A

IIA6TsatoBegSG©

K

1

dsdvt(a

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nm

1d

Biochemical Engineering Journal 38 (2008) 259–269

Application of two anaerobic digestion models to biofilm systems

Mauren Fuentes a,b, Nicolas J. Scenna a,c, Pıo A. Aguirre a,d,∗, Miguel C. Mussati a,c

a INGAR Instituto de Desarrollo y Diseno (CONICET-UTN), Avellaneda 3657, (3000) Santa Fe, Argentinab Depto de Ciencias Basicas - UTN FRSF, Lavaise 610, (3000) Santa Fe, Argentina

c CAIMI-UTN FRR, Zeballos 1341, (2000) Rosario, Argentinad Facultad de Ing. Qca, UNL, Santiago del Estero 2829, (3000) Santa Fe, Argentina

Received 7 August 2006; received in revised form 26 July 2007; accepted 28 July 2007

bstract

This work deals with a comparative analysis of two alternative anaerobic digestion models proposed by Batstone et al. [D.J. Batstone, J. Keller,. Angelidaki, S.V. Kalyuzhnyi, S.G. Pavlostathis, A. Rozzi, W.T.M. Sanders, H. Siegrist, V.A. Vavilin, Anaerobic Digestion Model No. 1 (ADM1)WA Task Group for Mathematical Modelling of Anaerobic Digestion Processes, IWA Publishing, London, UK, 2002] and Angelidaki et al. [I.ngelidaki, L. Ellegaard, B.K. Ahring, A comprehensive model of anaerobic bioconversion of complex substrates to biogas, Biotechnol. Bioeng.3 (5) (1999) 363–372], and their application to biofilm systems. Bioreactors are modeled as dynamic (gas–solid–liquid) three-phase systems.he experimental set-up consists of two mesophilic (36 ± 1 ◦C) lab-scale anaerobic fluidized bed reactors, which were loaded with sand as inertupport for biofilm development. The experimental protocol is based on step-type disturbances applied on the inlet substrate concentration (glucosend acetate-based feeding) and on the feed flow rate considering the criterion of maximum efficiency. The predicted and measured responses ofotal and soluble chemical oxygen demand (COD), volatile fatty acid concentrations, biogas production rate and pH are investigated. Under theperating conditions evaluated in this investigation, the anaerobic digestion model proposed by Angelidaki et al. [I. Angelidaki, L. Ellegaard,.K. Ahring, A comprehensive model of anaerobic bioconversion of complex substrates to biogas, Biotechnol. Bioeng. 63 (5) (1999) 363–372]

nsures the best prediction. Parameters related to non-active biomass composition, disintegration and hydrolysis should be revised to achieve aood agreement between experimental and predicted values using the model proposed by Batstone et al. [D.J. Batstone, J. Keller, I. Angelidaki,.V. Kalyuzhnyi, S.G. Pavlostathis, A. Rozzi, W.T.M. Sanders, H. Siegrist, V.A. Vavilin, Anaerobic Digestion Model No. 1 (ADM1) IWA Taskroup for Mathematical Modelling of Anaerobic Digestion Processes, IWA Publishing, London, UK, 2002].2007 Elsevier B.V. All rights reserved.

ee-ph

Atasthc

eywords: Anaerobic digestion model; Biofilms; Fluidized bed bioreactor; Thr

. Introduction

In anaerobic digestion, complex organics such as carbohy-rates, proteins, and lipids are first hydrolyzed by enzymes tougars, amino acids, and fatty acids, respectively. These interme-iate products are then degraded by acidogen microorganisms toolatile fatty acids (VFAs), which are further degraded by ace-

ogens forming acetate, carbon dioxide (CO2), and hydrogenH2). Last, acetate and H2/CO2 are converted by acetoclasticnd H2-utilizing methanogens, respectively, to methane (CH4).

∗ Corresponding author at: INGAR - Instituto de Desarrollo y Diseno - Avel-aneda 3657, (3000) Santa Fe, Argentina. Tel.: +54 342 4534451;ax: +54 342 4553439.

E-mail addresses: mfuentes@ceride.gov.ar (M. Fuentes),scenna@ceride.gov.ar (N.J. Scenna), paguir@ceride.gov.ar (P.A. Aguirre),mussati@ceride.gov.ar (M.C. Mussati).

sesbooodt

369-703X/$ – see front matter © 2007 Elsevier B.V. All rights reserved.oi:10.1016/j.bej.2007.07.013

ase system; Dynamic modeling and simulation

cidogens grow faster and are less sensitive to pH variationhan acetogens and methanogens. This usually results in theccumulation of organic acids and pH decreases, leading to theuppression of methanogenics activity and, in some cases, eveno process failure [1–7]. Thus, an anaerobic digestion schemeas to be adequately described by the biochemical and physico-hemical processes.

The model proposed by Batstone et al. [1] is known as atandard: the Anaerobic Digestion Model No. 1 (ADM1). How-ver, some processes related to glucose alternative products,ulfate reduction and sulfide inhibition, nitrate, weak acid andase inhibition, long chain fatty acid (LCFA) inhibition, acetatexidation, homoacetogenesis and solids precipitation have been

mitted. Several benefits were expected by authors from the usef this generalized model as a common basis for further modelevelopment and validation studies, and assisting technologyransfer from research to industry.

260 M. Fuentes et al. / Biochemical Engineering Journal 38 (2008) 259–269

Nomenclature

A aread diameterf biomass composition (fraction)g gravityH heightI inhibition functionICOD index, (g COD mol−1)k specific rate coefficient, GBWM parameterkLa liquid–gas mass transfer coefficientKH Henry’s coefficientn expansion coefficientP,p pressureQ flow rateR homogeneous reaction rateRe Reynolds numberS soluble species concentrationT mass transfer and transport process rate (inter-

face)V volumeU superficial velocityW particle loadX biomass concentration or non-soluble species

concentrationz axial direction

Greeks symbolsδ biofilm thicknessε porosity, phase holdup (volumetric fraction)φ mass or molar concentrationμ microorganism growth rate, viscosityν process rate coefficientνst gas molar volumeρ densityω specific energy dissipation rate

Subscriptsbh biomass hydrolysisbp bioparticlec reactor columnd biomass deathE detachmentf feedG, g gasHid hydrolysisi phase component indexin inletj biochemical and physico-chemical process indexL, l liquidout outletp particler recycleS solidT total

Fig. 1. Anaerobic degradation steps for model M1 [1]: (1) acidogenesisfrom sugars (glucose); (2) acidogenesis from amino acids; (3) acetogenesisfrom LCFA; (4) acetogenesis from propionate (HPr); (5) acetogenesis frombh

gpcsgdMsr

gdthdsaV

ssrgbIa

utyrate (HBu) and valerate (HVa); (6) acetoclastic methanogenesis; and (7)ydrogenotrophic methanogenesis.

Previously to the academic consensus among several researchroups on developing the ADM1 model, Angelidaki et al. [2]roposed one of the most widely used model that describes theomplex substrates degradation. In this model, some processesuch as lipid digestion (by including a microorganism trophicroup for glycerol acidogenesis) and LCFA inhibition, are wellescribed. By simplicity, these models are hereafter named as1 and M2, respectively. Figs. 1 and 2 represent the degradation

teps and microorganism trophic groups assumed in M1 and M2,espectively.

M1 and M2 were described taking into account suspendedrowth treatment systems. Differences between these two degra-ation schemes, concerning to the description of microorganismrophic groups, uptake kinetics including inhibitory factors, andydrogen stoichiometry, make them appropriate to be used inifferent scenarios (see Section 2.1). However, both modelshould be able to represent the macroscopic behavior of thenaerobic digestion system, even more if a simple (glucose andFA-based) substrate is fed to the bioreactor.In the present work, these models are applied to a biofilm

ystem. It requires the modeling of the interaction betweenuspended and attached biomass. An anaerobic fluidized bedeactor (AFBR) is used as reactor configuration. As microor-anisms attach on the bare support and develop forming

iofilm, a bioparticle model is required to model an AFBR.n the multispecies biofilm model, biomass is considered ascontinuum; that is, biomass is mathematically characterized

M. Fuentes et al. / Biochemical Enginee

Fig. 2. Anaerobic degradation steps for model M2 [2]: (1) acidogenesis fromglycerol combined with lipid (triglyceride) hydrolysis; (2) acidogenesis fromsL(

bs

cirmaci(t

atoapmio[

otitolc

2Ctiac

2

2

2p

aamoet

dis(

gpatscnif

dr

tadti

tv

itb

ugars (glucose); (3) acidogenesis from amino acids; (4) acetogenesis fromCFA; (5) acetogenesis from butyrate (HBu); (6) acetogenesis from valerate

HVa); (7) acetogenesis from propionate (HPr); (8) acetoclastic methanogenesis.

y average quantities such as the concentration of microbialpecies.

Some hydrodynamic considerations are needed. Fluidizationharacteristics such as holdup and velocity of each phase presentn the reactor are important due to their influence on the systemesidence time, reactor size, specific biofilm superficial area,ass transfer and biofilm detachment processes. Indeed, char-

cteristics of fluidization in a bioreactor are functions of biofilmoncentration. Therefore, an AFBR model is intended throughts four major modeling tasks: (1) the anaerobic digestion model;2) the biofilm model; (3) the bioparticle model, and finally (4)he hydrodynamic model.

The quantitative calibration of the model parameters is notn easy task with dynamic models in anaerobic digestion duehe lack of some measurements, the scarcity and uncertainty ofthers, and the uncertainty related to the process dynamics. M1nd M2 are based in accepted sets of kinetic expressions andarameters. In this work, parameters related to biofilm detach-ent processes need to be estimated in order to model the

nteraction between suspended and attached biomass. Resultsf parameter estimation from a previous paper are here used8].

The paper is oriented firstly to a simulation-based comparisonf models M1 and M2. Then, models predictions and experimen-al data from fluidized bed reactors containing microorganismsmmobilized on sand particles for anaerobic treatment of syn-

hetic substrates are analyzed. The focus is on the applicationf both models and their accuracy to predict the substrate uti-ization and gas production rates under different organic loadingonditions.

fr(i

ring Journal 38 (2008) 259–269 261

In this context, the paper is organized as follows: in Section, main hypotheses and mathematical equations are presented.haracteristics of bioreactors and inert support materials, and

he experimental protocol are described in Section 3. The exper-mental data and simulation results for both reactors using M1-nd M2-based models are presented in Section 4. Finally, con-lusions are drawn in Section 5.

. Mathematical model

.1. Main modeling hypotheses

.1.1. Anaerobic digestion model: biochemical andhysico-chemical processes

Basically, both M1 and M2 models include three over-ll biochemical (cellular) steps: acidogenesis (fermentation),cetogenesis (anaerobic oxidation of organic acids) andethanogenesis. All extracellular steps are assumed to be first

rder, which is an empirical function reflecting the cumulativeffect of a multi-step process. Cellular kinetics is described byhree expressions: uptake, growth and decay.

While M1 includes two extracellular stages: (non-biological)isintegration of particulate material and hydrolysis, M2 onlyncludes the last one. Processes of hydrolysis, acidogene-is and acetogenesis have a number of parallel reactionsFigs. 1 and 2).

For M1, composite particulate waste is assumed to be homo-eneous, which disintegrates to carbohydrate, protein and lipidarticulate substrate. The complex particulate pool is also useds a pre-lysis repository of dead biomass (Fig. 1). Therefore,he disintegration step is intended to include an array of stepsuch as lysis, non-enzymatic decay, phase separation and physi-al breakdown (e.g. shearing). On the other hand, M2 interpretson-active biomass decomposition through an instantaneous dis-ntegration step and hydrolysis of its carbohydrate and proteinractions (Fig. 2).

For M1, inert particulate and soluble materials are produceduring the disintegration process. In M2, inert particulate mate-ials are produced during carbohydrate and protein hydrolysis.

M2 includes a trophic group and couples the stoichiome-ry of lipid (triglyceride) hydrolysis and glycerol acidogenesis,nd considers LCFA and propionate as digestion products. M1oes not include a trophic group for these stages by consideringhem as high rate processes, and assumes LCFA and glucose asnstantaneous digestion products.

In M1, butyrate and valerate are thought to be degradated byhe same microorganisms. In M2, acetogenesis of butyrate andalerate is carried out by different microorganism groups.

M1 explicitly describes the stoichiometry of hydrogen andncludes a trophic group. On the contrary, M2 describe the syn-ropism among acetogens and hydrogenotrophic methanogensy combining stoichiometry of these degradation stages.

Separation of biocidal and biostatic inhibition is important

or modeling, as the first mainly influences biomass decayate, while the second influences kinetic uptake and growthmaximum uptake, yield, half saturation parameters). Biostaticnhibitions such as product (acetate, hydrogen) inhibition, weak

2 nginee

atiws

ibroabctV

oerPiie

mil

a

mabctVt

meMtmntvligpa

pmtcoefficients (acid/base coefficients K , and Henry’s coefficients

TH

φ

X

X

X

X

S

S

X

X

p

62 M. Fuentes et al. / Biochemical E

cid/base (including VFA and NH3) inhibition and pH inhibi-ion are included in M1 and M2. The biocidal effect of LCFA isncluded in M1. It has a significant impact on process operationhen a lipid-rich waste is fed. H2S inhibition is not included,

ince sulfate reduction is not modeled.Besides the competitive LCFA inhibition function only

ncluded in M2, there are some other differences between inhi-ition functions assumed in M1 and M2 (see Figs. 4 and 6,espectively). The empirical pH inhibition function considersnly low pH inhibition in M1 and it is different for acidogens,cetogens and methanogens. In M2, both high and low pH inhi-ition and the same function for all microorganism groups isonsidered. For acetogens in M1, a non-competitive H2 inhibi-ion function is assumed. In M2, the hydrolysis inhibition byFA is modeled.A “temperature controlled” system with small changes in

perating temperature (±3 ◦C) is assumed, and kinetic param-ters reported by Batstone et al. [1] for mesophilic and highate operating conditions are here used for both M1 and M2.arameters related with M2 degradation stages non-described

n Batstone et al. [1], such as glycerol acidogenesis and somenhibitions (e.g. LCFA inhibition) are extracted from Angelidakit al. [2].

The physico-chemical system, defined as non-biologicalediated processes but relating to the biochemical rates,

s described through the equilibrium in solution model and

iquid–gas mass transfer model.

Equilibrium in solution model includes the system charge bal-nce (electroneutrality condition) for calculating pH. It involves

Kwa

able 1omogeneous reaction rates and mass transfer and transport process rates for phase c

ik

∑j

Rj

ik+

∑j

Tj

ik

Sia

εS[μiXSia

− kdXSia

− kEωXSia

]Sina

εS[kdXSia

− kbhXSina

− kEωXSina

]Lia

εL[μiXLia

− kdXLia

] + εSkEωXSia

Lina

εL[kdXLia

− kbhXLina

] + εSkEωXSina

i

∑

j={

5−12(M1)

3−10(M2)

νi,jμj(εSXSja

+ εLXLja

) +∑

j={

2−4(M1)

1−2(M2)

νi,jkHid,jεLXj +

∑j=11−18(M2)

(εSXSjna

+ εLXLjna

)

i

∑

j={

5−12(M1)

3−10(M2)

νi,jμj

(εSXS

ja+ εLXL

ja

)

−εL(kLa)i(Si − ICODKH,ipgas,i

)i

∑

j={

2−4(M1)

1−2(M2)

νi,jkHid,jεLXj + νi,j=1(M1)kdisεLXC

C −kdisεLXC + kdis

∑j=13−19

(εSXSjna

+ εLXLjna

)

gas,i νstpgas,TεL(kLa)i(Si/ICOD − KH,ipgas,i)

a See sections “Nomenclature” and “Appendix A” for notation.

ring Journal 38 (2008) 259–269

ass balance equations for total concentration of volatile fattycids (acetic, propionic, butyric and valeric), inorganic car-on, inorganic nitrogen, phosphate, “other anions”, and “otherations”. Specifically in M2, as free LCFA inhibition is modeled,his component (as palmitic) is included in a similar manner toFAs. The relationships of the acid–base equilibrium model are

aken from Batstone et al. [1].Gas phase components differ in M1 and M2. Besides

ethane, carbon dioxide and water vapor mass transfer mod-led in both M1 and M2, hydrogen mass transfer is added in1. Because transfer of CH4, CO2 and H2 are liquid film con-

rolled, and the diffusivities are similar, they have liquid–gasass transfer coefficient (kLa) values of a similar order of mag-

itude. Values for kLa vary a great deal depending on mixing,emperature and liquid properties; for simplicity, the same kLaalue for all these gases is recommended to be used [1]. Theiquid–gas mass transfer is modeled assuming ideal gas behav-or, and constant total gas phase pressure. The mass balance foras phase component i is expressed as a function of its partialressure pgas,i (Table 1). Water vapor pressure is calculated byn Antoine-type equation.

The overall effect on the system due to changes inhysico-chemical parameters with temperature is generallyore important than that due to changes in biochemical parame-

ers. The van’t Hoff equation describes the variation of equilibria

a

H) with temperature, except the Ka values for the organic acids,hich vary by a small amount in operating temperature range,

nd can be assumed to be constant.

omponentsa

i=

17–23 (M1), 16–23 (M2)17–23 (M1), 16–23 (M2)17–23 (M1), 16–23 (M2)17–23 (M1), 16–23 (M2)∑

j=1−2(M2)

fbio,iνi,jkHid,j × 1–7, 11 (M1), 1–7, 10 (M2)

8–10 (M1), 8–9 (M2)

12, 14–16, 24 (M1), 11–15 (M2)

13 (M1)

8–10 (M1), 8–9 (M2)

ginee

2

npfivbbtito

g

taf

fcst

2

sg

td

2

pacmng

dfat

taa

mRdbaf

2

scossbwt

ε

w

k

octtppwumaRwa(nn

U

n

ch

avc

M. Fuentes et al. / Biochemical En

.1.2. Biofilm modelConstant wet biofilm density and homogeneous biofilm thick-

ess are assumed. Species distribution is the same in all biofilmoints. No mass transfer limitations in the biofilm and liquidlm are assumed. The substrate concentration has the samealue throughout the biofilm. From the conceptual relationshipetween substrate concentration and depth (thickness) within aiofilm proposed by Williamson and McCarty [9], the evalua-ion of substrate (glucose and acetate) mass transfer resistancen the bulk liquid and within biofilm resulted in non-limited sys-ems for the scenarios here studied [10]. This result is commonlybserved in thin anaerobic biofilms.

No bubble formation inside the biofilm, and instantaneousas transfer from biofilm to liquid bulk are assumed.

Specific growth (μ) and death (kd) rates are assumed to behe same for suspended and attached biomass of a species i. Inddition, the specific biomass hydrolysis rate (kbh) is the sameor all species.

The biofilm detachment rate is modeled as a first-orderunction on the specific energy dissipation rate (ω), and massoncentration of each attached microbial species i [10,11]. Thepecific detachment rate (kE) is an empirical coefficient assumedo be the same for all biological species (Table 1).

.1.3. Bioparticle modelHomogeneous biofilm distribution on support particles, con-

tant density and diameter of support particles, and sphericaleometry are assumed for the bioparticle model.

The number of support particles (i.e. the number of biopar-icles) is assumed constant, and these are homogeneouslyistributed within the entire reactor.

.1.4. Hydrodynamics and phase mixingA three-phase gas–solid–liquid system is assumed. The solid

hase consists of the inert support particles and the (activend non-active) attached biomass (biofilm). The liquid phase isomposed by the chemical species in solution (substrates, inter-ediates, products, enzymes, ions, and water) and (active and

on-active) suspended biomass. The gas phase is formed by theaseous products from degradation stages.

The liquid and gas phase densities can be assumed constanturing the biological transient. However, the solid density is aunction of the biofilm thickness. Biochemical transformationsre assumed to occur only in the fluidized bed zone but not inhe free-support material zone.

A heterogeneous and dynamic model of the three-phase sys-em is considered by assuming a totally developed flow conditionnd a complete mixture behavior for all phases, i.e. the propertiesre only time functions.

These simplifying assumptions were introduced to make theodel workable, although they do not completely reflect reality.evision of these assumptions to more realistic ones (e.g. intro-

uction of the size distribution of particles, variable density ofiofilm with time, and characteristics of support particles suchs shape, roughness, and material porosity) will be the next stepor model refinement.

i

tp

ring Journal 38 (2008) 259–269 263

.2. Main mathematical model equations

Here, the generalized bubble and wake model (GBWM) iselected to describe the three-phase system [12]. The wake con-ept considers the three-phase fluidized bed to be composedf: (1) the gas bubble region; (2) the wake region; and (3) theolid–liquid fluidization region. The wake region moves at theame velocity as the bubble, and the porosity of this region cane different as the solid–liquid fluidization one. The simplifiedake theory, i.e. the liquid wakes are particle-free, is used here

o calculate the liquid holdup [13]:

L =[Ul

Ut− k

Ug

Ut

]1/n

[1 − εG − kεG]1−1/n + kεG (1)

here parameter k is calculated as in Yu and Rittmann [14]:

= 3.5ε3L exp(−5.08εG) (2)

Researchers have studied the effects of biofilm accumulationn the terminal settling velocity (Ut) and the expansion coeffi-ient (n) in fluidized bed reactors [15–21]. Most of them reportedheir results from correlations originally derived for rigid par-icles that were modified for biofilm system applications. In arevious work [22], a sensitivity analysis of correlations pro-osed by these authors for estimation of parameters Ut and nas presented. The aim was to show the dispersion of resultssing the most quoted correlations. However, they have beenainly applied to aerobic biofilm systems, and seem to be less

ppropriate for anaerobic systems than the original equation ofichardson and Zaki [23], quite used to calculate n in mostorks focused on anaerobic fluidized bed reactors (Eq. (4)),

nd the equation provided by Foscolo et al. [24] to calculate UtEq. (3)). These correlations are valid for the range of Reynoldsumber (Ret) investigated, and are here used to calculate Ut and:

t = −17.3μL + [299.29μ2L + 1.344gd3

bpρL(ρS − ρL)]0.5

0.672dbpρL(3)

= 4.4Re−0.1t , Ret = UtdbpρL

μL, 1 < Ret < 500 (4)

The empirical equation used by Yu and Rittmann [14] foralculating the gas holdup directly from flow velocities and solidoldup is here used:

Ug

εG= Ug

1 − εS+ Ul

1 − εS+ 0.1016 + 1.488

(Ug

1 − εS

)0.5

(5)

A constant volumetric flow through the fluidized bed isssumed; the velocity in the bed cross-section is equal to the fluidelocity at the reactor inlet (Uo = Ul + Ug). Since the solid isonfined in the control volume, no-flux conditions at the reactor

nlet and outlet are assumed for solid phase.

The generated gas is assumed to be separated from the mul-iphase stream at the top of the reactor column, and thus, the gashase flow rate at the reactor inlet is equal to zero. At the reactor

2 ngineering Journal 38 (2008) 259–269

o

U

H

a

cap

dcn(p(

chf

3

3

cw

Table 2Specification data for bioreactors R1 and R2 and support materials

Specification R1 R2

Bioreactor:Static bed porosity (L L−1) 0.42 0.40Static bed volume (L) 2.32 2.49Initial expanded bed volume (L) 3.50 3.58Superficial flow velocity (m s−1) 1.91 × 10−2 4.68 × 10−2

Inert support:Weight of dry sand loaded (kg) 3.50 4.00

−1

cd

0aeaw

uteii

mmssc

3

ua

sat632a

p1wt

64 M. Fuentes et al. / Biochemical E

utlet the gas superficial velocity is calculated as:

g = Qgout

Ac= −dεGH

dt+ V

AcρG

∑i,j

TjiG (6)

The height of the fluidized bed is calculated as:

= W

ρpAcεS

(1 + 2δ

dp

)3

(7)

Mass balance equations for phase components are expresseds follows.

Liquid phase:

AcdεLφiLH

dt= Qlinφ

∗iL − QloutφiL + V

⎛⎝∑

j

RjiL +

∑j

TjiL

⎞⎠

(8)

where

φ∗iL = 1

AcUo[QfφiLf + QrφiL] (9)

Solid phase:

AcdεSφiSH

dt= V

⎛⎝∑

j

RjiS +

∑j

TjiS

⎞⎠ (10)

Gas phase:

AcdεGφiGH

dt= −Qgout

φiG + V

⎛⎝∑

j

RjiG +

∑j

TjiG

⎞⎠ (11)

The terms∑

j

Rjik and

∑j

Tjik related to mass transfer pro-

esses are summarized in Table 1. A description of variables (i)nd processes (j) involved in the anaerobic digestion model isresented in Appendix A.

Mass balances are expressed in grams of chemical oxygenemand per liter per day (g COD L−1 d−1), except for inorganicarbon and nitrogen (mol L−1 d−1) and gas phase compo-ents (atm L−1 d−1). In Table 1, biological species numerationi = 17–23 in M1 and i = 16–23 in M2) has been extended to sus-ended (L) and attached (S) biomass, active (a) and non-activena) of the same species i.

Biochemical rate equation matrixes, kinetic and physico-hemical parameters are described in Fuentes [10], and are notere included due to space restrictions. They can be extractedrom the original sources [1,2].

. Materials and methods

.1. Bioreactors and inert support materials

Two lab-scale anaerobic fluidized bed reactors were used toarry out the experiments (hereafter named R1 and R2), whichere loaded with sand as support material. Both R1 and R2 are

wp(c

Sand density (kg L ) 2.63 2.66Surface–vol. mean diameter (mm) 0.35 0.90Specific surface (m2 kg−1) 7.62 2.96

olumns consisting of a 2.0 m high acrylic cylinder with an inneriameter of 0.065 m.

The separation compartment placed over the column is a.18 m high cylinder with a 0.145 m inner diameter, where gasccumulation and particle sedimentation take place. The efflu-nt discharge, the feed input and the recycle suction point arelso placed in this compartment. The setup used for bioreactorsas schematized in Mussati et al. [25].A sample of sand used in bioreactors R1 and R2 was meshed

sing the Tyler sieve series. The material specific surface andhe surface volume mean diameter are calculated as in McCabet al. [26] and Perry and Chilton [27]. These values and othernert support characteristics, and bioreactor specifications arencluded in Table 2.

The differences in the operational conditions of R1 and R2 areainly based on the sand particle diameter used as inert supportaterial. As shown in Table 2, bioreactor R2, which contains

upport particles with higher size than R1, operates at a higheruperficial velocity of fluid than R1 to obtain similar fluidizationharacteristics (porosity varies from �o ≈ 0.4 to � ≈ 0.6).

.2. Analytical methods

The experimental measured variables were the total and sol-ble chemical oxygen demand (COD), VFA concentrations, pHnd gas composition and flow rate.

The amount of biogas produced by the bioreactors was mea-ured using a water replacement method after gas washing usingFeCl3 solution (pH 2) for removing H2S. The gas composi-

ion was analyzed by a gas chromatograph (Hewlett Packard890) equipped with a thermal conductivity detector and am carbosphere column. Hydrogen was used as carrier gas at0 mL min−1. The column was operated at 150 ◦C. The injectornd detector temperatures were 100 and 230 ◦C, respectively.

The effluent COD was measured according to the HACHotassium dichromate method approved by USEPA (Cat. 21259-5, 0-1500 ppm). The concentration of the released Cr3+ ionsas determined by spectrophotometry (Metrolab 330). Microfil-

ers of PTFE 0.2 �m (F2513-4) for soluble COD determination

ere used. The effluent pH was measured using a digitalHmeter (Horiba D-12). The acetic, propionic and butyric acidVFA) concentrations were measured by a high pressure liquidhromatograph (HPLC) Hewlett Packard Model Series 1050

gineering Journal 38 (2008) 259–269 265

e2(fiw

3

itpCoaem

sTt(astoNtr4ia

4

4

a(pnasmi(roraplh

gp

Fig. 3. Active attached biomass profiles from M1.

iagrowth exponential period, hydrogen partial pressure values arein a magnitude order of 0.5 × 10-4 atm. It causes an inhibitioneffect up to 15% in acetogenic degradation stages (Fig. 4). ApH inhibition of 30% for acetoclastic methanogens is predicted

M. Fuentes et al. / Biochemical En

quipped with a UV–vis detector (wavelength: 215 nm). A0 �L sample volume was injected into a Spherisorb ODS-1C18) Classic 5U (250 mm × 4.6 mm) column (Alltech, Deer-eld, IL). The mobile phase consisted of 50% acetonitrile–50%ater, sulfuric acid 0.01% (pH 3) at a flow rate of 0.7 mL min−1.

.3. Experimental protocol

Fractions of both solid and liquid phases taken from anndustrial anaerobic reactor were used as inoculums for reac-ors R1 and R2. The substrate consisted of a mixture of milkowder, acetate and glucose (10%, 20% and 70% of the totalOD, respectively) plus 0.1 g L−1 of NH4Cl and 0.66 g L−1

f NaHCO3, in order to provide the inoculums with the micrond macronutrients, and adequate environmental conditions nec-ssary for microbial growth. The operating temperature wasaintained at 36 ± 1 ◦C.The start-up policy was the same for both reactors. It involved

tepped increases in COD loading rate over a 4 months period.he organic loading rate (OLR) was gradually increased by

hree step-type disturbances on the inlet substrate concentrationP1–P3), keeping constant the percentage of COD composition,nd the feed flow rate at 3.2 L d−1. During this acclimatizationtage, the influent COD concentration was increased from 0.85o 1.75 and from 1.75 to 2.66 g L−1 containing around 2.5%f insoluble substrates mostly composed by milk proteins. TheaHCO3 consumption for pH adjustment was increased, respec-

ively, from 0.66 to 1.50 and from 1.50 to 2.43 g L−1 for botheactors. Finally, two steps in the feed flow rates from 3.2 to.3 L d−1 (P4) and from 4.3 to 6.2 L d−1 (P5), keeping the samenlet concentration at 2.66 g COD L−1 for R1 and R2, werepplied.

. Results and discussion

.1. Simulation results for comparing M1 and M2

Mathematical M1 and M2-based models were implementednd solved using the process modeling software tool gPROMSgeneral PROcess Modeling System; Process Systems Enter-rise Ltd.) [28,29]. Since the biofilm adsorption phenomenon isot modeled, low steady state concentration values are assigneds initial condition values for the biological and chemicalpecies. Firstly, simulated responses from M1 and M2-basedodels are investigated. As an example, a simple case study

s assumed taking into account characteristics of bioreactor R1Table 2), and the feed concentration and flow rate values cor-esponding to disturbance P1 described in Section 3.3. A valuef 1.87 × 10−10 m s2 kg−1 for the specific biofilm detachmentate kE is here used (see Section 4.2). Figs. 3–6 show the activettached biomass species concentration and inhibition functionrofiles obtained from M1 and M2, respectively. Values for theimitation function by inorganic nitrogen (secondary substrate)

ave been also included in Figs. 4 and 6.

As expected, for a glucose and acetate-based feeding, acido-enic degraders and acetoclastic methanogens are the speciesresent in the largest concentrations (Figs. 3 and 5). As shown

Fig. 4. Inhibition function profiles from M1.

n Fig. 7, slightly increased values for methane partial pressurere predicted by the M1-based model. Around day 60, after the

Fig. 5. Active attached biomass profiles from M2.

266 M. Fuentes et al. / Biochemical Engineering Journal 38 (2008) 259–269

fi

sarFcce

bmtvs

tct

FM

Fa

Flaci(ipatft

aba

Fig. 6. Inhibition function profiles from M2.

rom M1 (Fig. 4), while a pH inhibition of 13% for all speciess predicted from M2 (Fig. 6).

Although there are some differences in the degradationcheme, microorganism trophic groups and inhibition functionsssumed in M1 and M2, the macroscopic variables show similaresponses during the exponential growth period, as observed inig. 8. However, deviations of 0.7%, 11% and 42% in pH, biofilmoncentration and total COD predicted values, respectively, arealculated at the biological steady state. A higher COD removalfficiency is predicted by M2-based model (Fig. 8).

Untreated substrate and suspended (active and non-active)iomass are the components that mostly contribute to the totaleasured COD. In previous papers [8,25], it was observed that

he main discrepancies between predicted and measured CODalues arise in computing the suspended biomass leaving theystem.

High sensitivity values of the predicted suspended biomasso the biofilm detachment model and its specific rate coeffi-ient were observed from a sensitivity analysis carried out usinghe AFBR model presented in Section 2.2 [10]. As observed in

ig. 7. Partial pressure profiles for gas phase components predicted by M1 and2-based models.

stttF

ig. 8. Total COD, biofilm concentration (XST) and pH profiles predicted by M1

nd M2-based models.

ig. 9, predictions from M1 show that non-soluble species (inert,ipid, carbohydrate and protein) contribute to total COD valuess active and non-active suspended biomass. Since a soluble glu-ose and acetate-based feeding has been assumed, it seems tondicate that the disintegration scheme of particulate materialincluding non-active biomass) and hydrolysis rates assumedn M1 do not assure low COD levels from biomass decom-osition. On the other hand, as shown in Fig. 10, the schemessumed in M2 for non-active biomass hydrolysis (decomposi-ion to sugars and amino acids from its carbohydrate and proteinractions, respectively, see Fig. 2) assures low COD values fromhis biochemical process.

From both models, the COD removal efficiency reachespproximately 85% at day 40 (Fig. 8). At this point, it coulde a good decision to carry out a change in the organic loadttending to the long period for recovering a biological steadytate regimen. Thus, an optimal organic load policy guaran-ees the best reactor performance in the shortest period of

ime. The models are able to resist strong numerical discon-inuities to represent a step-by-step start up of the bioreactor.ollowing, the analysis is continued using the experimen-

Fig. 9. Components of total COD from M1.

M. Fuentes et al. / Biochemical Engineering Journal 38 (2008) 259–269 267

tS

4

tiaatd

cRdflbf(t

atrrad

thtmbio

mfh

Fm

oMsg9ikoe

Fig. 10. Components of total COD from M2.

al data obtained from two lab-scale AFBRs described inection 3.

.2. Experimental data and simulation results

Time variation of the experimental and predicted values ofotal and soluble COD, and biogas (as methane) flow rate dur-ng disturbances P1–P5 are depicted in Figs. 11 and 12, for R1nd R2, respectively. Although VFA concentrations and pH havelso been measured, are not here included due to space restric-ions. The dotted lines indicate the time duration of each P1–P5isturbances.

Unexpected failures and changes in the operating conditionsaused hydrodynamic disturbances around days 38 and 60 for1, and day 35 for R2. The disturbance at day 38 for R1, anday 35 for R2, was caused by an electrical failure. The feedow was interrupted, and the bed height fell dawn to its staticed condition during 4 h. After that, fluidization and bioreactoreeding levels were recovered. At day 60, a new fluidization levelporosity ε ≈ 0.69) was established in R1 due to an increase inhe inlet flow rate Uo from 1.91 × 10−2 to 2.71 × 10−2 m s−1.

High COD removal efficiency values (85% and 95% of totalnd soluble COD, respectively) were obtained during step-ype disturbances in the influent concentration and feed flowate (P1–P5). The biogas production rates showed the fastestesponses to disturbances and the system pH was self-regulatedt the typical operation range (6.6–7.2) of healthy methanogenicigesters for both reactors.

The simplified wake and bubble theory, used to calculatehe fluidization characteristics, simulated successfully the mainydrodynamic events that took place in the reactors. As men-ioned, kinetic parameters reported by Batstone et al. [1] for

esophilic and high rate operating conditions are here used foroth M1 and M2-based models, except for the specific biochem-cal process rate and inhibition coefficient values characteristicf M2, which have been taken from its original source [2].

The estimated values of the empirical specific biofilm detach-ent rate (kE equal to 1.87 × 10−10 and 1.12 × 10−10 m s2 kg−1

or R1 and R2, respectively) reported by Fuentes et al. [8] areere used. These values were obtained using the gEST tool

cdbc

ig. 11. Experimental and predicted values of total and soluble COD, andethane (CH4) flow rate for R1.

f gPROMS package by minimizing the deviation between2-based model predictions and measurements of total and

oluble COD, VFA (as acetate) concentration, pH and bio-as (as methane) flow rate during disturbances P1–P5. The5% Chi-square test for goodness of fit (0.05 level of signif-cance) was used to accept or reject the proposed model. ForE estimation, authors carried out some partial adjustments tobtain more appropriate initial conditions, evaluate deviation ofxperimental data, and improve model sensitivities. They con-

luded that the ω-function assumed for modeling the biofilmetachment rate resulted to be appropriate for representingioreactor behavior during non-highly disturbed hydrodynamiconditions.

268 M. Fuentes et al. / Biochemical Engineering Journal 38 (2008) 259–269

Fm

wmpasiahm

Ptp

iCaeiareu

5

pnfgeamswta

gbdddtb

A

ig. 12. Experimental and predicted values of total and soluble COD, andethane (CH4) flow rate for R2.

Even when simulation results shown in Figs. 11 and 12ere obtained using the kE values estimated from M2-basedodel, it is evident that differences between measured and

redicted total COD values obtained from M1-based modelre due to the large predicted concentrations of non-solublepecies (inert, lipid, carbohydrate and protein) produced dur-ng non-active biomass disintegration and hydrolysis, as wasnalyzed from Fig. 9. Lower soluble COD values and thus,igher methane flow rate values are obtained from M1-basedodel.

Predicted values of biofilm concentration during disturbances

1–P5 for R1 and R2 are depicted in Fig. 13. For both reactors,he largest values are predicted from M1 at the end of the start-upolicy.

CPv

Fig. 13. Predicted values of biofilm concentration for R1 and R2.

An increase in the organic loading rate caused an increasen the biofilm concentration and, as expected, predicted totalOD values from M1-based model and the experimental onesre extremely different (Fig. 11 and 12). Therefore, kE parameterstimation using the M1-based model and experimental data,ncluding total COD values, corresponding to disturbances P1nd P2 do not present significant differences with respect toesults obtained from M2-based model (results not shown). Asxpected, when experimental data from P3–P5 disturbances aresed, model sensitivities decrease and statistical methods fail.

. Conclusions

A comparative analysis of the anaerobic digestion modelsroposed by Batstone et al. [1] and Angelidaki et al. [2] (hereamed as M1 and M2, respectively) was made. The main dif-erences are given by assumptions on microorganism trophicroups, inhibition functions and hydrogen stoichiometry. Mod-ling hypotheses to apply both substrate degradation schemes tobiofilm system were exposed. A heterogeneous and dynamicodel was implemented, and the main macroscopic variables

uch as COD concentration, pH and biogas production rateere investigated using the experimental data obtained from

wo mesophilic anaerobic lab-scale fluidized bed reactors (R1nd R2).

The results show that kinetics and parameters for disinte-ration of composite particulate waste, including non-activeiomass, and hydrolysis of non-soluble substrates (carbohy-rates, lipids and proteins) described by Batstone et al. [1] (M1)o not assure a good agreement between experimental and pre-icted total COD values. A better prediction is obtained usinghe Angelidaki et al. [2] model (M2) by estimating the specificiofilm detachment rate parameter.

cknowledgements

Financial support from Consejo Nacional de Investigacionesientıficas y Tecnicas (CONICET), Agencia Nacional para laromocion de la Ciencia y la Tecnologıa (ANPCyT) and Uni-ersidad Nacional del Litoral of Argentina is acknowledged.

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M. Fuentes et al. / Biochemical En

ppendix A

ariable Description i (M1) i (M2)

C Composite 13 –

CH Carbohydrate 14 11

P Protein 15 12

Li Lipid 16 13

I-CH Inert carbohydrate – 14

I-P Inert protein – 15

I Inert particulate 24 –

I Inert soluble 12 –

Gl Glucose 1 1

AA Amino acid 2 2

LCFA LCFA 3 3

HVa Valerate 4 4

HBu Butyrate 5 5

HPr Propionate 6 6

HAc Acetate 7 7

H2 Hydrogen 8 –

CH4 Methane 9 8

IC Inorganic carbon 10 9

IN Inorganic nitrogen 11 10

bio Biomass 17–23 16–23

rocesses j (M1) j (M2)

isintegration 1 –arbohydrate hydrolysis 2 1rotein hydrolysis 3 2ipid hydrolysis 4 –ipid hydrolysis/glycerol uptake – 3lucose uptake 5 4mino acid uptake 6 5CFA uptake 7 6alerate uptake 8 7utyrate uptake 9 8ropionate uptake 10 9cetate uptake 11 10

2 uptake 12 –

Glic decay – 11

GL decay 13 12

AA decay 14 13

LCFA decay 15 14

Va decay – 15

Bu decay – 16

Va Bu decay 16 –

Pr decay 17 17

Ac decay 18 18

H2 decay 19 –

2 mass transfer T8 –H4 mass transfer T9 T8O2 mass transfer T10 T9

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[

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