TEST SOTKAMO SILVER OY

Transcription

1 TEST REPORT SORTING OF SILVER ORE SOTKAMO SILVER OY KIDEKUJA VUOKATTI FINLAND Peter Simons Wedel

2 Customer: Sotkamo Silver Oy Represented by: Ilkka Tuokko Test Operator: Peter Simons Sabine Noelte Sales contact: Jörg Schunicht, TOMRA Sorting Mining (Germany) Jörn Rohleder, Outotec (Finland) Site: TOMRA Sorting Mining Feldstr Wedel Germany Test date: July,

3 1. INTRODUCTION TOMRA Sorting Mining offers ore sorting solutions from initial amenability testing and characterization through equipment supply to complete sorting lines. TOMRA s global presence with offices in North America, Germany, South Africa and Australia guarantees local ore sorting expertise and service for mining operations. This report describes the results of the bulk ore sorting tests performed on two different samples provided by Sotkamo Silver Oy. The samples were sorted by automated ore sorters at the TOMRA Sorting test facility in Wedel, Germany. 2. SORTING TASK A COM Tertiary XRT belt production scale sorting system was used in order to eject silver bearing rocks and upgrade the concentration of silver at maximized recovery. The test procedures followed by discussion of results are elaborated in the following sections of this report. 3

4 3. MATERIAL Sotkamo Silver Oy provided two samples of silver ore. These samples were generated artificially (sample blending) for testing a low-grade-scenario and an average-grade-scenario. The sample for the low-grade-scenario is called Green, for the average-grade-scenario Yellow. The base material for the sample blending originates from a test tunnel of the Sotkamo mine. According to the customer s description it is composed of quartz-sericite-schist and approx. 5 % of sulphide minerals. Main sulphides are sphalerite (ZnS), galena (PbS) and pyrite (FeS 2 ). Silver-bearing minerals are mainly pyrargyrite (Ag 3 [SbS 3 ]), dyscrasite (Ag 3 Sb) and freibergite (Ag 6 [Cu 4 Fe 2 ]Sb 4 S 13-x ). After mining the base material was washed and screened into a mm, followed by subsampling. Based on chemical analysis data these sub-samples were subsequently blended into a low grade ore sample ( Green ) and an average ore sample ( Yellow ). A customer s estimation of the Green and the Yellow sample composition is shown in the table below. This is just based on a rough calculation of chemical analysis data of the sub-samples. Table 1: Estimated composition of the artificially generated samples Color Weight [kg] Ag [g/t] Au [g/t] Zn [%] Pb [%] S [%] SiO2 [%] Amount ore pieces Green % Yellow % +- 4

5 3. MATERIAL Due to the inhomogenous blending both samples were additionally mixed (shoveling) and then split into two sub-samples. The latter one allows to run two different settings. The figure below gives an impression of the sample mixing and splitting. Figure 2: Material cone 5

6 3. MATERIAL During mixing and splitting it was observed that the thickness ratio of both samples varies up to 1:5 (see figure 2). For good tailored sorting programs and thus high sorting performances a 1:2 thickness ratio is recommended. This can be achieved in our facility by screening with bar screen decks at 8 mm, 20 mm, 40 mm and 60 mm. Figure 3: Example of a 1:5 thickness ratio Due to the relatively low sample amount in combination of testing different sorting sensitivities, the samples were not screened. Thus, more amount for testing and further analysis should be available. 6

7 4. TEST SYSTEM DE-XRT The XRT sorter used for the sorting tests is shown below in figure 4. Figure 4: Mass balance of the generated grain size fractions The sorter uses a broad-band electrical x-ray source. The material to be sorted is exposed to the x- radiation while it is moving along a belt. The x-ray sensor system below the material produces a digital image of the material, using two different energy bands. The x-ray attenuation through the material is different within the two bands and depends on both, the material thickness and atomic density. Special transformation of the attenuation images of the two bands classifies each pixel according to the measured atomic density. Because the x-rays pass through the particles and are a measure of the attenuation through the entire rock, XRT separation is almost independent of surface quality of the material or its moisture. Surface properties such as color and texture and/or contaminations such as dirt, dust, paint, etc. are mainly irrelevant to the detection. The used sorter in this test work has a working width of 600 mm and was run at a belt speed of 2,7 m/s. The feed rate for each sorting test is calculated for a standard unit of 1200 mm sorting width. More information about the sorting units can be found in the Appendix A. 7

8 XRT-image Classified image XRT-image Classified image 5. TEST PROCEDURE Before sorting, a training set was created by passing a small sample of each sample through the DE- XRT sorter. The generated raw XRT images were subsequently simulated by TOMRA Sorting Mining in-house software. Hereby, 2 different XRT sorting approaches were simulated: Inclusions Rock Rock Rock Inclusion Inclusion Dual-Energy Rock Rock High dense Low dense Both solutions have advantages and disadvantages. On the generated database a combination of both, means 2-step-sorting, was determined as a good approach theoretically. Hereby, step 1 focuses on inclusion sorting, whereas the Dual-Energy sorting is the objective of step 2. The reason is, that probably not all silver bearing ore can be recovered by one XRT sorting approach. Thus, a combination of both technologies can ensure a maximum recovery. 8

9 Waste (low density) Accept Product (high density) Accept Waste (no inclusions) Product (inclusions) 5. TEST PROCEDURE The figure below visualizes the before mentioned solution approach by means of simulated XRT of the two sample types. Explanation simulation step 1 : Explanation simulation step 2 : Black pixel: Inclusion; White pixel: No inclusion Blue pixel: High density; Red pixel: Low density Sorting step 1: Inclusions Eject Sorting step 2: Dual-Energy Eject Figure 3: Schematic illustration of the sorting approach with simulated XRT-images 9

10 5. TEST PROCEDURE For both samples 2 test series with different sensitivities regarding the inclusion sorting step were carried out: Aggressive (A): Minimum 0,5 % inclusions per rock Maximum recovery, but less product quality expected Less aggressive (LA): Minimum 2 % inclusions per rock In comparison to A: Less recovery, but higher product quality expected The sensitivity of the dual-energy scavenger step was the same in all test series (minimum 5 % dense areas). The table below gives an overview. Table 2: Estimated composition of the artificially generated samples Test Sample type XRT-Technology Setting / Sensitivity Test 1 step 1 Green / Low grade Inclusions Aggressive Test 1 step 2 Green / Low grade Dual-Energy Test 2 step 1 Green / Low grade Inclusions Less aggressive Test 2 step 2 Green / Low grade Dual-Energy Test 3 step 1 Yellow / Average grade Inclusions Aggressive Test 3 step 2 Yellow / Average grade Dual-Energy Test 4 step 1 Yellow / Average grade Inclusions Less aggressive Test 4 step 2 Yellow / Average grade Dual-Energy After each test the mass of the generated products (Accept and Eject) were taken. On this data basis a mass balance is calculated. Hereby, the sum of the end-product mass fractions is equal to 100 %. By taking the chemical analysis data into account, recoveries and product qualities are determined 10

11 6. SORTING RESULTS Test 1: Sample type "Green", Setting A Feed Mass [kg] 398,9 Grain size [mm] Feed [t/h] 30 Air Pressure [bar] Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] Feed 53,1 0,29 0,20 0,43 2,45 1,43 Recovery step 1 [%] 84,17 83,78 83,37 78,14 39,55 56,76 Total Recovery [%] 87,06 87,04 84,74 82,66 44,67 63, mm 7 Accept Waste No inclusions Eject Product Inclusions Dist. [%] Mass [kg] Dist. [%] Mass [kg] 262,9 65,91 Mass [kg] 136,0 34,09 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 13 15,83 Ag [ppm] ,17 0,07 16,22 Au [ppm] 0,71 83,78 0,05 16,63 Pb [%] 0,50 83,37 0,14 21,86 Zn [%] 0,99 78,14 2,25 60,45 Fe [%] 2,84 39,55 0,94 43,24 S [%] 2,38 56,76 Accept Waste Low density Eject Product High density Dist. [%] Dist. [%] Mass [kg] 249,0 62,42 Mass [kg] 13,9 3,48 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 11 12,94 Ag [ppm] 44 2,89 0,06 12,96 Au [ppm] 0,27 3,26 0,05 15,26 Pb [%] 0,08 1,36 0,12 17,34 Zn [%] 0,56 4,52 2,17 55,33 Fe [%] 3,59 5,11 0,84 36,68 S [%] 2,69 6,56 11

12 6. SORTING RESULTS Test 2: Sample type "Green", Setting LA Feed Mass [kg] 405,0 Grain size [mm] Feed [t/h] 31 Air Pressure [bar] Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] Feed 65,3 0,41 0,23 0,47 2,58 1,64 Recovery step 1 [%] 72,14 68,29 74,09 69,44 28,87 47,48 Total Recovery [%] 80,45 78,03 77,11 79,06 35,69 55, mm 7 Accept Waste No inclusions Eject Product Inclusions Dist. [%] Mass [kg] Dist. [%] Mass [kg] 321,0 79,26 Mass [kg] 84,0 20,74 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 23 27,86 Ag [ppm] ,14 0,16 31,71 Au [ppm] 1,35 68,29 0,08 25,91 Pb [%] 0,82 74,09 0,18 30,56 Zn [%] 1,56 69,44 2,32 71,13 Fe [%] 3,59 28,87 1,09 52,52 S [%] 3,75 47,48 Accept Waste Low density Eject Product High density Dist. [%] Dist. [%] Mass [kg] 304,0 75,06 Mass [kg] 16,5 4,07 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 17 19,55 Ag [ppm] 133 8,30 0,12 21,97 Au [ppm] 0,98 9,74 0,07 22,89 Pb [%] 0,17 3,02 0,13 20,94 Zn [%] 1,10 9,62 2,21 64,31 Fe [%] 4,32 6,82 0,98 44,91 S [%] 3,06 7,61 12

13 6. SORTING RESULTS Test 3: Sample type "Yellow", Setting A Feed Mass [kg] 414,7 Grain size [mm] Feed [t/h] 31 Air Pressure [bar] Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] Feed 142,5 0,86 0,50 0,93 2,82 2,40 Recovery step 1 [%] 86,89 85,32 90,48 85,94 66,77 78,87 Total Recovery [%] 90,32 90,66 91,33 89,88 72,47 83, mm 7 Accept Waste No inclusions Eject Product Inclusions Dist. [%] Mass [kg] Dist. [%] Mass [kg] 166,7 40,20 Mass [kg] 248,0 59,80 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 46 13,11 Ag [ppm] ,89 0,31 14,68 Au [ppm] 1,22 85,32 0,12 9,52 Pb [%] 0,76 90,48 0,33 14,06 Zn [%] 1,34 85,94 2,33 33,23 Fe [%] 3,15 66,77 1,26 21,13 S [%] 3,17 78,87 Accept Waste Low density Eject Product High density Dist. [%] Dist. [%] Mass [kg] 150,5 36,29 Mass [kg] 16,2 3,91 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 38 9,68 Ag [ppm] 125 3,43 0,22 9,34 Au [ppm] 1,17 5,34 0,12 8,67 Pb [%] 0,11 0,86 0,26 10,12 Zn [%] 0,94 3,94 2,14 27,53 Fe [%] 4,12 5,70 1,08 16,31 S [%] 2,97 4,83 13

14 6. SORTING RESULTS Test 4: Sample type "Yellow", Setting LA Feed Mass [kg] 427,2 Grain size [mm] Feed [t/h] 32 Air Pressure [bar] Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] Feed 145,6 0,87 0,46 1,10 3,05 2,71 Recovery step 1 [%] 74,92 72,80 78,47 74,87 46,15 61,09 Total Recovery [%] 82,85 83,49 82,74 85,95 60,84 74, mm 7 Accept Waste No inclusions Eject Product Inclusions Dist. [%] Mass [kg] Dist. [%] Mass [kg] 269,2 63,01 Mass [kg] 158,0 36,99 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 58 25,08 Ag [ppm] ,92 0,38 27,20 Au [ppm] 1,71 72,80 0,16 21,53 Pb [%] 0,98 78,47 0,44 25,13 Zn [%] 2,22 74,87 2,61 53,85 Fe [%] 3,81 46,15 1,68 38,91 S [%] 4,48 61,09 Accept Waste Low density Eject Product High density Dist. [%] Dist. [%] Mass [kg] 227,0 53,14 Mass [kg] 42,2 9,88 Ag [ppm] Au [ppm] Pb [%] Zn [%] Fe [%] S [%] 47 17,15 Ag [ppm] 117 7,94 0,27 16,51 Au [ppm] 0,94 10,69 0,15 17,26 Pb [%] 0,20 4,28 0,29 14,05 Zn [%] 1,23 11,08 2,25 39,16 Fe [%] 4,54 14,69 1,28 25,08 S [%] 3,80 13,84 14

15 Tomra Sorting Mining Wedel, 12th August, 2015 i.a. Peter Simons Applications Engineer 15

16 APPENDIX A COM Series The COM (common belt) series sorting equipment covers the range of applications which require a belt feeding system. The belt principle allows the presentations of a non-uniform feed. The particles can stabilize on the belt before they are scanned by the sensor X-ray transmission technology enables materials to be recognized and separated based on their specific atomic density Feeding of unsorted material X-ray source X-ray camera Separation chamber Figure A1: Schematic illustration of a sorting unit (COM series) A broad-band electrical x-ray source (3) is applied to the material to be sorted while it is moving along the belt. The X-ray sensor system (2) below the material produces a digital image of the material being sorted, using two different energy bands. The X-ray attenuation through the material is different within the two bands and depends on both the material's thickness and density. An image transformation of the density images of the two bands then makes it possible to classify each pixel according to atomic density. Classification proceeds relative to a reference density, to which the system has been calibrated. Depending on the classification the selected particles are either ejects, diverted upwards by air jets (Material Stream A) or Accepts in the other stream (Material Stream B). It is important to note that Eject refers to the material that the system has been configured to blow out of the material stream; this can be either the waste or the product. 16

17 APPENDIX A Figure A2: Test 1 step 1 Eject (Product) Figure A3: Test 1 step 1 Accept (Waste) 17

18 APPENDIX A Figure A4: Test 1 step 2 Eject (Product) Figure A5: Test 1 step 2 Accept (Waste) 18

19 APPENDIX A Figure A6: Test 2 step 1 Accept (Waste) Figure A7: Test 2 step 1 Eject (Product) 19

20 APPENDIX A Figure A8: Test 2 step 2 Accept (Waste) Figure A9: Test 2 step 2 Eject (Product) 20

21 APPENDIX A Figure A10: Test 3 step 1 Eject (Product) Figure A11: Test 3 step 1 Accept (Waste) 21

22 APPENDIX A Figure A12: Test 3 step 2 Eject (Product) Figure A13: Test 3 step 2 Accept (Waste) 22

23 APPENDIX A Figure A14: Test 4 step 1 Accept (Waste) Figure A15: Test 4 step 1 Eject (Product) 23

24 APPENDIX A Figure A16: Test 4 step 2 Eject (Product) Figure A17: Test 4 step 2 Accept (Waste) 24