- Email: [email protected]
Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1
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 x x x ( 2 0 1 6 ) 1 e8
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1 Yameng Li a, Zhiping Zhang a, Yanyan Jing a, Xumeng Ge b, Yi Wang a, Chaoyang Lu a, Xuehua Zhou a, Quanguo Zhang a,* a Collaborative Innovation Center of Biomass Energy, Henan Province, Henan Agricultural University, Zhengzhou 450002, China b The Ohio State University, OH 44691, USA
article info
abstract
Article history:
Hydrogen is a promising alternative energy, and can be produced from various materials.
Received 13 September 2016
Platanus orientalis leaves (POL) was utilized to produce hydrogen through simultaneous
Received in revised form
saccharification fermentative (SSF) method with photosynthetic bacteria HAU-M1. The
3 November 2016
method was investigated using response surface methodology (RSM) with central com-
Accepted 28 November 2016
posite design (CCD). The PlacketteBurman design was first used to screen the factors
Available online xxx
(initial pH, light intensity, temperature, substrate concentration and initial inoculum) that influence SSF hydrogen production. Results indicated that initial pH, temperature and
Keywords:
inoculation amount had a statistically significant effect on SSF hydrogen production.
Platanus orientalis leaves
Central composite design was further carried out to evaluate interaction between the
Bio-hydrogen production
selected variables. The optimal conditions were determined to be initial pH of 6.18, tem-
Simultaneous saccharification
perature of 35.59 C, and inoculation amount of 26.29% (v/v). The predicted maximum
fermentative
hydrogen yield was 65.03 mL H2/g TS, which was very close to the experimental result of
Photosynthetic bacteria
64.10 mL H2/g TS obtained under the optimal conditions. The R2 value was 0.9396, indi-
Response surface methodology
cating a good model fit for SSF hydrogen production. © 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Increasing demand of energy and over consumption of finite fossil fuel resources have led to growing interest in development of energy from renewable resources [1e3]. Hydrogen is regarded as a promising renewable energy, due to its high energy density (122 KJ g1) [4,5] and no greenhouse gases
generation during combustion [6e8]. As one of the various hydrogen production methods, biological hydrogen production becomes increasingly attractive, because it can be carried out at ambient temperature and pressure with low energyinput and high efficiency [9,10]. Biological method mainly includes photo-fermentative hydrogen production and darkfermentative hydrogen production [11,12], and can use various types of substrates, such as sucrose, xylose, crop
* Corresponding author. Fax: þ86 371 63558040. E-mail address: [email protected] (Q. Zhang). http://dx.doi.org/10.1016/j.ijhydene.2016.11.182 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
2
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 x x x ( 2 0 1 6 ) 1 e8
residues, food waste, and animal waste. Alternatively, photosynthetic bacteria (PSB) can use small-chain organic acids as electron donors for H2 production. Shi XY found Rhodopseudomonas capsulate can use volatile fatty acids as electron donors for continuous H2 production at the expense of light energy, and a substrate conversion efficiency of 45% were achieved [13]. Tugba Keskin used sugar industry wastes to hydrogen production with single-stage photofermentation, and found 10.5 mol H2/mol sucrose for beet molasses (1 g/L sugar); 8 mol H2/mol sucrose for black strap (1 g/L sugar) and 14 mol H2/mol sucrose for pure sucrose [14]. So PSB with high degradation rate of organic matter and high efficiency of hydrogen production is being widely studied in renewable energy field [15e18]. When using biomass as substrate, the hydrogen production process usually has two steps: enzymatic hydrolysis, and fermentative bio-hydrogen production [19,20]. However, these two steps can also be conducted simultaneously, which is referred to as simultaneous saccharification fermentation (SSF). At present, the SSF technology is widely used in ethanol and lactic acid fermentation, B Erdei optimized ethanol production with Lignocellulosic by s-Pejo E studied nutrient addition on preSSF [26], Toma inoculum growth of Saccharomyces cerevisiae for application in SSF processes [27], but has limited application in the biohydrogen production process [21e25]. Compared with separate hydrolysis and fermentation (SHF), SSF has several advantages, including weakened feedback inhibition on cellulase by glucose and cellobiose, short processing time, reduced number of reactors, low experimentation cost, and high productivity [28,29]. The major challenge for SSF is that enzymatic hydrolysis and hydrogen fermentation are normally developed separately with different optimal conditions, such as initial pH and temperature. The optimal initial pH is 4.8 and 7, and the optimal temperature is 50 C and 30 C in the process of enzymatic hydrolysis and hydrogen fermentation, respectively [19]. As a result, further optimization of reaction conditions is necessary for SSF hydrogen production, to obtain the conditions in which hydrolysis and fermentation can run efficiently. Platanus orientalis is a main road greening tree, the annual leaves production of Chinese city could reach ten-million tones, normal processing mode for these leaves are compost, landfill and incineration mainly now, which will make more environment pollution and resource waste [30e32]. So proper disposal method is needed for a search. The P. orientalis leaves (POL) have a potential to be used as an abundant feedstock for the bio-hydrogen production, which contains abundant cellulose. In this study, SSF hydrogen production from POL was evaluated and optimized. The highefficient photosynthetic bacteria (PSB) HAU-M1, which was found to have wider adaptation range and higher hydrogen yield than single strain and have higher activity in temperature 30 C and neutral environment, and light intensity, substrate concentration and initial inoculation amount also existed certain appropriate range, 3000e3500 lx, 25e30 mg/ mL, 20e25% (v/v), respectively, in previous research [33,34], was adopted as hydrogen production strains. Initial pH, light intensity, temperature, substrate concentration and initial inoculum were analyzed firstly to find their effects on the
hydrogen production process. Then, PlacketteBurman design was applied to screen the important factors. Response surface methodology with central composite design was adopted to further determine optimal levels of selected factors for maximized simultaneous hydrogen production [35e37].
Materials and methods Microorganisms and media The PSB HAU-M1 was provided by key laboratory of new materials and facilities for rural renewable energy, including Rhodospirillum rubrum, Rhodobacter capsulatus and Rhodopseudomonas palustris [38]. When the bacteria's concentration reached OD660nm of 1.2 (the cell dry weight is 1.25 ± 0.02 g/L) in about 48 h after inoculation, which was used as inoculum for further experiments. The culture medium contained NH4Cl (1 g/L), NaHCO3 (2 g/L), K2HPO4 (0.2 g/L), CH3COONa (3 g/L), MgSO4$7H2O (0.2 g/L), NaCl (2 g/L), and 1 g/L of yeast extract and Micronutrient solution (1 mL) that contained FeCl3$6H2O (5 mg/L), ZnSO4$7H2O (1 mg/L), CuSO4$5H2O (0.05 mg/L), H3BO4 (1 mg/L), MnCl2$4H2O (0.05 mg/L), and Co(NO3)2$6H2O (0.5 mg/ L). The pH value was adjusted to 7.0 [19,38]. P. orientalis leaves were collected from street trees in Henan Agricultural University, crushed into powder, and screened with 40-mesh sieve. The composition of the processed sample is as follows: cellulose of 32.51%, hemicellulose of 19.65%, lignin of 30.13%, volatile solids content of 91.5% and other of 17.71%, The C, N, P and micro-nutrient of the fermentation medium were supplied in the following dosages: NH4Cl (0.4 g/ L), MgCI2 (0.2 g/L), yeast extract (0.1 g/L), K2HPO4 (0.5 g/L), NaCl (2 g/L), and sodium glutamate (3.5 g/L).
Photo-fermentative bio-hydrogen production system The PFHP system is shown in Fig. 1, which consists of a reactor, an incandescent lamp, an incubator, a purifier, a gas cylinder, and a correlation detection instrument.
Batch reactor and fermentation All experiments were carried out in 150 mL flasks. The reactors were filled with argon (Ar) gas then sealed with silicone rubber, in order to create an anaerobic condition. The enzyme employed was cellulase (Trichoderma Vride G), with an enzyme activity of 35 u/mg. pH was adjusted by adding HCl or NaOH to obtain the settled value.
Analytical methods The volume and composition of gas were measured every 12 h. The relative hydrogen concentration was determined by gas chromatography (6820 GC-14B, Agilent Technologies; Beijing, China). Nitrogen was used as the carrier gas at a flow rate of 45 mL/min. The operational temperatures of the injector, detector and column were 100, 80 and 150, respectively. The hydrogen standard curve of the regression equation as Eq. (1) (R2 ¼ 0.9991):
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
3
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 x x x ( 2 0 1 6 ) 1 e8
1. Incubator 2. Hydrogen production reactor 3. Incandescent lamp 4. Testing port 5. Airway 6. Gas purifier 7. Gas collecting bottle 8. Gas chromatograph 9. Water block 10. Argon gas valve
Fig. 1 e The experimental device of photosynthetic hydrogen production with Platanus orientalis leaves.
Y ¼ 0:00087x 0:7636
(1)
where, Y is the relative hydrogen concentration, and x is peak area of hydrogen.
Experimental design and optimization Plackette Burman design (PBD) The PBD method was used to screen the significant variables (Table 1) for hydrogen production by PSB HAU-M1. This is a very economical factorial design with the run number a multiple of four and comprises two-level screening design [39]. The level for each factor was determined according to results of single factor experiments, using unit mass production quantity (mL/g TS) as response value. Table 1 shows factors investigated to find the key significant factors influencing the hydrogen production. All the factors are prepared in two levels: 1 for low level and þ1 for high level. The design included twelve runs of various combinations of levels of the assessed factors and all runs were done in triplicate [36].
Central composite design (CCD)
Table 1 e The PlacketteBurman design for screening independent variables.
X1 X2 X3 X4 X5
Variables
Y ¼ C0 þ
X
Ci Xi þ
X
Ci Xi 2 þ
X
Cij Xi Xj
(2)
where, Y is the predicted response, C0 is the intercept, Ci is the linear coefficient, and Cij is the interaction coefficient. An analysis of variance (ANOVA) was performed, and three-dimensional response surface curves were plotted by Design Expert 8.0 software to study the interaction among these factors.
Result and discussion Screening of significant factors for SSF hydrogen production
Central composite design is a method that can fit the response surface model through a small amount of experiments. The three selected significant variables from PBD are Initial pH (X1), Temperature (X2), and Initial inoculation amount (X5). Light and Substrate concentration are no significant variables, maybe because photosynthetic bacteria have reached light saturation point and the substrate can be effectively hydrolyzed in the selected range. In order to maximize hydrogen production, CCD was used to optimize the experimental parameters. The minimum and maximum range of variables investigated and their values in actual and coded form are listed in Table 2. Five experimental levels: a, 1, 0, þ1, þa,
Code
where a ¼ 2n/4, n was the number of factors and 0 corresponds to the central level which was selected from the preliminary work [36,40]. Experimental design includes 20 runs, and fermentation was carried out separately for each with triplicates. Hydrogen yield was taken as the response (Y). Regression analysis was performed on the data obtained. This resulted in an empirical model that related the response measured to the independent variables of the experiment. For any system, the model equation is represented as Eq. (2):
Unit
Initial pH Temperature C Substrate concentration mg/ml Light intensity lx Inoculation amount %(v/v)
Low level High level (1) (þ1) 5.00 30.00 25.00 3000 20
7.00 40.00 50.00 4000 30
Plackette Burman design was carried out to screen the significant variables. Variables that had significant effect on SSF hydrogen production (p < 0.05) were selected and were used for further optimization. The results showed that the response varied from 54.54 mL H2/g TS to 64.24 mL H2/g TS (Table 3), which indicated the necessity of optimization. Initial pH (X1), temperature (X2), and inoculation amount (X5) were found to have the most significant influence on the biohydrogen production process. The results were confirmed by the results of the statistical analysis of PBD listing in Table 4, in which the p-value of X1, X2 and X5 were less than 0.05. Based on the results of PBD, a polynomial, first order equation was developed, excluding the insignificant variables, describing the
Table 2 e Levels of the variables of CCD. Code
X1 X2 X5
Variables
Initial pH Temperature ( C) Inoculation amount (%)
Levels a
1
0
þ1
þa
4.318 26.591 16.591
5 30 20
6 35 25
7 40 30
7.681 43.409 33.409
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
4
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 x x x ( 2 0 1 6 ) 1 e8
Table 3 e The PBD matrix representing the coded values for independent factors and the values of measured response. Run
X1
1 2 3 4 5 6 7 8 9 10 11 12
1 þ1 1 1 1 þ1 1 þ1 þ1 þ1 þ1 1
X2/ C X3/mg/mL X4/lx X5/%(v/v) Y/mL/g TS þ1 þ1 1 1 þ1 þ1 þ1 þ1 1 1 1 1
þ1 þ1 1 þ1 1 1 þ1 1 1 þ1 þ1 1
1 1 þ1 þ1 1 1 þ1 þ1 1 þ1 þ1 1
þ1 1 1 þ1 þ1 1 1 þ1 þ1 1 þ1 1
56.16 64.26 55.54 54.54 55.19 61.95 58.45 59.67 57.81 59.67 57.60 55.19
Table 4 e Statistical analysis of PBD. SS
Df
F-value
Prob > F
Model X1 X2 X3 X4 X5
95.55 55.86 19.58 2.37 1.20 16.54
5 1 1 1 1 1
47.43 138.65 48.61 5.88 2.97 41.06
<0.0001 <0.0001 0.0004 0.0516 0.1355 0.0007
R-Squared ¼ 0.9753
Adj R-Square ¼ 0.9548
Source
correlation between the variables used for study. The hydrogen production yield, Y (mL/g TS) could be represented as Eq. (2): Y ¼ 58:17 þ 2:16X1 þ 1:44X2 1:77X5
(2)
where, Y is the response, and X1, X2 and X5 are the coded values of Initial pH, temperature, and inoculation amount, respectively. The statistical significance of the model was evaluated by ANOVA. p-value (p < 0.05) indicated the significance of the experiment. The determination coefficient R2 value of the model was 0.9753, indicating that the equation optimization was successful. The correction factor R2 value of the model was 0.9548, indicating that 95.48% of the variability in the response could be explained by the model (Table 4).
Central composite design
Table 5 e Central composite design matrix with responses. Run
X1
X2
X5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
þa 0 1 1 0 þ1 a 1 0 0 0 0 0 þ1 0 þ1 1 1 0 0
0 þa þ1 þ1 0 1 0 1 0 0 a 0 0 þ1 0 1 þ1 1 0 0
0 0 1 1 0 1 0 1 a 0 0 0 0 þ1 0 þ1 þ1 1 0 þa
Hydrogen yield mL/g TS Observed values
Predicted values
53.83 55.20 59.29 54.25 61.91 52.93 44.54 57.29 53.27 65.22 49.67 67.15 65.16 56.01 64.32 55.36 52.72 47.27 64.07 61.43
53.78 55.93 59.28 51.74 64.57 51.08 46.97 55.62 55.91 64.57 51.32 64.57 64.57 55.76 64.57 56.18 52.89 45.84 64.578 61.17
As shown in Table 6, the data obtained were evaluated by ANOVA, which showed that the lack-of-fit was not significant (p ¼ 0.2121 > 0.05). Therefore, the responses are adequate for employing in this model. The R2 value of 0.9396 and adjusted R2 of 0.8852 showed that the model could effectively predict the response and explaining 94% of the variability in the SSF hydrogen production, The R2 closes to 1 indicated the better of the model predicting the response [40]. A low value of the coefficient of variation (C.V. % ¼ 3.37%) indicates a high precision and reliability of the experimental variables, “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable. In this case, the ratio of 12.309 indicates an adequate signal. This model can be used to navigate the design space. p-value denotes the importance of each coefficient, and is helpful in understanding the interactions among the variables. Linear terms of initial pH (X1), temperature (X2) and Initial inoculum (X5) showed a significant effect on SSF hydrogen production individually (p < 0.05). The quadratic
Table 6 e Analysis of variance of the model.
Based on the results of PBD, Initial pH (X1), temperature (X2), inoculation amount (X5) were selected for further exploring by the CCD. The responses obtained at different experimental runs are represented in Table 5. By applying multiple regression analysis on the experimental data, the following second-order polynomial equation was obtained to describe the hydrogen yield as a function of the significant variables as follow Eq. (3): Y ¼ 59:08 þ 1:85X1 þ 1:226X2 þ 1:43X5 þ 0:53X1 X2 1:07X1 X3 1:97X2 X5 4:59X21 3:54X22 1:95X25 (3) where, Y is the response, and X1, X2 and X5 are Initial pH, temperature, and inoculation amount, respectively.
Source
SS
Df
MS
F-value
p-value
Model X1 X2 X5 X1X2 X1X5 X2X5 X21 X22 X25 Residual Lack of fit Pure error Cor total
720.41 56.12 25.66 33.42 2.65 10.9 37.24 363.03 215.84 65.52 46.33 31.54 14.79 766.74
9 1 1 1 1 1 1 1 1 1 10 5 5 19
80.05 56.12 25.66 33.42 2.65 10.9 37.24 363.03 215.84 65.52 4.63 6.31 2.96
17.28 12.11 5.54 7.21 0.57 2.35 8.04 78.36 46.59 14.14
<0.0001 0.0059 0.0404 0.0229 0.4673 0.1560 0.0177 <0.0001 <0.0001 0.0037
2.13
0.2127
R-Squared ¼ 0.9396
Adj R-Square ¼ 0.8852
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
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 x x x ( 2 0 1 6 ) 1 e8
5
Fig. 2 e Three-dimensional surface plot and Contour plot for specific hydrogen yield. The response surface model was obtained by the CCD with the date shown in Table 5. (a) the effect of initial pH, temperature and their mutual interaction on hydrogen yield with level of inoculation amount 25%; (b) the effect of inoculation amount initial pH and their mutual interaction on hydrogen yield with level of temperature 35 C; (c) the effect of inoculation amount, temperature and their mutual interaction on hydrogen yield with level of initial pH 6. Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
6
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 x x x ( 2 0 1 6 ) 1 e8
model terms of initial pH (X21) and temperature (X22) are highly significant (p < 0.0001), inoculation amount (X25) is also significant (p < 0.0037). A significant interaction between the temperature and inoculation amount (X2X5) was observed (Table 6). Subsequently, from this optimization study, the maximum yield of 65.03 mL/g TS was predicted from Eq. (2), and the optimal conditions were initial pH of 6.18, temperature of 35.59 C, and initial inoculum of 26.29% (v/v). Further experiments obtained a hydrogen yield of 64.48 mL/g TS under the optimal condition, which was consistent with the predicted response. Response surface plots of the mode is the best way to visualize the influence of the independent variables on the dependent variable, which was done by varying two variables within the experimental range and holding the other three are constant at central level. The 3D response surface and contour plots were employed to determine the interaction of the fermentation conditions and the optimum levels for SSF hydrogen production. The color and shape of contour plots can identify the significant and the interaction between of variables, the faster the color changed, the bigger the gradient was, and the more significant the impact was on the experiment, strong interaction exists if contour lines are elliptical in shape, and no interaction is observed if contour lines are circular [41,42]. Fig. 2 shows the three-dimensional response surface and contour plot for specific hydrogen yield as a function of initial pH, temperature and inoculation amount, as emulated by Eq. (2). The mutual effects of initial pH and temperature on hydrogen production are shown in Fig. 2a. Under the studied conditions, specific hydrogen yield continuously increased with increasing initial pH and temperature until it reached its peak, and then decreased with a further increase in initial pH and temperature. The highest hydrogen yield was obtained at pH of 6.18 and temperature of 35.6 C. The reason may be that increasing pH inhibited the activity of cellulase, which affects substrate hydrolysis in the process of SSF. Moreover, temperature also have some impact both on the activity of cellulase and the activity of PSB, so temperature affect enzymatic synthesis and the metabolism of PSB, which results in the different substrate utilization rate and hydrogen yield. Besides, there was no significant interaction between the variables, which was also confirmed by the ANOVA results in Table 6 in which the p-value for X1X2 was 0.4673. Fig. 2b shows the combined effect of initial pH and inoculation amount on the response. An optimum point at 6.18, 26.29% (v/v) was found in the design boundary, and there was no significant interaction between initial pH and inoculation amount, according to the shape of contour lines and the p-value for X1X5 (0.1560 determined by ANOVA). The theoretical reason may be that photosynthetic bacteria could adapt to different acidebase conditions in a certain range by regulating the metabolic pathway (Acetic acid pathway, Propionate pathway, and so on) which could directly affect the pH of the medium to maintain a suitable fermentation condition [38]. As shown in Fig. 2c, strong interaction was observed between inoculation amount and temperature, as the contour is elliptical and p-value for X2X5 was 0.0177 (ANOVA result). The surface plot (Fig. 2c) also shows that hydrogen yield is more sensitive to inoculation amount and
temperature. The contour plots are elliptical spontaneously, which means that the interactive effects are momentous. According to the surface plot, the effect of temperature on hydrogen yield was similar to initial pH, with a steeper slope on the inoculation amount axis compared to the temperature axis. The optimum point was observed at inoculation amount 26.29% (v/v) and temperature 35.6 C. As shown in Fig. 2, the significant peaks of the surface plots for specific hydrogen yield and the clear maximal of the corresponding contour plots indicated that the maximum specific hydrogen yield could be obtained inside the design range. The maximum yield of 65.03 mL/g TS was predicted under the optimal conditions which were initial pH of 6.18, temperature of 35.59 C, and initial inoculum of 26.29% (v/v). The experimental result of 64.10 mL H2/g TS obtained under the optimal conditions by SSF which was higher than previous studies, the hydrogen yield reached 34.5 mL H2/g TS with POL by SHF [30], and 57.83 mL H2/g TS with enzymatic hydrolysate superna€ tants of corn stalk by SHF [43]. Ohgren K et al. [44] preformed comparison between SSF and SHF using steam-pretreated corn stover for ethanol. SSF gave a 13% higher overall ethanol yield than SHF. Because SSF could weaken feedback inhibition on cellulase by glucose and cellobiose and promote the positive reaction.
Conclusion Statistical optimization was proved to be an effective method for optimization of the SSF hydrogen production by HAU-M1. Three significant variables affecting SSF hydrogen production were initial pH, temperature and inoculation amount, which were selected by the PB experiments. Maximum SSF hydrogen production 65.03 mL/g TS was obtained under the optimized condition: initial pH of 6.18, temperature of 35.6 C, and inoculation amount of 26.29% (v/v).
Acknowledgments The authors gratefully acknowledged financial support from the National Natural Science Foundation of China (No. 51376056) and Quality control mechanism and mass transfer characteristics of straw dark fermentation effluent of biohydrogen production processes (No. U1504509).
references
[1] Chen HZ, Liu ZH. Steam explosion and its combinatorial pretreatment refining technology of plant biomass to biobased products. Biotechnol J 2015;10:866e85. [2] Chen HZ, Qiu WH. Key technologies for bio-ethanol production from lignocelluloses. Biotechnol Adv 2010;28:556e62. [3] Asif M, Muneer T. Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 2007;11:1388e413. [4] Nandi R, Sengupta S. Microbial production of hydrogen: an overview. Crit Rev Microbiol 1998;24:61e84.
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
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 x x x ( 2 0 1 6 ) 1 e8
[5] Mizuno O, Dinsdale R, Hawkes FR, Hawkes DL, Noike T. Enhancement of hydrogen production from glucose by nitrogen gas sparging. Bioresour Technol 2000;73:59e65. [6] Hosseini SE, Wahid MA, Jamil MM, Azli AAM, Misbah MF. A review on biomass-based hydrogen production for renewable energy supply. Int J Energy Res 2015;39:1597e615. [7] Nicoletti G, Arcuri N, Nicoletti G, Bruno R. A technical and environmental comparison between hydrogen and some fossil fuels. Energy Convers Manag 2015;89:205e13. [8] Tuna E, Kargi F, Argun H. Hydrogen gas production by electrohydrolysis of volatile fatty acid (VFA) containing dark fermentation effluent. Int J Hydrogen Energy 2009;34:262e9. [9] Kalinci Y, Hepbasli A, Dincer I. Biomass-based hydrogen production: a review and analysis. Int J Hydrogen Energy 2009;34:8799e817. [10] Mathews J, Wang G. Metabolic pathway engineering for enhanced bio-hydrogen production. Int J Hydrogen Energy 2009;34:7404e16. [11] Wang JL, Wan W. Effect of Fe2þ concentrations on fermentative hydrogen production by mixed cultures. Int J Hydrogen Energy 2008;33:1215e20. [12] Wang JL, Wan W. Comparison of different pretreatment methods for enriching hydrogen producing cultures from digested sludge. Int J Hydrogen Energy 2008;33:2934e41. [13] Shi XY, Yu HQ. Continuous production of hydrogen from mixed volatile fatty acids with Rhodopseudomonas capsulata. Int J Hydrogen Energy 2006;31:1641e7. [14] Keskin T, Hallenbeck PC. Hydrogen production from sugar industry wastes using single-stage photofermentation. Bioresour Technol 2012;112:131e6. [15] Zhao C, Karakashev D, Lu W, Wanga H, Angelidaki I. Xylose fermentation to biofuels (hydrogen and ethanol) by extreme thermophilic (70 C) mixed culture. Int J Hydrogen Energy 2010;35:3415e22. [16] Eroglu E, Melis A. Photobiological hydrogen production: recent advances and state of the art. Bioresour Technol 2011;102:8403e13. [17] Lu CY, Zhang ZP, Ge XM, Wang Y, Zhou XH, You XF, et al. Biohydrogen production from apple waste by photosynthetic bacteria HAU-M1. Int J Hydrogen Energy 2016;41:13399e407. [18] Ponsano EHG, Paulino CZ, Pinto MF. Phototrophic growth of Rubrivivax gelatinosus in poultry slaughter house wastewater. Bioresour Technol 2008;99:3836e42. [19] Zhang QG, Wang SL, You XF. Effects of the influencing factors of photosynthetic bacteria group on hydrogen production. Trans Chin Soc Agric Eng 2006;22:180e5. [20] Kootstra AMJ, Mosier NS, Scott EL, Beeftink HH, Sanders JPM. Differential effects of mineral and organic acids on the kinetics of arabinose degradation under lignocellulose pretreatment conditions. Biochem Eng J 2009;43:92e7. [21] Qi YL, Chang E, Du J, Xu HJ, Wu XB. Biohydrogen production from rice straw by simultaneous saccharification and fermentation using klebsiella oxytoca HP1. J Xiamen Univ 2007;46:708e10. s-Pejo E, Ibarra D, Ballesteros M, Olsson L. [22] Moreno AD, Toma Fed-batch SSCF using steam-exploded wheat straw at high dry matter consistencies and a xylose-fermenting Saccharomyces cerevisiae strain: effect of laccase supplementation. Biotechnol Biofuels 2013;6:160. [23] Huang LP, Jin B, Lent P, Zhou J. Simultaneous saccharification and fermentation of potato starch wastewater to lactic acid by Rhizopus oryzae and Rhizopus arrhizus. Biochem Eng J 2005;25:265e76. [24] Stenberg K, Galbe M, Zacchi G. The influence of lactic acid formation on the simultaneous saccharification and fermentation (SSF) of softwood to ethanol. Enzyme Microb Technol 2000;28:71e9.
7
[25] Jin MJ, Lau MW, Balan V, Dale BE. Two-step SSCF to convert AFEX-treated switchgrass to ethanol using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Bioresour Technol 2010;101:8171e8. [26] Erdei B, Hancz D, Galbe M, Zacchi G. SSF of steam-pretreated wheat straw with the addition of saccharified or fermented wheat meal in integrated bioethanol production. Biotechnol Biofuels 2013;6:169. s-Pejo E, Negro MJ, Sa ez F, Ballestrros M. Effect of [27] Toma nutrient addition on preinoculum growth of S. cerevisiae, for application in SSF processes. Biomass Bioenergy 2012;45:168e74. [28] Jin MJ, Gunawan C, Balan V, Lau MW, Dale BE. Simultaneous saccharification and co-fermentation (SSCF) of AFEXTM pretreated corn stover for ethanol production using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Bioresour Technol 2012;110:587e94. [29] Jin MJ, Gunawan C, Balan V, Yu XR, Dale BE. Continuous SSCF of AFEXTM pretreated corn stover for enhanced ethanol productivity using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Biotechnol Bioeng 2013;110:1302e11. [30] Zhang QG, Li YM, Jing YY, Jiang DP, Zhang ZP. Effect of acid and alkaline pretreatment on simultaneous saccharification fermentation for bio-hydrogen production from Platanus orientalis leaves. Trans Chin Soc Agric Mach 2015;46:202e7. [31] Wong JWC, Mak KF, Chen NW, Lam A, Fang M, Zhou LX. Cocomposting of soybean residues and leaves in Hong Kong. Bioresour Technol 2001;76:99e106. [32] Guerra BE, Dias RM, Vazquez M. Co-composting of chestnut burr and leaf litter with solid poultry manure. Bioresour Technol 2001;78:107e9. [33] Lazaro CZ, Varesche MBA, Silva EL. Effect of inoculum concentration, pH, light intensity and lighting regime on hydrogen production by phototrophic microbial consortium. Renew Energy 2015;75:1e7. [34] Zhang ZP, Yue JZ, Zhou XH, Jing YY, Jiang DP, Zhang QG. Photo-fermentative bio-hydrogen production from agricultural residue enzymatic hydrolyzate and the enzyme reuse. Bioresources 2014;9:2299e310. [35] Kennedy M, Krouse D. Strategies for improving fermentation medium performance: a review. J Ind Microbiol Biotechnol 1999;23:456e75. [36] O-Thong S, Prasertsan P, Intrasungkha N, Dhamwichukorn S, Birkeland NK. Optimization of simultaneous thermophilic fermentative hydrogen production and COD reduction from palm oil mill effluent by Thermoanaerobacterium-rich sludge. Int J Hydrogen Energy 2008;33:1221e31. [37] Liu F, Fang B. Optimization of bio-hydrogen production from biodiesel wastes by Klebsiella pneumonia. Biotechnol J 2007;2:374e80. [38] Jiang DP, Han BX, Wang Y, Wang SL, You XF, Zhang QG. Characteristic analysis of physiological feature and hydrogen production by Hau-M1 photo-synthetic bacteria. Acta Energiae Solaris Sin 2015;36:289e94. [39] Jia B, Jin ZH, Mei LH. Medium optimization based on statistical methodologies for pristinamycins production by Streptomyces pristinaespiralis. Appl Biochem Biotechnol 2008;144:133e43. [40] Wang Z, Quan Y, Zhou F. Optimization of medium composition for exopolysaccharide production by Phellinus nigricans. Carbohydr Polym 2014;105:200e6. [41] Acikel U, Ersan M, Acikel YS. Optimization of critical medium components using response surface methodology for lipase production by Rhizopus delemar. Food Bioprod Process 2010;88:31e9. [42] Androga DD, Sevinc¸ P, Koku H, Yu¨cel M, Gu¨ndu¨z U, Eroglu I. Optimization of temperature and light intensity for
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
8
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 x x x ( 2 0 1 6 ) 1 e8
improved photofermentative hydrogen production using Rhodobacter capsulatus DSM 1710. Int J Hydrogen Energy 2014;39:2472e80. [43] Zhang QG, Sun TL, Jing YY, Wang Y, Zhang Y, Hu JJ. Process optimization of bio-hydrogen production by anaerobic fermentation of enzymatic hydrolysate supernatants of corn stalk. Trans Chin Soc Agric Eng 2016;5:233e8.
€ [44] Ohgren K, Bura R, Lesnicki G, Saddler J, Zacchi G. A comparison between simultaneous saccharification and fermentation and separate hydrolysis and fermentation using steam-pretreated corn stover. Process Biochem 2007;42:834e9.
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1 Yameng Li a, Zhiping Zhang a, Yanyan Jing a, Xumeng Ge b, Yi Wang a, Chaoyang Lu a, Xuehua Zhou a, Quanguo Zhang a,* a Collaborative Innovation Center of Biomass Energy, Henan Province, Henan Agricultural University, Zhengzhou 450002, China b The Ohio State University, OH 44691, USA
article info
abstract
Article history:
Hydrogen is a promising alternative energy, and can be produced from various materials.
Received 13 September 2016
Platanus orientalis leaves (POL) was utilized to produce hydrogen through simultaneous
Received in revised form
saccharification fermentative (SSF) method with photosynthetic bacteria HAU-M1. The
3 November 2016
method was investigated using response surface methodology (RSM) with central com-
Accepted 28 November 2016
posite design (CCD). The PlacketteBurman design was first used to screen the factors
Available online xxx
(initial pH, light intensity, temperature, substrate concentration and initial inoculum) that influence SSF hydrogen production. Results indicated that initial pH, temperature and
Keywords:
inoculation amount had a statistically significant effect on SSF hydrogen production.
Platanus orientalis leaves
Central composite design was further carried out to evaluate interaction between the
Bio-hydrogen production
selected variables. The optimal conditions were determined to be initial pH of 6.18, tem-
Simultaneous saccharification
perature of 35.59 C, and inoculation amount of 26.29% (v/v). The predicted maximum
fermentative
hydrogen yield was 65.03 mL H2/g TS, which was very close to the experimental result of
Photosynthetic bacteria
64.10 mL H2/g TS obtained under the optimal conditions. The R2 value was 0.9396, indi-
Response surface methodology
cating a good model fit for SSF hydrogen production. © 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Increasing demand of energy and over consumption of finite fossil fuel resources have led to growing interest in development of energy from renewable resources [1e3]. Hydrogen is regarded as a promising renewable energy, due to its high energy density (122 KJ g1) [4,5] and no greenhouse gases
generation during combustion [6e8]. As one of the various hydrogen production methods, biological hydrogen production becomes increasingly attractive, because it can be carried out at ambient temperature and pressure with low energyinput and high efficiency [9,10]. Biological method mainly includes photo-fermentative hydrogen production and darkfermentative hydrogen production [11,12], and can use various types of substrates, such as sucrose, xylose, crop
* Corresponding author. Fax: þ86 371 63558040. E-mail address: [email protected] (Q. Zhang). http://dx.doi.org/10.1016/j.ijhydene.2016.11.182 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
2
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 x x x ( 2 0 1 6 ) 1 e8
residues, food waste, and animal waste. Alternatively, photosynthetic bacteria (PSB) can use small-chain organic acids as electron donors for H2 production. Shi XY found Rhodopseudomonas capsulate can use volatile fatty acids as electron donors for continuous H2 production at the expense of light energy, and a substrate conversion efficiency of 45% were achieved [13]. Tugba Keskin used sugar industry wastes to hydrogen production with single-stage photofermentation, and found 10.5 mol H2/mol sucrose for beet molasses (1 g/L sugar); 8 mol H2/mol sucrose for black strap (1 g/L sugar) and 14 mol H2/mol sucrose for pure sucrose [14]. So PSB with high degradation rate of organic matter and high efficiency of hydrogen production is being widely studied in renewable energy field [15e18]. When using biomass as substrate, the hydrogen production process usually has two steps: enzymatic hydrolysis, and fermentative bio-hydrogen production [19,20]. However, these two steps can also be conducted simultaneously, which is referred to as simultaneous saccharification fermentation (SSF). At present, the SSF technology is widely used in ethanol and lactic acid fermentation, B Erdei optimized ethanol production with Lignocellulosic by s-Pejo E studied nutrient addition on preSSF [26], Toma inoculum growth of Saccharomyces cerevisiae for application in SSF processes [27], but has limited application in the biohydrogen production process [21e25]. Compared with separate hydrolysis and fermentation (SHF), SSF has several advantages, including weakened feedback inhibition on cellulase by glucose and cellobiose, short processing time, reduced number of reactors, low experimentation cost, and high productivity [28,29]. The major challenge for SSF is that enzymatic hydrolysis and hydrogen fermentation are normally developed separately with different optimal conditions, such as initial pH and temperature. The optimal initial pH is 4.8 and 7, and the optimal temperature is 50 C and 30 C in the process of enzymatic hydrolysis and hydrogen fermentation, respectively [19]. As a result, further optimization of reaction conditions is necessary for SSF hydrogen production, to obtain the conditions in which hydrolysis and fermentation can run efficiently. Platanus orientalis is a main road greening tree, the annual leaves production of Chinese city could reach ten-million tones, normal processing mode for these leaves are compost, landfill and incineration mainly now, which will make more environment pollution and resource waste [30e32]. So proper disposal method is needed for a search. The P. orientalis leaves (POL) have a potential to be used as an abundant feedstock for the bio-hydrogen production, which contains abundant cellulose. In this study, SSF hydrogen production from POL was evaluated and optimized. The highefficient photosynthetic bacteria (PSB) HAU-M1, which was found to have wider adaptation range and higher hydrogen yield than single strain and have higher activity in temperature 30 C and neutral environment, and light intensity, substrate concentration and initial inoculation amount also existed certain appropriate range, 3000e3500 lx, 25e30 mg/ mL, 20e25% (v/v), respectively, in previous research [33,34], was adopted as hydrogen production strains. Initial pH, light intensity, temperature, substrate concentration and initial inoculum were analyzed firstly to find their effects on the
hydrogen production process. Then, PlacketteBurman design was applied to screen the important factors. Response surface methodology with central composite design was adopted to further determine optimal levels of selected factors for maximized simultaneous hydrogen production [35e37].
Materials and methods Microorganisms and media The PSB HAU-M1 was provided by key laboratory of new materials and facilities for rural renewable energy, including Rhodospirillum rubrum, Rhodobacter capsulatus and Rhodopseudomonas palustris [38]. When the bacteria's concentration reached OD660nm of 1.2 (the cell dry weight is 1.25 ± 0.02 g/L) in about 48 h after inoculation, which was used as inoculum for further experiments. The culture medium contained NH4Cl (1 g/L), NaHCO3 (2 g/L), K2HPO4 (0.2 g/L), CH3COONa (3 g/L), MgSO4$7H2O (0.2 g/L), NaCl (2 g/L), and 1 g/L of yeast extract and Micronutrient solution (1 mL) that contained FeCl3$6H2O (5 mg/L), ZnSO4$7H2O (1 mg/L), CuSO4$5H2O (0.05 mg/L), H3BO4 (1 mg/L), MnCl2$4H2O (0.05 mg/L), and Co(NO3)2$6H2O (0.5 mg/ L). The pH value was adjusted to 7.0 [19,38]. P. orientalis leaves were collected from street trees in Henan Agricultural University, crushed into powder, and screened with 40-mesh sieve. The composition of the processed sample is as follows: cellulose of 32.51%, hemicellulose of 19.65%, lignin of 30.13%, volatile solids content of 91.5% and other of 17.71%, The C, N, P and micro-nutrient of the fermentation medium were supplied in the following dosages: NH4Cl (0.4 g/ L), MgCI2 (0.2 g/L), yeast extract (0.1 g/L), K2HPO4 (0.5 g/L), NaCl (2 g/L), and sodium glutamate (3.5 g/L).
Photo-fermentative bio-hydrogen production system The PFHP system is shown in Fig. 1, which consists of a reactor, an incandescent lamp, an incubator, a purifier, a gas cylinder, and a correlation detection instrument.
Batch reactor and fermentation All experiments were carried out in 150 mL flasks. The reactors were filled with argon (Ar) gas then sealed with silicone rubber, in order to create an anaerobic condition. The enzyme employed was cellulase (Trichoderma Vride G), with an enzyme activity of 35 u/mg. pH was adjusted by adding HCl or NaOH to obtain the settled value.
Analytical methods The volume and composition of gas were measured every 12 h. The relative hydrogen concentration was determined by gas chromatography (6820 GC-14B, Agilent Technologies; Beijing, China). Nitrogen was used as the carrier gas at a flow rate of 45 mL/min. The operational temperatures of the injector, detector and column were 100, 80 and 150, respectively. The hydrogen standard curve of the regression equation as Eq. (1) (R2 ¼ 0.9991):
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
3
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 x x x ( 2 0 1 6 ) 1 e8
1. Incubator 2. Hydrogen production reactor 3. Incandescent lamp 4. Testing port 5. Airway 6. Gas purifier 7. Gas collecting bottle 8. Gas chromatograph 9. Water block 10. Argon gas valve
Fig. 1 e The experimental device of photosynthetic hydrogen production with Platanus orientalis leaves.
Y ¼ 0:00087x 0:7636
(1)
where, Y is the relative hydrogen concentration, and x is peak area of hydrogen.
Experimental design and optimization Plackette Burman design (PBD) The PBD method was used to screen the significant variables (Table 1) for hydrogen production by PSB HAU-M1. This is a very economical factorial design with the run number a multiple of four and comprises two-level screening design [39]. The level for each factor was determined according to results of single factor experiments, using unit mass production quantity (mL/g TS) as response value. Table 1 shows factors investigated to find the key significant factors influencing the hydrogen production. All the factors are prepared in two levels: 1 for low level and þ1 for high level. The design included twelve runs of various combinations of levels of the assessed factors and all runs were done in triplicate [36].
Central composite design (CCD)
Table 1 e The PlacketteBurman design for screening independent variables.
X1 X2 X3 X4 X5
Variables
Y ¼ C0 þ
X
Ci Xi þ
X
Ci Xi 2 þ
X
Cij Xi Xj
(2)
where, Y is the predicted response, C0 is the intercept, Ci is the linear coefficient, and Cij is the interaction coefficient. An analysis of variance (ANOVA) was performed, and three-dimensional response surface curves were plotted by Design Expert 8.0 software to study the interaction among these factors.
Result and discussion Screening of significant factors for SSF hydrogen production
Central composite design is a method that can fit the response surface model through a small amount of experiments. The three selected significant variables from PBD are Initial pH (X1), Temperature (X2), and Initial inoculation amount (X5). Light and Substrate concentration are no significant variables, maybe because photosynthetic bacteria have reached light saturation point and the substrate can be effectively hydrolyzed in the selected range. In order to maximize hydrogen production, CCD was used to optimize the experimental parameters. The minimum and maximum range of variables investigated and their values in actual and coded form are listed in Table 2. Five experimental levels: a, 1, 0, þ1, þa,
Code
where a ¼ 2n/4, n was the number of factors and 0 corresponds to the central level which was selected from the preliminary work [36,40]. Experimental design includes 20 runs, and fermentation was carried out separately for each with triplicates. Hydrogen yield was taken as the response (Y). Regression analysis was performed on the data obtained. This resulted in an empirical model that related the response measured to the independent variables of the experiment. For any system, the model equation is represented as Eq. (2):
Unit
Initial pH Temperature C Substrate concentration mg/ml Light intensity lx Inoculation amount %(v/v)
Low level High level (1) (þ1) 5.00 30.00 25.00 3000 20
7.00 40.00 50.00 4000 30
Plackette Burman design was carried out to screen the significant variables. Variables that had significant effect on SSF hydrogen production (p < 0.05) were selected and were used for further optimization. The results showed that the response varied from 54.54 mL H2/g TS to 64.24 mL H2/g TS (Table 3), which indicated the necessity of optimization. Initial pH (X1), temperature (X2), and inoculation amount (X5) were found to have the most significant influence on the biohydrogen production process. The results were confirmed by the results of the statistical analysis of PBD listing in Table 4, in which the p-value of X1, X2 and X5 were less than 0.05. Based on the results of PBD, a polynomial, first order equation was developed, excluding the insignificant variables, describing the
Table 2 e Levels of the variables of CCD. Code
X1 X2 X5
Variables
Initial pH Temperature ( C) Inoculation amount (%)
Levels a
1
0
þ1
þa
4.318 26.591 16.591
5 30 20
6 35 25
7 40 30
7.681 43.409 33.409
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
4
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 x x x ( 2 0 1 6 ) 1 e8
Table 3 e The PBD matrix representing the coded values for independent factors and the values of measured response. Run
X1
1 2 3 4 5 6 7 8 9 10 11 12
1 þ1 1 1 1 þ1 1 þ1 þ1 þ1 þ1 1
X2/ C X3/mg/mL X4/lx X5/%(v/v) Y/mL/g TS þ1 þ1 1 1 þ1 þ1 þ1 þ1 1 1 1 1
þ1 þ1 1 þ1 1 1 þ1 1 1 þ1 þ1 1
1 1 þ1 þ1 1 1 þ1 þ1 1 þ1 þ1 1
þ1 1 1 þ1 þ1 1 1 þ1 þ1 1 þ1 1
56.16 64.26 55.54 54.54 55.19 61.95 58.45 59.67 57.81 59.67 57.60 55.19
Table 4 e Statistical analysis of PBD. SS
Df
F-value
Prob > F
Model X1 X2 X3 X4 X5
95.55 55.86 19.58 2.37 1.20 16.54
5 1 1 1 1 1
47.43 138.65 48.61 5.88 2.97 41.06
<0.0001 <0.0001 0.0004 0.0516 0.1355 0.0007
R-Squared ¼ 0.9753
Adj R-Square ¼ 0.9548
Source
correlation between the variables used for study. The hydrogen production yield, Y (mL/g TS) could be represented as Eq. (2): Y ¼ 58:17 þ 2:16X1 þ 1:44X2 1:77X5
(2)
where, Y is the response, and X1, X2 and X5 are the coded values of Initial pH, temperature, and inoculation amount, respectively. The statistical significance of the model was evaluated by ANOVA. p-value (p < 0.05) indicated the significance of the experiment. The determination coefficient R2 value of the model was 0.9753, indicating that the equation optimization was successful. The correction factor R2 value of the model was 0.9548, indicating that 95.48% of the variability in the response could be explained by the model (Table 4).
Central composite design
Table 5 e Central composite design matrix with responses. Run
X1
X2
X5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
þa 0 1 1 0 þ1 a 1 0 0 0 0 0 þ1 0 þ1 1 1 0 0
0 þa þ1 þ1 0 1 0 1 0 0 a 0 0 þ1 0 1 þ1 1 0 0
0 0 1 1 0 1 0 1 a 0 0 0 0 þ1 0 þ1 þ1 1 0 þa
Hydrogen yield mL/g TS Observed values
Predicted values
53.83 55.20 59.29 54.25 61.91 52.93 44.54 57.29 53.27 65.22 49.67 67.15 65.16 56.01 64.32 55.36 52.72 47.27 64.07 61.43
53.78 55.93 59.28 51.74 64.57 51.08 46.97 55.62 55.91 64.57 51.32 64.57 64.57 55.76 64.57 56.18 52.89 45.84 64.578 61.17
As shown in Table 6, the data obtained were evaluated by ANOVA, which showed that the lack-of-fit was not significant (p ¼ 0.2121 > 0.05). Therefore, the responses are adequate for employing in this model. The R2 value of 0.9396 and adjusted R2 of 0.8852 showed that the model could effectively predict the response and explaining 94% of the variability in the SSF hydrogen production, The R2 closes to 1 indicated the better of the model predicting the response [40]. A low value of the coefficient of variation (C.V. % ¼ 3.37%) indicates a high precision and reliability of the experimental variables, “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable. In this case, the ratio of 12.309 indicates an adequate signal. This model can be used to navigate the design space. p-value denotes the importance of each coefficient, and is helpful in understanding the interactions among the variables. Linear terms of initial pH (X1), temperature (X2) and Initial inoculum (X5) showed a significant effect on SSF hydrogen production individually (p < 0.05). The quadratic
Table 6 e Analysis of variance of the model.
Based on the results of PBD, Initial pH (X1), temperature (X2), inoculation amount (X5) were selected for further exploring by the CCD. The responses obtained at different experimental runs are represented in Table 5. By applying multiple regression analysis on the experimental data, the following second-order polynomial equation was obtained to describe the hydrogen yield as a function of the significant variables as follow Eq. (3): Y ¼ 59:08 þ 1:85X1 þ 1:226X2 þ 1:43X5 þ 0:53X1 X2 1:07X1 X3 1:97X2 X5 4:59X21 3:54X22 1:95X25 (3) where, Y is the response, and X1, X2 and X5 are Initial pH, temperature, and inoculation amount, respectively.
Source
SS
Df
MS
F-value
p-value
Model X1 X2 X5 X1X2 X1X5 X2X5 X21 X22 X25 Residual Lack of fit Pure error Cor total
720.41 56.12 25.66 33.42 2.65 10.9 37.24 363.03 215.84 65.52 46.33 31.54 14.79 766.74
9 1 1 1 1 1 1 1 1 1 10 5 5 19
80.05 56.12 25.66 33.42 2.65 10.9 37.24 363.03 215.84 65.52 4.63 6.31 2.96
17.28 12.11 5.54 7.21 0.57 2.35 8.04 78.36 46.59 14.14
<0.0001 0.0059 0.0404 0.0229 0.4673 0.1560 0.0177 <0.0001 <0.0001 0.0037
2.13
0.2127
R-Squared ¼ 0.9396
Adj R-Square ¼ 0.8852
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
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 x x x ( 2 0 1 6 ) 1 e8
5
Fig. 2 e Three-dimensional surface plot and Contour plot for specific hydrogen yield. The response surface model was obtained by the CCD with the date shown in Table 5. (a) the effect of initial pH, temperature and their mutual interaction on hydrogen yield with level of inoculation amount 25%; (b) the effect of inoculation amount initial pH and their mutual interaction on hydrogen yield with level of temperature 35 C; (c) the effect of inoculation amount, temperature and their mutual interaction on hydrogen yield with level of initial pH 6. Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
6
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 x x x ( 2 0 1 6 ) 1 e8
model terms of initial pH (X21) and temperature (X22) are highly significant (p < 0.0001), inoculation amount (X25) is also significant (p < 0.0037). A significant interaction between the temperature and inoculation amount (X2X5) was observed (Table 6). Subsequently, from this optimization study, the maximum yield of 65.03 mL/g TS was predicted from Eq. (2), and the optimal conditions were initial pH of 6.18, temperature of 35.59 C, and initial inoculum of 26.29% (v/v). Further experiments obtained a hydrogen yield of 64.48 mL/g TS under the optimal condition, which was consistent with the predicted response. Response surface plots of the mode is the best way to visualize the influence of the independent variables on the dependent variable, which was done by varying two variables within the experimental range and holding the other three are constant at central level. The 3D response surface and contour plots were employed to determine the interaction of the fermentation conditions and the optimum levels for SSF hydrogen production. The color and shape of contour plots can identify the significant and the interaction between of variables, the faster the color changed, the bigger the gradient was, and the more significant the impact was on the experiment, strong interaction exists if contour lines are elliptical in shape, and no interaction is observed if contour lines are circular [41,42]. Fig. 2 shows the three-dimensional response surface and contour plot for specific hydrogen yield as a function of initial pH, temperature and inoculation amount, as emulated by Eq. (2). The mutual effects of initial pH and temperature on hydrogen production are shown in Fig. 2a. Under the studied conditions, specific hydrogen yield continuously increased with increasing initial pH and temperature until it reached its peak, and then decreased with a further increase in initial pH and temperature. The highest hydrogen yield was obtained at pH of 6.18 and temperature of 35.6 C. The reason may be that increasing pH inhibited the activity of cellulase, which affects substrate hydrolysis in the process of SSF. Moreover, temperature also have some impact both on the activity of cellulase and the activity of PSB, so temperature affect enzymatic synthesis and the metabolism of PSB, which results in the different substrate utilization rate and hydrogen yield. Besides, there was no significant interaction between the variables, which was also confirmed by the ANOVA results in Table 6 in which the p-value for X1X2 was 0.4673. Fig. 2b shows the combined effect of initial pH and inoculation amount on the response. An optimum point at 6.18, 26.29% (v/v) was found in the design boundary, and there was no significant interaction between initial pH and inoculation amount, according to the shape of contour lines and the p-value for X1X5 (0.1560 determined by ANOVA). The theoretical reason may be that photosynthetic bacteria could adapt to different acidebase conditions in a certain range by regulating the metabolic pathway (Acetic acid pathway, Propionate pathway, and so on) which could directly affect the pH of the medium to maintain a suitable fermentation condition [38]. As shown in Fig. 2c, strong interaction was observed between inoculation amount and temperature, as the contour is elliptical and p-value for X2X5 was 0.0177 (ANOVA result). The surface plot (Fig. 2c) also shows that hydrogen yield is more sensitive to inoculation amount and
temperature. The contour plots are elliptical spontaneously, which means that the interactive effects are momentous. According to the surface plot, the effect of temperature on hydrogen yield was similar to initial pH, with a steeper slope on the inoculation amount axis compared to the temperature axis. The optimum point was observed at inoculation amount 26.29% (v/v) and temperature 35.6 C. As shown in Fig. 2, the significant peaks of the surface plots for specific hydrogen yield and the clear maximal of the corresponding contour plots indicated that the maximum specific hydrogen yield could be obtained inside the design range. The maximum yield of 65.03 mL/g TS was predicted under the optimal conditions which were initial pH of 6.18, temperature of 35.59 C, and initial inoculum of 26.29% (v/v). The experimental result of 64.10 mL H2/g TS obtained under the optimal conditions by SSF which was higher than previous studies, the hydrogen yield reached 34.5 mL H2/g TS with POL by SHF [30], and 57.83 mL H2/g TS with enzymatic hydrolysate superna€ tants of corn stalk by SHF [43]. Ohgren K et al. [44] preformed comparison between SSF and SHF using steam-pretreated corn stover for ethanol. SSF gave a 13% higher overall ethanol yield than SHF. Because SSF could weaken feedback inhibition on cellulase by glucose and cellobiose and promote the positive reaction.
Conclusion Statistical optimization was proved to be an effective method for optimization of the SSF hydrogen production by HAU-M1. Three significant variables affecting SSF hydrogen production were initial pH, temperature and inoculation amount, which were selected by the PB experiments. Maximum SSF hydrogen production 65.03 mL/g TS was obtained under the optimized condition: initial pH of 6.18, temperature of 35.6 C, and inoculation amount of 26.29% (v/v).
Acknowledgments The authors gratefully acknowledged financial support from the National Natural Science Foundation of China (No. 51376056) and Quality control mechanism and mass transfer characteristics of straw dark fermentation effluent of biohydrogen production processes (No. U1504509).
references
[1] Chen HZ, Liu ZH. Steam explosion and its combinatorial pretreatment refining technology of plant biomass to biobased products. Biotechnol J 2015;10:866e85. [2] Chen HZ, Qiu WH. Key technologies for bio-ethanol production from lignocelluloses. Biotechnol Adv 2010;28:556e62. [3] Asif M, Muneer T. Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 2007;11:1388e413. [4] Nandi R, Sengupta S. Microbial production of hydrogen: an overview. Crit Rev Microbiol 1998;24:61e84.
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
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 x x x ( 2 0 1 6 ) 1 e8
[5] Mizuno O, Dinsdale R, Hawkes FR, Hawkes DL, Noike T. Enhancement of hydrogen production from glucose by nitrogen gas sparging. Bioresour Technol 2000;73:59e65. [6] Hosseini SE, Wahid MA, Jamil MM, Azli AAM, Misbah MF. A review on biomass-based hydrogen production for renewable energy supply. Int J Energy Res 2015;39:1597e615. [7] Nicoletti G, Arcuri N, Nicoletti G, Bruno R. A technical and environmental comparison between hydrogen and some fossil fuels. Energy Convers Manag 2015;89:205e13. [8] Tuna E, Kargi F, Argun H. Hydrogen gas production by electrohydrolysis of volatile fatty acid (VFA) containing dark fermentation effluent. Int J Hydrogen Energy 2009;34:262e9. [9] Kalinci Y, Hepbasli A, Dincer I. Biomass-based hydrogen production: a review and analysis. Int J Hydrogen Energy 2009;34:8799e817. [10] Mathews J, Wang G. Metabolic pathway engineering for enhanced bio-hydrogen production. Int J Hydrogen Energy 2009;34:7404e16. [11] Wang JL, Wan W. Effect of Fe2þ concentrations on fermentative hydrogen production by mixed cultures. Int J Hydrogen Energy 2008;33:1215e20. [12] Wang JL, Wan W. Comparison of different pretreatment methods for enriching hydrogen producing cultures from digested sludge. Int J Hydrogen Energy 2008;33:2934e41. [13] Shi XY, Yu HQ. Continuous production of hydrogen from mixed volatile fatty acids with Rhodopseudomonas capsulata. Int J Hydrogen Energy 2006;31:1641e7. [14] Keskin T, Hallenbeck PC. Hydrogen production from sugar industry wastes using single-stage photofermentation. Bioresour Technol 2012;112:131e6. [15] Zhao C, Karakashev D, Lu W, Wanga H, Angelidaki I. Xylose fermentation to biofuels (hydrogen and ethanol) by extreme thermophilic (70 C) mixed culture. Int J Hydrogen Energy 2010;35:3415e22. [16] Eroglu E, Melis A. Photobiological hydrogen production: recent advances and state of the art. Bioresour Technol 2011;102:8403e13. [17] Lu CY, Zhang ZP, Ge XM, Wang Y, Zhou XH, You XF, et al. Biohydrogen production from apple waste by photosynthetic bacteria HAU-M1. Int J Hydrogen Energy 2016;41:13399e407. [18] Ponsano EHG, Paulino CZ, Pinto MF. Phototrophic growth of Rubrivivax gelatinosus in poultry slaughter house wastewater. Bioresour Technol 2008;99:3836e42. [19] Zhang QG, Wang SL, You XF. Effects of the influencing factors of photosynthetic bacteria group on hydrogen production. Trans Chin Soc Agric Eng 2006;22:180e5. [20] Kootstra AMJ, Mosier NS, Scott EL, Beeftink HH, Sanders JPM. Differential effects of mineral and organic acids on the kinetics of arabinose degradation under lignocellulose pretreatment conditions. Biochem Eng J 2009;43:92e7. [21] Qi YL, Chang E, Du J, Xu HJ, Wu XB. Biohydrogen production from rice straw by simultaneous saccharification and fermentation using klebsiella oxytoca HP1. J Xiamen Univ 2007;46:708e10. s-Pejo E, Ibarra D, Ballesteros M, Olsson L. [22] Moreno AD, Toma Fed-batch SSCF using steam-exploded wheat straw at high dry matter consistencies and a xylose-fermenting Saccharomyces cerevisiae strain: effect of laccase supplementation. Biotechnol Biofuels 2013;6:160. [23] Huang LP, Jin B, Lent P, Zhou J. Simultaneous saccharification and fermentation of potato starch wastewater to lactic acid by Rhizopus oryzae and Rhizopus arrhizus. Biochem Eng J 2005;25:265e76. [24] Stenberg K, Galbe M, Zacchi G. The influence of lactic acid formation on the simultaneous saccharification and fermentation (SSF) of softwood to ethanol. Enzyme Microb Technol 2000;28:71e9.
7
[25] Jin MJ, Lau MW, Balan V, Dale BE. Two-step SSCF to convert AFEX-treated switchgrass to ethanol using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Bioresour Technol 2010;101:8171e8. [26] Erdei B, Hancz D, Galbe M, Zacchi G. SSF of steam-pretreated wheat straw with the addition of saccharified or fermented wheat meal in integrated bioethanol production. Biotechnol Biofuels 2013;6:169. s-Pejo E, Negro MJ, Sa ez F, Ballestrros M. Effect of [27] Toma nutrient addition on preinoculum growth of S. cerevisiae, for application in SSF processes. Biomass Bioenergy 2012;45:168e74. [28] Jin MJ, Gunawan C, Balan V, Lau MW, Dale BE. Simultaneous saccharification and co-fermentation (SSCF) of AFEXTM pretreated corn stover for ethanol production using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Bioresour Technol 2012;110:587e94. [29] Jin MJ, Gunawan C, Balan V, Yu XR, Dale BE. Continuous SSCF of AFEXTM pretreated corn stover for enhanced ethanol productivity using commercial enzymes and Saccharomyces cerevisiae 424A (LNH-ST). Biotechnol Bioeng 2013;110:1302e11. [30] Zhang QG, Li YM, Jing YY, Jiang DP, Zhang ZP. Effect of acid and alkaline pretreatment on simultaneous saccharification fermentation for bio-hydrogen production from Platanus orientalis leaves. Trans Chin Soc Agric Mach 2015;46:202e7. [31] Wong JWC, Mak KF, Chen NW, Lam A, Fang M, Zhou LX. Cocomposting of soybean residues and leaves in Hong Kong. Bioresour Technol 2001;76:99e106. [32] Guerra BE, Dias RM, Vazquez M. Co-composting of chestnut burr and leaf litter with solid poultry manure. Bioresour Technol 2001;78:107e9. [33] Lazaro CZ, Varesche MBA, Silva EL. Effect of inoculum concentration, pH, light intensity and lighting regime on hydrogen production by phototrophic microbial consortium. Renew Energy 2015;75:1e7. [34] Zhang ZP, Yue JZ, Zhou XH, Jing YY, Jiang DP, Zhang QG. Photo-fermentative bio-hydrogen production from agricultural residue enzymatic hydrolyzate and the enzyme reuse. Bioresources 2014;9:2299e310. [35] Kennedy M, Krouse D. Strategies for improving fermentation medium performance: a review. J Ind Microbiol Biotechnol 1999;23:456e75. [36] O-Thong S, Prasertsan P, Intrasungkha N, Dhamwichukorn S, Birkeland NK. Optimization of simultaneous thermophilic fermentative hydrogen production and COD reduction from palm oil mill effluent by Thermoanaerobacterium-rich sludge. Int J Hydrogen Energy 2008;33:1221e31. [37] Liu F, Fang B. Optimization of bio-hydrogen production from biodiesel wastes by Klebsiella pneumonia. Biotechnol J 2007;2:374e80. [38] Jiang DP, Han BX, Wang Y, Wang SL, You XF, Zhang QG. Characteristic analysis of physiological feature and hydrogen production by Hau-M1 photo-synthetic bacteria. Acta Energiae Solaris Sin 2015;36:289e94. [39] Jia B, Jin ZH, Mei LH. Medium optimization based on statistical methodologies for pristinamycins production by Streptomyces pristinaespiralis. Appl Biochem Biotechnol 2008;144:133e43. [40] Wang Z, Quan Y, Zhou F. Optimization of medium composition for exopolysaccharide production by Phellinus nigricans. Carbohydr Polym 2014;105:200e6. [41] Acikel U, Ersan M, Acikel YS. Optimization of critical medium components using response surface methodology for lipase production by Rhizopus delemar. Food Bioprod Process 2010;88:31e9. [42] Androga DD, Sevinc¸ P, Koku H, Yu¨cel M, Gu¨ndu¨z U, Eroglu I. Optimization of temperature and light intensity for
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182
8
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 x x x ( 2 0 1 6 ) 1 e8
improved photofermentative hydrogen production using Rhodobacter capsulatus DSM 1710. Int J Hydrogen Energy 2014;39:2472e80. [43] Zhang QG, Sun TL, Jing YY, Wang Y, Zhang Y, Hu JJ. Process optimization of bio-hydrogen production by anaerobic fermentation of enzymatic hydrolysate supernatants of corn stalk. Trans Chin Soc Agric Eng 2016;5:233e8.
€ [44] Ohgren K, Bura R, Lesnicki G, Saddler J, Zacchi G. A comparison between simultaneous saccharification and fermentation and separate hydrolysis and fermentation using steam-pretreated corn stover. Process Biochem 2007;42:834e9.
Please cite this article in press as: Li Y, et al., Statistical optimization of simultaneous saccharification fermentative hydrogen production from Platanus orientalis leaves by photosynthetic bacteria HAU-M1, International Journal of Hydrogen Energy (2016), http:// dx.doi.org/10.1016/j.ijhydene.2016.11.182