Enhancement of bioenergy production and effluent quality by integrating optimized acidification with submerged anaerobic membrane bioreactor

Bioresource Technology 101 (2010) S7–S12

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Enhancement of bioenergy production and effluent quality by integrating optimized acidification with submerged anaerobic membrane bioreactor Emma Jeong a, Hyun-Woo Kim b, Joo-Youn Nam a, Hang-Sik Shin a,* a b

Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea Center for Environmental Biotechnology, Biodesign Institute at Arizona State University, 1001 S. McAllister Ave., Tempe, AZ 85287, USA

a r t i c l e

i n f o

Article history: Received 28 October 2008 Received in revised form 23 April 2009 Accepted 27 April 2009 Available online 24 May 2009 Keywords: Organic waste Response surface methodology Optimization Submerged anaerobic membrane bioreactors

a b s t r a c t To ensure effluent quality in the treatment of high-strength organic waste and enhance CH4 production, this study investigates the applicability of process optimization and a submerged anaerobic membrane bioreactor (SAMBR) for a two-phase anaerobic digestion (TPAD) system. The use of response surface methodology (RSM) suggests that the optimum conditions for maximum volatile fatty acids (VFA) production were a hydraulic retention time (HRT) of 2.01 days and a substrate concentration of 29.30 g/L based on chemical oxygen demands (COD). A confirmation experiment showed that an empirical model could predict a VFA increase of 76% under the proposed conditions with a relative error of 4%. SAMBRs could convert the VFA in acidogenic effluent to CH4 with an average production rate of 0.28 m3/m3/d in an HRT of 14 days. All of the SAMBRs could achieve COD removal rates of over 99% by the increased solid retention time and secondary membrane formation. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Anaerobic digestion offers practical solutions for the treatment of high-strength organic wastes such as sewage sludge (Appels et al., 2008), municipal solid waste (Ismail and Abderrezaq, 2007), livestock wastewater (Cantrell et al., 2008) and food waste (Kim et al., 2007). This method provides several advantages, including low sludge production, low energy consumption, waste stabilization and, more significantly, biogas recovery (Speece, 1996; Mata-Alvarez et al., 2000). Recently, biomethanation from organic waste has come into the spotlight particularly due to the active research being conducted in alternative energy fields for reasons of energy security, diversity, and sustainability. Moreover, the anaerobic digestion of biomass is a widely commercialized technology, in contrast to other energy sources such as solar, wind, or geothermal. In anaerobic digestion, acidogenic and methanogenic microorganisms have different optimal growth conditions with different physiologies, growth kinetics, nutrient requirements, and sensitivity levels to environmental changes (Pohland and Ghosh, 1971). The imbalance between the two groups of microorganisms is the primary reason for digester failure and instability (Demirel and Yenigün, 2002). Since hydrolysis was found to be the rate-limiting step in the overall anaerobic digestion process, particularly in degrading particulate matter (Eastman et al., 1981), proper control * Corresponding author. Tel.: +82 42 350 3613; fax: +82 42 350 8460. E-mail address: [email protected] (H.-S. Shin). 0960-8524/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2009.04.064

of the acidogenesis phase is critical for the successful operation of two-phase anaerobic digestion (TPAD). Many factors, including the substrate concentration, hydraulic retention time (HRT), temperature, pH, and process configuration, affect the performance of the acidogenesis phase (Veeken et al., 2000). In conventional complete mixed digesters, HRT approaches the solid retention time (SRT). Therefore, in order to prevent the washout of microorganisms and untreated organic particles, acidogenesis requires sufficient time for biodegradation by HRT adjustment. Effective HRT for acidogenesis varies from 0.25 to 2 days owing to the use of different substrates, reactor configurations, and due to other environmental conditions (Demirel and Yenigün, 2004; Liu et al., 2006). Additionally, a substrate concentration that involves a half velocity concentration (Ks) in a Monod kinetics is the most important factor, as anaerobic processes are severely limited by the enzymatic, microbial activity and production rates (Speece, 1996). Conventional experimentation for process optimization is timeconsuming, and reaching a true optimum level is difficult because it only considers one variable at a time, implying that the interaction between variables is of no significance (Kim et al., 2007). This limitation can be overcome by simultaneous consideration of important variables using response surface methodology (RSM) (Hwang and Hansen, 1997). RSM is a collection of stepwise mathematical and statistical techniques that are useful for designing experiments for optimization and building empirical models for prediction, permitting evaluations of the relative significance of several independent factors (Montgomery, 2001). RSM has been

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Table 1 Central composite experimental design matrix to optimize the HRT and substrate concentration simultaneously. No.

Coded variables X1: HRT

X2: substrate concentration

X1: HRT (day)

X2: substrate concentration (g COD/L)

1 2 3 4 5 6 7 8 9a 10a

0 1 1.414 1 0 1 1.414 1 0 0

1.414 1 0 1 1.414 1 0 1 0 0

2.0 2.5 2.7 2.5 2.0 1.5 1.3 1.5 2.0 2.0

15 19 29 39 42 39 29 19 29 29

a

Experimental variables

A duplicate center point.

widely applied to optimize and evaluate the interactive effects of independent factors in numerous biochemical processes, including the methane fermentation from the effluent in a bio-hydrogen fermentation process (Wang et al., 2008); determining the influence of the pH, temperature and substrate concentration on the acidogenesis of sucrose-rich wastewater (Wang et al., 2005); acidogenesis of cattail by rumen cultures (Hu et al., 2006). However, little investigation has been done regarding the optimization of operating parameters in the acidogenic fermentation of organic waste, particularly in co-fermentation. The successful and cost-effective application of anaerobic digestion in waste and wastewater treatment can be attributed to an efficient uncoupling of the solid retention time from the hydraulic retention time. This is usually accomplished through biofilm and/ or granule formation due to the slow growth rate of methanogens. A submerged anaerobic membrane bioreactor (SAMBR) is an interesting alternative to the application of anaerobic biotechnology, as they can ensure biomass retention through the use of membrane modules, providing stable performance without biomass washout, small foot print, and solid free final effluent (Hu and Stuckey, 2006; Saddoud et al., 2007). The objective of the present study is to investigate an innovative TPAD system combining optimized acidogenesis and SAMBR for the treatment of mixed organic waste. For the acidogenesis process, RSM was applied to determine the optimum HRT and substrate concentration. The applicability of SAMBR for the subsequent methanogenesis process was evaluated in terms of CH4 gas production and organic removal at different HRTs.

Y ¼ b0 þ

k X i¼1

bi xi þ

k X

bii x2i þ

i¼1

k X k X

bij xi xj

ð1Þ

i¼1< j¼2

where Y is the predicted response, x denotes the independent variables, and b is the coefficient. 2.2. Operating procedures for acidogenesis Three types of organic waste–sewage sludge, food waste, and livestock wastewater–were mixed as a co-substrate according to the results of preliminary research (Jeong et al., 2008). The optimal sewage sludge:food waste:livestock wastewater mixing ratio as a substrate was 1.0:1.1:0.4 based on the chemical oxygen demands (COD) of each constituent, respectively. The sewage sludge was taken from a thickener in a publicly owned wastewater treatment plant in D city, South Korea, the food waste was collected from a university cafeteria, and the livestock wastewater was effluent from a centrifuge process in a livestock waste treatment plant in K city, South Korea. All substrates were stored in refrigerators at 4 °C prior to use. The physico-chemical characteristics of each substrate are presented in Table 2. Seed microorganisms in the range of 19.6–24.0 g/L of a volatile suspended solid (VSS) were taken from a full-scale egg-shaped anaerobic digester at the same publicly owned wastewater treatment plant. Reactors (Wheaton, USA) with an effective working volume of 0.5 L were operated in a mesophilic (35 °C) condition within a shaking incubator at 200 rpm (Vision Scientific Co., VS8480-SR) to provide better contact between the substrates and microorganisms. During the

2. Methods 2.1. Acidogenesis optimization To determine the optimum operating conditions for the acidogenesis process, a central composite design (CCD) was used. This design is known to be an efficient design for the fitting of a second-order response surface model. It consists of factorial runs (four experiments), axial runs (four experiments) and center runs (two experiments). Design-Expert (Stat-Ease, Inc., USA) software was utilized to discover the optimum conditions of two independent variables, HRT and the substrate concentration. The target response was the percent increase of the volatile fatty acids (VFA) concentration, as VFA are the major products of acidogenesis and are important precursors of methane in the preceding methanogenesis phase. The matrix corresponding to the CCD is presented in Table 1. The response was fitted using a polynomial quadratic equation in order to correlate the response to the independent variables. The general form of the predictive polynomial quadratic equation is

Table 2 Physical and chemical characteristics of the mixed organic substrates. Item

Unit

Substrate

pH Alkalinity Total COD (TCOD) Soluble COD (SCOD) Total solids (TS) Volatile solids (VS) Total suspended solids (TSS) Volatile suspended solids (VSS) Total kjeldahl nitrogen (TKN) Ammonia nitrogen (NHþ 4 –N) TCOD/TKN Total carbohydrates Soluble carbohydrates Total volatile fatty acids (TVFA) Acetate (HAc) Propionate (HPr) Butyrate (HBu) Valerate (HVa)

– mg/L as CaCO3 mg/L mg/L mg/L mg/L mg/L mg/L mg/L as NHþ 4 –N mg/L as NHþ 4 –N – mg/L as glucose mg/L as glucose mg COD/L mg COD/L mg COD/L mg COD/L mg COD/L

5.8 2635.0 44,598.4 21,417.3 31,588.9 24,683.6 26,816.7 21,166.7 2400.2 1069.9 18.6 11,514.6 9614.5 11,318.0 2954.8 2137.8 4419.4 1805.9

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start-up process, 200 mL of seed sludge was inoculated, and the optimized co-substrates were fed by adjusting the feed flow rate for microbial adaptation. Three inflow/outflow instances were conducted semi-continuously per day, and a different feed flow rate was applied for each reactor according to the HRT in the experimental design. A steady-state was assumed after five turnovers, and more than five samples of effluent were analyzed to determine the average performance parameters.

an Amicon model 8200 ultrafiltration stirred cell (200 mL process volume). An Amicon cellulose acetate membrane YM 30 (MWCO: 30,000 Dalton, diameter: 63.5 mm, Amicon Inc., USA) was used for the filtration of sludge following procedures indicated by Shin and Kang (2003). Other items were analyzed according to the procedures described in Standard Methods (APHA, 2005).

2.3. Experimental conditions – methanogenesis

3.1. Acidogenesis optimization

For efficient stabilization of the acidogenic effluent, the feasibility of using SAMBR was tested for the methanogenesis process to determine the best operational condition for coupling. The process performance was evaluated by decreasing the HRT from 20 days. A SAMBR with a working volume of 0.6 L was operated semi-continuously under mesophilic (35 °C) conditions. A U-shaped membrane module was placed in the middle of the reactor. The module consisted of a hollow fiber microfiltration membrane (Sumitomo Electric Fine Polymer Inc., Japan). The fiber, made of polypropylene, had a nominal pore size of 0.45 lm. The effective membrane filtration area was 0.003 m2 and the total flux (measured as the permeation rate) from each reactor was controlled using a peristaltic pump (Cole-Parmer, USA).

Fig. 1 shows the average increase in the VFA concentration in an acidogenic reactor under a steady-state. By applying regression analysis to the actual value, the experimental results were fitted to the following second-order polynomial equation (Eq. (2)):

2.4. Analytical methods To determine the VFA concentration, samples were initially filtered through a 0.45 lm polyvinylidene fluoride (PVDF) filter (Whatman, UK) and measured by high-performance liquid chromatography (HPLC; Spectra Physics P2000) with an Aminex HPX87H (300  7.8 mm) column and a UV (210 nm) detector. The pH was monitored using a pH meter (Orion 720A), and the carbohydrates were determined according to the phenol–sulfuric method with glucose as a standard. Absorbance was measured against a blank at 480, 484, and 490 nm with Beckman UV–visible spectrophotometer. The CH4 gas content was analyzed using a gas chromatograph (GC, Gow Mac series 580) equipped with a thermal conductivity detector (TCD) and a 2 m  2 mm stainless-steel column packed with a Porapak Q mesh (80/100). The produced CH4 was adjusted to the volume at the standard temperature (0 °C) and pressure (1 atm). Membrane resistance was analyzed using

3. Results and discussion

Y ¼ 531:40 þ 327:37X 1 þ 18:74X 2  0:21X 1 X 2  79:73X 21  0:31X 22

ð2Þ

Here, X1 is the HRT (day) and X2 is the substrate concentration (g COD/L). Table 3 shows analysis of variance (ANOVA) results; this method is important in determining the adequacy and significance of a predictive model (Eq. (2)). The second-order polynomial model obtained at a confidence level of 95% is significant with a model F-value of 10.81. This implies that there is only a 1.94% chance of variation due to noise. The goodness of fit can be confirmed by the determination coefficient (R2); a high determination coefficient (R2 = 0.931) indicates that 93.1% of the variability in the response is

Table 3 Analysis of variance (ANOVA) for a quadratic response surface model. Source

Sum of squares

DF

Mean square

F-value

Prob. > F

Model X1 X2 X1X2 X 21 X 22 Residual Lack of fit

4751.27 11.34 30.24 4.41 1816.02 4466.07 351.71 333.10

5 1 1 1 1 1 4 3

950.25 11.34 30.24 4.41 1816.02 4466.07 87.93 111.03

10.81 0.13 0.34 0.05 20.65 50.79

0.0194a 0.7376 0.5891 0.8338 0.0105 0.0020

5.97

0.2902

a

Significant under 95% level of confidence.

Fig. 1. Variations of VFA increase depending on the HRT and substrate concentration in each semi-continuous acidogenesis following the central composite designs of RSM: (a) average VFA increase and (b) contour plots of a fitted response surface modified to the proportion of the VFA increase.

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Predicted values of VFA increase (%)

explainable by the model, whereas the balance (6.89%) is explained by the presence of residues. The non-significant lack of fit explains that the quadratic model was valid in the present work. A P-value of less than 0.05 can determine the significance of each coefficient listed in Table 3. This implies that the second-order effects of the HRT (X 21 ) and substrate concentration (X 22 ) are highly significant. In addition, the interaction terms of the HRT and the substrate concentration were found to be insignificant, as is evident from their respective P-values. Although the first-order effect and interaction were not significant, they cannot be eliminated to support the hierarchy of the model. This model, therefore, can be used to predict an increase in the VFA using the information of the HRT and substrate concentration within the design space. A contour plot of the response surface (Fig. 1) shows the effect of the HRT, substrate concentration and the interactions between these two factors on the increase in the VFA. The maximum VFA increase obtained here is very close to the center of the experimental range. Moreover, the results show that the amount of VFA production increased as the HRT increased from 1.5 to 2.0 days. At an HRT longer than 2 days, the amount of VFA increase decreased, which may be explained by the growth of VFA scavengers such as methanogens. In this study, it was found that the VSS concentration increased from 11.0 to 13.3 g VSS/L as the HRT increased from 2.0 to 2.7 days at same substrate concentration (29 g COD/L). This is similar to the findings of other studies, one of which (Elefsiniotis and Oldham, 1994) reported that the VFA concentration increased as the HRT increased to 12 h during the acidogenesis of primary sludge, but started to decrease as the HRT increased further to 15 h. Moreover, they detected CH4 gas in the head space, which was most likely due to the onset of methanogenesis. The optimum operating conditions estimated by Eq. (2) were 2.01 days of HRT and 29.30 g COD/L of substrate concentration, respectively. A previous study indicated that the optimal HRTs were 0.22–0.40 days for partial acidification of cheese-whey wastewater (Yang et al., 2003), 0.5 days for dairy wastewater (Demirel and Yenigün, 2004), and1.25 days for a mixture of municipal and industrial wastewaters (Maharaj and Elefsiniotis, 2001). The longer optimum HRT in this study resulted from the rather complex particulate nature of the substrates. The initial pH of the substrate was 5.8. For all of the reactors, the pH remained in the range of 5.5–6.0 during the entire operation without alkalinity addition. This is the optimal pH range for acidogenic metabolism (Veeken et al., 2000; Hu et al., 2006). This was mainly due to the strong buffering effects of alkalinity of the raw livestock wastewater.

80

60

40

2

R =0.931

20

0 0

20

40

60

80

Actual values of VFA increase (%) Fig. 2. Parity plot showing the distribution of the experimental vs. predicted values of the VFA increase in the confirmation experiment.

Fig. 3. Profile of CH4 production rates (MPR) in SAMBRs at different HRTs.

3.2. Experimental verification of the statistical model To verify the model prediction, a confirmation experiment was carried out under the optimum conditions. It was observed that the increase in the percentage of VFA reached 76%. The parity plot shown in Fig. 2 shows a satisfactory correlation between the experimental and predicted values in terms of the VFA increase. These confirmations demonstrate that RSM with an appropriate experimental design can be applied effectively to the optimization of an acidogenic fermentation process of organic wastes. These findings also suggest that the RSM strategy can provide more efficient and meaningful experiments for optimization with reduced experimental runs and statistical analysis. 3.3. Methanogenesis using SAMBR Fig. 3 shows the CH4 production rate (MPR) of the three SAMBRs at different HRTs. Although the MPR in all of the SAMBRs shows a wide fluctuation, as reported in a previous study (Padmasiri et al., 2007), this may be partially explained by the loss of CH4 together with the permeate as the membrane suction shows an irregular fluctuation in the gas level. The average MPR at a steady state increased as the HRT decreased: 0.10, 0.15, and 0.28 m3/m3/d at HRT 20, 16, and 14 days, respectively. Although the SAMBR with an HRT of 14 days showed unstable performance during the initial stage due to the high organic loading rate (1.8 kg COD/m3/day), it showed superior performance after acclimation. Previous researchers showed that the performance of an anaerobic membrane bioreactor can be affected by operating conditions of the membrane morphology, substrate characteristics, microorganisms, OLR and other factors. He et al. (2005) attained a MPR value of 0.63 m3/m3/d with a COD removal rate of 81–94% from food factory wastewater using a UF membrane. Hu and Stuckey (2006) reported a low MPR value of 0.07 m3/m3/d using a polyethylene SAMBR. Saddoud et al. (2007) operated a side-stream anaerobic membrane bioreactor for treating municipal wastewater with an OLR of 2 kg COD/m3/d; they recorded a MPR value of 0.54 m3/m3/d. Considering these earlier anaerobic membrane bioreactor studies, the present SAMBR shows a comparable MPR under a short HRT when treating optimized acidogenic effluent. Fig. 4 shows profiles of the COD removal efficiency for all of the SAMBRs at different HRTs. Clearly, all achieved a high COD removal efficiency of over 99% regardless of the HRT. Although the COD removal efficiency tended to fluctuate at a shorter HRT, it stabilized within 60 days of acclimation. This fluctuation may be due to the microbial stress at a low HRT, which can lead to increases of solu-

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thy flux decline was observed in operation periods that exceeded 90 days; thus, a fouling control was not necessary (Fig. 4). 4. Conclusions

Fig. 4. Profile of COD removal efficiency and flux in SAMBRs at different HRTs.

ble microbial products (SMP) and extracellular polymeric substances (EPS) (Aquino et al., 2006; Meng et al., 2007). SMP and EPS can be expressed as soluble cellular macromolecules such as carbohydrates, proteins, and nucleic acids, which are released to liquid. They can be calculated as supernatant dissolved organic carbon (DOC) (Le-Clech et al., 2006). In addition, colloidal particles originated from hydrolysis and acidogenesis processes can be another source affecting the performance fluctuation. In this study, it was found that DOC increased as HRT decreased: 105.2, 291.4, and 357.1 mg DOC/L at HRT 20, 16 and 14 days, respectively. These results are in good agreement with the trend observed by Aquino et al. (2006). After 60 days of operation, however, the COD removal efficiency recovered up to 99% in all SAMBR operations. This can be attributed to a cake/gel formation on the membrane surface by those colloids and small particles. As a cake layer functioned as a secondary membrane, it reduced the direct contact of small colloidal particles to the membrane surface. In this study, the formation of a secondary membrane with respect to HRT verified by resistance analysis. Jeison et al. (2008) also showed the relationship between cake resistance and formation of a fouling layer that can act as a secondary membrane. Cake resistance increased as HRT decreased: 2.15, 2.54 and 2.96  1013 m1 at HRT of 20, 16, and 14 days, respectively, showing a good correlation with DOC concentration. Thus, it can be concluded that the operation of a SAMBR at a short HRT affects the increase of microbial products and untreated colloidal particles in the liquid. They ultimately accumulate on the membrane surface and form a secondary membrane together. The effect of a secondary membrane, effectively rejecting large particles and bringing high permeability, has been reported in not only an aerobic but also an anaerobic membrane bioreactor (Lee et al., 2003; Akram and Stuckey, 2008). During the operation of the reactor at a HRT of 20 days, there was a sudden drop in the COD removal efficiency (Fig. 4) caused by membrane damage due to a temporary malfunction of the mixing system. This decrease eventually recovered, most likely due to similar mechanisms on the membrane surface. This highlights the importance of secondary membrane formation in the operation of a SAMBR. Hu and Stuckey (2007) reported that a secondary membrane formed within 15 days of operation. This rapid formation is highly possible in that a shorter HRT can lead to greater production of microbial products owing to the change of the microbial activity under longer SRT (Tay et al., 2003). As a result, a SAMBR with a HRT of 14 days (1.8 kg COD/m3/day) showed the best performance in terms of the MPR and organic removal efficiency due to the combination of elongated SRT and secondary membrane formation. No notewor-

In this study, a TPAD system combining process optimization and SAMBR technology was applied in the effective treatment of high-strength organic wastes. RSM was used to verify the meaningful and precise optimum operating conditions in the acidogenesis phase. According to the RSM experiments, the optimum values of variables included a substrate concentration of 29.30 g COD/L with a HRT of 2.01 days. Under this obtained optimum condition, the predicted increase in the VFA was approximately 73%. The result of a confirmation experiment was in good agreement with the values predicted by the model within a relative error of 4%. During the methanogenesis phase, a maximum MPR value of 0.28 m3/m3/ d was achieved at a HRT of 14 days with a COD removal rate of over 99%. Increased amount of microbial macromolecules and untreated particles was caused by HRT decrease. However, they played an important role in the formation of the secondary membrane that contributed to maintaining high organic removal efficiency. From a sustainable viewpoint, this modified TPAD system provides a promising alternative to improve bioenergy recovery while decreasing the need for the additional treatments that are eventually required in conventional systems. Acknowledgements This work was supported by New & Renewable Energy R&D Program (Grant No. 2006-NBI-02P0130102007) under the Korea Ministry of Knowledge Economy (MKE). References Akram, A., Stuckey, D.C., 2008. Flux and performance improvement in a submerged anaerobic membrane bioreactor (SAMBR) using powdered activated carbon (PAC). Process Biochemistry 43, 93–102. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. APHA, AWWA and WPCF, Washington, DC. Appels, L., Baeyens, J., Degreve, J., Dewil, R., 2008. Principles and potential of the anaerobic digestion of waste-activated sludge. Progress in Energy and Combustion Science 34, 755–781. Aquino, S.F., Hu, A.Y., Akram, A., Stuckey, D.C., 2006. Characterization of dissolved compounds in submerged anaerobic membrane bioreactors (SAMBRs). Journal of Chemical Technology and Biotechnology 81, 1894–1904. Cantrell, K.B., Ducey, T., Ro, K.S., Hunt, P.G., 2008. Livestock waste-to-bioenergy generation opportunities. Bioresource Technology 99, 7941–7953. Demirel, B., Yenigün, O., 2002. Two-phase anaerobic digestion processes: a review. Journal of Chemical Technology and Biotechnology 77, 743–755. Demirel, B., Yenigün, O., 2004. Anaerobic acidogenesis of dairy wastewater: the effects of variations in hydraulic retention time with no pH control. Journal of Chemical Technology and Biotechnology 79, 755–760. Eastman, P.A., Rico, J.L., Polanco, F.Fdz., 1981. Anaerobic treatment of cheese whey in a two-phase reactor. Environmental Technology 12, 355–362. Elefsiniotis, P., Oldham, W.K., 1994. Anaerobic acidogenesis of primary sludge – the role of solids retention time. Biotechnology and Bioengineering 44, 7–13. He, Y.L., Xu, P., Li, C.J., Zhang, B., 2005. High-concentration food wastewater treatment by an anaerobic membrane bioreactor. Water Research 39, 4110– 4118. Hu, A.Y., Stuckey, D.C., 2006. Treatment of dilute wastewaters using a novel submerged anaerobic membrane bioreactor. Journal of Environmental Engineering-Asce 132, 190–198. Hu, A.Y., Stuckey, D.C., 2007. Activated carbon addition to a submerged anaerobic membrane bioreactor: effect on performance, transmembrane pressure, and flux. Journal of Environmental Engineering-Asce 133, 73–80. Hu, Z.H., Yu, H.Q., Zheng, H.C., 2006. Application of response surface methodology for optimization of acidogenesis of cattail by rumen cultures. Bioresource Technology 97, 2103–2109. Hwang, S., Hansen, C.L., 1997. Modeling and optimization in anaerobic bioconversion of complex substrates to acetic and butyric acids. Biotechnology and Bioengineering 54, 451–460. Ismail, Z.K., Abderrezaq, S.K., 2007. Employment of anaerobic digestion process of municipal solid waste for energy. Energy Sources Part a—Recovery Utilization and Environmental Effects 29, 657–668.

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E. Jeong et al. / Bioresource Technology 101 (2010) S7–S12

Jeison, D., Diaz, I., van Lier, J.B., 2008. Anaerobic membrane bioreactors: are membranes really necessary? Electronic Journal of Biotechnology 11, 9. Jeong, E., Kim, H., Nam, J., Shin, H., 2008. Application of statistical analysis for optimization of organic waste acidogenesis. Journal of Korean Society of Environmental Engineers 30, 781–788. Kim, H., Shin, H., Han, S., Oh, S., 2007. Response surface optimization of substrate for thermophilic anaerobic codigestion of sewage sludge and food waste. Journal of Air and Waste Management Association 57, 309–318. Le-Clech, P., Chen, V., Fane, T.A.G., 2006. Fouling in membrane bioreactors used in wastewater treatment. Journal of Membrane Science 284, 17–53. Lee, W., Kang, S., Shin, H., 2003. Sludge characteristics and their contribution to microfiltration in submerged membrane bioreactors. Journal of Membrane Science 216, 217–227. Liu, D.W., Liu, D.P., Zeng, R.J., Angelidaki, I., 2006. Hydrogen and methane production from household solid waste in the two-stage fermentation process. Water Research 40, 2230–2236. Maharaj, I., Elefsiniotis, P., 2001. The role of HRT and low temperature on the acidphase anaerobic digestion of municipal and industrial wastewaters. Bioresource Technology 76, 191–197. Mata-Alvarez, J., Mace, S., Llabres, P., 2000. Anaerobic digestion of organic solid wastes. An overview of research achievements and perspectives.. Bioresource Technology 74, 3–16. Meng, F.G., Shi, B.Q., Yang, F.L., Zhang, H.M., 2007. Effect of hydraulic retention time on membrane fouling and biomass characteristics in submerged membrane bioreactors. Bioprocess and Biosystems Engineering 30, 359–367. Montgomery, D.C., 2001. Design and Analysis of Experiment, fifth ed. John Wiley & Sons, New York. Padmasiri, S.I., Zhang, J.Z., Fitch, M., Norddahl, B., Morgenroth, E., Raskin, L., 2007. Methanogenic population dynamics and performance of an anaerobic

membrane bioreactor (AnMBR) treating swine manure under high shear conditions. Water Research 41, 134–144. Pohland, F.G., Ghosh, S., 1971. Developments in anaerobic stabilization of organic wastes-the two-phase concept. Environmental Letters 1, 255–266. Saddoud, A., Ellouze, M., Dhouib, A., Sayadi, S., 2007. Anaerobic membrane bioreactor treatment of domestic wastewater in Tunisia. Desalination 207, 205–215. Shin, H.S., Kang, S.T., 2003. Characteristics and fates of soluble microbial products in ceramic membrane bioreactor at various sludge retention times. Water Research 37, 121–127. Speece, R.E., 1996. Anaerobic Biotechnology for Industrial Wastewaters. Archae Press, Tennessee, USA. Tay, J.H., Zeng, J.L., Sun, D.D., 2003. Effects of Hydraulic Retention Time on System Performance of a Submerged Membrane Bioreactor. Marcel Dekker Inc.. pp. 851–868. Veeken, A., Kalyuzhnyi, S., Scharff, H., Hamelers, B., 2000. Effect of pH and VFA on hydrolysis of organic solid waste. Journal of Environmental Engineering-Asce 126, 1076–1081. Wang, G., Mu, Y., Yu, H.Q., 2005. Response surface analysis to evaluate the influence of pH, temperature and substrate concentration on the acidogenesis of sucroserich wastewater. Biochemical Engineering Journal 23, 175–184. Wang, X., Niu, D.J., Yang, X.S., Zhao, Y.C., 2008. Optimization of methane fermentation from effluent of bio-hydrogen fermentation process using response surface methodology. Bioresource Technology 99, 4292–4299. Yang, K., Yu, T., Hwang, S., 2003. Selective optimization in thermophilic acidogenesis of cheese-whey wastewater to acetic and butyric acids: partial acidification and methanation. Water Research 37, 2467–2477.