THE EFFECT OF PROCESS VARIABLES ON CYANIDE AND COPPER RECOVERY USING SART ABSTRACT

Transcription

1 THE EFFECT OF PROCESS VARIABLES ON CYANIDE AND COPPER RECOVERY USING SART By 1,2 Andrew Simons and 1 Paul Breuer 1 Parker CRC for Integrated Hydrometallurgy Solutions CSIRO Minerals Down Under National Research Flagship CSIRO Process Science and Engineering Australia 2 WA School of Mines, Curtin University, Australia Presenter and Corresponding Author Andrew Simons andrew.simons@csiro.au ABSTRACT Copper is a problem for gold processing using cyanidation as it causes increased cyanide consumption and creates significant environmental risk. This is due to the formation of toxic copper cyanide species. The SART (sulfidisation, acidification, recycle, and thickening) process is an effective way to overcome this problem as it can recover cyanide and copper as separate products from a clarified tailings stream after cyanidation. This has led to the SART process gaining popularity for cyanide and copper recovery around the world. There is, however, an apparent lack of information on the optimum operating conditions for SART. Factorial experimentation and economic analysis of the process has been conducted. Screening and factorial design investigations found that SART is largely affected by sulfide to copper molar ratio and cyanide to copper molar ratio. The economic analysis identified the optimum operating conditions for the SART process as a sulfide to copper molar ratio of around 0.56, a ph of 4, and minimised cyanide to copper molar ratio. Price variation of reagents and products has little impact on this optimum but does have a large effect on economics when SART is not operated at optimal conditions. 1

2 INTRODUCTION For the past century gold has been recovered using cyanidation processes. Of these, cyanidation followed by carbon adsorption has been the dominant technique for gold recovery since the 1980 s. The success of the cyanidation process is due to the selective leaching of gold by cyanide. Some copper minerals, however, also dissolve and complex with cyanide. This has two major effects on gold ore processing. First, the copper causes increased cyanide consumption, which results in increased operating costs. Second, copper cyanide complexes in the tailings are an environmental hazard. This is due to both the high stability and toxicity of copper cyanide species. Traditionally cyanide soluble copper has caused many copper bearing gold ores to be considered as too difficult to process. The exhaustion of simple to process gold resources has changed this view resulting in the development of both cyanide destruction and recovery technologies to treat copper cyanide species in tailings. Due to its simplicity, destruction of cyanide and copper cyanide species is the primary technique used to treat gold cyanidation tailings. Nevertheless, destruction technology means not only a loss of excess cyanide, but also additional reagent and operating costs to run the destruction process. The outcome is that while cyanide destruction makes processing copper bearing gold ores environmentally viable, many ores remain economically unviable. Alternatively, cyanide recovery technology offers the same environmental advantages as destruction technology with the additional benefit of cyanide recycle and in some cases the generation of a copper product. Numerous processes for cyanide recovery have been presented in the literature with reviews available from several authors [1-4]. Of these, few have been used industrially. The SART process is one of these few and has been demonstrated to be an effective way to recover both copper and cyanide from gold cyanidation tailings. This is partially due to the advantages it has over other technologies in that it both generates a saleable copper by-product and typically recovers more cyanide. THE SART PROCESS SART stands for sulfidisation, acidification, recycle, and thickening which describes the major steps involved in the process. The process exploits the reaction shown in Equation 1 which has been the key to three other patented processes since 1965 [5-7]. SART, which is unpatented, is the only one of these processes to be used commercially. 2Cu(CN) H + + S 2- Cu 2 S (s) + 6HCN (aq) 1 For most SART installations a clarified tailings stream containing copper cyanide is mixed in a pipe with a sulfide source, sulfuric acid, and seed recycled from within the process. In most cases sodium sulfide (Na 2 S) or sodium hydro sulfide (NaSH) are used as the sulfide source, although a growing number of plants using biologically produced hydrogen sulfide gas (H 2 S) are appearing [8]. Once mixed with sulfide and acid, the reaction represented by Equation 1 occurs rapidly precipitating copper as synthetic chalcocite (Cu 2 S) and releasing cyanide as hydrocyanic acid (HCN). The resultant slurry is then fed to a nucleation tank to allow precipitate size growth. After nucleation the slurry is thickened and filtered to separate the chalcocite from the hydrocyanic acid solution. The hydrocyanic acid solution is then re-neutralised, often with lime, converting the hydrocyanic acid back to cyanide as per Equation 2. Generally this whole system is covered and kept under a slight negative pressure to prevent the release of hydrogen cyanide gas. A generic SART flowsheet is shown in Figure 1. HCN (aq) + OH - CN - + H 2 O 2 The use of lime in the neutralisation step typically results in gypsum precipitation due to the large quantities of sulfate from the use of sulphuric acid for acidification. A thickener is often used to remove the gypsum before recycle of the cyanide back to leaching. 2

3 Figure 1: Generic SART flowsheet The results of bench scale and pilot plant studies of the SART process have shown that the technique can separate and recover more that 95% of copper and cyanide. This does not include cyanide that is oxidised or has reacted with a strongly complexing metal such as iron. In every case a ratio of around 0.5 to 0.55 stoichiometric sulfide to copper addition yielded a precipitate which analysed as being close to chalcocite (Cu 2 S) [9-11]. Operational data from the Yanacocha Gold Mill in Peru shows, however, that SART will mainly produce other minerals such as covellite (CuS) and digenite (Cu 1.8 S) (unpublished data, 2010). The same authors also show that SART is difficult to control on an industrial scale yielding lower precipitate grades and higher reagent consumption than expected. Such issues have not been reported in any bench or pilot studies of the process. Apart from copper, SART may also be useful in the separation of other metals which are complexed with cyanide. There is an understanding that zinc and silver precipitate at a higher ph than copper and that this could allow for a selective precipitation process to be established [7, 12, 13]. Nevertheless, separation efficiency and required ph ranges are not well defined. Selective precipitation processes could also be useful to separate toxic trace elements such as arsenic, antimony, and mercury to prevent these metals from being discharged to a TSF where they pose an environmental risk. Also, selectivity may be important in preventing these toxic elements from appearing in the copper sulfide precipitate where they would reduce the precipitates marketability. The unexpected problems encountered when using SART at an industrial scale, such as less than optimal reagent additions and precipitate grades, highlight a requirement for a better fundamental understanding of SART chemistry. The objective of this study was to determine the effects that different process variables have on the SART process and how this affects SART economics. FACTORIAL EXPERIMENTATION Factorial experimentation was selected as the best approach to analyse the various process parameters and the effect these have on SART chemistry. Factorial experimentation means that instead of varying only one factor (parameter) at a time, all factors are varied over their ranges simultaneously. The primary advantage of this approach over one factor at a time experimentation is that interaction of effects become visible. A second advantage is it allows for curves to be fitted to the data meaning a model of the system can be constructed. This is useful for determining optimum conditions and economic impacts. 3

4 Screening Experiment Results Before conducting factorial design experiments examining SART, screening experiments were performed to determine if some variables could be eliminated from the factorial investigations. Based off the literature the factors screened were ph, sulfide to copper molar ratio (S:Cu ratio), time, cyanide to copper molar ratio (CN:Cu ratio), and initial copper concentration. During screening copper recovery was the focus due to the ease of analysis using atomic adsorption spectrometry (AAS). Screening of ph, sulfide to copper ratio, and cyanide to copper ratio showed that all three of these variables have an impact on the SART process in terms of copper recovery, cyanide recovery, or acid consumption. Hence, these three variables were included in the factorial design. Initial copper concentration had little impact on copper recovery but did have an impact on acid consumption. With three factors having already been selected the factorial design required a minimum of 27 experiments which would increase to 81 if the initial copper concentration was included. Due to this, several experiments were run with initial copper concentration of 1250 and 2000 ppm while other variables were fixed to establish if the initial copper concentration could be removed as a factor by correlation of acid consumption with initial copper concentration. It was found that under any set of conditions, varying initial copper concentration had no effect on acid consumption per initial copper concentration. For this reason initial copper concentration was excluded from the factorial design. An interesting outcome during screening of initial copper concentration is that at a concentration of 500 ppm the precipitate is too fine to filter (<45 µm). This is not an issue with more concentrated solutions. This result indicates that whatever the mechanism of crystallisation, larger precipitate crystals can be formed at higher copper feed concentrations. As this effect has an impact on seeding and crystallisation reactor selection, it will be studied in greater detail at a later time. Screening for the effect of time showed (Figure 2) that excess sulfide reduces over time and once excess sulfide is depleted the precipitated copper begins to redissolve. It has not yet been determined if the loss of sulfide is due to oxidation of sulfide; a change of precipitate structure, such as from chalcocite (Cu 2 S) to a mineral with more sulfide per copper (ie CuS); or loss as hydrogen sulfide gas (H 2 S). Nonetheless, the data suggests that once excess sulfide is consumed the general SART equation is reversed (Equation 1) causing copper redissolution. Although time has an effect on the system it was not included in the factorial design. This is largely due to the unknown cause of the effects and the fact that the degree of the time effect can vary from experiment to experiment. These preliminary results, however, may have a relationship to the problems with reagent consumption observed at Yanacocha. Due to this, the effect of time will be studied in more detail at a later date. % Cu Precipitated 100% 99% 98% 97% 96% 95% 94% 93% 92% 91% 90% Time (min) Sulfide concentration (mm) % Cu Precipitated c(s) Figure 2: Change of species concentration over time in the SART reactor when sulfide is overdosed 4

5 Factorial Design Results The factors selected for the factorial design were ph, S:Cu ratio, and CN:Cu ratio which were varied over the ranges shown in Table 1. Four replicates of the centre point (ph=4.75, S:Cu=0.5, CN:Cu=4) were also performed so experimental error could be determined. The range selected is based on the typical SART operating range quoted in the literature. Table 1: Factor ranges for the factorial design Factor Low Mid High ph S:Cu CN:Cu Four responses were measured, these were: copper recovery, cyanide recovery, acid consumed per initial copper concentration, and oxidation/reduction potential (ORP). The first three of these responses were selected due to their obvious impact on SART efficiency. The last, ORP, was chosen to check if ORP could be used to monitor and control SART in any way. A four dimensional second order polynomial model was then fitted to the data and insignificant terms were removed. A generalised version of the polynomial is shown in Equation 3 where a x are the coefficients for each term. Response a a CN:Cu a S:Cu a ph a CN:Cu S:Cu a CN:Cu ph a S:Cu ph a CN:Cu a S:Cu a ph 3 All response models generated by the factorial design have good fit except for cyanide recovery. This problem also occurred for Barter et al. [14] and is likely due to the small changes in recovery being difficult to detect in the large concentration of cyanide measured. Cyanide recovery when calculated from copper recovery, however, did have good fit. The calculated cyanide recovery also took into account that any excess sulfide in solution would convert cyanide to thiocyanate once they are in the leaching circuit [15, 16]. Table 2 shows response equations, rounded to 3 significant figures, along with their coefficient of determination (R 2 ) and lack of fit p-values (LoF). The coefficient of determination shows how well the model fits the data points with numbers closer to 1 indicating a better fit. The lack of fit p-value shows the probability that variation between the data and the fitted response can be attributed to experimental error. This is important as low p-values mean the model fits poorly and variations may be caused by some other untested factor. Generally p-values above 0.1 are considered acceptable. Table 2 Factorial design response equations and fit statistics Response Equation R 2 LoF Copper recovery (%) CuRec CN:Cu 666 S:Cu ph 12.9 S:Cu ph 617 S:Cu Cyanide recovery CNRec CN:Cu 805 S:Cu (calculated, %) ph CN:Cu S:Cu 14.1 S:Cu ph CN:Cu S:Cu 2 Acid consumed ACu CN:Cu 4.55 S:Cu (mmol) / initial copper ph S:Cu ph S:Cu (mmol) ORP (mv) ORP S:Cu 228 ph 287 S:Cu ph Figure 3 shows the copper recovery when CN:Cu ratio is held at 4 and S:Cu ratio and ph are varied. Clearly, S:Cu ratio has the largest impact on copper recovery followed by ph. The effect of varying CN:Cu ratio is not shown as it has the least effect on copper recovery. Larger S:Cu ratios and low ph result in the best copper recovery with the optimum being on the boundary of the experimental range. 5

6 ed value Cu Recovery (%) S:Cu ph Figure 3: Copper recovery at a CN:Cu ratio of 4 when S:Cu and ph are varied Like copper recovery, Cu:S ratio has the largest impact on cyanide recovery compared to the other factors. Figure 4 shows cyanide recovery when ph is fixed at 4.75 and S:Cu ratio and CN:Cu ratio are varied. The effect of varying ph is not shown as it has a small effect on cyanide recovery. Unlike copper recovery there is an apparent optimum cyanide recovery within the experimental range. It is obtained at an S:Cu ratio somewhere between 0.55 and 0.6. CN:Cu ratio appears to have little effect once the S:Cu ratio is high only impacting largely on the system when the S:Cu ratio goes below 0.5. ign points below predicted value CN Recovery (%) S:Cu CN:Cu Figure 4: Cyanide recovery at a ph of 4.75 when S:Cu and CN:Cu are varied Surprisingly, both the S:Cu ratio and CN:Cu ratio has larger impacts on acid consumption than ph. Figure 5 shows the acid consumed per initial copper concentration at a ph of 4.75 when S:Cu and CN:Cu are varied. Acid consumption is minimised when less cyanide is present and less sulfide is added to the system. Figure 6 shows the small impact ph has on the response. 6

7 ed value H2SO4 (mmol) / Initial Cu (mmol) S:Cu CN:Cu Figure 5: Acid addition per initial copper concentration at a ph of 4.75 when S:Cu and CN:Cu are varied gn points below predicted value H2SO4 (mmol) / Initial Cu (mmol) CN:Cu ph Figure 6: Acid addition per initial copper concentration at a S:Cu ratio of 0.5 when CN:Cu and ph are varied The oxidation/reduction potential (ORP) response did not produce curves that could be useful in controlling the SART process. This does not mean that ORP is not useful for controlling SART but rather that a second order model failed to show anything useful. Nevertheless, due to this the graphs for ORP have been omitted. 7

8 ECONOMIC IMPACTS Figure 3, 4, 5, and 6 show that the different responses have different optimum positions with different key impacting factors. Hence, it becomes difficult to find the best operating conditions for SART when each response is considered separately. To avoid this problem all equations can be combined into an objective function which can be maximised. A desirability equation can be used to achieve this but has the inherent flaw that a user must decide which responses are more important than others. An economic analysis does not contain this flaw as it allows all the equations to be combined into a profit equation which can be optimised. In the case presented here the term profit doesn t take into account costs that are not influenced by SART operating conditions, such as power, labour, maintenance, etc. Hence, the profit value is not a true profit value but rather a good way to combine the responses from the factorial design into one objective function as an indicator for optimum conditions. Profit equation The profit equation is shown in Equation 4 where RCu is the revenue from copper recovered, LCN is the loss from cyanide not recovered, LA is the loss from acid consumed, LB is the loss from base consumed, and LS is the loss from sulfide consumed. P RCu LCN LA LB LS 4 Note that cyanide has been treated as cyanide lost from failure to recover cyanide rather than revenue from cyanide recovered. This is because there is a cost associated with purchasing the cyanide entering SART and thus cyanide recovered is not truly a revenue stream. If cyanide recovered were to be treated as revenue the economic model would suggest that operations should put in more cyanide to make more profit. Based on the output from the factorial design, Equations 5 to 11 can be deduced where Cu is the initial copper concentration (mm) and VCu, VCN, VA, VB, and VS are the dollar values of pure copper, sodium cyanide, acid, base, and sulfide in $/mmol respectively. Each of these equations give a value in $/L of feed, ultimately giving profit in the same units. As the values given using this technique are often small it was beneficial to use initial copper concentration in mmol/m 3 to give profit as $/m 3 of feed solution. Copper revenue equation: RCu VCu Cu Cyanide loss equation: LCN VCN Cu CN:Cu 5 6 Acid consumed if using Na 2 S: LA VA Cu ACu 7 Acid consumed if using NaSH: LA VA : 8 Base consumed if using CaO: LB VB Cu ACu 9 Base consumed if using caustic: LB 2 VB Cu ACu 10 Sulfide consumed: LS VS Cu S:Cu 11 System Optimum and Sensitivity A case study was conducted to investigate the SART process economics and determine the optimum operating conditions in different economic situations. One hundred random economic situations were generated between the maximum and minimum values shown in Table 3 and initial copper concentrations between 3 and 30 mm (~ ppm). These numbers were based on reagent values used by Adams and Lloyd [17]. The equations in Table 2 and Equations 4 to 11 were then used to give profit equations and to find the optimum operating conditions for each economic situation. In every case the optimum ph and cyanide to copper molar ratio was calculated as 4 and 3 respectively. Sulfide to copper molar ratio was on average 0.56 with a standard deviation of only 1.7%. These results indicate that the optimum conditions for SART are largely insensitive to price variation. 8

9 It s is worthy to note here that the optimum CN:Cu ratio of 3 in the feed (discharge from cyanidation) can be problematic for gold recovery and is discussed in more detail later. Table 3: Value ranges for the sensitivity analysis Item Cu NaCN H 2 SO 4 CaO NaOH Na 2 S NaSH Min (US$/t) Max (US$/t) Although the optimum conditions did not change with different economic situations the shape of the model in four dimensional space did. This indicates that the effect when operating away from the optimum varies significantly under different economic situations. To show this effect, three graphs were created where two of the factors were set at their optimum and the third factor was varied over its range. For each graph (Figure 7, 8, and 9), a curve was then generated for each of the 100 economic situations which shows the impact on profit of operating away from the optimum. A more mathematical way to perceive this process is that a two dimensional plane for each factor was cut through the four dimensional model for each of the 100 economic situations. Figure 7 shows the effect that changing CN:Cu ratio has on profit when S:Cu ratio and ph are held at their optimum values. It is apparent that economic variation has a large effect on profit variation with CN:Cu ratios above the optimumm of 3. The reason that high CN:Cu ratio causes a drop in profit is due to more acid and base being consumed when more cyanide is present (for the same copper concentration). The higher the acid and base costs are, the steeper the change in profit. The problem with this situation is that the optimum CN:Cu ratio of 3 has a negative impact during leaching as low ratios can cause copper cyanide species to load onto activated carbon. This means copper competes with gold during carbon adsorption and copper starts to appear in the gold recovery circuit. Hence, it is apparent that the CN:Cu ratio should be kept high enough to prevent copper cyanide from loading onto carbon but equally care should be taken not to let CN: :Cu ratio get too high as this can have a drastic impact on SART profitability. For the subsequent S:Cu ratio and ph graphs (Figure 8 and 9) a CN:Cu ratio of 4 was adopted as the optimum to reflect the typical ratio of operating gold plants. Figure 7: Variation of profit from its optimum for different economic situations when CN:Cu is varied and S:Cu and ph are held constant 9

10 Figure 8 shows the effect changing S:Cu ratio has on profit when CN:Cu ratio is 4 and ph is held at its optimum. Like CN:Cu ratio, economic variation has a large effect on profit variation when operating with the S:Cu ratio away from its optimum value. The reduction in profit at low S:Cu ratios is mainly caused by lower copper and cyanide recovery compared to the optimum. The reduction in profit at high S:Cu ratio, however, is caused by cyanide loss, due to thiocyanate (SCN - ) formation upon return of the cyanide with excess sulfide to the leach, and increased acid and base consumption. High acid, base, and sulfide costs coupled with low copper values appeared to cause a more drastic reduction in profit when operating at sub optimal conditions. Figure 8 highlights the importance of getting S:Cu ratio correct when operating a SART plant. S:Cu ratio, however, is often poorly controlled in SART due to the difficulty in measuring both sulfide and copper concentrations industrially and particularly on-line. Further research in controlling this factor is clearly warranted. Figure 8: Variation of profit from its optimum for different economic situations when S:Cu is varied and CN:Cu and ph are held constant The effect changing ph has on profit when CN:Cu ratio is 4 and the S:Cu is held at its optimum is shown in Figure 9. Unlike the other two factors ph has little impact on profit with most economic situations varying from the optimum by less than 20%. This is surprising as there is a perception in the literature that ph has a significant impact on the SART process and is therefore an important variable. The reason for this trend is that reducing the ph within the range investigated consumes a small amount of acid and base, which is of low cost, but slightly increases copper and cyanide recovery which is of greater value. No SART studies thus far have taken this effect into account and hence don t usually recommend ph values this low. While the optimum ph is on the boundary of the range tested, which could make it tempting to go lower, it is not recommended that a SART plant is operated at a ph much lower than 4. This is because copper cyanide (CuCN) can begin to precipitate causing marketability issues with the copper sulfide precipitate and reduced cyanide recovery. 10

11 Figure 9: Variation of profit from its optimum for different economic situations when ph is varied and CN:Cu and S:Cu are held constant CONCLUSIONS Based on the results from the economic analysis it is apparent that the SART process is best operated at sulfide to copper ratios slightly above stoichiometry (~0.56) with low ph (~4) and minimised cyanide to copper ratios (~4). The biggest impact of the result, however, is not the optimum conditions but the effect of operating at sub optimum conditions. Under the majority of economic situations tested when S:Cu ratio and CN:Cu ratio are not at their optimum, profit can drop considerably. The ph value didd not exhibit the same characteristic, having little impact on system economics when operated below its optimum. As sulfide to copper ratio and cyanide to copper ratio can have a drastic impact on SART economics, these two factors should be controlled where possible. Unfortunately, the factorial design also showed that ORP may not be a useful tool in monitoring sulfide addition to the reactor. Nevertheless, more research is required as this result may be due to the model not being detailed enough. Other important observations made in this work were that time appears to have an effect on the system with reductions in excess sulfide in the system followed by a reduction in copper recovery over time. The significance of this is that it may be related to some of the problems observed within industrial SART processes. The screening experiments also revealed that copper concentration can have an effect on precipitate particle size. Both the time effects and crystallisation mechanisms during SART are research topics that equire further investigation. ACKNOLEDGEMENTS The support of the CSIRO Minerals Down Under National Research Flagship and Parker CRC for Integrated Hydrometallurgy Solutions (established and supported under the Australian Government s Cooperative Research Centres Program) is gratefully acknowledged. REFERENCES 1. C.A. Fleming, "Cyanide Recovery", In: Advances in Gold Ore Processing, M.D. Adams, Editor, Amsterdam, Elsevier, pp , G.M. Ritcey, "Tailings management in gold plants", Hydrometallurgy 78 (1-2), 3-20, B. Sceresini, "Gold-copper ores", In: Advances in Gold Ore Processing, M.D. Adams, Editor, Amsterdam, Elsevier, pp , X. Dai, A. Simons, P. Breuer, "A review of copper cyanide recovery for the cyanidation of copper containing gold ores", In: ALTA Gold 2011, Perth,

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