlotationSFA

GravitySGA

LeachSFADLA

FlotationSGFB

Gravity

Flotation

FlotationGravityLeach

SGFDLB

Process Path 1

Process Path 7

Process Path 6

Process Path 4

Process Path 3

Process Path 2

CharacterizationRepresentitive sample of Starting end member orientation sample.

(in 4 size fractions)

Sample SC1-4Magnetic

Separation

Process Path 10

Process Path 11 Ore SortingFlotationSOSGFC

GravitySOSBGC

LeachSOSGFDLC

Ori

en

tati

on

Ste

p 1

Ori

en

tati

on

Ste

p 2

Analysis on what works and what does not

Process Path 5Ore Sorting

SOSA

FlotationMagneticSeparation

Process Path 8

FlotationMagnetic

SeparationLeach

SGFDLBProcess Path 9

OrientationStep 3

Chemical Assay

SEM Automated Mineralogy

X-Ray Diffraction

XRD

X-Ray Fluorescence

XRF

4 Acid Digest Multi-element analysis by ICP-MS

Fire Assay, Au, Ag, Pd, Pt determination by ICP-OES

Determination of Sulphurby sulphur S analyzer (Eltra)

Determination of carbon by carbon C analyzer (Eltra)

Particle Mineral Texture, Content & Association

Bulk Element Analysis

Bulk Mineral Analysis

SKC KonttijärviOrientation

Characterization Sample

SKC-PM1

SKC-PM2

SKC-PX1

SKC-PX2

SKC-MS1

SKC-MS2

SKC-BAS1

SKC-BAS2

Samples

Characterization analysis of each Orientation sample

Lead Collection Fire Assay (50-100g)

4 acid digest (to measure for 60

elements) (1g)

Ammonium Citrate leach analysis (to

measure supplied nickel minerals) (1g)

LECO/ELTRA (Suplhur combustion

test for high sulphur content) (1g)

XRF pellet (1g)

Bulk QXRD (50-100g)

Chem Assay XRD/XRF MLA – gangue

MLA – Value 1 MLA – Value 2MLA – Smelter

Penalty 1

FeedSample

A (flow)

ai (components)

B (flow)

bi (components)

C (flow)

ci (components)

Product Samples

SeparationProcess

Rotary divideeach sampleinto 4 parts

Examine Mawsondata

Reserve 1

Leachbackground

Flotation A

Gravity A

Select 4-10 samplesbased on extreme

data signatures

Concentrate

Tails

Heavy fraction

Light fraction

CSIRO

Characterization Point• Qemscan• XRD/XRF• Chemical Assay

Representitively sample acrosswhole sample size distribution. Sample prep in 4 size fractions

Size distributionmeasurement

Size by size handheld XRF &

Chemical Assay

Sample α

Sample δConc

Sample βHF

Sample βLF

Sample δTail


Examine Mawsondata

Reserve 1

Leachbackground

Flotation A

Gravity A


data signatures

Concentrate

Tails

Heavy fraction

Light fraction

CSIRO





Chemical Assay

Sample α

Sample δConc

Sample βHF

Sample βLF

Sample δTail


Examine Mawsondata

Reserve 1

Leachbackground

Flotation A

Gravity A


data signatures

Concentrate

Tails

Heavy fraction

Light fraction

CSIRO





Chemical Assay

Sample α

Sample δConc

Sample βHF

Sample βLF

Sample δTail

Make a rock type

mineral content profile

Konttijärvi (SAP) Orientation Samples

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

SKC-PM1 SKC-PM2 SKC-PX1 SKC-PX2 SKC-MS1 SKC-MS2 SKC-TZ1 SKC-TZ2 SKC-BAS1 SKC-BAS2

Konttijärvi (SAP) Whole Rock Mineralogy - XRD

Biotite Chlorite Quartz Amphibole Plagioclase Calcite Dolomite Magnesite Talc Magnetite

XRD has shown to be useful in

tracking rock type and mineral class

From MLA data SKC-PM1 SKC-PM2 SKC-PX1 SKC-PX2 SKC-MS1 SKC-MS2 SKC-TZ1 SKC-TZ2 SKC-BAS1 SKC-BAS2

Pyrrhotite (%) 0,69 0,68 0,42 0,01 0,62 0,32 0,62 1,37 1,28 0,26

Chalcopyrite (%) 0,28 0,32 0,38 0,05 0,56 0,49 0,54 0,48 0,45 1,84

Pentlandite (%) 0,29 0,36 0,10 0,00 0,20 0,19 0,15 0,23 0,39 0,04

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

SKC-

PM1

SKC-

PM2

SKC-

PX1

SKC-

PX2

SKC-

MS1

SKC-

MS2

SKC-

TZ1

SKC-

TZ2

SKC-

BAS1

SKC-

BAS2

Prec

ious

Met

al C

onte

nt (m

g/kg

)

Konttijärvi (SAP) Precious Metal Content - Fire Assay

Pd

Ag

Pt

Au

From MLA data SKC-PM1 SKC-PM2 SKC-PX1 SKC-PX2 SKC-MS1 SKC-MS2 SKC-TZ1 SKC-TZ2 SKC-BAS1 SKC-BAS2

Pyrrhotite (%) 0,69 0,68 0,42 0,01 0,62 0,32 0,62 1,37 1,28 0,26

Chalcopyrite (%) 0,28 0,32 0,38 0,05 0,56 0,49 0,54 0,48 0,45 1,84

Pentlandite (%) 0,29 0,36 0,10 0,00 0,20 0,19 0,15 0,23 0,39 0,04

0,00

0,10

0,20

0,30

0,40

0,50

0,60

SKC-PM1 SKC-PM2 SKC-PX1 SKC-PX2 SKC-MS1 SKC-MS2 SKC-TZ1 SKC-TZ2 SKC-BAS1 SKC-BAS2

(%)

Konttijärvi (SAP) Cu-Ni-Co Content - 4 Acid Digest Assay

Cu (%)

Ni (%)

Co (%)

Sample Mass Pull Pd Recovery Cu Recovery Ni Recovery Co Recovery

SKF-PM2 2,1 % 72,6 % 80,5 % 27,0 % 11,2 %

SKF-PX1 1,0 % 65,0 % 86,4 % 19,9 % 9,2 %

SKF-PX2 0,8 % 62,1 % 81,6 % 1,0 % 0,6 %

SKF-MS1 2,0 % 60,8 % 85,4 % 42,5 % 10,3 %

SKF-MS2 1,0 % 74,0 % 87,1 % 31,0 % 7,9 %

SKF-TZ1 1,5 % 55,6 % 77,1 % 45,8 % 11,1 %

SKF-TZ2 1,5 % 78,5 % 85,9 % 54,2 % 9,3 %

SKF-BAS1 2,8 % 71,6 % 87,9 % 71,3 % 21,9 %

SKF-BAS2 2,6 % 65,5 % 87,5 % 26,5 % 10,3 %

SAP Flotation

Palladium (2g/t),

Pt (0.5g/t),

Cu (0.16%),

Ni (0.08%),

Co (60-120 ppm)

Au (0.1g/t), Ag, Rhodium

Flotation at Konttijärvi

% R

ecov

ery

Time

Chemical Assay

Qemscan SEM

QXRD

Characterization

Data

Lead Collection Fire Assay (50-100g)

4 acid digest (to measure for 60

elements) (1g)

Ammonium Citrate leach analysis (to

measure supplied nickel minerals) (1g)

LECO/ELTRA (Suplhur combustion

test for high sulphur content) (1g)

XRF pellet (1g)

Bulk QXRD (50-100g)

Prepared

Sample

Bulk Sulphide

rougher

flotationBulk Sulphide

Cleaner Test 1

Flotation

Bulk Sulphide

Cleaner Test 2

Flotation

Bulk Sulphide

Cleaner Test 3

Flotation

Rougher Tails

Konttijärvi Palladium Flotation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25

Pd

Re

cove

ry (

%)

Time (min)

Palladium Flotation

SKF-PM2

SKF-PX1

SKF-PX2

SKF-MS1

SKF-MS2

SKF-TZ1

SKF-TZ2

SKF-BAS1

SKF-BAS2

50

55

60

65

70

75

80

85

90

0 50 100 150

Pd R

eco

very

(%

)

Pd Grade (g/t)

Palladium Grade Recovery

Test 2: SKF-PM2

Test 3: SKF-MS1

Test 4: SKF-MS2

Test 5: SKF-TZ1

Test 6: SKF-TZ2

Test 7: SKF-BAS1

Test 8: SKF-BAS2

Test 9: SKF-PX1

Test 10: SKF-PX2

Flotation data Pd – 20 minutesFe and Ni good indicators of Pd at 20

mins in most samples. Mo best

predicator in BAS-1 and TZ2

Correlation matrix of data, larger

squares = best correlation

PCA analysis of 20 min data indicate

elemental associations.

Pd group

Zn related to poor recovery

PM1

PM2

PX1

PX2

MS1

MS2

TZ1

TZ2

BAS2

BAS1

SAP Rock Types

Different mineral

control of flotation

Flotation data Pd – 20 minutes ( minerals)

Biotite and Plagioclase indicators of Pd

at 20 mins in most samples Chlorite

inverse of these two minerals.

Konttijärvi Copper Flotation

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Cu

Re

cove

ry %

Time, min

Cu Flotation Kinetics in Final Cleaning

Test 2: SKF-PM2, 2nd cleaning

Test 3: SKF-MS1, 1st cleaning


Test 5: SKF-TZ1, 1st cleaning


Test 7: SKF-BAS1, 1st cleaning


Test 9: SKF-PX1, 1st cleaning


70

75

80

85

90

95

0 2 4 6 8 10 12 14 16

Cu

re

cove

ry %

Cu Grade %

Copper grades and Recoveries

Test 1: SKF-PM2

Test 2: SKF-PM2

Test 3: SKF-MS1

Test 4: SKF-MS2

Test 5: SKF-TZ1

Test 6: SKF-TZ2

Test 7: SKF-BAS1

Test 8: SKF-BAS2

Test 9: SKF-PX1

Test 10: SKF-PX2

Flotation data Cu – 20 mins

Cu strongly associated with Mo,

W and Zn

Konttijärvi Nickel Flotation

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30

Ni R

eco

very

%

Time, min

Ni Flotation Kinetics in Final Cleaning

Test 2: SKF-PM2, 2nd cleaning









0

10

20

30

40

50

60

70

80

0 1 2 3 4 5

Ni r

eco

very

%

Ni %

Nickel grades and Recoveries

Test 1: SKF-PM2

Test 2: SKF-PM2

Test 3: SKF-MS1

Test 4: SKF-MS2

Test 5: SKF-TZ1

Test 6: SKF-TZ2

Test 7: SKF-BAS1

Test 8: SKF-BAS2

Test 9: SKF-PX1

Test 10: SKF-PX2

Konttijärvi Cobalt Flotation

0%

5%

10%

15%

20%

25%

0 5 10 15 20 25

Co

Re

cov

ery

(%

)

Time (min)

Cobalt Flotation SKF-PM2

SKF-PX1

SKF-PX2

SKF-MS1

SKF-MS2

SKF-TZ1

SKF-TZ2

SKF-BAS1

SKF-BAS2

0

10

20

30

40

50

60

70

80

0,00 0,10 0,20 0,30

Co r

ecov

ery

(%)

Co Grade (%)

Cobalt Grade Recovery

Test 2: SKF-PM2

Test 9: SKF-PX1

Test 10: SKF-PX2

Test 3: SKF-MS1

Test 4: SKF-MS2

Test 5: SKF-TZ1

Test 6: SKF-TZ2

Test 7: SKF-BAS1

Test 8: SKF-BAS2

Flotation data Co – 20 minsS good indicator of Co at 20 mins in

most samples. MnO is the opposite of S

Co strongly associated with S

Gravity data –general

2.9% of mass 40.8% of mass 56.3% of mass

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

9 000

Concentrate Middlings Tails

(mg/

kg)

Gravity Shaking Table Separation (Sample PM1)

Copper (Cu)

Nickel (Ni)

Cobalt (Co)

Cobalt (Co) Concentrate Middlings Tails

Sample mass 2,9 % 40,8 % 56,3 %

Grade 602 mg/kg 111 mg/kg 106 mg/kgRecovery 14,4 % 36,9 % 48,7 %

0 %

20 %

40 %

60 %

80 %

100 %

Concentrate Middlings Tails

(%)

SKG-PM1 Gravity Separation XRD

Chlorite Quartz Amhibole Plagioclase Calcite

Dolomite Magnesite Magnetite Talc Pyrrhotite

Pyrite Chalcopyrite Ilmenite Pentlandite

Gravity data – TZ1

Co conc relative to starting material

Gravity data – TZ1

Co & U removed

How do we look at down hole data? 26

0

1

2

3

4

5

6

7

8

9

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Fe %

, S

%

Depth (m)

Fe_pct

S_pct

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

12

8 -

13

0

13

2 -

13

4

13

6 -

13

8

14

0 -

14

2

14

4 -

14

6

14

8 -

15

0

15

2 -

15

4

15

6 -

15

8

16

0 -

16

2

16

4 -

16

6

16

8 -

17

0

17

2 -

17

4

17

6 -

17

8

18

0 -

18

2

18

4 -

18

6

18

8 -

19

0

19

2 -

19

4

19

6 -

19

8

20

0 -

20

2

20

4 -

20

6

20

8 -

21

0

21

2 -

21

4

21

6 -

21

8

22

0 -

22

2

22

4 -

22

6

22

8 -

23

0

23

2 -

23

4

Cu

%,

Cu

/S

Depth (m)

Cu_pct

Cu/S

0

5000

10000

15000

20000

25000

30000

35000

40000

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Pa

rts

pe

r m

illi

on

(p

pm

)

Depth (m)

Mg_ppmAl_ppmCa_ppmK_ppm

Need a statistically valid

method that can filter data

Case Study P

Cumulative Summation (cusum) analysis27

82

84

86

88

90

92

94

96

0 20 40 60 80 100 120 140 160

Day

Reco

very

(%

)

Change in Circuit

A change was made to a flotation plant

circuit at day 85 and the data was

analyzed to determine if there was a

change in recovery performance of the

circuit.

The time series plot does not provide any

visible indication of any change in the day

to day recovery data.

Example Source: T. Napier-

Munn

Time Series Recovery Chart

Cumulative Summation (cusum) analysis 28

-30

-25

-20

-15

-10

-5

0

5

0 20 40 60 80 100 120 140 160

Day

CU

SU

M

Change in Circuit

The cusum plot identifies four periods:

• two –ve gradients

• one horizontal gradient

• one positive gradient

Difference between lowest and

highest recoveries are only 1%

μ=88.87% (overall mean of

dataset) Example Source: T. Napier-Munn

Cumulative Summation (cusum) analysis 29

• A “cusum” chart is traditionally a time sequence plot of the cumulative sum of the

current value minus some mean value, plus the previous cusum

• Ct=Ct-1+Rt-μ

• Ct: cusum at time t

• Ct-1: cusum at time t-1

• Rt: value of variable at time t

• μ: mean/target value

Copper Rougher Recovery

(CUSUM Analysis)

-50

0

50

100

150

200

250

1100 1175 1250 1325 1400 1475

Depth (m)

CU

SU

M .

How do we look at down hole data? 30

0

1

2

3

4

5

6

7

8

9

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Fe %

, S

%

Depth (m)

Fe_pct

S_pct

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

12

8 -

13

0

13

2 -

13

4

13

6 -

13

8

14

0 -

14

2

14

4 -

14

6

14

8 -

15

0

15

2 -

15

4

15

6 -

15

8

16

0 -

16

2

16

4 -

16

6

16

8 -

17

0

17

2 -

17

4

17

6 -

17

8

18

0 -

18

2

18

4 -

18

6

18

8 -

19

0

19

2 -

19

4

19

6 -

19

8

20

0 -

20

2

20

4 -

20

6

20

8 -

21

0

21

2 -

21

4

21

6 -

21

8

22

0 -

22

2

22

4 -

22

6

22

8 -

23

0

23

2 -

23

4

Cu

%,

Cu

/S

Depth (m)

Cu_pct

Cu/S

0

5000

10000

15000

20000

25000

30000

35000

40000

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Pa

rts

pe

r m

illi

on

(p

pm

)

Depth (m)

Mg_ppmAl_ppmCa_ppmK_ppm

Need a statistically valid

method that can filter data

Case Study P

The CuSUM tool 31

-8,0

-6,0

-4,0

-2,0

0,0

2,0

4,0

6,0

8,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

4,0

5,0

12

8 -

13

0

13

2 -

13

4

13

6 -

13

8

14

0 -

14

2

14

4 -

14

6

14

8 -

15

0

15

2 -

15

4

15

6 -

15

8

16

0 -

16

2

16

4 -

16

6

16

8 -

17

0

17

2 -

17

4

17

6 -

17

8

18

0 -

18

2

18

4 -

18

6

18

8 -

19

0

19

2 -

19

4

19

6 -

19

8

20

0 -

20

2

20

4 -

20

6

20

8 -

21

0

21

2 -

21

4

21

6 -

21

8

22

0 -

22

2

22

4 -

22

6

22

8 -

23

0

23

2 -

23

4

Depth (m)

cusum S

cusum Fe

-1,4

-1,2

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

-2

-1,8

-1,6

-1,4

-1,2

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Depth (m)

cusum Cu

cusum Cu/S

-20000

-10000

0

10000

20000

30000

40000

-35000

-30000

-25000

-20000

-15000

-10000

-5000

0

5000

10000

15000

12

8 -

13

01

30

- 1

32

13

2 -

13

41

34

- 1

36

13

6 -

13

81

38

- 1

40

14

0 -

14

21

42

- 1

44

14

4 -

14

61

46

- 1

48

14

8 -

15

01

50

- 1

52

15

2 -

15

41

54

- 1

56

15

6 -

15

81

58

- 1

60

16

0 -

16

21

62

- 1

64

16

4 -

16

61

66

- 1

68

16

8 -

17

01

70

- 1

72

17

2 -

17

41

74

- 1

76

17

6 -

17

81

78

- 1

80

18

0 -

18

21

82

- 1

84

18

4 -

18

61

86

- 1

88

18

8 -

19

01

90

- 1

92

19

2 -

19

41

94

- 1

96

19

6 -

19

81

98

- 2

00

20

0 -

20

22

02

- 2

04

20

4 -

20

62

06

- 2

08

20

8 -

21

02

10

- 2

12

21

2 -

21

42

14

- 2

16

21

6 -

21

82

18

- 2

20

22

0 -

22

22

22

- 2

24

22

4 -

22

62

26

- 2

28

22

8 -

23

02

30

- 2

32

23

2 -

23

4

Depth (m)

cusum Mg

cusum Al

cusum Ca

cusum K

• The absolute value of the cusum at any point is not important

• The gradient of the line over a characteristic period indicates the prevailing mean.

Case Study P

[email protected]

www.gtk.fi

Simon P. MichauxAssociate Professor GeometallurgyUnit Minerals Processing and Materials Research - Circular Economy SolutionsOre Characterization, Process Engineering & Mineral Intelligence

Geological Survey of Finland/Geologian tutkimuskeskusPO Box 96, (Vuorimiehentie 2)F1-02151 Espoo, FINLAND

Landline: +358 (0)29 503 2158Mobile: +358 (0)50 348 6443

http://en.gtk.fi/

http://en.gtk.fi/

Documents

Konttijärvi Battery Mineral geometallurgical case study