extension of anaerobic digestion model no.1 with processes of … · 2003. 7. 18. · lydrogen...

Lokshina et al. [email protected] rights of any nature whatsoever reserved.

nhibits hydrolysis. 0273-2289/03/109/0033/$20.00

days) is needed to

Extension of Anaerobic Digestion Model No.1:hno/. 37, 808-812. with Processes of Sulfate Reduction9), Water Res. 32,1423-

1971), J. App/. Bacterio/.VYACHESLAV FEDOROVICH,*,1 PIET LENS,2

'eng. 23, 1591-1610.J/. 53,403-409. AND SERGEY KAL YUZHNYI1. (1994), Water Environ. 1 Department of Chemical Enzymology, Moscow State University,6), Arch. Microbio/. 145, Moscow, 119992, Russia, E-mail: [email protected];

and 2Sub-department of Environmental Technology,~. (1984), Water Res.1S, Wageningen University, 6700 EV Wageningen, The Netherlands

Abstractiron. Cont. 21,411-490.0/. Bioeng. 42, 159-166. In the present work, the Anaerobic Digestion Model No.1 (ADM1) for19), in Proceedings of the computer simulation of anaerobic processes was extended to the processeslata-Alvarez,}., Tilche, of sulfate reduction. The upgrade maintained the structure of ADM1 and15-18, vol. II, pp. 1-4. included additional blocks describing sulfate-reducing processes (multiple

~7~6. reaction stoichiometry, microbial growth kinetics, conventional materialeMe~l~gtrys of thf eE8dth In~er- balances for ideally mixed reactor, liquid-gas interactions, and liquid-phase

mlS 0 ucation .l 'b . h . ) Th d d d 1 1. d t d .b 1equi I num c emlstry. e exten e mo e was app Ie 0 escn e a ong-

99), Water Sci. Techno/. term experiment on sulfate reduction in a volatile fatty acid-fed upflowanaerobic sludge bed reactor and was generally able to predict the outcome

1ostathis, S. G., Rozzi, of competition among acetogenic bacteria, methanogenic archaea, and sul-, Anaerobic Digestion fate-reducing bacteria for these substrates. The computer simulations also)mwall, UK. showed that when the upward liquid velocity in the reactor exceeds 1 m/ d,,. (1993), Water Resour. the structure of the sludge becomes essential owing to bacterial detachment.

v, S. V. (1994), Biores. Index Entries: Mathematical modeling; sulfate reduction; methano-genesis; competition; Anaerobic Digestion Model No.1.

t., and Nozhevnikova,

l. V. (1995), Water Res. Introduction

).J. Environ. Eng.126, The Anaerobic Digestion Model No.1 (ADMl) is a structured modelthat includes disintegration and hydrolysis, acidogenesis, acetogenesis,

:nviron. Microbio/. 55, and methanogenesis as the steps in anaerobic biodegradation. A detailed,,' T h / 7 311-318 description of ADMI is given in ref. 1. At present, ADMI does not account;~'w:~t~O 4,427-441. . for the processes of sulfate reduction, and, hence, it is invalid to describe an

,chem. Biotechno/. 109, important part of the anaerobic degradation processes. The reasonable upgrad-ing procedure should incorporate a minimal number of equations to ADMl,which describe the main features of the sulfate-reducing processes.

* Author to whom all correspondence and reprint requests should be addressed.

Vol. 109, 2003 Applied Biochemistry and Biotechnology 33 Vol. 109, 2003

L{:~"~ , ;;';':

I

34 Fedorovich, Lens, and Kalyuzhnyi

! The fact that sulfate-reducing bacteria are capable of using many of thei intermediates formed during the methanogenic breakdown of organic

matter results in competition for these substances. In general, substratecompetition in anaerobic systems is possible on three levels: between sul-fate-reducing bacteria and fermentative (acidogenic) bacteria for sugarsand amino acids; between sulfate-reducing bacteria and acetogenic bacte-ria for syntrophic substrates, such as volatile fatty acids (VF A) and ethanol;and between sulfate-reducing bacteria and methanogenic archaea for thedirect methanogenic substrates-acetate and hydrogen.

The competition of the first level is won by the very fast growingfermentative (acidogenic) bacteria (2). Therefore, this part of ADM1 as wellas the disintegration/hydrolysis will be not modified. The Monod kinetic

I data of sulfate-reducing bacteria, acetogenic bacteria, and methanogenicarchaea for growth and conversion on VFA and hydrogen indicate thatsulfate-reducing bacteria successfully compete with acetogenic bacteriaand methanogenic archaea. This was confirmed experimentally for hydro-gen (3-6) and for propionate/butyrate (3). Different situations can appearfor acetate utilization. In some situations, sulfate-reducing bacteria couldsuccessfully outcompete methanogenic archaea for acetate (3,7), whereassome other results showed that the latter is preferentially degraded tomethane (4,8-10). These facts indicate that the mass balance equations ofADM1 should be supplemented with additional members, which describe

: VFA and hydrogen removal via sulfate reduction. In addition, the kineticexpressions should be upgraded by taking into account hydrogen sulfideinhibition, especially in its undissociated form (2,4,6).

Basic Principles of ADM1 Extensionto Sulfate-Reducing Processes

Stoichiometry of Sulfate-Reducing Processes

The methanogenic conversions of VF A are well described by ADM1(1). In particular, in molar basis, they are the following:

XlC3H7COOH + 2H2O ~ 2CH3COOH + 2H2 (1)

X2C2HsCOOH + 2H2O ~CH3COOH + CO2+ 3H2 (2)

X3CH3COOH ~ CH4 + CO2 (3)

X44H2 + CO2 ~ CH4 + 2H2O (4)

in which Xl and X2 are the groups of syntrophic acetogenic bacteria, and X3and X4 are the groups of methanogenic archaea.

Applied Biochemistry and Biotechnology Vol. 709,2003

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I, and Kalyuzhnyi ADM 1 in Sulfate Reduction 35

lsingmanyofthe Extension of the ADMI reaction sequences for the sulfate reduction:lown of organic process was done by incorporation of the following reactions (11-13):eneral, substrate X'els: between sul- slcteria for sugars C3H7COOH + 0.5H250 4 ~ 2CH3COOH + 0.5H25 (5)

acetogenic bacte- X'FA) and ethanol; 6

h f th C2HsCOOH + 0.75~50 4 ~ CH 3COOH + CO 2+ H 2O + 0.75H 25 (6)IC arc aea or e

~ry fast growing X7of ADM 1 as well CH3COOH + ~504 ~ 2C02+ 2H2O + H25 (7)

le Monod kinetic Xld methanogenic 4H + H 50 -:H 5 + 4H a (8)yen indicate that 2 2 4 2 2

,~togenic bacteria Thus, according to the proposed stoichiometric scheme (Eqs. 5-8), the!ntally for hydro- sulfate reduction process is carried out by four groups of microorganisms: thelnons can appear group Xs comprises all butyrate-degrading sulfate-reducing bacteria; X6' all19 bacteria could propionate-degrading sulfate-reducing bacteria; ~, all acetotrophic sulfate-lte (3,7), whereas reducing bacteria; and Xs' all hydrogenotrophic sulfate-reducing bacteria.

illy degraded to . .nce equations of Kinetics and Mass Balances

': ,,:hich de~cri~e -In this section, only expressions that correspond directly to the exten-llhon, the kmehc sion of ADMI model are introduced. All others are referenced in thelydrogen sulfide detailed description of ADMI (1). The kinetics of sulfate reduction pro-

cesses was introduced following the principles of ADMI taking into accountboth the concentration of electron donor (organic substrate or hydrogen)and concentration of electron acceptor (5042-):

k S, Sp. = max I. 804. X. I . I . (9)1 K + S. K + S pH sulfide

:ribed by ADMI 5 1 804 804

The first two terms on the right side of Eq. 9 are uninhibited Monod-type uptake. The I functions are the inhibition functions that describe the

2 (1) influence of excessive amounts of sulfide. The I H function was used in theform analogous to that applied in ADMI (1): p

IH (2) 1 + 2 x l00,5(pKt-p~)2 I = {10)pH 1 + 10(pH-p~) + 10(PH-pKt)

(3) Undissociated H25 inhibition is assumed to proceed according to first-order inhibition kinetics (14) for all bacteria. Because little reliable informa-tion about H25 inhibition kinetics is available, the inhibition factor Isulfide as

(4) given in Eq. 11 can be considered a reasonable approximation:

bacteria, and X3 H 5Isulfide = 1 - 1- (if H25 > KI, Isulfide = 0) (11)I

Vol. 109,2003 Applied Biochemistry and Biotechnology Vol. 109,2003

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-cf" A~.., ,., -, -' ~~~ r36 Fedorovich, Lens, and Kalyuzhnyi

For calculation of pH values and concentrations of undissociatedforms of VF A and other species during the process, the correspondingformulas 4.1-4.7 of ADM1 were applied (1). Material balances in the liquidphase were described according to formulas 5.2-5.3 of ADM1 accountingfor ideal mixing conditions (1). For dissolved gas components, additionalterms according to formula 4.10 of ADM 1 were used to describe mass trans-fer between gas bubbles and liquid (1).

The general mass balance equation used to describe the behavior ofeach microbial group in the reactor is presented in the form of Eq. 12following formula 5.3 in the description of AMDI (1):

dX/.. qX.. x /..~=~-~+~pv.. (12)dt V/iq SRT j 1 1,1

In ADMl, the solids retention time (SRT) function was accepted as

SRT = HRT + tj,x (13)

in which tj x is the residence time of the solids above the hydraulic retentiontime (HRf) (1). Following the aim of this work, the function for SRT wastaken to be dependent not only from the HRT but also from the upwardliquid velocity (V u):

1 [1 - ER.(V ) ]-= I up' (14)

SRT HRT

I in which ERj characterizes the efficiency of the retention of bacterial groupXj in the reactor. These parameters are functions of both the upwardliquid velocity, V u ' and the reactor design. The same description of theefficiency of biomlss retention was used previously for modeling up flowanaerobic sludge bed (UASB) reactors (11,15). Thus, the mass balance in thereactor for bacterial group Xj can be expressed as

ax. X.-!-=-~.[l-ER.(V )]-b..X.+~ pv.. (i=1,8 ) (15)dt HRT I up' I I ":- 1 I,)

J

d~ ~ i=8-=--.[l-ER(V )]+ ~ b..X. (16)dt HRT 9 up .L., 1 1

1=1

where

ERj(V up) = Function(V up) (17)

"" The term X9 is introduced as the so-called inactive biomass, which alsoit~ needs to be taken into account for the total chemical oxygen demand (COD)

.'0 balance of the system. X9 comprises the biomass of bacterial groups, which

:: ;.. are present in aggregated biomass (e.g., denitrifying bacteria) but are not

."i;; J Applied Biochemistry and Biotechnology Vol. 109,2003, ;;i I

Lens, and Kalyuzhny . .'I ADM 1 In Sulfate ReductIon 37

)

)ns of undissociated Table 1bs,lthe co~respo~ding Experimental Operating Regimes of Sludge Bed Reactora ances In the lIquidof ADM1 accounting OLRmin - OLRmax

nponents, additional Days (g COD/[L. d]) V uv (m/h) pHI describe mass trans- 1-58 1.9-3.5 2 8.0

59-108 3.8-6.15 2 8.0:ribe the behavior of 108-122 2.0-5.5 4 8.0l the form of Eq 12 123-168 3.8-15.4 2 8.0I:. 169-182 6.4-9.0 6 8.0

183-208 1.3-6.7 2 8.0209-274 3.76-12.0 1 8.0

(12) 275-325 3.8-17 1 7.0

III was accepted as considered in our stoichiometric scheme. Biomass arising from bacterial

(13) decay is also included in X9.h d l' . The gas-phase components include the concentrations and partialnY ti raufic ~tentIon pressures in the head space. Since ADM1 is developed for complete mixing0 ~ on au: RT was conditions, only partial pressures were needed. These components were

rom e upward calculated directly using formulas 5.5-5.10 of section 5.2 of ADM1 (1).

Experimental System Considered

(14) Results of the experimental study carried out by Omil et al. (16,17) thatf . considered different operating regimes of granular sludge bed reactor with

1 ~ b~ctenal group effluent recycle treating synthetic sulfate-containing wastewater are useddot :h: upward here to validate the extended ADM1 model. Operating regimes are summa-

eS~I~tIon of the rized in Table 1. Briefly, the granular sludge bed reactor operated in variousr mo elmg u~flow regimes that differed by average organic loading rate (OLR) values, feed:nass balance In the content, and recycling intensity. The variations in the recycling intensity

resulted in different upward liquid velocities inside the reactor. All periodsin the experiment maintained the pH value inside the reactor at 8.0 except

(i = 1, 8) (15) the l~st one, during which the inhibition effect of hydrogen sulfide wasstudIed at pH 7.0.

Criteria for Evaluation of Reactor Performance

i (16) The considered experimental system was created to investigate theprocesses of sulfate removal from wastewater by means of anaerobic granu-lar sludge, which was exposed to a sulfate-rich influent for a prolongedperiod of time. Following the main aim of the experimental work, the four

(17) types of criteria describing the removal efficiency of acetate, butyrate, pro-)m h. h pionate, and sulfate will be represented as output information. This enables

dass, WdIC(C also the input and output value of the extended ADM1 model to be plotted inn eman aD ) . .ial' one curve. Note that SInce acetate can be produced from vanous types oft ?r)obups,which reactions, the acetate removal efficiency, which is given by Eq. 19, can beena ut arenot less than zero:


~


CINREp = (1 -~) . 100 (for propionate) (18)r COUT

Pr

CINI REAc = (1-~) .100 (for acetate) (19)

COUTAc

I CINRE B = (1 -~) . 100 (for butyrate) (20)

U COUTBu

\ "

CINsoREso = (1 - ~) . 100 (for sulfate) (21)4 COUT

I 504II Computational Methods l

Simulations were performed on an IBM-compatible personal com-puter (processor Pentium-233) by numeric integration of the differential

I equations resulting from the structure of the ADM1 model, with an auto-matic selection of the time step by a computer program based on a Runge-Kutta (fourth-order) technique. The computer program was written as aWindows application in Fortran-90 using Fortran Power Station FPS-4.0 ina generalized form, in which a variable number of steps, organisms, com-ponents, substrates, and inoculum data could be specified through an inputfile. The program created an output data file in a format suitable for graphic

processing.

Model Parameters and Initial Conditions

The parametric values, which have been introduced in the programvia the input file, can be divided into initial conditions and adjustableparameters. The adjustable parameters contain the numerical values ofthe kinetic parameters, which specify Eq. 9 for each process. These valueswere chosen by fitting to the experimental data (16,17) in a range consistentwith the set of parameters recommended by ADM 1 report for non-sulfidogenic microorganisms (1) and with the experimental determina-tion/ estimation for sulfidogenic bacteria parameters reported in theliterature (6,15,18-24). Taking into account the large number of parametersto be fitted reveals that such an approach has difficulties dealing with amultiplicity of sets of parameter values that may give a similar quality ofdescription of the modeled experiment. However, an extensive pool ofexperimental data obtained in various operational regimes (Table 1) basedon the detailed monitoring of more than 10 variables as well as additionalbatch studies on sludge activities by others (16,17) substantially reduced


_11- .. I-I IS, and Kalyuzhnyi ADM 1 in Sulfate Reduction 39

the freedom in choosing parametric values and thus significantly justifye) (18) such a fitting approach.

The group of initial conditions contains the initial substrate concentra-tions, which were taken to be equal to the reactor inlet concentrations. Therange of initial concentrations of bacterial groups was estimated on the basis

(19) of the information from batch experiments used to determine the specificactivity with butyrate, propionate, and acetate as the substrates (16).

Table 1 contains information about the minimal and maximal experi-mental values of OLR in all the considered regimes. In the simulation pro-cedure, the experimental OLR values as a function of time from original

(20) works (16,17) were used as the input parameters.

Results and DiscussionThe simulation results, together with experimental data, are presented

(21) in Figs. 1-5. The parameters used in the simulation process are presentedin Tables 2 and 3. Figures 1-5 show the dynamics of the main processcomponents and removal efficiencies in time. As can be seen, the maintendencies of the experiment are approached by the model. Moreover,having used the minimal set of model parameters, it appeared to be pos-

tle pers~nal co~- sible to describe the long-term experiment, which contained various types:>f the .dIfferential of transition regimes. Nevertheless, within two periods, d 168-208 anddel, with an auto- d 275-325, one can find several deviations of the model from the experiment.lased on ,a Runge- Comparison of the results given in Figs. 1 and 2 reveals that duringwa~ WrItten as. a d 168-208, the model was not as sensitive to the changes of external condi-Statio~ FP5-4.0 m tions as in previous periods. This was most probably owing to temporary,orgarusms,.com- overloading of the system. However, as can be seen from Figs. 1-5, afterl ~oughanmp~t several HRT this perturbation was overcome. Note that the model alsoutable for graphIc reflects this fact, but with lesser amplitudes.

An interesting feature of the experiment used for model calibration is- the significant increase in the methane production rate after approx 150 d

of operation (Fig. 5A). This was related to a stepwise increase in the concen-d in the program tration of the methanogens in the sludge (16,17). The model took this intolS and adjustable account via slightly higher ER values for methanogenic species X7 and Xgme rica I values of compared to other bacterial groups in the system. This resulted in satisfac-:ess. These values tory agreement of model predictions with the experiment with respect to1 range consistent methane output throughout almost the entire study (Fig. 5A).report for non- A clear deviation of the model results from experimental values was

:ental determina- observed in the last period (d 275-325). This may be explained by the lowreported in the upward velocity and the decrease in the pH value of the liquid phase from

bel of parameters 8.0 to 7.0. These two factors increased the inhibition effect of H2S. Note thates dealing with a in the present work, the inhibition effect of the neutral form of H2S in thesimilar quality of bulk liquid was assumed. In UASB reactors, however, the major part of!xtensive pool of biotransformations takes place inside the granulated biomass. An insuffi-~s (Table 1) based cient mass transfer of H2S between the granulated biomass and surround-'lell as additional ing medium can occur, and the decreased V u may have caused additional:antially reduced inhibitory effects. P

Vol. 109,2003 Applied Biochemistry and Biotechnology Vol, 709,2003

I

-

~~~~..


6 A 100. .5 80 .'00 4 . 0aJ . ~ 60] 3 .. ~-3' ~ 40C/] 2

1 20. .0 0

0 50 100 150 200 250 300 0 50 100 150 200 250 300

T~, days T~, days

Fig. 1. Model vs experiment for sulfate: (A) effluent concentration; (B) removalefficiency. (0) Experiment; (-) model.

2,5 150

Q 2 1000ut)O 1,5 ~ 50

~ 0

Q) ~

'GJ 1 ,~ 0'GJ ~< 0,5 -50

0 -100. .0 50 100 150 200 250 300 .-150

T~, days TinK:, days

Fig. 2. Model vs experiment for acetate: (A) effluent concentration; (B) removalefficiency. (0) Experiment; (-) model.

0,6 ~ . 100\00§ 0,5 o' 80

~0,4 ~ 60

{0,3 r~I .~ 0,2 0 ~ 40

£ 0,1 20

0 00 50 100 150 200 250 300 0 SO 100 150 200 250 300

A TinK:, days 8 T~, days

Fig. 3. Model vs experiment for propionate: (A) effluent concentration; (B) removalefficiency. (0) Experiment; (-) model.

Taking into account the discussed enhancement of the inhibitoryeffect of HzS, one can explain the discrepancy between the model andexperiment regarding methane production during the last period (Fig. SA).


;

, and Kalyuzhnyi ADM 1 in Sulfate Reduction 41

. 0,6 120. 0 0,5 100

0. u 0,4 . . ~ 80~ 01) 0 I0 .., oS 0,3 i 60 0,~. ~.. ~:~. ~o §' 0,2 , 40

'., . . ~0 1 20. ,

, , , I 0 0

>0 200 250 300 0 50 100 150 200 250300 0 50 100 150 200 250 300

lays TmK:, days Titre, days

'ation; (B) removal Fig. 4. Model vs experiment for butyrate: (A) effluent concentration; (B) removalefficiency. «» Experiment; (-) model.

10 140

.~ 8 r/)>:. r/)

~ > 105~6 01)

J 4 i 70u 0

. ~ 2 :cE 35

. . 0 0

0 50 100 150 200 250 300 0 50 100 150200250300, days TmK:, days Titre, days

ation; (B) removal Fig. 5. Model vs experiment for (A) methane production and (B) biomass accumu-lation in reactor. «» Experiment; (-) model.

~~~j.n Note that some fluctuations in model variables at the final stage of simula-~ -- ~I tions (such as chaos) were caused by frequent variation in actual OLR

~ during the experimental study (16). These fluctuating experimental OLR{~ values were used as input parameters during simulations. Finally, in spite

0 of some discrepancies in the behavior pf the main components, the agree-

'1: ment between the experiment and the model for biomass dynamics was{ good throughout the study (Fig. 5B).

".', " According to the approach introduced earlier (Eqs.I4-17), the loga-0 200 250 300 rithmic dependence of all ER values from V u was introduced in the model0 da because the biomass retention inside the sludge bed reactor was high, even-, ys under high V up values in the considered experiment (Fig. 5B):

~ation; (B) removal ER.(V ) = Ki [1 - K . Log (V /2)] (22)I up ER V 10 up

The Kv value here was found by fitting to be equal to 0.028.f the inhibitory In conclusion, for engineering purposes in which sulfate removalthe model and efficiencies are of primary interest, the proposed extension of ADMI is

period (Fig. SA). generally valid, as evidenced from Figs. 1-5.


42 F

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ulfate Reduction

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I 44 Fedorovich, Lens, and Kalyuzhnyi

! Nomenclature

b = bacterial decay rate constant (d-l)ER = efficiency of retention of bacterial group in reactorKER = ER under V u = 2 m/h (dimensionless)

K[ = inhibition crinstant by undissociated hydrogen sulfide(moI/L)

kLa = mass transfer coefficient (d-l)kmax = maximum substrate rate uptake (lid)

Ks = Monod saturation constant (g COD IL for organic sub-strates and hydrogen or mol/L for sulfate)

Kv = ER dependency from V up (dimensionless)M = mass transfer rate to gas phase (g COD[mol]/[L. d])

I I' p = partial pressure of substrate in gaseous form (atm)

pKl' pK2 = parameters of pH inhibition functions"\ q = liquid flow rate (LI d)

,~~~ REA = removal efficiency of component A (dimensionless)" \. , '. S = substrate concentration in liquid phase (g COD IL for

, \ \ organic components and hydrogen or mol/L for sulfate,)~ sulfide, and soluble CO2 and its ionized form)I V G = volume of reactor gas phase (L)~, V R = volume of reactor liquid phase (L) ,~" V u = upward liquid velocity (m/h) t;~

I I X = bacterial concentration (g COD IL) ~..~I Y = bacterial yield (g COD I g COD consumed)

Vii = rate coefficients for component i on process jPi = conversion rate (g S-COD [g VSS-COD or mol]/[L. d])

l~eferences

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101/L for sulfate,

form)

.,.

d)ess jr mol]/[L . d])

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;ent60/4b,2669-2676..III, Wize, D. L.,ed.,

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