搜索
:2003-05-06:(962126);“”(1022051-3-01):(1955-),,,. PACIMBR傅金祥,苏锦明,徐 巍,赵玉华,梁建浩(,110168) :(PAC-MBR),,.,PAC-IMBRMBR,COD、BOD53.1%、2.4%.40-60200-300,80-100,CSPAC=1/4,,EPS,.:PAC-MBR;;;:X703 :A :1671-2021(2004)02-0143-04 (MBR),、、、,[1].,[2].,MBR[3,4].,(IMBR)(PAC)[5～7].J.S.Kim.,(EPS),[5].(EPS),,[6,7],.,,,.,IMBR,,、(TMP).:CSPAC=ρ(CPAC)ρ(MLSS):ρ(CPAC)———,g/L;CSPAC———;ρ(MLSS)———,g/L.1 1.1 ,2×10-4mm,0.5mm,0.8mm,1m2.1.2 ,1.,,0.30m×0.20m,0.75m. 1 20044202 ()JournalofShenyangArch.andCiv.Eng.Univ.Apr. 2004Vol.20,No.2,0.1MPa.1#,PAC-MBR;2#(),.1.3 、,,.1,2.1 、ρ(COD)/(mg·L-1)ρ(BOD)/(mg·L-1)ρ(NH3-N)/(mg·L-1)ρ(LAS)/(mg·L-1)/℃pH/NTU50～20030～1082～100.3～0.413～256.0～7.830～602 ()ρ(COD)/(mg·L-1)ρ(BOD)/(mg·L-1)ρ(NH3-N)/(mg·L-1)ρ(LAS)/(mg·L-1)/℃pH/NTU200～450140～25030～550.3～0.416～276.0～7.320～106 ,、、、、、、、.2 2.1 PAC-MBR,COD、BOD3.1%、2.4%.、,COD、NH3-N、BOD5、92.6%、86.6%、96.5%、99%,ρ(COD)25mg/L、ρ(NH3-N)1.5mg/L、ρ(BOD5)10mg/L、0.3NTU,《》(GB50335-2002)《》(GB50336-2002).2.2 (1)2,,1#PAC-MBR2#MBR5kPa,5,1#2#,0.51kPa/d0.3kPa/d,.,1#PAC-MBR,0.35kPa/d,2#MBR0.46kPa/d.40～60,,,.,PAC,,,,. 2 2060PAC (2)3,1#PAC-MBR5,,6.3kPa/d.1#2h(3a),0.175kPa,.200～300,.,,,.,.,.4400200～300PAC,. 3 200300PAC (3),5.,MBR,0.51kPa/d,PAC-MBR144 ()20 4 200～300PAC 5 80100PAC0.61kPa/d,、,.80-100,.,,.,,. (4)80-100,ρ(MLSS)4.79g/L4.18g/L,,PACEPS,PAC,EPS,EPS,,EPS,.2.3 620∶1、40∶1、100∶1.20∶1、PAC1/4,,.100∶1.PAC1/8,(100∶1),.40∶1、PAC1/6,.7100,80-100、1/4,1/6、1/8,.4,,,. 6 7 80～100PAC3 (1)PAC-MBRMBR,《》.PAC-MBR.(2)80-100,,EPS,,,.,80-100.1452:PACIMBR (3)CSPAC=1/4、CSPAC=1/6、CSPAC=1/8PAC-MBR,CSPAC=1/4,(20∶1).,CSPAC=1/4、20∶1,.:[1] ,.[J].,1998,11(1):40-44.[2] SheikholeslamiR.Foulingmitigationinmembranepro-cess[J].Desalination,1999,123(1):45-53.[3] ,,.IMBR[J].(),2003,19(1):53-58.[4] ,,.IMBR[J].(),2003,19(1):50-52.[5] Jae-Kim.Comparisonofultrafiltrationcharacteristicsbe-tweenactivatedsludgeandBACsludge[J].Wat.Res,1998,32(11):3443-3451.[6] ,,.SMSBR[J].,2001,20(1):24-27.[7] ,,.[J].,2001,17(2):1-4.EffectsofpowderedactivatedcarbononIMBReffluentandmembranefoulingFUJin-xiang,SUJin-ming,XUWei,ZHAOYu-hua(MunicipalandEnvironmentalEngineeringFaculty,ShenyangArch.andCiv.Eng.Univ.,Shenyang,110168,China)Abstract:Acomparisonbetweenmembranebioreactor(PAC-MBR)wascarriedoutundersimilaroperationalconditions.Poweredac-tivatedcarbonconcentrationandMLSSspecificConcentration(CSPAC)weredefined,andCSPAChadmadeagreatcontributiontomaintaininghighmembraneflux.PAC-MBRdemonstratedamorestableandexcellenteffluentqualitythanMBR.TheCODandBOD5removalratehadincreased3.1%and2.4%respectively.TheeffluentcouldmeetthequalitystandardforreusedwastewaterissuedbytheMinistryofConstruction.ThePACsizeof40-60meshand200-300meshledtoseriousmembranefouling.However,thePACsizeof80-100meshmitigatedmembranefoulingobviously.AndtheoptionalCSPACwas1/4,whichwasmorebeneficialtoform-ingbio-PACandmitigatingtheEPSinthemixedliquid.KeyWords:PAC-MBRcombinedprocess;membranefouling;CSPAC;membraneflux(123):[1] RumelhartD,MeclellandJ.ParallelDistributedProcess-ing:ExplorationsintheMicrostructureofCognition,1986,1(2):41-49.[2] .[M].:,1992.[3] .[M].:,1998.[4] .[D].:,1998.[5] JTJ073-1996[S].[6] ,,.[J].(),2004,20(1):14-16.AsphaltConcretePavementPerformanceForecastBasedonNeuralNetworkZHAOFu-xiao(SchoolofCivilEngineering,ShenyangArchitectureandCivilEngineeringUniversity,Shenyang,110168,China)Abstract:Thefeasibilityofneuralnetworkapplicationtoasphaltconcretepavementperformanceforecastissetforth.Threemainenvi-ronmentalfactors———traffic,rainfall,temperature,whichcanleadtothedirectchangeofpavementperformanceareusedastheinputlayerofneuralnetwork.Theperformanceindexesofasphaltumconcretepavement,i.e.dilapidationrate,smoothness,deflection,fric-tioncoefficientareusedasoutputlayer.TherelationbetweenenvironmentfactorandpavementperformanceisacquiredbyintroducingthemoduleofBPNeuralNetworkinMATLAB.Theforecastmethodofasphaltumconcretepavementperformanceisstudied.Theef-ficiencyofthecalculationmethodisprovedthroughillustration.KeyWords:neuralnetwork;networkmodel;asphaltconcretepavement;performance146 ()20