Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure

Bioresource Technology xxx (2015) xxx–xxx

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Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure Heejung Jung a, Gahyun Baek a, Jaai Kim a, Seung Gu Shin b, Changsoo Lee a,⇑ a b

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea School of Environmental Science and Engineering, POSTECH, Pohang, Gyungbuk 790-784, Republic of Korea

h i g h l i g h t s
Mild-temperature pretreatment of Ulva biomass with HCl or NaOH was investigated for enhanced AD.
Models to approximate solubilization rate and biogas production were generated for each method.
HCl/NaOH addition had positive influence on Ulva solubilization but negative on biogas recovery.
NaOH was more effective for Ulva hydrolysis but of a stronger negative effect on biogas production.
Variations in bacterial community structure correlated significantly with HCl and NaOH.

a r t i c l e

i n f o

Article history: Received 1 July 2015 Received in revised form 6 August 2015 Accepted 8 August 2015 Available online xxxx Keywords: Anaerobic digestion Macroalgae Pretreatment Response surface analysis Ulva

a b s t r a c t The effects of mild-temperature thermochemical pretreatments with HCl or NaOH on the solubilization and biomethanation of Ulva biomass were assessed. Within the explored region (0–0.2 M HCl/NaOH, 60–90 °C), both methods were effective for solubilization (about 2-fold increase in the proportion of soluble organics), particularly under high-temperature and high-chemical-dose conditions. However, increased solubilization was not translated into enhanced biogas production for both methods. Response surface analysis statistically revealed that HCl or NaOH addition enhances the solubilization degree while adversely affects the methanation. The thermal-only treatment at the upper-limit temperature (90 °C) was estimated to maximize the biogas production for both methods, suggesting limited potential of HCl/NaOH treatment for enhanced Ulva biomethanation. Compared to HCl, NaOH had much stronger positive and negative effects on the solubilization and methanation, respectively. Methanosaeta was likely the dominant methanogen group in all trials. Bacterial community structure varied among the trials according primarily to HCl/NaOH addition. Ó 2015 Published by Elsevier Ltd.

1. Introduction Global warming and oil depletion, along with ever increasing energy demand, are posing a serious threat to mankind today. With growing awareness of these issues, the exploitation of renewable energy sources replacing fossil fuels continues to attract great attention worldwide. A good example is the European Union’s binding target of a 20% share of renewable sources in its total energy consumption by 2020 (Renewable-Energy-Network, 2013). Among various renewable energy sources, e.g., sun light,

⇑ Corresponding author. Tel.: +82 52 217 2822; fax: +82 52 217 2819. E-mail address: [email protected] (C. Lee).

wind, falling water, tides, waves, and biomass, only biomass is capable of producing combustion fuels (i.e., biofuels) other than electricity or heat (Adams et al., 2009). As the demand for clean alternative fuels has rapidly grown, significant efforts have been paid to biofuel technologies in the past decades. Most of the commercially available biofuels today are ‘first-generation’ biofuels produced from food and oil crops, such as sugarcane and corn for bioethanol and soybean and rapeseed for biodiesel. However, the competition with food production is an inevitable issue which poses a serious problem from sustainability and economic perspectives (Zinoviev et al., 2010). This limitation has fostered the quest for more sustainable biofuel feedstocks, so recent research efforts mostly focused on the development of ‘nextgeneration’ biofuel technologies using, for example, lignocellulosic

http://dx.doi.org/10.1016/j.biortech.2015.08.014 0960-8524/Ó 2015 Published by Elsevier Ltd.

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx

biomass (agricultural and forestry residues), industrial or municipal organic wastes, and algal biomass as raw materials (Singh et al., 2011). Seaweeds (i.e., marine macroalgae) are often regarded as a promising feedstock for next-generation biofuels because they contain little lignin and high fractions of easily hydrolysable polysaccharides (Costa et al., 2012). Such characteristics make seaweeds especially attractive for fermentation technologies for producing, for example, bioethanol and biogas (Vanegas et al., 2013). Besides, cultivation of seaweeds requires neither arable land nor costly fertilizer, making seaweeds economically more feasible than terrestrial plants as a feedstock for energy production. Countries with long coastlines may also source a substantial amount of biomass feedstock from naturally occurring seaweeds. Eutrophication in coastal areas often results in massive algal growth and piles up a huge amount of seaweeds on beaches, which could lead to serious hygiene and environmental problems (Nkemka and Murto, 2010). Green tides (i.e., green macroalgal blooms), primarily caused by Ulva spp., repeatedly occur worldwide, with America, Europe, and the Asia–Pacific area being the most severely affected (Ye et al., 2011). The world’s largest green tide ever which covered approximately 3800 km2 (approximately 20 million wet tons of algal biomass) occurred in the Yellow Sea off China in the summer of 2008, and more than 1.5 million wet tons of seaweed biomass was dumped on the beaches of Qingdao (Gao et al., 2010). Converting unwanted seaweed waste into energy can therefore be an appealing option environmentally as well as economically. Anaerobic digestion (AD) is considered among the viable technologies for bioconversion of Ulva biomass into energy owing to its ability to produce methane, a valuable energy carrier, and reduce pollution load simultaneously. AD is a multi-step process consisting of hydrolysis, acidogenesis, and methanogenesis where diverse microbial groups of different functions and characteristics are involved. The bioavailability of the organic matter incorporated in seaweed biomass is hindered by the structural complexity and rigidity of seaweed matrices, and thus the hydrolysis rate is a significant factor determining the overall reaction rate. Pretreatment of feedstock is therefore required in seaweed AD for enhanced hydrolysis and fermentation rates. Different methods for seaweed pretreatment using physical, chemical, and biological methods have been extensively examined, and among the most popular approaches is thermochemical pretreatment using a combination of heat and chemicals, e.g., strong acids and bases (Jung et al., 2011; Borines et al., 2013; Jard et al., 2013). A major drawback of thermal pretreatment at high-temperatures (>100 °C) is high energy consumption which could offset the benefit from the enhanced biogas production by pretreatment. Thermochemical pretreatment, involving the addition of reactive chemicals, can reduce the need for high-temperature heating and provide an alternative at lower temperatures. Although many efforts have been devoted to investigate such a potential for other waste biomass, e.g., waste activated sludge (Kim et al., 2013; Jung et al., 2014), relatively little research has been performed on the effect of mild-temperature (<100 °C) thermochemical pretreatment on the disintegration and biomethanation of seaweed biomass including Ulva spp. Therefore, this study aimed to comparatively examine the effects of acid (HCl) and alkaline (NaOH) thermochemical pretreatments at mild temperatures on Ulva AD, in terms of biogas production as well as biomass solubilization. Additionally, the differences in microbial community structure between the AD trials of different pretreatment conditions were analyzed and compared to examine how the anaerobic microbiota was influenced by the pretreatment. This study provides a comprehensive insight into the changes in bioavailability and digestibility of Ulva biomass and also in AD microbial community structure with respect to the different pretreatment conditions.

2. Methods 2.1. Seaweed biomass Ulva biomass was collected from a coastal area near Ulsan, Korea in the spring of 2014. Collected biomass was rinsed twice with a small amount of tap water and finely ground in a household blender. The prepared seaweed slurry had total and soluble COD concentrations of 13.4 ± 0.3 and 4.3 ± 0.1 g/L, respectively, resulting in a soluble to total COD ratio of 31.9% (Table S1). 2.2. Experimental design for response surface analysis To examine the simultaneous influence of two pretreatment factors (i.e., temperature and chemical concentration), response surface analysis (RSA) with a two-factor, three-level facecentered design (FCD) was employed to generate the experimental design. The FCD matrix consisted of nine experimental points including a center point being replicated five times, i.e., a total of thirteen experimental trials each for the tested pretreatment methods (Fig. S1). RSA is a powerful statistical tool often used to study the combined effects of multiple independent variables on the responses of a complex system owing to its ability to build statistical models to approximate the responses at untested points with a minimal number of experimental trials. The explored ranges of temperature and chemical concentration were set to be 75 ± 15 °C and 0.1 ± 0.1 M, respectively (Table 1), with reference to literature values on the thermochemical pretreatment of different biomass feedstocks (Adams et al., 2009; Kim et al., 2013). A sequential procedure of collecting experimental data, estimating polynomial equations, and testing model adequacy was carried out to find the most adequate response surface model for each dependent variable. Increasingly higher-order polynomials, i.e., linear to partial cubic equations, were fitted to the experimental data for model calculation and validation using Design Expert 7 software (Stat-Ease, Minneapolis, MN). Independent variables (i.e., temperature and chemical concentration in this study) are converted to coded values for computational convenience in RSA: the upper limit of a factor to 1, the center level to 0, and the lower limit to 1. The relationship between the coded and actual value is described as below:

Cx ¼

Ax  Ac Au  Ac

ð1Þ

where Cx and Ax are respectively the coded and actual values of the independent variable x, Ac is the actual value of the independent variable x at the center level of the explored range, Au is the actual value of the explored upper limit of the independent variable x. The detailed design information is given as both actual and coded values for ease of comprehension in Table 1.

Table 1 Experimental design for response surface analysis. Run

1 2 3 4 5 6 7 8 9a a

HCl or NaOH (M)

Temperature (°C)

Coded

Actual

Coded

Actual

1 1 1 1 0 0 1 1 0

0 0 0.2 0.2 0.1 0.1 0 0.2 0.1

1 1 1 1 1 1 0 0 0

60 90 60 90 60 90 75 75 75

Center point was replicated five times.

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx

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Ulva biomass samples were treated at different temperatures and chemical concentrations using HCl or NaOH, which are commonly used for the disintegration of cellular structures, as the pretreatment conditions shown in Table 1. A desired concentration of chemical was added to 500 mL of the Ulva slurry in a 1-L glass beaker. The mixture was thoroughly agitated with a magnetic stirrer for 10 min and then placed in a thermostat oven at a designated temperature. Thermal treatment was carried out for 6 h with manual shaking for 1 min every half an hour. A total of thirteen trials including five replications of the center point were tested for the acid and alkaline thermochemical pretreatments each, with the trials not added with chemicals (i.e., Runs 1, 2, and 7) being shared. Consequently, the pretreatment experiments were carried out with twenty-three trials in total. For each trial, solubilization of Ulva biomass was determined by measuring the change in the portion of soluble organics, i.e., the soluble to total COD ratio (RS/T), before and after the pretreatment.

primer sets, respectively, as previously described (Kim et al., 2014). The obtained amplicons (20 lL) were electrophorized in 8% (w/v) polyacrylamide gels with 35–65% (archaea) or 25–60% (bacteria) denaturant gradients (100% denaturant = 7 M urea and 40% (v/v) formamide). Electrophoresis was run for 16 h at 80 V in a D-code system (Bio-Rad, Hercules, CA), and then the gels were stained with SYBR Safe Dye (Molecular Probe, Eugene, OR) to visualize the band patterns under blue light illumination. Bands of interest were cut out of the gels and eluted in 40 lL sterile water. The eluted DNA was re-amplified with the same primer pairs as for the DGGE analysis without GC clamp. The resulting PCR products were gel-purified and cloned into the pGEM-T Easy vector (Promega, Madison, WI). The cloned 16S rRNA genes were sequenced using the vector-specific T7 primer and compared against the GenBank and RDP databases. The RDP Classifier was used for taxonomic assignment of the retrieved band sequences at a bootstrap confidence threshold of 80%. The nucleotide sequences obtained in this study were deposited in the GenBank database: KR081310–KR081322.

2.4. Batch AD tests

2.6. Cluster analysis of DGGE profiles

To further investigate the pretreatment effect on the digestibility and biogas production, batch AD tests of pretreated Ulva biomass were performed. A total of 72 reactor bottles with a total volume of 120 mL were prepared and incubated in parallel: 23 pretreated samples and the untreated Ulva slurry tested in triplicate (24 samples  3 replications = 72 trials). A bottle was filled with 35 mL Ulva slurry and 70 mL inoculum. The anaerobic sludge (volatile solids (VS) concentration, 13.9 g/L) collected from a full-scale sewage sludge digester was used as the inoculum. To avoid the noise potentially derived from endogenous biogas production, the inoculum biomass was completely starved at 35 °C in batch mode for two weeks until biogas production ceased. Inoculation was made after cooling down the Ulva samples pretreated at design temperatures (Table 1) to room temperature. All pretreated samples were adjusted to neutral pH (7.0 ± 0.2) with HCl or NaOH solution prior to the inoculation, and no further pH adjustment was made afterwards. Each reactor bottle was flushed with nitrogen gas to remove oxygen in the headspace and sealed with a rubber stopper and an aluminum cap. The reactor bottles were incubated for 30 days at 35 °C with intermittent manual shaking, and the biogas production in each reactor bottle was periodically measured using a gas-tight syringe.

To analyze the relatedness between the DGGE community profiles, cluster analysis was conducted using unweighted pair group method with arithmetic means (UPGMA) algorithm. Distance calculation was performed using Sorensen distance measure on the binary matrices for archaea and bacteria generated by scoring the presence or absence of each DGGE band as 1 or 0, respectively, in the corresponding gels regardless of band intensity. The band patterns of different gels were normalized using the sample runs commonly included in the gels (i.e., the lanes corresponding to Runs 1, 2, 7, and control; Table 1). Cluster dendrogram was drawn using PAST 3.06 (Hammer et al., 2001). Additionally, influences of the pretreatment factors (i.e., chemical dose and temperature) on bacterial microbial community structure were further examined by canonical correspondence analysis (CCA) on the binary matrix prepared above. CCA was performed using the R package vegan (version 2.2-1; Oksanen et al., 2015).

2.3. Thermochemical pretreatment

2.5. Denaturing gradient gel electrophoresis and sequencing analysis After the batch AD tests, archaeal and bacterial community structures were examined by denaturing gradient gel electrophoresis (DGGE) in ten reactor bottles for each of the acid and alkaline thermochemical pretreatment methods tested: nine for the different pretreatment runs (Table 1) and one for the untreated control. For each experimental condition, one bottle of the corresponding triplicates was randomly selected for DGGE analysis. Total DNA was isolated from the digestates after 30 days of batch incubation using an automated extractor (Exiprogen, Bioneer, Daejeon, Korea) as per the manufacturer’s instruction. One milliliter of each sample was pelleted by centrifugation at 13,000g for 1 min and then washed by repeated suspending (in 1 mL distilled water) and pelleting (13,000g, 1 min) to remove cell debris and impurities. A 200-lL aliquot of the final suspension was loaded on the extractor with ExiProgen Bacteria Genomic DNA kit (Bioneer). The extracted DNA was recovered in 200 lL elution buffer and stored at 20 °C prior to use. Archaeal and bacterial 16S rRNA gene fragments were amplified by touch-down polymerase chain reaction (PCR) with ARC787F/1059R and BAC338F/805R

2.7. Analytical methods COD concentration was measured spectrophotometrically with HS-COD-MR kit (Humas, Daejon, Korea). Samples for soluble COD measurement were prepared by filtration through a 0.45-lmpore membrane filter. Solids were measured according to standard methods. The C, H, O, N, and S contents (dry weight basis) of Ulva biomass were determined using an organic elemental analyzer (Flash 2000, Thermo Scientific, Delft, The Netherlands). Methane content of the biogas produced in the batch AD trials was analyzed using a 7820A gas chromatograph (Agilent, Palo Alto, CA) equipped with a thermal conductivity detector and a ShinCarbon ST column (Restek, Bellefonte, PA). All analyses were performed at least in duplicate. 3. Results and discussion 3.1. Pretreatment effects A significant solubilization of Ulva biomass was observed for each pretreatment method tested. The RS/T values observed in the experimental runs ranged from 30.0% to 56.2% (up to 1.9-fold increase) for the thermo-acid pretreatment and from 33.4% to 72.6% (up to 2.2-fold increase) for the thermo-alkaline pretreatment (Table 2). These results indicate that both HCl- and NaOHbased pretreatment methods effectively disintegrated Ulva

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx Table 2 Observed data from the experimental trials for response surface analysis. Run

1 2 3 4 5 6 7 8 9b a b

Treatment conditions (coded)

RS/T (%)a

PB (mL)a

HCl or NaOH

Temperature

HCl-treated

NaOH-treated

HCl-treated

NaOH-treated

1 1 1 1 0 0 1 1 0

1 1 1 1 1 1 0 0 0

34.2 35.5 32.4 56.2 30.0 47.7 33.4 37.3 32.7 (0.5)

34.2 35.5 62.4 68.3 54.8 65.5 33.4 72.6 59.1 (2.4)

259.2 293.0 239.5 254.0 247.2 284.8 255.7 239.8 247.9

259.2 293.0 232.2 219.3 222.7 251.3 255.7 223.2 228.0

(8.3) (18.1) (3.3) (1.8) (4.4) (6.0) (6.7) (5.9) (6.6)

(8.3) (18.1) (3.7) (2.9) (5.4) (6.0) (6.7) (1.4) (7.0)

Standard deviations are in parentheses. Center point was replicated five times.

biomass under mild-temperature conditions. A thing to note here is that all runs without chemical addition showed no significant increase in RS/T irrespective of temperature while RS/T increased with increasing temperature when chemicals were added. These observations support that combining thermal with chemical pretreatments had a positive effect on the solubilization of Ulva biomass. In all tested conditions with chemical addition (Runs 3–6, 8, and 9 in Table 2), the NaOH-treated trials showed significantly greater RS/T values (1.2–1.9-fold) than the HCl-treated trials. On the other hand, the cumulative biogas production during the batch AD (PB) was higher in the HCl-treated trials than in the NaOHtreated trials. Furthermore, for each temperature tested, the highest PB value was observed in the thermal-only treated run, with 293 mL in Run 2 (1, 1) being the measured maximum. All trials with chemical addition, except the thermo-acid pretreatment runs 4 and 6, actually showed smaller PB value than that observed in the control (253 mL). This implies that the biomethanation of Ulva biomass was negatively affected in the experimental trials by the addition of HCl or NaOH. These observations suggest that, despite the increased RS/T, the soluble organics released from the disintegration of Ulva biomass were not efficiently utilized for biogas production in the trials treated with chemicals. The methane content of the produced biogas was 57–62% in all reactor bottles measured. 3.2. Response surface model for Ulva solubilization To find the most suitable model to approximate the response surface of RS/T, increasingly complex polynomials were tested to fit the experimental data (Table 2). Model adequacy was evaluated based on R2, p-value, lack-of-fit (LOF), and adequacy of precision (AP). Statistical significance of models and individual model terms

were investigated by analysis of variance (ANOVA). The thermoacid pretreatment data were best fitted to a partial cubic model (Table 3), and the resulting response surface plot is shown in Fig. 1A:

Y RS=T ¼ 33:01 þ 1:93X h þ 8:88X t þ 5:61X h X t þ 1:68X 2h þ 5:21X 2t  2:58X 2h X t þ 2:80X h X 2t

ð2Þ

where YRS/T is the estimated response of RS/T, Xh is HCl concentration in coded value, and Xt is temperature in coded value. The generated response surface model showed an excellent fit to the experimental data (R2 > 0.99, p < 0.001). No significant LOF for the model was found (p > 0.05), and the calculated AP value (=35.423) was sufficiently high to support the validity of the model. AP measures the range of estimated responses relative to the average error, i.e., a signal to noise ratio. A high AP value thus indicates a statistically sound regression, and its value of 4 or higher is typically desired for an adequate model (Kim et al., 2013). The model was further evaluated for the normality assumption by generating a normal probability plot of residuals for the regression equation. The residuals were randomly distributed along a straight line with no structure or pattern (data not shown), indicating that the obtained model provides responses with constant variance. Therefore, the produced model proved adequate to estimate the RS/T response within the explored space for the thermo-acid pretreatment experiments. As shown in the ANOVA results for individual model terms presented in Table 3, all model terms were significant at 5% a-level (p < 0.05) while especially Xt, XhXt, and Xt2 were significant at 0.1% a-level (p < 0.001). Corresponding to the high significance of the interaction term XhXt, the RS/T model formed a saddle-shaped response surface (Fig. 1A) which reflects a significant effect of the

Table 3 Statistical significance of the RS/T response surface model coefficients. Thermo-acid treatment (HCl)

a

F-value

Thermo-alkaline treatment (NaOH)

Terms

Coefficient

p-value

Terms

Coefficient

Intercept Xh Xt XhXt Xh2

33.01 1.93 8.88 5.61 1.68

F-value

p-value

Intercept Xn Xt XnXt

59.41 16.70 3.01 1.14 –8.34

8.40 178.26 142.49 8.49

0.0442 0.0002 0.0003 0.0435

170.74 5.53 0.53 22.94

<0.0001 0.0465 0.4861 0.0014

X t2 Xh2Xt

5.21 –2.58

81.93 10.02

0.0008 0.0340

XhXt2

2.80

11.84

0.0263

X 2n Xt XnXt2

Model

p-value

R2

APa

Model

p-value

R2

APa

Regression Lack-of-fit

0.0002 0.0750

0.9946

35.423

Regression Lack-of-fit

<0.0001 0.2145

0.9615

20.300

X 2n X t2

Adequate precision.

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx

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Fig. 1. Contour plots of the RS/T response surface for the thermo-acid (A) and thermo-alkaline (B) pretreatments.

interaction between the independent variables on the model output (Murthy et al., 2000). A large flat area (RS/T response <35%) appears in the bottom half of the plot, implying that the thermo-acid pretreatment has little effect on the solubilization of Ulva biomass under the lower-temperature conditions (ca. <75 °C). The contour level increases diagonally toward the upper right corner to reach the maximum response, indicating the interdependence of HCl concentration and temperature. Particularly in the higher-temperature region (ca. >75 °C), the model output changes more steeply along the temperature axis. This reflects the greater effect of temperature on the RS/T response than of HCl concentration, corresponding to the markedly lower p-values of the temperature-related terms. The maximum RS/T response predicted by the model was 56.4% at the upper right corner point (1, 1), and this value is close to the observed RS/T value in the corresponding experimental run (56.2%; Run 4 in Table 2). For the thermo-alkaline pretreatment data, a modified quadratic model was most suitable to approximate the response of RS/T (Table 3), and the model plot is shown in Fig. 1B:

Y RS=T ¼ 59:41 þ 16:70X n þ 3:01X t þ 1:14X n X t  8:34X 2n

ð3Þ

where YRS/T is the estimated response of RS/T, Xn is NaOH concentration in coded value, and Xt is temperature in coded value. The squared temperature term (Xt2) was eliminated because of its highly insignificant p-value (p > 0.8). The obtained response surface model was well fitted to the experimental data (R2 > 0.96, p < 0.0001). LOF was insignificant (p > 0.05), and the AP value (=20.300) was high enough to ensure the model adequacy. The normal probability plot of regression residuals was a random, structureless scatter about a linear line (data not shown), indicating that the prediction errors are distributed with constant variance. These suggest that the resulting model is suitable to navigate the response surface of RS/T within the design space for the thermoalkaline pretreatment experiments. All terms involved in the model, except for XnXt, were significant (p < 0.05), and particularly both the linear and squared terms of NaOH concentration (Xn and X 2n ) had significantly lower p-values (p < 0.0015) than the other terms (Table 3). These suggest that NaOH concentration has a greater influence on the RS/T response than temperature does, which is evidently mirrored in the response surface plot (Fig. 1B). The response value changes sharply along the NaOH concentration axis while the model contours are highly elongated along the temperature axis to

form a hill of concentric ellipses. Similarly to the thermo-acid pretreatment model, the thermo-alkaline pretreatment model also predicts high responses in the high-NaOH-concentration and hightemperature region. The maximum model output of 71.9%, estimated at the point for the harshest pretreatment conditions (1, 1), is close to the experimental RS/T value observed in the corresponding conditions (68.3%; Run 4 in Table 2). 3.3. Response surface model for biogas production A partial cubic model was selected as the best-fit model to illustrate the response surface of PB for the thermo-acid pretreatment (Table 4), and the constructed response surface plot is shown in Fig. 2A:

Y PB ¼ 251:43  7:92X h þ 18:83X t  4:83X h X t  4:04X 2h þ 14:21X 2t  6:75X 2h X t  6:75X h X 2t

ð4Þ

where YPB is the estimated response of PB, Xh is HCl concentration in coded value, and Xt is temperature in coded value. The obtained model showed a good fit (R2 > 0.83, p < 0.0001) to the experimental data (Table 2). LOF was not significant (p > 0.05), and the model AP value was 13.823 which is sufficiently high for an adequate model. The residuals showed a normal distribution of random errors (data not shown), indicating that the predicted model responses have constant variance. Consequently, the developed model for the thermo-acid pretreatment proved adequate to approximate the PB response within the explored space. Among the model terms, Xt and Xt2 were of the most significant p-values (<0.0001) markedly smaller than those of the HCl concentration-related terms (Table 4). This indicates that temperature has a greater influence on the PB response than HCl concentration does. Meanwhile, contrary to the model for RS/T model, HCl concentration has a negative effect (i.e., the negative coefficient value of Xh) on the model output. Such characteristics are clearly reflected in the response surface plot (Fig. 2A) where the response level increases steeply with increase in temperature, particularly in the low-HCl-concentration and high-temperature region. The model output shows the peak value of 293 mL around the upper left corner point where the Ulva biomass was pretreated by heating at 90 °C without adding HCl (1, 1). This is a very close estimation of the observed PB value in the corresponding test conditions (293 mL; Run 2 in Table 2).

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx

Table 4 Statistical significance of the PB response surface model coefficients. Thermo-acid treatment (HCl)

a

F-value

Thermo-alkaline treatment (NaOH)

Terms

Coefficient

Intercept Xh Xt XhXt Xh2

251.43 7.92 18.83 4.83 4.04

p-value

Terms

Coefficient

F-value

p-value

Intercept Xn Xt XnXt

230.12 16.25 14.33 11.67 11.14

5.59 32.22 4.24 1.95

0.0243 <0.0001 0.0491 0.1744

29.20 22.72 30.10 18.30

<0.0001 <0.0001 <0.0001 0.0002

Xt2 Xh2Xt

14.21 6.75

24.10 2.76

<0.0001 0.1083

XhXt2

6.75

2.76

0.1083

X 2n Xt XnXt2

8.72 9.08

11.22 6.08

0.0023 0.0200

8.92

5.86

0.0222

Model

p-value

R2

APa

Model

p-value

R2

APa

Regression Lack-of-fit

<0.0001 0.8527

0.8301

13.823

Regression Lack-of-fit

<0.0001 0.2949

0.9067

21.215

X 2n Xt2

Adequate precision.

Fig. 2. Contour plots of the PB response surface for the thermo-acid (A) and thermo-alkaline (B) pretreatments.

The observed PB data from the thermo-alkaline pretreatment trials were also best fitted to a partial cubic model (Table 4), and the resulting response surface plot is presented in Fig. 2B:

Y PB ¼ 230:12  16:25X n þ 14:33X t  11:67X n X t þ 11:14X 2n þ 8:72X 2t  9:08X 2n X t  8:92X n X 2t

ð5Þ

where YPB is the estimated response of PB, Xn is NaOH concentration in coded value, and Xt is temperature in coded value. The constructed model showed a good approximation (R2 > 0.90) of the PB response surface with a highly significant p-value of below 0.0001. The model was free from LOF (p > 0.05) and showed a sufficiently high AP value (=21.215) to confirm the model adequacy. The regression errors was further verified for the normality assumption (i.e., constant variance of the residuals) by drawing a normal probability plot (data is not shown). These suggest that the obtained model provides reliable predictions of PB response. All model terms were significant (p < 0.05), with Xn, Xt, and XnXt being of markedly greater significance (p < 0.0001). This indicates that both NaOH concentration and temperature have comparable, highly significant effects on the PB response. The strong influence of the interaction between the independent variables is mirrored in the saddle-

shaped response surface shown in Fig. 2B (Murthy et al., 2000). A large flat area (PB response < 235 mL) appears in the right half of the plot, indicating that changes in the pretreatment conditions in the higher-NaOH-concentration region (ca. >0.1 M) have limited effect on the biomethanation of Ulva biomass. The contours run diagonally up toward the upper left corner to show the maximum model output at (1, 1), suggesting the interdependence of the independent variables. Interestingly, as in the model for thermoacid pretreatment, the PB response is negatively related to NaOH concentration while being positively related to temperature. The estimated maximum response of 292 mL is very close to the observed PB value in the corresponding batch test run (293 mL; Run 2 in Table 2). For each pretreatment method tested, the obtained response surface models for RS/T and PB determined different points for the estimated maximum outputs (Figs. 1 and 2). Although the combined pretreatment with heat and chemicals proved more effective than thermal-only pretreatment for solubilizing Ulva biomass in the RS/T model (the maximum model output at (1, 1)), the PB model showed that chemical addition must be excluded for higher production of biogas (the maximum model output at (1, 1)). As the purpose of pretreatment for AD is to enhance the biodegradability

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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of feedstock and thus the biogas production, it seems reasonable to suggest the point for the maximum PB to be the optimum pretreatment condition (thermal-only treatment at 90 °C). 3.4. Microbial community structure

Fig. 3. PCR-DGGE profiles of archaeal 16S rRNA gene fragments from the HCltreated (A) and NaOH-treated (B) batch AD runs. Lanes are labeled with the pretreatment condition, i.e., chemical concentration (M) and temperature (°C). C indicates the untreated control.

Fig. 4. PCR-DGGE profiles of bacterial 16S rRNA gene fragments from the HCltreated (A) and NaOH-treated (B) batch AD trials. Each lane is labeled with chemical concentration (M) and temperature (°C). C indicates the untreated control.

Archaeal and bacterial community structures were investigated by DGGE analysis at the end of the batch AD test (Figs. 3 and 4). Four archaeal bands (UPA1 to 4) were sequenced for phylogenetic affiliation, and the retrieved sequences were all closely related (P97% sequence similarity) to known methanogen species (Table 5). UPA1 and 2 were affiliated with obligate aceticlastic Methanosaeta strains while UPA3 and 4 were closely related to hydrogenotrophic Methanolinea strains. UPA1 and 2 were detected with significantly stronger intensity than all other bands in all lanes, indicating that, although not robustly quantitative, the Methanoseata-related populations corresponding to these bands were likely the dominant methanogens in all AD trials tested with different pretreatment conditions. The Methanolinea-related populations deduced from UPA3 and 4, although likely minor in number, might be involved in syntrophic propionate oxidation as hydrogen scavenger (Kim et al., 2014). Methanolinea was originally isolated from an propionate-degrading culture enriched from AD sludge (Imachi et al., 2008). These suggest that, although aceticlastic pathway mediated by Methanosaeta was presumably the major methanogenic route, Methanolinea also likely contributed to the methanogenesis by converting hydrogen released, for example, from the syntrophic oxidation of propionate and other volatile organic acids, into methane in the batch AD trials. Nine bacterial bands (UPB1 to 9) were selected for further sequencing and phylogenetic analyses. Five out of them (UPB1, 3, 4, 6 and 7) were assigned to known genera by the RDP Classifier at a bootstrap confidence threshold of 80% (Table 5). UPB1 and 4 were assigned to the saccharolytic fermentative genus Parabacteroides producing acetic acid as the major end product (Krieg et al., 2011), suggesting that the corresponding population to these bands were likely responsible for the utilization of seaweed polysaccharides. Particularly, UPB4 was not detected or present with faint intensity in the NaOH-treated samples while it was a dominant band in all other lanes, suggesting that the corresponding population is potentially sensitive to alkaline conditions. This bacterium might be a critical player in digesting Ulva biomass given that biogas production was significantly lower in the NaOH-treated runs, where the growth of the population seemed to be suppressed, than in all other runs (Table 2). Ulva cell wall is very rich in ulvan (ca. 30% dry weight), a sulfated polysaccharide in which sulfate groups occupy about 20% of its total weight (Lahaye and Robic, 2007). This could be related to the common occurrence of UPB3 and 7 assigned to the sulfate-reducing genus Desulfomicrobium (Brenner et al., 2005). UPB6 was affiliated with the genus Treponema of the family Spirochaetaceae, a chemoorganotrophic family frequently observed in abundance in AD environments. Members of this genus are able to obtain energy by fermentation of carbohydrates and amino acids, and some homoacetogenic species can form acetic acid from carbon dioxide and hydrogen (Krieg et al., 2011). Among the remaining four sequences unclassifiable at the genus level, UPB2, 8, and 9 were also assigned to Spirochaetaceae at the family level. The Spirochaetaceae-related populations, including the one deduced from UPB6, were likely involved in the fermentation of substrate organics. On the other hand, UPB5 was classifiable only at the phylum level into Bacteroidetes, and the role of its corresponding population in AD environments is unclear. UPGMA cluster analysis based on the DGGE profiles revealed that archaeal community structure varied little among the digested samples from different batch AD runs including the control

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H. Jung et al. / Bioresource Technology xxx (2015) xxx–xxx

Table 5 Phylogenetic affiliation of archaeal and bacterial 16S rRNA gene sequences retrieved from DGGE bands. Band Archaea UPA1 UPA2 UPA3 UPA4 Bacteria UPB1

UPB2 UPB3

UPB4 UPB5 UPB6 UPB7

UPB8 UPB9 a

Closest relatives

Accession number

Similarity (%)

Classificationa

Methanosaeta concilii GP-6 Methanosaeta concilii Opfikon Methanosaeta concilii GP-6 Methanosaeta concilii Opfikon Methanolinea tarda NOBI-1 Methanolinea mesophila TNR Methanolinea tarda NOBI-1 Methanolinea mesophila TNR

CP002565 X51423 CP002565 X51423 NR028163 NR112799 NR028163 NR112799

99.3 99.3 100.0 100.0 98.2 97.8 98.5 98.1

genus Methanosaeta

Macellibacteroides fermentans LIND7H Iron-reducing enrichment clone CL-W2 Bacteroides sp. W7 Uncultured Spirochaetes bacterium clone SWHR3 Uncultured Spriochaetes bacterium clone RSg13–44 Desulfomicrobium sp. ADR26 Desulfomicrobium escambiense DSM10707 Desulfomicrobium apsheronum Bu2.2 Parabacteroides chartae strain NS31-3 Macellibacteroides fermentans LIND7H Uncultured bacterium clone 060B04_B_SD_P93 Uncultured bacteroidetes bacterium QEDN5AB01 Uncultured bacterium clone GB7 Spirochaetes bacterium SA-10 Desulfomicrobium baculatum DSM 1742 Desulfomicrobium hypogeium CN-A Desulfomicrobium sp. PR3_G08 Uncultured bacterium clone noFP_H9:3 Uncultured bacterium clone FP_C7 Uncultured bacterium clone MTSBac-D8 Uncultured bacterium clone FP_G10

NR117913 DQ677015 FJ862827 JQ346773 AB603822 AM419442 NR042018 AF228132 NR109439 NR117913 CT574199 CU926956 KJ679870 AY695841 AJ277896 NR114508 HE600847 FJ769500 FJ769496 EU591644 FJ769482

98.0 97.8 97.2 100.0 100.0 99.8 99.1 98.1 99.8 98.0 100.0 100.0 100.0 96.6 99.1 98.9 98.5 99.1 99.1 99.6 97.6

genus Parabacteroides

genus Methanosaeta order Methanomicrobiales order Methanomicrobiales

family Spirochaetaceae genus Desulfomicrobium

genus Parabacteroides phylum Bacteroidetes genus Treponema genus Desulfomicrobium

family Spirochaetaceae family Spirochaetaceae

The lowest rank assigned by the RDP Classifier at a bootstrap confidence threshold of 80%.

Fig. 5. Cluster dendrogram (A) and CCA biplot (B) constructed based on the bacterial DGGE profiles. Each community profile is labeled with chemical concentration (M) and temperature (°C): no chemical (h), HCl (H; ▲), NaOH (N; d). The proportion of the species-environment variance explained by each axis is shown in parentheses. Arrows indicate the direction (angle) and magnitude (length) of the correlation between the environmental parameters and the axes. Significant (p < 0.05) and insignificant parameters are represented by solid and dotted arrows, respectively.

(Sorensen distance (DS) <0.08). This indicates that both pretreatment methods tested had little influence on the formation and evolution of methanogen community structure in the Ulva AD tests. In contrast, as shown in the cluster dendrogram, significantly larger variations in bacterial community profile were observed among the examined reactor samples (Fig. 5A). This is attributable to the less diverse and dynamic nature of archaea, mostly methanogens in AD environments, compared to bacteria owing to the limited

substrate range of methanogens (Kim et al., 2014). Interestingly, the bacterial community profiles were clearly separated into three clusters according to the pretreatment conditions, particularly to whether HCl or NaOH was added. The profiles from the control and the thermal-only treated runs comprised a tight cluster, which also contained the profile from the NaOH-treated test at (0, 1) condition (Run 5 in Table 2). All other NaOH-treated tests showed similar bacterial community profiles very closely related in a

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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cluster while the profiles from the HCl-treated tests were all grouped in another cluster. These imply that the development of bacterial community structure during the batch AD test was significantly affected by the pretreatment conditions. Supportively, the CCA results also showed the same clustering pattern of the bacterial community profiles (Fig. 5B). The first and second canonical axes represented 69.1% and 23.7% of the variance of the relationship between species and environmental parameters, respectively. The significance of CCA ordination was assessed by permutation test of the canonical axes, and both axes proved significant (p = 0.005). For biplot analysis, the correlation significance of each environmental parameter (i.e., HCl, NaOH, temperature, RS/T, and PB) was assessed by permutation test, and HCl, NaOH, and RS/T were found significant (p < 0.05). HCl and NaOH, correlated negatively and positively, respectively, with Axis 1 representing the majority of the species-environment variance, and both showed a strong correlation with bacterial community assembly within the studied pretreatment conditions while temperature did not. This agrees well with the separate clustering of the community profiles according to the addition of HCl or NaOH, regardless of the pretreatment temperature in the cluster analysis (Fig. 5A). 3.5. Correlation between Ulva solubilization and biogas production Both thermo-acid and thermo-alkaline pretreatments were effective for hydrolyzing Ulva biomass at mild temperatures (Table 2). This is attributed to the synergistic effect of heat and reactive chemicals. Rigid cellular structure of biomass are first attacked by chemical reagents, and the loosened polymeric macromolecules are easily damaged by thermal treatment (Tyagi and Lo, 2012). The observed solubilization rates in the pretreatment trials (up to 72.6%; Table 2) were comparable to the rates reported in other studies on seaweed pretreatment (Jung et al., 2012; Jard et al., 2013). Within the experimental design region (Fig. S1), NaOH addition was significantly more effective than HCl addition for solubilizing Ulva biomass (Table 2). This is possibly related with the higher degree of cellulose degradation (i.e., reduction of crystallinity) by NaOH than by HCl (Wang et al., 2015). Cell wall polysaccharides account for about 38–54% of the dry matter in seaweeds, and Ulva cell wall includes four polysaccharide families: water-soluble ulvan and insoluble cellulose as the major ones and alkali-soluble xyloglucan and glucuronan as the minor ones (Lahaye and Robic, 2007). Therefore, effective hydrolysis of insoluble cellulose is crucial for solubilizing Ulva biomass. Although to a small degree, the existence of alkali-soluble xyloglucan may also have contributed to the higher solubilization by NaOH treatment. Interestingly, however, no corresponding increases in biogas production were observed despite such increases in soluble organics (Table 2 and Fig. S2). This indicates that the enhanced solubilization of Ulva biomass was not concluded to improve the anaerobic digestibility. Adding HCl or NaOH actually had an adverse effect on AD performance, which can be seen by comparing the PB values among the runs treated at the upper-limit temperature (Runs 2, 4, and 6 in Table 2). These experimental observations were further supported by the RSA results (Tables 3 and 4). The generated response surface models also clearly showed the positive effect on solubilization and negative effect on biogas production of adding chemicals, particularly NaOH (Figs. 1 and 2). This confirms that, for each pretreatment method, RS/T and PB respond in totally different manners to variations in pretreatment conditions (i.e., chemical concentration and temperature) within the experimental design region. Although the underlying reasons for such observations are unclear, it may be related with deterioration of methanogenic activity. Inhibitory effect of saline stress due to the addition of strong acid and alkali for pretreatment and neutralization might

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be a possible reason (Li et al., 2012). Previous works reported that >3 g/L Na+ can cause inhibition of methanogenic activity (Feijoo et al., 1995; Manu and Chaudhari, 2002). This may explain the low responses of PB in the higher-NaOH-concentration region (>0.1 M NaOH or 2.3 g/L Na+) where higher amounts of soluble organics (i.e., higher RS/T) are available for microbial utilization (Figs. 1 and 2B). Another possible reason may be the formation of inhibitory or refractory compounds during the pretreatment process. Acid hydrolysis of hemicellulose and/or cellulose produces various inhibitory byproducts, e.g., furfural, hydroxymethylfurfural (HMF), and phenolic compounds (Zheng et al., 2014; Wang et al., 2015). Alkali hydrolysis also reportedly produces monomers of hemicellulose that can be easily converted into other inhibitors at high temperatures (Wang et al., 2015). Given the high protein and carbohydrate contents of seaweeds, the formation of melanoidins via Maillard reaction between amino acids and reducing sugars might also have contributed to the negative effects of the thermochemical pretreatments on biogas production (Ajandouz et al., 2008; Monlau et al., 2013). A thing to note here is that no clear correlation was observed between biogas production and bacterial community assembly (Fig. 5). This may be attributed to the potential formation of inhibitory or refractory compounds during the pretreatment process as mentioned above. In the presence of inhibitory substances, microbial community structure would be affected more strongly by the inhibitors (i.e., suppression of the growth of sensitive populations) rather than by the increase in soluble organics (i.e., facilitation of the growth of fermentative bacteria). In line with this, the CCA ordination revealed that the before-inoculation parameters (i. e., HCl, NaOH, and RS/T) correlated significantly with the variations in bacterial community structures while the after-inoculation parameter (i.e., PB) did not (Fig. 5B). This seems to be a point that requires further clarification with quantitative approaches at different taxonomic or functional levels. There are several previous works on the pretreatment of seaweed biomass using different thermochemical methods (Jung et al., 2011; Borines et al., 2013; Jard et al., 2013). However, comparative examination of acid and alkaline thermochemical pretreatments in terms of both solubilization and biogas production, in relation to microbial community structure, as carried out in this study, is seldom reported. This study helps better understand the influence of mild-temperature thermochemical pretreatment on the bioavailability and anaerobic digestibility of Ulva biomass. 4. Conclusions The simultaneous effects of chemical concentration (i.e., HCl or NaOH) and temperature on RS/T and PB were investigated for the pretreatment of Ulva biomass by RSA. Both methods were effective in Ulva solubilization, particularly under high-temperature and high-chemical-dose conditions, within the explored space (0– 0.2 M HCl or NaOH, 60–90 °C). However, RS/T poorly correlated with PB, and no increase in biogas recovery was observed in the chemically treated trials for both methods. Response surface modeling further confirmed that HCl/NaOH positively affects RS/T while adversely PB. Molecular fingerprint-based analysis suggested that HCl/NaOH strongly influenced the development of bacterial community structure. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2014R1A1A1002329) and by the Ministry of Education

Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014

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Please cite this article in press as: Jung, H., et al. Mild-temperature thermochemical pretreatment of green macroalgal biomass: Effects on solubilization, methanation, and microbial community structure. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.08.014