- Email: [email protected]
Maximization of volatile fatty acids production from alginate in acidogenesis
Bioresource Technology 148 (2013) 601–604
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Short Communication
Maximization of volatile fatty acids production from alginate in acidogenesis Hong Duc Pham a, Jiyun Seon b, Seong Chan Lee a, Minkyung Song b,⇑, Hee-Chul Woo a,⇑ a b
Department of Chemical Engineering, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 608-739, Republic of Korea The Institute of Cleaner Production, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 608-739, Republic of Korea
h i g h l i g h t s The VFA production from alginate as a feedstock was approached for the first time. Optimization of alginate concentration and initial pH was determined using RSM. The effect of variables on VFAs production was evaluated. Acetic acid was the major component of the alginate fermentation.
a r t i c l e
i n f o
Article history: Received 22 June 2013 Received in revised form 20 August 2013 Accepted 21 August 2013 Available online 30 August 2013 Keywords: Alginate Anaerobic fermentation Marine brown algae Response surface methodology Volatile fatty acids
a b s t r a c t In this study, the response surface methodology (RSM) was applied to determine the optimum fermentative condition of alginate with the respect to the simultaneous effects of alginate concentration and initial pH to maximize the production of total volatile fatty acids (TVFAs) and alcohols. The results showed that the alginate fermentation was significantly affected by initial pH than by alginate concentration and there was no interaction between the two variables. The optimum condition was 6.2 g alginate/L and initial pH 7.6 with a maximum TVFAs yield of 37.1%. Acetic acids were the main constituents of the TVFAs mixtures (i.e., 71.9–95.5%), while alcohols (i.e., ethanol, butanol, and propanol) were not detected. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Fundamental issues in the current energy system such as dramatic increase in fossil fuel prices, sharp depletion of petrol, and climate change, have led scientists to investigate new alternative energy sources. Among these, marine biomass from sources such as microalgae and macro-algae, represent one of the most promising resources due to its cleanliness and sustainability (Kim et al., 2013). Recently, marine macro-algae, classified as green-, red-, and brown-algae, have attracted attention as biomass resources for biofuels and biomaterials (Chang et al., 2010). For instance, bioalcohol (Borines et al., 2013) and bio-hydrogen (Shi and Yu, 2006) can be produced from these algae by anaerobic fermentation and bio-oil (Ross et al., 2009) can be produced by pyrolysis. Among
⇑ Corresponding authors. Tel.: +82 51 629 7646; fax: +82 51 629 6429 (M. Song). Tel.: +82 51 629 6436; fax: +82 51 629 6429 (H.-C. Woo). E-mail addresses: [email protected] (M. Song), [email protected] (H.-C. Woo). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.08.128
the macro-algae, massive brown algae are primarily composed of polysaccharides such as alginate, laminaran, fucoidan, mannitol, and cellulose (Chang et al., 2010) and the feasibility of the fermentative conversion of these polysaccharides into liquid biofuels has demonstrated (Horn et al., 2000). Alginate is a polysaccharide, which accounts for up to 40% of the dry weight in brown algae and is a principal component of the cell wall (Draget et al., 2005; Jung et al., 2013). Although this new marine biomass presents a high potential as a polysaccharide feedstock for liquid biofuels, the use of alginate in fermentation processes is still a challenge due to its low solubility in water and its limited usage as a microbial substrate, compared to terrestrial biomass components (Pawar and Edgar, 2012). In this study, we investigated the experimental conditions that are necessary to maximize TVFA and alcohol production by the fermentation of alginate. The objective of this study was to identify the optimal conditions (alginate concentration and initial pH) required to maximize the efficiency of the bioconversion of alginate into TVFAs and alcohols by anaerobic fermentation.
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H.D. Pham et al. / Bioresource Technology 148 (2013) 601–604
2. Methods 2.1. Feedstock Sodium alginate (80–120 mPas, Wako Pure Chemical Industries Ltd., Japan) was dissolved in distilled water and autoclaved (121 °C for 15 min) then used as a microbial growth substrate. Alginate was the sole carbon source in the medium, which also contained NH4HCO3, 2.0 g/L; KH2PO4, 1.0 g/L; MgSO4.7H2O, 0.01 g/L; NaCl, 0.001 g/L; Na2MoO4.2H2O, 0.001 g/L; CaCl2.2H2O, 0.001 g/L; MnSO4.7H2O, 0.0015 g/L; and FeCl2.4H2O, 0.00388 g/L as nutrient additives. The initial pH was adjusted as required using 5 N NaOH or 5 N HCl. 2.2. Inoculum preparation and fermentation Anaerobically digested sludge was obtained from a municipal wastewater treatment plant in Busan, Korea. In order to enhance the activity of VFA-producing bacteria, an acid pre-treatment (2 N HCl) was applied at 35 °C for 24 h (Lee et al., 2009). A continuous anaerobic fermentation was operated in a 3 L bioreactor with a working volume of 2 L at 35 °C and pH was maintained at 5.5 with 5 N NaOH or 5 N HCl. Part of the fermentation broth was removed daily and replaced (the retention time was 1 day) with a fresh feed (Lee et al., 2009). The concentrations of TVFAs and alcohols in the system were maintained at 12–14 g/L. The effluent from the inoculum system was used as seed culture (equivalent to 10% of working volume) for a series of 500 mL amber reactors with a working volume of 400 mL. The alginate fermentation was operated at 35 °C and 120 rpm. Chloroform (CHCl3; 100 lM) was used as a methanogen inhibitor from both H2/CO2 and acetate. It also inhibited acetate consumption by sulfate reducers (Hu and Chen, 2007). 2.3. Experimental design and selection of variables Response surface methodology (RSM) was used to determine the effects of alginate concentration and initial pH. It was applied to evaluate the relative significance of the experimental variables and to find the optimum conditions within the design boundary of the independent variables, under which TVFA and alcohol yields were maximum. The experiment was based on the central composite in cube design (Montgomery et al., 2011) and consisted of a 2 2 orthogonal design (alginate concentration and initial pH) in order to minimize the number of trials needed to obtain statistically valid results (Table 1). The ranges of independent variables were set 4.0–9.0 g of alginate/L and pH 6.0–10.0 based on preliminary results (data not shown). Each trial with a center point (i.e., Table 1 Experimental design and observed total volatile fatty acids (TVFAs) production in the anaerobic alginate fermentation. Trials
Linear design
Quadratic design
Validation a
1 2 3 4 5a 6 7 8 9 10
Independent variables Alginate concentration (g/L)
Initial pH
4 9 4 9 6.5 2.9 10.0 6.5 6.5 6.2
6 6 10 10 8 8 8 5.2 10.8 7.6
Center point was repeated by three times.
TVFAs yield(%)
30.9 24.0 15.1 20.7 34.7 ± 0.2 23.9 21.6 13.6 8.0 37.0 ± 0.1
6.5 g of alginate/L and initial pH 8.0) was replicated 3 times as previously described. This type of design was used to minimize the number of trials needed to obtain statistically valid results (Song et al., 2007). A sequential procedure of collecting data, estimating polynomials, and checking the adequacy of the model was applied. The method of least squares was used to estimate the parameters in the approximating polynomials. For the statistical analysis, Minitab software (version 15.1.1.0, Minitab Inc., State College, Pennsylvania, USA) was applied to establish the experimental design and to test complex polynomials to model the data. 2.4. Analytical methods Liquid and gas samples were taken daily for analysis. The liquid samples were centrifuged for the detection of VFAs (C2–C6) and alcohols (ethanol, butanol, and propanol) at 3000 rpm for 10 min. The VFAs profile was detected by UV/VIS detector at 210 nm, and alcohols were determined by Refractive Index detector using HPLC (Ultimate 3000, Dionex, USA) with column Aminex HPX-87H. Every analysis was performed at 65 °C under isocratic condition with 2.5 mM H2SO4 as mobile phase. Total organic carbon (TOC) was analyzed by a TOC analyzer (TOC–VCPH, Shimadzu, Japan). The volatile solids (VS) concentration was determined according to the procedures in Standard Methods (APHA-AWWA-WEF., 1998). The carbohydrate concentration was determined using the phenol–sulfuric acid method (Dubois et al., 1956) and pH was monitored by a pH meter (Istek, model 720P, Korea). Gas samples for hydrogen were analyzed by GC-HP5890 with a packed column Hayesep Q (SS, 1.8 m 1/800 , and 80/60 mesh) and a thermal conductivity detector (TCD) of 90 °C, 35 °C, and 120 °C. And methane and carbon dioxide were measured by GC-HP5890 with a flame ionization detector of 180 °C, 35 °C, 280 °C, and 350 °C using Ni catalyst and a packed column Porapak Q (SS, 2 m, 1/800 , 80/100 mesh). 2.5. Calculation The yield of TVFAs (g carbon in TVFAs/g carbon in substrate) was calculated as the amount of carbon in the TVFAs produced divided by the amount of soluble carbon in the substrate feed.
TVFAs yield ð%Þ ¼
nTVFAs nalginate
ð1Þ
where: nTVFAs = the carbon amount of TVFAs produced as observed (mole carbon).nalginate = the carbon amount of alginate feeding as obtained (mole carbon). 3. Results and discussion In this study, acetic-, butyric-, propionic-, lactic-, and valeric acids were the bioconversion metabolites, whereas alcohols (i.e., ethanol, butanol, and propanol) were not detected. In individual VFA, acetic acid was the main constituent with 71.9–95.5% of the TVFAs. Ethanol production from lignocellulosic biomass which has similar chemical structure with alginate has been reported (Nakashima et al., 2011). In contrast, in our study, the utilization of alginate as acidogenic feedstock did not lead to the production of alcohols. A total of 11 trials, including a center point, were run to approximate the response surface for TVFAs production. To find the maximum bioconversion efficiency, increasingly complex equations from linear to quadratic were sequentially tested to model the data obtained from the trials in Table 1. When the data were analyzed using the various models, the P-value of regression was significant
H.D. Pham et al. / Bioresource Technology 148 (2013) 601–604
Fig. 1. Two- and three-dimensional contour plots of the quadratic model for the TVFAs yields (%) with the respect to alginate concentration and initial pH within the design boundary.
at the 1% a-level, whereas the lack of fit was not significant at the 5% a-level only for the quadratic model in Eq. (2).
gTVFAs ¼ 35:241 0:592 x1 3:397 x2 4:769 x21 10:745 x22 þ 3:130 x1 x2
603
where g = experimental value of the TVFAs yield (%), and xi = independent variables i (i = 1 for alginate concentration and 2 for initial pH). Thus, this equation was used to determine the condition that would maximize the bioconversion efficiency of TVFAs by setting the quadratic derivatives of the equation to zero with respect to the independent variables. The RSM model estimated a maximal TVFAs yield of 37.1% at 6.2 g of alginate/L and initial pH 7.6. Twodimensional response surfaces of the quadratic model for TVFAs yield with the corresponding estimated optimums are illustrated in Fig. 1. The response surface of TVFAs yield indicated a clear peak, which showed the optimum condition was inside the design boundary. Response surface analysis showed that the initial pH was significant for TVFAs yield at the 1% a-level, whereas the alginate concentration was not. And none of the interaction between these two variables was significant at the 1% a-level. The optimum pH of 7.6 indicates that slight alkaline environment was suitable ranges for enhancing alginate solubility (Borchard et al., 2005; Haug et al., 1963). The solubility of alginate depends strongly on the dissociation of the backbone carboxylic groups by pH. Alginate is a water soluble polysaccharide containing a linear unbranched chain of b (1 ? 4)-linked-D-mannuronic acid (M)and a (1 ? 4)-linked-L-guluronic acid (G)- residues. It consists of a widely varying composition of M- and G- residues with no regular repeating unit (Kim et al., 2010). The low soluble fractions are composed of molecules that are either predominantly M rich or G rich (i.e., MM and GG), whereas the hydrolysable fractions are made up of a high proportion of alternating MG residues (Pawar and Edgar, 2012). In this study, the maximum yield of TVFAs (i.e., 37.1%) was lower than that of lignocellulosic biomass as a polysaccharide feedstock i.e., 63.0%; (Hu et al., 2004). This suggests that the irregular sequences of alginate presented a challenge during the anaerobic fermentation. In order to verify the accuracy of the model predictions, an additional validation trial was run under the optimal conditions predicted by the model (6.2 g alginate/L and pH 7.6) (Table 1). The residual plots, another indication of the adequacy of the fit of the model, were randomly distributed without any patterns and trends (Fig. 2). Therefore, it was concluded that the model was able to accurately predict the maximum bioconversion conditions for TVFA production using alginate as a biofuels feedstock in anaerobic fermentation.
ð2Þ 4. Conclusions The RSM was successfully applied to determine the optimal conditions with respect to alginate concentration and initial pH for TVFAs production. In the quadratic model, the initial pH significantly affected the yield of TVFAs and no significant interaction between the independent variables was observed. The maximum TVFA yield of 37.1% was determined at 6.2 g/L of alginate and an initial pH of 7.6. Acetic acid was the predominant component of TVFAs mixture (i.e., 95.5%). This novel approach suggests that alginate is a potential biomass resource for biofuel production in anaerobic fermentation.
Acknowledgement
Fig. 2. Residual plots of the quadratic model for the TVFAs yields (%). Each residual was calculated using Eq. (2).
This work was financially supported by the Korea Fisheries Resource Agency of Ministry of Oceans and Fisheries (2012063125100).
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References APHA-AWWA-WEF, 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Health Association, Washington, DC. Borchard, W., Kenning, A., Kapp, A., Mayer, C., 2005. Phase diagram of the system sodium alginate/water: a model for biofilms. Int. J. Biol. Macromol. 35 (5), 247– 256. Borines, M.G., de Leon, R.L., Cuello, J.L., 2013. Bioethanol production from the macroalgae Sargassum spp. Bioresour. Technol. 138, 22–29. Chang, H.N., Kim, N.-J., Kang, J., Jeong, C.M., 2010. Biomass-derived volatile fatty acid platform for fuels and chemicals. Biotechnol. Bioprocess Eng. 15 (1), 1–10. Draget, K.I., Smidsrod, O., Skjak-Braek, G., 2005. Alginates from algae, . first ed.. In: Steinbuchel, A., Rhee, S.K. (Eds.), Polysaccharides and Polyamides in the Food Industry: Properties, Production, and Patents first ed., Vol. 1 Wiley-Blackwell, pp. 1–30. Dubois, M., Gilles, K.A., Hamilton, J.K., Rebers, P.A., Smith, F., 1956. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28 (3), 350–356. Haug, A., Larsen, B., Smidsrod, O., 1963. The degradation of alginates at different ph values. Acta Chem. Scand. 17 (5), 1466–1468. Horn, S.J., Aaaen, I., Ostgaard, K., 2000. Ethanol production from seaweed extract. J. Ind. Microbiol. Biotechnol. 25, 249–254. Hu, B., Chen, S., 2007. Pretreatment of methanogenic granules for immobilized hydrogen fermentation. Int. J. Hydrogen Energy 32 (15), 3266–3273. Hu, Z.-H., Wang, G., Yu, H.-Q., 2004. Anaerobic degradation of cellulose by rumen microorganisms at various pH values. Biochem. Eng. J. 21 (1), 59–62.
Jung, K.A., Lim, S.R., Kim, Y., Park, J.M., 2013. Potentials of macroalgae as feedstocks for biorefinery. Bioresour. Technol. 135, 182–190. Kim, S.J., Dwiatmoko, A.A., Choi, J.W., Suh, Y.W., Suh, D.J., Oh, M., 2010. Cellulose pretreatment with 1-N-butyl-3-methylimidazolium chloride for solid acidcatalyzed hydrolysis. Bioresour. Technol. 101 (21), 8273–8279. Kim, S.S., Ly, H.V., Kim, J., Choi, J.H., Woo, H.C., 2013. Thermogravimetric characteristics and pyrolysis kinetics of Alga Sagarssum sp. biomass. Bioresour. Technol. 139, 242–248. Lee, M.J., Song, J.H., Hwang, S.J., 2009. Effects of acid pre-treatment on bio-hydrogen production and microbial communities during dark fermentation. Bioresour. Technol. 100 (3), 1491–1493. Montgomery, D.C., Runger, G.C., Hubele, N.F., 2011. Engineering Statistics, fifth ed. John Wiley & Sons. Nakashima, K., Yamaguchi, K., Taniguchi, N., Arai, S., Yamada, R., Katahira, S., Ishida, N., Takahashi, H., Ogino, C., Kondo, A., 2011. Direct bioethanol production from cellulose by the combination of cellulase-displaying yeast and ionic liquid pretreatment. Green Chem. 13 (10), 2948. Pawar, S.N., Edgar, K.J., 2012. Alginate derivatization: a review of chemistry, properties and applications. Biomaterials 33 (11), 3279–3305. Ross, A.B., Anastasakis, K., Kubacki, M., Jones, J.M., 2009. Investigation of the pyrolysis behaviour of brown algae before and after pre-treatment using PY– GC/MS and TGA. J. Anal. Appl. Pyrol. 85 (1–2), 3–10. Shi, X., Yu, H., 2006. Continuous production of hydrogen from mixed volatile fatty acids with Rhodopseudomonas capsulata. Int. J. Hydrogen Energy 31 (12), 1641– 1647. Song, M., Kim, N., Lee, S., Hwang, S., 2007. Use of whey permeate for cultivating Ganoderma lucidum mycelia. J. Dairy Sci. 90 (5), 2141–2146.
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Short Communication
Maximization of volatile fatty acids production from alginate in acidogenesis Hong Duc Pham a, Jiyun Seon b, Seong Chan Lee a, Minkyung Song b,⇑, Hee-Chul Woo a,⇑ a b
Department of Chemical Engineering, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 608-739, Republic of Korea The Institute of Cleaner Production, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 608-739, Republic of Korea
h i g h l i g h t s The VFA production from alginate as a feedstock was approached for the first time. Optimization of alginate concentration and initial pH was determined using RSM. The effect of variables on VFAs production was evaluated. Acetic acid was the major component of the alginate fermentation.
a r t i c l e
i n f o
Article history: Received 22 June 2013 Received in revised form 20 August 2013 Accepted 21 August 2013 Available online 30 August 2013 Keywords: Alginate Anaerobic fermentation Marine brown algae Response surface methodology Volatile fatty acids
a b s t r a c t In this study, the response surface methodology (RSM) was applied to determine the optimum fermentative condition of alginate with the respect to the simultaneous effects of alginate concentration and initial pH to maximize the production of total volatile fatty acids (TVFAs) and alcohols. The results showed that the alginate fermentation was significantly affected by initial pH than by alginate concentration and there was no interaction between the two variables. The optimum condition was 6.2 g alginate/L and initial pH 7.6 with a maximum TVFAs yield of 37.1%. Acetic acids were the main constituents of the TVFAs mixtures (i.e., 71.9–95.5%), while alcohols (i.e., ethanol, butanol, and propanol) were not detected. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Fundamental issues in the current energy system such as dramatic increase in fossil fuel prices, sharp depletion of petrol, and climate change, have led scientists to investigate new alternative energy sources. Among these, marine biomass from sources such as microalgae and macro-algae, represent one of the most promising resources due to its cleanliness and sustainability (Kim et al., 2013). Recently, marine macro-algae, classified as green-, red-, and brown-algae, have attracted attention as biomass resources for biofuels and biomaterials (Chang et al., 2010). For instance, bioalcohol (Borines et al., 2013) and bio-hydrogen (Shi and Yu, 2006) can be produced from these algae by anaerobic fermentation and bio-oil (Ross et al., 2009) can be produced by pyrolysis. Among
⇑ Corresponding authors. Tel.: +82 51 629 7646; fax: +82 51 629 6429 (M. Song). Tel.: +82 51 629 6436; fax: +82 51 629 6429 (H.-C. Woo). E-mail addresses: [email protected] (M. Song), [email protected] (H.-C. Woo). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.08.128
the macro-algae, massive brown algae are primarily composed of polysaccharides such as alginate, laminaran, fucoidan, mannitol, and cellulose (Chang et al., 2010) and the feasibility of the fermentative conversion of these polysaccharides into liquid biofuels has demonstrated (Horn et al., 2000). Alginate is a polysaccharide, which accounts for up to 40% of the dry weight in brown algae and is a principal component of the cell wall (Draget et al., 2005; Jung et al., 2013). Although this new marine biomass presents a high potential as a polysaccharide feedstock for liquid biofuels, the use of alginate in fermentation processes is still a challenge due to its low solubility in water and its limited usage as a microbial substrate, compared to terrestrial biomass components (Pawar and Edgar, 2012). In this study, we investigated the experimental conditions that are necessary to maximize TVFA and alcohol production by the fermentation of alginate. The objective of this study was to identify the optimal conditions (alginate concentration and initial pH) required to maximize the efficiency of the bioconversion of alginate into TVFAs and alcohols by anaerobic fermentation.
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H.D. Pham et al. / Bioresource Technology 148 (2013) 601–604
2. Methods 2.1. Feedstock Sodium alginate (80–120 mPas, Wako Pure Chemical Industries Ltd., Japan) was dissolved in distilled water and autoclaved (121 °C for 15 min) then used as a microbial growth substrate. Alginate was the sole carbon source in the medium, which also contained NH4HCO3, 2.0 g/L; KH2PO4, 1.0 g/L; MgSO4.7H2O, 0.01 g/L; NaCl, 0.001 g/L; Na2MoO4.2H2O, 0.001 g/L; CaCl2.2H2O, 0.001 g/L; MnSO4.7H2O, 0.0015 g/L; and FeCl2.4H2O, 0.00388 g/L as nutrient additives. The initial pH was adjusted as required using 5 N NaOH or 5 N HCl. 2.2. Inoculum preparation and fermentation Anaerobically digested sludge was obtained from a municipal wastewater treatment plant in Busan, Korea. In order to enhance the activity of VFA-producing bacteria, an acid pre-treatment (2 N HCl) was applied at 35 °C for 24 h (Lee et al., 2009). A continuous anaerobic fermentation was operated in a 3 L bioreactor with a working volume of 2 L at 35 °C and pH was maintained at 5.5 with 5 N NaOH or 5 N HCl. Part of the fermentation broth was removed daily and replaced (the retention time was 1 day) with a fresh feed (Lee et al., 2009). The concentrations of TVFAs and alcohols in the system were maintained at 12–14 g/L. The effluent from the inoculum system was used as seed culture (equivalent to 10% of working volume) for a series of 500 mL amber reactors with a working volume of 400 mL. The alginate fermentation was operated at 35 °C and 120 rpm. Chloroform (CHCl3; 100 lM) was used as a methanogen inhibitor from both H2/CO2 and acetate. It also inhibited acetate consumption by sulfate reducers (Hu and Chen, 2007). 2.3. Experimental design and selection of variables Response surface methodology (RSM) was used to determine the effects of alginate concentration and initial pH. It was applied to evaluate the relative significance of the experimental variables and to find the optimum conditions within the design boundary of the independent variables, under which TVFA and alcohol yields were maximum. The experiment was based on the central composite in cube design (Montgomery et al., 2011) and consisted of a 2 2 orthogonal design (alginate concentration and initial pH) in order to minimize the number of trials needed to obtain statistically valid results (Table 1). The ranges of independent variables were set 4.0–9.0 g of alginate/L and pH 6.0–10.0 based on preliminary results (data not shown). Each trial with a center point (i.e., Table 1 Experimental design and observed total volatile fatty acids (TVFAs) production in the anaerobic alginate fermentation. Trials
Linear design
Quadratic design
Validation a
1 2 3 4 5a 6 7 8 9 10
Independent variables Alginate concentration (g/L)
Initial pH
4 9 4 9 6.5 2.9 10.0 6.5 6.5 6.2
6 6 10 10 8 8 8 5.2 10.8 7.6
Center point was repeated by three times.
TVFAs yield(%)
30.9 24.0 15.1 20.7 34.7 ± 0.2 23.9 21.6 13.6 8.0 37.0 ± 0.1
6.5 g of alginate/L and initial pH 8.0) was replicated 3 times as previously described. This type of design was used to minimize the number of trials needed to obtain statistically valid results (Song et al., 2007). A sequential procedure of collecting data, estimating polynomials, and checking the adequacy of the model was applied. The method of least squares was used to estimate the parameters in the approximating polynomials. For the statistical analysis, Minitab software (version 15.1.1.0, Minitab Inc., State College, Pennsylvania, USA) was applied to establish the experimental design and to test complex polynomials to model the data. 2.4. Analytical methods Liquid and gas samples were taken daily for analysis. The liquid samples were centrifuged for the detection of VFAs (C2–C6) and alcohols (ethanol, butanol, and propanol) at 3000 rpm for 10 min. The VFAs profile was detected by UV/VIS detector at 210 nm, and alcohols were determined by Refractive Index detector using HPLC (Ultimate 3000, Dionex, USA) with column Aminex HPX-87H. Every analysis was performed at 65 °C under isocratic condition with 2.5 mM H2SO4 as mobile phase. Total organic carbon (TOC) was analyzed by a TOC analyzer (TOC–VCPH, Shimadzu, Japan). The volatile solids (VS) concentration was determined according to the procedures in Standard Methods (APHA-AWWA-WEF., 1998). The carbohydrate concentration was determined using the phenol–sulfuric acid method (Dubois et al., 1956) and pH was monitored by a pH meter (Istek, model 720P, Korea). Gas samples for hydrogen were analyzed by GC-HP5890 with a packed column Hayesep Q (SS, 1.8 m 1/800 , and 80/60 mesh) and a thermal conductivity detector (TCD) of 90 °C, 35 °C, and 120 °C. And methane and carbon dioxide were measured by GC-HP5890 with a flame ionization detector of 180 °C, 35 °C, 280 °C, and 350 °C using Ni catalyst and a packed column Porapak Q (SS, 2 m, 1/800 , 80/100 mesh). 2.5. Calculation The yield of TVFAs (g carbon in TVFAs/g carbon in substrate) was calculated as the amount of carbon in the TVFAs produced divided by the amount of soluble carbon in the substrate feed.
TVFAs yield ð%Þ ¼
nTVFAs nalginate
ð1Þ
where: nTVFAs = the carbon amount of TVFAs produced as observed (mole carbon).nalginate = the carbon amount of alginate feeding as obtained (mole carbon). 3. Results and discussion In this study, acetic-, butyric-, propionic-, lactic-, and valeric acids were the bioconversion metabolites, whereas alcohols (i.e., ethanol, butanol, and propanol) were not detected. In individual VFA, acetic acid was the main constituent with 71.9–95.5% of the TVFAs. Ethanol production from lignocellulosic biomass which has similar chemical structure with alginate has been reported (Nakashima et al., 2011). In contrast, in our study, the utilization of alginate as acidogenic feedstock did not lead to the production of alcohols. A total of 11 trials, including a center point, were run to approximate the response surface for TVFAs production. To find the maximum bioconversion efficiency, increasingly complex equations from linear to quadratic were sequentially tested to model the data obtained from the trials in Table 1. When the data were analyzed using the various models, the P-value of regression was significant
H.D. Pham et al. / Bioresource Technology 148 (2013) 601–604
Fig. 1. Two- and three-dimensional contour plots of the quadratic model for the TVFAs yields (%) with the respect to alginate concentration and initial pH within the design boundary.
at the 1% a-level, whereas the lack of fit was not significant at the 5% a-level only for the quadratic model in Eq. (2).
gTVFAs ¼ 35:241 0:592 x1 3:397 x2 4:769 x21 10:745 x22 þ 3:130 x1 x2
603
where g = experimental value of the TVFAs yield (%), and xi = independent variables i (i = 1 for alginate concentration and 2 for initial pH). Thus, this equation was used to determine the condition that would maximize the bioconversion efficiency of TVFAs by setting the quadratic derivatives of the equation to zero with respect to the independent variables. The RSM model estimated a maximal TVFAs yield of 37.1% at 6.2 g of alginate/L and initial pH 7.6. Twodimensional response surfaces of the quadratic model for TVFAs yield with the corresponding estimated optimums are illustrated in Fig. 1. The response surface of TVFAs yield indicated a clear peak, which showed the optimum condition was inside the design boundary. Response surface analysis showed that the initial pH was significant for TVFAs yield at the 1% a-level, whereas the alginate concentration was not. And none of the interaction between these two variables was significant at the 1% a-level. The optimum pH of 7.6 indicates that slight alkaline environment was suitable ranges for enhancing alginate solubility (Borchard et al., 2005; Haug et al., 1963). The solubility of alginate depends strongly on the dissociation of the backbone carboxylic groups by pH. Alginate is a water soluble polysaccharide containing a linear unbranched chain of b (1 ? 4)-linked-D-mannuronic acid (M)and a (1 ? 4)-linked-L-guluronic acid (G)- residues. It consists of a widely varying composition of M- and G- residues with no regular repeating unit (Kim et al., 2010). The low soluble fractions are composed of molecules that are either predominantly M rich or G rich (i.e., MM and GG), whereas the hydrolysable fractions are made up of a high proportion of alternating MG residues (Pawar and Edgar, 2012). In this study, the maximum yield of TVFAs (i.e., 37.1%) was lower than that of lignocellulosic biomass as a polysaccharide feedstock i.e., 63.0%; (Hu et al., 2004). This suggests that the irregular sequences of alginate presented a challenge during the anaerobic fermentation. In order to verify the accuracy of the model predictions, an additional validation trial was run under the optimal conditions predicted by the model (6.2 g alginate/L and pH 7.6) (Table 1). The residual plots, another indication of the adequacy of the fit of the model, were randomly distributed without any patterns and trends (Fig. 2). Therefore, it was concluded that the model was able to accurately predict the maximum bioconversion conditions for TVFA production using alginate as a biofuels feedstock in anaerobic fermentation.
ð2Þ 4. Conclusions The RSM was successfully applied to determine the optimal conditions with respect to alginate concentration and initial pH for TVFAs production. In the quadratic model, the initial pH significantly affected the yield of TVFAs and no significant interaction between the independent variables was observed. The maximum TVFA yield of 37.1% was determined at 6.2 g/L of alginate and an initial pH of 7.6. Acetic acid was the predominant component of TVFAs mixture (i.e., 95.5%). This novel approach suggests that alginate is a potential biomass resource for biofuel production in anaerobic fermentation.
Acknowledgement
Fig. 2. Residual plots of the quadratic model for the TVFAs yields (%). Each residual was calculated using Eq. (2).
This work was financially supported by the Korea Fisheries Resource Agency of Ministry of Oceans and Fisheries (2012063125100).
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