Mathematical model for soluble carbonaceous substrate biosorption

e:>

Pergamon

Wa~

0273·1223(95)00181-6

Sci Tech. Vol. 31, No. 2, pp. 67-77, 1995.

Copyrigbt C 1995 1AwQ

Printed in Great Britain. All rigbts reserved.

0273-1223195 $9'50 + 0-00

MATHEMATICAL MODEL FOR SOLUBLE

CARBONACEOUS SUBSTRATE

BIOSORPTION

Libor Novak*, Luis Larrea** and liri Wanner* "'Department o/Water Technology and Environmental Engineering, Prague Institute o/Chemical Technology, Technickd 5, 16628 Prague 6, Czech Republic "''''Depanment 0/ Environmental Engineering, CElT, Apartado 1555, 20009 San Sebastidn, Spain

ABSTRACT An activated sludge mathematical model for soluble carbonaceous substrale biosorption has been developed. The model allows the soluble COD fractions in biological reactors of a waslewaler b'eaIment plant and/or in batch cultivations to be predicled more precisely than by using conventional models. Special attention was paid to inleractions between the biokinetic model structure and population dynatnics of microorganisms in activated sludge. The model was developed using observations made on a synthetic wastewater which was dosed to a lab-scale. continuously operaled plant with the D-R·D-N configuration. The development of the model advanced through several biokinetic models which resulted in a fmal version of the biosorption model. The model is constructed on a matrix structure. Adsorption. desorption and hydrolysis kinetics are incorporated into the model. Experimental results from batch cultivations carried out under anoxic and oxic conditions as well as some dynamic tests carried out with the continuously operated system served for the model calibration. Simulations of a wide diversity of kinetic Iests demonstrale that the model is able to accurately predict the soluble COD under different cultivation conditions.

KEYWORDS

Activated sludge process; biomass fractionation; biosorption; bilamentous microorganisms; mathematical modelling; NUR; OUR; population dynamics; simulation; wastewater fractionation.

INfRODUcnON Progress in activated sludge modelling is carried out by introducing new concepts into the existing activaled sludge biokinetic models. Nevertheless. some phenomena and their corresponding theoretical descriptions still need to be incorporated into the latest models. Soluble substrate biosorption. accumulation. storage and regeneration mechanisms are not included in present-day models such as. for example. in the Activated Sludge Model No. I (Henze et aI., 1987). Moreover. the term "soluble COD" is often used in wastewater treatment practice and even in activated sludge process modelling. Nevertheless, it has never been standardized and mathematical models do not incorporate it

In previous activated sludge mathematical models based on the single-component substrate approach attempts were made to incorporate some kinetics of adsorption and storage mechanisms (Marais and Ekama, 1976; Ekama and Marais. 1977. 1979). The substrate was generally considered of particulate nature and adsorption phenomena in wastewater practice were thus commonly observed. The situation changed when 67

68

L. NOVAle tt al.

bi-substrate theory was applied (Dold et al., 1980) and particulate and soluble biodegradable organic fractions were considered separately (Henze et al.• 1987). The overall reaction rate for particulate substrate breakdown is limited by extracellular enzymatic hydrolysis and the multi-step path "adsorption-breakdownstorage-synthesis" has been modelled as the slowest step: slowly biodegradable substrate hydrolysis. Later, with phosphorus removal modelling development (Marais et al.• 1983. Siebritz et aL. 1983; Wentzel et at., 1984) mechanisms of particulate substrate enmeshment, adsorption and storage have been incorporated into the nitrification-denitrification systems (Dold et al., 1991) and into the model for biological phosphorus removal (Wentzel et at., 1992). Nevertheless, the role of the soluble biodegradable substrate, its importance and appropriate prediction by means of mathematical modelling, have emerged when the first theories on activated sludge filamentous bulking and population dynamics appeared (Ekama and Marais. 1986). For the purpose of a better estimation of the activated sludge biocenosis composition even two fractions of soluble biodegradable organic substrate were proposed: a readily biodegradable and a rapidly hydrolyzable fraction (Sollfrank. and Gujer, 1991; Henze. 1992). Subsequently, the first models for population dynamics including floc-forming and filamentous microorganisms have been constructed (Gujer and Kappeler. 1992. Kappeler and Gujer. 1992). The aim of this paper is to show a possible way of how information of soluble COD may be used in mathematical modelling of activated slUdge processes. On this basis a new mathematical model for soluble COD biosorption is proposed. Interactions between Biokinetic MQdels and PQpulation Dynamics Activated sludge filamentous bulking is frequently related to readily biodegradable (RBCOD) andlor soluble COD concentrations in different zones of the activated sludge process. It is generaJly accepted that fllamentous microorganisms can proliferate more easily in an environment with low concentrations of RBCOD because their trichomes protrude from the floes and are in direct contact with easily utilizable substrates. Different models for nutrient removal have always considered the principles Qf filamentQUS bulking in relation to the RBCOD available. Three basic concepts based on different approaches tQ hydrolysis products destination have been used.

1. Particulate substrate is hydrolysed to RBCOD which diffuses back to the liquid phase before it is consumed (Dold and Marais, 1986; Ekama et al., 1986; Henze et at.. 1987). This approach favours flJamentous growth on hydrolysis products. 2. Particulate substrate enmeshed into the floes is directly utilized by microorganisms. Although formation of an intermediate readily biodegradable substrate from hydrolysis is supposed, it does not appear again in the bulk liquid and remains on the surface of flocs (Dold et al.• 1980; Dold et at.. 1991. Wentzel et at.. 1992). This approach disfavours the filamentous growth on hydrolysis products. 3. Hydrolysis products are destined to the bulk solution but their higher concentration is supposed within the floes rather than outside the floes. This concept is mathematically expressed by different half-saturation coefficients for filamentous and floc-forming microorganisms (Gujer and Kappeler, 1992; Kappeler and Gujer, 1992). This approach favours filamentous growth on primary RBCOD and disfavours filamentous growth on secondary readily biodegradable hydrolysis products. Although these three concepts consider different pathways of the substrate circulation, nQne of them is advanced in its prediction of the easily available or soluble COD in different zones of the activated sludge process. Using the information of soluble COD in different reactors of the activated sludge process seems to be an important step in the progress of population dynamics modelling. PRESENTATION OF BIOKINETIC MODELS The development of a new biokinetic model for soluble COD substrate biosorption has been based on labscale experiments carried out in a continuously operated systems fed with a synthetic wastewater. It was

Soluble carbonaceous substrate biosorption

69

observed that in configurations based on the R-D-N and D-R-D-N flow schemes, i.e. in systems which incorporated regeneration of return activated sludge and anoxic selectors, very strong elimination of the soluble COD in the head anoxic parts of these systems occurred. The dependence of the net specific biosorption upon the floc loading is depicted in Figure I (Novlik et aI., 1993).

--,

6O~

c

o

~

50

~

40

eo 0 ....

•-

o

•

ell

,gQ~

() E

!5-2O

•~

!

10 01--

o

o

0

-_--_--_--_----1

20

40

60

00

100

120

Floc loading (mg/gl Fig. I. Relationship between net specific biosorption and floc loading obtained from R-D-N{D}and D-R-D-N (.) lab-scale systems.

Basic Assumptions of the Model Deyelopment An attempt to model the soluble COD biosorption needed: 1. Definition of a category of soluble COD. 2. More detailed wastewater fractionation. 3. Incorporation of adsorption-regeneration kinetics for substrate elimination.

The soluble COD was defined as a fraction of wastewater which passes through Whatrnan GF/C filters. This soluble fraction is divided into three subfractions: (1) inert soluble COD (SI), (2) readily biodegradable soluble COD (SS), and (3) rapidly hydrolyzable soluble COD (SR). The following basic assumptions had to be accepted in order to simplify as much as possible the development of the biosorption model. eOnly biosorption of the soluble carbonaceous fraction of wastewater is considered. Particulate fractions are supposed to be immediately enmeshed onto the floes or separated when filtered. eOnly biosorption of SS and SR substrates is incorporated into the models. Inert soluble COD passes through the system without any change. eBiosorption of soluble organic nitrogen is not included. The nitrogen conversion follows the classical pathways of the Activated Sludge Model No.1. eNo energy requirements are associated with adsorption kinetics. eAlI adsorption kinetics are related to the active heterotrophic biomass. Model Components Several new soluble and particulate components had to be established for the model development Their nomenclature is given in Table 1. Their meaning will be evident from different biokinetic models presented in Figure 2. The total soluble and the total particulate COD is always the sum of the soluble and the particulate carbonaceous fractions involved in a certain model. The XIN su!.lstrate is supposed to be an unchanged SS only transferred from the bulk liquid onto the floc surface. The SR substrate is subjected to a similar conversion and appears as the XEX substrate. This simplification was necessary because it was very difficult to reveal experimentally the virtual substrate destination during biosorption which is a very complex process involving several mechanisms.

70

L. NOVAK tit al.

TABLE I. Nomenclature and Description of Model Components Soluble carbonaceous fractions lrog CODII)

Nitrogen fractions lmg N/I)

SI SS

SNO SNH SND

XND

nitrate nitrogen ammonia nitrogen soluble organic nitrogen particulate organic nitrogen

SO SALK

dissolved oxygen (mg -CC>DII) alkalinity (mmolll)

SR SH

soluble inert organics soluble readily biodegradable substrate soluble rapidly hydrolyzable substrate soluble readily biodegradable hydrolysis products

Particulate carbonaceous fractions lrog CODIl) XI

XS

XIN XEX

XBH XBA XP

particulate inert organics particulate slowly hydrolyzable substrate SS adsorbed SRadsorbed active heterotrophic biomass active autotrophic biomass products from biomass decay

Bjokinetic Models The net specific biosorption (Figure 1) was calculated in the anoxic selector compartments of the continuously operated system. Therefore, it was logical to begin the experimental studies on substrate biosorption under anoxic conditions. Based on the experimental results from anoxic batch tests carried out under different substrate to biomass (SIX) ratios, two principal biokinetic models (Models I and 2) with incorporated adsorption for two different forms of biodegradable soluble organic substrate were suggested (Figure 2). Model I considers that all substrate must be converted to SS prior to use for biomass synthesis. In relation to filamentous growth, Model I follows the approach used in the Activated Sludge Model No.1. Model 2 is constructed on an opposite concept All substrate must pass through pathways of adsorption with a subsequent direct utilization by heterotrophs. Filamentous growth on hydrolysis products is thus not favoured (UCT approach). Later, the investigation advanced with batch tests under oxic conditions. The structure of Model 3 depicted in Figure 2 was developed in order to fit the OUR curve shape observed in oxic batch tests. An example of such a test is depicted in Figure 4. It was hypothesized that the negative declination of both plateaus observed during this type of cultivations can be simulated only by means of a direct rapid hydrolysis expressed as a fust-order kinetics. This means that the pathway of the SR substrate hydrolysis should be directly destined to the heterotrophic biomass. Model 3 did not involve adsorption kinetics and served for fitting the OUR values only. Finally, a compromise between the presented models 1 to 3 was taken for constructing the final Model 4 (Figure 2). Model 4 also considers production of soluble COD under anaerobic conditions which were observed during dynamic tests with a continuously operated system (Figure 5), during kinetic tests under anaerobic conditions and in COO balances calculated over the selector compartments of the O-R-O-N system (Novlik, 1994). The SH substrate production from the XEX substrate hydrolysis is not stopped under anaerobic conditions. Nevertheless, the SH substrate can be consumed only when an electron acceptor is present in the mixed liquor. Process stoichiometry and kinetics of Model 4 are summarized in Tables 2 and 3. Model parameters and their recommended values are given in Table 4. Model Kinetics

Modell. The adsorption kinetics is mathematically expressed by equations of modified Langmuir isotherm relating COD in the liquid phase with COD adsorbed in the sludge mass, represented by the heterotrophic biomass. The formulation by Ekama and Marais (1979) was adopted for adsorption of the SR and SS substrates (see procesess to and 11 in Table 3).

Soluble carbonaceous substrate biosorplion

Modell

71

Model 1

XBA

Fig. 2. Biokinetic models 1,2,3, and 4. Mass flow path diagram of carbonaceous substrate conversion.

During the reverse mechanisms the substrates XEX and XIN are converted to the SS and destined to the bulk liquid. These mechanisms can be called desorption and rather hydrolysis or solubilization in the case of the XEX substrate. The XEX substrate is considered to be composed of organic molecules with larger carbon chains that need hydrolysis for their conversion to readily biodegradable organic matter. Consequently, these hydrolysis products are of a different nature than the primary substrate and should rather be considered secondary hydrolysis products or simply a secondary substrate. In this model this approach is simplified and the category SS is also used for the readily biodegradable COD arising from the XEX substrate hydrolysis. On the other hand, the molecules of the XIN substrate are very simple and need not be hydrolyzed prior to their utilization. They are simply desorbed and consequently utilized by heterotrophic biomass.

Model 2. Model 2 was developed as an opposite alternative to Model I from the point of view of filamentous

bulking on hydrolysis soluble products. Both substrates, SS and SR, are adsorbed and converted to XIN and XEX substrates, respectively. The next step consists of direct utilization of the adsorbed carbonaceous matter by heterotrophic biomass. Contrary to Modell, the substrate does not appear in the liquid phase again and thus no desorption mechanisms are involved. This approach is reasonable when considering that all substrate must pass through the adsorption state prior to its utilization and that the desorption is from the thermodynamic point of view an energy demanding process. This must be taken into consideration although no energy requirements are associated with the adsorption/desorption kinetics in these models. Once the substrate is adsorbed onto the floc, the SS substrate converted to the XIN substrate can be directly utilized. The SR substrate converted to the XEX substrate is within the radius of the bacterial hydrolytic enzymes and thus can be hydrolysed. It is supposed that the XEX substrate must firstly be hydrolysed to the readily biodegradable soluble hydrolysis products that remain on the surface of bacterial cells. The hydrolysis process rate is slow. This secondary substrate is consequently utilized for synthesis at a much higher rate than that of the hydrolysis. From the point of view of mathematical modelling, the pathway of the XEX substrate utilization is governed by the slowest process, i.e. the XEX hydrolysis. Thus the step of the hydrolysis products utilization can be omitted although the stoichiometry of this kinetics is adopted for heterotrophic growth on the XEX substrate.

t:i

TABLE 2. Model 4. Process Stoichiometry J I 1

Com_t-.I

I

1

3

4

5

6

7

Procea'"

SI

ss

SR

SH

XI

XS

XIN

AOl"ObkC.-th ofXBHODXIN AnoxkC.-th ofXBHonXIN AOl"ObkC.-th

3

ofXBH . . SR Aaod
4

ofXBR . . SR

6 7

•

,

10

11 13

10

II

11

13

14

15

16

17

XBA

XP

so

SNO

SNH

SND

XND

SALK

-IIY H

I

-IIY H

I I

-IIY H

I

ofXBHoaSH Anod
-IIYK

J

-IIY K

I

Dea,.

I-fp

ofXBH

Deay

.rsR

H,..""'"

-I

H,......,..

286Y H {I-YH)/YH

15

2.86 YH

Aa. aIIL1IIadaII:

(I-YHYI4-Z86 YH -iX!l"14 -iX!l"14

IIYA

(I-Y HYIN.86YH

-iXB

-iX!l"14

-lXB

-iX!l"14 0 Iny"

-IIY A

fp .\

~

ixB ofpi xp

1

-I

-I

1 I

01 I I

oj

~

,...c:o

-fpixp

fp

r

~

iXB

.fXND .rSND

-iX!l"14

';XB

H,..ro/yI& 14

-iX!l"14

';XB {I-YHY

AYY A

(I-YHYI4·1.86 YH

-ixB -iXB

{I-YH)/YH

{4.57-Y

-iX!l"14

I

.fXEX .fXS

2.86 YH

-I

I-fp

.rXBA A~

{I-YHY

I

orXBA

orss

-iXB

{I-YH)/YH

{I-YHY

"Ol"Oblec..-h

A......,uo. II

,

XBH

-IIY H

Aorobk CI'8Wtlo 5

•

XEX

-I 1/14

Soluble carbonaceous substrate biosorption

73

TABLE 3. Model 4. Process Kinetics

XIN SO XBH p~. KSlN +XIN' KOH +SO' XlN KOH . SNO .T],.XBH Pt!IH'KSIN +XlN'KOH +SO KNO+SNO

kh~.SR·K 4

","olk: IrowtIt

KOH SNO khS/l.SR'K08 +SO· KNO +SNO

orXBHoaSR

15

A..... We: lrowth orXBHHSH

,

PHSH'

AJooIk: I""'""

erXBH .. SH PHSH'

7

• ,

SH SO .XBH KSSH + SH . KOH + SO

XSH KON . SNO .T],.XBH KISH + XSH' KON + SO KNO + SNO

A.....We: lrowth orXBA

SO +SO

OH

SNH SO .XBA KNH +SNH' KOA +SO

J.'A·

Decay

orXBH

Decay orXBA

10

Adoorpdooo orss

II

Adoorplloll orSR

IJ

kass.SS. XBH.(jss - XlN I XBH) kaSll.SR.XBH.(jSll- XEX I XBH)

Hyd....lyob orXEX

IchEX • XEX. [ K

SO + SO + 11.· K

OH

13

15

+ SO

Hydrolyob orxs

14

KOH]

OH

-Hydrolyob

Idrxs·XS. [ K

SO KOH SNO] +SO +11.· KOH +SO· K +SNO NO

OH

orXND

PI3.(XNDI XS)

.rSND

k•. SND.XBH

74

L. NOVAK tral.

TABLE 4. Parameters used in Model 4

Name

Value

Dimension

Maximum specific growth rate for XBH on XIN Half-saturation coefficient for XBH and XlN Maximum specific growth rate for XBH on SH Half-saturation coefficient for XBH and SH Decay coefficient for XBH Correction factor for I-lHIN and I-lHSH under anoxic conditions Oxygen half-saturation coefficient for XBH Nitrate half-saturation coefficient for denitrifying XBH

2 - 2.S

d- I gCODm-3 d- I gCODm-3 d- I dimensionless g 0 2 m-3 gN03-Nm-3

Symbol Heterotrophs I-lHIN KSIN I-lHSH KSSH

bu

Tlg KQH KNO Autotrophs I-lA bA KoA KNH

Maximum specific growth rate for XBA Decay coefficient for XBA Oxygen half-saturation coefficient for XBA Ammonia half-saturation coefficient for XBA

Adsorption kaSS kaSR fS8 fSR

Adsorption rate constant for SS Adsorption rate constant for SR Maximum fraction of SS incorporated to XBH Maximum fraction of SR incorporated to XBH

Hydrolysis khXS TJh khEX TIs khSR ka

Hydrolysis rate constant for XS Correction factor for hydrolysis of XS under anoxic conditions Hydrolysis rate constant for XEX Correction factor for hydrolysis of XEX under anoxic conditions Hydrolysis rate constant for SR Ammonification rate

Stoichiometry YH YA fp iXB ixp

0.62

O.7S 0.7S

0.5 I 0.1 0.5

O.S

d- I d- I g 0 2 m-3 g NH3-Nm-3

0.25

m3 g-ICOD d- I m3 g-ICOD d- I dimensionless dimensionless

7 0.4 70 0.8 7-IU 0.02

d- I dimensionless d- I dimensionless d- I m3 g-ICOD d- I

0.67 0.24 0.08 0.086 0.06

dimensionless dimensionless dimensionless dimensionless dimensionless

0.6 0.1 0.2S

Yield for XBH Yield for XBA Fraction of biomass leading to particulate products Mass of nitrogen per mass of COD in biomass Mass of nitrogen per mass of COD in products from biomass

<00.---

7 2 - 7.S 7

--.,

l15 o Z

lEX

III

10

5 0

1.5

Q5

TIme (hI

2

0

Q5

1.5

TIme (hI

FIg. 3. Anoxic batcb test simulation using Model I. COD and SNO curves.

2

Soluble carbonaceous substrate biosorption

75

For mathematical expression of the heterotrophic growth on the XIN substrate, the standard Monod equation was chosen (see Table 3, process I and 2). The XIN substrate is added to the particulate substrate because its location is on the floes and not in the liquid phase. But it was considered that the XIN substrate's real nature is the same as in the liquid phase, i.e. soluble. This is the reason why the Monod equation was also adopted for the microorganisms growth on the XIN substrate.

Model 3. The heterotrophic growth on the SR substrate is expressed by a simple fust-order kinetics (Table 3,

processes 3 and 4). This fonnula is derived from the adsorption kinetics for particulate substrate hydrolysis considering the fraction of substrate in the biomass to be much lower than the value of the process halfsaturation coefficient Although the XBH concentration was incorporated to this fonnula in Model 3 (resembling the ammonification kinetics), Model 4 uses a simplified expression. This means that the coefficient kh SR will depend on the biomass concentration.

Model 4. The SS substrate elimination takes place through the adsorption step with the consequent direct

utilization of the XIN substrate. The SR substrate is both directly destined to the heterotrophic biomass and adsorbed and subsequently converted to secondary hydrolysis soluble products (SH). The SR substrate hydrolysis rate is equal in oxic and anoxic environment, but it ceases when no acceptor of electrons (oxygen, nitrates) is available. A similar approach is accepted for XIN and SH substrates utilization by heterotrophic biomass but they are corrected for anoxic growth by factor Tl g. The XEX substrate hydrolysis is corrected under anoxic conditions (TIS> and continues with the same correction factor (rate) under anaerobic conditions. This change enables an accumulation of the SH substrate in the liquid phase when no electron acceptor is available. Mode! Atllllicatjoos

Case studies. Both Models I and 2 could predict the soluble COD and nitrate nitrogen (SNO) curves in

anoxic batch tests with a similar precision. Figure 3 shows these predictions with Model 1. Model 3 has fitted the experimental OUR values in oxic batch tests (Figure 4). The final Model 4 was successfully applied to case studies under anoxic and oxic conditions. Moreover, the soluble COD elimination was predicted in the oxic batch tests in accordance with the experimental values (Novak, 1994). The dynamic behaviour of the continuously operated system, when a two-hour step increase in the COD and SNO concentrations was introduced, is simulated in the two compartments of the anoxic selector (Figure 5). The same values of model parameters (Table 4) could be used for the simulation of all case studies by Model 4(Novak, 1994).

60 50

F-~===------:=-==----I •••

~40

l

-~ ~

o

OURH (SRI

20 OURH (SSI 10 r-

01--

o

=-----=--=

-'.;:--__ OURA

- -_ _""-0.5

_-======1 - - +_ _- - - - '

1.5

2

2.5

Time (hi

Fig. 4. OUR curve simulated using Model 3.

OUR prediction. The comparison of actual OUR values prediction in nitrification and regeneration reactors of the D-R-D-N system was made using the Activated Sludge Model No.1 and the developed biosorption

76

L. NOVAK el aL

model (Model 4). The experimental and predicted OURs are shown in Table 5. Model 4 better predicts the actual OUR value in the regeneration tank. l00r--------------..,

234

234

6

5

5

6

TIme (hi

TIme (hi

Fig. S. Soluble COD and SNO curves during dynamic test simulall:d using Model 4.

TABLE 5. Comparison of Simulated and Measured Values of Actual OURs by Activated Sludge Model No. I and Model 4 Unit

Measured

Simulated (ASMNo.1)

Simulated (Model 4)

Actual OUR in N-tank

mglg VSS.d

Actual OUR in R-tank

mglg VSS.d

358 190

403 420

394 235

Rate

Soluble residual products formation. In simulations with Model 4, the fraction of the inert soluble carbonaceous matter (SI) in the influent wastewater had to be lowered to a minimum. The reason can be explained by a lack of the available substrate in the majority of simulations carried out with Model 4. It was revealed that under anoxic conditions, when the substrate elimination from the mixed liquor (that means also from the sludge) could be only associated with the consumption for denitrification, there was not enough available substrate for the denitrification, as indicated by the experimental results (see dynamic test in Figure 5). This situation seemed to indicate that almost all soluble COD could be considered as biodegradable (assumption valid only for this synthetic wastewater used!). This approach is in conformity with theories on the secondary microbial products' formation (Dennis and Irvine, 1981; Orhon et al., 1989). The primary soluble substrate can be converted to a secondary soluble substrate which can be inert or biodegradable. The primary substrate could also reach a residual equilibrium which could be characterized by residual concentrations of the SS, SR and SH substrates. A justification of this hypothesis can be shown when comparing the soluble COD concentrations in the biological reactors of the continuously operated system predicted by Model 4. In Table 6 soluble COD concentrations in the plant reactors in a steady state are shown. Although the soluble inert fraction is considered to be only 0.0 I (I %) of total COD, the soluble COD predicted is in accordance with the real experimental values. This approach could also serve as an alternative hypothesis to that of Orhon et al. (1989) for the modelling of residual soluble microbial products formation. TABLE 6. Comparison of Experimental and Predicted (Model 4) Soluble COD Concentrations (mg/l) in Biological Reactors of the Lab-Scale Plant Reactor

Experimental

Simulated

Denitrification I Regeneration Selector 1 Selector 2 Denitrification 2 Nitrification

37 28 91 85 32 24

37 24 97 93 61 22

SI

SS

5

0.3 0 5 2 0 0

S

5 5 5 5

SR

S8

5

26.7 2 44 56 48

17

43 30 8 16

I

Soluble carbonaceous substrate biosorption

77

CONCLUSIONS A mathematical model for soluble carbonaceous substrate biosorption has been developed. Two different concepts favouring and disfavouring the fIlamentous microorganisms' growth on readily biodegradable substrates have been considered and a compromise between them accepted for the final structure of the biosorption model. The biosorption model was able to predict the majority of experimental results obtained from case studies and the continuously operated system with high accuracy. The biosorption model was also calibrated using the experimental results from the steady state lab-scale plant performance. More reasonable results were achieved by using the biosorption model in comparison with the Activated Sludge Model No.1 application. The model also provides an alternative of modelling the residual soluble microbial products. ACKNOWLEDGEMENT The research was financially supported by the Basque Government. The first author wishes to thank to CElT San Sebastian in which laboratories he carried out all experimental work for his Ph.D. thesis. REFERENCES Dennis,R.W. and lrvine,R.L. (198 I): A stoicbiometric model of bacterial growth. Wat. Res. IS, 1363-1373. DoId,P.L., Ekama,G.A. and Marais,G.v.R. (1980): The activated sludge process Part I. A general model for the activated sludge process. Prog. Wat. Tech. 12(6), Toronto, 47-77. DoId,PL. and Marais,G.v.R. (1986): Evaluation of the general activated sludge model proposed by the IAWPRC task group. Wat. Sci. Teck 18(6), 63-89. Dold,P.L., Wentzel,M.C., Billing,A.E., Ekama,G.A., Marais,G.v.R. (1991): Actiyated Sludge Syslem Simulation PrQgnuns Water Researcb CQmmisiQn, Creda Press. Cape TQwn, South Africa Ekama,G.A. and Marais.G.v.R. (1977): Tbe activated sludge process Part II - Dynamic behaviour. Water SA 3, I, 17-50. Ekama,G.A. and Marais,G.v.R. (1979): Dynamic bebaviQr of the activated sludge process. J. Wat. Pollut. Control Fed. 51, 3, 534556. Ekama,G.A. and Marais.G.v.R. (1986): The implicatiQn Qf the IAWPRC bydrolysis bypothesis on IQW PIM bulking. Wat. Sci. Tech. 18(6), 11-19. Ekama,G.A., DoId,P.L. and Marais,G.v.R. (1986): Procedures for determining influent COD fractiQns and the maximum specific grQwth rate of beterotropbs in activated sludge systems. Wat. Sci. Tech. 18(6),91-114. Gujer,W. and Kappeler). (1992): ModelIing population dynamics in activated sludge systems. Wat. Sci. Teck 25(6), 93-103. Henze,M. (1992): Characterization of wastewater for modelling of activated sludge processes. Wat. Sci. Tech. 25(6), I-IS. Henze,M., Grady Jr,C.PL., Gujer,W., Marais,G.R. and MatsuQ,T. (1987): Actiyated sludge model No I. Scientific and IW1nkal RepQrt NQ I. IAWPRC, London, ISSN 1010-707X. Kappeler,J. and Gujer,W. (1992): Bulking in activated sludge systems: a qualitative simulatiQn model for Sphaerotilus natans, type 021N and type 0961. Wat. Sci. Tech. 26(3-4),473482. Marais,G.v.R. and Ekama,G.A. (1976): The activated sludge process Part I - Steady state behaviour. Water SA 2, 4, 163-200. Marais,G.v.R., Loewenthal,R.E. and Siebrltz, I.P. (1983): Review: Observations supporting pbQsphate removal by biQlogical excess uptake. Wat. Sci. Tech. 15(3/4), 15-41. Novak,L. (1994): Behaviour and modelling of advanced activated sludge wastewater treatment plants for nutrient removal. Pb.D.thesis, CElT San Sebastilin, Spain. Novak,L., Larrea,L., Wanner,J., Grau,P. and de Ia Sota,A. (1993): PQpulatiQn dynamics of R-D-N and D-R-D-N nutrient removal processes. Presented at the Two Day Workshop on Design and Operational Experience of Treatment Plants for Nutrient Removal from Wastewater, Perugia, June 28-29. OrhQn,D., Artan,N. and Cimsit,Y. (1989): The concept Qf soluble residual product fQnnation in the modelling of activated sludge. Wat. Sci. Tech. 21(4-5), 339-350. Siebritz,I.P., Ekama,G.A. and Marais,a.v.R. (1983): A parametric model for biological excess phosphorus removal. Wat. Sci. Tech. 15(3/4), 127-152. Sollfrank,U. and Gujer,W. (1991): Characterisation Qf dQmestic wastewater fQr mathematical modelling of the activated sludge process. Wat. Sci. Tech. 23(4-6), 1057-1066. WentzeI,M.C., DoId,P.L., Ekama,G.A. and Marais.G.v.R. (1984): Kinetics of biQIQgical phQspborus release. Presented at 12th IAWPRC PQst Conference Seminar on biolQgical phosphorus removal, Paris. September 1984. Wentzel,M.C., Ekama,G.A. and Marais.G.v.R. (1992): Processes and modelhng Qf nitrification denitrification bioligicaJ excess phQsphorus removal systems - a review. Wat. Sci. Tech. 25(6), 59-82.