Optimization of Process Variables for the Batch Degradation of Phenol by Pseudomonas fluorescence Using Response Surface Methodology
In this study, the classical method of one-variable at a time bioprocess design and Response Surface Methodology (RSM) was performed to evaluate the effects of aeration, agitation and temperature on phenol degradation by Pseudomonas fluorescence. Experiments were performed as a function of temperature (25-45°C), aeration (1.0-3.5 vvm) and agitation (200-600 rpm). The results of the one-variable at a time bioprocess design showed that percent phenol degradation increased with increased aeration, agitation and temperature up to a value of 3.0 vvm, 300 rpm and 30°C, respectively. Above these respective values, the percent phenol degradation decreased. Furthermore, phenol biodegradation was optimized by 23 full-factorial central composite design. Statistical analysis of results revealed that the linear and quadratic terms of these variables had significant effects and evident interactions existing between the temperature and agitation were found to contribute to the response at a significant level. More also, full-factorial central composite design used for the analysis of treatment combinations gave a second-order polynomial regression model, which was in good agreement with experimental results, with R2 = 0.9647 (p<0.05). By response surface methodology and multistage Monte- Carlo optimization technique, the optimal degradation (fermentation) parameters for enhanced phenol degradation were obtained. The optimum process conditions for maximizing phenol degradation (removal) were recognized as follows: temperature 30°C, aeration 3.0 vvm and agitation 300 rpm and the model predicted a maximum percent phenol degradation of 60.7% at these optimum process conditions. This confirmed the closeness of the model to the experimental result of 60.8%.
Phenol and its derivatives is the basic structural unit in a wide variety of
synthetic organic compounds (Annadurai et al., 2000).
It is an organic, aromatic compound that occurs naturally in the environment
(Prpich and Daugulis, 2005), but is more commonly produced
artificially from industrial activities such as petroleum processing, plastic
manufacturing, resin production, pesticide production, steel manufacturing and
the production of paints and varnish (Mahadevaswamy et
al., 1997; Bandyopadhyay et al., 1998).
This aromatic compound is water-soluble and highly mobile (Collins
and Daugulis, 1997) and as such waste waters generated from these industrial
activities contain high concentrations of phenolic compounds (Chang
et al., 1998) which eventually may reach down to streams, rivers,
lakes and soil, which represent a serious ecological problem due to their widespread
use and occurrence throughout the environment (Fava et
Phenol is a listed priority pollutant by the US Environmental
Protection Agency (1979) and is considered to be a toxic compound by the
Agency for Toxic substances and Disease Registry (2003).
The adverse effects of phenol on health are well documented (Calabrese
and Kenyon, 1991) and death among adults has been reported with ingestion
of phenol ranging from 1 to 32 g (Prpich and Daugulis, 2005).
The low volatility of phenol and its affinity for water make oral consumption
of contaminated water the greatest risk to humans (Prpich
and Daugulis, 2005).
A variety of techniques have been used for the removal of phenol from industrial
effluents and contaminated waters with bioremediation receiving the most attention
due to its environmentally friendly, its, ability to completely mineralize toxic
organic compounds and of low-cost (Kobayashi and Rittman,
1982; Prpich and Daugulis, 2005). Microbial degradation
of phenol have been actively studied and these studies have shown that phenol
can be aerobically degraded by wide variety of fungi and bacteria cultures such
as Candida tropicalis (Ruiz-Ordaz et al.,
1998, 2001; Chang et al.,
1998); Acinetobacter calcoaceticus (Paller et
al., 1995); Alcaligenes eutrophus (Hughes
et al., 1984; Leonard and Lindley, 1998);
Pseudomonas putida (Hill and Robinson, 1975;
Kotturi et al., 1991; Nikakhtari
and Hill, 2006) and Burkholderia cepacia G4 (Folsom
et al., 1990; Solomon et al., 1994).
It has been demonstrated that treatment of small volumes of toxic compounds
at the point of emission using specific microbial strains and better bioreactors
allows a higher control over the process and higher removal efficiencies (Schroeder
et al., 1997). The high dependence of enzymatic activity and cellular
maintenance requirements on temperature makes it an important quantity. Temperature
exerts an important regulatory influence on the rate of metabolism (Ghosh
and Swaminathan, 2003). The effects of oxygen supply vary from species to
species (Onken and Liefke, 1989). In addition, oxygen
mass transfer is aided by agitation rates that create turbulence and shear forces
in the cultivation system causing significant influences on the growth rate
and product formation (Hoq et al., 1995).
In a mechanically agitated bioreactor, agitation increases the rate of mass
and heat transfer operations and provides the required degree of mixing of the
reactor contents. Insufficient agitation may lead to limitations in the transfer
operations and the appearance of regions of insufficient nutrient content or
inadequate temperature or pH (Gonzalez et al., 2003).
As a result, the overall productivity of the process will decline (Namdev
et al., 1994). Regarding process variables, aeration and agitation
rates are the most pertinent. The most adequate aeration must be carefully determined
so, to provide oxygen as demanded by the cell population, but avoiding excessive
flow rates that can lead to impellers flooding and cause unnecessarily high
operating costs. On the other hand, an intense agitation must be provided, but
too high agitation rates should be avoided to prevent attrition and metabolic
stress in the bacterial population (Toma et al.,
1991; Enfors et al., 2001; Gonzalez
et al., 2003). So, the problem of optimization of the agitation and
aeration conditions is complex and should be addressed using a statistically
based experimental design. Thus, optimization of process variables is recognized
to be an essential aspect of successful fermentation (Kumar,
This study examined the effect of temperature, aeration and agitation on the degradation of phenol by local strains of Pseudomonas fluorescence using both the classical method of optimization that involves varying the level of one parameter at a time over a certain range while, holding the rest of the variables constant and statistical optimization technique for multivariable effect.
MATERIALS AND METHODS
The microorganism, Pseudomonas fluorescence being an indigenous bacteria
strain isolated from an oil-polluted area in Niger-Delta region of Nigeria was
procured from the Department of Microbiology, Obafemi Awolowo University, Ile-Ife,
Nigeria. The study was conducted in 2008. The microorganism was maintained on
nutrient agar slant and stored at 4±1°C for further use.
Culture Medium and Inoculum
The mineral salt medium used was modified from the one suggested by Bettmann
and Rehm (1984). The medium had the following composition per litre: 700
mL deionized water, 100 mL buffer solution A, 100 mL trace elements solution
B, 50 mL solution C and 50 mL solution D. Compositions of each solution were
as follows: Buffer solution A composition K2 HPO4 1.0
g, KH2PO4 0.5 g, (NH4)2SO4
0.5 g, deionized water 100 mL. Trace element solution B composition NaCl 0.5
g, CaCl2 0.02 g, MnSO4 0.02 g, CuSO4.5H2O
0.02 g, H3 BO3 0.01 g, deionized water 50 mL. Solution
C composition MgSO4.7H2O 0.5 g, deionized water 50 mL,
solution D composition FeSO4 0.02 g, Molybdenum powder 0.02 g, deionized
water 50 mL. To prevent the precipitation of CaSO4 and MgSO4
in storage, the water, buffer solution A, trace elements solution B, solution
C and solution D were autoclaved at 121°C for 15 min. After cooling, all
the solutions were then mixed together and kept as stock solution from which
known quantities were taken for the cultivation of the microorganisms.
A primary culture was prepared by transferring two loops full of microorganisms from an agar slant culture into 100 mL of feed medium containing 20 mL of mineral salt medium and 80 mL of 50 mg phenol solution in a 250 mL Erlenmeyer conical flask. This was then, incubated in a New Brunswick gyratory shaker (G25-R model, N.J., USA) for 48 h at a temperature of 30°C and agitated with a speed of 120 rpm. Thereafter, 10 mL of the primary culture was transferred into another 100 mL of feed medium in a 250 mL Erlenmeyer conical flask and the incubation process was repeated. This was the secondary culture that was used as the inoculum for the degradation studies as this ensures that the organisms had fully adapted to growth on the phenol as sole source of carbon and energy.
To optimize the range of experimentation for the 23 full-factorial
central composite design, the following experiments were carried out in a New
Brunswick Microferm Twin Bioreactor (PH-22 model, N.J., USA) with 4 L working
volume. Eight hundred milliliter of the autoclaved mineral salt medium and 3
L of phenol solution (100 mg L-1) were measured into the bioreactor
vessel and 200 mL of the inoculum was introduced aseptically to make up 4 L
of working volume. The bioreactor was operated for 48 h at different temperature
(25, 30, 35, 40 and 45°C); aeration (1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 vvm)
and agitation rates (200, 300, 400, 500 and 600 rpm).
||Experimental range and the levels of the variables
||Coded and uncoded full-factorial central composite design
for the three independent variables
Culture broth was withdrawn at every 6 h for phenol determination. From the
above experiments, the range of temperature, aeration and agitation rates were
chosen for further optimization of the design.
Design of Experiments
Temperature (25, 30 and 35°C); aeration (2.5, 3.0 and 3.5 vvm) and agitation
(200, 300 and 400 rpm) that served as critical variables X1, X2
and X3 respectively are the range and levels of the process variables
under study (Table 1). The actual design of experiments is
given in Table 2 for each experiment that was carried out
in a New Brunswick Microferm Twin Bioreactor containing 800 mL mineral salt
medium, 3 L of phenol solution (100 mg L-1) and 200 mL of inoculum
that was operated for 48 h. Samples were withdrawn after 48 h for phenol determination.
The undegraded phenol was estimated quantitatively by the spectrophotometric
method using 4-amino antipyrene as color indicator (Yang
and Humphrey, 1975) at an absorbance of 510 nm.
Response Surface Methodology and Statistical Optimization
A full-factorial central composite design using response surface methodology
(Khuri and Cornell, 1987; Montgomery,
1991; Ghosh and Swaminathan, 2003; Preetha
et al., 2007; Cao et al., 2008) is
a powerful tool for understanding complex processes. The full-factorial central
composite design consists of (a) a complete 2k factorial design where,
k (= 3) is the number of tests variables, (b) no center points (no>1)
and (c) two axial points on the axis of each design variables at a distance
of α (α = 2k/4 = 1.682 for k = 3) from the design center
(Khuri and Cornell, 1987; Ghosh and
Swaminathan, 2003). Hence, the total number of design points is N = 2k
+ 2k + no and these data are fitted in a second order polynomial
model (Ghosh and Swaminathan, 2003). An orthogonal 23
full-factorial central composite design with 6 replicates (no = 6)
at the central point, all in duplicates resulting in total of 20 experiments
were used to optimize the chosen key variables that have effect on phenol microbial
degradation are given in Table 1. The variables were coded
according to the Eq. 1 (Box and Behnken,
1960; Box and Draper, 1959; Annadurai
et al., 2000).
where, xi, Xi and Xo are the coded value, uncoded value and the value at the center point respectively of the ith test variable and Δxi is the step change value. The full experimental design in coded and uncoded form is given in Table 2. The behavior of the system was explained by the following second order polynomial equation:
where, Y is predicted response, βo is offset term, βi is linear effects, βii is quadratic effects, βij is interaction effects. Xi (i = 1, 2, 3) and Xj (j = 1, 2, 3) (i≠j) are the process variables.
The graphical representation of these equations are called response surfaces
which was used to describe the individual and cumulative effects of the test
process variables on the response and to determine the mutual interactions between
the test variables and their subsequent effect on the response (Ghosh
and Swaminathan, 2003).
A statistical program package, Design Expert (Start-Ease Inc, Minneapolis, MN) was used for regression analysis of the data obtained and to estimate the coefficient of the polynomial equation.
RESULTS AND DISCUSSION
Classical Method of Optimization: Effect of Aeration, Agitation and Temperature
The effects of aeration rate, agitation rate and temperature on phenol degradation
by indigenous (local strains) Pseudomonas fluorescence were studied.
It could be seen that the rate of degradation (percent degradation) increased
with increase in aeration up to an aeration rate of 3.0 vvm, above which it
decreased (Fig. 1). It was observed that the biomass concentration
increased up to an aeration rate of 3.0 vvm and above that, it decreased. The
increase in degradation rate may be due to the fact that as aeration rate increased
more dissolved oxygen (i.e., higher mass transfer) were made available for the
metabolism of the organism, while the decrease may be due to toxic effect of
the excess air (oxygen). Oboirien et al. (2005)
and Hannaford and Kuek (1999) also gave a similar report
that biodegradation rate increased with increase in aeration rate when they
considered an aeration rate of 1.5 to 2.5 vvm and 4 to 16 vvm, respectively.
Oboirien and his co-workers used freely suspended Pseudomonas fluorescence
NCIB 3756 and a New Brunswick Microferm Twin Fermentor for their studies, while;
Hannaford and Kuek (1999) used an immobilized Pseudomonas
putida ATCC 11172 and a bubble column reactor for their studies. Collins
and Daugulis (1997) reported that aeration rate greater than 0.5 vvm produce
excessive foaming and led to solvent and cell losses. In this work, no foaming
was observed and this is in agreement with the observation of Oboirien
et al. (2005).
||Effect of aeration rate on phenol degradation by P. fluorescence
||Effect of agitation rate on phenol degradation by P. fluorescence
It may be seen that the rate of phenol degradation increased from an agitation
rate of 200 to 400 rpm and above 400 rpm the rate decreased (Fig.
2). It was observed that the biomass concentration increased up to an agitation
rate of 400 rpm and above that, it decreased. Sobczuk et
al. (2006) reported a similar observation. The increase in biodegradation
rate may be due to adequate high mass transfer thus allowing more oxygen to
be dissolved and made available for the metabolism of the organism. While, the
decrease may be due to higher shear stress effect thus leading to cell loss
or lower biomass concentration (Hoq et al., 1995).
However, the optimum agitation rate was found to be 300 rpm at which the percent
degradation was highest.
Also, it could be seen that the phenol degradation rate (percent degradation)
increased with increase in temperature up to 40°C above which, it decreased
(Fig. 3). In this study, it was observed that the biomass
concentration increased up to a temperature of 40°C and above that, it decreased.
The decrease in the rate of degradation may be due to decrease in the effective
reactivity of the multi-enzyme complex system within the cell. A similar observation
was reported by Bandyopadhyay et al. (1998) that
biodegradation rate of phenol by Pseudomonas putida MTCC 1194 increased
with increase in temperature from 15 to 30°C above which it decreased.
||Effect of temperature on phenol degradation by P. fluorescence
||Experimental and theoretically predicted values for degradation
of phenol by Pseudomonas fluorescence
However, from the same Fig. 3, it may be observed that the
highest percent degradation was at a temperature of 30°C and thus represent
the optimum temperature.
Optimization of Temperature, Aeration and Agitation Using Response Surface
The quantitative description of the process variables (physical condition)
effects on phenol microbial degradation was performed. The optimum level of
aeration, agitation and temperature are as shown in Fig. 1-3.
Aeration (2.5, 3.0, 3.5) vvm, agitation (200, 300, 400) rpm and temperature
(25, 30, 35)°C, were optimized using the 23 full factorial central
composite design of experiments. Response surface methodology is an empirical
modeling technique involved in the evaluation of the relationship of a set of
controlled experimental factors and observed results (Annadurai
et al., 2000).
||Regression Analysis for the degradation of phenol by P.
fluorescence, quadratic response surface model fitting (ANOVA)
||Coefficient of the model for P. fluorescence
The average percent phenol degraded at each fermentation run is summarized
and presented in Table 3 along with predicted values. The
experimental data were fitted to a second order polynomial regression model
containing 3 linear, 3 quadratic and 3 interaction terms using the same experimental
design software. The regression equation obtained after analysis of variance
gives the level of degradation of phenol as a function of the different process
variables: aeration, agitation and temperature. All terms regardless of their
significance are included in the following Eq. 3:
where, X1, X2 and X3 represent coded values of temperature, aeration and agitation respectively and Y is the response variable (maximum degradation of phenol).
The optimum coded and uncoded value was obtained by solving equation
3 analytically. The optimum coded and uncoded value of temperature was found
to be 0.01 and 30°C, respectively. While for aeration, it was obtained as
-0.01 and 3.0 vvm, respectively and for agitation, it was found to be 0.01 and
300 rpm, respectively at maximum percent age of phenol degradation 60.7%. The
optimum temperature for the biodegradation of phenol has generally been reported
to be 30°C (Bandyopadhyay et al., 1998; Polymenakou
and Stephanou, 2005).
The Analysis of Variance (ANOVA) of the regression model demonstrates that
the model is highly significant (Table 4), as is evident from
Fisher Ftest (Fmodel = 30.39) and a very low failure probability
(p = 0.0001). Moreover, the computed F value (F 0.01(9, 10) = 30.39)
was greater than the tabular F value (F = 4.94) at the 1% level indicating that
the treatment differences are highly significant. The value of R (= 0.9822)
indicates a high degree of correlation between the observed value and predicted
values. The value of the determination coefficient (R2 = 0.9647)
being a measure of goodness of fit to the model indicates that only about 4%
of the total variations are not explained by the model. The Coefficient of Variation
(CV) indicates the degree of precision with which the treatment is compared.
Usually the higher the value of CV, the lower is the reliability of the experiment
(Ghosh and Swaminathan, 2003). Here, the low value of
CV (= 5.98%) indicates a greater reliability of the experiment performed.
The coefficient of the model (parameter estimation) and the corresponding p-values
(Table 5) suggest that among the test variables, linear and
quadratic effect of aeration and agitation are highly significant.
||Phenol degradation by Pseudomonas fluorescence on 3-D
graphics for response surface optimization vs. temperature and aeration
||Phenol degradation by Pseudomonas fluorescence on 3-D
graphics for response surface optimization vs. temperature and agitation
The quadratic effect of temperature was more pronounced than linear effect.
These observations can be interpreted as a consequence of proportional relationship
between the variables and phenol degradation. The mutual effects of temperature
and agitation and, aeration and agitation are approximately of equal significance
than the other interaction. This data analysis also substantiates the inference
that can be drawn from three-dimensional contour plots (3-D graphics) as shown
in Fig. 4-6, respectively. The interactions
amongst temperature, aeration and agitation are quite prominent from the elliptical
nature of the respective contour plots. These figures also suggest the optimum
range of the process variables.
||Phenol degradation by Pseudomonas fluorescence on 3-D
graphics for response surface vs. temperature and agitation
||Factor plot representing the individual variables effect on
phenol degradation of Pseudomonas fluorescence
The factor effect function plot (Fig. 7) was used to assess
the effect of each factor graphically. From the trace plot as shown in Fig.
7, it can be seen that each of the three variables used in the present study
has its individual effect on phenol biodegradation by P. fluorescence.
Gradual increase in temperature, aeration and agitation rates from low level
(coded value-1) to a higher level (coded value +1) resulted in both increase
and decrease of phenol degradation. Moreover, it is also to be noted from Fig.
7 that over the range of agitation (200 to 400 rpm) the phenol degradation
changed in a wide range, which was not the case for temperature and aeration.
This clearly indicates that keeping temperature and aeration at the optimum
level a change in agitation affects the process more severely than done otherwise.
The optimum values were found by solving the regression equation analytically.
The highest phenol degradation that can be achieved according to the model prediction
under the optimal experimental conditions is 60.7%. The experimental results
indicated, a degradation of about 60.8% under the optimal process conditions.
This confirms the closeness of the model to the experimental results.
The present study showed the potential of the isolated indigenous P. fluorescence for phenol wastewater treatment. The performance of the indigenous strain in biodegradation of phenol in the nutrient medium is excellent. The response surface methodology using 23 full-factorial composite designs was adopted to optimize the process variables like temperature, aeration and agitation for the microbial degradation of phenol using a mechanically agitated bioreactor. The design generated may be used for designing a treatment plant for phenol waste effluents where, collection can be achieved on a large scale. The statistical analyses and the closeness of the experimental results and model predictions show the reliability of the regression model.
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