The Optimization, Kinetics Model, and Lab-Scale Assessments of Phenol Biodegradation Using Batch and Continuous Culture Systems

: Phenol was considered a severe hazard to all ecosystems even at low concentrations. The bioremediation process is an eco-friendly process for complete phenol degradation and bioelectricity generation. In the present study, a consortium of native isolates was used for phenol biodegradation and bioenergy generation using nano-graphite electrodes. The optimization of nutritional and environmental parameters using batch culture revealed that the optimum conditions for maximum phenol degradation and energy generation were inoculum concentration, 1%; incubation period, 48 h; phenol, 6 ppm; MgSO 4 , 70 mg/L; K 2 HPO 4 , 175 mg/L; and CaCl 2 , 1 mg/L. Phenol biodegradation reached 93.34% with a power density of 109.419 mW/cm 3 . A lab-scale bioreactor was used as a continuous culture with aeration rate, agitation speed, and dissolved oxygen of 0.5 v / v / m , 750 rpm, and 30%, respectively. On using the continuous culture, phenol biodegradation and bioenergy production reached 97.8% and 0.382 W/cm 3 , respectively. A kinetics study using Haldane’s kinetics model reported the best ﬁt to achieve a signiﬁcant correlation coefﬁcient (R 2 ) value (0.9865) reaching maximum speciﬁc growth rate with initial phenol concentration of approximately 9 mg L − 1 where the speciﬁc growth rates ( µ , h − 1 ) varied with different initial phenol concentrations. In conclusion, the native isolated consortium could be considered as an economical and sustainable approach to phenol biodegradation in industrial wastewater as well as bioelectricity generation.


Introduction
Organic pollutants, especially phenolic compounds, are the most widely abundant pollutants in industrial wastewater.In general, petrochemicals, coke oven plants, coal mining, and petroleum oil refineries are the most important industries releasing phenolic pollutants to the ecological environment [1,2].Phenolic pollutants were reported as biologically unmanageable.Thus, they remain in the industrial wastewater with accumulated concentrations [3].Due to their carcinogenic effect and acute toxicity, phenolic pollutants cause serious human health hazards and harm the environment [4].Biodegradation is the most promising and popular approach to remove toxic and hazardous pollutants, owing to its eco-friendliness, economic viability, and practical feasibility [5].Consequently, the bioremediation strategy has emerged as a potential method for the complete biodegradation of phenolic pollutants.
On the other hand, the technique known as microbial fuel cells (MFC), which produces energy particularly from the oxidation of organic compounds brought about by the metabolic activity of microorganisms, seems to be appealing for the purpose of warranting energy power production.According to Mohan et al. [6], the use of MFC as a renewable energy source to produce electricity is seen as a dependable, clean, and effective process.This approach employs renewable ways as an alternative tool to the most widely used non-sustainable sources, and it does not result in the production of any hazardous byproducts.MFCs have been shown to be an effective technology in recent years for the recuperation and on-site conversion of chemical energy into electrical energy.According to Hemashenpagam and Selvajeyanthi [7], the microbial metabolism of wastes utilizing innovative bioremediation technologies such as MFC for energy generation is recognized as the effective and ecologically benign solution.Hejazi et al. [8] reported that the phenol degradation percentage in a conventional MFC was 75% only by changing the bioreactor to a granular-activated carbon (GAC) adsorption/MFC combined system (GAMFC); the phenol degradation percentage reached 95%.Another study built a two-chamber MFC in order to treat phenol and acetone wastewater and simultaneously produce electricity.They used proton exchange poly-sulfonated (ether ketone) membrane and reported an output voltage range between 240 and 250 mV [9].On the other hand, Moreno et al. [10] reported achieving maximum power density (777.8 mW m −3 ) upon using continuous flow MFCs with granular graphite electrodes compared to single-rod electrodes (0.8 mW m −3 power density).
Hence, the aim of the present study was to: − evaluate the phenol degradation and bioelectricity generation using a microbial consortium in batch and continuous systems; − assess the potential application of nano-electrodes; and − investigate the kinetic study of the tested consortium.
The McFarland standards were employed as a point of reference for the purpose of calibrating the turbidity of bacterial suspensions derived from cultures that were 16 to 24 h old.This calibration was necessary in order to standardize the bacterial count in either sterile saline or a nutritional broth, resulting in a concentration of 1.5 × 10 −6 colony-forming units per milliliter (CFU/mL).Subsequently, equivalent quantities of the six isolates, each with a numerical label, were combined to create a solution with a final concentration of 10.0% (v/v).The isolates were carefully measured to ensure that their optical density at a wavelength of 600 nm (OD600 nm) fell within a range of 1.00 ± 0.40 [12].
2.2.Nano-Graphite Synthesis, Characterization, and Electrode Plating 2.2.1.Nano-Graphite Synthesis Nano-graphite was synthesized through the utilization of a mixed microemulsion approach.This involved the combination of two distinct microemulsions, one containing a sucrose solution and the other containing sulfuric acid.The sulfuric acid microemulsion component consisted of 20 µL of sulfuric acid and 1 mL of a surfactant solution with a mass ratio of 1:7:8/2 for cetyltrimethylammonium chloride (CTAC), cyclohexane, and pentanol, respectively.The sucrose microemulsion component was made using 20 µL of aqueous sucrose, along with 1 g of the same surfactant solution in an aliquot.The aqueous sucrose microemulsion and sulfuric acid microemulsion were combined in equal proportions.The product was clear isotropic phase, which was then left for three days in room temperature for nano-graphite synthesis [13].

Nano-Graphite Characterization
The dynamic light scattering (DLS) technique (Malvern Zetasizer, Worcestershire) was the chosen method to assess the particle size (PS), polydispersity index (PDI), and zeta potential of the synthesized nano-graphite.Nano-structured graphite ultra-structure, size, and shape were examined using transmission electron microscopy (TEM, JEOL 2100F FEG-200 kV TEM operating at 80 kV) [14].

Nano-Graphite Electrode Plating
The cathode was prepared by grinding 70% graphite nanoparticles with 10% Super-P(Carbon) and 20% Polyvinylidene fluoride (PVDF) in a high-power ball milling machine at 500 rpm for 24 h in the presence of argon.N-methyl-2-pyrrolidinone (NMP) solvent was used for mixture dispersion with continuous stirring.The slurry was pasted on metal collectors using a minicoater (MC-20, Hohsen) and dried at 80 • C for 2 h [15].

Phenol Degradation and Bioelectricity Estimation
The phenol concentration was measured spectrophotometrically using the HACH ® phenol kit [16], while the bioelectricity biogenerated from a microbial fuel cell (MFC) was estimated using the synthesized nano-graphite as cathode and cupper as anode.To calculate the power density PD (w/cm 3 ), 15 pieces of MFC (connected to each other by a salt bridge) with an external resistance was connected in series and 3 sets of these connected cells were connected in parallel (Figure 1).The power density was calculated using the following equation: where V was the cell voltage (V), I was the current (A), and U was the anodic solution volume.

Optimization of Nutritional and Environmental Factors
The optimization of the key variables influencing phenol biodegradation and bioelectricity production processes was achieved by employing two statistical designs: firstly the Plackett-Burman design (PBD) and subsequently the central composite design (CCD), as applied by Boudraa et al. [17]

Optimization of Nutritional and Environmental Factors
The optimization of the key variables influencing phenol biodegradation and bioelectricity production processes was achieved by employing two statistical designs: firstly the Plackett-Burman design (PBD) and subsequently the central composite design (CCD), as applied by Boudraa et al. [17] The projected response Y represents the percentage of phenol degradation.The model intercept is denoted as β 0 , the linear coefficient as β i , and the level of the independent variable as X i .The current study aimed to examine the model fitting and the impact of all parameters through an analysis of variance (ANOVA) using t-tests and p-values.The researchers examined each independent variable across three levels, denoted as −1, 0, and +1, as shown in Table 1 [19].Further optimization of both phenol biodegradation and bioelectricity generation responses was conducted by taking into account the essential elements.This was achieved through the use of a central composite design (CCD).The optimization of the most effective factors and their interactions, including inoculum size, MgSO 4 , K 2 HPO 4 , CaCl 2 , phenol concentration, and incubation length, was conducted using response surface methodology (RSM) statistical multifactorial modelling.The parameters were varied at five levels (−2, −1, 0, 1, and 2) as shown in Table 2.
The quadratic relationship between the response value (y) and the independent parameters (x 1 , x 2 , x 3 , x 4 , x 5 , and x 6 ) was demonstrated using a second-degree polynomial equation (Equation

Lab-Scale Generation of Bioelectricity and Biodegradation of Phenol in a Continuous Stir Tank Bioreactor
A larger scale generation of bioelectricity and biodegradation of phenol was carried out in a 4-L fermenter (BIOCANVAS, Centrion bioreactor).Before inoculation, the optimized condition was set and standardized.Moreover, the optimum aeration rate, agitation speed, and the dissolved oxygen were tested and fixed at 0.5 v/v/m, 750 rpm, and 30%, respectively.During the whole fermentation phase, the feed flow rate was set to be 1 mL/min, while the sample collection was every 2 h for 4 days to measure the phenol degradation percentage.Furthermore, power density and potentiodynamic polarization curves were recorded in the potential region from open circuit potential −50 mV to +500 mV (vs.Ag/AgCl), while the electrochemical behavior was assessed using cyclic voltammetry at a potential range of −500 mV to +500 mV (vs.Ag/AgCl) at a scan rate of 10 mV/s using PARSTAT 226 [20,21].

Kinetic Model Development of Cells
The specific growth rate of cells (µ) under batch culture was determined through the exponential phase (by applying the optimum conditions obtained from CCD optimization) and it was expressed according to Bera et al. [22]: The variables X t and X 0 represent the concentration of cells at specified time points t and t 0 , respectively.The calculation of the parameter µ was derived by analyzing the gradient of a linear graph depicting the natural logarithm of the ratio of the final value (X t ) to the initial value (X 0 ) against the elapsed time (t) during the logarithmic growth phase of the curve.
In contrast, it was observed that phenol, when used as a substrate, exhibited a suppression of cell development when subjected to elevated starting phenol concentrations.The use of Haldane kinetics was employed to represent the cellular growth phenomenon, as elucidated using the subsequent equation: In the given context, µ represents the specific growth rate (h −1 ), µ max denotes the maximum specific growth rate (h −1 ), K s signifies the half-saturated constant of substrate (mg L −1 ), and K i represents the inhibition constant (mg L −1 ).Furthermore, the experimental results about the breakdown of phenol at different combinations of initial phenol concentrations were employed to calculate the yield of cellular development [23] using the subsequent equation: In the context of cellular growth, Y represents the growth yield of cells.X and X 0 denote the cell concentration and initial cell concentration, respectively, measured in milligrams per liter (mg L −1 ).Similarly, S and S 0 represent the substrate concentration and initial substrate concentration, respectively, with the substrate being phenol and the units being mg L −1 .

Nano-Graphite Synthesis and Characterization
After the nanoparticles' synthesis, the vessel containing suspended nanoparticles was dispersed in deionized water.The nanoparticles were ultra-centrifuged at 20,000 rpm for 30 min.The dynamic light scattering technique revealed that the nano-graphite had good stability and relevant homogeneity (zeta potential −38.2 mV and 0.34 PDI) (Figure 2a).The HR-TEM study showed a spherical shape in the nano-range with diameter 86.9 nm (Figure 2b).Yu et al. [24] synthesized graphite nanoparticles using the microemulsion technique and reported that nano-graphite had good dispersibility and agglutination with zeta size reached 200 nm.Moreover, it was reported that nano-graphite has more space for charge/discharge reactions than the porous graphite rod [25] hence it was used in the plated electrodes to enhance the conductivity.

Plackett-Burman Design (PBD)
Phenol biodegradation (response 1) and bioelectricity generation (response 2) was achieved by applying the Plackett-Burman design (PBD) using Minitab 19 ® (Table 3).Each individual factor was represented in the regression equations while the significance of each factor was determined using analysis of variance (ANOVA) through assessing ttest and p-values (Table 4).Some variables, namely: inoculum size, CaCl2, phenol concentration, and incubation period were the most significant factors for phenol biodegradation with model summary (S = 2. On the other hand, inoculum size, MgSO4, K2HPO4, and CaCl2 were found to be significant for bioelectricity generation with model summary (S = 2.06, R-sq = 89.92%,Rsq(adj) = 74.81%and R-sq(pred) = 21.53%) and regression equation:

Optimization of Nutritional and Environmental Factors 3.2.1. Plackett-Burman Design (PBD)
Phenol biodegradation (response 1) and bioelectricity generation (response 2) was achieved by applying the Plackett-Burman design (PBD) using Minitab 19 ® (Table 3).Each individual factor was represented in the regression equations while the significance of each factor was determined using analysis of variance (ANOVA) through assessing t-test and p-values (Table 4).Some variables, namely: inoculum size, CaCl 2 , phenol concentration, and incubation period were the most significant factors for phenol biodegradation with model summary (S = 2.3, R-sq = 92.56%,R-sq(adj) = 81.40%and R-sq(pred) = 44.85%)and regression equation: Furthermore, the production of Pareto charts was undertaken to visually represent the set of factors being tested in relation to their impact on two specific outcomes: phenol biodegradation (response 1) and bioelectricity generation (response 2) (as depicted in Figure 3).Hence, the aforementioned parameters were chosen to undergo further optimization using five coded levels (−2, −1, 0, 1, and 2) in order to conduct a comprehensive study of the entire process through the implementation of a central composite design (CCD).

Central Composite Design (CCD)
Parameters, namely: inoculum size (x1), incubation period (x2), phenol concentration (x3), MgSO 4 (x4), CaCl 2 (x5), and K 2 HPO 4 (x6) were investigated using Minitab 19 ® with phenol degradation and bioelectricity generation (Table 5) as responses.The results of the second-order response surface model fitting in the form of an analysis of variance (ANOVA) were calculated (Table 6).The phenol degradation model was shown to be significant with (S = 2.0, R-sq = 95.64%,R-sq(adj) = 90.93% and R-sq(pred) = 68.30%)and regression equation: However, the bioelectricity generation model was also shown to be significant with (S = 5.04, R-sq = 91.70%,R-sq(adj) = 82.74%and R-sq(pred) = 56.99%)and regression equation:    Furthermore, the 3D curve interpretation revealed that the phenol biodegradation percentage and bioelectricity power density increased by decreasing the inoculum size and incubation period (Figures 4 and 5).The correlation between the initial phenol concentration (x3) and the incubation period (x5) revealed that, upon increasing both factors, maximum phenol degradation and bioelectricity generation was noticed (almost in a similar pattern) (Figures 4 and 5), while a completely different pattern was noticed when correlating between the initial phenol concentration (x3) and pH (x4) (Figures 4 and 5).This is in accordance with Puig et al. [26], who stated that, by increasing the pH to be higher than the optimal one, anodic bacteria were affected, and power generation ceased.Moreover, it was reported that, when the cultural media pH decreased with growth, the phenol degradation was impeded below pH 5.4 [27].The optimization study of phenol degradation and bioelectricity generation revealed that the optimum values for each factor that yield the maximum percentage of phenol degradation (93.34%) and power density (109.419mW/cm 3 ) were: bacterial inoculum concentration, 1.0%; incubation period, 48 h; phenol concentration, 6.0 ppm; MgSO4 concentration, 70.0 mg/L; K2HPO4 concentration, 175 mg/L; and CaCl2 concentration, 1.0 mg/L.
Aisami et al. [28] considered Pseudomonas sp.strain AQ5-04 as a phenol degrader.Upon optimization, a pH value of 6.8 was identified as the best pH for phenol degradation.The bacterium was able to degrade up to 90% out of 5 mg/L phenol.However, the maximum percentage of phenol biodegradation has been shown to be 92.64% with the optimized conditions when compared to unoptimized conditions in the same unit volume [29].Furthermore, the biodegradation of phenolic compounds in MFC exhibited a power density of 67.2 mW/m 2 and phenol degradation of 83.2% [21].

Bioelectricity Generation and Phenol Biodegradation under Continuous Conditions Using a Stir Tank Bioreactor
The data in Figure 6a show the phenol biodegradation percentage within 96 h, and revealed a sharp reduction in phenol with maximum phenol degradation (97.8%) after 48 h incubation using a 4 L continuous stir tank bioreactor.Moreover, the data in Figure 6b show the bioelectricity generation revealing a significant increase in the power density during the first 24 h and reaching an optimum value (0.382 mW/cm 3 ) after 72 h incubation.However, the Potentiodynamic polarization curve showed a sharp decrease in the generated potential (V) while increasing the current density (Figure 6c).Furthermore, cyclic voltammogram showed a steady state with a peak current at about 0.98 and 0.62 V corresponding to the phenol biodegradation (Figure 6d).
Aisami et al. [28] considered Pseudomonas sp.strain AQ5-04 as a phenol degrader.Upon optimization, a pH value of 6.8 was identified as the best pH for phenol degradation.The bacterium was able to degrade up to 90% out of 5 mg/L phenol.However, the maximum percentage of phenol biodegradation has been shown to be 92.64% with the optimized conditions when compared to unoptimized conditions in the same unit volume [29].Furthermore, the biodegradation of phenolic compounds in MFC exhibited a power density of 67.2 mW/m 2 and phenol degradation of 83.2% [21].

Bioelectricity Generation and Phenol Biodegradation under Continuous Conditions Using a Stir Tank Bioreactor
The data in Figure 6a show the phenol biodegradation percentage within 96 h, and revealed a sharp reduction in phenol with maximum phenol degradation (97.8%) after 48 h incubation using a 4 L continuous stir tank bioreactor.Moreover, the data in Figure 6b show the bioelectricity generation revealing a significant increase in the power density during the first 24 h and reaching an optimum value (0.382 mW/cm 3 ) after 72 h incubation.However, the Potentiodynamic polarization curve showed a sharp decrease in the generated potential (V) while increasing the current density (Figure 6c).Furthermore, cyclic voltammogram showed a steady state with a peak current at about 0.98 and 0.62 V corresponding to the phenol biodegradation (Figure 6d).flow modes of operation.This was owing to the fact that graphite nanoparticles had an enlarged surface area, which made it easier for biofilm to grow and for electrons to be transferred.
Ziaedini et al. [30] reported that phenolic oxidation peaks at 120 ± 30 mV is in association with bacterial cell wall, while the peak at about 600 ± 20 mV can be attributed to a soluble active redox component secreted by electrogenic bacteria into the culture medium.

Kinetics Model for Monitoring the Drastic Effect of Phenol on Bacterial Cells
Using the optimum conditions obtained through the response surface methodology, the data in Figure 7 show the specific growth rate (μ, h −1 ) variations while using different phenol concentrations.Haldane's model gave the best fit to achieve a significant correla- It was reported that, in fed batch MFC, phenol biodegradation achieved 71.8%, while the bioelectricity reached 0.305 mW/m 3 [30].Furthermore, Moreno et al. [10] stated that biodegradation of phenol produces a concomitant generation of energy in the continuousflow MFCs.The biodegradation of phenol in batch-operated MFCs with single-rod electrodes was 71%.Nevertheless, changing the mode of operation from batch to continuous flow resulted in increased biodegradation rates as well as increased power and current densities.Additionally, the use of nano-graphite electrodes resulted in an enhancement in the performance of MFCs in terms of the electrochemical outputs produced by the device, and this improvement was seen for batch as well as continuous flow modes of operation.This was owing to the fact that graphite nanoparticles had an enlarged surface area, which made it easier for biofilm to grow and for electrons to be transferred.
Ziaedini et al. [30] reported that phenolic oxidation peaks at 120 ± 30 mV is in association with bacterial cell wall, while the peak at about 600 ± 20 mV can be attributed to a soluble active redox component secreted by electrogenic bacteria into the culture medium.

Kinetics Model for Monitoring the Drastic Effect of Phenol on Bacterial Cells
Using the optimum conditions obtained through the response surface methodology, the data in Figure 7 show the specific growth rate (µ, h −1 ) variations while using different phenol concentrations.Haldane's model gave the best fit to achieve a significant correlation coefficient (R 2 ) value (0.9865).It was revealed that the cells reported maximum specific growth rate with an initial phenol concentration of approximately 9 mg L −1 .According to Bera et al. [22], a decline in the specific growth rate might be related to the fact that increased concentrations of phenol could inhibit the metabolic activity of bacteria, which would ultimately result in cell death.Based on the results of the current growth kinetic investigation, it was determined that the bacterial cells were capable of efficiently breaking down phenol at concentrations of up to 9 mg L −1 .This may be attributed to the fact that the bacterial strains were naturally exposed for extended periods of time to quantities of hazardous petroleum compounds [31].Hasan et al. [32] reported that Pseudomonas and Bacillus sp.strains transformed phenol into catechol via the ortho-cleavage pathway.It was reported that Burkholderia sp. can use both the catechol and protocatechuate branches of the β-ketoadipate pathway during the early stage of phenol degradation, and only the catechol branch during the late stage [33].In another study, Burkholderia sp. was reported to use a meta-ring opening cleavage phenol degradation pathway [34].Hence, the observed high specific growth rate, initial phenol concentration, and high phenol degradation and bioelectricity generation could be attributed to the fact that we are using a bacterial consortium which may use different phenolic degradation pathways.tion coefficient (R 2 ) value (0.9865).It was revealed that the cells reported maximum specific growth rate with an initial phenol concentration of approximately 9 mg L −1 .According to Bera et al. [22], a decline in the specific growth rate might be related to the fact that increased concentrations of phenol could inhibit the metabolic activity of bacteria, which would ultimately result in cell death.Based on the results of the current growth kinetic investigation, it was determined that the bacterial cells were capable of efficiently breaking down phenol at concentrations of up to 9 mg L −1 .This may be attributed to the fact that the bacterial strains were naturally exposed for extended periods of time to quantities of hazardous petroleum compounds [31].Hasan et al. [32] reported that Pseudomonas and Bacillus sp.strains transformed phenol into catechol via the ortho-cleavage pathway.It was reported that Burkholderia sp. can use both the catechol and protocatechuate branches of the β-ketoadipate pathway during the early stage of phenol degradation, and only the catechol branch during the late stage [33].In another study, Burkholderia sp. was reported to use a meta-ring opening cleavage phenol degradation pathway [34].Hence, the observed high specific growth rate, initial phenol concentration, and high phenol degradation and bioelectricity generation could be attributed to the fact that we are using a bacterial consortium which may use different phenolic degradation pathways.
Figure 7. Bacterial specific growth rate as affected by initial phenol concentration.
As plotted in Figure 8, the bacterial growth yield (Y) was estimated using Equation (3).The calculated values of bacterial growth yield while using phenol as a sole carbon source were listed in Table 7.When the initial phenol concentration was between 100 and 600 mg/L, the growth yields ranged from 0.225547 to 0.254036 mg mg −1 with the average growth yield reaching 0.243177 ± 0.011498 mg mg −1 .Similar experiments were conducted and higher growth yields were reported while using lower initial phenol concentrations, e.g., Abuhamed et al. [35], who reported a 0.44 mg mg −1 average growth yield upon using 10-200 mg L −1 initial phenol concentrations.Another study by Lin and Gu [31] reported reaching a 0.340 mg mg −1 average growth yield while using 50 to 600 mg L −1 initial phenol concentrations.As plotted in Figure 8, the bacterial growth yield (Y) was estimated using Equation (3).The calculated values of bacterial growth yield while using phenol as a sole carbon source were listed in Table 7.When the initial phenol concentration was between 100 and 600 mg/L, the growth yields ranged from 0.225547 to 0.254036 mg mg −1 with the average growth yield reaching 0.243177 ± 0.011498 mg mg −1 .Similar experiments were conducted and higher growth yields were reported while using lower initial phenol concentrations, e.g., Abuhamed et al. [35], who reported a 0.44 mg mg −1 average growth yield upon using 10-200 mg L −1 initial phenol concentrations.Another study by Lin and Gu [31] reported reaching a 0.340 mg mg −1 average growth yield while using 50 to 600 mg L −1 initial phenol concentrations.

Figure 1 .
Figure 1.Scaling up the power density using Stacked MFC.

Figure 1 .
Figure 1.Scaling up the power density using Stacked MFC.

Figure 4 .
Figure 4. Three-dimensional surface plots for the effects of the significant tested parameter interactions that led to maximum phenol biodegradation.

Figure 4 .
Figure 4. Three-dimensional surface plots for the effects of the significant tested parameter interactions that led to maximum phenol biodegradation.

Figure 4 .
Figure 4. Three-dimensional surface plots for the effects of the significant tested parameter interactions that led to maximum phenol biodegradation.

Figure 5 .
Figure 5. Three-dimensional surface plots for the effects of the tested significant parameter interactions that led to maximum bioelectricity generation.

Figure 5 .
Figure 5. Three-dimensional surface plots for the effects of the tested significant parameter interactions that led to maximum bioelectricity generation.

Figure 7 .
Figure 7. Bacterial specific growth rate as affected by initial phenol concentration.

Table 1 .
Parameters under investigation using a PBD.

Table 2 .
Parameters under investigation using a CCD.

Table 3 .
PBD matrix for phenol biodegradation percentage and bioelectricity power density.

Table 5 .
CCD matrix for phenol biodegradation percentage and bioelectricity power density after significant parameters optimization.

Table 6 .
Analysis of variance of phenol biodegradation percentage and bioelectricity power density.

Table 7 .
Growth yield (Y) evaluation under various initial phenol concentrations.

Table 7 .
Growth yield (Y) evaluation under various initial phenol concentrations.