RESEARCH ARTICLE Open Access [631163]

RESEARCH ARTICLE Open Access
Revealing the factors influencing a fermentative
biohydrogen production process using industrial
wastewater as fermentation substrate
Iulian Zoltan Boboescu1,2, Mariana Ilie1, Vasile Daniel Gherman1, Ion Mirel1, Bernadett Pap2, Adina Negrea1,
Éva Kondorosi3, Tibor Bíró4and Gergely Maróti1,3*
Abstract
Background: Biohydrogen production through dark fermentation using organic waste as a substrate has gained
increasing attention in recent years, mostly because of the economic advantages of coupling renewable, cleanenergy production with biological waste treatment. An ideal approach is the use of selected microbial inocula that
are able to degrade complex organic substrates with simultaneous biohydrogen generation. Unfortunately, even
with a specifically designed starting inoculum, there is still a number of parameters, mostly with regard to thefermentation conditions, that need to be improved in order to achieve a viable, large-scale, and technologically
feasible solution. In this study, statistics-based factorial experimental design methods were applied to investigate
the impact of various biological, physical, and chemical parameters, as well as the interactions between them onthe biohydrogen production rates.
Results: By developing and applying a central composite experimental design strategy, the effects of the independent
variables on biohydrogen production were determined. The initial pH value was shown to have the largest effect on
the biohydrogen production process. High-throughput sequencing-based metagenomic assessments of microbial
communities revealed a clear shift towards a Clostridium sp.-dominated environment, as the responses of the variables
investigated were maximized towards the highest H
2-producing potential. Mass spectrometry analysis suggested that
the microbial consortium largely followed hydrogen-generating metabolic pathways, with the simultaneous degradation
of complex organic compounds, and thus also performed a bio logical treatment of the beer b rewing industry wastewater
used as a fermentation substrate.
Conclusions: Therefore, we have developed a comp lex optimization strategy for batc h-mode biohydrogen production
using a defined microbial consortium as the starting ino culum and beer brewery wastewater as the fermentation
substrate. These results have the potential to bring us closer t o an optimized, industrial-scale system which will serve the
dual purpose of wastewater pre-treatment and concomitant biohydrogen production.
Keywords: Biohydrogen, Central composite experimental design, Mic robial inocula, Metagenomics, Industrial wastewater
* Correspondence: [anonimizat]
1Polytechnic University of Timisoara, Timisoara, Romania
3Hungarian Academy of Sciences, Biological Research Centre, Temesvari krt.
62., Szeged 6726, HungaryFull list of author information is available at the end of the article
© 2014 Boboescu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public DomainDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,unless otherwise stated.Boboescu et al. Biotechnology for Biofuels 2014, 7:139
http://www.biotechnologyforbiofuels.com/content/7/1/139

Background
Our society today increasingly requires more energy to
maintain overall ascending economic trends. Althoughthe demand for energy is permanently growing, the re-
serves of our primary energy carriers will be depleted
within a few decades [1]. In addition, our fossil fuel-based economy is dramatically accelerating the process
of global warming with severe and permanent conse-
quences for the environment [2]. Therefore, novel andsafe energy carriers must be introduced. Hydrogen satis-
fies all the requirements for a clean and renewable fuel,
producing only water as a by-product upon combustionor direct use in fuel-cell technology. Hydrogen has the
highest energy content per unit weight of any known
fuel (142 kJ/g or 61,000 Btu/lb) and can be transportedfor domestic/industrial consumption through conven-
tional means [3,4]. In addition to this, H
2gas is safer to
handle than domestic natural gas, and can be used dir-ectly in internal combustion engines or in fuel cells to
generate electricity [3]. Hydrogen use in fuel cells is in-
herently more efficient than the combustion currentlyrequired for the conversion of other potential fuels to
mechanical energy [5]. However, most hydrogen is cur-
rently produced by conventional chemical or electrolyticmethods, which require high amounts of energy and ex-
pensive technologies [6].
In the last few years, attention has shifted towards
novel and less energy-intensive technologies for produ-
cing hydrogen [7-9]. Among the various hydrogen pro-
duction processes, the biological methods (direct andindirect photolysis, photo-fermentation, and dark fer-
mentation) appear to be the most promising [10-12]. In
addition, certain methods of producing biological hydro-gen such as dark fermentation can utilize various or-
ganic wastes as a substrate for fermentative hydrogen
production, thus coupling organic waste treatment withrenewable energy generation [13-16]. Recently, reducing
the cost of wastewater treatment and finding ways to
produce useful products from wastewater has been gainingimportance with regard to environmental sustainability.
One way to address both issues is to simultaneously gener-
ate bioenergy in the form of hydrogen by utilizing the or-
ganic matter present in wastewater [17]. In addition,
certain types of wastewater generated by various industrialprocesses are considered ideal substrates because they con-
tain high levels of easily degradable organic material [18].
The complete oxidation of glucose could yield a theor-
etical maximum of 12 moles of H
2per mole of glucose,
but in this case no energy can be utilized to support the
growth and metabolism of the hydrogen-producing or-ganism [7,19,20]. Strictly anaerobic hydrogen-producing
microbes are able to generate a maximum of 4 moles of
H
2per mole of degraded glucose [21-23]. Fermentative
H2production using complex microbial communitiestherefore has the advantage of a high hydrogen produc-
tion rate utilizing complex organic wastes as fermenta-
tion substrates with limited amounts of additionalexternal energy input [24]. Bacteria and other microbes
capable of hydrogen production exist widely in natural
environments rich in organic nutrients such as soil,wastewater sludge, and compost [25-27]. Microbial pop-
ulations sampled from these habitats can thus be used as
cheap and highly efficient inocula for fermentativehydrogen production. In addition, dark hydrogen pro-
duction processes using mixed microbial cultures as
starting inocula are more efficient than those using purecultures. The reason is that mixed cultures represent
more simple systems to operate which are easier to con-
trol, and may be able to degrade a broader range of feed-stock [28]. The use of mixed microbial cultures as
starting inocula also allows the use of unsterile fermen-
tation substrates, such as most types of wastewater.However, in a fermentative hydrogen production process
using mixed cultures, the hydrogen produced by
hydrogen-evolving bacteria can be consumed byhydrogen-consuming bacteria [29,30]. Strategies for pre-
treatment of the parent inocula are therefore required to
restrict or even terminate the methanogenic process toensure that H
2remains the end product in the metabolic
flow [31-33].
One of the major impediments in developing biohydro-
gen (bioH 2) production processes for commercialization is
low hydrogen production yield. The biological production
rate of hydrogen and its molar yield, like most other bio-processes, are dependent on several parameters including
the activity rate of hydrogen-producing and -consuming
bacteria, substrates, inorganic nutrients, and operationalconditions of the bioreactor, among other factors [34].
Identifying these influencing factors and optimizing the
fermentation conditions, particularly the nutritional andenvironmental parameters, is thus of primary importance
in the bioprocess development. The most widely used
screening and optimization strategy is the design of ex-periment (DOE) method, by which certain factors are se-
lected and deliberately varied in a controlled manner, in
order to study their effects, facilitate process comprehen-
sion, and even to improve performance [35-43]. The use
of such DOE methods for process optimization in fermen-tative hydrogen production processes is critical due to the
dynamic and complex nature of these systems.
In the present study, a central composite experimental
design was used to investigate the influence of the
process variables involved in batch-mode biohydrogen
production, as well as to optimize their response. Theexperiments were performed using a defined mixed mi-
crobial consortium as the starting inoculum and waste-
water obtained from a beer-brewing factory as thefermentation substrate. High-throughput metagenomicBoboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 2 of 15
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microbial community assessments as well as analysis of
substrate degradation rates were performed during the
experiments to understand the fermentation mecha-nisms involved. The results of this study have the poten-
tial to propel us closer to achieving and optimizing an
industrial-scale system which will serve the dual purposeof providing wastewater pretreatment coupled with bio-
hydrogen production.
Results and discussion
Batch experiments were conducted using mixed microbial
consortia able to degrade complex organic substrates likewastewater in addition to simultaneous biohydrogen pro-
duction. The involvement of biological, chemical, and
physical factors influencing the fermentative biohydrogenproduction process as well as the interactions of these
parameters were assessed by experiments designed by
statistics-based methods and metagenomic monitoring ofthis complex ecosystem.
Effect of various influencing factors and the interactions
between them on the biohydrogen production process
To assess the influence of various factors on the biohy-
drogen production process, as well as the level of inter-action between these factors, a cybernetic representation
of the dark biohydrogen fermentation process was devel-
oped, with an input-output structure (Figure 1). The in-put data are defined as influencing factors (IF) while the
system output data are defined as objective functions
(OF). By developing and applying this experimentalmodeling approach, connecting relationships (most fre-
quently with a predefined polynomial shape) between
the IF and the OF (in this case, H
2production rates) can
be identified for an experimental domain previously de-
fined as being of interest. The data obtained by applying
this experimental design strategy can be used to generatepredictions regarding the behavior of the system under
investigation. They can also provide deep insight into
the degree of influence of the investigated variables onthe objective functions. In addition, by applying ad-
vanced statistical approaches, the optimum region of a
system for a specific response can be identified (in thiscase, biohydrogen production rate).
Because of the steep curve generated by the response
surface approach during the investigation of an optimalarea within a specific system, using linear methods to
adequately identify the stationary point becomes obso-
lete. Therefore, depending on the number of investigatedvariables, further additional experimental points are re-
quired. One of the most recommended approaches to
address this issue is the central composite experimentaldesign method. By applying this strategy, one can use
the information obtained through a first-order mathem-
atical model, completing these insights by consecutivelyextending the experimental approach to explain a
second-order mathematical model. Thus, a central com-
posite design (CCD) consists of a standard first-orderdesign with a 2
kor 2k-porthogonal factorial point, where
k is the number of selected factors and p the number of
interactions replaced with influencing factors. 2kaxial
points are displayed in a "star pattern" at distance αfrom
the center of the design with n0center points. Based on
preliminary screening experiments, three IF were se-lected; fermentation temperature, starting pH value, and
degradable substrate availability (glucose addition;
Table 1). Each IF was defined between two physicalvalues, low and high, coded as -1 and +1 respectively,
with a center value coded as 0.
The relationship between the physical and coded
values is given by the following equation:
x
jcod¼xjphys−xj0phys
Ijphys;j¼1;2;3 ð1Ț
where:
–xjcodis the coded value of the j factor
–xjphys is the physical value of the j factor
Figure 1 Cybernetic representation of the investigated dark biohydrogen fermentation process with an input-output structure.
The anaerobic fermentation of the wastewater is considered a black-box system influenced by different variables, with consequences on the
objective function (in this case, bioH 2production).Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 3 of 15
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–xj0phys is the central level of the j factor
–Ijphys is the variation interval for the j factor; double
this value gives the variation range D (Equation 2)
D¼xjphys high −xjphys low ¼2Ijphys ð2Ț
The factorial portion of the CCD is a complete 23fac-
torial with eight runs, which contains all the possiblecombinations within the defined levels of the investi-
gated variables (runs 1-8; Table 2). The additional ex-
perimental runs (9-14) represent supplementary axialpoints displayed in a "star pattern" around the center of
the design, at distance αof 1.287 from the center. The
design is completed with n
0= two observations at the
experimental center (runs 15 and 16). Because of the dif-
ferent nature of the experimental design approaches
(runs 1-8 and runs 9-14 together with runs 15 and 16),we will separately discuss the results obtained for these
two main experimental groups. Biohydrogen productionwas monitored every 24 h for the duration of all
experiments.
Even though the same starting microbial inocula and
the same wastewater were used, notable differences inbiohydrogen production rates were observed for the fac-
torial portion of the CCD (runs 1-8; Figure 2A). This is
the first indication that the selected variables manifest,beyond doubt, a clear influence on the OF in the investi-
gated system. The highest biohydrogen production rates
observed during the factorial portion of the CCD weremeasured during experimental run 4 (a total production
of 25.2 mL H
2was measured at the end of the experi-
ment), followed closely by experimental run 3 (a totalproduction of 22.3 mL H
2was measured at the end of
the experiment). The other six experimental runs gener-
ated considerably smaller amounts of H 2, ranging from
14.6 mL (experimental run 7) to 8.2 mL (experimental
run 5) H 2at the end of the experiment (Figure 2A).
Strong similarities in the H 2production rates were ob-
served between similar experimental conditions (Table 2).
This was particularly true for most of the cases, where
only the glucose addition variable (X 3) differed.
Notable differences in volumetric biohydrogen produc-
tion were also observed for the supplementary star and
central points (experimental runs 9-16; Figure 2B). Thehighest biohydrogen production rates were obtained for
experimental runs 12 and 14 (a total H
2production of
27.1 mL and 25.9 mL was measured at the end of theexperiments respectively), while the lowest biohydrogen
production rate was obtained for experimental run 11 (a
total H
2production of 2.9 mL was measured at the end
of the experiment). Regarding the fermentation condi-
tions of the highest hydrogen-producing experimental
run (12), the fermentation temperature (X 1) as well as
the glucose addition (X 3) values were situated in the
center of the experimental model, while the initial pH(X
2) was fixed at a value of 6.74, representing a distance
of 1.287 from the experimental center (Table 2). These
preliminary findings suggest that the initial value of theenvironmental pH has a strong influence on the biohy-
drogen production rate.
The analysis of variance (ANOVA) test performed on
the central composite experimental design showed that
both linear and quadratic effects of the temperature and
p H ,a sw e l la st h e i rf i r s t – o r d e ri n t e r a c t i o n s ,a r es t a t i s t i c a l l ysignificant parameters with regard to the OF (Table 3).Table 1 Physical and coded values of the variables used in the central composite design
Coded
symbolVariables Values of coded levels
(-1.28) (-1) (0) (+1) (+1.28)
X1 Operating temperature (°C) 23.28 25 31 37 38.72
X2 Initial value of fermentation pH 4.56 4.8 5.65 6.5 6.74
X3 Glucose addition (g/L) 3.56 5 10 15 16.44
Table 2 Central composite experimental design matrix of
the three investigated variables, with the total measuredH
2production for each of the experimental runs
Run
numberVariable Response
X1 X2 X3 Total hydrogen production mean (mL)
1 -1 -1 -1 8.66
2 -1 -1 1 9.623 -1 1 -1 22.344 -1 1 1 25.235 1 -1 -1 8.286 1 -1 1 8.357 1 1 -1 14.67
8 1 1 1 11.92
9 -1.28 0 0 17.6710 1.28 0 0 10.4311 0 -1.28 0 2.9712 0 1.28 0 27.1813 0 0 -1.28 14.9514 0 0 1.28 25.9215 0 0 0 21.0816 0 0 0 21.66Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 4 of 15
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However, the main influence of initial glucose addition
(X3), its quadratic effect, and first-order interactions on
biohydrogen production are regarded as statistically insig-
nificant. The lack of fit has a P-value slightly higher than
α= 0.05, which makes it marginally significant (meaning
that it is significant at the 0.10 α- level).
The estimated effects of the IF on the OF together
with their order of magnitude were revealed through
additional data analysis (Figure 3A). The largest effect
on biohydrogen production for the investigated systemwas caused by a switch in initial pH value from lower to
higher values. Fermentation temperature also had a large
effect on the OF, followed by the interaction betweenthe fermentation temperature (X
1) and initial pH value
(X2). The interaction plot for X 1X2indicates a consider-
able increase in biohydrogen production at the pointwhen initial pH values move from low to high levels,
and temperature is low (Figure 3B). This demonstrates
that understanding the interactions among the variousprocess parameters in complex systems is crucial fordeveloping and optimizing an efficient biohydrogen pro-
duction process.
By analyzing the evolution of the main effects
throughout the experimental process (120 h), an explicitshift in the direction and intensity of the effect of these
variables on the biohydrogen production rate was de-
tected (Figure 4). The main effects of initial pH ( X
2) and
temperature (X 1) tended to increase over time, while the
effects of other variables, like glucose addition (X 3),
tended to maintain a relatively constant value. Thesefindings emphasize even more the dynamic characteris-
tics of such a complex environment. Thus, the initial en-
vironmental conditions as well as the fermentationprocess settings greatly influenced the development of
microbiological processes, and thereby the metabolic
end products, in the investigated system. Because of thedifferences in the direction and intensity of the effects
that investigated variables exerted on the objective func-
tions, multiple optimal situations may be identified de-pending on the point selected in the fermentation
Figure 2 Biohydrogen evolution measured during the full factorial (A) and additional orthogonal central composite (B) multifactorial
experiments. Clear differences can be observed between the different experimental lines, suggesting a strong influence of the investigated
variables on the OF.
Table 3 ANOVA test performed on the second-order polynomial model used to discriminate between the significant
linear (L) and quadratic (Q) effects of the investigated variables, as well as their interactions, for the investigatedsystem and response (biohydrogen production)
Sum of squares (SS) Degree of freedom Mean square F-value P-value
X1-L 270.935* 1* 270.935* 11.63704* 0.001725*
X1-Q 260.138* 1* 260.138* 11.17333* 0.002075*
X2-L 1314.436* 1* 1314.436* 56.45696* 0.000000*
X2-Q 185.342* 1* 185.342* 7.96069* 0.008033*
X3-L 61.992 1 61.992 2.66265 0.112237
X3-Q 0.236 1 0.236 0.01013 0.920420
X1X2 140.261* 1* 140.261* 6.02441* 0.019553*
X1X3 15.980 1 15.980 0.68634 0.413363
X2X3 0.291 1 0.291 0.01250 0.911647
Lack of fit 273.054 5 54.611 2.34561 0.062915Pure error 768.309 33 23.282 –
Total SS 3290.973 47 – –
*Values considered statistically significant.Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 5 of 15
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development process. Consequently, application of these
findings to an industrial-scale continuous biohydrogen
fermentation system is crucial for establishing an optimum
process development strategy with regard to influent feed-ing of the system among other factors. These findings par-
ticularly support the necessity for a complete exploration
of the effects manifested over time on the biohydrogenproduction process by the different influencing factors.
These crucial insights show that in complex biotech-
nological systems, both the effect of a particular variableat a specific time on the OF and the global effect of mul-
tiple influencing factors together with their different in-
teractions must be considered. In addition, the evolutionof the main effects of the IF throughout the biohydrogen
fermentation process must be considered. Using theseinsights, an optimized industrial-scale biohydrogen pro-
duction system can be successfully designed and oper-
ated in a feasible economic context.
Identification of the optimum conditions for biohydrogen
production in the investigated system
Once the estimated effects on biohydrogen production
rates and their magnitudes were established for each of
the analyzed independent variables as well as for their
interactions, a strategy to identify the optimum condi-tions of the investigated system to maximize hydrogen
production rates was developed. To understand the spe-
cific hydrogen production conditions and to confirm thevalidity of the statistical experimental strategies applied,
a response surface and contour plot methodology was
developed. Prediction of H
2production at any tested
parameter within the range of the applied experimental
design is achieved by employing a second-order polyno-
mial regression equation obtained from experimentaldata (Equation 3 and Figure 5). With respect to the
coded factor levels, the second-order model used to fit
the experimental data is:
Y¼20:95−2:83X
1ț6:22X2ț1:35X3
−2:42X1X2−0:82X1X3−0:11X2X3
−3:97X2
1−3:35X2
2−0:12X2
3ð3Ț
with 60% of the variation in biohydrogen production ex-
plained by the model (R2
adj= 0.60).
When considering the observed versus predicted
values of biohydrogen production, it can be assumed
that the statistical model developed is reasonably accur-
ate. The response surface and contour plot analysis wasdeveloped for an initial glucose concentration of 10 g/L
(Figure 6). The stationary point was found to be outside
the investigated experimental domain. The canonical
Figure 3 The effects and the interactions of the investigated variables on the biohydrogen production process. Pareto histogram depicting
the estimated linear (L) and quadratic (Q) effects of each of the analyzed independent variables (in decreasing order of magnitude) on the
biohydrogen production rate (A). Analysis of the effect of the interactions between the X 1and X2variables on biohydrogen production rate (B).
Figure 4 The evolution of the main effects of the investigated
variables and their interactions on the biohydrogen production
rate. The three investigated variables as well as the statistically
significant interactions between the operating temperature and the
initial pH are represented.Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 6 of 15
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analysis of the response surface indicates a possible rising
ridge, since two of the canonical coefficients are close to
zero ( λ1= -0.002995; λ2= -0.099786; λ3= -4.65529). In this
type of ridge system, inferences about the true surface of
the stationary point cannot be drawn because it is outside
the region where the model has been fitted. The contourplots for three levels of X
3(initial glucose concentration;
low, medium, and high) display an optimum around X 1=
27°C, X2= 6.7, which also indicates that the influence of
X3on biohydrogen production in the investigated experi-
mental domain is limited. Therefore, if the initial glucose
addition is pooled out from the model (according toEquation 4), considering that it was found statisticallyinsignificant in the investigated domain, the stationary
point is found at X
1=2 6 . 7 ° C a n d X2= 6.66, with a pre-
dicted optimal value of biohydrogen production of25.57 mL with a ±95% confidence interval.
Y¼−343 :22ț9:05X
1
ț74:42X2−0:47X1X2−0:11X2
1−4:64X2
2 ð4Ț
where the coefficients are calculated with uncoded (nat-
ural) factor values.
The stationary point was verified by carrying out ex-
periments in triplicate. The mean biohydrogen produc-
tion obtained was 28 mL, and this value was within the
±95% confidence interval for the predicted maximum.Therefore, the new empirical model, with a determin-
ation coefficient of R
2
adj= 0.62 and from which all the
terms containing the X 3independent variable have been
pooled out, was able to predict the behavior of the sys-
tem within the experimental domain (Figure 7).
Wastewater degradation
During the dark fermentative biohydrogen production
process, organic matter is converted from complex long-chain molecules to simple compounds. By using waste-
water as a fermentative organic substrate, at least partial
biodegradation of this waste can therefore be achieved.Mass spectrometry (MS) was used to evaluate the waste-
water biodegradation efficiency. Several organic com-
pounds including lactose, glucose, acetic acid, propionicacid, and furfurol, among others, were identified and
monitored during the wastewater degradation experi-
ments. The results allowed direct comparison of the ex-perimental runs by revealing the different degradation
Figure 5 Modeling the biohydrogen production using
beer-brewing wastewater as th e fermentation substrate.
Observed versus predicte d values of the biohydrogen
production process using a com plex microbial consortium as
the starting inoculum and beer-brewing wastewater as the
fermentation substrate.
Figure 6 Prediction of the optimum area for the highest biohydrogen production yields. Response surface (A)and contour plot
(B)analysis of H 2production as a function of temperature and pH, with a constant glucose addition value of 10 g/L.Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 7 of 15
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rates and metabolic pathways followed by the microbial
communities involved.
The analysis of three different experimental situations,
one with low hydrogen production (experimental run 11),one with medium hydrogen production (experimental run
13), and one with high hydrogen production (experimental
run 14), revealed significant differences in the wastewatercomposition at the end of the biohydrogen fermentation
process (Figure 8). As expected, comparison of low and
high hydrogen-producing experimental runs revealed ageneral decrease in concentration of most of the measured
components in the fermented wastewater with increasing
hydrogen production. This tendency was especially
marked for lactose, glucose, capric acid, maltose, lacticacid, furfurol, and caproic acid (Figure 8). These observa-
tions suggest that most of the macromolecules in the
wastewater are metabolized, with biodegradation efficiencyclearly correlating with the amount of hydrogen gener-
ated. Some components (glucose, maltose, and furfurol)
were fully consumed during fermentation, suggesting that
Figure 7 Confirmation of the predicted stationary point for the investigated system. The mean of the maximum biohydrogen production yield
of 28 mL measured at the end of the confirmation experiments is within the ±95% confidence interval for the predicted optimum area. *The
microbial population used as a starting inoculum during the wastewater degradation experiments was subjected to heat pretreatment prior to
the inoculation.
Figure 8 Wastewater chemical composition at the end of the fermentation period. Comparison between experimental runs which
generated low (experimental run 11), medium (experimental run 13), and high (experimental run 14) hydrogen production rates.Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 8 of 15
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these compounds represent easily accessible energy
sources which drive the hydrogen-producing fermentation
pathways. At the same time, accumulation of propionicacid and galactose was observed concomitant with the in-
creasing hydrogen yield (Figure 8).
The clear differences between experimental runs ob-
served during the biohydrogen-producing wastewater
degradation experiments indicate a strong correlation
between the consumption of the organic constituentsand hydrogen production rates. This suggests that the
technological process developed here has the potential
to generate biohydrogen using wastewater as an organicsubstrate.
Assessment of microbial community composition
The same mixed microbial consortium was used as a
starting inoculum in all experimental runs and stages.
This consortium was designed according to our previousresearch on the ability of various complex consortia to
degrade complex organic substrates and simultaneously
produce biohydrogen [44]. An organic nutrient-rich habitat(generated during the biological denitrification step at a
communal wastewater treatment plant) was sampled in
order to isolate the required microbial inoculum. To fur-ther increase the biohydrogen-generating abilities of the
population, heat pretreatment was applied. As a result, the
majority of the spore-forming hydrogen-producing bacteriasurvived the treatment, while the hydrogen-consuming
methanogens were largely eliminated.
To better understand the hydrogen-producing dark
fermentation processes, a sequencing-based metage-
nomic analysis was carried out on samples selected from
different experimental runs. This approach allows for adetailed taxonomic and functional characterization of
the selected ecosystem as a function of time, which is es-
sential when planning strategies for dark fermentation-based biohydrogen production using wastewater as a fer-
mentative substrate.
To fully understand the experimental model, the
complete factorial portion of the CCD was subjected to
metagenomic analysis. Additional samples from the cen-
ter as well as axial experimental points were analyzedmetagenomically, with the microbial composition of
samples with the lowest and highest H
2-producing capabil-
ities being investigated. This approach provided valuableinsight into the transitions and structural rearrangements
occurring in the microbial communities under different
fermentation conditions. The influence of these rearrange-ments on the OF (biohydrogen production rates) was also
elucidated.
The metagenomes extracted from the full factorial
experimental design approach (experimental runs 1-8)
revealed similarities in the microbial populations col-
lected from the different experimental setups (Figure 9).Two major clusters formed at the genus level. Cluster A
contained experimental runs 2, 3, 4, 6, and 7, while clus-
ter B contained experimental runs 1, 5, and 8. Inaddition, both clusters could be further divided into
smaller units. Most of the cluster A microbial communi-
ties developed under similar experimental conditionswith regard to the initial pH value ( X
2) and glucose
addition (X 3). A notable difference between these clus-
tered groups was the fermentation temperature (X 1). It
seems that in the temperature range investigated (25 to
37°C) the microbial composition in each experimental
run was similar at the same levels of X2and X 3, regardless
of X 1levels. Interestingly, the microbial communities lo-
cated at the closest Bray-Curtis distance (experimental
runs 5 and 8), which are isolated from the experimentalruns in cluster B, developed under the same temperature
conditions (37°C) and different levels of X
2and X 3. How-
ever, even in this cluster, the microbial community isolatedfrom experimental run 1 developed at the same levels of
X
2and X 3but at levels of X 1that differed from the com-
munity in experimental run 5. These findings suggest astrong correlation between the microenvironmental con-
ditions inside the bioreactor and the different develop-
mental pathways followed by the microbial community.
The cluster formed by samples from experimental
runs 3, 4, and 7 displayed the highest H
2production po-
tential (22.24 mL, 25.23 mL, and 14.67 mL, respectively).Certain key microorganisms identified in this cluster,
such as Aeromonas spp.,Clostridium spp.,Thermoanaer-
obacter spp.,and Alkaliphilus spp., were generally more
abundant in experimental runs 3 and 4, while Bacillus
spp. and Lactobacillus spp.were poorly represented in
these runs (Figure 9).
Samples from the supplementary points (star and cen-
tral points) in the experimental matrix (experimental
runs 9-16) were also analyzed using metagenomics, withsamples producing the lowest and highest amounts of
H
2being compared (Figure 2B and Figure 10). Strong
correlations were observed between the structuralchanges within these microbial communities and their
biohydrogen-producing potential. The microbial com-
munity with the lowest H
2production capacity was
dominated by Lactobacillus spp.and Tetragenococcus
spp.(Figure 10A). The microbial consortium which pro-
duced moderate quantities of H 2was dominated by
Citrobacter spp.and Aeromonas spp. (Figure 10B). The
microbial population which produced the highestamounts of H
2was clearly dominated by Clostridium
spp. (Figure 10C). A clear shift from a Lactobacillus
spp.-dominated microbial population towards a Clostrid-
ium spp.-dominated population concomitant with increas-
ing H 2production rates was thus observed. Interestingly,
the only factor that varied between these three experimen-tal runs was the initial pH value ( X
2; Table 2). Therefore,Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 9 of 15
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changing only one IF in the investigated system can result
in significant changes in the OF (the biohydrogen produc-
tion rate).
The results obtained from metagenomic investigations
have led to a better understanding of the impact of each
physicochemical parameter and the interactions between
them, all of which are essential for the fermentative bio-
hydrogen production process. To maximize the biohy-drogen production potential of a microbial consortium
during wastewater degradation, special emphasis has to
be made not only on the careful selection and pretreat-ment of these populations but also on the optimal fer-
mentation conditions for each system.
Conclusions
A central composite experimental design was used tounderline the involvement of the various biological, chem-
ical, and physical factors influencing the fermentativebiohydrogen production process. The experiments were
performed using a mixed microbial consortium as the
starting inoculum and beer-brewing wastewater as the fer-mentation substrate.
It was demonstrated that the selected variables have a
clear influence on the objective function in the investi-
gated system. Both linear and quadratic effects of the
fermentation temperature and initial pH, as well as theirfirst-order interactions, were statistically significant with
regard to the biohydrogen production rates for the sys-
tem considered. The largest effect was caused by achange in initial pH value from its lower to its higher
level. The fermentation temperature also had a strong
effect on the objective function, followed by the inter-action between the fermentation temperature (X
1) and
initial pH value ( X2). Analysis of the evolution of these
main effects over time during the experiment revealed astrong shift in the direction and intensity of the effect of
Figure 9 Partial heatmap calculated for samples from the full factorial experimental design approach (experimental runs 1-8). The heatmap
was redrawn using normalized values clustered by genus using a Bray-Curtis distance metric (divided into clusters AandBfor practical reasons).Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 10 of 15
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these variables on the biohydrogen production rate. Be-
cause of this, further optimal situations can be identifieddepending on the point in the fermentation process be-
ing investigated. These crucial insights show that this
complex biotechnological system is governed by thecombined effect of several influencing factors as well as
by their interactions with each other.
We successfully used response surface and contour
plot methodology to understand the specific hydrogen
production conditions of the system under study, and to
confirm the validity of the statistical experimental strat-
egies applied. In addition, confirmation experiments
yielded a mean biohydrogen production of 28 mL, avalue situated within the ±95% confidence interval of
the predicted maximum. The new empirical model was
thus able to predict the system behavior within the ex-perimental domain.
The mass spectrometry analyses performed on the de-
graded wastewater during each biohydrogen productionexperimental run revealed significant differences in the
biochemical composition between runs, confirming a
strong correlation between the consumption of most ofthe organic constituents and a high level of hydrogengeneration. This indicates the potential of the developed
technological process to generate biohydrogen by usingwastewater as an organic substrate.
To better understand the microbiological factors driving
the dark fermentative biohydrogen production processes inthe system studied, high-throughput next generation meta-
genomic analyses were carried out on samples taken from
different experimental runs. These analyses revealed strongcorrelations between the micro-environmental conditions
inside the bioreactors and th e developmental pathways
taken by the microbial communities, even though the same
microbial consortium was used as a starting inoculum in all
experimental runs. By analy zing the metagenomes of the
lowest and highest H
2-producing samples, population shifts
in hydrogen production potential within the microbial con-
sortia triggered by the different fermentation conditionscan be traced. These results have led to a better under-
standing of the impact which a mixed microbial consortium
h a so nt h ef e r m e n t a t i v eb i o h y d r o g e np r o d u c t i o np r o c e s s ,and at the same time, the influence of the factors involved
on the development of these populations over time.
Together the data generated by the present study re-
veal a strong interconnection between the investigated
Figure 10 Microbial population shifts during the biohydrogen production experiments. Microbial community composition of samples
from experimental runs 11 (A),1 6(B), and 12 (C), part of the central composite fractional factorial experimental design approach.Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 11 of 15
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variables, as well as a permanent and shifting influence
on the biohydrogen production process under the inves-
tigated conditions. Only by understanding these phe-nomena can an optimized industrial-scale biohydrogen
production system be successfully designed and operated
in a feasible economic context. This will bring us onestep closer to a clean, fossil fuel-free future and a devel-
oped H
2-based economy.
Methods
Seed inocula
Biological samples were collected from the denitrifica-
tion step at a municipal sewage wastewater treatment
plant. This ecosystem has a high microbial biodiversity
consisting of naturally formed populations of microflorasuitable for biodegradation of complex organic sub-
strates. Once collected, the samples were stored at 4°C
until inoculation.
Prior to inoculating the bioreactors, microbial samples
were subjected to a heat pretreatment process at 70°C
for one hour as described in our previous work [44].The purpose of the pre-treatment strategy is to enrich
with spore-forming hydrogen-producing microbes as
well as to reduce the abundance of hydrogen-consumingmicroorganisms.
Bioreactor design and operation
The experimental setup was conducted using wastewater
generated by the beer-brewing industry as the fermenta-
tion substrate in different experimental combinations asdetailed in Table 2. The composition of the wastewater
was: total COD – 6558 mg/L, soluble COD – 5066 mg/L,
total N – 75.8 mg/L, and total P – 58.4 mg/L. The batch-mode experiments were conducted in 100-mL serum
vials with 50 mL of wastewater and 10 mL pretreated
sediment samples as a starting inoculum. A granularbiofilm support material (sterilized river-bed rocks) was
introduced into the bioreactors to increase the contact
surface area of the microbial populations. The bottleswere capped with rubber septum stoppers and aluminum
rings. Incubation was performed at varying temperature
conditions for a period of 120 h (Table 2). During the fer-mentation period, response parameters including biogas
production and composition, metabolite concentration,
substrate degradation, and total microbial communitycomposition were monitored as described below. All
batch experiments were performed in triplicate.
Statistical experimental design methods
A central composite experimental design approach was
used to assess the IF and their effect on the OF (biohy-drogen production rate). In the early stage of the experi-
ments, a statistically based full factorial experimental
design approach was utilized to determine the influenceof the factors under study (fermentation temperature,
starting pH value, and glucose addition) on the response
(H
2production; Table 1). Use of this strategy helped to
avoid overlap of different effects and interactions among
these variables. The screened factors were chosen based
on our previous work and were tested at low, medium,and high levels, coded as -1, 0, and +1 (Table 1) [44].
The factorial portion of the design is a complete 2
3fac-
torial with eight runs, which contains all the possiblecombinations within the defined levels of the investi-
gated variables (runs 1-8; Table 2). In addition, a second
statistically based factorial experimental design methodwas applied in order to obtain a complete understanding
of the investigated process. These experimental runs
(9- 14) represent additional axial points displayed in a "starpattern" around the center of the design, at αdistance of
1.287 from the center, a value that ensures its orthogonal-
ity. The design also contains two observations at the ex-perimental center (runs 15 and 16; Table 2). This
approach provided N
0- 1 degrees of freedom in estimat-
ing the experimental error, and at the same time estab-lished the estimation precision of the OF (biohydrogen
production rate) around the central experimental point.
The biohydrogen production rate was monitored every24 h for the duration of the experiments. Experimental de-
sign approaches were developed and analyzed using the
Statistica 8 software suite (StatSoft Inc., USA).
Analytical methods
The quantity and composition of the biogas producedwas directly measured by gas chromatography in 24-h
intervals using an Agilent Technologies 7890A GC sys-
tem equipped with a thermal conductivity detector andargon as a carrier gas. The temperatures of the injector,
detector, and column were kept at 30°C, 200°C, and
230°C, respectively. An HP PLOTQ column (15 m ×530 mm × 40 mm) was used. Since a concentration gra-
dient of H
2gas can form in the headspace, gas samples
(0.5 mL) were taken out after mixing of the headspacegas by sparging several times with a gas-tight syringe.
Microbial degradation of wastewater was monitored at
the end of the experimental runs by mass spectrometryconducted on a High Capacity Ion Trap Ultra mass spec-
trometer (HCT Ultra, PTM discovery; Bruker Daltonics,
Bremen, Germany). All mass spectra were acquired in themass range 100 to 3000 m/z, with a scan speed of 2.1
scans/s. Tandem mass spectrometry was carried out by
collision-induced dissociation (CID) using He as the colli-sion gas. For MS/MS sequencing, precursor ions were
selected within an isolation width of 2 μm. The fully auto-
mated process was performed using a NanoMate 400robot incorporating ESI chip technology (Advion BioSci-
ences, Ithaca, USA) couplet on a High Capacity Ion Trap
Ultra mass spectrometer (HCT Ultra, PTM discovery;Boboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 12 of 15
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Bruker Daltonics, Bremen, Germany). The robot was con-
trolled and manipulated by ChipSoft software operated
under the Windows system. The position of the electro-spray chip was adjusted to the sampling cone potential to
give rise to an optimal transfer of the ionic species into
the mass spectrometer. To avoid contamination, a glass-coated microtiter plate was used in all experiments. Five
microliter aliquots of working sample solutions were
loaded onto a 96-well plate. The robot was programmedto aspirate the whole sample volume into the pipette tip
followed by 2 μL of air, and then to deliver the sample to
the inlet side of the microchip. Each nozzle had an in-ternal diameter of 2.5 μm, and under the given conditions
delivered a flow rate of approximately 200 nL/min. The
nano-ESI process was initiated by applying voltages of 1.5to 1.8 kV and a head pressure of 0.5 to 0.7 PSI. After spray
initialization, infusion parameters (ESI voltage in the pip-
ette tip, voltage, and desolvation gas flow) were optimized.
Values for ESI capillary, cone potential, and desolva-
tion gas (nitrogen) were optimized to achieve an efficient
ionization and to produce the optimum transfer of ionsduring MS. Measurement parameters were: capillary
voltage of 1 kV, contraelectrode voltage (cone voltage) of
60 V, acquisition time 2 min, scan speed 2.1 scans/s, anda mass range of 100 to 3000 m/z. The NanoMate HCT
MS system was tuned to operate in the positive ion
mode. This technique was chosen because glucideionization is highly efficient in this mode. The source
block maintained at a constant temperature of 80°C pro-
vided optimal desolvation of the generated dropletswithout the need for desolvation gas. To prevent any
cross-contamination or carry-over, the pipette tip was
ejected and replaced after every sample infusion and MSanalysis. All mass spectra were processed using Data Ana-
lysis 3.4 software (Bruker Daltonik, Bremen, Germany).
The mass spectra were calibrated using sodium iodide.Accurate determination of the average mass was 20 ppm.
Samples were dissolved in methanol at a concentration of
approximately 5 pmol/ μL. At the acquisition time of
2 min, the required volume of sample was approximately
2 pmol, a value indicating a highly sensitive analysis.
Total DNA extraction from samples
DNA from the complex samples was extracted and puri-
fied according to described methods with some modifi-cations [45]. Samples (0.5 g) were extracted with 1.3 mL
extraction buffer (100 mM Tris-Cl pH 8.0, 100 mM
EDTA pH 8.0, 1.5 M NaCl, 100 mM sodium phosphatepH 8.0, 1% CTAB). After thorough mixing, 7 μL of pro-
teinase K (20.2 mg/mL) was added. After incubation for
45 min, 160 μL 20% SDS was added and mixed by inver-
sion several times with further incubation at 60°C for
1 h with intermittent shaking every 15 min. Samples
were centrifuged at 13,000 RPM for 5 min, and thesupernatant was transferred into new Eppendorf tubes.
The remaining soil pellets were treated three times with
400μL extraction buffer and 60 μL SDS (20%) and kept
at 60°C for 15 min with intermittent shaking every
5 min. Supernatants collected from all four extractions
were mixed with an equal quantity of chloroform andisoamyl alcohol (25:24:1). The aqueous layer was sepa-
rated and precipitated with 0.7 vol isopropanol. After
centrifugation at 13,000 RPM for 15 min, the brown pel-lets were washed with 70% ethanol, dried at room
temperature, and dissolved in TE (10 mM Tris-Cl,
1 mM EDTA, pH 8.0).
Metagenomic characterization of microbial communities
The total DNA from selected samples was prepared forhigh-throughput next generation sequencing analysis
performed on the Ion Torrent PGM platform (Life
Technologies). An average of 291.322 sequencing readswere generated for each sample, with a mean read length
of 161 nucleotides. Bioinformatic analyses (taxonomic
profiling and assessment of metabolic potential) wereconducted using the public MG-RAST software package,
which is a modified version of RAST (Rapid Annotations
based on Subsystem Technology) [46]. The sequencedata were compared to M5NR using a maximum e-value
of 1 × 10
-5, a minimum identity of 95%, and a minimum
alignment length of 15, measured in amino acids forproteins and base pairs for RNA databases.
Abbreviations
BioH 2:biohydrogen; CCD: central composite design; COD: chemical oxygen
demand; DOE: design of experiments; IF: influencing factors; OF: objectivefunctions; RAST: Rapid Annotations based on Subsystem Technology.
Competing interests
The authors declare that they have no competing interests.
Authors ’contributions
IZB participated in the conception, design, experimental work, and data
collection and analysis, and also drafted the manuscript. MI participated inthe design of the experiment approaches and mathematical modeling. VDGcarried out the microbiological sampling and pretreatment procedures. IM
conceived the design and operation of the bioreactors. BP elaborated and
carried out the total DNA extractions as well as community compositionassessments by the 16S rRNA method and metagenomic approaches. ANdesigned and performed the wastewater analysis. EK participated in the
critical discussion of the results. TB contributed to the evaluation of the
analytics data. GM performed the metagenomics experiments, designed thestudy, and participated in the critical discussion of the results. All authorsread and approved the final manuscript.
Acknowledgments
This work was supported by the following international (EU) and domesticfunding bodies: "SYMBIOTICS" ERC AdG EU Grant, "BIOSIM" PN-II-PT-PCCA-2011-3.1-1129 European Fund (Romania), and by PIAC_13-1-2013-0145
supported by the Hungarian Government and financed by the Research and
Technology Innovation Fund.
Author details
1Polytechnic University of Timisoara, Timisoara, Romania.2Seqomics
Biotechnology Ltd, Szeged, Hungary.3Hungarian Academy of Sciences,
Biological Research Centre, Temesvari krt. 62., Szeged 6726, Hungary.4SzentBoboescu et al. Biotechnology for Biofuels 2014, 7:139 Page 13 of 15
http://www.biotechnologyforbiofuels.com/content/7/1/139

István University, Faculty of Economics, Agricultural and Health Studies,
Szarvas, Hungary.
Received: 17 April 2014 Accepted: 5 September 2014
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Cite this article as: Boboescu et al. :Revealing the factors influencing a
fermentative biohydrogen production process using industrialwastewater as fermentation substrate. Biotechnology for Biofuels
2014 7:139.
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