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Response Surface Methodology for Optimization of Zinc Biosorption By Grewia Orbiculata L.

Rakesh Namdeti^*, King Pulipati

Environmental Pollution Control Engineering Laboratory, Department of Chemical Engineering, Andhra University College of Engineering, Visakhapatnam - 530003, Andhrapradesh, INDIA.

^*Corresponding Address:

[email protected]

Abstract: Response surface methodology was used to study the effect of various parameters like initial zinc ion concentration, pH, Temperature, biosorbent dosage and to optimize process conditions for the maximum removal of zinc. Batch mode experiments were conducted to determine the isotherms (Langmuir, freundlich, temkin, dubinin radushkevich models), kinetics (Pseudo-first and pseudo second order) and thermodynamic parameters like standard Gibbs free energy (∆G⁰), enthalpy (∆H⁰), entropy (∆S⁰). A 2⁴ full factorial central composite design (CCD) using response surface methodology (RSM) was employed for obtaining the mutual interaction between the variables and optimizing these variables. The most influential factor on each experimental design response was identified from the analysis of variance (ANOVA). The model was statistically tested and verified by experimentation. The response surface methodology indicated that 53.07 mg/l initial zinc concentration, 5.86 pH, 0.27 g biosorbent dosage and a temperature of 39.42 ⁰C were optimal for biosorption of zinc by Grewia Orbiculata L., when 75.86 % of the zinc metal is removed from the solution.

Keywords: Biosorption, Grewia Orbiculata L., CCD (Response surface methodology), Isotherms, Kinetics, Thermodynamics.

1. Introduction

Research and development in effluent treatment processes has gained much importance in the recent years as environmental legislations for industrial discharges have become quite stringent. Heavy metal contamination of surface waters due to industrial effluents is a major problem, which needs to be tackled due to ecotoxicological effects of metals along with their accumulation in food chain ( EPA, 1976). Zinc is the one of the toxic metals that is available in industrial effluents involved in acid mine drainage, galvanizing plants, natural ores and municipal waste water treatment plants (L Nortan et al., 2004). Conventional technologies like oxidation/reduction, ion exchange, and membrane transfer methods available to tackle the problem of heavy metal contamination of waste waters have a major constraint that they are not so effective and economical for effluents containing concentration of metals less than 100mg/L (B. Volesky, 1990). Hence, there is a need to develop newer techniques, which are cost effective, environment friendly and have high efficacy for metal removal from waste waters with relatively lower metal concentration (S.Tunali et al., 2006). Biosorption is an innovative technology using inactive and dead biomasses to remove heavy metals from aqueous solutions. This biological phenomenon could be explained by considering different kinds of chemical and physical interactions among the functional group present in the cell wall and heavy metals in solution (A.Esposito et al., 2002) . The biosorbent used in this study is Grewia Orbiculata leaves powder, in which the adsorption takes place on surface of insoluble cell walls of the leaves. The insoluble cell walls of the Grewia Orbiculata leaves are largely made up of cellulose and hemi celluloses, lignin, condensed tannins and structural proteins (H.Christ Ray et al., 1981). Batch mode experiments were conducted to determine the biosorption isotherms, kinetics and thermodynamic parameters. The conventional method used for optimization is the “one factor at a time” method in which one independent variable is varied while fixing all others at a specific level. This may lead to unreliable results and less accurate conclusions. This method requires a large number of experiments which might result in predicting the false optimum values. These drawbacks can be eliminated by optimizing all the affecting parameters collectively by Central Composite Design (CCD) (Box GEP et al., 1951) using Response Surface Methodology (RSM). A detailed account of this technique has been outlined (Cochran WG et al., 1957). This optimization process involves three major steps: performing the statistically designed experiments, estimating the coefficients in a mathematical model and predicting the response and checking the adequacy of the model. This technique has been applied in biosorption studies for optimization of different parameters (Farshid G et al., 2008; Preetha B et al., 2007; Kumar R et al., 2009). The present study also applied CCD to optimize zinc biosorption in an aqueous solution onto Grewia Orbiculata L.  powder.

2. Material And Methods

2.1 Preparation of biosorbent

                The Grewia Orbiculata L. leaves were collected near K.L University campus of Guntur, Andhra Pradesh, India. Leaves were washed with deionized water several times to remove dirt particles. Then the dried leaves were powdered using domestic grinder and the powder size of 75-212 μm, which were used as biosorbent without any pretreatment for zinc adsorption.

2.2 Chemical 

                Analytical grades of ZnSO₄.7H₂O, HCl and NaOH were purchased from Merck (Mumbai, Maharastra, India). Zinc ions were prepared by dissolving its corresponding sulphate salt in distilled water. The pH of solutions was adjusted with 0.1 N HCl and NaOH.

2.3 Biosorption experiments

                Biosorption experiments were performed at room temperature (30 ± 1⁰C) in a rotary shaker at 180 rpm using 250 mL Erlenmeyer flasks containing 30 mL of different zinc concentrations. After 1 hr of contact (according to the preliminary sorption dynamics tests), with 0.1 g Grewia Orbiculata L. leaves biomass, equilibrium was reached and the reaction mixture was centrifuged for 5 min. The metal content in the supernatant was determined using Atomic Absorption Spectrophotometer (GBC Avanta Ver 1.32, Australia) after filtering the adsorbent with 0.45 μm filter paper. The amount of metal adsorbed by Grewia Orbiculata L. leaves was calculated from the differences between metal quantity added to the biomass and metal content of the supernatant using the following equation:

               (1)

Where q is the metal uptake (mg/g); C₀ and C_f  the initial and final metal concentrations in the solution (mg/L), respectively; V the solution volume (mL); M is the mass of biosorbent (g).The pH of the solution was adjusted by using 0.1N HCl and 0.1N NaOH.

                The Langmuir (I.Langmuir, 1916) sorption model was chosen for the estimation of maximum zinc sorption by the biosorbent. The Langmuir isotherm can be expressed as

           (2)

Where Q_max indicates the monolayer adsorption capacity of adsorbent (mg/g) and the Langmuir constant b (L/mg) is related to the energy of adsorption. For fitting the experimental data, the Langmuir model was linearized as

  (3)

The freundlich (H.M.F Freundlich, 1906) model is represented by the equation:

        (4)

Where K (mg/g) is the Freundlich constant related to adsorption capacity of adsorbent and 1/n is the Freundlich exponent related to adsorption intensity (dimensionless). For fitting the experimental data, the Freundlich model was linearized as follows:

   (5)

The Temkin (C.Aharoni et al., 1977) isotherm has generally been applied in the following form:

      (6)

Where A_T (L/mg) and b_T are Temkin isotherm constants.

The Dubinin–Radushkevich (D–R) (T.Santhi et al., 2010) model was also applied to estimate the porosity apparent free energy and the characteristics of adsorption. The D–R isotherm dose not assumes a homogeneous surface or constant adsorption potential. The D–R model has commonly been applied in the following Eq. (7) and its linear form can be shown in Eq. (8):

q_e=Q_m exp(−Kε²)     (7)

ln q_e=ln Q_m−Kε² (8)

where K is a constant related to the adsorption energy, Qm the theoretical saturation capacity, ε the Polanyi potential, calculated from Eq. (9).

ε=  RT (1+1/C_e)        (9)

The slope of the plot of ln qe versus ε² gives K (mol² (kJ²) ⁻¹) and the intercept yields the adsorption capacity, Qm (mg g−1). The mean free energy of adsorption (E), defined as the free energy change when one mole of ion is transferred from infinity in solution to the surface of the solid, was calculated from the K value using the following relation 10:

E= 1/√2K    (10)

The calculated value of D–R parameters is given in Table 1. The values of E calculated using Eq. (10) is below 1 KJ mol−1, which indicating that the physico-sorption process plays the significant role in the biosorption of zinc metal.

2.4 Biosorption Kinetics

                The kinetic studies were carried out by conducting batch biosorption experiments with different initial zinc concentrations. Samples were taken at different time periods and analyzed for their zinc concentration.

2.5 Thermodynamic Studies

The thermodynamic effect was studied by agitating 20 mg l⁻¹ of zinc solution with 0.1 g of adsorbent at temperature 30, 35, 40, and 45 ◦C for a time period of 15 min.

2.6 Central composite design (CCD)

With the identification of the parameters having the statistically significant influence on the response a CCD (Box gep et al., 1951) was used to optimize the levels of these parameters. The full CCD, based on three basic principles of an ideal experimental design, primarily consists of (i) a complete 2ⁿ factorial design, where n is the number of test parameters, (ii) n_o center points (n_o≥1) and (iii) two axial points the axis of each design parameters at a distance of 2^n/4 from the design center. The total number of design points is N= 2ⁿ+2n+n_o. For statistical calculations, the parameters X_i are coded as x_i according to Eq. (11):

X_i = X_i- x_i / Δx_j      (i=1, 2, 3, ….k)  (11)

where x_i is dimensional value of an independent parameter, X_i is the real value of an independent parameter, x_i^- is the real value of the independent parameter at the center point and x_j is the step change. The second degree polynomials (Eq. 12) are calculated with the statistical package STATISTICA 6.0 (Stat-Ease Inc., Tulsa, OK, USA) to estimate the response of the dependent variable:

Y = b₀ + b₁X₁ + b₂X₂ + b₃X₃ + b₄X₄ + b₁₁X₁² + b₂₂X_2­­­² + b₃₃X₃² + b₄₄X₄²

+ b₁₂X₁X₂ + b₁₃X₁X₃ + b₁₄X₁X₄ + b₂₃X₂X₃ + b₂₄X₂X₄ + b₃₄X₃X₄           (12)

Where Y is predicted response, X₁, X₂, X₃,X₄ are independent parameter,b₀ is offset term, b₁, b₂, b₃, b₄ are linear effects , b₁₁, b₂₂, b₃₃, b₄₄ are squared effects and b₁₂, b₁₃, b₁₄, b₂₃, b₂₄, b₃₄ are interaction terms.

3. Results and Discussion

3.1 The effect of contact time

The data obtained from the biosorption of zinc ions on the Grewia Orbiculata L. showed that a contact time of 15 min was sufficient to achieve equilibrium and the adsorption did not change significantly with further increase in contact time. Therefore, the uptake and un -adsorbed zinc concentrations at the end of 15 min are given as the equilibrium values (, mg/g;, mg/L), respectively (Fig. 1) and the other adsorption experiments were conducted at this contact time of 15 min (pH 6).

3.2 Effect of pH

The effect of pH on % biosorption was investigated and results were shown in Figure 2. It reveals that % biosorption increased from 15.5 % to 81.9 % as pH is increased from 2 to 6 and then decreased beyond pH value of 6 reaching 76.3 % for pH value of 7. Low pH depresses biosorption of zinc, which may be due to competition with H⁺ ions for appropriate sites on the biosorbent surface. However, with increasing pH, this  competition weakens and zinc ions replaces H⁺ ions bound to the biosorbent or forming part of the surface functional groups such as OH, COOH, etc. (R.Gong et al., 2005; F.A Abu Al-rub et al., 2004; P.X.Sheng et al., 2004; G.Ozdemir et al., 2004; A.Iyer et al., 2004)

3.3 Effect of metal ion concentration

                Fig. 3 shows the effect of metal ion concentration on the adsorption of zinc by Grewia Orbiculata L. The data shows that the metal uptake increases and the percentage adsorption of zinc decreases with increase in metal ion concentration. This increase is a result of increase in the driving force, i.e. concentration gradient. Though an increase in metal uptake was observed, the decrease in percentage adsorption may be attributed to lack of sufficient surface area to accommodate much more metal available in the solution. The percentage adsorption at higher concentration levels shows a decreasing trend whereas the equilibrium uptake of zinc displays an opposite trend. At lower concentrations, all zinc ions present in solution could interact with the binding sites and thus the percentage adsorption was higher than those at higher zinc ion concentrations. At higher concentrations, lower adsorption yield is due to the saturation of adsorption sites. As a result, the purification yield can be increased by diluting the wastewaters containing high metal ion concentrations.

3.4 Effect of adsorbent dosage

Fig. 4 shows the effect of adsorbent dosage on the % removed at equilibrium conditions. It was observed that the amount of zinc adsorbed varied with varying adsorbent dosage. The amount of zinc adsorbed increases with an increase in adsorbent dosage from 0.1 to 0.5 g. The increase in the adsorption of the amount of solute is obvious due to increasing biomass surface area. Similar trend was also observed for zinc removal using Azadirachta indica as adsorbent (K.G.Battacharya et al., 2004).

3.5 Effect of temperature

The effect of temperature on biosorption of zinc was studied between 303 K to 318 K and results were depicted in Figure 5. The results show that biosorption increased from with the rise in temperature.

4. Biosorption equilibrium

The equilibrium biosorption of zinc on the Grewia Orbiculata L. as a function of the initial concentration of zinc is shown in Fig. 6-9. There was a gradual increase of adsorption for zinc ions until equilibrium was attained. The Langmuir, Freundlich, Temkin and Dubinin–Radushkevich models are often used to describe equilibrium sorption isotherms. The calculated results of the Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherm constants are given in Table 1.

It is found that the adsorption of zinc on the Grewia Orbiculata L. was correlated well with the Langmuir and Temkin as compared to Freundlich, Dubinin–Radushkevich isotherm equations under the concentration range studied. Examination of the Freundlich, Dubinin–Radushkevich isotherm data shows that these isotherms were not modeled as well across the concentration range studied.

Table.1.The values of parameters and correlation coefficients for each isothermal model for zinc:

5.  Kinetics of adsorption

The prediction of adsorption rate gives important information for designing batch adsorption systems. Information on the kinetics of solute uptake is required for selecting optimum operating conditions for full-scale batch process. The adsorption rate within the first 10 min was observed to be very high and there after the reaction proceeds at a slower rate till equilibrium and finally a steady state was obtained after equilibrium. The saturation time was found to be 15 min based on the initial metal concentration. The kinetics of the adsorption data was analyzed using two kinetic models, pseudo-first- and pseudo-second-order kinetic model. These models correlate solute uptake, which are important in predicting the reactor volume. These models are explained as follows:

5.1 The pseudo-first-order equation

The pseudo-first-order equation of Lagergren (S.Lagergren et al, 1898) is generally expressed as follows:

              (13)

where and are the sorption capacities at equilibrium and at time t, respectively (mg/g) and  is the rate constant of pseudo first-order sorption (min−1). After integration and applying boundary conditions,   =0 to  =  at t = 0 to t = t; the integrated form of Eq. (13) becomes:

      (14)

                The pseudo-first-order rate constant  can be obtained from the slope of plot between log (q_e −q) versus time, t. The calculated  values and their corresponding linear regression correlation coefficient values are shown in Table 2. These linear regression correlation coefficient values () shows that this model cannot be applied to predict the adsorption kinetic model.

5.2 The pseudo-second-order equation

If the rate of sorption is a second-order mechanism, the pseudo-second-order chemisorption kinetic rate equation is expressed as (Y.S Ho et al., 1998):

                (15)

Where  and  are the sorption capacity at equilibrium and at time t, respectively (mg/g) and k is the rate constant of pseudo-second-order sorption (g/(mg min)). For the boundary conditions  =0 to   =  at t = 0 to t = t; the integrated form of Eq. (15) becomes:

                  (16)

The second-order rate constants were used to calculate the initial sorption rate, given by the following equation:

h = k.q_e²                    (17)

where t is the contact time (min),  (mg/g) and  (mg/g) are the amount of the solute adsorbed at equilibrium and at any time, t. Eq. (16) does not have the problem of assigning as effective . If pseudo-second-order kinetics is applicable, the plot of t/q_t against t of Eq. (16) should give a linear relationship, from which  and k can be determined from the slope and intercept of the plot (Fig. 10) and there is no need to know any parameter beforehand.

The pseudo-second-order rate constant k₂, the calculated q_e value and the corresponding linear regression correlation coefficient value are given in Table 2. At an initial zinc concentrations of 20, 40, 60, 80, 100 mg/L, the linear regression correlation coefficient  value was higher. The higher value confirms that the adsorption data were well represented by pseudo-second order kinetic model.

The values of initial sorption (h) that represents the rate of initial adsorption Eq. (17), is increased with increasing metal concentrations onto Grewia Orbiculata L. (Table 2).

Table.2. Kinetic constants for zinc onto Grewia Orbiculata L. :

6. Thermodynamic Parameters

In environmental engineering practice, both energy and entropy factors must be considered in order to determine what processes will occur spontaneously. Gibb’s free energy change, ΔG°, is the fundamental criterion of spontaneity. Reactions occur spontaneously at a given temperature if ΔG° is a negative value. The thermodynamic parameters of ΔG°, enthalpy change, ΔH°, and entropy change, ΔS°, for the adsorption processes are calculated using the following equations (A.E,Martel et al., 1977; J.M.Murray et al., 1979):

ΔG°=-RT1nK_a    (18)

and

ln K_d = ∆S⁰/R - ∆H⁰/RT           (19)

Where R is universal gas constant (8.314 J/mol K) and T is the absolute temperature in K.

A plot ln K_d(q_e/C_e) versus temperature 1/T, was found to be linear (Fig. 11). The values of ΔH° and ΔS° were respectively determined from the slope and intercept of the plot. ΔH° and ΔS° for the sorption process were calculated to be 101.4683 KJ/mol and 0.337119 KJ/mol K, respectively. The negative values (Table 3) of ΔG° confirm the feasibility of the process and the spontaneity of sorption with a high preference for zinc to adsorb onto Grewia Orbiculata L. The value of ΔH° and ΔS° were positive, indicating that the process is spontaneous at high temperatures, where exothermicity plays a small role in the sorption process (Zawani et al., 2009; Jing he et al., 2010).

Table. 3. Thermodynamic Parameters:

7. Optimization of the Selected Parameters Using CCD

The experiments with different pH values of 2– 10, different zinc concentrations of 20–100 mg/L, different biosorbent dosages of 0.1–0.5 g/L and different temperatures of 303–318 K were coupled to each other and varied simultaneously to cover the combination of parameters in the CCD. The levels and ranges of the chosen independent parameters used in the experiments for the removal of zinc were given in Table 4. A 2⁴ – factorial CCD design, with eight axial points (α= √4) and six replications at the center points (no=6) leading to a total number of 30 experiments (Table 5) was employed for the optimization of the parameters. The calculated regression equation for the optimization of medium constituents showed that percentage removal of zinc (Y) was function of the temperature (X₁), pH (X₂), biosorbent dosage (X₃) and initial concentration (X₄). Multiple regression analysis of the experimental data resulted in the following equation for the biosorption of zinc:

Y =  -212.740+ 12.065X₁ + 1.747X₂ + 67.310X₃ + 0.481X₄ -0.171X₁² -1.954X_2­­­² + 34.332X₃² -0.003X₄²+ 0.574X₁X₂ -2.085X₁X₃ -0.005X₁X₄ + 0.949X₂X₃ + 0.007X₂X₄  -0.118X₃X₄             (20)

Each response plot represents a number of combinations of two test parameters with the other parameter maintained at zero levels. The maximum percentage biosorption of zinc is indicated by the surface confined in the smallest curve (circular or elliptical) of the response plot. The optimal set of conditions for maximum percentage biosorption of zinc is pH = 5.86, initial concentration of zinc in aqueous solution = 53.07 mg/L, biosorbent dosage = 0.277 g/L and temperature = 39.42⁰C. The extent of biosorption of zinc at these optimum conditions was 75.865%.

Table.4. Experimental range and levels of the independent parameters:

Table 5: CCD matrix showing coded and real values along with the experimental values for percentage biosorption of zinc.