Biosorption of heavy metals from aqueous solutions onto peanut shell as a low-cost biosorbent

Desalination 265 (2011) 126–134

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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l

Biosorption of heavy metals from aqueous solutions onto peanut shell as a low-cost biosorbent Anna Witek-Krowiak ⁎, Roman G. Szafran, Szymon Modelski Department of Chemistry, Wrocław University of Technology, Norwida 4/6, 50-373 Wroclaw, Poland

a r t i c l e

i n f o

Article history: Received 24 February 2010 Received in revised form 19 July 2010 Accepted 19 July 2010 Available online 21 August 2010 Keywords: Biosorption Low cost sorbent Heavy metal Water treatment

a b s t r a c t Biosorption of Cu(II) and Cr(III) ions from aqueous solutions by peanut shell biomass was investigated as a function of initial pH, initial biomass concentration and temperature. The optimum sorption conditions were studied for each metal separately. The kinetics and equilibrium of biosorption were examined in detail. Four kinetic models (pseudo-first order, pseudo-second order, power function equation, and Elovich model) were used to correlate the experimental data and to determine the kinetic parameters. Four well-known adsorption isotherms were chosen to describe the biosorption equilibrium. The experimental data were analyzed using two two-parameter models (Langmuir and Freundlich) and two three-parameter models (Redlich–Peterson and Sips). The equilibrium biosorption isotherms showed that peanut shells possess high affinity and sorption capacity for Cu(II) and Cr(III) ions, with monolayer sorption capacities of 25.39 mg Cu2+ and 27.86 mg Cr3+ per 1 g biomass, respectively. All results showed that peanut shells biomass is an attractive, alternative low-cost biosorbent for removal of heavy metal ions from aqueous media. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Many industrial processes, such as mining, metal plating, or pigment and battery manufacturing, result in the release of heavy metals to aquatic ecosystems. Heavy metals are toxic pollutants, which can accumulate in living tissues causing various diseases and disorders. Removal of toxic contaminants from wastewaters is one of the most important environmental issues. Since all heavy metals are non-biodegradable, they must be removed from the polluted streams for the environmental quality standards to be met. Many physicochemical methods have been developed for the removal of heavy metals from aqueous solutions, such as extraction, ion exchange, chemical precipitation and membrane separation processes. These methods have several disadvantages like high operating costs, low selectivity, incomplete removal, and production of large quantities of wastes. Conventional methods are limited by technical and economical barriers, especially when concentration of metals in the wastewater is low (under 100 ppm). Development of efficient and low-cost separation processes is therefore of utmost importance. Another popular method for the removal of heavy metals from aqueous solutions is adsorption. Parameters taken into consideration when choosing an appropriate adsorbent are mainly the sorption

⁎ Corresponding author. Tel.: + 48 71 320 3813; fax: + 48 71 328 0475. E-mail address: [email protected] (A. Witek-Krowiak). 0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2010.07.042

capability, regeneration ability, kinetic parameters, price and market availability. Maximum sorption capability is the most important parameter that characterizes each sorbent. It is the maximum amount of the adsorbed substance available for the uptake per sorbent unit mass or unit volume (usually in mg/g or meq/g). The sorption capability is determined experimentally at constant temperature, and the results are presented as isotherms. Sorption capability is a very important parameter that allows for estimation of process costs, since the determined isotherms allow to predict the amount of sorbent required for effective sorption. Ability to regenerate the sorbent is very important in cyclic processes, especially when expensive and selective sorbents are used. Kinetic parameters allow to determine the rate of the sorption process. The choice of the sorbent is determined by its price and market availability. Prices of sorbents fall within a wide range. Waste materials from various industrial processes and biological materials—biosorbents—are also used. Biosorption is the natural capability of the biomass to immobilize dissolved components, e.g. heavy metal ions, on its surface. Biomass is composed mostly of polysaccharides, proteins and fats, and has many functional groups able to bind heavy metal ions. Recent studies showed that common agricultural waste products or natural polymers can be used as potential biosorbents for the removal of heavy metals. Biosorption can be used for the treatment of wastewater with low heavy metal concentration as an inexpensive, simple and effective alternative to conventional methods. Biosorption is the capability of active sites on the surface of biomaterials to bind and concentrate heavy metals from even the most dilute aqueous solutions. The process of metal ion binding is comprised of many

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physicochemical processes like ion exchange, complexation, microprecipitation, and electrostatic interactions [1]. Several studies showed that non-living microorganisms are effective in the removal of heavy metal ions. They include: bacteria [2,3], fungi [4], yeast [5], and algae [6,7]. Extracellular polysaccharides (EPSs) secreted by microorganisms are recommended as surface active agents for heavy metal removal [8]. Among various lignocellulose biomaterials, plant biomass [9,10], tobacco dust [11], coconut shells powder [12], short hemp fibers [13], rice [14,15], orange [16] and citrus [17] wastes, and nut shells [18] have been studied. Chitinrich wastes, such as crab carapaces [19] or arca shells [20] have also shown a potential for the removal of toxic metals. In recent years, some industrial and agricultural wastes, such as Neem oil cake (NOC) [21], sugar-beet pectin [22], waste activated sludge [23], and wineprocessing sludge [24] have been examined in the removal of heavy metals from aqueous solutions. Description of the sorption process makes use of numerous models to determine the sorbent volume. The total sorption effect may be described by empirical equations with constants determined in an experimental manner. There are many models that allow to describe both the equilibrium, and the kinetics of biosorption. They include two-, three- and four-parameter models. Models most commonly described in the literature include the Langmuir and Freundlich models describing the process equilibrium, and the pseudo-second order model describing the process kinetics. The simplest method for determination of constants in two-parameter models is transformation of the equation describing the equilibrium to a linear equation form. Linear regression is commonly used to determine the model parameters; however, non-linear regression was shown to allow for better accuracy and correlation of the model with the experimental results [25,26]. In addition, linear regression does not permit for determination of the parameters of a three-parameter model. The purpose of this study was to confirm the potentiality of the use of an agricultural waste material, such peanut shells, as a costeffective metal biosorbent for the removal of heavy metals from dilute aqueous solutions. Unfortunately, only a few studies have been carried out to date in this field [27–29]. In present work, uptake of Cu2+ and Cr3+ ions by peanut shells was investigated under different initial conditions (pH, temperature, biomass concentration, and sorption time). Four kinetic models (pseudo-first order, pseudo-second order, power function equation, and Elovich model) were used to correlate the experimental data and to determine the kinetic parameters. Five well-known adsorption isotherms were chosen to describe the biosorption equilibrium. The experimental data have been analyzed using 2 two-parameter models (Langmuir and Freundlich) and 2 three-parameter models (Redlich–Peterson and Sips). Non-linear analysis was performed to obtain the equilibrium and kinetic parameters and to find an appropriate model for the equilibrium and kinetics of removal of heavy metal ions (Cu2+ and Cr3+) by peanut shell biomass. 2. Experimental 2.1. Biosorbent preparation Peanut shells were extensively washed in running tap water for 1–2 h to remove coloration and dirt, and then washed with distilled water several times. The washed sorbent was transferred to an oven set at 50 °C for 24 h to reduce the water content. The dried sorbent was crushed and milled. The particle sizes were less than 30 μm. 2.2. Metal solution preparation Solutions of Cu2+ and Cr3+ ions were prepared by dissolving predefined amounts of CuSO4 and Cr(NO3)3, respectively, in distilled

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water, so as to achieve concentrations of 1000 mg/L in each flask. Different initial concentrations of metal ions were prepared by diluting the stock solution. The pH of the solutions was adjusted using 0.1 N HCl and 0.1 N NaOH to achieve the desired values. 2.3. Isotherm experiments Experiments were carried out in batch mode. 100 mL samples of aqueous solutions of metal ions at different initial concentrations (10–1000 mg/L) and at adjusted pHs were transferred into 250 mL Erlenmeyer flasks. Pre-defined amounts of the sorbent were added to these solutions. After 24 h, solutions were filtered and metal ion concentrations in the filtrate were determined. Concentrations of metal ions in samples were determined by atomic absorption spectroscopy (AAS). Concentration of metal retained in the sorbent phase (qe, mg/g) was calculated from the expression: qe ¼

ðC0 Ce Þ⋅V m

ð1Þ

where C0 and Ce are the initial and final (equilibrium) concentrations of the metal ion in solution (mg/L), V is the solution volume (L) and m is the mass of the sorbent (g). All experiments were conducted at a constant temperature. Throughout the study, the pH values varied from 2 to 5, the initial metal concentrations from 10 to 1000 mg/L, the initial biomass concentrations from 0.01 to 20 g/L, and the temperature from 20 to 60 °C. 2.4. Kinetics experiments Kinetic studies were carried out in order to determine the contact time required to reach the equilibrium. 200 mL samples of 100 mg/L metal ion solutions were adjusted to desired pH and then mixed with 0.1 g of each sorbent . Sorption processes were carried out in flasks placed in a thermostatic water-bath shaker at fixed temperature, until the equilibrium was reached. 3 mL samples were collected at specified intervals for the analysis of metal concentrations. 2.5. Modeling 2.5.1. Isotherm models Sorption equilibrium can be described by a number of models available in the literature. In this work, four different models: two two-parameter models and two three-parameter models, fall within a wide range. The Langmuir model assumes a monolayer adsorption of solutes onto a surface comprised of a finite number of identical sites with homogeneous adsorption energy. This model [30] is expressed as follows: q = qmax

b⋅Ce 1 + b⋅Ce

ð2Þ

where q is the metal uptake capacity (mg of metal/g dry weight of biosorbent) and Ce is the concentration of metal ions in the solution (mg/L) when equilibrium is reached, qmax is the uptake capacity when the surface is completely covered with metal ions (maximum uptake capacity), and b is a constant that represents the affinity between the biosorbent and the metal ion. The Freundlich isotherm is an empirical expression that takes into account the heterogeneity of the surface and multilayer adsorption to the binding sites located on the surface of the sorbent. The Freundlich [31] model is expressed as follows: 1

q = K⋅C n

ð3Þ

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Fig. 1. Effect of biomass concentration on biosorption pH 5.0, temp. 20 °C, initial metal concentration 100 mg/L.

Fig. 3. Biosorption kinetics (pH 5.0, temp. 20 °C) fitted by pseudo-first order model, R2 = 0.928 and 0.977 for Cr3+ ad Cu2+ respectively.

where K is the biosorption equilibrium constant and n is a constant indicative of biosorption intensity. The Freundlich model does not consider the biosorbent saturation. The Redlich–Peterson isotherm [32] approximates to the Henry's law at low concentrations and to the Freundlich isotherm at high concentrations.

2.5.2. Kinetic models The pseudo-first order model was proposed by Lagergren in 1898 [34]. This model describes the rate of sorption to be proportional to the number of sites unoccupied by the solutes.

kRP ⋅Ce qe = 1 + aRP ⋅Ceb

CeβS 1 + aS ⋅CeβS

ð6Þ

ð4Þ   −k t qt = qe 1−e 1

where kRP, aRP and b are the Re–P parameters. The value of b is between 0 and 1. The three-parameter Sips model [33] is the combination of Langmuir and Freundlich models and is given by Eq. (5):

q = KS

dqt = k1 ðqe −qt Þ dt

ð5Þ

where KS is the Sips model isotherm constant (L/g), aS the is Sips model constant (L/mg) and βS is the exponent of this model. At low sorbate concentrations, the model approaches the Freundlich isotherm, while at high sorbate concentrations, it predicts a monolayer characteristic for the Langmuir isotherm.

Fig. 2. Effect of pH on biosorption. temp. 20 °C, initial metal concentration 100 mg/L, biomass conc. 1 g/l.

ð7Þ

where q and qe are the metal ion concentrations (mg g− 1) at any time (t) and at the equilibrium (e) , respectively, and k1 is the first order rate constant (min− 1). The pseudo-first order model works well only in the region where biosorption process occurs rapidly. Ho [35] noticed that the use of the Lagergren model for prediction of the biosorption kinetics is not suitable for the entire sorption period. The pseudo-second order kinetic model as developed by Ho has the following form: dqt 2 = k2 ðqe −qt Þ dt

ð8Þ

Fig. 4. Biosorption kinetics (pH 5.0, temp. 20 °C) fitted by pseudo-second order model, R2 = 0.977 and 0.994 for Cr3+ ad Cu2+ respectively.

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Table 1 Kinetic model parameters.

k1 (min− 1) qe,cal (mg g− 1) R2 ERRSQ k2 (gmg− 1 min− 1) qe,cal (mg g− 1) R2 ERRSQ k n R2 ERRSQ a (mg g− 1 min− 1) b (g mg− 1) R2 ERRSQ

Pseudo-I-order

Pseudo-II-order

Power function equation

Elovich

Cu2+

Cr3+

1.804 7.26 0.977 96.07 0.578 7.48 0.994 24.2 6.594 0.041 0.995 20.24 4.94e08 3.306 0.996 15.72

0.663 7.89 0.928 408.79 0.141 8.45 0.977 128.1 5.719 0.102 0.953 265.36 884.7 1.278 0.964 206.3

Fig. 5. Biosorption kinetics (pH 5.0, temp. 20 °C) fitted by power function equation model, R2 = 0.953 and 0.995 for Cr3+ ad Cu2+ respectively.

qt = qe

qe k2 t 1 + qe k2 t

ð9Þ

where k2 the second order rate constant (g mg− 1 min− 1). The power function equation is an empirical model that describes the relation between the mass of the sorbate per unit mass of the adsorbent and time t to be as follows [36]: q = kt

ν

ð10Þ

where q adsorption capacity at given time (mg/g), k (mg g− 1 min− 1) and v are adjustment parameters. When v = 0.5, equation becomes the same as the Webber–Morris intraparticle diffusion model [37]. Elovich's equation has been widely used to describe the adsorption of gases onto solid materials; it has also been applied to describe the process of adsorption of dyes from aqueous solutions. The model describes the kinetics of the chemisorption process [38–41].

extent of surface coverage and to the activation energy for the adsorption. After integration with the boundary conditions of t = 0, q = 0; t = t, q = q, the equation becomes as follows:

q=

1 lnða⋅b⋅t + 1Þ b

2.5.3. Non-linear regression All model parameters were evaluated by non-linear regression using OriginPro8 software. In this study, commonly used non-linear error functions, i.e. the correlation coefficient (R2) and the sum of the squares of the errors, were examined for each set of experimental data. The sum of the squares of the errors (ERRSQ), this is the most commonly used error function (Eq. (13)). The better the fitting of the model, the smaller the difference between the experimental and the model data, and thus the smaller the square of that difference. m

dq −b⋅q = a⋅e dt

ð11Þ

ð12Þ

2

ERRSQ = ∑ ðxcal −xexp Þ i=1

ð13Þ

where a and b are constants. The parameter a represents the rate of chemisorption at zero coverage, the parameter b is related to the

where xexp is the experimental point, xcal is the estimation from the model, m is the number of observations in the experiment.

Fig. 6. Biosorption kinetics (pH 5.0, temp. 20 °C) fitted by Elovich model, R2 = 0.964 and 0.996 for Cr3+ ad Cu2+ respectively.

Fig. 7. Biosorption isotherm (pH 5.0, temp. 20 °C, biomass concentration 10 g/l) fitted by Langmuir model, R2 = 0.992 and 0.992 for Cr3+ ad Cu2+ respectively.

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Fig. 8. Biosorption isotherm (pH 5.0, temp. 20 °C, biomass concentration 10 g/l) fitted by Freundlich model, R2 = 0.904 and 0.936 for Cr3+ ad Cu2+ respectively.

Fig. 10. Biosorption isotherm (pH 5.0, temp. 20 °C, biomass concentration 10 g/l) fitted by Sips model, R2 = 0.994 and 0.993 for Cr3+ ad Cu2+ respectively.

3. Results

surface of the biosorbent, by the H+ and metal ions, started. Protonated active sites were incapable of binding the bind metal ions, leading to free ions remaining in the solution. When the initial pH of the solution was adjusted to a value higher than 5.5, Cr(III) and Cu(II) ions precipitated because of the higher concentration of hydroxyl anions in the solution. For this reason, the experiments were not conducted at pH values higher than 5.5.

Biosorption of heavy metal ions onto the surface of a biological material is affected by several factors, such as biomass concentration, initial pH, initial metal ion concentration, time and temperature. 3.1. Effect of biomass concentration The number of sites available for biosorption depends upon the amount of the adsorbent. The effect of the biosorbent concentration on the metal removal efficiency is presented in Fig. 1. The metal ions uptake was found to increase linearly with the increasing concentration of the biosorbent up to the biomass concentration of 10 g/L. Beyond this dosage, the increase in removal efficiency was lower. Increasing the biosorbent dosage caused a wise in the biomass surface area and in the number of potential binding sites. 3.2. Effect of pH It is well-known that pH can affect protonation of functional groups (i.e. carboxyl, phosphate and amino groups) in the biomass, as well as the chemistry of the metal (i.e. its solubility). As shown in Fig. 2, the metal uptake increased with the increasing pH in the range of 2 to 5. At pH values of about 5, sorption capacities achieved maximum values. When the pH decreased, concentrations of protons increased and competition in binding the active sites on the

3.3. Effect of contact time Typical biosorption kinetics exhibits a rapid initial uptake, followed by a slower process. It has been observed that maximum removal took place within the first 20 min (Figs. 3–6). After this period, the amount of bound metal ions did not change during the course of the process. In order to analyze the biosorption kinetics of Cr(III) and Cu(II) ions, kinetic models can be applied to fit the experimental data (Figs. 3–6). Four kinetic models with different reaction orders were used and nonlinear fitting was performed to determine the model parameters. The experimental results pertaining to the biosorption kinetics were compared to four kinetic models, allowing determining the parameters of these models (Table 1). Correlation coefficients and ERRSQ values allow for the assessment of the correlation of the model with the experimental data. The pseudo-first order model is poor in

Table 2 Isotherm model parameters.

Langmuir

Freundlich

Redlich–Peterson

Sips

Fig. 9. Biosorption isotherm (pH 5.0, temp. 20 °C, biomass concentration 10 g/l) fitted by Redlich–Peterson model, R2 = 0.992 and 0.994 for Cr3+ ad Cu2+ respectively.

qmax [mg/g] b [l/mg] R2 ERRSQ K [l/g] n R2 ERRSQ kRP [l/g] aRP [l/mg] b R2 ERRSQ KS [l/g] aS [l/mg] βS R2 ERRSQ

Cu2+

Cr3+

25.39 0.022 0.992 5.75 3.09 2.97 0.936 111.6 0.461 0.010 1.096 0.994 4.45 0.405 0.016 1.091 0.994 5.44

27.86 0.036 0.992 8.90 4.12 3.23 0.904 117.9 0.994 0.029 1.022 0.992 8.56 0.701 0.026 1.138 0.993 7.29

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Table 3 Linear forms of Langmuir isotherm. Model Langmuir Langmuir Langmuir Langmuir

1 (L-1) 2 (L-2) 3 (L-3) 4 (L-4)

Linearized form

Plot

Ce qe 1 qe qe Ce

Ce qe 1 qe qe Ce

1 = qmax Ce + b⋅q1max 1 = b⋅q1max ⋅ C1e + qmax = b⋅qmax −b⋅qe qe = qmax − 1b ⋅ Cqee

vs: Ce vs: C1e vs: qe qe vs: Cqee

describing the experimental data; the three remaining models are much better, particularly in the description of the adsorption of Cu(II) ions. In case of this metal, the correlation coefficients exceed 0.990, which suggests good fitting. In case of Cr(III) ions, the correlation coefficient are somewhat lower, and the pseudo-second order model provides the best fitting (R2 is 0.977). Comparison of the experimental results with model data confirmed good correlation of the experimental results with the pseudo-second order model, which proved to be the best for the description of biosorption kinetics.

Fig. 12. Fitting of linear function L-2 to the experimental points (R2 = 0.987for Cr3+ and 0.989 for Cu2+).

3.4. Isotherm of biosorption Biosorption isotherms describe the relationship between the mass of the adsorbed component per biosorbent mass and the concentration of this component in the solution. Determination of equilibrium parameters provides important information that allows for future design of adsorption systems. Four well-known adsorption isotherms were chosen to fit the experimental data regarding the sorption of metal ions on the surface of the biomass. The biosorption isotherm was characterized by Langmuir (Fig. 7.), Freundlich (Fig. 8.), Redlich–Peterson (Fig. 9.) and Sips (Fig. 10.) models, since the process was complex and included various physical and chemical interactions. Experimental data were compared to 4 models describing the biosorption equilibrium (Table 2). In case of all models excluding the Freundlich model, high correlation of the experimental and model data was obtained (the correlation coefficients were above 0.990). The values of the biosorption capacity qmax and the Langmuir constant b were calculated by nonlinear regression. The qmax value for Cr(III) ions was 27.86 mg/g, and showed the biosorption uptake to be slightly higher than that observed for Cu(II) ions (25.39 mg/g). The k constants in the Freundlich equilibrium were 4.12 and 3.09 L/g for Cr(III) and Cu(II) ions, respectively. The value of n was between 0 and 10, suggesting relatively strong adsorption of these ions onto the surface of peanut shells. However, low correlation coefficients suggest that this was not the best model to describe these equilibria.

The Redlich–Peterson model showed a high correlation with the experimental results, as evidenced by high correlation coefficients and low values of the ERRSQ function, particularly in case of biosorption of copper ions (ERRSQ was 4.45). The Sips model is also perfect for describing the biosorption of copper and chromium ions from aqueous solutions. A better correlation between the model and the experimental data was obtained than in the case of two-parameter models, such as the Langmuir model (lower ERRSQ and higher R2 values). The effect of the coefficient determination method on the fitting of the function to the experimental points was analyzed using the Langmuir isotherm as a model. In order to determine the Langmuir isotherm parameters using linear methods, Eq. (2) was transformed into linear forms in 4 possible ways (Table 3). Figs. 11–14 present the fitting of the linear functions to the experimental points for all 4 variants of the linearized Langmuir equation. Linear equation parameters were determined by the least squares method using the Origin 8 software. The linear equation forms were found to have a significant effect on the determined parameters and the on fitting of the function. Best fitting was obtained in case of equation L-1, while in case of equations L-3 and L-4, R2 values were definitely lower than 0.900, which indicated that the function was not fitted. Figs. 15 and 16 compare the model curve fitting for the linear methods and for a nonlinear regression method. Function parameters

Fig. 11. Fitting of linear function L-1 to the experimental points (R2 = 0.997 for Cr3+ and 0.997 for Cu2+).

Fig. 13. Fitting of linear function L-3 to the experimental points (R2 = 0.694 for Cr3+ and 0.839 for Cu2+).

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Fig. 14. Fitting of linear function L-4 to the experimental points (R2 = 0.694 for Cr3+ and 0.809 for Cu2+).

Fig. 16. Comparison of the model curve fitting for the linear methods and for a nonlinear regression method (Langmuir model for Cu2+).

determined by the nonlinear regression method are well correlated with the experimental points and are close to the values determined by linear regression using equation L-1. For the remaining linear methods, the curve fitting was definitely worse. The nonlinear regression method yields very good fitting results in all cases, while in case of the linear methods, the fitting strongly depends on the form of the equation, and therefore the results obtained by the nonlinear method can be considered more reliable. Table 4 presents the comparison of Cu(II) and Cr(III) ions biosorption uptakes for peanut shells and various biosorbents described in the literature. The biosorption capacity of peanut shells is higher or similar than that of the majority of other biosorbents reported. The differences in sorption capabilities of individual biological materials may be due to different surface characteristics, associated with the presence of different functional groups.

region. The decrease at higher temperatures may be due to the damage of active binding sites in the biomass. The process involved the transfer of metal ions from the bulk liquid to the biosorbent and the sorption of metal ions onto the biosorbent surface.

3.5. Effect of temperature The equilibrium uptake of metal ions to peanut shells was affected by temperature (Fig. 17) and increased with the increasing temperature up to 50 °C. It was observed that 10.03 mg Cr(III) and 9.77 mg Cu(II) per 1 g of biomass was adsorbed at equilibrium at 50 °C. The quantities adsorbed passed through a maximum at 50 °C and then started to decrease with the temperatures increasing to 60 °C. It could suggest predominance of physical sorption over chemical sorption in this

Fig. 15. Comparison of the model curve fitting for the linear methods and for a nonlinear regression method (Langmuir model for Cr3+).

4. Conclusions Peanut shells are an environmentally friendly potential biosorbent for heavy metals. This work examined the efficiency of this sorbent in removal of Cu(II) and Cr(III) ions from aqueous environment. Biosorption is affected by various parameters, such as biomass concentration, pH and temperature. Several mathematical models were used to describe the equilibrium and kinetics of biosorption of heavy metals onto the surface of peanut shells. This study demonstrated that under optimum conditions (pH = 5.0, biomass concentration = 10 g/L; temperature= 20 °C, and contact time = 1 h), maximum biosorption capacities of 25.39 mg/g and

Table 4 Comparison of the biosorption capacity of different biosorbents. Biosorbent

qmax [mg/g]

Reference

Cu(II) Peanut shell Hyacinth roots Crab shell biomass Arca shell biomass Fungal biomass Botrytis cinerea Wheat shell Cone biomass Thuja orientalis Orange residue Pinus silvestris biomass Sawdust Brown alga Fucus vesiculosus Terrestrial moss Pleurozium schreberi

25.4 22.7 38.62 17.64 9.23 8.34 19.23 23.47 28.83 4.9 23.4 11.1

This study [42] [20]

Cr(III) Peanut shell Algal biomass Gelydium Tropical peat Palm flower Reed mat, aquatic weed Microalga, Chlorella minata S. cerevisiae P. aeruginosa Algal biomass spirogyra spp Leersia hexandra Swartz biomass Modified peanut husks Sawdust

27.86 18 5.60 6.24 7.18 41.12 13.26 7.07 28.16 28.64 7.67 5.52

[43] [44] [45] [46] [47] [48]

This study [49] [50] [51] [52] [53] [54] [55] [56] [57] [58]

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Fig. 17. Effect of temperature on biosorption at pH 5.0, initial metal concentration 100 mg/L, biomass conc. 10 g/l.

27.86 mg/g were obtained in the Langmuir model for Cu(II) and Cr(III) ions, respectively. Kinetic experiments proved that the biosorption process is rapid, with equilibrium achieved practically as early as after 20 min. The kinetics of the process was best described using the pseudo-second order model. The obtained results and their comparison to various biosorbents reported in the literature showed that peanut shells biomass was an efficient biosorbent for those metal ions.

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