research on modeling and simulation of activated sludge process

8/2/2019 Research on Modeling and Simulation of Activated Sludge Process

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Research on Modeling and Simulation of Activated Sludge Process

Wei Yao, Wu Li, Qiao Junfei

Beijing University of Technology, Beijing, 100124, China

[email protected]

AbstractDue to the complexity of wastewater treatment

process, it is difficult to apply the existing mathematical models

in practice. A new model is presented for the wastewater

treatment process in this paper. This model is based on

Benchmark Simulation Model no.1 (BSM1) modeling method,

and then simplifies Activated Sludge Model No. 1 (ASM1)

which was set up to connect the secondary settler model

dynamically. Meanwhile, the parameters of the model are

adjusted by the experiment data. Finally, the practical data

was used to predict the COD values of the water quality. The

results demonstrate that this proposed model is useful.

Keywords-Dynamic model; Benchmark Simulation Model no.1(BSM1); Simulation; Activated Sludge Process

I. INTRODUCTIONGenerally speaking, wastewater treatment process is a

biological process. However, due to the instability of the

influent quantity and quality, the complexity of biochemicalreactions, as well as various factors such as regional

differences, it is difficult to choose an appropriate modeling

method for the whole wastewater treatment. Therefore, how

to simulate the wastewater treatment process accurately is

still an open problem. Activated sludge wastewater

treatment process, which is the major approach of treatingindustrial organic wastewater and urban sewage in the

current, has been widely used as the object of mathematical

modeling for a long time. With a view to guide the practical

wastewater treatment plant operation and design process, a

well-known wastewater treatment process model, named

Activated Sludge Model No. 1 (ASM1) [1] was issued by

the International Water Association. In ASM1, Matrix form

was used to describe the various biochemical reaction

processes. In order to facilitate the computer simulation, the

factors in the matrix can precisely and intuitively reflects the

changes of each component in wastewater treatment

process. However, this model is difficult for practical

application due to various parameters and reaction

processes.

Based on ASM1, many domestic and foreign scholars haveproposed some methods to establish simple and effective

models. Eveline et al [2] proposed a parallel ASM1 model,

which performance better when the parameters and

microbial biomass is not fully given in ASM1. However, a

large quantity of calculation is still necessary in this model.

And the forward the assumption did not distinguish solubleCOD and particulate COD. In order to save the

computational time, Jeppsson [3] carried out ROM model

based on ASM1; Llse Y. Smets et al [4] selected typical

input and output data for reasonable linear approximation,

which greatly simplifies the ASM1 parameters identification

and model calibration process. Ji et al [5] implemented

ASM-CN, a simplified ASM1 model of carbon oxidation

which is suitable for common plug-flow reactor, and Yu etal [6] introduced a corrected and simplified model ASP-CR

for carbon removal. However, the above models only

consider the effect of aeration reactor without conducting a

comprehensive simulation of the overall wastewatertreatment process.

In order to obtain a universally applicable model of the

wastewater treatment process for countries around the world,

the Europe Scientific and Technological Co-operation

launched the Benchmark Simulation Model no.1 (BSM1)

[7]. It provides a set of rigorous criteria to improve theapplication of control strategy, including simulation models,

engineering indicators, controllers, and performance

indicators and testing processes. Yuan et al [8] combined

Matlab and C++ to simulate the overall processes of BSM1,

and verified the stability and consistency of software

calculating process. In recent years, the intelligent modeling

methods [9-10] are also used a certain degree ofdevelopment and become a research hotspot. Compared

with the classical method of mathematical modeling, the

most serious problem of intelligent modeling is that a

universal model of the wastewater treatment process can not

be given. Therefore, how to obtain an exact mathematical

model is still an open problem for the wastewater treatment

process simulation.

In this paper, a new model of the whole wastewatertreatment process is proposed based on the modeling idea ofBSM1. This new simplified model not only reduces theparameters and computation, but it also shows the validitythrough simulation results. The rest paper is organized asfollows: In the next section, the whole architecture of the

new model is described. This modeling shows the wholewastewater treatment process. In Section , the proposed

model is used to predict the key parameters in the wastewatertreatment. The simulation results show that the new modelhas a high accuracy with compact structure. Finally, the

paper is concluded in Section .

II. WASTEWATERTREATMENT PROCESS MODELINGThe main process of BSM1 is shown in Fig. 1. Using

A/O process, the benchmark plant is composed of a five

2010 International Conference on Intelligent Computation Technology and Automation

978-0-7695-4077-1/10 $26.00 2010 IEEE

DOI 10.1109/ICICTA.2010.482

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compartment activated sludge reactor consisting of two

anoxic tanks followed by three aerobic tanks. ASM1 model

is used to express the reaction mechanism in aeration reactor,and then the water flow into the secondary settler after

reaction, the secondary settler was expressed by solid flux

theory and Takcs double-exponential settling equation

[11].

Figure 1. The flowchart of BSM1

A. Aeration Reactor ModelingASM1 Consists of three major processes: carbon

oxidation process, nitrification process and denitrification

process. Then, the treatments are promoted the presentation

of eight biochemical reaction processes via a matrix format

of wastewater in aerobic and anoxic conditions, including

hydrolysis, microbial growth, decay, etc. The model

includes thirteen components, five stoichiometric

parameters and 14 kinetic parameters. Due to the

complexity of ASM1 itself, a certain degree of

simplification can be simulated according to different

treatment target. The model in this article only considers the

carbon oxidation process, without regard to nitrification

process and denitrification process, so the biochemical

reactions part of BSM1 model can be reduced to aerobic

reactor, only consider the removal of carbon; and ASM1

biochemical reaction processes has been simplified for threereactions. The total influent COD of aerobic reactor consists

of the following five components, while XBA , XP are

negligible due to the low concentration in the influent

wastewater.

TO S S I I BH COD S X X S X = + + + +

where SS is the readily biodegradable substrate, XS is slowly

biodegradable substrate, both of them flow into the

secondary settler along with effluent water after beingdegraded in the aerobic reactor. XI is the particulate non-

biodegradable material which is settled in the secondary

settler without precipitating biochemical reactions in aerobic

reactor. XBH is the heterotrophic biomass which is in the

secondary settler for settlement as well after reacted in the

aerobic reactor. SI is the soluble non-biodegradable material

and can not be degraded or settled in the whole process.

Accordingly, the observed conversion rate of each

component in the activated sludge process can be

established as follows:

( )

1

,

,

S S OH BH

S S O H O

S BH O

h BH

X S BH O H O

dS S S r X

dt K S K S

X X Sk X

K X X K S

= = + + +

+ +

( )

2

,

(1 )S p H BH

S BH O

h BH

X S BH O H O

dXr f b X

dt

X X Sk X

K X X K S

= =

+ +

3

,

S OBHH BH

S S O H O

H BH

S SdXr X

dt K S K S

b X

= = + +

The mass balance in aerobic reactor can be described as

follows:

0 0

1( )k r r k f f

dZQ Z Q Z r V Q Z

dt V= + +

where V is the volume of the aerobic reactor, rk is the

reaction rate of the kth component Zk, Qr is the quantity of

reflux in secondary settler, Q0 is the influent flow rate, Qf is

the effluent flow rate from the aerobic reactor, Zf is the

effluent component concentration from the aerobic reactor,

Zr represents the reflux concentration of the secondary

settler.

Based on the above analysis, the modeling conditions

and basis are:

This model primarily for inspection of, nitrogen andphosphorus removal is not considered;

This model only considered carbon oxidationprocess of heterotrophic bacteria under aerobic

conditions and particulate organic material in the

hydrolysis process; the corresponding parameters of

other processes take zero value;

According to the Benchmark data provided by IWA,the components which account for small proportion

of effluent COD indicators are negligible;

The dissolved oxygen concentration in the aerobicreactor is assumed to be constant, based on

experience set SO = 2mg/l;Assuming heterotrophic microbial species is unitary and

stability.B. Secondary Settler Modeling

As an important part of a wastewater treatment system,secondary settler can be divided into two regions accordingto different functions: clarification zone and thickening zone.In secondary settler, soluble components can be considereduniformly distributed, namely, the concentration of solublecomponents do not change through sedimentation. And for

Q0, Z0

Qa, Za

Qe, Ze

Qf, Zf

Qw, ZwQu, Zu

Qr, Zr

Biological ReactorClarifier

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particulate components, the sedimentation process can bedescribed by the following differential equation:

( ) xX X

D rt z z z

= +

whereX is the total sludge concentration at any height, is the solids flux at corresponding height, D is the

diffusion coefficient, rx is the biochemical reaction rate.That is, the changing rate with time of the mass

concentration of solid particles is tantamount to the changealong the height of solid flux concentration which caused bythe transfer and diffusion effect and the conversion rate ofactivated sludge components.

Equation (4) can be transformed and simplified as thefollowing format:

The secondary settler only consider the physicalsedimentation while ignore biochemical reactions, rx

= 0;

Without regard to the impact of diffusion, let thediffusion coefficientD = 0;

According to solid flux theory, the solid flux insecondary settler consists of gravitationalsedimentation of solid particles and the flux

generated by the flow of sludge water.

Then equation (4) was transformed into:

( )( ) ups V XV XX

t z z

= +

( ) ( )s dnV X V X X

t z z

= +

Equation (5) and (6) are respectively the expressions of

Thickening zone and Clarification zone. Where /up e

V Q A=

and /dn uV Q A= are cross-sectional velocity respectivelygenerated by effluent water and effluent sludge, gravity

sedimentation rate Vs is represented by Takcs double-

exponential settling equation. X is the total sludge

concentration, and the standard values in the original model

are used as the value of other parameters.In this model, the secondary settler is divided into five

layers from the bottom to top, the bottom is the 1st layer

while the top is the 5th layer, and feed layer is the 3rd layer.

Sludge concentrations in each layer are written as:

For the bottom layer:

2 1 ,2 ,11( ) min( , )dn s sV X XdX

dt h

+=

For the layers below the feed layer:

12

,3 ,2 ,2 ,1

( )

min( , ) min( , )

dn m m

s s s s

V X XdX

dt h

h

+

= +

For the feed layer:

33

,4 ,3 ,2

( )

min( , )

f f e u

clar s s

Q X Q Q X dX

dt V

h

= +

For the clarification layers above the feed layer:

3 4 ,5 ,44( )up clar clar V X XdX

dt h h

= +

For the top layer:

4 5 ,55( )up clar V X XdX

dt h

=

while, , 1 1 1

,

, 1

min( , )s j j s j j j tclar j

s j j j t

V X V X X X

V X X X

>=

where Vis the volume of the secondary settler, h is theheight of each layer,Xi is the sludge concentration of eachlayer, s,i is sedimentation flux in each layer. The threshold

concentrationXt is equal to 3000g/m3.

C. Dynamic CouplingThe coupling of aeration reactor and secondary settler

lies in the unit conversion of the particulate components. Thesludge concentrationXf which flow into the secondary settlercan be expressed as:

, , , ,( )f S f I f BH f BA fX k X X X X= + + + where k=0.75. In order to simplify the wastewater treatmentmodel, the secondary settler is considered as the idealcondition. This process main refers to the sedimentation. Therecycle parameters are given as:

, ,S f S u

f u

X X

X X=

whereXu is the underflow concentration.

In the same way, the parameters XP,uXI,uXBH,u

XBA,u can be calculated by (12).

D. Some Common MistakesIn order to obtain satisfying simulation results, the

parameters of simplified model must be calibrated. ASM1includes more than a dozen parameters, most of which arenot constant. These parameters may change its valueaccording to the change of environmental conditions.However, if each one was calibrated under specific practicalwastewater quality conditions and process characteristics, it

would result in model application difficulties.The first step of parameter calibration is to analyze each

parameters impact on effluent indicators. Synthesizingliterature [12, 13] about parameter sensitivity analysis, theheterotrophic yield coefficient YH has the greatestsensitivity, followed by the heterotrophic maximum specificgrowth rate H , half-saturation coefficient for heterotrophyKs and maximum specific hydrolysis rate constant Kh. Thecalibration ofYHcan be expressed by following formula:

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XO TO SOCOD COD COD= +

XOH

SO

CODY

COD

=

where CODSO and CODXO respectively represents the CODconcentration of soluble and particulate components. Due to

the small impact of other parameters, their values can be

directly using the recommended ones at 20 for calculating.

III. SIMULATION AND RESULTS ANALYSISThe processing model was built according to the

treatment process of some small-scale wastewater treatment

plant, the treating capacity of which is 60,000 tons/day. The

plant is designed for meeting the target in accordance with

national second level effluent standards. Since the model is

mainly considered for COD removal of the plant, in whichthe effluent COD concentration should be less than 100

mg/l.

Simulation steps are given as: First of all, obtain steady-state effluent data of both aeration reactor and secondarysettler through steady-state simulation, then implement theentire wastewater treatment process through dynamicsimulation, which used to observe the fitting results bycomparing actual data and experimental data.

A. Steady-state SimulationIn the actual operation of wastewater treatment plants,

the system's steady-state is only relative, the water quantityand quality constantly keep changing every day even everymoment, it just stay relatively stable in the absence ofexceptional circumstances (for example, heavy rainfall andtemperature impact). Thus, the average running data of thewastewater treatment plant was selected as steady-stateoperation data. In this case, flow rate is 50, 000m3/d, and

influent COD concentration is 338.5mg/l.Based on the experience, the analysis was carried out to

measure the flow components. Therefore, the influent CODcan be decomposed by he ratio of 54%, 12.8%, 16.8%,11.5%, 4.9%, corresponding to the components respectivelywhich required in the model: XS, XI , SS , SI , XBH. Thus,through calculation, the concentrations of the abovecomponents are listed in order: 182.9mgCOD/l,43.23mgCOD/l, 56.53mgCOD/l, 39.25mgCOD/l and16.625mgCOD/l.

Finally, the concentration of effluent component will besuperimposed to be the effluent COD concentration bymodeling principles. The results of the steady-statesimulation are shown in Figure 2 and Figure 3 below.

0 0.1 0.2 0.3 0.4 0.50

10

20

30

40

50

60

70

80

90

t(day)

Concen

tration(mgCOD/l)

Xs

Ss

Figure 2. Steady-state simulation in aeration reactor

In Fig. 2: Under steady-state condition, aeration reactoreffluent XS , SS concentrations are 38.93mgCOD/l,2.08mgCOD/l respectively, which shows that aerationreactor model can effectively degrade the organic material ofwastewater.

In Fig. 3: When the secondary settler reaches steadystate, the concentration of the 1st layer to the 5th layer isrespectively: 6765.6mgCOD/l, 182.91mgCOD/l, 182.91mgCOD/l, 25.91mgCOD/l and 8.36mgCOD/l, which showsa good settlement performance as well.

0 0.1 0.2 0.3 0.4 0.50

2000

4000

6000

8000

10000

t(day)

Co

ncentration

gSS/m3

The 1st Layer

(a) Sludge concentration of the 1st layer

0 0.1 0.2 0.3 0.4 0.50

500

1000

1500

2000

2500

t(day)

Concen

tration

gSS/m3

The 2nd layer

The 3rd layer

(b) Sludge concentration of the 2nd and the 3rd layer

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0 0.1 0.2 0.3 0.4 0.50

10

20

30

40

50

60

t(day)

Concent

ration

mgSS/l

The 4th layer

The 5th layer

(c) Sludge concentration of the 4th and the 5th layer

Figure 3. Steady-state simulation in secondary settlerUnder the steady-state conditions, the effluent COD

concentration was degraded to 52.6mg/l after calculation,which is close to the first emission standards at the nationallevel, so the model is valid.

B. Dynamic SimulationTo verify the performance of model capability of

simulating practical operation, using the 18-day data ofwastewater treatment plant from April 1st to April 18th fordynamic simulation. According to the literature, the influentof a specific wastewater treatment plant is constant and notsubject to flow rate fluctuations for relative content of eachcomponent for urban wastewater, in dynamic simulation,components determination still follows the experience valuein steady-state, and then obtains effluent data through modelcalculation. The simulation results are shown in Fig. 4:

0 2 4 6 8 10 12 14 16 1830

40

50

60

70

80

90

100

t(day)

COD(mg/l)

Measured Effluent CODSimulating Effluent COD

Figure 4. Comparison of effluent CODFrom Figure 4, we can see a similar trend in the two

curves and the average value from April 1st to April 18th

of simulated effluent COD can be calculated from the result,of which is 54.78mg/l, while the average value of measuredeffluent COD is 56.93mg/l, the error is 3.78%. Therefore, themodel has capability of predictability on the actual operationof wastewater treatment plants.

The daily simulated and measured values are shown inTable 1, in which CODIN represents the concentration ofCOD in influent wastewater, and CODM and CODPrespectively represents the measured and the simulated CODconcentration in effluent water.

As the results shown in Table 1, the model is relativelyweak in anti-interference ability, when the influent

concentration changed greatly compared to the last day, thedifference between effluent COD of the simulated andmeasured values is comparatively large, therefore, the modelshould be further improved robustness . For example, onApril 9th and April 11th, the errors were 15.4% and 12.48%respectively. At other time, the simulated values can be fittedwell with the measured values.

TABLE I. SIMULATION RESULTS OF THE EFFLUENT CODDATE

CODIN

(mg/l)

CODM(mg/l)

CODP

(mg/l)

ERROR

(%)

4.1 323 51.3 46.6 9.16

4.2 312 53 47.4 10.57

4.3 339 53 53.6 1.13

4.4 335 58.6 57.7 1.54

4.5 306 56.4 53.6 4.96

4.6 323 62 61.4 0.97

4.7 277 56.3 56 0.53

4.8 242 56 49.4 11.79

4.9 326 57 65.8 15.44

4.10 224 52 49.4 5

4.11 310 62.5 54.7 12.48

4.12 320 62 65.5 5.65

4.13 275 55.5 52.5 5.41

4.14 350 61 56.2 7.87

4.15 331 55.1 53.3 3.27

4.16 372 56 57.1 1.96

4.17 354 60 53.6 10.67

4.18 347 57 51.4 9.82

IV. CONCLUSIONThis paper proposed a mathematical model for

wastewater treatment plants which was established throughreasonable simplification on the aeration reactor and thesecondary settler. Not only the model's components, processand the number of model parameters are greatly reduced, butalso achieved the dynamic connection of aeration reactor andthe secondary settler. At the same time, by simulating thepractical wastewater treatment plants of COD removal, theability of prediction on urban wastewater treatment processis improved. Through the effluent COD simulation of actual

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process using this model, the obtained simulation results canfollow the trend in measured values, and the error is within acertain range, which indicates the validity of the model andcalculation process.

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