Copper Concentrate Sampling, control over final product.

Transcription

1 Copper Concentrate Sampling, control over final product. Patricio Valenzuela Fuenzalida Codelco-Chile Div. Andina/ Los Andes Chile/ Jefe Unidad Muestreo_Pesaje, , Vicente Esparza González Minerals Sampling/Automation & Electrical Engineering/ FLSmidth, Chile, Business Manager, , 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.

2 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.

3 FLOWSHEET Concentrated Flow (Copper) (930 ton/hr) Primary sample (30 Kg/cutting) Secondary Sample Final Sample (16 Kg/Final lot Sample) Concentrated to Train Wagon (loading less than 50'' per container)

4 Productive process of Codelco s Andina Division Primary Crushing Don Luis Fine Material Belt 4F North Primary Crushing South Primary Crushing Secondary Crushing A6-A7 Belt Pannel Belt 5 Thick Hoppers SAG Grind Feeding Hopper Tertiary Crushing Quaternary Crushing SAG Grinding Unitary Grinding Conventional Grinding Collective Flotation Selective Flotation Los Leones Dam Molybdenum Concentrate Copper Concentrate Ovejería Dam

5 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: s 2 ( CE) Sy Z( j) Q Sy Stratified random sampling: Completely random sampling: s 2 ( CE) Ra Z( j) Q Ra 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: Z( j) 2W ( j / 2) W Sy, j 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.

6 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.

7 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).

8 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 5.5e-005 Rel Variogram: C1 Registro dual Vario 4.4e-005 V_trend 3.3e e-005 V_trend_j 1.1e-005 V(0) X-axis 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

9 Cº2 Mass Campaign Rel Variogram: Registro Dual C2 Dual Record C2 Vario V_trend 8.4e e-005 V_trend_j 2.8e-005 V(0) X-axis 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 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.

10 Determination of Maximum Total Error Acceptable Minutes Stratified random Systematic Error 1 0,4777% 0, ,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, ,2091E-04 1,100% 6 1,2277% 1,4960E-04 1,223% 7 1,3312% 0, ,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.

11 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.

12 TABLES DUAL1 MASS RECORD Id Campaign Total weight (g) Cu Grade (%) 1 Dual ,5 27,72 2 Dual ,3 28,24 3 Dual ,9 28,30 4 Dual ,1 28,37 5 Dual ,2 28,22 6 Dual ,3 28,16 7 Dual ,6 28,33 8 Dual ,6 27,98 9 Dual ,9 28,16 10 Dual ,0 28,26 11 Dual ,8 28,24 12 Dual ,9 28,19 13 Dual ,0 28,27 14 Dual ,1 28,14 15 Dual ,0 28,30 16 Dual ,1 28,14 17 Dual ,1 28,34 18 Dual ,9 28,60 19 Dual ,5 28,26 20 Dual ,7 28,33 21 Dual ,4 28,32 22 Dual ,6 28,19 23 Dual ,4 28,20 24 Dual ,2 28,33 25 Dual ,0 28,14 26 Dual ,4 27,94 27 Dual ,1 28,51 28 Dual ,5 28,54 29 Dual ,9 28,53 30 Dual ,8 28,40 31 Dual ,2 28,46 32 Dual ,2 28,20 33 Dual ,6 28,61 34 Dual ,1 28,24 35 Dual ,5 28,30 36 Dual ,2 28,16 37 Dual ,8 28,65 38 Dual ,8 28,50

13 Id Campaign Total weight (g) Cu Grade (%) 39 Dual ,2 28,30 40 Dual ,5 28,45 41 Dual ,6 28,42 42 Dual ,3 28,51 43 Dual ,1 28,59 44 Dual ,3 28,49 45 Dual ,5 28,29 46 Dual ,9 28,52 47 Dual ,5 28,33 48 Dual ,0 28,52 49 Dual ,8 28,53 50 Dual ,2 28,33 51 Dual ,8 28,43 52 Dual ,6 28,55 53 Dual ,7 28,59 54 Dual ,6 28,53 55 Dual ,1 28,50 56 Dual ,7 28,44 57 Dual ,7 28,52 58 Dual ,1 28,59 59 Dual ,1 28,51 60 Dual ,1 28,46 Average 1513,9 28,4

14 DUAL2 MASS RECORD Id Campign Total weight (g) Cu Grade (%) 1 Dual ,8 28,40 2 Dual ,7 28,35 3 Dual ,2 28,01 4 Dual ,4 28,20 5 Dual ,8 28,35 6 Dual ,6 28,20 7 Dual ,2 28,07 8 Dual ,4 28,54 9 Dual ,4 28,13 10 Dual ,4 28,20 11 Dual ,2 28,30 12 Dual ,1 28,10 13 Dual ,1 28,14 14 Dual ,6 27,91 15 Dual ,0 28,12 16 Dual ,8 28,08 17 Dual ,9 28,54 18 Dual ,5 28,14 19 Dual ,0 28,41 20 Dual ,0 28,68 21 Dual ,2 28,21 22 Dual ,8 28,53 23 Dual ,1 28,32 24 Dual ,4 27,93 25 Dual ,9 28,35 26 Dual ,1 28,02 27 Dual ,1 28,70 28 Dual ,1 28,24 29 Dual ,8 28,32 30 Dual ,3 28,18 31 Dual ,3 28,28 32 Dual ,9 28,07 33 Dual ,5 28,48 34 Dual ,6 28,53 35 Dual ,4 28,60 36 Dual ,3 28,32 37 Dual ,2 28,09 38 Dual ,8 28,42 39 Dual ,0 28,98 40 Dual ,7 28,23 41 Dual ,6 28,18 42 Dual ,9 28,02

15 Id Campign Total weight (g) Cu Grade (%) 43 Dual ,5 27,88 44 Dual ,0 28,22 45 Dual ,5 28,14 46 Dual ,8 28,00 47 Dual ,2 28,03 48 Dual ,7 27,94 49 Dual ,4 28,08 50 Dual ,4 28,13 51 Dual ,7 28,19 52 Dual ,1 27,98 53 Dual ,2 28,05 54 Dual ,4 27,94 55 Dual ,7 27,84 56 Dual ,1 27,68 57 Dual ,73 58 Dual ,3 27,21 59 Dual ,6 27,85 60 Dual ,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.

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

17 Photo # 6, Loading process to stacker #15

18 Photo # 7, Primary Cutter, bottom dump design