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Lokshinaet al.
nhibits hydrolysis. days) is needed to

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Extension of Anaerobic Digestion Model No.1
:hno/. 37, 808-812.

9),WaterRes.32,14231971), App/. Bacterio/. J.

with Processes Sulfate Reduction of
VYACHESLAV FEDOROVICH,*,1 PIET LENS,2

'eng. 23, 1591-1610.

J/.53,403-409.

AND

SERGEY KAL YUZHNYI1

. (1994), Water Environ.

6), Arch. Microbio/. 145,
~.

(1984), Res.1S, Water

1 Department of Chemical Enzymology, Moscow State University, Moscow, 119992, Russia,E-mail: vfedorovich@enz.chem.msu.ru; and 2Sub-departmentof Environmental Technology, Wageningen University, 6700 EV Wageningen, The Netherlands

Abstract
iron.Cont.21,411-490. 0/.Bioeng. 159-166. 42, 19), Proceedings the in of lata-Alvarez,}., Tilche, 15-18,vol. II, pp. 1-4. ~7~6. e M e~l~gtry th e 8 th In~ers of f E d mlS 0 ucation In the computer of sulfate included present work, the Anaerobic Digestion Model No.1 (ADM1) for simulation of anaerobic processes was extended to the processes reduction. The upgrade maintained the structure of ADM1 and additional blocks describing sulfate-reducing processes (multiple

reaction stoichiometry, microbial growth kinetics, conventional material balances for ideally mixed reactor, liquid-gas interactions, and liquid-phase equi .l Ib num chemlstry. ) Th e exten d ed mo d e1was app 1 d t 0 d escn.b e a 1 Ie . ong' . .

99),WaterSci.Techno/.
1ostathis,S. G., Rozzi, , Anaerobic Digestion )mwall, UK. ,. (1993), Water Resour.

term experiment on sulfate reduction in a volatile fatty acid-fed upflow
anaerobic sludge bed reactor and was generally able to predict the outcome of competition among acetogenic bacteria, methanogenic archaea, and sulfate-reducing bacteria for these substrates. The computer simulations also showed that when the upward liquid velocity in the reactor exceeds 1 m/ d, the structure of the sludge becomes essential owing to bacterial detachment.

v,S.V. (1994), Biores.
t., and Nozhevnikova,
l. V. (1995), Water Res.

Index Entries: Mathematical modeling; sulfate reduction; methanogenesis; competition;AnaerobicDigestion Model No.1.

Introduction
The Anaerobic Digestion Model No.1 (ADMl) is a structured model that includes disintegration and hydrolysis, acidogenesis, acetogenesis, and methanogenesis as the steps in anaerobic biodegradation. A detailed

).J. Environ. Eng.126, :nviron. Microbio/. 55,
,,' T h / 7 311-318

;~'w:~t~O 4,427-441. .
,chem. Biotechno/. 109,

for theprocesses sulfatereduction,and,hence, is invalid to describe of it an

description ADMI is givenin ref. 1.At present, of ADMI does account not

importantpart of theanaerobic degradation processes. reasonable The upgrading procedureshouldincorporatea minimal numberof equations ADMl, to which describe main featuresof the sulfate-reducing the processes.
*Author to whom all correspondence and reprint requests should be addressed.

Vol. 109,2003

Applied Biochemistry Biotechnology and

33

Vol. 109, 2003


L{:~"~

,

;;';':

I

34 !
i

Fedorovich,Lens,and Kalyuzhnyi Thefactthat sulfate-reducing bacteriaarecapable using many of the of

intermediates formed during the methanogenic breakdown of organic

I

:

matter results in competition for these substances. In general, substrate competition in anaerobic systems is possible on three levels: between sulfate-reducing bacteria and fermentative (acidogenic) bacteria for sugars and amino acids; between sulfate-reducing bacteria and acetogenic bacteria for syntrophic substrates, such as volatile fatty acids (VFA) and ethanol; and between sulfate-reducing bacteria and methanogenic archaea for the direct methanogenic substrates-acetate and hydrogen. The competition of the first level is won by the very fast growing fermentative (acidogenic) bacteria (2). Therefore, this part of ADM1 as well as the disintegration/hydrolysis will be not modified. The Monod kinetic data of sulfate-reducing bacteria, acetogenic bacteria, and methanogenic archaea for growth and conversion on VFA and hydrogen indicate that sulfate-reducing bacteria successfully compete with acetogenic bacteria and methanogenic archaea.This was confirmed experimentally for hydrogen (3-6) and for propionate/butyrate (3). Different situations can appear for acetate utilization. In some situations, sulfate-reducing bacteria could successfully outcompete methanogenic archaea for acetate (3,7), whereas some other results showed that the latter is preferentially degraded to methane (4,8-10). These facts indicate that the mass balance equations of ADM1 should be supplemented with additional members, which describe
VFA and hydrogen removal via sulfate reduction. In addition, the kinetic

expressions should be upgraded by taking into account hydrogen sulfide inhibition, especially in its undissociated form (2,4,6). Basic Principles of ADM1 Extension to Sulfate-Reducing Processes Stoichiometry of Sulfate-Reducing Processes The methanogenic conversions of VFA are well described by ADM1 (1). In particular, in molar basis, they are the following: Xl C3H7COOH+ 2H2O ~ 2CH3COOH + 2H2 X2 C2HsCOOH + 2H2O ~CH3COOH + CO2+ 3H2 X3 CH3COOH ~ CH4 + CO2 X4 4H2 + CO2 ~ CH4 + 2H2O (1) (2)

(3) (4)

in which Xl and X2are the groups of syntrophic acetogenicbacteria, and X3 and X4 are the groups of methanogenic archaea.
Applied Biochemistry Biotechnology and Vol. 709,2003

"', c


r'
I, and Kalyuzhnyi

ADM 1 in Sulfate Reduction Extension of the ADMI reaction process was done by incorporation of X C3H7COOH+ 0.5H2504 ~ X
6

35 sequencesfor the sulfate reduction the following reactions (11-13): s 2CH3COOH + 0.5H25 (5)

lsingmanyofthe :lown of organic eneral, substrate 'els:between sullcteria for sugars acetogenic bacte'FA) and ethanol; h f th

C2HsCOOH

+ 0.75~50

4~

CH 3COOH

+ CO 2+ H 2O + 0.75H 25

(6)

IC arc aea or

e X7 CH3COOH + ~504 ~ 2C02+ 2H2O + H25 X 4H + H 50 -:H
2 2 4

~ry fast growing of ADM 1 aswell le Monod kinetic ld methanogenic yen indicate that , ~togenicbacteria !ntally for hydrolnonscan appear
19bacteria could

(7) (8)

2

5 + 4H a
2

Thus, according to the proposed stoichiometric scheme (Eqs. 5-8), the sulfate reduction processis carried out by four groups of microorganisms: the group Xs comprises all butyrate-degrading sulfate-reducing bacteria; X6' all
propionate-degrading sulfate-reducing bacteria;

~,

all acetotrophic sulfate-

lte (3,7),whereas illy degraded to nce equations of ': ,,:hich de~cri~e llhon, the kmehc lydrogen sulfide

reducing bacteria; and Xs' all hydrogenotrophic sulfate-reducing bacteria. . . Kinetics and Mass Balances -In this section, only expressions that correspond directly to the extension of ADMI model are introduced. All others are referenced in the detailed description of ADMI (1). The kinetics of sulfate reduction processes was introduced following the principles of ADMI taking into account both the concentration of electron donor (organic substrate or hydrogen) and concentration of electron acceptor (5042-): p.
1

=

k S, S maxI. 804. K + S. K + S
5 1 804 804

X. I

pH

.I

sulfide

.

(9 )

:ribed by ADMI

2

(1) (2) (3)

The first two terms on the right side of Eq. 9 are uninhibited Monodtype uptake. The I functions are the inhibition functions that describe the influence of excessiveamounts of sulfide. The I H function was used in the form analogous to that applied in ADMI (1): p
I

IH2

pH

=

1 + 2 x l00,5(pKt-p~)

1 + 10(pH-p~) + 10(PH-pKt)

{10)

(4) bacteria,and X3
Vol. 109,2003

Undissociated H25inhibition is assumed to proceed according to firstorder inhibition kinetics (14) for all bacteria. Becauselittle reliable information about H25 inhibition kinetics is available, the inhibition factor Isulfide as given in Eq. 11 can be considered a reasonable approximation:

Isulfide1 =

H5

1-

(if H25 > KI, Isulfide 0) =

(11)
Vol. 109,2003

I Applied Biochemistry Biotechnology and

-


-cf"

A~..,

,.,

-,

-'

~~~

r
Fedorovich, Lens, and Kalyuzhnyi

36

For calculation of pH values and concentrations of undissociated forms of VF A and other species during the process, the corresponding formulas 4.1-4.7 of ADM1 were applied (1). Material balances in the liquid phase were described according to formulas 5.2-5.3 of ADM1 accounting for ideal mixing conditions (1). For dissolved gas components, additional terms according to formula 4.10of ADM 1were used to describe masstransfer between gas bubbles and liquid (1). The general mass balance equation used to describe the behavior of each microbial group in the reactor is presented in the form of Eq. 12 following formula 5.3 in the description of AMDI (1): dX/ ..

qX..

x /..
SRT j
1 1,1

~=~-~+~pv.. dt V/iq

(12)

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 retention time (HRf) (1). Following the aim of this work, the function for SRT was taken to be dependent not only from the HRT but also from the upward liquid velocity (V u):
-=

1

SRT I

[ 1 - ER.Vup') ] ( I HRT

(14)

in which ERjcharacterizes efficiencyof the retention of bacterialgroup the Xj in the reactor. These parameters are functions of both the upward liquid velocity, Vu ' and the reactor design. The same description of the efficiency of biomlss retention was used previously for modeling up flow anaerobic sludge bed (UASB) reactors (11,15).Thus, the massbalance in the reactor for bacterial group Xj can be expressed as ax. X.
I up'

-!-=-~.[l-ER.(V dt HRT

)]-b..X.+~I I

":- p1v.. I,)
J

(i=1,8 )

(15)

d~
-=--.[l-ER(V

~
HRT
9 up

i=8

dt

)]+

~
.L.,

b..X.
1 1

(16)

1=1

where
ERj(V up)= Function(V up)
""

(17)

Theterm X9is introduced asthe so-called inactive biomass, which also
balance of the system. X9comprises the biomass of bacterial groups, which are present in aggregated biomass (e.g., denitrifying bacteria) but are not
I
J

it~
.'0

needs to be taken into account for the total chemical oxygen demand (COD)

::

;..

, ;;i

."i;;

Applied Biochemistry Biotechnology and

Vol. 109,2003


y y Lens, and Kal uzhn I.
)ns of undissociated

.' ADM 1 In Sulfate ReductIon
)

37

s, the b l co~respo~ding
a ancesIn the lIquid of ADM1 accounting

Table 1 ExperimentalOperating Regimesof SludgeBed Reactor OLRmin OLRmax -

nponents, additional
I describe mass trans-

Days
1-58 59-108

(g COD/[L. d])
1.9-3.5 3.8-6.15

Vuv (m/h)
2 2

pH
8.0 8.0

:ribe the behavior of l the form of Eq 12 I:. (12)

108-122 123-168 169-182 183-208 209-274 275-325

2.0-5.5 3.8-15.4 6.4-9.0

4 2 6

8.0 8.0 8.0

1.3-6.7
3.76-12.0 3.8-17

2
1 1

8.0
8.0 7.0

III was accepted as (13) h d l' . nYti raufic ~tentIon 0 ~ on au: RT was rom e upward

considered in our stoichiometric scheme. Biomass arising from bacterial decay is also included in X9. The gas-phase components include the concentrations and partial pressures in the head space.SinceADM1 is developed for complete mixing conditions, only partial pressures were needed. These components were calculated directly using formulas 5.5-5.10 of section 5.2 of ADM1 (1). Experimental System Considered

(14) . 1 b~ctenal group ~ dot :h: upward eS~I~tIon of the r mo elmg u~flow :nass balanceIn the f

Results of the experimental study carried out by Omil et al. (16,17)that considered different operating regimes of granular sludge bed reactor with effluent recycle treating synthetic sulfate-containing wastewater are used here to validate the extended ADM1 model. Operating regimes are summarized in Table 1.Briefly, the granular sludge bed reactor operated in various regimes that differed by average organic loading rate (OLR) values, feed content, and recycling intensity. The variations in the recycling intensity resulted in different upward liquid velocities inside the reactor. All periods in 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 was
studIed at pH 7.0. Criteria for Evaluation of Reactor Performance

i

(16)

The considered experimental system was created to investigate the

(17) )m
d W dIC also ass, (C n eman aD ) ial' t ?rob ) ups,which

h. h

processes sulfateremovalfrom wastewater means anaerobic of by of granular sludge, which was exposedto a sulfate-rich influent for a prolonged period of time. Following the main aim of the experimentalwork, the four typesof criteria describingthe removalefficiencyof acetate, butyrate, propionate,and sulfatewill berepresented output information. Thisenables as
the input and output value of the extended ADM1 model to be plotted in . . one curve. Note that SInceacetate can be produced from vanous types of

ena ut arenot
Vol. 109,2003

reactions,the acetate removal efficiency,which is given by Eq. 19,canbe lessthan zero:
Applied Biochemistry Biotechnology and Vol. 109,2003


~

38
CIN REp = (1 -~)
r COUT Pr

Fedorovich,Lens,and Kalyuzhnyi

.

100

(for propionate)

(18)

I

REAc

=

(1-~) CIN
COUT Ac
CIN

.100

(for acetate)

(19)

I

RE B
U
\

= (1 -~) . 100 COUT
Bu CIN

(for butyrate)

(20)

"

REso = (1 - ~)
4 I I
I

so

.

100

(for sulfate)

(21)

COUT 504
l

Computational Methods
Simulations were performed on an IBM-compatible personal computer (processor Pentium-233) by numeric integration of the differential
I

equationsresulting from the structure of the ADM1 model, with an automatic selection of the time step by a computer program based on a RungeKutta (fourth-order) technique. The computer program was written as a Windows application in Fortran-90 using Fortran Power Station FPS-4.0in a generalized form, in which a variable number of steps, organisms, components, substrates, and inoculum data could be specified through an input file. 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 program via the input file, can be divided into initial conditions and adjustable parameters. The adjustable parameters contain the numerical values of the kinetic parameters, which specify Eq. 9 for each process. These values were chosenby fitting to the experimental data (16,17)in a range consistent with the set of parameters recommended by ADM 1 report for nonsulfidogenic microorganisms (1) and with the experimental determination/ estimation for sulfidogenic bacteria parameters reported in the literature (6,15,18-24).Taking into account the large number of parameters to be fitted reveals that such an approach has difficulties dealing with a multiplicity of sets of parameter values that may give a similar quality of description of the modeled experiment. However, an extensive pool of experimental data obtained in various operational regimes (Table 1) based on the detailed monitoring of more than 10 variables as well as additional batch studies on sludge activities by others (16,17) substantially reduced
Applied Biochemistry Biotechnology and Vol. 109,2003


_11IS, and Kalyuzhnyi
e)

..

I-I

ADM 1 in Sulfate Reduction

39

(18)

(19)

(20)

the freedom in choosing parametric values and thus significantly justify such a fitting approach. The group of initial conditions contains the initial substrate concentrations, which were taken to be equal to the reactor inlet concentrations. The range of initial concentrations of bacterial groups was estimated on the basis of the information from batch experiments used to determine the specific activity with butyrate, propionate, and acetateas the substrates (16). Table 1 contains information about the minimal and maximal experimental values of OLR in all the considered regimes. In the simulation procedure, the experimental OLR values as a function of time from original works (16,17) were used as the input parameters.

Results and Discussion
(21) The simulation results, together with experimental data, are presented in Figs. 1-5. The parameters used in the simulation process are presented in Tables 2 and 3. Figures 1-5 show the dynamics of the main process components and removal efficiencies in time. As can be seen, the main tendencies of the experiment are approached by the model. Moreover, having used the minimal set of model parameters, it appeared to be possible to describe the long-term experiment, which contained various types of transition regimes. Nevertheless, within two periods, d 168-208 and d 275-325,one can find several deviations of the model from the experiment. Comparison of the results given in Figs. 1 and 2 reveals that during d 168-208, the model was not as sensitive to the changes of external conditions as in previous periods. This was most probably owing to temporary overloading of the system. However, as can be seen from Figs. 1-5, after several HRT this perturbation was overcome. Note that the model also 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 concentration of the methanogens in the sludge (16,17). The model took this into account via slightly higher ER values for methanogenic speciesX7 and Xg compared to other bacterial groups in the system. This resulted in satisfactory agreement of model predictions with the experiment with respect to methane output throughout almost the entire study (Fig. 5A). A clear deviation of the model results from experimental values was observed in the last period (d 275-325). This may be explained by the low upward velocity and the decreasein the pH value of the liquid phase from 8.0 to 7.0. Thesetwo factors increased the inhibition effect of H2S.Note that in the present work, the inhibition effect of the neutral form of H2Sin the bulk liquid was assumed. In UASB reactors, however, the major part of biotransformations takes place inside the granulated biomass. An insufficient mass transfer of H2Sbetween the granulated biomass and surrounding medium can occur, and the decreased V u may have caused additional inhibitory effects. P
Applied Biochemistry Biotechnology and Vol, 709,2003

tle pers~nal co~:>fthe .dIfferential del,with an autolasedon ,aRungewa~ WrItten as.a Statio~FP5-4.0m ,orgarusms,.coml ~oughanmp~t utablefor graphIc d in the program lS and adjustable me I values of rica :ess. Thesevalues 1rangeconsistent report for non:ental determinareported in the bel of parameters es dealing with a similar quality of !xtensive pool of ~s (Table1)based 'lell as additional :antially reduced
Vol. 109,2003 I

-


~~~~..
40
6A
5 '00 4 aJ

Fedorovich,Lens,and Kalyuzhnyi
100. 80

.

]

3
2 1 0 0

. ..
.
50 100 150 200 250 300 T~, days

.
.

0 ~ 60

-3'
C/]

~

~

40

20. 0 0

50 100 150 200 250 300 T~, days

Fig. 1. Model vs experiment for sulfate: (A) effluent concentration; (B) removal efficiency. (0) Experiment; (-) model. 2,5
Q 0
~ Q)

150
100

2

u 1,5 t)O
'GJ 1 'GJ < 0,5 0

~
0 ~

50
0 -50

,~ ~

0 50 100150200250300
T~, days

-100.

.
TinK:, days

.

-150

Fig. 2. Model vs experiment for acetate: (A) effluent concentration; (B) removal efficiency. (0) Experiment; (-) model.
0,6 ~
\00

.

100 80

§
I

0,5

o'

~0,4
.~ 0,2

~ 60 r~
0

{0,3

~ 40
20 0

ё0,1
0 0 50 100 150 200 250 300
TinK:,days

0

SO 100 150 200 250 300

A

8

T~, days

Fig. 3. Model vs experiment for propionate: (A) effluent concentration; (B) removal efficiency. (0) Experiment; (-) model. Taking into account the discussed enhancement of the inhibitory effect of HzS, one can explain the discrepancy between the model and experiment regarding methane production during the last period (Fig. SA).
Applied Biochemistry Biotechnology and Vol. 109,2003

;


, and Kalyuzhnyi
.

ADM 1 in Sulfate Reduction 0,6 120
100

41

~ ,~.
.
0

.
..,

0
0 u
01)

0,5

0,4

.

.

:~. .
,

~o
,

'., .
,

.
I

~..
oS 0,3

§' 0,2
0,1 0

,
50 100 150 200 250300 TmK:,days

~ 80 i 60 ~ 40
0

I
0

~

20 0 0 0 50 100 150 200 250 300 Titre, days

>0 200 250 300 lays 'ation; (B) removal

Fig. 4. Model vs experiment for butyrate: (A) effluent concentration; (B) removal efficiency. «» Experiment; (-) model. 10 140
r/) r/)

.~ >:.

8

~ ~6

> 105

J
u

4

i
0

01)

70

. . .

~2 0 0 50 100 150 200 250 300 TmK:,days

:cE 35 0 0 50 100 150200250300 Titre, days

, days ation; (B) removal

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

~~~j.n -- ~I
~

Note that some fluctuations in model variables at the final stage of simula-

tions (such as chaos)were causedby 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 of some discrepancies in the behavior pf the main components, the agreement 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 logarithmic dependence of all ER values from Vu was introduced in the model becausethe biomass retention inside the sludge bed reactor was high, even under high Vup values in the considered experiment (Fig. 5B):
ER.(V
I up

~

{~ 0

0

-,

'1: { ".', " 0 200250 300 da
ys

~ation; removal (B)

)

=

Ki
ER

[1 - K . Lo
V

g10(Vup/2)]

(22)

f the inhibitory the model and period (Fig.SA).
Vol. 109,2003

The Kv value here was found by fitting to be equal to 0.028. In conclusion, for engineering purposes in which sulfate removal efficiencies are of primary interest, the proposed extension of ADMI is generally valid, as evidenced from Figs. 1-5.
Applied Biochemistry Biotechnology and Vol. 109,2003


42
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Fedorovich, Lens, and Kalyuzhnyi
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Fedorovich,Lens,and Kalyuzhnyi

Nomenclature
b = bacterial decay rate constant (d-l) ER = efficiency of retention of bacterial group in reactor KER= ERunder Vu = 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 CODIL for organic subKv
I
I

=

strates hydrogen mol/L for sulfate) and or
ER dependency from Vup (dimensionless)

'

M = mass transfer to gasphase COD[mol]/[L. d]) rate (g p = partial pressure of substrate in gaseous form (atm)

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,~~~ " \. ,

\

pKl' pK2= parametersof pH inhibition functions
REA
'.

=removal efficiencyof componentA (dimensionless) S = substrate concentration in liquid phase (g CODIL for
organiccomponents hydrogenor mol/L for sulfate, and
and soluble CO2and its ionized form) of reactor gas phase (L) of reactor liquid phase (L) liquid velocity (m/h)

q = liquid flow rate (LI d)

,\
)~ I
~,

\

I

"
I

sulfide, V G= volume VR = volume Vu = upward

,~

I

X = bacterialconcentration(g CODIL) Y = bacterialyield (g CODI g CODconsumed) Vii =ratecoefficients componenton process for i j Pi = conversionrate (g S-COD[g VSS-COD mol]/[L. d]) or

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l~eferences
1. Batstone, J.,Keller, J.,Angelidaki, I., Kalyuzhnyi, S.V., Pavlostathis, G., Rozzi, D. S. A., Sanders, T. M., Siegrist,H., and Vavilin, V. A. (2002), W. Scientificand Technical Report No. 13,IWA, London. 2. Wid del, F. (1988), Biology Anaerobic in of Microorganisms, ZehnderA. J.B.,ed.,Wiley, New York, pp. 469-585. 3. Alphenaar,P.A., Visser,A., andLettinga,G. (1993), Bioresour. Technol. 43(3),249-258. 4. Mulder, A. (1984), Frog.Ind. Microbiol.20,133-143. 5. Rinzema,A., Paaderkooper, H., De Vegt,A. L., and Lettinga,G. (1986), ProceedA. in ings of the EWPCAConference Anaerobic Treatment, Grown Up Technology, V. a B. Schiedam, Amsterdam,pp. 205-217. 6. van Houten,R.T., HulshoffPol, L. W., and Lettinga,G. (1994), Biotechnol. Bioeng. 44, 586-594. 7. Rin~ma, A. andSchultz,C. E.(1987), Finalreport preparedfor the Ministry of Housing, 1-1lysical Planningand Environment,Agricultural University Wageningen, Departmtnt of Water Pollution Control, Wageningen, The Netherlands. 8. Hoeks,F.W.J.M.M.,TenHoopen,H.J.G.,Roels,J. and Kuenen,J. (1984),Prog. A., G. Ind. Mic.obiol. 113-119. 20, 9. O'Flahen~, andColleran, (1995),Med. Landbouww. Gent V. E. Fac. Univ. 60/4b,2669-2676. 10. Rinzema,}. and Lettinga,G. (1988), Biotreatment in Systems, III, Wize,D. L., ed., vol. CRCPress,locaRaton,FL, pp. 65-109. ,
1

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Vol. 109,2003

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IS,and Kalyuzhnyi

ADM 1 in Sulfate Reduction

45

,in reactor
. sulfIde

11. Kalyuzhnyi, S. V. and Fedorovich, V. V. (1998), Bioresour. Technol.65,227-242. 12. Kalyuzhnyi, S. and Fedorovich, V. (1997), Water Sci. Technol.36(6-7),201-208. 13. Kalyuzhnyi, 5., Fedorovich, V., Lens, P., Hulshoff Pol, L., and Lettinga, G. (1998), Biodegradation9, 187-199. 14. Levenspiel, O. (1980), Biotechnol.Bioeng. 22, 1671-1687.
15. Bolle, W. L., van Breugel, J., van Eybergen, G. C., Kossen, N. W. F., and van Gils, W. (1986), Biotechnol. Bioeng. 28, 1621-1636.

lydrogen

16. Omil, F., Lens, P., HulshoffPol, L. W.,andLettinga,G.(1997),EnzymeMicrob. Technol. 20,229-236. 17. Omil, F., Lens, P., Hulshoff Pol, L. W., and Lettinga, G. (1996), FroG.Biochem.31,

for organIC sub. te) ) nol]/[L . d])
~

18. 19. 20. 21.
22.

699-710. (1985), Bryers, D. J. Biotechnol. Bioeng. 27,638-649. Costello, J.,Greenfield, F.,andLee,P.L. (1991), D. P. Water 25,859-871. Res. Kalyuzhnyi, V. (1997), S. Bioresour. Technol. 59,249-256. Mosey, E.(1983), F. Water Technol. Sci. 15,209-217.
Stucki, G., Hanselmann, 303-315. K. W., and Hurzeler, R. A. (1993), Biotechnol. Bioeng. 41,

orm

( tm) a

lensionless) :e (g COD/L

for

23. Vavilin, V. A., Vasiliev, V. B., Ponomarev, A. V., and Rytov, S. V. (1994), Bioresour. Technol.48, 1-8. 24. Vavilin, V. A., Vasiliev, V. B., Rytov, S. V., and Ponomarev, A. V. (1994), Bioresour. Technol.49, 105-119.

101/L for sulfate, form)
.,.

d) essj r mol]/[L

.

d])

ostathis, S. G., Rozzi, entific and Technical er A. J. B., ed., Wiley, 'chnol.43(3),249-258. G. (1986), in Proceedlp Technology, B. V. 3iotechnol.Bioeng. 44, he Ministry of Housity Wageningen, Derlands. len,J. G. (1984), Frog. ;ent60/4b,2669-2676. .III, Wize, D. L.,ed.,

Vol. 109,2003

Applied Biochemistry and Biotechnology

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