optimization of hydrothermal and diluted acid

Research ArticleOptimization of Hydrothermal and Diluted Acid Pretreatmentsof Tunisian Luffa cylindrica (L) Fibers for 2G BioethanolProduction through the Cubic Central Composite ExperimentalDesign CCD Response Surface Methodology

Kaouther Zaafouri1 Manel Ziadi12 Aida Ben Hassen-Trabelsi3 Sabrine Mekni12

Balkiss Assi12 Marwen Alaya13 Latifa Bergaoui4 and Moktar Hamdi1

1Laboratory of Microbial Ecology and Technology (LETMi) The National Institute of Applied Sciences and Technology (INSAT)Carthage University 2 Boulevard de la Terre BP 676 1080 Tunis Tunisia2Department of Biotechnology and Environment Sciences High Institute of Environmental Science and Technology (HIEST) ofBorj-Cedria Borj-Cedria Technopark BP 1003 2050 Hammam-Lif Tunisia3Laboratory of Wind Energy Control and Waste Energy Recovery (LMEEVED) Research and Technology Center ofEnergy (CRTEn) Borj-Cedria Technopark BP 95 2050 Hammam-Lif Tunisia4Laboratory of Materials Chemistry and Catalysis Faculty of Sciences of Tunis El Manar University Tunis Tunisia

Correspondence should be addressed to Kaouther Zaafouri kaoutherzaafourioutlookcom

Received 8 July 2016 Revised 28 November 2016 Accepted 25 December 2016 Published 24 January 2017

Academic Editor Khanh-Quang Tran

Copyright copy 2017 Kaouther Zaafouri et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This paper opens up a new issue dealing with Luffa cylindrica (LC) lignocellulosic biomass recovery in order to produce 2Gbioethanol LC fibers are composed of three principal fractions namely 120572-cellulose (4580 plusmn 13) hemicelluloses (2076 plusmn 03)and lignins (1315 plusmn 06) The optimization of LC fibers hydrothermal and diluted acid pretreatments duration and temperaturewere achieved through the cubic central composite experimental design CCDThe pretreatments optimization was monitored viathe determination of reducing sugars Then the 2G bioethanol process feasibility was tested by means of three successive stepsnamely LC fibers hydrothermal pretreatment performed at 96∘C during 54 minutes enzymatic saccharification carried out bymeans of a commercial enzymeAP2 and the alcoholic fermentation fulfilled with Saccharomyces cerevisiae LC fibers hydrothermalpretreatment liberated 3355 gkg of reducing sugars Enzymatic hydrolysis allowed achieving 594 gkg of reducing sugars Theconversion yield of reducing sugar to ethanol was 8866 After the distillation step concentration of ethanol was 158 with avolumetric yield about 70

1 Introduction

The environmental crisis due to the increasing level ofCO2 and the greenhouse gases emissions (GHG) in theatmosphere is linked to the global warming which isdirectly associated with the combustion of fossil fuels [1]Consequently to overcome these environmental and fossilenergy issues the development and utilization of alternativenonpetroleum based renewable sources of energy becamemandatory [1 2] Biomass and its byproducts with a global

production reaching 200 billion metric tons a year representgreat potential feedstocks for energy conversion technologiesin order to produce biofuels [3] Moreover lignocellulosicbiomass is renewable more abundant and the cheapestresource in the world This biomass could be providedthrough food cultures (ie sugar cane beets corn sorghumand starch) energy or nonfood crops (ie switchgrassMiscanthus giganteus poplar willow sweet sorghum wildsugarcane bitter cassava alfalfa hemp and water hyacinth)as well as from agricultural forest and industrial residues

HindawiBioMed Research InternationalVolume 2017 Article ID 9524521 14 pageshttpsdoiorg10115520179524521

2 BioMed Research International

(ie corn stover sugarcane bagasse rice straw [1] cassavapulp palm residues soybean residues wheat straw wheatbran [3] straw bark used edible oil and black liquor)woody feedstocks softwood (pine and spruce) herbaceousbiomass and cellulose wastes (waste office paper) [4] andseaweeds (brown algae) [5] In the Mediterranean regionespecially in Tunisia Luffa cylindrica (LC) is a promisinglignocellulosic feedstock for 2G bioethanol recovery [1] LCis an annual herbaceous plant from the cucurbitaceous family[6] It is a fibrous plant largely distributed in the tropical andsubtropical countries and countries with moderate climate[7] with a plant growth yield reaching 62000 LC fruitsha(20 to 25 fruitsplant) However this yield depends highly onthe climate [8] Many common end-uses of LC fibers werelisted as follows the disposal of copper from food industrywastewater [9] the biofilm supporting medium in tricklingfilters for wastewater treatment the handicraft activitiessome other industrial applications and pharmacology [10]the bath sponges manufacturing the use as a basic stampfor the chemical and biological immobilization andor as asupport with fixed bed of biological culture or for chemicalsynthesis the use as a support of discoloration of the reagentsandor as a thermal support and the use as a basic materialfor the insulation and the extraction of the chemical andbiological compounds [6] As listed previously and to thebest of our knowledge no published report exists on LCfibers recovery for 2G bioethanol production In fact theworks dealing with this subject are still unknown andorunwell and thorough studies are lacking Thus this work isconsidered as a novelty in terms of 2G bioethanol productionfrom LC biomass The second generation biofuels producedfrom renewable resources ldquoplant biomassrdquo are made withthe lignocellulosic biomass since it is a cheap and abundantnonfood material available from plants [1] Lignocellulosicsubstrate is mainly composed of two types of carbohydratesand onemore complex polymer namely 30ndash55 of cellulose20ndash40 of hemicelluloses 10ndash35 of lignins and theirratio varies extremely depending on the plant species Theseunits are strongly linked and chemically bonded in factcellulose is the backbone structure while hemicelluloses andlignins are the binding networks Cellulose (consisting ofD-glucose only) and hemicelluloses which is composed ofmainly pentoses (like xylose and arabinose) and hexoses (likemannose glucose galactose etc) are bioconvertible [11 12]

Three main steps are required to obtain 2G bioethanolfrom lignocellulosic biomass namely pretreatment enzy-matic saccharification and fermentation and distillation [211 13 14] Many pretreatment methods of lignocellulosicbiomass are listed in the literature including physical pre-treatment (grinding milling microwave and extrusion)chemical pretreatment (hydrothermal-aqua Solv [1] alkali[1 2] acid organosolv ozonolysis and ionic liquid) physic-ochemical pretreatment (steam explosion liquid hot waterammonia fiber explosion AFEX wet-oxidation and CO2explosion) and biological pretreatment (delignification oflignocellulosic substrate by Basidiomycota fungi) [15] Pre-treatment step plays three important roles that is ligninsdestruction hydrolysis of hemicelluloses and modificationof cellulose which will improve enzymatic hydrolysis [15]

Particularly hydrothermal pretreatment of lignocellulosicmaterial for the enhancement of biofuels 2G productionbecomes more and more important in the 21st centuryWater under high pressure and temperature can get intothe biomass moisturize cellulose enhance its accessible andsusceptible surface area and improve its accessibility tothe hydrolytic enzymes indeed it removed hemicellulosesand part of lignins The main advantages of hydrothermalpretreatment are as follows no addition of chemicals norequirement of corrosion resistant materials for hydrolysisreactors and no need for size reduction of biomass it requiresmuch lower need for chemicals for neutralization of theproduced hydrolyzate and it produces lower amounts ofneutralization residues compared to many processes [1]

However pretreatment methods have some weaknesseslimiting their applications so combined pretreatment meth-ods are recently developed to curb this challenge by increas-ing efficiency of sugars liberation decreasing the formationof inhibitors and making the process time shorter Thusbioethanol yield becomes higher and the process becomesmore economical [1]

In order to destroy cellulose chains the subsequentenzymatic hydrolysis is catalyzed by the synergistic actionof four cellulase enzymes operating at 40ndash50∘C and pH 4-5namely endo-14-120573-glucanases cellobiohydrolases exo-14-120573-glucanasesmdashthat will hydrolyze cellulose into cellobiosemdashand 120573-glucosidases that will hydrolyze cellobiose into glu-cose Cellulolytic enzymes play a critical role in lignocellulosesaccharification and bioconversion of pretreated lignocel-lulosic material that requires multiple enzyme activitiesThe monomeric sugars (glucose galactose mannose xyloseand arabinose) released from enzymatic saccharification areconverted into ethanol thanks to some microorganismsSaccharomyces cerevisiae is the most used yeast for ethanolproduction from hexoses given that it is well-known for itsresistance to low pH high temperatures high ethanol con-centration and various inhibitors Otherwise one amylolyticSaccharomyces cerevisiae strain was employed for bioethanolproduction from wheat bran [3] Other yeasts could produceethanol through hexoses recovery mainly from xylose forexample Pichia stipitis Candida shehatae Kluyveromycesmarxianus and Pachysolen tannophilus Some bacteria couldalso ferment monomeric sugars to produce alcohols such asZymomonas mobilis and Escherichia coli [13] Several typesof fermentation processes have been tested for examplebatch continuous continuous with cell recycling fed-batchand repeated-batch culture designs [15] In order to obtaina fuel grade or anhydrous ethanol many distillation anddehydration processes are used [13] Nevertheless the scale-up of the whole lignocellulosic biomass conversion process isvery expensive In order to reduce the biofuels 2G productioncost suitable processes available were listed in the literaturenamely the implementation of simultaneous saccharificationand fermentation SSF which integrates enzymatic saccharifi-cation and ethanol fermentation in one system saving bothprocess and time cost [1 2 15] the using of a recombinantcellulase cocktail (RCC) which contains two cellobiohydro-lases an endoglucanase and a 120573-glucosidase with S cerevisiaein SSF condition [3] and the continuous recycling of enzymes

BioMed Research International 3

during production of lignocellulosic bioethanol by using highdry matter content and low enzymes dosage and so reducingthe enzyme consumption and as a result reducing their cost[16]

Otherwise the success key of the bioethanol 2G processis the optimization of the different production steps [17]Response surface methodology (RSM) is an optimizationmethodology commonly employed In this methodology theinteraction effects between factors on the response of ananalytical system could be illustrated by a surface in threedimensions called the response surface Among the severalRSM design classes central composite design (CCD) isamong the most popular methods due to its simple structureand efficiency [18]

Regarding the aforementioned problematics the maingoal of this study is to optimize the hydrothermal and dilutedacid pretreatments of Tunisian Luffa cylindrica fibers for 2Gbioethanol production through the cubic central compositeexperimental design CCD and RSM The effects of the maininfluencing factors which are temperature reaction time andH2SO4 concentration on sugars concentrations are studiedBesides the subsequent 2G bioethanol fromLCfibers processfeasibility is carried out by means of maximizing the enzy-matic saccharification of the pretreated substrate and testingthe alcoholic fermentation of biomass hydrolysate

The new concern of the current work is to explain themechanisms of both hydrothermal and diluted acid pretreat-ments of Tunisian Luffa cylindrica fibers for 2G bioethanolproduction and thus to highlight the beneficial effects ofhydrothermal pretreatment in favor of 2G bioethanol processeffectiveness in terms of 2-G fermentable sugars

2 Materials and Methods

21 Raw Material Sampling and Preprocessing Luffa cylin-drica fresh fruits used for this study were sampled fromthe region of Monastir that is located in the Tunisian Sahel(center-east coast of Tunisia) in January 2014 The sampleswere milled with a kitchen grinder Then they were stockedin glass bottles at 4∘C for both characterization analysisand subsequent experimental procedure This preprocessingstep is considered as a mechanical pretreatment made beforediluted acid and hydrothermal pretreatments steps and enzy-matic saccharification and fermentation of LC fibers

22 Analytical Methods

221 Proximate and Ultimate Analysis of LC Fibers Proxi-mate analysis of LCfiberswas carried out by themeasurementof the dry matter volatile matter and the ash contentaccording to the protocols described by Boussarsar et al(2009) [19] Ultimate CHN analysis of LC fibers was achievedwith Perkin Elmer 2400 CHN elemental analyzer in richoxygen atmosphere The sulfur percentage was measured forthe studied fibers via Horiba Jobin Yvon elemental sulfuranalyzer [20] However the oxygen content was calculated bydifference as follows

O () = 100 minus (C +H +N + S + ash) (1)

222 Density of LC Fibers The LC fibers density was mea-sured according to the protocol described by Hamza et al(2013) [21]

223 pH of LCMilled Fibers The pH of LCmilled fibers wasdetermined according to the method detailed by Mukherjeeet al (2011) [22]

224 Lignocellulosic Characterization of LC Fibers The lig-nocellulosic characterization including cellulose hemicellu-loses and lignins of LC fibers has been fulfilled according toa gravimetric method employing specific chemical reagentsdescribed by Sun et al (2003) [23] with some modificationsrelated to the initial sample size for simplicity and repeata-bility At first 10 grams of LC milled fibers is defatted byusing toluene and ethanol mixture (2 vv) during 6 hoursat ambient temperature to determine the lipids contentSecondly for water-soluble polysaccharides extraction thedefatted LC fibers were treated with 200mL of water at80∘C for 2 hours After that a simultaneous treatment withsodium hypochlorite and acetic acid at pH 4 during 2hours at 75∘C was applied to the collected solid fractionfrom the previous step in order to determine the ligninscontent Then the holocellulose fraction obtained from theprevious acid treatment was purified with 600mL of sodiumhydroxide (10 weightvolume) for 10 hours at 20∘C understirring conditions to extract and purify the 120572-celluloseAfter filtration of the previous reaction mix the collectedliquid fraction was neutralized with HCl chlorhydric acid(6M) until reaching a pH about 55 and then precipitatedwith 450mL of ethanol (95∘ alcoholic degree) The obtainedpellets were washed with ethanol (70∘ alcoholic degree) thendried in a ventilation oven at 50∘C in order to obtain thehemicellulosic fraction All the experiments related to thelignocellulosic characterization of LC fibers were carried outin triplicate

225Thermogravimetric Analysis TG-DTG of LC Fibers TheTGDTG analysis of LC fibers was carried out using Setaramthermogravimetric analyzer type labsys thermo-balanceThe operating conditions were as follows inert atmosphere(N2 nitrogen gas flow) temperature varying from 30∘C to900∘C heating rate about 10∘Cmin and initial sample weightof 66mgThe data were taken and recorded every 11 seconds[24]

226 Fourier Transform Infrared Spectroscopy (FTIR) ofLC Fibers Fourier transform infrared spectroscopy (FTIR)analysis was performed for LC fibers The translucent pellets(5mmOslash) were done by blending and pressing LC milledfibers with KBr powder (5 100ww) The FTIR spectra wasrecorded in absorbance mode at a spectral range of 4000 and400 cmminus1 with an accumulation of 15 scans using spectropho-tometer type Perkin Elmer Spectrum BX equipped with aHe-Ne laser andwith detectorMCT type broadband and highsensitivity The spectra acquisition was made via spectrumv531 software The bands identification was accomplishedaccording to the data cited by Feng and Donghai [25]


Table 1 The coded levels of the studied factors for both LC fibers hydrothermal and diluted acid pretreatments

Pretreatment Diluted acid pretreatmentHydrothermal pretreatments

Acid concentration ()Factors Temperature (∘C) Reaction time (min)Coded levelsminus1 80 30 050 100 45 275+1 120 60 5

227 Total Sugars Determination The total sugars concen-tration of the studied samples was performed accordingto Dubois et alrsquos (1956) method [26] by adding a phenolsolution (5 wv) and concentrated sulfuric acid H2SO4(96ndash98 vv) Then the samples incubation was achievedin a boiling water bath at 100∘C for 5minutesThe absorbanceof each sample was measured at a wavelength 120582 = 480 nmusing a spectrophotometer UV-visible type Jenway Thetotal sugars concentration of each sample was determinedreferring to the standard curve previously established withthe same protocol detailed above

228 Reducing Sugars Determination The reducing sug-ars concentration was measured referring to the methoddescribed by Miller (1959) [27] by mixing the studied samplewith the 3-5dinitrosalicylic acid DNS reagent prepared withthe potassium sodium tartrate (KNaC4H4O6sdot4H2O) and thesodium hydroxide (NaOH) The reaction happened in aboiling water bath at 100∘C for 15 minutes After the reactioncooling the absorbance of each sample was determined ata wavelength about 540 nm using a spectrophotometer UV-visible type Jenway The reducing sugars concentration ofeach sample was measured according to the standard curvepreviously elaborated with Miller protocol described above

229 Ethanol Determination The ethanol concentrationof distilled samples resulting from fermentation step wasdetermined through high-performance liquid chromatog-raphy using Agilent equipment with inverse C18 columnPRONTOSIL 120-5-C18-AQ 50Xm (250mm times 40mm) andsulfuric acid 1mM as mobile phase at 25∘C with a flowof 03mLmin (analysis time 30min and injection volume20120583L) [28]

23 Experimental Methodology

231 Optimization of Diluted and Hydrothermal Pretreat-ments of LC Crude Fibers The cubic central compositeexperimental design (CCD) which is the most popularsecond-order designs was adopted as detailed previously inthe literature [29 30] in order to optimize process parametersfor LC fibers hydrothermal and diluted acid pretreatmentsCCD is a very commonly used form of response surfacemethodology (RSM) in order to evaluate the interaction ofpossible influencing parameters on the appropriate responsewith a limited number of planned experiments [29 30]The three-level (minus1 0 +1) operating factors (independentvariables) and their respective coded levels for both LC

fibers hydrothermal and diluted acid pretreatments are sum-marized in Table 1 The (+1) value is the highest level ofthe operating factor and (minus1) value is its lowest level Theaverage of these two values is assigned to (0) which is thecentral level of the studied factor The coded level of eachfactor was selected according to their direct influence on thelignocellulosic biomass pretreatment according to previousstudies [15 31 32]The selected response for LC fibers dilutedacid pretreatment is solely the variation (Δ) of total sugarsconcentration to which the variation (Δ) of reducing sugarsamount was added for LC fibers hydrothermal pretreatment

The theoretical matrix of cubic central composite exper-imental design CCD for optimization of diluted acid andhydrothermal pretreatments of LC fibers showing runs instandard order are respectively illustrated by Tables 2 and3 For both LC fibers hydrothermal and diluted acid pretreat-ments the experiments are performed in Erlenmeyer flasks of250mL loaded until 40 of their volume using suspensionof LC milled fibers at 033 (ww) of dry matter in staticconditionsThe experiments requiring the temperature about120∘C are carried out in autoclave type Labtechmodel LAC-5040S at a pressure of 12 bar

Statistical Analysis and Mathematical Model NemrodWsoftware version 9901 was used for the statistical analysis ofthe output variables obtained for CCD experiments and forthe regression coefficients calculation [33] In order to explorethe functional relationship between the operating factors (119883)and the responses (119884) a second-order polynomial model wasadopted The coded mathematical equation of the studiedmodel is expressed as follows

119884 = 1205730 +119896

sum119894=1

120573119894119883119894 +119896

sum119894=1

1205731198941198941198831198942 +sumsum119894lt119895

120573119894119895119883119894119883119895 + 120598 (2)

where Y is the response 1205730 is the model intercept coefficient119883119894 and 119883119895 are the operating factors (independent variables)(119894 and 119895 range from 1 to 119896) 120573119895 120573119895119895 and 120573119894119895 are the interactioncoefficients of linear quadratic and the second-order termsrespectively 119896 is the number of independent variables (119896 =3 for diluted acid pretreatment and 119896 = 2 for hydrothermalpretreatment) and 120598 is the error [29 30]

The interactive effects of the factors were examined usingresponse surface plots derived from the chosen model Theoptimal conditions showing the best yields of total and reduc-ing sugars from hydrothermal pretreatment were adoptedfor the subsequent experiments of enzymatic saccharificationand fermentation


Table 2 Theoretical matrix of cubic central composite experimental design CCD for optimization of diluted acid pretreatment of Luffacylindrica fibers

Experiments Temperature (∘C) Reaction time (min) Acid concentration ()

A1 minus1 minus1 minus12 minus1 minus1 minus13 minus1 minus1 minus1

B4 +1 minus1 minus15 +1 minus1 minus16 +1 minus1 minus1

C7 minus1 +1 minus18 minus1 +1 minus19 minus1 +1 minus1

D10 +1 +1 minus111 +1 +1 minus112 +1 +1 minus1

E13 minus1 minus1 +114 minus1 minus1 +115 minus1 minus1 +1

F16 +1 minus1 +117 +1 minus1 +118 +1 minus1 +1

G19 minus1 +1 +120 minus1 +1 +121 minus1 +1 +1

H22 +1 +1 +123 +1 +1 +124 +1 +1 +1

I25 minus1 0 026 minus1 0 027 minus1 0 0

J28 +1 0 029 +1 0 030 +1 0 0

K31 0 minus1 032 0 minus1 033 0 minus1 0

L34 0 +1 035 0 +1 036 0 +1 0

M37 0 0 minus138 0 0 minus139 0 0 minus1

N40 0 0 +141 0 0 +142 0 0 +1

O43 0 0 044 0 0 045 0 0 0


Table 3Theoreticalmatrix of cubic central composite experimentaldesign CCD for optimization of hydrothermal pretreatment of Luffacylindrica fibers

Experiments Temperature (∘C) Reaction time (min)

A10158401 minus1 minus12 minus1 minus13 minus1 minus1

B10158404 +1 minus15 +1 minus16 +1 minus1

C10158407 minus1 +18 minus1 +19 minus1 +1

D101584010 +1 +111 +1 +112 +1 +1

E101584013 minus1 014 minus1 015 minus1 0

F101584016 +1 017 +1 018 +1 0

G101584019 0 minus120 0 minus121 0 minus1

H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0

232 Enzymatic Saccharification of Pretreated LC FibersFirstly pretreated LC fibers suspensions were neutralizedusing (1 N) NaOH solution to reach pH 4 Then the enzy-matic saccharification was carried out in 100mL of total reac-tion mix volume of LC pretreated fibers using separately twocommercial enzymes Sumizyme AP2 (pectinase cellulaseand hemicellulase activities 54000 unitg) and SumizymeSPC (pectinase and cellulase activities 6000 ugminus1000 ug)provided as a powder compacted in zipped plastic bagsby Shin Nihon Chemical Co Ltd (Japan) AP2 and SPCwere respectively added at a rate about 02 (ww) and0005 (ww) relative to the dry matter content of the lig-nocellulosic substrate and they were previously dissolved in1mL of sodium acetate buffer solution (01M) (pH 40 plusmn 02)The reaction time is about 1 hour at a temperature of 60∘CFinally the enzymatic hydrolysis was stopped by increasingthe temperature to 85∘C for 15 minutes The monitoringof enzymatic saccharification was fulfilled through reducingsugars measurement (as described in the Section 228)For the subsequent fermentation step the best enzymaticsaccharification condition giving the maximum reducingsugars content was selected

Table 4 Physicochemical properties and proximate and ultimateanalysis and lignocellulosic composition of Luffa cylindrica fibers

Physicochemical propertiespH 475 plusmn 02Density 093 plusmn 045

Proximate analysis (wt)Dry matter 55 plusmn 033Volatile matter 356 plusmn 13Ash 2 plusmn 01Lipids 1244 plusmn 05

Ultimate analysis (wt)H 5626 plusmn 03C 47667 plusmn 1S 1498 plusmn 01N 1245 plusmn 01O 41964

Lignocellulosic composition (wt)Water-soluble polysaccharides 786 plusmn 01Lignins 1315 plusmn 06120572-cellulose 4580 plusmn 13Hemicelluloses 2076 plusmn 03

233 Alcoholic Fermentation of LC Hydrolysates A 250mLErlenmeyer flask containing 100mL of LC hydrolysates wasinoculated with 10 (vv) of 12 hours old preculture (expo-nential growth phase) of commercial yeast strain Saccha-romyces cerevisiae supplied by Rayen food company (Beja-Tunisia) grown on Sabouraud broth The fermentation wasconducted at 30∘C and pH 48 plusmn 02 during 24 hours atshaking conditions (250 rpm)

234 Distillation In order to increase the final ethanolconcentration the fermentation broth distillationwas carriedout at 785∘Cby using standard column for simple distillation

3 Results and Discussion

31 Characterization of LC Lignocellulosic Biomass

311 Physicochemical Characterization of LC Fibers Ultimateand proximate analyses of LC fibers as far as their lignocel-lulosic composition including determination of pH densitydry and volatile matter contents ash lipids polysaccha-rides cellulose hemicelluloses and lignins percentages aredetailed in Table 4 As it can be seen the pH value of LC fibersis about 475 plusmn 02 which is considered as acidic pH valuesuitable for subsequent thermochemical and biochemicalconversion of LC biomass for bioenergy recovery LC fibershave a density value around 093plusmn045 which is slightly lowerthan that of the same substrate (148) studied by Laidani etal (2012) [6] and it is higher than some other lignocellulosicfibers studied byHamza et al (2013) [21] such as Alfa (0672plusmn0011) Rush (0417 plusmn 0010) Palm (0578 plusmn 0036) and Palmstipe (0220 plusmn 0055) Although LC fibers density is lowerthan those of some lignocellulosic substrates for example


coconut (1150) sisal (1500) and banana (1350) fibers [21]the density values of vegetal fibers are significantly lowerthan glass fibers (2500) [21] LC fibers are classified as wetlignocellulosic feedstocks since their dry matter content isaround 55 plusmn 033 with the same tendency of Algerian LCfibers having a dry matter content around 75 [6] which issignificantly lower than those of some dry fibers such as Alfa(9258) Rush (9061) Palm (9272) Palm stipe (9138)[21] The volatile matter of Tunisian LC fibers is around356 plusmn 13 with 2 plusmn 01 of ash This value is considerablyhigher than Brazilian LC fibers ash content (07 plusmn 02)studied by Siqueira et al (2010) [7] relatively to the growingconditions variability (climate soil nutrients etc) and plantbiology The ash content of the studied fibers is similar tosome other annual and perennial plants for example Parthe-nium argentatum (20) kenaf (Hibiscus cannabinus) (22)cotton stalks (22) [34] and corncob (1-2) [12] The lipids(ethanol-toluene extractives) content of LC fibers (1244 plusmn05) are higher than those of wood nonwood and annualor perennial plants which are ranging from 12 to 107[34] The ultimate analysis of the Tunisian LC fibers showsthat carbon hydrogen sulfur nitrogen and oxygen contentsare about 47667 5626 1498 1245 and 41964respectively These previous findings are almost similar tothe ultimate analysis of some lignocellulosic feedstocks suchas Brazilian [35] and Algerian [6] LC fibers Poplar Populusnigra L Fern Pteris vittata L and cellulose pulps [36] withsome differences due to the geographical conditions andplant physiology The studied fibers are rich in 120572-cellulose(4580 plusmn 13) and in hemicelluloses (2076 plusmn 03) with asmall amount of polysaccharides not exceeding 786 plusmn 01Besides their lignins content is about 1315 plusmn 06 Thislignocellulosic composition is slightly similar to those ofBrazilian LC fibers (63ndash655 of 120572-cellulose) as well as to thefibers of Algerian LC core (45 of cellulose) [6 7 34] andalso to those of some other lignocellulosic biomasses havingan 120572-cellulose content ranging from 3923 to 559 [21 33]such as Tunisian and Algerian Alfa (Stipa tenacissima) stemsPosidonia oceanica fibers nonwood substrate (Prosopis albaetc) some other wood (Pinus pinaster) rush and palmleaflets and stipe Certainly LC composition depends onvarious factors such as species variety soil type weatherconditions and plant age [35]These previous results confirmthat LC fibers are suitable for 2G bioethanol production

312 Fourier Transform Infrared (FTIR) Spectra of LCFibers Figure 1 outlines the FTIR spectra of LC fibers Thebands assigned to the lignins are those around 3400 cmminus12924 cmminus1 1632 cmminus1 and 1384 cmminus1 which are respec-tively attributed to O-H stretching vibration C-H stretchingvibration C=O stretching (unconjugated) and C-H bendingvibration The functional groups attributed to the hemicellu-loses of LC are shown through three vibration bands existingaround 1700 cmminus1 1384 cmminus1 and 1103 cmminus1 which arerespectively attributed to ketone or aldehyde C=O stretchingvibration C-H bending vibration and C-O-C asymmetricalstretching vibration Cellulose fraction of LC fibers is empha-sized by C-H bending vibration and C-O-C asymmetrical

stretching vibration bands are detected respectively around1384 cmminus1 and 1103 cmminus1 Consequently these structuraland functional characterizations of LC fibers confirm theiraliphatic and oxygenated nature and thus their ability forbioethanol recovery as lignocellulosic feedstocksThese FTIRfindings show the same tendency observed for some otherlignocellulosic feedstocks namely Brazilian LC fibers [7 35]Tunisian Alfa stem fibers [34] and rice straw [37]

313 Thermogravimetric Analysis TG-DTG of LC Fibers Thethermal behaviour result of LC fibers is given in Figure 2The black curve (TG) illustrates the mass loss (expressed inmg) of LC fibers while the red curve gives the mass lossderivative (expressed in mgmin) and the blue curve presentsthe heat flow (expressed in 120583V) applied during the thermalanalysis As shown in Figure 2 the thermal degradation curveof LC fibers shows three main decomposition stagesThe firststage corresponds to the evaporation or drying process of thesample which happened from 30∘C to 120∘C with a slightweight loss about 38 due to water removal and releaseof some light volatile molecules This step is endothermicThe second stage of LC fibers thermal degradation generatesa considerable weight loss about 530 which is observedbetween 120∘C and 360∘C This second event which isexothermic is related to the thermal degradation of hemicel-luloses and cellulose occurring respectively at temperaturesvarying from 200∘C to 350∘C and from 350 to 400∘C Thissecond region is considered as themain active pyrolytic stageThe maximum decomposition yield of LC fibers happensat 300∘C given that the glycosidic linkage depolymerisationprovokes the major weight loss [37] Several previouslystudied lignocellulosic feedstocks show that the major massloss rate is between 200 and 450∘C for example Posidoniaoceanica (L) fibers (330∘C) and sugar cane bagasse (395∘C)and olive stones (380∘C) [38] and rice straw (320∘C) [37]Thethird region corresponds to a continuous devolatilisation andlignins degradation occurring between 360∘C and 510∘Cwitha mass loss about 341 (exothermic stage) This third zone(400∘Cndash700∘C) is attributed to the passive pyrolysis zone[39] From 510∘C to 900∘C LC fibers thermal degradationprogresses at a slow rate 76 because of the carbonaceousfraction decomposition of the residual solid sample Thesefindings confirm that the thermogravimetric analysis of LCfibers is in the right agreement with the thermal behaviourof the previous studied LC fibers [35 40ndash42] and some otherlignocellulosic biomass fromdifferent herbaceous plants [40ndash42]

32 Optimization of Diluted Acid and Hydrothermal Pretreat-ments of LC Fibers The optimization of hydrothermal anddiluted acid pretreatments process parameters of LC fiberswere carried out by means of two cubic central compositeexperimental design (CCD) matrixes (as described above inthe Section 231)

321 Diluted Acid Pretreatment of LC Fibers The studiedresponse for LC fibers diluted acid pretreatment is thevariation Δ of total sugars concentration It is important


4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700

Wavenumbers (cmminus1)

Figure 1 FTIR spectra of Luffa cylindrica crude fibers

0 200 400 600 800 1000

minus8minus7minus6minus5minus4minus3minus2minus1

01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)

TG (mg)dTG (mgmin)Heat flow (120583V)

Figure 2 Thermogram DTATGDTG of Luffa cylindrica crudefibers

to note that the variation Δ of reducing sugars concentra-tion measurement for LC fibers diluted acid pretreatmentdemonstrates that the reducing sugars are absents Figure 3(a)illustrates the variationΔ of total sugars concentration duringdiluted acid pretreatment for all CCD experiments Fromthis figure it can be seen that the optimum and the bestvariation Δ of total sugars concentration (8039 plusmn 1052) gkgis obtained for the experience O [43ndash45] performed at 100∘Cduring 45 minutes with diluted H2SO4 at 275 In order toconclude the effect of each factor influencing the diluted acidpretreatment of LC fibers Table 5 summarizes the regressioncoefficients CT for the variation Δ of total sugars concentra-tion calculated by means of NemrodW software [33] As isclear from Table 5 the regression coefficients b1 (+03821)and b2 (+01193) have positive signs so both temperatureand reaction time have positive effect on the variation Δ oftotal sugars concentration and they should be kept at theirhighest levels respectively at 120∘C and 60 minutes Whilethe regression coefficient b3 has a negative sign (minus04087)

0123456789

10

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

Δ Total sugars (gkg)

ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments

Δ Total sugarsΔ Reducing sugars

A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)

Figure 3 Variations Δ of sugars concentration for both diluted acid(a) and hydrothermal (b) pretreatments for all CCD experiments

then H2SO4 concentration has a negative effect on thevariation Δ of total sugars concentration Consequently theH2SO4 concentration should be used at 05 The regressioncoefficients b13 (temperaturelowast acid concentration = +08708)and b23 (reaction time lowast acid concentration = +00872)


Table 5 The regression coefficients 119862119879 for Δ total sugars concen-tration calculated for diluted acid pretreatment of Luffa cylindricabiomass

Regressioncoefficients Factors and interactions 119862119879

1198870 Squared effect term 482211198871 Temperature 038211198872 Reaction time 011931198873 Acid concentration minus0408711988711 Temperature2 0907511988722 Reaction time2 minus1028311988733 Acid concentration2 minus0812011988712 Temperature lowast Reaction time minus0423011988713 Temperature lowast Acid concentration 0870811988723 Reaction time lowast Acid concentration 00872

have positive effects on the variation Δ of total sugarsconcentration whereas the interaction between temperatureand reaction time (b12 = minus04230) has a negative effect on thevariation Δ of total sugars concentration Besides only thetemperature increasing (b11 = +09075) has a positive effecton the variation Δ of total sugars concentration which is theopposite for both reaction time (b22 = minus10283) and H2SO4concentration (b33 = minus08120) effects

The mathematical model describing the variation Δ oftotal sugars concentration is established according to thecalculated regression coefficients CT as follows

[Δ total sugars = 48221 + 03821 lowast Temperature

+ 01193 lowast Time minus 04087 lowast [H2SO4] + 09075

lowast Temperature2 minus 10283 lowast Time2 minus 08120

lowast [H2SO4]2 minus 04230 lowast Temperature lowast Time

+ 08708 lowast Temperature lowast [H2SO4] + 00872

lowast Time lowast [H2SO4]]

(3)

Figure 4 shows the responses surfaces of temperature-reaction time (a) [H2SO4]-temperature (b) [H2SO4]-reaction time (c) interaction effects on the variation Δ oftotal sugars concentration for CCD As can be seen theeffect of interaction between different factors (temperaturereaction time and H2SO4 concentration) on the variationΔ of total sugars concentration confirms that the optimalconditions of LC fibers diluted acid pretreatment are asfollows 100∘C 45 minutes and H2SO4 at 275 to reachΔ total sugars concentration around 8 gkg Theoreticallyglucose is the main sugar present in LCrsquos acid hydrolysisresidue [7] Choudhary et al (2015) [43] confirm thatthe glucose concentration liberated after sulfuric acidpretreatment of sorghum (YSS-10R variety) with 05of H2SO4 at 100∘C during 10 minutes reaches 64 gkgwhich is significantly higher than the optimal total sugarsconcentration released during this study Otherwise the

Table 6 The regression coefficients 1198621198791015840 and 119862119877 for respectively Δtotal and reducing sugars concentrations calculated for hydrother-mal pretreatment of Luffa cylindrica biomass

Regressioncoefficients Factors and interactions 1198621198791015840 119862119877

1198870 Squared effect term 161811 1127701198871 Temperature minus06586 minus029521198872 Reaction time 20452 minus0405111988711 Temperature2 minus40547 minus0799111988722 Reaction time2 13913 minus0068711988712 Temperature lowast Reaction time minus03982 minus01878

acid thermal pretreatment of raw wheat bran was performedusing 1 (wv) of sulfuric acid in the autoclave at 121∘C for30min [3]

The absence of reducing sugars released after LC dilutedacid pretreatment could be explained by three main argu-ments namely (i) their degradation (ii) their repolymer-ization or redistribution andor (iii) their transformation tothe ldquoenzymes and fermentationrdquo toxic inhibitor compoundsgenerated due to the thermo-acid conditions for examplefurfural hydroxymethylfurfural HMF levulinic acetic andformic acids phenolics and aldehyde components [13]Consequently avoiding the use of acid during the thermalpretreatment of LC fibers seems to be a better solution topreserve the structure of the reducing sugars

322 Hydrothermal Pretreatment of LC Fibers The selectedresponses for LC fibers hydrothermal pretreatment are thevariationsΔ of both total and reducing sugars concentrationsFigure 3(b) represents the variations Δ of both total andreducing sugars during hydrothermal pretreatment for allCCD experiments As shown the LC biomass hydrothermalpretreatment achieved at 100∘C during 60 minutes (Exper-iment 1198671015840) allows reaching the optimal variation Δ of totalsugars concentration about 24161 plusmn 2150gkg Otherwisethe optimal variation Δ of reducing sugars amount (12490 plusmn0191 gkg) is obtained for the experiment (1198681015840) which wascarried out at 100∘Cduring 45min In order to study the influ-ence of each factor affecting the hydrothermal pretreatmentof LC fibers Table 6 outlines the regression coefficients 1198621198791015840andCR for respectively the variationsΔ of total and reducingsugars concentrations for hydrothermal pretreatment of LCbiomass calculated using NemrodW software [33] Table 6indicates that the two b0 values (1198621198791015840 = minus06586 and CR =minus02952) have negative signs so the raising of the temperaturedecreases both variations Δ of total and reducing sugars con-centrations although the reaction time increasing enhancesthe total sugars liberation given that b2 (1198621198791015840= +20452)has a positive sign But the variation Δ of reducing sugarsconcentration decreases while the reaction time increasessince b2 (119862119877 = minus04051) has a negative sign Besides theincrease of the interaction between the temperature and thereaction time decreases both the variations Δ of total andreducing sugars concentrations since b12 (1198621198791015840= minus03982 andCR = minus01878) have negative signs


Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)

Δ Total sugars concentration (gkg)

X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()

(b) Reaction time = 45min

Reaction time (min)9

243

477

7

032

1


X2

X3[H2SO4] ()

(c) Temperature = 100∘C

Figure 4 Responses surfaces of temperature-reaction time (a) [H2SO4]-temperature (b) [H2SO4]-reaction time (c) interaction effects onthe variation Δ of total sugars concentration for CCD

The mathematical models describing the variations Δ ofboth total and reducing sugars concentrations are elaboratedaccording to the calculated regression coefficients1198621198791015840 andCRas follows[Δ Total sugars = 161811 minus 06586 lowast Temperature

+ 20452 lowast Reaction time minus 40547

lowast Temperature2 + 13913 lowast Reaction time2

minus 03982 lowast Temperature lowast Reaction time]

[Δ Reducing sugars = 112770 minus 02952

lowast Temperature minus 04051 lowast Reaction time minus 07991

lowast Temperature2 minus 00687 lowast Reaction time2


(4)

Figure 5 represents the responses surfaces of (temperature-reaction time) interaction effect on the variations Δ of both

total sugars (a) and Δ reducing sugars (b) concentrations forCCD As shown the effect of interaction between the twomain independent variables (temperature-reaction time) onthe variation Δ of both total and reducing sugars concentra-tions demonstrates that the hydrothermal pretreatment of LCfibers performed at a temperature not exceeding 100∘Cduring45 minutes allows obtaining the variation Δ of total andreducing sugars concentrations respectively around 20 gkgand 12 gkg

The reducing sugars concentration obtained afterthe hydrothermal pretreatment of LC fibers (3355 gkg)(Figure 6) is higher than the sugars amount (glucose plusxylose) released during thermal autoclaving pretreatments(121∘C for 60 minutes) of wheat straw (148 plusmn 001 gkg) usedas substrate for biogas production [44]Thus LC fibers couldbe used as a potential source for bioethanol 2G recoverySanchez and Cardona (2008) [15] explain that the reducingsugars liberated after hydrothermal pretreatment (LiquidHotWater LHWpretreatmentmethod) aremainly obtained fromhemicelluloses depolymerization and water-soluble polysac-charides hydrolysis Besides the hydrothermal pretreatment


258

53

154

30

500

6


Reaction time(min)

Reaction time(min) X2

X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)

Δ Reducing sugars concentration (gkg)

(b)

Figure 5 Responses surfaces of (temperature-reaction time) interaction effect on the variations Δ of total sugars (a) and Δ reducing sugars(b) concentrations for CCD

Table 7 Desirability function of the variations Δ of the total and Δ reducing sugars concentrations for CCD studying the optimization ofhydrothermal pretreatment of Luffa cylindrica biomass

Parameters Δ Total sugars concentration Δ Reducing sugars concentration

Temperature (∘C) 96 95(1198831 = minus0216062) (1198831 = minus0260133)

Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)

1198841 (Δ Total sugars) (gkg) 19541 195711198842 (Δ Reducing sugars) (gkg) 10882 10880Desirability (1198841) () 5170 5239Desirability (1198842) () 5515 4831Desirability () 5396 5070

strength is the low or the absence of toxic inhibitors whichmakes it one of the favorite pretreatment methods for thescientists to avoid inhibition of enzymatic saccharificationand fermentation during bioethanol production

Table 7 illustrates the desirability function of the varia-tions Δ of both total and reducing sugars concentrations forCCD studying the optimization of hydrothermal pretreat-ment of LC biomass As given byNemrodWsoftware [33] thehydrothermal pretreatment performed in the optimal condi-tions should be fulfilled at a temperature about 95∘C-96∘Cduring 54ndash60 minutes Furthermore Chandra et al (2012)found that the rice straw biomass hydrothermal pretreatmentshould be carried out for 10min at 200∘C [1]

33 Enzymatic Saccharification of LC Pretreated FibersThe enzymatic saccharification of LC pretreated fibers wasperformed according to the protocol described above inSection 232 The highest reducing sugars concentration wasabout 594 gkg it records for the saccharification carriedout by enzyme AP2 However the enzymatic assay of LC

pretreated fibers performed with enzyme SPC generatesaround 37 gkg of reducing sugars

The reducing sugars recovery after enzymatic sacchar-ification performed with enzyme AP2 reaching 9329 ishigher than those of steam pretreated agricultural residues(triticale Canadian prairie spring wheat (SW) durumwheatfeed barley malt barley oat and flax straws) ranging from30 to 70 [45] Besides the current finding is in perfectagreement with the maximum glucose yield around 8589 and 95 respectively obtained after enzymatic hydrol-ysis of hydrothermal pretreated switchgrass fibers dilute acidpretreated cotton fibers and steam pretreated Arundo donax[32 46 47] Moreover the current reducing sugars amountreleased after the enzymatic saccharification of LChydrother-mal pretreated fibers with the commercial enzymes AP2and SPC (594 gkg or 55242 gl and 37 gkg or 3441 gl) issignificantly higher than the reducing sugars liberated duringSSF of alkali pretreated (80∘C 05M NaOHg dry weightfor 2 h with agitation) sugarcane bagasse (12468 gl) aiming


Milled Luffa cylindrica biomass (55 plusmn 033) dry matter

Lipids = (1244 plusmn 05)Water-soluble polysaccharides = (786 plusmn 01)Lignins = (1315 plusmn 06)120572-cellulose = (4580 plusmn 13)Hemicelluloses = (2076 plusmn 03)

LC hydrothermal pretreatmentReaction time 54 minutes

Biomass (55 plusmn 033) dry matterEnzyme AP2 (54000 unitg) 02

Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL

Time 24hShaking 250 rpm

Pretreated biomass Reducing sugars = 3355gkg

Hydrolysate

Reducing sugars recovery = 9329

Reducing sugars = 5940gkgΔ Reducing sugars = 2585 gkg

Fermented biomass

Reducing sugars conversion yield = 8866Reducing sugars = 6733gkg

Ethanol conversion efficiency

Volumetric yield = 7035mL ethanol (158)

(w (enzyme) w (substrate))pH 40 plusmn 02 temperature 60∘C time 1 hour

Figure 6 Process flowchart of Luffa cylindrica fibers pretreatment and enzymatic saccharification for 2G bioethanol conversion

at the sugars and 120573-glucosidase production during singleand mixed culture by Trichoderma reesei and Penicilliumdecumbens for 72 h [2]

34 Alcoholic Fermentation of Hydrolysate and DistillationThe alcoholic fermentation feasibility of AP2 hydrolysategiving the best reducing sugars rate (Δ reducing sugars =2585 gkg) and the alcoholic broth distillation were carriedout referring to themethodology detailed above respectivelyin Sections 233 and 234 After enzymatic saccharificationthe reducing sugars conversion yield is around 8866Thus the ethanol conversion efficiency is 158 and itsvolumetric yield 70 which is higher than the ethanolconversion efficiency obtained fromhydrothermal pretreatedand enzymatic hydrolysate lucerne ranging from 417

and 628 [48] This amount is also more important thanthose reported for the alkali pretreated sugarcane bagassefermentation which is about 4084 (of theoretical yield)achieved after 24 h using Saccharomyces cerevisiae [2] andfor other lignocellulosic feedstocks that is dried carob podparticles fermented with Zymomonas mobilis (43) [14] andmicrowave hydrothermal pretreated (900W for 2min) sagopith waste fermented with Saccharomyces cerevisiae (156)[49] However the hydrothermal pretreated LC fibers ethanolconversion yield is slightly lower than the ethanol efficiencyof wheat branrsquos starch fermentedwith S cerevisiae yeast (81ndash89) [3] To have a general overview of the studied biofuelrefinery Figure 6 outlines the process flowchart of LC fiberspretreatment and enzymatic saccharification for bioethanol2G conversion


4 Conclusion

From this research it was highlighted that the hydrother-mal pretreatment of LC fibers performed at 96∘C during54 minutes seems to be the suitable way to liberate theoptimal amount of reducing sugars (3355 gkg) Then theenzymatic saccharification was carried out via AP2 enzymeand the reducing sugars concentration reached 594 gkgThen 8866 of reducing sugars were converted to alcoholThe potential conversion yield of 2G bioethanol is 1 Ton (drymatter) of LC fibers to 138545 kg (=36599 Gallon) of biofuelThus the current study approves that Luffa cylindrica could beconsidered as an energy crop for 2G bioethanol production inNorth Africa especially in Tunisia

Nomenclature

LC Luffa cylindricaSSF Simultaneous saccharification and

fermentationCCD Cubic central composite experimental

designRSM Response surface methodologyAP2 Commercial cellulolytic enzymeSPC Commercial cellulolytic enzymeFTIR Fourier transform infrared spectroscopyΔ Variation119862119879 The regression coefficients for Δ total

sugars concentration calculated for dilutedacid pretreatment of Luffa cylindricabiomass

1198621198791015840 and 119862119877 The regression coefficients forrespectively Δ total and reducing sugarsconcentrations calculated forhydrothermal pretreatment of Luffacylindrica biomass

Additional Points

Highlights (1) Luffa cylindrica hydrothermal pretreatmentis performed at 96∘C for 54 minutes (2) The optimalreducing sugars amount obtained from pretreated biomassis 3355 gkg (3) After enzymatic saccharification reducingsugars recovery is 9329 (4) Reducing sugars conversionyield is 8866 after alcoholic fermentation (5) Bioethanol2G conversion efficiency is 70

Competing Interests

The authors declare that they have no competing interests

References

[1] R Chandra H Takeuchi and T Hasegawa ldquoHydrothermalpretreatment of rice straw biomass a potential and promisingmethod for enhanced methane productionrdquo Applied Energyvol 94 pp 129ndash140 2012

[2] Y Liu Y Zhang J Xu Y Sun Z Yuan and J Xie ldquoConsolidatedbioprocess for bioethanol production with alkali-pretreatedsugarcane bagasserdquo Applied Energy vol 157 pp 517ndash522 2015

[3] R Cripwell L Favaro S H Rose et al ldquoUtilisation of wheatbran as a substrate for bioethanol production using recombi-nant cellulases and amylolytic yeastrdquo Applied Energy vol 160pp 610ndash617 2015

[4] P Phitsuwan K Sakka and K Ratanakhanokchai ldquoImprove-ment of lignocellulosic biomass in planta a reviewof feedstocksbiomass recalcitrance and strategicmanipulation of ideal plantsdesigned for ethanol production and processabilityrdquo Biomassand Bioenergy vol 58 pp 390ndash405 2013

[5] P Fasahati H CWoo and J J Liu ldquoIndustrial-scale bioethanolproduction from brown algae effects of pretreatment processeson plant economicsrdquo Applied Energy vol 139 pp 175ndash187 2015

[6] Y Laidani S Hanini G Mortha and G Heninia ldquoStudy of afibrous annual plantLuffa cylindrica for ppaper application partI characterization of the vegetalrdquo Iranian Journal of Chemistryand Chemical Engineering vol 31 no 4 pp 119ndash129 2012

[7] G Siqueira J Bras and A Dufresne ldquoLuffa cylindrica as alignocellulosic source of fiber microfibrillated cellulose andcellulose nanocrystals Luffa as a cellulose sourcerdquoBioResourcesvol 5 no 2 pp 727ndash740 2010

[8] UNFAO Food and Agriculture Organization of the UnitedNations Minor Oil Crops Part ImdashMinor Edible Oil CropsPrepared by BL Axtell from Research by RM FairmanIntermediate Technology Development Group Rugby LondonUK 1992

[9] Y Laidani S Hanini and G Henini ldquoValorization of Luffacylindrica for water treatment copper chargers study of thepossibility of regeneration by chemical desorptionrdquo in Proceed-ings of the 1st International Conference on Applied MechanicsMaterials and Manufacturing (ICAMMM rsquo10) Sultan QaboosUniversisty Muscat Oman December 2010

[10] M R Vianna G C B de Melo andM R V Neto ldquoWastewatertreatment in trickling filters using Luffa cyllindrica as biofilmsupporting mediumrdquo Journal of Urban and EnvironmentalEngineering vol 6 no 2 pp 57ndash66 2012

[11] S Liu ldquoA synergetic pretreatment technology for woodybiomass conversionrdquoApplied Energy vol 144 pp 114ndash128 2015

[12] A Abdolali W S Guo H H Ngo S S Chen N C Nguyenand K L Tung ldquoTypical lignocellulosic wastes and by-productsfor biosorption process in water and wastewater treatment acritical reviewrdquoBioresource Technology vol 160 pp 57ndash66 2014

[13] A Limayem and S C Ricke ldquoLignocellulosic biomass forbioethanol production current perspectives potential issuesand future prospectsrdquo Progress in Energy and CombustionScience vol 38 no 4 pp 449ndash467 2012

[14] DMazaheri S A Shojaosadati S MMousavi P Hejazi and SSaharkhiz ldquoBioethanol production from carob pods by solid-state fermentation with Zymomonas mobilisrdquo Applied Energyvol 99 pp 372ndash378 2012

[15] O J Sanchez and C A Cardona ldquoTrends in biotechnologicalproduction of fuel ethanol from different feedstocksrdquo Biore-source Technology vol 99 no 13 pp 5270ndash5295 2008

[16] M Oslash Haven J Lindedam M D Jeppesen et al ldquoContinuousrecycling of enzymes during production of lignocellulosicbioethanol in demonstration scalerdquoApplied Energy vol 159 pp188ndash195 2015

[17] M Han K E Kang Y Kim and G-W Choi ldquoHigh efficiencybioethanol production from barley straw using a continuouspretreatment reactorrdquo Process Biochemistry vol 48 no 3 pp488ndash495 2013

[18] P Izadiyan and B Hemmateenejad ldquoMulti-response optimiza-tion of factors affecting ultrasonic assisted extraction from


Iranian basil using central composite designrdquo Food Chemistryvol 190 pp 864ndash870 2016

[19] H Boussarsar B Roge and M Mathlouthi ldquoOptimization ofsugarcane bagasse conversion by hydrothermal treatment forthe recovery of xyloserdquo Bioresource Technology vol 100 no 24pp 6537ndash6542 2009

[20] ASTM ldquoStandard method for ultimate analysis of coal andcokerdquo ASTM D3176-74 American Society for Testing andMaterials ASTM Philadelphia Pa USA 1997

[21] S Hamza H Saad B Charrier N Ayed and F Charrier-El Bouhtoury ldquoPhysico-chemical characterization of Tunisianplant fibers and its utilization as reinforcement for plaster basedcompositesrdquo Industrial Crops and Products vol 49 pp 357ndash3652013

[22] A Mukherjee A R Zimmerman and W Harris ldquoSurfacechemistry variations among a series of laboratory-producedbiocharsrdquo Geoderma vol 163 no 3-4 pp 247ndash255 2011

[23] X F Sun R C Sun J Tomkinson andM S Baird ldquoPreparationof sugarcane bagasse hemicellulosic succinates using NBS asa catalystrdquo Carbohydrate Polymers vol 53 no 4 pp 483ndash4952003

[24] W-H Chen and P-C Kuo ldquoTorrefaction and co-torrefactioncharacterization of hemicellulose cellulose and lignin as well astorrefaction of some basic constituents in biomassrdquo Energy vol36 no 2 pp 803ndash811 2011

[25] X Feng and W Donghai ldquoAnalysis of lignocellulosic biomassusing Infrared methodologyrdquo in Pretreatment of Biomass Pro-cesses and Technologies A Pandey S Negi P Binod and CLarroche Eds vol 154 chapter 2 pp 7ndash25 Elsevier EducationAmstedam The Netherlands 1st edition 2015

[26] M Dubois K A Gilles J K Hamilton P A Rebers and FSmith ldquoColorimetric method for determination of sugars andrelated substancesrdquoAnalytical Chemistry vol 28 no 3 pp 350ndash356 1956

[27] G L Miller ldquoUse of dinitrosalicylic acid reagent for determina-tion of reducing sugarrdquo Analytical Chemistry vol 31 no 3 pp426ndash428 1959

[28] V B Hugo and L I Felipe ldquoThe unified metabolism ofGluconacetobacter entanii in continuous and batch processesrdquoProcess Biochemistry vol 42 no 8 pp 1180ndash1190 2007

[29] D C Montgomery Design and analysis of experiments JohnWiley and Sons New York NY USA 5th edition 2001

[30] J Goupy and L Creighton Introduction Aux PlansdrsquoExperiences Dunod Paris France 3rd edition 2006

[31] B B Hallac P Sannigrahi Y Pu M Ray R J Murphy andA J Ragauskas ldquoEffect of ethanol organosolv pretreatmenton enzymatic hydrolysis of Buddleja davidii stem biomassrdquoIndustrial and Engineering Chemistry Research vol 49 no 4 pp1467ndash1472 2010

[32] H Amiri and K Karimi ldquoEfficient dilute-acid hydrolysis ofcellulose using solvent pretreatmentrdquo Industrial amp EngineeringChemistry Research vol 52 no 33 pp 11494ndash11501 2013

[33] M Diller J Nonny and R Phan-than-Luu NemrodW-399Version 9901 2009

[34] Z Marrakchi R Khiari H Oueslati E Mauret and F MhennildquoPulping and papermaking properties of Tunisian Alfa stems(Stipa tenacissima)mdasheffects of refining processrdquo IndustrialCrops and Products vol 34 no 3 pp 1572ndash1582 2011

[35] V O A Tanobe T H D Sydenstricker M Munaro and SC Amico ldquoA comprehensive characterization of chemicallytreated Brazilian sponge-gourds (Luffa cylindrica)rdquo PolymerTesting vol 24 no 4 pp 474ndash482 2005

[36] M Carrier A Loppinet-Serani D Denux et al ldquoThermogravi-metric analysis as a new method to determine the lignocellu-losic composition of biomassrdquo Biomass and Bioenergy vol 35no 1 pp 298ndash307 2011

[37] J Liang Y Lin S Wu C Liu M Lei and C Zeng ldquoEnhancingthe quality of bio-oil and selectivity of phenols compoundsfrom pyrolysis of anaerobic digested rice strawrdquo BioresourceTechnology vol 181 pp 220ndash223 2015

[38] M C Ncibi V Jeanne-Rose BMahjoub et al ldquoPreparation andcharacterisation of raw chars and physically activated carbonsderived from marine Posidonia oceanica (L) fibresrdquo Journal ofHazardous Materials vol 165 no 1ndash3 pp 240ndash249 2009

[39] G Mishra J Kumar and T Bhaskar ldquoKinetic studies on thepyrolysis of pinewoodrdquo Bioresource Technology vol 182 pp282ndash288 2015

[40] S NMonteiro V Calado F MMargem and R J S RodriguezldquoThermogravimetric stability behavior of less common ligno-cellulosic fibersmdasha reviewrdquo Journal of Materials Research andTechnology vol 1 no 3 pp 189ndash199 2012

[41] A Hlavsova A Corsaro H Raclavska S Vallova and DJuchelkova ldquoThe effect of feedstock composition and taxonomyon the products distribution from pyrolysis of nine herbaceousplantsrdquo Fuel Processing Technology vol 144 pp 27ndash36 2016

[42] P O Biney M Gyamerah J Shen and B Menezes ldquoKineticsof the pyrolysis of arundo sawdust corn stover and switchgrass biomass by thermogravimetric analysis using a multi-stage modelrdquo Bioresource Technology vol 179 pp 113ndash122 2015

[43] S J Choudhary S Mehmood H Naz H Z E Jaafar and MZia-Ul-Haq ldquoEffect of sulfuric acid on pretreatment of YSS-10R variety of sorghum and analysis of its interaction withtemperature and timerdquo BioResources vol 10 no 2 pp 2103ndash2112 2015

[44] S Bolado-Rodrıguez C Toquero J Martın-Juarez R Travainiand P A Garcıa-Encina ldquoEffect of thermal acid alkaline andalkaline-peroxide pretreatments on the biochemical methanepotential and kinetics of the anaerobic digestion of wheat strawand sugarcane bagasserdquo Bioresource Technology vol 201 pp182ndash190 2016

[45] R P Chandra V Arantes and J Saddler ldquoSteam pretreatmentof agricultural residues facilitates hemicellulose recovery whileenhancing enzyme accessibility to celluloserdquo Bioresource Tech-nology vol 185 pp 302ndash307 2015

[46] Z Hu and A J Ragauskas ldquoHydrothermal pretreatment ofswitchgrassrdquo Industrial amp Engineering Chemistry Research vol50 no 8 pp 4225ndash4230 2011

[47] I De Bari F Liuzzi A Villone and G Braccio ldquoHydrolysis ofconcentrated suspensions of steam pretreated Arundo donaxrdquoApplied Energy vol 102 pp 179ndash189 2013

[48] S T Thomsen M Jensen and J E Schmidt ldquoProductionof 2119899119889 generation bioethanol from lucernemdashoptimization ofhydrothermal pretreatmentrdquo BioResources vol 7 no 2 pp1582ndash1593 2012

[49] S K Thangavelu A S Ahmed and F N Ani ldquoBioethanol pro-duction from sago pith waste using microwave hydrothermalhydrolysis accelerated by carbon dioxiderdquo Applied Energy vol128 pp 277ndash283 2014

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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International Journal of

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GenomicsInternational Journal of


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Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

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Enzyme Research



Microbiology


(ie corn stover sugarcane bagasse rice straw [1] cassavapulp palm residues soybean residues wheat straw wheatbran [3] straw bark used edible oil and black liquor)woody feedstocks softwood (pine and spruce) herbaceousbiomass and cellulose wastes (waste office paper) [4] andseaweeds (brown algae) [5] In the Mediterranean regionespecially in Tunisia Luffa cylindrica (LC) is a promisinglignocellulosic feedstock for 2G bioethanol recovery [1] LCis an annual herbaceous plant from the cucurbitaceous family[6] It is a fibrous plant largely distributed in the tropical andsubtropical countries and countries with moderate climate[7] with a plant growth yield reaching 62000 LC fruitsha(20 to 25 fruitsplant) However this yield depends highly onthe climate [8] Many common end-uses of LC fibers werelisted as follows the disposal of copper from food industrywastewater [9] the biofilm supporting medium in tricklingfilters for wastewater treatment the handicraft activitiessome other industrial applications and pharmacology [10]the bath sponges manufacturing the use as a basic stampfor the chemical and biological immobilization andor as asupport with fixed bed of biological culture or for chemicalsynthesis the use as a support of discoloration of the reagentsandor as a thermal support and the use as a basic materialfor the insulation and the extraction of the chemical andbiological compounds [6] As listed previously and to thebest of our knowledge no published report exists on LCfibers recovery for 2G bioethanol production In fact theworks dealing with this subject are still unknown andorunwell and thorough studies are lacking Thus this work isconsidered as a novelty in terms of 2G bioethanol productionfrom LC biomass The second generation biofuels producedfrom renewable resources ldquoplant biomassrdquo are made withthe lignocellulosic biomass since it is a cheap and abundantnonfood material available from plants [1] Lignocellulosicsubstrate is mainly composed of two types of carbohydratesand onemore complex polymer namely 30ndash55 of cellulose20ndash40 of hemicelluloses 10ndash35 of lignins and theirratio varies extremely depending on the plant species Theseunits are strongly linked and chemically bonded in factcellulose is the backbone structure while hemicelluloses andlignins are the binding networks Cellulose (consisting ofD-glucose only) and hemicelluloses which is composed ofmainly pentoses (like xylose and arabinose) and hexoses (likemannose glucose galactose etc) are bioconvertible [11 12]

Three main steps are required to obtain 2G bioethanolfrom lignocellulosic biomass namely pretreatment enzy-matic saccharification and fermentation and distillation [211 13 14] Many pretreatment methods of lignocellulosicbiomass are listed in the literature including physical pre-treatment (grinding milling microwave and extrusion)chemical pretreatment (hydrothermal-aqua Solv [1] alkali[1 2] acid organosolv ozonolysis and ionic liquid) physic-ochemical pretreatment (steam explosion liquid hot waterammonia fiber explosion AFEX wet-oxidation and CO2explosion) and biological pretreatment (delignification oflignocellulosic substrate by Basidiomycota fungi) [15] Pre-treatment step plays three important roles that is ligninsdestruction hydrolysis of hemicelluloses and modificationof cellulose which will improve enzymatic hydrolysis [15]

Particularly hydrothermal pretreatment of lignocellulosicmaterial for the enhancement of biofuels 2G productionbecomes more and more important in the 21st centuryWater under high pressure and temperature can get intothe biomass moisturize cellulose enhance its accessible andsusceptible surface area and improve its accessibility tothe hydrolytic enzymes indeed it removed hemicellulosesand part of lignins The main advantages of hydrothermalpretreatment are as follows no addition of chemicals norequirement of corrosion resistant materials for hydrolysisreactors and no need for size reduction of biomass it requiresmuch lower need for chemicals for neutralization of theproduced hydrolyzate and it produces lower amounts ofneutralization residues compared to many processes [1]

However pretreatment methods have some weaknesseslimiting their applications so combined pretreatment meth-ods are recently developed to curb this challenge by increas-ing efficiency of sugars liberation decreasing the formationof inhibitors and making the process time shorter Thusbioethanol yield becomes higher and the process becomesmore economical [1]

In order to destroy cellulose chains the subsequentenzymatic hydrolysis is catalyzed by the synergistic actionof four cellulase enzymes operating at 40ndash50∘C and pH 4-5namely endo-14-120573-glucanases cellobiohydrolases exo-14-120573-glucanasesmdashthat will hydrolyze cellulose into cellobiosemdashand 120573-glucosidases that will hydrolyze cellobiose into glu-cose Cellulolytic enzymes play a critical role in lignocellulosesaccharification and bioconversion of pretreated lignocel-lulosic material that requires multiple enzyme activitiesThe monomeric sugars (glucose galactose mannose xyloseand arabinose) released from enzymatic saccharification areconverted into ethanol thanks to some microorganismsSaccharomyces cerevisiae is the most used yeast for ethanolproduction from hexoses given that it is well-known for itsresistance to low pH high temperatures high ethanol con-centration and various inhibitors Otherwise one amylolyticSaccharomyces cerevisiae strain was employed for bioethanolproduction from wheat bran [3] Other yeasts could produceethanol through hexoses recovery mainly from xylose forexample Pichia stipitis Candida shehatae Kluyveromycesmarxianus and Pachysolen tannophilus Some bacteria couldalso ferment monomeric sugars to produce alcohols such asZymomonas mobilis and Escherichia coli [13] Several typesof fermentation processes have been tested for examplebatch continuous continuous with cell recycling fed-batchand repeated-batch culture designs [15] In order to obtaina fuel grade or anhydrous ethanol many distillation anddehydration processes are used [13] Nevertheless the scale-up of the whole lignocellulosic biomass conversion process isvery expensive In order to reduce the biofuels 2G productioncost suitable processes available were listed in the literaturenamely the implementation of simultaneous saccharificationand fermentation SSF which integrates enzymatic saccharifi-cation and ethanol fermentation in one system saving bothprocess and time cost [1 2 15] the using of a recombinantcellulase cocktail (RCC) which contains two cellobiohydro-lases an endoglucanase and a 120573-glucosidase with S cerevisiaein SSF condition [3] and the continuous recycling of enzymes










O () = 100 minus (C +H +N + S + ash) (1)


















119884 = 1205730 +119896

sum119894=1

120573119894119883119894 +119896

sum119894=1

1205731198941198941198831198942 +sumsum119894lt119895

120573119894119895119883119894119883119895 + 120598 (2)













H22 +1 +1 +123 +1 +1 +124 +1 +1 +1


J28 +1 0 029 +1 0 030 +1 0 0


L34 0 +1 035 0 +1 036 0 +1 0


N40 0 0 +141 0 0 +142 0 0 +1

O43 0 0 044 0 0 045 0 0 0







D101584010 +1 +111 +1 +112 +1 +1


F101584016 +1 017 +1 018 +1 0


H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0




















4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology










O () = 100 minus (C +H +N + S + ash) (1)


















119884 = 1205730 +119896

sum119894=1

120573119894119883119894 +119896

sum119894=1

1205731198941198941198831198942 +sumsum119894lt119895

120573119894119895119883119894119883119895 + 120598 (2)













H22 +1 +1 +123 +1 +1 +124 +1 +1 +1


J28 +1 0 029 +1 0 030 +1 0 0


L34 0 +1 035 0 +1 036 0 +1 0


N40 0 0 +141 0 0 +142 0 0 +1

O43 0 0 044 0 0 045 0 0 0







D101584010 +1 +111 +1 +112 +1 +1


F101584016 +1 017 +1 018 +1 0


H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0




















4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













119884 = 1205730 +119896

sum119894=1

120573119894119883119894 +119896

sum119894=1

1205731198941198941198831198942 +sumsum119894lt119895

120573119894119895119883119894119883119895 + 120598 (2)













H22 +1 +1 +123 +1 +1 +124 +1 +1 +1


J28 +1 0 029 +1 0 030 +1 0 0


L34 0 +1 035 0 +1 036 0 +1 0


N40 0 0 +141 0 0 +142 0 0 +1

O43 0 0 044 0 0 045 0 0 0







D101584010 +1 +111 +1 +112 +1 +1


F101584016 +1 017 +1 018 +1 0


H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0




















4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology











H22 +1 +1 +123 +1 +1 +124 +1 +1 +1


J28 +1 0 029 +1 0 030 +1 0 0


L34 0 +1 035 0 +1 036 0 +1 0


N40 0 0 +141 0 0 +142 0 0 +1

O43 0 0 044 0 0 045 0 0 0







D101584010 +1 +111 +1 +112 +1 +1


F101584016 +1 017 +1 018 +1 0


H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0




















4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







D101584010 +1 +111 +1 +112 +1 +1


F101584016 +1 017 +1 018 +1 0


H101584022 0 +123 0 +124 0 +1

I101584025 0 026 0 027 0 0




















4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400014015016017018019020021022023024025026027028029030031

Abso

rban

ce

OH

292405C-H

C-H163278

C=O

C=O

138424 110340 C-O-C

591751700



0 200 400 600 800 1000


01

minus04

minus03

minus02

minus01

00

01

minus15

minus10

minus5

0

5

10

15

Temperature (∘C)




0123456789

10



ΔTo

tal s

ugar

s (g

kg)

(a)

0

5

10

15

20

25

30

Con

cent

ratio

n (g

kg)

Experiments


A998400 B998400 C998400 D998400 E998400 F998400 G998400 H998400 I998400

(b)















(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













(3)









Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Reaction time(min)

924

3

477

7

032

1

Temperature (∘C)


X1

X2

(a) [H2SO4] = 275

924

3

477

7

032

1

Temperature (∘C)


X1

X3

[H2SO4] ()



243

477

7

032

1


X2

X3[H2SO4] ()











(4)





258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


258

53

154

30

500

6


Reaction time(min)


X1

Temperature ( ∘C)

(a)

137

80

897

4

416

8

Reaction time (min)

X2

X1

Temperature ( ∘C)


(b)





Time (min) 60 60(1198832 = 0976316) (1198832 = 0990424)












Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






Yeast inoculum 10

pH 48 plusmn 02

Distillation

Temperature 96∘C

Temperature 30∘C

Temperature 785∘C

Volume 100mL

Volume 100mL

Volume 50mL

Hydrolysate 100mL



Hydrolysate



Fermented biomass










4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


4 Conclusion


Nomenclature






Additional Points


Competing Interests


References



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

optimization of hydrothermal and diluted acid

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