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Bioethanol production by Miscanthus as a lignocellulosic biomass: Focus on high efficiency conversion to glucose and ethanol

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Current ethanol production processes using crops such as corn and sugar cane have been well established. However, the utilization of cheaper lignocellulosic biomass could make bioethanol more competitive with fossil fuels while avoiding the ethical concerns associated with using potential food resources. In this study, Miscanthus, a lignocellulosic biomass, was pretreated using NaOH to produce bioethanol. The pretreatment and enzymatic hydrolysis conditions were evaluated by response surface methodology (RSM). The optimal conditions were found to be 145.29 degrees C, 28.97 min, and 1.49 M for temperature, reaction time, and NaOH concentration, respectively. Enzymatic digestibility of pretreated Miscanthus was examined at various enzyme loadings (10 to 70 FPU/g cellulose of cellulase and 30 CbU/g of beta-glucosidase). Regarding enzymatic digestibility, 50 FPU/g cellulose of cellulase and 30 CbU/g of beta-glucosidase were selected as the test concentrations, resulting in a total glucose conversion rate of 83.92%. Fermentation of hydrolyzed Miscanthus using Saccharomyces cerevisiae resulted in an ethanol concentration of 59.20 g/L at 20% pretreated biomass loading. The results presented here constitute a significant contribution to the production of bioethanol from Miscanthus.
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Han et al. (2011). “Miscanthus to glucose & ethanol,” BioResources 6(2), 1939-1953. 1939
BIOETHANOL PRODUCTION BY MISCANTHUS AS A
LIGNOCELLULOSIC BIOMASS: FOCUS ON HIGH EFFICIENCY
CONVERSION TO GLUCOSE AND ETHANOL
Minhee Han,a Gi-Wook Choi,a,* Yule Kim,a and Bon-cheol Koo b
Current ethanol production processes using crops such as corn and
sugar cane have been well established. However, the utilization of
cheaper lignocellulosic biomass could make bioethanol more competitive
with fossil fuels while avoiding the ethical concerns associated with using
potential food resources. In this study, Miscanthus, a lignocellulosic
biomass, was pretreated using NaOH to produce bioethanol. The
pretreatment and enzymatic hydrolysis conditions were evaluated by
response surface methodology (RSM). The optimal conditions were
found to be 145.29 °C, 28.97 min, and 1.49 M for temperature, reaction
time, and NaOH concentration, respectively. Enzymatic digestibility of
pretreated Miscanthus was examined at various enzyme loadings (10 to
70 FPU/g cellulose of cellulase and 30 CbU/g of β-glucosidase).
Regarding enzymatic digestibility, 50 FPU/g cellulose of cellulase and 30
CbU/g of β-glucosidase were selected as the test concentrations,
resulting in a total glucose conversion rate of 83.92%. Fermentation of
hydrolyzed Miscanthus using Saccharomyces cerevisiae resulted in an
ethanol concentration of 59.20 g/L at 20% pretreated biomass loading.
The results presented here constitute a significant contribution to the
production of bioethanol from Miscanthus.
Keywords: Bioethanol; Miscanthus; Response surface methodology (RSM); Enzymatic hydrolysis;
Fermentation
Contact information: a: Changhae Institute of Cassava and Ethanol Research, Changhae Ethanol Co.,
Ltd, Jeonju, 561-203, Korea; b: Bioenergy Crop Research Center, National Institute of Crop Science,
Rural Development Administration, Muan, 534-833, Korea; *Corresponding author: changrd@chethanol.com
INTRODUCTION
Over the last few decades, the excessive consumption of fossil fuels has led to an
increasing demand for alternative sources of fuel (Zaldivar et al. 2001). These alternative
sources usually rely upon the production of renewable energy sources such as ethanol.
Currently, ethanol is mainly produced from sugar or starch for use as a fuel. However, the
availability of raw materials that are also food sources is not sufficient to meet the need
for ethanol fuel production (Hahn-Hagerdal et al. 2006). Cellulosic ethanol is one of the
most promising technological approaches available for reducing the emission of
greenhouse gases from the transportation sector (Lynd 1996). Further, lignocellulosic
biomass is a widely available, low-cost feedstock that is not subject to the ethical
concerns associated with the use of a potential food resource (Rass-Hansen et al. 2007).
For this reason, the development of a process for converting lignocellulosic biomass into
ethanol is imperative. However, such a process is challenging due to the complex
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structure of the plant cell wall. Pretreatment is required to alter the structural and
chemical composition of lignocellulosic biomass to facilitate rapid and efficient
hydrolysis of carbohydrates in the cell wall into fermentable sugars (Chang and
Holtzapple 2000).
A variety of physical (comminution, hydrothermolysis), chemical (acid, alkali,
solvents, ozone), physico-chemical (steam explosion, ammonia fiber explosion), and
biological pretreatment techniques have been developed to improve the accessibility of
enzymes to cellulosic fibers (Moiser et al. 2005). Acid pretreatment includes the use of
sulfuric, nitric, or hydrochloric acids to remove hemicellulosic components and expose
cellulose to enzymatic digestion (Schell et al. 2003). Agricultural residues such as
corncobs and stovers are particularly well suited for dilute acid pretreatment (Torget et al.
1991). Alkali pretreatment refers to the application of alkaline solutions for the removal
of lignin and various uronic acid substitutions present on hemicellulose that lower
enzyme accessibility (Chang and Holtzapple 2000). Generally, alkaline pretreatment is
more effective for agricultural residues and herbaceous crops than wood materials (Hsu
1996). Peroxide pretreatment enhances enzymatic conversion through oxidative
delignification and reduction of cellulose crystallinity (Gould 1985). Increased lignin
solubilization and cellulose availability have been observed during the peroxide
pretreatment of wheat straw (Martel and Gould 1990), Douglas fir (Yang et al. 2002), and
oak (Kim et al. 2001). Ozonation is another attractive pretreatment method that does not
leave strong acidic, basic, or toxic residues in treated materials (Neely 1984). The effect
of ozone pretreatment is essentially limited to lignin degradation. Specifically,
hemicellulose is attacked, while cellulose is barely affected (Sun and Cheng 2002).
Further, ozonation has been widely used to reduce the lignin content of both agricultural
and forestry wastes (Neely 1984).
Miscanthus utilizes the C4 photosynthetic pathway. Compared to C3 plants,
which make up the majority of plants, C4 plants have a higher carbon dioxide fixation
rate that results in high rates of photosynthesis. Therefore, C4 plants can grow very fast.
Additionally, C4 plants have a very low compensation point, enabling them to conduct
photosynthesis at high light intensity when only low concentrations of carbon dioxide are
available. Furthermore, since the concentration of CO2 relative to O2 in the cells of C4
plants is high, the rate of photorespiration in C4 plants is significantly lower than in C3
plants (Theese 1995). Miscanthus can grow up to 4 m tall (Eitzinger and Kossler 2002).
The height of the plant is dependent on the species as well as the growing conditions. A
benefit of Miscanthus is its high biomass yield, which depends on the season during
which it is harvested (Himken et al. 1997). In Northern Europe, M. sinesis hybrids have
been found to yield up to 25 t/ha, whereas in middle and Southern Europe M. x giganteus
yields up to 38 t/ha, and M. sinensis hybrids yield up to 41 t/ha (Lewandowski et al.
2003).
To fully utilize Miscanthus as a feedstock for ethanol production, pretreatment is
required to render the cellulose fibers more amenable to the action of hydrolytic enzymes.
In this study, response surface methodology (RSM) was used to determine the optimal
pretreatment with NaOH solution and enzymatic hydrolysis conditions that produce high
concentrations of bioethanol. RSM is a statistical technique used to model and optimize
multiple variables, and it can be used to determine the optimum conditions by combining
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experimental design with interpolation of first- or second-order polynomial equations in a
sequential testing procedure. This methodology has already been successfully applied in
optimizing the enzymatic hydrolysis of several substrates, including cellulose
(Kunamneni and Singh 2005; Tengborg et al. 2001; Marques et al. 2007; Ribeiro and
Ribeiro 2008; Gouveia et al. 2008; Lu et al. 2007; Lebo et al. 2004). Also, enzymatic
hydrolysis and fermentation experiments were carried out using the pretreated biomass to
determine the appropriate enzyme concentration and biomass loading. As the enzyme
cost is high relative to other factors, the enzyme dosage has a great effect on the
economics. Although high biomass loading could lead to high ethanol concentration, it is
too difficult to agitate as the solid content and viscosity increase. In addition, the enzyme
can be exposed to product inhibition by glucose that is produced in the course of
enzymatic hydrolysis. Under these conditions, bioethanol was produced through separate
hydrolysis and fermentation (SHF) processes, which is the most basic process used for
fermentation in order to minimize contamination.
MATERIALS AND METHODS
Raw Materials
Miscanthus was harvested at a local site in Korea (Jeonju City) during the winter
of 2009 and air-dried at temperatures below 45 °C to obtain a dry matter content of 92 to
94%. Dried Miscanthus was chopped and hammer-milled to a particle size of 1 to 3 mm,
then stored in sealed plastic bags at room temperature until used.
Enzymes were provided by Novozymes, Korea. A cellulase complex (NS50013)
and β-glucosidase (NS50010) were used to investigate enzymatic digestibility. The
cellulase complex had an activity of 70 filter paper units (FPU)/g cellulose. The β-
glucosidase had 250 cellobiase units (CbU)/g. All reagents used in this study were of
analytical grade.
NaOH Pretreatment and Enzymatic Hydrolysis
In this study, 10 mL of NaOH solution was used to pretreat 2 g of ground
Miscanthus samples in order to determine the optimum pretreatment and enzymatic
hydrolysis conditions. The treatments were performed at various temperatures and for
various times in an oil bath. The reaction time was estimated after approaching the set
temperature. After cooling, the treated biomasses were washed with deionized water
several times. Then, the biomass was dried at 45 °C in order to fix the moisture for
enzymatic hydrolysis.
Two enzyme solutions, the cellulase complex and β-glucosidase, were used to
investigate the effects of enzyme concentration (cellulase activity of 5 to 70 FPU/g
cellulose and β-glucosidase activity of 30 CbU/g) and biomass concentration (1 to 30%
loading) on enzymatic hydrolysis. Hydrolysis was conducted at 50 °C and 150 rpm for 72
h. After the reaction, 1.0 mL aliquots were taken and centrifuged at 5,000 rpm for 10
min. The supernatant was removed for sugar content analysis (Yang and Wyman 2004).
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Experimental Design
Response surface methodology (RSM) is a collection of mathematical and
statistical techniques that are used to model and analyze problems in which the response
of interest is influenced by several variables, and the objective is to optimize this
response (Montgomery 2001). In this study, many variables could potentially affect the
efficiency of the pretreatment process. Central composite rotatable design (CCRD) was
employed to determine the effects of independent variables on the response and factor
interactions using different combinations of variables. Three independent variables,
namely temperature (X1), reaction time (X2), and NaOH solution concentration (X3), were
studied at three levels with three repetitions at the central point and three replications at
the axial and factorial points (Table 1).
Table 1. Coded and Decoded Values for each Variable of the Central Composite
Rotatable Design
Coded levels of the
experimental factors
X1:
Temperature
(C)
X2:
Time
(min)
X3:
NaOH concentration
(M)
-2
-1
0
1
2
99.95
120
150
180
200.45
3.18
10.00
20.00
30.00
36.82
0.16
0.50
1.00
1.50
1.84
For each of the five variables studied, high (coded +2) and low (coded −√2) set
points were selected according to the results obtained in the preliminary tests. The results
of each CCRD were analyzed using Design Expert® software version 7.1.3 from Stat-
Ease, Inc., Minneapolis, USA. The quadratic effects of the five variables were calculated,
as well as their possible interactions, with the conversion rate of the biomass to glucose.
The significance of these variables was evaluated using variance analysis (ANOVA).
Three-dimensional surface plots were drawn to illustrate the effects of the
independent variables on the dependent variables, as described by a quadratic polynomial
equation fitted to the experimental data. The fit of the models was evaluated by
determining the R-squared coefficient and the adjusted R-squared coefficient. To verify
the models, optimum values for the selected variables were obtained by solving the
regression equation using Design Expert® software version 7.1.3.
Fermentation with Industrial Microorganism Saccharomyces cerevisiae
CHY 1011
After enzymatic hydrolysis, S. cerevisiae CHY 1011 was inoculated, and solid
caps were replaced with silicone septa caps pierced with 22 g needles in order to exhaust
the CO2 that was released during fermentation. The bottles were then placed back on the
shaker/incubator, and the temperature was set to 32 °C. These bottles were sampled
periodically for the next 48 h, after which the final ethanol concentration was estimated.
S. cerevisiae inoculum was prepared by growing strain CHY 1011 on solid YPD
medium containing 10 g of yeast extract, 20 g of protease peptone, and 10 g of dextrose
per liter supplemented with 15 g of Bacto agar. The solid culture was incubated at 32 °C
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for 48 h, after which a single colony was transferred to a 50 mL Erlenmeyer flask
containing 10 mL of YPD. Then, the colony was grown at 32 °C with agitation (150 rpm)
for 12 h. This culture was used to inoculate the seed culture, which consisted of 200 mL
of YPD in a 500 mL Erlenmeyer flask incubated for 12 h.
Analytical Methods
The total solids, acid-soluble lignin, and acid-insoluble lignin contents of
Miscanthus were determined by the National Renewable Energy Laboratory (NREL)
using Standard Biomass Analytical Procedures (National Renewable Energy Laboratory).
The carbohydrate content of Miscanthus was estimated by measuring the hemicellulose
(xylan, galactan, and arabinan)- and cellulose (glucan)-derived sugars. The composition
of the hydrolysate produced by enzymatic hydrolysis was determined by measuring
glucose and xylose by high performance liquid chromatography (HPLC).
The HPLC (Waters, USA) system was equipped with a Bio-Rad Aminex HPX-
87P column, a guard column, an automated sampler, a gradient pump, and a refractive
index detector. The mobile phase was deionized water at a flow rate of 0.6 mL/min at 85
°C. Prior to HPLC injection, all samples (derived from solids and hydrolysate) were
neutralized with calcium carbonate, centrifuged at 5,000 g for 10 min, and filtered
through 0.2 µm syringe filters. The concentration and impurities of ethanol were deter-
mined using a Density/Specific Gravity Meter (DA-510, KEM Co, Ltd., Japan) and gas
chromatography (GC) with a Supelco 6.6 % CARBOWAX 20M column, Agilent, USA.
RESULTS AND DISCUSSION
Characteristics of Miscanthus
The chemical composition of Miscanthus varies according to its growth location,
season, harvesting method, as well as analysis procedure (National Renewable Energy
Laboratory). The composition of Miscanthus used in this study is listed in Table 2.
Based on the HPLC carbohydrate analysis, the sugar fraction was 59.08% and the
lignin fraction was 23.31% of the dry biomass. Glucan, which was derived from both the
Miscanthus fiber and plant cell wall, was the major component (36.96%). Xylan, as the
major hemicellulose constituent, constituted up to 22.12%. Lignin is a complex chemical
compound derived from biomass that protects against enzyme attack. Arabinan accounted
for only a small portion (>1%) of the biomass, whereas galatan and mannan were not
detected. Additionally, Miscanthus contained little ash (2 to 3%) and other unknown
components. Glucan and xylan can be converted to ethanol using organisms capable for
fermenting pentoses and hexoses. However, digestion of pentose by S. cerevisiae CHY
1011 is difficult, and therefore, cellulose was retained during pretreatment and utilized to
ferment hexoses derived from the biomass.
Optimization of Pretreatment and Enzymatic Hydrolysis with Central
Composite Rotatable Design (CCRD)
Following pretreatment and enzymatic hydrolysis, the total glucose conversion
rate (TGCR) was evaluated as a function of temperature, time, and NaOH solution
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concentration. The TGCR was expressed as the efficiency of pretreatment and enzymatic
hydrolysis at the same time, and it was determined according to Eq. 1,
%   %%
%%   (1)
in which Sp is the solid ratio after pretreatment, GE is the glucose concentration after
enzymatic hydrolysis, BL is the pretreated biomass loading at enzymatic hydrolysis, and
GR is the raw biomass glucose concentration. The temperature ranged from 120 to 180
°C, time ranged from 10 to 30 min, and the pretreatment solution concentration ranged
from 0.5 to 1.5 M in the optimal CCRD test (Table 3).
Table 2. Major Components of Miscanthus
Component [%]*
Cellulose 36.96(±0.94)
Hemicellulose 22.12(±0.75)
Acid-insoluble lignin 20.43(±0.86)
Acid-soluble lignin 2.88(±0.12)
Moisture 7.02(±0.14)
Ash 2.84(±0.08)
*Values indicate the mean of triplicate observations
Table 3. Central Composite Design for the Optimization of Three Variables in
Determining Total Glucose Conversion Rate (TGCR) after Enzymatic Hydrolysis
Runs Reaction
temperature
Residual
time
NaOH
solution
concentration
TGCR [%]*
1 -1.68 0.00 0.00 76.72 (± 0.58)
2 -1.00 -1.00 -1.00 64.24(± 0.74)
3 -1.00 -1.00 1.00 81.79(± 0.32)
4 -1.00 1.00 -1.00 66.25(± 0.63)
5 -1.00 1.00 1.00 77.17(± 0.91)
6 0.00 -1.68 0.00 74.35(± 0.53)
7 0.00 0.00 -1.68 38.00(± 0.28)
8 0.00 0.00 0.00 77.19(± 0.26)
9 0.00 0.00 0.00 76.35(± 0.41)
10 0.00 0.00 0.00 76.20(± 0.82)
11 0.00 0.00 1.68 85.64(± 0.39)
12 0.00 1.68 0.00 77.49(± 0.47)
13 1.00 -1.00 -1.00 74.73(± 0.61)
14 1.00 -1.00 1.00 73.46(± 0.29)
15 1.00 1.00 -1.00 68.50(± 0.64)
16 1.00 1.00 1.00 81.54(± 0.57)
17 1.68 0.00 0.00 74.96(± 0.53)
* Values indicate the mean of triplicate observations
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Variance analysis (ANOVA) was performed to evaluate the effects of the
variables and their possible interactions. The coefficients of the full model were
evaluated using regression analysis, and their significance was tested. The insignificant
coefficients were excluded from the model using backward elimination. The analysis of
variance performed on the reduced models (Table 4) demonstrated that they were
statistically significant with p-values lower than 0.0001.
Table 4. ANOVA Results for Response of Total Glucose Conversion Rate
Source Sum of
Squares d.f. Mean of
Square F Value p-value
(Prob > F)
Model 2154.984 13 165.768 938.943 < 0.0001
Temp. 1.550 1 1.550 8.778 0.0594
Time 0.333 1 0.333 1.884 0.2634
NaOH conc. 1134.247 1 1134.247 6424.599 < 0.0001
Temp. X Time 10.805 1 10.805 61.199 0.0043
Temp. X NaOH conc. 0.006 1 0.006 0.037 0.8601
Time X NaOH conc. 57.517 1 57.517 325.789 0.0004
Temp.
2
0.187 1 0.187 1.061 0.3788
Time
2
4.027 1 4.027 22.808 0.0174
NaOH conc.
2
291.643 1 291.643 1651.923 < 0.0001
Temp. X Time X NaOH conc. 229.466 1 229.466 1299.741 < 0.0001
Temp.
2
X Time 22.283 1 22.283 126.214 0.0015
Temp.
2
X NaOH conc. 213.912 1 213.912 1211.640 < 0.0001
Temp. NaOH conc.
2
0.913 1 0.913 5.169 0.1076
Residual 0.530 3 0.177
Lack of Fit 0.206 1 0.206 1.276 0.3759
Pure Error 0.323 2 0.162
Total 2155.514
R2 = 0.9998; adj. R2 = 0.9987; d.f.=degree of freedom.
The NaOH concentration produced the lowest p-values (<0.0001) among all
factors, which indicates that NaOH concentration was the dominant factor affecting the
TGCR. Equation (2) describes the correlation between the significant variables and
glucose release rate for the pretreated biomass in terms of the decoded values,
Ya = 76.056 – 0.523 X1 -0.243 X2 + 14.16 X3
- 1.162 X1X2 + 0.029 X1X3+ 2.681 X2X3
- 0.129 X1
2 + 0.598 X2
2 -5.086 X3
2 + 5.356 X1X2X3
- 2.593 X1
2X2 -8.035 X1
2X3+ 0.525 X1X2
2 (2)
where X1 is temperature, X2 is time, and X3 is the NaOH concentration.
The relationship between the response and controlled variables was visualized
using the response surface or contour plots. Response surface plots were used to estimate
the TGCR as a function of two factors, based on Eq. 2, while maintaining all of the other
factors at a fixed level of zero. The convex response surfaces suggest that there were
well-defined optimal variables. Graphic representation of the response surface shown in
Fig. 1 helps visualize the effects of temperature and NaOH solution concentration.
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30
40
50
60
70
80
90
100
120
140
160
180
200
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
TGCR [%]
Temp. ['C]
NaOH conc. [M]
30
40
50
60
70
80
90
FIG. 1. Response surface plots show the effects of temperature and NaOH solution
concentration. The value of the variable time was fixed at the central point.
The proportion of the total variation attributed to each fit was evaluated using the
R-squared value (noting that R2 > 0.75 indicates a suitable model) (Haaland 1989). For
the pretreated Miscanthus, the regression equation resulted in an R2 value of 0.9998,
which is in good agreement with the adjusted R2 of 0.9987. These results ensure that the
theoretical values were well adjusted to the experimental data using this model.
Therefore, the model was suitable for predicting the TGCR.
The optimum values of the selected variables were obtained by solving the
regression equation, and results are shown in Table 5. To validate the model, the
optimum values for Equation (2) were used in triplicate sets of experiments, and the
maximum response obtained for each parameter is presented in Table 5. The experi-
mental response for Miscanthus was 83.92% of the TGCR. This value is in good
agreement with the predicted value of 86.89 (82.55-91.23) with a 95% confidence
interval. This behavior shows that the model could be adapted to the experimental results,
confirming the validity and adequacy of the models.
Table 5. Optimal Values of the Test Variables in Decoded Units, and the
Predicted Maximum of the Total Glucose Conversion Rate (TGCR) from Dry
Biomass at a 95% Confidence Interval
Variables Value
X1: Temperature [C] 145.29
X2: Time [min] 28.97
X3: NaOH concentration [M] 1.49
TGCR of predicted response with 95%
confidence interval 86.89 (82.55-91.23)
TGCR of experimental response [%] 83.92 ± 0.35
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Enzymatic Hydrolysis of Pretreated Miscanthus
Enzymatic hydrolysis experiments were carried out using the pretreated biomass
in order to determine the effects of enzyme concentration and biomass loading. Equation
3 describes enzymatic digestibility,
Enzymaticdigestibility%%
%   (3)
in which GE is the concentration of enzyme-converted glucose and GP is the glucose
concentration after pretreatment. Figure 2 shows the enzymatic digestibility of pretreated
Miscanthus at 1% (w/v) biomass loading with cellulase complex and β-glucosidase
enzyme loadings of 5 to 70 FPU/g cellulose and 30 CbU/g, respectively. The conversion
rate was enhanced in accordance with an increase in enzyme dosage. However, there was
little difference in enzymatic digestibility when the enzyme dosage was greater than 50
FPU/g cellulose. Based on these results, an enzyme loading of 50 FPU/g cellulose was
chosen to examine the enzymatic digestibility of pretreated Miscanthus, due to its high
stability and effective reaction.
Time [h]
0 20406080
Enzymatic digestibility [%]
0
20
40
60
80
100
FIG. 2. Enzymatic digestibility of pretreated Miscanthus at 1% (w/v) biomass loading with cellulase
complex and β-glucosidase enzyme loadings of 5 to 70 FPU/g cellulose and 30 CbU/g,
respectively. 5; 10; 20; 30; 40; 50; 60; 70 FPU g-1 cellulose
Figure 3 (a and b) shows the converted glucose concentration and enzymatic
digestibility of pretreated Miscanthus at various biomass loadings (1 to 24% (w/v)) with
50 FPU/g of cellulose enzyme. A biomass loading of over 24% was impractical due to
difficulties in stirring. In early enzymatic hydrolysis, enzymatic digestibility could not be
analyzed due to the high viscosity of the biomass. The converted glucose concentration
showed an upward trend, but enzymatic digestibility decreased with increased biomass
loading. There was little difference in enzymatic digestibility (ca. 90%) when the biomass
loading was greater than 10% (w/v) of the biomass concentration, whereas greater than
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20% (w/v) biomass loading resulted in 80% enzymatic digestibility. One explanation is
that the enzyme activity decreased in accordance with the increasing conversion of
glucose concentration.
Time [h]
0 2040608
Converted glucose conc. [%]
0
2
4
6
8
10
12
14
(a)
Time [h]
0 2040608
0
Enzymatic hydrilysis [%]
0
20
40
60
80
100
(b)
FIG. 3. Converted glucose concentration (a) and enzymatic digestibility (b) of pretreated
Miscanthus at 1~24 % (w/v) biomass loading with cellulase complex and β-glucosidase enzyme
loadings of 50 FPU/g cellulose and 30 CbU/g, respectively. 1; 5; 10; 15; 17; 20; 22;
24 % biomass loading
Separate Hydrolysis and Fermentation (SHF) of Pretreated Miscanthus with
S. cerevisiae
The fermentative potentials of the pretreated materials were evaluated using S.
cerevisiae. The pretreatment and enzymatic hydrolysis conditions were applied according
to the results of the RSM. To evaluate the ethanol concentration as a function of biomass
loading (10 to 24% (w/v)), the pretreated biomass was mixed with the cellulase complex
and β-glucosidase for 72 h at 50 °C. Fermentation was subsequently carried out for 48 h
at 32 °C with inoculation of 7% S. cerevisiae. Sterilization was conducted before and
after enzymatic hydrolysis to prevent contamination of the SHF process.
Figure 4 (a) shows the ethanol concentrations of the pretreated Miscanthus at
various biomass loadings (10 to 24 % (w/v)) after enzymatic hydrolysis using 50 FPU/g
cellulose before and after sterilization. As the biomass loading increased, the ethanol
concentration accordingly increased. The final ethanol concentrations for biomass
loadings of 10% and 20% were 27.01 and 49.30 g/L, respectively. However, the ethanol
yields decreased upon increasing biomass loading. Ethanol yields were calculated by
dividing the experimental ethanol concentration by the theoretical amount of ethanol
produced by glucose conversion from pretreated biomass. When the biomass dosage was
10%, the ethanol yield was 80.73%, which is 1.3 times higher than that of the 20%
biomass loading (61.40%). As previously mentioned, this indicates that enzyme
activation was inhibited by the amount of converted glucose, which increased with
increasing biomass loading.
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Figure 4 (b) shows the results of the fermentation process carried out under the
same conditions of (a), but without sterilization after enzymatic hydrolysis, to preserve
enzyme activity. As a result, ethanol production increased considerably. Compared to the
data of (a), the rates of increase in ethanol concentration were found to be 4.92% for 10%
and 20.08% for 24% biomass loading, respectively. This suggests that the ethanol
concentration was increased by additional enzymatic hydrolysis along with consumption
of glucose by yeast during fermentation. These results are summarized in Table 6.
Time [h]
0 1020304050
Ethanol concentration [g/L]
0
20
40
60
(a)
Time [h]
0 1020304050
Ethanol concentration [g/L]
0
20
40
60
(b)
FIG. 4. Ethanol fermentation as a function of biomass loading (10~24% (w/v)), (a): Sterilization
before and after enzymatic hydrolysis, (b): Sterilization before enzymatic hydrolysis. 10; 15;
20; 22; 24 % biomass loading
Table 6. Converted Glucose Concentration after Enzymatic Hydrolysis, and
Ethanol Production from Fermentation
Pretreated
biomass dosage
[%]
Sterilization after enzymatic hydrolysis Sterilization before enzymatic hydrolysis
10 15 20 22 24 10 15 20 22 24
Max. converted
glucose conc.
[g/L of theoretical]
65.50 98.20 130.90 144.00 157.10 65.50 98.20 130.90 144.00 157.10
Max. ethanol
conc. [g/L] 27.01 36.8 44.96 48.62 49.3 28.34 41.22 51.91 56.97 59.2
Ethanol yield
[% of theoretical] 80.73 73.33 67.19 66.05 61.40 84.70 82.13 77.57 77.40 73.72
Rate of increase
in ethanol [%] - - - - - 4.92 12.01 15.46 17.17 20.08
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Han et al. (2011). “Miscanthus to glucose & ethanol,” BioResources 6(2), 1939-1953. 1950
Table 7. Overall Process for Bioethanol Production from Miscanthus
Raw
biomass
[g]
1000
Components [%] Conditions
Cellulose 36.96 Temp. [oC] 145.29
Hemicellulose 22.12 Time [min] 28.97
Pretreatment
Lignin 23.31 NaOH conc. [M] 1.49
Moisture 7.02 Biomass: NaOH 1:5
Pretreated
biomass
[g]
550
(Solid ratio:
55%)
Components [%] Loss1) Ratio[%]*
Cellulose 63.36 Cellulose 5.71
Hemicellulose 18.07 Hemicellulose 55.07
Lignin 9.92 Lignin 76.59
Enzymatic
hydrolysis
Moisture 7.02 *Base on raw biomass
Biomass
conc.
[%]
Pretreated
biomass
[g]
Add water
& enzymes
[ml]
Max. glucose
[g/L of
theoretical]
Conditions
10 10 90.00 65.47 Temp. [oC] 50
15 15 85.00 98.21 Time [h] 72
20 20 80.00 130.94 Agitation [rpm] 150
Fermentation
22 22 78.00 144.04 Cellulase [FPU] 50
24 24 76.00 157.13 β-glucosidase [CBU] 30
Biomass
conc.
[%]
Max.
Ethanol
[g/L of
theoretical]
Max. Ethanol
[g/L of
experimental]
Ethanol
yield [%] Conditions
10 33.46 28.34 84.69 S.cerevisiae CHY 1011
15 50.19 41.22 82.12 Temp. [oC] 32
20 66.93 51.91 77.56 Time [h] 48
22 73.62 56.97 77.39 Agitation [rpm] 150
24 80.31 59.2 73.71 Inoculation [%] 7
1) Loss%   

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Han et al. (2011). “Miscanthus to glucose & ethanol,” BioResources 6(2), 1939-1953. 1951
CONCLUSIONS
The overall process of Miscanthus pretreatment for production of bioethanol was
examined in this study (Table 7). Pretreatment was essential to the production of ethanol
from the lignocellulosic biomass, which was achieved through saccharification by
breaking the tangled structure of cellulose, hemicellulose, and lignin so that the enzyme
could easily permeate into the biomass. In this study, NaOH solution was used for
pretreatment, and the optimal pretreatment and enzymatic hydrolysis conditions were
determined through response surface methodology (RSM). The results reveal that the
optimal temperature was 145.29°C with a reaction time of 28.97 min and a NaOH
concentration of 1.49 M. After pretreatment, 50 FPU/g of cellulose of the cellulase
complex and 30 CbU/g of β-glucosidase were added and mixed together at 150 rpm and
50 °C for 72 h. Following inoculation of S. cerevisiae, ethanol was produced after 48 h of
fermentation at 32 °C and 150 rpm. The ethanol concentration was 59.20 g/L at a
pretreated biomass loading of 20%, which is relatively higher than those of other
lignocellulosic materials. Overall, the ethanol production process from Miscanthus using
NaOH pretreatment was effective, and it may be feasible for the commercial production
of bioethanol in the near future.
ACKNOWLEDGEMENTS
This study was financially supported by the Rural Development Administration
(20100401-030-800-001-06-00).
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Article submitted: February 17, 2011; Peer review complete: March 20, 2011; Revised
version received and accepted: April 12, 2011; Published: April 18, 2011.
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The aim of this study was the modelling, under high pressure, of naringin hydrolysis by naringinase. Response surface methodology (RSM) was used to compare the effects of the selected variables on the bioconversion under study. The combined action of temperature (13–61 °C) and pressure (80–216 MPa) on the catalytic activity of naringinase was investigated at pH 4.0 using naringin as the substrate. The choice of experimental domains resulted from preliminary studies.Naringinase activity, for naringin hydrolysis at pH 4.0, could be described by a convex surface with a maximum of 0.13 mM min−1, at 41 °C and 158 MPa. After 1 h of reaction time, reducing sugars production could also be described by a convex surface, with a maximum reducing sugars concentration of 8 mM at 38 °C and 168 MPa. The interaction temperature–pressure had a significant effect on both naringinase activity and reducing sugar formation after 1 h.Under the optimized conditions, the naringin hydrolysis by naringinase was evaluated.
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