Optimization of ethanol production from microwave alkali pretreated rice straw using statistical experimental designs by Saccharomyces cerevisiae

Industrial Crops and Products 37 (2012) 334–341

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Optimization of ethanol production from microwave alkali pretreated rice straw using statistical experimental designs by Saccharomyces cerevisiae Anita Singh ∗ , Narsi R. Bishnoi ∗ Department of Environmental Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India

a r t i c l e

i n f o

Article history: Received 18 November 2011 Received in revised form 18 December 2011 Accepted 25 December 2011 Available online 20 January 2012 Keywords: Energy Enzyme Ethanol Fermentation Hydrolysis

a b s t r a c t Ethanol production from agro-waste provides an alternative energy-production system. Statistical experimental designs were used for optimization of critical nutrients and process variables for ethanol production. The critical nutrients and process variables were initially selected according to a Placket–Burman (PB) design. Selected factors (inoculum level 1–5%, pH 4.5–7, temperature 25–35 ◦ C and urea concentration 0.25–0.75 g/L) were optimized by response surface methodology (RSM) based on a three-level four-factor Box–Behnken design (BBD). Under optimum conditions of inoculum level 3%, pH 5.75, temperature 30 ◦ C and urea concentration 0.50 g/L maximum ethanol production obtained 13.2 g/L from microwave alkali pretreated rice straw with ethanol productivity 0.33 g/L/h. Under optimum conditions ethanol production studied at fermenter level and obtained ethanol concentration 19.2 g/L, ethanol productivity 0.53 g/L/h and ethanol yield to consumed sugar 0.50 g/g. These results indicated that ethanol production can be enhanced by optimization of nutritional and process variables. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Industrial revolution has changed the world to its sophisticated edge, excessive dependent on fossil fuels as the main source of energy has led to the diminishing of this non-renewable supply (Goh et al., 2010). During last few decades energy crisis leads to an increased demand for alternative and renewable energy source, as ethanol. The lignocellulosic biomass is of great availability, low cost feedstock and does not have the ethical concerns associated with the use of potential food resources. Lignoellulosic ethanol is one of the most promising technological approaches available to reduce emissions of greenhouse gases from the transportation sector (Ferreira et al., 2009). It gives an estimation of about 650–975 million tons of rice straw produced per year globally and a large part of this is going as cattle feed and rest as waste. The options for the disposition of rice straw are limited by the low bulk density, slow degradation in the soil, harboring of rice stem diseases, and high mineral content (Singh et al., 2011). For the effective conversion of lignocellulosic material into ethanol, there are three major steps involved firstly, pretreatment – a preprocessing step that improves enzyme access to the cellulose; secondly, enzymatic hydrolysis/saccharification – use of acid or enzymes (cellulases and hemicellulases); and thirdly, fermentation of released sugars

∗ Corresponding authors. Tel.: +91 1662 263321; fax: +91 1662 276240. E-mail addresses: [email protected] (A. Singh), [email protected] (N.R. Bishnoi). 0926-6690/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.indcrop.2011.12.033

by specialized organism. Around 60% of the total ethanol is produced by fermentation (Kim et al., 2008). Ethanol can be derived from fermentation processes from any material that contains sugars or sugar precursors. Simultaneous hydrolysis and fermentation reduce the cost and increasing the yield (Lee, 2007). Lignocellulosic cell walls have a natural resistance, often called “recalcitrance”, to microbial and enzymatic deconstruction. Therefore, the biomass pretreatment is necessary to catalyze the hydrolysis of hemicellulose and make the cellulose fraction more accessible to enzymatic digestion prior to the ethanol fermentation (Singh et al., 2011). Enzymatic hydrolysis of cellulose is carried out by the cellulose hydrolyzing enzyme cellulases, a mixture of several enzymes that act hydrolyzing crystalline cellulose to its monomeric components, glucose (Ferreira et al., 2009). The released glucose is fermented by microorganism (bacteria, fungi etc.) into ethanol. Saccharomyces cerevisiae, the yeast commonly used for ethanolic fermentations, can ferment increased amounts of sugars in the medium when all the nutrients and process conditions are provided in adequate amounts (Pereira et al., 2010). Medium and process parameters optimization by the classical method of changing one variable while fixing the others at a certain level is laborious and time consuming, especially when the number of variable is large. An alternative and more efficient approach in microbial systems is the use of statistical methods. Plackett–Burman designs allow testing of largest number of factors with the least number of observations, and allow random error variability estimation and testing of the statistical significance of the parameters (Plackett and Burman, 1946). RSM is also

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Table 1 Plackett–Burman design matrix for the screening of variables influencing ethanol production. Sr. no.

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

Eth

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

0.006 0.006 0.006 0.002 0.002 0.002 0.002 0.006 0.006 0.002 0.002 0.002 0.006 0.006 0.006 0.006 0.002 0.002 0.002 0.006

0.003 0.003 0.001 0.001 0.001 0.001 0.003 0.003 0.001 0.003 0.001 0.003 0.001 0.001 0.003 0.003 0.001 0.003 0.003 0.001

0.006 0.002 0.002 0.006 0.002 0.002 0.006 0.002 0.002 0.006 0.006 0.006 0.006 0.006 0.006 0.002 0.002 0.002 0.002 0.006

0.006 0.002 0.006 0.006 0.006 0.002 0.006 0.002 0.002 0.002 0.002 0.002 0.006 0.002 0.002 0.006 0.002 0.006 0.006 0.006

0.001 0.005 0.005 0.001 0.001 0.001 0.005 0.001 0.001 0.001 0.005 0.005 0.005 0.005 0.001 0.005 0.005 0.005 0.001 0.001

0.0005 0.0015 0.0005 0.0015 0.0015 0.0005 0.0005 0.0005 0.0015 0.0005 0.0005 0.0015 0.0015 0.0015 0.0015 0.0005 0.0005 0.0015 0.0015 0.0005

0.75 1.5 0.75 0.75 1.5 0.75 1.5 1.5 0.75 0.75 1.5 0.75 1.5 0.75 1.5 0.75 1.5 0.75 1.5 1.5

0.75 1.5 0.75 0.75 1.5 0.75 1.5 1.5 0.75 0.75 1.5 0.75 1.5 0.75 1.5 0.75 1.5 0.75 1.5 1.5

2 6 2 6 6 2 2 6 2 6 6 2 2 6 2 6 2 6 2 6

5 2.5 2.5 2.5 5 2.5 2.5 2.5 5 5 5 2.5 5 2.5 5 5 5 5 2.5 2.5

0.003 0.001 0.001 0.001 0.003 0.001 0.003 0.003 0.003 0.001 0.001 0.003 0.001 0.003 0.001 0.001 0.003 0.003 0.001 0.003

4 4 4 5.5 4 4 5.5 5.5 5.5 5.5 4 4 5.5 5.5 4 5.5 5.5 4 5.5 4

27 27 37 27 27 27 27 37 37 37 37 37 37 27 27 27 27 37 37 37

0.075 0.0375 0.075 0.075 0.0375 0.0375 0.0375 0.075 0.0375 0.0375 0.075 0.0375 0.0375 0.075 0.075 0.0375 0.075 0.075 0.075 0.0375

20 20 40 20 40 20 20 20 20 40 20 40 20 40 40 40 40 20 40 40

6.5 5.9 6.7 7.9 6.1 6.8 5.1 5.5 6.2 6.8 5.3 4.2 5.6 8.6 6.3 8.8 8.3 5.9 6.3 5.4

Eth = ethanol concentration (g/L).

a statistical approach for designing experiments, building models, evaluating the effects of many factors and finding the optimal conditions for desirable responses and reducing the number of required experiments (Singh et al., 2011). In previous paper, microwave-alkali pretreatment, saccharification and lignocellulolytic enzyme production studied (Singh et al., 2011), present study describe the statistical optimization of critical nutrients and process variables for ethanol production by statistical methods from enzymatic hydrolysate of pretreated rice straw by S. cerevisiae. Under optimum conditions ethanol production at 3 L fermenter level also studied. 2. Materials and methods 2.1. Microorganism, growth conditions and material Yeast S. cerevisiae MTCC 174 was purchased from Institute of Microbial Technology, Chandigarh (IMTEC), India used for present study. Stock cultures were maintained on YPD [yeast extract 1% (w/v), bacto peptone 2% (w/v) and glucose 2% (w/v)] agar plates at 4 ◦ C. Yeast for inoculation was grown in 1 L Erlenmeyer flasks filled with 400 mL of medium containing glucose 50 g/L, peptone 20 g/L and yeast extract 10 g/L. After incubation at 30 ◦ C and 150 rpm for 24–32 h, the cell suspension was aseptically collected by centrifugation (10 min at 7500 × g, 4 ◦ C) and suspended in 0.9% NaCl for further use. Microwave alkali pretreated rice straw used for present study (Singh et al., 2011). 2.2. Enzymatic hydrolysis Enzymatic hydrolysis was done by crude unprocessed enzyme. Enzymatic hydrolysis of microwave alkali pretreated rice straw (50 g) was carried out in 3 L bioreactor (BioAge, Mohali, India) (equipped with agitator for stirring and temperature controller) containing 1.5 L citrate buffer (50 mM, pH 5.0 ± 0.2, 50 ± 0.5 ◦ C) at 100 rpm. The cellulosic substrate was soaked in citrate buffer for 2 h before adding enzymes. Sodium azide was also added at a concentration of 0.005% to restrict any microbial growth during the course of enzymatic hydrolysis. The substrate soaked in citrate buffer was supplemented with cellulase 14 FPU/g, endoglucanase (CMCase) 225 IU/g, ␤-glucosidase 186 IU/g and xylanase 160 IU/g of the dry substrate (Singh et al., 2011). Samples were withdrawn at various intervals, centrifuged and supernatant analyzed for total reducing

sugar released. The hydrolysate was concentrated by evaporation in rotatory evaporator to reducing sugar content of 5%. 2.3. Fermentation Initial reducing sugar content for fermentation was maintained at 50 g/L throughout the experiments. For nutrient and process variables, screening and optimization tests were performed in the basic medium supplemented with nutrients according to the experimental designs (Tables 1 and 2). Fermentation experiments for optimization of ethanol production were conducted in 250 ml flasks with a working volume of 100 ml. All medium components and laboratory tools used in experimentation were autoclaved (121 ◦ C, 15–30 min). Samples were withdrawn at regular intervals and analyzed. 2.4. Optimization of medium and culture conditions by PBD Plackett–Burman design (PBD) was used to screen variables in experiments (Pereira et al., 2010; Singh et al., 2011) resulting in a tremendous decrease in the number of total experiments. PBD was employed to evaluate the relative importance of various components in promoting ethanol production and to screen the important variables affecting the ethanol production. The preliminary information for the trails was taken from the literature survey (Palukurty et al., 2008; Leus¸tean et al., 2010; Uncu and Cekmecelioglu, 2011). This model does not describe interaction among factors and it is used to screen and evaluate the important factors that influence the response. For present study, selected variables were: pH, incubation temperature, inoculum size, FeSO4 . 7H2 O, KH2 PO4 , MnCl2 ·4H2 O, (NH4 )2 SO4 , MgSO4 ·7H2 O, CaCl2 . 2H2 O, ZnSO4 ·7H2 O, NaCl, CoCl2 , CuSO4 , urea conc., fermentation time. The PBD was set up for 15 variables in 2 levels, high and low (Tables 1 and 2). The high level of each variable was set far enough from low level to identify the ingredients of the media having significant influence on ethanol production. Tables 1 and 2 showed the factors under investigation as well as levels of each factor used in the experimental design. 2.5. Response surface methodology Response surface methodology (RSM) is an empirical modeling technique used to evaluate the relationship between a set of controllable experimental factors and observed results (Singh et al.,

336

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Table 2 Actual level of variables tested with the Plackett–Burman design and their effect on ethanol production. Sr. no.

Factors

Low level

High level

A B C D E F G H I J K L M N O

FeSO4 ·7H2 O (g/L) CaCl2 ·2H2 O (g/L) MnCl2 ·4H2 O (g/L) ZnSO4 ·7H2 O (g/L) MgSO4 ·7H2 O (g/L) CoCl2 (g/L) Urea conc. (g/L) KH2 PO4 (g/L) Inoculum level (%) (NH4 )2 SO4 (g/L) NaCl (g/L) pH Temperature (◦ C) CuSO4 (g/L) Fermentation time (h)

0.002 0.001 0.002 0.002 0.001 0.0005 0.75 0.75 2 2.5 0.001 4 27 0.037 20

0.006 0.003 0.006 0.006 0.005 0.0015 1.5 1.5 6 5 0.003 5.5 37 0.075 40

1.25 −2.50 −2.14 0.18 0.27 −0.98 2.86 −0.54 −3.84 1.52 −2.06 4.47 −5.54 1.88 3.04

p value

Confidence level, %

0.279 0.067 0.099 0.867 0.802 0.381 0.046 0.620 0.018 0.203 0.109 0.011 0.005 0.134 0.038

72.1 93.3 90.1 13.3 19.8 61.9 95.4 38 98.2 79.7 89.1 98.9 99.5 86.6 96.2

response surfaces of the variables inside the experimental domain were analyzed using Design Expert software (Stat Ease, 6.0 trial Version). Subsequently, additional 5 confirmation experiments were conducted to verify the validity of the statistical experimental strategies.

2011). Based on the results from PBD, inoculum level, urea conc., temperature and pH were selected for further evaluation of their effects on ethanol production by BBD, a very useful tool for determining optimal level and interaction of medium constituents and culture conditions. In this study, BBD was used to evaluate the main and interaction effects of the factors: inoculum level (A), pH (B), temperature (C) and urea conc. (D) on ethanol production (Y). The range and levels of the variables investigated were given in Table 3, whereas the experimental designs with the observed responses for ethanol production presented in Table 3. A polynomial quadratic equation was fitted to evaluate the effect of each independent variable to the response:

2.6. Ethanol production in 3 L fermenter under optimum conditions About 1.5 L of concentrated enzymatic hydrolysate obtained from the enzymatic hydrolysis was collected in a 3 L batch fermenter (BioAge, India). Hydrolysate was neutralized and supplemented with a nutrient solution as optimized by RSM, respectively, in the fermentation medium. The fermenter-containing hydrolysate was heated to a temperature of 80 ◦ C for 30 min and agitated at 250 rpm, followed by exposure to UV light in the biosafety cabinet for about 30 min, prior to inoculation. This was done for uniform mixing of the nutrient solution with the fermentation medium and elimination of any contamination. Fermentation

Y = ˇ0 + ˇ1 A + ˇ2 B + ˇ3 C + ˇ4 D + ˇ11 A2 + ˇ22 B2 + ˇ33 C 2 + ˇ44 D2 + ˇ12 AB + ˇ13 AC + ˇ14 AC + ˇ23 BC + ˇ24 BD

t value

(1)

where Y is the predicted response; ˇ0 is a constant; ˇ1 , ˇ2 , ˇ3 , ˇ4 are the linear coefficients; ˇ12 , ˇ13 , ˇ14 , ˇ23 , ˇ24 are the crosscoefficients; ˇ11 , ˇ22 , ˇ33 , ˇ44 are the quadratic coefficients. The

Table 3 Box–Behnken design matrix for optimization of parameters identified by Plackett–Burman design. Sr. no.

Inoculum level (%)

pH

Temperature (◦ C)

Urea conc. (g/L)

Ethanol production (g/L)

Ethanol productivity (g/L/h)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1 5 1 5 3 3 3 3 1 5 1 5 3 3 3 3 1 5 1 5 3 3 3 3 3 3 3 3 3

4.5 4.5 7 7 5.75 5.75 5.75 5.75 5.75 5.75 5.75 5.75 4.5 7 4.5 7 5.75 5.75 5.75 5.75 4.5 7 4.5 7 5.75 5.75 5.75 5.75 5.75

30 30 30 30 25 35 25 35 30 30 30 30 25 25 35 35 25 25 35 35 30 30 30 30 30 30 30 30 30

0.5 0.5 0.5 0.5 0.25 0.25 0.75 0.75 0.25 0.25 0.75 0.75 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.25 0.25 0.75 0.75 0.5 0.5 0.5 0.5 0.5

5.7 8.7 4.9 4.4 5.4 3.9 8.3 4.2 5.2 7.5 7.9 7.9 8.3 3.1 2.9 2.9 5.3 6.8 2.8 3.8 8.3 3.5 7.6 7.3 13.2 12.2 11.2 12.2 12.1

0.143 0.218 0.123 0.11 0.135 0.098 0.208 0.105 0.13 0.188 0.198 0.198 0.208 0.078 0.073 0.073 0.133 0.17 0.07 0.095 0.208 0.088 0.19 0.183 0.33 0.305 0.28 0.305 0.305

Fermentation time 40 h.

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was performed at temperature and pH optimized through RSM experiments for 72 h. The fermenter was inoculated with 3% of yeast inoculum. The agitation speed was maintained at 200 rpm, and the pH was maintained using sterilized 5 N HCl and 10 N NaOH solutions throughout the experiments. Samples were drawn at 12 h intervals and analyzed for residual sugar and ethanol concentration. 2.7. Analytical methods

2.8. Fermentation parameters The ethanol volumetric productivity (g/L/h) was calculated as the ratio of ethanol concentration (g/L) at the end of the run to the fermentation time (t, h). The yield of ethanol to consumed sugar (g/g) was defined as ratio of ethanol concentration to the sugar consumption (So –Sf , So initial sugar concentration and Sf final sugar concentration). Sugar conversion (%) calculated as a ratio of sugar consumption to the initial sugar concentration.

3. Result and discussion 3.1. Enzymatic hydrolysis Enzymatic hydrolysis of cellulose is carried out by the cellulosehydrolyzing enzyme cellulases, a mixture of several enzymes that act hydrolyzing crystalline cellulose to its monomeric components, glucose (Singh et al., 2011). There are three classes of enzymes acting synergistically in cellulose hydrolysis: endoglucanase, exoglucanase and ␤-glucosidase. The endoglucanase attacks the amorphous regions of the cellulose cleaving internal ␤-1,4-glucosidic bonds and providing chain ends for the action of exoglucanases. The exoglucanases act on the reducing and non-reducing ends of cellulose chains to release mainly soluble cellobiose. Finally, ␤-glucosidases hydrolyse the soluble celluboise to glucose (Ferreira et al., 2009). In the present study crude unproceesed celluluases and xylanases enzymes used for hydrolysis of microwave alkali pretreated rice straw. Rice straw contains hemicelluloses, so xylanase enzyme also used for conversion of hemicelluloses to pentose sugars (Sukumaran et al., 2009). Crude unprocessed enzymes used for hydrolysis of biomass in the present study as cellulase 14 FPU/g, endoglucanase (CMCase) 225 IU/g, ␤glucosidase 186 IU/g and xylanase 160 IU/g of the dry substrate (Singh et al., 2011). Maximum reducing sugars obtained after 72 h was 24.7 g/L as shown in Fig. 1. Uncu and Cekmecelioglu (2011) hydrolysed kitchen waste hydrolysis within 6 h at pH 5.5, Wang et al. (2009) hydrolyzed dried distiller’s grain solubles for 72 h at pH 5 and 50 ◦ C, while Varga et al. (2005) reported hydrolysis conditions for corn stover as 24 h, pH 4.8 and 50 ◦ C. Lever et al. (2010) also studied ethanol production from lignocellulosic biomass using crude unprocessed cellulase and found 20 g/L of ethanol concentration. It can be speculated that the difference in hydrolysis time might be due to differences in raw material, solid loadings, enzyme loadings and other hydrolysis conditions. The hydrolysate contains reducing

Fig. 1. Reducing sugar production during enzymatic hydrolysis over time.

sugar was concentrated by evaporation in rotatory evaporator to reducing sugar content of 5% used for ethanol production. 3.2. Optimization by PB design Tables 1 and 2 represent the independent variables and their respective lower and higher concentration used in the present study, effect of parameter, t value, p value and confidence (%) for the design. Regression analysis showed that the model for ethanol production (R2 = 0.96; adjusted R2 = 0.82) was adequate. The significance of each coefficient was determined by Student’s t-test. The p-value was used as indicator of the statistical significance of the test. Table 2 represented the PBD for 15 parameters with the experimental results of ethanol production. It can be seen from Table 1 that pH, fermentation time, temperature, inoculum level and urea conc. were significant at confidence level of more than 95% and rest were non-significant. Fig. 2 showed that pH, fermentation time, temperature, inoculum level and urea conc. have significant effect among all studied variables. It can be seen that effect of parameters, temperature and inoculum level were significant at lower level and could be used for further optimization studied at lower level (Fig. 2). Fig. 2 also showed that pH, fermentation time, and urea conc. significant at upper level and could be used for further optimization studied at upper level. The effect of urea (Tables 1 and 2) was calculated to be 2.86 units means that from 0.0375 to 0.075 g/L of urea conc., ethanol production increases by 2.86 units. pH is very important for enzyme activity, pH influences the metabolic activity of the organism and as the pH level increases from 4 to 5.5

Effect Type Not Significant Significant

Normal Plot of the Standardized Effects (response: Ethanol production, Alpha = 0.05) 99

L

95 90

O G

80

Percent

Yeast growth was monitored by measuring the optical density of the culture at 600 nm (OD600 ). Cell dry weight was determined by centrifugation (10 min at 7500 × g, 4 ◦ C) of 20 mL of the yeast culture in a pre-weighed dried tube, washing of the pellet with 20 mL of distilled water, drying overnight at 105 ◦ C and weighing. Estimation of total reducing sugar in enzymatic hydrolysate of microwave alkali pretreated biomass was done by DNS method (Miller, 1959). The estimation of ethanol was done by spectrophotometer (Caputi et al., 1968).

70 60 50 40 30 20 I

10 5

M

1 -5.0

-2.5

0.0

2.5

Standardized Effect Fig. 2. The normal probability plot for ethanol production.

5.0

338

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ethanol production increases by 4.47 units. pH also has a strong positive effect on the biotechnological process because the pH affects both the cellulase activity and the yeast fermentation (Leus¸tean et al., 2010). Increase in incubation temperature from 20 to 40 ◦ C decreases ethanol production by −5.54 units. Increase in incubation time from 20 to 40 h increases ethanol production by 3.04 units. The inoculum size has a positive effect on the process, a very small inoculum concentration increases the fermentation time and a too high concentration can lead to losses in ethanol yield due to sugar consumption by the multiplying yeast cells (Uncu and Cekmecelioglu, 2011). Increase in inoculum level decreases ethanol production by −3.84 units, so lower level of inoculum used for further optimization studies. The parameters selected for optimization studies were pH, incubation temperature, inoculum level and urea conc. 3.3. Optimization by RSM After the screening of parameters affecting ethanol production by PBD, Box–Behnken design (BBD) was adopted to know the optimum response region of ethanol production and optimize the variables: inoculum level, urea conc., temperature and pH. The results of BBD experiments for studying the effect of four independent variables (optimized by Placket–Burman design) were presented along with the observed responses in Tables 3 and 4. In this study, BBD was used to evaluate the main and interaction effects of the factors: inoculum level (A), pH (B), temperature (C) and urea conc. (D) on ethanol production. The equation that relates the ethanol production as the dependent variable (Y, g/L) to other significant terms, as listed in Tables 3 and 4, can be expressed as follows: Ethanol production (g/L) = +17.38 + 1.54 ∗ A − 2.53 ∗ B − 2.90 ∗ C + 1.79 ∗ D − 3.07 ∗ A2 − 3.74 ∗ B2 − 6.36 ∗ C 2 − 1.20 ∗ D2 − 0.87 ∗ A ∗ B − 0.12 ∗ A ∗ C − 1.33 ∗ A ∗ D + 2.55 ∗ B ∗ C + 2.62 ∗ B ∗ D + 0.075 ∗ C ∗ D

(2)

Analysis of variance (ANOVA) of the quadratic equation for ethanol production has been summarized in Table 4. ANOVA of regression model demonstrates that the model is highly significant, as is evident from the Fisher’s F-test with a very low probability value [(p model > F) = 0.001]. The p-value denoting the significance of the coefficients was also important in understanding the pattern of the mutual interactions between the variables. The goodness of the fit of the model can be checked by the ‘determination coefficient’ R2 , the value of R2 and adjusted R2 are 0.996 and 0.985 respectively, which shows a high correlation between the observed values and the predicted values. This means that regression model provides an excellent explanation of the relationship between the independent variables (factors) and the response (ethanol production). No abnormality was observed from the diagnoses of residuals. Thus, it can be concluded that the model was statistically sound. The interactions between inoculum level and pH, between inoculum level and urea conc., between temperature and initial pH, between pH and urea conc., and between temperature and urea conc. were significant (Table 4). The exception in the interaction between inoculum level and temperature was non-significant (Table 4). Table 4 summarizes regression parameters used in the ethanol production model. As the data in Table 4 showed, linear terms (A, B, C, D), square terms (A2 , B2 , C2 , D2 ), and two-way interaction terms (AB, AD, BC, BD, CD, all except AC) are the major factors, with p-values of under a ˛ = 0.05, significantly affecting ethanol production. The graphic representation of the regression Eq. (2) is presented in Fig. 3a–e. The response surface model was used to predict the

result by isoresponse contour plots and three dimensional surface plots. Fig. 3a showed a plot at varying pH and inoculum level at fixed incubation temperature and urea conc. pH is very important factor in fermentation process, ethanol production low at pH 4.0 and increases with increase in pH up to 5.75 and further increases in pH decreases ethanol production. This finding is in consistence with Mishima et al. (2008). Fig. 3b represents a plot at varying urea conc. and inoculum level, at fixed incubation temperature and pH. Ethanol production increases with increase in inoculum level up to a certain level after that level ethanol production rate decreases as shown by Fig. 3b. The ethanol concentration reached a peak value at the mid-value of inoculum volume. Powchinda et al. (1999) stated that up to a critical amount, the increase in inoculum size increases ethanol yield due to better utilization of the sugars. However, a high amount of inoculum can adversely affect ethanol production due to the fact that high increase in inoculum level decreases the viability of yeast population and causes inadequate development of biomass and ethanol production (Powchinda et al., 1999). Fig. 3c illustrates graphically a plot at varying pH and incubation temperature with fixed inoculum level and urea conc. Temperature has a profound effect on ethanol production, since ethanol is volatile. Ethanol production is less at 20 ◦ C temperature but gradually increases up to 30 ◦ C, at this temperature, maximum ethanol yield obtained after that it start decreasing. This is in agreement with work reported by other workers (Strand, 1998; Bajaj et al., 2001; Raines-Casselman, 2005). Temperature between 28 and 30 ◦ C has been usually employed for culturing of yeast and temperature above 30 ◦ C has been found inhibitory to ethanol fermentation due to yeast growth inhibition at higher temperatures (Alegre et al., 2003). Fig. 3d showed a plot at varying pH and urea conc. at fixed inoculum level and incubation temperature. Fig. 3e showed a plot at varying temperature and urea conc. with fixed inoculum level and pH. Under suitable environment and nutritional conditions, yeast can bear alcohol in high concentration, but the vigor of yeast was lower when nitrogen was scarce in culture medium. Ethanol production increases by increase in urea level up to a certain level after which ethanol production decreases; this may be due to inhibitory effect of urea conc. (Vu and Kim, 2009). Since urea enhances ethanol production and is a more economical source of nitrogen, so the effect of the urea concentration on ethanol production studied. This finding is similar to Vu and Kim (2009) and Pereira et al. (2010). 3.4. Confirmatory experiments The developed model was verified by an additional 5 runs under different combinations of pH, urea conc., inoculum level, and temperature (Table 5). The results of the verification experiments were presented in Table 5 in terms of predicted versus experimental ethanol concentrations. A high value of coefficient of determination (R2 = 0.91) showed that the model was successful in predicting ethanol concentration. 3.5. Ethanol production in 3 L Laboratory Batch Fermenter under optimum conditions pH and temperature maintained throughout the experiments at 5.75 and 30 ◦ C respectively as changes in physical or chemical parameters affected cell growth and product formation during fermentation. To improve yield, substrates, metabolite products, and conditions should be maintained or controlled at optimal levels under bioreactor fermentation (Phisalaphong et al., 2007). Fig. 4 showed the time course of ethanol production, sugar consumption, ethanol yield and ethanol productivity. Fermentation of the hydrolysate obtained after enzymatic hydrolysis proceeded vigorously during the first 36 h with nearly 76% of sugars getting consumed with a corresponding increase in ethanol concentration

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a

b

12.27

10.36

10.49

8.37

8.70

Ethanol (g/l)

Ethanol (g/l)

12.35

6.37 4.38

6.92 5.14

0.75

7.00 5.00

6.38

pH

2.00 4.50

4.00 0.50

3.00 5.13

5.00

0.63

4.00 5.75

Urea conc. (g/l)

3.00 0.38

Inoculum level (%)

2.00 0.25

1.00

c

Inoculum level (%)

1.00

d 12.34

12.45

10.13

10.06 7.66

Ethanol (g/l)

Ethanol (g/l)

339

5.27 2.87

7.92 5.71 3.50

0.75

35.00 7.00

32.50

6.38 30.00 5.13 25.00

6.38 0.50

5.75

Temperature (oC) 27.50

7.00

0.63

Urea conc. (g/l)

5.75 0.38

pH

5.13 0.25

4.50

pH

4.50

e 12.37

Ethanol (g/l)

10.26 8.15 6.04 3.93

0.75 35.00

0.63

32.50 0.50

Urea conc. (g/l)

30.00 0.38

27.50 0.25

Temperature (oC)

25.00

Fig. 3. (a) Surface plot showing the effect of inoculum level and pH on ethanol production. (b) Surface plot showing the effect of inoculum level and urea conc. on ethanol production. (c) Surface plot showing the effect of temperature and pH on ethanol production. (d) Surface plot showing the effect of pH and urea conc. on ethanol production. (e) Surface plot showing the effect of temperature and urea conc. on ethanol production.

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Table 4 ANOVA for selected Box–Behnken design. Source

Sum of squares

DF

Mean square

F-Value

Model A B C D A2 B2 C2 D2 AB AC AD BC BD CD Residual Lack of fit Pure error Cor total R2 Adj R2 Cofficient of variance

271.381 4.44083 19.7633 23.2408 7.36333 55.2748 72 135.42 30.17 3.0625 0.0625 1.3225 6.76 5.0625 1.69 2.0175 0.0175 2 273.4 0.9926 0.9852 5.57

14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 10 4 28

19.3844 4.44083 19.7633 23.2408 7.36333 55.2748 72 135.42 30.17 3.0625 0.0625 1.3225 6.76 5.0625 1.69 0.14411 0.00175 0.5

134.514 30.8162 137.143 161.275 51.0962 383.568 499.628 939.719 209.358 21.2516 0.43371 9.1772 46.9095 35.1301 11.7274

Prob > F <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0004 0.5209 0.009 <0.0001 <0.0001 0.0041

0.0035

1

significant

not significant

Table 5 Validation of the model. Sr. no.

Inoculum level (%)

pH

Temperature (◦ C)

Urea conc. (g/L)

Ethanol concentration (g/L) observed

Ethanol concentration (g/L) predicted

Ethanol productivity (g/L/h)

1 2 3 4 5

3.47 3.40 3.65 3.48 3.07

4.84 5.77 5.02 4.59 5.47

27.98 29.27 26.61 28.07 29.01

0.44 0.69 0.43 0.37 0.58

12.3 13.2 12.1 12.5 12.4

12.52 13.29 11.97 12.18 12.49

0.307 0.33 0.302 0.312 0.31

Fermentation time 40 h.

(Fig. 4). This could be attributed to the early entry of cells into the log phase because of use of high initial inoculum. It is possible that the cells might have reached the stationary phase around 36–48 h, after which the fermentation rate declined. Beyond 36 h, no significant increase in ethanol concentration was observed, this may be decline in cell biomass due to lack of nutrients and production of toxic metabolites, resulting in death of a few cells. 76% sugar consumed during ethanol production and 24% remains

Sugar consumption (g/l) Ethanol production (g/l) Yield of ethanol on consumed sugar (g/g)

0.6

50

0.5

40

0.4

30

0.3

20

0.2

10

0.1

0

Yield (g/g), productivity (g/l/h)

Sugar, Ethanol (g/l)

Ethanol volumetric productivity (g/l/h)

60

0 0

12

24

36

48

72

Time (hrs) Fig. 4. Time course of sugar utilization, ethanol production, ethanol yield and ethanol productivity from microwave alkali pretreated rice straw by Saccharomyces cerevisiae.

uncomsumed (Table 6). The remaining sugar of 24% (w/v) is may be xylose. Xylose is pentose sugar that cannot be digested by S. cerevisiae (Yoswathana et al., 2010). The ethanol product yield and volumetric productivity play a decisive role in commercial adoption of any process. In present study ethanol yield and volumetric ethanol productivity reached up to 0.55 g/g and 0.50 g/L/h respectively. Yoswathana et al. (2010) studied ethanol production from pretreated rice straw and found that after 3 days 55–65% sugar will be converted to bioethanol and ethanol yield in this study was about 0.42 g/g. Sukumaran et al. (2009) used enzymatic hydrolysate of pretreated rice straw as substrate for ethanol production by S. cerevisiae and yield of ethanol was 0.093 g/g. Karimi et al. (2006) reported an ethanol concentration of 10.20 g/L in 48–72 h using rice straw hydrolysate and S. cerevisiae capable with ethanol yield of 66.7%. Abedinfar et al. (2009) have investigated the fermentation of rice straw (pretreated with diluted acid and subsequent enzyme treatment) using Mucor indicus, S. cerevisiae and Rhizopus oryzae. They have found an ethanol yield of 0.36–0.43 g/g using Mucor indicus, which was comparable with the corresponding yield

Table 6 Batch fermentation for ethanol production from microwave alkali pretreated rice straw hydrolysate by free cells of Saccharomyces cerevisiae in a 3 L fermenter. Parameters Initial sugar concentration (g/L) Residual sugar (g/L) Sugar consumed (%) Ethanol (g/L) Volumetric ethanol productivity (g/L/h) Ethanol yield on consumed sugar (g/g) Fermentation time 40 h.

50 12 76 19.2 0.53 0.50

A. Singh, N.R. Bishnoi / Industrial Crops and Products 37 (2012) 334–341

by S. cerevisiae (0.37–0.45) Rhizopus oryzae produce 0.33–0.41 g/g ethanol. This indicates that the results obtained through the present study are encouraging in terms of product yield and volumetric ethanol productivity for further scale-up studies and commercial exploitation of such a process. This study evaluated the use of rice straw as a resource to produce ethanol. 4. Conclusion The present study presented enzymatic hydrolysis by crude unprocessed on-site produced enzymes. Response surface methodology was successfully employed to optimize ethanol production medium and process variables for ethanol production by S. cerevisiae using microwave alkali pretreated rice straw. A maximum ethanol concentration of 13.2 g/L under optimum conditions as suggested according to the developed model. Under optimum conditions ethanol production at fermenter level ethanol concentration reached up to 19.2 g/L, ethanol productivity 0.53 g/L/h and ethanol yield 0.50 g/g. The screening and optimization methodologies represent a valuable tool for optimization of nutrients and process parameters for ethanol production using low cost agrowaste rice straw. Acknowledgments The authors acknowledge the financial assistance to Ms. Anita Singh by CSIR, New Delhi in the form of Senior Research Fellowship (SRF), and University Grant Commission, New Delhi, India for providing financial support under the Major Research Project scheme F-33-144/2007(SR). References Abedinfar, S., Karimi, K., Khananhmadi, M., Taherzadeh, M., 2009. Ethanol production by Mucor indicus and Rhizopus oryzae from rice straw by separate hydrolysis and fermentation. Biomass Bioenergy 33, 828–833. Alegre, R.M., Rigo, M., Jokes, 2003. Ethanol fermentation of a diluted molasses medium by Saccharomyces cerevisiae immobilized on chrysotile. Brazil. Arch. Biol. Technol. 46 (4). Bajaj, K.B., Yousef, S., Thakur, L.R., 2001. Selection and characterization of yeasts for desirable fermentation characteristics. Indian J. Microbiol. 41 (2), 107–110. Caputi, A.J., Ueda, M., Brown, T., 1968. Spectrophotometric determination of ethanol in wine. Am. J. Enol. Viticult. 19, 160–165. Ferreira, S., Duarte, A.P., Ribeiro, M.H.L., Queiroz, J.A., Domingues, F.C., 2009. Response surface optimization of enzymatic hydrolysis of Citrus ladanifer and Cytisus striatus for bioethanol production. Biochem. Eng. J. 45, 192–200. Goh, C.S., Tan, K.T., Lee, K.T., Bhatia, S., 2010. Bio-ethanol from lignocellulose: status, perspectives and challenges in Malaysia. Biores. Technol. 101, 4834–4841. Karimi, K., Emtiazi, G., Taherzadeh, M.J., 2006. Ethanol production from dilute-acid pretreated rice straw by simultaneous saccharification and fermentation with

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