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Application of Plackett–Burman experimental design to optimize biohydrogen fermentation by E. coli (XL1-BLUE)
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 6 ( 2 0 1 1 ) 1 3 9 4 9 e1 3 9 5 4
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/he
Application of PlacketteBurman experimental design to optimize biohydrogen fermentation by E. coli (XL1-BLUE) P. Bakonyi*, N. Nemesto´thy, E´. Lo¨vitusz, K. Be´lafi-Bako´ Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem Street 10, 8200 Veszpre´m, Hungary
article info
abstract
Article history:
Escherichia coli is attractive for biotechnological hydrogen production. Compared to other
Received 21 January 2011
biohydrogen producing bacteria e.g. Clostridium species, E. coli is able to tolerate oxygen,
Received in revised form
fast growing and well-characterized in physiological and biochemical terms. According to
9 March 2011
the well known metabolic pathways of E. coli, the hydrogen production from different
Accepted 11 March 2011
substrates is dependent on the membrane-boundary formate-hydrogen lyase (FHL)
Available online 16 April 2011
enzyme complex. The efficiency and economic success of hydrogen fermentation are influenced by the applied operational conditions. In this work the optimal conditions
Keywords:
(composition of broth, inoculum size, stirring speed) for biohydogen fermentation using
Biohydrogen
E. coli (XL1-BLUE) were investigated by experimental design. We found that among the
E. coli
several variables only formate compound plays a key role in hydrogen formation and the
Experimental design
optimal conditions for biohydrogen production were identified as follows: 30 mM formate,
Process optimization
5 g/l yeast extract, 10 g/l tryptone, 3.33 g/l NaCl, 0.05 g dry cell weight/l initial cell density and 220 rpm stirring rate, where productivity and yield were 426 ml H2 l1 d1 and 0.41 mol H2/mol formate, respectively. Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
One of the greatest challenges that mankind has to face in the 21st century is to find “green” energy sources for the replacement of fossil fuels and ensuring sustainable development. The global warming and the rapidly depleting reservoirs of fossil energy carriers have induced the intensive research for renewable sources [1]. Biologically produced hydrogen has the potential to be the fuel of the future due to its high energy content and environmentally friendly characteristic [2e4]. The bioprocesses for hydrogen production can be divided into two main categories: the photosynthetic and the dark fermentation processes [5,6]. Nowadays, compared to light-driven hydrogen bioproduction, anaerobic dark
hydrogen fermentation is more feasible for practical application due to its higher efficiency, higher stability, simpler control requirements, etc. [7,8]. Microbes for fermentative hydrogen production either belong to strict anaerobes and facultative anaerobes [9]. Recently, a large number of microorganisms and substrates have been used and investigated for biohydrogen formation and Escherichia coli is considered as an attractive strain for bacterial hydrogen production [10e12]. E. coli can provide hydrogen using different substrates e.g. glucose, lactose, formic acid, etc. [12e14]. Among these alternatives, formate was shown to ensure the highest hydrogen productivity [15]. Furthermore, formic acid can be derived from low cost renewable materials, such as biomass [16]. Biological hydrogen production from formate is catalyzed
* Corresponding author. E-mail address: [email protected] (P. Bakonyi). 0360-3199/$ e see front matter Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2011.03.062
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by the membrane-boundary formate-hydrogen lyase (FHL) complex. This enzyme complex of E. coli consists of formate dehydrogenase (FDH-H), hydrogenase (Hyd-3), and numerous electron transfer mediators [15,17,18]. In fact, formate is the sole precursor of H2 evolution in E. coli and it is converted to H2 and CO2 by the FHL enzyme complex [19,20]. This apparently simple transformation of formate compound to molecular hydrogen and carbon-dioxide relies on complex genetic, biochemical and physiological regulation and control but the particular description of that is beyond the scope of this paper. According to recent reported data several factors e.g. fermentation conditions like temperature, pH, the composition of broth, nutrient availability, gas partial pressure in the headspace of the fermenter, metabolites in liquid phase, etc. can affect the rate and yield of hydrogen production [21e25]. The economic and industrial success of the hydrogen producing biosystems significantly depends on the reaction conditions used and generally its optimization represents a significant cost and time factor in the bioprocess development [26]. Due to the wide range of process variables and the metabolic complexity of cells, experimental design procedures are often used to determine the optimal operational conditions. Among reaction conditions the composition of fermentation media (including carbon sources, nitrogen sources, mineral salts, trace elements, vitamins and other growth factors) is a very important factor that determines the nutrimental and chemical environment for the whole cell biocatalysts in the reactor and thus may influence the hydrogen generation potential of the strains [27]. To optimize a fermentative hydrogen production process the following experimental design strategy is highly recommended: Firstly, employ PlacketteBurman experimental design to identify the key factors of the respective hydrogen production system (Screening). Secondly, gradient method needs to be used to find the optimum variable ranges (Narrowing). Finally, the genuine optimal in the optimum range has to be determined (Optimum search) [28]. However, a complete statistical experimental design for the optimization of a bioprocess including hydrogen fermentation is surprisingly not frequently carried out [27,28]. According to earlier studies and our best knowledge only a few papers have dealt with the described 3-step procedure in order to optimize hydrogen production and neither of them applied E. coli strain [28]. In this paper the effect of the components in sodium-formate supplied LuriaeBertani (LB) medium and the influence of initial cell concentration and stirring speed were studied as well. Firstly PlacketteBurman experimental design was employed to identify the key factors for biohydrogen production using E. coli (XL1-BLUE) strain (derivative of E. coli K-12). After screening test we aimed to determine the optimum range and optimum value of the key parameters in order to improve hydrogen productivity and yield.
2.
Materials and methods
2.1.
Microorganism
E. coli (XL1-BLUE) was used in this research. The strain was provided by the University of Szeged (Prof. Kornel Kovacs
et al.). The culture was maintained in 70% glycerol at 80 C. The bacteria was periodically revived on Petri dishes using agar supported LB medium (10 g/l tryptone, 5 g/l yeast extract, 10 g/l sodium-chloride, 30 g/l agar) in which 10 mg/ml tetracycline was added. The dishes were incubated at 37 C and after 24 h of growth the fresh colonies were used to prepare the inoculum.
2.2.
Inoculum preparation
The seed culture was prepared aerobically in a 500 ml flask (working volume 300 ml) containing LB medium (in which 10 mg/ml tetracycline was added) and placed in a shaking incubator at 110 rpm agitation speed and 37 C for 24 h. Prior to inoculation, the effective cell concentration (g dry cell/l) in the obtained inoculum was measured spectrophotometrically (OD600, PG Instruments, model T80).
2.3.
Experimental conditions
Batch experiments were conducted in a 3500 ml bioreactor. The reactor was filled with the fermentation medium containing sodium-formate, tryptone, yeast extract, NaCl. Prior to inoculation the reactor was sterilized in autoclave for 1 h at 120 C. After sterilization the fermenter was cooled to ambient temperature under a sterile box and the appropriate amount of inoculum was added to obtain the desired cell density. The total volume of liquid phase and the headspace was 3100 ml and 400 ml, respectively. The bioreactor was incubated at 37 C and purged with high purity nitrogen (>99.9%) for 20 min before start-up in order to ensure anaerobic circumstances for biohydrogen production. Finally the reactor was sealed, the pH controller and the stirrer were attached to the fermentation set-up. The pH was adjusted and maintained at 6.5 by 5 M NaOH and 5 M sulfuric acid. In order to measure the volume of total evolved gas it was continuously released from the reactor and delivered to a volumetric gasmeter which was connected to a computer.
2.4.
Analytical methods
Concentrations of hydrogen, carbon-dioxide, nitrogen and oxygen were determined in the headspace of the bioreactor by gas chromatography. The gas samples were taken from the reactor using a gastight Hamilton syringe. H2, N2, O2 were analysed by gas chromatograph (Gow-Mac Series 600) equipped with a thermal conductivity detector and a molecular sieve column (zeolite filled X13, 2 m length * 28 mm diameter) with helium as carrier gas. CO2 was also measured by gas chromatograph (Hewlett Packard Instruments, model 5890) equipped with a TCD and capillary column (CarbonPlot, Agilent Technologies) with nitrogen as carrier gas. The injection volume was 1 ml. The volumetric hydrogen gas production rate YV (ml H2 l1 d1) for each time interval was calculated from headspace measurements and the total volume of the gas mixture produced using the following equation: VH;i ¼ V0 CH;i þ
X
VG;i CH;i
where VH,i is the total volume of biohydrogen produced at
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Fig. 1 e The time course of the fermentation. Dark square e gas production rate; light square e cumulative gas volume.
2.5.
Experimental design and statistical analysis
where YV (ml H2 l1 d1) is the response or dependent variable, Mi is the linear regression coefficient, Xi is the level of the independent variable, and I is the model intercept. According to PlacketteBurman design all factors were tested at a low and a high level coded as (1) and (þ1), respectively (Table 1). The design matrix is shown in Table 2 where it can be seen the effect of 6 variables was investigated in 11 independent experimental runs including triplicate measurements in the center point in order to estimate the standard deviation. Statistica 8 software was used to analyze the experimental design. The measurements were carried out in random order.
2.5.1.
PlacketteBurman design
2.5.2.
(i) time interval, V0 is the volume of the headspace, CH,i is the volume fractions of biohydrogen gas at the current (i) time interval in the headspace of the fermenter and VG,i is the total volume of gas produced at the current (i) time intervals, respectively. As an example the time course of the fermentation is illustrated in Fig. 1 where the conditions were as follows: 30 mM formate, 5 g/l yeast extract, 3.33 g/l tryptone, 3.33 g/l NaCl, 220 rpm stirring speed, 0.05 g (dry cell weight)/l initial cell density.
The goal of applying PlacketteBurman experimental design was to identify which ingredients of the medium (sodiumformate, tryptone, yeast extract, sodium-chloride) and fermentation parameters (inoculum size, stirring speed) have significant effect on the biohydrogen producing capability of E. coli (XL1-BLUE). When this kind of statistical experimental design is employed it is assumed that no interactions between different factors occur in the range of variables under consideration [29]. A first order, linear approach is sufficient for the screening procedure: YV ¼
X
Table 2 e The PlacketteBurman experimental design matrix for evaluating data.
Mi Xi þ I
Table 1 e The codes and levels of the variables. Code X1 X2 X3 X4 X5 X6
Variable Formate (mM) Cell density (g dry cell/l) Yeast extract (g/l) NaCl (g/l) Tryptone (g/l) Stirring speed (rpm)
One-factor-at-a-time design
One-factor-at-a-time design is a traditional experimental design where only one factor is studied in a certain time period, while keeping the levels of other factors constant. The level of the factor to be investigated is then changed over a desired range to study its effects on the response. After the experimental results are obtained, certain graphs are generally constructed showing how the response is influenced by the factor studied [28].
Low level (1) High level (þ1) 30 0.01 1.66 3.33 3.33 110
60 0.05 5 10 10 220
Run
X1
X2
X3
X4
X5
X6
YV (ml H2 l1 d1)
1 2 3 4 5 6 7 8 9 10 11
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
268 438 357 289 250 682 557 443 310 370 320
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Table 3 e The result of ANOVA. Code X1 X2 X3 X4 X5 X6
3.
Variable
Significance (a)
Formate conc. Cell density Yeast extract conc. NaCl conc. Tryptone conc. Stirring speed
0.006 0.218 0.066 0.992 0.2 0.394
Results and discussion
3.1. Evaluation of PlacketteBurman experimental design The influence of the previously described fermentation parameters was studied by PlacketteBurman statistical experimental procedure. Analysis of variance (ANOVA) was performed to estimate the effect of each factor regarding our system (Table 3). ANOVA indicated that more or less every factors (except NaCl) can affect biohydrogen production using E. coli (XL1-BLUE) (Table 3). The relative importance of the variables was found as follows: X1 > X3 > X5 > X2 > X6 [ X4. Parameters above 95% confidence levels (a < 0.05) were considered significant and according to this assumption only formate concentration (carbon source) was found to be a significant parameter for hydrogen production. Based on ANOVA it can be stated formate concentration plays a key role in the hydrogen fermentation probably due to hydrogen metabolism in E. coli depends on formate compound. The results showed that higher hydrogen productivities can be achieved at higher formate concentrations. The effect of formate has to be further investigated in order to find the optimal substrate concentration. The results suggested that
Fig. 2 e The effect of formate and yeast extract on biohydrogen production.
nitrogen source concentration is also an important factor for hydrogen formation. Nitrogen is one of the main constituents of the organic compounds occurring in cells. Yeast extract and tryptone are widely employed as complex sources of proteins, amino acids, trace elements for microorganisms in different fermentation media. However, proteins are not sufficient for hydrogen fermentation. These materials are hydrolyzed to amino acids which are principally fermented in couples (by so-called Strickland reactions) where one amino acid serves
Fig. 3 e The correlation between bacterial growth and gas production. Dark square e optical density (600 nm); light square e cumulative gas volume.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 6 ( 2 0 1 1 ) 1 3 9 4 9 e1 3 9 5 4
Table 4 e Results of investigating the optimal formate concentration. Formate conc. (mM) 15 30 45 75
Productivity (ml H2 l1 d1)
Yield (mol H2/mol formate)
210 426 562 725
0.4 0.41 0.37 0.28
as the electron acceptor for the oxidation of the other amino acid. Thus, these reactions do not lead to hydrogen formation [8,30]. These findings were confirmed by our previous results, as well [31]. Nevertheless, high amount of nitrogen source is important for biohydrogen productivity since it is essentially required for the efficient microbial growth. The effect of formate and yeast extract on hydrogen productivity can be seen in Fig. 2 which was created by Statistica 8 software. The response surface was fitted to the 11 measured values (white circles) where some of the circles are above while the others are under the surface area. As reported in the literature, high Naþ concentration may have an inhibitory effect on hydrogen production [32]. The results of ANOVA clearly showed that our system was not suffered from sodium inhibition. Hydrogen fermentation by wild-type E. coli (XL1-BLUE) is basically a growth-associated procedure (Fig. 3) and thus the highest formation rates could be reached in the growing period. The results demonstrated that the correlation between the inoculum size and the rate of hydrogen production was not significant. However, higher rate could be achieved at higher inoculum concentration so it seems to be useful to maintain higher biomass density in the bioreactor. It is well known that the performance of a biosystem can be strongly limited by inefficient mass transfer
13953
properties. Therefore, stirring speed was chosen for the study of the diffusion circumstances in the system. Data obtained from the statistical analysis indicated that hydrogen generation in our system is not inhibited by diffusion but a slight increase in hydrogen productivity could be observed at faster stirring speed.
3.2.
Determination of optimal formate concentration
Based on PlacketteBurman design, only formate was found to be statistically significant on biohydrogen production and in order to find the optimal value of formate concentration the one-factor-at-a-time design was used. In all trials the insignificant variables were applied at their levels where they had positive contributions for biohydrogen production. In the optimization experiments firstly we aimed to find the optimum range of formate concentration therefore measurements were carried out at 15, 45, 75 mM formate concentrations and the productivities and yields were calculated. The data showed that higher productivities could be achieved at higher formate concentration but there could be observed a decrease in yields above 15 mM on the other hand. These results implied that we have to make a trade-off between productivity and yield and the optimal range of formate has to be between 15 and 45 mM. As the final step the optimum search of formate concentration was performed at 15, 30, 45 mM. The results are listed in Table 4 and graphically presented in Fig 4. The obtained data indicated that the increase in hydrogen productivity was linear and the yield was not decreased up to 30 mM, but at higher levels formate seems to have an inhibitory impact on biohydrogen production. Hence, 30 mM can be considered as optimum concentration of formate. This finding was confirmed by Yoshida et al. who have reported that
Fig. 4 e The effect of formate concentration on hydrogen productivity and yield. Dark square e volumetric productivity; light square e yield.
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formate concentration above 25 mM is disadvantageous for hydrogen fermentation using E. coli [11].
4.
Conclusions
In this study we focused on bioprocess optimization by experimental design using E. coli (XL1-BLUE) bacteria. PlacketteBurman design (screening procedure) was applied and formate (substrate) was identified as the key factor for hydrogen fermentation. After screening the optimum range and value of formate concentration were studied and 30 mM was determined as optimum where the productivity and yield were 426 ml H2 l1 d1 and 0.41 mol H2/mol formate, respectively. The optimal conditions for biohydrogen production were found to be 30 mM formate, 5 g/l yeast extract, 10 g/l tryptone, 3.33 g/l NaCl, 0.05 g dry cell weight/l initial cell density and 220 rpm stirring rate. Based on this result our further aim is to create a continuous hydrogen producing bioreactor and finally it is planned to build-up an integrated system where e coupling the fermenter and the membrane gas separation e purified biohydrogen will be produced, which can be used directly in fuel cells.
Acknowledgement The authors would like to thank Prof. K.L. Kovacs and coworkers (University of Szeged, Hungary) for supplying the strain.
references
[1] Chen SY, Chu CY, Cheng MJ, Lin CY. The autonomous house: a bio-hydrogen based energy self-sufficient approach. Int J Environ Res Public Health 2009;6(4):1515e29. [2] Liying X, Nanqi R, Xingzu W, Yongfeng J. Biohydrogen production by Ethanoligenens harbinense B49: nutrient optimization. Int J Hydrogen Energy 2008;33:6962e7. [3] Nanqi R, Jianzheng L, Baikun L, Yong W, Shirui L. Biohydrogen production from molasses by anaerobic fermentation with a pilot-scale bioreactor system. Int J Hydrogen Energy 2006;31:2147e57. [4] Mei-Ling C, Nor’Aini AR, Phang LY, Suraini AA, Raha AR, Yoshihito S, et al. Effects of pH, glucose and iron sulfate concentration on the yield of biohydrogen by Clostridium butyricum EB6. Int J Hydrogen Energy 2009;34:8859e65. [5] Nandi R, Sengupta S. Microbial production of hydrogen: an overview. Crit Rev Microbiol 1998;24:61e84. [6] Kova´cs KL, Maro´ti G, Ra´khely G. A novel approach for biohydrogen production. Int J Hydrogen Energy 2006;31:1460e8. [7] Krupp M, Widmann R. Biohydrogen production by dark fermentation: experiences of continuous operation in large lab scale. Int J Hydrogen Energy 2009;34:4509e16. [8] Hallenbeck PC. Fermentative hydrogen production: principles, progress, and prognosis. Int J Hydrogen Energy 2009;34:7379e89. [9] Jong-Hwan S, Jong HY, Eun KA, Mi-Sun K, Sang JS, Tai HP. Fermentative hydrogen production by the newly isolated Enterobacter asburiae SNU-1. Int J Hydrogen Energy 2007;32: 192e9.
[10] Redwood MD, Mikheenko IP, Sargent F, Macaskie LE. Dissecting the roles of Escherichia coli hydrogenases in biohydrogen production. FEMS Microbiol Lett 2008;278(1):48e55. [11] Yoshida A, Nishimura T, Kawaguchi H, Inui M, Yukawa H. Enhanced hydrogen production from formic acid by formate hydrogen lyase-overexpressing Escherichia coli strains. Appl Environ Microbiol 2005;71(11):6762e8. [12] Ghosh D, Hallenbeck PC. Fermentative hydrogen yields from different sugars by batch cultures of metabolically engineered Escherichia coli DJT135. Int J Hydrogen Energy 2009;34:7979e82. [13] Seol E, Kim S, Raj SM, Park S. Comparison of hydrogen production capability of four different Enterobacteriaceae strains under growing and non-growing conditions. Int J Hydrogen Energy 2008;33:5169e75. [14] Redwood MD, Macaskie LE. A two-stage, two-organism process for biohydrogen from glucose. Int J Hydrogen Energy 2006;31:1514e21. [15] Yoshida A, Nishimura T, Kawaguchi H, Inui M, Yukawa H. Efficient induction of formate hydrogen lyase of aerobically grown Escherichia coli in a three-step biohydrogen production process. Appl Microbiol Biotechnol 2007;74:754e60. [16] Yun J, Jin F, Kishita A, Tohji K, Enomoto H. Formic acid production from carbohydrates biomass by hydrothermal reaction. J Phys Conf Ser 2010;215:121e6. [17] Maeda T, Sanchez-Torres V, Wood TK. Enhanced hydrogen production from glucose by metabolically engineered Escherichia coli. Appl Microbiol Biotechnol 2007;77:879e90. [18] Leonhartsberger S, Korsa I, Bo¨ck A. The molecular biology of formate metabolism in enterobacteria. J Mol Microbiol Biotechnol 2002;4(3):269e76. [19] Sawers RG. Formate and its role in hydrogen production in Escherichia coli. Biochem Soc Trans 2005;33:42e6. [20] Mathews J, Wang G. Metabolic pathway engineering for enhanced biohydrogen production. Int J Hydrogen Energy 2009;34:7404e16. [21] Be´lafi-Bako´ K, Bu´csu´ D, Pientka Z, Ba´lint B, Herbel Z, Kova´cs KL, et al. Integration of biohydrogen fermentation and gas separation processes to recover and enrich hydrogen. Int J Hydrogen Energy 2006;31:1490e5. [22] Collet C, Adler N, Schwitzguebel JP, Peringer P. Hydrogen production by Clostridium thermolacticum during continuous fermentation of lactose. Int J Hydrogen Energy 2004;29:1479e85. [23] Khanal SK, Chen W-H, Ling L, Sung S. Biological hydrogen production: effects of pH and intermediate products. Int J Hydrogen Energy 2004;29:1123e31. [24] Kim DH, Han SK, Kim SH, Shin HS. Effect of gas sparging on continuous fermentative hydrogen production. Int J Hydrogen Energy 2006;31:2158e69. [25] Van Ginkel S, Logan BE. Inhibition of biohydrogen production by undissociated acetic and butyric acids. Environ Sci Technol 2005;39:9351e6. [26] Kennedy MJ, Reader SL, Davies RJ. The kinetics of developing fermentation media. Process Biochem 1994;29:529e34. [27] Weuster-Botz D. Experimental design for fermentation media development: statistical design or global random search? J Biosci Bioeng 2000;90(5):473e83. [28] Wang J, Wan W. Experimental design methods for fermentative hydrogen production: a review. Int J Hydrogen Energy 2009;34:235e44. [29] Plackett RL, Burman JP. The design of optimum multifactorial experiments. Biometrika 1946;33:305e25. [30] Gottschalk G. Bacterial metabolism. 2nd ed. Springer; 1986. [31] Be´lafi-Bako´ K, Bakonyi P, Nemesto´thy N, Pientka Z. Biohydrogen production in integrated system. Des Water Treat 2010;14:116e8. [32] Dong-Hoon K, Sang-Hyoun K, Hang-Sik S. Sodium inhibition of fermentative hydrogen production. Int J Hydrogen Energy 2009;34:3295e304.
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/he
Application of PlacketteBurman experimental design to optimize biohydrogen fermentation by E. coli (XL1-BLUE) P. Bakonyi*, N. Nemesto´thy, E´. Lo¨vitusz, K. Be´lafi-Bako´ Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem Street 10, 8200 Veszpre´m, Hungary
article info
abstract
Article history:
Escherichia coli is attractive for biotechnological hydrogen production. Compared to other
Received 21 January 2011
biohydrogen producing bacteria e.g. Clostridium species, E. coli is able to tolerate oxygen,
Received in revised form
fast growing and well-characterized in physiological and biochemical terms. According to
9 March 2011
the well known metabolic pathways of E. coli, the hydrogen production from different
Accepted 11 March 2011
substrates is dependent on the membrane-boundary formate-hydrogen lyase (FHL)
Available online 16 April 2011
enzyme complex. The efficiency and economic success of hydrogen fermentation are influenced by the applied operational conditions. In this work the optimal conditions
Keywords:
(composition of broth, inoculum size, stirring speed) for biohydogen fermentation using
Biohydrogen
E. coli (XL1-BLUE) were investigated by experimental design. We found that among the
E. coli
several variables only formate compound plays a key role in hydrogen formation and the
Experimental design
optimal conditions for biohydrogen production were identified as follows: 30 mM formate,
Process optimization
5 g/l yeast extract, 10 g/l tryptone, 3.33 g/l NaCl, 0.05 g dry cell weight/l initial cell density and 220 rpm stirring rate, where productivity and yield were 426 ml H2 l1 d1 and 0.41 mol H2/mol formate, respectively. Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
One of the greatest challenges that mankind has to face in the 21st century is to find “green” energy sources for the replacement of fossil fuels and ensuring sustainable development. The global warming and the rapidly depleting reservoirs of fossil energy carriers have induced the intensive research for renewable sources [1]. Biologically produced hydrogen has the potential to be the fuel of the future due to its high energy content and environmentally friendly characteristic [2e4]. The bioprocesses for hydrogen production can be divided into two main categories: the photosynthetic and the dark fermentation processes [5,6]. Nowadays, compared to light-driven hydrogen bioproduction, anaerobic dark
hydrogen fermentation is more feasible for practical application due to its higher efficiency, higher stability, simpler control requirements, etc. [7,8]. Microbes for fermentative hydrogen production either belong to strict anaerobes and facultative anaerobes [9]. Recently, a large number of microorganisms and substrates have been used and investigated for biohydrogen formation and Escherichia coli is considered as an attractive strain for bacterial hydrogen production [10e12]. E. coli can provide hydrogen using different substrates e.g. glucose, lactose, formic acid, etc. [12e14]. Among these alternatives, formate was shown to ensure the highest hydrogen productivity [15]. Furthermore, formic acid can be derived from low cost renewable materials, such as biomass [16]. Biological hydrogen production from formate is catalyzed
* Corresponding author. E-mail address: [email protected] (P. Bakonyi). 0360-3199/$ e see front matter Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2011.03.062
13950
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 6 ( 2 0 1 1 ) 1 3 9 4 9 e1 3 9 5 4
by the membrane-boundary formate-hydrogen lyase (FHL) complex. This enzyme complex of E. coli consists of formate dehydrogenase (FDH-H), hydrogenase (Hyd-3), and numerous electron transfer mediators [15,17,18]. In fact, formate is the sole precursor of H2 evolution in E. coli and it is converted to H2 and CO2 by the FHL enzyme complex [19,20]. This apparently simple transformation of formate compound to molecular hydrogen and carbon-dioxide relies on complex genetic, biochemical and physiological regulation and control but the particular description of that is beyond the scope of this paper. According to recent reported data several factors e.g. fermentation conditions like temperature, pH, the composition of broth, nutrient availability, gas partial pressure in the headspace of the fermenter, metabolites in liquid phase, etc. can affect the rate and yield of hydrogen production [21e25]. The economic and industrial success of the hydrogen producing biosystems significantly depends on the reaction conditions used and generally its optimization represents a significant cost and time factor in the bioprocess development [26]. Due to the wide range of process variables and the metabolic complexity of cells, experimental design procedures are often used to determine the optimal operational conditions. Among reaction conditions the composition of fermentation media (including carbon sources, nitrogen sources, mineral salts, trace elements, vitamins and other growth factors) is a very important factor that determines the nutrimental and chemical environment for the whole cell biocatalysts in the reactor and thus may influence the hydrogen generation potential of the strains [27]. To optimize a fermentative hydrogen production process the following experimental design strategy is highly recommended: Firstly, employ PlacketteBurman experimental design to identify the key factors of the respective hydrogen production system (Screening). Secondly, gradient method needs to be used to find the optimum variable ranges (Narrowing). Finally, the genuine optimal in the optimum range has to be determined (Optimum search) [28]. However, a complete statistical experimental design for the optimization of a bioprocess including hydrogen fermentation is surprisingly not frequently carried out [27,28]. According to earlier studies and our best knowledge only a few papers have dealt with the described 3-step procedure in order to optimize hydrogen production and neither of them applied E. coli strain [28]. In this paper the effect of the components in sodium-formate supplied LuriaeBertani (LB) medium and the influence of initial cell concentration and stirring speed were studied as well. Firstly PlacketteBurman experimental design was employed to identify the key factors for biohydrogen production using E. coli (XL1-BLUE) strain (derivative of E. coli K-12). After screening test we aimed to determine the optimum range and optimum value of the key parameters in order to improve hydrogen productivity and yield.
2.
Materials and methods
2.1.
Microorganism
E. coli (XL1-BLUE) was used in this research. The strain was provided by the University of Szeged (Prof. Kornel Kovacs
et al.). The culture was maintained in 70% glycerol at 80 C. The bacteria was periodically revived on Petri dishes using agar supported LB medium (10 g/l tryptone, 5 g/l yeast extract, 10 g/l sodium-chloride, 30 g/l agar) in which 10 mg/ml tetracycline was added. The dishes were incubated at 37 C and after 24 h of growth the fresh colonies were used to prepare the inoculum.
2.2.
Inoculum preparation
The seed culture was prepared aerobically in a 500 ml flask (working volume 300 ml) containing LB medium (in which 10 mg/ml tetracycline was added) and placed in a shaking incubator at 110 rpm agitation speed and 37 C for 24 h. Prior to inoculation, the effective cell concentration (g dry cell/l) in the obtained inoculum was measured spectrophotometrically (OD600, PG Instruments, model T80).
2.3.
Experimental conditions
Batch experiments were conducted in a 3500 ml bioreactor. The reactor was filled with the fermentation medium containing sodium-formate, tryptone, yeast extract, NaCl. Prior to inoculation the reactor was sterilized in autoclave for 1 h at 120 C. After sterilization the fermenter was cooled to ambient temperature under a sterile box and the appropriate amount of inoculum was added to obtain the desired cell density. The total volume of liquid phase and the headspace was 3100 ml and 400 ml, respectively. The bioreactor was incubated at 37 C and purged with high purity nitrogen (>99.9%) for 20 min before start-up in order to ensure anaerobic circumstances for biohydrogen production. Finally the reactor was sealed, the pH controller and the stirrer were attached to the fermentation set-up. The pH was adjusted and maintained at 6.5 by 5 M NaOH and 5 M sulfuric acid. In order to measure the volume of total evolved gas it was continuously released from the reactor and delivered to a volumetric gasmeter which was connected to a computer.
2.4.
Analytical methods
Concentrations of hydrogen, carbon-dioxide, nitrogen and oxygen were determined in the headspace of the bioreactor by gas chromatography. The gas samples were taken from the reactor using a gastight Hamilton syringe. H2, N2, O2 were analysed by gas chromatograph (Gow-Mac Series 600) equipped with a thermal conductivity detector and a molecular sieve column (zeolite filled X13, 2 m length * 28 mm diameter) with helium as carrier gas. CO2 was also measured by gas chromatograph (Hewlett Packard Instruments, model 5890) equipped with a TCD and capillary column (CarbonPlot, Agilent Technologies) with nitrogen as carrier gas. The injection volume was 1 ml. The volumetric hydrogen gas production rate YV (ml H2 l1 d1) for each time interval was calculated from headspace measurements and the total volume of the gas mixture produced using the following equation: VH;i ¼ V0 CH;i þ
X
VG;i CH;i
where VH,i is the total volume of biohydrogen produced at
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Fig. 1 e The time course of the fermentation. Dark square e gas production rate; light square e cumulative gas volume.
2.5.
Experimental design and statistical analysis
where YV (ml H2 l1 d1) is the response or dependent variable, Mi is the linear regression coefficient, Xi is the level of the independent variable, and I is the model intercept. According to PlacketteBurman design all factors were tested at a low and a high level coded as (1) and (þ1), respectively (Table 1). The design matrix is shown in Table 2 where it can be seen the effect of 6 variables was investigated in 11 independent experimental runs including triplicate measurements in the center point in order to estimate the standard deviation. Statistica 8 software was used to analyze the experimental design. The measurements were carried out in random order.
2.5.1.
PlacketteBurman design
2.5.2.
(i) time interval, V0 is the volume of the headspace, CH,i is the volume fractions of biohydrogen gas at the current (i) time interval in the headspace of the fermenter and VG,i is the total volume of gas produced at the current (i) time intervals, respectively. As an example the time course of the fermentation is illustrated in Fig. 1 where the conditions were as follows: 30 mM formate, 5 g/l yeast extract, 3.33 g/l tryptone, 3.33 g/l NaCl, 220 rpm stirring speed, 0.05 g (dry cell weight)/l initial cell density.
The goal of applying PlacketteBurman experimental design was to identify which ingredients of the medium (sodiumformate, tryptone, yeast extract, sodium-chloride) and fermentation parameters (inoculum size, stirring speed) have significant effect on the biohydrogen producing capability of E. coli (XL1-BLUE). When this kind of statistical experimental design is employed it is assumed that no interactions between different factors occur in the range of variables under consideration [29]. A first order, linear approach is sufficient for the screening procedure: YV ¼
X
Table 2 e The PlacketteBurman experimental design matrix for evaluating data.
Mi Xi þ I
Table 1 e The codes and levels of the variables. Code X1 X2 X3 X4 X5 X6
Variable Formate (mM) Cell density (g dry cell/l) Yeast extract (g/l) NaCl (g/l) Tryptone (g/l) Stirring speed (rpm)
One-factor-at-a-time design
One-factor-at-a-time design is a traditional experimental design where only one factor is studied in a certain time period, while keeping the levels of other factors constant. The level of the factor to be investigated is then changed over a desired range to study its effects on the response. After the experimental results are obtained, certain graphs are generally constructed showing how the response is influenced by the factor studied [28].
Low level (1) High level (þ1) 30 0.01 1.66 3.33 3.33 110
60 0.05 5 10 10 220
Run
X1
X2
X3
X4
X5
X6
YV (ml H2 l1 d1)
1 2 3 4 5 6 7 8 9 10 11
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 0
268 438 357 289 250 682 557 443 310 370 320
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Table 3 e The result of ANOVA. Code X1 X2 X3 X4 X5 X6
3.
Variable
Significance (a)
Formate conc. Cell density Yeast extract conc. NaCl conc. Tryptone conc. Stirring speed
0.006 0.218 0.066 0.992 0.2 0.394
Results and discussion
3.1. Evaluation of PlacketteBurman experimental design The influence of the previously described fermentation parameters was studied by PlacketteBurman statistical experimental procedure. Analysis of variance (ANOVA) was performed to estimate the effect of each factor regarding our system (Table 3). ANOVA indicated that more or less every factors (except NaCl) can affect biohydrogen production using E. coli (XL1-BLUE) (Table 3). The relative importance of the variables was found as follows: X1 > X3 > X5 > X2 > X6 [ X4. Parameters above 95% confidence levels (a < 0.05) were considered significant and according to this assumption only formate concentration (carbon source) was found to be a significant parameter for hydrogen production. Based on ANOVA it can be stated formate concentration plays a key role in the hydrogen fermentation probably due to hydrogen metabolism in E. coli depends on formate compound. The results showed that higher hydrogen productivities can be achieved at higher formate concentrations. The effect of formate has to be further investigated in order to find the optimal substrate concentration. The results suggested that
Fig. 2 e The effect of formate and yeast extract on biohydrogen production.
nitrogen source concentration is also an important factor for hydrogen formation. Nitrogen is one of the main constituents of the organic compounds occurring in cells. Yeast extract and tryptone are widely employed as complex sources of proteins, amino acids, trace elements for microorganisms in different fermentation media. However, proteins are not sufficient for hydrogen fermentation. These materials are hydrolyzed to amino acids which are principally fermented in couples (by so-called Strickland reactions) where one amino acid serves
Fig. 3 e The correlation between bacterial growth and gas production. Dark square e optical density (600 nm); light square e cumulative gas volume.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 6 ( 2 0 1 1 ) 1 3 9 4 9 e1 3 9 5 4
Table 4 e Results of investigating the optimal formate concentration. Formate conc. (mM) 15 30 45 75
Productivity (ml H2 l1 d1)
Yield (mol H2/mol formate)
210 426 562 725
0.4 0.41 0.37 0.28
as the electron acceptor for the oxidation of the other amino acid. Thus, these reactions do not lead to hydrogen formation [8,30]. These findings were confirmed by our previous results, as well [31]. Nevertheless, high amount of nitrogen source is important for biohydrogen productivity since it is essentially required for the efficient microbial growth. The effect of formate and yeast extract on hydrogen productivity can be seen in Fig. 2 which was created by Statistica 8 software. The response surface was fitted to the 11 measured values (white circles) where some of the circles are above while the others are under the surface area. As reported in the literature, high Naþ concentration may have an inhibitory effect on hydrogen production [32]. The results of ANOVA clearly showed that our system was not suffered from sodium inhibition. Hydrogen fermentation by wild-type E. coli (XL1-BLUE) is basically a growth-associated procedure (Fig. 3) and thus the highest formation rates could be reached in the growing period. The results demonstrated that the correlation between the inoculum size and the rate of hydrogen production was not significant. However, higher rate could be achieved at higher inoculum concentration so it seems to be useful to maintain higher biomass density in the bioreactor. It is well known that the performance of a biosystem can be strongly limited by inefficient mass transfer
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properties. Therefore, stirring speed was chosen for the study of the diffusion circumstances in the system. Data obtained from the statistical analysis indicated that hydrogen generation in our system is not inhibited by diffusion but a slight increase in hydrogen productivity could be observed at faster stirring speed.
3.2.
Determination of optimal formate concentration
Based on PlacketteBurman design, only formate was found to be statistically significant on biohydrogen production and in order to find the optimal value of formate concentration the one-factor-at-a-time design was used. In all trials the insignificant variables were applied at their levels where they had positive contributions for biohydrogen production. In the optimization experiments firstly we aimed to find the optimum range of formate concentration therefore measurements were carried out at 15, 45, 75 mM formate concentrations and the productivities and yields were calculated. The data showed that higher productivities could be achieved at higher formate concentration but there could be observed a decrease in yields above 15 mM on the other hand. These results implied that we have to make a trade-off between productivity and yield and the optimal range of formate has to be between 15 and 45 mM. As the final step the optimum search of formate concentration was performed at 15, 30, 45 mM. The results are listed in Table 4 and graphically presented in Fig 4. The obtained data indicated that the increase in hydrogen productivity was linear and the yield was not decreased up to 30 mM, but at higher levels formate seems to have an inhibitory impact on biohydrogen production. Hence, 30 mM can be considered as optimum concentration of formate. This finding was confirmed by Yoshida et al. who have reported that
Fig. 4 e The effect of formate concentration on hydrogen productivity and yield. Dark square e volumetric productivity; light square e yield.
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formate concentration above 25 mM is disadvantageous for hydrogen fermentation using E. coli [11].
4.
Conclusions
In this study we focused on bioprocess optimization by experimental design using E. coli (XL1-BLUE) bacteria. PlacketteBurman design (screening procedure) was applied and formate (substrate) was identified as the key factor for hydrogen fermentation. After screening the optimum range and value of formate concentration were studied and 30 mM was determined as optimum where the productivity and yield were 426 ml H2 l1 d1 and 0.41 mol H2/mol formate, respectively. The optimal conditions for biohydrogen production were found to be 30 mM formate, 5 g/l yeast extract, 10 g/l tryptone, 3.33 g/l NaCl, 0.05 g dry cell weight/l initial cell density and 220 rpm stirring rate. Based on this result our further aim is to create a continuous hydrogen producing bioreactor and finally it is planned to build-up an integrated system where e coupling the fermenter and the membrane gas separation e purified biohydrogen will be produced, which can be used directly in fuel cells.
Acknowledgement The authors would like to thank Prof. K.L. Kovacs and coworkers (University of Szeged, Hungary) for supplying the strain.
references
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