Environ. Sci. Technol. 2001, 35, 4726-4730
Biohydrogen Production as a Function of pH and Substrate Concentration STEVEN VAN GINKEL AND SHIHWU SUNG* Department of Civil & Construction Engineering, Iowa State University, Town Engineering Building, Ames, Iowa 50011-3232 JIUNN-JYI LAY Department of Safety, Health, and Environmental Engineering, National Kaohsiung First University of Science and Technology, 1 University Road, Yanchau, Kaohsiung, Taiwan, ROC
The conversion of organics in wastewaters into hydrogen gas could serve the dual role of renewable energy production and waste reduction. The chemical energy in a sucrose rich synthetic wastewater was recovered as hydrogen gas in this study. Using fractional factorial design batch experiments, the effect of varying pH (4.5-7.5) and substrate concentration (1.5-44.8 g COD/L) and their interaction on hydrogen gas production were tested. Mixed bacterial cultures obtained from a compost pile, a potato field, and a soybean field were heated to inhibit hydrogen-consuming methanogens and to enrich sporeforming, hydrogen-producing acidogens. It was determined that the highest rate (74.7 mL H2/(L*h)) of hydrogen production occurred at a pH of 5.5 and a substrate concentration of 7.5 g COD/L with a conversion efficiency of 38.9 mL H2/(g COD/ L). The highest conversion efficiency was 46.6 mL H2/(g COD/ L).
Introduction The recent rise in oil and natural gas prices may drive the current economy toward alternative energy sources. In addition, oil and natural gas are finite resources, and the combustion of these fossil fuels contributes to air pollution and the greenhouse effect. Expensive fossil fuel prices, in addition to pollution concerns, warrant the need for alternative, nonpolluting energy sources. Hydrogen may be an ideal fuel (1). Hydrogen gas has been deemed the fuel of the future, and it is believed that a hydrogen fuel based economy would be less polluting than a fossil fuel based economy (2). There are significant advantages of using hydrogen gas in place of conventional fossil fuels to produce energy. The combustion of hydrogen with oxygen produces only water vapor, a nongreenhouse gas. The combustion of hydrogen in automobiles is 50% more efficient than gasoline, and hydrogen gas has 2.75 times the energy content of any hydrocarbons (3). In addition, the conversion efficiency of hydrogen to electricity could be doubled using fuel cells other than gas turbine. Hydrogen can also be easily stored as a metal hydride (4). According to Kloeppel and Rogerson (5), the transmission of hydrogen through natural gas pipelines * Corresponding author phone: +1 515 294 3896; fax: +1 515 294 8216; e-mail:
[email protected]. 4726
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would be more efficient than the transmission of electricity down power lines. Current production of hydrogen can be divided into physical/chemical methods and biological methods. Physical/chemical methods cannot be considered as an alternative, nonpolluting energy source since traditional nonrenewable fossil fuels are used to produce the hydrogen gas. In contrast, complex substrates (sugary wastewater, cellulose, municipal solid waste, sugar cane juice, corn pulp, and paper) have been used in anaerobic fermentation to produce hydrogen gas (6-9). According to Benemann (10), the most promising method to produce hydrogen is the production of hydrogen from low cost substrates using anaerobic fermentation methods. Using the same hardware as methane fermentation, hydrogen fermentation could be economical at less than stoichiometric yields. Much research has been performed on anaerobic fermentation with methane as the final byproduct. This method is well-known as a system requiring low energy input and little mixing. Hydrogen gas, carbon dioxide, and volatile acids are all intermediary products produced by acidogenic bacteria that would traditionally be used by methanogens to produce methane. Some of the bacteria known to produce hydrogen include Clostridia, Escherichia, Citrobacter, and Bacillus (6, 7, 11). Earlier research by Lay et al. (6) suggests that if the final step or methanogenesis were blocked by inhibiting methanogens, only acidogens would be left to produce hydrogen gas, carbon dioxide, and volatile acids. An enrichment procedure, heat-shock, inhibits or kills nonsporeforming bacteria (hydrogen-consuming methanogens) and enriches sporeforming bacteria (hydrogen-producing acidogens). In the past, much research concerning hydrogen gas production produced a small net amount of hydrogen gas since the methanogens quickly utilized the hydrogen to produce methane (6). In reality, significant amounts of hydrogen gas were produced, but it was quickly utilized. Inhibiting methanogens will enable the hydrogen gas to be recovered. Subsequently, the acids can be converted by methanogens into methane, also an energy source (10). Furthermore, in a continuous process producing hydrogen gas from a waste, such as a wastewater treatment reactor, the bacterial culture would have to be mixed culture since the wastewater itself contains a mixed culture. Research in the anaerobic method has used many mixed cultures sewage, anaerobic digestion sludge, landfill sediments, hydrogenexplosion soybean silos, and sludge compost (6-9). The objectives of this research were to find the best initial pH, substrate concentration, inocula, and enrichment procedure to start up a hydrogen-producing reactor. According to Gottschalk (12), spores can be found in almost any anaerobic environment, so three different types of natural inocula were tested to find inocula rich in hydrogenproducing, sporeforming anaerobes. One inoculum type was then selected to confirm the effect of pH and substrate concentration on biohydrogen production.
Materials and Methods Natural Inocula and Heat Shock Treatment. Compost from the Iowa State University Composting Facility and potato and soybean soil from the Iowa State University Student Organic Farms were used as inocula in this study. The compost was obtained from ∼30 cm from the top of the compost pile. The compost was broken up and placed onto a 41 cm aluminum pizza pan to a depth of 1 cm and heat shocked (baked) at 104 °C for 2 h. This dry compost mix was 10.1021/es001979r CCC: $20.00
2001 American Chemical Society Published on Web 11/02/2001
TABLE 1. Inocula Experimental Design factorial design design
[S] g COD/L
A
14.9-44.8
B C D
1.5-7.5 1.0-4.0 0.5-1.5
step change
inocula
no. of batches
potato soil soybean soil compost compost compost compost
3 1 2 1 1 1
pH
1
4.5-6.5
0.2 0.1 0.03
5.5-7.5 5.0-7.0 4.5-6.5
then used as inoculum. Similarly, potato and soybean soil were obtained from an agricultural field in early March from a depth of roughly 20 cm and subjected to heat shock treatments. The volatile solids content of the compost, potato soil, and soybean soil inocula were approximately 54, 12, and 14 g/L reactor volume, respectively. Procedure. Five batch experiments using compost, three using potato soil, and one using soybean soil were performed as shown in Table 1. The experiments were conducted in a random fashion so as not to introduce any biased error as described by Steele et al. (13). Thirty grams of dry inocula mix were added to 250 mL Wheaton batch serum bottles. A prescribed amount of sucrose and 0.5 mL of nutrient stock solution were added to the batch bottles. Each liter of nutrient stock solution, modified from Lay et al. (6), contained 200 g of NH4HCO3, 100 g of KH2PO4, 10 g of MgSO4•7H2O, 1.0 g of NaCl, 1.0 g of Na2MoO4•2H2O, 1.0 g of CaCl2•2H2O, 1.5 g of MnSO4•7H2O, and 0.278 g of FeCl2. The bottles were then filled to 150 mL using deionized water. The pH was adjusted using either 1 M HCl or 1 M KOH, and the bottles were immediately capped with a Wheaton rubber septum stopper. The bottles were then flushed with nitrogen gas for 15 s to remove oxygen from the solution, and the stoppers were replaced and tied down with plastic fasteners. The batch was then placed in an incubator/shaker at 37 °C and a horizontal rotational speed of 180 rpm. After 24 h, biogas production was sampled using 2 to 100 mL syringes according to the Owen approach (14). Fractional Factorial Design. To describe the effects of initial sucrose concentration and initial pH and their interaction, the batches described previously were subjected to a fractional factorial central composite design (15) as shown in Table 1. In the first design (design A), 12 bottle batch experiments using compost, potato soil, and soybean soil as inocula were conducted where the sucrose concentration varied from 14.9 to 44.8 g COD/L with a central value of 29.8 g COD/L, while the initial pH varied from 4.5 to 6.5 with a central value of 5.5. To fit the factorial design, the initial pH and sucrose ([S]) values were evenly distributed by the means of coded units. The levels of [S] and pH in coded units were determined by the following equation
xi ) (Xi - Xi*)/∆XI
(1)
where xi is the coded value of the ith test variable, Xi is the uncoded value of the ith test variable, Xi* is the uncoded value of the ith test variable at the center point, and ∆Xi is the step change value as described by Lay et al. (6). The coded values were (-2, -1, 0, 1, 2) corresponding to sucrose values of (2-6) in g/150 mL bottle or (14.9, 22.4, 29.9, 37.3, 44.8 g COD/L). The step change value (∆Xi) was set at certain values by an assumed degree of change in the factorial response based on the levels of pH and [S] as shown in Table 1. The larger the step change value, the greater the variability of the results. As the step change value decreases, the differences in the initial conditions become smaller, and it
FIGURE 1. Typical cumulative hydrogen production curves and the background hydrogen control study: regular batch bottle, O; first blank, 1; second blank delayed 2 days, b. becomes harder to see any variability in the results. In this research, decreasing the step change value can be compared to focusing in on the optimum combination of pH and substrate concentration. The center point was replicated three times to achieve an accurate value at this point. The last designs (B, C, and D) of Table 1 were conducted similar to design A described previously, yet only compost was used as the inoculum, and the sucrose concentration was decreased substantially. In addition, after baking, the dry compost was crushed into a finer material using a mortar and pestle and sieved through a 9 mm sieve mesh. This sieved material was used as inoculum. These designs were buffered using a 0.06 M H2PO4/HPO4 buffer solution. Analytical Methods. The hydrogen gas percentage (H2%) was measured using a 1.0 mL gastight syringe by comparing the sample biogas with a standard of pure hydrogen using a GOW-MAC Series 350 gas chromatograph (GC) equipped with a thermal conductivity detector. The column was a 2.43 m × 0.64 cm stainless steel (SS) 350A Molesieve 13 × 80/100. The operational temperatures of the injection port, the oven, and the detector were 100, 50, and 100 °C, respectively. Nitrogen was used as the carrier gas with a flowrate of 75 mL/min. The minimum amount of hydrogen detectable was 1%. Nitrogen, carbon dioxide, and methane were measured by comparing the sample biogas with a standard of pure nitrogen, carbon dioxide, and methane using a GC of the same model noted previously with a 2.43 m × 0.64 cm SS 350B Hayesep DB 80/100 column. The operational temperatures of the injection port, the oven, and the detector were 150, 50, and 100 °C, respectively. Helium was used as the carrier gas at a flowrate of 140 mL/min. The minimum amount of methane detectable was 1%. Data Analysis. Once cumulative hydrogen production curves were obtained over the course of an entire batch experiment, a curve was modeled to the data using the following modified Gompertz eq 2
{
H(t) ) Hmax • exp - exp
[
R•e (λ - t) + 1 Hmax
]}
(2)
Figure 1 shows a cumulative hydrogen production curve best fitted by minimizing the ratio of the sum of square error to the correlation coefficient (SSE/R 2) using the “Solver” function in the “Tools” menu in Microsoft Excel 1995. The curve was initially fit by eye and was subjected to the constraints of hydrogen production potential, Hmax (or total amount of hydrogen produced in mL), the hydrogen production rate, R (mL hydrogen produced/h), and the lag phase, λ (h) or the time to exponential hydrogen production. In the tables and figures, the rate values are normalized and VOL. 35, NO. 24, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Fractional Factorial Central Design Matrix with Observed Responses (Ps and R) for Each Natural Inoculum in Design A inoculum compost
potato
soybean
[S]a
pH
Psb
Rc
Ps
R
Ps
R
14.9 22.4 22.4 29.9 29.9 29.9 37.3 37.3 44.8
5.5 5.0 6.0 4.5 6.5 5.5 5.0 6.0 5.5
17.9 14.9 15.8 14.7 17.0 15.7 ( 1.4 8.4 9.0 7.9
21.7 18.6 15.4 13.7 11.4 10.7 ( 1.4 16.1 12.4 13.5
16.0 11.9 12.1 7.9 8.3 8.4 ( 0.03 6.6 6.5 5.2
34.56 39.37 38.71 30.23 33.85 38.4 ( 2.9 38.22 35.20 34.60
14.9 8.9 11.4 6.5 10.6 8.2 ( 0.3 5.7 7.4 7.1
15.3 12.0 15.3 13.3 18.7 15.3 ( 0.0 13.3 15.3 18.7
13.5 ( 3.9
14.8 ( 3.5
35.9 ( 3.0
9.0 ( 2.9
15.3 ( 2.3
a
Units in g COD/L.
b
Averages 9.2 ( 2.4
c
Units in mL H2/(g COD/L). Units in mL H2/(L‚hr).
expressed as (mL hydrogen produced/(L reactor volume*h)). H(t)is hydrogen production as a function of time. In addition, Ps, the specific hydrogen production potential or conversion efficiency was determined in this study. Ps is defined as Hmax/ [S] (mL H2/(g COD/L)). Stepwise regression was performed using the statistical program SAS. In the regression, the Ps and R values were regressed with respect to [S] and pH. Subsequently, threedimensional response surface plots were constructed using the SigmaPlot Version 3 software package to give visual insight to the effect of [S] and pH on hydrogen production.
Results and Discussion Control Study. To confirm that there was no background hydrogen production resulting from degradation of the organic matter in the inoculum, two blank batch bottles and one regular batch bottle were incubated as shown in Figure 1. No hydrogen production occurred from the first blank reactor (Figure 1). The second blank was delayed by adding the 2 g of sucrose 48 h after the start of the experiment. The curves show that the only difference between the curves with two grams of sucrose added is the lag phase. In addition, a batch was delayed 24 h by withholding nutrients. By hour 48, the entire batch behaved as normalsthe only difference being the extension of lag phase for an additional 24 h (data not shown). These three batch bottles confirm the lack of hydrogen production from the natural inocula. These control experiments suggest that sucrose and nutrients may be necessary to germinate the spores in the inoculum. Ability of Natural Compost Bacteria Converting Sucrose to Hydrogen. From the various batches conducted, the use of compost, potato soil, and soybean soil as inoculum confirmed the effect of pH and [S] on hydrogen production. It also confirmed that hydrogen producing bacterial species could be easily obtained from nature. In design A, the use of potato soil yielded the highest rate (35.9 ( 3.0 H2/(L* h)), and the use of compost resulted in the highest conversion efficiency (13.5 ( 3.9 mL H2/(g COD/L)) (Table 2). This may be due to differences in bacterial species in the inoculum as well as the buffering capacity of the organic matter in the compost. Measurements of Conversion Efficiency and Rate. Conversion efficiency and rate represent the hydrogengenerating ability and the hydrogen-producing bacterial activity on the defined substrate, respectively. Using the Gompertz model, it was found that the correlation coefficients for Hmax and R were high. For example, R 2 > 0.99 for the potato soil, >0.93 for compost, and >0.85 for the soybean soil. The soybean curves were quite different in shape than the other inocula as shown in Figure 2. Hence, the Gompertz 4728
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FIGURE 2. Cumulative H2 production curve shapes using the curves with the highest potential (their initial conditions are stated as ([S] (g COD/L), pH): compost, design A, 0 (30, 5.5); potato soil, design A, 3 (22, 6); soybean soil, design A, 9 (45, 5.5); compost, design B, b (7.5,5.5); compost, design C, O (4,5); and compost, design D, 1 (1.5,5.5).
TABLE 3. Polynomial Regression Equations for Figures 3-6 pH*[S]
pHb
[S]b
pH
[S]
R2
F d.f.
-0.12 0.01 0.06 -0.06 4.13 0.99 β/s.e.b -2.21 3.54 0.21 -0.20 2.62 all designs β 0.25 0.00 -2.07 -1.77 16.19 0.91 β/s.e. 4.70 -0.60 18.0 5.20 21.90 potato soil β 2.70 -13.97 -0.04 76.2 -12.40 0.93 β/s.e. 7.91 -7.96 -3.72 8.12 -7.17 designs β -4.03 0.50 -0.18 -3.61 33.85 0.91 B-D β/s.e. -7.31 1.82 -0.90 -2.16 10.21
59
a Coefficients for each variable in the regression equation. ficient divided by the standard error of the coefficient.
b
design A
βa
4
53 27 14
5
81 39
Coef-
model did not fit as well. The reasons for this are unknown. Conversion Efficiency. For the batch experiments conducted using design A, Ps, and R values are shown in Table 2. To evaluate the relationship of [S] and pH on conversion efficiency, the design matrix with the corresponding averages of all three inoculum in Table 2 were subjected to stepwise regression analysis, and the regression equation and figure were generated as shown in Table 3 and Figure 3, respectively. As shown in Table 3, since most of the values of β/s.e. are greater than one (not equal to zero), [S], pH, and their interaction are shown to have an effect on hydrogen production. For the batch experiments conducted using design B, C, and D, Ps and R values are shown in Table 4. Similarly, to evaluate the relationship between [S] and pH
FIGURE 3. Conversion efficiency of all three inocula using data from design A.
TABLE 4. Fractional Factorial Central Design Matrix with Observed Responses (Ps and R) in Designs B, C, and D design B
C
D
S
pH
Ps
R
S
pH
Ps
R
S
pH
1.5 1.5 1.5 3.0 3.0 3.0 3.0 4.5 4.5 4.5 6.0 6.0 7.5 7.5 7.5
5.5 6.5 7.5 5.5 6.0 7.0 7.5 5.5 6.5 7.5 6.0 7.0 5.5 6.5 7.5
39.6 15.7 2.7 33.1 35.5 10.6 4.7 39.7 30.0 11.2 38.0 18.5 38.9 18.8 7.9
15.1 6.0 0.8 25.7 27.2 8.2 3.6 47.2 34.5 12.8 59.2 29.5 74.7 36.0 16.2
1.0 1.0 1.0 1.7 1.7 2.5 2.5 2.5 2.5 3.2 3.2 4.0 4.0 4.0
5.0 6.0 7.0 5.5 6.5 5.0 6.0 6.0 7.0 5.5 6.5 5.0 6.0 7.0
25.2 18.8 16.0 24.7 32.4 16.7 20.8 20.7 11.4 24.3 17.7 24.9 24.3 15.9
9.6 7.2 6.1 16.6 21.8 16.2 20.1 20.0 11.0 30.6 22.3 38.7 37.7 24.7
0.5 0.5 0.5 0.7 0.7 1.0 1.0 1.0 1.0 1.0 1.2 1.2 1.5 1.5 1.5
4.5 5.5 6.5 5.0 6.0 4.5 5.5 5.5 5.5 6.5 5.0 6.0 4.5 5.5 6.5
R
Ps
26.8 4.2 46.6 4.0 13.8 2.2 31.9 7.5 21.6 5.1 22.5 7.0 25.4 6.4 25.6 8.1 25.2 5.3 15.0 4.7 20.9 7.1 17.9 7.1 18.9 8.2 25.2 9.3 23.8 11.3
Averages Ps
R
Ps
R
Ps
R
23.0 ( 13.9 26.1 ( 21.0 21.0 ( 5.4 20.1 ( 10.2 24.1 ( 7.8 6.5 ( 2.3
on the conversion efficiency of compost inoculum using design A (15-45 g COD/L) as well as at the lower substrate concentrations of designs B, C, and D (0.5-7.5 g COD/L) the design matrix with the compost data from Tables 2 and 4 were subjected to stepwise regression analysis, and the regression equation and figure were generated as shown in Table 3 and Figure 4, respectively. As can be seen from Figures 3 and 4, the conversion efficiency significantly increases with a decrease in substrate concentration. However, the average conversion efficiencies of designs B, C, and D (23.0, 21.0, and 24.1 mL H2/(g COD/L), respectively) are relatively constant as shown in Table 4 suggesting that if the sucrose concentrations were increased, the conversion efficiency could be maintained. Furthermore, the lower conversion efficiency of using compost in design A (13.5 mL H2/(g COD/L) indicates that lower substrate concentrations (0.5-7.5 g COD/L) were more effectively utilized. This is in agreement with earlier research where higher substrate concentrations “shock load” to the system caused increased initial hydrogen production, simultaneous acid/pH inhibition, and increased hydrogen partial pressures (7). If “shock loading” persists, substrate may be left unutilized or be converted to alcohols (6, 16). As shown in Figure 4, Ps
FIGURE 4. Conversion efficiency of inocula from compost of all designs. increases with decreasing pH. As can be seen in Table 4, the highest Ps values always occurred around a pH of 5.5-6.0 and were much lower at pH values of 6.5-7.5. This agrees with earlier work by Lay et al. (6). Lay et al. (6) suggests a pH of 5.6 to be optimum and a dividing line between alcohol and acid production for undefined inocula. The results obtained in this research are in agreement with this earlier work. Doremus et al. (16) suggested that when the hydrogen partial pressure (PH2) increases to a certain level in the reactor headspace, the culture will switch to alcohol production and produce much less hydrogen. Thus, based on the above two conclusions, the effect of [S] on pH, PH2, and conversion efficiency is important. For example, in this research, those batches with the highest initial [S] (design A, 15-45 g COD/ L) yielded the highest hydrogen partial pressure, but the lowest conversion efficiency suggesting that the [S] was in excess and the excess may have been converted to alcohols rather than acids and hydrogen (data not shown). In those batches conducted using design A (highest [S]) the PH2 rose quickly to > 40%, while in the batch conducted using design B (1.5-7.5 g COD/L) the PH2 rarely rose > 40%. The highest hydrogen gas percentages of 71, 45, 35, and 24% in the biogas decreased with decreasing substrate concentration from design A (15-45 g COD/L), design B (1.5-7.5 g COD/L), design C (1.0-4.0 g COD/L), to design D (0.5-1.5 g COD/L), respectively. Hydrogen Production Rate. Since the use of potato soil as inoculum achieved the highest hydrogen production rate of 39.37 mL H2/(L*h) under design A, rate data obtained using potato soil as inoculum was used to produce the regression equation as shown in Table 3. The potato soil rate data from Table 2 was used to produce Figure 5. As can be seen from Figure 5, the highest hydrogen production rate occurred at a pH of around 5.5 and a substrate concentration of roughly 15-35 g COD/L. Similarly, to evaluate the relationship between [S] and pH on the hydrogen production rate using the compost data at the lower substrate concentrations of designs B, C, and D (0.5-7.5 g of COD/L), the data of Table 4 were subjected to stepwise regression analysis, and the regression equation and figure were generated as shown in Table 3 and Figure 6, respectively. As can be seen from Figure 6 and Table 4, it is evident that hydrogen production rate reaches its maximum as the [S] increases in the pH range of 5.0-6.0. Table 4 shows that a [S] of 7.5 g COD/L (design B) and a pH value of 5.5 yield the highest hydrogen production rate of 74.7 mL H2/(L*h). In addition, the average rate of using compost as inoculum in design B (26.1 mL H2/(L*h)) is much higher than that of design VOL. 35, NO. 24, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Hydrogen production rate using potato soil as inocula.
mately be halved to 2.45 mol H2/mol glucose. The highest conversion known is 2.6 mol H2/mole glucose by Taguchi et al. (17) using Clostridia. Lay et al. (6) suggests the genus Clostridium to be the dominant hydrogen producer in natural undefined inocula. Indeed, Clostridia are sporeformers and are expected to survive the heat shock enrichment treatment used in this study. In conclusion, to start up a hydrogen producing reactor two factors have to be considered. First, the inocula used to seed the reactor must be free of hydrogen consuming methanogens. This inocula can be obtained from almost any natural source, since, according to Gottschalk (12), spores can be obtained from almost any anaerobic environment. Results from this research show that the selection for spores can be accomplished by heat shocking the inocula. Second, since acids are produced simultaneously with hydrogen gas, the substrate concentration should not be too high to shock load the system. Finally, the results of this research suggest a pH range of 5.0-6.0 to be optimum for hydrogen production.
Acknowledgments This study was supported by a grant from the U.S. Department of Energy, contract number DE-FC36-00GO10530, through the Hydrogen Program.We gratefully acknowledge Kari Jovag for her assistance in statistical analysis and Dr. Bruce E. Logan at Pennsylvania State University for his helpful comments in revising this manuscript.
Literature Cited
FIGURE 6. Hydrogen production rate using compost as inocula of designs B, C, and D. A (14.8 mL H2/(L*h)) suggesting that lower substrate concentrations yield higher hydrogen production rates. This may indicate that higher substrate concentrations may quickly become inhibitory through pH depletion, acid production, or increased hydrogen partial pressures. Hence, the removal of these inhibitory mechanisms may be necessary to achieve high hydrogen conversion efficiencies and production rates at higher substrate concentrations. However, as suggested earlier, since the conversion efficiencies of designs B, C, and D are relatively constant, increasing the [S] slightly above that of design B (>7.5 g COD/L) may achieve not only the same conversion efficiency but also a greater hydrogen production rate (>74.7 mL H2/(L*h)) since the hydrogen production rate increases as the [S] increases from 0.5 to 7.5 g COD/L (Table 4). In comparison to previous anaerobic fermentation research, this research was able to demonstrate higher production rates than most other methods. The highest molar conversion efficiency was 4.9 mol H2/mol sucrose (46.6 mL H2/(g COD/L)) at a pH of 5.5. Since sucrose is fructose and glucose combined, this molar conversion could approxi-
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(1) Gregory, D. P. Sci. Am. 1973, 228(1), 13. (2) Gregoire-Padro, C. E. Energy Fuels. Special section on hydrogen 1998, 12(1), 1. (3) Ramachandran, R.; Raghu, K. M. Int. J. Hydrogen Energy 1998, 23(7), 593. (4) Billings, R. E. The hydrogen worldview, 1st ed.; American Academy of Science: 1991. (5) Kloeppel, J.; Rogerson, S. Science 1991, 668. (6) Lay, J. J.; Lee, Y. J.; Noike, T. Water. Res. 1999, 33(11), 2576. (7) Roychowdhury, S.; Cox, D.; Levandowsky, M. Intl. Assoc. Hydrogen Energy 1988, 584. (8) Ueno, Y.; Kawai, T.; Sato, S.; Otsuka, S.; Morimoto, M. J. Ferment. Bioeng. 1995, 79(4), 395. (9) Sparling, R.; Risbey, D.; Poggi-Varaldo, H. M. Int. J. Hydrogen Energy 1997, 22(6), 563. (10) Benemann, J. Nature Biotechnol. 1996, 14, 1101. (11) Nandi, R.; Sengupta, S. Crit. Rev. Microbiol. 1998, 24(1), 61. (12) Gottschalk, G.; Andreesen, J. R.; Hippe, H. The Prokaryotes; Springer-Verlag: New York, 1981; Vol. 2(138), p 1767. (13) Steele, R. G. D.; Torrie, J. H.; Dickey, D. A. Principles and procedures of statistics: a biometrical approach, 3rd ed.; McGraw-Hills Series: 1997; p 126. (14) Owen, W. F.; Stuckey, D. C.; Healy, J. B., Jr.; Young, L. Y.; McCarty, P. L. Water Res. 1979, 13, 485. (15) Box, G. E. P.; Hunter, W. G.; Hunter, J. S. Statistics for experimenters: an introduction to design, data analysis, and model building; Wiley: New York, 1978; Chapter 12, pp 347417. (16) Doremus, M. G.; Linden, J. C.; Moreira, A. R. Biotech. Bioeng. 1985, 27, 852. (17) Taguchi, F.; Yamada, K.; Hasegawa, K.; Taki-Saito, T.; Hara, K. J. Ferment. Bioeng. 1996, 82(1), 80.
Received for review December 18, 2000. Revised manuscript received September 19, 2001. Accepted September 19, 2001. ES001979R
