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Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Preshanthan Moodley, E.B. Gueguim Kana*

UniversityofKwaZulu-Natal,SchoolofLifeSciences,Pietermaritzburg,SouthAfrica

ARTICLE INFO

Articlehistory:

Received29September2018

Receivedinrevisedform3January2019 Accepted21March2019

Keywords:

Lignocellulosicbioethanol Microwavepretreatment Sugarcane

Fermentationkinetics Inorganicsaltpretreatment

ABSTRACT

This studyexamines the kinetics of S. cerevisiae BY4743 growth and bioethanol production from sugarcane leaf waste (SLW), utilizing two different optimized pretreatment regimes; under two fermentation modes: steam salt-alkali filtered enzymatic hydrolysate (SSA-F), steam salt-alkali unfiltered(SSA-U),microwavesalt-alkalifiltered(MSA-F)andmicrowavesalt-alkaliunfiltered(MSA- U).ThekineticcoefficientsweredeterminedbyfittingtheMonod,modifiedGompertzandlogistic modelstotheexperimentaldatawithhighcoefficientsofdeterminationR²>0.97.Amaximumspecific growthrate(m^max⁾ôf^0.153^h¹^wasôbtainedûnder^SSA-Fând^SSA-U^whereas,^0.150^h¹^wasôbserved withMSA-FandMSA-U.SSA-Ugaveapotentialmaximumbioethanolconcentration(Pm)of31.06g/L comparedto30.49, 23.26and 21.79g/LforSSA-F, MSA-Fand MSA-Urespectively. Aninsignificant differencewasobservedinthem^maxând^P^m^for^thefilteredandunfilteredenzymatichydrolysateforboth SSA and MSA pretreatments, thus potentially reducing a unit operation. These findings provide significantinsightsforprocessscaleup.

creativecommons.org/licenses/by-nc-nd/4.0/).

1.Introduction

Globalenergydemandiscurrentlymetbyfossilfuelswithmore than 80% of the total energy market comprising of these conventional sources. The transport sector alone accounts for 60%ofthetotalusage[1].However,theﬁnitesupplyofthesefossil fuels and their contribution to greenhouse gas emission upon combustion are major challenges. It is therefore necessary to obtainanalternativesourceofenergy[2].

Lignocellulosicbiomassisconsideredanimportantfeedstock forbiofuelproductioninmitigatingfossilfueldependenceandits relatedgreenhousegasemissions([3,4]).Agriculturalwastes,such assugarcaneleavesarecurrentlyamajorproblemforagriculture fromanenvironmentalstandpoint,thusitsconversiontobiofuels ishighlyadvantageous[5].Secondgenerationbioethanolisone suchfuelandisconsideredclean,affordableandsustainablewith theinherentcapacitytoreplaceconventionalfuel[6].Incontrast, ﬁrst generation bioethanol utilizes edible feedstocks thereby contributingtothefoodversusfueldebate[7].

Microorganisms such as Saccharomyces cerevisiae are often employedintheproductionofbioethanol,thusplayingakeyrole in the fermentation process. However, due to the recalcitrant properties of lignocellulosicbiomass, thesemicroorganismsare unable to hydrolyse or access the glucose polymer, cellulose.

Furthermore,enzymatic hydrolysisis alsohampered due tothe complexitiesinthelignocellulosicstructure[1].For thisreason, the biomass hasto undergoan effective pretreatment prior to fermentation [8]. Our previous work established a steam and microwave-assistedsequentialsalt-alkali pretreatment(SSAand MSArespectively)whicheffectivelyenhancedenzymatichydroly- sis[9].However,theeffectofsteamandmicrowavepretreatment couldsigniﬁcantlyimpactontheprocesskineticsandultimately the scale-up efﬁciency and productivity. Currently, there is a scarcityofstudiescomparingtheeffectofsteamandmicrowave pretreatment of sugarcane leaves on bioethanol production kineticsusingSaccharomycescerevisiaeBY4743.

Twomainfermentationmodeshavebeenfrequentlyreported forbioethanol production,separatehydrolysisandfermentation (SHF) andsimultaneous sacchariﬁcation and fermentation(SSF) [10].IntheSHFprocess,thepretreatedmaterialishydrolysedto simplesugarsandsubsequentlyundergoesfermentation.Amajor advantageofthisprocessisitallowsindependentoptimizationof the enzymatic and fermentation phase to maximizesugar and

* Correspondingauthor.

E-mailaddress:[email protected](E.B. GueguimKana).

https://doi.org/10.1016/j.btre.2019.e00329

BiotechnologyReports (2019)

ContentslistsavailableatScienceDirect

Biotechnology Reports

j o u r n a l h o m e p a g e : w w w . e l s ev i er . c o m / l o c a te / b t r e

2 22 e00329

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ethanol yield respectively [10]. However, there is high cost to separating the solid and liquid fractions of the hydrolysate, particularlyatlargescale [11].Several studieshavehighlighted the SSF system as a potential solution. In this strategy, the hydrolysis and fermentation occur in the same reactor, thus negatingtheneedforaseparationstage.Thistechniquealsohas significant drawbacks such as specialised equipment require- ments, high concentration of inhibitor formation and non- reusabilityof theyeastdue tolignin separation [10]. However, the main drawback is the different optimum temperatures requiredfor theenzyme(usuallycellulase) and thefermenting microorganism,usually50and30Crespectively[12].Ultimately, preference is given to the microorganism resulting in a sub- optimalsaccharificationprocess.Anotherpotentialsolution,isto remove the separation stage from the SHF process. There is a dearth of knowledge on the effect of filtered and unfiltered enzymatic hydrolysateon fermentation process kinetics.More- over,thesolidwasteresiduefromtheSHFprocesseffluentcouldbe anattractiveadditionalrevenuestreamforanimalfeedsincethe plant material has been delignified to enhance digestibility.

Furthermore,there couldbeanincrease inproteincontentdue totheyeastcellbiomass[13].

With increasinginterestinthe commercialapplications of batchbioethanolprocesses,severalkineticsmodelshavebeen proposedwhichdescribemicrobialgrowth,productformation andsubstrate consumption[14]. These modelsare extremely useful in the process development of bioethanol production, since they assist in predicting fermentation performance in responsetochangesinvariousfactors[15].Thisstudyemploys theMonod,logisticandmodiﬁedGompertzmodelstocompar- ativelydescribethe microbialgrowthandbioethanolproduc- tionfrompretreatedSLW.

Theaimofthisstudywastothereforeexaminethekineticsof SHF bioethanol fermentation from two previously optimized pretreatment techniques of sugarcane leaf waste, under two fermentationmodesusingSaccharomycescerevisiaeBY4743.These includeSSAfilteredenzymatichydrolysate(SSA-F),SSAunfiltered enzymatichydrolysate(SSA-U),MSAfilteredenzymatichydroly- sate(MSA-F)andMSAunfilteredenzymatichydrolysate(MSA-U).

Inaddition,thepotentialofthefermentationefﬂuentasanimal feedwasalsoexplored.

2.Methods

2.1.Feedstockandpretreatment

Sugarcane leaf waste (SLW) employed in this study was harvested from a sugarcane plantation located in the North Coast of South Africa. Prior to pretreatment, the leaves were dried at 60C for 72h and milled to 1mm. The substrate pretreatment protocols have been described in our previous study[9].Forthesteamsalt-alkalimethod(SSA),SLWwasfirst treatedwith1.73MZnCl2for30minat121Cfollowedby1.36M NaOH at 121C for 30min. For the microwave-assisted salt- alkali(MSA),SLWwaspretreatedwith1.67MZnCl2at400Wfor 5mininthefirststagefollowedby1.52MNaOHinthesecond stage. All pretreated samples were washed thoroughly with deionizedwateruntilaneutralpHwasobtained.Briefly,theSSA andMSApretreatmentexhibitedalignin removalof80.5 and 73% and hemicellulose removal of 51.9 and 62% respectively.

Similarcelluloserecoveriesof88and87%wereobservedforthe SSA and MSA pretreatments respectively [9]. Enzymatic hydrolysis was performed in sodium citrate buffer (pH 4.8, 0.05M)with asolid andenzyme loadingof10% (w/v)and10 FPU/grespectively.Thecommercialcellulaseenzymeprepara- tion, Cellic CTec 2, was kindly supplied by Novozymes (Novozymes A/S, Denmark). Sacchariﬁcationwas achieved at 50Cfor72hat120rpminashakingincubator.

2.2.Microorganismandinoculumdevelopment

This study employed Saccharomyces cerevisiae BY4743 and was supplied by the Department of Genetics, University of KwaZulu-Natal, Pietermaritzburg,SouthAfrica. The culturewas asepticallymaintainedonadoublestrengthYPDslant(20g/Lyeast extract,40g/Lpeptoneand40g/Ldextrose).Priortofermentation, the stock culture was streaked onto YPD media (10g/L yeast extract,20g/Lpeptoneand20g/Ldextrose)andincubatedat30C for24hthereafterasinglecolonywasinoculatedintoYPDbroth and incubated at 30C overnight in a shaking incubator at 120rpm.

2.3.Batchfermentation 2.3.1.Fermentationmedium

Bioethanolproductionwasinvestigatedwithtwopretreatment typesundertwo differentfermentationmodes.Theseincluded:

steam salt-alkali filtered enzymatic hydrolysate (SSA-F), steam salt-alkali unfilteredenzymatic hydrolysate(SSA-U), microwave salt-alkalifilteredenzymatichydrolysate(MSA-F)andmicrowave salt-alkaliunfilteredenzymatichydrolysate(MSA-U).Forexperi- mentsexaminingtheeffectsofunfilteredenzymatichydrolysate onprocesskinetics,theenzymatichydrolysatedidnotundergoa filteringprocesstoremovethesolidresidues.PretreatedSLWwas added to give an initialglucose concentrationof 60g/L in the fermentation media. Additional nutrients consisted of yeast extract5g/L,peptone5g/L,KH2PO42g/L,MgSO4

^7H2O1g/Land (NH4)2SO41g/L.

2.3.2.Fermentationconditions

Bioethanolfermentationwasperformedin100mlErlenmeyer ﬂasks.A10%(v/v)inoculationwasusedwithaninitialcellcountof between10⁶and10⁸cells/ml.ThepHofthefermentationmedium wasadjustedto4.5andfermentationwascarriedoutat30Cwith anagitation of120rpmfor approximately24hor untilethanol productionceased.Aliquotswerewithdrawnforsugar,ethanoland biomassanalysisevery2h.

Nomenclature

SSA-F Steamsaltsalt-alkaliﬁlteredenzymatichydrolysate SSA-U Steam saltsalt-alkaliunﬁlteredenzymatic hydroly-

sate

MSA-F Microwavesaltsalt-alkaliﬁlteredenzymatichydro- lysate

MSA-U Microwave saltsalt-alkaliunﬁlteredenzymatic hydrolysate

X Cellconcentration,g/L X0 Initialcellconcentration,g/L Xmax Maximumcellconcentration,g/L

m

^max Maximumspeciﬁcgrowthrate P Ethanolconcentration,g/L

Pmax Maximumpotentialethanolconcentration,g/L r_p,m Maximumethanolproductionrate,g/L.h t Fermentationtime,h

tL Lagphase,h

S Substrateconcentration,g/L KS Monodconstant,g/L

2 P.Moodley,E.B.GueguimKana/BiotechnologyReports22²(2019)e00329

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2.4.Analyticalmethods

Totalreducing sugarwas quantiﬁed withthe3,5-dinitrosali- cylicacidmethod[16]andglucosewasmeasuredusingaglucose kit assay (Megazyme). The yeastbiomass concentrationin the fermentation broth was determined using a pre-established correlationdependenceonbiomassdryweightasa functionof cellcount[14].Theconcentrationofbioethanolwasdetermined using a Vernier Ethanol sensor interfaced with the Vernier LabQuest monitor (ETH-BTA, VernierSoftware and Technology, USA).Thesensoremploysametaloxidesemiconductortodetect ethanol. In the measuring principle, ethanol is consumed in a combustion reaction with the metal oxide, thus reducing the internalresistanceofthesensorelement.Thechangeinresistance is converted to a response voltage corresponding to ethanol concentration.Thesensorwascalibratedandtestedwithknown concentrationsofethanolpriortoanalysis.Crudeprotein,ashand fat content in the fermentation efﬂuent was analysed using previousestablishedprotocols[17,18].

2.5.Kineticmodelsandcalculationofkineticparameters

Thekineticstudiesofthefourfermentationtypes(SSA-F,SSA- U,MSA-FandMSA-U)wereinvestigated.Thegrowthkineticswas described using Monod’s equationwith the following conven- tions:(a)thebrothcultureintheﬂaskwashomogenous,(b)yeast cellswere viable and(c) the mixing speedof 120rpm wasin excess of the needs for the fermentation process to provide adequate mass transfer. The Monod equation describes the relationshipbetweencell growthrateandsubstrateconcentra- tion.ToobtaintheMonodkineticparametersKSand

m

max,ﬁve experimentswithvaryinginitial glucoseconcentration(10, 20, 40,50,70g/L)wereconductedinduplicate.Theprocesseswere sampledevery2h,andsugarconsumptionandcellgrowthwere monitored.Thespeciﬁcgrowthrates(

m

⁾^were^estimated^using

experimental data obtained during the exponential phase by linearregressionfromtheslopeofnaturallogofbiomassvstime (Eq.(1)):

m

¼lnðX2X1Þ

t2t1 ð1Þ

Themaximumspeciﬁcgrowthrate(

m

max)andMonodconstant (K_S) were subsequently estimated using the non-linear least squaresmethod[19].

m

¼

m

maxS

KsþS ð2Þ

InadditiontotheMonodkineticmodel,thelogisticmodelis increasinglybeingusedtodescribemicrobialgrowthsystems.A termconsideringinhibitionof growthbyethanol concentration was not included, since the maximum ethanol concentration obtainedinthisstudyisfarbelowthe15%thresholdwhichinhibits yeastcells[20].ThedifferentialformofthelogisticEq.(3)isshown below:

dX

dt ¼

m

max

¹ X Xmax

^X ð3Þ

whereXmaxisthemaximumyeastcellconcentration(g/L)and

m

^max

isthemaximumspeciﬁccellgrowthrate(h¹).Withthefollowing boundaryconditions:t=0,;X=X0,asigmoidalvariationofXis givenasafunctionoft.Eq.(3)canthenbeintegratedtogivethe logistic Eq. (4) which describesthe exponentialand stationary phase.Theexperimentaldatawasusedtoﬁtthisequation.

X¼ X0

^expð

m

max

^tÞ

1ðX0=XmaxÞ

ð1expð

m

max

^tÞÞ ð4Þ

Theabovelogisticmodeldoesnotpredictthedeathphaseof microorganismsafterthestationaryphase[14].

The modified Gompertz model was adoptedtodescribe the kineticsofbioethanolformation(Eq.(5)).Thismodeldefinesthe changeinethanolconcentrationduringthecourseoffermenta- tion. Experimentaldatawas usedtofitthemodified Gompertz equationusingtheleastsquaresmethod(CurveExpertV1.5.5):

P¼Pm

^exp exp rp;m

^expð1Þ Pm

ð^t^LtÞþ1

ð5Þ

wherePisthebioethanolconcentration(g/L),Pmisthepotential maximum bioethanol concentration(g/L), rp,m is the maximum bioethanolproductionrate(g/Lh)andtListhelagphase(h).

Thesugarutilization,ethanol(EtOH)productivityandfermen- tationefﬁciencywerecalculatedusingEqs.(6)–(8)respectively:

Sugarutilizationð%Þ¼ AmountofInitialsugarfinalsugar Amountofinitialsugar

100 ð6Þ

Ethanolproductivityðg=L:hÞ¼Maximumethanolconcentrationðg=LÞ FermentationtimeðhÞ

ð7Þ

Fermentationefficiencyð%Þ¼ Experimentalethanolyieldðg=LÞ Theoreticalethanolyieldðg=LÞ 100

ð8Þ 3.Resultsanddiscussion

3.1.GrowthkineticsofS.CerevisiaeBY4743onpretreatedSLW Thechangeinbiomassconcentrationovertimeunderthefour differentfermentationconditionsisshowninFig.1.Ashorterlag timeof4hwasobservedunderSSA-FandSSA-Uwhereas,slightly longerlagtimesof6hwereobservedunderMSA-FandMSA-U.

This can be attributed to the presence of trace amounts of certain inhibitory products in the MSA enzymatic hydrolysate.

Microwave-assisted pretreatment has been reported to have a higherseverity factor;thusproducingahigherconcentrationof

Fig.1.Timecourseofbiomassconcentrationunderthefourexaminedfermenta- tionconditions.

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inhibitorycompoundscompared tosteampretreatment[21]. In addition,theremaybeproductionofinhibitorsduringenzymatic hydrolysisduetothehydrothermalbreakdownofthelignocellu- losiccomponentsowingtotheprocesstemperatureandduration.

Someof thereportedinhibitorsfrommicrowave-assistedmetal chloride pretreatment of SLW include acetic acid, furfural and hydroxymethyl furfural (HMF) [22]. Acetic acid increases the intracellularpHofS.cerevisiaeleadingtoanincreasein thelag phaseanddecreaseinthegrowthrate[23].Similarly,furfuraland HMFcansynergisticallyaffectthegrowthrateofS.cerevisiaeby affectingglycolyticactivity,causingoxidativestressandreducing theactivityofvariousdehydrogenases[24].

Thelogisticmodelswereinagreementwiththeexperimental values,showinghighcoefﬁcientsofdetermination(R²>0.97).The estimatedvaluesforthekineticparameterswerefoundtobein close range with the empirical values. These values and the developedmodelsforeachfermentationtypearesummarizedin Table1.SlightlyhigherXmaxvalueswereobtainedunderSSA-Fand SSA-U fermentation conditions (4.70 and 4.56g/L respectively) comparedto4.36and4.35g/LforMSA-FandMSA-Urespectively, further suggesting the steam pretreated substrate was more favourable and promoted cell growth. Phukoetphim et al. [14]

reportedasimilarmaximumcellconcentration(Xmax)of5.145g/L fromsweetsorghumjuicewhereas,Dodicetal.[25]reporteda valueof8.381g/Lfromsugarbeetjuice.ThesevaryingX_maxvalues couldbeaccountedforbydifferencesinyeaststrain,substrateand working volume. The maximum speciﬁc growth rate (

m

^max⁾

obtained from the logistic model under SSA-F and SSA-U conditionswere0.24 and 0.26h¹ respectivelywhereas,MSA-F and MSA-U were 0.28 and 0.29h¹ respectively. This was an indicationthatfilteredandunfilteredenzymatichydrolysatehada negligibleeffectonthemaximumspecificgrowthrate.Thisfurther impliesthenon-requirementofaseparationstage,thusenhancing processeconomicsandproductivityatalargescale.However,the

m

^max^values^obtained^from^the^Monod^model^were^0.153^h¹^for

SSA-F and SSA-U, and 0.150h¹ for MSA-F and MSA-U. The differenceinthe

m

^max^values^between^model^types^(logistic^and

Monod)canbeascribedtotheintrinsicparametersandboundaries employedbyeachmodel.Forinstance,thelogisticmodelconsiders thebiomassconcentrationfromthelagphasetostationaryphase, disregardingthesubstrateutilizationwhereas,Monodconsiders both the biomass concentration (however only in exponential phase)and theratelimiting substrate[26]. Differencesin

m

^max

valuesfromthelogisticandMonodmodelshavebeenpreviously reported. Manikandan and Viruthagiri [27] observed a

m

^max^of

0.307 and 0.095h¹ using the Monod and logistic model respectivelyfor ethanol production fromwheatﬂour. Likewise, the Monod and logistic model gave

m

max values of 0.65 and 0.45h¹usingglucoseforethanolproduction[28].Themaximum speciﬁcgrowth rates obtainedin the present study arewithin rangeofpreviousstudies.Srimachaietal.[29]reporteda

m

maxof 0.15h^-1 from oil palm frond juice and a

m

^max ^of ^0.27^h^-1 ^was

reportedfromsweetsorghumjuice[14].Theobtained

m

maxvalues are highlydesirable, particularly for commercialscale upsince growthrates>0.025h¹havebeenshowntolinearlyincreasethe fermentative capacity of Saccharomyces species. Furthermore, highergrowthratesmaytriggerrespirofermentativemetabolism, thus resulting in an increase in fermentative capacity [30].

Moreover,the

m

maxvaluesarewithinrangeofpreviouspilotscale studies. For example, a

m

^max ^of ^0.34^h^-1 ^was ^reported ⁱⁿ ^the

productionofethanolfrommolassesat300000L[31].

Monod constants (KS) of 4.91g/L for SSA-Fand SSA-U, and 5.61g/LforMSA-FandMSA-Uwereobtained(Table2).AlowerKS

value indicates the microorganism’s inherent afﬁnity to the substrate since its reciprocal describes the cells afﬁnityto the substratetype. The higher K_Svalue obtainedunderMSA-Fand MSA-Uconditionscouldbeexplainedbythepresumptivepresence ofinhibitorycompoundsinthefermentationmedium.Overall,S.

cerevisiae showed a higher substrate afﬁnity with the steam (0.20g/L¹)andmicrowave(0.17g/L¹)pretreatedSLWcompared topreviousstudiesonsweetsorghumjuice(0.021g/L¹,[32])and sorghumleaves(0.10g/L¹,[33]).ThedifferenceinKSisaffectedby substratetypeandconcentration,andyeaststrainandconcentra- tion[34].

3.2.KineticsofbioethanolfermentationfrompretreatedSLW The experimental profiles for bioethanol production and glucose consumption with S. cerevisiae from SSA-Fand MSA-F are shown in Fig. 2. Ethanol production commenced almost immediatelyfromtheinitialhoursoffermentationandincreased graduallyuntil it peaked at 18h intotheprocess. Under SSA-F conditions,aconsiderablyhigherethanolconcentration(28.47g/L) was achievedcomparedtoMSA(23.01g/L).Thishigherethanol production under SSA-F compared to MSA-F conditions is substantiatedbythehighermaximum specificgrowth rateand substrate affinityobserved in the Monodmodels.Li et al. [35]

reportedasigniﬁcantlylowerethanolconcentration(17.5g/L)from acidpretreatedcornleaves.Similarly,alowerethanolconcentra- tion(4.71g/L)fromacidpretreatedsugarcaneleaveswasreported by Jutakanoke et al. [36]. Under SSA-F conditions, S. cerevisiae

Table1

Thelogisticmodelsdescribingcellgrowthunderdifferentfermentationcondition.

Fermentationconditions Xo(g/L) Xmax(g/L) m^max^(h¹⁾ ^Logistic^equation ^R²

Pred Exp Pred Exp Pred Exp

SSA-F 0.27 0.26 4.70 4.41 0.24 0.24 X¼1ð0:27=4:70Þ^0:27^expð0:24ð1expð0:24^tÞ tÞ 0.98

SSA-U 0.23 0.20 4.56 4.54 0.26 0.24 X¼1ð0:23=4:56Þ^0:23^expð0:26ð1expð0:26^tÞ tÞ 0.98

MSA-F 0.20 0.16 4.36 4.27 0.28 0.26 X¼1ð0:20=4:36Þ^0:20^expð0:28ð1expð0:28^tÞ tÞ 0.98

MSA-U 0.18 0.14 4.35 3.15 0.29 0.27 X¼1ð0:18=4:35Þ^0:18^expð0:29ð1expð0:29^tÞ tÞ 0.97

Pred–Predicted.

Exp–Experimental.

Table2

TheeffectofdifferentfermentationconditionsandsubstratetypeonMonodkinetic parameters.

Substrate Kineticparameter Reference

mmax(h¹) Ks(g/L)

SLW(SSA-FandSSA-U) 0.153 4.19 Thisstudy

SLW(MSA-FandMSA-U) 0.150 5.61 Thisstudy

Sorghumleaves 0.176 10.11 [33]

Sweetsorghumjuice 0.119 2.08 [37]

Sweetsorghumjuice 0.313 47.51 [32]

Glucose 0.133 3.7 [38]

Oilpalmfrondjuice 0.15 10.21 [29]

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showedahigherglucoseconsumptionrateof4.5g/L.hfrom0to6h intothefermentationwhereasalowerglucoseconsumptionrateof 4.0g/L.h was observed with MSA-F between 0 and 8h of fermentation.

The proﬁle of ethanol production and glucose consumption fromSLWwithSSA-UandMSA-UisshowninFig.3.Theethanol productionandsugarconsumptiontrends,showedsimilaritiesto theSSA-FandMSA-FconditionspresentedinFig.2.Theethanol productioncommenced2hintothefermentationandpeakedat 18h with 28.81g/L (SSA-U) and 16h with 22.72g/L (MSA-U).

Similarly,S.cerevisiaereachedamaximumglucoseconsumption rate of 5g/L.h during the ﬁrst 6h of fermentation with SSA-U whereas, a glucose consumption rate of 4.5g/L.h was obtained duringtheﬁrst7hoffermentationwithMSA-U.Thelowerethanol concentrationobservedunderMSA-Uconditionscouldbeascribed tothedecline inpH from4.54to3.90 compared totheSSA-U processwitharelativelystablepHslightlydecreasingfrom4.55to 4.23(Fig.4).ThedeclineinpHcouldbeattributedtothegeneration of acetic acid from thehydrothermal breakdown of the hemi- cellulosicacetylgroupspresentinthepretreatedsugarcaneleaf waste biomass [39]. Furthermore, the severity of microwave pretreatment would infer a higher concentrationof acetic acid release compared to steam pretreatment. In addition, the presumptive presence of furfuralin the enzymatic hydrolysate couldbeacontributortothedeclineinpHsinceS.cerevisiaehas beenshowntometabolizefurfuralcompoundsintofuroicacidand

furfuryl alcohol [40]. The decline in pH from the 11^th h of fermentation(MSA-U)alsocoincidedwiththedecreaseinglucose consumptionandethanolproductionratesfrom2.5to1g/L.hand 2.3to0g/L.hrespectively.Furthermore,S.cerevisiaeisknownto produceethanoloptimallyatpHof4.5.ApHbeyondthisrange affects the activity of plasma membrane-bound proteins and includesbothenzymesandtransportproteins[41].ThepHofthe SSA-F and SSA-U experiments showed a relatively slower drift remainingclosetotheoptimumvalueof4.5comparedtoMSA-F andMSA-UwiththeﬁnalpHvaluesbelow4.Thiscouldaccountfor thelowerethanolconcentrationandglucoseconsumption.

ThemodifiedGompertzmodelfittheexperimentaldatawell under the four fermentation conditions (SSA-F, SSA-U, MSA-F, MSA-U) with high coefficients of determination (R²) > 0.99 (Table3).Thepotentialmaximumbioethanolconcentration(Pm) rangedfrom31.06g/L(SSA-U)to21.79g/L(MSA-U).ThehighP_m valueobtainedunderSSA-Ucanbeattributedtoitshigher

m

^max

and1/KSvaluescomparedtoMSA-UthusinferringtheS.cerevisiae cells hadahigherafﬁnityforthesteampretreatedsubstrate.In addition, taking into account the aforementioned factors that contributedtothedeclineinpH,thehigherPmobtainedforSSA-U wasexpected.Theundissociatedformofweaklipophilicacidssuch as acetic acid induces acidiﬁcation of the cell cytoplasm by accumulating inside the cells. This leads to a decrease in cell metabolicactivity[42].

Yanetal.[43]reportedahigherPm(104g/L)fromfoodwaste using S. cerevisiae HO58 whereas, a lower Pm (17.15g/L) was recordedfromsorghumleavesusingS.cerevisiaeBY4743[33].The differences in Pm can be attributed to different sugar concentrations and yeast strainsemployed.Comparedtothereported Fig.2. TimescourseofbioethanolproductionandglucoseconsumptionfromSLW

underSSA-FandMSA-Ffermentationconditions.

Fig.3. TimescourseofbioethanolproductionandglucoseconsumptionfromSLW underSSA-UandMSA-Ufermentationconditions.

Fig.4.TimecourseofpHevolutionduringethanolfermentationfromSLWunder SSA-F,SSA-U,MSA-FandMSA-Uconditions.

Table3

ComparisonofthekineticvaluesinthemodiﬁedGompertzmodelfromSLWand otherlignocellulosicbiomass.

Substrate ModiﬁedGompertzmodel References

Pm(g/L) rp,m(g/L.hr) tL(h) R²

SLW(SSA-F) 30.49 2.81 3.39 0.99 Thisstudy

SLW(SSA-U) 31.06 2.44 3.14 0.99 Thisstudy

SLW(MSA-F) 23.26 2.85 3.17 0.99 Thisstudy

SLW(MSA-U) 21.79 2.79 3.22 0.99 Thisstudy

Sorghum 17.15 0.52 6.31 0.98 [33]

Sugarbeetrawjuice 73.31 4.39 1.04 0.99 [25]

Sweetsorghumjuice 60.04 2.09 3.07 0.99 [14]

Foodwaste 104 2.22 6.41 0.99 [43]

Oilpalmfrondjuice 3.79 0.08 0.77 – [29]

P.Moodley,E.B.GueguimKana/BiotechnologyReports22(2019)e00329 5

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maximumethanol productionrate (rp,m)of 2.09g/L.hr obtained fromsweetsorghumjuiceand0.08g/L.hrreportedfromoilpalm frond[29],therp,mobtainedinthisstudybetween2.44and2.85g/

L.h,aredesirable sincehigherproduction ratesarepreferredat large scale. Likewise, a shorter lag time is favoured thereby implying the yeast cells have acclimated to the fermentation conditions.Thelagtime(tL)forbioethanolproductioninthisstudy ranged from 3.14 to 3.39h thereby indicating no signiﬁcant variationbetweenthe SSA-F,SSA-U,MSA-Fand MSA-U experiments.HigherlagtimeshavebeenreportedbyRorkeandGueguim Kana[33]fromsorghumleaves(6.31h)andYanetal.[43]from enzymaticallypretreatedfoodwaste(6.41h).Asimilarlagtimeof 3.07h was observed by Phukoetphim et al. [14] from sweet sorghumjuicewhereasalowlagtime(1.04h)wasreportedfrom sugarbeetrawjuice[25].Lagtimecanbeaffectedbyfactorssuch asworkingvolume,inoculumtypeandsize,andsubstratetypeand concentrations.

TheSSA-FandSSA-Uexperimentsgaveasimilarfermentation efﬁciency(92.86and93.97%respectively)andethanolproductivity (1.095 and 1.11g/L h respectively), as shown in Table 4. Sugar utilizationfolloweda similartrendwith86.67% and83.33% for SSA-F and SSA-U respectively. MSA-F and MSA-U gave lower fermentation efﬁciencies of 75.05 and 74.10% respectively and ethanol productivities of 0.885 and 0.874g/L h respectively.

Therefore,nosignificantdifferencewasobservedbetweenfiltered andunfilteredenzymatichydrolysate,indicatingthatthepresence ofsugarcaneleafbiomassdidnothinderbioethanolproduction.In fact, under SSA-U conditions, ethanol production was slightly higher.Reportedethanolproductionfromoilpalmfrondjuiceand sugarcanejuiceshowedalowerfermentationefficiencycompared toSSAbutahigherefficiencycomparedtoMSA(Table4;[29,44]).

Theunfilteredenzymatichydrolysateresults(SSA-UandMSA- U) are comparable to previous studies where the enzymatic hydrolysatewasfilteredpriortofermentation.Forinstance,Mishra etal.[46]observedanethanolconcentrationof29g/Lfromfiltered enzymatichydrolysateofacidpretreatedricestraw.Amaximum ethanolconcentrationof2.95g/Lwasreportedfromthefiltered enzymatic hydrolysateof alkalipretreated hazelnut shells[47].

Thisis anindicationthat unﬁlteredenzymatichydrolysategave similarethanolconcentrationstoﬁlteredenzymatichydrolysate in a separate hydrolysis and fermentation (SHF) system. The

separation of the solid biomass requires an additional unit operationthatcancontributetoabout5%oftheannualoperating costs;thusimpactingontheprocesseconomicsatlargescale[11].

Inaddition,theprocesstimeforcentrifugationorfilteringreduces theproductivityoftheprocessandimpactsthenumberofbatch runs annually. Some studies have employed the simultaneous saccharificationandfermentation(SSF)systemtocircumventthe needforafilteringstagehowever,previousreportsonSSFhave givensignificantlylowerethanolconcentrationscomparedtothe concentrationsobtainedinthisstudy.Forexample,aSSFsystem usingsteamexplodedacornproduced1.97g/Lethanol[48]while acidpretreatedSaccharinajaponicagave6.65g/Lethanol [49].A slighthigherethanolconcentrationof13.6g/Lwasreportedfrom Arundodonax[50].Althoughtheseloweryieldscouldbeattributed tomanyfactorssuchasyeaststrainandsubstrate,theSHFsystem doesoffer someattractivefeatures. Thisincludes theability to optimizethesaccharificationandfermentationprocessseparately, therebyimprovingtherespectiveproductyields.

3.3.Feedanalysis

Effluent from the SSA-U process (ethanol concentration of 28.81g/Landfinalbiomassconcentrationof4.56g/L)underwent compositionalandnutritionalanalysistodetermineitspotentialas animalfeed(Table5).ThesolidbiomassfromtheSSA-Ueffluent wasshowntocontain6.0%crudeprotein.Proteincontentvaluesof between 1.6 and 26% are commonly reported in feedstock compositionsandthereforetheobtainedproteincontentof6.0%

fell withinthis range [51]. Othercommon animalfeed suchas wheatandcorncobshavereportedaproteincontentof4.8and 3.0%respectively[51].ThehighproteincontentfromtheSSA-U biomasscanbeaccountedforbythenitrogenrichyeastbiomass.

Furthermore,theSSA-Uprocessgaveafatcontentof2.57%,which was wellwithin thereportedrange of 0.1 to19.3% fromother lignocellulosic biomass [51]. Cotton seeds and wheat have previously been reported to contain a similar fat content, 2.5 and 1.9% respectively. Since fat provides more than twice the energycomparedtocarbohydratesandproteins,itisanessential component inanimal feed[52]. A major bottleneck withmany animalfeedsisthelowdigestibilityduetothehighlignincontent [13]. The SSA pretreatment of SLW caused signiﬁcant (80.5%)

Table4

ComparisonofbioethanolproductionfromSLWandotherreportedlignocellulosicbiomass.

Substrate Sugarutilization(%) Maxethanolproduction(g/L) Ethanolproductivity(g/Lhr) Fermentationefﬁciency(%) Reference

SLW(SSA-F) 86.67 28.47 1.095 92.86 Thisstudy

SLW(SSA-U) 83.33 28.81 1.11 93.97 Thisstudy

SLW(MSA-F) 78.33 23.01 0.885 75.05 Thisstudy

SLW(MSA-U) 76.27 22.72 0.874 74.10 Thisstudy

Oilpalmfrondjuice 94.05 11.50 0.12 76.52 [29]

Sugarcanejuice 98.00 67.00 0.93 78.43 [44]

Sweetsorghumjuice 100 72.43 1.01 94.60 [45]

Table5

ComparisonofthefeedanalysisfortheSSA-Usolidresiduesandothercommonanimalfeed.

Substrate Ash Fat CP Ca Mg K Na P Zn Cu Mn Fe Ref

% mg/kg

SLW(SSA-U) 6.27 2.57 6.0 0.11 0.09 0.33 0.60 0.29 33 4 30 132 Thisstudy

Wheat 7.6 1.9 4.8 0.31 0.14 1.55 0.12 0.10 ND ND ND ND [51]

Corncob 2.2 0.6 3.0 0.10 0.06 0.90 0.04 0.06 ND ND ND ND [51]

Cottonseeds 2.8 2.5 6.2 0.18 0.17 1.16 0.02 0.12 ND ND ND ND [51]

CP-Crudeprotein.

ND–Notdetermined.

6 P.Moodley,E.B.GueguimKana/BiotechnologyReports22(2019)e00329

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delignification,therebyenhancingthedigestibility[9].Theeffluent canbesupplementedwithadditionalnutrients,dependingonthe specificrequirements.Developingasuitablemethodologyforthe useof this waste-streamfor animal feedingcouldenhance the environmental and economic outlook of this process since no wastetreatmentanddisposalwillberequired.

4.Conclusion

Inthisstudy,threeempiricalmodels,i.e.Monod,logisticand modiﬁed Gompertz, were employed to describe S. cerevisiae BY4743 growth and ethanol production from pretreated SLW underSSA-F,SSA-U,MSA-FandMSA-Ufermentationconditions.

Allmodelsfittheexperimentaldatawellwithhighcoefficientsof determinationR²>0.98,indicatingtheirpotentialapplicationfor largescaleoperations.Steamsalt-alkalipretreatedSLWproduced 25%morebioethanolcomparedtomicrowavesalt-alkali.Further- more,nodifferencewasobservedbetweenfilteredandunfiltered enzymatichydrolysateexperimentsforbothpretreatments.These findings provide crucial insights into enhancing the cost, productivityandenvironmentaloutlookforscaleupprocesses.

Conﬂictofinterest None.

Acknowledgements

TheﬁnancialassistanceoftheNationalResearchFoundationof SouthAfrica(NRF)[grantnumber101275]towardsthisresearchis hereby acknowledged.Opinionsexpressedandconclusionsarrivedat, arethoseoftheauthorandarenotnecessarilyattributedtotheNRF.

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