- Email: [email protected]
Technology Research on Bio-Hydrogen Production
Available online at www.sciencedirect.com
Procedia Engineering 43 (2012) 53 – 58
International Symposium on Safety Science and Engineering in China, 2012 (ISSSE-2012)
Technology Research on Bio-hydrogen Production Wang Boa, *, Liu Yongye a, Qiao Yahuaa, Yang Yanga, Shi Qiang b, Wan Weic, Wang Jianlongc a
Nuclear and Radiation Safety Center, Ministry of Environmental Protection, Beijing, China, 100082 b Capital University of Economics and Business, Beijing, China, 100070 c Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China, 100084
Abstract Hydrogen as a clean and renewable energy plays very important role in both of relief severe shortage of petroleum and deterioration of ecological environment. Fermentative hydrogen production from organic wastes plays the dual role of waste reduction and energy production. In this paper, the characteristics of biological hydrogen production by mixed cultures were investigated in batch tests; several modified kinetic models describing the biological hydrogen production were developed. The results show that the modified Logistic model could describe the variation of hydrogen production potential with time; the modified Ratkowsky model could describe th e effects of temperature and initial pH on average hydrogen production rate successfully.
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology. . Keywords:National security, Hydrogen, Fermentative hydrogen production
1. Introduction Nowadays, with the increase of population and development of society, issues of energy shortage have become increasingly prominent. Fossil energy as main energy sources of China is non-renewable and highly dependent on the import. The development of renewable energy should be encouraged especially for the national energy strategy. Hydrogen is clean, efficient, renewable and highly focused, even honored as “Future Energy” [1]. Comparing with the traditional method of hydrogen production, such as fossil fuel conversion and water-electrolytic method, biological method is known to be less energy intensive, for it is carried out at ambient temperature and pressure, therefore it has been received increasing attention in recent years. Many factors such as temperature and initial pH can influence the fermentative hydrogen production, because they can affect the activity of hydrogen-producing bacteria by influencing the activity of some essential enzymes such as hydrogenases for fermentative hydrogen production [2]. Although, here have been several studies on how temperature and initial pH affected fermentative hydrogen production, their results differed considerably. The optimal temperature reported by Xing et al. was 37ć [3], while that reported by Othong et al. was 60 [4]. The optimal initial pH reported by Khanal et al. was 4.5 [5], while that reported by Lee et al. was 9.0 [6]. The possible reasons why their results were different greatly are the differences among their studies in terms of the seed sludge, substrates, or the ranges of the initial pH studied.
*e-mail: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.010
54
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
2. Materials and methods The digested sludge collected from a primary anaerobic digester at Beijing Gaobeidian Sewage Treatment Plant (China) was used as the seed sludge. The volatile suspended solid (VSS) of the sludge was 11.8 g/L. Batch tests were conducted in 150 mL glass bottles. The initial pH of the mixed solution in each bottle was regulated by 1 mol/L HCl or 1 mol/L NaOH. Each bottle was flushed with argon for 3 min to provide anaerobic condition, capped with a rubber stopper, and placed in a reciprocal shaker (reciprocation: 150 strokes/min). Each batch test was done three fold. 3. Results and discussion 3.1. Comparison of the modeling abilities of modified Logistic and modified gompertz models Some kinetic models such as modified Gompertz model (eq.1) developed by Zwietering et al.[7] have been proposed to describe the progress of substrate degradation, bacteria growth, hydrogen production and some soluble metabolite production in a batch fermentative hydrogen production process.
½ ª R e º° ° H max exp ®exp « max O t 1» ¾ ° ¬ H max ¼° ¯ ¿
H
(1)
Where H (mL) is the cumulative hydrogen production at the reaction time t (h), Hmax (mL) is the hydrogen production potential, Rmax (mL/h) is the maximum hydrogen production rate and λ (h) is the lag time.
+˄mL˅
Hmax
Rmax
0
O
t ˄h˅
Fig.1. A curve for modified Gompertz model
As shown in Fig 1, in a batch test, H increases very slowly with increasing cultivation time from 0 to λ, and then increases rapidly almost at the rate of Rmax and finally reaches an asymptotic value Hmax with further increasing the cultivation time. When Eq. 1 was used to describe the progress of substrate degradation in batch tests, H and Hmax denote the cumulative degraded substrate value and the maximum degraded substrate value, respectively. When Eq. 1 was used to describe the progress of bacteria growth in batch tests, H and Hmax denote the cumulative bacteria growth value and the maximum bacteria growth value, respectively. Comparatively, the modified Logistic model has a similar property as the modified Gompertz model and using it can also obtain some constants that have biological meanings, it has been not used widely as the modified Gompertz model. Thus, using it to describe the progress of a batch fermentative hydrogen production process is recommended.
55
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
H
H max 1 exp > 4 Rmax (O t ) / H max 2@
(2)
Fig.2 shows fitting results of the two models, the dynamic parameters obtained from the experimental data were as follows:
350 300
+PD[˄mL˅
250 200 150 H[SHULPHQWDOGDWD PRGLILHGLogisticPRGHO PRGLILHGGompertzPRGHO
100 50 0 0
10
20
30
40
50
60
W˄h˅
Fig.2. Fitting results comparison of two models.
For the modified Gompertz model, the coefficients of determination (R2) was 0.9978, the significance level (p) was 0.0005˖
H
ª14.84 u e º½ 297.55 u exp ®exp « u 14.08 t 1» ¾ ¬ 297.55 ¼¿ ¯
(3)
For the modified Logistic model, the coefficients of determination (R2) was 0.9992 and the significance level (p) was 0.0002˖
H
291.4 1 exp[4 u15.02 u (14.83-t)/291.4+2]
(4)
Analysis of variance (ANOVA) of the fitting models showed that both of the fitting models were highly significant (p<0.01), could describe the progress of fermentative hydrogen production successfully. However, the coefficients of determination (R2) of the modified Logistic model was higher than of the modified Gompertz model, likewise the significance level (p) was lower, and the fitting dynamic parameters were more accurate, the fitting effect of the modified Logistic model was better. 3.2. Application of Ratkowsky model in fermentative hydrogen production 3.2.1. Effect of temperature on average hydrogen production rate Fig.3 shows the effect of temperature on average hydrogen production rate. The results showed that the hydrogen production rate in batch tests increased with increasing temperature from 20 to 40ć, and then it decreased with further increasing temperature from 40 to 55ć.
56
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
$YHUDJHK\GURJHQSURGXFWLRQO mL/hP
35
30
25
20
15
10
5 15
20
25
30
35
40
45
50
55
60
7HPSHUDWXUHO ćP Fig.3. Effect of temperature on average hydrogen production rate
In this study, the Ratkowsky model (Eq.5) was used to describe the effect of temperature on average hydrogen production rate. [8]
R
^
`
ª¬ A u T Tmin º¼ u 1 exp ª¬ B u T Tmax º¼ 2
2
(5)
Where R (mL/h) is the average hydrogen production rate, A (mL0.5/(ćЬh0.5)) and B (1/ć) are constant, Tmin is the minimum temperature when the average hydrogen production rate is becoming zero, Tmax is the maximum temperature when the average hydrogen production rate is becoming zero. The fitting result is as follows.
R
^
`
ª¬0.4 u T 14.3 º¼ u 1 exp ª¬0.02 u T 58.5 º¼ 2
2
(6)
The coefficient of determination (R2) was 0.932, with the significance level (p) being less than 0.05, which indicated that the Ratkowsky model could describe the effect of temperature on average hydrogen production rate in the batch tests of this study successfully. The scope of the temperature fitted by Ratkowsky model was 14.3-58.5; this result is in accord with most other researches and covers the majority of optimal temperatures. The activity of hydrogen-producing bacteria may be completely suppressed under too high or too low temperature. 3.2.2. Effect of initial pH on average hydrogen production rate Fig.4 shows the effect of initial pH on average hydrogen production rate. The results showed that the hydrogen production rate in batch tests increased with increasing initial pH from 4.0 to 8.0, and then it decreased with further increasing initial pH from 8.0 to 10.0. In this study, the modified Ratkowsky model (Eq.7) was used to describe the effect of initial pH on average hydrogen production rate. [8].
R
^
`
ª¬ A u T Tmin º¼ u 1 exp ª¬ B u T Tmax º¼ 2
2
(7)
Where R (mL/h) is the average hydrogen production rate, A (mL0.5/(ćЬh0.5)) and B are constant, pHmin is the minimum temperature when the average hydrogen production rate is becoming zero, pHmax is the maximum temperature when the average hydrogen production rate is becoming zero.
57
$YHUDJHK\GURJHQSURGXFWLRQUDWH˄mL/hP
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
18 16 14 12 10 8 6 4 2 0
4
5
6
7
8
9
10
,QLWLDOpH
Fig.4. Effect of initial pH on average hydrogen production rate.
The fitting result is as follows.
R
^
`
ª¬0.8 u pH 2.3 º¼ 1 exp ª¬0.8 u pH 10.6 º¼ 2
2
(8)
The coefficient of determination (R2) was 0.988, with the significance level (p) being less than 0.05, which indicated that the modified Ratkowsky model could describe the effect of initial pH on average hydrogen production rate in the batch tests of this study successfully. The scope of the initial pH fitted by modified Ratkowsky model was 2.3-10.6; this result is in accord with most other researches and covers the majority of optimal initial pH. The activity of hydrogen-producing bacteria may be completely suppressed under too high or too initial pH. 4. Conclusion x The modified Logistic model could describe the variation of hydrogen production potential with time very well. x The Ratkowsky model could describe the effect of temperature on average hydrogen production rate successfully. x The modified Ratkowsky model could describe the effect of initial pH on average hydrogen production rate successfully. Acknowledgements We would like to thank the National Key Projects “Regulatory technology and independent verification test for CAP1400 security” (No. 20777045). References [1] J. A. C. Leite, B. S. Fernandes, E. Pozzi, M. Barboza, and M. Zaiat, 2008. Application of an anaerobic packed-bed bioreactor for the production of hydrogen and organic acids, Int J Hydrogen Energy 33, pp. 579-586. [2] C. L. Li and H. H. P. Fang, 2007. Fermentative hydrogen production from wastewater and solid wastes by mixed cultures, Crit Rev Env Sci Technol 37(3), pp. 1-39. [3] D. F. Xing, N. Q. Ren, A. J. Wang, Q. B. Li, Y. J. Feng, and F. Ma,2008. Continuous hydrogen production of auto-aggregative Ethanoligenens harbinense YUAN-3 under non-sterile condition, Int J Hydrogen Energy 33, pp. 1489-1495. [4] S.O-Thong, P. Prasertsan, N. Intrasungkha, S. Dhamwichukorn, and N.K. Birkeland, 2008. Optimization of simultaneous thermophilic fermentative hydrogen production and COD reduction from palm oil mill effluent by Thermoanaerobacterium-rich sludge, Int J Hydrogen Energy 33, pp. 1221-1231.
58
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58 [5] S. K. Khanal, W. H. Chen, L. Li, and S. Sung, 2004. Biological hydrogen production, Effects of pH and intermediate products, Int J Hydrogen Energy 29, pp. 1123-1131. [6] Y. J. Lee, T. Miyahara, and T. Noike,2002. Effect of pH on microbial hydrogen fermentation, J Chem Technol Biotechnol 77, pp. 694-698. [7] M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. VAN’T RIET, Modeling of the bacterial growth curve, Appl Environ Microbiol, vol. 56, pp. 1875-1881, 1990. [8] D. A. Ratkowsky, R. K. Lowry, T. A. Mcmeekin, A. N. Stokes, and R. E. Chandler, 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range, J Bacteriol 154, pp. 1222-1226.
Procedia Engineering 43 (2012) 53 – 58
International Symposium on Safety Science and Engineering in China, 2012 (ISSSE-2012)
Technology Research on Bio-hydrogen Production Wang Boa, *, Liu Yongye a, Qiao Yahuaa, Yang Yanga, Shi Qiang b, Wan Weic, Wang Jianlongc a
Nuclear and Radiation Safety Center, Ministry of Environmental Protection, Beijing, China, 100082 b Capital University of Economics and Business, Beijing, China, 100070 c Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China, 100084
Abstract Hydrogen as a clean and renewable energy plays very important role in both of relief severe shortage of petroleum and deterioration of ecological environment. Fermentative hydrogen production from organic wastes plays the dual role of waste reduction and energy production. In this paper, the characteristics of biological hydrogen production by mixed cultures were investigated in batch tests; several modified kinetic models describing the biological hydrogen production were developed. The results show that the modified Logistic model could describe the variation of hydrogen production potential with time; the modified Ratkowsky model could describe th e effects of temperature and initial pH on average hydrogen production rate successfully.
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology. . Keywords:National security, Hydrogen, Fermentative hydrogen production
1. Introduction Nowadays, with the increase of population and development of society, issues of energy shortage have become increasingly prominent. Fossil energy as main energy sources of China is non-renewable and highly dependent on the import. The development of renewable energy should be encouraged especially for the national energy strategy. Hydrogen is clean, efficient, renewable and highly focused, even honored as “Future Energy” [1]. Comparing with the traditional method of hydrogen production, such as fossil fuel conversion and water-electrolytic method, biological method is known to be less energy intensive, for it is carried out at ambient temperature and pressure, therefore it has been received increasing attention in recent years. Many factors such as temperature and initial pH can influence the fermentative hydrogen production, because they can affect the activity of hydrogen-producing bacteria by influencing the activity of some essential enzymes such as hydrogenases for fermentative hydrogen production [2]. Although, here have been several studies on how temperature and initial pH affected fermentative hydrogen production, their results differed considerably. The optimal temperature reported by Xing et al. was 37ć [3], while that reported by Othong et al. was 60 [4]. The optimal initial pH reported by Khanal et al. was 4.5 [5], while that reported by Lee et al. was 9.0 [6]. The possible reasons why their results were different greatly are the differences among their studies in terms of the seed sludge, substrates, or the ranges of the initial pH studied.
*e-mail: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.010
54
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
2. Materials and methods The digested sludge collected from a primary anaerobic digester at Beijing Gaobeidian Sewage Treatment Plant (China) was used as the seed sludge. The volatile suspended solid (VSS) of the sludge was 11.8 g/L. Batch tests were conducted in 150 mL glass bottles. The initial pH of the mixed solution in each bottle was regulated by 1 mol/L HCl or 1 mol/L NaOH. Each bottle was flushed with argon for 3 min to provide anaerobic condition, capped with a rubber stopper, and placed in a reciprocal shaker (reciprocation: 150 strokes/min). Each batch test was done three fold. 3. Results and discussion 3.1. Comparison of the modeling abilities of modified Logistic and modified gompertz models Some kinetic models such as modified Gompertz model (eq.1) developed by Zwietering et al.[7] have been proposed to describe the progress of substrate degradation, bacteria growth, hydrogen production and some soluble metabolite production in a batch fermentative hydrogen production process.
½ ª R e º° ° H max exp ®exp « max O t 1» ¾ ° ¬ H max ¼° ¯ ¿
H
(1)
Where H (mL) is the cumulative hydrogen production at the reaction time t (h), Hmax (mL) is the hydrogen production potential, Rmax (mL/h) is the maximum hydrogen production rate and λ (h) is the lag time.
+˄mL˅
Hmax
Rmax
0
O
t ˄h˅
Fig.1. A curve for modified Gompertz model
As shown in Fig 1, in a batch test, H increases very slowly with increasing cultivation time from 0 to λ, and then increases rapidly almost at the rate of Rmax and finally reaches an asymptotic value Hmax with further increasing the cultivation time. When Eq. 1 was used to describe the progress of substrate degradation in batch tests, H and Hmax denote the cumulative degraded substrate value and the maximum degraded substrate value, respectively. When Eq. 1 was used to describe the progress of bacteria growth in batch tests, H and Hmax denote the cumulative bacteria growth value and the maximum bacteria growth value, respectively. Comparatively, the modified Logistic model has a similar property as the modified Gompertz model and using it can also obtain some constants that have biological meanings, it has been not used widely as the modified Gompertz model. Thus, using it to describe the progress of a batch fermentative hydrogen production process is recommended.
55
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
H
H max 1 exp > 4 Rmax (O t ) / H max 2@
(2)
Fig.2 shows fitting results of the two models, the dynamic parameters obtained from the experimental data were as follows:
350 300
+PD[˄mL˅
250 200 150 H[SHULPHQWDOGDWD PRGLILHGLogisticPRGHO PRGLILHGGompertzPRGHO
100 50 0 0
10
20
30
40
50
60
W˄h˅
Fig.2. Fitting results comparison of two models.
For the modified Gompertz model, the coefficients of determination (R2) was 0.9978, the significance level (p) was 0.0005˖
H
ª14.84 u e º½ 297.55 u exp ®exp « u 14.08 t 1» ¾ ¬ 297.55 ¼¿ ¯
(3)
For the modified Logistic model, the coefficients of determination (R2) was 0.9992 and the significance level (p) was 0.0002˖
H
291.4 1 exp[4 u15.02 u (14.83-t)/291.4+2]
(4)
Analysis of variance (ANOVA) of the fitting models showed that both of the fitting models were highly significant (p<0.01), could describe the progress of fermentative hydrogen production successfully. However, the coefficients of determination (R2) of the modified Logistic model was higher than of the modified Gompertz model, likewise the significance level (p) was lower, and the fitting dynamic parameters were more accurate, the fitting effect of the modified Logistic model was better. 3.2. Application of Ratkowsky model in fermentative hydrogen production 3.2.1. Effect of temperature on average hydrogen production rate Fig.3 shows the effect of temperature on average hydrogen production rate. The results showed that the hydrogen production rate in batch tests increased with increasing temperature from 20 to 40ć, and then it decreased with further increasing temperature from 40 to 55ć.
56
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
$YHUDJHK\GURJHQSURGXFWLRQO mL/hP
35
30
25
20
15
10
5 15
20
25
30
35
40
45
50
55
60
7HPSHUDWXUHO ćP Fig.3. Effect of temperature on average hydrogen production rate
In this study, the Ratkowsky model (Eq.5) was used to describe the effect of temperature on average hydrogen production rate. [8]
R
^
`
ª¬ A u T Tmin º¼ u 1 exp ª¬ B u T Tmax º¼ 2
2
(5)
Where R (mL/h) is the average hydrogen production rate, A (mL0.5/(ćЬh0.5)) and B (1/ć) are constant, Tmin is the minimum temperature when the average hydrogen production rate is becoming zero, Tmax is the maximum temperature when the average hydrogen production rate is becoming zero. The fitting result is as follows.
R
^
`
ª¬0.4 u T 14.3 º¼ u 1 exp ª¬0.02 u T 58.5 º¼ 2
2
(6)
The coefficient of determination (R2) was 0.932, with the significance level (p) being less than 0.05, which indicated that the Ratkowsky model could describe the effect of temperature on average hydrogen production rate in the batch tests of this study successfully. The scope of the temperature fitted by Ratkowsky model was 14.3-58.5; this result is in accord with most other researches and covers the majority of optimal temperatures. The activity of hydrogen-producing bacteria may be completely suppressed under too high or too low temperature. 3.2.2. Effect of initial pH on average hydrogen production rate Fig.4 shows the effect of initial pH on average hydrogen production rate. The results showed that the hydrogen production rate in batch tests increased with increasing initial pH from 4.0 to 8.0, and then it decreased with further increasing initial pH from 8.0 to 10.0. In this study, the modified Ratkowsky model (Eq.7) was used to describe the effect of initial pH on average hydrogen production rate. [8].
R
^
`
ª¬ A u T Tmin º¼ u 1 exp ª¬ B u T Tmax º¼ 2
2
(7)
Where R (mL/h) is the average hydrogen production rate, A (mL0.5/(ćЬh0.5)) and B are constant, pHmin is the minimum temperature when the average hydrogen production rate is becoming zero, pHmax is the maximum temperature when the average hydrogen production rate is becoming zero.
57
$YHUDJHK\GURJHQSURGXFWLRQUDWH˄mL/hP
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58
18 16 14 12 10 8 6 4 2 0
4
5
6
7
8
9
10
,QLWLDOpH
Fig.4. Effect of initial pH on average hydrogen production rate.
The fitting result is as follows.
R
^
`
ª¬0.8 u pH 2.3 º¼ 1 exp ª¬0.8 u pH 10.6 º¼ 2
2
(8)
The coefficient of determination (R2) was 0.988, with the significance level (p) being less than 0.05, which indicated that the modified Ratkowsky model could describe the effect of initial pH on average hydrogen production rate in the batch tests of this study successfully. The scope of the initial pH fitted by modified Ratkowsky model was 2.3-10.6; this result is in accord with most other researches and covers the majority of optimal initial pH. The activity of hydrogen-producing bacteria may be completely suppressed under too high or too initial pH. 4. Conclusion x The modified Logistic model could describe the variation of hydrogen production potential with time very well. x The Ratkowsky model could describe the effect of temperature on average hydrogen production rate successfully. x The modified Ratkowsky model could describe the effect of initial pH on average hydrogen production rate successfully. Acknowledgements We would like to thank the National Key Projects “Regulatory technology and independent verification test for CAP1400 security” (No. 20777045). References [1] J. A. C. Leite, B. S. Fernandes, E. Pozzi, M. Barboza, and M. Zaiat, 2008. Application of an anaerobic packed-bed bioreactor for the production of hydrogen and organic acids, Int J Hydrogen Energy 33, pp. 579-586. [2] C. L. Li and H. H. P. Fang, 2007. Fermentative hydrogen production from wastewater and solid wastes by mixed cultures, Crit Rev Env Sci Technol 37(3), pp. 1-39. [3] D. F. Xing, N. Q. Ren, A. J. Wang, Q. B. Li, Y. J. Feng, and F. Ma,2008. Continuous hydrogen production of auto-aggregative Ethanoligenens harbinense YUAN-3 under non-sterile condition, Int J Hydrogen Energy 33, pp. 1489-1495. [4] S.O-Thong, P. Prasertsan, N. Intrasungkha, S. Dhamwichukorn, and N.K. Birkeland, 2008. Optimization of simultaneous thermophilic fermentative hydrogen production and COD reduction from palm oil mill effluent by Thermoanaerobacterium-rich sludge, Int J Hydrogen Energy 33, pp. 1221-1231.
58
Wang Bo et al. / Procedia Engineering 43 (2012) 53 – 58 [5] S. K. Khanal, W. H. Chen, L. Li, and S. Sung, 2004. Biological hydrogen production, Effects of pH and intermediate products, Int J Hydrogen Energy 29, pp. 1123-1131. [6] Y. J. Lee, T. Miyahara, and T. Noike,2002. Effect of pH on microbial hydrogen fermentation, J Chem Technol Biotechnol 77, pp. 694-698. [7] M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. VAN’T RIET, Modeling of the bacterial growth curve, Appl Environ Microbiol, vol. 56, pp. 1875-1881, 1990. [8] D. A. Ratkowsky, R. K. Lowry, T. A. Mcmeekin, A. N. Stokes, and R. E. Chandler, 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range, J Bacteriol 154, pp. 1222-1226.