MATERIALS AND METHODS

Adsorbents and dye:Citrus reticulata (orange) peels were obtained from a local fruit stall at Tirunelveli, India on November, 2009. The peels were cut into small pieces, crushed and washed thoroughly with deionized water to remove the adhering dirt. They were air dried in an oven at 40-50°C for 48 h. The BET surface areas of orange peels were in the range 0.0263-0.6719 m² g^-1 obtained from N₂ adsorption isotherms by sorptiometer (Quantochrome NOVA 1000). Bromophenol blue was obtained from Merck Co. The solution pH was adjusted by adding a small amount of 0.1 M HCl or NaOH.

Box-Behnken design experiments: To optimize the range of experimentation the Box-Behnken design was utilized during the investigation. The following were measured dye concentration, X₁ (40, 60, 80 mg L^-1); pH, X₂ (5.7, 6.95, 8.2); Temperature X₃ (30, 45, 60°C). These served as the critical variables for the actual experiments as shown in Table 1. For each experimental batch a known weight of nano porous sorbent (1 g L^-1) was transferred to a 250 mL flask, then it was incubated on an orbital shaker (150 rpm) at 30°C for 24 h in Lab-line environ shaker. The dye solution was filtered before it was subjected to spectrophotometer analysis. After incubation period, the amount of sorbed dye was calculated by taking samples. All experiments were carried out in triplicate and average values are reported.

Design of experiments: Response surface methodology is an empirical modelization technique derived to the evaluation of the relationship of a set of controlled experimental factors and observed results (Cochran and Cox, 1964; Box and Behnken, 1960; Box and Hunter, 1957). It requires a prior knowledge of the process to achieve statistical model. Basically this optimization process involves three major steps, performing the statistically designed experiments, estimating the coefficients in a mathematical model, predicting the response and checking the adequacy of the model.

(1)

The true relationship between Y and X_k may be complicated and, in most cases, it is unknown; however, a second-degree quadratic polynomial can be used to represent the function in the range of interest;

Table 1:	Adsorption of dyes calculated from the three variables at each experimental point along experimental with theoretical values

(2)

where, X₁,X₂,X₃,…,X_k are the input variables which affect the response Y, R₀, R_i, R_ii and R_ij (i = 1-k, j = 1-k) are the known parameters, ε is the random error. A second-order model is designed such that variance of Y is constant for all points equidistant from the center of the design. The parameters and their values (in brackets) were four levels like dye concentration (40, 60, 80 mg L^-1), pH (5.7, 6.95, 8.2), temperature (°C) (30, 45, 60). This also enabled the identification of significant effects of interactions for the batch studies. In system involving four significant independent variables X₁, X₂ and X_3, the mathematical relationship of the response of these variables can be approximated by quadratic (second degree) polynomial equation:

(3)

Y = Predicted value, b_o = Constant, b₁X₁ = dye concentration (mg L^-1), b₂X₂ = pH, b₃X₃ = Temperature (°C), b₁X₁, b₂X₂ and b₃X₃ = linear coefficients, b₁₂X₁X₂, b₁₃X₁X_3, b23X2X3 = cross product coefficients and X₁², X₂² and X₃³ = quadratic coefficients. These parameters were chosen as the critical variables and designed X₁, X₂ and X₃, respectively. The low middle and high levels of each variable were designated as -1, 0 and +1, respectively, as given in Table 2. A total of 17 treatments were necessary to estimate the coefficients of the model using multiple linear regressions. The design of experiments was carried out for analysis using the design expert by Stat Ease lnc., Statistics Made Easy, Minneapolis, MN Version 5.7.1. A total of 17 experiments were necessary to estimate the 10 coefficients of the model. Adsorption in a solid-liquid system results in the removal of solutes from solution and their concentration at the surface of the solid, to such a time as the concentration of the solute remaining in solution are in dynamic equilibrium with that at the surface. At this position of equilibrium there is a defined distribution of solute between the liquid and solid phases which is generally expressed by one (or) more of a series of isotherms.

Table 2:	The box–behnken design for the three independent variables adsorption dye in actual and predicted values
*Values of “Prob>F” less than 0.0500 indicate model terms are significant

RESULTS AND DISCUSSION

Adsorption of dyes from the synthetic solution was carried out by the batch equilibrium studies. In the present investigation adsorption of dyes is optimized by Box-Behnken design method. The influence of three factors such as dye concentration (mg L^-1), temperature and pH three level investigated. The regression equation obtained after analysis of variance gives the level of adsorption of dye as a function of different dye concentration (mg L^-1), temperature and pH. All terms regardless of their significance are included in the following equation:

(4)

The adsorption of dyes from the model at each experimental point is summarized in Table 1 along with experimental and theoretical observed values. The coefficients of Eq. 3 are calculated using design expert and their values and Analysis of Variance (ANOVA) are listed in Table 2. The results of Analysis of Variance (ANOVA) are shown in Table 2 which indicates that the predictability of the model is at 99% confidence interval. The model was found to be adequate for prediction within the range of variable chosen Fig. 1A-C, observed adsorption Bb versus those from the statistical model (Eq. 3). The figure confirmed that the predicted data of the response from the empirical model is in good agreement with the experimentally obtained data.

Adsorption of dye from the synthetic solution was carried out by the batch equilibrium studies. In the present investigation adsorption of dyes is optimized by Box-Behnken design method. The influence of three factors such as dye concentration (mg L^-1), temperature and pH three level investigated. The predicted response fit well with those of the experimentally obtained response. The Pred R-Squared of 0.99985 is in reasonable agreement with the Adj R-Squared of 0.9996 which showed that the equation is highly reliable. Values of Prob>F less than 0.0500 indicate model terms are significant (Table 2). The model was found to be adequate for prediction within the range of variable chosen. Figure 1 shows observed adsorption Bb versus those from the statistical model (Eq. 3).

Fig. 1:	Adsorption of dye on 3-D graphics (A) for response surface optimization versus dye concentration (mg g¯1) vs pH; (B) for response surface optimization versus pH vs temperature (°C) and (C) for response surface optimization versus temperature (°C) vs dye concentration (mg L¯1)

The figure confirmed that the predicted data of the response from the empirical model is in good agreement with the experimentally obtained data.

The contour plot represents maximum percentage of dye adsorption against dye concentration (mg L^-1) vs pH. The maximum percentage of dye adsorption is 32.4 mg g^-1 a particular of dye concentration (mg L^-1) (50 to 70 mg L^-1) and pH (6.8, 6.95 to 7.6) which is also clearly shown in Fig. 1A. The dye concentration was increased the adsorption also decreased. The optimum level of dye adsorption occurs with 32.4 mg g^-1 at dye concentration (60.0 mg L^-1) and pH (6.95) calculated by derivation of the Eq. 4 and by solving the inverse matrix. The alkaline pH of the present study suggests that the strong chemical reaction occurred between the adsorbate and the adsorbent for more removal of dye (Tripathi et al., 2009).

Figure 1B shows the maximum percentage of dye adsorption was found with pH (6.3 to 7.6) and Temperature (37.5 to 52.5°C) level of adsorption of dye 15 mg g^-1. Optimum level of pH (6.8) and Temperature (45°C) showed the maximum percentage of dye adsorption. The result of pH (alkaline) is fit with result of Islam et al. (2009) where it’s contradict with finding of Tripathi et al. (2009). For obtaining maximum adsorption of dye the particle size must be decreased (Chitra and Gowri, 1996; Panday et al., 1986). At lower pH, the lone-pair of electrons of the oxygen atom coordinates with the highly positively charged groups in surface. An increase in the rate with decreasing pH may be caused by an alteration in the adsorbent surface; particularly variation in its electrokinetic’s character. That the three variables selected in this study have their individual effect on dye adsorption. The amount of dye adsorption gradually increases with decreasing dye concentration and pH but increasing temperature. Increase of concentration in the solutions decreases the removal percentage of dye. At low concentration, surface activity of used nano-porous adsorbent is higher and resulted more removal of % but at high concentration, the active sites of adsorbent gradually covered by the molecules and show lower removal percent.

Figure 1C shows the maximum percentage of dye adsorption was found to occur with Temperature (37.5 to 52.5°C) and dye concentration (mg L^-1) (50 to 70) level of adsorption of dye 32.4 mg g^-1. Optimum level temperature (45°C) and dye concentration (60 mg L^-1) showed the maximum percentage of dye adsorption. The optimum values of process parameters were dye concentration 60 mg L^-1, pH 6.8-6.95 and temperature 45°C for maximum removal efficiency. We are reporting first about orange peel adsorbent for removal of Bb using Box-Behnken model. Tripathi et al. (2009) optimized the values like dye concentration 15.7 g L^-1, pH 2 and temperature 40°C for maximum removal of Methyl orange dye using coconut based activated carbon-commercial grade adsorbent. In this results the dye concentration and temperature comparable to the present study where the pH in contradiction. The adsorption of dye increases with increasing temperature. An increased adsorption at higher temperature is difficult to explain, although many authors have also reported (Chitra and Gowri, 1996; Panday et al., 1986). The higher removal due to increasing temperature may be due to chemical reaction occurring between the functional groups of the adsorbent and dye (Panday et al., 1989; Chitra et al., 1996).

CONCLUSION

Results from this study suggest that the orange cellulose-based waste peels are suitable adsorbents for Bb. Dyestuff removal by adsorbents of environmental nanomaterial is strongly influenced by physicochemical parameters such as pH, temperature and the concentration of Bb. The model employed provided good quality of predictions for the above variables in terms of effective adsorption of Bromophenol blue and good correlation coefficient (0.99967 was obtained).

The data reported here should be useful for the design and fabrication of an economically viable treatment process using the response for the above variables at any time and also it’s revealed that the waste orange peels were the potential materials for dyes removal from aqueous solutions.

ACKNOWLEDGMENT

Authors gratefully acknowledge the University Grants Commission (UGC), New Delhi, India for financial support (Major Research Project - F. No. 36-173-2008(SR)) to carry out this work.