Variography applied as a tool to monitor metallurgy. EMPV software application to gold and silver recovery

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1 Paper 02:text 10/7/09 1:33 PM Page 73 TELLO, A. Variography applied as a tool to monitor metallurgy. EMPV software application to gold and silver recovery. Fourth World Conference on Sampling & Blending, The Southern African Institute of Mining and Metallurgy, Variography applied as a tool to monitor metallurgy. EMPV software application to gold and silver recovery A. TELLO y Compañía Limitada, Consultores en Calidad, muestreo y Control de Procesos, Chile Variography was introduced by Dr. Pierre Gy to monitor a process, in the sixties. Later on, Francis Pitard extended this concept, building a complete system of variographic controls for different applications. Nevertheless, the existence of information about its use as a way to effectively control plants has been limited. There are infinite areas where variography and chronostatistics can be used. In this paper, two different cases related to gold and silver recovery, through well known but complex metallurgical routes are studied. The use of effective management of process variability software (EMPV) has shown to be a suitable solution to clarify problems at the plant and, consequently, a solution to the encountered problems has been proposed. Chronostatistics is like opening a new world to the metallurgist, making the process variability speak in a more comprehensive and in depth way. Once the ore has been extracted from the mine, sampling is necessary at each sequence of the metallurgical process involved. As a first example, leaching of the ore with cyanide in agitated tanks is another case of a complex metallurgy. The aim of the process is to know about residual values in the tailings in order to optimize process. As a second example, leaching of gold and silver in ore heaps, has to be optimized in terms of time, reagents consumption and, of course, in predicting a reasonable recovery. This last issue appears to be crucial. After cyanide leaching at the heaps, a carbon in columns process is involved, where there are a series of carbon loaded columns that are submitted to a dynamic process of load and unload, to finally deliver an enriched solution to the precious metal electro winning plant. In both cases, variography has been a good approach to show metallurgists ways to improve the operation. Introduction The use of Variography in chronostatistics has been shown by Pitard 2. When we look at the total estimation error we summarize it as follows: The first part of the equation is well known and used, at the central limit theorem (CLT). Secondly, covariances, is used in chronostatistics studies or variographic analysis. There is strong evidence that covariances are not zero and that is the main reason to take this tool to interpret and optimize processes. As a routine basis, data is collected at regular time intervals in any industrial process. Usually, information collected represents an 8 or 12 hours period, or shifts. We can handle this data to evaluate our process, taking care that they are homogeneous, and no important changes have been applied to the process in the way it works. Calculating a variogram Let us agree in a nomenclature to write our data: Each of the collected values is a i. The natural way to compare two values of a variable measured at regular times in a process is to measure their difference d: [1] d = a 1 a 2 [2] In a set of data we are interested in the average difference between many measurements N a given interval j apart It is well known that such difference converges toward zero. To avoid this inconvenience the squared differences should be used instead. The average squared difference is divided by 2. The averaged squared differences represent an absolute semi-variogram which is written as follows: A relative, dimensionless semi variogram can be calculated as well, making it easier to compare variograms from different experiments, simply dividing by the squared average content of the lot a 2 L: In order to accomplish all calculations required, in an easy way, an existing software was used: effective management of process variability (EMPV). This software is friendly to use, as no programming is required. [3] [4] [5] VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 73

2 Paper 02:text 10/7/09 1:33 PM Page 74 Gold and silver recovery The Zinc cementation process has been used extensively in the gold mining industry for more than 100 years. The process is very efficient and relatively low cost, and still produces favourable economics versus carbon based process in a number of applications. Carbon based processes ( CIP, CIL, CIC) have assumed a dominant role in the last 30 years and account for more than 70% of annual production today. The economics of treating high grade solutions (particularly those containing high silver concentration) favour Merril Crowe over carbon, because of the relatively high capital and operating cost to elute and regenerate carbon. The economics of treating large volumes of low grade solution or pulp strongly favor Carbon over Merril Crowe because of the high cost of solid liquid separation clarification. This allowed lower cut off ore grades to be economically treated after the 1970s. Overall, gold recovery is generally higher with carbon/pulp based processes than MC because: Soluble gold and silver are lower, particularly with ores that are difficult to filter or thicken There is additional leaching time in CIP It is possible to overcome the weak preg-robbing effects that occur in certain ore types. Practical aspects of gold recovery at Kinross Gold Company Kinross is an important and well known commercial Company, which operates two plants for Gold recovery in Chile. The first plant is Mantos de Oro, MDO and the second plant is Maricunga, CMM. Each of them applies one of the two technologies presented above. It is interesting to know that each of the processes has particular difficulties and both of them can be optimized using a process control criteria, based on statistical studies by variography as the tool to show the key areas to improve. Case Study #1: gold and silver recovery by agitated leaching followed by Merrill Crowe cementation Typical flowsheet involves several stages: Ore stock pile coming from the mine Crushing Milling, sometimes cyanide addition starts here Leaching in agitated tanks, air injection Solid liquid Separation (CCD or Filtration). Tailings collection after filter press process Pregnant leach liquor goes to clarification De-aeration in vacuum tower Clarified solution is submitted to zinc addition (Merril Crowe cementation) Solid liquid separation by filtration. Liquid returned to CCD stage Solid goes to calcining Smelting Doré. Process optimization As in any metallurgical process, the efficiency of the whole process is measured by quantifying presence of values in the tailings. In this case, after the stage where the pulp is treated in CCD; the solids undergo the filtration process in filter press, and tailings are sampled to check on the extent of Gold and Silver dissolution by cyanide leaching. Sampling system actually used, consists of manual sampling collecting grab samples from each of the filters, every 15 minutes to get a composite sample over the 12 hours shift. It is expected that tailings comply with specifications or wish list: US = Upper specification TA = Targeted Average LS = Lower Specification Gold: statistical data analysis in tailings The following pages show information gathered in 2004 to 2005, for grab sampling in tailings after filter press. Data collected means 12 hours shifts. EMPV software allows displaying data in graphics, showing histograms, control charts and Variograms. Figure 1 shows data collected and wish list as specifications. Data distribution is important, as we can see real data and its behavior in terms of mode, on overall distribution. Figure 2 shows data distribution. We can see that mode (0.16) is different from average (0.19), probably because this average is affected by some extreme values, which is what usually happens when dealing with gold ores. It is important to note these extreme values and to evaluate how frequently do they appear in the process. A good explanation about the interpretation of a variogram can be found in a paper by A.G. Royle 4, who explains this tool in a descriptive way. In the Variogram, there is a range corresponding to the range of influence of the variable, a well known concept that the variogram puts on a rational and numerical basis. The variogram splits the total variance into two parts. One represents the spatial differences between the values of samples taken at points separated by increasingly larger distances. The other represents local or short range variances. In actual case, samples are taken a time-lag apart, but the concept appearing in the variogram is the same as above: variance is split into two parts. The local or random variance is called the nugget variance, while the larger scale one is the spatial variance. Thus, any assay measured in mineral deposits, or time-lag in a chronological study, exhibits a partly random variation and a partly spatial variation. From variogram in Figure 3, we find the spatial variance. Variance has a trend to increase reaching its maximum when J=14, or 7 days. Probably this is related to change of shifts (people). We also detect a cycle with about same period (6 days). V(0) is the short range variance, or random variance, projected at J=0. Following Pitard 5, this variance mainly represents effects from sampling, sample preparation and chemical analysis. It is remarkable that if this variance is big, there is no way of improving the process, as any modification would be masked by a high variance. In this case, V(0) is moderate, but even so, it should be minimized. Table I Wish List: gold and silver specifications in tailings US TA LS Au (g/t) Ag(g/T) FOURTH WORLD CONFERENCE ON SAMPLING & BLENDING

3 Paper 02:text 10/7/09 1:33 PM Page 75 Table II EMPV Report: Agitated Leaching Tailings Au Relative Absolute V(0) 3,88E-02 1,38E-03 * (Au (ppm))^2 V(0)^.5 1,97E-01 3,72E-02 * (Au (ppm)) V[process j=1] 1,97E-02 7,02E-04 * (Au (ppm))^2 V[process j=1]^.5 1,40E-01 2,65E-02 * (Au (ppm)) V[cyclic] 3,23E-02 1,15E-03 * (Au (ppm))^2 V[cyclic]^.5 1,80E-01 3,39E-02 * (Au (ppm)) In relative terms V(0) is 19.7% of the average content. From the variogram, we get control limits to be applied in the chart. V(0) is a Variance; square root of V(0) is a standard deviation, showing variability in the measurement step. Control Limits are: UCL = Average +3 times Square root of [V(0)] LCL = Average - 3 times Square root of [ V(0)] Silver: statistical data analysis in tailings From Figure 4 to Figure 7 we can see data analysis for silver in tailings. Control chart is built by using square root of [V(0)] determined from variogram. This chart shows some events where Ag goes higher, meaning that leaching was not effective for the ore at that time Distribution chart shows that average and mode are a little different also. Variogram shows the range where it is possible to find. Figure 1. Data control chart for gold in tailings Figure 2. Data distribution gold in tailings Figure 3. Absolute variogram, gold in tailings VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 75

4 Paper 02:text 10/7/09 1:33 PM Page 76 some correlation between samples, which appears to be 18 shifts or 9 days. V(0) is lower in comparison to V(J=1) considered as variance of the process. This fact means that sampling variance is relatively low in comparison to process variability. Overall conclusion to improve the process The sampling system for gold and silver in tailings is the same. However, sampling variance for gold is higher than sampling variance for silver. This is a remarkable conclusion. Probably, gold distribution in the ore is more heterogeneous than silver. Sampling variance for Gold can be diminished if sample increments are bigger, and sampling system is flawless by design. If the plant manager wants to increase gold recovery, he should consider different heterogeneity for gold and silver. Variograms for gold and silver are able to determine V(0) separated from each other, allowing a clear screening of sampling effect. On the other hand, if sampling for gold is more difficult, due to heterogeneity in the ore, it is clear that a manual system will not allow a good control of tailings due to probable segregation happening on the belt where manual sampling is performed. As a recommendation, a complete automatic, sampling system should be installed. Figure 4. Data control chart for silver in tailings Figure 5. Distribution of Ag in tailings Figure 6. Absolute variogram for Ag in tailings 76 FOURTH WORLD CONFERENCE ON SAMPLING & BLENDING

5 Paper 02:text 10/7/09 1:33 PM Page 77 Table III EMPV report agitated leaching tailings: Ag Relative Absolute V(0) 5,62E-03 1,68 * (Ag (ppm))^2 V(0)^.5 7,49E-02 1,30 * (Ag (ppm)) V[process j=1] 1,31E-02 3,93 * (Ag (ppm))^2 V[process j=1]^.5 1,15E-01 1,98 * (Ag (ppm)) V[cyclic] 2,33E-02 6,96 * (Ag (ppm))^2 V[cyclic]^.5 1,52E-01 2,64 * (Ag (ppm)) Case Study #2: gold and silver recovery by activated carbon in columns, followed by electrowining Typical flowsheet involves several stages: Ore stock pile coming from the mine Crushing (primary and secondary) Crushing (tertiary, up to particle size suitable for heap leaching) Conditioning with lime Heap Leaching by adding cyanide in solution Solution pumping to absorption desorption refining plant (ADR plant) Valuable metals (Au and silver) are adsorbed in Carbon in columns. Solution goes in counter current with carbon in the columns. Loaded carbon is advanced to elution column Loaded carbon is eluted. Downloaded carbon goes to regeneration step. Solution goes to EW cells Figure 7. Diagram of different flows in balance Smelting Doré Mass Balance: Au in PLS transferred to adsorption trains To get a mass balance some concepts should be defined Fixed parameters (Au) s 0 = Gold concentration in feed (PLS) F s = Flow of solution F C = Flow of Carbon (Au) C 0 = Gold concentration in eluted carbon Design parameters (Au) s n = Gold concentration in barren alter stage n (Au) C 1 = Gold concentration in loaded carbon Variable parameters N = Number of stages (C) = Carbon tonnage in each stage We may establish a mass balance equation Mass balance Fs ([Au] s 0 -[Au] s 1) = F C ([Au] C 1 - [Au] C 2) [6] Equation [6] simply states that there should be a balance for the gold transferred from solution (left side) into carbon in columns (right side). Data analysis. Carbon columns at ADR plant To calculate a balance, it is needed to know precision of sampling systems at each of the trains existing in ADR plant. ADR plant consists of three trains of carbon in column. Trains A and B are fed by same PLS solution, but sampling system at each train is independent. Train C is fed with intermediate solution. Barren solution is independent for each train also. As we do not have replicate sampling in an industrial plant, we need to know the effect of random variables affecting average content determined at each composite sample taken at each sampling point. The only way to accomplish this is to run variograms with existing data, which give us a good picture of what is happening at the sampling point, and what is the contribution to total Figure 8. Relative variogram: feed train A VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 77

6 Paper 02:text 10/7/09 1:33 PM Page 78 variance. If we run classic statistics of the data, calculating averages, standard deviation and number of samples, we will never know the effect of sampling in our data. Standard deviations taken from traditional analysis account for the total variability and do not decompose variability into its components. Variability when feeding trains with rich solution Sampling systems at each of the trains is almost automatic. Solution is taken by introducing a wire at the flow exiting the column; each of the collected drops by the wire is received in an open recipient, that sometimes overflows. If the system is controlled and supervised, avoiding overflow, the sample is good enough for process control purposes. Variograms show this relatively good sampling system, with low values for V(0). Figures 8, 9 and 10 show the variograms obtained from operational data. It is remarkable that the shape of variograms in Trains A and B are very similar, because they are fed with the same PLS solution. Train C (Figure 10) shows a different shape. Each of the figures is a relative variogram because it is easier to compare relative results when looking at operational data. Anyway, the shape of the variograms is exactly the same when dealing with absolute variograms. Table IV shows a summary of absolute and relative parameters obtained from variographic analysis. In Table IV there is also a column explaining the term rv(0) which is used in graphs in the next page. rv(0) is Square root of V(0) or V(0)^.5. Table V shows a global summary of classic statistics and variographic statistics. We can see absolute parameters as average and standard deviation. We also find relative parameters such as coefficient of variation. In the same table, appears data obtained from variograms. Coefficient of variation is a relative measurement expressed as: CV = Standard deviation / Average In Figures 11 and 12, we can see graphs of the information presented in Tables IV and V. It is evident that standard deviation is considering total variances of the whole set of data, while rv(0) is showing just the variability at the sampling point. In the same graph, we can compare the three trains. The conclusion is that train C has more variability at the sampling point than trains A and B. Figure 12 shows relative comparisons, where coefficient of variation is used instead of standard deviation. As a conclusion in this analysis, we find that biggest sampling variability in columns is given by Train C. Reducing this variability is an important task to perform, to get accurate Figure 9. Relative variogram: feed train B Figure 10. Relative variogram: feed train C 78 FOURTH WORLD CONFERENCE ON SAMPLING & BLENDING

7 Paper 02:text 10/7/09 1:33 PM Page 79 Table IV Variogram data summary: gold fed to carbon columns TRAIN EMPV report names Graph names Relative Absolute A V(0) 1,79E-03 3,22E-04 * (G/T AU)^2 V(0)^.5 rv(0) 4,23E-02 1,79E-02 * (G/T AU) Average 0,419(G/T AU) B V(0) 1,35E-03 2,30E-04 * (G/T AU)^2 V(0)^.5 rv(0) 3,67E-02 1,51E-02 * (G/T AU) Average 0,416(G/T AU) C V(0) 3,07E-03 1,00E-04 * (G/T AU)^2 V(0)^.5 rv(0) 5,54E-02 1,00E-02 * (G/T AU) Average 0,172(G/T AU) Table V Complete statistics summary. Gold fed to carbon columns Au Au Au (PLS) (PLS) (Intermediate. solution.) Train A B C Absolute Data parameters Average (mg/l) 0,419 0,416 0,172 Std. Deviation (mg/l) 0,041 0,037 0,024 Absolute rv(0) 1,79E-02 1,51E-02 1,00E-02 Absolute rv(p) 8,01E-03 8,07E-03 5,45E-03 Absolute rv(res) 1,67E-02 1,63E-02 9,22E-03 Relative Data Parameters Coefficient of Variation (CV) 0,10 0,09 0,14 Relative rv(0) 4,23E-02 3,67E-02 5,54E-02 Relative rv(p) 2,02E-02 1,88E-02 3,65E-02 results from the balance of solutions, as final the result, is the addition of the three trains. Variability for Barren solution from trains Figures 13, 14, and 15 show variograms from Barren solutions, in trains A, B and C. Cyclic behaviour in train A is evident. Every 2 shifts there is a change at the plant, where handling solutions increase recovery in each train. This situation is well known at the plant but is much more evident when looking at the variograms. Train B is a little different from A, but handling is the same. Train C has a longer cycle and every 6 shifts or three days solutions are handled. Data in Tables VI and VII show the data obtained from V(0) at each of the variograms. When comparing precision data it is evident trains A and B are with higher V(0) than train C. However, despite this relative high variation, absolute values are very low, with a minor incidence in the overall balance at the plant. As a recommendation, sampling system at trains A and B, should Figure 11. Absolute dispersion: feed to trains of activated carbon in columns VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 79

8 Paper 02:text 10/7/09 1:33 PM Page 80 Figure 12. Relative dispersion: feed to trains of activated carbon in columns Figure 13. Variogram, barren solution train A Figure 14. Variogram, barren solution train B 80 FOURTH WORLD CONFERENCE ON SAMPLING & BLENDING

9 Paper 02:text 10/7/09 1:33 PM Page 81 Figure 15. Variogram, barren solution train C be improved as V(0) accounts for 26 and 24% of total variability at the sampling point. Variability from loaded and downloaded carbon As we already mentioned, a mass balance can be calculated with Equation [6]: F s ([Au] s 0 -[Au] s 1) = FC ([Au] C 1 - [Au] C 2) [6] Now it is the time to look at the right side of the equation: balance with loaded and downloaded carbon. Table VI Variogram data summary: gold in barren from carbon columns Relative Absolute Train A V(0) 6,64E-02 4,31E-05 * (G/T AU)^2 V(0)^.5 2,58E-01 6,57E-03 * (G/T AU) Au Average Train 0,0255(G/T AU) Train B V(0) 5,75E-02 2,78E-05 * (G/T AU)^2 V(0)^.5 2,40E-01 5,28E-03 * (G/T AU) Au Average Train 0,0220(G/T AU) Train C V(0) 5,77E-03 4,84E-06 * (G/T AU)^2 V(0)^.5 7,59E-02 2,20E-03 * (G/T AU) Au Average Train 0,029(G/T AU) Figure 16 shows absolute variogran for loaded carbon. It is evident there is no splitting of variability between short range or random variability and long term or spatial variability; this variogram is completely random, and V(0) is equal to total variability found. Table VIII is a summary of classic and variographic statistics. In this table, it is seen that coefficient of variation and relative rv(0) are about the Table VII Summary statistics Barren solutions trains A, B, C Au Barren Au Barren Au Barren Train A B C Absolute data parameters aaverage (mg/l) 0,025 0,022 0,029 Std. Dev. (mg/l) 0,009 0,008 0,007 Absolute rv(0) 6,57E-03 5,28E-03 2,20E-03 Absolute rv(p) 2,70E-03 2,01E-03 3,59E-03 Absolute rv(res) 2,88E-03 1,18E-03 4,73E-03 Relative Data parameters Coefficient of Variation (CV) 0,36 0,35 0,24 Relative rv(0) 2,58E-01 2,40E-01 7,59E-02 Relative rv(p) 1,06E-01 9,14E-02 1,24E-01 Relative rv(res) 1,13E-01 5,34E-02 1,63E-01 Figure 16. Variogram loaded carbon VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 81

10 Paper 02:text 10/7/09 1:33 PM Page 82 Figure 17. Absolute variogram: downloaded carbon Table VIII Summary statistics, loaded and downloaded carbon Loaded Downloaded Cuenta promedio desv std Absolute rv(0) 4,29E+02 3,98E+01 Coefficient of Variation (CV) 2,36E-01 1,10E+00 Relative rv(0) 2,18E-01 6,37E-01 Findings: Loaded Relative Absolute V(0) 4,76E-02 1,84E+05 * (Au G/T)^2 V(0)^.5 2,18E-01 4,29E+02 * (Au G/T) V[process j=1] 7,64E-03 1,98E+04 * (Au G/T)^2 V[process j=1]^.5 8,74E-02 1,41E+02 * (Au G/T) V[resid] 8,83E-03 3,68E+04 * (Au G/T)^2 V[resid]^.5 9,40E-02 1,92E+02 * (Au G/T) Findings: Downloaded Relative Absolute V(0) 4,05E-01 1,59E+03 * (Au G/T)^2 V(0)^.5 6,37E-01 3,98E+01 * (Au G/T) V[process j=1] 8,83E-02 5,88E+02 * (Au G/T)^2 V[process j=1]^.5 2,97E-01 2,42E+01 * (Au G/T) V[cyclic] 4,53E-01 2,04E+03 * (Au G/T)^2 V[cyclic]^.5 6,73E-01 4,52E+01 * (Au G/T) same, showing that 100% of relative variability is random. The fact that variability in loaded carbon is completely random, does not allow a good balance because of lack of precision. Figure 17 shows a variogram of downloaded carbon. This variogram presents marked cycles, and it is different from loaded Carbon. V(0) is very high and implies that results are highly imprecise, and this situation confirms that carbon balance is much more variable than solutions balance, where precision is in of the order of 4,5%. As a Global conclusion, it can be recommended to trust in the balance of solutions instead of using carbon in columns to reach a balance. Conclusions The two case studies, show the tremendous capability of variographic analysis to find areas of process optimization Variographic analysis is an easy and well developed technique which, used in conjunction with a suitable software such as EMPV, allows quick understanding of the key points in a metallurgical process. It is the author s experience that variographic studies have no limit to be used as an optimizing technique. Care should be taken to respect natural limits of variograms already published by other authors. However, main features allow identification of at least two kind of variances; namely, short range variance or random variance, usually associated to nugget effect, and long term or spatial variability associated with the process itself and which is the main variance to consider to learn from the process. The advantage of chronostatistics is clear when considering time as an important part of process control. Acknowledgement The author wishes to acknowledge the support and permission to publish the results shown here to KINROSS Company. This publication wouldn t be a reality without its support. References 1. TELLO, A. y Compañía Ltda., Consultores en Calidad, muestreo y Control de Procesos, Vital Apoquindo 1380, Santiago, Chile, atello51@yahoo.com 2. PITARD, F. Chronostatistics, wcsb2 3. FLEMING, C., and WYSLOUZIL, B.W Workshop of Gold Ores, Lakefield Research ROYLE, A.G. Why Statistics?, Geostatistics, McGraw-Hill, Inc., New York 1980, pp PITARD, F. The in situ nugget effect: a major component of the random term of a Variogram, wcsb3 Porto Alegre, 2007, pp FOURTH WORLD CONFERENCE ON SAMPLING & BLENDING

11 Paper 02:text 10/7/09 1:33 PM Page 83 Alberto Tello Manager, Alberto Tello, y Compa ~ nia Limitada 20 years at Mining and Metallurgy Research Center (CIMM in spanish), Chile. 6 years as General manager at Cognis chemical products in South America (Solvent extraction for mining purposes in South America). 10 years as Consultant on sampling and process control. This work has been developed to main mining Companies working in Chile, Peru and Venezuela. Among Others: Codelco, BHPB, Anglo America, Kinross, SQM, Antofagasta Minerals, Freeport Mac Moran, Aur Resources, etc. VARIOGRAPHY APPLIED AS A TOOL TO MONITOR METALLURGY 83

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