Extraction of Rubidium from Gold Waste by Optimization of Sulfation Roasting-Water Leaching Process

Extraction of Rubidium from Gold Waste by Optimization of Sulfation Roasting-Water Leaching Process

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Specific physical and chemical properties of rubidium have attracted the attention of researchers for extraction of rubidium from its resources, which as a rare alkali metal in recent years the expansion of its commercial uses has been experienced. The main research conducted in this field has been related to recovery of this element as a byproduct of mining process of lithium minerals. In this study, we have discussed the extraction of rubidium from tailings dam of Mouteh gold processing plant in Iran by modeling the hydrometallurgical procedure used for lithium processing. For this purpose, first the pickling process of sample was done using 5M nitric acid in 85°C for 5 hours to remove most of the impurities. Then, the roasting process was optimized using central composite experiment design in two steps. 81.11% recovery rate of rubidium per 1/0.29/0.51 mass ratio of sample/sodium sulfate/calcium chloride was increased to 90.95% recovery rate per 1/0.11/0.45 mass ratio of sample/sodium sulfate/calcium chloride at the same temperature. Finally, 97.14% recovery rate of rubidium was achieved by optimization of water leaching process of roasted sample in 1.69 solid to liquid ratio, 58.51°C temperature within 31.36 minutes.
Rubidium is a rare alkali metal in the first group in periodic table, which was discovered in 1861 by Robert Bunsen and Gustav Kirchhoff using flame spectrometry. Chemical reactions of this element resemble those of the alkali metals of potassium and cesium, and acetate, bromide, carbonate, chloride, chromate, fluoride, formate, hydroxide, iodide, nitrate and sulfate salts of this element can be easily solved in water. The most striking physical properties of this silvery white element include softness, malleability and low melting point (39°C), and it is also the fourth light metallic element [1].
The application of rubidium in ionic engines (like spacecraft engines) as fuel, in photocell and photoelectric appliances due to photonic effect, its use to produce methanol and other alcohols, styrene and butadiene as catalyst, use of various rubidium compounds in electrical appliances due to semiconducting and piezoelectric properties, in analytical chemistry for identification of manganese, zirconium and noble metals, in incandescent and cathode ray lamps because of transparency of infrared radiation as well as many other applications indicate the unique properties of this valuable element [1,2].
The annual production of rubidium in the world amounts to several tens of kilograms. Because of the variety of applications, supply and demand of rubidium has been constantly growing since 1990, and its price has increased in the international market.

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The price range of this precious element from 1990 until 2010 was increased from 40 to 80 dollars per gram [3].
Rubidium is the sixteenth element in terms of frequency in the Earth's crust, but despite its high frequency (more than chromium, copper, nickel or zinc), it is widely dispersed in the Earth’s crust. This element is not found but in its original form in any mineral, and is mainly produced together with cesium as a byproduct of lithium ore mining process [2]. Therefore, identification of lithium containing mines and different processing methods of them is required for the extraction of this valuable metal.
Despite the presence of lithium in clays, seawaters and oil residues, the most significant resources for economic development of lithium include natural brines with a high lithium chloride content as well as pegmatite minerals such as Lepidolite, Spodumene, Petalite and Zinwaldite [4-8]. Valuable ingredients of lithium minerals are shown in Table 1. According to this table, rubidium and cesium are only found in lithium minerals of Lepidolite and Zinwaldite [9-10].
Table 1: Valuable mineral ingredients of lithium containing ores [9-10]
Processing of lithium minerals includes upgrading the ground ores using beneficiation techniques like gravity concentration, flotation and wet magnetic separation [11], optical sorting [12] or heavy media separation [13]. The resulting concentrates contain 4 to 6 percent lithium oxide. The next processing stage is chemical roasting of the concentrate using a variety of chemical agents with the aim of converting lithium minerals to a soluble form for the following leaching step [8, 14].Leaching by sulfuric and hydrochloric acids entails 70 to 97 percent recovery of lithium depending on the processed minerals. However, the resulting leach solutions contain most of the metals present in the ore, especially iron and aluminum, which subsequently require complex purification [6, 8, 14]. Obviously, roasting by hydrochloric acid is more expensive because of the need for highly corrosion resistant equipment and higher energy consumption, and is more difficult to control relative to sulfuric acid roasting [8, 15].
In recent years, other chemical agents like limestone, gypsum as well as alkali salts or sulfates have been used in concentrate roasting process. The main advantage of these processes is using non-invasive salts to convert low-solubility lithium minerals to compounds which can be leached into the water. Reducing the impurities in leach solution facilitates the purification process of lithium compounds [16, 17].
Studies have been reported for each of the above-mentioned processes. Table 2 summarizes the processes that lead to rubidium recovery during lithium processing.
In 2012, Shan et al roasted the Muscovite mineral in different conditions with the sole aim of rubidium recovery [23]. Their results showed that optimum recovery (90.12%) was achieved for the sample roasted with 1/0.25/0.25 mass ratio of Muscovite/sodium chloride/calcium chloride in 850°C for half an hour.
Mouteh gold processing plant north to the province of Isfahan is one of the largest gold processing projects in Iran. In this plant, gold is extracted from the ore using cyanidation process, and then the resulting gold-cyanide complexes (in the presence of oxygen and alkaline environment) are adsorbed on carbon. The type of processed ore and operational process leads to transfer of some toxic compounds like cyanide and heavy metals including arsenic and mercury to the wastewater and their accumulation in tailings dam, which can result in negative environmental impacts in the long run. Regardless of the environmental issues, tailings dam samples of the plant contain valuable metals such as rubidium, cesium, neodymium, lanthanum, strontium, titanium, tungsten, etc. The extraction of these elements as byproducts can create added value for the plant, and can reduce the considerable volume of tailings due to processing of low-grade gold ores. In the present study, we investigate the recovery of rubidium from tailings dam samples of Mouteh gold plant by optimizing roasting and leaching processes by modeling the processing methods of rubidium-containing lithium minerals.
The representative sample obtained from six trenches dug in a depth of 2 m in tailings dam of Mouteh gold plant was subject to dry granulation, and d70 of the samples was found to be nearly 150 microns. Since activated carbon has been used in cyanide leaching process for the recovery of gold, the sample was passed from a 150 micron sieve to remove the remainder carbon in order to prevent the re-uptake of metal ions by activated carbon in the leaching process. No need for further sample grinding for the leaching process can be important to justify cost-effectiveness of the extraction process of valuable elements in small volumes from tailings dam samples. ICP was used in this study to analyze rubidium, and ICP_MS was used to analyze 34 ingredient elements. The results are shown in Table 3.
Table 3: Analysis of the elements contained in the sample of the tailings dam
According to XRD analysis results of raw samples, the main constituent phases of the sample include quartz and minor phases include orthoclase, dolomite, gypsum and pyrite, which are silicate and carbonate compounds insoluble in the process of cyanide leaching
To avoid impurities with a significant negative effect in rubidium recovery in the leach solution such as iron, magnesium, calcium, manganese and aluminum, pickling operation of the sample was done using 5M sulfuric, hydrochloric, phosphoric and nitric acids. Roasting optimization process was conducted by placing the filtrate sample mixed with sodium sulfate, sodium chloride or calcium carbonate along with calcium chloride (AR grade) in an electric furnace in given times and temperatures. Then, the roasted samples were leached by water in different temperatures, times and liquid to solid ratios. Finally, after sample filtration and several times rinsing the filtrate by distilled water, the rubidium content was analyzed in the aqueous phase. Figure 1 shows the complete flow sheet of this procedure.
As mentioned, the appearance of such impurities as iron, magnesium, calcium, manganese and aluminum in the leach solution is an essential problem in rubidium recovery process. According to XRD analysis results indicating the alumosilicate nature of the studied sample, the effect of pickling process on elimination of impurities and rubidium content was investigated. Table 4 shows the analysis results of sample pickling process with 5M sulfuric, hydrochloric, phosphoric and nitric acids in ambient temperature within two hours.
Table 4: The analysis results of pickling process using 5M acids at room temperature within 2 hours (ppm)
According to Table 2, nitric acid was the most effective acid in removing impurities with no impact on rubidium level. Afterwards, 5M nitric acid was used for pickling the samples at 85 °C for 5 hours, with the results indicating further removal of impurities from the sample under study (Table 5). The sample subject to pickling under these conditions was used later in the analyses.
3.2.1 – Selection of effective Factors
In this study, sodium sulfate, sodium chloride and calcium carbonate were used as sulfation, chloridation and carbonation agents, respectively, and calcium chloride was used as sintering agent. We set out to select the factors effective in roasting process and their levels in process optimization operations, and the results are given in Tables 6 and 7. The values for the agents under study were chosen according to the best results from similar studies.
Table 6: Selection of chemical agents of roasting process at 850 °C (per 10 g sample)
Table 7: Selection of temperature range in roasting process
According to Table 6, calcium carbonate and sodium chloride did not have an important role in recovery of rubidium. Thus, the sample was combined with sodium sulfate and calcium chloride to optimize the roasting process. In addition, according to Table 7, the temperature range of 750 to 950 °C was selected for the roasting process to be effective.
In the sample roasting process using the studied salts, calcium chloride was combined with water vapor at high temperatures to produce hydrogen chloride, which converted the rubidium oxide present in the sample to hydrogen chloride soluble in water, and then the sodium ions were replaced with rubidium by ionic exchange mechanism. Roasting reactions are shown in Equations 1 to 3. It seems that CaCl2 contributes to muscovite degradation by forming the stable mineral of CaSiO3.
The melting point of a mixture of calcium and sodium salts is lower than the melting point of each of them, resulting in increased melt fluidity and reduced viscosity of the liquid phase. Therefore, improving the penetration of these salts to sample surface facilitates the recovery of rubidium [15, 23].
3.2.2 - Initial optimization of the process
In this study, central composite design experiment was used to optimize the roasting process. Since the rubidium concentration was lower than detection limit of XRD instrument, there was no opportunity to identify the main phase containing rubidium. Therefore, the maximum values considered for sulfation and sintering agents were assumed to be similar to that of the sample under study (10 g). Table 13-5 shows the experiments conducted to investigate the combined effect of the three parameters studied and their results.
Based on software results, the models with optimum compliance with recovery data were the modified combined quadratic and bi-level Factorial (2FI) models. ANOVA results of this model are shown in Table 8.
According to Table 3, the model and effect of all the three operating parameters were significant, with temperature being the most important factor and sodium cyanide concentration the less effective factor in recovery. In addition, lack of fit in the model was not significant. If this factor becomes significant, it means that either the reproduced tests are problematic or the model fitted on responses is not suitable [24].
Table 8: The proposed central composite statistical design and its results
Table 9: ANOVA results for the recovery
Table 10: Regression results of the model for recovery
According to Table 10, in addition to high correlation coefficient of the model, which is a determining factor in its suitability, Pred R-Squared and Adj-R-Squared values were close, and the difference between them was less than 0.2, which is crucial in terms of suitability of the statistical model. Table 11 shows the optimum conditions predicted by software for maximum recovery in roasting process and minimum amounts of sodium sulfate and calcium chloride consumption. Closeness of the actual value of recovery under these conditions to the predicted values expresses good compliance of the model proposed by software with conditions in the laboratory.
Table 11: Verification of the model and laboratory conditions
Figures 2 to 4 show the reciprocal effect of operating parameters on the recovery of roasting process
Figure 2: The reciprocal effect of temperature and Na2 SO4 concentration on recovery of rubidium
According to the results, increasing the roasting process temperatures up to 910 °C improves the reaction conditions of roasting process due to flow facilitation of molten salts and more efficient sequestration of the studied sample. According to Table 11 and figures 2 to 4, the optimal values for temperature and sodium sulfate were boundary values. Thus, to complete optimization operations of roasting process, choosing newer levels for these two parameters and repetition of the experiments was needed.
3.2.3 – Selection of roasting time and temperature
Under optimal conditions of the previous step (2.9 g of sodium sulfate and 5.1 g calcium chloride), experiments were conducted at 910 and 950°C temperatures and different roasting times, and the results are shown in Figure 5. In both temperatures, roasting time was increased up to half an hour recovery, and then had a downward trend, which was more evident for 950°C. The reason for this phenomenon and lower recovery temperature for 950°C was probably due to changing nature of recovery. Therefore, 910°C temperature and 30 min was selected for the final optimization step of roasting process.
Figure 5: Effect of roasting time and temperature on the recovery of rubidium
3.2.4 – Selection of sodium sulfate concentration
According to Figure 2, increasing concentration of sodium sulfate decreased recovery of the roasting process, which was in in complete violation of the research done in this field. Therefore, we investigated the effect of lower concentrations of sodium sulfate in the recovery process of roasting at 910°C and 5.1 g concentration of calcium, and the results are shown in Figure 6. According to this figure, decreased concentration of sodium sulfate to 1 g increased the recovery rate, and had a downward trend afterwards.
3.2.5 - Final optimization of the roasting process
At this point, central composite experiment design was used to optimize the roasting process at 910°C. Table 12 shows the results of experiments conducted to evaluate the combined effect of concentration of sodium sulfate and calcium chloride parameters. Modified combined quadratic and bi-level factorial (2FI) models were in compliance with the above data and ANOVA results in Table 13.
The ANOVA results indicate the significance of model and both operational parameters, and the effect of calcium chloride concentration on recovery was much higher than sodium sulfate concentration. Lack of fit in the model indicates proper model fit on responses, which is confirmed with model regression results in Table 14.
The model proposed by software for recovery in the optimized roasting process is as follows:
Figure 6 shows the Box-Cox diagram of Equation 2, which confirms the suitability of power (λ) for this equation. In this graph, the horizontal axis (lambda) shows the power values for the model, and the vertical axis indicates the logarithmic values of the residual sum of squares. Minimum point of the curve represents the best value of model power (λ), giving the lowest residual sum of squares in the model. According to this figure, optimum power in 95% confidence level was in 0.88-1.46 range, and the software suggested 1.17 as the optimum value for λ.
Figure 7 shows the interaction between sodium sulfate and calcium chloride concentrations on recovery. The curve showing the effect of sodium sulfate concentration on the recovery for two different values of calcium sulfate is also shown in Figure 8. According to Figure 8, recovery was increased and then decreased for a given concentration of sodium sulfate.
Maximum recovery rate predicted by the software for the lowest amount of sodium sulfate (1.12 g) and calcium carbonate (4.52 g) consumption was 92.35%, which was close to the recovery rate obtained under real conditions (90.95), confirming good fit of the model.
Figure 8: Effect of sodium sulfate concentration on recovery for two different concentrations of calcium chloride
After final optimization of the roasting process and determination of the appropriate value of parameters, the composite experiment design was used for optimizing the effective parameters in the leaching process. Table 15 shows the results of experiments conducted to evaluate the combined effect of liquid to solid ratio, temperature and time on leaching process recovery. The shaker rotation was assumed 500 rpm in all the experiments.
Based on software results, the modified combined quadratic and bi-level Factorial (2FI) models were consistent with recovery results of the leaching process as well as ANOVA and regression results in Tables 16 and 17.
The results of Tables 16 and 17 indicate good compliance of the proposed model on experimental data. In addition, based on the obtained results, temperature, time and liquid to solid ratio were the most effective parameters of the leaching process in this study, respectively. The software suggested the following parameter for recovery in the optimized process of leaching.
The Box-Cox curve in Figure 9 shows that the best power in 95% confidence level lies in -2.56-5.41 range, and the best value for λ suggested by software was 1.34.
Figures10-12 show the reciprocal effect of factors per constant value of the third factor.
Optimum conditions predicted by the software to have the maximum recovery for the leaching process and minimum values for time, temperature and liquid to solid ratio are shown in Table 18. After optimization of both roasting and leaching processes, rubidium recovery rate obtained in the laboratory was 97.14%, which was close to the value predicted by the software, indicating a good fit of the proposed model on experimental data.
The purpose of this study was extraction of rare alkali metal of rubidium from Mouteh gold plant tailings dam samples containing 60 ppm of this valuable element. No need for further sample grinding and possibility of simultaneous extraction of other valuable metals provides the economic justification, and is the main reason to conduct this study. The hydrometallurgical process used in this study has been application of pickling, roasting and water leaching processes by modeling the hydrometallurgical processes used for extraction of lithium, with rubidium as the byproduct. Four different acids were used in the pickling process to remove impurities associated with rubidium, and the highest rate of removal of impurities was achieved by application of 5M nitric acid at 85°C for 5 h. Following the initial tests, the factors affecting roasting of the studied sample were found, and in 18 experiments designed by central composite experiment, 81.11% rubidium recovery rate was achieved with sample/sodium sulfate/calcium chloride mass ratio of 1/0.29/0.51 in 910°C. Due to boundary nature of the obtained optimized values for temperature and concentration of sodium sulfate, we tried to address optimization of roasting process at lower concentrations of sodium sulfate. The final optimization results were roasting process at 910°C for 30 minutes, indicating 90.95% recovery of rubidium in the mass ratio of sample/sodium sulfate/calcium chloride equivalent to 0.45/0.11/1. Ultimately, we addressed the evaluation of liquid to solid ratio, time and temperature conditions for constant shaking (500 rpm) in order to optimize the leaching process. 97.14% rubidium recovery rate for 1.69 liquid to solid ratio, 58.51°C temperature within 31.63 minutes indicates the suitability of the hydrometallurgical process suggested for solution of rubidium.
In conclusion, the authors take this opportunity to appreciate Tarbiat Modarres University, Mouteh gold complex and Presidential Scholars and Technologists Fund because of providing the conditions to perform this research.




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