A coupling model for EGSB bioreactors: Hydrodynamics and anaerobic digestion processes

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Chemical Engineering and Processing 50 (2011) 316324Contents lists available at ScienceDirectChemical Engineering and Processing:Process Intensicationjourna l homepage: www.e lsev ier .coA coup dydigestiMauren Fa INGAR - (CONb Depto de Cienc CAIMI-UTN FRd Facultad de Ina r t i c lArticle history:Received 31 JaReceived in reAccepted 23 JaAvailable onlinKeywords:Dynamic modThree-phase sEGSB reactorsWastewater trouplgranic (gaed on. A nophences wood aropoctor. 2011 Elsevier B.V. All rights reserved.1. IntroductionThe exphigh rate teby De Manin the lastbe considerblanket (UAanaerobic das proteins,amino acidtion to acetnally utiliz[46].Both EGto form denthe EGSB reby a high resludge expcontact witSince highedemand CO Correspon3657 (3000) SE-mail addduction is also higher, improving even more the mixing inside thereactor.0255-2701/$ doi:10.1016/j.anded granular sludge bed (EGSB) reactor is a superchnology for wastewater treatment. It was introducedet al. [1] and has attained an increasing popularitydecade [2,3]. The conception of the EGSB reactor caned as an improvement of the upow anaerobic sludgeSB) reactor developed by Lettinga et al. [3]. In theigestion processes, complex organic substrates suchlipids, and carbohydrates are hydrolyzed into solubles, fatty acids, and sugars, followed by the fermenta-ate, formate, hydrogen, and carbon dioxide which areed by methanogenic microorganism to form methaneSB and UASB are based on the ability of microorganismse aggregates by autoimmobilization. However, insideactor, the higher upow velocities, which are causedcycle rate and a high height/diameter ratio, cause theansion through the whole reactor thus improving itsh the efuent and reduce the unit dead volume [7,8].r organic loading rates (up to 40kg of chemical oxygenDm3 d1) can be accommodated in EGSB, the gas pro-ding author at: INGAR - Instituto de Desarrollo y Diseno- Avellanedaanta Fe, Argentina. Tel.: +54 342 4534451; fax: +54 342 4553439.ress: mfuentes@santafe-conicet.gov.ar (M. Fuentes).The exact mixing pattern cannot be generalized, and it must beevaluated in each reactor by assessing the reactor hydrodynam-ics [9]. However, a fully expanded EGSB reactor can be consideredas a completely mixed tank [10,11], and liquid lm mass transferresistance seems to be negligible [12]. The knowledge of the bedexpansion plays an important role in the design and operation ofan EGSB reactor, because it is the key point to reach a compromisebetween bed expansion and sludge washout, and the stability andperformance of the EGSB system would be sensitive to the degreeof expansion [13].Some ideas from solid dynamics in a three-phase uidizedbed reactor can be used to describe the multiple solidliquidgasinteractions that take place in EGSB reactors. In previous works,a heterogeneous model for three-phase anaerobic uidized bed(AFB) reactors was developed and validated [1416]. The aimof this work is to apply this methodology for modeling EGSBreactors. Thus, a heterogeneous model for EGSB bioreactors is pre-sented here. This modeling approach combines the dynamics ofthe three phases present in the reactor including biochemical,physico-chemical and hydrodynamic processes. Several details ofreactor design, sludge and wastewater characteristics, and opera-tional conditions are required formodel adjustment and validation.Almost all works published describe better the biochemical perfor-mance of EGSB reactors than their hydrodynamics. Due to the lackof information on some parameters that can be measured and aresee front matter 2011 Elsevier B.V. All rights reserved.cep.2011.01.005ling model for EGSB bioreactors: Hydroon processesuentesa,b,, Nicols J. Scennaa,c, Po A. Aguirrea,dICET-UTN), Avellaneda 3657 (3000) Santa Fe, Argentinacias Bsicas - UTN FRSF, Lavaise 610 (3000) Santa Fe, ArgentinaR, Zeballos 1341 (2000) Rosario, Argentinag. Qca, UNL, Santiago del Estero 2829 (3000) Santa Fe, Argentinae i n f onuary 2010vised form 10 January 2011nuary 2011e 1 February 2011eling and simulationystemseatmenta b s t r a c tThe aim of this paper is to present a cobic digestion processes in expandedThe bioreactor is modeled as a dynamimental data of three case studies basused to adjust and validate the modelfor calculating the biomass transportthis parameter. Bioreactor performanCOD, VFA and VSS concentration. A gvalues. It seems to indicate that the phydrodynamic successes in the bioream/locate /cepnamics and anaerobicing model for calculating both the hydrodynamic and anaer-ular sludge bed (EGSB) bioreactors for treating wastewaters.ssolidliquid) three-phase system. An existing set of exper-the start-up and operational performance of EGSB reactors isvel parameter, the specic rate of granule rupture, is denedomena. Values around 11020 dmd2 g1 are calculated forere analyzed through the main variable proles such as pH,greement was obtained among experimental and predictedsed EGSB model is able to reproduce the main biological andM. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324 317not reported, the authors should solve the initialization problemvia simulation, i.e. a sensitivity analysis of model results related tothese parameters is required.The paper is organized as follows: in Section 2, main mathe-matical equhypothesesdiscussed.solution usment and vcase studieof EGSB readrawn in Se2. MathemAs mengassolidl(granules) cphase is comproducts, esingle suspThe gas phastages.The globmodel prevare: (1) theand (3) thebetween bophase and hcuss theseequations h2.1. AnaeroThe ana(growth-upphysico-chegasliquidbioreactor.model para[9,1416].Hare summaA novel aaggregationmodeling threlated tobe considerbioreactor.than in granuid velociTiwari etemperaturconcentraticoncluded talkalinity is(2) the natugranular difof the graniron may encellular polcementatioand mass trIn this work, the parameter specic rate of granule rupture krhas been dened for calculating the biomass transport phenom-ena. This parameter explains how the process of granulation isaffected by all these environmental andoperational conditions, andof thenzye invoderegaergyn, of eable AkrXeteritivitted iopareralh con. Thus, tch, het bss traed. Tbiopmake diah thusedn [12dmbesed be of oVSVbputincalcudbp3XST is(iydrodractesed bations of the global model are presented, and somerelated to thebioparticlemodel andhydrodynamics areComputational aspects of model implementation anding gPROMS are described in Section 3. Model adjust-alidation using an existing set of experimental data ofs based on the start-up and operational performancectors is presented in Section 4. Finally, conclusions arection 5.atical modeltioned, the EGSB is modeled as a three-phaseiquid system. The solid phase consists of bioparticlesomposed by active and non-active biomass. The liquidposed by the chemical species in solution (substrates,nzymes, ions, and water) and (active and non-active)ended cells, which are assumed to behave as solutes.se is formed by the gaseous products from degradational EGSB model results analogous to the AFB reactoriously published [1416]. The main subsystem modelsanaerobic digestion model; (2) the bioparticle model;hydrodynamics model. Since the main differencesth technologies are based on characteristics of the solidydrodynamics, the paper is essentially oriented to dis-aspects. Some hypotheses and the main mathematicalave been rewritten for better comprehension.bic digestion modelerobic digestion model involves the biochemicaltake, death, hydrolysis and disaggregating) andmical (system charge balance for calculating pH,mass transfer) processes which take place in theAll terms related with mass transfer processes,meters and constants have been previously describedowever, themain rate expressionsjRjik +jT jikrized in Table A.1 of Appendix A.spect in the EGSB model is related to modeling the dis-of single suspended cells from biomass granules. Fore disintegration of aggregates, some physical aspectsthe structure and compactness of granules need toed. Hydrodynamics affects biomass processes inside aThese effects are more pronounced in uidized bedsular sludge bed reactors, attending to the operationalty ranges [7,17].t al. [18] studied the inuence of some factors such ase, alkalinity, natureandstrengthof substrate, andcationon on granule formation in sludge bed reactors. Theyhat: (1) a careful temperature control and an adequaterequired for generation and maintenance of granules;re and strength of substrate in conjunction with intra-fusion to a large extent determines the microstructureules; and (3) the divalent cations such as calcium andhance granulation by ionic bridging and linking exo-ymers. However, their presence in excess may lead ton due to precipitation leading to increased ash contentansfer limitation.differsto thetors arother m(disaggthe enfunctiotration(see Tarri = SParamA senspresen2.2. BiGenthe asfractioand thapproastant wNo maassumout theForgranuling witrate isfunctioddbpdt=The nuexpresvolumNbp =Substitcan beddbpdt=wherephase2.3. HChaexprese specic rate of biomass hydrolysis, which is relatedme attack on non-active biomass. Since several fac-olved in the stability and formation of granule, and noling approach has been published, the rate of granuleting) rupture is modeled as a rst-order function ondissipation parameter (which is an upow velocity=Uo( p/ z), p/z = gkkk), and mass concen-ch microbial species present in the biomass aggregates.1):Si (1)kr has been assumed the same for all biological species.y analysis of model results related to this parameter isn Section 4.ticle modelly, the granule composition has been described fromntent, moisture and (active and non-active) biomassese parameters can vary during the granule formation,he dry density of granules varies. As a rst modelingomogeneous biomass distribution on granules, con-iomass density and spherical geometry are assumed.nsfer limitations in the granule and the liquid bulk arehe substrate concentration has the same value through-article.ing the model workable, a simplifying assumption onmeter dbp is considered. A simple relationship deal-e net biomass growth-decay-hydrolysis-disaggregatingto calculate the average granule diameter as a time9]:2bpdVbpdt= 2d2bpVi,jRjiS +i,jT jiSNbps(1 ACbp/100)(1 MCbp/100)(2)r of bioparticles Nbp in the control volume (V) can bey dividing the total sludge volume (VS) by the averagene aggregate (Vbp):= VSXSTS(1 ACbp/100)(1 MCbp/100)6d3bp(3)g Eq. (3) into Eq. (2), time variation of granule diameterlated as:i,jRjiS +i,jT jiSSXST(4)the total concentration of biological species in the solid(XSi a + XSina)).ynamics modelristics of phase mixture and ow patterns arey the hydrodynamic model. Since the EGSB is assumed318 M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324Table 1Details of experimental studies used in the adjustment and validation of the model.Parameter (dimension)/case studya R1 R2 R3Reactor designWorking r 157Inner diam 2Height of t 50Sludge charaBiomass c 4.6Initial load ndAsh conte ndMoisture c ndGranule m nd (Granule de nd (Steps (distur ITime horiz 113WastewaterInuent TC 126Inuent SC 56Inuent V 42Inuent pH 6.9Substrate XLOperationalTemperatu 29.7HRT (d) 0.17OLR (gCOD 0.64Biomass c 6.4HydrodynamStatic bed nd (Bed poros nd (Inuent o 945Liquid up 300Recycle o 0a R1-Verone itten bb See sectionto behave aof the highconsidered.In EGSBsettling chasuggest thadancewithow rangewith the Allintermediatof the granabove workLiu et alheight assucategory ofknown Richexpansion,velocity (Utstatement oselected hetions for calsupercial vFrom thand makingcalculated aH = H0(1 +Since param(see Table BHo and o aBiochemexpanded gtion, the coalgebraic eqase cputmats moeactor volume (dm3) 1.3eter (dm) 0.53he water level (dm) 6.2cteristicsoncent. (Gvss dm3) 43.4(dm3) 0.23nt (%) 19ontent (%) 94.8ean diameter (102 dm) 2.30.2nsity (gdm3) 1026bances) I II IIIon/step (d) 28 30 20characteristicsOD (mgdm3) 44451 44451 44451OD (mgdm3) nd, 262 as SHAc nd ndSS (mgdm3) 7536 7536 75368.90.4 8.90.4 8.90.4composition (%COD)b XCHXPXLiSHAc: 3944116characteristicsre (C) 302.0 302.0 302.00.33 0.25 0.17dm3 d1) 1.33 1.78 2.66oncent. (gVSSdm3) 10.00 10.06 10.43ic characteristicsheight (dm) nd (2.48)ity (dm3 dm3) nd (0.4)w rate (dm3 d1) 3.89 5.21 7.78ow velocity (dmd1) 2568 1968 2353w rate (dm3 d1) 563 429 511z et al. [28], R2-Kato et al. [29], R3-Brito and Melo [12]. The assumed values are wrnomenclature for notation.s a continuous stirred tank reactor due to the effectsrecirculation rate, a completely mixed ow pattern isreactors, the bed expansion is closely related to theracteristics of the sludge. Almost all works publishedt the mean settling velocity of the granules is in accor-Stokes lawbecause the settlingprocess is in the laminarfor phB.3. ComTheproces[2022], whilst others suggest that it is in accordanceen formula because the settling process falls within thee ow range [23]. However, the settling characteristicsules have scarcely been discussed theoretically by theers.. [11] proposed the Eq. (5) for calculating the EGS bedming that the settling process of the granules is in theintermediate ow regime. The authors used the wellardson and Zaki [24] correlations to calculate the bedthe expansion coefcient (n) and the terminal settling). This approach is in accordancewith themathematicalf the generalized bubble and wake model (GBWM [25])re to describe the three-phase system. GBWM equa-culating uidization characteristics (phase holdup andelocity) are summarized in Table B.1 of Appendix B.e denition of bed expansion, using the ow equation,mathematical transformations, the bed height can bes [11]:e(ln U0/Ut)/n 01 e(ln U0/Ut)/n)(5)eters Ut and n are functions of biomass concentration.1), the bed height is also a biomass function. Propertiesre calculated at the static bed condition.ical processes are assumed to occur only in theranular sludge zone. As the bed height is a time func-ntrol volume assumed to compute the differential anduation (DAE) system varies. Mass balance equationsprise Ltd.).[26,27]. Lowinitial valustudies, 68%(XGL, XAA) aspecies areies describePentium IV4. Results4.1. Case stAn exist[28], Kato eup and opeadjust andstudies havof reactor dational con4.2. ModelThere arfor model aare not repoters related.5 0.480.4183.531170.09ndnd2.3) 1.61026) nd (1026)II III I92 124 nd53 1800.150 15648 128022 7931 5519 128027 5429 6330 00.1 6.70.2 6.90.2 7XCHXPSHAc: 2991151 SHAc: 1001.7 30.32.0 321.9 240.17 0.17 0.070.92 0.80 18.299.2 8.0 1912.06) 0.660.4) 0.4945 945 6.85600 900 4800945 1890 652etween parentheses.omponents are described in Table B.1 of Appendixational aspectshematicalmodelwas implementedandsolvedusing thedeling software tool gPROMS (Process Systems Enter-A high-index DAE system (index>1) was veriedsteady state concentration values are considered ases for the biological and chemical species. In all caseof total non-active biomass, 6% for active acidogensnd methanogens (XAc), and 2.8% for the other activeassumed. The total CPU time required to solve case stud-d in the following section is about 7 s on an 800MHzPC.and discussionudiesing set of experimental data obtained by Veronez et al.t al. [29] and Brito and Melo [12], based on the start-rational performance of EGSB reactors, is used here tovalidate the proposed model. By simplicity, the casee been named as R1, R2 and R3, respectively. Detailsesign, sludge and wastewater characteristics, and oper-ditions are summarized in Table 1.adjustmente two types of parameters that have to be calculateddjustment: (1) parameters that can be measured andrted in the original sources; and (2) empirical parame-to kinetics and biomass transport in EGSB reactors. AsM. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324 319Table 2Experimental and predicted values of the efuent stream for R1 [28].Step pH Total COD (mgdm3) Soluble COD (mgdm3) VFA (mgHAcdm3) VSS (mgdm3)Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod.I 7.0 0.1 6.89 119 15 117 98 16 93 16 2 19 19 6 19II 6.9 0.2 6.91 143 25 136 106 10 103 16 1 25 35 9 29III 7.7 0.1 7.75 181 40 190 130 25 112 23 1 29 32 9 69Table 3Experimental and predicted values of the efuent stream for R2 [29].Step pH Total COD (mgdm3) Soluble COD (mgdm3) VFA (mgHAcdm3) VSS (mgdm3)Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod.I nd 6.90 68 29 67 42 21 36 nd 19 13 8 22II nd 6.73 87 21 84 55 22 44 nd 21 8 3 29III nd 6.87 79 26 86 44 15 44 nd 20 24 17 30mentioned, the lack of information on granule characteristics andstatic bed conditions requires a sensitivity analysis ofmodel resultsto calculate these parameters and make the model workable. Thevalues nally assumed are written between parentheses in Table 1.Some aspects on the inuence of these parameters are discussed atthe end of Section 4.3.Here theeters. Only(kr) is estimphysico-chesists in an ivalue.Arbitrarmodel. Biorwith a meaposed by c(meat extrawas used toin three steby increasiconcentratiFig. 1related to(krg=7.3200 kr wera decreaseFig. 1. Sensitirupture (kr).050005000400450R1Step IIIStep IIStep Ig dm-3) Influent TCOD Effluent TCOD Effluent SCOD Effluent VSS010203040500,00,10,20,30,46,06,57,07,58,0 Biogas (dm3 d-1) pH,0,5,0150020002500 Uo (dainterest is focused in calculating the empirical param-the novel parameter specic rate of granule ruptureated via simulation, and no other biochemical andmical parameters are modied. The procedure con-terative calculation of model variables changing the krily, the rst case study (R1) was selected to adjust theeactor R1 was used for treating a synthetic wastewatern COD concentration of 444mgdm3, basically com-arbohydrates (sucrose, starch and cellulose), proteinsct) and lipids (soy oil) [28]. A commercial detergentemulsify the lipid. The experimental setup consistedpped disturbances in the organic loading rate (OLR)ng the feed ow rate, keeping the same inuent CODon (Table 1).shows the sensitivity analysis of model resultskr for R1. An initial value of 11022 dmd2 g11011 dm2 g1, see Eq. (1)) was used, and values up toe evaluated. An increase in the parameter kr causesin the granule mean diameter and solid holdup; the112Concentration (m VFA (mg dm-3)334 H (dm)b cvity analysis of model results related to the specic rate of granule8060402000,00,20,42,50,000,020,045001000 LTime (d) dbp (dm)m d-1)Fig. 2. R1 simulation results: (a) TCOD, SCOD and VSS concentration values; (b)VFA (as acetate) concentration, biogas ow rate and pH; and (c) bed height, liquidholdup, liquid upow velocity and granule diameter.320 M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324050100150200R2Concentration (mg dm-3) Influent TCOD Effluent TCOD Effluent SCOD Effluent VSS6,06,57,07,51520253035 pH VFA (mg dm-3)00,00,20,45101520 L H (dm)abcFig. 3. R2 simVFA (as acetatholdup, liquidterminal seheight of rein the eftion of singbioreactor.As obsertal and predobtained foresponses fvariables su(VFA, as acevertical dotdisturbanceTable 2dicted valueheight of 2.equal to 0.402004006008000001200R31,52,02,53,0aStep IIIStep IIStep I101214 Bio1TCOD (mg dm-3) Biogas (dm3 d-1))b02468gas (dm3 d-1)300250200150100500,000,010,020,030,04300600900Time (d) dbp (dm) Uo (dm d-1)ulation results: (a) TCOD, SCOD and VSS concentration values; (b)e) concentration, biogas ow rate and pH; and (c) bed height, liquidupow velocity and granule diameter.ttling velocity decreases and thus, an increase in theactor is veried. In practice, the total COD concentrationuent stream increases due to a higher concentra-le suspended cells, and small granules can leave theved in Fig. 1, a good agreement between experimen-icted values of total COD at steady state conditions isr a value around 100 kr (11020 dmd2 g1). Modelor the COD concentration and other main macroscopicch as volatile suspended solids (VSS), volatile fatty acidstate), biogas ow rate and pH, are depicted in Fig. 2. Theted lines indicate the time duration of each step-types.shows a comparison between experimental and pre-s measured at the end of each step-type disturbance. A48dm (40% of the maximum height) and bed porositywere assumed for calculating the static bed condition.00,00,51,0 L H (dmFig. 4. R3 simuand (b) biogas4.3. ModelCase sturesults areshow a comeach case. Athe end of emodied, i.R2 expethe efuent[29]. The bvarying the0, and 40%Vand suspentively. SoluDuring twith a UASulation valalthough aever, the auunits.The origthe feasibilsoluble wa[12] conduthe bioreacapproach thin the originat the react70mgdm3In Sectioculate the bassumed fo Influent TCOD XsAc 1.6XsAc 2.3XsAc403020100,000,020,040,066,06,57,07,5Time (d) dbp (dm) pHlation results: (a) TCOD concentration for different initial conditions;ow rate, pH, bed height, liquid holdup and granule diameter.validationdies R2 an R3 are used for model validation. Simulationdepicted in Figs. 3 and 4, respectively. Tables 3 and 4parison between predicted and experimental data fors mentioned, the simulated values were measured atach step-type disturbance. Model parameters were note. they are the same as used in case R1.rience was carried out in a pilot EGSB reactor fed withfrom a full-scale UASB treating municipal wastewaterioreactor was monitored in three different periods byupow velocity according to Table 1. Values of 30, 30,SS were assumed to model carbohydrate, protein, lipiddedbiomass composition in the inuent stream, respec-ble COD was measured as acetic acid concentration.he rst step-type disturbance, bioreactor R2 operatedB conguration (recirculation ow equal to zero). Sim-ues resulted also appropriate for this stage (Fig. 3),completely mixed ow pattern was considered. How-thors do not recommend using this model for UASBinal research in the case study R3 was focused onity of EGSB reactors for the treatment of low strengthstewaters; acetic acid was used as substrate. Authorscted a study of the substrate concentration prole intor and concluded that the EGSB mixing propertiesose of an ideal continuous stirred tank reactor. Fig. 1al source [12] shows that the acetic acid concentrationor inlet is 770mgdm3 and immediately falls down toat the reactor dimensionless length equal to 0.01.n 3 are described the initial conditions assumed to cal-iomass composition in the granule. Same values werer all case studies; however, the total biomass concen-M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324 321Table 4Experimental and predicted values of the efuent stream for R3 [12].Step pH Total COD (mgdm3) SolubleCOD(mgdm3) VFA (mgHAcdm3) VSS (mgdm3)Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod. Exp. Mod.I 7.0 0.2 7.09 nd 87 nd 74 70 70 nd 14Table A.1Homogeneous reaction rates (R) and mass transfer and transport process rates (T) for phase componentsa.ikjRjik+jT jikiXSiaS[iXSia kdiXSia krXSia ] 1623XSinaS[kdiXSia kbhXSina krXSina] 1623XLiaL[iXLia kdiXLia] + SkrXSia 1623XLinaL[kdiXLia kbhXLina ] + SkrXSina1623Xij=12i,jkHid,jLXj 1115Sij=310i,jj(SXSja + LXLja) +j=12i,jkHid,jLXj +j=12fbio,ii,jkHid,j j=1118(SXSjna + LXLjna) 17,10Sij=310i,jj(SXSja + LXLja) L(kLa)i(Si ICODKH,ipgas,i) 89Eij=45i,jj(SXSja + LXLja) kde,iEi 2425pgas,i stpgas,TL(kLa)i(Si/ICOD KH,ipgas,i) 89a See Appentration andthe granuleup. The conthe substragrowth-decsame time,the initial csumption (uare reachedAlthougthe hydrodhydrodynamFigs. 2(c),bic rexas inholdevenses.n exlysised toancsamTable A.2Variables (i) anVariableXCHXPXLiXICHXIPSGlSAASLCFASHVaSHBuSHPrSHAcSCH4SICSINXbioECHEPa Mass balacomponents (adix C for notation.substrate composition vary. It explains the decrease inmean diameter at the beginning of the reactor start-centration of some biological species decreases whente composition results insufcient, and the net biomassay-hydrolysis-disaggregating rate is negative. At thewhen the substrate is rich in a specic metabolite andoncentration of the degrader species increases, the con-ptake) rate increases although steady state conditionsat the same time (Fig. 4).h the original sources are not oriented to analyzeynamic performance of EGSB bioreactors, the mainanaerocan bechangeliquidtheseprocesAs aity anaassumperforming theic variables for R1, R2 and R3, have been added in3(c) and 4(b), respectively. Since the gas holdup into 2Ho, a dothe COD remd processes (j) taken into account in the anaerobic digestion modela.Description iCarbohydrate 11Protein 12Lipid 13Inert carbohydrate 14Inert protein 15Glucose 1Amino acid 2LCFA 3Valerate 4Butyrate 5Propionate 6Acetate 7Methane 8Inorganic carbon 9Inorganic nitrogen 10Biomass 1623Enzymes in process j=1 24Enzymes in process j=2 25nces are expressed in grams of chemical oxygen demand per liter per day (gCODL1 dtmL1 d1) and enzymes (AUL1 d1). Enzymatic activity is measured in Anson Unit (AUeactors is less than 0.03, changes in the bed porositymined from the liquid holdup (L) variations. Suddenthe liquid upow velocity (Uo) cause variations in theup and thus, in the bed height prole. As observed,ts are more marked than those caused by biologicalample, the case study R3 is selected to show a sensitiv-of model results related to some parameters that werecarry out the simulation. Fig. 5 shows the bioreactore for different initial values of bed height (Ho) keep-e initial biomass concentration. For a value equivalentuble amount of biomass is present in the reactor, andoval rate increases during the rst days. Contrarily, forProcesses jCarbohydrate hydrolysis 1Protein hydrolysis 2Lipid hydrolysis/glycerol uptake 3Glucose uptake 4Amino acid uptake 5LCFA uptake 6Valerate uptake 7Butyrate uptake 8Propionate uptake 9Acetate uptake 10XGlic decay 11XGL decay 12XAA decay 13XLCFA decay 14XVa decay 15XBu decay 16XPr decay 17XAc decay 18CH4 mass transfer T8CO2 mass transfer T91), except for inorganic carbon and nitrogen (mol L1 d1), gas phase).322 M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324Fig. 5. Sensitivity analysis of model results related to the initial value of bed height(Ho) for R3.initial values less thanHo, the bioreactor efciency decreases. How-ever, the time to reach the steady state conditions and the efuentCOD values are the same for all cases.At less bed height, the OLR increases for the same feed owrate, and aIn Fig. 5, higare observeIn generbut the meatypically rasludge gran1000 and 1are depicteconcentratiExperimattending tocles with leextension hbed expanswith granulbiomass cosame in botvalues. Thewashout wsion.Fig. 6. Sensitimean diamete5. ConclusionsA dynamic model was proposed and adjusted to simultaneouslycompute the dynamics of the phases and their components in anexpanded sbioreactor pproles sucinformationincompleteysis. Basedconclusions(1) The EGrate of gter. A vaAn incrremovasuspend(2) A sensibed heibetweethe bioring thethe steacasecausle dgeneerims ofedoki etavioreace EGSiablen. Thbeturbawledautal decionyT) adix Ahigh biomass concentration is obtained in the reactor.her values of solid holdup and granule mean diameterd for this condition.al, the granular diameter ranges between 0.5 and 5mm,n diameter of sludge granules in full-scale installationsnges between 1 and 3mm. On the other hand, largerules have low density whose values range between050gdm3 [11]. The effects of granule mean diameterd in Fig. 6; the same sludge density and initial biomasson are considered.ental data on hydrodynamic behavior of EGSB reactorssettling characteristics of granules is scarcely. Bioparti-ss mean diameter are assumed to be uidized in higheraving a less terminal settling velocity. Thus, a higherion is predicted for granules of 1.6mm when comparedes of 2.3mm (Fig. 6). The solid holdup, bed height andncentration varies so that the total biomass results theh cases and a similar prole is obtained for total CODrisk of working with small bioparticles is the biomasshen high upow velocities are applied for bed expan-allterpro(3) Inexpciebasidabehbio(4) ThvartiocandisAcknoTheNacioncia Na(ANPCAppenvity analysis of model results related to the initial value of granuler (dbp) for R3.A.1. BiocheThe anaeis selectedinvolves twcarbohydraism trophicbacteria-aceride) hydacetogens;; (6) butyra(8) aceticlavious resulmodel, andproposed bprocesses oludge bed reactor. Three case studieswere investigated;erformances were analyzed through the main variableh as pH, COD, VFA and VSS concentration. Since theon sludge characteristics and static bed condition is, some values had to be assumed after a sensitivity anal-on the results obtained during this study, the followingcan be drawn:SB model can be adjusted by calculating the specicranule rupture, without modifying any other parame-lue of 11020 dmd2 g1 was obtained via simulation.ease in this parameter predicts a decrease in the CODl efciency caused by a higher concentration of singleed cells.tivity analysis of model results related to the staticght and bioparticle diameter, showed the interactionn hydrodynamic events and biological performance ofeactor. The former modies the COD removal rate dur-rst days of the reactor start-up but the time to reachdy state conditions and COD values are the same fors. On the other hand, an increase in the granule diame-es a decrease in the bed expansion, although the CODoes not vary for the same biomass initial conditions.ral, model predictions agree satisfactorily with theental observations obtained during the start-up poli-bioreactors. It seems to indicate that the EGSB model,n the anaerobicdegradation schemeproposedbyAngel-al. and the GBW model to explain the hydrodynamicr of reactor, is able to reproduce the main successes oftor.B model allows predicting proles of non-macroscopics such as substrates and biological species concentra-us, additional information of the investigated systemobtained. The model is able to resist strong numericalnces to represent a stepby step start upof the reactor.gementshors acknowledge nancial support from ConsejoInvestigacionesCientcas yTcnicas (CONICET), Agen-al para la Promocin de la Ciencia y la Tecnologand Universidad Nacional del Litoral of Argentina..mical and physico-chemical processesrobic digestion model proposed by Angelidaki et al. [4]here to represent the degradation scheme. The modelo enzymatic processes: hydrolysis of (a) undissolvedtes and (b) undissolved proteins, and eight microorgan-groups: (1) glucose-fermenting acidogens; (2) lipolyticidogenesis from glycerol combined with lipid (triglyc-rolysis-; (3) long-chain fatty acid (LCFA)-degrading(4) amino acid-degrading acidogens; (5) propionate-te-; and (7) valerate-degrading acetogens; and nallystic methanogens. This selection is sustained by pre-ts on model application and simplicity of the biomasshydrogen stoichiometry [30,31]. The hydrolysis modely Fuentes et al. [16] is used to represent the enzymaticf biopolymers.M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324 323Fig. A.1. Biochemical processes and interaction between aggregated and suspended biomass.Fig. A.1 shows a schematic representation of the interac-tion between aggregated and suspended biomass involving thegrowth-uptake, death, hydrolysis and disaggregating processes.Mathematical relationships on reaction and mass transfer processrates are summarized in Table A.1. Variables (i) and processes (j)taken into account in the anaerobic digestion model are describedin Table A.2.In Table A.1, biological species numeration (i=1623) has beenextended to suspended single cells and aggregated biomass ofthe same (active and non-active) species i. The specic growthand death rates are assumed to be the same for suspended andaggregated biomass. Non-active biomass is considered as particu-late material subjected to hydrolysis. Aspects related to the novelparameter specic rate of granule rupture kr, were discussed inSection 2.1.Equilibrium in solution model involves mass balance equationsfor total concentration of LCFA, volatile fatty acids (acetic, pro-pionic, butyric and valeric), inorganic carbon, inorganic nitrogen,phosphate,of the acidare taken frThe liqubehavior, anfor gas phavapor) is exWater vapor pressure is calculated by an Antoine-type equation.The gasliquid mass transfer coefcient (kLa) varies a great dealdepending on mixing, temperature and liquid properties; for sim-plicity and as recommended by Batstone et al. [32], the same kLavalue is used for all gases.Biochemical rate equation matrixes, kinetic and physico-chemical parameters are described in the original sources[4,1416,3032].Appendix B.B.1. Generalized bubble and wake model (GBWM)Thewake concept considers the three phase expanded bed to becomposedof: (1) the gas bubble region; (2) thewake region; and (3)the solidliquid uidization region. The porosity in the solidliquiduidization regioncanbeexpressedby theRichardsonandZaki [24]equation, thewake regionmoves at the samevelocity as thebubble,e porationticlearizeterisonsted, tTable B.1GBWM equati ompoGBW ModelLiquid phaseHoldup k = 3VelocitySolid phaseHoldupVelocityGas phaseHoldupVelocityParameters:Terminal sExpansionPhase compLiquid phaSolid phasGas phaseother anions, and other cations. The relationshipsbase equilibrium model and temperature dependenceom Batstone et al. [32].idgas mass transfer is modeled assuming ideal gasd constant total gas phase pressure. The mass balancese components (methane, carbon dioxide and waterpressed as a function of its partial pressure (Table A.1).and thuidizare parsummcharacA cassumons to calculate uidization characteristics and mass balance equations for phase cL =[UlUt k UgUt]1/n[1 G kG]11/n + kG,Uo =Ul +Ug = lUl + gUgS + L + G =1US = 0UgG= Ug1S +Ul1S + 0.1016 + 1.488(Ug1S)0.5Ug = QgoutAc = dGHdt+ VAcGi,jT jiGettling velocity Ut = 17.3L+[299.29L2+1.344gdag3L(SL)]0.50.672dagLcoefcient n = 4.4Re0.1t , 1 < Ret < 500onent mass balance equationsse AcdLiLHdt= QliniL QloutiL + V(jRjiL+jT jiLe AcdSiSHdt= V(jRjiS+jT jiS)AcdGiGHdt= QgoutiG + V(jRjiG+jT jiG)osity of this region can be different as the solidliquidone. The simplied wake theory, i.e. the liquid wakesfree, is used to calculate the liquidholdup [33]. Table B.1s the mathematical equations to calculate uidizationtics.ant volumetric ow through the uidized bed ishe velocity in the bed cross section is approximatelynents..53L exp(5.08G)), iL= 1AcUo [QfiLf + QriLz=H ], Qlin = Qo = UoAc, Qlout = UlAc324 M. Fuentes et al. / Chemical Engineering and Processing 50 (2011) 316324equal to uid velocity at the reactor inlet (Uo =Ul +Ug). The gen-erated gas is assumed to be separated from the multiphase streamat the top of the reactor column, and thus, the gas phase ow rateat the reactor inlet is equal to zero. Since the solid is conned in thecontrol voluare assumebeen includAppendix CA ard dif big grH heICOD ink spkLa liqKH Hen exP, p prQ oR hoRe ReS soT mV voU suX bitioz axGreeks ph be m prst ga de sp mSubscriptsbh bibp bic red bif feG, g gaHid hyi phin inj biL, l liqo upout our reS sot teT toReferences[1] A.W.A. Danaerobicperature ranging from 8 to 30 C, in: E.R. Hall, P.N. 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Eng. 10 (1970) 3741.A coupling model for EGSB bioreactors: Hydrodynamics and anaerobic digestion processesIntroductionMathematical modelAnaerobic digestion modelBioparticle modelHydrodynamics modelComputational aspectsResults and discussionCase studiesModel adjustmentModel validationConclusionsAcknowledgementsBiochemical and physico-chemical processesGeneralized bubble and wake model (GBWM)NomenclatureReferencesNomenclatureReferences


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