Statistical optimization of key process variables for enhanced hydrogen production by newly isolated Clostridium tyrobutyricum JM1

international journal of hydrogen energy 33 (2008) 5176–5183

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Statistical optimization of key process variables for enhanced hydrogen production by newly isolated Clostridium tyrobutyricum JM1 Ji Hye Joa, Dae Sung Leeb,*, Donghee Parkc, Jong Moon Parka,c,1 a

Advanced Environmental Biotechnology Research Center, School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk 790-784, Republic of Korea b Department of Environmental Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Republic of Korea c Department of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk 790-784, Republic of Korea

article info

abstract

Article history:

A fermentative hydrogen-producing bacterium was isolated from a food waste treatment

Received 8 May 2008

process. The biological hydrogen production rate by the pure isolate (designated as Clostrid-

Accepted 26 May 2008

ium tyrobutyricum JM1) was dependent on various nutritional and environmental condi-

Available online 8 August 2008

tions. In this study, to enhance hydrogen production rate, the individual and mutual effects of three key process variables such as glucose concentration, pH and temperature

Keywords:

were investigated through response surface methodology (RSM) in a batch system. A

Clostridium tyrobutyricum

Box–Behnken design was employed to determine the effect of the three independent vari-

Hydrogen production rate

ables on the hydrogen production rate and to find the optimum condition of each variable

Optimization

for improved hydrogen production. Experimental results showed that a maximum hydro-

Response surface methodology

gen production rate of 5089 ml H2 (g dry cell h)1 was obtained under the condition of glu-

(RSM)

cose concentration of 102.08 mM, temperature 35  C and pH 6.5, and all three factors had

Box–Behnken design

significant influences on the specific hydrogen production rate. The RSM with the Box– Behnken design was a useful tool for achieving the high rate of hydrogen production by C. tyrobutyricum JM1. ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Hydrogen (H2) is an alternative energy source that is cost-effective, environment-friendly and renewable because it has high specific energy content per unit mass and produces no carbon-based emissions that contribute to greenhouse effect. Among various hydrogen production processes, biological

methods are known to be less energy intensive than chemical or electrochemical ones since they are carried out at an ambient temperature and pressure [1,2]. A fermentative hydrogen production process which utilizes strict/facultative anaerobes is prospective and advantageous due to its higher evolution rate of hydrogen than those of photo fermentation and bio-photolysis, and it

* Corresponding author. Tel.: þ82 53 950 7286; fax: þ82 53 950 6579. E-mail addresses: [email protected] (D.S. Lee), [email protected] (J.M. Park). 1 Tel.: þ82 54 279 2275; fax: þ82 54 279 2699. 0360-3199/$ – see front matter ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2008.05.012

international journal of hydrogen energy 33 (2008) 5176–5183

involves no inhibitory effect of oxygen on FeFe-hydrogenase under anoxic or anaerobic conditions [3,4]. It can produce hydrogen continuously without light sources unlike photosynthetic bacteria and can use various kinds of substrates such as refuse and food waste products [5]. Until recently, clostridia and enteric bacteria, such as Clostridium butyricum and Enterobacter aerogenes have been known to be strong and efficient producers of hydrogen [3,6]. E. aerogenes, which are representative of facultative anaerobes, can rapidly consume oxygen and recover the activity of FeFe-hydrogenase under anoxic conditions in contrast to strict anaerobes which are extremely sensitive to oxygen. Hence, the facultative anaerobe does not require any devices for degassing the feed in the continuous hydrogen production process [7]. Therefore, a few studies including modeling and optimization of fermentative hydrogen production with pure E. aerogenes have been carried out (Table 1). Meanwhile, studies using Clostridium sp. responsible for hydrogen production have been rarely reported. The optimization of hydrogen yield or hydrogen production rate has been solely performed using C. butyricum in a pure culture (Table 2) [11,12]. This is in part due to the difficulty in cultivating strict anaerobes because they are especially sensitive to small amounts of dissolved oxygen [13]. Therefore, addition of expensive reducing agents such as L-cysteine$HCl may be required to grow Clostridium sp. for hydrogen production. This results in low feasibility for practical applications. However, because H2 production using Clostridium sp. such as C. butyricum is frequently found in hydrogen-producing bacterial consortia and is also very effective in producing H2 from organic substrates, it is still of great value in revealing hydrogen production characteristics of Clostridium sp. to maintain or improve hydrogen production performance [12]. In addition, there is a shortage in the literature to date regarding the statistical optimization of process parameters on hydrogen production by pure clostridial species. In this study, we isolated a hydrogen-producing anaerobe (designated as Clostridium tyrobutyricum JM1) from a food waste treatment process that is very efficient in H2 production. Previous research relevant to C. tyrobutyricum focused on butyrate fermentation of xylose using immobilized cells [14]. Up to now, however, nothing is reported concerning the optimization study on hydrogen production from glucose by C. tyrobutyricum through statistically designed approaches. Effects of glucose concentration, pH and temperature on hydrogen production performance were investigated to identify the proper conditions for H2 production with the pure isolate. The distribution of soluble end products (volatile fatty acids and

Table 1 – The experimental conditions and hydrogen production rates by Enterobacter aerogenes from pure carbohydrates pH6.0 5.8 6.1–6.6 6.13

Temp.  C

SHPRa ml H2 (g DW h)1

38 38 40 38

274 11.3 9.68 425.8

a SHPR: specific hydrogen production rate.

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alcohol) was discussed with regards to pH levels. A Box– Behnken design using response surface methodology was employed to investigate the effects of the key process variables and to improve the hydrogen production rate by C. tyrobutyricum JM1.

2.

Materials and methods

2.1. Isolation of C. tyrobutyricum JM1 from a food waste treating reactor The bacterial samples were collected from the effluent of a food waste treatment process at 35  C and pH 5.2–5.5. For the operation of the reactor, sewage sludge as a seed was obtained from an anaerobic digester of a domestic sewage treatment plant at Pohang in Korea and was heat-treated to harvest spore-forming bacteria. Food waste, as a medium, was collected from a dining hall at Pohang University of Science and Technology. The averaged characteristics of food wastes used in this study have been described previously [15]. The samples for isolation were diluted with sterile distilled water, spread onto agar plates, and then incubated at 35  C under anaerobic conditions. The RCM (Reinforced Clostridial Medium, Merck) was used for isolation and cultivation of H2-producing bacteria. The unique colonies which appeared on the plates were selected by amplified ribosomal DNA restriction analysis (ARDRA) and C. tyrobutyricum JM1 responsible for hydrogen production was isolated [15].

2.2.

Media preparation for optimization study

All experiments were performed in 160 ml Wheaton serum bottles with 98 ml medium. The media used for hydrogen production was the same as that used for inoculum preparation except for glucose concentration. The pure cells harvested from pre-cultivation were washed with sterile phosphatebuffered saline (PBS) before being used as inoculum. Experiments were carried out with the cells of 1.19 g dry weight per liter and 7 ml of a suspension of the seed obtained by aseptic centrifugation was inoculated to the sterilized medium. The initial pH of medium was adjusted using 5 N HCl and/or 5 N NaOH solution(s). These serum bottles were immediately air-sealed with butyl rubber stoppers and tied with aluminum seal caps. They were then flushed with nitrogen gas to remove oxygen in the headspace of the bottles and to ensure the anaerobic condition. The bottles were put into operation in an orbital shaker with a rotation speed of 150 rpm to provide better contact among the substrates. At each 2 h interval, the total gas volume was measured by releasing the pressure in the bottles using a syringe.

2.3.

Analytical methods

References [6] [8] [9] [10]

The biogas composition was analyzed by gas chromatography (model 6890 N, Agilent Inc.), using pulsed discharge detector. Hydrogen gas production was calculated from the headspace measurement of gas composition and the total volume of biogas produced, at each time (2 h) interval according to the following equation based on mass balance [16]:

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Table 2 – The optimized experimental conditions and hydrogen productivity by pure (Clostridium sp.) or mixed cultures (predominantly Clostridium sp.) Microorganism Carbon Type Mixed Mixed Mixed Mixed Mixed Mixed C. C. C. C.

cultured culturee culture culture culture culturef

butyricum butyricum butyricum tyrobutyricumg

Sucrose Glucose Molasses Glucose Glucose Sucrose Sucrose Sucrose Glucose Glucose

SHPRa

VHPRa

– 330–360 ml/g-VSS d – 4600  400 ml/g-VSS d 0.456 ml/g-DW d –

– 4460–5540 ml/l d 4835 ml/l d – – 5.5 ml/l h

3.43 – – 2.1  0.1 1.7 –

[20] [2] [21] [22] [23] [24]

– – – 5089 ml/g-DW d

– 209 ml/l h – –

2.78 – 1.4–2.3 –

[12]

Optimized condition

pH 6.55 and temp. 35  1  C pH 5.7, temp. 34.5  0.5  C and HRTc 8 h pH 4.1–4.4 and temp. 35  C pH 5.5, temp. 36  C and HRT 6 h pH 5.7, temp. 35  1  C and SRTc 0.25 d pH 5.4  0.2, temp. 36  1  C and sucrose 4.0  0.5 g/l Batch pH 5.5, temp. 37  C and sucrose 17.8 g/l Batch pH 6.0, temp. 37  C and sucrose 17.8 g/l CSTR pH 6.7, temp. 30  C and HRT 8 h Batch pH 6.5, temp. 35  C and glucose 102.08 mM Batch ASBRb CSTR CSTR CSTR Batch

Yield References (mol/mol)

[11] This study

d,e,f, and g correspond the optimization study using response surface methodology. a SHPR: specific hydrogen production rate; VHPR: volumetric hydrogen production rate. b ASBR: Anaerobic sequencing batch reactor. c HRT: hydraulic retention time; SRT: solid retention time. d Taguchi fractional design. e Full factorial design. f Central composite design. g Box–Behnken design.

V ¼ Vo gi þ

X

Vi gi

(1)

where V is the cumulative hydrogen gas volumes at the current (i); V0 is the volume of headspace of serum bottles; Vi is the biogas volume discharged from the serum bottles at the time interval (i); gi is the fraction of hydrogen gas discharged from the serum bottles at the time interval (i). The dry weight of cells was determined by centrifuging the culture broths (3000 g, 10 min), washing twice with distilled water and then filtering the broth with 0.45 mm membrane filters. The filters were dried in an oven at 105  C, until no weight changes between consecutive measurements were observed.

2.4.

Response surface methodology (RSM)

A 3K factorial Box–Behnken model was used as an experimental design model to optimize the key process parameters for enhanced hydrogen production in this study. For the three factors, the Box–Behnken design offers some advantages in requiring a fewer number of runs and is rotatable if the variance of the predicted response at any point x depends only on the distance of x from the design center point [17,18]. The 3K factorial design also allows efficient estimation of seconddegree quadratic polynomial and obtains the combination of values that optimizes the response within the region of the three-dimensional observation space [19]. In developing the regression equation, the relation between the coded values and actual values are described according to the following equation:   (2) xi ¼ Xi  Xi DXi where xi is the coded value of the ith independent variable, Xi is the uncoded value of the ith independent variable, Xi is the uncoded value of the ith independent variable at the center point and DXi is the step change value. The levels of the

variables and the experimental design are shown in Table 3. The hydrogen production rate was associated with simultaneous changes in glucose (11.1, 88.8, 166.5 mM), temperature (30, 37, 44  C) and pH (5.0, 6.0, 7.0) of the culture medium. A total of fifteen experimental runs decided by the 3K Box– Behnken design were carried out, and the center point was replicated three times to estimate the experimental errors. For predicting the optimal condition, the quadratic polynomial equation was fitted to correlate the relationship between

Table 3 – The Box–Behnken experimental design with three independent variables Run #

Glucose (mM) X1

1 2 3 4 5 6 7 8 9 10 11 12 13a 14a 15a

11.10 166.52 11.10 166.52 11.10 166.52 11.10 166.52 88.81 88.81 88.81 88.81 88.81 88.81 88.81

pH

Code X2 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0

5.0 5.0 7.0 7.0 6.0 6.0 6.0 6.0 5.0 7.0 5.0 7.0 6.0 6.0 6.0

Temperature H2 production ( C) rate Y [ml H2 (g dry Code X3 Code cell h)1] 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0

37 37 37 37 30 30 44 44 30 30 44 44 37 37 37

0 0 0 0 1 1 1 1 1 1 1 1 0 0 0

2563 1944 3908 4178 2922 3662 1792 1195 2482 4413 1767 1321 4642 4663 4655

a The center point was replicated three times to estimate the experimental errors.

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variables and response (i.e., hydrogen production rate) and estimated as the following equation: Y ¼ ao þ

3 X

ai X i þ

i¼1

3 X i¼1

aii X2i þ

3 3 X X

Factors aij Xi Xj

(3)

i¼1 i
where Xi are the input variables, which influence the response variable Y, a0 the offset term, ai the ith linear coefficient, aii the quadratic coefficient and aij is the ijth interaction coefficient. The input values of X1, X2 and X3 corresponding to the maximum value of Y were solved by setting the partial derivatives of the functions to zero.

3.

Results and discussion

Model X1 X2 X3 X1X2 X2X3 X1X3 Error Total

Statistics Sum of squares

Degree of freedom

Mean square

30913 957 1357 948 8526 900 20229 600 197 606 447 307 154 596 467 252.5 31381 209.5

9 2 2 2 1 1 1 5 14

3434 884.1 678 974 4263 450 10114 800 197 606 447 307 154 596 93 450.5

F-value P-value 36.756 7.266 45.623 108.237 2.115 4.787 1.654

0.0005 0.0332 0.0006 0.0001 0.2057 0.0403 0.2547

a Coefficient of determination (R2) ¼ 0.984.

3.1. Optimization study for maximum hydrogen production rate The optimum levels of the key factors and the effect of their interactions on hydrogen production were determined by the Box–Behnken design of RSM. Many studies have shown the effects of substrate concentration, pH and temperature on hydrogen productivity. However, the optimum conditions reported so far were inconsistent (Table 2). The values were based on the ‘one-variable-at-a-time’ approach and could not explain the mutual interactions among the independent variables and guarantee the determination of optimal conditions. Therefore, the interactive effects of the three factors selected as key parameters were investigated to maximize the hydrogen production rate. The hydrogen production rate is based on the dry cell weight with the unit of ml (g dry cell h)1. Table 3 represents the design matrix of the variables together with the experimental results. The response of the center point (glucose ¼ 88.8 mM, temperature ¼ 37  C, pH ¼ 6.0) was 4653 ml H2 (g dry cell h)1. The regression equation obtained after the analysis of variance gave the level of response as a function of three independent variables. A quadratic model was attempted to fit the data by least-squares, and all terms regardless of their significance were included in the following equation. Y ¼ 4653:480  1:847X1 þ 831:812X2  160:616X3  3:088X21  900:125X22  33:782X23 þ 15:876X1 X2  3:412X1 X3  28:085X2 X3

Table 4 – ANOVA for the hydrogen production ratea

(4)

where Y is the predicted hydrogen production rate; X1, X2, and X3 are the coded values of initial glucose concentration, pH and temperature, respectively. Analysis of variance (ANOVA) was conducted to test the significance of fit of the second order polynomial equation for the experimental data as shown in Table 4. ANOVA for hydrogen production indicated that the F-value was 36.756, which implies that the model was significant because model terms which have values of ‘Prob > F’ less than 0.05 are considered to be significant, but values higher than 0.1 are insignificant [25]. The P-value of the regression coefficient was significant at the 1% a level, while the lack of fit was not significant. Evidence of significant interaction was found and this was confirmed by a statistical result for the interaction

between X2 and X3 as 0.0403 [2]. The coefficient of determination (R2) was 0.984, which can explain up to 98.4% variability of the response. It indicates a good agreement between experimental and predicted values and implies that the model was reliable for hydrogen production. The optimum level of each variable and the effects of their interactions on the hydrogen production were studied by plotting three-dimensional response surfaces and two-dimensional contour lines (Fig. 1). The figures are based on Eq. (4) with one variable kept constant at its optimum level and with variation of the other two variables within the experimental range. A contour plot indicates that the effects of process variables on the rate of hydrogen production were significant. The three-dimensional curves of the calculated responses show the interactions between pH and temperature, initial glucose concentration and temperature, and initial glucose concentration and pH in Fig. 1(a)–(c), respectively. In Fig. 1(a), the effect of pH (X2) and temperature (X3) on the hydrogen production rate (Y ) was investigated keeping initial glucose concentration (X1) constant. Y increased as pH level increased from 5.0 to 6.5 and then decreased beyond the level. The pH is one of the most important factors in hydrogen production due to its effects on FeFe-hydrogenase activity, metabolic pathways, and the duration of lag phase [6,26,27]. Therefore, if the initial pH does not inhibit bacterial growth or residence, a higher initial pH value would delay the beginning of the pH inhibition caused by the metabolic shift from acidogenesis to solventogenesis [14,28,29]. Khanal et al. [30] also reported that low initial pH values of 4.0–4.5 cause longer lag periods. On the other hand, high initial pH values such as 9.0 decrease lag time, but have a lower yield of hydrogen production [31]. It was reported that the effect of pH is due to the change of the ionization state of the components in enzymatic reactions [9]. Temperature also affects the maximum specific growth, substrate utilization rate and the metabolic pathway of microorganisms, resulting in a shift of by-product compositions [27,32,33]. In this study, Y increased as temperature increased from 30 to 35  C and then rapidly decreased beyond the level. The angle of inclination of the axis was not biased towards either X2 or X3, showing that Y was almost dependent on both process variables. Fig. 1(b) shows that the effect of initial glucose concentration (X1) and temperature (X3) on Y, while pH (X2) was constant at an optimal value. Initial glucose

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Fig. 1 – Two-dimensional contour plots and three-dimensional response plots showing (a) the effect of pH (X2), temperature (X3) and their mutual interaction on Y, with constant level of glucose (102.08 mM); (b) the effect of glucose (X1), temperature (X3) and their mutual interaction on Y, with constant level of pH (6.5); (c) the effect of glucose (X1), pH (X2), and their mutual interaction on Y, with constant level of temperature (35 8C).

concentration plays an important role on the yield and production rate of hydrogen [9]. Low initial glucose concentration results in a low rate of the fermentation steps, but as starting substrate concentration increases, fermentation time increases and substrate inhibition phenomenon may occur [9]. As glucose concentration increased from 11.10 to 102.08 mM, Y also increased and the angle of inclination of the axis was slight compared with that of temperature. It means that the positive effect of increased temperature on Y was stronger than glucose concentration increased. Finally, Fig. 1(c) indicates the effect of initial glucose concentration (X1) and pH (X2) with constant level of temperature (X3). The response surface of the hydrogen production rate Y indicated a clear peak, which means that the optimum point (X1: 102.08 mM and X2: 6.5) at 35  C was inside the design boundary well. Fig. 2 shows the residual plots of the observed value. The residuals of the model, which compared the predicted value with the experimental response, were randomly distributed without any patterns. The constant variance indicated accurate predictions of experimental values [34]. The optimum conditions for the hydrogen production rate were initial glucose concentration of 102.08 mM, temperature 35  C, and initial pH 6.5. The maximum predicted value of the hydrogen production rate was 5089 ml H2 (g dry cell h)1, which was comparable with those of strict anaerobe, other

clostridium spp. and facultative anaerobe, E. aerogenes reported in previous researches (Tables 1 and 2). Therefore, the response surface optimization could be successfully used to evaluate the hydrogen production performance and to achieve a higher rate of hydrogen production. In addition, the maximum hydrogen yield (not optimized) of 2.0 mol/mol

Fig. 2 – Residual plot of quadratic model for the hydrogen production rate.

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glucose was observed in the condition (not shown data) with a glucose conversion of 100% (Table 5). The hydrogen yield found in this study is comparable (or higher than) to the optimized yields of other reported results, i.e., 1.4–2.3 mol/mol glucose and 2.78 mol/mol sucrose (¼1.46 mol/mol glucose) [11,12]. This clearly indicates that C. tyrobutyricum JM1 used in this study is an effective hydrogen-producer.

3.2.

Metabolic characteristics of C. tyrobutyricum JM1

Anaerobic hydrogen production from a carbon source produces volatile fatty acids (VFAs) and alcohols as fermentation end products along with hydrogen [26,35]. Table 5 lists the change of pH, the removal efficiency of substrate, and the distribution of the major VFAs (acetate, butyrate and lactate) and ethanol in the final products of each ‘Run’ in the experimental design. The pH decreased correspondingly with production of hydrogen and formation of acidic metabolites expressed by VFAs [12]. It showed that pH had an important effect on the variation of VFAs, and that the final pH was related to the fractions of VFAs and ethanol (Fig. 3). Commonly, the majority of fractions of the metabolites with the evolution of hydrogen at pH 6.3 were occupied with butyrate, which could explain why C. tyrobutyricum is a good producer of butyrate, i.e., the butyric clostridia [36]. Butyrate is known to result from an anaerobic metabolism of clostridial species such as C. butyricum and C. tyrobutyricum because anaerobic bacteria cannot use the tricarboxylic acid (TCA) cycle as a complete pathway [37]. In this study, the decrease of the final pH to below 4.0 (Run # 2, 4, 6, 9, 10, 13, 14, and 15), regardless of the initial pH, resulted in increases (31.37–51.62 C-mmol) and major fractions (78.6– 91.9% on carbon basis) of lactate among the VFAs with stoppage of hydrogen. It was coincident with the result that lactate became the major fermentation product when the pH was further dropped to 5.0 [14,36]. Glucose in the medium was converted into biomass, carbon dioxide in the biogas, VFAs, and ethanol. Table 5 also summarizes the overall carbon mass

Fig. 3 – The fractions of soluble end products (VFAs and ethanol).

balance, which was satisfactory as the error of the carbon recovery compared to the consumption of glucose (C-mmol) was in the range of 0.1–3.6%. In other words, the carbon balance calculated by taking into account the conversion of glucose into biomass and metabolites was in the range of 96.4–100.9%. Glucose was completely degraded in the ‘Runs’ (# 1, 3, 5, and 7) containing glucose concentration of 11.1 mM and a center point. Meanwhile, the two lowest removal efficiencies of glucose (<30%) were shown in the Runs (# 8 and 11) with glucose concentration of 88.8 and 166.5 mM, respectively at 44  C.

4.

Conclusion

The present work focused on the optimization of key parameters for improving hydrogen production rates using statistical methodology. Experimental results showed that glucose

Table 5 – pH change and glucose degradation during the operation, and VFAs and CO2 distribution in the final product Run #

1 2 3 4 5 6 7 8 9 10 11 12 13a 14a 15a

Glur a (%)

pH Initial

Final

5.0 5.0 7.0 7.0 6.0 6.0 6.0 6.0 5.0 7.0 5.0 7.0 6.0 6.0 6.0

4.98 3.47 5.93 3.65 5.74 3.77 5.44 4.23 3.68 3.95 4.20 4.30 3.65 3.64 3.63

a Glucose removal.

100.0 53.5 100.0 60.7 100.0 48.9 100.0 27.4 71.1 98.5 24.4 38.9 100.0 100.0 100.0

Major VFA component (C-mmol) Acetate

Lactate

Butyrate

5.24 6.05 5.63 4.95 5.52 5.50 5.34 6.14 5.81 2.27 6.61 6.07 4.70 4.70 4.78

0.04 51.62 0.03 56.98 0.01 44.46 3.26 22.42 31.37 33.27 11.36 19.76 41.89 41.79 41.85

3.95 0.06 3.20 0.05 3.64 1.82 3.18 5.45 2.73 13.63 2.27 0.03 5.68 5.45 5.59

Ethanol (C-mmol)

CO2 (C-mmol)

Biomass (C-mmol)

Carbon recovery (%)

0.22 0.23 0.24 0.21 0.25 0.18 0.15 0.13 0.22 0.21 0.17 0.18 0.18 0.18 0.19

1.96 1.31 1.80 2.37 1.74 2.01 1.16 0.94 1.48 2.57 0.23 0.86 2.57 2.60 2.60

4.01 7.59 4.67 8.81 4.77 7.70 2.60 3.70 7.17 7.58 2.93 3.60 7.74 7.74 7.58

97.9 99.6 96.4 99.0 99.4 99.0 97.9 97.9 100.2 96.94 99.25 99.32 100.89 100.40 100.62

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concentration, temperature, and pH had significant influences on hydrogen production rates (P < 0.05). Evidence of significant interaction between temperature and pH was found, and this was confirmed by a statistically significant result for the interaction. Accurate prediction of the maximum value of the experimental response and the constant variance of residuals indicated that the quadratic model had been adequately selected to describe the response surface within the experimental region. A maximum hydrogen production rate of 5089 ml H2 (g dry cell h)1 was obtained under the optimum condition of glucose concentration of 102.08 mM, temperature 35  C and pH 6.5. Therefore, the RSM was useful in optimizing the hydrogen production process and improving the hydrogen production rate by C. tyrobutyricum JM1 isolated from a food waste treatment process. Moreover, the maximum hydrogen yield (not optimized) of 2.0 mol/mol glucose indicated that the isolate used in this study was an effective hydrogen-producer. Acetate, lactate, butyrate, and ethanol were major soluble metabolites. The fraction of lactate among VFAs was relatively high when the final pH was below 4.0 with stoppage of hydrogen production. The carbon balance based on the metabolites, CO2 and biomass was satisfied with carbon recovery of 96.4–100.9%.

Acknowledgement This work was financially supported by the Korea Science and Engineering Foundation through the Advanced Environmental Biotechnology Research Center (AEBRC) at Pohang University of Science and Technology.

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