The Activated Sludge Model No 1ASM1

Bengt Carlsson

The Peterson process matrixGeneral expression for a component z in a completely mixed reactor

zin

zin

rDzDzz

VrQzQzzV

+−=

+−=.

.

where V=volume of the reactor, Q=inflow rate=outflowrate, D=Q/V=dilution rate, and rz is the process rate Note that for simple biomass growth.

In the process matrix discussed below, only process rates are described.

XSrZ )(µ=

Biomass with Monod growth rate and decay:

bXXXK

SrS

X −+

= maxµ

If we assume that all decayed biomass becomes substrate the process rate for substrate is:

bXXXK

SY

rS

S ++

−= max1 µ

Example 1

X S r

Growth 1

Decay -1 1 bX

Y1

− XXK

S

S +maxµ

Peterson matrix for Example 1

↓

→

ProcessComponents

The bisubstrate model

Slowly biodeg substrateXs

Easily biodeg substrateSs

BiomassX

Inert matterXP

Hydrolysis

Growth of biomass

Decay of biomass1-ff

Example 2. A Bisubstrate model

Assumptions:

•Biomass X has growth rate r and a decay bX.A typical example of r is

• The fraction (1-f) of the decayed biomass becomes slowly biodeg matter Xs, and fraction f becomes inert matter Xp.

•The yield is Y and hydrolysis of Xs has growth rate kXS

XSK

SSK

SrOO

O

S ++= maxµ

SS kXr

YdtdS

+−=1

SS kXbXf

dtdX

−−= )1(

XbrdtdX 11 −=

Xbfdt

dX P =

Substrate dynamics:

Change of slowly biodeg substrate:

Accumulation of inert matter:

Growth and decay of biomass

SS XS XP X rate

Growth -1/Y 1 r

Decay (1-f) f -1 b X

Hydro-lysis

1 -1 k XS

• In ASM1, a bisubstrate model is used for the carbon oxidation process.

• If the substrate and biomass is measured inCOD [g COD/m3] the oxygen consumption is easy to calculate by adding the factors in the growth process:

rY

YrrY

)1(11 −−=+−

ASM1

• ASM1 proposed 1987, probably the most used modelfor the ASP. Previously called IAWQ Model No 1

• ASM2: 1995, model for biological P removal

• ASM3 1999, improvements of ASM1. Substrate goesthrough a storage process…

• ADM1 2001, Anaerobic Digester Model No 1

• ASM1 describes an activated sludge system withcarbon oxidation, nitrification, denitrification.

• In total 8 processes are modelled:Growth of biomass (3), decay (2), ammonificationof organic N (1), hydrolysis (2)

•Heterotrophic biomass XB,H:- Oxidise carbon under aerobic conditions- Denitrify (SNO =>N2) under anoxic

conditions (if substrate available).

•Autotropic biomass XB,A:- Nitrify (SNH =>SNO) under aerobic

conditions.

Divisions of carbonaceous material

Total COD

Biodeg. COD:

Soluble SSParticulate XS

NonbiodegCOD:

Soluble SIParticulate XIand XP

Active biomass

HeterotrophsXB,H

AutothropsXB,A

Nitrogeneous componentsMost important components:

•Ammonia SNH•Nitrate (and nitrite) SNO

•Biodeg N: SND and XND

In summary: 12 components (+ alkalinity)

Growth rates and processes:

growth of heterotrophs XB,H- Aerobic growth- Anoxic growth- Decay

HBH

HBNOS

NO

OHO

HO

SS

SgH

HBOO

O

SS

SH

HB

Xb

XSK

SSK

KSK

S

XSK

SSK

Sdt

dX

,

,,

,

,,

−

++++

++=

∧

ηµ

µ

Growth of autotrophs XB,A- Aerobic growth- Decay

ABA

ABOAO

O

NHNH

NHA

AB

Xb

XSK

SSK

Sdt

dX

,

,,

,

−

++=

∧

µ

Model parameters

ASM1 has 19 model parameters, where some (typically max growth rates) are temperature dependent.

Default values exist, but several parameters may needto be tuned to mimic a specific plant.

Some set of parameters may lead to (approximately) the same model behaviour!

Also the influent water needs to be characterized.

See: Calibrating, identifiability and optimal experimental design of activated sludge models,B Peterson, PhD 2000. Thesis available for download

Limitations of ASM1

Examples:

• The pH is assumed to be near neutrality

• Many of the relations are empirical or based on hypothesis

• Cells need nutrients to grow which are not modelled

• Constant temperature. In order to allow for temperature variations, Arrerenius relations may be used

Model useExample of use:

• Testing and evaluation new control strategies

• Education and process understanding

• Evaluating new processes and/or operating modes

• Process optimization

• Prediction

The goal of the modelling should determine how careful the model should be calibrated.