Chemical Engineering Journal 228 (2013) 871–881

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Chemical Engineering Journal

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New fractionation for a better bioaccessibility description of particulateorganic matter in a modified ADM1 model

1385-8947/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.cej.2013.05.082

⇑ Corresponding author. Tel.: +33 468 425 151; fax: +33 468 425 160.E-mail address: helene.carrere@supagro.inra.fr (H. Carrère).

A. Mottet a, I. Ramirez a, H. Carrère a,⇑, S. Déléris b, F. Vedrenne b, J. Jimenez b, J.P. Steyer a

a INRA, UR0050, Laboratoire de Biotechnologie de l’Environnement, Avenue des Etangs, Narbonne F-11100, Franceb Veolia Environnement R&D, Centre de Recherche sur l’Eau, F-78603 Maisons-Laffitte, France

h i g h l i g h t s

� A new disintegration/hydrolysis structure is introduced to the ADM1.� Two hydrolysable composite fractions of particulate organic matter were implemented.� A anaerobic batch test was used to calibrate model parameters.� Estimated parameters were representative of a continuous full-scale digester.

a r t i c l e i n f o

Article history:Received 19 November 2012Received in revised form 9 May 2013Accepted 18 May 2013Available online 29 May 2013

Keywords:ADM1ModelingAnaerobic digestionHydrolysis kineticWaste activated sludge

a b s t r a c t

A new model structure for the hydrolysis step is introduced in the IWA anaerobic digestion model no 1(ADM1) in order to better represent the bioaccessibility of particulate organic matter. Two particulateorganic matter fractions for waste activated sludge (WAS) samples were defined: a readily hydrolysablefraction (Xcr) and a slowly hydrolysable fraction (Xcs). These fractions were hydrolyzed according to a sur-face-limiting reaction. Batch anaerobic digestion test of untreated WAS was used to develop the modeland calibrate the kinetic parameters and biomass concentrations. The validation was carried out witha similar substrate than the calibration but a thermal pretreatment was applied at two different condi-tions (110 �C and 220 �C). The behavior of thermophilic anaerobic digestion of WAS samples was effec-tively represented by the proposed model. No changes among kinetic parameter sets were done andthe model is able to represent produced methane volume following the intrinsic changes of the WAScomposition through the different thermal pretreatment conditions. Moreover, estimated parametersin 20 days of batch anaerobic digestion test were representative of continuous anaerobic digestion in afull-scale digester.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The waste activated sludge (WAS) has a complex structure thatincludes several types of organic and inorganic compounds, such asextracellular polymeric substances (EPS), microorganism and mul-tivalent cations [1]. Jorand et al. [2] studied sludge disintegrationby sonication and proposed a floc model where the structure iscomposed of three levels of microflocs linked by EPS. Moreover,Appels et al. [3] emphasized that hydrolysis is the rate-limitingstep in the WAS anaerobic digestion, since EPS have to be degradedand cell walls have to be ruptured resulting in the release of intra-cellular material. Therefore, various disintegration methods havebeen studied as a pre-treatment to improve the biogas production:these methods lead to a more accessible organic matter by disrupt-ing cell wall and the sludge flocs [4]. The current pretreatment

methods include sonication [5], microwave [6], ozone [7], hydro-thermal treatment [8]. The organic matter (OM) in WAS is there-fore more or less accessible to anaerobic microorganismsresulting in a large impact on the anaerobic degradation rates.

After stopping sewage sludge feed in a continuously fed labora-tory digester, Siegrist et al. [9] observed two gas production pro-files: the first gas production phase occurred within hours fromreadily degradable matter and the second phase resulted fromslowly degradable matter. Thus the complex structure of theWAS organic matter involves different degradation rates accordingto the matter accessibility.

The WAS biodegradability is therefore closely linked to by thechemical nature and the complex structure of the organic matter.Consequently, the modeling of particulate matter degradation isdifficult. Tomei et al. [10] pointed out that accurate anaerobichydrolysis modeling is necessary and would be very useful forthe simulation and the design of anaerobic digesters.

872 A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881

Currently, the integration of particulate composite such as WASin the input variables of the IWA anaerobic digestion model no 1(ADM1) and modeling of the disintegration and hydrolysis stepsare two key issues. Indeed, in the ADM1 model [11], a complex or-ganic waste is introduced as a simple variable Xc which is disinte-grated into particulate proteins, carbohydrates and lipidsaccording to a first-order reaction.

Myint and Nirmalakhandan [12] observed that the chemicaloxygen demand (COD) solubilization curve during anaerobic deg-radation of cattle manure presented two distinct phases. They pro-posed that this substrate is composed of a readily hydrolysablefraction and a slowly hydrolysable fraction. They integrated thisnew feature in a model able to simulate the hydrolysis and acido-genesis steps.

Using batch anaerobic respirometry tests, Yasui et al. [13] alsoshowed that the anaerobic degradation of activated sludge organicmatter follows different kinetic behaviors: a rapidly degraded frac-tion in the initial phase of methane production and a slowly de-graded fraction. Therefore, Yasui et al. [14] proposed a modifiedADM1 structure for modeling municipal primary sludge with thedegradation of readily hydrolysable fraction and the degradationof large-size particles. However, the simulation of individual vola-tile fatty acid and individual biomass was not included in theirmodel.

From these different experimental observations, the implemen-tation of a readily hydrolysable fraction and a slowly hydrolysablefraction in a generalized model such as ADM1 appears to be veryconsistent with the WAS organic matter structure and degradabil-ity. Moreover, it is in line with the recent ASM model adaptationproposed by Fenu et al. [15]. Indeed, it seems reasonable to con-sider some organic matter such as endogenous products with aslow hydrolysis process in order to better predict sludge produc-tion. Spérandio et al. [16] pointed out that the organic matteraccessibility and chemical nature are the main factors influencingthe degradation rates of the hardly biodegradable fractions. Thebiological processes with long solid retention times are also con-cerned [17].

The most common approach in modeling the hydrolysis of com-plex organic matter is a first-order reaction based on the substrateconcentration, which has been selected in the ADM1 model. Thisfunction is an empirical expression that is assumed to reflect thecumulative effects of many processes [9,18–21]. However, thefirst-order reaction is the simplest description of the hydrolysisstep. Indeed the first-order reaction assumes that the hydrolysisreaction is independent of the colonization of hydrolytic bacteriaand that the biomass responsible for producing hydrolysis en-zymes is available in excess for immediate and spontaneous con-tact with particulate substrate. This obviously represents anoversimplification, since the biomass might not have direct accessto substrate particles [22].

The hydrolysis step of the anaerobic digestion of particulatesubstrate is actually a very complex process that includes (i) phys-ical disintegration of sludge flocs, particulate organic matter anddead biomass and (ii) enzymatic hydrolysis of the polymers tomonomers. Vavilin et al. [23] suggested a description of the hydro-lysis process in two steps: the first step is a bacterial colonization,where hydrolytic bacteria cover the surface of solids and the sec-ond step is the production of enzymes by bacteria on or near theparticle surface allowing the production of monomers. A moreappropriate equation form is therefore necessary to improve thehydrolysis description.

In the IWA Activated Sludge Model no 2 [24], the Contois model[25] has been selected to better describe slowly biodegradable or-ganic hydrolysis. This function links hydrolytic biomass growthand substrate degradation. The Contois model integrates hydro-lytic biomass concentrations and is able to represent the mass

transfer limitations due to the limited surface area of particulatesubstrates. Consequently, the substrate-microorganism ratio maybe a better limiting factor in the hydrolysis of particulate substrate.

In anaerobic digestion, Chen and Hashimoto [26] used a modi-fied Contois model for the hydrolysis of dairy waste and sewagesludge. von Münch et al. [27] observed that the hydrolysis ratewas reduced when the biomass concentration increased above acertain level, leading these authors to use the Contois equationwhich takes into account the limiting effect of mass transfer be-tween particulate substrate and biomass.

Vavilin et al. [28] also used the Contois function to represent theperformance of anaerobic digestion of different substrates, such assludge, cattle manure, swine waste and cellulose. The results corre-sponded well to the experimental data and it was concluded thatthe Contois model is best suited to describe the hydrolysismechanisms.

Tomei et al. [29] compared different kinetic equations takinginto account the dependence of the reaction rates on the biomassconcentration or not for the hydrolysis step of particulate organicmatter at different feed/inoculum ratio. The degradation kineticshave been effectively modeled by hydrolytic model including bio-mass concentration such as Contois model. On the opposite thefirst order equation is unable to represent the particulate COD con-centration profiles. Moreover, Tomei et al. [10] emphasized that,surprisingly, the effect of biomass concentration is not taken intoaccount in the sludge hydrolysis step of the main models devel-oped in the last decades.

Another critical point is that the application of kinetic parame-ters estimated from lab-scale experiments to the modeling of full-scale digesters is still controversial [10]. Indeed Batstone et al. [30]demonstrated that the first order hydrolysis rate coefficients (khyd)in ADM1 determined in anaerobic batch tests are not appropriatewhen applied to continuous digester modeling. The assumptionthat the sludge disintegration step is described as a homogeneoussubstrate (Xc) simulated by a single kinetic parameter is thereforetoo simple and an enhanced representation of hydrolysis step inADM1 could provide an effective modeling of full-scale digesters.

Thus, the objectives of this study were to introduce a new dis-integration/hydrolysis step structure to the traditional ADM1 mod-el in order to implement the description of the differenthydrolysable fractions of the particulate organic matter. In addi-tion, this study demonstrated that the calibration procedure car-ried out from batch experiments allows one to quite accuratelydetermine model parameters which are applicable in full-scale di-gester modeling.

2. Materials and methods

2.1. Thermal pretreatment and batch anaerobic degradations

The WAS sample was taken from a full-scale highly loaded ur-ban wastewater treatment plant in France.

The thermal pretreatment and the batch anaerobic degradationtests were performed in agreement with Mottet et al. [31]. A 10 Lagitated autoclave (Autoclave, class IV) was used to carry out ther-mal pretreatments, the temperature increase was performed byelectric mode. Sludge sample volume was around 6 L. A low tem-perature and a high temperature thermal pretreatment were cho-sen, respectively at 110 �C and 220 �C in order to have animportant impact on the intrinsic characteristics of WAS sampleorganic matter. The duration of treatments was 30 min once thedesired temperature was reached.

The batch anaerobic degradation tests were carried out in fourreactors with a volume of 3.5 L used in parallel. Anaerobic batchreactors were kept at 55 �C in thermophilic condition. Three

0100200300400500600700800900

1000

0 5 10 15 20Time (days)

Untreated WAS WAS pretreated at 110°C WAS preatreated at 220°C

Readily hydrolysable organic matter

Slowly hydrolysable organic matter

Xcr Xcs

Met

hane

pro

duct

ion

rate

(m

LCH

4.d-1

)

Fig. 1. Fractionation of biodegradable organic matter in different WAS samplesuntreated and thermally pretreated.

A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881 873

reactors were fed with untreated and pretreated (110 �C and220 �C) WAS samples and the last one was used to estimate endo-geneous methane production of inoculum. The inoculum was takenfrom the pilot scale continuous digester treating WAS (see Sec-tion 2.2). Biogas production and pH were measured continuouslyand biogas composition was daily measured during the batch tests.

2.2. Pilot scale continuous digester

The pilot scale unit located at a municipal wastewater treatmentplant (WWTP) of Veolia was used. It was a thermophilic pilot scaledigester of 8 m3 equipped with an axial mixing. The biogas produc-tion was measured with a drum-type gasmeter (Ritter, type TG50).The composition was monitored on-line with an integrated ana-lyzer system (ABB) composed of an ABB Limas11 for H2S analyzis(by UV) and an ABB Uras14 for CH4 and CO2 analyzis (by IR). Theinoculation was realized with digested sludge from the full scale di-gester located at the WWTP. The feed of both full scale and pilotscale digesters was similar and composed only of WAS. The massloading rate of the pilot scale digester was 35 kgCOD m�3 d�1.

Experimental data on continuous anaerobic digestion of WASwere generated to evaluate the optimized model parameters whichwere obtained from the degradation batch kinetics. The digesterwas continuously fed with the same kind of WAS used in batchtests under thermophilic condition. The sludge retention time(SRT) was maintained constant at 8 days over a period of 12 daysthen it was varied in order to test the robustness of the model.The inlet WAS was accurately characterized in accordance withthe characterization procedure used in model calibration. The mea-sured parameters were: soluble and total COD concentrations, sol-uble and total concentrations of proteins, carbohydrates and lipids,individual VFA concentrations and carbon and nitrogen inorganicconcentrations.

2.3. Analytical methods

The soluble and particulate fractions were separated by centri-fugation at 50,000g, for 15 min and at 5 �C, then by filtrationthrough a cellulose acetate membrane with 0.45 lm pore size.Substrate characterization was carried out on the sludge samplesto determine initial values of the model variables. Some measure-ments were performed on total and soluble fractions: COD, pro-teins measured according to the Lowry method [32] and totalsugars measured with the anthrone reduction method [33]. Lipidswere measured according to the Soxhlet method using petroleumether as solvent, on both total and particulate fractions. Ammonianitrogen (measured by Kjeldahl method), inorganic carbon (ana-lyzed with a Carbon TOC-V module, Shimadzu) and VFA were mea-sured only in the soluble fraction.

VFA concentrations were measured using a gas chromatograph(GC-8000 Fisons instrument) equipped with a flame ionizationdetector with an automatic sampler AS 800. The biogas composi-tion was determined with a gas chromatograph (Shimadzu GC-8A) equipped with a CTRI Alltech column in which argon was thecarrier gas and a thermal conductivity detector connected to anintegrator.

2.4. Statistical analyzis

Statistical analyzis was carried out using the software R version2.15.1 for windows with the package ‘‘Rcmdr’’. The following crite-ria were used to assess the performance of the model during thecalibration and validation phases on the batch anaerobic degrada-tion tests: intercept (a), slope (b) and R2 values of linear regressionbetween simulated and measured data for each modeling. The 95%confidence interval for the intercept and the slope was used to iden-

tify the strength of the linear correlation. In the case of the pro-duced methane volume parameter, the 95% confidence intervalfor the intercept has to contain 0 and for the slope have to contain1. A significant level of 0.05 means that 95% of the observed confi-dence interval will hold the true value of the parameter. And finally,the significance levels of the slope were indicated using the proba-bility (p) levels: �0.05 P p > 0.01, ��0.01 P p > 0.001, ���0.001 -P p > 0. Values of p higher than 0.05 are considered non-significant.

3. Results and discussion

3.1. Modifications to ADM1 model

Fig. 1 representing the methane production rates for untreatedand thermally pretreated WAS, gives relevant information con-cerning the COD conversion during anaerobic digestion of a WASsample for which its organic matter structure was changed bythermal pretreatment. Indeed, the thermal pretreatment has beenstrongly studied as a pretreatment in order to make the organicmatter more accessible to the anaerobic microorganisms and soto improve methane production rate [31,34]. We observed that alow thermal pretreatment at 110 �C increases the methane produc-tion rate and a larger quantity of organic matter seems to be de-graded during the first 4 days compared to the untreated WAS.This confirms that the thermal pretreatment leads to a particulateorganic matter more accessible to the anaerobic microorganisms.At a high thermal pretreatment of 220 �C, the methane productionrate was also increased but a lower methane volume was producedthan with untreated WAS. The formation of recalcitrant compo-nents at high thermal pretreatment can explain the anaerobic deg-radation performance decrease [35].

Another interesting information can be obtained from thesemethane production rates. Indeed, the methane production ratecan be divided into two distinct phases: the first phase has a highmethane production rate from day 0 to day 8, due to the degrada-tion of readily hydrolysable organic matter; and the second phasehas a slower methane production rate from day 8 to day 20, due tothe degradation of slowly hydrolysable organic matter. Theseobservations confirm the presence of different degradation rateswhich depend on the intrinsic characteristics of the organic matterand can be driven by the accessibility of the organic matter. There-fore, we propose a new disintegration/hydrolysis structure in thetraditional ADM1 where two different hydrolysable fractions ofparticulate organic matter are included. We suggest changing theunique fraction of complex particulate fraction, called compositeXc in the standard ADM1, into two compartments of matter: areadily hydrolysable fraction (Xcr) and a slowly hydrolysable frac-tion (Xcs). The traditional fractionation into particulate proteins

874 A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881

Xpr, carbohydrates Xch, lipids Xli, particulate inert and soluble inertfractions is maintained. Fig. 2 schematically represents the modi-fied biochemical conversion processes for the disintegration stepaccording to ADM1. The COD flux coming from Xcr and Xcs is frac-tionated into proteins, carbohydrates, lipids and inerts accordingto yield coefficients. The following conversion pathways are similarto those implemented in the ADM 1 model.

The Contois kinetic was used to represent the disintegrationand hydrolysis steps as also successfully performed in the studyof Ramirez et al. [36]. Indeed they represented the hydrolytic activ-ities through a modified ADM1 disintegration/hydrolysis struc-tures by using Contois model associated to hydrolytic biomassconcentrations.

The disintegration and hydrolysis rates are expressed as:

qprocess ¼ km;processXS

kS;processX þ S¼ km;processX

S=XkS;process þ S=X

where qprocess is the process rate (kgCOD m�3 d�1), km,process is themaximum specific uptake rate of the process (d�1), KS,process is thehalf-saturation coefficient for the ratio S/X, X is the biomass concen-tration (kgCOD m�3) and, S is the particulate compound concentra-tion (kgCOD m�3).

However, disintegration and hydrolysis reactions are carriedout by specific extracellular enzymatic activities released byhydrolytic biomass [28]. We consider therefore a hydrolytic bio-mass concentration in Contois kinetic to represent the extracellularenzymatic activities. The implementation of hydrolytic biomass inorder to represent the hydrolytic activities is confirmed by Kimet al. [37]. Indeed, they showed that the increase of extracellularenzymatic activities corresponds to the hydrolysis of protein, lipidand carbohydrate polymers during the first days of anaerobicdigestion. Moreover they pointed out that the fast hydrolysis inthermophilic condition is strongly depended on higher level ofhydrolytic extracellular enzyme activities which are in direct pro-portion to the amount of enzymes. Thus it seems realistic that theevolution of extracellular enzyme activities can be described by aContois model which implements a new variable as hydrolytic bio-mass in order to represent the mass transfer limitations due to thelimited surface area of particulate substrates and the bacterialcolonization.

For the following hydrolysis step, in order to be consistent withthe modeling of the disintegration step, Contois kinetics associatedto specific biomass concentrations are also used to describe thehydrolytic enzymatic activities on particulate biochemical compo-nents (carbohydrates, proteins and lipids).

To summarize, the new structures of the disintegration stepwith two different hydrolysable fractions and the hydrolysis stepinvolves several additional parameters that can be found in Appen-dices A and B, where only disintegration, hydrolysis and decayreactions are described for simplicity.

3.2. Initial variable implementation

3.2.1. Fractionation of organic matterAs already presented in Ramirez et al. [36], we directly obtained

the initial conditions of most of the model variables from

Fig. 2. Disintegration COD flux in the modified model inclu

measurements characterizing the WAS samples. The soluble vari-ables were determined from the VFAs, amino acid and sugar con-centrations in the soluble phase of WAS samples as Sac, Spro, Sbu,Sva, Saa and Ssu respectively. According Rosen and Jeppsson [38] an-ion concentration (San) was equal to SIN and cation concentration(Scat) was adjusted in the case of untreated WAS based on theexperimental pH and was kept similar for the pretreated WAS sam-ples. The initial values of Xi and Si were assumed to be equal to theparticulate and soluble COD concentrations initially present in thereactor before feeding.

In our approach, the particulate organic matter was assumedto be initially present in both fractions of composite Xcr and Xcs

instead of in only Xc. We propose a simple method based onthe methane production curves which are obtained from batchexperiments to divide the WAS particulate COD concentrationin Xcr and Xcs fractions. This strategy is consistent with Giraultet al. [39] who concluded that biogas production data should beused for a more detailed characterization of the substrate andthe percentage of readily and slowly degradable fractions canbe better estimated. As already discussed, the methane produc-tion curve shows two distinct phases. During the first phase,the produced methane volume was divided by the total methanethat gave a percentage of methane ‘‘readily’’ produced from thesubstrate (Fig. 1). Obviously, this part of methane volume is alsoproduced from soluble components initially present in the sub-strate. This is especially the case when a thermal pretreatmentis applied. However, the maximal methane production rates ob-served during the first phase were reached after 6 days for theuntreated WAS and 5 days for the pretreated WAS. This indicatesthat the hydrolysis step was limiting since a large proportion ofparticulate COD should be hydrolyzed before to be converted intosoluble components, VFAs and biogas.

The calculation of the two fractions Xcr and Xcs is presented withthe case of untreated WAS as an example. In Fig. 1, the first phaseof methane production from day 0 to day 8 represents 69% and thesecond phase represented 31% of the total produced methane. TheWAS particulate COD which was equal to 3.90 kgCOD m�3, was thusdivided into 2.69 kgCOD m�3 for Xcr and 1.21 kgCOD m�3 for Xcs. Asthe WAS has a complex organic matter structure, we also assumedthat the particulate organic matter is initially only present into thetwo hydrolysable fractions and so Xch, Xpr and Xli were taken equalto 0.

Finally, the stoichiometric coefficients of the disintegration stepwhich are strongly correlated to the composition and intrinsiccharacteristics of the sludge have to be specified for each sample.We assumed that the fractionation of the two fractions Xcr andXcs into particulate biochemical components and inert compositefractions is similar. The particulate inert stoichiometric coeffi-cients, i.e. fXi_Xcr and fXi_Xcs, were directly determined using the finalmeasured biodegradability (BD) of the batch test. The inert fractionwas 1-BD. The value was therefore equal for the two coefficientssince it is not currently possible to determine the inert matter inthe readily and in the slowly hydrolysable fractions. Since ourobjective was to analyze the dynamics of non-inert materials, itwas decided for simplicity to set all inert material to theparticulate variable and fSi_Xcr and fSi_Xcs were taken equal to 0.

ding two different fractions of composite, Xcr and Xcs.

A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881 875

The degradable particulate COD is thus divided into carbohydrates,proteins and lipids. Similarly we also assumed that stoichiometriccoefficients, i.e. fch_Xcr, fpr_Xcr, fli_Xcr and fch_Xcs, fpr_Xcs, fli_Xcs of the tworespective fractions Xcr and Xcs were equal. They were calculatedfrom the particulate COD carbohydrate, protein and lipid contentsof the WAS samples. The biochemical components stoichiometriccoefficients were normalized to keep the sum of the stoichiometriccoefficients of the disintegration step equal to 1 in order to main-tain the COD balance. Table 1 presents the fractionation results forthe three WAS samples.

3.3. Model calibration

The model calibration was carried out using experimental datafrom the degradation batch test of untreated WAS. Initially, theparameter values corresponded to those proposed in the IWA Sci-entific and Technical Rapport [11]. The new kinetic parameters andrates for the Xcr and Xcs fractions were chosen close to the values ofXc fraction proposed in the modified model by Ramirez et al. [36]who implemented a Contois model for the disintegration andhydrolysis steps. According to Blumensaat and Keller [40], a heu-ristic method procedure was applied to optimize the parametersvalues of the modified model. The values of kinetic parameterswere reevaluated following a curve-fitting process between thesimulated and the measured data. The reason for choosing thisheuristic calibration procedure was to obtain minimal changes inthe kinetic parameters between the values proposed in the litera-ture and our model. The biomass initial values are key parametersin the model calibration. We used the typical strategy proposed byDonoso-Bravo et al. [41] to optimize the biomass initial values. Itconsists by simulating the process several times until the biomassconcentrations were constant.

Table 2 presents the main changes for the anaerobic degrada-tion modeling of the untreated WAS. As it was already observedin Fig. 1, the maximal methane production rates are reached aftera long lag time and some methane is produced from day 8 to day20. These observations suggest a limiting hydrolysis step and a sec-ond COD flux from slowly hydrolysable organic matter. The appliedstrategy to well represent these variations was to calibrate the ini-tial hydrolytic biomass concentrations for XXcr and XXcs and Monod

Table 1Ratio of fractionation of readily and slowly hydrolysable substrates and initial conditionssamples.

Stoichiometric coefficients (kgCOD kgCOD�1) Untreat

fSi_Xcr/fSi_Xcs 0.0fXi_Xcr/fXi_Xcs 0.38fch_Xcr/fch_Xcs 0.13fpr_Xcr/fpr_Xcs 0.31fli_Xcr/fli_Xcs 0.18

Dynamic states variables Units Initial ckgCOD m�3 0.0

Xcr 2.69Xcs 1.21Xch 0Xpr 0Xli 0Xi 12.21Si 1.25Ssu 0.009Saa 0.034Sfa 0.0Sva 0.027Sbu 0.074Spro 0.112Sac 0.127SIC kg moleC m�3 0.014SIN kg moleN m�3 0.034

maximum specific uptake rates (km,Xcr and km,Xcs) in order to haveslow initial disintegration rates. It is consistent with the hydrolysismechanisms involving colonization of particulate organic matterand release of enzymes. Moreover, the decay rates were increasedfrom 0.04 to 0.10 in the case of valerate and butyrate degradersand 0.50 in the case of propionate and acetate degraders comparedwith the values of the standard ADM1 (Table 2). The reason forincreasing these parameters was to feed the Xcs fraction and to pro-duce more slowly hydrolysable organic matter. The new decay ratevalues have a similar order of magnitude to that used by Siegristet al. [9]. This appears to be more realistic than the low values pre-viously used in the standard ADM1 [42].

Fig. 3a presents the comparison between measured and simu-lated methane volume for untreated WAS. The modified modelled to a correct representation of the main product of the anaerobicdigestion for the untreated WAS. The agreement between the sim-ulated and the measured values was statistically significant(0.001 P p > 0) with correlation coefficients of 0.99. Moreover,the 95% confidence intervals contain 0 for the intercept and 1 forthe slope respectively (Table 3).

3.4. Model validation

3.4.1. Application on anaerobic degradation of pretreated WASsamples

The experimental data from the anaerobic degradation batch ofpretreated WAS samples at 110 �C and 220 �C were used tovalidate the model. We used the same kinetic parameters (see inTable 2) but also biomass concentrations identical to those ob-tained during the calibration step. Indeed, the inocula used in thethree anaerobic batch tests were taken from the same reactorand run in parallel.

The only changes thus concerned the variable initial conditionsand the stoichiometric coefficients of the disintegration step sincethey depend on the intrinsic characteristics of the WAS samples(Table 1). As it was already discussed, thermal pretreatment atlow temperature leads to a better accessibility of organic matterby the anaerobic biomass but high thermal pretreatment decreasesthe performance. Therefore, hydrolysable fractions, yield coeffi-cients and the soluble variables were directly impacted. At a

values of the model variables determined from experimental for the different WAS

ed WAS WAS pretreated at 110 �C WAS pretreated at 220 �C

0.0 0.00.39 0.560.15 0.130.27 0.120.19 0.19

ondition values0.0 0.02.27 1.361.12 0.900 00 00 012.21 12.211.25 1.250.024 0.0180.177 0.7640.0 0.00.012 0.0150.042 0.0490.078 0.0870.099 0.1380.014 0.0140.034 0.034

Table 2Main changes of kinetic parameters and rates for the degradation batch tests of WAS samples. Comparison of the values with Ramirez et al. [36] and standard ADM1.

Kinetic parameters and rates Units Default values Untreated WAS WAS pretreated at 110 �C WAS pretreated at 220 �C

YXcr kgCOD_X kgCOD_S�1 ND 0.1 0.1 0.1YXcs ND 0.09 0.09 0.09Ych 0.1a 0.1 0.1 0.1Ypr 0.1a 0.1 0.1 0.1Yli 0.1a 0.1 0.1 0.1

km,Xcr kgCOD_S kgCOD_X�1 d�1 ND 9 9 9km,Xcs ND 5.7 5.7 5.7km,ch 10a 10 10 10km,pr 10a 10 10 10km,li 10a 10 10 10km,c5 ND 20 20 20km,c4 30b 20 20 20km,pro 20b 40 40 40km,ac 16b 35 35 35

KS,Xcr kgCOD_S m�3 ND 0.4 0.4 0.4KS,Xcs ND 0.3 0.3 0.3KS,ch 0.5a 0.5 0.5 0.5KS,pr 0.5a 0.5 0.5 0.5KS,li 0.5a 0.5 0.5 0.5

kdec,Xcr d�1 ND 0.20 0.20 0.20kdec,Xcs ND 0.40 0.40 0.40kdec,c5 ND 0.10 0.10 0.10kdec,c4 0.04b 0.10 0.10 0.10kdec,pro 0.04b 0.50 0.50 0.50kdec,ac 0.04b 0.50 0.50 0.50

a Default values from Ramirez et al. [36].b Default values from standard ADM1.

0 500 1000 1500 2000 2500

050

010

0015

0020

0025

00

Measured methane volume (mL)

Sim

ulat

edm

etha

nevo

lum

e (m

L)

(a) y = 1.018 x –40.587 R2 = 0.992

0 500 1000 1500 2000 2500

050

010

0015

0020

0025

00

Measured methane volume (mL)

Sim

ulat

edm

etha

nevo

lum

e (m

L)

(b) y = 1.034 x –169.827 R2 = 0.992

0 500 1000 1500

050

010

0015

00

Measured methane volume (mL)

Sim

ulat

edm

etha

nevo

lum

e (m

L)

(c) y = 1.058 x + 16.034 R2 = 0.989

Fig. 3. Relation between simulated and measured cumulative CH4 production for untreated WAS (a), pretreated WAS at 110 �C (b) and pretreated WAS at 220 �C (c).

876 A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881

Table 3Statistical performance for predicting produced methane volume (CH4) from the anaerobic digestion of the three different WAS samples.

Linear regression na a 95% Confidence interval b 95% Confidence interval R2 p

Untreated WAS 19 �40.6 �128.6/47.4 1.02 0.97–1.06 0.99 0 < p < 0.001WAS pretreated at 110 �C 18 �169.8 �288.9/�50.7 1.03 0.97–1.10 0.99 0 < p < 0.001WAS pretreated at 220 �C 14 16.0 �70.5/102.6 1.06 0.99–1.12 0.99 0 < p < 0.001

In linear regressions between simulated and measured data: intercept (a), slope (b), correlation coefficient (R2) and signification (p).a n is the number of observed values.

A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881 877

pretreatment of 110 �C, the biodegradability was not modified sincethe inert fraction remained similar to the one obtained for un-treated WAS with 0.386 and 0.383 respectively. The fractionationof particulate COD was also unchanged since the particulate matterwas divided into 67% in Xcr and 33% in Xcs compared to 69% and 31%in the case of untreated WAS. However, we observe a different sol-ubilization of particulate COD since amino acids and sugar variablevalues increased. At 220 �C, the intrinsic characteristics of the WASwere largely changed. The inert fraction increased inducing a yieldcoefficient value of 0.56. The particulate matter was mainly solubi-lized into amino acids but some part of COD was not measured dueto the formation of recalcitrant and components at this extremecondition.

The set of kinetic parameters and fractionation for the pre-treated WAS samples presented in Tables 1 and 2 were thereforeused to simulate the methane production and the pH. Fig. 3b andc shows that the simulated data follow very closely the perfor-mance of both pretreated WAS anaerobic digestion. The agreementbetween the simulated and the measured values was statisticallysignificant in both cases (0.001 P p > 0) with high correlation coef-ficients. In the case of pretreated WAS at 110 �C, the 95% confi-dence interval is [�289/�51] and it does not contain 0 but thedeviation is very low. Indeed, Fig. 3c presents a slight underestima-tion of the simulated data during the first days of anaerobic degra-dation. However, the last values are well represented as it isconfirmed by the statistical significance of the slope.

Simulated data of the acetate concentration for WAS samples,shown in Fig. 4, were selected to confirm the robustness of themodified model since the acetate is the most important intermedi-ate by-product. As can be seen the acetate concentration is wellrepresented since the concentration remained in a coherent rangeand no inhibition threshold was reached. This is consistent withthe good modeling of the methane production for the WAS sam-ples. Moreover, the acetate concentrations show two productionphases: a high acetate concentration of around 0.6 gCOD L�1 wasreached during the first days from day 0 to day 8 and a lower var-iation was observed from day 8 to day 20. The second acetate var-iation allowed to approximately obtain the final measured acetateconcentration. This observation confirmed that our proposed mod-el is more realistic than the implementation of an ammonia inhibi-tion to simulate the second acetate variation as presented in

0

0.2

0.4

0.6

0.8

1

Acet

ate

(kgC

OD

.m-3

)

Acet

ate

(kgC

OD

.m-3

)

Time (days)0 10 20

0

0.2

0.4

0.6

0.8

1

Time0

(a)

Fig. 4. Simulated acetate concentration for untreated WAS (a), pretreated WAS at 110 �Cline: modified ADM1).

Ramirez et al [36]. Indeed, additional anaerobic batch tests (notpresented in this paper) showed that ammonia concentrations de-crease during the degradation. The simulations were not consistentwith the experimental ammonia variations. These results confirmthat our proposed model is able to well represent two COD fluxthrough acetate and methane variation coming from two differenthydrolysable organic matter compartments, Xcr and Xcs fractionswhich are led by the accessibility of the matter in complex sub-strate. Therefore, this model can be applyed for a number of com-plex carbon sources such as lignocellulosic biomass. However,stoichiometric coefficients and initial variables have to be mea-sured and calculated since their values depend on the substratecomposition.

3.4.2. Application on continuous pilot scale digesterAn important issue of this study was to demonstrate whether

the model parameters estimated in batch condition were represen-tative of continuous anaerobic digestion. In other words, whetherour degradation batch experiment procedure allowed us to deter-mine the main biodegradable and inert variables, Xcr, Xcs and Xi, aswell as determining the kinetic parameters which could be useddirectly in continuous process modeling to predict performanceand to test specific conditions?

Consequently, the pilot scale digester fed with the same kind ofWAS used in degradation batch experiments was monitored for30 days. Moreover, it is important to notice that the digestedsludge of this reactor was the inoculum source used to performthe study of degradation batch tests.

Over the period of study i.e. at steady influent flow-rate andvariable flow-rate periods, the WAS characteristics showed a totalCOD concentration equal to 49.0 ± 5.1 gCOD L�1 and the solubleCOD content equal to 9.0 ± 2.3% (Table 4). Thus, the organic matterwas mainly present in the particulate fraction. We also observedthat the amplitude of total and soluble COD concentrationvariations was high: from 39.5 to 60.1 gCOD L�1 and from 1.0 to12.2 gCOD L�1, respectively. The main biochemical compound inparticulate fraction was proteins with a content equal to23.9 ± 2.8%. The particulate content of carbohydrates and lipidswere 7.9 ± 3.9% and 8.1 ± 2.3%, respectively. Some variations inthe biochemical composition were also noticed. This could bedue to the wastewater composition which was not constant,

Acet

ate

(kgC

OD

.m-3

)

(days)10 20

0

0.2

0.4

0.6

0.8

1

Time (days)0 10 20

(b) (c)

(b) and pretreated WAS at 220 �C (c). (Circles: experimental data points, black plain

Table 4Influent characteristics and performances of continuous pilot scale thermophilic anaerobic digester at different flow rates.

Days(d)

Flow rate(m3 d�1)

Influent characteristics Effluent characteristics

Influent CODt(gCOD L�1)

CODs(%CODt)

Proteins(%CODp)

Carbohydrates(%CODp)

Lipids(%CODp)

Effluent CODt(gCOD L�1)

Methane yield(LCH4 gCODintro

�1)

Steady flow rate0–12 1.01 ± 0.02 52.2 ± 3.4 9.9 ± 1.1 23.8 ± 4.0 7.4 ± 5.7 9.1 ± 2.9 35.6 ± 2.3 117.4 ± 3.8

[0.98–1.04]a [49.2–60.1]a [7.5–11.8]a

[29.8–14.2]a [2.5–20.9]a [4.4–12.3]a [31.7–40.4]a [109.7–122.1]a

Variable flow rate13–28 0.97 ± 0.41 46.5 ± 4.9 8.3 ± 2.7 24.0 ± 1.4 8.3 ± 1.6 7.2 ± 1.1 28.5 ± 4.1 116.0 ± 32.2

[0.50–1.91]a [39.5–52.9]a [1.0–12.2]a

[22.5–26.8]a [5.5–11.3]a [5.3–9.1]a [23.2–36.4]a [67.2–178.6]a

a Values in [ ] are minimal and maximal values.

878 A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881

inducing variability in the sludge quality and to the standard errorof analytic method equal to 10–15%.

The COD balance showed a difference between COD input andoutput equal to 2.5% during the studied period. Moreover, the spe-cific methane production was 325 mLCH4 gCODremoved

�1, represent-ing a low variation of 7% against the theoretical value(350 mLCH4 gCODremoved

�1).During the steady flow-rate period (1.01 ± 0.02 m3 d�1 or a SRT

of 8 days), the digester performance was low with a mean methaneyield of 117.4 ± 3.8 mLCH4 gCODintro

�1. The flow-rate variation led toa change in methane yield increasing to 178.6 mLCH4 gCODintro

�1 ata flow-rate of 0.68 m3 d�1 (SRT: 11.8 days and OLR: 3.1 kgCOD mreac-

tor�3 d�1) and decreasing to 67.2 mLCH4 gCODintro

�1 at a flow-rate of1.91 m3 d�1 (SRT: 4.2 days and organic loading rate: 8.3 kgCOD -mreactor

�3 d�1). As a result, increasing the SRT improved perfor-mance since it led to a higher conversion of organic matter.However, the highest conversion performance into methane re-mained low with a value of 179 mLCH4 gCODintro

�1 due to the com-plex organic matter structure.

The experiment was conducted with the aim of obtaining datain order to evaluate calibrated model parameters in continuoussimulation and also to observe the model dynamic behavior whenfaced with a process disturbance.

The modeling was carried out by keeping the kinetic parameterset calibrated from the untreated WAS anaerobic degradation per-formance. The inert fraction (fXi_Xcr and fXi_Xcs) and the hydrolysablefractions, Xcr and Xcs, were also determined from the experiment inbatch condition since the substrate was similar. Indeed, the meth-ane production curve is necessary to calibrate these parameters.The yield coefficients fch_Xcr, fpr_Xcr, fli_Xcr, fch_Xcs, fpr_Xcs and fli_Xcs weredetermined from the characterization performed on the WASentering into the continuous digester. The measurements of carbo-hydrates, proteins and lipids were performed three times per weekduring the experimental period. The yield coefficients were there-fore used as input variables in the model. However, the yield coef-ficient values between Xcr and Xcs were kept constant since it isdifficult to distinguish the different conversion pathways of thereadily and slowly particulate matter in proteins, carbohydratesand lipids. The initial values of the microbial population wereadapted from the initial biomass concentrations calibrated withthe batch test degrading untreated WAS. Then the strategy pro-posed by Donoso-Bravo et al. [41] was used. The process was sim-ulated several times over a 28 days period with a steady SRT stateof 8 days, until the biomass concentrations were constant.

Fig. 5 shows the experimental and simulated data in terms ofQbiogas, QCH4, QCO2, pH, total COD as well as also acetate and propi-onate concentrations respectively.

The results show that the proposed model effectively simulatesthe dynamic behavior of the main variables in both the liquid and

gas phases. The model accurately predicted the biogas flow-ratedynamics in response to changes of the sludge composition andthe imposed flow-rate. Slight deviations were found when predict-ing the biogas production. The differences can be explained sincethe applied kinetic parameters came directly from the batch testexperiment and no further parameters optimization was carriedout.

The experimental pH did not change due to a strong bufferingcapacity of the medium. The pH was underestimated (Fig. 5c) sincethe simulated VFA concentrations were higher than in reality(Fig. 5e and f). Moreover, pH prediction is closely related to the cat-ion and anion concentrations in the digester. These concentrationswere calculated from the inorganic carbon and nitrogen, accordingto Rosen and Jeppsson [38] and this can explain the difference be-tween simulated and measured pH. However, the model was ableto reflect the pH behavior in response to the simulated VFAvariations.

During the steady flow-rate period, the model over-predictedVFA concentrations (mainly propionate) and then during the vari-able flow-rate period, the simulated VFA concentrations wereunderestimated (Fig. 5e and f). This may result from either under-estimation of the uptake coefficients for propionate degraders oran underestimation of uptake coefficients for soluble compounds,butyrate and valerate degraders. Nevertheless, the dynamics ofsimulated VFAs concentration was similar to the experimentaldata.

Fig. 5d shows that the model is also able to well represent thetotal COD variation which is an important parameter for the man-agement of full scale digesters in order to assess the COD balanceand to optimize the COD removal.

These observations confirm that the model with two hydrolysa-ble fractions and the parameter values determined from a batchexperiment procedure could well represent a continuous pilotscale digester.

3.4.3. Prediction of Xcr and Xcs fractions when sludge feeding is stoppedIn the standard ADM1 model, the complex substrate disintegra-

tion is described by a single kinetic parameter. Batstone et al. [30]concluded that the first order hydrolysis rate coefficients (khyd)determined in anaerobic batch tests are not appropriated when ap-plied to continuous digester modeling. In fact they showed that thehydrolysis coefficient value should be higher (order of 5 d�1) thanthat found in BMP tests (where most WAS hydrolysis coefficientsare 0.1–0.5 d�1: [43,44]), in order to accurately describe dynamicdata of a continuous digester. In order to explain the differencesin parameters estimated from batch and continuous conditions,Batstone et al. [30] emphasized the fact that the models treat thecomplex solid hydrolysis step as a homogeneous substrate de-scribed by a single kinetic parameter. However, the WAS has a

A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881 879

complex structure which makes the organic matter more or lessaccessible to anaerobic biomass which involves different degrada-tion rates.

As already quoted, after stopping sewage sludge feed in a con-tinuously fed laboratory digester, Siegrist et al. [9] observed twogas production profiles: the first gas production from readilydegradable matter occurred within hours and the second gas pro-duction was from slowly degradable matter. According to Batstoneet al. [30], short-term dynamics of gas production in a full-scalesystem are strongly impacted by highly degradable substrate. Forthis reason, when the full-scale digesters are stopped, a rapid ini-tial drop in gas production is typically observed, followed by a longtail of residual gas production.

The new model structure proposed is consistent with theseobservations. It seems interesting to observe the behavior of thenew model with the help of methane production and Xcr and Xcs

fraction variations after stopping feed substrate during the model-ing and compared the results with the literature observations.

We simulated the performance of a thermophilic digester treat-ing WAS in continuous condition over 100 days. Several assump-tions were made: kinetic parameters set and sludge composition

0 5 10 15 20 250

2

4

6

8

10

12

14

16

18

20

Biog

as fl

ow (m

3 .d-1

)

Time (days)

5

5.5

6

6.5

7

7.5

8

pH

0 5 10 15 20 25Time (days)

0 5 10 15 20 25Time (days)

0

0.2

0.4

0.6

0.8

1

Acet

ate

(kgC

OD

.m-3

)

(a)

(c)

(e)

Fig. 5. Simulated vs. experimental Qbiogas, QCH4, QCO2, pH, total COD, acetate and propionexperimental data points, black plain line: modified ADM1, blue dashed thin line: SRT). (Fto the web version of this article.)

were similar to those presented in Section 3.4.2. The biochemicalcomposition of the substrate was kept constant throughout thesimulation. For the first 30 days the feeding flow-rate was constantand equal to 1 m3 d�1, then it was stopped.

The methane production shows a rapid decrease when sludgefeed is stopped which is strongly associated with the degradationof Xcr (Fig. 6a). An increase in Xcs concentration is observed whichcan be explained by the biomass degradation due to a substratelimitation (Fig. 6b). From day 40, the slowly hydrolysable fractionis degraded providing a residual methane production (Fig. 6a andb). These results comply with the observations of Siegrist et al.[9] and Batstone et al. [30]. Consequently, the new structure withtwo hydrolysable fractions effectively represents the different deg-radation rates of complex substrate and biogas production ob-served when sludge feeding is stopped in a continuous digester.

Thus, it seems reasonable to think that sludge complex organicmatter has different degradation rates involved by physical acces-sibility and biochemical composition but fundamental degradationmechanisms are still not clear. More efforts have to be done for thecomplex organic matter fractionation and the determination of themodel kinetic and stoichiometric parameters.

0 5 10 15 20 2502468

10

QC

H4

(m3 .

d-1Q

CO

2 (m

3 .d-1

Time (days)

0 5 10 15 20 2502468

10

Time (days)

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25Time (days)

Tota

l CO

D (k

gCO

D.m

-3)

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Prop

iona

te (k

gCO

D.m

-3)

Time (days)

(b)

(d)

(f)

ate for pilot scale digester in thermophilic condition with SRT changes. (Red circles:or interpretation of the references to color in this figure legend, the reader is referred

0

2

46

810

QC

H4

(m3 .

j-1)

0 10 20 30 40 50 60 70 80 100Time (days)

90

Con

cent

ratio

ns (k

gCO

D.m

-3)

5

10

15

20

25

30

0

Time (days)0 10 20 30 40 50 60 70 80 90 100

XcrXcs

(a)

(b)

Fig. 6. Simulated methane flow-rate (a) and Xcr and Xcs concentrations and (b) afterstopping feed sludge for continuous pilot scale thermophilic digester.

880 A. Mottet et al. / Chemical Engineering Journal 228 (2013) 871–881

4. Conclusions

A new structure was proposed for the ADM1 model with twohydrolysable composite fractions. It was developed from the obser-vation of methane production in WAS anaerobic digestion. The rep-resentation of particulate organic matter bioaccessibility in theADM1 model was improved by implementing two compartmentsof matter with a readily hydrolysable fraction Xcr and a slowlyhydrolysable fraction Xcs using a Contois model to represent thedisintegration and hydrolysis steps. The intrinsic fractionation ofthe particulate organic matter (Xcr, Xcs, fch_Xcr, fpr_Xcr, fli_Xcr, fch_Xcs,fpr_Xcs, fli_Xcs, fXi_Xcr and fXi_Xcs) is therefore an important parameterthat strongly impacts the calibration model. A detailed character-ization of the particulate organic matter is necessary to properlyimplement the stoichiometric parameters. Some methods of frac-tionation by centrifugation separation or chemical extractionshould be proposed and linked to accurate characterization meth-ods as infrared [45] and fluorescence methods [46] and theirintrinsic biodegradability [47,48].

Furthermore, an anaerobic digestion batch test may be consid-ered to be a biological and physicochemical calibration procedureused to determine inert and biodegradable fractions, Xi, Xcr andXcs as well as the main kinetic parameters. These variables andmodel parameters could indeed well represent a continuous anaer-obic digestion process facing several dynamic changes.

Acknowledgments

Financial support from CIFRE convention (no 68/2006) and Veo-lia Environment Research & Innovation are gratefully acknow-ledged.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.cej.2013.05.082.

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