1. 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)

2. WASTEWATER TREATMENT
 Removal of Physical, Biological and Chemical
constituents
 Complex process
 Quality fluctuation
 Composition fluctuation
 Adaptive behaviour of microorganisms

3. MODELING IN WASTEWATER TREATMENT
Models
Describe
Predict
Control
This Review focuses on the models using for
biological treatment process.

4. 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

5. MODELS
Models are classified into three groups
 Aerobic Process – ASM models
Anaerobic Process – ANN and UASB
Hybrid models

6. 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

7. 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).

8. 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.

9. PARAMETERS

10. 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).

11. 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
+
PO4
3+ concentrations

12. 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

13. 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

14. 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

15. 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.

16. 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

17. 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

18. 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.

19. 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

20. 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.

21. 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.