Optimization of key factors affecting hydrogen production from food waste by anaerobic mixed cultures

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Optimization of key factors affecting hydrogen production from food waste by anaerobic mixed cultures Chakkrit Sreela-or a, Tsuyoshi Imai b, Pensri Plangklang a, Alissara Reungsang a,c,* a

Department of Biotechnology, Faculty of Technology, Khon Kaen University, A. Muang, Khon Kaen 40002, Thailand Division of Environmental Science and Engineering, Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi 755-8611, Japan c Fermentation Research Center for Value Added Agricultural Products, Khon Kaen University, A. Muang, Khon Kaen 40002, Thailand b

article info

abstract

Article history:

Key factors (inoculums concentration, substrate concentration and citrate buffer concen-

Received 25 January 2011

tration) affecting hydrogen yield (HY) and specific hydrogen production rate (SHPR) from

Received in revised form

food waste in batch fermentation by anaerobic mixed cultures were optimized using

13 April 2011

Response Surface Methodology with Central Composite Design. The experiments were

Accepted 15 April 2011

conducted in 120 ml serum bottles with a working volume of 70 mL. Under the optimal

Available online 31 May 2011

condition of 2.30 g-VSS/L of inoculums concentration, 2.54 g-VS/L of substrate concentration, and 0.11 M of citrate buffer concentration, the predicted maximum HY and SHPR of

Keywords:

104.79 mL H2/g-VSadded and 16.90 mL H2/g-VSS.h, respectively, were obtained. Concentra-

Optimization

tions of inoculums, substrate and citrate buffer all had an individual effect on HY and SHPR

Biohydrogen

(P < 0.05). The substrate concentration and citrate buffer concentration had the greatest

Response surface methodology

interactive effect on SHPR (P ¼ 0.0075) while their effects on HY (P ¼ 0.0131) were profound.

Food waste

These results were reproduced in confirmation experiments under optimal conditions and generated an HY of 104.58 mL H2/g-VSadded and an SHPR of 16.86 mL H2/g-VSS.h. This was only 0.20% and 0.24%, respectively, different from the predicted values. Microbial community analysis by PCR-DGGE indicated that Clostridium was the pre-dominant hydrogen producer at the optimum and worst conditions. The presence of Lactobacillus sp. and Enterococcus sp. might be responsible for the low HY and SHPR at the worst condition. Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Hydrogen is a promising alternative renewable energy and is considered to be a clean energy. It produces only water when combusted with oxygen and has 2.75 times higher energy content than hydrocarbon fuels [1,2]. Conventional methodologies for producing hydrogen include steam reforming of natural gas, thermal cracking of natural gas, partial oxidation

of heavier than naphtha hydrocarbon, and coal gasification [2]. These methods emit a mixture of gases including H2, CH4, CO2, CO, and N2. Biomass can be exploited as a substrate for producing hydrogen, mainly through the pyrolysis process, while water can be employed to produce hydrogen by electrolysis, photolysis, thermo-chemical process, direct thermal decomposition, and biological production. The processes of hydrogen production on a large industrial scale are coal

* Corresponding author. Khon Kaen University, Faculty of Technology, Department of Biotechnology, A. Muang, Khon Kaen 40002, Thailand. Tel./fax: þ66 43 362 121. E-mail address: [email protected] (A. Reungsang). 0360-3199/$ e see front matter Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2011.04.136

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gasification and electrolysis of water. These processes deplete fossil fuels and consume high energy to produce hydrogen [3] e.g. the thermo-chemical process has to be operated under high temperatures (>850  C) [4]. As a result of high energy consumption, the biological hydrogen process is exploited to resolve this limitation. Biological hydrogen production can be divided into two types. First, photo-fermentation is a process in which high energy content is gained from the conversion of short chains of organic acids to generate hydrogen under suitable sunlight by cyanobacteria, algae, photosynthetic and chemosynthetic-fermentative bacteria [5]. However, the fermentative process is not feasible if the system has no sunlight and results in a low hydrogen production rate [6]. A high hydrogen production rate can be obtained when hydrogen is produced via the dark fermentation process [6]. Microorganisms commonly used in the dark fermentation process are mixed cultures from sewage sludge [7], anaerobic sludge [8], and soil [9]. A mixed culture study by Fang et al. [10] found that approximately 70% of the hydrogen producers were of genus Clostridium and 14% were of Bacillus species. Dark fermentation has advantages over the photo-fermentation process because of its ability to continuously produce hydrogen from a variety of feed stocks and without an input of external energy [11] These advantages have drawn the attention of researchers to apply this fermentation process on practical and abundant waste products, such as food waste [12e14] In Thailand, the generation of food waste reached about 20,041 tons per day in 2006 accounting for 50% of municipal solid waste [15]. Food waste consists mainly of starch, protein, and fat, with a small amount of cellulose and hemicellulose which are possible sources for bioenergy production [16]. Due to its high organic content and its easily hydrolysable nature, food waste is a good candidate to be used as the substrate for producing hydrogen by dark fermentation. The efficiency of hydrogen production is greatly influenced by environmental factors such as temperature, pH, nutrient addition, ferrous iron and substrate concentration [17]. Using food waste for hydrogen production may however, result in a problem where the inoculums would be outgrown by the normal flora in food waste. Therefore, the proper inoculums concentration needs to be optimized. An increase in substrate concentration could increase hydrogen production to a certain level. However, an excessive substrate concentration can cause a build up of volatile fatty acids (VFAs) in the system, leading to a decline of pH in the reactor and could inhibit the growth of hydrogen producers [18]. Therefore, the optimum substrate concentration as well as an addition of the buffer at suitable concentration to counteract a decrease in pH would remove this limitation. In this research, the effects of inoculums, substrate and citrate buffer concentrations on hydrogen production from food waste were investigated using the response surface methodology (RSM) with central composite design (CCD) in order to optimize the hydrogen yield (HY) and the specific hydrogen production rate (SHPR). The information from the optimized parameters could pave the way toward a scaling up of the hydrogen production process and/or a continuous hydrogen fermentation process from food waste.

2.

Material and methods

2.1.

Preparation of feed stocks

Food waste was collected from a cafeteria on the Khon Kaen University campus, Khon Kaen, Thailand and was mainly made up of rice, vegetables, fruits and meats. Bones were removed from the food waste before being mixed with tap water at the volumetric ratio of 1:3 and then ground in a food blender. The pH and the volatile solid (VS) of the resulting food waste slurry were 7.2 and 10,100 mg/L, respectively. The chemical characteristics of the food waste are shown in Table 1. The resulting food waste slurry was stored at 17  C and thawed in a refrigerator before being used.

2.2.

Seed sludge and preparation of inoculums

Anaerobic seed sludge was obtained from a full-scale anaerobic digester of the Upflow Anaerobic Sludge Blanket (UASB) reactor of a brewery company in the Northeastern part of Thailand. The UASB is used to produce methane from the wastewater of the beer production process. The obtained sludge was pre-heated at 105  C for 3 h in a drying oven (LDO100E) in order to deactivate methanogens which are hydrogen consumers. The pH and volatile suspended solids (VSS) concentration of the sludge were 6.8 and 7.4 g/L, respectively. For inoculums preparation, pre-heated sludge was cultivated in food waste at 20 g-COD/L and supplemented with nutrient solution at a rate of 0.5 mL/L [19]. The culture was incubated for 36 h, 150 rpm, before being used as the inoculums in the batch experiment. The composition of the nutrient solution was as follows (all in g/L): NH4HCO3 200; KH2PO4 100; MgSO4.7H2O 10; NaCl 1; Na2MoO4.2H2O 1; CaCl2.2H2O 1; FeCl2 0.278 [19].

2.3.

Bio-hydrogen production

The biohydrogen production experiment was conducted in 120 mL serum bottles with a working volume of 70 mL. The fermentation broth contained different concentrations of inoculums (g-VSS/L), food waste (g-VS/L) and citrate buffer (M) according to the design. Citrate buffer solution, 3 M, was added to the fermentation broth to achieve a designated final

Table 1 e Chemical characteristics of food waste used in this study. Parameter Total chemical oxygen demand (COD) Total carbohydrate Total nitrogen Total phosphate Magnesium Manganese Iron Copper Sodium Cobalt Volatile solid (VS)

Concentration (mg/L) 110,000 64,093 14,081 20.41 6.76 0.26 0.84 0.13 109.02 Not detected 10,100

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concentration. Serum bottles were flushed with nitrogen gas to remove oxygen in the headspace of the bottles, in order to create anaerobic conditions, and capped with rubber stoppers. The bottles were incubated at room temperature (30  3  C) and operated in an orbital shaker with a rotation speed of 150 rpm. At designed time, the total gas volume was measured by releasing the pressure in the bottles using a wetted glass syringe [20] and then analyzed for gas content by gas chromatography equipped with a thermal conductivity detector (TCD). Effluent was collected by using a glass syringe and analyzed for VFAs and alcohol by gas chromatography equipped with a flame ionization detector (FID). All treatments were conducted in four replications. The hydrogen production was continued until the biogas volume could not be measured.

2.4.

Experimental design and data analysis

The range and level of independent input variables including inoculums concentration, substrate concentration and citrate buffer concentration based on RSM with a CCD are shown in Table 2. The HY and SHPR were selected as the dependent output variables. For statistical calculations, the test factors (Xi) were coded as xi according to the following transformation equation (Eq. (1)): xi ¼ ðXi  Xo Þ=DXi ;

(1)

where xi is the coded value of the variable Xi, Xi is the actual value of the ith independent variable, Xo is the actual value of Xi at the center point and DXi is the step change value. A quadratic model (Eq. (2)) [21] was used to evaluate the optimization of key factors: Yi ¼ b0 þ

X

bi xi þ

X

bii x2i þ

X

bij xi xj

(2)

where Yi is the predicted responses, xi is the parameters, b0 is an offset term, bi is the linear coefficients, bii is the squared coefficients, and bij is the interaction coefficients. HY (mL H2/ g-VSadded) was calculated as the total volume of hydrogen (mL H2) divided by g-VSadded. SHPR (mL H2/g-VSS.h) was calculated by dividing the rate of hydrogen production (mL H2/h) by the amount of biomass in terms of VSS (g-VSS). The response variable (YHY and YSHPR) was fitted using a predictive polynomial quadratic equation (Eq. (2)) in order to correlate the response variable to the independent variables [22]. YHY is the hydrogen yield response and YSHPR is the specific hydrogen

production rate response. The YHY and YSHPR values were regressed with respect to inoculums concentration (X1), substrate concentration (X2) and citrate buffer concentration (X3). The software Design Expert version 7.0, Stat-Ease Inc., MN, USA, was used for regression and graphical analysis of the experiment. Table 3 illustrates the coded values of the variables, the experimental design and the corresponding results.

2.5.

Biogas composition was measured by a gas chromatography (GC-2014, Shimadzu) equipped with a TCD and a 2 m stainless column packed with Unibeads C (60/80 mesh). The operational temperatures of the injection port, the column oven and the detector were 150, 145 and 150  C, respectively. Argon was used as the carrier gas at a flow rate of 25 mL/min. For VFAs and alcohols analysis, the collected effluents were first centrifuged at 6000 rpm for 10 min then acidified by 0.2N oxalic acid and finally filtered through 0.45 mm cellulose acetate membrane. The same GC model with a FID and a 30 m  0.25 mm  0.25 mm capillary column (Stabiwax) was used to analyze the VFAs and alcohols concentrations. The temperatures of the injector and detector were 250  C. The initial temperature of the column oven was 50  C for 2 min; this was followed with a ramp of 15  C/min for 12.6 min and raised to a final temperature of 240  C for 1 min. Helium was used as the carrier gas with a flow rate of 66 mL/min. Total nitrogen, total phosphate, total carbohydrate, magnesium, manganese, iron, copper, sodium, cobalt, VS and VSS, were measured according to the procedures described in standard methods [23]. Hydrogen gas production was calculated from the headspace measurement of gas composition and the total volume of hydrogen produced, at each time interval, using the mass balance equation (Eq. (3)) [24]:     VH;i ¼ VH;i1 þ CH;i VG;i  VG;i1 þ VH;0 CH;i  CH;i1 ;

Variable

X1: inoculums concentration (g-VSS/L) X2: substrate concentration (g-VS/L) X3: Citrate buffer concentration (M)

Parameter value 1.682

1

0

1

1.682

0.98

1.48

2.22

2.96

3.46

1.21

1.83

2.75

3.67

4.29

0.02

0.05

0.10

0.15

0.18

(3)

where VH;i is the cumulative hydrogen gas volumes at the current (i), VH;i1 is the previous time interval (i-1), VG;i is the total biogas volume at the current time interval, VG;i1 is the total biogas volume at the previous time interval, CH;i is the fraction of hydrogen gas in the headspace at the current time interval, CH;i1 is the fraction of hydrogen gas in the headspace at the previous time interval and VH is the volume of the headspace of the serum bottles (50 mL).

2.6. Table 2 e Experimental variables and levels investigated by central composite design.

Analytical methods

Kinetic analysis

The modified Gompertz equation (Eq. (4)) was used to determine the cumulative hydrogen production [25].    Rm e H ¼ P exp  exp ðl  tÞ þ 1 P

(4)

Where, H is the cumulative volume of hydrogen produced (mL), Rm is the maximum hydrogen production rate (mL H2/h), l is the lag-phase time (h), t is the incubation time (h), P is the hydrogen production potential (mL H2) and e is 2.718281828. Parameters (P, Rm and l) were estimated using the solver

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Table 3 e Central composite experimental design matrix defining inoculums concentration (X1), substrate concentration (X2) and citrate buffer concentration (X3) on hydrogen yield (HY) and specific hydrogen production rate (SHPR). Run

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

HY (mL H2/g-VSadded)

Parameter X1

X2

X3

Observed

Predicted

Observed

Predicted

0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1.682 0 1 1 1.682

0 0 0 1.682 0 1 1 1 1 1 0 0 1.682 0 1 0 0 1 1 0

0 0 0 0 0 1 1 1 1 1 0 0 0 1.682 1 0 1.682 1 1 0

103.66 96.05 101.15 54.89 101.59 48.68 36.53 19.76 14.60 29.22 102.24 100.17 20.82 49.59 71.85 30.05 40.93 27.39 21.60 34.38

102.61 102.61 102.61 52.99 102.61 50.83 32.29 16.91 22.11 27.73 102.61 102.61 21.78 56.91 65.01 23.40 32.63 30.93 26.51 40.05

15.46 14.43 15.05 8.17 15.12 7.24 5.44 2.94 2.17 4.35 15.30 16.39 3.10 7.38 10.69 4.47 6.09 4.08 3.21 5.12

15.30 15.30 15.30 7.72 15.30 7.47 4.91 2.61 3.39 4.25 15.30 15.30 3.42 8.47 9.57 3.48 4.87 4.51 3.85 5.97

function in Microsoft Excel version 5.0 (Microsoft, Inc.) as explained by Khanal et al. [26].

2.7.

SHPR (mL H2/g-VSS.h)

DNA isolation and PCR-DGGE analysis

Total genomic DNA was extracted from sludge collected at the end of fermentation of the worst and optimal conditions using a modified phenol/chloroform method [27]. Briefly, one mL of the mixed liquor was centrifuged at 12,000 rpm for 5 min and the cell pellets were re-suspended in 560 mL of saline-EDTA (150 mM NaCl, 100 mM EDTA [pH 8.0]). A volume of 7 mL of freshly prepared 50 mg/mL lysozyme was added to the mixture and incubated at 50  C for 1 h. Next, 30 mL of 10% (w/v) sodium dodecyl sulfate and 3 mL of 2% (w/v) proteinase K were added to the mixture and incubated at 50  C for 1 h. Nucleic acid, 500 mL, from the aqueous phase was taken from the top part of the mixture. The DNA was then extracted by adding 800 mL of phenol chloroform isoamyl alcohol (25:24:1) (v/v) and then hand-mixed for 10 min. The top part was transferred to the fresh tube and the DNA was precipitated by adding 50 mL of sterile 3 M sodium acetate and 1 mL of ice-cold 100% ethanol and incubated for 2 h at 20  C. The DNA pellet was recovered by centrifuging the solution at 12,000 rpm for 20 min at 4  C. The pellet was washed by adding 1 mL of 70% ice-cold ethanol and was recovered by centrifuging at 12,000 rpm for 10 min at 4  C. The pellet was then air dried, and the nucleic acids were dissolved in 50 mL of sterile milliQ-purified (mQ) water. The DNA was visualized by agarose gel electrophoresis. After DNA extraction, two steps of PCR amplification were employed in this study. For the 16S rDNA analysis, a universal primer set including forward primer PA19-38 (50 -AGAGTTTGATCCTGGCTCA G-30 ) and reverse primer PH1541-1561 (50 -AAGGAGGTGATCCAGCCGCA-30 ) were used for amplifying an approximately 1500 bp fragment of bacterial 16S rDNA. PCR

amplification was conducted in an automated thermal cycler using the following protocol, that is; initial denaturation for 3 min at 95  C, 30 cycles of denaturation for 45 s at 95  C, annealing for 1 min at 55  C, extension for 2 min at 72  C, followed by a final extension for 7 min at 72  C. For the DGGE profile analysis, PCR amplification was used on the primer set of 357f with GC clamp (50 CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGGCCTACGGGAGGCGCAG-30 ) and 518 r (50 -ATTACCGCGCTGCTGG-30 ) [28]. PCR amplification was conducted in a P X 2 thermal cycler (PX2, USA) using the following protocol, that is; initial denaturation for 3 min at 95  C, 30 cycles of denaturation for 45 s at 95  C, annealing for 1 min at 57  C, extension for 2 min at 72  C, followed by a final extension for 7 min at 72  C. The DGGE analysis of PCR products was performed by electrophoresis for 20 min at 20 V and 16 h at 70 V through a 7.5% polyacrylamide gel containing a linear gradient of denaturant ranging from 30% to 70% in 0.5x TAE buffer at a constant temperature of 60  C. The gel was stained with SYBR-Gold (1000 ng/mL) for 20 min and visualized on a UV transilluminator. Most of the bands were excised from the gel and re-amplified with the forward primer without a GC clamp and the reverse primer. After re-amplification, the PCR products were purified using the QIAquick PCR purification Kit (QIAGEN, USA) and sequenced using primer 518r and 357f and an ABI PRISM Big Terminator Cycle Sequencing Kit version 3.1 (Applied Biosystems, USA) in accordance with the manufacturer’s instructions. Closest matches for partial 16S rRNA gene sequences were identified by database searches in GenBank using BLAST [29]. CLUSTAL X was used to align obtained sequences with sequences of reference microorganisms retrieved from GenBank [30]. A phylogenetic tree was then constructed using the neighbor-joining method [31] with PHYLIP 3.69 [32]. Bootstrapping analysis [33] for 1000 replicates was performed to estimate the confidence of tree topologies.

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Results and discussion

3.1. Effects of inoculums concentration, substrate concentration and citrate buffer concentration on HY The effects of key factors i.e. concentrations of inoculums (X1), substrate (X2) and citrate buffer (X3) on HY were evaluated. Regression analysis of the data from Table 3 resulted in the quadratic (Eq. (5)) as follows: YHY ¼ 102:61 þ 4:95X1  9:28X2 þ 7:22X3 þ 0:30X1 X2 þ 2:44X1 X3  7:38X2 X3  25:06X21  23:06X22  20:45 X23 (5) The model shows a high determination coefficient (R2 ¼ 0.98) which indicates a statistically significant model. The ANOVA with quadratic regression model demonstrates that the model is significant; this is indicated by a low probability (P < 0.0001) together with an insignificant lack of fit model (P ¼ 0.0945). The predicted HY ranged from 16.91 to 102.61 mL H2/g-VSadded (Table 3). The optimum conditions for maximum HY were calculated by setting the partial derivatives of Eq. (5) to zero in association with the corresponding variables. The optimal condition for maximum HY, 102.61 mL H2/g-VSadded, was observed at an inoculums concentration of 2.22 g-VSS/L, a substrate concentration of 2.75 g-VS/L and a citrate buffer concentration of 0.10 M, under moderate conditions (runs 1, 2, 3, 5, 11 and 12) (Table 3). The main factors of inoculums concentration, substrate concentration and citrate buffer concentration all had individual significant influences on HY. The significance of each coefficient was determined by probability values that are listed in Table 4. Three-dimensional response surfaces plot based on Eq. (5) were plotted in order to determine the optimum level of each variable and the effects of their interactions on the HY (Fig. 1 aec). The response surface of HY indicates that the optimum condition fell well inside the design boundary (Fig. 1). Fig. 1a shows the response surface plot on independent variables: inoculums concentration (X1) and substrate concentration (X2) while the citrate buffer concentration (X3), was kept at the optimal level. The threedimensional response surfaces plot suggests that inoculums concentration and substrate concentration had no

significant interaction on HY (P ¼ 0.9037) (Fig. 1a and Table 4). HY increased with an increase in inoculums concentration from 1.48 to 2.22 g-VSS/L but HY decreased when the inoculums concentration were further increased from 2.22 to 2.96 g-VSS/L. An increase in inoculums concentration could increase the bacterial metabolites (i.e. VFAs) resulting in a drop of pH in the fermentation broth. This would make the culture more favorable for solvent production (second growth phase) and stop the first metabolites production (acids and gases) [34]. Therefore, the inoculums concentration should be compatible with the available substrate for maximizing bacterial activity. Fig. 1a shows that HY increased with the increase in substrate concentration from 1.83 to 2.75 g-VS/L and then HY decreased when the substrate concentration was greater than 2.75 g-VS/L. This might be due to a shift from an acidogenic to a solventogenic pathway, where hydrogen was consumed to reduce the acids to alcohols [35]. In addition, an increase in substrate concentration could lead to a partial pressure in the fermentation system. When the partial pressure accumulates in the headspace of the reactor to a certain level, the hydrogen production will switch to solvent production, thus inhibiting the hydrogen production [36]. Therefore, the substrate concentration should not be so high that it shock loads the system [35]. Fig. 1b illustrates the response surface plot and corresponding contour curves based on independent variables i.e., inoculums concentration (X1) and citrate buffer concentration (X3), while the third independent variable i.e. substrate concentration (X2) was kept at the optimal level. The threedimensional contour plot along with a P value of 0.3432 (Table 4) suggested that the inoculums concentration (X1) and citrate buffer concentration (X3) had no significant interaction on HY. The HY increased when the citrate buffer concentration increased from 0.05 to 0.10 M. The buffering capacity of a citrate buffer can reduce the pH fluctuations caused by VFAs accumulated in the fermentation broth thus enhancing hydrogen generation and acidogenesis in the first stage of an acid-gas digestion system [37,38]. However, a further increase in citrate buffer concentration to greater than 0.10 M resulted in a decrease in HY. This may be due to the negative effect of increased cytoplasmic osmotic pressure that occurs at high citrate buffer concentrations [39].

Table 4 e Model coefficients estimated by multiple linear regression (significance of regression coefficients). Factor

Intercept X1 X2 X3 X12 X22 X32 X1 X2 X1 X3 X2 X3

Hydrogen yield (YHY)

Specific hydrogen production rate (YSHPR)

Coefficient estimate

Probability

Coefficient estimate

Probability

102.61 4.95 9.28 7.22 25.06 23.06 20.45 0.30 2.44 7.38

e 0.0248 0.0006 0.0032 <0.0001 <0.0001 <0.0001 0.9037 0.3432 0.0131

15.30 0.74 1.38 1.07 3.74 3.44 3.05 0.05 0.36 1.10

e 0.0235 0.0005 0.0030 <0.0001 <0.0001 <0.0001 0.9025 0.3378 0.0123

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Fig. 1 e Three response surface plots showing the effects of inoculums concentration, substrate concentration, and their mutual interaction on hydrogen yield with optimum level of citrate buffer concentration (0.10 M) (a); the effects of inoculums concentration, citrate buffer concentration and their mutual interaction on hydrogen yield with optimum level of substrate concentration (2.75 g-VS/L) (b); the effects of substrate concentration, citrate buffer concentration and their mutual interaction on hydrogen yield with optimum level of inoculums concentration (2.22 g-VSS/L) (c).

Fig. 1c depicts the effect of substrate concentration (X2), citrate buffer concentration (X3) and their mutual interaction on HY when the optimum level of inoculums concentration (X1) was fixed. The three-dimensional response plots indicated that the interaction between substrate concentration (X2) and citrate buffer concentration (X3) significantly affects HY with a P-value of 0.0131 (Table 4). The shape of the circular contour plot of HY was observed. Results illustrated that substrate concentration (X2) and citrate buffer concentration (X3) significantly affected HY. HY peaked when the substrate and citrate buffer concentrations were 2.75 g-VS/L and 0.1 M, respectively. Concentrations of substrate greater than 2.75 gVS/L and citrate buffer less than 0.1 M could result in a low HY. Zhao et al. (2010) [40] explained that at a high phosphate buffer concentration the bacteria lack energy because ATP could not be produced normally. This would lead to the inhibition of cell growth and hydrogen production. In addition, a high concentration of buffer can result in osmotic shock [39].

The efficiency of biohydrogen production is closely related to the optimal control of substrate to biomass (S/X) ratio [11]. This ratio significantly affects the metabolic and kinetic characteristics of microorganisms [41]. When the citrate buffer concentration was kept at the optimum level, the S/X ratio obtained were 0.79 (run 20), 1.24 (runs 1, 2, 3, 5, 11, 12), 1.93 (run 13) and 2.81 (run 16) which yielded an HY of 40.05, 102.61, 21.78 and 23.40 mL H2/g-VSadded, respectively (Table 3). The highest HY was obtained at an S/X ratio of 1.24. A higher S/X ratio greater than 1.24 (optimum S/X ratio) resulted in a significant decrease in HY. Results implied that when a certain substrate threshold is exceeded and the partial pressure of hydrogen is increased, bacteria may shift their metabolism from hydrogen to alcohol production [36] resulting in a low HY. Our results coincided with the findings of Lay [42]. They found that a low substrate (cellulose) to cell density facilitated high hydrogen production by a mixed hydrogen producers,. However, the optimum S/X ratio attained in this

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study was relatively low compared to the findings of Chen et al. [11] who conducted the hydrogen production from food waste by anaerobic sludge. They reported that the highest HY of 180 mL/g-VSadded peaked at an S/X ratio of 7.3. In addition, the work conducted by Pan et al. [43] revealed that the optimum food to microorganisms (F/M) ratio for hydrogen production from food waste at mesophilic temperature (35  C) was 6 and yielded an HY of 38.8 mL H2/g-VSadded. In addition, they reported that at an F/M ratio higher than 7, the final pH of the fermentation broth were all lower than 4.8 and the hydrogen production was inhibited. Different pH levels resulted in different fermentation rates and products because the pH not only affects the bacteria activities and growth rates, but also changed the metabolic pathways [44]. Results indicated that citrate buffer supplementation at optimum levels (runs 1, 2, 3, 5, 11 and 12) could enhance HY up

to 3.14 times compared to the treatments with the same level of inoculums concentration and substrate concentration but with a different citrate buffer concentration (Table 3) (runs 14, 17). Therefore, citrate buffer is needed in the hydrogen fermentation process in order to counteract a decrease in pH due to the accumulation of VFAs.

3.2. Effects of inoculums concentration, substrate concentration and citrate buffer concentration on SHPR The efficiency of SHPR was obtained through the design matrix of the three variables. Results showed that SHPR ranged from 2.61 to 15.30 mL H2/g-VSS.h (Table 3). The maximum SHPR of 15.30 mL H2/g-VSS.h was obtained at moderate levels (runs 1, 2, 3, 5, 11 and 12) whereas the lowest SHPR of 2.61 mL H2/g-VSS.h was found in run 8 (Table 3).

Fig. 2 e Three response surface plots showing the effects of inoculums concentration, substrate concentration, and their mutual interaction on specific hydrogen production rate with optimum level of citrate buffer concentration (0.10 M) (a); the effects of inoculums concentration, citrate buffer concentration and their mutual interaction on specific hydrogen production rate with optimum level of substrate concentration (2.75 g-VS/L) (b); the effects of substrate concentration, citrate buffer concentration and their mutual interaction on specific hydrogen production rate with optimum level of inoculums concentration (2.22 g-VSS/L) (c).

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86 77 121 80 0 0 0 89 2 30 5 168 509 472 476 459 1167 985 1010 940 16.90 11.02 16.69 5.22 104.79 60.95 92.43 28.96 4.4 4.3 4.2 4.1 5.5 5.5 5.5 5.5 0.11 0.15 0.10 0.05 Optimala High Medium Worst e 7 1 19

a Based on HY and SHPR.

Acetic acid (mg/L) Butyric acid (mg/L) SHPR (mL H2/ g-VSS.h) HY (mL H2/ g-VSadded) Final pH Initial pH Citrate buffer concentration (M) Substrate concentration (g-VS/L) Inoculums concentration (g-VSS/L) Condition Run

The quadratic regression model indicated that the model is significant with a low probability (P < 0.0001) accompanied by an insignificant lack of fit model (P ¼ 0.0773). All variables i.e. inoculums concentration, substrate concentration and citrate buffer concentration showed probability values of less than 0.05, which indicated their significant effect on SHPR (Table 4). Results also demonstrated the significant interaction effect between substrate concentration and citrate concentration (X2X3) (P ¼ 0.0123) while the interaction effects between inoculums concentration and substrate concentration (X1X2) (P ¼ 0.9025), inoculums concentration and citrate concentration (X1X3) (P ¼ 0.3378) are not significant (Table 4). Three-dimensional response surfaces based on Eq. (6) were plotted in order to determine the optimum level of each variable and the effects of their interactions on the SHPR (Fig. 2aec). Fig. 2a shows the response surface plot on independent variables: inoculums concentration (X1) and substrate concentration (X2) while citrate buffer concentration (X3) was kept at the optimal level. The three-dimensional response surfaces plot suggested that inoculums concentration and substrate concentration had no significant interactive effect on SHPR (P ¼ 0.9025) (Table 4). SHPR increased when inoculums concentration increased from 1.48 to 2.96 g-VSS/L and then SHPR slightly decreased with the further increase in inoculums concentration. A significant increase in SHPR could be observed when the substrate concentration was increased from 1.83 to 2.75 g-VS/L and SHPR decreased when the substrate concentration was higher than 2.75 g-VS/L, which indicated the occurrence of substrate inhibition. The interaction effect between inoculums concentration and substrate concentration (X1X2) was not significant, suggesting that variation in inoculums concentration and substrate concentration led to the change in SHPR. Hydrogen production by the predominant hydrogen producers, Clostridium sp., in our study (see Section 3.4), is primarily a growth-associated process and it is therefore expected that the highest hydrogen production rate be obtained during the growth phase. Thus, for the same substrate concentration, an initially high substrate to cell ratio would prolong the growth phase and facilitate a longer duration of high rates of hydrogen production by the growing cells and would result in high hydrogen production [45]. The model coefficients estimated by multiple linear regression (Table 4) and the response surface plots (Fig. 2b and c) showed no significant interaction effect between inoculums concentration and citrate buffer concentration (X1X3). The SHPR was rapidly increased when the citrate buffer concentration was increased up to 0.1 M then SHPR gradually decreased when the citrate buffer concentration was higher than 0.1 M (Fig. 2b and c). A maximum SHPR of 15.30 mL/gVSS.h, obtained from runs 1, 2, 3, 5, 11 and 12, was up to 3.14 times higher than runs 14 and 17 by using the same levels of

Propionic acid (mg/L)

ð6Þ

Table 5 e Experimental design and results of confirmation test obtained at 96 h (data are given as mean; n [ 3).

þ 0:36X1 X3  1:10X2 X3  3:74X21  3:44X22  3:05X23

2.54 3.67 2.75 1.83

YSHPR ¼ 15:30 þ 0:74X1  1:38X2 þ 1:07X3 þ 0:05X1 X2

i-

2.30 2.96 2.22 1.48

Lactic acid (mg/L)

Ethanol (mg/L)

Multiple regression analysis was applied on the data in Table 3 and the obtained second-order polynomial equation (Eq. (6)) could well explain the SHPR with a high determination coefficient and adjusted coefficient of 0.99 and 0.98, respectively.

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Table 6 e Comparison of hydrogen yield (HY) and specific hydrogen production rate (SHPR) in various types of organic waste and inoculums concentration in batch experiments. Organic waste Food waste Food waste Food waste and sludge Food waste and sludge Potato Rice Rice Food waste

HY (mL H2/g-VSadded)

Inoculums

Inoculums concentration (g-VSS/L)

Clostridium-rich composts Aged refuse Anaerobic digester Anaerobic digester Anaerobic digester Anaerobic digester Anaerobic digester Anaerobic digester

NA

77 mL H2/g-TVS

NA NA 5.50 3.37 NA 3.37 2.30

194 112 59 106 96 134 105

added

SHPR

Reference

21.66 mL/g-TVS.h

[46]

94.35 mL/g-VS.h NA 111.2 mL/g-VSS.h 68.75 mL/g-VSS.h NA 82.91 mL/g-VSS.h 16.86 mL/g-VSS.h

[47] [12] [48] [49] [50] [49] This study

NA: not available.

3.3.

Confirmation experiment

The maximum HY and SHPR were obtained when inoculums concentration, substrate concentration and citrate buffer

concentration were 2.30 g-VSS/L, 2.54 g-VS/L, and 0.11 M, respectively (Table 5). Under optimal conditions, the model predicted an HY of 104.58 mL H2/g-VSadded and an SHPR of 16.86 mL H2/g-VSS.h. In order to confirm the validity of the statistical experimental strategy, three replications of batch experiments were performed under optimal conditions. The results of the optimum point are in close agreement with the observed values of an HY of 104.79 mL H2/g-VSadded and an SHPR of 16.90 mL H2/g-VSS.h (Table 5). Our results showed 60

Cumulative hydrogen production Hydrogen yield Specific hydrogen production rate

250

140 120

200

100 80

150

60 100 40 50

20

0

0

Hydrogen yield (mL H2/g-VS added)

Cumulative hydrogen production (mL)

300

50

40

30

20

10

Specific hydrogen production rate (mL H2/g-VSS.h)

noculums concentration and substrate concentration. Results suggested that citrate buffer concentration under the optimum level of inoculums concentration and substrate concentration improved HY and SHPR.

0

Soluble metabolite produce concentration (mg/L)

1400 1200 1000 800 600 400 200 0 0

20

40

60

80

Time (h)

Fig. 3 e Cumulative hydrogen production, hydrogen yield, specific hydrogen production rate in the confirmation experiment at optimum condition (a); and development of soluble metabolites product (b).

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that the observed maximum HY and SHPR obtained from the optimal condition were favorable in comparison to results found in the literature search (Table 6). Fig. 3 shows cumulative hydrogen production, HY, SHPR and soluble metabolites product (SMP) in a confirmation experiment at the optimal condition. Cumulative hydrogen production and HY at the optimal condition sharply increased from approximately 3 h (lag phase) to 48 h of incubation time and achieved a steady state after 72 h of incubation time (Fig. 3a). SHPR at the optimal condition increased after the start up the experiment and reached the maximum value of 43.46 mL H2/g-VSS.h at 24 h of incubation time; the SHPR then sharply decreased until the end of incubation time (Fig. 3a). The concentration of SMP was sharply increased after the lag phase of the incubation time (Fig. 3b). The major SMPs were butyric acid (1167 mg/L) and acetic acid (509 mg/L) with a small amount of ethanol and propionic acid detected (Fig. 3b). Lactic acid was not found at this condition. This result coincided with the results from PCR-DGGE analysis in which lactic acid bacteria were not present in the fermentation broth at optimal conditions (see section 3.4). Butyric acid and acetic acid constituted 66.18% and 28.86% of the total SMP, respectively. Theoretically, 4 mol of hydrogen are produced from glucose concomitantly

with 2 mol of acetate while 2 mol of hydrogen are produced when butyrate is the main metabolite [51]. The presence of greater amounts of butyrate than acetate in the system indicated a butyrate type fermentation. Therefore, it can be concluded that high percentages of butyric acid and acetic acid along with low amounts of lactic acid, propionic acid and ethanol contributed to high HY and SHPR at optimal conditions. At the worst condition, the cumulative hydrogen production, HY and SMP slightly increased from approximately 5 h (lag phase) to 96 h of incubation time and tended to stabilize thereafter (Fig. 4a). The SHPR peaked at 24 h of incubation with a maximum value of 11.67 mL H2/g-VSS.h (Fig. 4a). SMPs detected were butyric acid (940 mg/L) and acetic acid (459 mg/L). At this condition, high amounts of propionic acid (168 mg/L), lactic acid (89 mg/L) and ethanol (80 mg/L) (Fig. 4b, Table 5) were detected. The existence of these three solvents could contribute to low hydrogen production, HY and SHPR (Fig. 4a) at this condition. The production of lactic acid did not yield hydrogen [51]. In addition, 2 mol H2 would be consumed for every 1 mol of propionic acid produced [52]. PCR-DGGE results coincided with the detection of lactic acid in which lactic acid bacteria (LAB) were detected at the worst condition (see section 3.4).

250

140 120 100

200

80

150

60 100 40 50

20

0

0

50

40

30

20

10

Specific hydrogen production rate (mL H2/g-VSS.h)

60 Cumulative hydrogen production Hydrogen yield Specific hydrogen production rate

Hydrogen yield (mL H2/g-VS added)

Cumulative hydrogen production (mL)

300

0

Soluble metabolite produce concentration (mg/L)

1400 1200 1000 800 600 400 200 0 0

20

40

60

80

Time (h)

Fig. 4 e Cumulative hydrogen production, hydrogen yield, specific hydrogen production rate in the confirmation experiment at worst condition (a); and development of soluble metabolites product (b).

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Clostridium sp. At the worst condition, the dominating populations can be divided into two groups. The first group is affiliated with Clostridium sp in which bands 5, 7, and 13 showed 98e99% similarity to Clostridium sp. The second group is affiliated with LAB in which bands 3 and 9 showed 92% and 95% similarity, respectively, to Enterococcus faecalis while bands 10 and 12 showed 97% and 93% similarity to Lactobacillus sp. Band 11 showed 99% similarity to uncultured bacterium. Clostridium sp. have been reported as the active hydrogen producing bacteria that could increase hydrogen production efficiency [54e57]. The Clostridia group is known to produce acetic acid, n-butyric acid and hydrogen via dark fermentation. Eqs. (7) and (8) are used to explain the direct conversion of carbohydrate-rich substrate into hydrogen, carbon dioxide, acetic acid and n-butyric acid [58]: C6 H12 O6 þ 2H2 O/2CH3 COOH þ 2 CO2 þ 4H2

(7)

C6 H12 O6 /CH3 CH2 CH2 COOH þ 2 CO2 þ 2H2

(8)

The pre-dominance of LAB i.e., Lactobacillus sp. and Enterococcus sp. might be responsible for the low hydrogen production efficiency at the worst condition. These microorganisms can utilize carbohydrate-rich substrate for the production of lactic acid, instead of hydrogen [59], and other metabolites such as bacteriocins that can inhibit the activity of hydrogen producing bacteria [60]. There are two categories of LAB i.e. homofermentative bacteria (Lactococcus and Lactobacillus genera) and heterofermentative bacteria (Lactobacillus and Leuconostoc genera). Lactic acid is the only end product of glucose metabolism by homofermentative bacteria through the Embden-Meyerhof-Parnas (EMP) pathway as showed in the Eq. (9): Fig. 5 e DGGE profile of the PCR-amplified 16S rDNA gene fragment extracted from sludge at the end of fermentation of worst (a); and optimum conditions (b).

3.4.

PCR-DGGE analysis

An image of a DGGE gel of the PCR-amplified 16S rDNA segments extracted from the sludge sampled at the end of fermentation of the worst (Lane a) and optimal conditions (Lane b) is depicted in Fig. 5. The number of bands detected from the worst condition was greater than that from the optimum condition, suggesting significant differences of microbial community between the worst and the optimal conditions. Differences in dominant microbial species in the community reveal the differences in fermentation type, the distribution and the dominant fermentation products [53]. To identify anaerobic microorganisms corresponding to bands 1e13, the bands were excised and purified to determine their 16S rDNA sequences. A phylogenetic tree (Fig. 6) was then constructed by comparing the obtained sequences with data base sequences using the BLAST search (data not shown). Only Clostridium sp. was detected at the optimum condition. Bands 4 and 6 showed 97% and 99% similarity, respectively, to uncultured Clostridium sp. Bands 1 and 2 showed 96% and 94% similarity, respectively, to C. beijerinckii, C. butyricum, and

C6 H12 O6 þ2NADþ þ2ADPþ2Pi/2C3 H6 O3 þ2NADþ þ2ATP

(9)

Lactic acid, ethanol and CO2 are the end products of glucose metabolism by heterofermentative bacteria. The difference in the end products are caused by the presence or absence of the enzyme, fructose-1,6-diphosphate aldolase in the EMP pathway [61]. Therefore, the presence of Lactobacillus sp. coincides well with the detection of lactic acid as well as ethanol in the fermentation broth obtained at the worst condition (Table 5). The difference in citrate buffer concentration used may be the main factor in the difference between the microbial populations in the fermented broth at the worst and optimal conditions. At the worst condition with the low citrate buffer concentration of 0.05 M, the pH of the fermented broth decreased rapidly after the start up of the fermentation process (data not shown). Under acidic conditions, the inhibitory activity of the bacteriocins produced by LAB was reported to increase against other bacteria [62], which could result in a decrease in the number of hydrogen producers, hence the LAB became dominant. The use of a high concentration of citrate buffer (0.11 M) at the optimum condition could maintain the pH value in the range of 5.0e5.5 for 48 h of incubation; this could facilitate the activity of hydrogen producing bacteria in the group of Clostridia [18,63,64]. The high concentration of sodium citrate used for buffer preparation could inhibit the activity of LAB. For example, Branen and Kenan. 1970 [60] reported that 12e18 mM of sodium citrate could stimulate the

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 6 ( 2 0 1 1 ) 1 4 1 2 0 e1 4 1 3 3

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Clostridium acetobutylicum (CP002118.1) Clostridium sp. (GU191346.1) Clostridium sp. (EU305673.1) 84

Clostridium acetobutylicum (AB595131.1) Clostridium sp. (GU097452.1)

75 92 60 65

Uncultured Clostridium sp. (GU255485.1) Clostridium sp. (HQ012837.1) Uncultured Clostridium sp. (FN689577.1)

76

72 60

Clostridium butyricum (EEP53083.1)

94

Clostridium beijerinckii (NR029230.1) Clostridium sp. (HQ677442.1) Clostridium sp. (HM217767.1) 63

Uncultured bacterium (AB559694.1) 61

74 80

Uncultured bacterium (HQ176308.1) Clostridium sp. (DQ196630.2)

Clostridium sp. (AY862517.1) 75

Uncultured Clostridium sp. (FJ982843.1) 75

99 90

Enterococcus faecalis (GU994772.1) Enterococcus faecalis (FR717853.1)

93

Lactobacillus sp. (AY363378.2) 60

Lactobacillus reuteri (JF268325.1)

61 82 76

Lactobacillus rhamnosus (HQ293100.1) Uncultured Lactobacillus sp. (HQ889761.1) Lactobacillus parabuchneri (HQ293093.1) Escherichia coli (CP000243.1)

Fig. 6 e Phylogenetic tree of microbial at worst and optimal conditions and their close relatives based on partial 16S rDNA genes (E. coli position 357e518). The tree based on JukeseCantor distance was constructed using neighbor-joining algorithm with 1000 bootstrappings. The scale bar represents 0.1 substitutions per nucleotide position. Numbers at the nodes are the bootstrap values.

growth of LAB whereas 40 mM of sodium citrate or more could cause an inhibitory effect on growth of LAB. Therefore, it could be concluded that the disappearance of LAB in the fermentation broth at the optimum condition and Clostridium sp. becoming dominant might be due to the high concentration of citrate buffer used at the optimum condition.

4.

Conclusion

The RSM results indicated that the optimum conditions for maximizing HY and SHPR were 2.30 g-VSS/L inoculums concentration, 2.54 g-VS/L substrate concentration, and 0.11 M citrate buffer concentration in which the predicted HY and SHPR were 104.58 mL H2/g-VSadded and 16.86 mL H2/gVSS.h, respectively. The predicted values were relatively close to the experimental values of an HY of 104.79 mL H2/g-VSadded

and an SHPR of 16.90 mL H2/g-VSS.h obtained from the confirmation experiment. The difference was only 0.20% and 0.24%, respectively, from the predicted values. Hydrogen producing microorganisms detected at the worst and optimum conditions are affiliated with Clostridium sp. Lactobacillus sp. and Enterococcus sp. and were responsible for a low HY obtained at the worst condition. The presence of LAB coincided with lactic acid and ethanol being detected in the fermentation broth at the worst condition.

Acknowledgements The authors gratefully received the research funds from Research Group for Development of Microbial Hydrogen Production Process from Biomass, the Higher Education Research Promotion and National Research University Project

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for Thailand, Office of the Higher Education Commission through Biofuels Research Cluster of Khon Kaen University, and Research Centre for Environmental and Hazardous Substance Management, Khon Kaen University Innovative Biosciences and the Special Coordination Funds for Promoting Science and Technology, Ministry of Education, Culture, Sports, Science and Technology, Japan. Graduate school, Khon Kaen University is very much appreciated for Ph.D. Scholarship to CS.

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