Bioresource Technology 159 (2014) 365–372

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Application of a novel enzymatic pretreatment using crude hydrolytic extracellular enzyme solution to microalgal biomass for dark fermentative hydrogen production Yeo-Myeong Yun a, Dong-Hoon Kim b, You-Kwan Oh c, Hang-Sik Shin a, Kyung-Won Jung d,⇑ a

Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea Waste Energy Research Center, Korea Institute of Energy Research, 102 Gajeong-ro, Yuseong-gu, Daejeon 305-343, Republic of Korea Bioenergy Center, Korea Institute of Energy Research, 102 Gajeong-ro, Yuseong-gu, Daejeon 305-343, Republic of Korea d Center for Water Resources Cycle Research, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650, Republic of Korea b c

h i g h l i g h t s  A novel enzymatic pretreatment of microalgal biomass using CHEES.  CHEES has a dual role as the hydrolysis enhancer and the co-subsrate supplier.  Lactate and acetate in CHEES acted as co-substrate for DFHP.  The accumulated butyrate in CHEES was not affected.

a r t i c l e

i n f o

Article history: Received 9 December 2013 Received in revised form 27 February 2014 Accepted 28 February 2014 Available online 12 March 2014 Keywords: Chlorella vulgaris Food waste Crude hydrolytic extracellular enzyme solution Co-substrate

a b s t r a c t In this study, a novel enzymatic pretreatment of Chlorella vulgaris for dark fermentative hydrogen production (DFHP) was performed using crude hydrolytic extracellular enzyme solution (CHEES) extracted from the H2 fermented effluent of food waste. It was found that the enzyme extracted at 52 h had the highest hydrolysis efficiency of microalgal biomass, resulting in the highest H2 yield of 43.1 mL H2/g dry cell weight along with shorter lag periods. Even though a high amount of VFAs was accumulated in CHEES, especially butyrate, the fermentative bacteria on the DFHP was not affected from product inhibition. It also appears that the presence of organic acids, especially lactate and acetate, contained in the CHEES facilitated enhancement of H2 production acted as a co-substrate. Therefore, all of the experimental results suggest that the enhancement of DFHP performance caused by CHEES has a dual role as the hydrolysis enhancer and the co-substrate supplier. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Due to political, economic, and environmental issues that have risen over the past several decades, biofuels have been in the spotlight as an economically viable and environmentally clean energy. During the past decade, hydrogen (H2) has garnered huge interest as a promising alternative energy carrier among various alternative energy sources. This is due to its tremendous potential as a clean and renewable energy currency. Among various H2 generation processes, dark fermentative H2 production (DFHP), a term referring to biological hydrogen production, from renewable biomass has been one of the focal points in this research field due to its valuable ⇑ Corresponding author. Tel.: +82 2 958 6859; fax: +82 2 958 6854. E-mail address: [email protected] (K.-W. Jung). http://dx.doi.org/10.1016/j.biortech.2014.02.129 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.

inherent ability to produce energy simultaneously with waste degradation (Jung et al., 2011a). Among the various affecting factors for DFHP, the selection of feedstock is the most rudimentary progress for determining whether successful H2 production or not. Up to date, various types of renewable biomass have been considerably employed as feedstock for DFHP such as agricultural crops and waste biomass, referring to first- and second- generation biomass, respectively, due to their large potential for reduction of environmental issues (IEA, 2010; Singh et al., 2010). On the other hand, recently, the third-generation biomass, micro- and macro algae, has been paid most attention as a technologically viable future energy source due to it can overcome the major drawbacks of the previous generation biomass regarding land availability and carbon debt (Joseph et al., 2008; Jung et al., 2011b). Microalgal

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biomass can be converted into a number of different biofuels, e.g. into biodiesel, bioethanol, or biogas (hydrogen and methane) (Singh et al., 2010; Yang et al., 2011). However, most research has been one sided on biodiesel production from the lipid extraction of microalgal biomass. According to the reports, even though the main composition of the cell wall of Chlorella vulgaris, one of the most popular microalgal biomass, depends on the species or condition of cultivation, the major accumulated constituent of microalgal biomass is starch and it makes algae become a very potential feedstock source for biological processes to produce biofuels. For a more sustainable development of third-generation biofuels, researching DFHP is also important (Blumreisinger et al., 1983; Yun et al., 2012). Taking into consideration the abovementioned issues, the considerable efforts to improve DFHP have focused on pretreating the substrate over the last three decades by using chemical (mostly acid/base), thermal, microwave, hydrodynamic cavitation, and ultrasonication (pretreatment or combined pretreatment) (Carlsson et al., 2012; Cheng et al., 2011; Pilli et al., 2011; Wu et al., 2012). Even though the effective hydrolysis performance can be anticipated via physical and chemical pretreatment technique, their strong activation also can lead to form inhibitory materials under the severe conditions of pretreatment (Datar et al., 2007; Jung et al., 2011b) such as furfural and hydroxylmethylfurfural. On the other hand, the enzymatic hydrolysis, often referred to as biological pretreatment, has been suggested as a more environmentally- and economically-friendly perspective to release easily fermentable sugars from a biomass due to the low energy requirement, no corrosion issues, less byproduct production, and a higher yield under mild environmental conditions, since the first application of microbial enzymes in the food industry in the early 1960s (Balat et al., 2008). Up to now, a large number of commercial enzymes (e.g. peroxidase, oxidoreductase, cellulase, protease, and amylase.) from a variety of different sources have been reported to play an important role in an array of waste treatment applications and fermentation industry (Nguyen et al., 2010; Sangave and Pandit, 2006). However, due to the high cost, the application of commercial enzymes for hydrolysis makes the entire DFHP process become a non-costeffective process. In addition, the enzyme generally reacts to only the specific target material and it needs optimum environmental conditions (Choi et al., 2010). For instance, the optimal active conditions of amylase, the hydrolytic enzyme of starch, are similar to the DFHP condition, where there is a pH of 5.5–7.0 and temperature of 30–55 °C (Pandya et al., 2005). Ideally, when using the starch enriched microalgal biomass as feedstock for DFHP, if the enzyme can be extracted from the H2 fermented effluent by using biomass as feedstock, there would be no need to consider the above limiting issues and it can be directly applied to the DFHP process because the extracted enzyme was produced from the same operational conditions as DFHP. In light of the above research background, a novel enzymatic pretreatment of microalgal biomass on DFHP was performed by using a crude hydrolytic extracellular enzyme solution (CHEES) extracted from the H2 fermented effluent. To minimize the effect of seeding sludge during the CHEES production on further procedures, including hydrolysis and DFHP, and to maximize the economic and environment values, the DFHP of food waste was conducted without the addition of external inoculum to produce CHEES as described in previous work (Kim et al., 2009). The reduced sugar concentration was monitored for the evaluation of hydrolysis and the optimal sampling time of CHEES from the H2 fermenter. To ascertain the effect of CHEES on DFHP, several batch tests were carried out. To the best of the authors’ knowledge, this is the first report regarding a novel enzymatic pretreatment of microalgal biomass optimized for DFHP. Lastly, the H2 fermented effluent using CHEES was continuously treated using

anaerobic sequencing batch reactor (ASBR) for methane (CH4) production. 2. Methods 2.1. Inoculum and feedstock preparation The source of the anaerobic mixed culture was collected from an anaerobic digester at a local wastewater treatment plant (Daejeon, Korea). The pH, alkalinity, and volatile suspended solid (VSS) concentration of the sludge were 7.6, 2.83 g CaCO3/L, and 5.5 g/L, respectively. The sludge was heat-treated at 90 °C for 20 min and then cooled to room temperature in an attempt to harvest only spore-forming anaerobic bacteria such as Clostridium sp. (Jung et al., 2010). C. vulgaris, freshwater microalgal biomass, was used as feedstock for this DFHP experiment, and it was stored at 4 °C to preserve its characteristics. The total chemical oxygen demand (COD) concentration of C. vulgaris was 1.37 g COD/g dry cell weight (dcw). In order to produce CHEES via DFHP from food waste, food waste was collected from a KAIST cafeteria and was shredded by a grinder to be smaller than 5 mm in diameter. The characteristics of C. vulgaris and food waste used in this experiment are shown in Table 1. 2.2. Batch fermentation In batch test I, the H2 fermentation of food waste without the addition of inoculum was conducted to produce CHEES, as described in previous work (Kim et al., 2009). In detail, prior to the addition to the reactor, food waste was boiled at 90 °C for 20 min. A certain amount of food waste and tap water were added into the batch fermenter to the carbohydrate concentration of 30 g carbo. COD/L in order to reach a working volume of 2.0 L (total volume = 3.5 L). N2 gas was purged in order to provide an anaerobic condition. By using a pH sensor and pH controller, the initial pH was adjusted at 7.0 ± 0.1, and the operational pH during fermentation was maintained at higher than 5.5 ± 0.2 by adding 3 N of KOH. During H2 fermentation (batch test I), the mixed liquors were directly taken from sampling ports in the reactor at determined time intervals from 10 h to 52 h of operation time. In order to obtain the supernatant (enriched enzyme solution), the centrifugation at 7000 rpm for 10 min was applied, and then all samples were immediately filtered through a 0.45 lm GF/C paper (Whatman, USA) to enrich CHEES. Batch test II was performed to check the pretreatment efficiency of CHEES. Every enriched CHEES at different sampling times was added to the 7.61 g dcw microalgal biomass in order to make it be 100 mL and easier to prepare the substrate concentration of Table 1 Characteristics of microalgal biomass and food waste. Items

Units

C. vulgaris

Carbohydrate Protein Lipid Ash Etc.

Nutritional components of cell (g/100 g)

38.8 49.6 0.7 9.0 1.9

TCOD SCOD TS VS Carbohydrate TN TKN Ammonia pH

g COD/L g COD/L g/L g/L g COD/L g N/L g N/L mg NH4-N/L -

Food waste

145.1 ± 15.2 52.7 ± 3.4 171.5 ± 5.2 123.7 ± 10.1 85.6 ± 9.8 4.1 ± 2.5 2.9 ± 0.2 340 ± 17.2 5.2 ± 0.1

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76.1 g dcw/L for future batch tests, which were optimized in a previous study (Yun et al., 2012). In order to define the optimum enzyme extraction point, changes in reducing the sugar concentration were monitored at 2 h intervals. Batch test III was conducted using the enzymatic pretreated microalgal biomass in the batch test II as feedstock for DFHP. A total volume of 250 mL serum bottles (working volume = 100 mL) was seeded with the heat-pretreated sludge equivalent to 30% of the working volume, and filled with a specified amount of microalgal biomass with CHEES and tap water. There was no addition of an external nutrient. The substrate concentration was fixed at 76 g dcw/L, and the initial pH was adjusted at 7.4 by adding 3 N of KOH. The pH was not controlled during fermentation. All batch tests were conducted in a temperature controlled room of 35 °C ± 1, and the mixing rate was 150 rpm. Batch tests were carried out in triplicate and average values were determined for each set. 2.3. CH4 production: ASBR system For the CH4 production, an ASBR system (working volume 3.0 L; 380 mm high by 135 mm ID) was applied. One cycle period for the ASBR was 24 h: 18 h reaction time, 5 h settling time, and 1 h filling and decanting. The OLR was controlled by HRT (30–12 d or 1.0–2.5 g COD/L/d) and substrate concentration (30–50 g COD/L or 2.0–3.34 g COD/L/d). The solid retention time (SRT) has been calculated as the ratio of the mass of VSS within the reactor to the mass of VSS in the effluents removed from the ASBR (Luo et al., 2013). As a result, the calculated SRT at each HRT conditions were 27.7, 22.5, 19.3, 16.9, and 9.2 d at HRT of 30, 25, 20, 15, and 12 d, respectively. The seed sludge was taken from anaerobic digestion. Reactor was installed in a temperature controlled room at 35 °C ± 1. 2.4. H2 fermentation analysis The measured biogas production was adjusted to the standard conditions of temperature (0 °C) and pressure (760 mmHg) (STP). To describe the H2 production, a cumulative H2 production curve was described by the modified Gompertz Eq. (1) (Chen et al., 2006).

  0  R e HðtÞ ¼ P  exp  exp ðk  tÞ þ 1 P

ð1Þ

where H(t) = cumulative H2 production (L) at cultivation time t (h); P = ultimate H2 production (L); R0 = H2 production rate (L/L/h); k = lag phase (h); and e = exp(1) = 2.71828. H2 production was calculated from the headspace measurements of gas composition and the total volume of biogas produced at each time interval using the mass balance Eq. (2).

VH2 ;i ¼ VH2 ;i1 þ VW CH2 ;i þ VG;i CH2 ;i  VG;i1 CH2 ;i1

ð2Þ

where VH2 ;i and VH2 ;i1 are the volumes of cumulative hydrogen (mL) calculated after the ith and the previous measurement; VW is the total gas volume measured by the water displacement method (mL); CH2 ;i is the concentration of H2 gas in the total gas measured by the water displacement method (%); VG,i and VG,i  1 are the volumes of gas in the headspace of the bottle for the ith and previous measurement (mL); CH2 ;i and CH2 ;i1 are the percent H2 in the headspace of the bottle for the ith and the previous measurement (Argun et al., 2008). 2.5. Analytical method To determine the H2 content in the biogas, a gas chromatography (GC, Cow Mac series 580, Gow-Mac Instrument Co., USA)

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equipped with a thermal conductivity detector and a 1.8 m  3.2 mm stainless-steel column packed with molecular sieve 5 A was employed with N2 as a carrier gas. The contents of CH4, N2, and CO2 were measured using a GC of the same model noted previously with a 1.8 m  3.2 mm stainless-steel column packed with porapak Q (80/100 mesh) using helium as a carrier gas. The concentrations of organic acids (VFAs, C2–C6) and lactic acid were measured by a high-performance liquid chromatography (HPLC) (Finnigan Spectra SYSTEM LC, Thermo Electron Co.) using an ultraviolet (210 nm) detector (UV1000, Thermo Electron) and an 100  7.8 mm Fast Acid Analysis column (Bio-Rad Lab.) with 0.005 M H2SO4 as a mobile phase at a flow rate of 0.6 mL/min. The COD and pH of the samples were measured according to Standard Methods (APHA, 1998). The chemical components of microalgae biomass were analyzed by the Korea Food Research Institute according to the Korean Food Standards Codex (2009) and the Kjeldahl method (Jones and Woods, 1986; Merill and Watt, 1973; Schakel et al., 1996). The concentration of reducing sugar was measured by a 3,5-dinitrosalicylic acid (DNS) method (Miller, 1959). Microalgal biomass observation was conducted via digital microscopy (Axioscpoe, Zeiss) and use of a digital camera (Axiocam MRm, Zeiss).

2.6. Microbial analysis To identify the microbial communities in batch test I and III, the DNA samples were taken from each batch fermenter and then it was extracted using an Ultraclean Soil DNA Kit (Cat # 12800-50; Mo Bio Laboratory Inc., USA). The 16S rDNA fragments were stored at 20 °C before being amplified by polymerase chain reaction (PCR). The region corresponding to positions 357F and 518R in the 16S rDNA of Escherichia coli was PCR-amplified using the forward primer EUB357f (50 -CCTACGGGAGGCAGCAG-30 ) with a GC clamp (50 -CGCCCG CCGCGCCCCGCGCCCGGCCCGCCGCCCCCGC CCC-30 ) at the 50 end to stabilize the melting behavior of the DNA fragments and the reverse primer UNIV518r (50 -ATTA CCGCGGCTGCTGG-30 ). PCR amplification was conducted in an automated thermal cycler (MWG-Bio TECH, Germany) using the following protocol: initial denaturation for 4 min at 94 °C, annealing for 40 s at 55 °C, extension for 1 min at 72 °C, followed by a final extension for 8 min at 72 °C. PCR mixtures had a final volume of 50 ll of 10  PCR buffer, 0.8 mM MgSO4, 0.5 mM of each primer, 0.1 mM dNTP, 25 pg template, and 1 U polymerase. PCR products were electrophoresed on 2% (wt./vol) agarose gel in 1  TAE for 30 min for 50 V, and then checked with ethidium bromide staining to confirm the amplification. Denaturing gradient gel electrophoresis (DGGE) was carried out using a Dcode Universal Mutation Detection System (BioRad, USA) in accordance with the manufacturer’s instruments. PCR products were electrophoresed in 1  TAE buffer for 480 min at 70 V and 60 °C on a polyacrylamide gel (7.5%) containing a linear gradient ranging from 40% to 60% denaturant. After electrophoresis, the polyacrylamide gel was stained with ethidium bromide for 30 min, and then visualized on a UV transilluminator. Most bands were excised from the DGGE polyacrylamide gel for 16S rDNA sequencing. DNA was eluted from the excised bands by immersion in 20 ll of Tris EDTA buffer (pH 8.0) for one day, and then PCR-amplified with the forward primer EUB357f without a GC clamp and the reverse primer UNIV518r. After PCR amplification, PCR products were purified using a Multiscreen Vacuum Manifold (MILLIPORE com., USA). All strands of the purified PCR products were sequenced with primers EUB357f by an ABIPRISM Big Terminator Cycle Sequencing Kit (Applied Biosystems, USA) in accordance with the manufacturer’s instructions. Search of the GenBank database was conducted using the BLAST program.

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3. Results and discussion 3.1. Batch test I: DFHP from food waste without external inoculum addition The objective of batch test I was to obtain CHEES during DFHP from food waste without the addition of an external inoculum. Fig. 1 shows the time frame of cumulative H2 production from the food waste. During the entire H2 fermentation (tests were done three times), CH4 was not detected and the curves were well fitted by the modified Gompertz equation with a high R2 value of 0.99. Although there was no addition of inoculum, the fermentation began in about 10 h of operation and it was completed within 52 h. Finally, the H2 yield of 181.3 mL H2/g hexoseadded, or 1.6 mol H2/ mol hexoseconsumed was obtained, as shown in Table 1, which was similar with previous research (Kim et al., 2009). In terms of organic acid production, DFHP could be accomplished by acetate and butyrate production with propionate and lactate, which are known to be byproducts that are not related to H2 fermentation (Hawkes et al., 2002). As provided in Table 1, acetate and butyrate were the main VFA components with a carbohydrate removal rate of 92.0 ± 0.6% (data not shown). These results indicated that the DFHP of food waste without the addition of an external inoculum was successfully reproduced in this study. 3.2. Batch test II: Extraction of a CHEES from H2 fermeter According to the report, the various extracellular enzymes could be produced by H2-producing bacteria from a substrate (carbohydrate), mainly Clostridium sp., including amylase, cellulase, lipase, and protease (HPA, 2008). In order to detect dominant microorganisms, a mixed sample was taken from the batch fermenter and the bacterial diversity was monitored by polymerase chain reaction– denaturing gradient gel electrophoresis (PCR–DGGE). From the DGGE profile (Fig. 2), a total of 10 bands were detected, where each band represents one microbial species. The results of 16S rDNA sequences shown in Table 3 reveal that 8 matched well with H2proudcing bacteria, showing a high similarity level of about 97%. Clostridium butyricum (band #2), Clostridium saccharobutylicum (band #3), and Clostridium acetobutyricum (band 9) were known to be producers of amylase and cellulase. Even though there was no knowing how much of them exist in the fermenter, this means that the crude enzyme from the DFHP of food waste could be

Fig. 2. DGGE profiles of the 16S rDNA gene fragment after batch test I and III.

applied as an enzymatic pretreatment material to improve the hydrolysis of microalgal biomass with an economic and viable process. However, among Clostridium sp., Clostridium perfringens (band #7) and Clostridium sporogenes (band #12) were also detected, which are well-known phospholipase and protease producers from feedstock (HPA, 2008). This means that, unlike the application of a commercial specific enzyme, the most considerable factor in this

Fig. 1. Cumulative H2 production of food waste without external inoculum addition (Red line: sampling time). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Y.-M. Yun et al. / Bioresource Technology 159 (2014) 365–372 Table 2 Average gas and liquid phase parameters in batch fermentation of food waste without inoculum addition. Time (h)

H2 yield (mL H2/g hexoseadded)

k (h)

Rm (mL H2/L/h)

Organic acids production (%) HLa

10 20 30 40 52 a

9.6 118.6 169.3 176.7 181.3

– – – – 10.4

– – – – 1,075

a

3 8 13 13 15

HAc

a

36 34 33 32 30

HPr

Total organic acids (mg COD/L)

a

a

– – 3 2 2

a

HBu

EtOH

57 52 44 45 45

4 6 7 8 8

969 ± 23 11,344 ± 102 15,851 ± 113 16,920 ± 122 18,540 ± 115

HLa = Lactate; HAc = acetate; HPr = propionate; HBu = butyrate; EtOH = ethanol.

Table 3 Closet match of DGGE fragments determined by their 16S rDNA and isolated microorganisms. Sample

Band

Closet match

Accession number

Length (bp)

Similarity (%)

Batch test I

1 2 3 4 5 6 7 8 9 10 11 12

Lactobacillus delbrueckii Clostridium butyricum Clostridium saccharobutylicum Clostridium baratii Clostridium botulinum Lactobacillus fermentum Clostridium perfringens Clostridium acetobutyricum Clostridium sp. TERIGK12 Eubacterium pyruvativorans Clostridium peptidivorans Clostridium sporogenes

HQ293115 AJ458421 NR_036951 JN048942 FR773526 EU931242 L77965 DQ831124 EF605259 AJ310135 AF156796 JN048943

149 145 156 166 156 147 150 172 169 168 166 150

99 97 99 99 95 99 93 96 98 90 98 96

Batch test III

10 20 30 40 50 60 70

Clostridium saccharobutylicum Clostridium acetobutylicum Eubacterium pyruvativorans Clostridium diolois Clostridium beijerinckii Not matched Clostridium sp.

NR_036951 DQ831124 AJ310135 NR_025542 AJ458421 – AY827856

139 166 152 150 172 – 170

97 96 90 98 97 – 95

Table 4 Average gas and liquid phase parameters in batch fermentation of microalgal biomass with CHEES. Time (h)

Control 10 30 40 52 52b a b

H2 yield (mL H2/g dcw)

30.3 31.6 37.0 39.7 43.1 30.6

k (h)

9.9 8.7 6.9 5.6 4.8 9.6

Rm (mL H2/L/h)

20.6 21.7 16.8 18.8 21.8 21.1

Reducing sugar removal (%)

90.6 91.3 92.9 90.2 93.0 91.1

Organic acids production (%)

Total organic acids (mg COD/L)

HLaa

HAca

HPra

HBua

EtOHa

0 3 3 4 5 0

33 34 29 28 26 39

6 5 2 3 2 5

54 53 59 60 61 54

7 5 7 5 6 2

11.959 ± 80 12.110 ± 90 19.410 ± 230 21.521 ± 100 23.512 ± 120 12.234 ± 65

HLa = Lactate; HAc = acetate; HPr = propionate; HBu = butyrate; EtOH = ethanol. Addition of boiled CHEES.

study is the existence of protease when using a crude enzyme solution because this can degrade and destroy other enzymes (Satoshi et al., 1986). In addition, enzyme concentration is also the decisive factor for enzymatic hydrolysis efficiency, and thus, for these reasons, the optimal sampling time during DFHP was performed at 10 (starting point), 20, 30, 40, and 52 h (finishing point). Fig. 3 shows the time frame of reducing sugar concentration that originated from C. vulgaris using various collected CHEES, and consequently, the highest improvement of hydrolysis efficiency was observed at over 5200 mg/L after 8 h of reaction time (over 560% increased), where the CHEES of 52 h (control = 920 mg/L). Even though the total enzyme concentration was not monitored because it was hard to analyze the conjugated enzyme, it can be indirectly assumed that the higher concentration of enzymes derived from the higher microbial population than the initial fermentation period leads to the enhancement of hydrolysis efficiency. In addition, as is illustrated clearly in Fig. 4, microalgal biomass cell wall was

disrupted by CHEES of 52 h after 8 h of reaction time. Therefore, these results clearly indicate that the CHEES derived here from the DFHP of food waste is a potent material for the alternative enzymatic pretreatment of microalgal biomass, as well as the enhancement of H2 production performance. This is discussed further in the next chapter. 3.3. Batch test III: DFHP of microalgal biomass with CHEES In order to feasibly enhance H2 productivity, several batch tests were conducted using the extracted CHEES at different times from the H2 fermenter. Moreover, unusually, CHEES was directly added to the batch fermenter in this study, and then fermentation began because the hydrolysis reaction of CHEES and the lag period of the batch test (control: raw C. vulgaris as feedstock) were similarly within 10 h, as shown in Fig. 5 and Table 4, respectively. As a result, the highest H2 yield was 43.1 mL H2/g dcw and the highest H2

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Fig. 3. Change of hydrolysis rate using different collected CHEES from batch fermenter (Control: mixing with DI water and C. vulgaris).

Fig. 4. Electro-microscopic images of microalgal cell: (a) microalgal biomass without CHEES; (b) microalgal biomass with CHEES.

production rate was 21.8 mL H2/L/h. The lag period was approximately halved compared to the control test. These results coincided with the hydrolysis rates observed in different extracted CHEES samples, and they suggest that a higher enzyme activity could result in a more complete hydrolysis of C. vulgaris, which further leads to higher H2 productivity. Additionally, in order to know the reason for enhancing the hydrolysis and DFHP caused by the sole enzyme activity, or whether those microorganisms externally injected cannot be sifted out during the extraction process, an additional batch test was conducted using boiled CHEES at 90 °C for 20 min. As presented in Table 4, the H2 fermentation performances were similar with the control test, which might have resulted from the destruction of enzymes. In other words, it could be concluded that the enhancement of hydrolysis and DFHP performance was caused by only CHEES, not microorganisms, because it is well known that Clostridium sp. could make spores when environments are not suitable for them, but the germination can occur after a while in a favorable condition. Interestingly, the concentration of butyrate was significantly increased after fermentation compared to the control test, while the concentration of acetate was decreased. Moreover, the lactate concentration of CHEES was 2630 ± 120 mg COD/L (Table 2), but it was decreased to 1170 ± 50 mg COD/L after DFHP (Table 4). According to the reports, the DFHP efficiency is lower when metabolic flows to the lactate rather than acetate and butyrate, while lactate can be oxidized to butyrate by C. acetobutylicum (also detected in this

study, as shown in Fig. 2 and Table 3) coupled with acetate reduction to become energetically feasible (Agler et al., 2010; Juang et al., 2011; Kim et al., 2012) via Eq. (3), resulting in 1.5 mol butyrate production.

Acetate þ 2 Lactate ! 1:5 Butyrate þ H2 þ CO2

ð3Þ

Furthermore, in comparison to the H2 productivity in previous studies, the H2 yield obtained in this study was higher than acid (37.0 mL H2/g dcw), ultrasonication (36.5 mL H2/g dcw), and combined (acid + ultrasonication, 42.1 mL H2/g dcw) pretreatments with a low energy requirement for pretreatment (Yun et al., 2013). Therefore, all of the experimental results suggest that the enhancement of DFHP performance caused by CHEES has a dual role as a hydrolysis enhancer and a co-substrate supplier. 3.4. Continuous test: CH4 production from H2 fermented effluent To maximize bioenergy recovery, ASBR system was operated as a second-stage fermentation system for CH4 production from H2 fermented effluent during 234 days, and CH4 content was around 65–73% during whole operation period. Fig. 6 shows the daily variations of CH4 yield and CH4 production rate and the average reactor performance at various operational conditions was arranged in Table 5. The overall COD removal efficiency indicates that the H2 fermented effluent of this study is a favorable feedstock for CH4 production. At the first 35 days, the reactor was operated at

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Fig. 5. Cumulative H2 production of C. vulgaris using extracted CHEES at different time (Control: mixing with DI water and C. vulgaris).

Fig. 6. Daily CH4 production from the H2 fermented effluent at different HRTs and substrate concentration.

Table 5 Average CH4 production performance at various operational conditions. Conditions

CH4 yield (mL CH4/g COD)

CH4 production rate (mL CH4/L/d)

COD removal (%)

VS reduction (%)

1.0 1.2 1.5 2.0 2.5

81.8 ± 6.9 108.6 ± 3.8 173.9 ± 7.3 230.6 ± 3.5 –

– 130.4 ± 6.5 261.2 ± 8.4 461.3 ± 4.8 –

90 90 93 92 50

64 65 75 64 52

2.0 2.67 3.34

229.6 ± 5.2 315.9 ± 4.8 330.2 ± 8.6

462.5 ± 7.5 592.4 ± 10.1 436.3 ± 9.0

93 91 82

74 71 53

HRT (day)

OLR (g COD/L/d)

30 25 20 15 12 15

a HRT of 30 days (OLR = 1.0 g COD/L/d) as a start-up period, and subsequently, the HRT condition was gradually decreased to 12 days (OLR = 2.5 g COD/L/d), when stable biogas productivity was obtained over 15 days in each condition. As the OLR increased

up to 2.0 g COD/L/d (HRT 15 days), both CH4 yield and production rate increased simultaneously, resulting in the highest CH4 yield and its production rate of 230.6 ± 3.5 mL CH4/g COD and 461.3 ± 4.8 mL CH4/L/d with 90% COD removal (64% VS reduction),

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respectively. However, the both decrease of COD removal and VS reduction of around 50% lead to failure of reactor performance at a HRT of 12 days. Hence, the HRT was increased to 15 days, and then, the reactor recovered rapidly after 14 days. After optimizing HRT condition, the OLR was gradually increased up to 3.34 g COD/ L/d by increasing substrate concentration from 30 g COD/L to 50 g COD/L. Even though the maximum CH4 yield of 330.2 ± 8.6 mL CH4/g COD was obtained at 3.34 g COD/L/d, the OLR of 2.67 g COD/L/d was optimal for maximizing the CH4 production rate (592.4 ± 10.1 mL CH4/L/d), and thus, the latter condition was selected as optimal condition for CH4 production because production rate is more important factor in real field in economic point of view. Based on the experimental results of H2 and CH4 production, the bioenergy recovery efficiency of the whole system were evaluated using the H2 yield of batch test I, III, and the highest CH4 yields (Jung et al., 2012). It was found that 56.7% biogas conversion (6.2% of the influent COD (batch test I), 1.6% of the influent COD (batch test III), and 48.9% of the influent COD (CH4 production) was achieved in this system; however, further additional treatment such as fertilization or dry digestion on the sediment derived from centrifugation (about 35% of influent COD) should be required to obtain more bioenergy and to achieve zero-waste emission system. 4. Conclusion A novel enzymatic pretreatment of microalgal biomass on DFHP was performed by using CHEES derived from the H2 fermenter. It was found that the CHEES extracted at 52 h had the highest hydrolysis efficiency of microalgal biomass, resulting in the highest H2 yield of 43.1 mL H2/g dcw along with shorter lag periods. In addition, it appears that the presence of lactate and acetate contained in the CHEES facilitated the enhancement of H2 production by changes in the metabolic pathway. These results suggest that the CHEES is a potent material for the alternative enzymatic pretreatment of microalgal biomass for DFHP. Acknowledgements This work was supported by Grants from the Eco-STAR Project Program of the ministry of Korean Environmental Technology (EW21-07-11) and the National Research Foundation of Korea (NRF) Grant funded by the Korea government Ministry of Education, Science and Technology (MEST) (NRF-2012M1A2A2026587). References Agler, M.T., Wrenn, B.A., Zinder, S.H., Angenent, L.T., 2010. Waste to bioproduct conversion with undefined mixed cultures: the carboxylate platform. Trends Biotechnol. 29, 70–78. APHA, 1998. Standard methods for the examination of water and wastewater, 20th ed. USA American Public Health Association, Washington, DC. Argun, H., Kargi, F., Kapdan, I.K., Oztekin, R., 2008. Batch dark fermentation of powdered wheat starch to hydrogen gas: effects of the initial substrate and biomass concentrations. Int. J. Hydrogen Energy 33, 6109–6115. Balat, M., Balat, H., Oz, C., 2008. Progress in bioethanol processing. Prog. Energy Combust. Sci. 34, 551–573. Blumreisinger, M., Meindl, D., Loos, E., 1983. Cell wall composition of chlorococcal algae. Phytochemistry 22, 1603–1604. Carlsson, M., Lagerkvist, A., Morgan-Sagastume, F., 2012. The effects of substrate pre-treatment on anaerobic digestion systems: a review. Waste Manage. 32, 1634–1650. Chen, W.H., Chen, S.Y., Khanal, S., Sung, S., 2006. Kinetic study of biological hydrogen production by anaerobic fermentation. Int. J. Hydrogen Energy 31, 2170–2178.

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Application of a novel enzymatic pretreatment using crude hydrolytic extracellular enzyme solution to microalgal biomass for dark fermentative hydrogen production.

In this study, a novel enzymatic pretreatment of Chlorella vulgaris for dark fermentative hydrogen production (DFHP) was performed using crude hydroly...
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