Development of a Submerged Membrane Bioreactor simulator: a useful tool for teaching its functioning

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Identification number: DOI : 10.1016/j.ece.2014.03.001

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Eprints ID: 11427

To cite this version:

González Hernández, Yusmel and Jáuregui Haza, Ulises Javier and Albasi,

Claire and Alliet-Gaubert, Marion Development of a Submerged Membrane

Bioreactor simulator: a useful tool for teaching its functioning. (2014)

Education for Chemical Engineers, vol. 9 (n° 2). pp. e32-e41. ISSN 1749-7728

O

pen

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rchive

T

oulouse

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OATAO

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Development

of

a

Submerged

Membrane

Bioreactor

simulator:

a

useful

tool

for

teaching

its

functioning

Yusmel

González

Hernández

_,

_Ulises

_Javier

_Jáuregui

_Haza

_,

Claire

Albasi

,

Marion

Alliet

a_{InstitutoSuperiordeTecnologíasyCienciasAplicadas(InSTEC),Cuba}

b_{UniversitédeToulouse,INPT,UPS,LGC,4,AlléeEmileMonso,BP84234,F-31432ToulouseCedex4,France} c_{CNRS,LaboratoiredeGénieChimique,BP84234,F-31432ToulouseCedex4,France}

a

b

s

t

r

a

c

t

Amongthe technologiesused totreat wastewater,the Submerged MembraneBioreactor (SMBR) hasexcellent prospectsbecauseofthepossibilityitprovidesforwaterreuse.Inthiswork,anSMBRcomputersimulatoris devel-oped.Amathematicalmodelwasimplemented,whichintegratedthebiologicaldegradationprocessusingactivated sludgeswiththephysicalseparationprocessusingmembranes.Thesimulatorfunctioningwasvalidatedwith exper-imentalresultsanditsuseinteachingwasevaluatedthroughthedevelopmentofasimulatedlaboratoryrunning forthreeandahalfhours.Thisgaveaccesstotrendsandordersofmagnitudethatwouldtakemorethanfifteen monthstoobtainwithrealexperiments.Itwassuccessfullyusedandacceptedbythestudents.

Keywords: Improvingclassroomteaching;Interactivelearningenvironments;Simulations;SubmergedMembrane Bioreactor

1.

Introduction

Fresh water is becoming known as the “blue gold” of the 21stcentury.Itisanaturalresourcealreadyinshortsupply and it will become even scarcer with increased urbaniza-tionandpopulation,climatechange,andindustrialpollution, makingithumanity’smostpreciousresourceandoneofthe majorenvironmentalissuesofthiscentury(BuzatuandLavric, 2011).For thisreason, manygovernments todayare devot-ingconsiderableresourcesandeffortstothedevelopmentof newtechnologiesforwastewatertreatmentandthe decon-taminationof contaminated sources.An example of these technologiesistheSubmergedMembraneBioreactor(SMBR).

TheSMBRcanbedefinedasasystemthatcombines bio-logicaldegradationofwastewatereffluentswithmembrane filtration(Ciceket al.,1999).Formanyyears,thesesystems haveshown theireffectivenessinthe treatmentof munic-ipalandindustrial wastewater(Jimenezet al.,2010;Santos

∗

Correspondingauthorat:UniversitédeToulouse,INPT,UPS,LGC,4,AlléeEmileMonso,BP84234,F-31432ToulouseCedex4,France. Tel.:+330534323630;fax:+330534323700.

E-mailaddress:[email protected](M.Alliet).

et al.,2011).Inthe lasttwo decades,SMBRtechnologyhas grownexponentiallyduetoitsadvantagesoverconventional wastewater treatmentprocesses, such asreduced environ-mentalimpact,improvedeffluentqualityandbetterprocess control(BuerandCumin,2010;Drews,2010).Themajor poten-tial advantage of this technology is found in the field of waterreuse.ThisisbecausetheSMBRcanuseultrafiltration membranesandthusretainbacteria,somevirusesandmany organicandinorganiccomponentsthatareoftenfoundinthe effluentfromconventionalbiologicaltreatments(Lobosetal., 2007;DeLucaetal.,2013).

Therefore, the effluent ofan SMBR may be suitable for directreuseor watersupply forareverseosmosisprocess. ThatisoneofthereasonswhyresearchintheSMBRfieldis increasingcontinuouslyatpresent,duethecommercialand scientificinterestthatithasaroused(Stephensonetal.,2000; Van Nieuwenhuijzen et al., 2008). Nevertheless, the effec-tiveapplicationofmembranebioreactors(MBRs)islimitedby

Nomenclature

A membranearea(m2₎

C sludgeconcentration(kg/m3₎

Cd coefficientofdragandliftingforces

dp particlesize(m)

Fl liftingforce

Fa suctionforce

G apparentshearintensityofthefluidturbulence (s−1₎

g gravitationalconstant(m/s2₎

Gi apparentshearintensityofthefluidturbulence

ontheithsectionofthemembranesurface(s−1₎

J overallflux(m3_/m2_s)

Ji localfiltrationfluxthroughtheithmembrane

section(m3_/(m2_day))

Mtd massofsludgeinthedynamicsludgefilmcake

adheringtothemembranesurface(kg/m2₎

Mtf(i) mass of sludge in the stable sludge cake

attachedtotheithmembranesection(kg/m2₎

Mtd(i) massofsludgeinthedynamicsludgefilmcake

intheithmembranesection(kg/m2₎

n totalnumberofsectionsinthemembrane sur-facearea

qa aerationintensity(Lm−2s−1)

QBG coarsebubbleflow(L/s)

Rm intrinsicresistanceofthemembrane(m−1)

Rp porefoulingresistance(m−1)

rp specificporefoulingresistanceintermsof

fil-tratevolume(m−2₎

RT overallfiltrationresistance(m−1)

rtd specificfiltrationresistanceofdynamicsludge

film(m/kg)

Rtd resistanceofdynamicsludgefilm(m−1)

Rtf resistanceofstablesludgecakelayer(m−1)

rtf specificfiltrationresistanceofsludgecakelayer

(m/kg)

RTS(i) filtrationresistancefortheithmembrane

sec-tion(m−1₎

Si asectionofthemembranesurfacearea

SI concentrationofsolubleundegradableorganics

(gCOD/m3₎

SMBR SubmergedMembraneBioreactor SMP solublemicrobialproducts

SO2 concentrationofdissolvedoxygen(g/m3)

SRT sludgeretentiontime(days)

SS concentration of easily biodegradable

sub-strates(gCOD/m3₎

SSMP concentration of soluble microbial products

(gCOD/m3₎

t time(s)

taBG timeofcoarsebubbleaeration(min)

tf filtrationtime(min)

TMP trans-membranepressure(Pa)

tpaBG timewithoutcoarsebubbleaeration(min)

tpf relaxationtime(min)

tSTOP timetosimulate(days)

V bioreactorvolume(m3₎

Vf water productionwithin afiltrationperiodof

anoperationcycle(m3_/m2₎

XH concentrationofordinaryheterotrophic

orga-nisms(gCOD/m3₎

XI concentration of particulate undegradable

organics(gCOD/m3₎

XS concentration of slowly biodegradable

sub-strates(gCOD/m3₎

XTSS concentration of total suspended solids

(gTSS/m3₎

˛ stickinessofthebiomassparticles

ˇ erosionratecoefficientofthedynamicsludge film

1t timestep(s)

compressioncoefficientforthedynamicsludge film(kgm−3_s−1₎

εa fractionofthemembranesurfacearea(or

dis-tance ratio to the bottom of the membrane module)wheretheshearintensityisincreasing ε fractionofthemembranesurfacearea(or dis-tance ratio to the bottom of the membrane module)

f filtrationtimeinanoperationcycle(min)

s densityofsludgesuspension(kg/m3)

reduction index of cake compression coeffi-cient

s viscosityofsludgesuspension(Pas)

membranefoulingandtheassociatedcostandenergyburdens (MennitiandMorgenroth,2010).Atthesametime, experimen-tationinthesetypesofinstallationsisveryexpensiveandtime consuming.

Ontheotherhand,itisnecessarytotakealltheelements mentionedaboveintoaccountinthetrainingofengineersand ofthestaffthatwilloperatetheSMBR.Itisessentialtodevelop toolsthatcanhelpinthelearningprocess,bothatuniversities andatoperatortrainingcentres.Thedevelopmentof simula-torsisanecessitysincetheyconstituteaplatformtoenhance virtuallaboratories(Corteret al., 2011). Virtuallaboratories can provideadynamic Problem-BasedLearningexperience wherestudentsengageinanauthentic,industriallysituated task.Theysimulatewhatexpertengineersdoinpractice,and areverydifferentincharacterfromthephysicallaboratoryat university(Koretskyetal.,2011).Anotheradvantageofa simu-latorisitsvalueinthetrainingprocessfromtheresearchpoint ofview:tohelptosolveproblemsthatareasyetunsolved. Sim-ulatorsarealsoanimportantsupportforthestudyofprocess optimization.

Theuseofsimulatedexperimentscanconsiderablyreduce thecostofalaboratorycourse,increasethenumberof exper-imentsinthelearningprocessandenableexperimentstobe carriedoutthatwouldotherwiseinvolveworkingwith dan-gerousmaterialsand/orindangerousconditions(Skorzinski etal.,2009).Forallthesereasons,themathematicalmodelling ofanSMBRandthedevelopmentofasimulatorofthis pro-cessprovidesanalternative thatcansolvemany problems. Theobjectiveofthisworkistodevelopacomputersimulator ofanSMBRandtoshowitspotentialinteachinghowsuch processeswork.

2.

Materials

and

methods

A computer simulator consists of three main parts: the mathematical model, the numerical solution method and the graphical interface. The integrated model proposed by

Fig.1–ConceptualschemeoftheintegratedmodelproposedbyZarragoitiaetal.(2008).

Zarragoitiaetal.(2008)wasusedtobuildtheSMBRsimulator. ThemodelwasimplementedusingthePascalprogramming language.Thedifferentialequationsthatappearinthemodel were solvedusing the fourth-order RungeKutta numerical method.

Finally,the simulatorgraphicalinterfacewasdeveloped. Designingeducationalsoftwareinterfacesisacomplextask, given its strong domain dependency and multidisciplinary nature.Itrequirestheteachers’knowledgeandpedagogical beliefstobeincorporated intothe interface,posinga chal-lengetobothteachersanddesignersastheyhavetoactas partnersfromtheearliestphasesoftheprocess,sharingtheir knowledge (Perry and Schnaid, 2012). In the present case, thesimulatorgraphical interfacewasbuiltusing the facili-tiesprovidedbyDelphi2009forobject-orientedprogramming, inordertoachieveafriendlygraphicalinterfaceenablingthe assignationandmanipulationofdifferentoperating param-eters,aswellasobservationofhowthevariablesofinterest behaveovertime.

2.1. Mathematicalmodelanditsimplementation

ThemathematicalmodelproposedbyZarragoitiaetal.(2008)

integrates the biological degradation process by activated sludges with the physical separation process using mem-branes.

Inordertofacilitatetheevaluationofthemodel,the selec-tionofequationsandbiologicalprocessesconsideredduring modellingwaslinkedtothecharacteristicsofthe experimen-tal reactor and its operatingconditions. However, thefinal structureofthemodeloffersthepossibilityofaddingother process rates and stoichiometries. The conceptualscheme ofthemodeldevelopedisshowninFig.1.Itgivesthemain

relationsoperatingduringsimulationandalsothe informa-tionflowestablishedamongthedifferentpartsofthemodel duringcalculation.Themodelisdividedintothreesections, the first considers the biological behaviour (stoichiometry and kinetics), the second is related to membrane fouling evolutionandthebehaviourofallfiltrationresistances,and thelastconsistsofasetofperiodicequationsthatrepresent theprocess associatedwithcoarsebubbleaeration,feeding anddiscontinuousfiltration.

Tosimulatetheactivatedsludgeprocess,amodifiedmodel wasestablishedconsideringtheformation-degradation kinet-icsofsolublemicrobialproductsproposedinthemodification ofASMldevelopedbyLuetal.(2001),butadaptingthese equa-tionstoastrictlyaerobicSMBR.Thebiologicalmodelconsists ofasystemofdifferentialequationsobtainedfromthe Peter-sonmatrix(Zarragoitiaetal.,2008).

Ontheotherhand,inthephysicalseparationmodel,the mainprocessisthemassattachedtothemembranesurface. ThisprocessisdescribedbyEq.(1):

dMtd dt = 24CJ2 24J+CddpG −ˇ(1−˛)GM 2 td Vft+Mtd (1)

ThefirsttermofEq.(1)representsthenetmassdepositedon the membranesurfaceduetothe equilibriumbetweenthe suctionandliftingforces(Fig.2),whilethesecondterm rep-resentsthemassremovedbytheshearforcescausedbythe coarsebubbleaeration(LiandWang,2006).

Shearforcesarenotuniformlydistributedoverthe mem-brane surface.When modellingthedepositionofsludgeon themembranesurface,inthecasewherethemembrane mod-uleresemblestoacylindricalobject,thevariationofGvalues

Fig.2–Modellingthemembranefoulingprocess.

inthevicinityofthemembranemodulecanbeestimatedas follows:

- atthebottomofthemembranemodule,Gvalueisassumed thesmallestpossiblevalue,whichisone-tenthofits maxi-mumvalue,

- fordistanceratiostothebottomhigherthanεa,Gvalueis

assumedtotakeitsmaximumvalue,

- betweenthetwo,sinusoidalgrowthisassumed(Fig.2). Therefore, Eqs. (2) and (3) are used to calculate the Gi

values. These equations must be modified for the case of moduleswith differentgeometryor differentaeration sys-tems,e.g.withairnozzleslocatedatmultipleheightsofthe membranemodule,whicharenotfrequentduethestructural complexitiesandchangesthatthehydrodynamicsofthe sys-temintroduces(Zarragoitiaetal.,2008).

G(ε)=





















q

q















Forthemodellingofmembranefouling,themembrane sur-face was divided into sections of equal area (Si). It was

consideredthateachareaSiwastraversedbyaflowofequal

magnitude,whichwascalculatedbythefollowingequation: Ji=

J

n (4)

Eq. (4) was adaptedso as to calculate the massof sludge depositedintheithsectionofthemembranesurface.Liand Wang,(2006)foundthat128sectionsofequalarea(Si),

guar-anteedagoodapproximationtosystembehaviour.

Also,theoverallflux(J)inEq.(1)isreplacedbythelocal filtrationfluxthroughtheithmembranesection(Ji)andthe

apparentshearintensityofthefluidturbulence(G)isreplaced bytheactualshearintensityontheithsectionofthe mem-branesurface(Gi).Hence,withthesetransformationsEq.(1)

canbeexpressedas: dM_td(i) dt = 24CJ2 i 24Ji+CddpGi − ˇ(1−˛)GiM2_td(i) Vf(i)t+Mtd(i) (5)

Thistypeofinstallationworksinperiodiccyclesoffiltration andcleaningbycoarsebubbleaerationso,whenthesystemis filtering,themassattachedtothemembranesurfaceis calcu-latedusingEq.(5),butwhenthesystemisnotfiltering,theonly processthatcanoccurisremovalofthesludgefromthe mem-branesurface.InthiscaseEq.(6)isusedtocalculatethemass attachedtothemembranesurface,whichisamodificationof Eq.(5).

dM_td(i) dt =−

ˇ(1−˛)GiM2_td(i)

Vf(i)f+Mtd(i) (6)

Foreachnewtimestep:Mtf(i)(t+1t)=Mtf(i)(t)+Mtd(i)(t).

Another parametertobetakeninto accountisthe pore foulingresistance.Porefoulingoccursasaresultofthe adhe-sionofsomesludgeparticleshavingadiameterlessthanor oftheorderofthediameteroftheporesontheinteriorwalls (Mengetal.,2009).Then,thefiltrationresistancefortheith

membranesectionisdeterminedbythefollowingexpression: R_TS(i)=R_m(i)+R_p(i)+R_td(i)+Rtf(i) (7)

where R_m(i)=const. (8) R_p(i)=rp m

X

Initiation

Input data

t =0

Determination of the sludge

concentration (C)

Determination of the mass

to the membrane (M

)

Determination of the overall

resistance (R

)

t < t

t=t+ t

Output parameters

End

Yes

No

Determination of the

trans

-membrane pressure (TMP

)

Fig.3–Generalalgorithmfortheimplementationofthe mathematicalmodel.

Oncethevalueofthefiltrationresistancefortheith mem-branesectionhasbeendetermined,thevalueoftheoverall resistancecanbecalculatedbymeansoftheexpression(12).

1 RT = n

X

Finally,thevalueofTMPcanbedeterminedusingDarcy’slaw

(13):

TMP=sJRT (13)

Thegeneralschemeofthealgorithmforimplementingthe mathematicalmodelisshowninFig.3.

Forthecalculation,thesludgeconcentrationisdetermined bysolvingthe systemofdifferential equationsforthe bio-logical system. Then, the mass of sludge attached to the membranesurfaceanditsresistancetofiltrationare deter-mined. Later, the overall resistance value is evaluated in theexpressionofDarcy’slawsothattheTMPvaluecanbe obtained.Theoutputparametersofthesimulatorareshown ateach1stimeinterval,whichisthestepusedbythe fourth-orderRungeKuttanumericalmethodduringthecalculation.

Nowadays,manyunitprocessmodelsareavailableinthe fieldofwastewatertreatment.Allofthesemodelsusetheir own notation, which causes problems for documentation, implementation and connectionofdifferent models (using differentsetsofstatevariables)(Corominasetal.,2010).For thisreason,inthiswork,theuniversalnotationproposedby

Corominasetal.(2010)todescribemathematicalmodelsis used.

2.2. Evaluationofthesimulator

Todemonstratethesimulatorcapacitytodescribethe perfor-manceofarealmembranebioreactor,theexperimentaldata reportedbyZarragoitiaetal.(2008)wereused.Tables1–3show theworkingconditionsinwhichtheexperimentswere con-ducted,the characteristics ofthe wastewaterusedand the characteristicsoftheactivatedsludgeatthebeginningofthe experiments,respectively.

2.3. Practicalapplicationofthesimulatorinchemical engineeringeducation

The simulator was used in chemical engineering educa-tion in December 2012and inDecembre 2013atthe Ecole Nationale Supérieure des Ingénieurs en ArtsChimiques Et Technologiques(ENSIACET),France.Theuseofthissoftware wasincorporatedintothesyllabusofthe“Watertreatment” courseinthefifthyearofhighereducationcorrespondingto theChemicalEngineeringCareersunit.Theobjectivesofthe practicalactivityare thatthestudentsbecomefamiliarized withawater treatmentprocess,that theyunderstand how theSMBRprocessworksandthattheyanalyzetheinfluence ofoperatingparametersonthefunctioningoftheprocess.For thispurposepracticalworklastingthreeandahalfhoursina simulatedlaboratorywasdeveloped.Itwasstructuredinthree steps:

1. Analysisofthemathematicalmodelimplementedinthe simulatorinordertobetterunderstandtheresultsobtained withthesimulator.

2. Resolutionofapracticalproblemusingthesimulator. 3. Writingofthereportbythestudents,withtheresultsand

discussionofallthesimulatorpredictions. 2.3.1. Practicalactivity

Apracticalexercisewasdevelopedusingexperimentaldata from a real plant (Zarragoitia et al., 2008). Theexercise is describedbelow.

AnMBRpilotplantwithasubmergedmembrane configu-rationislocatedinthetownofBrax,France.Thisplantisfed withavolumetricflowof0.09m3_day−1_{ofrealwastewater,the}

characteristicsofwhichareshowninTable1.Specifically,a polysulfonehollow-fibremembranemodulesuppliedby Poly-men (pore size=0.2mm, surface area=0.3m2_{) is immersed}

directlyinanaerobictankwithavolumeof10L.Thesludge retentiontimeisaround30days.Thebiologicalreactoris aer-atedandstirredbyafinebubbleairdiffuserlocatedatthe bottomofthereactor.Asecond airblower,locateddirectly atthebottomofthemembranemodule,producesanairflow rateof6Lm−2_s−1_{togeneratecoarsebubblesthatcausestrong}

turbulencesoastocleanthesurfaceofthemembraneand thuslimitmembranefouling.Membranefiltrationiscarried outinconventionalsequentialcyclemode:9minwith filtra-tionand1minwithoutfiltration(relaxationtime).Duringthe relaxationtime,thecoarsebubbleaerationison,sothe mem-branecleaningiscarriedoutinacyclemode:1minofcoarse bubbleaerationand9minwithoutsuchaeration.

Themaintenanceofthesystemisperformedwhen trans-membranepressure reachesitscriticalvalue,whichinthis caseisfixedat60kPa.ThemeantemperatureinsideofMBR

Table1–WorkingconditionsinwhichtheexperimentswereperformedintheSBRM.

Temperature(◦_{C) q}

a(Lm−2s−1) tf(min) tpf(min) taBG(min) tpaBG(min) SRT(days) J(m3/(m2day))

7.3 11 10 4 2 8 40 0.25

Source:Zarragoitiaetal.(2008).

Table2–Characteristicsofthewastewatertobetreated.

XSST(mg/L) XS(mg/L) XI(mg/L) XH(mg/L) SS(mg/L) SI(mg/L) SPMS(mg/L) SO2(mg/L)

50 70 40 10 220 10 60 0.2

Source:Zarragoitiaetal.(2008).

Table3–Characteristicsoftheactivatedofsludgeatthebeginningoftheexperiments.

XSST(mg/L) XS(mg/L) XI(mg/L) XH(mg/L) SS(mg/L) SI(mg/L) SPMS(mg/L) SO2(mg/L)

5500 10 10 550 130 10 90 10

Source:Zarragoitiaetal.(2008).

is20◦_{Cduringtheentirecampaign.Tables2–4showthedata}

necessaryfortheMBRoperation:thesupplycharacteristics, the characteristics ofthe mixture insidethe MBR and the propertiesofthe activatedsludge and cakeformed onthe membranesurface,respectively.

2.3.1.1. Activities. Usingthesoftware

1. SimulatetheoperationoftheMBRover8days.Describethe behaviourovertimeofthefollowingparameters:

• Trans-membranepressure. • Totalsuspendedsolids. • Chemicaloxygendemand. • Filteredwatervolume. • Solublemicrobialproducts. • Overallresistancetofiltration. • Dissolvedoxygen.

2. Determinethetimenecessaryforthesystemtoreachthe criticaltrans-membranepressureworkingintheoperating conditionsdescribedabove.

3. Whatneedstobedoneifthesystemreachesthecritical trans-membranepressure?

4. In order to understand the influence of the following variablesonthetrans-membranepressure,performa sen-sitivityanalysisintheindicatedranges:

• Filtrationflux(±5%ofestablishedvalue).

• Specificfiltrationresistanceofthesludgecakelayer(±5% ofestablishedvalue).

Discusstheresultsofthesensitivityanalysis.

Table4–Propertiesoftheactivatedsludgeandcake formedonthemembranesurface.

Parameters Value

Erosionratecoefficientofthedynamicsludgefilm 3.5×10−4

Stickinessofthebiomassparticles 0.7

Particlesize(m) 1×10−4

Specificfiltrationresistanceofthedynamicsludge film(m/kg)

1×1015

Specificfiltrationresistanceofthesludgecakelayer (m/kg)

1×1015

Compressioncoefficientforthedynamicsludgefilm (kgm−3_s−1₎

2.5×10−5

Coefficientofthedragandliftingforce 0.4 Reductionindexofthecakecompressioncoefficient 0.1

5. For the purposeof determiningthe best operating con-ditions,plot thetimenecessary forthe systemtoreach thecriticaltrans-membranepressureversusthefollowing variables(maintainingtherestoftheoperatingconditions constant):

• Filtration frequency (Filtration time/Relaxation time=9:1,7:3,5:5,3:7and1:9)

• Aerationintensity(3,6,9,12and15Lm−2_s−1₎

Discusstheresultsobtained. 2.3.2. Anonymousquestionnaire

Attheendofthepracticalactivity,thestudentswereaskedto fillinananonymousquestionnairegivingtheiropinionabout thesimulatedlaboratory(Table5).

3.

Results

and

discussion

3.1. Descriptionandoperationofthesimulator

For teaching use, the SBRM computer simulatordeveloped shouldbeuserfriendlyandprovideaneasilyaccessible intro-ductiontothesubject.Sinceotherusesareadvancedtraining andresearch,manyparametersshouldbeeasilymodifiable.

The simulatorshows ageneral standardscheme ofthe SMBR,whichallowsthemainstructuralcomponentsofthe systemtobeapprehended,sothattheusercangainabetter understandingoftheinstallationperformanceandthusa bet-terunderstandingoftheprocessesthatareinvolvedinthese typesofinstallations(Fig.4).

Thesimulatorallowstheusertostudyoftheinfluenceof the35modelinputvariables(thosepresentedinTables1–4, bioreactor volume, membranesurfaceand time tobe sim-ulated) on 16 output parameters, which can be displayed graphicallyornumerically.Theseoutputparametersare:

1. Trans-membranepressure.

2. Resistanceofthestablesludgecakelayer. 3. Resistanceofthedynamicsludgefilm. 4. Porefoulingresistance.

5. Overallresistance. 6. Chemicaloxygendemand. 7. Dissolvedoxygen.

Table5–Questionspresentedtothestudents.

No Questions Stronglyagree Agree Disagree Stronglydisagree Idon’tknow

1 Thislaboratoryisinteresting     

2 Thislaboratoryhelpedmetorepresent therealfunctioningofamembrane bioreactor

    

3 Withthisexercise,Ihaveabetter understandingoftheinfluenceofthe membranebioreactorfunctioning parametersontheprocessperformance

    

4 Iwasabletounderstandthefunctioning ofthesimulatorbymyself

    

5 Theparticipationoftheteacherhelped meunderstandthefunctioningofthe simulatorbetter

    

6 Theproblempresentedtomewasclearly defined

    

7 Thisactivityenabledmetounderstand whyacomputersimulatorsisanuseful toolinchemicalengineering

    

8 Thislaboratoryiswellsituatedinmy educationprogramme

    

9 Thislaboratoryisrelevanttomy programme

    

10 Theactivityhelpedmetoconsolidatethe conceptsexposedintheclassroom

    

9. Solubleundegradableorganics. 10. Solublemicrobialproducts. 11. Particulateundegradableorganics. 12. Slowlybiodegradablesubstrates. 13. Ordinaryheterotrophicorganisms. 14. Totalsuspendedsolids.

15. Massattachedtothemembrane. 16. Filteredwatervolume.

The simulator enables the user to select, before calcu-lating,the time-scale overwhich resultswillbepresented.

Thistime-scalecanbeseconds,minutes,hoursordays.The precisionofcalculationdoesnotchangewiththetimescale selectedbecauseitisalwaysperformedinseconds.

Also, the simulator allows the numerical results to be exported toa txt file. Thisoption may permitthe user to process theseresultsusingother computationaltools. Sim-ilarly,thegraphicalresultscanbesavedinbmpimageformat. Anotheradvantageisthateachcalculationcanbesavedina fileusingbmsformat,anextensioncreatedforthissoftware. Finallythesimulatoropensthepossibilityofasensitivity anal-ysiswithrespecttovariousparameters.

Fig.5–Comparisonbetweentheexperimentaldata(points)andsimulationresults(line).Theleft-handdiagramshowsthe workingconditionsoftheexperiments.

3.2. Comparisonofsimulatorperformancewith experimentaldata

Tosubstantiateandjustifytheuseofthecomputersimulator tostudyanSMBR,itisessentialtoknowthelevelof approxi-mationtowhichthemathematicalmodelcanreproduceSMBR operation.Forthisreason,thesimulationresultswere com-paredwithexperimentaldata.

Theparameterchosenwasthetrans-membranepressure becauseofitsimportanceintheoperationoftheSMBR(Meng etal.,2009;Fenuetal.,2010).Fig.5showstheexperimentaland calculatedvaluesofTMP.Ameanrelativeerrorofestimation of15%wasobtained.

This result can be considered acceptable for predicting the behaviour ofa systemof suchcomplexity because, as explainedabove,thesimulatorwasbuiltbyimplementinga modelthatcombinesbiologicaldegradationwiththefiltration process.Thebiologicalsystemmodelling introducesahigh percentageoferrorastheinputvaluesofbiologicalvariables correspondtothemeanvaluesmeasuredduringthe experi-ment(Zarragoitiaetal.,2008).

Even with15% oferror, thesimulator already givesthe trendsintheevolutionofphysicalquantitiesandtheorderof magnitudeoftheirvalues,whichistheinformationsought.

3.3. Practicalapplicationofthesimulatorinchemical engineeringeducation

Beforebeginningthepracticalactivity,theteachergavethe studentsabriefoverviewofthemodelimplementedinthe simulatorasa“blackbox”.Oncethestudentshadreceived theorientationinformationandthenecessarymaterials,they werereadytostarttheproposedexercise.Theinstructorwas athandduringtheentirepracticalactivity,andwasavailable toclarifyanypointsthestudentshaddoubtsabout.Finally,the studentspreparedareportwiththeexercisesolution.When theyhad finishedtheworkinthesimulatedlaboratory,the studentsansweredtheanonymousquestionnaire,whichwas drawnupintheaimofknowingthestudents’opinionsabout thesimulatedlaboratory.

Theevaluationsofhowwelltheobjectivesofthispractical activitywereattainedweremade:

- duringtheactivityitself,bythediscussionbetweenthe stu-dentsandtheteachingstaff,

- fromthereportsgivenbythestudents, - bythestudents’answerstothequestionnaire.

3.3.1. Considerationsaboutthepracticalactivity

Thepracticalactivitywascarriedoutsuccessfullybythe stu-dents. They showed their abilities in the use of computer programsand,ingeneral,theymanagedthesimulatorwith success. Nevertheless, there were some students who had difficultysolvingthistaskbecausetheydidnotunderstand the functioning of anSMBR correctly and others who had problems with the simulator language. However, with the instructor’shelp,theyfinishedtheproposedexercisecorrectly. Thestudents’correctuseofthedifferentsimulatortoolsand their understanding of the SMBR operation was evaluated fromtheiranalysisoftheresultstheyreported.

Thereportswere correctedand gradedaccordingtothe Frenchnorm,whichgivespointsoutof20,withthe follow-ingappreciation:10=pass,12=quitegood,14=good,16=very good,18=excellent,and20=congratulations.Theaveragewas 13.6/20withaminimumof12/20andamaximumof16/20, whichisarathergood result.Parts1–4were achievedvery well,withonlyminormistakes. Part5was completedina morevariable way,mainlyduetoalackoftime(andtothe Frenchwayofteaching,whichdiscriminatesusingtime). 3.3.2. Students’opinions

Thestudents’responsestothequestionnairearepresentedin

Fig.6.Agradingscaleobtainedbyusingnumericalequivalents forthe opinions:“Strongly agree”=20,“Agree”=13.33, “Dis-agree”=6.67,“Strongly disagree”=0 (in orderto correspond totheFrenchgradingsystem, whichisoutof20)hasbeen added.Foreachofthestatementsproposedinthe question-naire,a“grade”isindicated,whichwasobtainedbyaveraging theanswers.Toanalyzetheseresponses,thequestionswith themostnumerousanswers“Disagree”were consideredas wellastheoneswithlessgoodgrades.Thestudents’ evalu-ationswereverypositive.Thissimulatedlaboratoryaroused greatinterestinmorethan95%ofthestudents.

As noticed by the teaching staff during the practical activity, although some students had some difficulties in understandingthefunctioningofthesimulatorbythemselves (Q4),theparticipationoftheteacherhelpedtheminthistask (Q5).

Slightlymorethan10%ofthestudentsdidnotagreethat thislaboratorywasrelevanttotheirprogrammeandthe low-est evaluationconcerned thesituation ofthis laboratoryin theeducationprogramme(Q8).Adiscussionwiththestudents showedthatanadditionalexperimentalactivitymayhelpto improvethisimpression.Theteachingstaffisthinkingabout aconvenientandnottooexpensivewaytoincludeit(visittoa

Fig.6–Resultsoftheanonymousquestionnaire(seeTable5). watertreatmentplant,visittoaresearchexperimentaldevice,

shortexperimentalpracticalactivity,etc.)

3.4. Advantagesofsimulatorinteaching

Laboratory exercises, field observations and field trips are a fundamental part of many earth science and environ-mental science courses. Field observations and field trips cansufferfrom constraintsofdistance,time, expense,and the scale,safety,or complexity ofreal-worldenvironments (Ramasundaram etal., 2005). Corteret al.(2011) statethat themajorityofcomparativestudieshaveconcludedthat sim-ulation is a good substitute forhands-on labsin teaching courseconceptsandtheirapplicationbut someresearchers haveproposedthatsimulationmightbemosteffectivewhen itisintegratedasacomplementarypartofacourseinvolving hands-onlaboratoryactivity.

In comparison with textbooks and lectures, a learning environment with a computer simulation has the advan-tagesthatstudentscansystematicallyexplorehypothetical situations,interactwithasimplifiedversionofaprocessor system,changethetime-scaleofevents,and practicetasks andsolveproblemsinarealisticenvironmentwithoutstress (vanBerkumanddeJong,1991).

El-Naas (2011) developed a course following the active learning approach, wherestudents are heavily involved in classactivitiesandtheycandirectlyassesstheeffectofinput variablesonthedesignparameters,allowingthemtocarryout “WhatIf”orparametersensitivityanalysis.Forthisheused ExcelandEz-Solve indesigningandanalysing desalination processes.Simulationshaveearnedaplaceintheclassroom asrobustadditions tothe teachers’repertoire,eitherasan additiontothetraditionalteachingmethodsavailableorasa replacementofpartsofthecurriculum(Ruttenetal.,2012).As shownhere,theSMBRcomputersimulatordevelopedisable topredictthebehaviourofvariousoutputparametersinthe short-and long-term.Itallowsuserstostudytheinfluence ofthebioreactorinitialworkingconditionsonthebehaviour ofthemainparametersthatdescribetheplantperformance.

ItalsoallowstheoptimumSMBRoperatingconditionstobe determinedsoastoincreasethemembranelifespan.Atthe sametime,itfacilitatesstudiesrelatedtothesearchforthe optimaldesignparametersoftheplantandtheinfluenceof sludgeproperties,whichwillleadtobetterefficiencyinthe processofwastewatertreatment.Italsofacilitates sensitiv-itystudiesonthemostimportantparametersinthesystem, whichisafundamentalaspecttobeconsideredinfuture mod-ellingworksothatthemajorcontributorstotheestimation errorofthemodelcanbedetermined.Lastbutnotleast,it givesaccesstointermediateparametersthatcannotbe reg-ularlymeasured inrealconditions, suchastheresistances ofthestablesludgecakelayerandthedynamicsludgefilm, ortheconcentrationofsolublemicrobialproducts,butwhich contributetoabetterunderstandingofthewaytheprocess functions (membranefouling,COD,etc.). Itisimportantto notethatthesimulatorwasbuiltinamannerthatmakesit aninvaluabletoolforteachinghowanSMBRworks,sinceit allowstheusertointeractwiththeoperatingconditionsofthe bioreactorandobservetheinfluenceoftheseparameterson thebehaviourofthemaincontrolvariablesofthesystemover time.

Computer modelling has become a helpful tool in the analysisoftheperformanceandeffectivenessofwastewater treatmentsystems(Korniluketal.,2008).Nowadays,industrial firms havebecomevery interested because computer sim-ulationssignificantlylowercostscomparedtoexperimental studies (Kraft et al., 2005).The use ofthe SMBR simulator allows considerable savingof resources and time since,in general,performingexperimentsinrealtimeonanSMBRis veryexpensiveandtimeconsuming.Forthepractical activ-ity, wewere abletoplace24 studentsinfrontoftheirown 12“devices”.Thisallowedthemtovirtuallyimplement sev-eraloperatingconditionsandtoanalyzetheconsequencesof thiscontrol.Theuserfriendlinessofthesimulatorhelpedin this,inatimemuchshorterthanintherealworldandata costcompatible withthe university’sresources.Theroleof theteacherswastosupplythestudentswithallthe practi-calproceduresthatarenotincludedinthesimulator,suchas

chemicalcleaningormembranereplacement,andtokeepthe studentsawareoftherealcontextofinsituexperiments com-paredtoinsilicoones.Inthecaseofthetreatmentplantunder study,theplantneedsaperiodofalmost20daysforthesludge mixturetobecomestabilizedand,eachtimethecriticalvalue oftrans-membranepressureisreached,thesystemmustbe stoppedtocarryoutexpensivechemicalcleaningor replace-mentofthemembrane,oftenbeforetheendofitslifespan (Zarragoitiaetal.,2008;Kimetal.,2011).However,thisstudy canbeperformedinthesimulatorinashorttimeandwith considerablesavingofresources.

4.

Conclusions

An SMBR computer simulatorwas built with recent mod-ellingknowledgeandafriendlyinterface.Theresultsgiven bythesimulatorareaccurateenoughtoprovidethetrends andordersofmagnitudesofphysicalquantitiesneededfor theteaching applicationofthe simulatedMBR.The practi-caluseofthesimulatorwasevaluatedwiththedevelopment ofsimulatedlaboratoryworklastingthreeandahalfhours, whichgaveresultsthatwouldtakemorethanfifteenmonths ofreal-worldexperiments.Itwassuccessfully applied,and achievedthemostdifficultobjectivesofenablingthestudents toanalyzetheinfluenceofoperatingparametersontheSMBR functioningandbeinglargelyacceptedbythestudents.While thishasnotbeentested, itappears clearthatthe dynamic modelusedwouldpermitthetrainingofprofessionals.

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