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ARTICLE

Ultrasound Pretreatment of Cassava ChipSlurry to Enhance Sugar Release for

Subsequent Ethanol ProductionSaoharit Nitayavardhana,1 Sudip Kumar Rakshit,2 David Grewell,3

J. (Hans) van Leeuwen,4 Samir Kumar Khanal1

1Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa,

Honolulu, Hawaii 96822; telephone: 808-956-3812; fax: 808-956-3842;

e-mail: [email protected] of Food Engineering and Bioprocess Technology, School of Environment,

Resources and Development, Asian Institute of Technology, Pathumthani, Thailand3Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa4Department of Civil, Construction and Environmental Engineering,

Iowa State University, Ames, IowaReceived 2 October 2007; revision received 16 January 2008; accepted 20 March 2008

Published online 3 April 2008 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/bit.21922

ABSTRACT: The use of ultrasound pretreatment to enhanceliquefaction and saccharification of cassava chips wasinvestigated. Cassava chip slurry samples were subjectedto sonication for 1040 s at three power levels of low(2 W/mL), medium (5 W/mL), and high (8 W/mL). Thesamples were simultaneously exposed to enzymes to convertstarch into glucose. The cassava particle size declined nearly40-fold following ultrasonic pretreatment at high power

input. Scanning electron micrographs of both unsonicated(control) and sonicated samples showed disruption offibrous material in cassava chips but did not affect thegranular structure of starch. Reducing sugar releaseimproved in direct proportion to the power input andsonication time. The reducing sugar increase was as muchas 180% with respect to the control groups. The slurrysamples with enzyme addition during sonication resultedin better reducing sugar release than the samples withenzyme addition after sonication. The heat generated duringsonication below starch gelatinization temperature appar-ently had no effect on the reducing sugar release. Thereducing sugar yield and energy efficiency of ultrasoundpretreated samples increased with total solids (TS) contents.The highest reducing sugar yield of 22 g/100 g of sample and

efficiency of 323% were obtained for cassava slurry with 25%TS at high power. The reducing sugar yield at thecompletionof reaction (R

1) were over twofold higher compared to the

control groups. The integration of ultrasound into a cassava-based ethanol plant may significantly improve the overallethanol yield.

Biotechnol. Bioeng. 2008;101: 487496.

2008 Wiley Periodicals, Inc.

KEYWORDS: cassava chip; ethanol yield; reducing sugarrelease; particle size distribution; ultrasonic pretreatment

IntroductionThe dependence of the global economy on fossil-derivedfuels coupled with political instability in oil producingcountries has pushed petroleum prices near all-time highs.The energy demand is expected to increase by more than50% by 2025, mostly due to increase in demand fromemerging developing nations such as India and China(Ragauskas et al., 2006). The increasing use of fossil fuelsalso generates excessive greenhouse gases, which areconsidered to be the main cause of global warming(Newton, 2007). Thailand, for example, consumes around20 million liters of petroleum fuel daily, the majority of

which is imported. The contribution of renewable energy onthe nations total energy demand is barely 0.5% and the Thaigovernment plans to increase this to 8% in 45 years time(Mogg, 2004).

Bioethanol is considered as one of the most promisingclean and alternative renewable automotive fuels. Bio-ethanol is mainly produced by yeast fermentation offermentable sugars derived from sugar and starch-basedfeedstocks, such as sugarcane, corn, cassava, milo, and sugarbeet. In the United States, ethanol is mainly produced fromcorn starch; whereas Brazil obtains a quarter of its liquid

Correspondence to: S.K. Khanal

Contract grant sponsor: Council for International Program (CIP)

2008 Wiley Periodicals, Inc. Biotechnology and Bioengineering, Vol. 101, No. 3, October 15, 2008 487


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transportation fuel from sugarcane-derived ethanol. One of

the starch-rich raw materials suitable for ethanol production

is cassava (Manihot esculenta). Thailand produces about

25 million tons of cassava annually and it is the third

most important cash crop after rice and sugar cane (KAPI,

2003). Cassava contains relatively low protein and other

nutrients. Thus it could serve as an ideal feedstock for

ethanol production. Additionally, it can be grown in

otherwise infertile land with minimal input of chemicals,

such as fertilizers, herbicides and insecticides, which makesit one of the cheapest agro-based feedstocks (Hill and Hay,

2004).

Ethanol is produced mainly from molasses in Thailand.

The production of molasses is finite and limited as a sugarco-product. Thus, cassava could become a very useful

additional major low-cost feedstock for ethanol production.

Cassava-based ethanol plants can run year-round because

of the unbounded growing and harvesting times plus its

capability to be stored as dried chips; unlike sugar-based

distilleries that are seasonally operated (Nguyen et al., 2007).

The Thai government issued permission to build 12 cassava

plants by 2007 and 2008 with a total output of 3.4 million

liters ethanol per day (Sukphjsal, 2005).

Cassava roots need to be crushed to release starch

followed by enzymatic treatment for liquefaction and

saccharification to obtain fermentable sugars. The starchrelease, however, is incomplete due to association of starch

within the fiber. Thus, there are ample opportunities toenhance the recovery of starch from cassava chips. The

reduction in particle size and opening up of cassavasfibrous structures could essentially reduce the dose of costlyenzymes, shorten the processing time, improve the starch

hydrolysis, enhance the overall sugar yield, and eliminate

some of the unit processes, which will ultimately result in

overall improvement in ethanol yield. One such option is theuse of ultrasound technology. The application of ultrasound

in dry corn milling ethanol plants showed improvements in

sugar yield by nearly 30% and rate of reaction by 2.5-fold

with respect to control (Khanal et al., 2007).

Ultrasound is a sound wave with frequency above the

normal hearing range of humans (>16 kHz). When

ultrasound is applied in the liquid phase, it generates a

series of compression and rarefaction waves. In the zone of

rarefaction, the liquid is unduly stretched, thereby generat-

ing excessive negative pressure. This results in the formation

of microbubbles, which subsequently implode producing

strong hydrodynamic shear forces in the aqueous phase.The shear force facilitates the disintegration of coarse

particles/fibers in cassava slurry intofine particles, therebyexposing a much larger surface area to enzymes during the

enzymatic hydrolysis. As a result, the enzymatic activity is

greatly enhanced. By integrating ultrasound in cassava-

based ethanol plants, the overall ethanol yield could be

significantly increased with short processing time. Thus,ultrasonic technology could provide a practical means of

cutting down the production cost of emerging cassava-to-

ethanol plants. Based on these premises, the objectives of

this study were: (i) Quantify the effect of different sonication

conditions, for example, power input, sonication time,

and mode of enzyme addition on reducing sugar yield

from cassava chip slurry. (ii) Determine reducing sugar yield

from sonicated cassava chip slurries with and without

temperature control. (iii) Examine the effect of total solids

(TS) on reducing sugar yield for ultrasound pretreated

samples.

Materials and Methods

Cassava Samples and Enzyme Preparation

Cassava chip samples were obtained from Sui Heng Lee Co.

Ltd. (Bangkok, Thailand). The chips were ground and sieved

through 10 mesh screen to obtain particle sizes of less than

2 mm. Cassava slurries were prepared by adding 0.05 M

acetate buffer at pH 4.3. The cassava chip composition

is shown in Table I. The enzyme STARGENTM 001 was

obtained from Siam Victory Chemical Co. Ltd. (Bangkok,

Thailand). STARGENTM 001 (456 granular starch hydro-

lyzing unit (GSHU)/g) contains bothAspergillus kawachi a-amylase expressed inTrichoderma reeseiand a glucoamylase

fromAspergillus nigerthat work synergistically to hydrolyze

starch into glucose.

Ultrasonic Equipment

The cassava chip slurries were sonicated using a Branson

2000 Series bench-scale ultrasonic unit (Branson Ultrasonics

Corporation, Danbury, CT). The ultrasound unit has a

maximum power output of 2.2 kW and operates at a

constant frequency of 20 kHz. The components of theultrasound system include the booster (gain 1:2) and the

catenoidal titanium horn (gain 1:8), with a flat 13 mmdiameter face.

Two sonication chambers, one without temperature

control and one with temperature control, were used.

The sonication tests without temperature control were

conducted using a series of 50-mL polypropylene centrifuge

tubes. For sonication tests with temperature control, a

specially designed glass sonication chamber was employed

(Fig. 1). The temperature of inlet water was approximately

108C.

Table I. Composition of cassava chips.

Composition Percentage

Starch 69.7

Moisture 12.0

Fiber 3.4

Sand/silica 2.4

Others 12.5

Data provided by Sui Heng Lee Co. Ltd., Bangkok, Thailand.

488 Biotechnology and Bioengineering, Vol. 101, No. 3, October 15, 2008


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Ultrasonic Pretreatment and Incubation

Sonication tests were conducted using 35 mL cassava chip

slurries with 5% TS. The slurry samples were treated in a

batch-mode at three different amplitude (power) levels:

low, medium, and high. The power levels were changed

by varying the amplitude at the horn tip through pulse

width modulation voltage regulation to the converter. The

amplitudes at the tip of the horn were 96, 208, and 320 mmpp(peak to peak amplitude in mm) at low, medium, and high

power levels, respectively. The respective ultrasonic densities

were 2.03 0.04, 5.22 0.21, and 8.20 0.37 W/mL. Thedetailed procedure is summarized in Figure 2. The enzyme

was added to cassava slurry samples after sonication (SA) orprior to sonication (SD). The control samples were not

subjected to sonication. In order to examine the effect of

different TS contents of 5%, 15%, and 25% on sugar release,

cassava slurry samples were sonicated at high power level for

30 s without temperature control.

Reducing Sugar Determination

The enzyme activity was terminated with addition of 10%

(v/v) 4 M TrisHCl buffer (pH 7.0) into the samples

following incubation. The samples were centrifuged at10,000g for 20 min. Supernatant was then analyzed for

reducing sugar concentration using a modified dinitrosa-licylic acid (DNS) colorimetric method (Miller, 1959). A

sample size of 100 ml was mixed thoroughly with 1 mL of

DNS reagent. The solution was heated to 1008C for 10 min

and then cooled down in an ice bath. The absorbance of the

sample was measured at a wavelength of 570 nm using a

spectrophotometer (ThermoSpectronic Genesys 2-model

W1APP11, Rochester, NY). Reducing sugar concentrations

were calculated from the standard calibration curve

obtained using standard solutions of D-glucose and the

DNS reagent.

Particle Size Distribution

The cassava slurry samples were subjected to sonication at

low, medium, and high power levels. For each power input,

the samples were sonicated for 10, 20, and 30 s without

Figure 1. Schematic of glass sonication chamber for temperature control experiment.

Figure 2. Summary of the experimental procedure.

Nitayavardhana et al.: Ultrasound Pretreatment of Cassava Chip 489

Biotechnology and Bioengineering


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temperature control. The samples were then analyzed for

particle size distribution using a laser diffractometer,

Malvern particle size analyzer (Mastersizer 2000, Malvern

Inc., Worcestershire, UK). The samples under analysis were

contained in a 600-mL glass beaker with water, which was

magnetically agitated during the experiment. All analyses

were performed in triplicates with the same batch of cassava

slurry samples.

Scanning Electron Microscopic (SEM) Examination

The cassava slurries were sonicated at low and high power

levels. For each power level, the samples were sonicated

for 20 and 40 s with temperature control. Prior to SEM

examination, the samples werefixed with 3% glutaraldehyde(w/v) and 2% paraformaldehyde (w/v) in 0.1 M cacodylate

buffer (pH 7.2) for 48 h at 48C. Samples were rinsed 3 times

in this buffer and post-fixed in 1% osmium tetroxide for 1 h,followed by two 5-min washes in buffer. The samples

were then dehydrated in a graded ethanol series up to

100% ultra-pure ethanol followed by substitution intohexamethyldisilazane and allowed to air dry. When dried,

the samples were placed onto carbon adhesive coated

aluminum stubs, sputter coated (Denton Desk II sputter

coater, Denton Vacuum, LLC, Moorestown, NJ) with

palladium/gold alloy (60/40), and imaged using a JEOL

5800LV SEM (Japan Electron Optics Laboratory, Pea-

body, MA) at 10 kV with a SIS ADDA II for digital image

capture (Soft Imaging Systems Inc., Lakewood, CO).

The scanning electron microscopy (SEM) images of cassava

slurry samples (after fixing) were taken using a JEOL5800L V SEM.

Determination of Reducing Sugar Release Rate

The reducing sugar contents of the sonicated cassava slurries

at three different TS contents of 5%, 15%, and 25%, and

control at each TS level were determined. The samples

were sonicated at high power level for 30 s. After sonication,

0.5% (v/w) enzyme was added. The samples were incubated

up to 8 h, and analyzed for reducing sugar release at

1-h intervals. The samples were incubated using the

protocol elucidated earlier. In order to evaluate the effect

of sonication on sugar release rate, the data were thenfitted into the standard reaction rate kinetics of Arrheniusform:

Rt R11 ekt (1)

whereR(t) is the reducing sugar yield as a function of time

(t) (g/100 g of sample), R1

is the reducing sugar yield at

time infinity (g/100 g of sample), and kis the reaction ratecoefficient (1/h).

Calculation of Ultrasonic Efficiency

In order to evaluate the ultrasonic efficiency, the total energydissipated and delivered were calculated. The total energy

dissipated (Ein) into each sample was calculated based on the

average power and sonication time

Pavg Pinitial Pfinal

2

Pair (2)

Ein

Ztft0

Pdt Ein Pavgt (3)

where Pis the power (W), tis the sonication time (s), t0 is the

initial time during sonication (s), and tf is the final timeduring sonication (s).

The initial and final powers ( Pinitialand Pfinal) were thepowers indicated by the power supply system. The static

power ( Pair) which is the power required to run the system

in an unloaded condition (in air) was subtracted from these

values prior to energy calculation.In addition, the total energy delivered during sonication

(Eout) was calculated based on the chemical energy of the

sugar yield. The energy of the sugar was estimated by

assuming a conservative energy density of 15,740 kJ/kg for

glucose if fully oxidized (Patzek, 2004). The overall

sonication efficiency (Eff) was calculated using the followingequation:

EffEout Ein

Ein 100% (4)

Results and Discussion

Particle Size Distribution and Scanning ElectronMicroscopy Examination

In order to examine the efficacy of ultrasound to disintegratecassava chip slurries, particle size distribution and SEM

images were examined. Based on Figures 3ac, three peaks of600, 200, and 15 mm were observed in the particle size

distribution curves. The peak shifted from 600 to 200 and

15 mm following sonication especially at medium and high

power levels. The particle size reduction was directly relatedto power level and sonication time. The amount of 600 mm

particle decreased whereas the amount of 15 and 200 mm

particles increased in direct proportion of sonication time

and power input. The original peak (600 mm) is expected to

represent milled cassava particles that were not affected by

ultrasound treatment. The 200 mm peak corresponds to cell

morphologies of the cassava chips affected by sonication.

The 15 mm peak essentially represents the individual starch

granules. This was further substantiated by scanning

electron microscopy as discussed later.



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The SEM images of cassava slurry samples after sonication

for 20 and 40 s at low and high power levels are presented in

Figures 4 and 5. The sonication experiments were conducted

under temperature-controlled condition in order to eli-

minate the effect of temperature on particle disintegration.

For control (unsonicated) sample, the cassava cells were

completely intact, and there were starch granules confinedwithin the cell structures as seen in Figure 4a. SEM images in

Figure 4be apparently show disintegration of cassava cellstructures in direct proportion to power level and sonication

time. As expected, the changes in cell structures were more

prominent at high power setting. With ultrasonic pretreat-ment for 40 s at high power, near-complete disintegration

of cassava cell structures was observed (Figure 4e). The

destruction of cassava cell structures resulted in release of

more individual starch granules in the aqueous phase, which

enhanced the enzymatic hydrolysis as discussed later.

Figure 5 displays the SEM images of cassava starch

granules for control and sonicated samples. The starch

granules appeared to be unaffected by ultrasound pretreat-

ment at all conditions. The granules, however, were not of

spherical shape. Owing to the granular structure of cassava

starch, which was a cluster of numbers of starch granules,

indents were observed at the attachment points of individual

starch granules. The average size of cassava starch granules

was around 15 mm.

Effect of Ultrasound Pretreatment onReducing Sugar Release

The reducing sugar yields and temperature increases of the

cassava slurry samples under various sonication conditionsare presented in Figure 6. Because all the experiments were

conducted in a batch-mode, the comparisons were only

made in relation to the control batch at each power level in

order to eliminate errors associated with continuous

enzymatic reaction. The results showed an improvement

in reducing sugar release following sonication in direct

proportion to the power input and sonication time. The

highest reducing sugar yields of 51 and 46 g/100 g of sample

were obtained for SA 40 and SD 40, respectively, at high

power level.

Figure 3. Particle size distribution of cassava slurry samples at (a) low power (2 W/mL); (b) medium power (5 W/mL); (c) high power (8 W/mL).




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Figure 7 shows the percentage reducing sugar increase of

sonicated samples with respect to the control group at each

power level under various conditions. The highest increases

in reducing sugar yield were 17%, 181%, and 736% with

respect to the controls for SD 40 at both low and medium

power inputs, and SA 40 at high power input, respectively.

Moreover, the reducing sugar yields of SD samples were

higher than SA samples at the same sonication duration

for all sonication conditions except at high power level for

40 s. The higher yield for SD samples was attributedto stimulation of enzyme activity due to streaming effect

(Khanal et al., 2007). At high power input, there was a drop

in reducing sugar yield for SD 40. This was most likely

attributed to denaturation/degradation of enzyme due to

excessive ultrasound pretreatment and the heat generated

during sonication. Thisfinding was in close agreement withthat of corn (Khanal et al., 2007).

Temperature increase was observed during sonication.

Based on temperature profile (Fig. 6), the highesttemperature increase above 708C was observed at high

power level during long sonication duration. Interestingly,

the reducing sugar yields of SA 40 and SD 40 at high power

level were significantly higher than that at other sonicationconditions. This increase in sugar yield was primarily

attributed to starch gelatinization due to significant increasein temperature.

Effect of Temperature-Controlled Sonication on

Reducing Sugar Release

In order to evaluate the effect of temperature on reducing

sugar yield, the cassava slurry samples were sonicated under

temperature-controlled conditions. The reducing sugar

release and temperature increase at high power setting for

10 to 40 s of sonication with and without temperature

control are shown in Figure 8. The temperature profileshows an appreciable increase in temperature (708C) ofcassava slurries when sonicated without temperature

control. The temperature increase of cassava slurries for

Figure 4. SEM images of cassava slurries: (a) control; (b) low power (20 s); (c) low power (40 s); (d) high power (20 s); (e) high power (40 s).



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Figure 6. Reducing sugar yield and temperature increase at various sonication conditions.

Figure 5. SEM images of cassava starch granules: (a) control; (b) low power (20 s); (c) low power (40 s); (d) high power (20 s); (e) high power (40 s).




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respectively. For the sonicated samples, the respective k

values were 0.31, 0.33, and 0.36 h1. The comparison ofk

was only made in relation to the control at each TS

concentration. The results apparently show that the reaction

rate coefficients of sonicated samples were about 1.2-fold

higher than the respective control at all TS levels. The

reducing sugar yields at the completion of reaction (R1

)

were 11.12, 11.59, and 12.02 g/100 g of sample at 5%, 15%,

and 25% TS levels, respectively. For sonicated samples, the

respective R1

values were 15.4, 20.3, and 24.0 g/100 g of

sample at 5%, 15%, and 25% TS levels.

It is obvious that unsonicated samples require more

time for the completion of reaction. However,R1

values of

sonicated samples were much higher than those of the

controls. The highest R1 of sonicated sample was twofoldhigher compared to control at 25% TS level. Bothkand R

1

values of sonicated samples increased with increase in TS

levels. At high TS level, there were more particles (starch

granules) available for enzyme to react with, which resulted

in both higher reaction rate coefficients and overall reducingsugar yields at completion of reaction (R

1). Sonication with

30% TS was aborted due to mixing problem.

Energy Balance

To evaluate the effectiveness of ultrasound system in terms

of net energy release, the energy balance calculation was

conducted using Equations (2)(4). All experiments wereconducted under temperature-controlled conditions in

Figure 10. The efficiency of sonicated samples at high power level for 30 swithout temperature control at different TS contents.

Figure 11. Reducing sugar release from cassava slurries at different incubation times: (a) 5% TS sample; (b) 15% TS sample; (c) 25% TS sample.




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order to eliminate the effect of temperature on reducing

sugar yield. As seen in Figure 12, the overall efficiencyof the ultrasonic system ranged from 63% to 177%,depending on the treatment conditions. The negative

efficiency indicates that the energy equivalent of releasedsugar was less than the energy input for sonication.

Efficiencies of ultrasonic system were negative for most ofthe sonication conditions, except for shorter sonication

times at lower power level. Although the reducing sugar

yield at high power and long sonication time improved by

180% with respect to control, the chemical energy gained

from the reducing sugar release was not sufficient to meetthe ultrasonic energy demand at high power level. The

highest efficiencies of 162% and 177% were obtained at low

power setting and sonication duration of 10 s with enzymeaddition after (SA10) and before sonication (SD10),

respectively. Thus, from energy efficiency standpoint, themost effective ultrasound pretreatment appears to be low-

power input with a short sonication time. For more accurate

economic analysis, energy balance calculation should be

based on continuous sonication tests with ethanol yield data.

Research is currently underway to examine this aspect of

sonication.

Summary

This study examined the feasibility of ultrasound pretreat-

ment to enhance sugar release from cassava chips. Ultrasonic

pretreatment of cassava chip slurry resulted in nearly 40-fold

reduction in particle size. Scanning electron micrographs

showed disruption offibrous structure in cassava samplesbut did not affect the starch granules. The enzyme addition

before sonication yielded higher reducing sugar release

than with enzyme addition after ultrasonic pretreatment.

Improvement in reducing sugar released was in direct

proportion to the power input and sonication time. The

reducing sugar release of sonicated samples improved as

much as 181% with respect to control group. The heat

generated during sonication had no effect on reducing

sugar yield unless the temperature reached the starch

gelatinization point. The drop in reducing sugar yield at

high power level during longer sonication time was

attributed to denaturation and degradation of the enzymes

due to excessive ultrasound pretreatment. The reducing

sugar release and sonication efficiency increased with TScontent. The efficiency, however, declined when the

power input and sonication time increased. In addition,the reaction rate coefficient (k) for sonicated samples in-creased as much as 1.2-fold compared to control group.

Overall, the results of this study show that the use of

ultrasound facilitates the release of starch granules from

cassavafiber and thus enhances the reducing sugar yield. Ina starch-to-ethanol plant, starch has to be released using

physical/thermal treatment. Starch hydrolysis requires

gelatinization of starch for efficient enzyme hydrolysis.Ultrasound pretreatment of cassava chips serves the dual

purpose of starch granule release and starch gelatinization.

This project was partly funded by Council for International Program(CIP) grant at Iowa State University. The authors would like

to express special thanks to Branson Ultrasonics for providing

high-power ultrasonic equipment. Thanks are also extended to

Dr. Lawrence Johnson of Center for Crops Utilization and Research

(CCUR) and Dr. Anthony L. Pometto of the Fermentation Facility for

making laboratory space available. Sincere appreciation is also

extended to Sui Heng Lee Co. Ltd., and Siam Victory Chemical

Co. Ltd., Bangkok, Thailand for providing the cassava chip samples

and the enzymes.

References

Hill A, Hay M. 2004. Biofuels in AsiaThailand relaunches Gasohol forautomotive use. Refocus 5:4447.

Kasetsart Agricultural and Agro-Industrial Product Improvement Institute

(KAPI). 2003. Executive Summary, Development of raw material

management plan for the ethanol industry in Thailand, submitted

to the National Ethanol Committee, Ministry of Industry, Kasetsart

University, July 2003.

Khanal SK, Montalbo M, Van Leeuwen J, Srinivasan G, Grewell D. 2007.

Ultrasound enhanced glucose release from corn in ethanol plants.

Biotechnol Bioeng 98(5):978985.

Miller GL. 1959. Use of dinitrosalicylic acid reagent for determination of

reducing sugar. Anal Chem 31:426428.

Mogg R. 2004. Biofuels in AsiaThailand relaunchesGasohol for auto-

motive use. Refocus 5:4447.

Newton. 2007. http://newton.nap.edu/execsumm/pdf/11676.

Nguyen TLT, Gheewala SH, Garivait S. 2007. Full chain energy analysis offuel ethanol from cassava in Thailand. Environ Sci Technol 11:4135

4142.

Patzek TW. 2004. Thermodynamics of the corn-ethanol biofuel cycle. Crit

Rev Plant Sci 23:517567.

Ragauskas AJ, Williams CK, Davison BH, Britovsek G, Cairney J, Eckert CA,

Frederick WJ, Hallett JP, Leak DJ, Liotta CL, Mielenz JR, Murphy R,

Templer R, Tschaplinski T. 2006. The path forward for biofuels and

biomaterials. Science 311:484489.

Sukphjsal B. 2005. Market development of biofuels in Thailand; The

renewable energy committee parliamentAsia biomass conference;

Bangkok, Thailand.

Figure 12. Energy efficiency of ultrasound pretreatment at various conditions.


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