CHARACTERIZATION OF PARTICLE DAMAGE AND SURFACE

EXPOSURE OF A COPPER ORE PROCESSED BY JAW CRUSHER,

HPGR AND ELECTRO-DYNAMIC FRAGMENTATION

Otávio da Fonseca Martins Gomes

1,2, Debora Monteiro de Oliveira

1, Luis Gonzaga Santos

Sobral1, Eric Pirard

2

1Centre for Mineral Technology – CETEM,

Av. Pedro Calmon 900, Rio de Janeiro, RJ, 21.941-908, Brazil 2University of Liege, GeMMe, Sart-Tilman B52, Liege, 4000, Belgium

Keywords: bioleaching, copper ore, digital microscopy, image analysis.

Abstract

Heap bioleaching is gaining importance as a low cost technology for processing low grade ores

and tailings. Furthermore, it can be employed to process large particles and aggregates.

Nevertheless, only grains at the surface are exposed to the leaching solution. The present work

aims at investigating the effect of different crushing methods in the generation of cracks in order

to improve the surface exposure of copper sulphides. A method for the microstructural

characterization of samples composed by macroscopic particles, i.e., around 1 mm or more in a

resolution better than 1 µm, is employed. This method involves large cross-sections preparation,

image acquisition on SEM, automatic mosaicing, and image processing and analysis. The results

show that it is possible to increase the copper sulphide minerals exposure through the generation

of cracks by secondary crushing for a given particle size range.

Introduction

The availability of high grade ores is decreasing in the world. Moreover, the mining industry has

suffered with the rise on energy cost and the growing environmental constraints. Therefore, there

has been a growing interest to search and develop more efficient and sustainable processes, such

as the ones capable to exploit low grade mineral resources and tailings.

In this context, heap bioleaching is gaining importance as a low cost technology for processing

low grade ores. It is currently used for extracting base metals such as copper, nickel, cobalt etc.,

and precious metals such as gold and silver. In fact, bioleaching constitutes an alternative process

to roasting, smelting and pressure leaching, avoiding their high energy consumption, with less

pollution and safety risks.

Furthermore, bioleaching is generally employed for processing large particles and aggregates.

Despite its energy efficiency, this fact leads to a limitation as only grains at the surface of

particles are exposed to the leach solution.

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The present work aims at investigating the effect of different crushing methods in the generation

of cracks in large particles of a copper ore in order to increase the copper sulphide exposure. The

increasing of surface exposure can potentially enhance bioleaching rates.

The challenge is the microstructural characterization of samples composed by macroscopic

particles, i.e., particles in the order of 1 mm or more in a resolution better than 1 µm in which

thin cracks can be analyzed.

Experimental

Sample selection and preparation

A copper sulphide ore was used as case-study. It was processed through a primary jaw crushing.

Subsequently, some samples were processed in a secondary crushing respectively by electro-

dynamic fragmentation (Selfrag), and high pressure grinding rolls (HPGR) under 20 and 50 bar.

Then, the samples were classified, and the fraction -6+20# between sieves 6 and 20# (3.35 and

0.853 mm) from: (i) the ore after the primary crushing; (ii) the ore crushed by Selfrag; (iii) the

ore crushed by HPGR under 20 bar; and (iv) the ore crushed by HPGR under 50 bar were

analyzed.

The samples were embedded in epoxy resin to form blocks with 5 cm of diameter. The blocks

were ground and polished. Then, the cross-sections were covered by evaporated carbon to make

them conductive and suitable for conventional SEM analysis.

Image Acquisition and Mosaicing

The cross-sections were automatically imaged at SEM with a resolution of 0.5 µm/pixel using a

back-scattered electron detector. For each cross-section: 35 x 40 = 1400 images of 2000 x 1726

pixels from partially overlapped fields were acquired. The overlapping was about 10% of the

field length in both X and Y directions.

The images of each sample were then combined in an extended field image [1] through

mosaicing that was performed using an automatic stitching procedure, implemented in the

Matlab environment, based on the normalized cross-correlation [2]. Figure 1 presents the

extended field image of the fraction -6+20# of the sample crushed by Selfrag.

Image Analysis

The image analysis procedure basically comprises the segmentation of sulphide grains, the

segmentation of cracks, and some measurements in 2D related to surface exposure.

The segmentation of sulphide grains was easy. In the ore under study, copper sulphides presented

a quite different grey level in the acquired back-scattered electron images. They were brighter

than other phases; therefore, their segmentation was accomplished through grey level

thresholding with a fixed threshold value.

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On the other hand, the segmentation of cracks was a more complex task, since cracks cross

diverse minerals with different grey levels and also occurs in interfaces between them.

Furthermore, the thickness of cracks varies widely. Thus, the segmentation of cracks by

thresholding or even through modeling of their grey level as a signal combination of surrounding

phases and background was not possible.

The segmentation of cracks required the development of a specific method, which must be robust

in relation to grey level variations and at the same time sensitive enough to detect thin cracks.

Thus, a region growing algorithm that uses intensity and intensity gradient as criteria was

implemented in the Matlab environment [3].

Figure 2 exemplifies the segmentation procedures. It shows a particle (Fig. 2-a) extracted from

center-right of the image presented in Figure 1; its segmented cracks (Fig. 2-b); and the

segmented sulphide grains and their exposed perimeters superimposed with pseudo-colors (green

and red, respectively) to this particle (Fig. 2-c).

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Figure 1. The extended field image of the fraction -6+20# of the sample crushed by Selfrag with

scale bars in millimeters and in pixels.

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Figure 2. A segmentation result sample: (a) a particle extracted from center-right of the image

presented in Figure 1, with scale bars in millimeters and in pixels; (b) its segmented cracks; and

(c) the segmented sulphide grains and their exposed perimeters superimposed with pseudo-colors

(green and red, respectively) to this particle.

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(a)

(b) (c)

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After the segmentation of sulphide grains and cracks, the measurements of specific perimeter of

particles, specific exposed perimeter, and exposed perimeter ratio were carried out in Axiovision

based on Crofton perimeters [4] and area, as defined below:

���������perimeter ������� ���= Perimeter of particles

Area of ����� ��

����������xposed perimeter�= Exposed perimeter of sulphide grains

Area of sulphide grains

Exposed perimeter ratio (%) = 100 × Exposed perimeter of sulphide grains

Perimeter of sulphide grains

Results and Discussion

Table 1 presents the obtained measurements of specific perimeter of particles, specific exposed

perimeter, and exposed perimeter ratio for the fraction -6+20# of the studied samples. First line

shows the results for the ore after primary crushing through jaw crusher. Second, third and forth

lines show respectively the results for the samples processed through a secondary crushing with

Selfrag, HPGR under 20 and 50 bar.

Table 1. Results of measurements.

Crushing method Specific perimeter of

particles (µm-1

)

Specific exposed

perimeter (µm-1

)

Exposed perimeter

ratio (%)

Jaw crusher 0.1270 0.0615 48.44

+ Selfrag 0.1433 0.0817 56.99

+ HPGR @ 20 bar 0.1300 0.0665 51.14

+ HPGR @ 50 bar 0.1859 0.1124 60.46

Specific perimeter of particles is defined as the ratio between perimeter (µm) and area of

particles (µm2). Therefore, it can be used in the present work to measure the particle damage due

to generation of cracks by secondary crushing methods. The results have shown that Selfrag and

HPGR under 50 bar were capable of creating cracks. Nevertheless, the sample processed by

HPGR under 20 bar and the sample after primary crushing presented similar results, showing

that this pressure in HPGR was not enough to increase the generation of cracks.

Specific exposed perimeter computes the ratio between exposed perimeter (µm) and total area

(µm2) of sulphide grains. Exposed perimeter ratio denotes the fraction (%) of sulphide grains

(3)

(1)

(2)

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perimeter that is exposed. They are measurements directly related to sulphide exposure. Thus,

they can be employed to compare different samples regarding the surface exposure of sulphide

grains.

As observed for the generation of cracks, the results of sulphide exposure were similar for jaw

crusher and HPGR under 20 bar. The sample out of HPGR under 20 bar presented a slightly

better copper sulphide exposure in comparison with the sample processed only through the

primary jaw crushing. On the other hand, Selfrag and HPGR under 50 bar increased particle

damage due to cracks and copper sulphide exposure.

Conclusion

The effect of electro-dynamic fragmentation (Selfrag) and HPGR as secondary crushing methods

in the generation of cracks in large particles of a copper ore was investigated.

A method for the microstructural characterization of ore samples composed by macroscopic

particles was employed to analyze the generation of cracks and measure the exposure of copper

sulphide minerals.

The results have shown that it is possible to increase the exposure of sulphide minerals grains by

generating cracks through a secondary crushing. Nevertheless, these results are preliminary once

they were obtained for only one particle size range. Besides, for ore processing purposes, other

factors must be considered such as the generation of ore fines, and the energy consumption in the

crushing operations. These remain as further works.

Acknowledgements

The support of CNPq and MCTI is gratefully acknowledged. O.D.M. Gomes also wishes to

thank the support of the program "Ciência sem Fronteiras".

References

1. S. Paciornik, and M. H. P. Maurício, “Digital Imaging”, ASM Handbook, vol. 9:

Metallography and Microstructures, ed. G. F. Vander-Voort (Materials Park: ASM International,

2004), 368-402.

2. R. A. Johnson, and D. W. Wichern, Applied multivariate statistical analysis, 6th

ed. (New

Jersey: Pearson Education, 2007).

3. O. da F. M. Gomes, L. G. S. Sobral, and E. Pirard, “Evaluation of HPGR and electro-dynamic

fragmentation regarding generation of cracks through 2D image analysis” (Paper presented at the

11th

European Congress of Stereology and Image Analysis, Kaiserslautern, July 2013).

4. M. P. do Carmo, Differential geometry of curves and surfaces (Englewood Cliffs: Prentice-

Hall, 1976).

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