Optimization of phenol degradation by the microalga Chlorella pyrenoidosa using Plackett–Burman Design and Response Surface Methodology

Bioresource Technology 207 (2016) 150–156

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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech

Optimization of phenol degradation by the microalga Chlorella pyrenoidosa using Plackett–Burman Design and Response Surface Methodology S. Dayana Priyadharshini, A.K. Bakthavatsalam ⇑ National Institute of Technology, Tiruchirappalli 620015, India

h i g h l i g h t s
Degradation of phenol using Chlorella pyrenoidosa demonstrated.
Chlorella pyrenoidosa proved to be an efficient phenol degrading microalga.
The experimentation performed under ambient condition.
4 g/L of Chlorella pyrenoidosa degraded 97% of 0.8 g/L-phenol within 4 days.
3 significant factors identified- concentration of algae, phenol and reaction time.

a r t i c l e

i n f o

Article history: Received 12 December 2015 Received in revised form 25 January 2016 Accepted 31 January 2016

Keywords: Biodegradation Phenol Chlorella pyrenoidosa Central Composite Design Plackett–Burman Design

a b s t r a c t Statistical optimization designs were used to optimize the phenol degradation using Chlorella pyrenoidosa. The important factor influencing phenol degradation was identified by two-level Plackett– Burman Design (PBD) with five factors. PBD determined the following three factors as significant for phenol degradation viz. algal concentration, phenol concentration and reaction time. CCD and RSM were applied to optimize the significant factors identified from PBD. The results obtained from CCD indicated that the interaction between the concentration of algae and phenol, phenol concentration and reaction time and algal concentration and reaction time affect the phenol degradation (response) significantly. The predicted results showed that maximum phenol degradation of 97% could be achieved with algal concentration of 4 g/L, phenol concentration of 0.8 g/L and reaction time of 4 days. The predicted values were in agreement with experimental values with coefficient of determination (R2) of 0.9973. The model was validated by subsequent experimentations at the optimized conditions. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Phenol is one of the common organic pollutants released by various industries like petroleum refineries, plastic, paper & pulp, pharmaceuticals and coal processing. Phenol and its types are listed as priority pollutants by EPA – US Environmental Protection Agency (Agarry et al., 2008); Central Pollution Control Board (CPCB) of India norms for phenolic compounds in effluents is

Abbreviations: RSM, Response Surface Methodology; CCD, Central Composite Design; PBD, Plackett–Burman Design; mg/L, milligrams per Liter; ppm, parts per million; g/L, grams per Liter; BHEL, Bharat Heavy Electrical Limited; PTFE, Poly Tetra Fluoro Ethylene. ⇑ Corresponding author. Fax: +91 431 2501081. E-mail address: [email protected] (A.K. Bakthavatsalam). http://dx.doi.org/10.1016/j.biortech.2016.01.138 0960-8524/Ó 2016 Elsevier Ltd. All rights reserved.

0.5 mg/L (max). Hence, treatment of phenol and its derivatives present in industrial effluents being released to the environment assumes significance. Various conventional methods viz. physico-chemical methods, anaerobic digestion and biological methods are adopted for removal of phenol. Amongst various treatment methods, biodegradation is gaining importance as versatile, inexpensive and potential alternative to the conventional treatment methods (Annadurai et al., 2008; Sivasubramanian et al., 2015) for effective elimination of formation of toxic substances during the degradation process. The bioremoval of phenols by bacteria and fungi has been extensively studied (Patil and Jena, 2015).There has been recent increased interest in investigating the capabilities of algae for phenol degradation. The biodegradation of phenol by microalgae

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occurs only under aerobic conditions. While some algae have low tolerance to the acute toxicity of phenols, some others like cyanobacteria and eukaryotic microalgae (e.g., Chlorella sp., Scenedesmus sp., Selenastrum capricornutum, Tetraselmis marina, Ochromonas danica, Lyngbya gracilis, Nostoc punctiforme, Oscillatoria animalis, Phormidium foveolamm) are capable of biotransforming phenolic compounds (Al-khalid and El-Naas, 2012). The phenol remediation ability of algae coupled with potential applicability of the spent biomass as a biofuel feedstock and animal feed makes it a potential candidate for an environmentally sustainable process (Das et al., 2015; Komolafe et al., 2014). Anabaena cylindrica degrades 2,4-dinitrophenol completely (Hirooka et al., 2006). Klekner and Kosaric (1992) reported that Chlorella sp., Scenedesmus obliquus and Spirulina sp., degraded phenol completely. Chlorella vulgaris and Coenochloris pyrenoidosa were found to degrade pchlorophenol supplemented with zeolite up to 150 ppm (Lima et al., 2004). However, no previous work has been reported on optimization of phenol degradation using algae. The degradation potential of microorganisms on toxic compounds is strongly influenced by different factors such as nutrients, pollution load and physico-chemical cultivation conditions. The experimental factors influencing bioremediation need to be optimized for maximum removal efficiency. Biodegradation of phenol is sensitive to many factors which could affect the degradation ability/metabolism of microorganisms by preventing or stimulating growth of the organisms. These factors include inoculum size, pH, temperature, carbon and nitrogen sources and pollution load. Optimization of such nutritional and physical parameters is of primary importance for effective biodegradation by the selected organism especially when concentration of phenol is high. Optimized factors would enhance the degradation efficiency and significantly reduce the process time and cost. Therefore, it is necessary to design the process so as to maximize phenol degradation by the chosen alga viz. Chlorella pyrenoidosa. A number of modeling and optimization methodologies are available ranging from simple models like one factor at a time (OFAT) to complex statistical designs such as Plackett–Burman Design (PBD), Central Composite Design (CCD), and Box–Behnken Design (BBD) (Silveira et al., 2015; Amara and Salem, 2010). The one-factor-at-a time (OFAT) approach is laborious, time consuming and less capable of finding true optimum levels due to the interactions among factors (Jo et al., 2008). On the other hand, statistically designed experiments could effectively solve such issues and minimize the error in determining the effect of factors and interaction between factors (Sahoo et al., 2011; Suhaila et al., 2013). The design of experiment (DOE) offers reduced number of experiments and increased process efficiency (Saqib et al., 2012; Sivasubramanian et al., 2015). Statistical experimental designs such as PBD and CCD have been successfully applied to optimize many bioprocesses (Elboughdiri et al., 2015; Jin et al., 2013; Lakshmikandan et al., 2014; Liu et al., 2011; Lopez-Linares et al., 2015; Saqib et al., 2012; Suhaila et al., 2013). Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques that is applied for the modeling and analysis of various processes in which the response of interest is influenced by several factors and the response is optimized (Jabeen et al., 2015; Lakshmikandan et al., 2014). Central Composite Design (CCD) used in RSM, is more advantageous over the classical approach in terms of the information gained and the accuracy of the experiment conducted (Ahmadi et al., 2006). Obtaining informative results depends on finding the effects between key parameters of the particular process and the combined effects of these controllable factors. To achieve the same, residual plots of the CCD are used to check the uniformity of the error distribution

and the adequacy of the proposed model. The regression model obtained from the least squares technique provides a reasonable explanation for the effect of each individual factor and for all possible relationships that exist between the dependent and independent variables. Accordingly, the objective set out for the present work was to optimize the variables using statistical techniques for achieving enhanced phenol degradation by the microalga C. pyrenoidosa and further validate the predicted response with actual experimentation. 2. Methods 2.1. Materials All chemicals used in this study were of the highest purity grade purchased from Sigma–Aldrich/Merck-India. 2.2. Organism and cultivation A culture of targeted algal species was isolated from the effluent treatment plant of Bharat Heavy Electrical Limited (BHEL) located at Trichy and characterized at molecular level (18S rRNA) as C. pyrenoidosa. C. pyrenoidosa exhibited fast growth under ambient conditions with high phenol content and further cultivated in an optimized production medium (potassium bicarbonate: urea in the ratio of 2:1). A rectangular plastic transparent trough with a capacity of 40 L was used for cultivation of seed culture under ambient condition and its growth was monitored by analyzing absorbance at 600 nm using UV-vis Spectrophotometer (UV–vis Spectroquant Pharo 300 Spectroquant, Merck Millipore). The pH of the culture was maintained in the range of 7–8. The culture environment was kept in a closed room with sufficient openings (windows and doors) for natural light and air. No artificial lamps were used for light source. Seed culture was always maintained in logarithmic phase. 2.3. Experimentation The optimization experiments were performed in 250 ml Erlenmeyer flasks; for optimization studies, algal concentration, phenol concentration, urea, potassium bicarbonate and reaction time were varied according to the PBD of experiments (Table 1). The wet biomass of C. pyrenoidosa used in the optimization study was prepared by centrifuging 15 day old culture of C. pyrenoidosa at 10000 g for 10 min and washed with distilled water for removing adsorbed nutrients, if any. The initial pH of the samples was adjusted to 7.5 using 30% NaOH prior to autoclaving. Phenol was sterilized separately by 0.45 lm PTFE membrane filter and added to the sterilized medium at room temperature. The experiments were carried out at ambient conditions. During the cultivation, 10 mL of culture samples were withdrawn every day for analysis. The concentration of phenol was analyzed by using Indian Standard procedure IS3025 part No. 43.

Table 1 Levels of the factors tested in Plackett–Burman Design. Factors

Algal concentration (Wet biomass) (g/L) Phenol concentration (g/L) Urea (g/L) Potassium bicarbonate (g/L) Reaction time (days)

Symbol

A B C D E

Experimental value Low (1)

High (+1)

2 0.8 0.1 0.5 1

4 1.6 0.5 1 5

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The percentage of phenol degradation (degradation efficiency or biodegradation yield) was calculated using Eq. (1):

ðC i  C f =C i Þ 100

ð1Þ

where, Ci and Cf are initial and final concentration of phenol respectively. 2.4. Design of experiments and statistical analysis 2.4.1. Plackett–Burman Design The Plackett–Burman Design (PBD) is an efficient screening method to identify the important factors among large number of factors that influences a process (Patil and Jena 2015; Ungureanu et al., 2015). PBD was used to select the significant factors out of five factors considered in this study which influence phenol degradation by C. pyrenoidosa. For mathematical modeling the following first-order polynomial model was used (Eq. (2)):

Y ¼ b0 þ

X

b i xi

ð2Þ

where Y is the predicted response (percentage of phenol degradation), b0 is the model intercept and bi is the linear coefficient and xi is the level of the independent variable. Five factors (independent variables) including physical and nutritional factors viz. (i) algal concentration (wet biomass) (A); (ii) concentration of phenol (B); (iii) concentration of urea (C); (vi) concentration of potassium bicarbonate (D) and (v) reaction time (E) were investigated to identify the significant factors for the phenol degradation (response) at 95% confidence level. In this study, a 12 run Plackett–Burman Design was used to evaluate the five factors. Each independent variable was evaluated at two levels: 1 for the low level and +1 for high level. Determination of phenol concentration (response) is average of three trials. The experimental design of PBD (factors and tested range) is shown in Table 2. The factors with confidence levels of more than 95% (P < 0.05) were considered to have significant effect on phenol degradation and were considered for further optimization using Response Surface Methodology (RSM). 2.4.2. Central Composite Design (CCD) of experiments and Response Surface Methodology (RSM) After selection of three significant factors using PBD, a factorial CCD and RSM were performed to get information about the significant effects and the interactions between the selected factors with positive influence on degradation of phenol and to identify the

Table 2 Plackett–Burman Design of factors (in coded levels) with phenol degradation as response. Run order

1 2 3 4 5 6 7 8 9 10 11 12

A

1 1 1 1 1 1 1 1 1 1 1 1

B

1 1 1 1 1 1 1 1 1 1 1 1

C

1 1 1 1 1 1 1 1 1 1 1 1

D

1 1 1 1 1 1 1 1 1 1 1 1

E

1 1 1 1 1 1 1 1 1 1 1 1

Phenol degradation (%) Experimental value

Predicted value

93.4 68.7 82.7 78.1 79.4 60.8 95.7 86.7 79.4 81.7 62.9 92.6

92.3 67.1 83.6 76.9 79.9 62.3 98 84.6 80.2 81.4 63.6 92.1

A – Algal concentration (Wet biomass) (g/L); B – Phenol concentration (g/L); C – Urea (g/L); D – Potassium bicarbonate (g/L); E – Reaction time (days)

optimal value of each variable that would degrade phenol to the maximum. In this study, a three factor, five levels CCD with 20 runs was employed. The three factors selected from PBD, for further optimization were algal concentration (g/L), phenol concentration (g/L) and reaction time (days) which were denoted as X1, X2 and X3 respectively. Each factor was assessed at five different levels: combining factorial points (1,+1); axial points (a, +a) and central point (0) (Table 3). Phenol degradation was analyzed by using a second order polynomial equation and the data was fitted by multiple regression procedure. The mathematical relationship of the response (Y) to the significant independent variables viz. X1, X2 and X3 is given by the following quadratic polynomial equation:

Y ¼ b0 þ b1 X 1 þ b2 X 2 þ b3 X 3 þ b12 X 1 X 2 þ b13 X 1 X 3 þ b23 X 2 X 3 þ b11 X 21 þ b22 X 22 þ b33 X 23

ð3Þ

where Y is the response (percentage phenol degradation); X1, X2 and X3 are significant independent variables; b1, b2, b3 are linear regression coefficients; b11, b22, b33 are quadratic regression coefficients; b12, b13, b23 are interactive regression coefficients; b0 is a constant term. 2.4.3. Statistical analysis Statistical analysis of the model was performed to evaluate the analysis of variance (ANOVA). Analysis includes Fisher test (F-test), its associated probability P(F) and the coefficient of determination (R2) which measure the goodness of fit of the regression model. The response surface and contour plots of predicted responses of the model were used to assess the interaction between the significant factors. 2.5. Data analysis MinitabÒ 17.1, PA, USA software was used for designing experiments as well as for regression analysis of the experimental data obtained. 3. Results and discussion 3.1. Screening of significant factors affecting the degradation of phenol using Plackett–Burman Design (PBD) The impact of the five factors considered in this study on phenol degradation was statistically analyzed using PBD, The results (Table 4) showed that the effect of algal concentration (A), phenol concentration (B) and reaction time (E) have a positive influence on degradation of phenol and hence were included for the next optimization stage. The concentration of urea (C) and potassium bicarbonate (D) were found to have no significant effect on phenol biodegradation. The factors above 95% confidence level was considered as significant and included for the next stage of optimization. The model equation for phenol degradation (%) (Y) could be written as:

Y ¼ 80:175 þ 8:242A  6:642B  0:575C  0:075D þ 2:308E

ð4Þ

Table 3 Levels of the factors tested in Central Composite Design (CCD). Factors

Symbol

Algal concentration (g/L) Phenol concentration (g/L) Reaction time (days)

X1 X2 X3

Coded level a

1

0

1

a

1 0.4 1

2 0.8 2

3 1.2 3

4 1.6 4

5 2 5

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Term constant A B C D E

Table 7 ANNOVA for CCD.

Effect

Coefficient

Standard error

T-value

P-value

Source

DF

Adj SS

Adj MS

F-value

P-value

16.483 13.283 1.15 0.15 4.617

80.175 8.242 6.642 0.575 0.075 2.308

0.529 0.529 0.529 0.529 0.529 0.529

151.5 15.57 12.55 1.09 0.14 4.36

0.000 0.000 0.000 0.319 0.892 0.005

Model Linear X1 X2 X3 Square X1 * X1 X2 * X2 X3 * X3 2-Way Interaction X1 * X2 X1 * X3 X2 * X3 Error Lack of fit Pure error Total

9 3 1 1 1 3 1 1 1 3 1 1 1 10 5 5 19

5884.08 2062.52 772.71 331.38 958.44 2481.18 1026.18 141.65 1661.22 1340.37 406.13 528.12 406.13 15.85 10.44 5.41 5899.93

653.79 687.51 772.71 331.38 958.44 827.06 1026.18 141.65 1661.22 446.79 406.13 528.12 406.13 1.59 2.09 1.08

412.42 433.69 487.43 209.04 604.60 521.72 647.33 89.35 1047.92 281.84 256.19 333.15 256.19

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.93

0.244

R2 0.9859; Adjusted-R2 0.9742; Predicted-R2 0.9437; 95% significant level.

Table 5 ANNOVA for PBD (Plackett–Burman Design) model. Source

DF

Adj SS

Adj MS

F-value

P-value

Model Linear A B C D E Error Total

5 5 1 1 1 1 1 6 11

1412.42 1412.42 815.1 529.34 3.97 0.07 63.94 20.16 1432.58

282.484 282.484 815.101 529.341 3.968 0.067 63.941 3.361

84.05 84.05 242.53 157.5 1.18 0.02 19.03

0.000 0.000 0.000 0.000 0.319 0.892 0.005

Adj SS – Adjusted Sum of Square; Adj MS – Adjusted Mean Square; DF – Degrees of Freedom.

The R2 (coefficient of determination) value of 0.9859 indicated that up to 97.5% variability in phenol degradation could be calculated. The predicted R2 value of 0.9437 was in reasonable agreement with the adjusted R2 value of 0.9742. Experimental data was statistically analyzed using F-test for ANOVA and the results were presented in Table 5. The model F value of 84.05 implies that the model was significant and the ‘‘Model F-value’’ to occur due to noise was only 0.01%. P values (P (probability) > F) were used as a tool to check the significance of each parameter. Thus, P values less than 0.05 denoted the significance of the factors on the phenol degradation (Y).

Adj SS – Adjusted Sum of Square; Adj MS – Adjusted Mean Square; DF – Degrees of Freedom. R2 0.9973; Adjusted R2 0.9949; Predicted R2 0.985; 95% significant level.

considered for further optimization using CCD. The levels chosen for the factors were set based on the previous PBD analysis. Each variable was studied at five coded levels (a, 1, 0, 1, a) and all variables were taken at a central point of coded value zero. The matrix for CCD along with the experimental and predicted results is shown in Table 6. By applying multiple regression analysis on the experimental data, the following second order polynomial equation was obtained to describe the phenol biodegradation efficiency (Eq. (5)):

Yð% phenol degradationÞ ¼ 89:639 þ 7:522X 1  4:926X 2 þ 8:377X 3  8:438X 21  3:135X 22  10:736X 23  7:125X 1 X 2 þ 8:125X 1 X 3 þ 7:125X 2 X 3

ð5Þ

3.2. Mathematical modeling for process optimization The significant factors chosen from PBD viz. algal concentration (X1), phenol concentration (X2) and reaction time (X3) were

where Y is the predicted phenol degradation (%), X1-algal concentration (g/L), X2-phenol concentration (g/L) and X3-reaction time (days).

Table 6 Experimental design and results of CCD. Run order

X1

X2

X3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0 0 1.68179 (a) 0 1 1 0 1 0 0 0 0 1 0 1 1 1 0 1.681793 (a) 1

0 0 0 0 1 1 0 1 0 0 0 0 1 1.681793 (a) 1 1 1 1.68179 (a) 0 1

0 0 0 0 1 1 1.68179 (a) 1 0 0 1.681793 (a) 0 1 0 1 1 1 0 0 1

X1 – Algal concentration (g/L); X2 – Phenol concentration (g/L); X3 – Reaction time (days).

Phenol Degradation (%) Experimental value

Predicted value

90 91 52 90 40 51 45 71 89 90.1 72 88 65 71 87 77 97 89 78 55

89.6 89.6 53.1 89.6 39.2 50.7 45.2 69.4 89.6 89.6 73.4 89.6 64.5 72.5 86.4 77.5 96.3 89.1 78.4 54.6

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Amongst the three parameters, the reaction time (X3) was found to have the highest regression coefficient (8.377), followed by the concentration of algae (X1) (7.522) followed by phenol concentration (X2) (4.926). These three variables are considered since they have positive impact on phenol degradation under ambient conditions. The adequacy of the model was checked using ANOVA, as shown in Table 7. The ‘‘F-value” of the model was 412.42, lack of fit value 2.09 and the value of ‘‘prob > F00 < 0.0001, suggesting that the model was highly significant. Linear terms X1, X2, X3, and quadratic terms X21, X22 and X23 were significant for phenol degradation. Interactive terms X1X2, X2X3, and X1X3 were also significant for phenol degradation. The coefficient of determination (R2) was 0.9973 for phenol degradation, indicating good agreement between the experimental and the predicted values. The predicted R2 of 0.985 was in reasonable agreement with adjusted R2 of 0.9949.

3.3. Mutual interactions between the significant factors

3.4. Experimental validation of the model The statistical model was validated by experiments using 4 g/L of algal concentration, 0.8 g/L of phenol concentration for a period of 4 days of reaction time under ambient conditions. Under these optimized conditions, the predicted model response for phenol degradation was 97%, against the observed experimental value of 96.3% (average of three trials). The experimental value was close to the predicted value, thus validating the model. 3.5. Discussion Phenol was consumed as the sole carbon and energy source, for the growth of C. pyrenoidosa in the degradation study. Therefore,

Y-Phenol degradation (%)

X1-Algal concentration (g/L)

The optimum level of each variable and the effect of their interaction on phenol degradation were studied by constructing response surface plots and their corresponding contour plots (graphical representations of the regression model) (Figs. 1–3). The figures are based on Eq. (5) with one variable kept constant at its optimum level and varying the other two variables within the experimental range. The shape of the corresponding contour plot indicated if the mutual interaction between the independent factors was significant or not. As shown in Figs. 1–3, each response surface for phenol degradation indicated a clear peak, which means that the optimum point was inside the design boundary level. The effects of algal concentration (X1) and phenol concentration (X2) on phenol degradation while keeping reaction time (X3) constant at zero level was depicted in Fig. 1. The response surface plot showed that the optimum degradation of phenol could be attained while the algal concentration was high and the phenol concentration was low. The two dimensional contour plot showed uniformly elongated diagonal running pattern, suggesting that the interaction between algal concentration and phenol concentration was significant for phenol degradation.

The response surface plot and the corresponding contour plot of initial phenol concentration (X2) vs. reaction time (X3), keeping algal concentration (X1) at zero level is shown in Fig. 2. It could be noticed from the plots that the optimum phenol degradation (Y) was observed when phenol concentration was low (a) and reaction time (X3) to maximum (a). The two dimensional contour plot of phenol concentration vs. reaction time showed an elongated pattern, suggesting that the interaction between phenol concentration and reaction time was significant on Y. Phenol concentration and reaction time affects the phenol degradation (response) while keeping algal concentration at zero level. Fig. 3 showed that the effect of initial algal concentration (X1) and reaction time (X3) on phenol degradation while the other independent variable viz. phenol concentration (X2) was kept at zero level. The response surface plot and corresponding contour plot (Fig. 3) of algal concentration vs. reaction time showed that the increase of phenol degradation (Y) could be observed as algal concentration (X1) and reaction time (X3) increase to maximum levels. This is in agreement with the work reported by Zhou et al., 2011 and Ungureanu et al., 2015, wherein the inoculum size was the important factor for phenol degradation. The two dimensional contour plot showed an elongated running diagonal pattern, implying that the interaction between algal concentration and reaction time is significant on phenol degradation (Y). Algal concentration and reaction time affect the phenol degradation (response) when the initial phenol concentration was at zero level.

X2-Phenol concentration (g/L)

X2- Phenol concentration (g/L)

X1-Algal concentration (g/L) Fig. 1. Surface and contour plot showing interactions between algal concentration (X1) and phenol concentration (X2) on phenol degradation by Chlorella pyrenoidosa while reaction time (X3) at Zero level.

155

Y-Phenol degradation (%)

X3-Reaction time (days)

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X2-Phenol concentration (g/L)

X2-Phenol concentration (g/L)

X3-Reaction time (days)

Y-Phenol degradation (%)

X3-Reaction time (days)

Fig. 2. Surface and contour plot showing interactions between phenol concentration (X2) and reaction time (X3) on phenol degradation by Chlorella pyrenoidosa while algal concentration (X1) at Zero level.

X3-Reaction time (days) X1-Algal concentration (g/L)

X1-Algal concentration (g/L)

Fig. 3. Surface and contour plot showing interactions between algal concentration (X1) and reaction time (X3) on phenol degradation by Chlorella pyrenoidosa while phenol concentration (X2) at Zero level.

the production of any cell mass is a function of the exhaustion of phenolic compounds. The results of this study suggested that growth of algae in phenol rich effluent offers a new option of applying algal process in treatment of phenolic effluent to manage the pollution load of the aeration tank to which the centrate is returned. The application of statistical design strategy for screening and optimization of process parameters enables quick identification of the key factors and interactions between them. The novelty of the present work is statistical optimization of phenol degradation using C. pyrenoidosa and subsequent validation through experimentation. In the present study, Plackett–Burman Design methodology was found to be useful in studying the nutritional and physico-chemical factors which have a positive influence on phenol biodegradation. Statistical experimental design of experiments and mathematical modeling were applied to optimize the process of phenol degradation by the microalgae C. pyrenoidosa. Algal concentration, phenol concentration and reaction time proved to have a significant effect in phenol degradation. Interac-

tions between these factors were also analyzed in order to analyze the degradation process. The optimal phenol degradation conditions predicted from the RSM-CCD was validated by experimental results. This optimization study will encourage further use of this microalga in the treatment of effluents with high phenolic concentration. Moreover, the results suggest that the chosen statistical optimization strategy is an effective tool for the bioremediation of media contaminated with phenol. C. pyrenoidosa proves to be an efficient phenol degrading microalga under ambient conditions of Trichy, Tamilnadu, India. 4. Conclusion The primary goal of this study is to determine important factors influencing phenol degradation by C. pyrenoidosa and to achieve maximum phenol degradation. Among the five factors, PBD determines algal concentration, phenol concentration and reaction time as the significant factors influencing degradation of phenol by

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C. pyrenoidosa. The optimal phenol degradation conditions predicted from the RSM-CCD are as follows: 4 g/L, 0.8 g/L and 4 days for algal concentration, phenol concentration, and reaction time respectively. The model is validated experimentally. The removal efficiency of phenol by C. pyrenoidosa under these conditions is about 97%.

Acknowledgements Authors thank the National Institute of Technology, Trichy and the World Bank funded Technical Education Quality Improvement Program.

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