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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-Gaubert
To cite this version:
Yusmel González Hernández, Ulises Javier Jáuregui Haza, Claire Albasi, Marion Alliet-Gaubert.
De-velopment of a Submerged Membrane Bioreactor simulator: a useful tool for teaching its functioning.
Education for Chemical Engineers, Elsevier, 2014, vol. 9, pp. e32-e41. �10.1016/j.ece.2014.03.001�.
�hal-01069368�
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Identification number: DOI : 10.1016/j.ece.2014.03.001
Official URL:
http://dx.doi.org/10.1016/j.ece.2014.03.001
This is an author-deposited version published in:
http://oatao.univ-toulouse.fr/
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
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Development
of
a
Submerged
Membrane
Bioreactor
simulator:
a
useful
tool
for
teaching
its
functioning
Yusmel
González
Hernández
a,
Ulises
Javier
Jáuregui
Haza
a,
Claire
Albasi
b,c,
Marion
Alliet
b,c,∗aInstitutoSuperiordeTecnologíasyCienciasAplicadas(InSTEC),Cuba
bUniversitédeToulouse,INPT,UPS,LGC,4,AlléeEmileMonso,BP84234,F-31432ToulouseCedex4,France cCNRS,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:marion.alliet@ensiacet.fr(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/m2s)
Ji localfiltrationfluxthroughtheithmembrane
section(m3/(m2day))
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−3s−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)=
0.1+0.45 1+sin(2ε−εa) 2εaq
sgqa s ,ε<εaq
sgqa s ,ε≥εa
(2) where qa=QBG A (3)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: dMtd(i) dt = 24CJ2 i 24Ji+CddpGi − ˇ(1−˛)GiM2td(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).
dMtd(i) dt =−
ˇ(1−˛)GiM2td(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: RTS(i)=Rm(i)+Rp(i)+Rtd(i)+Rtf(i) (7)
where Rm(i)=const. (8) Rp(i)=rp m
X
k=1 Jif(k) (9) Rtd(i)=rtdMtd(i) (10) Rtf(i)=rtfMtf(i) (11)Initiation
Input data
t =0
Determination of the sludge
concentration (C)
Determination of the mass
attachedto the membrane (M
td)
Determination of the overall
resistance (R
T)
t < t
STOPt=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
i=1 Si RTS(i) (12)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 withavolumetricflowof0.09m3day−1ofrealwastewater,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−2s−1togeneratecoarsebubblesthatcausestrong
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−3s−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−2s−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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