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International Journal of Environmental Research
University of Tehran
ISSN: 1735-6865 EISSN: 2008-2304
Vol. 3, Num. 2, 2009, pp. 229-238

International Journal of Environmental Research, Vol. 3, No. 2, Spring 2009, pp.229-238

Biosorption of Cr (III) from Aqueous Solutions Using BacteriumBiomass Streptomyces rimosus

Sahmoune, M.N.1*, Louhab, K.1 and Boukhiar, A.2

1Laboratoire Matériaux, revêtement et environnement (LMRE), Faculty of Science ofthe engineer, University of Boumerdes, 35000-Boumerdes, Algeria
2Département technologie alimentaire, Faculty of Science of the engineer, University ofBoumerdes, 35000-Boumerdes, Algeria

*Corresponding author E-mail:sahmoune63@yahoo.fr

Received 16 April 2008;
Revised 15 Oct 2008;
Accepted 25 Dec 2008

Code Number: er09025

ABSTRACT

In the present investigation, dead bacterium biomass Streptomyces rimosus was used as an inexpensive and efficient biosorbent for Cr (III) removal from aqueous solution. The bacterial biomass was treated with 0.1 M NaOH. Sorption level of 65 mg/g was observed at pH 4.8 while precipitation effect augmented this value at higher pH range. Chromium desorption increased with decreasing desorption agents pH (including HCl and H2SO4) to a maximum value of 95% at approximately zero pH. Langmuir, Freundlich and Temkin models were applied to describe the biosorption isotherm of the metal ions by Streptomyces rimosus biomass. Langmuir model fitted the equilibrium data better than the Freundlich isotherm. Maximum metal uptake qmax was observed as 83.33 mg g”1 indicate good biosorbents than other biomass. Experimental data were also tested in terms of biosorption kinetics using fractional power, Elovich, pseudo-first order and pseudo-second order rate expressions. The results showed that the biosorption processes followed well pseudosecond-order kinetics and the intra-particle diffusion is not the rate-limiting step for the whole reaction.

Key words: Biosorption, Chromium (III), Isotherms, Kinetics, Streptomyces rimosus

INTRODUCTION

Trivalent chromium is evacuated to the environment by the effluents of a large number of industries such as mining, iron sheet cleaning, chrome plating, leather tanning and wood preservation (Krishna & Philip, 2005). The maximum levels permitted in wastewater are 5 mg/l for trivalent and 0.05 mg/L for hexavalent chromium (Richard and Bourg, 1991). Although chromium (III) is an essential element, it can be toxic at elevated concentrations in the environment. Cr (III) is selected instead of Cr (VI) because of following facts Cr (III) is toxic if excess quantity is taken and cause abnormalities in organisms. Chromium (III) sulphate salts are mainly used in tanning (Mant et al., 2005).

The removal of chromium employing conventional methodologies (Tiranvanti et al., 1997) like ion exchange, chemical precipitation or reverse osmosis suffer from limitations like high operating cost, incomplete precipitation, sludge generation, etc. On the other hand biosorption is receiving increasing attention as an emerging technology for the removal of heavy metals from contaminated effluents (Kumari et al., 2006). The process is based on the adsorption behaviour of certain biological materials towards organic or inorganic substances from their solution.

Fungal biomass and seaweed biomass have been found to be excellent biosorbents for sequestering heavy metals (Lewis and Kiff 1988, Holan and Volesky 1994, Voleskyand Holan 1995).

Fungal biomass has been used to sequester copper, lead, zinc, nickel, cadmium, gold, silver and various actinide elements, such as thorium, uranium and plutonium (Tsezos and Volesky 1981, Gadd and White 1989, Luef et al., 1991, Kapoor and Viraraghavan 1995, Meyer and Wallis 1997, Kapoor and Virraghavan1998).

Streptomyces rimosus, mycelial bacterium is classified Gram-positive. In general, Gram-positive bacteria have a greater sorptive capacity due to their thicker layer of peptidoglycan which contains numerous sorptive sites (Van Hullebusch et al., 2003).Metal removal treatment systems using micro-organisms is a cheap and practical alternative to conventional processes, since low cost sorbent materials are used. Micro-organisms based technologies must compete with both operational and economical terms in existing metal removal treatment systems. Non-living biomass appears to present specific advantages in comparison to the use of living micro-organisms(Öztürk et al., 2004).

The objective of the present work is to investigate the biosorption potential of Streptomyces rimosus biomass in the removal of Cr (III) ions from aqueous solution. An optimum biosorption condition was determined as a function of pH. The Langmuir, Freundlich and Temkin models were used to describe equilibrium isotherms. Various kinetic models were tested to describe the sorption data and desorption processes were also investigated to facilitate metal recovery.The study of adsorption dynamics describes the solute uptake rate. This rate controls the residence time of adsorbate uptake at the solid solution interface. The kinetics of chromium biosorption on dead biomass were analyzed using Fractional power (Basha, and Murthy, 2007), pseudo-first-order (Ho, 2004). , Elovich (Ho, 2006), pseudo-second-order (Ho, 2006), and intra-particle diffusion (Weber and Morris, 1963) kinetics models.The sorption kinetics is described by power function model as follows:

qt= ktν (1)

Where qt is the amount of chromium mg/g sorbed at time t and ν the rate constant of power function (min-1). k is constant of power function model (mg/g).Equation (1) can be rearranged to obtain equation (2), which has a linear form:

ln qt = ln k +ν ln t (2)

The plot ln qt and ln t should give linear relationship from which ν and k can be determined from the slope and intercept of the plot, respectively.The pseudo-first order kinetic rate equation is expressed as:

Where k1 is the rate constant of pseudo first order adsorption (min-1), qe is the amount of chromium sorbed at equilibrium (mg/g) and qt the amount of chromium sorbed at time t (mg/g). Integrating Equation (3) for the boundary conditions t=0 to t=t and qt=0 to qt = qe and rearranging yields the linear time-dependent function.

The intercept of the straight-line plots of log (qe − qt ) against t should equal log qe.

However, if the intercept does not equal qe, then the reaction is not likely to be first-order, irrespective of the magnitude of the correlation coefficient.The pseudo-second- order kinetic rate equation is expressed as:

Where k2 is the rate constant of pseudo second order adsorption (g.mg-1.min-1). Taking into account, the boundary conditions t=0 to t=t and qt=0 to qt = qt . The integrated form of equation (5) can be rearranged to obtain equation 6.

The initial adsorption rate h (mg.g-1.min-1) is expressed as:

h=k 2 qe2 (7)

The plot of (t/ qt) and t of equation (6) should give a linear relation ship from which qe and k2 can be determined from the slope and intercept of the plot, respectively.The Elovich model equation is generally expressed as:

Where aE is the initial adsorption rate (mg/g .min1). βE is the desorption constant (g.mg-1) during any one experiment. To simplify the Elovich equation the authors (Chien and. Clayton, 1980) assumed aE βE >> 1 and by applying the boundary condition qt=0 at t=0 and qt = qt at t=t.

Equation (8) becomes:

If Chromium biosorption fits the Elovich model, a plot of qt versus ln t should yield a linearrelationship with a slope of 1/ βE and an intercept of ln aE βE / βE .The intra-particle diffusion model presented here refers to the theory proposed by the authors (Weber and Morris, 1963), who concluded that the uptake is proportional to the square root of contact time during the course of adsorption. Accordingly:

qt = Kp t 0.5 (10)

Where Kp is the intra-particle diffusion rateconstant (mg/g.min0.5)

Equilibrium data, commonly known as adsorption isotherms, are the basic requirement for the design of adsorption systems. Classical adsorption models, such Langmuir (1916), Freundlich (1906) and Temkin (1940) models were used to describe the equilibrium between adsorbed chromium on the biomass and chromium in solution( Ce) at constant temperature.The Freundlich isotherm is a non linear sorption model. This model proposes a monolayer sorption with a heterogeneous energetic distribution of active sites, accompanied by interaction between adsorbed molecules. The general form of this model is:

Where KF is the extent of the adsorption and n the degree of non-linearity between chromium concentration and adsorptionEquation (11) can be linearized in logarithmic form and the Freundlich constants can then be determined:

The Langmuir equation which is valid for monolayer sorption on a surface with a finite number of identical sites, is given by:

qmax is attributable to the maximum metal uptake upon complete saturation of the sorbent, and b is a coefficient attributed to the affinity between the sorbent and sorbate. qmax and b can be determined from the linear plot Ce/ qe versus Ce.The essential characteristic of the Langmuir isotherm can be expressed in terms of dimension less constant separation factor for equilibrium parameter RL (Hall and. Vermeylem, 1966), which is defined by:

Hall and Vermeylem (1966) show, using mathematical calculation, that the parameter RL indicates the shape of isotherm as follows (Table 1).

Temkin (1940) model was also used. This isotherm was first developed by Temkin and Pyzhev, and it is based on the assumption that the heat of adsorption would decrease linearly with the increase of coverage of adsorbent [30].

Where R is the gas constant, T the absolute temperature in Kelvin, bt the constant related to the heat of adsorption and at is the Temkin isotherm constant. Equation (15) can be rearranged to obtain equation (16):

MATERIALS & METHODS

The dead Streptomyces rimosus biomass was obtained from the SAIDAL-complex manufacturing unit of antibiotics Medea-Algeria The biomass was washed with deionised water, dried at 50°C for 24 h in a drying oven, then the activated biomass was prepared by treating the raw biomass with 0.1 N NaOH solution for 30 min at ambient temperature, at once again washed, dried and then screened through a set of sieves to get geometrical size 50-160 µm. The effluent used in the experiment was the deep blue colour characteristic of Cr (III) and had a pH of 4. Chromium content, as determined by atomic absorption spectroscopy, was found to be 2.4 g/L. No Cr(VI) was present in the effluent as indicated by phenylcarbazide testing. Sodium concentration was 30 g/L. Control experiments confirmed that no signal interference occurred between the two metals.

Chromium uptake serial dilutions of the tanning effluent were prepared using deionised distilled water to give solution ranging in concentration from full strength to 1 in 40 dilutions. Aliquots of 100 ml were contacted with 0.3 g quantities of Streptomyces rimosus biomass. Samples were filtered (0.45 µm filters) and the filtrates were analysed for remaining metals using a Perkin Elmer 2380 atomic absorption spectrometer. The amount of Chromium accumulated by biomass was calculated as the difference between the amount present in the initial solution and that in the final solution after equilibration with biomass using the following formula (Holan and Volesky, 1994).

All experiments were conducted at a constant temperature of 20 ± 1 °C to be representative of environmentally relevant conditions. Kinetic experiments were conducted on a rotary shaker with constant agitation speed of 300 rpm; using conical flasks (250 ml) containing 100 ml of solution and 0.3 g of biomass and an initial pH 4.8 with initial concentration of 2400 mg l”1 for 80 min. Toavoid shifts in pH due to biomass addition, the pH was adjusted with 0.1N HCl or 1N NaOH after the solution had been in contact with the biosorbent. For sorption isotherm experiments, flasks were agitated on a rotary shaker (300 rpm) until no additional metal was removed (5 h). The samples were filtered through 0.45 µm millipore filters and were taken periodically to analyse the chromium concentration.To make the biosorption process more economical, it would be necessary to regenerate the spent biosorbent.To evaluate the desorption efficiency the Chromium loaded biomass was dried at 60°C for 24 h after equilibrium sorption at pH 4.8. The dried biomass was contacted with 1 M H2SO4 or 1 M HCl for 4 h to allow chromium to be released from the biomass, there after, the desorbed chromium was analysed and desorption efficiency was calculated as follows (Choi. and Yun, 2004):

All the model parameters were evaluated by non-linear regression using Excel 2007® software. The optimization procedure requires an error function to be defined in order to be able to evaluate the fit of the equation to the experimental data (Ho et al., 2002, Kundu, and Gupta, 2006). Apart from the correlation coefficient (R2), the residual root mean square error (RMSE) and the chi-square test were also used to measure the goodness-of-fit. RMSE can be defined as:

Where the observation from the batch experiment is qi , qie is the estimate from the isotherm for corresponding qi and m is the number of observations in the experimental isotherm. The smaller RMSE value indicates the better curve fitting Tsai and Juang, 2000). The chi-square test can be defined as:

RESULTS & DISCUSSION

The pH of the metal solution usually plays an 60important role in the biosorption of metals (Vijayaraghavan et al., 2005). As can be seen in (Fig. 1). the uptake of Cr3+ by Streptomyces rimosus biomass was obtained by varying th initial concentration of chromium. When the pH value was raised from 1 to 4.8, the adsorption capacity was enhanced significantly from 27 to 64 mg/g biomass. Uptake was enhanced, probably because of proton competition to Cr3+ binding (Yun and Volesky, 2003),Adsorption at pH 5.5 marked precipitation effects augmented the biosorption removal of chromium, from solution resulting in apparent sequestration levels of in excess of 93 mg/g as illustrated in fig. 1. As can be seen in the figure, when the precipitation component is subtracted the net biosorption values are in good agreement with each other and those observed at pH 4.8 (Tobin and Roux, 1998). If the biosorption takes place under conditions that chromium may precipitate, a chemical sludge is generated which should be treated via appropriate solid waste management methods. From a practical point of view, therefore, chromium biosorption process is better operated at pH 4.8.

The batch experimental data, shown in (Figs. 2 & 3) on kinetic and equilibrium studies for the biosorption of Cr (III) on Streptomyces rimosus were tested to fit the various kinetic and equilibrium models, respectively As shown in (Fig. 2). the removal of chromium increases rapidly in the beginning (first 30 min) and than more slowly until the equilibrium.

As seen in (fig.3). it readily appears that, isotherm is somewhat curved and the equilibrium is established between chromium ions and the biomass The metal-binding properties of Grampositive bacteria are largely due to the existence of specific anionic polymers in the cell wall structure, consisting mainly of peptidoglycan, teichoic acids (Hancock, 1986, Hughes and Poole, 1989, Remacle et al., 1992). Due to this high fixed anionic content of the cell forms which are obviously present in Streptomyces rimosus, they may exhibit high sorption capacities that would be very important aspect in future because of its industrial application as biosorbent for the metal cations.A simple kinetic analysis of biosorption of Cr (III) on Streptomyces rimosus has been tested according to fractional power model (Basha, and Murthy, 2007) and Table 2 shows the estimated parameters of the model. The results indicate that the power function model described the timedependent Cr (III) on sorbent as the value of constant v was less than 1(Basha, and Murthy, 2007). The chromium biosorption data do not correlate well with fractional power model and this confirmed by high RMSE and chi-square values (Table 2).

The kinetic constant, k1,, of the pseudo-firstorder equation (Ho, 2004) for the biosorption of Cr (III) on to biomass is given in (Table 2). The results demonstrated that Lagergren model (Ho, 2004) is not applicable in the present case as high RMSE and chi-square values were observed. The kinetic constants obtained from the Elovich equation (Ho, 2006) are listed in (Table 2). The results demonstrate a significant relationship between Cr (III) sorbed, qt and t in this study with acceptable regression coefficient 0.95 and low values of RMSE and chi-square. The low values of standard error reflect agreement between the sets of data studied. In other words, the data also show satisfactory compliance with the Elovich equation.The results in (Table 2) show the biosorption rate constant, k2 , initial biosorption rate, h, and equilibrium biosorption capacity, qe, of the pseudo-second-order model (Ho, 2006). These results show a very good compliance with the pseudo-second-order equation with high regression coefficients (>0.99).The rate of a biosorption reaction nonlinearly decreased with time. For example, the instantaneous rates at 50 and 90% of Cr (III) biosorption (SR50 and SR90, respectively) can be calculated from pseudosecond order rate equation as follows:

Therefore, SR50 and SR90 values are onefourth and one-hundredth of initial biosorption rate, h, respectively (see Table5), and comparisons reported here based on h values can be extended to the entire experiment duration.Based on the pseudo-second-order kinetic model the half-life of Cr (III) biosorption (the time at which half of the biosorption process is completed) directly depends on the biosorption capacity of the biosorbent and inversely relates to the initial biosorption rate:

Biosorption capacity of the biosorbent determined from the fitted pseudo-second-order kinetic model was comparable to the maximum biosorption capacity calculated from the Langmuir isotherm as can be seen later.

The intraparticle diffusion coefficient for the biosorption of Cr (III) was calculated from the slope of the plot (Fig.4). between the amounts of Cr (III) sorbed, qt (mg/g) vs. t1/2 (min1/2). Based on this plot, the biosorption process of the Cr (III) is comprised by two phases, suggesting that the intraparticle diffusion is not the rate-limiting step for the whole reaction (Ho and Ofomaja, , 2005), the initial portion of the plot indicated an external mass transfer whereas the second linear portion is due to intraparticle or pore diffusion. Similar results were reported by the authors (Aguilar-Carrillo et al., 2006). The intercept of the plot provides an estimation of the thickness of the boundary layer (Oubagaranadin et al., 2007).The slope of the second linear portion of the plot has been identified as the intra-particle diffusion rate constant (KP = 12,90 mg/g min -1/2). The high value of Kp corresponded to low value of pseudo- second order rate constant, k2 (Table 2), indicating that the intra-particle diffusion retards the biosorption process. This also indicates, the biosorption process is rather complex and involves more than one diffusive mechanism.

Analysis of equilibrium data is important for developing an equation that can be used to design and optimize an operating procedure. To examine the relationship between biosorption and aqueous concentration at equilibrium, various biosorption isotherm models are widely employed for fitting the data.The Temkin model parameters are compared with value for the Langmuir and Freundlich model in (Table 3).

The Freundlich isotherm is originally empirical in nature (Freundlich ,1906), but was later interpreted as biosorption to heterogeneous surfaces or surfaces supporting sites of varied affinities and has been used widely to fit experimental data (Aksu and Kutsal, 1991) . The value of n, of this model, falling in the range of 1– 10 indicates favourable biosorption (Aksu, 2002). The numerical value of 1/n < 1 indicates that adsorption capacity is only slightly suppressed at lower equilibrium concentrations. This isotherm does not predict any saturation of the adsorbent by the adsorbate. Thus infinite surface coverage is predicted mathematically, indicating multilayer adsorption on the surface (Hasany et al., 2002).The present study results indicate that the Freundlich model does not fit the experimental data well. It is not the suitable model for describing these biosorption processes, as RMSE and χ 2 values are higher than 10.

Temkin model is unable to describe the data, as low correlation coefficients and high RMSE and chi-square values were observed.The adsorption data provided an excellent fit to the Langmuir isotherm with high values of the R2 (see Table3). The separation factor (RL) value indicates that Cr (III) biosorption of biosorbent in this study is favourable.Comparisons of maximum experimental biosorption capacities of Cr (III) for some biomasses were also given in (Table 4). It can be seen from the table, Streptomyces rimosus used in this study has high biosorption capacity.

To make the adsorption process more economical it is necessary to regenerate the spent adsorbent. In this study, the chromium bearing biomass was contacted with hydrochloric acid or sulphuric acid (Fig. 5).When a low pH was used the desorption efficiency was 95 %. At pH values of 0.5 and 1.0 only 66 and 51 % chromium was recovered respectively.Chromium desorption increased with decreasing desorption agents pH (including HCl and H2SO4) to a maximum value of Cr 95% at approximately zero pH.Variation of the hydrochloric acid or Sulphuric acid caused nondifference in desorption over the experimental range investigated as shown in (fig.5).

CONCLUSIONS

The present study showed that Streptomyces rimosus a biological sorbent of mycelial bacteria origin can find an application as a biological sorbent of Cr (III) ions from aqueous solution. The process kinetics was found to follows the pseudo-second order rate equation. Langmuir model presents good fits of the experimental data. The application of this model to complex biological system may not to explain the biosorption behaviour. Hence, conclusion derived only from good fit of the model should be avoided. The metal ion could be stripped by addition of HCl or H2SO4 at low pH, making the adsorbent regeneration and its reutilization possible.

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