Dissolution Optimization of Copper from Anode Slime in H2SO4

Jun 20, 1998 - Processing of copper electrorefining anode slime: a review. J. Hait , R. K. Jana , S. K. Sanyal. Mineral Processing and Extractive Meta...
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Ind. Eng. Chem. Res. 1998, 37, 3382-3387

Dissolution Optimization of Copper from Anode Slime in H2SO4 Solutions Bu 1 nyamin Do1 nmez Department of Chemical Engineering, Atatu¨ rk University, 25240 Erzurum, Turkey

Cafer C ¸ elik* Department of Industrial Engineering, Atatu¨ rk University, 25240 Erzurum, Turkey

Sabri C ¸ olak and Ahmet Yartas¸ i Department of Chemical Engineering, Atatu¨ rk University, 25240 Erzurum, Turkey

In the present study, aimed at the extraction of copper and the concentration of precious metals in anode slime, the optimum process conditions were sought for the dissolution anode slime from the Sarkuysan Co. in Turkey. The blade number of the mechanical stirrer, reaction temperature, O2 flow rate, stirring speed, acid concentration, solid-to-liquid ratio, reaction period, and roasting temperature were chosen as parameters. Using the Taguchi method, the optimum process conditions, at which 99.67% Cu conversion was reached, were found as follows: blade number 1, reaction temperature 70 °C, O2 flow rate 1.24 × 10-6 m3‚s-1, stirring speed 450 rpm, acid concentration 5.43 wt %, solid-to-liquid ratio 0.125 g/mL, reaction period 3600 s, and roasting temperature 300 °C. 1. Introduction In nature, copper, generally found in the form of its minerals such as cuprite, malachite, azurite, chalcopyrite, and bornite, is widely used in many fields from electricity-electronics to the architecture sector and from vehicle to the coin production. The most important minerals of copper are sulfides, oxides, and carbonates. In the process of copper electrorefining, undissolved metals, oxides, and the other compounds such as Cu2-xSe, CuAgSe, or AgCI deposit on the bottom of the electrolytic cell and form anodic slime. This slime contains mainly Cu, Pb, Se, Ag, Te, Au, and other several elements. Elements such as As, Sb, and Bi are also distributed between the anodic slime and electrolyte (Petkova, 1990, 1994; Scott, 1990; Sawicki et al., 1993). The composition of copper in the anode slime depends on the characteristics of blister copper and the conditions of refining. Copper in the anode slime is generally found as Cu, Cu2Se, Cu2Te, Cu2O, CuSO4‚5H2O, Cu(Te, Se)2, CuAgSe, and CuAgS (Chen and Dutrizac, 1989a; Yı´ldı´rı´m, 1988). Various studies on the anode slimes have been carried out (Subramanian et al., 1980; Belen’kii et al., 1987; Hoffmann, 1990; Sanuki et al., 1990; Abe et al., 1984; Stratigakos and Jennings, 1966). De Decker et al. (1976) report aeration in dilute H2SO4 to be a satisfactory technique for copper removal with most slimes except those with a high selenium and/or nickel content. Nippon Mining (1980) has developed a H2SO4 leaching process in which anode slimes are suspended in 15% H2SO4 and reacted with O2 in the presence of sodium * To whom correspondence should be addressed. Mailing address: Engineering Faculty, Industrial Engineering Department, Atatu¨rk University, 25240 Erzurum, Turkey. Fax: + (442) 2336961. E-mail: [email protected].

nitrite. Copper and selenium extractions of 99% and 95%, respectively, were obtained. To accommodate slimes characteristically high in NiO, Inco designed a sulfation reactor in which the slimes are contacted with concentrated H2SO4. The bulk of the copper and nickel is solubilized (98% and 95%, respectively) as well as a small quantity of selenium and tellurium (Monahan and Loewen, 1972). The use of an ozone-air mixture in the H2SO4 leaching of anode slimes can greatly enhance the dissolution of copper, selenium, and tellurium. Thus, the aeration of a 1% suspension of slimes in 1.35 M H2SO4 for 90 min with air containing 6.75 × 10-4 M ozone resulted in the solubilization of copper 99%, selenium 98%, tellurium 96%, and arsenic 31% (Ctyan et al., 1975). In another study, copper anode slime was dissolved in concentrated H2SO4, and the optimum parameters were confirmed to be as follows: reaction temperature 210 °C, solid-to-liquid ratio 0.5, and the reaction period 2 h (Chen and Dutrizac, 1989b). In an autoclave leaching all the copper and the major part of tellurium were dissolved within 2-3 h; the optimum conditions were as follows: reaction temperature 110 °C, O2 partial pressure 350 kPa, and H2SO4 concentration 30% (Habashi, 1979). During O2-H2SO4 pressure leaching at 180 °C, it was determined that significant amounts of Cu, Ni, Ag, Se, and Te were dissolved (Chen and Dutrizac, 1990). In a study two-step, first, the copper in anode slime was converted to CuSO4 in concentrated H2SO4 under 8 atm O2 pressure at 210-220 °C and in 60 min. Second, metallic selenium has been oxidized to the soluble SeO32- and SeO42- under 8-10 atm O2 pressure and in H2SO4 at 300 °C (Ziyadanogˇulları´ and Bu¨yu¨ks¸ ahin, 1988). Before the precious metals in anode slime are recovered, extracting the high-grade copper in the study will

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Ind. Eng. Chem. Res., Vol. 37, No. 8, 1998 3383

be beneficial for both the recovering of the copper and concentrating of the precious metals. In the present study, the effects of various parameters on the dissolution of copper from raw anode slime in H2SO4 solutions have been investigated, and the optimum working conditions have been determined by the Taguchi method (Kackar, 1985; Ross, 1988; Taguchi, 1987; Phadke, 1989; Phadke et al., 1983). There is a wide range of applications about the Taguchi method, from microbiology to agriculture and from chemistry to engineering. Recently, one study carried out by the Taguchi method was that the optimum conditions for the dissolution of stibnite in HCI solutions were determined (C¸ opur et al., 1997). Another study, also, was the optimization of the dissolution of phosphate rock with CI2-SO2 gas mixtures in an aqueous medium (Abalı´ et al., 1997). One of the advantages of the Taguchi method on the conventional experimental design methods, in addition to keeping the experimental cost at minimum level, is that it minimizes the variability around the target when bringing the performance to the target value. Another advantage is that optimum working conditions determined from the laboratory work can also be reproduced in the real production environment. 2. Taguchi Method The Taguchi method is well-documented (Kackar, 1985; Byrne and Taguchi, 1987; Nair, 1992). Genichi Taguchi, to obtain products (or processes) more robust to the varying environmental conditions and the variability in components (subproducts), after bringing the mean performance value to the target value, has shown that experimental designs could be utilized in making the variability around the target minimum. Thus, when the optimum working conditions are being determined, environmental conditions in which the product will be used and details of components used in its production should be taken into account. Parameters affecting the product may be divided into two groups as follows: controllable and uncontrollable. Because of the very high cost, instead of determining uncontrollable parameters and to get rid of them, values of controllable parameters which will get rid of or reduce the negative effects of uncontrollable parameters should be investigated. Taguchi, during this process, suggests the use of new tools different from those conventional ones such as the orthogonal array (OA), performance statistics (signal-to-noise ratio), loss function, etc. The performance statistics was chosen as the optimization criterion. The performance statistics were evaluated by using the following equation (Kackar, 1985):

( )

ZB ) -10 log

1

n

∑ n i)1

1

Yi2

(1)

where ZB is the performance statistics, n the number of repetitions done for an experimental combination, and Yi the performance value of the ith experiment. In the Taguchi method, the experiment corresponding to optimum working conditions might have not been done during the whole period of the experimental stage. In such cases the performance value corresponding to optimum working conditions can be predicted by utilizing the balanced characteristic of OA. For this, the additive model may be used (Phadke et al., 1983):

Yi ) µ + Xi + ei

(2)

Table 1. Chemical Composition of Anode Slime Used in the Study component

raw slime (%), 20 °C

roasted slime (%), 300 °C

decopperized slime (%)

Cu Au Pb Ag SO42SiO2 Ba Sr Ni Fe Zn Sn Sb As Co Cd Ca humidity the others

14.440 0.087 22.70 1.420 18.410 1.400 0.87 0.46 0.105 0.110 0.440 10.770 16.970 0.610 0.018 0.0017 0.035 4.010 7.1433

15.120 0.092 21.480 1.860 21.290 1.470 0.89 0.50 0.102 0.132 0.520 11.020 14.760 0.790 0.016 0.0013 0.044 2.260 7.6923

0.330 0.133 29.000 2.054 28.700 1.720 0.96 0.57 0.0302 0.160 0.275 15.950 17.160 0.930 0.0017 0.0014 0.055 0.700 1.2697

where µ is the overall mean of performance value, Xi the fixed effect of the parameter level combination used in the ith experiment, and ei the random error in the ith experiment. Because eq 2 is a point estimation, which is calculated by using experimental data in order to determine whether results of the confirmation experiments are meaningful or not, the confidence interval must be evaluated. The confidence interval at the chosen error level may be calculated by (Ross, 1988)

Yi (

x

(

1+m 1 + N ni

FR;1,DFMSeMSe

)

(3)

where F is the value of the F table, R the error level, DFMSe the degrees of freedom of the mean square error, m the degrees of freedom used in the prediction of Yi, N the number of total experiments, and ni the number of repetitions in the confirmation experiment. If the experimental results are in a percentage (%), before evaluating eqs 2 and 3 first Ω transformation of percentage values should be applied using the following equation. Then, later interested values are determined by carrying out reverse transformation by using the same equation (Taguchi, 1987):

(P1 - 1)

Ω(db) ) -10 log

(4)

where Ω(db) is the decibel value of the percentage value subject to Ω transformation and P percentage of the product obtained experimentally. 3. Material and Methods The anode slime used in the experiments was provided from Sarkuysan Co. in Turkey. The chemical composition of the raw, roasted at 300 °C and decopperized, anode slimes blended in a homogeneous way was determined by volumetric and gravimetric methods (Furman, 1963). Trace elements were analyzed by using atomic absorption spectroscopy. The analytical results are given in Table 1. In addition, X-ray diffraction analysis illustrating basic contents of the raw anode slime is given in Figure 1. The dissolution experiments were carried out in a glass reactor of 1000 mL capacity equipped with a mechanical stirrer having a digital controller unit

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Figure 1. X-ray diffractogram of the raw anode slime. Table 2. Parameters and Their Values Corresponding to Levels To Be Studied in Experiments levels parameters A B C D E F G H Figure 2. The schematic diagram of the experimental apparatus. (1) O2 tube. (2) Rotameter. (3) Mechanical Stirrer. (4) Reactor. (5) Thermostat. (6) Back Cooler.

providing an adjustable speed, a thermostat, and a back cooler minimizing aqueous loss when the system is heated. The temperature of the reaction medium could be controlled within (1 °C. The schematic diagram of the experimental apparatus used in the study is shown in Figure 2. After putting into the reactor 500 mL of H2SO4 solution with a certain concentration, the reaction medium was brought to a desired temperature by the thermostat while stirring the contents of the reactor with a certain speed. A predetermined amount of the solid was added into the reaction medium. Leaching experiments were performed by/without bubbling oxygen through the reaction medium. At the end of the reaction period, the contents of the vessel were filtered at once, and the filtered solution was then analyzed volumetrically for Cu2+ (Furman, 1963). X-ray diffractogram of the decopperized slime obtained at the end of a dissolution experiment with 99.67% Cu recovery is illustrated in Figure 3. As seen from XRDs, several compounds of Sb are detected. But Sb and Sn compounds in considerable quantity disappeared in XRDs. This can be explained by the fact that these compounds have amorphous structure (Petkova, 1990). Experimental parameters and their levels to be studied, which were determined in the light of preliminary experiences, are given in Table 2. The orthogonal array (OA) experimental design method was chosen to determine the experimental plan, L18 (21 × 37) (Table 3), as it is the most suitable for the conditions being investigated; one parameter with two

blade number reaction temperature (°C) O2 rate (m3‚s-1 ) × 106 stirring speed (rpm) acid concentration (%, in weight) solid-to-liquid ratio (g/mL) reaction period (s) roasting temperature (°C)

1

2

3

1 20 0 300 5.43 0.125 1800 20

2 50 1.24 450 10.86 0.200 3600 300

70 5.03 600 16.29 0.333 7200 600

Table 3. Chosen L18 (21 × 37) Experimental Plan parameters and their levels experiment no.

A

B

C

D

E

F

G

H

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 2 3 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2

1 2 3 2 3 1 1 2 3 3 1 2 3 1 2 2 3 1

1 2 3 2 3 1 3 1 2 2 3 1 1 2 3 3 1 2

1 2 3 3 1 2 2 3 1 2 3 1 3 1 2 1 2 3

1 2 3 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1

levels and seven parameters with three levels. To observe the effects of noise sources such as the temperature and the humidity of the laboratory medium on the dissolution process, each experiment was repeated three times under the same conditions at different times. The order of the experiments was obtained by inserting parameters into columns of OA, L18 (21 × 37), chosen as the experimental plan given in Table 3. But the order of experiments was made random in order to avoid noise sources which had not been considered initially and which could take place during an experiment and affect results in a negative way. Except between the blade number and reaction temperature, the interactive effects of parameters were not

Ind. Eng. Chem. Res., Vol. 37, No. 8, 1998 3385

Figure 3. X-ray diffractogram of the decopperized slime obtained at the end of 99.67% Cu recovery.

taken into account in the theoretical analysis because some preliminary tests showed that they could be neglected. The validity of this assumption was checked by confirmation experiments conducted at the optimum conditions. 4. Results and Discussion Dissolution Reactions. During the dissolution of copper from anode slime in H2SO4 solutions with/ without an oxygen medium, a lot of reactions take place. Some of them, related to our study, are as follows (Cooper, 1990; Chen and Dutrizac, 1989a; Kosyakov et al., 1995; Habashi, 1979):

O2(g) h O2(aq)

(5)

Cu2Se(s) + 4H2SO4(aq) f 2CuSO4(aq) + Se(s) + 2SO2(g) + 4H2O (6) Cu2Se(s) + 2H2SO4(aq) + O2(aq) f 2CuSO4(aq) + Se(s) + 2H2O (7) Cu(s) + 2H2SO4(aq) f CuSO4(aq) + SO2(g) + 2H2O (8) Cu(s) + H2SO4(aq) + 1/2O2(aq) f CuSO4(aq) + H2O (9) 2CuAgS(s) + 2H2SO4(aq) + O2(aq) f 2CuSO4(aq) + Ag2S(s) + S(s) + 2H2O (10) Se(s) + 2H2SO4(aq) h H2SeO3(aq) + 2SO2(g) + H2O (11) Se(s) + 2H2SO4(aq) + 1/2O2(aq) h H2SeO4(aq) + 2SO2(g) + H2O (12) CuO(s) + H2SO4(aq) f CuSO4(aq) + H2O (13) Cu2O(s) + H2SO4(aq) f Cu2SO4(aq) + H2O

(14)

Determination of the Optimum Conditions. The collected data were analyzed by an IBM-compatible PC

Table 4. Optimum Working Conditions, Predicted Dissolved Quantity of Copper parameters A B C D E F G H

blade number reaction temperature (°C) O2 rate (m3‚s-1 ) stirring speed (rpm) acid concentration (%, in weight) solid-to-liquid ratio (g/mL) reaction period (s) roasting temperature (°C) predicted dissolved quantity (%) predicted confidence interval (%)

optimum levels

optimum value

1 3 2 2 1 1 2 2

1 70 1.24 × 10-6 450 5.43 0.125 3600 300 99.67 97.30-99.99

using the ANOVA-TM computer software package for evaluation of the effect of each parameter on the optimization criteria. The results obtained are given in Figure 4. The order of graphs in Figure 4 is according to the degree of the influence of parameters on the performance statistics. At first sight, it is difficult and complicated to deduct experimental conditions for the graphs given in this figure. We shall try to explain it with an example. Let us take Figure 4b which shows the variation of the performance statistics with reaction temperature. Now, let us try to determine experimental conditions for the first data point. The reaction temperature for this point is 20 °C which is level 1 for this parameter. Now, let us go to Table 3 and find the experiments for which reaction temperature level (column B) is 1. It is seen in Table 3 that experiments for which column B is 1 are experiments with experiments no. 1-3 and 10-12. The performance statistics value of the first data point is thus the average of those obtained from experiments with experiments no. 1-3 and 10-12. Experimental conditions for the second data point thus are the conditions of the experiments for which column B is 2 (i.e., experiments no. 4-6 and 13-15), and so on. The numerical value of the maximum point in each graph marks the best value of that particular parameter and is given in Table 4 for each parameter. That is, parameter values given in Table 4 are the optimum conditions.

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Figure 4. The effect of each parameter on the optimization criteria.

If the experimental plan given in Table 3 is studied carefully together with Table 2, it can be seen that the experiment corresponding to optimum conditions (A, 1; B, 3; C, 2; D, 2; E, 1; F, 1; G, 2; H, 2; see Table 4) has not been carried out during the experimental work. Thus, it should be noted that the dissolution percentage given in Table 4 has predicted results by using eqs 2-4. Also, a 95% significance level confidence interval of prediction is given in Table 4. To test the predicted

results, confirmation experiments have been carried out three times at optimum working conditions. From the fact that the dissolution percentages obtained from confirmation experiments (99.11%, 99.93%, and 99.97%) are within the calculated confidence interval (see Table 4), it can be said that experimental results are within (5% in error. This proves that interactive effects of parameters are indeed negligible except between the blade number and reaction temperature.

Ind. Eng. Chem. Res., Vol. 37, No. 8, 1998 3387

5. Conclusions The optimum conditions for the dissolution of copper from anode slime in H2SO4 solutions, at which 99.67% Cu conversion was reached, have been determined by the Taguchi method. The major conclusions derived from the present work are the following. (1) The most effective parameters affecting the solubility are the roasting temperature and reaction temperature, respectively (see Figure 4). The solubility increases with increasing roasting temperature and reaction temperature and with decreasing solid-to-liquid ratio. (2) The dissolved copper in the final solution can be recovered as pure copper or CuSO4‚5H2O by well-known methods. (3) After high-grade copper in the raw anode slime is removed, the remainder solid can be processed economically to obtain the precious metals such as Au and Ag. (4) Since optimum conditions determined by the Taguchi method in the laboratory environment is reproducible in real production environments as well, the findings of the present laboratory-scale study may be very useful for the treatment of anode slime on an industrial scale. Literature Cited Abalı´, Y.; C¸ olak, S.; Yapı´cı´, S. The Optimization of the Dissolution of Phosphate Rock with Cl2-SO2 Gas Mixtures in Aqueous Medium. Hydrometallurgy 1997, 46, 27. Abe, S.; Imamura, T.; Goto, S. The Origin of Cu2O and Cu0 Powder Detected in the Layer of Anode Slimes Formed During Electrorefining of Copper. CIM Bull. 1984, 77, 65. Belen’kii, A. M.; Petrov, G. V.; Greiver, T. N. The Behaviour of the Principal Metals in the Sulfation of Copper Electrolyte Slimes. Kompleksn. Ispol’z. Miner. Syr’ya 1987, 7, 38. Byrne, D.; Taguchi, S. The Taguchi Approach to Parameter Design. Qual. Prog. 1987, 12, 19. Chen, T. T.; Dutrizac, J. E. A Mineralogical Study of the Department and Reaction of Silver During Copper Electrorefining. Metall. Trans. B 1989a, 20, 345. Chen, T. T.; Dutrizac, J. E. Mineralogical Characterization of Anode Slimes-IV. Copper-Nickel-Antimony Oxide in CCR Anodes and Anode Slimes. Can. Metall. Q. 1989b, 28, 127. Chen, T. T.; Dutrizac, J. E. Mineralogical Characterization of Anode Slimes: Part V-Nickel-Rich Copper Anodes from the CCR Division of Noranda Minerals Inc. Can. Metall. Q. 1990, 29, 27. Cooper, W. C. The Treatment of Copper Refinery Anode Slimes. Miner., Met. Mater. Soc. 1990, 42, 45. C¸ opur, M.; Pekdemir, T.; C ¸ elik, C.; C¸ olak, S. Determination of the Optimum Conditions for the Dissolution of Stibnite in HCl Solutions. Ind. Eng. Chem. Res. 1997, 36, 682. Ctyan, G. S.; Vartanyants, S. A.; Babayan, G. G. Processing Copper Electrolysis Sludges by the Action of Ozone-Air Mixtures. PromSt. Arm. 1975, 12, 27. De Decker, T.; Backx, A.; Van Peteghem, A. Leaching of Copper Refinery Slimes. In Proceedings of the AIME Annual Meeting, Las Vegas, NV, 1976.

Furman, N. H. Scott’s Standard Method of Chemical Analysis, 6th ed.; D. Van Nostrand Co. Inc.: New York, 1963. Habashi, F. Recent Advances in Pressure Hydrometallurgy. Proceedings of the International Conference on Advances in Chemical Metallurgy, Atomic Research Centre, Bombay, India, Jan 3-6, 1979; pp 26-30. Hoffmann, J. E. Processing Slimes: The Base Case and Opportunities for Improvement. Miner. Met. Mater. Soc. 1990, 42, 38. Kackar, R. N. Off-Line Quality Control, Parameter Design and Taguchi Methods. J. Qual. Technol. 1985, 17, 176. Kosyakov, A.; Hamalainen, M.; Gromov, P.; Kasikov, A.; Masloboev, V.; Neradovski, Yu. Outoclave Processing of Low Grade Copper-Concentrates. Hydrometallurgy 1995, 39, 223. Monahan, R. K.; Loewen, F. Treatment of Anode Slimes at the Inco Copper Refinery. In Proceedings of the Annual Conference of the Canadian Institute of Mining and Metallurgy, Halifax, Nova Scotia, Canada, 1972. Nair, V. V., Ed. Taguchi’s Parameter Design: A Panel Discussion. Technometrics 1992, 2, 127. Nippon Mining Co. Ltd. Treatment of Anode Slime From Copper Electrolysis. Jpn. Tokkyo Koho 1980, 80, 138. Petkova, E. N. Microscopic Examination of Copper Electrorefining Slimes. Hydrometallurgy 1990, 24, 351. Petkova, E. N. Hypothesis About the Origin Copper Electrorefining Slime. Hydrometallurgy 1994, 34, 343. Phadke, M. S. Quality Engineering Using Robust Design; Prentice Hall: Englewood Cliffs, NJ, 1989. Phadke, M. S.; Kackar, R. N.; Speeney, D. V.; Grieco, M. J. OffLine Quality Control in Integrated Circuit Fabrication Using Experimental Design. Bell Syst. Tech. J. 1983, 62, 1273. Ross, P. J. Taguchi Techniques for Quality Engineering; McGrawHill: New York, 1988. Sanuki, S.; Minami, N.; Arai, K.; Izaki, T.; Majima, H. Oxidative Leaching Behavior of Copper Anode Slime in a Nitric Acid Solution Containing Sodium Chloride. J. Jpn. Inst. Met. 1990, 54, 442. Sawicki, J. A.; Dutrizac, J. E.; Friedl, J.; Wagner, F. E.; Chen, T. T. Au Mo¨ssbauer Study of Copper Refinery Anode Slimes. Metall. Trans. B 1993, 24, 457. Scott, J. D. Electrometallurgy of Copper Refinery Anode Slimes. Metall. Trans. B 1990, 21, 629. Stratigakos, E. S.; Jennings, P. H. The Leaching of Copper Refinery Slimes in Aerated Dilute Sulfuric Acid. In Proceedings of the 95th AIME Annual Meeting, New York, 1966. Subramanian, K. N.; Bell, M. C. E.; Thomas, J. A.; Nissen, N. C. Recovery of Metal Values From Anode Slimes. U.S. Patent 4,229,270, 1980. Taguchi, G. System of Experimental Design; Quality Resources: New York, 1987; Vol. 1. Yı´ldı´rı´m, G. Selen ve Tellu¨r Bakı´mı´ndan Zengin Bakı´r Anod C¸ amurunun Konsantre Su¨lfat Asitte C¸ o¨zu¨mlendirilmesi. Proceedings of the 4th National Metallurgy Congress, Ankara, Turkey, 1988; pp 328-338. Ziyadanogˇulları´, R.; Bu¨yu¨ks¸ ahin, M. Anod C¸ amurundaki Selenyumun C¸ o¨zu¨nen Biles¸ ikler Halinde Kazanı´lması´. Dogˇ a TU 1988, 12, 108.

Received for review January 15, 1998 Revised manuscript received April 13, 1998 Accepted April 29, 1998 IE9800290