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JEB Journal of Environmental Biology
ISSN: 0254-8704 CODEN: JEBIDP
S.Sivasubramanian1* and S. Karthick Raja Namasivayam2 1
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Optimization of parameters for phenol degradation using immobilized Candida tropicalis SSK01 in batch reactor
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Department of Chemical Engineering, Sathyabama University, Chennai - 600 119, India 2 Department of Biotechnology, Sathyabama University, Chennai - 600 119, India *Corresponding Author E-mail:
[email protected]
Publication Info
Abstract
Bioremediation of phenol was done using Candida tropicalis SSK01 immobilized cells isolated from petroleum contaminated soil. Optimization of phenol degradation studies was carried at 30°C to 40°C, pH 6 to 8 and initial concentration of 300 mgl-1 to 900 mgl-1. Candida tropicalis SSK01cells immobilized using sodium alginate were used in phenol degradation studies. Optimization of phenol degradation was performed by Central Composite Design (CCD). A total of 20 experiments were carried out and the optimal degradation of 95.2% was observed at 34.20 ˚C at pH 6.86 with initial concentration of 610 mgl-1.The R2, adjusted R2 and Predicted R2 values were 0.9976, 0.9955 and 0.9919 respectively which indicates that experimental values are in good agreement with the predicted values.
Paper received: 04 August 2012 Revised received: 06 August 2013 Accepted: 27 September 2013
Key words
Introduction
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Bioremediation, Candida tropicalis SSK01, Immobilized cells, Phenol degradation
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Phenol is a common and important pollutant found in a variety of effluent streams from chemical industries such as resins, plastics, textiles, pulp and paper industries (Uberoi et al., 1997).Careful treatment of wastewater containing phenol and phenolic compounds is required before final discharge into the environment. Many technologies have been investigated for removal of phenol from industrial wastewater. They included adsorption (Carmona et al., 2006), enzymatic treatment (Mao et al., 2006), oxidation (Seredynska-Sobecka et al., 2005) and combined techniques (Sakurai et al., 2005).Using microorganism to degrade phenol is the most efficient and prevalent way for removal of phenol from industrial wastewater. Some members of yeast genera such as Candida, Rhodotorula, Trichosporon and others can metabolize phenolic compounds as a sole carbon and energy source (Sampio et al., 1999). Biological treatment of phenol has attracted great attention due to its environmental friendly approach and its ability to mineralize toxic organic compounds completely (Prpich et al., 2005). However, phenol degradation by microorganisms is generally limited by substrate (phenol) inhibition and low specific conversion rates (Lob et al., 2000). Various methods have been proposed to overcome substrate inhibition in order to treat high
concentration of phenol. They include adaptation of the cells to higher concentration of phenol (Loh et al., 1998) and immobilization of the cell (Viggiani et al., 2006). Immobilization of microorganism offers several advantages like protection of microorganisms from adverse environmental conditions, easier recycling of microorganisms and higher cell density (Kampf et al., 2002).
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In conventional experiments, optimization is usually carried out by varying a single factor while keeping all the other factors fixed at a specific set of conditions. This method requires a large number of experiments and is time consuming. Statistical experimental design can be used for optimization of process parameters in order to avoid the limitations of the classical method. Response surface methodology (RSM) is an effective statistical technique for designing such experiments, building models and investigating complex processes for optimization of target value (Rashid et al., 2009).
In the present study, optimization of phenol degradation was carried out by immobilized Candida tropacalisSSK01 cells. The optimization physical parameters have been carried to optimize parameters like temperature, pH and initial phenol concentration on phenol degradation. Journal of Environmental Biology, Vol. 35, 531-536, May 2014
S.Sivasubramanian and S.K.R. Namasivayam
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Phenol estimation : Concentration of phenol in samples was analyzed spectrophotmetrically using 4-aminoantipyrene method ASTM (1979) (Greenberg et al., 1972).The concentration of phenol was calculated from OD values using standards. Central composite design experiments : Central composite design (CCD) was adopted for optimizing the physical parameters for phenol degradation using immobilized Candida tropicalisSSK01 cells. The three variables temperature (X1), pH (X2) and initial phenol concentration (X3) was optimized. Each variable was studied at three different levels as shown in Table 1. All the variables at a central coded value were considered as zero. 23 factorial central composite experimental design leading to a set of 20 experimental runs was used to optimize phenol degradation.
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Microorganism and culture maintenance : Candida tropicalis SSK01 isolated from petroleum contaminated soil was used in this study. The strain was grown and maintained on nutrient agar slant at 4ºC. Inoculum was prepared by suspending the spores from slant. Candida tropicalis SSK01cells were grown in 250 ml Erlenmeyer flasks containing 100 ml medium containing glucose 1.0 mgl-1, yeast extract- 0.3 mgl-1, peptone - 0.5 mgl-1, malt extract 0.3 mgl-1 supplemented with 500 mgl-1 of phenol for adapting phenol degradation in further studies. The cell suspensions were aseptically transferred to experimental flasks for degradation studies. The phenol degradation studies were carried out in mineral medium containing : 2.0 mgl-1 NH4NO3, 0.01 mgl-1 CaCl2, 0.5 mgl-1 KH2PO4, 1.0 mgl-1 K2HPO4, 0.5 mgl-1 MgSO4, 0.1 mgl-1 KCl and 0.06 mgl-1 yeast extract, respectively.
supernatant was discarded. The pellet was taken and mixed aseptically into sodium alginate solution and dropped into 100 ml of 3% (w/v) CaCl2 solution with the help of a micropipette. The beads formed were stirred for 90 minutes to calcium alginate formation. Beads were collected, washed thoroughly with 25 mM Tris–acetate buffer (pH 7.5) to remove excess Ca2+ and stored in same buffer at 4ºC.
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Materials and Methods
Preparation of immobilized cells : 2% (w/v) of sodium alginate solution was prepared in 25 mM Tris–acetate buffer (pH 7.5) by stirring for 2 hr at room temperature and was stored at 4ºC. The inoculum was centrifuged at 10000 rpm for 20 min and Table 1 : Coded levels for independent factors used in the experimental design Symbol
Coded level -1
Temperature (ºC) pH Initial concentration of phenol in (mg l-1)
X1 X2 X3
30 6 300
0
+1
35 7 600
40 8 900
Phenol degradation studies : Optimization experiments were carried out in 250 ml Erlenmeyer flask containing 100 ml of minimal medium inoculated with immobilized Candida tropicalis SSK01 cells and maintained according to the experimental conditions given in Table 2. The flask was incubated 72 hr at 150 rpm. Then cell free supernatant obtained by centrifugation at
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Factor
Table 2 : Results of central composite design showing observed and predicted response for optimization of physical parameters
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Temperature °C
pH
40(1) 26.59(-1.681) 35(0) 43.41(1.681) 30(-1) 35(0) 35(0) 30(-1) 35(0) 40(1) 35(0) 30(-1) 30(-1) 35(0) 40(1) 35(0) 40(1) 35(0) 35(0) 35(0)
6 7(0) 7(0) 7(0) 6(-1) 7(0) 7(0) 6(-1) 7(0) 6(-1) 8.68(1.681) 8(1) 8(1) 7(0) 8(1) 7(0) 8(1) 5.32(-1.681) 7(0) 7(0)
Initial concentration of Phenol mgl-1
Experimental value (%)
Predicted value (%)
Residue
300((0) 600(0) 1104.54(1.681) 600(0) 300(-1) 600(0) 600(0) 900(1) 600(0) 900(1) 600(0) 900(1) 300(-1) 600(0) 300(-1) 600(0) 900(1) 600(0) 600(0) 95.46(-1.681)
76.5 86.3 86.5 81 84 95.6 94.6 83.5 94.5 78.54 75.7 77.8 80.3 95.8 78.5 94.5 78.9 78.8 95 82.5
75.789 86.961 87.321 82.102 83.174 96.894 93.68 83.047 93.359 77.568 76.449 77.151 79.912 97.536 77.594 93.359 78.366 79.814 94.965 83.443
-0.711 0.661 0.821 1.102 -0.826 1.294 -0.92 -0.453 -1.141 -0.972 0.749 -0.649 -0.388 1.736 -0.906 -1.141 -0.534 1.014 -0.035 0.943
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Run No
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Table 3 : Analysis of variance (ANOVA) for the fitted quadratic polynomial model for optimization of physical parameters Sum of squares
df
Mean square
F-value
p-value Prob > F
Model X1-Temperature X2-pH X3-Intial phenol concentration X1 X2 X1 X3 X2 X3 X12 X22 X32 Residual Lack of Fit Pure Error Cor Total
1025.05 35.68 10.99 0.083 17.29 3.7 1.66 244.03 586.19 301.59 2.45 0.79 1.66 1027.5
9 1 1 1 1 1 1 1 1 1 10 5 5 19
113.89 35.68 10.99 0.083 17.29 3.7 1.66 244.03 586.19 301.59 0.24 0.16 0.33
465.32 145.76 44.92 0.34 70.63 15.11 6.77 996.98 2394.87 1232.13
< 0.0001 < 0.0001 < 0.0001 0.5733 < 0.0001 0.0030 0.0264 < 0.0001 < 0.0001 < 0.0001
significant
0.47
0 .78370
not significant
5000 rpm for 15 min was assayed for phenol content. Results and Discussion
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R2 =0.997; Adj R2=0.9955; Pred R2 =0.9919
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Source
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Temperature has strong effect on phenol degradation as shown in Fig. 1(a). The percentage degradation of phenol increased from 70.4% to 96.7% as the temperature increased from 20⁰C to 35⁰C. Further increase in temperature from 40⁰C to 50⁰C decreased degradation percentage from 91.2% to 75% .This might be due to inhibition of enzyme secretion at higher temperature.So for optimizatation of phenol degradation was carried out at 30⁰C, 35⁰C and 40⁰C, respectively.
phenol degradation. The experimental data obtained from central composite design were analyzed by using response surface methodology. A second-order polynomial equation including all interaction terms was used to calculate the predicted response. The data were analyzed using Design Expert Software (version 8.0.0) and the coefficients were interpreted using F-test. Analysis of variance (ANOVA), regression analysis and 3-D plots were used to establish optimum conditions for percentage phenol degradation. The relationship between predicted and experimental values of phenol degradation is shown in Fig. 2. It was observed that there was a high correlation (R2=0.9994) between the predicted and experimental values of phenol degradation.
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The effect of pH on phenol degradation is shown in Fig. 1(b). Phenol degradation decreased as the medium pH deviated from neutral condition. The percentage degradation of phenol increased from 83.5% to 85.6% as pH increased from 5 to 6 and reached maximum of 96.5% at pH 7. Further, increase in pH from neutral condition decreased phenol degradation from 90% to 84.6%. The increase in pH from neutral condition significantly affected the biochemical reactions required for phenol degradation. The effect of initial concentration of phenol degradation was studied at an interval of 100 mgl-1 from 100 mgl-1 to 1000 mgl-1 as shown in figure 1(c).The percentage degradation of phenol increased from 92.68% to 95.15% with increase in initial concentration of phenol from 100 mgl-1 to 700 mgl-1. Further, increase in phenol concentration beyond 700 mgl-1 to 1000 mgl-1, phenol degradation decreased from 96.5% to 89% substantially resulting from intense substrate inhibition at high substrate concentration(Jing Bai et al., 2007). Central composite design (CCD) provides important information regarding the optimum level of each variable along with their interactions with other variable and their effect on
A regression model consisted of three linear term (X1, X2, X3), three quadratic term (X12, X22, X32),and three cross-interactions (X1X2, X1X3, X2X3) terms plus one block term. Multiple regression analysis (Analysis of Variance) was carried out considering full quadratic model on the responses to evaluate the adequacy of fit and results are reported in Table 3. The mathematical expression in coded term. related to phenol degradation with variables like temperature, pH and initial phenol concentration is shown below in equation 1.
Percentage degradation = 95.02-1.62X1-0.90X2-0.078X3+1.47X1 X2+0.68X1X3+0.45X2X3-4.11X12-6.38X22-4.57X32 .......... (1) The t and P-values were employed to evaluate the significance of each coefficient. Larger t-test value and smaller Pvalue suggests higher significance of the corresponding coefficient. The coefficient of determination (R2) was 0.997 indicating that the response model could explain 99.19 % of total variation. Value of R (0.9976 ) indicated high agreement between the experimental and predicted value. High value of adjusted determination coefficient (R 2 adj = 0.9955) indicated the significance of model.The results of ANOVA as shown in Table 3 Journal of Environmental Biology, May 2014
S.Sivasubramanian and S.K.R. Namasivayam
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(a)
86
7.00
20
30 Temperature (ºC)
40
50 6.50
(b)
90
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70
100
92
94
88
90
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Percentage degradation
88
7.50 80
60 10
95
86
84
6.00
30.00
90 85
96
80 0
2
4
6
8
32.00
34.00
36.00
38.00
X1 : Temperature
(b)
Percentage Degradation
89
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(c)
98 96 94 92
82
8.00
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90 88 0
40.00
10
pH 100 Percentage degradation
86 (a)
90
X2 : pH
Percentage degradation
100
100 200 300 400 500 600 700 800 900 1000 Initial phenol concentration
Fig. 1 : Optimium (a) temperature (b) pH and (c) initial phenol concentration for phenol degradation 100
40.00 35.00 X1 : Temperature
7.00 X2 : pH 6.00
30.00
Fig. 3 : (a) Contour and (b) Surface plot showing the interactive effect of temperature(X1) and pH(X2) on percentage degradation
100
indicates that predictability of model was at 99% confidence interval. A p value less than 0.0001 indicates that the model was statistically significant. P value lower than 0.05, indicates that the model was statistically significant. It was observed from Table 3 that the coefficients for linear terms except for initial phenol concentration term (p=0.5733), the square terms and the interaction terms were highly significant. The lack of fit (p=0.78370) was also not significant, confirming the applicability of the predicted model.
Fig. 2 : Graphical comparison between experimental and predicted percentage of phenol degradation
RSM allows investigation of interaction between parameters that influence the response of a process significantly. In this study, RSM was used to optimize and understand the
Predicted
95 90 85 80 75 70 70
75
80
85 90 Experimental
Journal of Environmental Biology, May 2014
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Phenol degradation using immobilized Candida tropicalis in batch reactor
825.00
825.00
(a)
750.00 675.00 92 600.00
90
525.00 94
450.00
88 86
90 300.00 32.00
34.00
36.00
38.00
90
525.00
94
450.00
88
92
86
86
6.00
96
(b)
88
600.00 90
375.00
Percentage Degradation
Percentage Degradation
675.00
40.00
X1 : Temperature
96
750.00
300.00
30.00
(a)
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375.00
84 86
83
88
X3 : Initial phenol concentration
X3 : Initial phenol concentration
900.00
90
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900.00
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6.50
7.00 X2 : pH
7.50
8.00
(b)
89
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89
82
900.00
40.00
35.00 X1 : Temperature
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600.00 X2 : Initial phenol concentration 300.00
30.00
82
900.00
8.00 7.00
600.00 X2 : Initial phenol concentration 300.00
X2 : pH 6.00
Fig. 4 : (a) Contour and 4.(b) Surface plot showing the interactions effect of Temperature (X1) and initial concentration of phenol (X3) on percentage degradation
Fig. 5 : (a) Contour and 7.(b) Surface plot showing the interactions effect of pH (X2) and initial concentration of phenol (X3) on percentage degradation
relationship between important parameters in phenol degradation. The relationship between most significant parameters was investigated by 2D contour and 3D surface plots. Contour plot is the projection of the response surface as two dimensional plane whereas 3D surface plot is a projection of the response surface in three dimensional plane (Box et al., 1957). The 3D response surface is a three dimensional graphic representation used to determine the individual and cumulative effect of the variable and the mutual interaction between variable and the dependent variable. The response surface analyzes the geometric nature of the surface, the maxima and minima of the response and the
significance of the coefficients of the canonical equation. The polynomial response surface model obtained may be maximized or minimized to obtain the optimum points. Whereas, a contour plot is a graphical technique for representing three dimensional surface by plotting constant z-slices called contours, on a two dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where z value occurs (Ravikumar et al., 2007). To investigate the individual and interactive effect of two factors on phenol degradation, three dimensional and contour plots were drawn with the help of Design Expert 8.0.0 software and the inferences obtained are discussed below.
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S.Sivasubramanian and S.K.R. Namasivayam References
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The interactive effect of two independent variables with another variable at fixed level on the phenol degradation with immobilized Candida tropicalis SSK01 was shown in 2-D plots and 3-D plots. Fig. 3 (a, b) shows the interactive effect of two variables temperature (X1) and pH (X2) in the range of 30–40 ºC and 6 -8 respectively on phenol degradation. The optimum level of phenol degradation occurs with 95.8% at temperature of 34.84ºC and 6.85 pH respectively. Further, increase in temperature decreases the rate of phenol degradation which might be due to decrease in effective reactivity of multi-enzyme complex system within the cell (Bandyopadhyay et al., 1998). The elliptical nature of the contour plot indicated that the mutual interaction between two independent variables temperature and pH was significant, which meant that the effect of phenol degradation was highly dependent on the level of temperature and pH used.
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Effect of two variables temperature (X1) and initial phenol concentration (X3) in the range of 30–40 ◦C and 300-900 mgl-1 on degradation is shown in Fig. 4.(a, b) respectively. The optimum level of 95.13% phenol degradation occured at 34.48 ◦C and initial phenol concentration of 608 mgl-1 respectively. The increase in initial phenol concentration inhibited the growth of Candida tropicalis SSK01 (Bandhyopadhyay et al., 2001; Ho et al., 2009). The elliptical nature of the contour plot indicated that the mutual interaction between two independent variables temperature and initial phenol concentration was significant. A rapid decrease in phenol degradation at higher initial phenol concentration might be due to substrate inhibition. Substrate inhibition was well known to predominate at a higher phenol concentration owing to its toxicity to cell.
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Fig. 5. (a, b) shows the interactive effect of pH (X2) and initial phenol concentration (X3) on percentage degradation. The optimum level of 95% degradation occured with at pH 6.92 and initial phenol concentration of 627.88 gl-1, respectively. Further, increase in pH significantly affected the biochemical reactions required for phenol degradation. The slightly elongated maxima and ellipse running along the initial concentration axis on respective plots point out the influence of initial phenol concentration was higher than the temperature.
Response surface methodology (RSM) was applied successfully for the optimization of operational conditions for phenol degradation. All three parameters, initial phenol concentration, temperature and pH had a significant effect on phenol degradation. A true functional relationship between independent variable like initial phenol concentration, temperature, pH and maximum percentage of phenol degradation was found out. A maximum degradation of 95.2% was observed at 34.20 ˚C and pH 6.86 with initial phenol concentration of 610 mg l1 . From the present study, it was observed that use of statistical optimization approach (RSM), had helped to identify the most significant operating factors and optimum levels for phenol degradation with minimum effort by Candida tropicalis SSK01. Journal of Environmental Biology, May 2014
