Bioresource Technology 101 (2010) S17–S22

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Bioresource Technology

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Effect of high temperature on bacterial community dynamics in anaerobicacidogenesis using mesophilic sludge inoculum

Woong Kim, Kwanghyun Hwang, Seung Gu Shin, Seungyong Lee, Seokhwan Hwang *

School of Environmental Science and Engineering, POSTECH, Nam-gu, Pohang, Gyeongbuk 790-784, Republic of Korea

a r t i c l e i n f o a b s t r a c t

Article history:Received 6 November 2008Received in revised form 27 February 2009Accepted 12 March 2009Available online 11 April 2009

Keywords:Anaerobic digestionAcidogenResponse surface methodologyHydraulic retention timeThermophilic

0960-8524/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.biortech.2009.03.029

* Corresponding author. Tel.: +82 54 279 2282; faxE-mail address: shwang@postech.ac.kr (S. Hwang)

In this study, we investigated the microbial community dynamics in thermal acidogenesis using meso-philic sludge. From the result of optimization with a response surface methodology, the acidogenic opti-mum conditions predicted were a hydraulic retention time of 2.0 days and 51 �C. Denaturing gradient gelelectrophoresis (DGGE) profiles shows that the monitored bacterial community present consists of Pseu-domonas mendocina, Bacillus halodurans, Clostridium hastiforme, Gracilibacter thermotolerans, and Thermo-monas haemolytica. Among these, B. halodurans, G. thermotolerans, and T. haemolytica are reported toferment carbohydrates thermotolerantly. In contrast, P. mendocina disappeared in the acidogenesis pro-cess because of its mesophilicity. In addition, C. hastiforme, G. thermotolerans originating from mesophilicanaerobic sludge were detected in the thermal acidogenesis. Based on this finding, we inferred that mostthermophiles detected as DGGE bands could grow catalyzing carbohydrates metabolism in swine waste-water to produce volatile fatty acids thermotolerantly.

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1. Introduction

Swine wastewater has high organic matter, animal nitrogen,and also malodorous compounds (Spoelstra, 1978). However, thereare certain difficulties in dealing with swine manure because of thehigh proportion of suspended solids resistant to biological degra-dation (Beaudet et al., 1990). Consequently, it is necessary to stim-ulate a high biodegradation rate in treating swine wastewater.

Anaerobic digestion, widely used to treat high-strength organ-ic wastewater such as swine wastewater, consists of three stagesin which the complex organic components of the waste are sol-ubilized, broken down, and fermented into intermediate prod-ucts such as oligosaccharides, amino acids, or glycerol andlong-chain fatty acids that are subsequently reduced into meth-ane and carbon dioxide. The stages are called hydrolysis or liq-uefaction, acidogenesis, and methanogenesis, respectively(Hwang et al., 2001; Ahn et al., 2006). These stages contain con-siderably varied species of symbiotic microorganisms, which canbe broadly classified as two groups: acidogens and methanogens.For designing the optimum anaerobic process yielding a maxi-mum efficiency, a two-phase anaerobic digestion is often pre-ferred because these two major microbial groups in theseprocesses are different in their physiology, biokinetics, andgrowth environment (Han and Dague, 1997). Since the methano-genic reaction is believed to be the rate-limiting step in the

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overall anaerobic digestion, proper control of the methanogenicphase has been a key factor in the successful operation of mostanaerobic processes (Grady et al., 1999). However, overall pro-cess enhancement must be based on an understanding of opti-mum growth conditions and behavior of acidogens in a two-phase process because they play a primary role in producing ma-jor substrates, short-chain organic acids, for methanogens. Whileorganic acids are important substrates in methanogenesis, highconcentrations of acids can cause anaerobic digestion systemsto fail (Stronach et al., 1986). Furthermore, the acidogens includemany analogous microbial species and little information abouttheir characteristics and growth conditions is available relativeto the methanogens.

Thermophilic acidogenesis can provide many operationalbenefits, such as increases in the rates of digestion and hydro-lysis, decrease in digester volume, and ease of liquid–solid sep-aration, with the efficient production of volatile fatty acids(VFAs) (Aitken and Mullennix, 1992). In practice, a thermophilicacidogenesis may start-up with mesophilic sludge inoculum be-cause the thermophilic process is less prevalent in field-scaleapplications (Yilmaz et al., 2008). Thermophilic anaerobicdigesters generally harbor less microbial diversity, however,with thermophilic bacteria as dominant species. Since microbialdiversity is closely related to functional stability, it is necessaryto study the effects of high temperature on the bacterial com-munity dynamics in the anaerobic digestion process duringacidogenesis using mesophilic sludge inoculum (Curtis andSloan, 2004).

Table 1Experimental design and observed total volatile fatty acids production in theacidogenesis using swine wastewater.

Trial Independent variables TVFAs (g/L)

Temperature (�C) HRT (days)

1 40 0.5 0.292 40 2.5 1.44

Linear 3 60 0.5 0.72Design 4 60 2.5 1.35

5a 50 1.5 1.70 ± 0.036 40 1.5 1.55

Quadratic 7 60 1.5 1.72Design 8 50 0.5 0.71

9 50 2.5 1.77

a Center point was repeated by three times.

S18 W. Kim et al. / Bioresource Technology 101 (2010) S17–S22

Bacterial communities can be investigated with denaturing gra-dient gel electrophoresis (DGGE) analysis. The DGGE method is oneof the most widely used fingerprinting technique, with the benefitof describing microbial communities by the recovery and sequenc-ing of amplification products (Curtis and Craine, 1993). The mainobjective of the present research was to investigate the microbialcommunity dynamics in thermal acidogenesis using mesophilicsludge inoculum.

2. Methods

2.1. Reactor operating conditions

Three identical automated anaerobic continuously stirred tankreactors (CSTR), each with a working volume of 4 L and equippedwith temperature controllers, were operated in batch mode andcontinuous mode sequentially for acidogenesis. Anaerobic seedsludge from a local municipal wastewater treatment plant in Po-hang, South Korea, was mixed with the swine wastewater as thesubstrate for acidogens as 1% of the total suspended solids (w/v),and cultivated in batch mode in the three bioreactors for 36 h toenrich a mixed population of acidogens. For the response surfaceanalysis (RSA), three trials were carried out using three identicalautomated CSTR at the same time, and then totally eleven trialswere performed through four times operations of three CSTR.When the production of total volatile fatty acids (TVFAs) was max-imized in 36 h (data not shown) after the batch mode process, thecontinuous process of acidogenesis was started by turning on thepump. TVFAs as a dependent variable for RSA was measured withthe shift of temperature and hydraulic retention time (HRT) asindependent variables. In order to monitor the well-operated pro-cess, physical properties such as total solids (TS), volatile solids(VS), total suspended solids (TSS), volatile suspended solids (VSS),and chemical properties such as chemical oxygen demand (COD),soluble chemical oxygen demand (sCOD) of reactors process wasmeasured, and the measurements of pH and the biogas productionwere also taken (in 2.4 methods). According to the preliminaryexperiments, pH nearly didn’t change during the process, so pHis not usually controlled in the optimization study.

2.2. Central composite in cube design and selection of variables

Microbial growth and enzyme activity have specific optimumconditions with regard to many variables, including pH, tempera-ture, hydraulic or microbial residence time in the bioreactor, andthe presence of inhibitors. For example, the pH and dilution rateof the bioreactor are also important in the formation of acetic, pro-pionic, and butyric acids (Hwang et al., 2001).

Response surface methodology (RSM) was applied to analyzeand to optimize the factors affecting TVFAs production associatedwith simultaneous changes in HRT and temperature in the thermo-philic acidogenesis phase. A sequential procedure of collectingdata, estimating polynomials (Eq. (1)), and checking the adequacyof the model was used (Yang et al., 2004).

ga ¼ co þXn

i¼1

aixi þXn

i¼1

aiix2i þ

Xi

Xj

i<j

aijxixj ð1Þ

where, ga is the experimental value of acetic acid concentration(mg/l); xk is the independent variable k (k = HRT and temperaturein order); co is the regression constant and ak is the regression coef-ficients of the independent variable k (k = HRT and temperature inorder).

The method of least squares was used to estimate the parame-ters in the approximating polynomials (Eq. (1)). The orthogonal de-sign, which consists of a 22 factorial design augmented by a center

and 2 � 2 axial points, was employed in this research (Lee et al.,2003; Box and Draper, 1987).

Based on the growth rate of anaerobes on swine wastes in aCSTR, the range of HRT was decided to be 0.5–2.5 days to give suf-ficient residence time for acidogenic activity (Hwang and Hansen,1992). The effect of temperature was also investigated at a rangeof 40–60 �C because it is possible to inactivate pathogenic bacteria,or to shorten retention time in the thermophilic temperature pro-cess more than 50 �C rather than the mesophilic process (Aitkenand Mullennix, 1992). Eleven trials (Table 1) were run to approxi-mate the response of the TVFAs production.

2.3. DNA extraction and DGGE analysis

Extraction of DNA from anaerobic sludge and bioreactor sam-ples was performed as described (Yu and Hwang, 2006). Appropri-ate dilutions were performed to assure the VSS concentration waslower than 1.5 g/L. Cells from each 500 uL samples were harvestedby centrifugation at 14,000 rpm for 5 min, followed by decantationof the supernatant. The residual pellet was resuspended with 1 mLof deionized and distilled water (DDW), and was centrifuged againin the same manner to ensure a maximal removal of residual med-ium. The supernatant was carefully removed, and the pellet wasresuspended by addition of 100 uL of DDW prior to the DNAextraction.

A fully automated nucleic acid extractor (Magtration System6GC, PSS Co., Chiba, Japan) employing magnetic bead technology(Obata et al., 2001) was used to extract and purify genomic DNA.The automated instrumentation has been reported to extractDNA with a consistent efficiency and high purity by eliminatingthe steps of manual preparation that may cause cross contamina-tion (Yu et al., 2005). Genomic DNA extracted from the sampleswas kept at �20 �C until used for further analysis.

16S rRNA gene clone libraries were constructed using theextracted DNA. Two universal primers for bacteria, BAC 338F (50-ACTCCTACGG GAGGCAG-30) and BAC 805R (50-GACTACCAGGGTATCTAATCC-30) (Yu et al., 2005), were used to amplify the bacterialsmall subunit (SSU) rRNA gene. A PCT (MJ Research, Waltham, MA)with an AccuPower PCR Mix (Bioneer, Daejeon, Korea) was used forthe DNA amplification. For each PCR PreMix tube, the followingswere added for final volume of 50 uL: 2 uL (5–20 ng) of the geno-mic DNA, 2 uL (final concentration, 400 mM) of the forward and re-verse primers, and PCR-grade sterile water. The PCR programconsists of an initial 10 min denaturation step at 94 �C, 35 cyclesof repeated denaturation at 94 �C for 30 s, annealing at 55 �C for30 s, and extension at 72 �C for 30 s, followed by final extensionstep of 7 min at 72 �C.

The PCR fragments were purified using a purification kit(General biosystem, Seoul, Korea) and cloned in Escherichia coliDH5 alpha using a commercial cloning vector (pGEM-T Easy

W. Kim et al. / Bioresource Technology 101 (2010) S17–S22 S19

Vector, Promega, Mannheim, Germany) according to the manufac-turer’s instruction. Cloned plasmids were isolated from randomlyselected colonies of the library using commercial kit (General bio-system, Seoul, Korea) and used as templates for the DNA sequenc-ing analysis. Sequencing was performed with T7 primers using anautomated sequencer (3730X DNA Analyzer, Perkin–Elmer, CA,USA). The sequencing results were compared with the referencesequences in the GenBank and Ribosomal Database Project II(RDP II) databases. The libraries sequences were classified usingthe Library Compare Tool provided by the RDP II databases. TheDNAMAN (Lynnon Biosoft, Vaudreuil, Canada) sequence analysissoftware was used to analyze similarity of sequences and estimatetheir phylogenetic relationship.

For DGGE analysis, the DNA samples from an acidogenesis bio-reactor were used. The V3 to V5 region of 16S rRNA genes in theextracted DNA was amplified by PCR with a set of universal bacte-rial primers, BAC 228F with a 40-bp GC-clamp (50-CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGG-30) (Muyzer et al.,1993) and BAC 805R. A touch-down PCR program was used forall amplifications to minimize non-specific amplification (Kowal-chuk et al., 2004). After an initial denaturation at 94 �C for10 min, 20 cycles of touch-down PCR were performed (denatur-ation at 94 �C for 30 s, annealing for 30 s with an 0.5 �C/cycle dec-rement at 65–55 �C and extension at 72 �C for 1 min), followed by15 cycles of regular PCR (94 �C for 30 s, 55 �C for 30 s, and 72 �C for1 min and a final extension step for 7 min at 72 �C).

Fifteen microliters of each PCR product was loaded onto 8%acrylamide gel containing a linear gradient ranging from 30% to55% denaturant (100% denaturants is a mixture of 7 M urea and40% [vol/vol] formamide). The DGGE was performed for 7 h at150 V in 1X TAE electrophoresis buffer with a D-code system(Bio-Rad, Inc., Hercules, CA, USA). Following the electrophoresis,the gel was stained in ethidium bromide solution for 20 min,rinsed for 20 min in DDW, and photographed under UVtransillumination.

For further identification of representative DGGE bands in indi-vidual samples, DGGE fragments were excised directly from thegels with a sterile blade, mixed with 40 uL of DDW, and incubatedovernight at 4 �C. Two microliters of each band elution solutionwere re-amplified with no GC-clamp BAC 338F and BAC 805R.The resulting PCR products were purified and cloned as previouslydescribed. After the DNA sequencing with T7 primers, the resultswere compared with reference sequences in the GenBank andRibosomal Database Project II (RDP II).

2.4. Source of wastewater and physicochemical analysis

Swine wastewater (500 L) was collected from a local pig farm(the Sansugol pig farm) in Kyungju, South Korea, and was pre-screened through an 850 lm sieve. The concentration of total sol-ids was 22.6 g/L. The sample was mixed homogeneously.

Physicochemical parameters were periodically analyzedthroughout the operation of three reactors. The chemical proper-ties such as COD, SCOD, and protein concentrations were deter-mined according to the procedures in Standard Methods (APHA-AWWA-WEF, 2005); in the same manner, the physical propertiessuch as TS, VS, TSS, VSS were measured. A gas chromatograph(6890 plus, Agilent, Palo Alto, CA) equipped with an Innowax cap-illary column and a flame ionization detector was used to deter-mine the concentrations of Volatile Fatty Acids (VFAs). Heliumwas the carrier gas at a flow rate of 2.5 mL/min with a split ratioof 10:1. Another identical gas chromatograph with a HP-5 capillarycolumn and a thermal conductivity detector was used to analyzegas composition in the biogas. Helium was the carrier gas at a flowrate of 8 mL/min with a split ratio of 70:1. All analyses were dupli-cated, and the results quoted as mean values.

3. Results and discussion

3.1. Response surface methodology

The substrate concentration was maintained at 70 g chemicaloxygen demand (COD)/L and steady-state was assumed after tenturn-overs. The operating condition at the center point was 1.5days HRT and 50 �C. Repeated observations at the center were usedto estimate the experimental error.

First, seven trials from trials 1–5 in Table 1 were run initially. Afirst-order model, gi = bo + b1x1 + b2x2, was used to fit this data byleast squares and the following models were generated for theTVFAs production.

gTVFAs ¼ 0:18þ 8:50� 10�3x1 þ 4:47� 10�1x2 ð2Þ

where gi is the concentration of i produced (g/L, where i = TVFAs)and xj is the corresponding variable term (j = temperature [�C] andHRT [days] in order).

The regression coefficient, the lack of fit of the first-order model,and P-values of parameter estimations were used to validate themodels. The P-value of the lack of fit for TVFAs was significant atthe 5% a level, while the regression coefficient was not significantat the 5% a level. Therefore, it was concluded that the first-ordermodel was not an adequate approximation for both TVFAs. Anadditional four trials (from 6 to 9 in Table 1) were augmented withprevious trials to make a central composite design to fit a second-order model.

To find out an optimum condition for TVFAs production, variousmodels from linear to quadratic were tested with these augmentedtrials. Eq. (3)–(5) represent first-order, interaction, and quadraticmodels, respectively.

gTVFAs ¼ 0:18þ 8:83� 10�3x1 þ 4:75� 10�1x2 ð3Þ

gTVFAs ¼ �0:78þ 2:80� 10�2x1 þ 1:11x2 � 1:28� 10�2x1 � x2 ð4Þ

gTVFAs ¼ �6:2þ 2:1� 10�1x1 þ 2:9x2 � 1:3� 10�2x1 � x2 � 1:8

� 10�3x21 � 5:8� 10�1x2

2 ð5Þwhere gai is the concentration of TVFAs produced with the model i(i = first, interaction, and quadratic models in order).

Two-dimensional response surfaces of the quadratic model forTVFAs production with corresponding estimated optimums areshown in Fig. 1. The response surface of TVFAs showed a clear peak,which indicated the optimum condition was inside the designboundary.

P-values of regression, lack of fit, and corresponding coefficientswere investigated. Lack of fit was not significant but regression wassignificant at the 1% a level only for the quadratic models, whichindicated the models fit the response surface. Therefore, the qua-dratic model was selected to describe the response surface ofTVFAs within this region. The residual plot of the each model se-lected randomly didn’t show any pattern, which was another indi-cation of the adequacy of the fit of the model (Fig. 2).

From further statistical inspection of the coefficients of themodels [Eq. (5)], it was shown that the interaction terms in TVFAsproduction were not also significant at the 1% level, whereas thetemperature and HRT terms were significant at the 5% and 1% con-fidence level, respectively. This means that the two variables (tem-perature and HRT) were slightly interdependent, but the effect onthe response surface was not significant. An ANOVA using Eq. (5)was carried out to investigate the possible interaction betweentemperature variable and HRT variable (Table 2). None of the inter-action between two variables was significant at the 1% a-level.

The conditions that maximized the production of TVFAs,previously defined as the optimum conditions, were calculated

Fig. 1. Two- and three-dimensional plots of the quadratic model for the amount of TVFAs produced in acidogenesis.

S20 W. Kim et al. / Bioresource Technology 101 (2010) S17–S22

by setting the partial derivatives of the function to zero with re-spect to the corresponding variables. The optimum conditionswere 2.0 days HRT and 51 �C, and the acidogenic reactor was oper-ated at this condition for the following experiment. As a result ofestimation, the response surface of TVFAs had a clear peak forwhich a quadratic model with a same design boundary could befit to in the general vicinity of this point. The response surface ofTVFAs exhibited rounded ridges running diagonally from lowerto upper left on the plot from the model (Fig. 1).

The unbounded polynomial models that defined the responsesurface might predict the location of the optimum response welloutside the design boundary; however, these models cause thepolynomial models to extend to infinity. Accordingly, the modelsused to predict the response surface should be restricted to withinspecified boundaries.

3.2. Performance of acidogenesis

The acidogenesis process was monitored from the batch step tothe point at which all the VFAs were produced continuously within

ECHIP

-0.10

-0.05

0.00

0.05

0.10

0.15

Residuals

0.0 0.5 1.0 1.5 2.0Observations (g/L)

Fig. 2. Residual plots of a quadratic model for total volatile fatty acids production.

Table 2ANOVA results of quadratic model for optimization of acidogenesis using swine wastewith two independent variables and their interactions.

Source Mean square DF P-value

Temperature 0.0633 2 0.0318HRT 1.1024 2 0.0000Temperature vs. HRT 0.0645 1 0.0403

a variation of 10%, a period of 20 days. The concentration of TVFAsreached 20.3 g/L. Of all these VFA products, acetate, propionate,and n-butyrate were the major fermentation products, comprising84.2% of TVFAs.

As shown in Fig. 3, the concentrations of acetate, propionate,and n-butyrate were 10.3, 3.5, and 3.3 g/L, respectively. The netconcentration of TVFAs was 1.7 g/L, from which we implied thatintermediary compounds (e.g., substrates of the methanogens)were produced effectively in the acidogenic process for subsequentmethanogenesis.

3.3. PCR-based DGGE analysis of the bacterial community duringacidogenesis

Fig. 4 shows the profiles of acidogenic microbial communitystructure using DGGE analysis. Table 3 also summarizes acidogen-inc species indentified using the DGGE band. Bands 5, 6, 7, 8, 12,and 13 were observed in the anaerobic sludge, and bands 1, 2, 3,6, 9, 10, and 13 were observed in the swine wastewater. Bands 1and 9, absent from anaerobic sludge, were observed in the swinewastewater, and are closely related to the swine effluent bacte-rium, which has yet to be unclassified. This species was detectedin the overall process of acidogenesis. Bands 6, 8, 13, 15, 18, 19,20, and 21 are closely related to Bacillus halodurans, and were de-tected during the entire process of acidogenesis. B. haloduransgrows thermotolerantly in the presence of sodium ions and ammo-

Fig. 3. Profile of VFAs production in an anaerobic acidogenic bioreactor treatingswine wastewater under optimal conditions of a temperature of 51 �C and an HRTof 2 days (negative numbers show the batch sampling time).

Fig. 4. Acidogenic bacterial DGGE banding profiles (W, anaerobic sludge; S, swine wastewater; lane labels show the sampling time [days]; negative and positive numbersshow the batch and continuous sampling time, respectively; positive numbers show the sampling time in the continuous system; numbers on the DGGE gel indicate thebands excised for sequencing).

Table 3Identification of amplified 16S rRNA gene sequences of acidogens in this experiment.

Bacterial DGGE band (based on the bacterial 16S rDNA) Highest similarity GenBank accession number Homology(%)

6, 8, 13, 15, 18, 19, 20, 21 Bacillus halodurans EU118675 452/454 (99%)7 Clostridiium hastiforme (Tissierella praecuta) X80833 413/428 (96%)11, 12 Gracillibacter thermotolerans DQ117469 386/430 (89%)1, 9 Swine effluent bacterium DQ337540 467/468 (99%)14 Thermomonas haemolytica AF508110 464/468 (99%)2, 3, 4, 5, 11, 12, 16, 17, 22, 23 Uncultured bacterium10 Pseudomonas sp. 98S1 EU370416 451/453 (99%)

The numbers show the bands whose sequences were identified with the BLAST program in the National Center for Biotechnology Information (NCBI) database.BA denote the domain Bacteria.

W. Kim et al. / Bioresource Technology 101 (2010) S17–S22 S21

nia, and can use polysaccharide as an energy source, catalyzing thehydrolysis with b-galactosidase (Takami and Horikoshi, 1999). Be-cause this species was observed throughout the entire process, weinfer that the swine effluent bacterium and B. halodurans played asteady and constant role in the acidogenesis. In contrast, band 10was observed only in the swine wastewater, and is closely relatedto Pseudomonas mendocina, a mesophilic bacterium (Verdoni et al.,1990). Because acidogenesis was performed at a high temperatureof 50 �C, P. mendocina must have been absent from the entire pro-cess. Band 7, which was observed in the anaerobic sludge, was de-tected for continuous periods and is closely related to Clostridiumhastiforme, which is a slender, rod-shaped bacterium with roundedends, with no ability to ferment carbohydrates (Bae et al., 2004).Although band 11 was observed in neither the anaerobic sludgenor the swine wastewater, it was detected in the continuous stageof acidogenesis. In contrast, band 12 was observed in the continu-ous process, as well as in the anaerobic sludge. According to DGGEanalysis, bands 11 and 12 were both closely related to Gracilibacterthermotolerans, a thermotolerant, obligatorily anaerobic asporo-genic bacterium. G. thermotolerans, observed at 25–54 �C, growsand can produce ethanol, acetate, or lactate as fermentative prod-ucts using carbohydrates (Lee et al., 2006). Band 14 was also de-tected continuously in the acidogenesis, despite its absence inthe anaerobic sludge and swine wastewater, and is closely relatedto Thermomonas haemolytica. T. haemolytica grows in the tempera-ture range between 18 and 50 �C and can produce acetate and pro-pionate from carbohydrate fermentation (Busse et al., 2002).Therefore, because C. hastiforme, and G. thermotolerans, were ob-served at intensities weaker than those of other bands in the anaer-obic sludge or were absent from the swine wastewater, and wereonly observed in the continuous process as comparatively strongerbands, we infer that these species originating from the mesophilicsludge can grow thermotolerantly at the comparatively high tem-perature of 50 �C, while other microorganisms had been washedout with the effluent of the swine wastewater.

4. Conclusion

Mathematical modeling of acidogenesis demonstrated that theoptimal temperature and HRT were calculated to be 51 �C and2.0 days, respectively. According to DGGE analysis, B. haloduransand a swine effluent bacterium worked constantly in the overallprocess of acidogenesis. In contrast, P. mendocina, present in theswine wastewater, disappeared because of its mesophilicity. Clos-tridium hastiforme, G. thermotolerans, and T. haemolytica were ob-served in the continuous process of acidogenesis, and arethought to grow because of their thermotolerance, whereas othermicroorganisms were washed out. Bacillus halodurans, G. thermo-tolerans, and T. haemolytica can ferment carbohydrates as priorsubstrates. Finally, when mesophilic sludge was used for acidogen-esis under these thermal conditions, the mesophilic bacteria disap-peared, but most of the thermotolerant species detected in theDGGE bands could ferment carbohydrates during acidogenesis.The results for this microbial community can be applied to thethermal fermentation of swine manure in the future.

Acknowledgements

This work was financially supported in part by the Korea Min-istry of Environment (MOE) as Human Resource Development Pro-ject for Waste to Energy, Manpower Development Program forEnergy and Resources by the Ministry of Knowledge and Economy(MKE), and by the BK-21 program.

References

Ahn, J.-H., Do, H.T., Kim, S., Hwang, S., 2006. The effect of calcium on the anaerobicdigestion treating swine wastewater. Biochem. Eng. 30, 33–38.

Aitken, M.D., Mullennix, R.W., 1992. Another look at thermophilic anaerobicdigestion of wastewater sludge. Water Environ. Res. 64 (7), 915–919.

Bae, J.W., Park, J.R., Rhee, S.K., Kim, B.C., Park, Y.H., 2004. Clostridium hastiforme is alater synonym of Tissierella praeacuta. Int. J. Syst. Evol. Microbiol. 54, 947–949.

S22 W. Kim et al. / Bioresource Technology 101 (2010) S17–S22

Beaudet, R., Gagnon, C., Bisaillon, J.G., Ishaque, M., 1990. Microbial aspects ofaerobic thermophilic treatment of swine waste. Appl. Environ. Microbiol. 56 (4),971–976.

Box, G.E.P., Draper, N.R., 1987. Empirical Model-building and Response Surfaces.John Wiley and Sons, New York.

Busse, H.J., Kampfer, P., Moore, E.R.B., Nuutinen, J., Tsitko, I.V., Denner, E.B.M.,Vauterin, L., Valens, M., Rossello-Mora, R., Salkinoja-Salonen, S., 2002.Thermomonas haemolytica gen. Nov., sp. Nov., a c-proteobacterium fromkaolin slurry. Int. J. Syst. Evol. Microbiol. 52, 473–483.

Curtis, T.P., Craine, N.G., 1993. The comparison of the diversity of activated sludgeplants. Water Sci. Technol. l37, 71–78.

Curtis, T.P., Sloan, W.T., 2004. Prokaryotic diversity and its limits: microbialcommunity structure in nature and implications for microbial ecology. Curr.Opin. Microbiol. 7 (3), 221–226.

Grady, C.P.L., Daigger, G.T., Lim, G.C., 1999. Biological Wastewater Treatment.Marcel Dekker, New York. p. 1076.

Han, V., Dague, R.R., 1997. Laboratory studies on temperature-phased anaerobicdigestion of domestic primary sludge. Water Environ. Res. 69, 1139–1143.

Hwang, S.H., Hansen, C.L., 1992. Biokinetics of an upflow anaerobic sludgeblanket (UASB) reactor treating whey permeate. Biores. Technol. 41, 223–230.

Hwang, S.H., Lee, Y., Yang, K., 2001. Maximization of acetic acid production in partialacidogenesis of swine wastewater. Biotechnol. Bioeng. 75 (5), 521–529.

Kowalchuk, G.A., de Bruijn, F.J., Head, I.M., Antoon, D., Akkermans, L., Van Elsas, J.D.,2004. Molecular Microbial Ecology Manual. Academic Publishers, Kluwer,Dordrecht. pp. 743–770.

Lee, H., Song, M., Yu, Y., Hwang, S., 2003. Production of Ganoderma lucidummycelium using cheese whey as an alternative substrate: response surfaceanalysis and biokinetics. Biochem. Eng. J. 15, 93–99.

Lee, Y., Romanek, C.S., Mills, G.L., Davis, R.C., Whitman, W.B., Wiegel, J., 2006.Gracilibacter thermotolerans gen. Nov., sp. Nov., an anaerobic, thermotolerant

bacterium from a constructed wetland receiving acid sulfate water. Int. J. Syst.Evol. Microbiol. 56, 2089–2093.

Muyzer, G., de Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbialpopulations by denaturing gradient gel electrophoresis analysis of polymerasechain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol.59 (3), 695–700.

Obata, K., Segawa, O., Yakabe, M., Ishida, Y., Kuroita, T., Ikeda, K., Kawakami, B.,Kawamura, Y., Yohda, M., Matsunaga, T., Tajima, H., 2001. Development of anovel method for operating magnetic particles, magtration technology, andits use for automating nucleic acid purification. J. Biosci. Bioeng. 91 (5), 500–503.

Spoelstra, S.F., 1978. Degradation of tyrosine in anaerobically stored piggery wastesand in pig feces. Appl. Environ. Microbiol. 36, 631–638.

Stronach, S.M., Rudd, T., Lester, J.N., 1986. Anaerobic Digestion Processes inIndustrial Wastewater Treatment. Springer, New York. p. 184.

Takami, H., Horikoshi, K., 1999. Reidentification of facultatively alkaliphilic sp. C-125 to B. Halodurans. Biosci. Biotechnol. Biochem. 63 (5), 943–945.

Verdoni, N., Aon, M.A., Lebeault, J.M., Thomas, D., 1990. Proton motive force, energyrecycling by end product excretion, and metabolic uncoupling during anaerobicgrowth of Pseudomonas mendocina. J. Bacteriol. 172 (12), 6673–6681.

Yang, K., Oh, C., Hwang, S., 2004. Optimizing volatile fatty acid production in partialacidogenesis of swine wastewater. Water Sci. Technol. 50 (8), 169–176.

Yilmaz, T., Yuceer, A., Basibuyuk, M., 2008. A comparison of the performance ofmesophilic and thermophilic anaerobic filters treating papermill wastewater.Biores. Technol. 99 (1), 156–163.

Yu, Y., Hwang, S., 2006. Use of real-time PCR for group-specific quantification ofaceticlastic methanogens in anaerobic processes: population dynamics andcommunity structure. Biotechnol. Bioeng. 93 (3), 424–433.

Yu, Y., Lee, C., Kim, J., Hwang, S., 2005. Group-specific primer and probe sets todetect methanogenic communities using quantitative real-time polymerasechain reaction. Biotechnol. Bioeng. 89 (6), 670–679.