Mineral Insights measured grades for gold, pyrite, chalcopyrite, galena and sphalerite in polished samples of the tailings from a gold mine.
Mineral Insights measured that 97% of the gold particles in the tailings are on or near a chalcopyrite grain, showing the strong association of gold with chalcopyrite. This graph shows that gold recovery will be increased with a chalcopyrite-focused floatation process.
- Mineral Insights can measure gold particle area and shape distribution in ore
- Use gold distribution data to optimize ore processing and increase gold recovery
Case Study – Tailings Measurement
Mineral Insights measured the grades shown in the table below for gold, pyrite, chalcopyrite, galena and sphalerite in four polished samples of the tailings from a gold mine. As shown in the graph below, these results indicate that there is an approximately linear relationship between the gold grade and the chalcopyrite grade in the tailings that quantifies the increase in gold recovery from increased efficiency of the floatation process that extracts the chalcopyrite from the ore. Mineral Insights found that 97% of the gold particles are on or near a chalcopyrite grain, showing the strong association of gold with chalcopyrite. For this gold mine, every reduction of 0.1 ppm in the average grade of the tailings resulting from an improved floatation process would produce $1.6 million US more gold per year at $1100 US per ounce. The Gold Sniffer can provide the data to facilitate this increase in profits.
Mineral Insights can use the same measurement techniques to identify which minerals gold is associated with in any gold deposit to show geologists which minerals to explore for.
This report presents Mineral Insights measurements of polished samples from the tailings of a gold mine. Mineral Insights measured the grade of gold, pyrite, chalcopyrite, galena and sphalerite for these samples. These measurements show the quantity of gold and sulphide minerals that were missed by the floatation process. It may be possible to use this data to improve the gold recovery of the floatation process.
Mineral Insights uses visible light to detect gold particles as small as 1.6 microns. It does this by analyzing high resolution digital pictures of the surface of a mineral sample using proprietary algorithms that process the digital image data. These algorithms analyze the colour of each photosite in the image, where a photosite consists of one red, one blue and two green pixels. The dimensions of an area viewed by a photosite on the mineral surface are 1.6 microns x 1.6 microns. There are 5 million photosites in the Gold Sniffer’s digital image. The detection algorithm also uses spatial data near each photosite, and angular reflection data, to identify gold. Mineral Insights has been developed to detect gold grades as low as 0.25 ppm. Mineral Insights can also detect other minerals using their reflectance data that defines the colour that the detection algorithm must seek. In this report Mineral Insights measurements are presented for gold mine tailings that measure the grade of gold, pyrite, chalcopyrite, galena and sphalerite.
Discussion of Mineral Insights Measurements
The polished samples of the gold mine tailings are 30 millimeters in diameter. The polished area permits Mineral Insights to take 24 pictures in a 4 picture by 6 picture pattern where each picture has an area of 3 mm x 4.5 mm. Mineral Insights measures the grade for gold and each of the four minerals using the fraction of each picture covered by each of these minerals, and then multiplying this fraction by the ratio of the specific gravity of the mineral divided by the specific gravity of the host rock which is estimated to be 3.0. From the measurements it is estimated that the gold is an alloy of 94% gold and 6% silver with a specific gravity of 18.79. Examples of the gold, pyrite, chalcopyrite, galena and sphalerite detected for one measurement are shown in Figures 1 to 5.
Figure 1 A Mineral Insights picture of a 3 mm x 4.5 mm area on the surface of a polished sample with a single photosite of gold detected that measures 1.6 microns x 1.6 microns and is marked by a red cross. The gold grade of this picture is 1.21 ppm and the grade of the sample is 0.28 ppm.
Figure 2 A Mineral Insights picture of a 3 mm x 4.5 mm area on the surface of a polished sample with pyrite detected shown using the false colour magenta. The pyrite grade of this picture is 0.073%.
Figure 3 A Mineral Insights picture of a 3 mm x 4.5 mm area on the surface of a polished sample with chalcopyrite detected shown using the false colour magenta. The chalcopyrite grade of this picture is 0.061%.
Figure 4 A Mineral Insights picture of a 3 mm x 4.5 mm area on the surface of a polished sample with galena detected shown using the false colour magenta. The galena grade of this picture is 0.110%.
Figure 5 A Mineral Insights picture of a 3 mm x 4.5 mm area on the surface of a polished sample with sphalerite detected shown using the false colour magenta. The sphalerite grade of this picture is 0.083%.
In Figures 4 and 5 it is clear that there is some overlap between the galena and sphalerite. The reason for this is that some of the particles are composed of both galena and sphalerite. To understand how Mineral Insights distinguishes between galena and sphalerite it must first be explained how the detection is done. The Gold Sniffer does its detection in two stages. The first stage uses only colour for detection. The second stage uses spatial data around the primary detection photosites to seek the boundaries of the mineral particle. In Figure 6 the primary detection photosites of galena are shown, and in Figure 7 the primary detection photosites of sphalerite on the same particle are shown. Mineral Insights is able to distinguish between primary detection galena and sphalerite because they are different colours. However, if these two minerals occur on the same particle then the spatial algorithm that extends the detection to the boundaries of the particle causes the same particle to be detected as both minerals as shown in Figure 7. This potential double counting is resolved by assigning the measured grade to the mineral with the lower grade, usually galena for these samples, and then subtracting this grade from the mineral with the higher grade, usually sphalerite. Occasionally, some measurements have galena and sphalerite grades that are nearly equal. In this case the highest grade is divided equally between galena and sphalerite. This approach provides reasonable estimates of the galena and sphalerite grades. A sense of the scale in Figures 5, 6 and 7 is obtained by noting that each photosite measures 1.6 microns x 1.6 microns.
Figure 6 This picture shows the primary detection photosites for galena that are detected using only the colour of galena.
Figure 7 This picture shows the primary detection photosites for sphalerite that are detected using only the colour of sphalerite.
Figure 8 This picture shows the full detection photosites for sphalerite that are detected using both the colour of sphalerite and spatial data. This detected area is divided between galena and sphalerite by using the measured grade for galena and subtracting this value from the measured grade for sphalerite to obtain the true grade for sphalerite.
Each of the gold particles detected on the four samples was examined to find out what mineral it is associated with. As shown in Table 1, all but 2 of the gold particles, which is 97% of the total, were associated with chalcopyrite. The remaining 3% were associated with pyrite.
Table 1 The measured grades for gold, pyrite, chalcopyrite, galena and sphalerite for the four polished samples of tailings from the gold mine. The number of gold particles associated with chalcopyrite and pyrite are presented, and it is found that 97% of the gold particles are associated with chalcopyrite.
84% of the gold particles are in contact with a chalcopyrite grain. This is demonstrated in Figure 9 which shows a gold particle, coloured magenta, composed of a single photosite located on a chalcopyrite grain. The chalcopyrite is visible as a brown streak. In Figure 10 the gold particle is marked with a yellow cross and the chalcopyrite is detected and marked as magenta. This shows how 84% of the gold particles are in contact with chalcopyrite.
Figure 9 A gold particle consisting of a single photosite that is on top of a chalcopyrite grain visible as a brown streak.]
Figure 10 A gold particle marked by a yellow cross on top of the same chalcopyrite grain shown in Figure 9. The chalcopyrite is detected by Mineral Insights and marked as the false colour magenta.
Due to the close association of gold with chalcopyrite it is instructive to note that for the tailings there is an approximately linear relationship between the gold grade and the chalcopyrite grade, as shown in Figure 11. This linear function quantifies the gains in gold recovery that can be obtained with a more efficient floatation process that recovers both more gold and more chalcopyrite. Mineral Insights is a useful tool to support the refinement of the floatation process because it can measure the gold and chalcopyrite grades in ore and tailings samples, and the relationship between these two grades.
Figure 11 A plot of gold grade versus chalcopyrite grade for the four polished samples of tailings from the gold mine. The linear fit quantifies the gains in gold recovery that can be obtained from a more efficient floatation process that recovers both more gold and more chalcopyrite.
Using the detection techniques described in this report each of the 24 measurements taken for each of the four polished samples was analyzed for gold, pyrite, chalcopyrite, galena and sphalerite. The measured grades for each sample were averaged for gold and the four minerals, and the results shown in Table 1 were obtained. It was found that 97% of the gold particles detected are either in contact with a chalcopyrite grain or microns away from a chalcopyrite grain. The results of this study have produced a linear function that quantifies the relationship between recovering more chalcopyrite in the floatation process and recovering more gold as a consequence of improvements to the floatation process. For this gold mine every 0.1 ppm reduction in the gold grade of the tailings produces $1.6 million US more gold each year at a gold price of $1100 US per ounce. The data measured by Mineral Insights can facilitate this increase in profits. This demonstrates the value of Mineral Insights measurements and analysis.