682
Ind. Eng. Chem. Res. 1997, 36, 682-687
Determination of the Optimum Conditions for the Dissolution of Stibnite in HCl Solutions M. C ¸ opur,† T. Pekdemir,*,† C. C ¸ elik,‡ and S. C ¸ olak† Departments of Chemical Engineering and Industrial Engineering, University of Atatu¨ rk, 25240 Erzurum, Turkey
The Taguchi method has been used to determine optimum conditions for the dissolution of stibnite in HCl solutions. Chosen experimental parameters and their ranges were (i) reaction temperature, 25-70 °C; (ii) solid-to-liquid ratio (in weight), 0.1-0.25; (iii) acid concentration (in weight), 23.71-37%; (vi) mean particle size; 0.1061-0.8426 mm; (v) stirring speed, 200800 rpm; (vi) reaction time, 5-60 min. The optimum conditions were found to be reaction temperature, 70 °C; solid-to-liquid ratio, 0.125; acid concentration, 37%; mean particle size, 0.1061 mm; stirring speed, 700 rpm; reaction time, 60 min. Under these optimum conditions the dissolution of stibnite was approximately 99%. Next, for the experimental conditions of a time value 15 min less than its optimum value, a value of solid-to-liquid ratio twice its optimum value, and the remaining parameters at their optimum values, it was found that a benefit of 0.023 $/g SbCl3 could be gained from the cost of acid and electricity against a loss of 7.5 × 10-4 $/g SbCl3 as a result of the 1.5% decrease in the Sb recovery due to the changes in optimum conditions. Sb2S3(s) + 8HCl(aq) T
Introduction Antimony trioxide (Sb2O3) is largely used in the production of plastics, cable, latex, and flame resistant materials (Morizot and Winter, 1980; Weast, 1986). Sb2O3 consumed in the production of flame resistant materials used in the U.S. car industry forms 57% of the total antimony consumption in the U.S. (MTA, 1985). Antimony is mainly obtained from stibnite (Sb2S3), which is a sulfuric mineral, and in small quantities from oxidized ores such as cervantite (Sb2O3‚Sb2O5), valentinite (Sb2O3), and kermesite (2Sb2S3‚Sb2O3) (Dennis, 1974; Kirk and Othmer, 1952). Stibnite has an orthorhombic crystal system and is available in status in lead-grey color, sometimes tarnished and iridescent, and opaque. It melts readily even in a match flame. Stibnite, the most common antimony mineral, is commonly found with quartz in hydrothermal veins, as replacement bodies in lime stone, and in hot spring deposits (Hamilton et al., 1981). The production of Sb2O3 requires production of antimony trichloride (SbCl3) with a certain purity (Morizot and Winter, 1980). SbCl3 is used in the production of other antimony compounds, in the processes of organic chlorination and polymerization, in electroplating, and in the coloration of metals such as iron and zinc (Kirk and Othmer, 1952). The most common SbCl3 production method is the reaction of chlorine with antimony ores followed by purification processes such as distillation and volatilization. Stibnite gives the following equilibrium reaction in HCl solutions (Gilreath, 1954)
Sb2S3(s) + 6HCl(aq) T 2SbCl3(aq) + 3H2S(g)
(1)
Depending upon the HCl concentration, the following reactions may also take place in the system † ‡
Department of Chemical Engineering. Department of Industrial Engineering. S0888-5885(96)00258-8 CCC: $14.00
2[SbCl4]-(aq) + 3H2S9(g) + 2H+(aq) (2) Sb2S3(s) + 12HCl(aq) T 2[SbCl6]3-(aq) + 3H2S(g) + 6H+(aq) (3) Because H2S is dissolved very little in strong acidic solutions, H2S formed at the end of these reactions will totally leave the reaction medium. Thus it will not have a significant effect on the dissolution of Sb2S3. A wide range of technology exists which may be used to convert H2S into a readily usable product, such as Na2S, instead of releasing it into the atmosphere. C¸ opur et al. (1995) proposed a semiempirical equation for the dissolution of stibnite in HCl solutions for small solid-to-liquid ratios (less than 4/100)
-ln(1 - x) ) 9.79 × 10-10(D)-0.908(C)10.6(S/L)-0.321e-6244/Tt (4) where x is the transformation fraction, D the particle size, C the HCI concentration, S/L the solid-to-liquid ratio, e the exponential function coefficient, T the reaction temperature, and t the time. Various optimization studies for the dissolution of antimony ores have been found in the literature. In a study done on the extraction of antimony from stibnite, optimum working conditions have been found to be FeCI3/Sb2S3 molar ratio, 9; HCI, 1.3 mol; NaCI, 2.5 mol; reaction temperature, 103 °C (Kim and Kim, 1975). In another study, optimum working conditions in the dissolution of concentrate antimony have been found to be 300 g Na2S‚9H2O/L, 80-90 °C reaction temperature, 1/5 solid-to-liquid ratio, and 2 h reaction time (Djurkovic and Ilic, 1979). In a different study investigating working conditions for the leaching of sulfur concentrates in alkali glycerine solution for antimony production, optimum leaching conditions have been found as 150-200 g NaOH/L, 150-200 g C3H8O3/L, 1.10 solid© 1997 American Chemical Society
Ind. Eng. Chem. Res., Vol. 36, No. 3, 1997 683
Figure 1. X-ray diffractograms of (a) the row ore and (b) the residue of the ore from a leaching process with 99% Sb recovery.
to-liquid ratio, 90 °C reaction temperature, and 60 min reaction time (Abdurrakhmanov, 1990). To the authors’ knowledge no experimental research on the optimization of the dissolution of stibnite in HCl solutions exists in the related literature. Thus the present study aims to determine optimum working conditions for the dissolution of stibnite in HCI solutions by using the Taguchi method. One of the advantages of the Taguchi method in the conventional experimental design methods, in addition to keeping the experimental cost at a minimum level, is that it minimizes the variability around the target when bringing the performance value to the target value. Its other advantage is that optimum working conditions determined from the laboratory work can also be reproduced in the real production environment. As the point of this study is not the Taguchi method, it will not be explained here. Those readers who are interested in the method are referred to Kackar (1985), Pignatiello (1988), Ross (1987), Taguchi (1987), and Phadke (1989).
Materials and Methods The antimony ore used in the experiments was obtained from the Nigde region in Turkey. After being crushed and ground, the ore was sieved by using ASTM standard sieves and separated into the proper fractions. It was determined by an X-ray diffractometer that the ore contained mainly Sb2S3 and SiO2. An X-ray diffractogram of the ore is given in Figure 1a. The chemical composition of the ore was determined by volumetric and gravimetric methods. Trace elements were analyzed by using an atomic absorption spectrophotometer. The results obtained from a sample of 0.1061 mm particle size are given in Table 1. The dissolution experiments were carried out in a glass reactor of 250 mL volume equipped with a mechanical stirrer having a digital controller unit and timer, a thermostat, and a back-cooler. The temperature of the reaction medium could be controlled within (1 °C. First 100 mL of HCI solution at a given concentration was put into the reactor, and then when
684 Ind. Eng. Chem. Res., Vol. 36, No. 3, 1997 Table 1. Chemical Composition of Stibnite Ore Used in the Study for a Particle Size of 0.1061 mm element
Sb
Ca
Pb
Co
Fe
Mn
Ag
Cu
Zn
As
S
SiO2
%
60.56
2.21
0.029
0.013
0.091
0.008
0.005
0.014
0.022
0.01
23.12
13.2
Table 2. Parameters and Their Values Corresponding to Their Levels To Be Studied in Experiments levels A B C D E F
parameters
1
2
3
4
5
reaction temperature (°C) solid-to-liquid ratio (in weight) acid concentration (% in weight) particle size (mm) stirring speed (rpm) reaction time (min)
25 0.250 23.71 0.1061 200 5
40 0.200 27.70 0.1500 400 10
50 0.167 30.58 0.4213 500 20
60 0.125 33.84 0.5958 700 45
70 0.100 37.00 0.8426 800 60
Table 3. L25 (56) Experimental Plan Table parameters and their levels exp no.
A
B
C
D
E
F
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
1 2 3 4 5 2 3 4 5 1 3 4 5 1 2 4 5 1 2 3 5 1 2 3 4
1 2 3 4 5 3 4 5 1 2 5 1 2 3 4 2 3 4 5 1 4 5 1 2 3
1 2 3 4 5 4 5 1 2 3 2 3 4 5 1 5 1 2 3 4 3 4 5 1 2
1 2 3 4 5 5 1 2 3 4 4 5 1 2 3 3 4 5 1 2 2 3 4 5 1
the desired temperature of the reactor content was reached, a predetermined amount of the ore was added into the solution while the contents of the vessel was stirred with a certain speed. At the end of the reaction period, the contents of the vessel was filtered, and the filtered solution was then analyzed volumetrically for Sb+3 (Furman, 1963). An X-ray diffractogram of the residue obtained at the end of a leaching test with 99% Sb recovery is shown in Figure 1b. Experimental parameters and their levels to be studied which were determined in the light of preliminary tests are given in Table 2. The orthogonal array (OA) experimental design method was chosen to determine the experimental plan, L25 (56) (Table 3), since it is the most suitable for the conditions being investigated: six parameters, each with five values (Phadke, 1989). In order to observe effects of noise sources on the dissolution process, each experiment was repeated twice under the same conditions at different times. The performance statistics was chosen as the optimization criterion. The performance statistics was evaluated by using the following equation (Pignatiello, 1988)
( )
ZB ) -10 log
1
n
∑ ni)1
1
Yi2
(5)
where ZB is the performance statistics, n the number
of repetition done for an experimental combination, and Yi the performance value of 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
(6)
where µ is the overall mean of the performance value, Xi the fixed effect of the parameter level combination used in ith experiment, and ei the random error in ith experiment. Because eq 6 is a point estimate, which is calculated by using experimental data in order to determine whether results of the confirmation experiments are meaningful or not, the confidence inverval must be evaluated. The confidence interval at a chosen error level may be calculated by (Ross, 1987)
Yi (
(
x
FR;1,DFMSe(MSe)
)
1+m 1 + N ni
(7)
where F is the value of the F table, R the error level, DFMSe the degrees of freedom of 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 experimental results are in percent (%), before evaluating eqs 6 and 7 Ω transformation of the percentage values should be applied first using the following equation. Values of interest are then later determined by carrying out reverse transformation by using the same equation (Taguchi, 1987)
(P1 - 1)
Ω(db) ) -10 log
(8)
where Ω(db) is the decibel value of percentage value subject to Ω transformation and P the percentage of the product obtained experimentally. The order of the experiments was obtained by inserting parameters into the columns of OA, L25 (56), 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. The interactive effects of parameters were not taken into account in the theoretical analysis because some preliminary tests showed that they could be neglected.
Ind. Eng. Chem. Res., Vol. 36, No. 3, 1997 685
Figure 2. Effect of each parameter on the optimization criteria.
The validity of this assumption was checked by confirmation experiments conducted at the optimum conditions. Results and Discussion The collected data were analyzed by an IBM compatible PC 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 2. The order of the graphs in Figure 2 is according to the degree of the influence of parameters on the performance statistics. At first sight it is difficult and complicated to deduce experimental conditions for
graphs given in this figure. We’ll try to explain it with an example. Let us take Figure 2a which shows the variation of the performance statistics with acid concentration. Now let us try to determine the experimental conditions for the first data point. The acid concentration for this point is 23.71% which is level 1 for this parameter. Now let us go to Table 3 and find the experiments for which the acid concentration level (column C) is 1. It is seen in Table 3 that experiments for which column C is 1 are experiments with experiment numbers 1, 10, 14, 18, and 22. The performance statistics value of the first data point is thus the average of those obtained from experiments with experiment
686 Ind. Eng. Chem. Res., Vol. 36, No. 3, 1997 Table 4. Optimum Working Conditions, Predicted Dissolved Quantities of Stibnite, and a Comparative Cost Analysis for Two Different Experimental Conditions case 1 value A reaction temperature (°C) B solid-to-liquid ratio (g/mL) C acid concentration (% in weight) D particle size (mm) E stirring speed (rpm) F reaction time (min) predicted dissolved quantity (%) predicted confidence interval (%) quantity of product (SbCl3, 99.5% pure) to be obtained (g) (UP:a 0.046 $/g) cost of spent HCI ($) (UP:a 6.25 $/L) cost of spent electrical energy ($) (UP:a 0.08623 $/kW h) cost of product ($/g) loss due to amount of product gone to waste ($/g) difference between the cost for case 2 and that for case 1 ($/g) difference between the loss due to amount of product gone to waste for case 2 and that in case 1 ($/g) a
70 0.125 37 0.1061 700 60 99.07 92.25-99.98 112.39
case 2 level 5 4 5 1 4 5
value 70 0.250 37 0.1061 700 45 97.5 90.09-99.89 110.61
5 0.25 0.0467 4.36 × 10-4
level 5 1 5 1 4 4
2.5 0.121 0.0237 1.19 × 10-3 -0.023 7.54 × 10-4
UP: unit price
numbers 1, 10, 14, 18, and 22. Experimental conditions for the second data point thus are the conditions of the experiments for which column C is 2 (i.e. experiments with experiment numbers 2, 6, 15, 19, and 23), 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 the second column of Table 4 (case 1) for each parameter. That is, parameter values given in case 1 column of Table 4 are the optimum conditions. Next we wanted to investigate how Sb recovery would change by changing the optimum values of those parameters which make up most of the total cost in such a way that it would reduce the total cost significantly. The first parameters that come to mind were time and solid-to-liquid ratio; the smaller the time and the higher the solid-to-liquid ratio, the smaller the total cost. Thus we selected a time value about 15 min less than the optimum time value and increased the value of the solidto-liquid ratio by a factor of 2 from its optimum value. The result was, as shown in the case 2 column of Table 4, that a benefit of 0.023 $/g SbCl3 could be gained from the cost of acid and electricity against a loss of 7.54 × 10-4 $/g SbCl3 due to the 1.5% decrease in the Sb recovery. Although case 2 is economically more advantageous, which experimental conditions should be chosen is left to the reader. The cost analysis results given in Table 4 were evaluated by taking the price of chemicals from the Aldrich 1990-1991 catalog and the price of the electricity power from Turkish Electric. If the experimental plan given in Table 3 is studied carefully together with Table 2, it can be seen that experiments corresponding to optimum conditions (A, 5; B, 4; C, 5; D, 1; E, 4; F, 5; see the case 1 column of Table 4) have not been carried out during the experimental work. Thus it should be noted that dissolution percentages given in Table 4 are the predicted results obtained by using eqs 6-8. Also given in Table 4 are the 95% significance level confidence intervals of the predictions. In order to test predicted results, confirmation experiments were carried out twice at optimum working conditions. From the fact that the dissolution percentages obtained from confirmation experiments (for case 1, 98.6% and 99.0%; for case 2, 96.3% and 97.1%) are within the calculated confidence intervals (see Table 4), it can be said that experimental results
are within (5% error. This proves that interactive effects of parameters are indeed negligible. Conclusions The optimum conditions for the dissolution of stibnite ore in HCI solutions have been determined. The major conclusions derived from the present work are (1) The most important parameter affecting the solubility is acid concentration. The solubility increases with increasing acid concentration. The dissolution of stibnite also increases with increasing temperature, time, and stirring speed and decreasing solid-to-liquid ratio and particle size. (2) The cost of the leaching operation can be significantly reduced against a very small amount of reduction (about 1.5%) in the dissolution of stibnite (see Table 4). Thus it is recommended that in decision-making stage the trade-off between the reduction in solubility and gained economical benefits should be studied carefully. (3) Since optimum conditions determined by the Taguchi method in laboratory environment are reproducible in real production environments as well, the findings of the present laboratory scale study may be very useful for production of SbCl3 on an industrial scale. Literature Cited Abdurrakhmanov, S. A.; Artykbaev, T. D.; Valiev, K. R.; Baev, S. A. Leaching of antimony from sulphide concentrates by an alkali-glycerine solution. Izv. Vyssh. Uchebn. Zaved., Tsvetn. Metall. 1990, 4, 46-50; Chem. Abstr. 1991, 114, 147650t. C¸ opur, M.; C¸ olak, S.; Yapici, S. Solubility of stibnite in HCI solutions. Ind. Eng. Chem. Res. 1995, 34, 3995-4002. Dennis, W. H Demirden Gayri Metaller Metalurjisi, (Trans. Tulgar E.); ITU Press: Istanbul, 1974; Section II, pp 555-584. Djurkovic, B.; Ilic, I. Results of a study of hydrometallurgical method for processing polymettallic antimony concentrates II. Tecnica (Belgrade) 1979, 34 (1), RGM4-RGM9 (Serbo-Croation); Chem. Abstr. 1979, 91, 24602c. Furman, N. H. Standard Method of Chemical Analysis, 6th ed.; D. Van Nostrant Company Inc.: New York, 1963; pp 92-93.
Ind. Eng. Chem. Res., Vol. 36, No. 3, 1997 687 Gilreath, E. S. Qualitative Analysis, 6th ed.; McGraw-Hill Book Company Inc.: New York, 1954; pp 198-202. Hamilton, W. R.; Wooley, A. R.; Bishop, A. C. Country Life Guides, Minerals, Rocks and Fossils, 4th impression; The Hamlyn Publishing Group Ltd.: Twickenham, Middlesex, England, 1987; p 28. Kackar, R. N.; Off-line quality control, parameter design and Taguchi methods. J. Quality Technol. 1985, 17, 176-209. Kim, S. S.; Kim, J. I. Ferric chloride leaching of antimony. Taechan Kwangsan Hakhoechi. 1975, 12 (1), 35-39; Chem. Abstr. 1975, 83, 135410t. Kirk, R. E.; Othmer, D. F. Encyclopaedia of Chemical Technology; Interscience Encyclopaedia Inc.: New York, 1952; Vol. 1, p 61. Morizot, G.; Winter, J. M.; Barbery, G. Volatilization chloridization with calcium chloride of complex sulphide minerals and concentrates. Proceedings of the Congress on Complex Sulphide Ores, Rome, Italy, 1980; pp 151-158. MTA Bulletin. General Directory of Mineral Research of Turkey, Antimony, No. 192; AnKara, 1985; pp 1-22. 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, (5), 12731309.
Phadke, M. S. Quality Engineering using Robust Design; Prentice Hall: Englewood Cliffs, NJ, 1989; p 292. Pignatiello, J. J. J. An overview of the strategy and tactics of Taguchi. IEEE Trans. 1988, 20 (3), 247-254. Ross, P. J. Taguchi Techniques for Quality Engineering; McGrawHill: New York, 1987; pp 123-124. Weast, R. C. CRC Handbook of Chemistry and Physics, 66th ed.; CRC Press Inc.: Boca Raton, FL, 1986; p B-8. Taguchi, G. System of Experimental Design; Quality Resources: New York, 1987; Vol. 1, pp 108-115.
Received for review May 8, 1996 Revised manuscript received September 16, 1996 Accepted September 23, 1996X IE960258R
X Abstract published in Advance ACS Abstracts, November 1, 1996.
