Optimization of thermal-dilute sulfuric acid pretreatment for enhancement of methane production from cassava residues

Bioresource Technology 102 (2011) 3958–3965

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Optimization of thermal-dilute sulfuric acid pretreatment for enhancement of methane production from cassava residues Qinghua Zhang a,b, Lei Tang a, Jianhua Zhang a, Zhonggui Mao a,⇑, Li Jiang a a b

The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, PR China College of Bioscience and Engineering, Jiangxi Agricultural University, Nanchang 330045, PR China

a r t i c l e

i n f o

Article history: Received 21 September 2010 Received in revised form 1 December 2010 Accepted 6 December 2010 Available online 13 December 2010 Keywords: Cassava residues Thermal-dilute sulfuric acid pretreatment Response surface methodology Methane Optimization

a b s t r a c t In this study, the pretreatment of cassava residues by thermal-dilute sulfuric acid (TDSA) hydrolysis was investigated by means of a statistically designed set of experiments. A three-factor central composite design (CCD) was employed to identify the optimum pretreatment condition of cassava residues for methane production. The individual and interactive effects of temperature, H2SO4 concentration and reaction time on increase of methane yield (IMY) were evaluated by applying response surface methodology (RSM). After optimization, the resulting optimum pretreatment condition was 157.84 °C, utilizing 2.99% (w/w TS) H2SO4 for 20.15 min, where the maximum methane yield (248 mL/g VS) was 56.96% higher than the control (158 mL/g VS), which was very close to the predict value 56.53%. These results indicate the model obtained through RSM analysis is suit to predict the optimum pretreatment condition and there is great potential of using TDSA pretreatment of cassava residues to enhance methane yield. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction With the increasing shortage of fossil fuels, there is world-wide effort to create new alternative energy sources. Increased attention has been focused on biogas production from waste lignocellulosics as a promising alternative to fossil fuels. In general, anaerobic digestion of lignocellulosic materials to produce biogas is a multiple-stage process utilizing three steps: hydrolysis, acidogenesis and methanogenesis. The hydrolysis reaction has been the ratelimiting step in the overall anaerobic digestion process (Kim et al., 2003; Park et al., 2005). The major components of lignocellulosics are cellulose, hemicellulose and lignin, which form a recalcitrant ligno-cellulose complex that resists assault of the microbial and enzymatic process (Himmel et al., 2007). Therefore, it is necessary to pretreat these lignocellulosic materials prior to anaerobic digestion to enhance their bio-digestibility. Various pretreatment methods, such as acid (Liu and Cheng, 2009), alkaline (Lin et al., 2009; Zheng et al., 2009), thermal (Wang et al., 2010) and ultrasound (Hogan et al., 2004) pretreatment, which have different effects on enhancing the digestibility of lignocellulosic substrates have been carried out, obtaining higher biogas yields. Every method has its own advantages and disadvantages, but for methane production, acid pretreatment is more attractive because methanogens can handle compounds like furfural and 5-hydroxymethylfurfural (HMF) to a ⇑ Corresponding author. Tel./fax: +86 510 85918279. E-mail address: [email protected] (Z. Mao). 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.12.031

certain concentration with an acclimatization period (Hendriks and Zeeman, 2009). Usually, acid pretreatment can increase the porosity of the substrate by solubilization of hemicellulose, making the substrate more accessible to the enzymes (Cara et al., 2007; Kim et al., 2009) and ultimately increase their anaerobic digestibility. Hydrolysis of lignocellulosic materials by dilute sulfuric acid is a well-known method to obtain fermentable hydrolysates, but using this pretreatment method to enhance biogas yield has rarely been found in practical application. A recent study showed that the highest increase of methane production from steam-treated manure biofibers compared to untreated samples was 67% and was achieved at 155 °C with addition of 2.1% w/w H2SO4 for 15 min (Bruni et al., 2010). Cassava residues, a waste industrial byproduct with main components of cellulose, hemicellulose and lignin, are generated during the distilling step of cassava-based bioethanol production. The decay of these residues will cause serious environmental pollution. Due to the low nutrition and high lignocellulosic components of these wastes, they are not suitable to produce DDGS (distillers dried grains with solubles) utilizing traditional technology. Every year, more than 3.6 million tons of stillage is generated from the production of 3 million tons of cassava-bioethanol in China, and the main components of stillage are cassava residues, therefore, effective disposal of these residues is a significant issue. Further, in our previous studies (Zhang et al., 2010), the production of cassava-bioethanol through the technology coupled with ethanol and biogas fermentation preliminarily realized zero discharge of the wastewater, i.e., the stillage obtained during the distilling

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step of bioethanol production, was treated by two stages of anaerobic digestion and the resulting digestion liquor could be totally utilized for next batch bioethanol production. When these cassava residues, without any pretreatment, were directly applied for biogas production in this process, a large number of lignocellulosic components of the residues could not be effectively degraded by anaerobic microbes, and the residual un-degraded lignocellulosic components were then directly discharged at the subsequent solid–liquid separation step, resulting in a huge waste of the lignocellulosic resources. Therefore, in order to effectively utilize these residues and obtain maximum methane yield, a promising pretreatment method must be applied to treat these materials prior to anaerobic digestion. It appears that there is little recent literature discussing the use of TDSA hydrolysis of cassava residues as a pretreatment strategy to enhance biogas production. Because temperature, H2SO4 concentration and reaction time played key roles in the pretreatment process (Talebnia et al., 2009), the optimum pretreatment condition should be optimized to obtain maximum methane yield. To the best of the authors’ knowledge, few attempts have been tried to optimize the pretreatment condition of cassava residues to enhance methane yield using TDSA pretreatment method. Therefore, the objective of this study was to optimize the pretreatment conditions using TDSA pretreatment of cassava residues to increase methane yield.

300 mL-serum glass vials with 150 mL of working volume. 50 mL of the thermophilic sludge was added into the glass vials containing 100 mL of these pretreated cassava residues samples. NaHCO3 was then utilized to adjust the pH to 7.2 and offer buffer during the anaerobic digestion. The control experiments were set up with the same methodology; the only difference being the cassava residues used in the control reactors had not gone through any pretreatment. All experimental glass vials were purged with N2 for 5 min to remove the oxygen and sealed with butyl rubber stoppers. These vials were maintained at 55 ± 0.5 °C in a water bath and shook by hand several times per day to assure sufficient mixing to keep the feedstock from setting. Each vial was treated in triplicates and the average results were reported in this paper. These results were compared with the blank tests, where only the biogas produced by the inoculum was measured. Methane volume produced was monitored using water replacement method with the removal of carbon dioxide and H2S by 2 N sodium hydroxide solutions (Wang et al., 2010), and the increase of methane yield (IMY) was calculated as follows:

2. Methods

For enhancement of methane yield from the pretreated cassava residues, the TDSA pretreatment conditions were optimized by CCD under RSM. RSM is a mathematical and statistical based technique for designing experiments, building models, evaluating the effects of factors, and optimizing the target function. It has advantages in terms of reductions in the number of experiments, improved statistical interpretation possibilities and reduced time requirements from overall analysis, and has been successfully applied in the field of biotechnology. In this study, the effect of three main parameters (temperature, H2SO4 concentration and reaction time) was investigated. The range and levels of the independent variables studied were selected based on the results of single factor tests (data not shown), and these variables and levels are listed in Table 1. The value of a (which means the distance from the central of design to axial point) for this CCD was fixed at 1.682. All variables at zero level constitute to the center points and the combination of each of the variables at either its lowest (1.682) or highest (+1.682) level with the other variables at zero level constitute the axial points. The coded and actual values of the design are calculated by Design expert 7.0 (Stat-Ease, Inc., Minneapolis, MN, USA) software in this study. IMY was taken as the response of the design experiment. The second-order polynomial coefficients were also determined using this software. By using RSM, the experimental response obtained was analyzed with the following second-order polynomial, Eq. (1):

2.1. Materials Cassava residues were collected from Taixing fuel ethanol Co. Ltd., Jiangsu Province, China. The sun-dried cassava residues were used as raw materials for TDSA pretreatment. The major components of cassava residues (obtained as average values of three replicates and expressed as weight percent on a dry basis) were as follows: cellulose, 24.92 ± 0.43; hemicellulose, 17.84 ± 0.28; lignin, 12.28 ± 0.22; total nitrogen (TN), 1.4 ± 0.08; crude fat, 4.6 ± 0.09; total solids (TS), 95.60 ± 1.41; and volatile solids (VS), 84.32 ± 1.34. 2.2. Thermal-dilute sulfuric acid pretreatment TDSA pretreatment of cassava residues was performed in a specially-built stainless steel pressure vessel. This vessel was equipped with a temperature sensor, a valve for steam release and a valve for collection of the hydrolysates. The desired temperature was reached by introducing steam and maintained for the duration of the treatment. The pretreatments with 6.0 g of cassava residues (dry weight) and a constant solid–liquid ratio (1:10 w/v) were conducted under different conditions (Table 2), where the concentration of sulfuric acid was in range of 1.32–4.68% (w/w TS), reaction times were 3.18–36.82 min and temperature was 143.18–176.82 °C. After accomplishing of these pretreatments, the hydrolysates and solid fractions were separated and cooled to ambient temperature. These hydrolysates was then sampled for components analysis and the residual solid fractions contained in the vessel was washed with 40 mL of distilled water to avoid biomass loss. Finally, these hydrolysates, solid fraction and washing liquor were mixed for subsequent anaerobic digestion. 2.3. Anaerobic digestion Digested thermophilic sludge from a thermophilic anaerobic reactor treating cassava-stillage was used in our laboratory with an average concentration of 39.2 g/L TS and 28.5 g/L VS, as inoculum for experiments. Batch experiments were conducted in

IMY ð%Þ ¼

ðMethane yieldÞpretreated  ðMethane yieldÞcontrol  100% ðMethane yieldÞcontrol

2.4. Experimental design and data analysis

Y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b11 x21 þ b22 x22 þ b33 x23 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3

ð1Þ

Table 1 Coded and uncoded values for each variables of the CCD. Variables

Coded level

Level 1.682

1

0

1

+1.682

Temperature (°C) H2SO4 concentration (%, w/w TS) Reaction time (min)

x1 x2 x3

143.18 1.32 3.18

150 2 10

160 3 20

170 4 30

176.82 4.68 36.82

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Table 2 Compositions of hydrolysates obtained under different pretreatment conditions. Run

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Pretreatment conditions

Components (g/L)

Temperature (°C)

CH2SO4 (w/w, %)

Reaction time (min)

Glucose

Xylose

Arabinose

Acetic acid

Furans

sCOD

150 170 150 170 150 170 150 170 143.18 176.82 160 160 160 160 160 160 160 160 160 160

2 2 4 4 2 2 4 4 3 3 1.32 4.68 3 3 3 3 3 3 3 3

10 10 10 10 30 30 30 30 20 20 20 20 3.18 36.82 20 20 20 20 20 20

1.49 ± 0.02 3.12 ± 0.03 2.22 ± 0.06 2.44 ± 0.05 2.27 ± 0.07 3.80 ± 0.11 2.22 ± 0.04 5.05 ± 0.18 1.07 ± 0.03 5.38 ± 0.12 1.25 ± 0.02 3.12 ± 0.06 2.52 ± 0.04 4.59 ± 0.10 4.25 ± 0.08 4.32 ± 0.04 4.29 ± 0.11 4.26 ± 0.06 4.32 ± 0.04 4.29 ± 0.08

4.07 ± 0.10 6.04 ± 0.13 5.01 ± 0.12 10.50 ± 0.34 4.54 ± 0.12 7.18 ± 0.23 4.43 ± 0.10 6.97 ± 0.18 4.55 ± 0.12 7.98 ± 0.22 4.34 ± 0.12 3.24 ± 0.07 3.26 ± 0.08 7.02 ± 0.13 6.45 ± 0.15 6.57 ± 0.11 6.39 ± 0.20 6.42 ± 0.18 6.34 ± 0.16 6.28 ± 0.23

1.02 ± 0.02 2.12 ± 0.04 1.42 ± 0.03 1.45 ± 0.02 1.35 ± 0.01 1.82 ± 0.04 1.49 ± 0.04 2.42 ± 0.05 0.79 ± 0.02 2.67 ± 0.06 1.08 ± 0.02 0.99 ± 0.01 1.53 ± 0.06 1.74 ± 0.03 1.19 ± 0.02 1.39 ± 0.04 1.28 ± 0.03 1.36 ± 0.04 1.32 ± 0.06 1.41 ± 0.04

1.27 ± 0.02 1.76 ± 0.03 1.44 ± 0.06 4.12 ± 0.12 1.39 ± 0.03 2.60 ± 0.05 2.37 ± 0.04 5.09 ± 0.11 1.31 ± 0.02 3.95 ± 0.13 1.27 ± 0.03 2.53 ± 0.05 1.12 ± 0.02 2.06 ± 0.02 1.53 ± 0.11 1.56 ± 0.13 1.56 ± 0.08 1.54 ± 0.09 1.50 ± 0.14 1.58 ± 0.08

0.06 ± 0.00 0.64 ± 0.01 0.17 ± 0.01 1.54 ± 0.03 0.11 ± 0.00 1.25 ± 0.03 0.13 ± 0.01 2.05 ± 0.06 0.09 ± 0.00 1.28 ± 0.04 0.11 ± 0.01 0.94 ± 0.02 0.13 ± 0.00 0.67 ± 0.04 0.24 ± 0.01 0.25 ± 0.01 0.29 ± 0.01 0.26 ± 0.00 0.27 ± 0.00 0.25 ± 0.01

20.40 ± 0.69 38.11 ± 0.83 39.40 ± 1.13 46.00 ± 1.43 26.27 ± 0.46 38.02 ± 0.82 31.28 ± 0.54 46.92 ± 1.23 23.60 ± 0.43 44.47 ± 1.03 22.11 ± 0.53 43.64 ± 1.22 33.20 ± 0.84 41.52 ± 1.81 39.81 ± 1.42 40.45 ± 1.26 40.56 ± 1.44 40.48 ± 0.68 40.59 ± 1.02 40.47 ± 0.82

where Y is the response (IMY,%), x1, x2 and x3 are the coded levels of the three variables respectively, and b0, b1, b2, b3, b11, b22, b33, b12, b13 and b23 are the model coefficients calculated from the experimental data. The responses and variables (in coded values) were analyzed by the response surface function to obtain the values of the coefficients of Eq. (1). The design of 20 experiments was formulated with six replicates at the central points. The regression equation was solved to determine the optimum values of the selected variables. Meanwhile, the effects of the influential parameters on IMY were investigated by analyzing the response surface plots. In addition, statistical analysis of the model was performed using the analysis of variance (ANOVA). Response surfaces and contour plots were developed using the fitted quadratic polynomial equation obtained from regression analysis, holding one of the independent variables at a constant value corresponding to the center point and changing the other two variables. 2.5. Analytical methods Glucose, xylose, arabinose and acetic acid were determined by HPLC (Biorad Aminex HPX-87H carbohydrate column), with a flow rate of 0.6 mL/min at 60 °C and a mobile phase of 0.05 M H2SO4. TS, VS and soluble COD (sCOD) were determined according to standard methods (APHA, 1995). Furan compounds (HMF and furfural) were determined according to the previous report (Martinez et al., 2000). TN was determined by the total Kjeldahl nitrogen analyzer (Model KDN-2C, Shanghai, China). The pH value was tested by pH meter (PHSJ-4A, Shanghai Kangyi Instrument Co. Ltd., China). The contents of lignin, cellulose, and hemicellulose were determined according to the procedures proposed by Van Soest et al. (1991). Volatile fatty acids (VFA) were analyzed by distillation– titration method and the result was expressed in acetic acid (He, 1998). Crude fat was determined according to the standard method. 2.6. Scanning electronic microscope (SEM) analysis All solid samples were dried at 105 °C overnight, and the untreated and pretreated cassava residues were then observed with a SEM (FEI Quanta 200, Holland).

3. Results and discussion 3.1. Compositions of cassava residues hydrolysates As shown in Table 2, the compositions of the cassava residues hydrolysates were determined at different pretreatment conditions. Glucose and xylose were the main sugar products in the hydrolysates, where they were decomposed from the cellulose and hemicellulose, respectively. Meanwhile, arabinose was found relative minor in quantity compared with glucose and xylose. As shown in Table 2, the sugar concentrations increased with temperature, acid concentration and reaction time, but further increase of the reaction temperature, acid concentration and/or reaction time resulted in the reduction of the sugar concentrations. The quantities of inhibitors (furan compounds) increased with increasing reaction temperature, sulfuric acid concentration and reaction time. These inhibitors were generated by decomposition of the sugars produced during the hydrolysis process at harsh pretreatment conditions. Within the experimental range, the highest concentrations of furan compounds (furfural and HMF) were obtained at 170 °C, 4.0% H2SO4 and 30 min (Table 2). At severe pretreatment conditions, part of the glucose and xylose were degraded into HMF and furfural, respectively. Acetylated xylose units of hemicellulose may be released as acetic acid during steam treatment (Allen et al., 2001; Duff and Murray, 1996; Sassner et al., 2008). Generally, acetic acid has apparent inhibitive effects on ethanol fermentation (Dien et al., 2003), while it has no negative effects on biogas fermentation because acetic acid can be utilized as the precursor of methane formation (Zhao et al., 2010). On the contrary, inhibitors such as furan compounds (furfural and HMF) and phenolic compounds, which are formed by the degradation of sugars or lignin respectively, in the hydrolysates have in many cases an inhibitory or toxic effect on bacteria, yeast and methanogens (Palmqvist and Hahn-Hägerdal, 2000). However, these compounds with moderate concentrations can be converted to methane after a period of adaptation (Fox et al., 2003; Rivard and Grohmann, 1991). According to Table 2, it was also found that sCOD is higher at severe pretreatment conditions and sulfuric acid concentration had evident effects on cassava residues solubilization. sCOD was apparently increased at the pretreatment condition with higher H2SO4 concentration, temperature and longer reaction time. As shown

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in Table 2, the maximum sCOD (46.92 g/L) was obtained at 170 °C, 4.0% H2SO4 and 30 min. Usually, the hydrolysis is the rate-limiting step during the anaerobic digestion of the solid feedstocks (Tiehm et al., 2001), therefore, increasing the solubility of the feedstocks through pretreatment would benefit for subsequent anaerobic digestion (Chou et al., 2010) and eventually enhance biogas yield. Otherwise, the inhibitors of furan compounds with the highest concentration of 2.05 g/L were obtained when sCOD reached the maximum level at current experimental range. Therefore, it could be estimated that the maximum biogas yield might not be achieved at the severest pretreatment conditions due to the high inhibitive effects of furan compounds with high concentrations. 3.2. Modeling for methane production The reaction temperature, H2SO4 concentration and reaction time were selected as influential parameters on methane production. CCD was employed to investigate the interactions between these parameters and also to determine their optimum levels. The actual values and the corresponding coded levels of the independent variables are summarized in Table 1. The experimentally obtained activities and statistically predicted data for a total of 20 experiments are presented in Table 3. Eq. (2), describing the relationship between the significant variables and IMY in coded units, was derived by the model. The equation is shown as follows:

IMY ð%Þ ¼ 56:22  2:85x1  1:06x2  0:11x3  6:53x21  6:83x22  4:68x23  4:38x1 x2  1:03x1 x3  2:00x2 x3

ð2Þ

The adequacy of the regression equation and the significance of the coefficients are shown in Table 4. The significance of each coefficient is determined by F- and P-values. The F-value (522.30) with a low probability value (P < 0.0001) implies a high significance of the model. The accuracy of the model was also checked by the multiple correlation coefficients (R2). In this case, the multiple correlation coefficient of 0.9979 indicates that this model is statistically significant and only 0.21% of the total variations is not defined by the regression. The predicted multiple correlation coefficient (R2Pre: = 0.986) is in reasonable agreement with the adjusted multiple correlation coefficient (R2Adj: = 0.996). Moreover, the coefficient

of variance (C.V = 1.36%) was low, which implies significant precision and reliability of the experimental data. Low P-values of linear and quadratic terms for temperature, H2SO4 concentration showed high linear and quadratic effects of these parameters on the response of IMY. Also, reaction time had high quadratic effect on IMY, but the P-value (0.522) for the linear term of this variable was high indicating insignificant linear effect. Further, the adequate precision value reflects that the probability of the model was interfered by noise, and the adequate precision value of this model was 69.932 (higher than 4), which indicates this model could reasonably reflect the experiment. Table 4 also indicates that the lack of fit of the model is insignificant. It suggests that the established model is adequate for the prediction. From Eq. (2), the optimal values of x1, x2 and x3 in the coded units were found to be 0.216, 0.010 and 0.015, respectively. Correspondingly, the maximum points of the models were temperature 157.84 °C, 2.99% sulfuric acid and 20.15 min, respectively. The maximum predicted value of IMY was 56.53%. 3.3. RSM analysis The regression model developed can be represented in response surfaces and contour plots to understand the interaction among the three variables and to determine the optimum level of each variable for maximum response of IMF. Contour plot and response surface plot (Fig. 1) were generated based on Eq. (2). The plots represent interactions of two variables while the other variable constant was held at zero level. As shown in Fig. 1A, the effect of both acid concentration and temperature was studied with reaction time kept as constant at the center point value. With the increase of H2SO4 concentration and temperature during the pretreatment process, the IMY increased to some extent after the pretreatment. However, further increase of these two variables resulted in a decrease of IMY in subsequent anaerobic digestion. Similarly, the same phenomenon was found in Fig. 1B and C, where Fig. 1C illustrates the effect of reaction time and temperature with acid concentration being set at center point, while Fig. 1C depicts the effect of reaction time and acid concentration with temperature kept as constant. As shown in Fig. 1B and C, with the prolongation of the reaction time and the increase of temperature or sulfuric acid concentration, the IMY was also underwent a process of first increasing, then

Table 3 The design and results of CCD. Run

x1

Temperature (°C)

x2

Concentration (%, w/w TS)

x3

Time (min)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 1 1 1 1 1 1 1 1.682 1.682 0 0 0 0 0 0 0 0 0 0

150 170 150 170 150 170 150 170 143.18 176.82 160 160 160 160 160 160 160 160 160 160

1 1 1 1 1 1 1 1 0 0 1.682 1.682 0 0 0 0 0 0 0 0

2 2 4 4 2 2 4 4 3 3 1.32 4.68 3 3 3 3 3 3 3 3

1 1 1 1 1 1 1 1 0 0 0 0 1.682 1.682 0 0 0 0 0 0

10 10 10 10 30 30 30 30 20 20 20 20 3.18 36.82 20 20 20 20 20 20

IMY (%) Experiment

Predicted

34.79 ± 1.22 40.17 ± 0.82 45.34 ± 1.04 32.68 ± 0.78 41.41 ± 1.06 42.12 ± 1.14 43.44 ± 0.62 27.19 ± 0.45 42.21 ± 0.84 32.67 ± 0.68 37.94 ± 0.81 35.22 ± 0.56 43.46 ± 0.62 41.89 ± 1.32 56.32 ± 0.46 55.85 ± 0.94 56.45 ± 0.89 55.97 ± 0.74 56.03 ± 1.09 56.78 ± 1.32

34.78 39.9 45.41 33.03 40.62 41.61 43.27 26.76 42.53 32.96 38.66 35.11 43.16 42.8 56.22 56.22 56.22 56.22 56.22 56.22

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Table 4 Analysis of variance (ANOVA) for the quadratic modela.

a

Source

Sum of squares

Model x1 x2 x3 x1x2 x1x3 x2x3 x21

1667.47 110.60 15.21 0.16 153.13 8.53 31.84 614.38

x22 x23 Residual Lack of fit Pure error Cor. total intercept

Coefficient

Mean square

F-value

P-value (prob. > F)

9 1 1 1 1 1 1 1

2.85 1.06 0.11 4.38 1.03 2.00 6.53

185.27 110.60 15.21 0.16 153.13 8.53 31.84 614.38

522.30 311.78 42.89 0.44 431.67 24.04 89.76 1731.95

<0.0001 <0.0001 <0.0001 0.5220 <0.0001 0.0006 <0.0001 <0.0001

672.93

1

6.83

672.93

1897.02

<0.0001

315.43

1

4.68

315.43

889.21

<0.0001

3.55 2.94 0.61 1671.02

10 5 5 19

0.35 0.59 0.12

4.81

2

DF

0.0550

56.22 2

Coefficient of determination (R ) = 0.9979; predicted (R ) = 0.986; adjusted (R2) = 0.996; Coefficient of variation (C.V.) = 1.36%; adequate precision value = 69.932.

decreasing. In other words, lower or higher pretreatment intensity both had negative effects on IMY. Generally, when the pretreatment intensity was very low, a small amount of soluble compounds would be produced during the hydrolysis process, and the solubilization of the hemicellulose contained in the cassava residues was limited, which resulted in little changes of the rigid structure of the lignocellulose, the residual lignocellulosic components of cassava residues could not be effectively degraded during the anaerobic digestion process, therefore, the increase of methane yield was relatively lower. On the contrary, the harsh pretreatment conditions with high temperature, sulfuric acid concentration and long reaction time, resulted in glucose and xylose generated from the hydrolysis process were degraded into HMF and furfural, respectively. Therefore, the high concentration of these furan compounds inhibited the methanogens in subsequent anaerobic digestion and eventually resulted in the decrease of methane yield. In addition, according to Tables 2 and 3, it was also found that the apparent decrease of methane yield was accompanied with the high concentration (1.25 –2.05 g/L) of the inhibitors (furan compounds) generated from the severe pretreatment. The maximum IMY only obtained at moderate pretreatment condition, at that time, a large amount of soluble low molecular weight substances were generated from the hydrolysis process. These soluble substances, hydrolyzed from cassava residues, mainly contained sugars and acetic acid, while the inhibitors of furan compounds kept at a very low level and had no apparently negative effects on anaerobic digestion. In addition, with little inhibitors formed under moderate pretreatment conditions, the solubilization of hemicellulose resulted in changing of the structure of residual lignocellulose and producing a lot of porous. These changes would benefit the enzymes and bacteria attacking (Jørgensen et al., 2007), which resulting in the increase of methane yield during the anaerobic digestion process after the pretreatment. 3.4. Characteristics of methane production under optimized pretreatment condition In order to confirm the predicted results of the model and Eq. (2), three repeated experiments for subsequent methane fermentation under optimal hydrolysis conditions (157.84 °C, 2.99% sulfuric acid for 20.15 min) were carried out. Fig. 2 demonstrates the time course profiles and characteristics of methane production from the pretreated cassava residues samples under optimized hydrolysis conditions, which contains (A) cumulative methane production; (B) the changing curves of pH and VFA during anaerobic digestion. As shown in Fig. 2A, the cumulative methane volume of both pretreated and control reactors increased with the prolonging of

fermentation time. At the prophase of anaerobic digestion, the methane production velocity of the pretreated reactor was apparently faster than the control, which was mainly due to a large number of soluble minor molecular compounds (sugars and acetic acid) contained in the pretreated reactor and these soluble compounds could be easily utilized and formed biogas. However, the methane production velocity of the control with little soluble compounds was relatively lower, because the anaerobic digestion of this reactor would first undergo a hydrolysis process by the anaerobic microbes. With the proceeding of the anaerobic digestion, the methane yields of both pretreated and control reactors slowly increased. Finally, the maximum methane yield (248 mL/g VS) of the pretreated reactor was obtained at 14th day, which was 56.96% higher than the control (158 mL/g VS). Fig. 2B presents the changing of VFA concentration and pH during anaerobic digestion. pH of the control and pretreated digester both decreased first and then slowly increased, which was opposite to the changing of VFA concentration, where the VFA concentration varied in a range of 45–732 mg/L during anaerobic digestion. For control run, as well as for reactor fed with pretreated cassava residues samples, the highest performance of VFA production in terms of concentrations was observed in pretreated reactor, and the lowest VFA concentration was observed in the control reactor. As shown in Fig. 2B, at the beginning of the anaerobic digestion, some sugars and acetic acid produced during the hydrolysis process were contained in the cassava residues hydrolysate. These acetic acid could be easily transformed into methane during the initial stage of the anaerobic digestion and sugars could be utilized by the anaerobic microbes and producing a large number of VFA, and these VFA would be then utilized to form methane, therefore the cumulative VFA was not very excessive at the initial stage of the anaerobic digestion of the pretreated samples. Accordingly, pH of the pretreated reactor decreased step by step during the initial stage of the anaerobic digestion, but after the 4th day, the VFA concentration decreased slowly corresponding with the increase of pH. Similarly, the changing of pH and VFA concentration of the control showed the same tendency, while the changing range of its pH and VFA was relatively minor compared to the pretreated. The resulting reason is that the insoluble organic compounds and rigid structure of the lignocellulose of the unpretreated sample constrained its accessibility to enzymes and anaerobic microbes, eventually resulted in the low hydrolysis and acidification velocity. 3.5. SEM of cassava residues Based on observation of the SEM images of cassava residues, it was found that the structure of the untreated cassava residues has a continuous even and smooth flat surface area, while the TDSA

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Fig. 1. Contour and response surface plot of IMY. (A) Effect of acid concentration and temperature; (B) effect of temperature and reaction time; (C) effect of reaction time and acid concentration.

pretreated cassava residues has a rugged and partially broken or degraded face. The fragments were separated from the initial connected structure and fully exposed, thus increasing the external surface area and porosity. This change would obviously favor the enzymes contacting the inner linkage, hence accelerating the biodegradation process.

3.6. Discussion TDSA pretreatment of cassava residues can increase the porosity of the substrate by solubilization of hemicellulose and part of lignin, making the substrate more accessible to the enzymes (Gregg and Saddler, 1996; Palonen et al., 2004). When reaction

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Fig. 2. Time-course profile of methane production under optimized hydrolysis conditions. (A) The cumulative methane volume of pretreated and unpretreated samples during anaerobic digestion; (B) the changing of VFA concentration and pH during anaerobic digestion.

temperatures are above 150 °C, hemicellulose is easily hydrolyzed (Hendriks and Zeeman, 2009) and passes into the hydrolysates. Removal of hemicellulose and part of lignin results in a weakening of the lignocellulosic structure and in an increase of pore size, thus allowing enzymatic attack (Mussatto et al., 2008) and conversion into methane during the biogas process. The adverse effects of TDSA pretreatment of cassava residues on biogas production were also estimated. The main concerns are coming from two parts: (1) the inhibitors contained in the hydrolysates including furan compounds and sulfate ions; (2) the high energy input when treated at high temperature. Although some of the inhibitors generated from the degradation of sugars at harsh pretreatment conditions, if the pretreatment condition was probably controlled to avoid high concentration of inhibitors formation, the subsequent anaerobic digestion could be proceed successfully. The main inhibitors of anaerobic digestion after TDSA pretreatment are furan compounds and the residual sulfate ions, however, the furan compounds with low concentration could be effectively converted to methane and carbon dioxide in acclimated anaerobic system (Rivard and Grohmann, 1991). The existing of sulfate ions with high concentrations is more adverse on methane fermentation, for sulfate is a stronger electron acceptor compared to CO2 and the reduction of sulfate to H2S competes with the biogas process (Zehnder and Stumm, 1988). However, in practical application, the sulfate concentration can be controlled through the reduction of the additional volume of sulfuric acid during the pretreatment process. Therefore, the pretreatment with

high temperature, less residence time and low H2SO4 addition is very attractive in practical application. In this study, the SO2 concentration in the anaerobic digestion was only 0.035 g 4 SO2 (g VS)1, which was significant lower compared to the VS 4 content of the substrate and would have no obvious negative effects on methane production (Damianovic and Foresti, 2009). Otherwise, high energy input is no longer a significant obstacle because of the short reaction time (20.15 min), extra energy repay (56.96% improvement of methane yield can be burned to generate energy) and the possibility to use waste heat from a CHP unit (combined heat and power) as energy input for steam production. Therefore, the industrial application of TDSA pretreatment of cassava residues to increase methane yield seems to have great potential.

4. Conclusions In this study, the RSM was applied to optimize the pretreatment conditions of temperature, H2SO4 concentration and reaction time on the increase of methane yield. The experimental results showed the maximum methane yield (248 mL/g VS) was obtained under the optimum pretreatment conditions (157.84 °C, 2.99% w/w TS H2SO4 and 20.15 min), which was 56.96% higher than the control (158 mL/g VS). This experimental value was in excellent agreement with the predicted value (56.53%). The present results demonstrate

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that the TDSA pretreatment of cassava residues under appropriate condition have great potential to increase methane yield. Acknowledgements This work is financially supported by the National High Technology Research and Development Program of China (863 Program) (No. 2008AA10Z338). The authors would also like to thank Mr. Jim Wright for his kind assistances during the paper preparation. References Allen, S.G., Schulman, D., Lichwa, J., Antal, M.J., 2001. A comparison between hot liquid water and steam fractionation of corn fiber. Ind. Eng. Chem. Res. 40, 2934–2941. APHA, 1995. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, Washington, DC, USA. Bruni, E., Jensen, A.P., Angelidaki, I., 2010. Steam treatment of digested biofibers for increasing biogas production. Bioresour. Technol. 101, 7668–7671. Cara, C., Moya, M., Ballesteros, I., Negro, M.J., González, A., Ruiz, E., 2007. Influence of solid loading on enzymatic hydrolysis of steam exploded or liquid hot water pretreated olive tree biomass. Process Biochem. 42, 1003–1009. Chou, K.W., Norli, I., Anees, A., 2010. Evaluation of the effect of temperature, NaOH concentration and time on solubilization of palm oil mill effluent (POME) using response surface methodology (RSM). Bioresour. Technol. 101, 8616–8622. Damianovic, M.H.R.Z., Foresti, E., 2009. Dynamics of sulfidogenesis associated to methanogenesis in horizontal-flow anaerobic immobilized biomass reactor. Process Biochem. 44, 1050–1054. Dien, B.S., Cotta, M.A., Jeffries, T.W., 2003. Bacteria engineered for fuel ethanol production: current status. Appl. Microbiol. Biotechnol. 63, 258–266. Duff, S.J.B., Murray, W.D., 1996. Bioconversion of forest products industry waste cellulosics to fuel ethanol: a review. Bioresour. Technol. 55, 1–33. Fox, M.H., Noike, T., Ohki, T., 2003. Alkaline subcritical-water treatment and alkaline heat treatment for the increase in biodegradability of newsprint waste. Water Sci. Technol. 48, 77–84. Gregg, D., Saddler, J.N., 1996. A techno-economic assessment of the pretreatment and fractionation steps of a biomass-to-ethanol process. Appl. Biochem. Biotechnol. 57 (58), 711–727. He, Y.L., 1998. Anaerobic Digestion of Waste Water. Light Industry Press, Beijing, China. Hendriks, A.T.W.M., Zeeman, G., 2009. Pretreatments to enhance the digestibility of lignocellulosic biomass. Bioresour. Technol. 100, 10–18. Himmel, M.E., Ding, S.-Y., Johnson, D.K., Adney, W.S., Nimlos, M.R., Brady, J.W., Foust, T.D., 2007. Biomass recalcitrance, engineering plants and enzymes for biofuels production. Science 315, 804–807. Hogan, F., Mormede, S., Clark, P., Crane, M., 2004. Ultrasound sludge treatment for enhanced anaerobic digestion. Water Sci. Technol. 50, 25–32. Jørgensen, H., Kristensen, J.B., Felby, C., 2007. Enzymatic conversion of lignocellulose into fermentable sugars: challenges and opportunities. Biofuels Bioprod. Biorefin. 1, 119–134.

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