Optimization of physicochemical parameters for

Hydrometallurgy 111 (2012) 22–28

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Optimization of physicochemical parameters for bioleaching of sphalerite by Acidithiobacillus ferrooxidans using shaking bioreactors Davoud F. Haghshenas a, Babak Bonakdarpour b, c,⁎, Eskandar Keshavarz Alamdari a, Bahram Nasernejad b, c a b c

Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Iran Department of Chemical Engineering, Amirkabir University of Technology, P.O. Box. 15875−4413, Tehran, Iran Food Process Engineering and Biotechnology Research Centre, Amirkabir University of Technology, Tehran, Iran

a r t i c l e

i n f o

Article history: Received 19 June 2011 Received in revised form 25 August 2011 Accepted 5 September 2011 Available online 1 October 2011 Keywords: Sphalerite Bioleaching Response surface methodology Central composite design Acidithiobacillus ferrooxidans Optimization

a b s t r a c t Bioleaching processes for extraction of zinc from sphalerite are more environmentally friendly and consume less energy than conventional technologies but are as yet less economic. One necessary step towards arriving at a cost-effective sphalerite bioleaching process is the use of appropriate methodology for the optimization of pertinent factors in such processes. Previous studies on Zn bioleaching systems have reported a fairly wide range of values as the optimum level of relevant physicochemical parameters for Zn bioleaching processes. This is partly due to the different strains and Zn source type employed but another reason could be that important parameters in this process interact with each other. In order to shed more light on this matter, in the present work Response Surface Methodology was employed for the study and optimization of important factors in a sphalerite bioleaching process by Acidithiobacillus ferrooxidans using shaking bioreactors. The effect of change in the levels of temperature, pH, initial Fe(II) concentration and pulp density – in the range 30–36 °C, 1.4–2.0, 3–11 g L−1 and 4–6% wt/vol respectively – on the rate Zn bioleaching was studied using a Central Composite Design. The results showed a statistically significant effect of pH and pulp density – and to a lesser extent temperature and initial Fe(II) concentration – on the rate of bioleaching of Zn. A statistically significant interaction was found between pH and temperature, which means that the optimum values of these two parameters can only be correctly obtained through the use of factorial design of experiments. Additionally, the optimum level of temperature and pH was found to depend on the level of pulp density. This means that when employing shaking bioreactors for optimization of these parameters the level of pulp density should be carefully chosen. However, there was no statistically significant interaction between initial Fe(II) concentration and the other three factors studied. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Identification of the correct optimum values of the effective parameters in biotechnological processes is a prerequisite for their successful commercial exploitation. This is especially true when these processes have to compete with alternative chemically or physically based processes. An example of such a process is the bioleaching of sphalerite. Alternative hydrometallurgical or pyrometallurgical technologies for extraction of Zn from zinc sulfide sources are currently available and operating at commercial scale. However, processes based on these technologies are energy intensive and also cause environmental pollution (Haghshenas et al., 2009a; Rodriguez et al., 2003). Bioleaching, on the other hand, is a less energy intensive and an environmentally friendly process (Haghshenas et al., 2009a; Rodriguez et al., 2003; Shi et al., 2006); however, it will only be commercially applied for extraction of Zn from sphalerite if it also proves to be economic.

⁎ Corresponding author. Tel.: + 98 64543169; fax: + 98 21 6640 5847. E-mail address: [email protected] (B. Bonakdarpour). 0304-386X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.hydromet.2011.09.010

Physicochemical parameters identified in previous research as significantly influencing the rate of Zn dissolution from zinc sulfide sources such as sphalerite are pH, temperature and initial ferrous iron concentration (Mousavi et al., 2006; Mousavi et al., 2007; Pina et al., 2005). The proper choice of the value of these parameters is necessary if an economic sphalerite bioleaching process is to be developed. There is an extensive literature on bioleaching of Zn from sulfide sources but an answer to the question of what is the optimum level of these parameters cannot be reliably found by reference to these reported studies. Below this is illustrated by the survey of literature on studies for Zn bioleaching employing Acidithiobacillus ferrooxidans — the most common bacterial species used in bioleaching in general and sphalerite bioleaching in particular. Different temperatures have been employed in studies on Zn dissolution from various sources by pure culture of A. ferrooxidans such as 26 °C (Hsu and Harrison, 1995), 30 °C (Lombardi and Garcia, 2002; Solisio et al., 2002), 32–35 °C (Olubambi et al., 2007), 32±2 °C (Tipre and Dave, 2004), 33 °C (Haghshenas et al., 2009b; Mousavi et al., 2006; Mousavi et al., 2007) and 35 °C (Gupta et al., 2003; Shi et al., 2006; Wang et al., 2008). The values of pH employed in previous studies

D.F. Haghshenas et al. / Hydrometallurgy 111 (2012) 22–28

also shows a wide range including 1.2 (Gupta et al., 2003), 1.5 (Mousavi et al., 2006), 1.5–1.8 (Sampson et al., 2005), 1.8±0.2 (Tipre and Dave, 2004), 2 (Olubambi et al., 2007; Shi et al., 2006; Wang et al., 2008), 2.5–3.0 (Solisio et al., 2002), 2.8 (Hsu and Harrison, 1995) and 3 (Lombardi and Garcia, 2002). There is also a range of initial ferrous iron concentration – between 2 and 12 g L −1 – employed in Zn bioleaching studies using A. ferrooxidans (Deveci et al., 2004; Haghshenas et al., 2009a; Haghshenas et al., 2009b; Mousavi et al., 2006; Pina et al., 2005; Shi et al., 2006; Wang et al., 2008). The wide range of values used or reported as optimum for the above-mentioned physicochemical parameters can partly be explained by the different strains of A. ferrooxidans, and to a lesser extent by the type of Zn sulfide source, employed in these studies. However, the wide range of values cannot be fully explained by these factors and, furthermore, the optimum level of these parameters might also be influenced by the level of the other important parameters in the sphalerite bioleaching process. In statistical parlance, there might be an interaction between effective parameters in such a process. A survey of previous literature on sphalerite bioleaching provides no clues as to whether such interaction between the important process parameters in a sphalerite bioleaching process exists or not. This is because in previous studies one-factor-at-a-time methodology has been used to optimize the abovementioned parameters (Deveci et al., 2004; Mousavi et al., 2006; Pina et al., 2005; Rodriguez et al., 2003; Solisio et al., 2002; Tipre and Dave, 2004). This methodology is very inefficient and furthermore gives absolutely no information about interactions between parameters in a process. The only methodology capable of providing an answer to this question is factorial design of experiments (DOE), which – through the use of techniques such as Response Surface Methodology (RSM) – is able to simultaneously consider several factors at different levels, and give a suitable model for the relationship between the various factors and the response (Montgomery, 2006). However, RSM has been applied in only a few cases to a bioleaching process (Chen and Lin, 2010; Simate et al., 2009; Xu and Ting, 2004). There are full as well as fractional factorial DOEs; the former gives the most complete information regarding interaction between parameters but the number of experiments becomes excessive when the number of factors or their levels becomes relatively large. Additionally, higher order interactions are usually statistically insignificant and, consequently, information about them is not very useful (Montgomery, 2006). Fractional factorial designs (FFD) – such as central composite design (CCD) or Box–Behnken (Chen and Lin, 2010; Khalili and Bonakdarpour, 2010) – can give information regarding parameter interactions with the use of less experimentation; however, reliable information about first order interactions can only be obtained from the results of DOEs which are not highly fractionated (Montgomery, 2006). Another important point regarding optimizing parameters in a biotechnological process such as sphalerite bioleaching is the correct choice of the cultivation method. Physicochemical parameters such as pH, temperature and initial ferrous iron concentration are best optimized using shaking bioreactors (also termed shake flask cultivation); this cultivation method is less time consuming and cheaper compared to stirred bioreactor cultivation but nevertheless gives conclusions applicable to the larger scale (Büchs, 2001). On the other hand, pulp density is best optimized in a stirred bioreactor; this is because, due to the higher gas transfer rates, much higher pulp densities can be employed in stirred bioreactors compared to shaking bioreactors (Tipre and Dave, 2004). The aim of the present work was to identify and quantify interaction between important physicochemical parameters – i.e. pH, temperature and initial ferrous iron concentration – in a sphalerite bioleaching process by using appropriate methodology, namely RSM. Shake flask cultivation was employed as it is the method commonly used for preliminary optimization of parameters in a bioleaching process (Pina et al., 2005;

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Simate et al., 2009; Xu and Ting, 2004). A half fractional factorial CCD was chosen as the design matrix since it allows reliable identification of first order interaction between factors and provides a second order polynomial model which can be used to predict optimum level of these parameters (Montgomery, 2006). The level of pulp density – as the fourth factor – was also varied in the CCD, not to optimize it but to see whether there is an interaction between this very important process parameter and the above-mentioned physicochemical parameters. The possible existence of this interaction has important implications regarding the use of shake flask cultivation for optimizing physicochemical parameters for a sphalerite bioleaching process. 2. Materials and methods 2.1. Concentrate and bacteria The sphalerite concentrate was obtained from the Kooshk mine (Yazd, Iran). The sample contained 47.7% Zn, 6.7% Fe, 2.76% Pb, 32.4% S, 1.95% CaO, 1.25% MgO and 1.85% Al2O3. X−Ray Diffraction (XRD) analysis showed the concentrate was mainly composed of sphalerite and quantities of pyrite and galena. 80% and 100% of the concentrate particles sizes fell in the range 38–150 μm and 0–250 μm, respectively. A. ferrooxidans PTCC 1647, adapted to both Zn ions (30 g L−1) and sphalerite concentrate (1.5%wt/vol), was used throughout this study. The adaptation procedure employed is described elsewhere (Haghshenas et al., 2009a). The mineral nutrient media employed was a ferrous iron based 9 K medium containing (g L−1): (NH4)2SO4 =3; KCl =1; MgSO4.7H2O=0.5; KH2PO4 =0.5; FeSO4.7H2O=44.8 (Silverman and Lundgren, 1959). The medium minus FeSO4 was autoclaved at 120 °C for 20 min. The FeSO4 medium was separately sterilized through a 0.2 μm filter and added aseptically to the iron free medium. 2.2. Experimental procedures All experiments were carried out in 500 mL Erlenmeyer flasks containing 150 mL solution. Experiments were conducted in a rotary shaker at 210 RPM at varying temperatures in the range 30–36 °C. The initial bacterial concentration in all the experiments was approximately 10 8 cells/mL. The pH of the media was monitored and regulated daily. During the experiments, make-up distilled water was added periodically to the flasks to compensate for evaporation loss; afterwards the pH of the solution was adjusted back to its initial value with 18 M sulfuric acid. All experiments were carried out in duplicate. 2.3. Experimental design for RSM A central composite design (CCD) was adopted to study four factors at three levels. Twenty eight experimental runs consisting of 8 star points (star distance was 0) and 4 center points were generated with 4 factors and 3 levels by the principle of RSM using MINITAB Release 15. The levels employed for the different factors, according to CCD design, are listed in Table 1. The levels of pulp density (PD) were chosen to simulate relatively high to relatively low gas supply conditions in the shake flasks (Haghshenas et al., 2009a). The levels of pH, temperature (T) and initial ferrous iron concentration (Fe(II)) were chosen to be in the range employed by previous investigators (see the Introduction section). The quadratic polynomial regression model (Eq. (1)) was chosen for predicting the response variable in terms of the four independent variables:

4

4

i¼1

i¼1

2

3

4

Y ¼ b0 þ ∑ bi Xi þ ∑ bii Xi þ ∑ ∑ bij Xi Xj i¼1 j¼iþ1

ð1Þ

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Table 1 Central composite design arrangement and response (Zn concentration at the end of 12 days of incubation expressed as mean ± standard deviation). Experiment number 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 26 27 28

Factors

Response (mean ± SD) Zn (g L−1)

T (°C)

Fe(II) (g L−1)

PD (%wt/vol)

pH

30 36 30 36 30 36 30 36 30 36 30 36 30 36 30 36 30 36 33 33 33 33 33 33 33 33 33 33

3 3 11 11 3 3 11 11 3 3 11 11 3 3 11 11 7 7 3 11 7 7 7 7 7 7 7 7

4 4 4 4 6 6 6 6 4 4 4 4 6 6 6 6 5 5 5 5 4 6 5 5 5 5 5 5

1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 2 2 2 2 2 2 2 2 1.7 1.7 1.7 1.7 1.7 1.7 1.4 2 1.7 1.7 1.7 1.7

10.7 ± 0.20 12.7 ± 0.30 11.3 ± 0.25 12.9 ± 0.30 9.1 ± 0.15 10 ± 0.20 9.4 ± 0.20 9.8 ± 0.25 14.7 ± 0.20 15 ± 0.15 14.9 ± 0.25 15.4 ± 0.35 9.7 ± 0.2 10.3 ± 0.3 10.1 ± 0.25 10.4 ± 0.35 13.1 ± 0.25 13.6 ± 0.35 13.2 ± 0.15 13.3 ± 0.35 15.6 ± 0.4 11.5 ± 0.2 12.5 ± 0.3 13.4 ± 0.15 13.5 ± 0.25 13.3 ± 0.35 13.7 ± 0.4 13.3 ± 0.3

Table 2 Values of regression coefficients calculated for the Zn recovery during sphalerite bioleaching by A. ferrooxidans. Independent factor

Regression coefficient

Standard error

T-value

P-value

Constant Linear T Fe(II) PD pH Quadratic T.T Fe(II).Fe(II) PD.PD pH.pH Interactive T.Fe(II) T.PD T.pH Fe(II).PD Fe(II).pH PD.pH

− 110.284

20.566

− 5.363

0.000

3.846 0.700 5.573 51.690

1.344 0.291 2.031 7.471

2.862 2.406 2.744 6.918

0.007 0.021 0.009 0.000

− 0.047 − 0.032 − 0.219 − 9.098

0.020 0.011 0.181 2.016

− 2.308 − 2.860 − 1.206 − 4.513

0.026 0.007 0.235 0.000

− 0.005 − 0.046 − 0.222 − 0.013 0.010 − 2.125

0.006 0.024 0.081 0.018 0.061 0.243

− 0.858 − 1.887 − 2.745 − 0.686 0.172 − 8.751

0.396 0.066 0.009 0.496 0.865 0.000

In Eq. (1) Y is the response variable (i.e. concentration of Zn in g L −1 at the end of 12 days of bioleaching), b0, bi, bii, and bij are the coefficients of the intercept, linear, quadratic and interaction terms, respectively, and Xi and Xj represent the four independent variables (i.e. T, PD, pH and Fe(II)). The experiments were carried out with two replicates and conducted in a randomized order to avoid systematic bias. The statistical significance of the full quadratic models predicted was evaluated by the analysis of variance (ANOVA). The significance and the magnitude of the effects estimates for each variable and all their possible linear and quadratic interactions were also determined. Unless otherwise stated, the significance level employed in the analysis was 5%. Finally, the model was used to predict both the optimum value and optimum region of the level of the factors which results in maximum or fairly high Zn dissolution rates. All the analysis was carried out using MINITAB Release 15.

second order polynomial model is necessary to represent the data. All the second order terms of the independent parameters, apart from PD, were significant together with all the linear terms. Main effect plots (not shown) indicated that increase in the level of all the three physicochemical factors – within the range studied – had a statistically significant effect on the rate of Zn dissolution after 12 days; increase in the level of these factors up to a certain (optimum) value led to an increase in the rate of Zn dissolution whereas further increases resulted in the opposite effect. The statistical analysis of the interaction terms showed that, at 5% significance level, there is significant interaction between pH with both T and pulp density. At 10% significance level, there is also significant interaction between T and pulp density. This means that the optimum level of T in sphalerite bioleaching process depends both on the level of pulp density as well as the level of pH employed in the process. The optimum level of pH, on the other hand, depends on the level of temperature and pulp density. The results presented in Table 2, furthermore, show that the interaction between Fe(II) with the other three factors is statistically insignificant; in other words, the optimum level of Fe(II) in a sphalerite bioleaching process does not depend on the level of pulp density, temperature and pH employed. Based on the calculated values of the regression coefficients (Table 2) a polynomial regression model equation that fitted 96.7% of the variation in the data was proposed as follows:

2.4. Measurements and analysis

−1 ¼−105:654 þ 4:046T þ 0:539FeðIIÞ þ 1:785PD þ 54:158pH Zn gL

The concentration of Zn and was determined by Atomic Absorption Spectrophotometry (AAS). The number of free cells in the inocula to the shake flasks was determined through direct counting by a phase contrast microscope using Neuber counting chamber. The pH of the solutions was measured using a pH meter.

−0:054T⋅T−0:036FeðIIÞ·FeðIIÞ−9:803pH·pH−0:222T·pH −2:125PD·pH

ð2Þ

The low values of P determined for the regression (P b 0.001), as well as the fact that the lack of fit of the model was not significant (P N 0.05), revealed the suitability of the model (Table 3).

3. Results 3.1. Model fitting

3.2. Study of interaction amongst factors in the sphalerite bioleaching process

Table 1 lists the values of Zn concentration after 12 days of incubation at each of the 28 combination of factor levels with the values ranging from 9.1 to 15.6 g L−1. The values given are the mean of two independent experiments. The values of the regression coefficients are presented in Table 2. Both the linear and quadratic terms are significant, indicating that a

In the cases where interaction between factors is statistically significant, surface plots give more complete information regarding the effect of a factor on the response. Examination of the surface plot presented in Fig. 1 shows that, as the pH decreases the optimum temperature for the Zn bioleaching process shifts to higher values. According to Fig. 2a, the effect of pH on the Zn dissolution rate depends on the pulp density.


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Table 3 ANOVA table.

Total Regression Residual error Lack of fit (model error) Pure error (replicate error) R2

df

SS

MS

F-values

P-values

55 14 41 10 31 96.75

214.329 207.366 6.963 2.568 4.395

14.812 0.170 0.257 0.142

87.22

0.000

1.81

0.100

Abbreviations: df = degrees of freedom; SS = sum of squares; MS = mean squares.

At low pulp densities, increase in pH (in the range 1.4–2.0) leads to an increase in Zn dissolution rate; however, with an increase in pulp density the optimum value of pH shifts to the lower values. The contour plot presented in Fig. 2b shows that the effect of the variation of pH on the rate of Zn dissolution during the high grade bioleaching process diminishes with an increase in pulp density over the range studied. Since, according to a previous study by the authors (Haghshenas et al., 2009b), such an increase results in lower gas transfer rates, this suggests that the reduction of oxygen and carbon dioxide transfer rates inside a bioreactor will diminish the importance of pH control. However, it should be pointed out that if the sphalerite bioleaching process is carried out in a bioreactor gas transfer limitations would occur at higher pulp densities compared to that observed in the present study (Tipre and Dave, 2004). Fig. 3 shows the trend of change in Zn concentration during the sphalerite bioleaching process with A. ferrooxidans at two pulp densities. It can be seen that at both pulp densities the rate of Zn dissolution is initially low, but, after a certain period the rate of Zn dissolution reaches a higher value. Furthermore, the length of this initial period increases with increase in pulp density. The surface plot presented in Fig. 4a shows that despite a statistically significant interaction between temperature and pulp density, the optimum temperature for a sphalerite bioleaching process only marginally depends on the level of the pulp density. The corresponding contour plot presented in Fig. 4b shows that the importance of temperature control, within the range studied, diminishes with increase in pulp density in the range 4–6% wt/vol. Furthermore, a comparison of Fig. 4b and Fig. 2b shows that, in a sphalerite bioleaching process at relatively low pulp densities, pH control is more critical than temperature control.

Fig. 2. a. Surface plots for Zn concentration with respect to pulp density and pH (with temperature and initial ferrous iron fixed at their middle values). b. Contour plots for Zn concentration with respect to pulp density and pH (with temperature and ferrous iron concentration fixed at their middle values).

Percent of Zn Extraction

100

4% wt/vol 6% wt/vol

80

60

40

20

0 0

4

8

12

16

20

24

28

time (day)

Fig. 1. Surface plots for Zn concentration with respect to temperature and pH (with pulp density and initial ferrous iron fixed at their middle values).

Fig. 3. Zn extraction percent versus time during bioleaching of sphalerite concentrate at pulp densities of 4 and 6%wt/vol in shake flask culture (T = 33.7 °C, Fe(II): 7.4 (g L−1), pH: 1.94, RPM = 180).

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Examination of these plots showed that pulp density and pH had the greatest effect on the Zn dissolution rate, whereas change in levels of temperature and initial ferrous iron concentration had a much lower effect. Contour plot analysis showed that final Zn concentrations ≥ 16 g L −1 can only be obtained when pulp density is set at around 4% wt/vol, pH is ≥ 1.8 and the level of the other two factors varied within a certain range. To examine this matter, it was decided to set the values of pulp density and pH at their optimum values and explore the possible combination of levels of the two other factors which results in fairly high rates of Zn dissolutions (Fig. 5). It can be seen that under these conditions Zn concentrations ≥ 16 g L −1 can be attained at the following combination of the level of T and Fe(II): 1) T = 33.7 °C, Fe(II) = 5.6–9.2 g L −1, 2) Fe(II) = 7.3 g L −1 and T =32.2– 35.2 °C. The first scenario is better from an economic perspective since it allows the use of a lower initial ferrous iron concentration which will decrease the cost of subsequent steps in the production of Zn. However, since an exact temperature control is not very feasible under industrial conditions, the second scenario (i.e. raising initial ferrous iron concentration but letting temperature vary within a range) would be a more realistic way of achieving fairly high Zn dissolution rates during a sphalerite bioleaching process. 4. Discussion

Fig. 4. a. Surface plots for Zn concentration with respect to pulp density and temperature (with pH and initial ferrous iron fixed at their middle values). b. Contour plots for Zn concentration with respect to pulp density and temperature (with pH and ferrous iron concentration fixed at their middle values).

3.3. Optimization of parameters in the sphalerite bioleaching process Initially, the optimization of the levels of the four factors for achieving maximum Zn dissolution after 12 days for shake flask cultivation of A. ferrooxidans, in the presence of high grade sphalerite concentrate, was carried out using the proposed second order polynomial model (Eq. (2)). This exercise predicted that the maximum concentration of Zn after 12 days of bioleaching of sphalerite is 16.1 g L −1 under the following conditions: T of 33.7 °C; Fe (II) of 7.4 (g L −1); PD of 4%wt/vol; pH of 1.94. To confirm this prediction, and therefore the applicability of the proposed second order model for further optimization exercises, confirmation runs (i.e. runs at the predicted optimum level of the factors) were carried out in triplicate. The 90% confidence interval for Zn concentration after 12 days of incubation under optimized conditions was obtained as 16.52 + 0.62 g L −1. Since the value predicted by the model is within this interval, this can be taken as the confirmation of the suitability of the regression model for predictive purposes (Montgomery, 2006). As the second order polynomial equation indicated that the four factors did not have the same effect on the response, a second optimization exercise was carried out in which the relative effect of these factors was examined using contour plots. To this end, eighteen contour plots (not shown) were generated between pairs of parameters.

One important reason for the statistically significant interactions identified between some of the physicochemical and process parameters in the sphalerite bioleaching process using RSM in the present study is related to the fact that during a sphalerite bioleaching process there are two mechanisms of Zn dissolution in operation, namely purely chemical (2ZnS + O2 + 4H + → 2Zn 2+ + 2H2O + 2S) and biologically-mediated (ZnS + 2Fe 3+ → Zn 2+ + 2Fe 2+ + S) Zn dissolution. The former mechanism occurs via proton (H +) attack whereas the latter occurs through the action of bacterially produced Fe(III) (Sand et al., 2001; Schippers and Sand, 1999). The former mechanism contributes to Zn dissolution throughout the bioleaching process but is slower and only dominates the overall Zn dissolution process in the early part when the bacterial population is going through their lag or acceleration phase of growth (Haghshenas et al., 2009b; Shi et al., 2006). Any condition that lengthens the period of this early phase, such as increase in pulp density as illustrated in Fig. 3 – or favors the rate of chemical dissolution over that of biological growth (see below)– would lead to increase in the contribution of the purely chemical dissolution to overall Zn dissolution in the process. It should be pointed out that although the biologically mediated Zn dissolution mechanism is also inherently a chemical process; it is directly dependent on the rate of bacterial activity since Zn dissolution rate is limited by the concentration of Fe (III) — which is regenerated through bacterial

Fig. 5. Contour plots for Zn concentration with respect to temperature and ferrous iron concentration (Optimum levels of pulp density and pH).


activity. Also, in the latter phases of the bioleaching process bacterial oxidation of the sulfur layer formed on the concentrate particles can contribute to the generation of protons and hence acid attack. However, according to our previous study (Haghshenas et al., 2009b) the rate of Zn dissolution is controlled by the rate of diffusion of ions through this non-porous elemental sulfur which suggests that microbial oxidation of sulfur is very inefficient, and, hence, the generation of protons by microbial mechanism might be negligible in this system. However, since pH was controlled throughout the biooxidation runs we do not have any experimental evidence for this. The change of temperature and pH has dissimilar effects on the rate of chemical leaching and that of biological growth. An increase in temperature and a decrease in pH both lead to an increase in the rate of chemical leaching of sphalerite (Gomez et al. 1999; Rawlings, 2004; Sampson et al., 2005). On the other hand, there is an optimum value of pH and temperature for the activity of A. ferrooxidans cells, and, therefore, any increase or decrease in the value of these parameters away from the optimum value should lead to decrease in the rate of biologically-mediated Zn dissolution (Das et al., 1999; Gomez and Cantero, 1998 ; Nemati et al., 1998). Since the biological mediated Zn dissolution is the faster one of the two mechanisms, the levels of pH and temperature which were predicted to result in the highest rate of Zn dissolution (i.e. pH = 1.94 and T = 33.7 °C at 4% wt/vol pulp density) should correspond to conditions which results in optimum activity of A. ferrooxidans. Therefore any change in level of these parameters away from these values should result in increase in the contribution of proton attack to overall Zn dissolution during the sphalerite bioleaching process. This explains why with decrease in pH – in the range studied – the optimum value of temperature for the process shifted to higher values. If the purely chemical dissolution mechanism had become dominant at low pH, then – at such pH – the effect of temperature would have been positive over the entire range of temperature studied. The fact that this did not happen suggests that, even at pH as low as 1.4, bacterially-mediated Zn dissolution is still the dominant Zn extraction mechanism during the sphalerite bioleaching process. Another reason why the temperature optimum did not shift more to the right with a decrease in pH is that, although with an increase in temperature the rate of chemical dissolution of Zn increases (Haghshenas et al., 2009b) but, concurrently, the bacterial thermal inactivation, which is more sensitive to temperature (Schuler and Kargi, 2002), also rises. The statistically significant interaction between pH and pulp density can be explained by the increase in the contribution of the purely chemical dissolution mechanism to the overall Zn extraction, as a result of increase in pulp density. This is illustrated by the data presented in Fig. 3 which show a lengthening of the initial slow Zn dissolution rate phase when pulp density is increased from 4% to 6% w/v. This happens because when pulp density is increased above 4% w/v, as a consequence of decrease in the rates of oxygen/carbon dioxide transfer, bacterial activity decreases (Haghshenas et al., 2009a). Since it has been previously shown that with increase in pulp density the relative population of cells attached to the ore particles compared to the suspended cells increases (Haghshenas et al., 2009a), another factor that might have contributed to the decrease in pH optima with an increase in pulp density is the higher tolerance of the immobilized bacterial cells to low pH conditions (Schuler and Kargi, 2002). The identification of interactions between important physicochemical and process parameters in the sphalerite bioleaching process in the present study has important implications regarding the correct method of optimizing such parameters for a sphalerite bioleaching process. The fact that a statistically significant interaction was identified between pH and temperature suggests that the correct optimum value for these parameters can only be obtained using fractional factorial DOEs such as CCD, although care should be taken to avoid highly fractionated design matrices. However, since no statistically significant interaction was identified between initial ferrous iron concentration and the other

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parameters studied the use of one-factor-at-a-time methodology or highly fractionated factorial DOEs gives correct optimum values for this parameter for a sphalerite bioleaching process, although the latter is considered to be a more efficient methodology (Montgomery, 2006). The fact that pulp density showed statistically significant interaction with pH and temperature for a sphalerite bioleaching process casts doubt on the suitability of shake flask cultivation for optimizing such parameters in a sphalerite bioleaching process even though this has been the usual method for optimizing physicochemical parameters for biotechnological processes — including sphalerite bioleaching (Mousavi et al., 2006; Pina et al., 2005; Simate et al., 2009; Xu and Ting, 2004). This is because much higher pulp densities are usually employed in industrial bioreactors compared to that attainable in shake flasks, which mean that the temperature and pH values predicted as optimum from the result of shake flask studies do not necessarily hold at the higher pulp densities employed in industrial bioreactors. However, based on the results of the previous studies by the authors (Haghshenas et al., 2009a, b), in the present study the three levels of pulp density used in the CCD matrix in the shake flask studies were chosen to simulate conditions of “low” to “high” gas transfer rates and the results seem to suggest that the interaction between pulp density and the physicochemical parameters occurs as a result of decreasing gas transfer rates when the level of pulp density is increased. Based on this finding we suggest that in shake flask studies aimed at optimizing physicochemical parameters for a sphalerite bioleaching process the level of pulp density should be matched to that in bioreactors in such a way that “equivalent gas transfer rate conditions” are obtained. Identification of such a match does not necessarily require quantitative knowledge of the gas transfer rates inside shake flasks and bioreactors since it can be deduced from the trend of change in Zn dissolution rate with pulp density in shake flask and bioreactor studies, with a decline in the trend being indicative of transition from “high” to “low” gas transfer rate conditions. Although the present study was carried out with pure culture of A. ferrooxidans – and a particular high grade sphalerite concentrate – there is no reason to believe that the conclusions of the present study – within the range of values of the physicochemical parameters considered – does not hold for other mesophilic autochemolithotrophic bacteria – or mixed cultures of such bacteria – or other Zn sources. However, the lack of significant interaction between the initial ferrous iron concentration and pulp density might not hold if Zn sources containing higher iron content, such as marmatite, are used. As a result of the leaching of iron into the bioleaching media when these Zn sources are used, a statistically significant interaction between pulp density and initial ferrous iron concentration might exist in such cases. Also, change in initial cell population might also affect the nature or extent of the interaction effects observed between pulp density with pH and temperature. However, the confirmation of these matters requires further experimental study. 5. Conclusions In the present study CCD coupled with RSM was used to study the interaction between factors in a high grade sphalerite concentrate bioleaching process using A. ferrooxidans with the following results: – Increase in pulp density, in the range 4–6% wt/vol, had the effect of shifting the optimum level of pH to lower values; furthermore, this also diminished the importance of pH control during the process. – The optimum level of temperature during the process was found to depend on both the level of pH and pulp density. Decrease in pH shifted the temperature optima to higher values, whereas an increase in pulp density diminished the importance of temperature control during the process. – There was no statistically significant interaction between the initial Fe (II) concentration in the bioleaching media and the other three factors studied.

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– Pulp density and pH had a more significant effect on the concentration of Zn after 12 days of incubation compared to temperature and initial ferrous iron concentration. It was predicted that when pH and pulp density are set at 4% w/v and 1.94 respectively, Zn concentrations ≥ 16 g L −1 can be attained if initial Fe(II) is set at 7.3 g L −1 and temperature is controlled within the range 32.2– 35.2 °C. – Optimization of the levels of pH and temperature for sphalerite bioleaching processes should be carried out using factorial DOEs such as CCD but the use of the conventional shake flask cultivation for this purpose only yields reliable results if the level of pulp density is chosen appropriately. References Büchs, J., 2001. Introduction to advantages and problems of shaken cultures. Biochemical Engineering Journal 7, 91–98. Chen, S.Y., Lin, P.L., 2010. Optimization of operating parameters for the metal bioleaching process of contaminated soil. Separation and Purification Technology 71, 178–185. Das, T., Ayyappan, S., Chaudhury, G.R., 1999. Factors affecting bioleaching kinetics of sulfide ores using acidophilic micro-organisms. Biometals 12, 1–10. Deveci, H., Akcil, A., Alp, I., 2004. Bioleaching of complex zinc sulphides using mesophilic and thermophilic bacteria: comparative importance of pH and iron. Hydrometallurg 73, 293–303. Gomez, J.M., Cantero, D., 1998. Modelling of ferrous sulphate oxidation by Thiobacillus ferrooxidans in discontinuous culture: influence of temperature, pH and agitation rate. Journal of Fermentation and Bioengineering 86, 79–83. Gomez, C., Blazquez, M.L., Ballester, A., 1999. Bioleaching of a spanish complex sulfide ore bulk concentrate. Minerals Engineering 12, 93–106. Gupta, A., Birendra, K., Mishra, R., 2003. Study on the recovery of zinc from Moore cake: a biotechnological approach. Minerals Engineering 16, 41–43. Haghshenas, D.F., Keshavarz Alamdari, E., Amouei Torkmahalleh, M., Bonakdarpour, B., Nasernejad, B., 2009a. Adaptation of Acidithiobacillus ferrooxidans to high grade sphalerite concentrate. Minerals Engineering 22, 1299–1306. Haghshenas, D.F., Keshavarz Alamdari, E., Bonakdarpour, B., Darvishi, D., Nasernejad, B., 2009b. Kinetics of sphalerite bioleaching by Acidithiobacillus ferrooxidans. Hydrometallurgy 99, 202–208. Hsu, C.H., Harrison, R.G., 1995. Bacterial leaching of zinc and copper from mining wastes. Hydrometallurgy 37, 169–179. Khalili, Z., Bonakdarpour, B., 2010. Statistical optimization of anaerobic biological processes for dye treatment. Clean Water Soil and Air 38 (10), 942–950. Lombardi, A.R., Garcia Jr., O., 2002. Biological leaching of Mn, Al, Zn, Cu and Ti in an anaerobic sewage sludge effectuated by Thiobacillus ferrooxidans and its effect on metal partitioning. Water Research 36, 3193–3202. Montgomery, D.C., 2006. Design and Analysis of Experiments, 6th Edition. John Wiley & Sons, New York.

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