INTERNATIONAL CONFERENCE ON ARCHITECTURE AND

ENGINEERING IN URBAN DEVELOPMENT 2013

The Development of Modeling Techniques for

Biological Wastewater Treatment : A review

Musfique Ahmed

Lecturer

Department of Environmental Science

Independent University, Bangladesh (IUB)

WASTEWATER TREATMENT

Removal of Physical, Biological and Chemical

constituents

Complex process

Quality fluctuation

Composition fluctuation

Adaptive behaviour of microorganisms

MODELING IN WASTEWATER TREATMENT

Models

Describe

Predict

Control

This Review focuses on the models using for

biological treatment process.

MODEL DEVELOPMENT

`Model Goals Select model purpose, required

model accuracy, model boundaries

Data Collection

Data Analysis

Model Setup &

Calibration

Model Verification

Model Simulation

tank dimensions, piping and inter

connections, flow dynamics and

influent characterization.

data screening, mass balances,

and design hand calculations

Modifying input parameters

MODELS

Models are classified into three groups

Aerobic Process – ASM models

Anaerobic Process – ANN and UASB

Hybrid models

ACTIVATED SLUDGE MODEL NO. 1(ASM1)

First model of ASM family

Developed by International Water Association (1987)

Developed to describe organic carbon removal, nitrification and de-

nitrification with instantaneous use of oxygen and nitrate as electron

acceptors

Useful as a predictor of oxygen demand and sludge production in an

activated sludge system

ACTIVATED SLUDGE MODEL NO. 2(ASM2)

Henze et al. first introduced the ASM2 model in 1995 by including biological

phosphorus (bio-P) removal in ASM1

Increase the capability of ASM1 model

Introduced a new group of organisms to the biomass – PAOs

Phosphorus Accumulating Organisms

Capable of gathering phosphorus and stocking them in the form of cell

internal polyphosphates (XPP) and poly-hydroxyalkanoates (XPHA).

ACTIVATED SLUDGE MODEL NO. 3(ASM3)

Developed with the same objectives as ASM1 for biological N removal

Insertion of internal cell storage compounds in heterotrophs

Developed by considering the importance of storage polymers in the

heterotropic activated sludge alteration.

All readily biodegradable substrate (SS) first taken up and stored into an

internal cell component (XSTO) prior to growth.

PARAMETERS

ANAEROBIC PROCESS MODELLNG

Very complex and complicated to model

high sensitivity to the influent characteristics

operational conditions

different environmental conditions

Most powerful methods for modelling the complex and non liner

anaerobic system is using artificial neural networks (ANN).

ARTIFICIAL NEURAL NETWORK

Predict the performance of the process

Develop a precise nonlinear mapping from input-output

couples of data without recognizing their functional

relationship

Models Reason Inputs & Output

Parameters

Hanbay,

Turkoglu &

Demir (2007)

Prediction and analysis of the COD

removal in effluent

Temperature,pH, COD,

TN, TSS

Hamed,

Khalafallah &

Hassanien

(2004)

Performance prediction of a WWTP

in Cairo, Egypt

BOD

SS concentrations

Hong et al.

(2007)

For the real time estimation of

nutrient concentrations to

overcome the problem of delayed

measurements

NO3-

NH4+

PO43+ concentrations

UPFLOW ANAEROBIC SLUDGE BLANKET (UASB)

To remove carbonaceous BOD

To stabilize the waste and

Conduct denitrification

Models for describing the aspects

• fluid flow

• rheological behavior of the sludge

• extremely long start-up period

• transport phenomena

UPFLOW ANAEROBIC SLUDGE BLANKET (UASB)

Bolle et. al. (1985) developed a hydrodynamic

model of the fluid flow based on previous scale

model experience and some physical intuition.

Assumption: both the sludge bed and sludge

blanket were behaving like completely stirred tank

reactors and the liquid flow settler volume was

explained as a plugflow reactor.

Outcome: the short-circuiting flow over the sludge

bed increases with the increasing superficial gas

velocity

UPFLOW ANAEROBIC SLUDGE BLANKET (UASB)

Skiadas and Ahring (2002) proposed a model for

UASB reactors by using Cellular Automata (CA)

concept.

A cellular automation is a simulation, which is

discrete in time, space and state

The CA theory is used to predict the granules’

structure which appears different in outer and inner

granule layers

HYBRID MODELS

Integration of two different models

Improved in predicting process dynamics

variability of bacteria growth rates variable retention times for phosphorus and nitrogen removal

A group from Taiwan National University gave the solution

By incorporating a biofilm model into the general dynamic model

To predict the effluent quality of a combined activated sludge and biofilm process.

HYBRID MODELS

Neural Fuzzy System – Fuzzy system + Neural Netwroks

Adaptive neuro-fuzzy interference system (ANFIS) –

functional neural fuzzy models

Tay & Zhang developed a fast predicting neural fuzzy model

for high rate wastewater anaerobic system to simulate and

predict the response of a system to different system

disturbances

HYBRID MODELS

Input and Output Parameters

Liquid Phase- include pH,

volatile fatty acids (VFA), alkalinity,

COD or TOC,

COD reduction and

redox potential (ORP)

Gas Phase - Gas production rates

CH4

CO2

H2

CO

DISCUSSION

Aerobic Process Modelling – Deterministic in nature

- derive a direct link between the inputs, outputs,

state variables and parameters

the state variables are represented by the

parameters and previous states of the model

ANN modelling - Stochastic model - use random

data generation for non linear mapping

Calibration is easier than the conventional

deterministic models.

DISCUSSION

Models applied in UASB reactors-

Deterministic - the model developed by Skiadas

and Ahring (2002) by using CA theory

Used real data and mathematical equations

Stochastic – Using artificial neural networks in

UASB reactors for the prediction of COD removal

efficiency

LIMITATIONS

Experimental basis of activated sludge modeling is very significant

The experimental backup lagged behind because of the fast pace of progressing in the modeling of activated sludge.

Over parameterized - a given parameter is treated with minor significance that can cause major propagation towards all estimated parameters

ANN training data - The problem of overfitting occurs in case of noisy and uncertain training data

Models for UASB reactors usually do not consider non-ideal conditions in full-scale reactors.

REFERENCES

Bolle, WL, Breugel, Jv, Eybergen, GCv, Kossen, NWF & Zoetemeyer, RJ 1985, 'Modeling the Liquid Flow in Up-Flow Anaerobic Sludge Blanket Reactors', Biotechnology and Bioengineering, vol. 28, pp. 1615-20.

Hamed, MM, Khalafallah, MG & Hassanien, EA 2004, 'Prediction of Wastewater Treatment Plant Performance Using Artificial Neural Networks', Environmental Modeling & Software, vol. 19, no. 10, pp. 919-28.

Hanbay, D, Turkoglu, I & Demir, Y 2007, 'Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks', Clean – Soil Air Water, vol. 35, no. 3, pp. 250 – 4.

Ng, ANL & Kim, AS 2006, 'A mini-review of modeling studies on membrane bioreactor (MBR) treatment for municipal wastewaters', Desalination, vol. 212, no. 1-3, pp. 261-81.

Pena-Tijerina, AJ & Chiang, W 2007, 'WHAT DOES IT TAKE TO MODEL A WASTEWATER TREATMENT PLANT?', paper presented to TEXAS WATER 2007, Texas.

Tay, J-H & Zhang, X 2000, 'A FAST PREDICTING NEURAL FUZZY MODEL FOR HIGH-RATE ANAEROBIC WASTEWATER TREATMENT SYSTEMS', Water Research, vol. 34, no. 11, pp. 2849-60.