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Optimization of biological treatment of a dye solution by macroalgae Cladophora sp. using response surface methodology
Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
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Optimization of biological treatment of a dye solution by macroalgae Cladophora sp. using response surface methodology A.R. Khataee a,*, G. Dehghan b a b
Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran Department of Biology, Faculty of Science, University of Tabriz, Tabriz, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 November 2009 Received in revised form 1 March 2010 Accepted 7 March 2010
In this paper biological treatment of Malachite Green (MG) solution using macroalgae Cladophora sp. has been reported. Response surface methodology (RSM) was employed to investigate the effect of key factors on the biological treatment. The variables investigated were the initial pH, initial dye concentration, algae amount and reaction time. Central composite design (CCD) was used for the optimization of biological decolorization process. Predicted values were found to be in good agreement with experimental values (R2 = 0.9740 and Adj-R2 = 0.9512), which indicated suitability of the model employed and the success of CCD in optimizing the conditions of biological decolorization process. The results of optimization predicted by the model showed that maximum decolorization efficiency was achieved at the optimum condition of the initial pH 8, initial dye concentration 10 mg/l, algae amount 4 g and reaction time 75 min. Biological degradation of MG was revealed on the basis of live and dead biomass comparison, repeated-batch operations and UV–Vis, FT-IR spectra of MG solution during biological decolorization treatment. ß 2010 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keywords: Experimental design Central composite design Decolorization Algae Biodegradation
1. Introduction Synthetic dyes are a major part of our life as they are found in the various products ranging from clothes to leather accessories to furniture. An unfortunate side effect of their widespread use is the fact that up to 12% of these dyes are wasted during the dyeing process, and that approximately 20% of this wastage enters the environment (mostly in water supply). Most of these dyes are toxic and potentially carcinogenic in nature and their removal from the industrial effluents is a major environmental problem (Khataee, 2009; Khataee et al., 2009b; Rauf and Ashraf, 2009). In recent years a number of studies have focused on some micro/macro-organisms that are able to biodegrade and biosorb dyes in wastewaters. A wide variety of organisms are capable of decolorization a wide range of dyes include bacteria (Banat et al., 1996; You and Teng, 2009); fungi (Kaushik and Malik, 2009); yeasts (Adav et al., 2009; Ertugrul et al., 2009); and algae (Daneshvar et al., 2007a; Jinqi and Houtian, 1992; Khataee et al., 2009a). Algae are photosynthetic organisms, which distributed in nearly all parts of the world and in all kinds of habitats (Daneshvar et al., 2007b). Algae can degrade number of dyes, postulating that the reduction appears to be related to the molecular structure of
* Corresponding author. Tel.: +98 411 3393165; fax: +98 411 3340191. E-mail addresses: [email protected], [email protected] (A.R. Khataee).
dyes and the species of algae used. In this work, macroalgae Cladophora sp. was used in order to decolorize a dye solution containing Malachite Green which is a common cationic dye for dyeing wool, silk, leather, cotton and fungicide, ectoparasiticide in aquaculture and fisheries (Khataee et al., 2009a). As it has been reported previously, biological removal of MG in the presence of different microorganisms is dependent on various parameters such as initial dye concentration, pH, reaction time and amount of biomass (Daneshvar et al., 2007a,b; Jadhav and Govindwar, 2006; Khataee et al., 2009a; Kumar et al., 2006; Parshetti et al., 2006; Sedighi et al., 2009). In most of the previous reports, conventional methods used to determine the influence of each one of these parameters. In the conventional methods, experiments were carried out varying systematically the studied parameter and keeping constant the others. This should be repeated to all the influence parameters, resulting in an unreliable number of experiments. In addition, this exhaustive procedure is not able to find combined effect of the effective parameters. In order to optimize the effective parameters with the minimum number of experiments, application of the experimental design methodologies can be useful. One of the experimental design techniques commonly used for process analysis and modeling is response surface methodology. Using RSM, it is possible to estimate linear, interaction and quadratic effects of the factors and a prediction model for the response. In this way, RSM designs could be used to find improved or optimal process settings in an efficient use of the resources (Zhang et al., 2009).
1876-1070/$ – see front matter ß 2010 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jtice.2010.03.007
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
In this work, the central composite design based on RSM has been applied to the optimization of biological treatment of the dye solution containing MG. The key factors (variables) investigated were the initial pH, initial dye concentration, algae amount and reaction time. UV–Vis spectra and FT-IR analysis was used to study the degradation of MG.
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Table 1 Characteristics of Malachite Green. Chemical structure
[TD$INLE]
2. Experimental 2.1. Algal species and biological treatment operation The algal species was acquired from Azna-lake in the north of Iran. The algal species was washed with distilled water to remove macro/microscopic contaminations. According to its morphology and microscopic observations, it was identified as Cladophora species belongs to Charophyta (Fig. 1). The microscopic picture was taken by Olympus BX41 microscope (Japan). The triphenylmethane dye used in this study was Malachite Green whose characteristics have been reported in Table 1. The biological treatment experiments were performed in the Erlenmeyer flasks containing 250 ml of the synthetic dye solution and algal biomass under controlled temperature environment in the incubator (Sanyo, Ogawa Seiki Co., Japan). At appropriate reaction times of biological treatment process, samples were taken and the remaining MG was determined with a spectrophotometer (UV/Vis spectrophotometer WPA Lightwave S2000, England) at maximum absorption wavelengths, lmax = 619 nm and calibration curve. The color removal efficiency (CR(%)) was expressed as the percentage ratio of decolorized dye concentration to that of the initial one. The pH was adjusted using diluted NaOH and H2SO4 solutions and measured by pH meter (654 pH meter Metrohm, Switzerland). Fourier Transform Infrared (FT-IR) spectroscopy was performed on a Bruker Tensor 27 spectrometer, Germany. For FT-IR analysis, biological treatment process was performed with 250 ml solution containing 10 mg/l of MG and 4 g of algal biomass. At the reaction times of 0 h (control) and 24 h samples were taken and the biological degradation products were extracted with 30 ml of diethyl ether in three times, then crystallized and used for analysis. The algal cells were killed by autoclaving (1 kg f/cm2) for 20 min in Kavoush-megha Autoclave, Iran.
[(Fig._1)TD$IG]
C.I. number C.I. name Class Ionization lmax (nm)
42000 C.I. Basic Green 4 Triarylmethane Basic 619
2.2. Experimental design In the present study, central composite design, which is a widely used form of RSM was employed for the optimization of biological color removal efficiency. In order to evaluate the influence of operating parameters on the decolorization efficiency of MG, four main factors were chosen: the initial pH (X1), initial dye concentration (X2), algae amount (X3) and reaction time (X4). Totally of 31 experiments were employed in this work, including 24 = 16 cube points, 7 replications at the center point and 8 axial points. Experimental data were analyzed using the Minitab 15 software. For statistical calculations, the variables Xi were coded as xi according to the following relationship: xi ¼
Xi X0 dX
(1)
where X0 is the value of Xi at the center point and dX presents the step change (Aleboyeh et al., 2008; Kasiri et al., 2008). The experimental ranges and the levels of the independent variables for MG color removal are given in Table 2.
3. Results and discussion 3.1. Central composite design model CCD involves the following steps: performing the statistically designed experiments according to the design, factors and levels selected; estimating the coefficients of the mathematical model to predict the response and check its adequacy (Kasiri et al., 2008; Santos and Boaventura, 2008). The 4-factor CCD matrix and experimental results obtained in the biological color removal runs are presented in Table 3. The second-order polynomial response equation (Eq. (2)) was used to correlate the dependent and Table 2 Experimental ranges and levels of the independent test variables. Variables
Fig. 1. Light microscopic picture of Cladophora sp. used in this study.
Initial pH (X1) Initial dye concentration (mg/l) (X2) Algae amount (g) (X3) Reaction time (min) (X4)
Ranges and levels 2
1
0
+1
+2
3 5 1 45
4.5 7.5 2 60
6 10 3 75
7.5 12.5 4 90
9 15 5 105
[(Fig._2)TD$IG]
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Table 3 The 4-factor central composite design matrix and the value of response function (CR(%)). Run
Initial pH
Initial dye concentration (mg/l)
Algae amount (g)
Reaction time (min)
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 29 30 31
1 1 1 2 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 2
1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 2 1 0 0 1 1 1 0 1 2 1 0 0 0
1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 2 1 1 1 2 1 0 1 0 0 0
1 1 1 0 0 1 1 0 2 1 2 1 1 0 0 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 0
Decolorization efficiency (%) Experimental
Predicted
41.33 34.74 38.86 14.00 48.36 42.25 31.34 53.39 57.80 19.36 36.34 64.78 55.19 49.96 51.31 55.33 17.00 46.24 26.00 48.54 35.18 79.91 44.00 28.53 69.40 56.29 42.99 69.22 52.40 50.30 54.00
37.96 38.36 42.15 12.93 50.61 48.04 31.93 50.61 55.77 21.61 36.07 64.96 51.77 50.61 50.61 53.94 18.41 49.46 28.85 50.61 34.28 73.74 40.25 32.35 68.01 55.58 37.48 64.48 50.61 50.61 58.17
Fig. 2. Comparison of the experimental results of decolorization efficiency with those calculated via central composite design resulted equation.
We used two lines to show the success of the prediction. The one is the perfect fit (predicted data equal to experimental data), on which all the data of an ideal model should lay. The other line is the line that best fits on the data of the scatter plot with equation Y = ax + b and it is obtained with regression analysis based on the minimization of the squared errors. The regression coefficient of this line is also presented (R2). An R2 closer to 1.0 indicates that the regression line perfectly fits the data. As can be seen, the predicted values match the experimental values reasonably well with R2 of 0.974. The obtained R2 values suggest good adjustments to the experimental results since these indicate that 0.974 of the variability in the response could be explained by the models. This implies that 97.4% of the variations for dye removal efficiency are
independent variables: Y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4 þ b12 x1 x2 þ b13 x1 x3 þ b14 x1 x4 þ b23 x2 x3 þ b24 x2 x4 þ b34 x3 x4 þ b11 x21 þ b22 x22 þ b33 x23 þ b44 x24
(2)
where Y is a response variable of decolorization efficiency. The bi are regression coefficients for linear effects; bik the regression coefficients for quadratic effects; xi are coded experimental levels of the variables. Based on these results, an empirical relationship between the response and independent variables was attained and expressed by the following second-order polynomial equation: Y ¼ 50:6086 þ 12:8321x1 2:6213x2 þ 8:0613x3 þ 4:9112x4
Source of variations
Sum of squares
Degree of freedom
Adjusted mean square
F-value
Regression Residuals
6980.92 186.54
14 16
498.64 11.66
42.77
Total
7167.46
R2 = 0.9740, adjusted R2 = 0.9512.
Table 5 Estimated regression coefficients and corresponding t- and P-values from the data of central composite design experiments.
4:2331x1 x2 1:5793x1 x3 þ 0:3394x1 x4 0:9656x2 x3 0:3331x2 x4 þ 1:4794x3 x4 þ 0:8619x21 2:2119x22 þ 0:0106x23 1:0869x24
Table 4 Analysis of variance (ANOVA) for fit of decolorization efficiency from central composite design.
(3)
The decolorization efficiencies (CR(%)) have been predicted by Eq. (3) and presented in Table 3. These results indicated good agreements between the experimental and predicted values of decolorization efficiency. 3.2. Analysis of variance and residuals The regression coefficient (R2) quantitatively evaluates the correlation between the experimental data and the predicted responses. Experimental results and the predicted values obtained using model (Eq. (3)) are given in Fig. 2.
Coefficient
Parameter estimate
Standard error
t-Value
P-value
b0 b1 b2 b3 b4 b12 b13 b14 b23 b24 b34 b11 b22 b33 b44
50.6086 12.8321 2.6213 8.0613 4.9112 4.2331 1.5793 0.3394 0.9656 0.3331 1.4794 0.8619 2.2119 0.0106 1.0869
1.2905 0.6970 0.6970 0.6970 0.6970 0.6385 0.6385 0.6385 0.6385 0.8536 0.8536 0.8536 0.8536 0.8536 0.8536
39.215 18.411 3.761 11.566 7.047 6.630 2.473 0.532 1.512 0.390 1.733 1.010 2.591 0.012 1.273
0.000 0.000 0.002 0.000 0.000 0.000 0.025 0.602 0.150 0.701 0.102 0.328 0.020 0.990 0.221
[(Fig._3)TD$IG]
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Fig. 3. Residual plots for biological decolorization efficiency of MG.
explained by the independent variables and this also means that the model did not explain only about 2.6% of variation. Adjusted R2 (Adj-R2) is also a measure of goodness of a fit, but it is more suitable [(Fig._4)TD$IG] comparing models with different numbers of independent for
Fig. 4. The response surface and contour plots of the decolorization efficiency (%) as the function of initial dye concentration (mg/l) and amount of algae (g).
[(Fig._5)TD$IG]
Fig. 5. The response surface and contour plots of the decolorization efficiency (%) as the function of reaction time (min) and amount of algae (g).
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A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
variables. It corrects the R2-value for the sample size and the number of terms in the model by using the degrees of freedom on its computations, so if there are many terms in a model and not very large sample size, Adjusted R2 may be visibly smaller than R2 (Box and Behnken, 1960; Santos and Boaventura, 2008). Here, adjusted R2 value (0.9512) was very close to the corresponding R2 value. Table 4 shows the results of the quadratic response surface model fitting in the form of analysis of variance (ANOVA). ANOVA is required to test the significance and adequacy of the model (Harrelkas et al., 2009; Liu and Chiou, 2005). ANOVA subdivides the total variation of the results in two components: variation associated with the model and variation associated with the experimental error, showing whether the variation from the model is significant or not when compared with the ones associated with residual error (Santos and Boaventura, 2008). This comparison is performed by the F-value, which is the ratio between the mean square of the model and the residual error. If the model is a good predictor of the experimental results, F-value should be greater than the tabulated value of the F-distribution for a certain number of degrees of freedom in the model at a level of significance a. Fratios obtained, 42.77, is clearly greater than the tabulated F (2.352 at 95% significance) confirming the adequacy of the model fits. The student t distribution and the corresponding values, along with the parameter estimate, are given in Table 5. The P-values were used as a tool to check the significance of each of the coefficients, which, in turn, are necessary to understand the pattern of the mutual interactions between the test variables. The
[(Fig._6)TD$IG]
larger the magnitude of t-value and smaller P-value, the more significant is the corresponding coefficient (Zarei et al., 2010). In addition to regression coefficient, the adequacy of the models was also evaluated by the residuals (difference between the observed and the predicted response value). Residuals are thought as elements of variation unexplained by the fitted model and then it is expected that they occur according to a normal distribution. Normal probability plots are a suitable graphical method for judging the normality of the residuals. The observed residuals are plotted against the expected values, given by a normal distribution (see Fig. 3). The residuals from the analysis should be normally distributed. In practice, for balanced or nearly balanced designs or for data with a large number of observations, moderate departures from normality do not seriously affect the results. The normal probability plot of the residuals should roughly follow a straight line. Trends observed in Fig. 3, reveal reasonably well-behaved residuals. 3.3. Effect of key factors as surface and counter plots In order to gain insight about the effect of each variable, the three dimensional (3D) and contour (2D) plots for the predicted responses were formed, based on the model polynomial function to analyze the change the response surface. The surface and contour plots of the quadratic model with two variables kept constant and the other two varying within the experimental ranges are shown in Figs. 4–9. Response surface plots provide a method to predict the decolorization efficiency for different values of the tested variables
[(Fig._7)TD$IG]
Fig. 6. The response surface and contour plots of the decolorization efficiency (%) as the function of reaction time (min) and initial dye concentration (mg/l).
Fig. 7. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and initial dye concentration (mg/l).
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
and the contours of the plots help in identification of the type of interactions between these variables (Li et al., 1996). In Fig. 4, the response surface and contour plots were developed as a function of initial dye concentration and amount of algae while the initial pH and reaction time were kept constant at 6 and 75 min, respectively, being the central levels. The increase in dye removal efficiency with increasing amount of algae has been observed (Figs. 4 and 5). The reason of this observation is thought to be the fact that increase of biomass of algae gives more surface area for sorption of the dye molecules on the surface of algae (Khataee et al., 2009a). As can be seen in Figs. 4, 6 and 7, biological decolorization efficiency increases with an increase in initial dye concentration, reaches the higher value and then decreases. The reason of this observation is thought to be the fact that initial dye concentration provides an important driving force to overcome all mass transfer resistances of the dye between the aqueous and solid phases. Hence, increasing initial concentration of dye may enhance the decolorization efficiency. But, when all dye molecules are adsorbed on constant amount of algae, the addition of higher quantities of MG molecules would have no effect on the decolorization efficiency (Daneshvar et al., 2007a; Khataee et al., 2009a). In addition, the response surface and contour plots in Figs. 5–7 clearly show that dye removal efficiency increased with increasing reaction time. The effect of initial pH on dye removal efficiency as response surface and contour plots is illustrated in Figs. 7–9. Previously, several researches proved that biological processes using algae were highly pH dependent (Daneshvar et al., 2007b; Jinqi and
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Houtian, 1992). As can be seen in these figures, the decolorization efficiency was rapidly increased with increasing initial pH of the dye solution. This can be explained on the basis of zero point of discharge for biomass. In general, an isoelectric point of around pH 3–4 is determined for all algal species (Crist et al., 1981; Donmez et al., 1999; Kumar et al., 2006). According to the zero point of charge of algae species, their surfaces are presumably positively charged in acidic solution and negatively charged in alkaline solution. Since MG is a cationic dye, the alkaline solution favors adsorption of it onto algae surface, thus the decolorization efficiency increases. The similar observations were previously reported for removal of Malachite Green using Pithophora sp., Cosmarium sp. and Chlorella sp. (Daneshvar et al., 2007b; Khataee et al., 2009a; Kumar et al., 2006). 3.4. Response optimization and confirmation The main objective of the optimization is to determine the optimum values of variables for biological decolorization using Cladophora sp. from the model obtained using experimental data. The desired goal in term of decolorization efficiency was defined as ‘‘maximize’’ to achieve highest treatment performance. The optimum values of the process variables for the maximum decolorization efficiency are shown in Table 6. After verifying by a further experimental test with the predicted values, the result indicated that the maximal decolorization efficiency was obtained
[(Fig._9)TD$IG]
[(Fig._8)TD$IG]
Fig. 8. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and reaction time (min).
Fig. 9. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and amount of algae (g).
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[(Fig._1)TD$IG]
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Table 6 Decolorization efficiency at optimum values of the process parameters. Parameter
Optimum value
Initial pH Initial dye concentration (mg/l) Algae amount (g) Reaction time (min) Predicted decolorization efficiency (%) Observed decolorization efficiency (%)
8 10 4 75 70.57 71
when the values of each parameter were set as the optimum values, which was in good agreement with the value predicted from the model. It implies that the strategy to optimize the decolorization conditions and to obtain the maximal decolorization efficiency by CCD for the biological removal of MG in the presence of macroalgae Cladophora sp. is successful. 3.5. UV–Vis and FT-IR spectra changes Fig. 10 shows a typical time-dependent UV–Vis spectrum of MG solution during biological decolorization under the optimized conditions. The absorbance peaks corresponding to the dye diminished indicating that the dye had been removed. The spectrum of MG in visible region exhibits a main peak with a maximum at 619 nm. The decrease of absorbance peak of MG at l = 619 nm in this figure indicated a rapid degradation of the dye. According to the pervious literature (Chang and Kuo, 2000; Chen et al., 2003), biological decolorization of dyes can be due to adsorption onto biomass or biodegradation. In the adsorption process, all peaks of the dye, in UV–Vis region, decrease approximately in proportion to each other. However, if the dye removal is attributed to biodegradation, either the major visible light absorbance peak will disappear or a new peak will appear. As seen in Fig. 10, the main absorbance peak approximately disappeared within 24 h. Therefore, this process seems to be a biodegradation. Fig. 11 illustrates FT-IR spectra of MG biodegraded by Cladophora sp. before and after biological treatment process under optimized conditions. FT-IR spectrum of Malachite Green before removal showed the specific peaks in fingerprint region (2500–500 cm1) for the mono-substituted and para-disubstituted benzene rings which is supporting to the peak at 1585 cm1 for the C5 5C stretching of the benzene ring. Also the peak at 1170 cm1 for the C–N stretching vibrations and peak at 2923 cm1 for C–H stretching of asymmetric –CH3 group gives the perception of structure of MG. The FT-IR spectrum of extracted product at 24 h reaction time showed that
Fig. 11. FT-IR spectra of MG (10 mg/l) biodegraded by Cladophora sp. before treatment (0 h) and after 24 h of biological treatment. T = 25 8C under the optimized conditions.
most of the peak has been disappeared or significantly diminished. Remarkable variations in the fingerprint region (2500–500 cm1) of the FT-IR spectroscopy of MG before removal and after 24 h indicate [(Fig._12)TD$IG] biodegradation of MG by Cladophora sp.
[(Fig._10)TD$IG]
Fig. 10. UV–Vis spectra of MG (10 mg/l) biodegraded by Cladophora sp. at times: (1) 0 h, (2) 2 h, (3) 7 h, and (4) 24 h. T = 25 8C under the optimized conditions.
Fig. 12. (a) Comparison of live and dead algal biomass in removal of MG; (b) biological decolorization profiles during repeated-batch operations. T = 25 8C under the optimized conditions.
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
3.6. Comparison of live and dead biomass efficiency with reusability of live algae Biological treatment of MG solution by live and dead algae has been compared in Fig. 12(a). The algal cells were killed by autoclaving (1 kg f/cm2) for 20 min. As can be seen, the dye removal efficiency using live algae is higher than that of dead biomass. The reason for this observation is thought to be the fact that the biodegradation of MG was occurred in the presence of the live algae biomass, while the dye removal in the presence of the dead biomass was attributed to adsorption process. In order to confirm this claim, repeated-batch operations were performed to examine the reusability of live Cladophora sp. in MG decolorization process. As can be seen in Fig. 12(b), during five repeated runs, Cladophora sp. showed the same decolorization efficiency that obtained from the first run. The results indicated that Cladophora sp. possessed reasonable reusability in repetitive decolorization operations. On the basis of these findings, it can be concluded that the treatment of MG solution by live Cladophora sp. is biological degradation process. Chang and Kuo (2000) performed repeated-batch decolorization of C.I. Reactive Red 22 by Escherichia coli NO3. Their results showed that, after the first batch run, the decolorization rate of E. coli NO3 increased dramatically for runs 2–5. This might be attributed to biological degradation and an adaptation effect, since E. coli NO3 cells were repeatedly exposed to the azo dye. 4. Conclusions In the present study, the performance of macroalgae Cladophora sp. in biological decolorization MG solution was studied focusing on the influence of key factors such as the initial pH, initial dye concentration, algae amount and reaction time by using RSM with CCD. Analysis of variance showed a high coefficient of determination value (R2 = 0.974), ensuring a satisfactory adjustment of the second-order regression model with the experimental data. The optimum values of the initial pH, initial dye concentration, algae amount and reaction time were found to be 8, 10 mg/l, 4 g and 75 min, respectively. Under the optimized conditions, the experimental values agreed with the values predicted by the ridge analysis. These results implicate that the optimization using a response surface methodology based on the central composite design can save the time and effort by the estimation of the optimum conditions of the maximum removal of dye. Comparison of live and dead biomass, repeated-batch operations and UV–Vis, FT-IR spectra of MG solution during biological decolorization revealed that this process was biodegradation. Acknowledgments The authors would like to thank the University of Tabriz for the financial support of this project. This paper reports the results of the project entitled ‘‘Optimization of biological treatment of dye solution containing Malachite Green in the presence of macroalgae Cladophora sp. using response surface methodology’’, which has been funded by research grants of University of Tabriz.
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Pourhassan, ‘‘Biological Decolorization of C.I. Basic Green 4 Solution by Chlorella sp.: Effect Operational Parameters,’’ Chin. J. Appl. Environ. Biol., 15, 110 (2009a). Khataee, A. R., V. Vatanpour, and A. Amani, ‘‘Decolorization of C.I. Acid Blue 9 Solution by UV/Nano-TiO2, Fenton, Fenton-like, Electro-Fenton and Electrocoagulation Processes: A Comparative Study,’’ J. Hazard. Mater., 161, 1225 (2009b). Kumar, K. V., V. Ramamurthi, and S. Sivanesan, ‘‘Biosorption of Malachite Green a Cationic Dye onto Pithophora sp., A Fresh Water Algae,’’ Dyes Pigments, 69, 74 (2006). Li, Y., C. Chang, and T. Wen, ‘‘Application of Statistical Experimental Strategies to H2O2 Production on Au/Graphite in Alkaline Solution,’’ Ind. Eng. Chem. Res., 35, 4767 (1996). Liu, H. L. and Y. R. Chiou, ‘‘Optimal Decolorization Efficiency of Reactive Red 239 by UV/ TiO2 Photocatalytic Process Coupled with Response Surface Methodology,’’ Chem. Eng. J., 112, 173 (2005). Parshetti, G., S. Kalme, G. Saratale, and S. Govindwar, ‘‘Biodegradation of Malachite Green by Kocuria rosea MTCC 1532,’’ Acta Chim. Slov., 53, 492 (2006). Rauf, M. A. and S. S. Ashraf, ‘‘Fundamental Principles and Application of Heterogeneous Photocatalytic Degradation of Dyes in Solution,’’ Chem. Eng. J., 151, 10 (2009). Santos, S. C. R. and R. A. R. Boaventura, ‘‘Adsorption Modelling of Textile Dyes by Sepiolite,’’ Appl. Clay Sci., 42, 137 (2008). Sedighi, M., A. Karimi, and F. Vahabzadeh, ‘‘Involvement of Ligninolytic Enzymes of Phanerochaete Chrysosporium in Treating the Textile Effluent Containing Astrazon Red FBL in a Packed-Bed Bioreactor,’’ J. Hazard. Mater., 169, 88 (2009). You, S. J. and J. Y. Teng, ‘‘Anaerobic Decolorization Bacteria for the Treatment of Azo Dye in a Sequential Anaerobic and Aerobic Membrane Bioreactor,’’ J. Taiwan Inst. Chem. Engrs., 40, 500 (2009). Zarei, M., D. Salari, A. Niaei, and A. R. Khataee, ‘‘Application of Response Surface Methodology for Optimization of Peroxi-Coagulation of Textile Dye Solution Using Carbon Nanotube-PTFE Cathode,’’ J. Hazard. Mater., 173, 544 (2010). Zhang, Z., J. Peng, W. Qu, L. Zhang, Z. Zhang, W. Li, and R. Wan, ‘‘Regeneration of HighPerformance Activated Carbon from Spent Catalyst: Optimization Using Response Surface Methodology,’’ J. Taiwan Inst. Chem. Engrs., 40, 541 (2009).
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Journal of the Taiwan Institute of Chemical Engineers journal homepage: www.elsevier.com/locate/jtice
Optimization of biological treatment of a dye solution by macroalgae Cladophora sp. using response surface methodology A.R. Khataee a,*, G. Dehghan b a b
Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran Department of Biology, Faculty of Science, University of Tabriz, Tabriz, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 November 2009 Received in revised form 1 March 2010 Accepted 7 March 2010
In this paper biological treatment of Malachite Green (MG) solution using macroalgae Cladophora sp. has been reported. Response surface methodology (RSM) was employed to investigate the effect of key factors on the biological treatment. The variables investigated were the initial pH, initial dye concentration, algae amount and reaction time. Central composite design (CCD) was used for the optimization of biological decolorization process. Predicted values were found to be in good agreement with experimental values (R2 = 0.9740 and Adj-R2 = 0.9512), which indicated suitability of the model employed and the success of CCD in optimizing the conditions of biological decolorization process. The results of optimization predicted by the model showed that maximum decolorization efficiency was achieved at the optimum condition of the initial pH 8, initial dye concentration 10 mg/l, algae amount 4 g and reaction time 75 min. Biological degradation of MG was revealed on the basis of live and dead biomass comparison, repeated-batch operations and UV–Vis, FT-IR spectra of MG solution during biological decolorization treatment. ß 2010 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keywords: Experimental design Central composite design Decolorization Algae Biodegradation
1. Introduction Synthetic dyes are a major part of our life as they are found in the various products ranging from clothes to leather accessories to furniture. An unfortunate side effect of their widespread use is the fact that up to 12% of these dyes are wasted during the dyeing process, and that approximately 20% of this wastage enters the environment (mostly in water supply). Most of these dyes are toxic and potentially carcinogenic in nature and their removal from the industrial effluents is a major environmental problem (Khataee, 2009; Khataee et al., 2009b; Rauf and Ashraf, 2009). In recent years a number of studies have focused on some micro/macro-organisms that are able to biodegrade and biosorb dyes in wastewaters. A wide variety of organisms are capable of decolorization a wide range of dyes include bacteria (Banat et al., 1996; You and Teng, 2009); fungi (Kaushik and Malik, 2009); yeasts (Adav et al., 2009; Ertugrul et al., 2009); and algae (Daneshvar et al., 2007a; Jinqi and Houtian, 1992; Khataee et al., 2009a). Algae are photosynthetic organisms, which distributed in nearly all parts of the world and in all kinds of habitats (Daneshvar et al., 2007b). Algae can degrade number of dyes, postulating that the reduction appears to be related to the molecular structure of
* Corresponding author. Tel.: +98 411 3393165; fax: +98 411 3340191. E-mail addresses: [email protected], [email protected] (A.R. Khataee).
dyes and the species of algae used. In this work, macroalgae Cladophora sp. was used in order to decolorize a dye solution containing Malachite Green which is a common cationic dye for dyeing wool, silk, leather, cotton and fungicide, ectoparasiticide in aquaculture and fisheries (Khataee et al., 2009a). As it has been reported previously, biological removal of MG in the presence of different microorganisms is dependent on various parameters such as initial dye concentration, pH, reaction time and amount of biomass (Daneshvar et al., 2007a,b; Jadhav and Govindwar, 2006; Khataee et al., 2009a; Kumar et al., 2006; Parshetti et al., 2006; Sedighi et al., 2009). In most of the previous reports, conventional methods used to determine the influence of each one of these parameters. In the conventional methods, experiments were carried out varying systematically the studied parameter and keeping constant the others. This should be repeated to all the influence parameters, resulting in an unreliable number of experiments. In addition, this exhaustive procedure is not able to find combined effect of the effective parameters. In order to optimize the effective parameters with the minimum number of experiments, application of the experimental design methodologies can be useful. One of the experimental design techniques commonly used for process analysis and modeling is response surface methodology. Using RSM, it is possible to estimate linear, interaction and quadratic effects of the factors and a prediction model for the response. In this way, RSM designs could be used to find improved or optimal process settings in an efficient use of the resources (Zhang et al., 2009).
1876-1070/$ – see front matter ß 2010 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jtice.2010.03.007
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
In this work, the central composite design based on RSM has been applied to the optimization of biological treatment of the dye solution containing MG. The key factors (variables) investigated were the initial pH, initial dye concentration, algae amount and reaction time. UV–Vis spectra and FT-IR analysis was used to study the degradation of MG.
27
Table 1 Characteristics of Malachite Green. Chemical structure
[TD$INLE]
2. Experimental 2.1. Algal species and biological treatment operation The algal species was acquired from Azna-lake in the north of Iran. The algal species was washed with distilled water to remove macro/microscopic contaminations. According to its morphology and microscopic observations, it was identified as Cladophora species belongs to Charophyta (Fig. 1). The microscopic picture was taken by Olympus BX41 microscope (Japan). The triphenylmethane dye used in this study was Malachite Green whose characteristics have been reported in Table 1. The biological treatment experiments were performed in the Erlenmeyer flasks containing 250 ml of the synthetic dye solution and algal biomass under controlled temperature environment in the incubator (Sanyo, Ogawa Seiki Co., Japan). At appropriate reaction times of biological treatment process, samples were taken and the remaining MG was determined with a spectrophotometer (UV/Vis spectrophotometer WPA Lightwave S2000, England) at maximum absorption wavelengths, lmax = 619 nm and calibration curve. The color removal efficiency (CR(%)) was expressed as the percentage ratio of decolorized dye concentration to that of the initial one. The pH was adjusted using diluted NaOH and H2SO4 solutions and measured by pH meter (654 pH meter Metrohm, Switzerland). Fourier Transform Infrared (FT-IR) spectroscopy was performed on a Bruker Tensor 27 spectrometer, Germany. For FT-IR analysis, biological treatment process was performed with 250 ml solution containing 10 mg/l of MG and 4 g of algal biomass. At the reaction times of 0 h (control) and 24 h samples were taken and the biological degradation products were extracted with 30 ml of diethyl ether in three times, then crystallized and used for analysis. The algal cells were killed by autoclaving (1 kg f/cm2) for 20 min in Kavoush-megha Autoclave, Iran.
[(Fig._1)TD$IG]
C.I. number C.I. name Class Ionization lmax (nm)
42000 C.I. Basic Green 4 Triarylmethane Basic 619
2.2. Experimental design In the present study, central composite design, which is a widely used form of RSM was employed for the optimization of biological color removal efficiency. In order to evaluate the influence of operating parameters on the decolorization efficiency of MG, four main factors were chosen: the initial pH (X1), initial dye concentration (X2), algae amount (X3) and reaction time (X4). Totally of 31 experiments were employed in this work, including 24 = 16 cube points, 7 replications at the center point and 8 axial points. Experimental data were analyzed using the Minitab 15 software. For statistical calculations, the variables Xi were coded as xi according to the following relationship: xi ¼
Xi X0 dX
(1)
where X0 is the value of Xi at the center point and dX presents the step change (Aleboyeh et al., 2008; Kasiri et al., 2008). The experimental ranges and the levels of the independent variables for MG color removal are given in Table 2.
3. Results and discussion 3.1. Central composite design model CCD involves the following steps: performing the statistically designed experiments according to the design, factors and levels selected; estimating the coefficients of the mathematical model to predict the response and check its adequacy (Kasiri et al., 2008; Santos and Boaventura, 2008). The 4-factor CCD matrix and experimental results obtained in the biological color removal runs are presented in Table 3. The second-order polynomial response equation (Eq. (2)) was used to correlate the dependent and Table 2 Experimental ranges and levels of the independent test variables. Variables
Fig. 1. Light microscopic picture of Cladophora sp. used in this study.
Initial pH (X1) Initial dye concentration (mg/l) (X2) Algae amount (g) (X3) Reaction time (min) (X4)
Ranges and levels 2
1
0
+1
+2
3 5 1 45
4.5 7.5 2 60
6 10 3 75
7.5 12.5 4 90
9 15 5 105
[(Fig._2)TD$IG]
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
28
Table 3 The 4-factor central composite design matrix and the value of response function (CR(%)). Run
Initial pH
Initial dye concentration (mg/l)
Algae amount (g)
Reaction time (min)
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 29 30 31
1 1 1 2 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 2
1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 2 1 0 0 1 1 1 0 1 2 1 0 0 0
1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 2 1 1 1 2 1 0 1 0 0 0
1 1 1 0 0 1 1 0 2 1 2 1 1 0 0 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 0
Decolorization efficiency (%) Experimental
Predicted
41.33 34.74 38.86 14.00 48.36 42.25 31.34 53.39 57.80 19.36 36.34 64.78 55.19 49.96 51.31 55.33 17.00 46.24 26.00 48.54 35.18 79.91 44.00 28.53 69.40 56.29 42.99 69.22 52.40 50.30 54.00
37.96 38.36 42.15 12.93 50.61 48.04 31.93 50.61 55.77 21.61 36.07 64.96 51.77 50.61 50.61 53.94 18.41 49.46 28.85 50.61 34.28 73.74 40.25 32.35 68.01 55.58 37.48 64.48 50.61 50.61 58.17
Fig. 2. Comparison of the experimental results of decolorization efficiency with those calculated via central composite design resulted equation.
We used two lines to show the success of the prediction. The one is the perfect fit (predicted data equal to experimental data), on which all the data of an ideal model should lay. The other line is the line that best fits on the data of the scatter plot with equation Y = ax + b and it is obtained with regression analysis based on the minimization of the squared errors. The regression coefficient of this line is also presented (R2). An R2 closer to 1.0 indicates that the regression line perfectly fits the data. As can be seen, the predicted values match the experimental values reasonably well with R2 of 0.974. The obtained R2 values suggest good adjustments to the experimental results since these indicate that 0.974 of the variability in the response could be explained by the models. This implies that 97.4% of the variations for dye removal efficiency are
independent variables: Y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4 þ b12 x1 x2 þ b13 x1 x3 þ b14 x1 x4 þ b23 x2 x3 þ b24 x2 x4 þ b34 x3 x4 þ b11 x21 þ b22 x22 þ b33 x23 þ b44 x24
(2)
where Y is a response variable of decolorization efficiency. The bi are regression coefficients for linear effects; bik the regression coefficients for quadratic effects; xi are coded experimental levels of the variables. Based on these results, an empirical relationship between the response and independent variables was attained and expressed by the following second-order polynomial equation: Y ¼ 50:6086 þ 12:8321x1 2:6213x2 þ 8:0613x3 þ 4:9112x4
Source of variations
Sum of squares
Degree of freedom
Adjusted mean square
F-value
Regression Residuals
6980.92 186.54
14 16
498.64 11.66
42.77
Total
7167.46
R2 = 0.9740, adjusted R2 = 0.9512.
Table 5 Estimated regression coefficients and corresponding t- and P-values from the data of central composite design experiments.
4:2331x1 x2 1:5793x1 x3 þ 0:3394x1 x4 0:9656x2 x3 0:3331x2 x4 þ 1:4794x3 x4 þ 0:8619x21 2:2119x22 þ 0:0106x23 1:0869x24
Table 4 Analysis of variance (ANOVA) for fit of decolorization efficiency from central composite design.
(3)
The decolorization efficiencies (CR(%)) have been predicted by Eq. (3) and presented in Table 3. These results indicated good agreements between the experimental and predicted values of decolorization efficiency. 3.2. Analysis of variance and residuals The regression coefficient (R2) quantitatively evaluates the correlation between the experimental data and the predicted responses. Experimental results and the predicted values obtained using model (Eq. (3)) are given in Fig. 2.
Coefficient
Parameter estimate
Standard error
t-Value
P-value
b0 b1 b2 b3 b4 b12 b13 b14 b23 b24 b34 b11 b22 b33 b44
50.6086 12.8321 2.6213 8.0613 4.9112 4.2331 1.5793 0.3394 0.9656 0.3331 1.4794 0.8619 2.2119 0.0106 1.0869
1.2905 0.6970 0.6970 0.6970 0.6970 0.6385 0.6385 0.6385 0.6385 0.8536 0.8536 0.8536 0.8536 0.8536 0.8536
39.215 18.411 3.761 11.566 7.047 6.630 2.473 0.532 1.512 0.390 1.733 1.010 2.591 0.012 1.273
0.000 0.000 0.002 0.000 0.000 0.000 0.025 0.602 0.150 0.701 0.102 0.328 0.020 0.990 0.221
[(Fig._3)TD$IG]
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29
Fig. 3. Residual plots for biological decolorization efficiency of MG.
explained by the independent variables and this also means that the model did not explain only about 2.6% of variation. Adjusted R2 (Adj-R2) is also a measure of goodness of a fit, but it is more suitable [(Fig._4)TD$IG] comparing models with different numbers of independent for
Fig. 4. The response surface and contour plots of the decolorization efficiency (%) as the function of initial dye concentration (mg/l) and amount of algae (g).
[(Fig._5)TD$IG]
Fig. 5. The response surface and contour plots of the decolorization efficiency (%) as the function of reaction time (min) and amount of algae (g).
30
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
variables. It corrects the R2-value for the sample size and the number of terms in the model by using the degrees of freedom on its computations, so if there are many terms in a model and not very large sample size, Adjusted R2 may be visibly smaller than R2 (Box and Behnken, 1960; Santos and Boaventura, 2008). Here, adjusted R2 value (0.9512) was very close to the corresponding R2 value. Table 4 shows the results of the quadratic response surface model fitting in the form of analysis of variance (ANOVA). ANOVA is required to test the significance and adequacy of the model (Harrelkas et al., 2009; Liu and Chiou, 2005). ANOVA subdivides the total variation of the results in two components: variation associated with the model and variation associated with the experimental error, showing whether the variation from the model is significant or not when compared with the ones associated with residual error (Santos and Boaventura, 2008). This comparison is performed by the F-value, which is the ratio between the mean square of the model and the residual error. If the model is a good predictor of the experimental results, F-value should be greater than the tabulated value of the F-distribution for a certain number of degrees of freedom in the model at a level of significance a. Fratios obtained, 42.77, is clearly greater than the tabulated F (2.352 at 95% significance) confirming the adequacy of the model fits. The student t distribution and the corresponding values, along with the parameter estimate, are given in Table 5. The P-values were used as a tool to check the significance of each of the coefficients, which, in turn, are necessary to understand the pattern of the mutual interactions between the test variables. The
[(Fig._6)TD$IG]
larger the magnitude of t-value and smaller P-value, the more significant is the corresponding coefficient (Zarei et al., 2010). In addition to regression coefficient, the adequacy of the models was also evaluated by the residuals (difference between the observed and the predicted response value). Residuals are thought as elements of variation unexplained by the fitted model and then it is expected that they occur according to a normal distribution. Normal probability plots are a suitable graphical method for judging the normality of the residuals. The observed residuals are plotted against the expected values, given by a normal distribution (see Fig. 3). The residuals from the analysis should be normally distributed. In practice, for balanced or nearly balanced designs or for data with a large number of observations, moderate departures from normality do not seriously affect the results. The normal probability plot of the residuals should roughly follow a straight line. Trends observed in Fig. 3, reveal reasonably well-behaved residuals. 3.3. Effect of key factors as surface and counter plots In order to gain insight about the effect of each variable, the three dimensional (3D) and contour (2D) plots for the predicted responses were formed, based on the model polynomial function to analyze the change the response surface. The surface and contour plots of the quadratic model with two variables kept constant and the other two varying within the experimental ranges are shown in Figs. 4–9. Response surface plots provide a method to predict the decolorization efficiency for different values of the tested variables
[(Fig._7)TD$IG]
Fig. 6. The response surface and contour plots of the decolorization efficiency (%) as the function of reaction time (min) and initial dye concentration (mg/l).
Fig. 7. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and initial dye concentration (mg/l).
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
and the contours of the plots help in identification of the type of interactions between these variables (Li et al., 1996). In Fig. 4, the response surface and contour plots were developed as a function of initial dye concentration and amount of algae while the initial pH and reaction time were kept constant at 6 and 75 min, respectively, being the central levels. The increase in dye removal efficiency with increasing amount of algae has been observed (Figs. 4 and 5). The reason of this observation is thought to be the fact that increase of biomass of algae gives more surface area for sorption of the dye molecules on the surface of algae (Khataee et al., 2009a). As can be seen in Figs. 4, 6 and 7, biological decolorization efficiency increases with an increase in initial dye concentration, reaches the higher value and then decreases. The reason of this observation is thought to be the fact that initial dye concentration provides an important driving force to overcome all mass transfer resistances of the dye between the aqueous and solid phases. Hence, increasing initial concentration of dye may enhance the decolorization efficiency. But, when all dye molecules are adsorbed on constant amount of algae, the addition of higher quantities of MG molecules would have no effect on the decolorization efficiency (Daneshvar et al., 2007a; Khataee et al., 2009a). In addition, the response surface and contour plots in Figs. 5–7 clearly show that dye removal efficiency increased with increasing reaction time. The effect of initial pH on dye removal efficiency as response surface and contour plots is illustrated in Figs. 7–9. Previously, several researches proved that biological processes using algae were highly pH dependent (Daneshvar et al., 2007b; Jinqi and
31
Houtian, 1992). As can be seen in these figures, the decolorization efficiency was rapidly increased with increasing initial pH of the dye solution. This can be explained on the basis of zero point of discharge for biomass. In general, an isoelectric point of around pH 3–4 is determined for all algal species (Crist et al., 1981; Donmez et al., 1999; Kumar et al., 2006). According to the zero point of charge of algae species, their surfaces are presumably positively charged in acidic solution and negatively charged in alkaline solution. Since MG is a cationic dye, the alkaline solution favors adsorption of it onto algae surface, thus the decolorization efficiency increases. The similar observations were previously reported for removal of Malachite Green using Pithophora sp., Cosmarium sp. and Chlorella sp. (Daneshvar et al., 2007b; Khataee et al., 2009a; Kumar et al., 2006). 3.4. Response optimization and confirmation The main objective of the optimization is to determine the optimum values of variables for biological decolorization using Cladophora sp. from the model obtained using experimental data. The desired goal in term of decolorization efficiency was defined as ‘‘maximize’’ to achieve highest treatment performance. The optimum values of the process variables for the maximum decolorization efficiency are shown in Table 6. After verifying by a further experimental test with the predicted values, the result indicated that the maximal decolorization efficiency was obtained
[(Fig._9)TD$IG]
[(Fig._8)TD$IG]
Fig. 8. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and reaction time (min).
Fig. 9. The response surface and contour plots of the decolorization efficiency (%) as the function of initial pH and amount of algae (g).
32
[(Fig._1)TD$IG]
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Table 6 Decolorization efficiency at optimum values of the process parameters. Parameter
Optimum value
Initial pH Initial dye concentration (mg/l) Algae amount (g) Reaction time (min) Predicted decolorization efficiency (%) Observed decolorization efficiency (%)
8 10 4 75 70.57 71
when the values of each parameter were set as the optimum values, which was in good agreement with the value predicted from the model. It implies that the strategy to optimize the decolorization conditions and to obtain the maximal decolorization efficiency by CCD for the biological removal of MG in the presence of macroalgae Cladophora sp. is successful. 3.5. UV–Vis and FT-IR spectra changes Fig. 10 shows a typical time-dependent UV–Vis spectrum of MG solution during biological decolorization under the optimized conditions. The absorbance peaks corresponding to the dye diminished indicating that the dye had been removed. The spectrum of MG in visible region exhibits a main peak with a maximum at 619 nm. The decrease of absorbance peak of MG at l = 619 nm in this figure indicated a rapid degradation of the dye. According to the pervious literature (Chang and Kuo, 2000; Chen et al., 2003), biological decolorization of dyes can be due to adsorption onto biomass or biodegradation. In the adsorption process, all peaks of the dye, in UV–Vis region, decrease approximately in proportion to each other. However, if the dye removal is attributed to biodegradation, either the major visible light absorbance peak will disappear or a new peak will appear. As seen in Fig. 10, the main absorbance peak approximately disappeared within 24 h. Therefore, this process seems to be a biodegradation. Fig. 11 illustrates FT-IR spectra of MG biodegraded by Cladophora sp. before and after biological treatment process under optimized conditions. FT-IR spectrum of Malachite Green before removal showed the specific peaks in fingerprint region (2500–500 cm1) for the mono-substituted and para-disubstituted benzene rings which is supporting to the peak at 1585 cm1 for the C5 5C stretching of the benzene ring. Also the peak at 1170 cm1 for the C–N stretching vibrations and peak at 2923 cm1 for C–H stretching of asymmetric –CH3 group gives the perception of structure of MG. The FT-IR spectrum of extracted product at 24 h reaction time showed that
Fig. 11. FT-IR spectra of MG (10 mg/l) biodegraded by Cladophora sp. before treatment (0 h) and after 24 h of biological treatment. T = 25 8C under the optimized conditions.
most of the peak has been disappeared or significantly diminished. Remarkable variations in the fingerprint region (2500–500 cm1) of the FT-IR spectroscopy of MG before removal and after 24 h indicate [(Fig._12)TD$IG] biodegradation of MG by Cladophora sp.
[(Fig._10)TD$IG]
Fig. 10. UV–Vis spectra of MG (10 mg/l) biodegraded by Cladophora sp. at times: (1) 0 h, (2) 2 h, (3) 7 h, and (4) 24 h. T = 25 8C under the optimized conditions.
Fig. 12. (a) Comparison of live and dead algal biomass in removal of MG; (b) biological decolorization profiles during repeated-batch operations. T = 25 8C under the optimized conditions.
A.R. Khataee, G. Dehghan / Journal of the Taiwan Institute of Chemical Engineers 42 (2011) 26–33
3.6. Comparison of live and dead biomass efficiency with reusability of live algae Biological treatment of MG solution by live and dead algae has been compared in Fig. 12(a). The algal cells were killed by autoclaving (1 kg f/cm2) for 20 min. As can be seen, the dye removal efficiency using live algae is higher than that of dead biomass. The reason for this observation is thought to be the fact that the biodegradation of MG was occurred in the presence of the live algae biomass, while the dye removal in the presence of the dead biomass was attributed to adsorption process. In order to confirm this claim, repeated-batch operations were performed to examine the reusability of live Cladophora sp. in MG decolorization process. As can be seen in Fig. 12(b), during five repeated runs, Cladophora sp. showed the same decolorization efficiency that obtained from the first run. The results indicated that Cladophora sp. possessed reasonable reusability in repetitive decolorization operations. On the basis of these findings, it can be concluded that the treatment of MG solution by live Cladophora sp. is biological degradation process. Chang and Kuo (2000) performed repeated-batch decolorization of C.I. Reactive Red 22 by Escherichia coli NO3. Their results showed that, after the first batch run, the decolorization rate of E. coli NO3 increased dramatically for runs 2–5. This might be attributed to biological degradation and an adaptation effect, since E. coli NO3 cells were repeatedly exposed to the azo dye. 4. Conclusions In the present study, the performance of macroalgae Cladophora sp. in biological decolorization MG solution was studied focusing on the influence of key factors such as the initial pH, initial dye concentration, algae amount and reaction time by using RSM with CCD. Analysis of variance showed a high coefficient of determination value (R2 = 0.974), ensuring a satisfactory adjustment of the second-order regression model with the experimental data. The optimum values of the initial pH, initial dye concentration, algae amount and reaction time were found to be 8, 10 mg/l, 4 g and 75 min, respectively. Under the optimized conditions, the experimental values agreed with the values predicted by the ridge analysis. These results implicate that the optimization using a response surface methodology based on the central composite design can save the time and effort by the estimation of the optimum conditions of the maximum removal of dye. Comparison of live and dead biomass, repeated-batch operations and UV–Vis, FT-IR spectra of MG solution during biological decolorization revealed that this process was biodegradation. Acknowledgments The authors would like to thank the University of Tabriz for the financial support of this project. This paper reports the results of the project entitled ‘‘Optimization of biological treatment of dye solution containing Malachite Green in the presence of macroalgae Cladophora sp. using response surface methodology’’, which has been funded by research grants of University of Tabriz.
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