Implementation of Activated Sludge

Models (ASMs) for an Aerobic Sludge

Digestion Process

Maryam Ghorbani (M.A.Sc.)

Cigdem Eskicioglu (Ph.D.)

University of British Columbia Okanagan Campus

2

Introduction

Problem Definition

Research Objectives

Model Assumptions

Experimental Design

AquaSim© Software

Parameter Estimation/Sensitivity Analysis

Results

Conclusion & Future Work

Activated Sludge Process

3

Wastewater(Influent)

Air

Treated Water(Effluent)

Recycled WAS

Waste Activated Sludge (WAS)

Aerobic Sludge Digestion Tank

Aeration Tank Clarifier/Settler

Reference: http://www.answers.com/topic/sewage-treatment

To River

Landfill

Advanced Activated Sludge Models (ASMs)

4

Aim:

To create a common platform for future carbon and

nutrient removal activated sludge processes.

History:

The first model, Activated Sludge Model No: 1 (ASM 1), was

published in 1987 with carbon/nitrogen removal.

In 1995, Activated Sludge Model No: 2 (ASM 2) was

published with biological phosphorous removal.

In 1998, ASM3 was published with bacterial internal storage

compounds.

Possible Applications of ASMs

5Reference: www.jswa.go.jp/english/r_d/major_pdf/06.pdf

Master Planning• Process Comparison/Selection

• Planning Future Construction

Detailed Design• Reactor Capacity Consideration

• Equipment Selection

• Upgrading Consideration

• Planning RetrofitRetrofit/Upgrading

• Plant Evaluation

• Making O & M Plan

Operation & Maintenance Assistance

Matrix Representation of ASM1 (Henze et al.,1986)

6

Matrix Representation of ASM3 (Henze et al., 2000)

7

ASM1 versus ASM3

8

Problem Definition

9

ASMs have not been tested against a large variety of

data.

Studies focused on modeling of wastewater

processes, rather than sludge digestion.

Some parameters are correlated, individually non-

identifiable.

The majority of parameters can not be

experimentally measured.

Practitioners need parameter identification for

model calibration purposes.

Research Objectives

10

To investigate:

Performance of ASMs for aerobic sludge digestion

under different flow regimes.

Kinetic & stoichiometric parameters for batch

and semi-continuous flow runs.

Most sensitive model parameters.

If kinetic parameters determined from batch could

predict semi-continuous flow digester performances at

different operating conditions, i.e. retention time.

Model Assumptions

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DO > 2 mg/L

Anoxic growth of heterotrophic

biomass is not applicable and

So/(KOH+So) ≈ 1

Autotrophic biomass

concentration is a small

percentage of heterotrophs

concentration

Autotrophs (nitrification) are

negligible

12

Process Rate, ρj

ML-3T-1

5

XH

4

XS

3

XI

2

SS

1

SI

Component, i

Process, j

1Aerobic Growth of

Heterotrophs

-11- fXIfXI

Decay of

Heterotrophs

-11

Hydrolysis of

Entrapped Organics

COD (Chemical Oxygen Demand) Parameter, ML-3

HY

1

Example for batch reactor:

H

SS

S

Hm XSK

S

HH Xb

H

HSX

HS

h XXXK

XXk

HHH

SS

SHm

HX XbX

SK

S

dt

dXr

H

13

Particulate COD = XI + XS+ XHSoluble COD = SS + SI

H

H

SX

H

S

hHHXIS X

XX

K

XX

kXbfdt

dX

1

HHXII Xbf

dt

dX

HHH

SS

SHm

H XbXSK

S

dt

dX

0dt

dS I

H

H

SX

H

S

hH

SS

SHm

H

SX

XX

K

XX

kXSK

S

Ydt

dS

1

Total COD = Soluble COD + Particulate COD

Component, i

Process, j

1

SI

2

SS

3

XI

4

XS

5

XH

6

XSTO

7

XV

Process Rate, ρj

ML-3T-1

Hydrolysis fSI 1- fSI -1 -iVSSXS

Aerobic Storage

of COD

-1 YSTOO2 0.6YSTOO2

Aerobic Growth

of Heterotrophs

1

Aerobic

Endogenous

Respiration of

Heterotrophs

fXI -1 fXI iVSSXI –

iVSSBM

Aerobic

Endogenous

Respiration of

Stored Organics

-1 -0.6

Parameter, ML-3 COD (Chemical Oxygen Demand)

Soluble COD = SI + SS

Particulate COD = XI + XS+ XH+ XSTO 14

HY

1

H

HSX

HSh X

XXK

XXk

H

SS

SSTO X

SK

Sk

H

VSSBMY

i6.0

H

HSTOSTO

HSTOHm X

XXK

XX

HH Xb

STOSTOO Xb 2

15

Soluble COD = SI + SSParticulate COD = XI + XS+ XH+ XSTO

HHXII Xbf

dt

dX

H

H

SX

H

S

hS X

XX

K

XX

kdt

dX

HHH

H

STOSTO

H

STO

HmH XbX

XX

K

XX

dt

dX

H

H

SX

H

S

hSII X

XX

K

XX

kfdt

dS

H

SS

SSTOH

H

SX

H

S

hSIS X

SK

SkX

XX

K

XX

kfdt

dS

1

HHH

HSTOSTO

HSTOHm

H

H

SS

SSTOSTOO

STO XbXXXK

XX

YX

SK

SkY

dt

dX

12

HHVSSBMVSSXIXIH

HSTOSTO

HSTOHm

H

VSSBMH

SS

SSTOSTOOH

HSX

HShVSSXS

V XbiifXXXK

XX

YiX

SK

SkYX

XXK

XXki

dt

dX

6.06.0 2

Digester Name 1- B12 1-B5

TSS initial (mg/L) 12 500 5000

Duration (days) 30 30

Digester Name 2- B12 2-B5-1 2-B5-2

TSS initial (mg/L) 12 500 5000 5000

Duration (days) 70 70 70

Digester Name 1-C5 1-C10 1-C20

Solid Ret. Time

(days)

5 10 20

Flowrate (mL/d) 1000 500 250

TSS initial (mg/L) 5000 5000 5000

Duration (days) 53 53 53

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Batch digesters

Dissolved oxygen ≥ 2 mg/L

Temperature = 20 ± 2 OC

Sludge was taken from municipal

wastewater treatment plant

(ROPEC) in Ottawa

Volume = 20 L

Semi-continuous digesters

Volume = 5 L

1st

Run

2 nd

Run

The sludge was taken from municipal WWTP in Kuwait

Dissolved oxygen ≥ 4 mg/L

Temperature = 20 ± 20C

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Volume = 10 L

Batch digester (Automated fermentor)

Al-Ghusain et al., 2002

18

Developed by EAWAG (Switzerland)

Simulation (solving differential

equations)

Parameter estimation

Sensitivity analysis

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Calibrate and validate the model

Determine best identifiable parameter

subsets

Determine reduced set

Computer sensitivities

Computer simulation

Assume parameters from literature

Select the kinetic model, i.e. ASM1

Step 4:

Step 2:

Step 1:

Step 0:

Step 3:

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ERROR EQUATION

where:

is the mean of observed values, yi is the observed value, represents the

value of predicted variable (such as digester COD)

n is number of the data points

n

i

i

n

i

ii

yy

yy

R

1

2

1

2

2

ˆ

1

yiy

Batch Digesters

21

ERROR EQUATION

where:

is the residual, the difference between the predicted and observed value in

mg/L.

n is number of predication/observation pairs.

ir

Semi-Continuous Reactors

n

i

irn

RMSR1

2 )(1

22

where:

y : arbitrary variable calculated by AquaSim© (such as digester COD)

p: model parameter (such as YH, bH)

Measures the relative change in “y”

for a 100% change in “p”.

RELATIVE- RELATIVE SENSITIVITY FUNCTION:

p

y

y

prr

py

,

,

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Batch Runs

Parameter- Units

Estimation

Range

Starting

Value

Reactor 2-B12 Reactor 2-B52

Before

identification

After

identification

Before

identification

After

identificationRepresentative

bHd-1 0.1-1.6 0.62 0.24 0.28 0.43 0.44 0.39 ± 0.24

fxig COD/g COD 0.04-0.2 0.08 0.06 0.11 0.07 0.07 0.09 ± 0.03

khd-1 0.5-20 3 0.58 0.58 0.77 0.77 0.66 ± 0.13

YHg COD/g COD 0.3-0.8 0.67 0.79 0.80 0.84 0.85 0.80 ± 0.04

Kx- 0.01-0.1 0.03 0.10 0.10 0.10 0.10 0.10 ± 0.00

μHmd-1 3-15 6 14.39 14.39 14.70 14.70 14.46 ± 0.30

Ksmg COD/L 10-200 20 10.00 10.00 10.00 10.00 10.00 ± 0.00

Overall R2 0.98 0.98 0.96 0.95

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Parameter- Units

Estimation

Range

Starting

Value

Reactor 2-B12 Reactor 2-B52

Before

identification

After

identification

Before

identification

After

identificationRepresentative

bHd-1 0.1-2 0.2 0.10 0.10 0.11 0.10 0.10 ± 0.00

fxig COD/g COD 0.15-0.25 0.2 0.25 0.25 0.24 0.24 0.22 ± 0.04

khd-1 0.5-5 3 0.51 0.52 0.71 0.54 1.32 ± 0.80

Kx- 0.03-5 1 4.76 4.84 4.57 4.53 4.86 ± 0.20

YHg COD/g COD 0.3-0.8 0.63 0.79 0.79 0.74 0.75 0.79 ± 0.02

μHmd-1 1-9 2 8.51 8.51 1.03 1.03 5.49 ± 4.04

Ksmg COD/L 1-50 2 1.00 1.00 1.00 1.00 1.00 ± 0.00

KSTOg COD/g COD 0.5-2 1 1.83 1.83 1.90 1.90 1.68 ± 0.56

kSTOd-1 2-10 5 2.00 2.00 2.00 2.00 2.00 ± 0.01

fSIg COD/g COD 0.005-0.05 0.01 0.005 0.005 0.005 0.005 0.005 ± 0.000

bSTOO2d-1 0.1-4 0.2 0.13 0.13 0.10 0.10 0.11 ± 0.01

YSTOO2g COD/g COD 0.6-0.9 0.85 0.89 0.90 0.61 0.60 0.81 ± 0.13

Overall R2 0.98 0.98 0.94 0.94

25

26

ASM1

ASM3

27

ASM1

ASM3

28

24

ASM1

ASM3

29

24

ASM1

ASM3

30

31

24

ASM1

ASM3

32

33

24

ASM1

ASM3

34

ERROR EQUATION

where:

and are the model predictions for validation and calibration data sets.

and represent the experimental (observed) values validation set and

with calibration values, respectively.

n and m are number of the calibration and validation data points.

n

i

cici

m

i

vivi

n

yy

m

yy

J

1

2

1

2

2

)ˆ(

)ˆ(

viy ciy

viy ciy

Between 0 and ∞

ASM1 ASM3J=1.44 J=0.65

35

36

Digester COD and VSS are sensitive to YH, fiini, fxi, bH, fxsini, fssini, kh, KX, µHm, and

KS ranging from the most to the least sensitive parameter.

-0.5

0.0

0.5

1.0

1.5

2.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Re

lati

ve-R

ela

tive S

en

s. (

CO

D )

Time (days)

b_H f_xi Y_H f_i_ini f_xs_ini

f-i-inif-xi

b-H

f-xs-ini

(Yield Coefficient)

(Decay Coefficient)

(endogenous fraction of

biomass leading to inert part)

(ratio of initial inert PCOD

to initial TCOD)

(ratio of initial particulate

degradable COD to initial TCOD)Y-H

37

Both ASM1and ASM3 predicted COD and VSS concentrations

successfully.

ASM3 parameters were more consistent throughout the runs.

ASM1 overestimated COD & VSS after15 d during validation.

Batch kinetic coefficients can successfully simulate continuous-

flow aerobic digesters.

Future work:

Validating semi-continuous results with independent data set.

Checking the performance of ASMs on industrial WAS.

Checking the performance of ASMs on a full-scale WWTP.

38

Special thanks to:

Dr. Cigdem Eskicioglu, UBC Okanagan

Dr. Ronald L. Droste, University of Ottawa

Dr. Mohammad Hamoda, University of Kuwait

UBC Okanagan for Start-up Funds

THANK YOU

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