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Contribution to dynamic simulation of activated slu dge wastewater treatment plants

Sara Patrícia da Silva Batista Pinto

Dissertação para obtenção do Grau de Mestre em

Engenharia do Ambiente

Júri

Presidente: José Manuel de Saldanha Gonçalves Matos, DECivil, IST-UTL

Orientadora: Filipa Maria Santos Ferreira, DECivil, IST-UTL

Co-orientador: António João Carvalho de Albuquerque, DECA, UBI

Vogal: Helena Maria Vasconcelos Rodrigues Pinheiro, DEQB, IST-UTL

Julho de 2010

I

AKNOWLEDGEMENTS

First of all, I would like to express my gratitude to my supervisors Professors Filipa

Ferreira (IST-UTL) and António Albuquerque (UBI) for all their support, advice and

explanations about the challenges of wastewater treatment modeling and respirometry, as

these were new areas to me. Furthermore, I must give special thanks to Professor Filipa

Ferreira for given me this opportunity, for our productively discussions and for her careful

proof reading of this thesis which improved its quality considerably.

I am very grateful to Sabrina Semitela (UBI) for collaborating with me in the respirometric

tests and especially for her patience when answering to all my questions (over and over

again). I also would like to thank my fellow colleague Marta Matos, who helped me during

an intensive and long day of the campaign, and who gave me enthusiastic support

throughout this project.

I wish to thank Águas do Zêzere e Côa for allowing me to use Valhelhas wastewater

treatment plant as my case study, and especially to staff for their technical support.

I am also grateful to Professor Pedro Rodrigues (IPG) and his assistant, for their

collaboration with the laboratory analysis.

Thanks are also due to IGAOT, namely to Tiago Sameiro, Pedro Lourenço and Filipe

Vitorino, for providing me valuable information presented in Chapter 2.2.

I also would like to express my thanks to Will Kirwin for his careful reading of this thesis

and for helping me improving my linguistic skills.

This work has been supported financially by the Fundação para a Ciência e Tecnologia

(FCT) as part of the MOGIS project, reference number PPCDT/AMB/56349/2004. This

support is gratefully acknowledged.

III

ABSTRACT

In Portugal, many wastewater treatment plants are presently operating in accordance to

predetermined schemes, with few concerns to variations of the activated sludge process

and without optimizing its performance to achieve a better effluent quality. Little attention

has been given to the activated sludge models as powerful tools for wastewater treatment

process understanding, design, control and optimization.

The main goal of this study is to contribute to the understanding of the activated sludge

process, to the simulation of organic carbon removal based on ASM3 and to the use of

respirometric assays in order to obtain kinetic and stoichiometric coefficients for model

calibration.

Respirometric assays were carried out using raw wastewater (as substrate source) and

return activated sludge (as biomass source) from the Valhelhas wastewater treatment

plant (WWTP); the values 2.88 d-1, 4.32 d-1, 6.4 d-1, 0.7 g CODVSS/g COD and 523 g

COD/m3 were subsequently obtained for parameters ��, ��, ��, �� and ,

respectively. Monitoring campaigns were conducted in order to characterize the

composition of flows from seven different sections of the WWTP and to investigate the

dissolved oxygen concentrations in the biological reactors.

The dynamic simulation of the WWTP was confronted with several limitations related to

the treatment plant performance and the desired stability for modeling was not verified. An

alternative academic approach was performed as an attempt to understand the

consequences of different operation methodologies, in terms of process efficiency. As a

result, the global quality of the final effluent could theoretically be improved and the

operation costs minimized if only one treatment line was used.

Keywords: activated sludge; ASM3; modeling; wastewater treatment; respirometry.

V

RESUMO

Em Portugal, muitas estações de tratamento de águas residuais (ETAR) operam

actualmente de acordo com esquemas pré-determinados, sem considerarem as

variações do processo de lamas activadas e sem optimizarem o seu desempenho de

forma a atingir uma melhor qualidade do efluente. Tem sido dada pouca atenção aos

modelos de lamas activadas enquanto ferramentas importantes para a compreensão,

concepção, controlo e optimização do processo de tratamento de águas residuais por

lamas activadas.

Este trabalho pretende contribuir para a compreensão do processo de lamas activadas,

para a simulação da remoção de carbono orgânico baseada no modelo ASM3 e para a

utilização de ensaios respirométricos, destinados à obtenção de coeficientes cinéticos e

estequeométricos para calibração do modelo.

Foram realizados ensaios respirométricos tendo como fonte de substrato a água residual

afluente e, como fonte de biomassa, as lamas activadas da ETAR de Valhelhas;

posteriormente foram obtidos os valores 2.88 d-1, 4.32 d-1, 6.4 d-1, 0.7 g CODVSS/g COD e

523 g COD/m3 para os parâmetros: ��, ��, ��, �� e , respectivamente.

Realizaram-se campanhas de monitorização para caracterizar a composição dos caudais

em sete secções diferentes da ETAR e para averiguar as concentrações de oxigénio

dissolvido nos reactores biológicos.

A simulação dinâmica da ETAR deparou-se com algumas limitações resultantes da

própria operação da ETAR, pelo que não foi possível obter a estabilidade desejada para

a modelação. Deste modo, optou-se por uma abordagem alternativa, de natureza

académica, numa tentativa de compreender as consequências de diferentes

metodologias de operação na eficiência do processo. Como resultado, observou-se que a

qualidade global do efluente final poderia ser, teoricamente, melhorada e os custos

operacionais reduzidos, se apenas uma linha de tratamento estivesse em operação.

Palavras-chave: ASM3; lamas activadas; modelação; respirometria; tratamento de águas

residuais.

VII

TABLE OF CONTENTS

AKNOWLEDGEMENTS……………………………………………………………….……….………I

ABSTRACT…………………………………………………………………………….…….……...III

RESUMO………………………………………………………………………………..………..….V

TABLE OF CONTENTS ………………………………………………………………….………….VII

LIST OF TABLES …………………………………………………………..……………..…………IX

LIST OF FIGURES………………………………………………………………………………....…X

LIST OF TABLES OF APPENDICES ………………………………………………………………….XI

LIST OF FIGURES OF APPENDICES………………………………………………………………...XI

NOTATION AND ABBREVIATION ………………………………………………………………..…XIII

1. INTRODUCTION .................................................................................................................. 1

1.1. Background and motivation of this thesis .............................................................. 1

1.2. Objective ............................................................................................................... 2

1.3. Outline of the thesis ............................................................................................... 2

2. LEGAL FRAMEWORK AND SANITATION IN PORTUGAL .......................................................... 5

2.1. Legal framework .................................................................................................... 5

2.2. Sanitation in Portugal ............................................................................................ 9

3. BIOLOGICAL TREATMENT ................................................................................................. 13

3.1. Composition of urban wastewater ....................................................................... 13

3.1.1 Chemical and physical properties ................................................................ 13

3.1.2 Organic components .................................................................................... 14

3.1.3 Inorganic non-metallic constituents .............................................................. 14

3.2. Basic aspects of microbiology ............................................................................. 16

3.3. Removal of Pollutants ......................................................................................... 18

3.3.1 Removal of organic constituents .................................................................. 18

3.3.2 Biological removal of nutrients ..................................................................... 20

3.4. Activated Sludge Process ................................................................................... 23

3.4.1 Historical perspective ................................................................................... 23

3.4.2 Oxidation ditch process ................................................................................ 24

3.5. Sedimentation ..................................................................................................... 28

4. RESPIROMETRY ............................................................................................................... 29

4.1. Respirometers ..................................................................................................... 30

4.2. Respirometric experiments .................................................................................. 32

4.2.1 Measurement conditions .............................................................................. 32

4.2.2 Measurement and deduction of variables .................................................... 34

5. MODELING OF WASTEWATER TREATMENT PLANTS ............................................................ 37

VIII

5.1. General considerations of modeling ................................................................... 37

5.2. Biological model: Activated sludge models ......................................................... 38

5.2.1 Description of the Activated sludge model Nº3 (ASM3) ............................... 39

5.3. Sedimentation models ........................................................................................ 44

5.4. Model calibration and validation .......................................................................... 46

6. CASE STUDY ................................................................................................................... 47

6.1. Overview of the work performed ......................................................................... 47

6.2. Characterization of the wastewater treatment system ........................................ 48

6.3. Respirometric assays .......................................................................................... 51

6.3.1 General considerations ................................................................................ 51

6.3.2 Materials and methods ................................................................................ 52

6.3.3 Results of the respirometric experiments .................................................... 55

6.4. Monitoring campaigns ......................................................................................... 61

6.4.1 General considerations and constraints ...................................................... 61

6.4.2 Description and methods ............................................................................. 63

6.4.3 Results of the measuring campaigns ........................................................... 65

6.5. Dynamic simulation of Valhelhas WWTP ............................................................ 73

6.5.1 General considerations ................................................................................ 74

6.5.2 Model construction ....................................................................................... 74

6.5.3 Simulation results ........................................................................................ 75

7. CONCLUSIONS ................................................................................................................ 77

REFERENCES ...................................................................................................................... 80

APPENDICES…………………………………………………………………………………..A1

Appendix A.1 – Respirometer classification ………………………………………………A3

Appendix A.2 – Respiration rate of substrate oxidation……………...…………………..A5

Appendix A.3 – Determination of the oxygen mass transfer coefficient (��)..…….….A7

Appendix A.4 – Simplified ASM3 process equations………….………………………….A9

Appendix A.5 – ASM3 model: Matrix of Petersen, typical values and components....A11

Appendix A.6 – Map of Valhelhas wastewater drainage system………………………A13

Appendix A.7 – Plant of operation of Valhelhas wastewater treatment plant…….…..A15

Appendix A.8 – Detailed measurements carried out at Valhelhas wastewater treatment

plant………………………………………………………………………....A17

IX

LIST OF TABLES

Table 2.1 | Requirements for discharges of WWTPs in sensitive areas (adopted from INAG, 2002) 7

Table 2.2 | Microbiological parameters according to their classification (adopted from Law

nº135/2009) (MPN: most probable number) .................................................................... 8

Table 2.3 | Problems of the wastewater drainage and treatment sector in Portugal presented in

PEAASAR II (adopted from MAOTDR, 2007) ............................................................... 12

Table 3.1 | Composition values of raw wastewater (adopted from Henze, 1997; quoted by Ferreira,

2006) ............................................................................................................................. 16

Table 3.2 | Main bacterial reactions in wastewater according with the environmental conditions

(adopted from Ferreira, 2006) ....................................................................................... 19

Table 4.1 | Typical values for stoichiometric and kinetic parameters for heterotrophic biomass ..... 37

Table 6.1 | Physical characteristics of the most relevant treatment units of Valhelhas WWTP ....... 49

Table 6.2 | Average daily flows of wastewater influent and return activated sludge (RAS) registered

from June 2008 to April 2009 ........................................................................................ 50

Table 6.3 | Summary of historical wastewater influent and final effluent analytical composition (data

related to the period from June 2008 to December 2009); full data is reported in Table

A.8.1 in Appendix A.8; q.l.: quantification limit of the method ........................................ 51

Table 6.4 | Composition and used volumes of the mineral solutions .............................................. 53

Table 6.5 | Characterization of the influent wastewater (substrate) and the volume of the

respirometric cell after substrate injection ..................................................................... 56

Table 6.6 | Stoichiometric and kinetic parameters obtained from the respirometric assays ............ 57

Table 6.7 | Comparison of several parameter set obtained through model based interpretation and

empiric calculation of respirogram R1-2; Legend: ......................................................... 60

Table 6.8 | Analytical methods used for physical-chemical and microbiological measurements

during the campaign at Valhelhas WWTP ..................................................................... 63

Table 6.9 | Average hourly flows for wastewater influent and RAS from 2 to 17 December ........... 65

Table 6.10 | Average concentrations of influent wastewater components (average values) during

the campaign of 16/17 December, 2009 ....................................................................... 67

Table 6.11 | Results of the measured dissolved oxygen in the oxidation ditches during the

campaign and in accordance with the sections of measurement as indicated in Figure

6.12 (n.a.: not assessed) ............................................................................................... 69

Table 6.12 | Summary of measurements of wastewater influent and final effluent carried out during

the campaign of 14/15 December at Valhelhas WWTP and the percentage of

component removal ....................................................................................................... 71

Table 6.13 | Summary of measurements of wastewater influent and final effluent carried out during

the campaign of 16/17 December at Valhelhas WWTP ................................................ 71

Table 6.14 | Comparison between dimension values adopted for design and historical operation

values relative to 2008/2009 from Valhelhas WWTP .................................................... 72

X

Table 6.15 | Comparison between the volume of the aeration tank of Valhelhas WWTP relative to

design parameters and different operation scenarios ................................................... 73

LIST OF FIGURES

Figure 2.1 | Compliance with Articles 4 and 5 of the UWWT Directive (adapted from Commission of

the European Communities, 2009) .................................................................................. 7

Figure 2.2 | Index of population served with wastewater drainage and treatment systems (adopted

from IRAR, 2009) ............................................................................................................ 9

Figure 2.3 | Indexes of distribution of population served with wastewater drainage (left) and

wastewater treatment (right), by municipalities and Hydrographic Regions (RH)

(adopted from INAG, 2009) ........................................................................................... 10

Figure 2.4 | Distribution of a) treatment systems (adopted from INSAAR, 2007 – data for 2007); b)

influent wastewater subject to each level of treatment (adopted from INE, 2009 – data

for 2008). Data includes WWTP and septic tanks ......................................................... 11

Figure 2.5 | Statistical results of a WWTP inspection relatively to 2006/2007; a) Fulfillment of all

legal requirements; b) Percentage of WWTPs that exceeded in more than 100% the

limit value for emission of each component analyzed ................................................... 11

Figure 3.1 | Microbial growth curve of a pure (a) and mixed (b) batch cultures, respectively

(adapted from Metcalf & Eddy, 1991) ............................................................................ 17

Figure 3.2 | Effect of a limiting substrate ( �) on the specific growth rate (�), according to Monod 17

Figure 3.3 | Typical oxidation ditch activated sludge system ........................................................... 24

Figure 4.1 | DO and OUR curves of a LFS respirometer test .......................................................... 35

Figure 5.1 | Wastewater characterization COD components in ASM3 (modified from Jeppsson,

1996) ............................................................................................................................. 40

Figure 5.2 | Nitrogen components in ASM3 (modified from Jeppsson, 1996) ................................. 41

Figure 5.3 | Substrate flows of COD in ASM3 for nitrifiers and heterotrophs (adopted from Gujer et

al., 2000) ....................................................................................................................... 42

Figure 5.4 | Solids balance around the settler layers (adopted from Hydromantis, 2006) ............... 44

Figure 5.5 | Graphical representation of the settling velocity model of Takács (adopted from

Hydromantis, 2006) ....................................................................................................... 46

Figure 6.1 | Flow diagram of the liquid and solid phases of the Valhelhas WWTP ......................... 49

Figure 6.2 | Schematic layout of the respirometer ........................................................................... 53

Figure 6.3 | Respirometer device for measurement of OUR ........................................................... 54

Figure 6.4 | Oxygen uptake rate evolution over time for all the respirometric tests (1 minute

measurements) ............................................................................................................. 56

Figure 6.5 | Estimation of �� and � based on the data presented in Table 6.6 ..................... 58

Figure 6.6 | Heterotrophic OUR variation over time of R1-2 (OUR simulated considering Set 4 of

Table 6.7) ...................................................................................................................... 61

Figure 6.7 | Foaming sludge in the oxidation ditch (left) and rising of sludge in the clarifier (right) . 62

XI

Figure 6.8 | View from the sampling locations in Valhelhas WWTP ................................................ 64

Figure 6.9 | Concentrations of influent wastewater components and average influent flow during the

campaign of 14/15 December, 2009 (fulfilled points: measured values; unfulfilled points:

estimated values) .......................................................................................................... 66

Figure 6.10 | Concentrations of nitrogen compounds in the influent wastewater during the

campaign of 14/15 December, 2009 (fulfilled points: measured values; unfulfilled points:

estimated values) .......................................................................................................... 67

Figure 6.11 | Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2,

according to Table 6.11 (the arrows indicate the direction of the flow) ......................... 68

Figure 6.12 | Samples collected in 16 December ............................................................................ 72

Figure 6.13 | Simplified layout of Valhelhas WWTP used for modeling .......................................... 75

Figure 6.15 | Example of application: Results of the dynamic simulation with ASM3 (T=10 ºC),

considering: 1 line; Qras/Qinf= 0.6; Qes/Qinf≈0.02 ....................................................... 76

LIST OF TABLES OF THE APPENDICES

Table A.1.1 | Respirometer classification (adapted from Spanjers et al., 1998)…………………….A3

Table A.1.2 | Respirometer description (adapted from Spanjers et al., 1998)……………………….A4

Table A.3.1 | Measured DO concentration values of the respirometric experiment R1-14………...A7

Table A.5.1 | Typical values of kinetic parameters for ASM3

(adopted from Gujer et al., 2000)……………………………………………………...…A11

Table A.5.2 | Typical stoichiometric and composition parameters for ASM3

(Source: Gujer et al. (2000))…………...……………………………………………...….A11

Table A.5.3 | Stoichiometric matrix ��,� , composition matrix ��,� and kinetic rate expressions �� for

ASM3 (adopted from Gujer et al., 2000)….………………………………………...…..A12

Table A.8.1 | Historical wastewater influent and final effluent compositions……….……..………A17

Table A.8.2 | Results of measurements carried out during the campaign of 14/15

December at Valhelhas WWTP…………………………………………………………..A18

Table A.8.3 | Results of measurements carried out during the campaign of 16/17 of

December at Valhelhas WWTP…………………………………………………………..A19

LIST OF FIGURES OF THE APPENDICES

Figure A.2.1 | DO curve of a LFS respirometer test (illustration of ��)………………..………A5

Figure A.3.1 | Decline curve of Ln(DOS-DO) in function of time…………………………………A7

Figure A.4.1 | Simplified model for aerobic conditions (adapted from Avcioglu et al. (2003))..A9

XIII

NOTATION AND ABBREVIATION

Symbol Description Units �� decay rate of heterotrophic biomass

[d-1] �� respiration rate for !� [d-1] BOD5 biochemical oxygen demand after five days g COD/m3 BODu ultimate biochemical oxygen demand g COD/m3 COD chemical oxygen demand g COD/m3

CODt concentration of total chemical oxygen demand g COD/m3

CODs concentration of soluble chemical oxygen demand g COD/m3 "#; � % dissolved oxygen concentration g O2/m3

"# &'(�)* dissolved oxygen consumed for substrate oxidation during growth period

g O2/m3 "# saturation DO in the liquid phase g O2/m3 +, inert fraction of soluble COD - .#", -⁄ .#"0 +1, production of !2 in endogenous respiration g COD78 g⁄ COD79:

HRT hydraulic retention time h jT total flux of solids g TSS/m3 ; solids flux due to settling g TSS/m3 ;< water flux due to bulk movement g TSS/m3 �� oxygen mass transfer coefficient h-1

substrate concentration at one-half the maximum growth rate

g COD/m3 )� storage rate constant g COD=> (g⁄ COD7@ ∙ d) � half saturation constant for !� g COD7>DE g⁄ COD7@

MLSS mixed liquor suspended solids g TSS/m3 MLVSS mixed liquor volatile suspended solids g VSS/m3 n number of moles of gas mol Ntotal total concentration of nitrogen g N/m3 OUR oxygen uptake rate g O2/(m

3·h) P pressure atm Ptotal total concentration of phosphorous g P/m3 F flow rate; wastewater flow rate [L3/T] GHIJ endogenous respiration rate g O2/(m

3·h) GKLI hindered zone settling parameter m3/g TSS GMNOP flocculant zone settling parameter m3/g TSS GQRS respiration rate of substrate oxidation g O2/(m3·h) GT total respiration rate of the biomass in the liquid g/(m3·h)

R ideal gas constant J/(K·mol) SRT sludge retention time d SVI sludge volume index mL/g F/M food-to-microorganism ratio g VSS/g COD � substrate concentration g COD/m3 �2 inert soluble matter concentration g COD/m3 �U� concentration of ammonia nitrogen g N/m3 �U concentration of nitrate nitrogen g N/m3 �U% concentration of nitrogen gas g N/m3 � Initial concentration of substrate after injection g COD/m3 � readily biodegradable substrate concentration g COD/m3 ( time variable [T]

XIV

T temperature ºC; K ThOD theoretical oxygen demand g COD/m3 TKN total Kjeldahl nitrogen g N/m3 TSS total suspended solids g TSS/m3 V vertical bulk velocity m/d WX volume of the gas phase m3 W� volume of the liquid phase m3 V�� maximum Vesilind settling velocity m/d V settling velocity of the sludge m/d VSS volatile suspended solids g VSS/m3 ! suspended solids concentration of the layer g TSS/m3 !� heterotrophic biomass concentration g COD/m3 !2 inert suspended matter g COD/m3 !�LI minimum attainable suspended solids concentration g TSS/m3 ! Initial concentration of biomass after injection g COD/m3 ! slowly biodegradable substrate g COD/m3 ! suspended solids g COD/m3 !� internal storage product g COD/m3 �� heterotrophic yield coefficient g CODVSS/g CODS �� storage yied g CODXSTO/g CODVSS YU 1 anoxic reduction factor [-] �� specific growth rate [d-1] �� maximum specific growth rate [d-1]

ASM activated sludge model GPS-X General Purpose Simulator p.e. population equivalent RAS return activated sludge VFA volatile fatty acids WWTP wastewater treatment plant

1

1. INTRODUCTION

1.1. BACKGROUND AND MOTIVATION OF THIS THESIS

Modern wastewater treatment techniques have been in use for over a century. Today, the

activated sludge process is one of the most widespread biological wastewater purification

technologies. In this process, wastewater is mixed with a concentrated bacterial biomass

suspension (the activated sludge) which degrades the pollutants. Originally, the concern

was mainly to remove the organic carbon substances from the wastewater, which could

be easily achieved by simple process designs. However, during the last three decades the

increased public awareness about the quality of waters and the management of hydric

resources has considerably increased the requirements imposed on treatment plants,

reflected in more stringent effluent regulations. As a consequence, the design and

operation of activated sludge plants had to be modified to more advanced levels to make

the treatment plants suited for biological nitrogen and phosphorus removal.

These more stringent requirements, and the associated technological improvements

resulted in an increase of knowledge about the biological degradation processes and in

the development and use of advanced dynamic mathematical models that are be able to

describe the biological removal processes, known as the Activated Sludge Models (Henze

et al., 1987; Henze et al., 1995; Henze et al., 1999; Gujer et al., 2000). These activated

sludge models allow one to study and increase the understanding of the influence of

process modifications on treatment process efficiency.

The activated sludge process is required to meet effluent standards while minimizing

investment, sludge production and energy consumption. A problem inherent in achieving

this aim is that the activated sludge process is highly dynamic due to variations in the

influent flow rate and its composition. Many wastewater treatment plants are presently

operated according to predetermined schemes with very little consideration to these

variations. In general, the combination of a better understanding of the dynamic behavior

of the processes, efficient monitoring control systems, adequate mathematical models and

identification of model parameters, have a significant potential for solving operational

problems and meet effluent quality standards at low operational costs.

In Portugal, little attention has been given to the activated sludge models as powerful tools

for wastewater treatment process understanding, design, control and optimization

(Ferreira, 2006).

2

1.2. OBJECTIVE

The aim of this study is to contribute to the understanding (theoretical and practical) and

to the assessment of the activated sludge treatment process, by combining dynamic

model simulation with respirometric tests in order to determine relevant kinetic and

stoichiometric parameters of these models. To this end, basic knowledge had to be

developed concerning:

♦ Sensitive analysis of wastewater treatment plant data;

♦ Planning and management of campaigns in the field (e.g. methodology and materials);

♦ Modeling construction and simulation of the wastewater treatment process;

♦ Interpretation of respirograms.

1.3. OUTLINE OF THE THESIS

The thesis is divided into 7 chapters and 8 appendices:

Chapter 1 introduces the scope and background, including the goals and structure of the

work.

Chapter 2 summarizes the legal framework of wastewater treatment in the European

Union and in Portugal, including the required effluent discharges and the degree of

compliance with legislation among some European countries. The evolution of sanitation

in Portugal is also presented, in particular regarding drainage and wastewater treatment

systems, and some inherent problems are highlighted in the perspective of the objectives

defined in the Strategic Plan of Distribution of Water and Drainage of Wastewater 2007-

2013 (PEAASAR II).

Chapter 3 reviews basic aspects of microbiology and biological treatment, namely the

composition of urban wastewater (physical properties, organic and inorganic non-metallic

constituents). Furthermore, it describes the biological removal of carbon, nitrogen and

phosphorous, focusing on the activated sludge process.

Chapters 4 and 5 include a review of literature on respirometry and modeling of

wastewater treatment plants, respectively. Chapter 4 presents basic concepts of

respirometric experiments and measurement conditions. It also describes what

parameters can be measured or deduced from the interpretation of a respirogram.

Chapter 5 deals with the main aspects of modeling biological wastewater treatment and

sedimentation processes and focuses on the Activated Sludge Model Nº3 as the selected

model used in this study. Model calibration and validation are also briefly discussed.

3

Chapter 6 includes all aspects related to the experimental work that was carried out in the

case study, Valhelhas wastewater treatment plant. The characterization of the system, the

respirometric assays, the measuring campaigns and the dynamic simulation are

presented separately. Firstly, the case study is described and the available operation data

is presented and analyzed. Secondly, the materials and methods used in the respirometric

assays are described, followed by the presentation and discussion of results concerning

the measurements of oxygen uptake rates (OUR) of activated sludge. A model based

interpretation of the obtained OUR curves is applied with the purpose of estimating kinetic

and stoichiometric parameters. Next, the monitoring campaigns conducted in Valhelhas

WWTP are described, regarding to its dynamic simulation. The problems which occurred

in the treatment plant in that period are highlighted and their influence in the overall result

of this work is discussed. Finally, the dynamic simulation of Valhelhas WWTP is

presented. Taking the mentioned constraints into account, it was only possible to perform

a simplified and academic dynamic simulation of this treatment plant.

Lastly, Chapter 7 summarizes the work that was carried out and the obtained results

which were obtained in this thesis. Perspectives for future work development and

research are also outlined.

Appendix 1 indicates the classification and a brief description of respirometers according

to Spanjers et al. (1998), including the oxygen measuring phase, regimes, mass balances

and a diagram illustrating each class.

Appendix 2 shows how the respiration rate of substrate oxidation can be estimated from a

dissolved oxygen curve.

Appendix 3 explains the determination of the oxygen mass transfer coefficient used in the

respirometric experiments.

Appendix 4 includes the simplified model equations of ASM3 considered in the model

based interpretation (presented in Chapter 6.3.3) of the measurements of OUR.

Appendix 5 presents all the information relative to the Activated Sludge Model Nº3,

including the stoichiometric and composition matrixes of Petersen, kinetic rate equations

and typical values of kinetic and stoichiometric parameters of the model.

Appendix 6 shows the map of Valhelhas wastewater drainage system, including all the

civil parishes served.

Appendix 7 presents the Valhelhas wastewater treatment plant, including all the treatment

units. The locations where the samples, relative to the measuring campaigns, were

collected are also indicated.

4

Appendix 8 contains all the detailed data resulted from the monitoring campaigns.

5

2. LEGAL FRAMEWORK AND SANITATION IN PORTUGAL

2.1. LEGAL FRAMEWORK

Ecosystems are vulnerable to various pressures caused by human activities such as

wastewater discharges. These can lead to over-fertilization and speed up biodiversity

loss, and can affect drinking water supplies and thereby have important impacts in public

health. Those impacts may in turn have serious negative consequences for economic

sectors such as tourism. This has been recognized by many countries and therefore,

since the 1970s, a range of environmental directives have been adopted by the European

Union (EU) in order to protect and improve the quality of water. The most important

legislations in the EU concerning wastewater treatment are:

♦ Directive 2000/60/EC – The Water Framework Directive establishes a framework for

community action in the field of water policy;

♦ Directive 91/271/EEC – The Urban Wastewater Treatment Directive concerns the

collection, treatment and discharge of urban and industrial wastewater and was altered

by Directive 98/15/EC;

♦ Directive 2006/7/EC – The Bathing Water Directive concerns the management of

bathing water quality and revokes Directive 76/160/EEC;

♦ Directive 91/676/EEC – The Nitrates Directive concerns the protection of waters

against pollution caused by nitrates from agricultural sources;

The Water Framework Directive (WFD) is considered to be the most important

legislation in Europe for water protection. It sets up a new legislative approach

establishing very ambitious objectives for the quality and protection of waters, and relies

on a river basin approach for water management. This directive of the Council of 23

October 2000 commits EU Member States to achieve good qualitative and quantitative

status for all water bodies (inland surface waters such as rivers and lakes, groundwater,

coastal and transitional waters) by 2015. It also regulates the sustainable use of water

resources throughout Europe. It was transposed for the National Portuguese Law by Law

nº 58/2005 of 29 December.

The WFD is also based on the following key principles:

♦ Waters should be managed at a river basin level through River Basin Management

Plans, which in the case of transboundary water bodies implies co-operation between

countries. These plans should enhance the characteristics of each hydrographic

6

region, including the analysis of the impact of human activities, characterization of

water bodies and identification of sources for drinking water.

♦ Active participation of all stakeholders, including NGOs (Non Governmental

Organizations) and local communities in water management activities has to be

ensured.

♦ Water pricing policies based on the “user pays” principle are required.

♦ The interests of the environment with those who depend on it should be maintained in

balance.

The aim of the Urban Wastewater Treatment Directive (UWWTD) of 21 May 1991 is to

protect the environment from the adverse effects of wastewater discharges. Urban

wastewater is considered any domestic wastewater, mixture of domestic and industrial

wastewater, and/or runoff or rainwater. The directive sets out guidelines and legislation on

how urban wastewater is collected, treated and discharged. The directive requires that all

European agglomerations with more than 2000 population equivalents (p.e.) are equipped

with collecting and treatment systems for their wastewaters. According to Article 4 of

UWWTD, the basic level of treatment is secondary treatment (i.e. removal of organic

pollution) whereas in sensitive areas, a more stringent treatment is required (for instance,

the removal of nutrients which are responsible for eutrophication) – Article 5. The

timetable for implementation of the directive depends on the sensitivity of the area into

which wastewaters are discharged and the population equivalents served.

In 2009, an assessment of the implementation of UWWTD was carried out by the

Commission of the European Communities reporting data from December 2005

(Commission of the European Communities, 2009). It was noticed that there were large

discrepancies in the compliance of agglomerations with the requirements settled in Article

4 and Article 5 of the UWWTD, in relation to the load subject to compliance, between

individual member states (Figure 2.1). The compliance rates of Austria, Germany and

Netherlands achieved 100% for both Articles, by contrast France and especially Portugal

had significantly lower compliance rates. It was also reported that, from the generated

load of all the 404 agglomerations of Portugal with more than 2000 p.e. (corresponding to

a generated load of 11 255 420 p.e.), around 95% were collected in collecting systems in

compliance with Article 4 of the UWWTD Directive. From those, only 41% fully complied

with the requirements of the Directive. Furthermore, from the 46 agglomerations subject to

compliance with Article 5, as few as 13% fully accomplished the requirements.

7

Figure 2.1 | Compliance with Articles 4 and 5 of the UWWT Directive (adapted from Commission of the European Communities, 2009)

The UWWTD was implemented as national law through Law nº 152/97 of 19 June, later

altered by Laws nº 348/98, 149/2004 and 198/2008. Law nº 348/98 of 9 November

corresponds to Directive 98/15/EC, which clarifies the rules relating to discharges from

urban WWTPs in sensitive areas subject to eutrophication and defines the concentrations

or the minimum percentage reduction for total phosphorus and total nitrogen. Table 2.1

presents the legal requirements for discharges of WWTPs in sensitive areas.

Table 2.1 | Requirements for discharges of WWTPs in sensitive areas (adopted from INAG, 2002)

Type of Treatment BOD5 with no Nitrification

COD TSS Pt Nt

Primary Concentration (mg/L) - - - - -

Reduction (%) 20 - 50 - -

Secondary Concentration (mg/L) 25 125 35 - -

Reduction (%) 70-90 75 90 - -

Terciary Concentration (mg/L) 25 125 35

2 (10000-100000 p.e.) 15 (10000-100000 p.e.)

1 (>100000 p.e.) 10 (>100000 p.e.)

Reduction (%) 70-90 75 90 80 70-80

Law nº 149/2004 of 22 June concerns not only the identification of sensitive areas

(superficial waters, estuaries and coastal lagoons) and less sensitive (coastal waters) but

its distribution as well. Law nº 198/2008 of 8 October changed the list of less sensitive

areas in the Portuguese mainland and defined the influence area of all sensitive areas.

One of the first European water protection laws, the Bathing Water Directive (BWD) –

Directive 76/160/EEC , came into force on the 8 December, 1975. It established minimum

quality criteria to be met by bathing waters in order to safeguard public health and protect

the aquatic environment in coastal and inland areas. Directive 2006/7/EC of 15 February

(transposed for the National Portuguese Law by Law nº 135/2009 on the 3 June) updated

67

13

50

34

88

94

98no data

41

100

95

100

64

88

100

67

100

0 20 40 60 80 100

Sweden

Slovakia

Portugal

Netherlands

Luxemburg

Germany

France

Finland

Denmark

Belgium

Austria

Degree of compliance with Article (% of the generated load - p.e.)

Article 4 (secondary treatment requirements)

Article 5 (more stringent treatment)62

8

the original BWD and simplified the imposed management and surveillance methods. It

also provided a more proactive approach to inform the public on water quality and created

four quality categories for bathing waters – ‘poor’, ‘sufficient’, ‘good’ and ‘excellent’. The

water concerned in this directive is surface water that can be used for bathing (except for

swimming pools and spa pools), confined waters subject to treatment or used for

therapeutic purposes and confined waters artificially separated from surface water and

groundwater. The directive introduced two parameters for analysis (intestinal enterococci

and Escherichia coli) instead of nineteen (physical, chemical and microbiological)

parameters as in the previous BWD, minimizing analysis costs. These parameters are

used for monitoring and assessment of the water quality and classification of bathing

waters according to their quality. Other parameters may be taken into account, such as

the presence of cyanobacteria or microalgae, if necessary to prevent any public health

risk. The microbiological parameters corresponding to their classification are presented in

Table 2.2.

Table 2.2 | Microbiological parameters according to their classification (adopted from Law nº135/2009) (MPN: most probable number)

Parameter Excellent quality Good quality Sufficient

For inland waters

Intestinal enterococci (MPN/100 mL) 200 (*) 400 (*) 330 (**)

Escherichia coli (MPN /100 mL) 500 (*) 1000 (*) 900 (**)

For coastal waters and transitional waters

Intestinal enterococci (MPN /100 mL) 100 (*) 200 (*) 185 (**)

Escherichia coli (MPN /100 mL) 250 (*) 500 (*) 500 (**)

(*) Based upon a 95-percentile evaluation. (**) Based upon a 90-percentile evaluation.

The category ‘sufficient’ is the minimum quality threshold that all member states should

attain by the end of the 2015 season at the latest. This directive also emphasizes the

need for public information and participation through Internet, geographical information

systems (GIS) or annual reports on member states, particularly during the bathing season,

with an obligation for member states to actively and promptly disseminate information on

bathing water quality.

As the proposed measures of this directive are implemented, the monitoring costs will

naturally increase. However, it is assumed that the global costs will decrease once the

waters start to show less progressive pollution and the frequency of analysis is reduced.

Despite the quality thresholds imposed by Law nº 135/2009, wastewater treatment plants

discharging into bathing waters must meet the requirements set out in Law nº 236/98

9

concerning fecal coliforms. The concentration of fecal coliforms in the final effluent should

be in the range of 100-2000 MPN/100 mL.

Directive 91/676/CEE , the “Nitrates Directive” concerning the protection of waters against

pollution caused or induced by nitrates from agricultural sources, was transposed to the

Portuguese Law by Law nº 235/97 of 3 September and altered by Law nº 68/99 of 11

March. This directive introduced a set of measures in order to reduce and prevent water

pollution, including the requirements for identification of polluted areas and areas which

contribute to pollution, the establishment of codes of good agricultural practices and the

implementation of action programmes by member states. The vulnerable areas in

Portugal, which drain into waters that are polluted or contribute to pollution, were identified

in Portaria nº 1100/2004 of 3 September, Portaria nº 833/2005 of 16 September, Portaria

nº 1433/2006 of 27 December and Portaria nº 1366/2007 of 18 October.

2.2. SANITATION IN PORTUGAL

Between 1980 and 2007 a considerable effort was made to improve the sanitation system

all over the country, by spreading out drainage systems and implementing treatment

systems (Ferreira, 2006). Figure 2.2 presents the evolution of the index of population

served with drainage systems and wastewater treatment (including WWTP and septic

tanks) in the Portuguese mainland.

Figure 2.2 | Index of population served with wastewater drainage and treatment systems (adopted from IRAR, 2009)

Although most of the population is nowadays served with a drainage system there is a

clear asymmetric development across the country due to the geographic distribution of the

population, as illustrated in Figure 2.3. In the 1990s, the tendency of population to move to

the coast, especially to Lisbon and Oporto areas and the northern districts of Braga,

Aveiro and Coimbra, was accentuated. According to the report of INSAAR 2008 (INAG,

2009), by 2007 about 34% of the municipalities located mainly in the north and in the

62% 61% 64% 68%73% 77% 80%

31%41%

58%66%

72% 70%

0

20

40

60

80

100

1990 1994 1998 2002 2005 2006 2007

Po

pu

lati

on

(%

)

Population served with wastewater drainage

Population served with wastewater drainage and treatment

10

middle of Portugal presented a wastewater drainage index below 90%, of which 55% have

less than half of population served with drainage systems. Only 16% of municipalities of

the Portuguese mainland showed an index of wastewater drainage above 90%.

Concerning wastewater treatment systems, nearly 70% of municipalities had an index of

treatment above 50%, but just 31 municipalities achieved 100%.

Figure 2.3 | Indexes of distribution of population served with wastewater drainage (left) and wastewater treatment (right), by municipalities and Hydrographic Regions (RH) (adopted from INAG, 2009)

Figure 2.4a (adopted from INAG, 2007) presents the distribution of treatment systems in

the country considering both WWTPs and septic tanks. It can be observed that primary

systems are by far the most common systems in Portugal (mainly due to septic tanks). On

the other hand, Figure 2.4b (adopted from INE, 2009), where the percentage of influent

wastewater subject to each level of treatment is displayed, shows that considerably 23%

of the wastewater influent is only subject to preliminary or primary treatment. However,

according to IRAR (2009), from the population served with wastewater treatment systems

only 6% is served with septic tanks.

Figure wastewater subject to each level of treatment (

According to data from IGAOT (

Território

more inhabitants

did not fulfill

limit value for emission of each component

exceeded in more than 100%, as analyzed in

parameters BOD

respectively.

requirements;

The most recent data report

during which

performing secondary treatment were inspected. Within these 24 WWTP

operated in

2006

legal quality requirements and

8526%



erritório

more inhabitants

did not fulfill



parameters BOD

respectively.

Figure requirements;


during which


operated in

2006


8526%



erritório

more inhabitants

did not fulfill



parameters BOD

respectively.

Figure requirements;


during which


operated in

2006) that


Figure 2.4wastewater subject to each level of treatment (


erritório), from the 328 WWTP

more inhabitants

did not fulfill



parameters BOD

respectively.

Figure 2requirements;


during which


operated in

) that


4 | Distribution of wastewater subject to each level of treatment (


), from the 328 WWTP

more inhabitants

did not fulfill



parameters BOD

respectively.

2.5 requirements; b)


during which


operated in total

) that most of WWTP


a)

Distribution of wastewater subject to each level of treatment (



more inhabitants

all legal requirement



parameters BOD

| Statistical results of b) Percentage of WWTP


during which 24 WWTP serving populat


total

most of WWTP


a)




more inhabitants, only 20% satisfied all legal requirements (




parameters BOD5

Statistical results of Percentage of WWTP


24 WWTP serving populat


total accordance to the law

most of WWTP


20%




only 20% satisfied all legal requirements (




5 and





accordance to the law

most of WWTP


6620%

17754%

Distribution of a)wastewater subject to each level of treatment (







and






most of WWTP


17754%

a) treatment systems (wastewater subject to each level of treatment (







and C






most of WWTP


treatment systems (wastewater subject to each level of treatment (







COD/


The most recent data reported by IGAOT resulted from




infractions are due to lack of


Satisfied all legal requirements

Did not satisfy all legal requirements

Waiting for verification



), from the 328 WWTPs





OD/B

Statistical results of a WWTP inspPercentage of WWTPs

ed by IGAOT resulted from





legal quality requirements and breach

Satisfied all legal requirements





s inspected in 2006/2007


all legal requirements (177 WWTP



BOD

WWTP insps that exceeded in more than 100% the limit value for emission of each






breach

Satisfied all

requirements




According to data from IGAOT (Inspecção Geral do Ambiente e Ordenamento do

inspected in 2006/2007


s (177 WWTP



OD5,

WWTP inspthat exceeded in more than 100% the limit value for emission of each

component analyzed






breach

Satisfied all

requirements

Did not satisfy

requirements


treatment systems (wastewater subject to each level of treatment (adopted from

and septic tanks

Inspecção Geral do Ambiente e Ordenamento do



s (177 WWTP



correspond

WWTP inspthat exceeded in more than 100% the limit value for emission of each

component analyzed






breach of license obligations. It was also reported that most

Did not satisfy

treatment systems (adopted from INSAAR, 2007 adopted fromand septic tanks




s (177 WWTP



correspond

WWTP inspection relatively to 2006/2007that exceeded in more than 100% the limit value for emission of each

component analyzed




accordance to the law. It has been reported (


of license obligations. It was also reported that most

0%

10%

20%

30%

40%

50%

adopted from INSAAR, 2007 adopted fromand septic tanks




s (177 WWTP


exceeded in more than 100%, as analyzed in Figure

correspond

ection relatively to 2006/2007that exceeded in more than 100% the limit value for emission of each

component analyzed


24 WWTP serving populations from 1200 to 51 528 inhabitants and


It has been reported (



0%

10%

20%

30%

40%

50%

adopted from INSAAR, 2007 adopted fromand septic tanks




s (177 WWTPs

limit value for emission of each component (previously

Figure

correspond


component analyzed


ions from 1200 to 51 528 inhabitants and





adopted from INSAAR, 2007 adopted from INE, 2009 and septic tanks




s corresponding to 54%), in 75 cases the

previously

Figure

corresponding to 47% and 27% of the cases


component analyzed







12%

adopted from INSAAR, 2007 INE, 2009

and septic tanks




corresponding to 54%), in 75 cases the

previously

Figure 2.

ing to 47% and 27% of the cases


component analyzed







12%

47%

adopted from INSAAR, 2007 INE, 2009





previously

.5b.



ed by IGAOT resulted from an inspection campaign




infractions are due to lack of the


47%

adopted from INSAAR, 2007 INE, 2009 –


inspected in 2006/2007 serving



previously

b. Most infracti



an inspection campaign




the


4%

adopted from INSAAR, 2007 data for 2008). Data includes WWTP


serving

only 20% satisfied all legal requirements (Figure


previously presented in

Most infracti







the discharge license


4%

b)

adopted from INSAAR, 2007 data for 2008). Data includes WWTP


serving

Figure


presented in

Most infracti







discharge license


27%

b)

adopted from INSAAR, 2007 – data for 2007); data for 2008). Data includes WWTP


serving a population of 2000 or

Figure 2


presented in

Most infracti


ection relatively to 2006/2007; a)that exceeded in more than 100% the limit value for emission of each




It has been reported (ERSAR, 2009;

discharge license


27%

5%

data for 2007); data for 2008). Data includes WWTP


a population of 2000 or

2.5a


presented in

Most infracti


a) Fulfillment of that exceeded in more than 100% the limit value for emission of each




ERSAR, 2009;

discharge license


5%




a). F


presented in

Most infractions were related to


Fulfillment of that exceeded in more than 100% the limit value for emission of each




ERSAR, 2009;

discharge license


5%




). From those that


Table

ons were related to






ERSAR, 2009;

discharge license


5%




rom those that


Table

ons were related to






ERSAR, 2009;

discharge licenses


COD

BOD5

TSS

COD and BOD5

COD, BOD5 and TSS

Non

data for 2007); b)data for 2008). Data includes WWTP



rom those that


Table 2

ons were related to


Fulfillment of all legal that exceeded in more than 100% the limit value for emission of each



performing secondary treatment were inspected. Within these 24 WWTPs,

ERSAR, 2009;

s, breach of


COD

BOD5

TSS

COD and BOD5

COD, BOD5 and TSS

Non-specified

b) influent data for 2008). Data includes WWTP



rom those that


2.1)

ons were related to


all legal that exceeded in more than 100% the limit value for emission of each

of 2009,


only 4

ERSAR, 2009; IGAOT

, breach of


BOD5

COD and BOD5

COD, BOD5 and TSS

specified

influent data for 2008). Data includes WWTP



rom those that


) was

ons were related to


all legal that exceeded in more than 100% the limit value for emission of each

2009,


only 4

IGAOT

, breach of


COD, BOD5

specified

11

influent data for 2008). Data includes WWTP



rom those that


was

ons were related to


that exceeded in more than 100% the limit value for emission of each

2009,


only 4

IGAOT,

, breach of


11

data for 2008). Data includes WWTP



rom those that


was

ons were related to


that exceeded in more than 100% the limit value for emission of each

2009,


only 4

,

, breach of


12

WWTPs are over dimensioned, which difficult their operation and management. A more in

depth analysis of the problems concerning the whole sector was presented in the

Strategic Plan of Distribution of Water and Drainage of Wastewater 2007-2013

(PEAASAR II – MAOTDR, 2007). Some of the problems identified in PEAASAR II

according to their nature are listed in Table 2.3.

Table 2.3 | Problems of the wastewater drainage and treatment sector in Portugal presented in PEAASAR II (adopted from MAOTDR, 2007)

Nature Problem

Structural ♦ Insufficient level of attendance to population, both regarding quantity and quality.

♦ Deficient environmental regulation and implementation of legislation.

Operational

♦ Lack of management capacity and services operation due to an inexistent entrepreneurial policy or to shortage of specialized human resources;

♦ Rain water infiltration into the sewage systems, which affects treatment plants operation and may result on polluted wastewater discharges.

♦ Deficient conception or construction of some systems components, such as WWTPs and drainage networks, resulting in unconformity of legal parameters.

♦ Deficient investment planning concerning the implementation of infrastructures which should complete the whole system.

♦ Lack of a strategy concerning industrial and agro industrial wastewater collection and treatment and the regulation of its discharge into urban sewage systems.

Economic and Financial

♦ Great diversity of tariff policy at national and regional levels with no correlation with the population served or the service quality.

Environmental

♦ Breach of environmental legislation due to lack of investments on infrastructure maintenance and improvement of the treatment process performance.

♦ Need for the adaptation of actual infrastructure to the Water Framework Directive (Directive 2000/60/EC) and the Sewage Sludge Directive (Directive 86/278/EEC) requirements.

♦ Lack of confidence in treated effluent quality for potential reuse

The strategy outlined in PEAASAR II defines objectives and proposes means to optimize

the management of wholesale and retail services, including a tariff policy. One of the

objectives of PEAASAR II is to ensure that by 2013, 90% of the population is served with

wastewater management services. Given the figures recorded for 2006, when the

treatment index in Portugal was 10% under the fixed value on PEAASAR I for the period

of 2000-2006, there is still a considerable effort to be made in order to accomplish the

objectives set out in PEAASAR II.

In view of the described situation and objectives set by PEAASAR II and other relevant

legislation, dynamic modeling of WWTP gain new importance as a powerful tool for

control and optimization of the treatment performance.

13

3. BIOLOGICAL TREATMENT

3.1. COMPOSITION OF URBAN WASTEWATER

The characteristics of wastewaters influent to WWTPs vary due to a combination of

several factors such as (Almeida, 2000):

♦ wastewater characteristics influent to the sewer system (surface runoff, household

effluents or foul wastewater, presence of commercial/industrial wastewater, infiltration,

social characteristics of the connected population);

♦ drainage system type and features (separate and/or combined, extension, slope, etc.);

♦ physical, chemical and biochemical processes occurring within the sewer (that depend

on temperature, transport time, oxygen supply, among others) and

♦ dry/wet weather flow fluctuations (time of day, day of the week and month).

The most important components found in wastewaters are solids, biodegradable organics

(proteins, carbohydrates and fats), nutrients (nitrogen and phosphorus), dissolved

inorganics (such as calcium and sulfate), pathogens, heavy metals and other toxic

pollutants (from industrial activities). The most relevant parameters used to characterize

wastewaters are presented below.

3.1.1 Chemical and physical properties

Redox potencial

The redox potential is a measure that can be used to indicate which oxidation-reduction

reactions can occur (Almeida, 2000) and thus is very useful to identify the environmental

conditions in the water.

Temperature

Temperature is a very important parameter since it influences dissolved oxygen

concentration, chemical and biological processes and their respective rates.

Total suspended solids (TSS)

TSS comprise volatile suspended solids (VSS) (organic matter) and cellular residues from

endogenous respiration (inorganic matter) and are usually used in the control of WWTPs

operation. The VSS give an estimation of the amount of organic matter present in the

wastewater.

14

3.1.2 Organic components

Biochemical Oxygen Demand (BOD)

The BOD measures the amount of oxygen consumed for the biochemical degradation of

organic matter (carbonaceous demand) and for the oxidation of inorganic material such as

sulphides and ferrous iron, during a specified incubation period (usually 5 days at 20ºC)

(Almeida, 2000). It also measures the oxygen used to oxidize reduced forms of nitrogen

(nitrogenous demand) unless an inhibitor is used (Almeida, 2000). After a 5-day period of

measurement, only 60-70% of the total carbonaceous BOD is measured. Therefore, in

order to estimate up to 95-99% of the oxidized carbonaceous organic matter, this period is

extended to 20 days and the ultimate BOD (BODu) is measured. For ordinary wastewater

the ratio BOD5/BODu is 0.5 to 0.7 (Metcalf & Eddy, 1991).

Chemical Oxygen Demand (COD)

The COD test measures the oxygen equivalent of the organic matter that can be oxidized

by using a strong chemical oxidizing agent (commonly potassium dichromate) in an acid

solution. It is based upon the fact that that all organic compounds, with a few exceptions,

and some inorganic substances (Cl-, NO2-, S2

-, S2O32-, Fe2+, SO3

2-) are oxidized. This

measurement is a good estimation of the total content of organic matter as the mentioned

organic compounds are not present in significant concentrations (Henze et al., 1995) and

considering that the oxidation of most organic compounds is 95-100% of the theoretical

value (ThOD).

The biodegradability of the wastewater is given by the relation BOD5/COD: values

between 0.2 and 0.6 indicate wastewaters biodegradable by selected and adapted

organisms, values lower and higher than this range indicate hard and easy biodegradable

wastewaters, respectively.

Oxygen Uptake Rate (OUR)

The OUR is the rate at which microorganisms use oxygen as they consume food. It can

be used as a measure of the biological activity; high OURs indicate high biological activity.

It can be measured using respirometry, as described in Chapter 4.

3.1.3 Inorganic non-metallic constituents

Dissolved oxygen (DO)

The concentration of DO is an indicator for water pollution control. The DO levels depend

on physical, chemical and biochemical conditions in the waters. In equilibrium with air, the

15

solubility of DO in water is referred to as its saturation value; it decreases with the

increase of both temperature and salinity and with the decrease of pressure (Almeida,

2000).

pH

Measurement of the pH value is very important since most biological processes take place

in the pH range of 6.5-8.5 (Ferreira, 2006).

Alkalinity

The alkalinity of the wastewater results in the presence of OH-, CO32- and HCO3

2- ions. In

activated sludge treatment plants, there are many biochemical processes that change the

alkalinity of wastewaters, which influences the pH value and consequently environmental

conditions for biological activity.

Nitrogen

In wastewater, nitrogen is generally in the forms of nitrate (NO3-), nitrite (NO2

-), ammonia

(NH3), ammonium ion (NH4+) and organic nitrogen. All of these forms of nitrogen, as well

as nitrogen gas (N2), are biochemically interconvertible and are components of the

nitrogen cycle. Analytically, organic nitrogen and ammonia can be determined through the

Kjeldahl nitrogen method, which measures the total unoxidised nitrogen (Almeida, 2000).

Exactly as for COD components, nitrogen components can be divided into a number of

fractions further presented in Chapter 5.2.1.1.

Phosphorous

The presence of phosphorous in water and wastewater is almost always in the form of

organic phosphorous and polyphosphate (PO43-), which is used for cell synthesis and

energy transport. It occurs in solution in particle or detritus or in the bodies of aquatic

organisms. Detergents from domestic wastewater and fertilizers dragged by storm water

runoff are the main sources of this contaminant.

Table 3.1 presents typical values of the parameters that characterizes raw domestic

wastewater in European countries and vary from very weak to strong wastewaters.

16

Table 3.1 | Composition values of raw wastewater (adopted from Henze, 1997; quoted by Ferreira, 2006)

Parameter Units Very weak Weak Moderate Strong

COD mg/L 210 320 525 740

BOD5 mg/L 100 150 250 350

TSS mg/L 120 190 300 450

Ntotal mg/L 20 30 50 80

Ptotal mg/L 4 6 10 14

Alkalinity (CaCO3) mg/L 5 5 5 5

pH - 7 7 7 7

Coliforms MPN/100mL 107 107 107 107

3.2. BASIC ASPECTS OF MICROBIOLOGY

In a reactor, the existing mixed population of microorganisms is the result of its adaption

to environmental conditions. Therefore, the control of biological wastewater processes

depend on factors such as temperature, pH, dissolved oxygen, proper mixing, sludge age

(in activated sludge processes), hydraulic retention time (HRT) and ratio of food to

microorganisms (F/M). These last three factors are further described in Chapters 3.4.2,

3.4.2.1 and 4.2.1, respectively. Microorganisms found in wastewaters can be divided as:

♦ organisms which promote flocculation – as a result of bacteria growth and segregation

of biopolymers;

♦ heterotrophic bacteria – responsible for organic matter removal or denitrification;

♦ nitrifying bacteria – autotrophic bacteria responsible for nitrification;

♦ predators (e.g. rotifers and protozoa) which feed on bacteria and flocs.

The growth of bacteria in a batch culture (e.g. under limitations of food supply) comprises

the following four phases, also presented in Figure 3.1:

I. Lag phase: time that microorganisms need to adapt themselves, when introduced

into a new culture environment;

II. Exponential phase: microorganisms grow at the maximal rate possible, given

their genetic potential and the conditions under which they are growing,

considering excess of substrate;

III. Stationary phase: population growth ceases mainly due to substrate limitation

and hence metabolism decreases;

IV. Endogenous phase: under substrate deprivation, microorganisms are forced to

use substrate stored in their cells and their own cell material leading to death of

cells.

17

Figure 3.1 | Microbial growth curve of a pure (a) and mixed (b) batch cultures, respectively (adapted from Metcalf & Eddy, 1991)

The growth of microorganisms has been mainly described by kinetic models. In a batch

culture, growth is limited by the effect of a limiting substrate (�) and the specific growth

rate can be defined using the expression proposed by Monod (Equation (1)):

� = �� ( + �) (1)

where: �� = maximum specific growth rate [d-1]; � = specific growth rate [d-1]; � = substrate concentration [g/m3]; = substrate concentration at one-half the maximum growth rate [g/m3].

The effect of substrate concentration on the specific growth rate is shown in Figure 3.2. As

depicted in the figure, despite the initial concentration of substrate (�\), �� designates

the maximum value of �. As � approaches ��, the flatness or sharpness of the curve is

related to the term .

Figure 3.2 | Effect of a limiting substrate ( �) on the specific growth rate (�), according to Monod

S0

18

3.3. REMOVAL OF POLLUTANTS

The main goal of the biological treatment of wastewaters is to remove pollutant loads (e.g.

organics, nutrients and trace elements) that could cause significant environmental impacts

on water bodies and their uses (e.g. depletion of dissolved oxygen and eutrophication)

when released (Ferreira, 2006).

3.3.1 Removal of organic constituents

Autotrophic and heterotrophic microorganisms are responsible for the decomposition of

organic material in the influent, whereas protozoa and rotifers contribute for a better

effluent quality (Metcalf & Eddy, 1991). Protozoa consume dispersed bacteria that have

not flocculated and rotifers consume small biological floc particles that have not settled.

Microorganisms need sources of energy, carbon and nutrients in order to reproduce and

fulfill their role in the process. For heterotrophs, the organic matter supplies carbon and

energy, while for autotrophs CO2 and inorganic matter act as a carbon and energy

sources, respectively. The following equations, where .��]#^ and ._�`#ab represent

organic matter and cells respectively, correspond to the metabolic reactions which occur

in a well-aerated environment (Ferreira, 2002):

♦ Substrate oxidation (Catabolism)

.��]#^ + (� + 1 4⁄ e − 1 2⁄ h)#a S�PTHiL�jkkkkkl �.#a + 1 2⁄ e �a# + *m*G-e (2)

The soluble readily biodegradable organic matter (normally described as COD) goes

through the cell walls and is quickly metabolized. Both the slowly biodegradable

particulate and the colloidal organic matter are absorbed by the organisms, stored and

over time is broken down by hydrolysis and metabolized.

♦ Cellular synthesis (Anabolism)

.��]#^ + b�n + (� − 1 4⁄ e − 1 2⁄ h − 5)#a + *m*G-e S�PTHiL�jkkkkkl ._�`#ab+ (� − 5).#a + 1 2⁄ (e − 4)�a# (3)

Some of the metabolized organic matter is converted into new cells, while the

remainder is lost as heat in the energy process required for the new cell synthesis.

♦ Endogenous respiration

._�`#ab + 5#a S�PTHiL�jkkkkkl 5.#a + 2�a# + b�n + *m*G-e (4)

19

When all the substrate is consumed and microorganisms use their own stored food

materials and dead cells, endogenous respiration takes place with a net loss of

biomass.

Nutrients, either organic (e.g. aminoacids and vitamins) or inorganic (e.g. nitrogen,

phosphorus, sulfur and some metals), are oxidized in the presence of an electron

acceptor. This electron acceptor depends on the environmental conditions as indicated in

Table 3.2. Under aerobic conditions, oxygen is the electron receptor through the

respiration process. In the absence of oxygen, this role is played by nitrate and nitrite

through denitrification under anoxic conditions or by CO2 and SO4 under anaerobic

conditions. Fermentation of readily biodegradable organic matter also occurs under

anaerobic conditions by strictly, facultative and aerotolerant anaerobic heterotrophs

(Metcalf & Eddy, 1991; Ferreira, 2006).

The greatest yield of energy comes from the use of dissolved oxygen in oxidation,

whereas least energy results from strict anaerobic metabolism. With a mixed culture of

microorganisms, as it is found in wastewater treatment, they seek the greatest energy

yield in order to achieve maximum synthesis (Gray, 2004).

Table 3.2 | Main bacterial reactions in wastewater according with the environmental conditions (adopted from Ferreira, 2006)

Conditions Bacteria Reaction Carbon source

Electron donor

Electron receptor

Reaction products

Biomass yield (Y)

Aerobic

Heterotrophic Aerobic oxidation Organic matter

Organic matter

O2 New cells, CO2

and H2O 0.4 g VSS/g COD

Autotrophic Nitrification CO2 Ammonia or

NO2-

O2 New cells, NO2

- or NO3

- 0.12 g VSS/g NH4-N

Autotrophic Sulphur oxidation

CO2 H2S, S, S2O3

2- O2

New cells, SO4

2- —

Anoxic Heterotrophic

facultative Denitrification

Organic matter

Organic matter

NO2- or

NO3-

New cells, N2, CO2 and H2O

0.3 g VSS/g COD

Anaerobic Heterotrophic

anaerobic

Acid fermentation

Organic matter

Organic matter

Organic matter

New cells 0.06 g VSS/g COD

Sulfate reduction

Organic matter

Organic matter

SO42-

New cells, H2S, CO2 and H2O

—

Methane formation

Organic matter

VFA (acetate)

CO2 New cells,

Methane (CH4) 0.05 g VSS/g COD

The mixed microbial cultures degrade and subsequently remove colloidal and dissolved

organic substances from solution by enzymatic reactions (Gray, 2004). The enzymes are

highly specific, catalyzing only a particular reaction and are sensitive to environmental

factors such as temperature, pH and the presence of metallic ions or toxic substances.

20

3.3.2 Biological removal of nutrients

Although the stabilization of carbonaceous matter is the main objective of wastewater

treatment, nutrients such as nitrogen and phosphorus contribute greatly to the

eutrophication of the receiving waters. Consequently, many countries have legislation that

imposes the removal of those compounds from wastewater.

The biological processes for nutrient removal have high potential removal efficiency,

increase the sludge settleability and reduce sludge production and oxygen demand in the

aeration tank. They also reduce the chemical products consumed, when compared to

chemical precipitation processes. The main difficulty lies in the fact that microorganisms

performing nitrification, denitrification and enhanced biological phosphorus removal

require very different biochemical environments to function effectively, that is, a

combination of aerobic, anoxic and anaerobic conditions (Jeppsson, 1996).

3.3.2.1 Biological nitrogen removal

The biological removal of nitrogen is carried out through a three-step mechanism: i)

ammonification, production of ammonia from organic nitrogen by hydrolysis; ii) nitrification,

aerobic conversion of ammonia to nitrate by reacting with oxygen and iii) denitrification,

conversion of nitrate to nitrogen gas by reacting with organic carbon under anoxic

conditions.

The formation of ammonia from organic nitrogen is expressed by Equation (6). The other

processes are explained in more detail below.

bpG- + �a# S�PTHiL�jkkkkl b�qr + .#a (5)

The ion NH4+ is undesirable in receiving water because it causes excessive oxygen

demand and is toxic for fish.

Nitrification

In nitrification, ammonium is converted to nitrate by nitrifying bacteria (autotrophs) in two

steps. In the first step, called nitritation, Nitrosomonas oxidize ammonia-nitrogen to nitrite

(Equation (6)). In the second step, denominated nitratation, nitrite is converted to nitrate

by Nitrobacter (Equation (7)).

b�qr + 3 2t #a ULTiOQO�OI�Qjkkkkkkkkkl 2�r + �a# + b#au + *m*G-e (6)

b#au + 1 2t #a ULTiOS�PTHijkkkkkkkl b#nu + *m*G-e (7)

21

All acidity and most energy are produced in nitritation (Stypka, 1998). It should be noted

that there is a considerable oxygen demand by autotrophic bacteria during nitrification, of

4.57 g O2 per g of ammonium oxidized. If the dissolved oxygen is not replaced, then

aerobic growth will eventually stop when the oxygen is exhausted, allowing only the slow

anaerobic processes to continue (Gray, 2004).

Nitrifying bacteria are very sensitive to environmental conditions, such as temperature, pH

(optimal range between 7.5 and 8) and alkalinity (between 50 and 100 g CaCO3/m3).

Nitrification normally requires a long retention time, a low F/M ratio, a high mean cell

residence time and adequate buffering alkalinity (the process produces acid that lowers

the pH and may reduce the growth rate of nitrifying bacteria). The concentration of

dissolved oxygen should be above 2 g/m3 and DO levels below 0.5 g/m3 inhibit nitrification

(Ferreira, 2006). Finally, the growth rate of Nitrobacter is higher than the growth rate of

Nitrossomonas, which means that ammonia-nitrogen may accumulate in the process (if

the ammonification rate is high) and become an inhibitor.

Nitrification may be sufficient as a nitrogen removal process, if the receiving waters are

less sensitive, because it ensures the limitation of toxicity by ammonium and the reduction

of oxygen demand.

Denitrification

Denitrification is biologically accomplished under anoxic conditions (in the absence of

oxygen and when nitrite or nitrate act as electron receptors). This process converts

nitrate-nitrogen into nitrogen gas by a sequence of reactions for nitrate reduction:

b#nu v b#au v b# v ba# v ba (8)

Equation (9) shows that in the conversion of nitrate to nitrogen gas, organic matter is

consumed although in a smaller proportion than in aerobic conditions.

.��]#^ + (4� + e − 2h)5 �r + (4� + e − 2h)5 b#nu S�PTHiL�jkkkkl �.#a+ (2� + 3e − 2h)5 �a# + (4� + e − 2h)10 ba

(9)

Heterotrophic bacteria, such as Pseudomonas, Bacillus, Microccoccus and Aerobacter,

are responsible for denitrification and are sensitive to changes in temperature since it

influences their growth rate. The optimal pH lies between 7 and 8, with different optimum

values for different bacterial populations (Metcalf & Eddy, 1991). The main inhibitor of

denitrification is oxygen and therefore dissolved oxygen concentration should not exceed

22

0.2 g/m3 (Ferreira, 2006). This process produces alkalinity that increases the buffering

power and needs a carbon source.

Moreover, nitrous oxide (N2O) produced during the process is a pollutant gas and care

must be taken in the WWTP operation to avoid its production in the system (Gray, 2004;

Ferreira, 2006).

Although nitrification and denitrification require different environmental conditions for the

efficient action of nitrifying and denitrifying bacteria, both processes may occur

simultaneously in the reactor. In one hand, in the biological reactor both anoxic and

aerobic zones can exist, depending on the stirring conditions of operation. On the other

hand, when analyzing the floc (composed by TSS, autotrophs and heterotrophs) at a

microscopic scale, the denitrifying bacteria can be involved in anoxic conditions despite

any dissolved oxygen in the mixed liquor (mixture of wastewater and activated sludge, as

explained in Chapter 3.4).

3.3.2.2 Biological phosphorus removal

Phosphorus appears in wastewater as orthophosphate (PO43-), polyphosphate (P2O7) and

organically bound phosphorus (Metcalf & Eddy, 1991).

Biological phosphorus removal works by encouraging the growth of phosphate-

accumulating organisms (PAOs), usually Acinetobacter species, which are subjected to

both anaerobic and aerobic conditions. Under anaerobic conditions, the microbes break

the high-energy bonds in internally accumulated polyphosphate, resulting in the release of

phosphate and the consumption of organic matter in the form of volatile fatty acids (VFAs)

or other easily biodegradable organic compounds. Under aerobic conditions,

microorganisms take up phosphate and store it as polyphosphate. According to Pattarkine

and Randall (1999), the involved equations in biological phosphorus removal under

anaerobic and aerobic conditions, respectively, are as follows:

xy#z + z(pG*{ 'p|e'ℎpz'ℎ�(* + ~-ar + r + -|e�p-*m + W�y v xy#z+ z(pG*{ ��p'p|e�*Gz + ~-ar + r + .#a + �a# + x#qnu (10)

xy#z + z(pG*{ ��p'p|e�*Gz + ~-ar + r + #a(pG b#nu) +x#qnu v xy#z+ z(pG*{ 'p|e'ℎpz'ℎ�(* + ~-ar + r + .#a + �a# + -|e�p-*m (11)

This luxury uptake results in more phosphate being included in the cells than was

released in the anaerobic zone, so the total phosphate concentration is reduced. When

these microorganisms enriched in polyphosphate are removed, the contained phosphate

is also removed.

23

3.4. ACTIVATED SLUDGE PROCESS

The activated sludge process is the most generally applied biological wastewater

treatment method and has been extensively used in all sort of modified forms (plug-flow,

extended aeration, deep shaft, etc.). Since the case study presented in Chapter 6 consists

of a WWTP having an oxidation ditch, this process will be subject to a detailed description.

The activated sludge process consists of the maintenance of suspended material

(bacteria and other microorganisms, organic and inorganic particles) in wastewater in the

reactor by stirring and/or aeration. Through hydrolysis organic particles are broken down

into simpler components and used by the microorganisms in the system as an energy

source, i.e., the organic material from the raw wastewater is removed whereas more

biomass is produced. This biological conversion of organic material produces CO2, NO3

and SO4, among other end products (Stypka, 1998).

In order to control the amount of suspended biomass in the system, a sedimentation tank

is placed at the end of the process, where the biomass is transported towards the bottom

by gravity settling as excess sludge, while the purified wastewater is withdrawn from the

top of the sedimentation tank and released either for further treatment or directly into a

receiving water body. A fraction of the sludge is returned to the aeration tank containing a

high density of biomass.

3.4.1 Historical perspective

In 1914, Edward Arden and William T. Lockett from the River Committee of the

Manchester Corporation were the pioneers of one of the most popular processes in

sewage treatment. After aeration and sedimentation of wastewater, they saved the

flocculent solids, which they called activated sludge, and studied the effect of their

repeated use in sewage treatment by aeration (Jeppsson, 1996). Arden and Lockett

proved that the use of activated sludge could appreciably increase the purification

capacity of simple aeration depending on the proportion of activated sludge to the sewage

treated. Over the following years, this batch process was converted into a continuous

process using an aeration tank, a sedimentation tank and a sludge recycle system, once it

was proven to be the best practical method for activated sludge.

However, the characteristics and quantity of raw wastewater were altered due to rapid

population growth and industrial development and the existing WWTP became

inadequate. This problem, along with the higher effluent quality requirements, encouraged

the development of modified processes, such as the oxidation ditch process.

24

3.4.2 Oxidation ditch process

3.4.2.1 Introduction

The oxidation ditch concept was first developed by A. Pasveer in the Netherlands in 1953

and the first full scale plant was installed in Voorrschoten in 1954 (EPA, 2000) for a

population equivalent of 369 persons (Nelson, 1984). This type of process became

increasingly popular worldwide since it could greatly reduce the excess sludge to be

treated and disposed, while producing a highly stabilized sludge at low cost. Oxidation

ditches allow significantly lower operation and maintenance costs (low energy

requirements and no chemical addition needed) than other secondary treatment

processes.

An oxidation ditch is a modified activated sludge process used in extended aeration, that

utilizes long solids retention times (also referred to as sludge age) and a low organic load

to remove biodegradable organic material, since it operates in the endogenous respiration

phase of the growth curve of microorganisms, as presented in Chapter 3.2. As a

consequence, the volume requirements of this process are superior to other activated

sludge processes, resulting in higher land occupation. It is typically a complete mix system

with a single or multi-channel configuration within a ring, oval or horseshoe-shaped basin

(EPA, 2000). In order to provide circulation, mixing and oxygen transfer in the ditch,

horizontal or vertical aerators are mounted. Primary settling prior to a ditch may also be

applied, but is not typical in this design. A ditch is usually coupled to a previous

preliminary treatment, such as bar screens and grit removal, and to a following secondary

clarifier. A typical process flow diagram for an activated sludge plant using an oxidation

ditch is shown in Figure 3.3.

Figure 3.3 | Typical oxidation ditch activated sludge system

Depending on the effluent requirements, an anaerobic tank may be added prior to the

ditch for phosphorus removal and/or a tertiary treatment, such as filtration and

disinfection, may be necessary prior to final discharge. This system can be applicable in

25

plants that require nitrification since the basin can be sized using an appropriate solids

retention time (STR) in order to achieve nitrification at the mixed liquor minimum

temperature and has proved to be very effective. It is particularly recommended in small

communities and industrial installations, because it requires more land than conventional

treatment plants.

3.4.2.2 Design parameters, operation and control

Screened wastewater enters the ditch and is aerated by surface aerators, such as brush

rotors, disc aerators, draft tube aerators or fine bubble diffusers. The dissolved oxygen

concentration sharply increases with aeration but decreases as the mixed liquor travels

through the ditch. The stirring process entrains oxygen into the mixed liquor to enhance

microbial growth and ensures an adequate velocity, which enables the contact of

microorganisms with the incoming wastewater and maintains the solids in suspension. In

oxidation ditches, horizontal velocity can vary between 0.25 and 0.60 m/s, with typical

values from 0.25 to 0.35 m/s (Metcalf & Eddy, 1991). A minimum velocity of 0.25 m/s is

usually required to prevent the organic particles from settling on the channel surface

(Abusam et al., 2002).

The hydraulic retention time within the oxidation ditch ranges from 6 to 30 hours for most

municipal WWTPs (EPA, 2000), which minimizes the impact of a shock load or hydraulic

surge. A BOD loading rate of 80 to 480 g/m3 per day is commonly used as a design

loading rate. The typical oxygen coefficient for BOD removal ranges from 1.1 to 1.5 g of

O2 per g of BOD removed and 4.57 g of O2 per g of TKN oxidized (EPA, 2000). Sludge

production for the oxidation ditch process ranges from 0.2 to 0.85 g TSS per g BOD

applied, with typical values of 0.65 g TSS per g of BOD (EPA, 2000), which is less than

conventional activated sludge facilities due to long solids retention times (SRT). The SRT

is selected as a function of nitrification requirements at the minimum mixed liquor

temperature, with design values varying from 4 to 48 or more days. In case where

nitrification is required, SRT usually ranges from 12 to 24 days (EPA, 2000).

The operation of a WWTP based in activated sludge processes should take into account

the following parameters: sludge age; substrate/microorganisms relation (F/M); sludge

production; aerobic and anoxic conditions (in order to allow the biological reactions for

organic matter and nutrients removal to take place); alkalinity and settleability of the mixed

liquor (Ferreira, 2006). Control of the system is mainly done through aeration and recycle

of activated sludge/removal of excess sludge.

In order to enable aerobic and anoxic conditions in the ditch, cycles of aeration on/off

combined with continuous operation of stirrers are advised (Dayton and Knight, 2001).

26

Nevertheless, in most conventional activated sludge plants mechanical aerators are

responsible for both aeration and mixing. As soon as the blowers are shut off at the end

of the aerobic phase, the concentration of DO in the mixed liquor will typically decline

rapidly to near zero, as bacteria continue to oxidize BOD and ammonia, exhausting the

residual DO in the process liquid. As DO disappears from solution, facultative bacteria

turn to nitrate as an alternative electron acceptor to carry out their metabolic processes,

resulting in continued BOD removal and denitrification of nitrates. If the air-off period is

allowed to continue after all available nitrates are removed from solution by denitrification,

the environment will become anaerobic at that point (i.e. neither free dissolved oxygen nor

nitrates are present). Under anaerobic conditions, oxidation of BOD by facultative

organisms will cease and fermentation of organic matter starts. The removal rate of BOD

by fermentation is negligible compared to that under aerobic and anoxic conditions;

consequently, if the anaerobic phase is allowed to continue while untreated wastewater

flows into the bioreactor, the BOD concentration in the process effluent will begin to

increase. Therefore, to achieve optimum BOD removal and to prevent nuisance odors, the

aeration blowers should be restarted immediately after all of the nitrates disappear from

solution.

Due to the high internal recirculation rate, significant amounts of nitrate and dissolved

oxygen are recirculated from the last compartment (effluent) to the first compartment

(influent). These amounts will obviously affect the DO profile along the ditch and

consequently the ditch performance (Abusam et. al., 2002).

The return activated sludge (RAS) recycle ratio varies from 75 to 150% (to maintain the

necessary F/M relation in the ditch) and the mixed liquor suspended solids (MLSS)

concentration is usually between 3000 and 6000 g/m3 (Metcalf & Eddy, 1991). Nearly 1 to

6% of the WWTP influent is removed as excess sludge from the bottom of the secondary

clarifier.

3.4.2.3 Sludge properties

The ability of microorganisms to form flocs along with the adsorptive capacity of these

flocs is vital for the activated sludge treatment process. The floc structure enables the

adsorption of soluble substrates, colloidal matter and macro-molecules usually found in

most wastewater. However, the ability of the flocs to settle in a relatively short time under

quiescent conditions is also very important, in order to avoid the discharge of biomass,

produced as a result of oxidation of the waste, into the receiving waters.

A number of parameters have been developed to obtain a quantitative measure of the

settleability of activated sludge. Among others, the most applied of these is the Sludge

27

Volume Index (SVI) (also known as Mohlman sludge index), which measures the sludge

volume after 30 minutes of sedimentation in a 1 liter sedimentation vessel (the higher the

SVI value, the lower the sludge settleability). However, as explained by Dick and Vesilind

(Stypka, 1998), this index is not related to the sludge yield strength, the plastic viscosity or

the initial velocity of the sludge. Hence, the variation of SVI values with the total

suspended solids concentration (SST) and the sludge volume makes it difficult to compare

the SVI values between different plants.

In cases where the settleability conditions are not verified, there are four main

phenomena that lead to a decrease of the quality of the effluent due to the escape of

flocs (Stypka, 1998):

♦ Bulking sludge , when the sludge settles poorly. There are two types of bulking:

i) Filamentous bulking: Filamentous microorganisms (such as Microthrix parvicella and

Nocardia sp. Type 0041) extended from the floc particle decrease its settling rate

and hold the particles apart, preventing them from compacting;

ii) Zoogleal bulking: when filamentous organisms are completely missing, this is

related to the viscous extracellular polymers produced in excess by some kind of

bacteria (Zooglea sp., Acinetobacter sp.), in which the exocellular slime capsule

absorbs a lot of water increasing the volume of the floc. In these cases the sludge

exhibits high SVI and loses the ability to settle.

♦ Pin-point sludge or dispersed sludge, consisting of small floc particles present in the

supernatant after the sludge has settled. This phenomenon results in an environmental

shock (such as excessive turbulence in the aeration basin): microorganisms form a

non-settleable suspended solid (pin-point floc) and do not agglomerate or incorporate

into a floc.

♦ Foaming sludge due to microbial activity. Some bacteria (usually filamentous

microorganisms) produce a lipid material (hydrophobic compounds) which is excrete

into the mixed liquor and collects on the surface of air bubbles. The bubbles mesh

together including some microorganisms and float on the surface forming scum.

♦ Rising of the sludge after settlement in the clarifier due to the nitrogen produced in

denitrification process.

A detailed description of the factors that influence the growth of filamentous bacteria and

its control strategies can be found in Stypka (1998).

28

3.5. SEDIMENTATION

In the activated sludge process the efficiency of the whole treatment system and

consequently the quality of the final effluent are based on a strong relationship between

the design of the aeration tank and of the secondary clarifier. The performance of the

aeration tank varies with the return sludge concentration and flow rate, while the

clarification and thickening functions of the clarifier depend on the characteristics of the

effluent formed in the aeration tank. These functions translate the solid-liquid separation

process that happens once the aeration tank effluent enters the sedimentation tank. A part

of the load flows to the surface of the clarifier where it is discharged as a clarified effluent

(overflow). The remaining part settles down by gravity and concentrates on the bottom of

the clarifier, from where it is removed as return activated sludge or as excess sludge

(underflow). In some cases, the settler may also be considered for sludge storage in its

bottom part or as a reactor where additional biological conversions can occur (e.g.

denitrification).

The sedimentation tank can be divided into three functioning areas (clarification zone,

settling zone and sludge zone) according to four different types of settling:

♦ Discrete particle settling: in the clarification zone, particles settle individually without

interaction with neighboring particles. The settling velocity regime is based on the

Stokes equation.

♦ Discrete flocculent particle settling: in the clarification zone, flocculation causes the

particles to increase in mass and settle at a faster rate.

♦ Hindered settling: in the settling zone, the particles are close enough so that

interparticulate forces may hold them fixed relative to one another so that the whole

mass tends to settle as a single "blanket" of sludge.

♦ Compression: at the bottom of the tank (sludge zone) the concentration of particles is

so high that their weight leads to a progressively greater compression with depth and

thickening of sludge.

There are many factors that influence the clarification and thickening functions of the

secondary clarifiers. Ekama et al. (1997) reported that excessive solids in secondary

clarifier effluents occur primarily because of one or more of the following aspects:

♦ Hydraulic short-circuiting or resuspension of solids from the surface of the sludge

blankets by high velocity currents.

♦ Thickening overloads, resulting in high sludge blankets and potential loss of solids

when the blanket reaches the effluent weir.

29

♦ Denitrification, causing solids to float to the surface of the clarifier due to the release of

nitrogen gas.

♦ Flocculation problems due to either flocs breakup or poor floc formation.

♦ Insufficient capacity of the sludge collection system.

In addition, external dimensions of the clarifier (such as area and depth), internal features

(such as inlet, outlet sludge collection and baffling arrangement) as well as flow

disturbances (short circuiting, turbulence) also influence these functions.

4. RESPIROMETRY Respirometry is the measurement and interpretation of the oxygen consumption rate per

unit of time and volume. Because oxygen consumption is directly associated with both

biomass growth and substrate removal, respirometry is a useful technique for modeling

and operating the activated sludge (Spanjers et al., 1998). This technique was first applied

to wastewater in 1890 but only later in the 1960s it raised interest in the scientific

community for process control. It can provide very diverse information, such as

composition of the wastewater (concentration, COD fractionation and toxicity), indication

of specific activity of organisms (e.g. kinetic parameters), information on how the biomass

interacts with the wastewater components and purification performance. During the past

two decades, respirometry has been widely used to obtain biokinetic characteristics and

gained most relevance in activated sludge modeling, as in the work of Copp et al. (2002).

Although respirometry is based on the same biochemical principles, depending on the

purpose of the respirometric assay different procedures may be considered, and the direct

or indirect use of the generated information differs from author to author.

According to Domingos (1999) (quoted by Ferreira, 2004) in a batch reactor of a mixed

culture, the kinetic parameters are observed to be slightly heterogeneous and may be

related with the existing bacterial species and with the history of the culture. The history of

the culture, which comprises many different aspects such as the selection of

microorganisms, substrate affinity and the capacity to adapt to different environmental

conditions, influences the initial physiologic state of the culture. Hence, biodegradation

kinetic parameters, such as the maximum specific growth rate (��), Monod saturation

constant () and the heterotrophic yield (��), can also be affected and should be

analyzed carefully.

30

4.1. RESPIROMETERS

A respirometer is a device used to measure the respiration rate, i.e. the variation of

dissolved oxygen as a result of its consumption by biomass over time. This can be done

directly by measuring DO or indirectly by measuring gaseous oxygen or pressure. A

respirometer consists of a reactor or respiration cell (where the sample is placed) and

sensorial instruments coupled with equipment data acquisition system. In the reactor

substrate, biomass and dissolved oxygen are brought together, among other components

that allow the control of reactions in the system. Usually an air pump or compressor and a

magnetic stirrer are also used to provide enough oxygen and ensure complete mix in the

reactor, respectively. Many different configurations of respirometers have been developed

with more or less complexity, from a simple and manually operated bottle equipped with a

DO sensor to complex instruments that operate fully automatically.

Ros (1993) (quoted by Ferreira, 2002, and Ferreira, 2004) divided respirometers into two

groups: closed respirometers (subdivided into manometric, volumetric and combined) and

open respirometers (continuous or descontinuous, according to the measuring continuity).

Spanjers et al. (1998) have pointed out that respirometers can be grouped in accordance

with these basic criteria: (1) the phase where oxygen concentration is measured (gas or

liquid) and (2) whether or not there is an input and output of gas and liquid (flowing or

static regime). In Table A.1.1 and Table A.1.2Table A.1. of Appendix A.1 the classification

and a brief description of respirometers according to Spanjers et al. (1998) is presented,

respectively, considering oxygen measuring phase, regime, mass balance and diagram.

The gas phase includes oxygen dispersed as bubbles in the liquid phase.

According to Spanjers et al. (1998) and Rozich & Gaudy (1992) the respirometric devices

generate a profile that registers the variation of DO over time (the respirogram).

Conceptually, this curve may contain four phases (as showed in Figure A.2.1. of Appendix

A.2), the first two developed to evaluate the endogenous respiration, the second to

determine and control the oxygen rate (��) and the last one to estimate the kinetics and

stoichiometric parameters. The area A of the respirogram only is developed if a

continuous aeration procedure is applied. Some authors (Ros, 1993; Spanjers et al.,

1998; and Mathieu & Etienne, 2000), have used continuous aeration in order to developed

a OD recovery curve and, therefore, to estimated some parameters through an integration

graphic method.

However, the studies of Rozich & Gaudy (1992), Spanjers et al. (1998), Ferreira (2002),

Ferreira (2004) and Mhlanga et al. (2009) the aeration was not used after substrate

injection since most of parameters may be estimated from the DO decay curve. If the

31

substrate source is not properly removed it may be performed additional cycles of

aeration/no aeration in order to compare the values obtained for each decay curve.

Respirometers based on the principle of measuring the DO concentration in the liquid are

based on the DO mass balance over the liquid phase, given by Equation (12). The liquid

in the respirometer must be a complete mixture (CSTR reactor), in order to avoid

concentration gradients.

�(W�"#)�( = FLI"#LI − FORT"# + W��("# − "#) − W�GT (12)

where: "# = DO concentration in the liquid phase [g O2/m3]; "#LI = DO concentration in the liquid phase entering the system [g O2/m

3]; "# = saturation DO in the liquid phase [g O2/m3]; FLI = flow rate of the liquid entering the system [m3/h]; FORT = flow rate of the liquid leaving the system [m3/h]; W� = volume of the liquid phase [m3]; �� = oxygen mass transfer coefficient (based on the liquid volume) [h-1]; GT = total respiration rate of the biomass in the liquid [g O2/m

3·h].

Some respirometers are based on measurements of gaseous oxygen. Therefore, in

addition to the DO mass balance in the liquid phase, an oxygen mass balance for the gas

phase has to be considered as expressed by Equation (13):

�(WX#a)�( = �LI#a,LI − �ORT#a − W��("# − "#) (13)

where: #a = O2 concentration in the gas phase [g O2/m3]; #a,LI = O2 concentration in the gas phase entering the system [g O2/m

3]; "# = DO concentration in the liquid phase [g O2/m3]; "# = saturation DO concentration in the liquid phase [g O2/m

3]; �LI = flow rate of the gas entering the system [m3/h]; �ORT = flow rate of the gas leaving the system [m3/h]; WX = volume of the gas phase [m3]; W� = volume of the liquid phase [m3]; �� = oxygen mass transfer coefficient (based on the liquid volume [h-1].

Some respirometers measure the variation of volume or pressure instead of the oxygen.

The oxygen concentration is subsequently obtained according to the ideal gas law

32

(xW = m��). Since continuous aeration is used, one must control very well the oxygen

transfer rate (��), which is applied in the expressions for parameters estimation.

4.2. RESPIROMETRIC EXPERIMENTS

4.2.1 Measurement conditions

According to Spanjers et al. (1998), in respirometry a respiration rate value or a

percentage of inhibition calculated from respiration rate measurements cannot be

interpreted without additional information about some measurement attributes. The most

relevant attributes are: biomass source, type of substrate, time of measurement and initial

substrate-to-biomass ratio (S0/X0). Other environmental conditions such as pH,

temperature and pressure are also important for the measurement result. Therefore, they

are assumed similar to the conditions in the treatment plant or kept constant in order not

to influence the results. The previous attributes are briefly described as follows:

Biomass

There are several sources of respirometer biomass: raw/settled sewage, activated sludge

from the aeration tank, returned activated sludge, effluent from the final settlement tank

and specific cultures grown on a synthetic substrate. As reported in Spanjers et al. (1998),

activated sludge sampled from the aeration tank often contains dissolved oxygen and a

varying and mostly unknown quantity of substrate. Return activated sludge has a high

biomass concentration and usually low dissolved oxygen and substrate concentrations,

whereas raw sewage has a low biomass and high substrate concentrations. The initial

concentration of biomass (X0) should be known (as explained later) and corresponds to

the concentration of volatile suspended solids, converted into units of COD by

multiplication of a conversion factor (+�V).

Most of the experiments in respirometers use the biomass from the activated sludge

process, since the objective is to get information on kinetic activity in the bioreactor. The

experiments should be carried out in a short time in order to avoid changes in the biomass

dynamics. The introduction of an external source may not be homogeneous with respect

to past history of environmental conditions, and the growth of this biomass will not be a

balanced (steady-state) growth even when the bioreactor operates in steady state

conditions.

According to Dang et al. (1989) (quoted by Ferreira, 2004), the consumption of DO is

associated to the removal of substrates and for biomass growth (both aerobic

heterotrophics and autotrophic nitrifiers). In order to better defined the respiration rate for

33

heterotrophics, nitrifiers and protozoa activities need to be inhibited (e.g. with

allylthiourea). Prior the beginning of the assay, the residual substrate must be exhausted

in order to allow the growth of biomass and the removal of substrates immediately after

substrate inoculation.

Substrate

As described by Spanjers et al. (1998), four substrates types have been considered in

respirometry: wastewater (raw or settled), treated effluent, return liquors from the sludge

treatment and specific substrates. A specific substrate such as acetate or ammonium can

be used to mimic the oxidation of particular components is wastewater. The initial

concentration of substrate (S0) (expressed as units of COD) and the time of measurement

(long term or short term experiments) are both very important conditions since the OUR

measurements depend on their variability. Moreover, when the respiration rate is

measured immediately after sampling, the respirogram can provide more accurate

information on biomass and substrate and closely reflect the condition of the treatment

process.

The ratio So/Xo is usually measured after the injection of the substrate and may result of

the sum of the mass of the input pulse and the mass already existent in the respirometer.

In most of the cases this evaluation uses the experimental values determined after the

pulse injection (i.e. in the mixed volume).

Time of measurement

The respiration rate is a function of time. Therefore, in a respirometric assay, the

respiration rate is measured for some time to obtain a time series of respiration rate

values. Depending on the characteristics of the sample to be analyzed and the aim of the

experiment (i.e. the set of parameters to be estimated from the respirogram), the time of

measurement usually varies between 5 to 6 hours for a short term experiment (Mathieu &

Etienne, 2000; Ferreira, 2004) and between 1 to 2 days for long term experiments (Ekama

et al., 1986).

Initial substrate to biomass ratio (S0/X0)

The outcome of the experiments and its quality is influenced by the initial substrate to

biomass ratio (Chudoba et al., 1992; Grady Jr. et al., 1996; Mathieu and Etienne, 2000).

S0/X0 influences the history of a culture and the kinetic parameter estimation in correlation

with the Monod parameters � and . If S0/X0 is very high, significant changes in the

community structure will happen and the measured kinetics parameters will reflect the

34

characteristics of faster growing species rather than the ones of the original culture (Grady

Jr. et al., 1996). On the other hand, if S0/X0 is very small, the storage of substrate into the

cells is enhanced and at some point the growth is limited by the shortage of a carbon

source (Ferreira, 2004). If kinetic parameter estimation is done with the aim of modeling, a

value of S0/X0 below 4 is usually used, since it is considered to be more representative of

the kinetics in the source environment (Chudoba et al., 1992).

4.2.2 Measurement and deduction of variables

In order to obtain relevant parameters of a wastewater treatment process, respirometric

measurements must first be converted to deduced variables. A deduced variable is

defined as a variable that results from a calculation (for instance arithmetic, integration,

parameter estimation, comparison and/or model based interpretation) performed with one

or more measured respiration rate values and possibly other measured variables

(Spanjers et al., 1998). Considering the set of parameters to be determined by

respirometry and its utilization in the calibration of an ASM, different approaches for the

deduction of such parameters may be undertaken. In this work, a combination of both

model based interpretation and equations was used in order to deduce stoichiometric and

kinetic coefficients based on respirometric experiments.

Figure 4.1 illustrates the DO curve pattern corresponding to an operation cycle of a

respirometer based on the LFS principle (see Table A.1.1, Appendix A.1), but with

aeration stopped after injection as suggested by Rozich & Gaudy (1992), Spanjers et al.

(1998) and Ferreira (2004). The DO curve is divided into four phases. In phase I, the DO

concentration increases quickly to saturation levels (6-8 g/m3) as a consequence of a

rapidly aeration (Melcer et al., 2003). Aeration is then turned off (t0) and the consequent

decrease of DO concentration is observed (phase II). The endogenous respiration rate

(GHIJ) may be obtained from the slope of the linear section of DO response. During phase

III, the sample is reaerated (t1) and the oxygen mass transfer coefficient (��) can be

calculated from the curve (as described in Appendix A.3). The main goal of these three

stages is to remove any residual substrate from the system before the addition of a known

substrate. When DO reaches again the saturation level (t2), the substrate is injected (t3) in

order to obtain the respirogram – that is, a graphical representation of the OUR as a

function of time (phase IV).

35

Figure 4.1 | DO and OUR curves of a LFS respirometer test

The OUR curve is obtained from the DO curve as given in Equation (14), considering the

period of time after substrate addition.

#��(() = "#Ir� − "#I(Ir� − (I (14)

The interpretation of the respirogram allows the direct extraction and/or the estimation of

several parameters calculated through empirical equations. The OUR curve or respiration

can be expressed as in Equation (15):

GT = GQRS + GHIJ (15)

where: GT = total respiration rate [g O2/m3·h]; GQRS = respiration rate of substrate oxidation [g O2/m

3·h]; GHIJ = endogenous respiration rate [g O2/m3·h].

The respiration rate of substrate oxidation, GQRS, corresponds to the oxygen consumed for

substrate oxidation and is obtained from the decay curve shown in Figure 4.1 (phase IV).

The GHIJ is associated to the consumption of DO in the absence of substrate and it can be

estimated from the slope of the curve DO vs. time in phase III of Figure 4.1 (Ros, 1993;

Spanjers et al., 1998).

OU

R [

g/m

3 �h

]

DO

[g

/m3 ]

t4 t5

t5

36

The kinetic and the stoichiometric parameters are evaluated after the injection of substrate

in the beginning of phase IV.

According to Henze et al. (1987), the heterotrophic decay rate (��) is obtained by plotting

the OUR values, corresponding to the period of time (t4-t5) in the OUR curve of Figure 4.1,

on a logarithmic scale. The slope of the curve ln(OUR(t)) vs. t represents the heterotrophic

decay (Ekama et al., 1986).

The �� is defined as the ratio of generated biomass per organic substrate utilized during

the logarithmic growth phase of the heterotrophic culture. As proposed by Rozich &

Gaudy (1992), �� can be obtained as given in Equation (16):

(!T − !\) = ("# − "#T(1 ��⁄ − +�V) (16)

where: �� = heterotrophic yield coefficient [g CODVSS/g CODS]; !T = biomass concentration during the biomass growth phase (t3-t5)

[g VSS/m3]; !((n) = initial biomass concentration (after substrate addition, t3)

[g VSS/m3]; +�V = 1.42 g CODVSS/g VSS (conversion factor).

The � (d-1) represents the growth of biomass (X, g SSV/m3) over the removal of substrate

(S, g COD/m3) and it is proportional to ∆x/∆t. According to Rozich & Gaudy (1992), it

corresponds to the slope of the curve ln X vs. t in logarithmic scale. �, and �� are related through Equation (1). (g/m3) is equal to the substrate concentration (S)

that equals � = ½ ��. This two parameters may be obtained through curve-fitting the

experimental results to Equation (1) by using a optimization methods such as the

minimum squares. If the value of was lower than the S value correspondent to ��/2,

the substrate is considered limiting biomass growth.

Table 4.1 presents typical values of stoichiometric and kinetic parameters found in the

literature for heterotrophic biomass.

37

Table 4.1 | Typical values for stoichiometric and kinetic parameters for heterotrophic biomass

Parameter Unit Typical Minimum Maximum Reference �� g CODVSS/g COD 0,67 0,6 0,75 Grady Jr. et al. (1999), Drolka et al. (2001), Insel et al. (2002) �� d-1 6 4 - Henze et al. (1987), Ekama et al. (1986) �� d-1 4,8 3,4 6,5 Sozen et al. (1998) �� d-1 0,05 0,03 0,07 Grady Jr. et al. (1999), Drolka et al. (2001), Insel et al. (2002) �� d-1 - 0,1 0,4 Henze et al. (1987), Ekama et al. (1986) � g COD/m3 40.0 15 70 Metcalf & Eddy (1991) �� h-1 12 - - Droste (1997)

5. MODELING OF WASTEWATER TREATMENT PLANTS

5.1. GENERAL CONSIDERATIONS OF MODELING

Modeling of biological wastewater treatment processes is currently a very active research

area. Generally, mathematical models are used, where equations of various types are

defined to relate inputs, outputs and characteristics of a system.

Overall, a model aims to describe as accurately as possible the behavior of a given

system. Models are therefore a valuable tool which enables the investigation of the static

and dynamic behavior of a system, thereby reducing the number of practical experiments

necessary, which may be rather expensive and time-consuming (Jeppsson, 1996).

However, no model illustrates the whole reality. The system of interest may be complex

and models of it may need to be simplified in order for the model to become useful for

modellers and practioners (i.e. assumptions have to be made, boundary conditions have

to be established and the consequent propagation of errors has to be considered and

evaluated).

WWTP model studies can have different purposes: (1) learning, i.e. use of simulations to

increase process understanding; (2) design, i.e. evaluate several design alternatives for

new WWTP or extension of existing ones; (3) process optimization and control, i.e.

evaluate several scenarios that might lead to improve operation and/or reduce its costs.

For the WWTP operator, simulations might be useful to indicate the consequences of

process operation modifications on the activated sludge composition and the WWTP

effluent quality.

In the simulation of an activated sludge WWTP, a number of factors have to be

considered, such as the ultimate goals of the model and the level of accuracy desired: a

38

step-wise approach is needed to move from the definition of the modeling goals to the

point where a WWTP model is ready for simulations.

5.2. BIOLOGICAL MODEL: ACTIVATED SLUDGE MODELS

One of the most widespread biological wastewater treatment techniques is the activated

sludge process. The increased knowledge about the mechanisms of different biological

processes that occur in activated sludge plants was translated into dynamics models

which describe the degradation processes. In 1982, the International Water Association

(IWA) formed a task group aiming to create a common platform that could be used for

future development of models for nitrogen-removal activated sludge processes, with a

minimum of complexity and which could give realistic results. Since then, a set of models

known as Activated Sludge Models (ASMs) has been developed. ASM1 (Henze et al.,

1987) is considered the reference model, since it triggered the general acceptance of

WWTP modeling and has been widely used. This model includes organic carbon and

nitrogen removal processes. ASM2 (Henze et al., 1995) extended the capability of ASM1

by including biological and chemical phosphorus removal processes. The ASM2d model

(Henze et al., 1999), built on the previous ASM2, added the denitrifying activity of PAOs

(phosphorus-accumulating organisms). ASM3 (Gujer et al., 2000) was intended to

become the new standard model, correcting for a number of defects of ASM1 and further

including internal storage compounds processes, which have an important role in the

metabolism of organisms.

Koch et al. (2000) concluded that ASM1 and ASM3 are both capable of describing the

dynamic behavior in common municipal WWTPs. Furthermore ASM3 performs better in

situations where the storage of readily biodegradable substrate is significant (industrial

wastewater) or in WWTPs with substantial anoxic zones. In accordance with

Vanrolleghem et al. (1999), the use of the endogenous respiration concept in the ASM3

model should allow easier comparisons between the results of kinetic parameters (derived

from respirometric batch experiments with the activated sludge of the plant to be modeled)

and the activated sludge model used to describe the phenomena in the full-scale plant.

Taking these two factors into account, as well as the characteristics of the biological

reactor considered in this thesis, the use of ASM3 model was decided upon.

A description of ASM2 and ASM2d is therefore not included since phosphorus removal is

not dealt with in this study. Also the description of ASM1 has been excluded because of

the reasons presented above. Consequently, the review presented in this chapter focuses

on the model concepts of Activated Sludge Model Nº3.

39

5.2.1 Description of the Activated sludge model Nº3 (ASM3)

ASM3 is presented in a matrix format in Appendix A.5 according to Gujer et al. (2000).

The matrix consists of 13 components and 12 process rate equations, which translate the

biological transformation of each component. Default kinetic and stoichiometric model

parameters are also presented in Table A.5.1 and presented in more detail in Table A.5.2

of Appendix A.5, respectively. Many of the basic concepts of ASM were adapted from the

activated sludge model defined by Dold (1980).

5.2.1.1 Components in ASM3

The components in the model are basically divided into COD and nitrogen compounds, as

described below. The other two components are the suspended solids (!) and the

dissolved oxygen concentration (� %), which is subject to gas exchange.

COD components

COD is selected as the most suitable parameter for defining the carbon substrates as it

provides a link between electron equivalents in the organic substrate, the biomass and the

oxygen utilized. In this model the COD is divided based on (i) solubility, (ii)

biodegradability, (iii) biodegradation rate and (iv) viability (biomass) (Petersen, 2000):

(i) The COD is divided into soluble (�) and particulate (!) components.

(ii) The COD is further subdivided into non-biodegradable and biodegradable matter. The

non-biodegradable matter is inert and its form is unaffected by the system. The inert

soluble matter (�2) leaves the system at the same concentration as it enters, by the

secondary clarifier effluent. Inert suspended matter (!2) from the wastewater influent

or produced via biomass decay becomes enmeshed in the sludge mass and

accumulates as inert VSS (volatile suspended solids). It is removed from the system

through sludge wastage.

(iii) A cell internal storage product (!� ) is considered in the model, although only as a

functional component required for modeling. The biodegradable COD is divided into

readily biodegradable substrate (�) and slowly biodegradable substrate (!). The

readily biodegradable substrate is formed by hydrolysis of particulate organic matter

entrapped in the biofloc. � is assumed to be first directly taken up by heterotrophic

organisms, storage in the form of !� and latter used for growth of new biomass.

The slowly biodegradable substrate appears in the wastewater influent as a result of

hydrolysis. It consists of complex molecules that require enzymatic breakdown prior

to absorption and utilization. It should be stressed that a fraction of ! may actually be

soluble although it is treated as a particulate material in the model (Jeppsson, 1996).

40

(iv)

In summary, the total

Figure

Nitrogen components

The characterization of the nitr

nitrogen incorporated in

components. This fraction is consumed or produced when the corresponding COD

fractio

ammonia nitrogen (

ammonia nitrogen via ammonification

biomas

source for biomass growth, especially as the energy supply for aerobic growth of

autotrophic biomass. The autotrophic conversion of ammonia results in nitrate nitrogen

(�U allowing for a closed nitrogen mass balance and is assumed to be the only product of

denitrification.

stoichiometric computations it is considered to be

nitrogen mass bala

Figure

40

(iv) The heterotrophic biomass (

growth on the readily biodegradable substrate (

(

heterotrophs are assumed to have no anaerobic act

hydrolysis, which is the only anaerobic process in ASM3 (


Figure

Figure

Nitrogen components




fractio

ammonia nitrogen (


biomas



U )

allowing for a closed nitrogen mass balance and is assumed to be the only product of

denitrification.


nitrogen mass bala

Figure

The heterotrophic biomass (


(�U�heterotrophs are assumed to have no anaerobic act



Figure

.#"

Figure

Nitrogen components




fraction is formed or degraded, respectively. B

ammonia nitrogen (


biomass. Ammonia nitrogen (ammonium plus ammonia nitrogen) serves as the nit



), with the requirement of oxygen.


denitrification.


nitrogen mass bala

Figure 5



U��). The growth of autotrophs occurs only in aerobic conditions, whereas




Figure 5.1

.#"TOT�N

Figure 5

Nitrogen components




n is formed or degraded, respectively. B

ammonia nitrogen (


s. Ammonia nitrogen (ammonium plus ammonia nitrogen) serves as the nit



, with the requirement of oxygen.


denitrification.


nitrogen mass bala

5.2.

Soluble (S



). The growth of autotrophs occurs only in aerobic conditions, whereas




1.

TOT�N

5.1 |

Nitrogen components





ammonia nitrogen (







denitrification.


nitrogen mass bala

.

Biodegradable

Soluble (SS)







TOT�N =

| Wastewater characterization COD components i

Nitrogen components





ammonia nitrogen (







denitrification. �stoichiometric computations it is considered to be

nitrogen mass bala

Biodegradable

Soluble







= �

Wastewater characterization COD components i

Nitrogen components





ammonia nitrogen (






allowing for a closed nitrogen mass balance and is assumed to be the only product of �U stoichiometric computations it is considered to be

nitrogen mass bala

Biodegradable







+


Nitrogen components





ammonia nitrogen (�ammonia nitrogen via ammonification






U is assumed to include nitrate and nitrite nitrogen, althoug


nitrogen mass balance i

Biodegradable

Particulate (X






In summary, the total COD balance is defined in Equation

+ �2


Nitrogen components




n is formed or degraded, respectively. B�U�ammonia nitrogen via ammonification






is assumed to include nitrate and nitrite nitrogen, althoug


nce i

Biodegradable

Particulate (XS)






COD balance is defined in Equation

+ !



nitrogen incorporated in �components. This fraction is consumed or produced when the corresponding COD


U�) and organic nitrogen. The organic nitrogen is converted to









nce in ASM3 is defined by Equation

Particulate )







! +


The characterization of the nitr�2, components. This fraction is consumed or produced when the corresponding COD


) and organic nitrogen. The organic nitrogen is converted to









n ASM3 is defined by Equation

Particulate







+ !


The characterization of the nitrogenous matter is based on the composition of COD as t

, �,













Soluble

The heterotrophic biomass (!growth on the readily biodegradable substrate (





!2 +


ogenous matter is based on the composition of COD as t

, !components. This fraction is consumed or produced when the corresponding COD












Soluble (SI

!�) and autotrophic bi






+ !�


ogenous matter is based on the composition of COD as t!2, components. This fraction is consumed or produced when the corresponding COD












biodegradable

Soluble

I)

) and autotrophic bi






!� +



, !,




ammonia nitrogen via ammonification (Equation 6)









Nonbiodegradable

Soluble







+ !�



, !components. This fraction is consumed or produced when the corresponding COD



(Equation 6)




, with the requirement of oxygen. A nitrogen gas component (





Non-biodegradable

Particulate







� +


ogenous matter is based on the composition of COD as t!�




(Equation 6)




A nitrogen gas component (





Total COD

biodegradable

Particulate (X







+ !�



and


n is formed or degraded, respectively. Biodegradable nitrogen is subdivided into


(Equation 6)









Total COD

biodegradable

Particulate (XI)







�



and !components. This fraction is consumed or produced when the corresponding COD

iodegradable nitrogen is subdivided into


(Equation 6)









Total COD

Particulate


growth on the readily biodegradable substrate (�) or by growth on ammonia nitrogen





Wastewater characterization COD components in ASM3 (modified from Jeppsson,

ogenous matter is based on the composition of COD as t!�




(Equation 6)







stoichiometric computations it is considered to be NO


Total COD


) or by growth on ammonia nitrogen





n ASM3 (modified from Jeppsson,


is defined as a fraction of these




and is removed by growth of the







NO3–


Heterotrophs

) and autotrophic biomass (



















–N

n ASM3 is defined by Equation (

Heterotrophs (XH

omass (




hydrolysis, which is the only anaerobic process in ASM3 (Gujer

COD balance is defined in Equation (17














N only (Gujer

(18

Heterotrophs

H)

omass (



heterotrophs are assumed to have no anaerobic activity except cell external

Gujer

17)














only (Gujer

18) and further illustrated in

Activ biomass

Heterotrophs

omass (!) or by growth on ammonia nitrogen


ivity except cell external

Gujer et al.

and further illustrated in














only (Gujer


Activ biomass

!�) are generated by




et al.















only (Gujer


biomass

Autotrophs

) are generated by




et al.,












A nitrogen gas component (�allowing for a closed nitrogen mass balance and is assumed to be the only product of


only (Gujer et al.


Autotrophs (XA

) are generated by




, 2000











autotrophic biomass. The autotrophic conversion of ammonia results in nitrate nitrogen �U%allowing for a closed nitrogen mass balance and is assumed to be the only product of


et al.


Autotrophs

A)

Storage (X

) are generated by




2000).












%) is included



et al., 2000


Autotrophs

Storage (XSTO

) are generated by




).


n ASM3 (modified from Jeppsson, 1996)










) is included


is assumed to include nitrate and nitrite nitrogen, although for all

2000


Storage

STO)

) are generated by





1996)







s. Ammonia nitrogen (ammonium plus ammonia nitrogen) serves as the nitrogen



) is included


h for all

2000).


) are generated by





(17)

1996)







rogen



) is included


h for all

). The


) are generated by





(17)

ogenous matter is based on the composition of COD as the






rogen



) is included


h for all

The


) are generated by




he






rogen



) is included


h for all

The

5.2.1.2

According to Gujer

particulate organic matter, storage of readily biodegradable substrate, growth and decay

of biomass. The substrates flows are

♦

♦

♦

5.2.1.2

According to Gujer



♦ Hydrolysis:

contained in the influent and is assumed to be electron donor independent.

♦ Aerobic storage of readily biodegradable substrate:

storage�and growth process, allowing growth to take place on external substrate directly, is not

considered. It is realized that this is not in accordance with rea

ASM3 was published there was no model available to predict the separation of

direct growth and storage. Therefore, Gujer

storage yied (

Monod relationship is used to describe the growth of heterotrophic and autotrophic

organisms.

♦ Anoxic storage of readily biodegradable substrate:

aerobic storage, but denitrification rathe

energy required. A correction factor (

heterotrophic biomass may be capable of denitrifying.

bTOT�N

5.2.1.2

According to Gujer



Hydrolysis:


Aerobic storage of readily biodegradable substrate:

storage

first

and growth process, allowing growth to take place on external substrate directly, is not




storage yied (


organisms.

Anoxic storage of readily biodegradable substrate:




bTOT�N

Processes in ASM3

According to Gujer



Hydrolysis:



storage

first becomes stored material before used for growth. Thus, a division of the storage





storage yied (


organisms.





Ammonia

TOT�N =

Processes in ASM3

According to Gujer



Hydrolysis:



storage of

becomes stored material before used for growth. Thus, a division of the storage





storage yied (


organisms.





Ammonia (SNH

= �U�

Figure

Processes in ASM3

According to Gujer



Hydrolysis:



of �becomes stored material before used for growth. Thus, a division of the storage





storage yied (


organisms.





Biodegradable

Ammonia

NH)

Soluble (iNSS

U��

Figure

Processes in ASM3

According to Gujer



This process makes available all slowly biodegradable substrates



in the form of






storage yied (�� Monod relationship is used to describe the growth of heterotrophic and autotrophic





Biodegradable

Ammonia

Soluble

NSS�S

� +·

Figure 5

Processes in ASM3

According to Gujer et al.






in the form of






��






Biodegradable

Soluble �SS)

�U

· ?!

5.2 |

Processes in ASM3

et al.






in the form of






� ) and a higher growth yied (






Biodegradable

Organic nitrogen

[

?!� [

| Nitrogen components in ASM3 (modified from Jepp

Processes in ASM3

et al.






in the form of






) and a higher growth yied (






Biodegradable

Organic nitrogen

[ �U

? [ !�

Nitrogen components in ASM3 (modified from Jepp

Processes in ASM3

(2000






in the form of












Organic nitrogen

Particulate (XNI

U%[

!�C [


Processes in ASM3

2000






in the form of !�












Particulate

NI=iNSS

[ �U

C [ �U


2000) in ASM3 there are four main processes: hydrolysis of






�












Particulate

NSS�X

(S

U,,

C U,1,


) in ASM3 there are four main processes: hydrolysis of


of biomass. The substrates flows are represented




� with the consumption of oxygen. It is assumed tha












Particulate �XS)

Soluble (SNI=i

· �2

,· !




represented




with the consumption of oxygen. It is assumed tha










energy required. A correction factor (η


Soluble =iNXI

�2 [

!2




represented













aerobic storage, but denitrification rather than aerobic storage respiration provides the

ηNOX


TKN

biodegradable

Soluble

NXI�SI)

[ �U,




represented













r than aerobic storage respiration provides the

NOX) is used to indicate that only a fraction of the


TKN

Nonbiodegradable

,0·




represented














) is used to indicate that only a fraction of the


TKN

Non-biodegradable

(X

· �




represented in









direct growth and storage. Therefore, Gujer et al

) and a higher growth yied (�






biodegradable

Particulate (XNI

[




in Figure









et al. (

��) to approximate direct growth. The






Particulate

NI=iNXI

�U,1




Figure









et al. (2000

) to approximate direct growth. The






Total Nitrogen

Particulate

NXI�XI)

Nitrate/Nitrite

10·




Figure 5









2000






Total Nitrogen

Particulate )

Nitrate/Nitrite (SNO

· !




5.3



Aerobic storage of readily biodegradable substrate: This process describes the






2000) suggested applying a low



Anoxic storage of readily biodegradable substrate: This process is identical to



Total Nitrogen

Heterotrophs

Nitrate/Nitrite

NO)

[




3.



This process describes the






) suggested applying a low



This process is identical to



Heterotrophs (iN,BM

Nitrate/Nitrite

�U.<�

Nitrogen components in ASM3 (modified from Jeppsson, 1996)









considered. It is realized that this is not in accordance with reality. However,








Heterotrophs

N,BM�X

<�

sson, 1996)









lity. However,








Active mass

Heterotrophs �XH)

Nitrogen gas (S

sson, 1996)









lity. However,








Active mass

Heterotrophs

Nitrogen gas (S

sson, 1996)









lity. However,








Active mass

Nitrogen gas (SN2)









lity. However,








Active mass

Autotrophs (iN,BM

Nitrogen )








lity. However, at








Autotrophs

N,BM�X








the time

ASM3 was published there was no model available to predict the separation of �







Autotrophs �XA)

(18)





with the consumption of oxygen. It is assumed that all



the time

� into







Autotrophs

41

(18)



!


t all



the time

into







41

(18)




t all



the time

into







42

Figure 5.3 | Substrate flows of COD in ASM3 for nitrifiers and heterotrophs (adopted from Gujer et al., 2000)

♦ Aerobic growth of heterotrophs: Aerobic heterotrophic growth takes place by

degradation of !� with the consumption of oxygen. Ammonia nitrogen (�U�) is

incorporated into cell mass.

♦ Anoxic growth of heterotrophs: This process is similar to aerobic growth but

respiration is based on denitrification. A correction factor (ηNOX) is applied to account for

the observation of reduced anoxic respiration rates compared to aerobic respiration.

♦ Aerobic endogenous respiration: This process describes all forms of biomass loss

and energy requirements not associated with growth but including processes such as

maintenance, lysis, endogenous respiration, predation and decay, according to simple

first order reaction kinetics.

♦ Anoxic endogenous respiration: This process is similar to aerobic endogenous

respiration but typically slower.

♦ Aerobic and anoxic respiration of storage products: These processes are

analogous to endogenous respiration and ensure that the storage product !� decays

together with the biomass.

5.2.1.3 Model restrictions and assumptions

A certain number of simplifications and assumptions must be made in order to make a

model of a wastewater treatment system practically useful. Some of these are associated

with the physical system itself, while others concern the mathematical model. These

restrictions are summarized below (Gujer et al., 2000):

♦ The system must operate at constant temperature.

♦ The pH is constant and nearly neutral (6.5-7.5). pH has an influence on many

parameters. Therefore, the inclusion of alkalinity in the model allows for detection of pH

problems. Alkalinity must be dominated by bicarbonate (HCO3).

♦ No consideration has been given to changes in the nature of the organic matter within

any given wastewater fraction (e.g. �). Therefore, the coefficients in the rate

expressions have been assumed to have constant values. This means that only

43

concentration changes in the wastewater components can be handled by the model

whereas changes in the wastewater character cannot.

♦ The effects of nutrient limitations (e.g. N and P) on the cell growth have not been

considered.

♦ The correction factor for denitrification (ηNOX) is fixed and constant for a given

wastewater, even though it is possible that its value depends on the system

configuration.

♦ The coefficients for nitrification are assumed to be constant and to incorporate any

inhibitory effects that wastewater constituents may have on them.

♦ The heterotrophic biomass is homogenous and does not undergo changes in species

diversity with time. This means that effects of substrate concentration gradients, reactor

configuration, etc. on sludge settleability are not considered.

♦ The storage of readily biodegradable substrate in the biomass is assumed to be

instantaneous.

♦ Hydrolysis of organic matter and organic nitrogen are coupled and occur

simultaneously with equal rates.

♦ The model does not include processes that describe behaviors under anaerobic

conditions. Therefore, simulations of systems with large fractions of anaerobic reactor

volume may lead to errors.

♦ It is not advised to apply the model to systems where industrial contributions dominate

the characteristics of the wastewater.

♦ ASM3 cannot deal with elevated nitrite concentrations.

♦ The model is not design to deal with activated sludge systems with very high load or

small retention times (SRT<1 day) where flocculation/adsorption of XS and storage may

become limiting.

♦ The theoretical oxygen demand (ThOD) is extensively used in the continuity check of

the stoichiometric coefficients. For organic compounds COD may analytically

approximate this ThOD. For some inorganic compounds ThOD must be calculated

based on redox equations in which each reactive electron is equivalent to a ThOD of 8

g/mole.

♦ The user of the model is responsible for the identification of applicable parameters and

the wastewater characterization. However, a set of typical model parameters and

concentrations of model compounds is provided in Table A.5.2 and Table A.5.3 in

Appendix A.5.

44

5.3. SEDIMENTATION MODELS

Sedimentation is one of the most important unit processes in activated sludge treatment

plants. The settler (or secondary clarifier) provides clarification and thickening functions as

present in Chapter 3.5, operating under continuous flow and load conditions. Nowadays

there are plenty of existing models for the secondary clarifier performance and their

complexity range from very simple empirical models to some very complicated ones, as

presented in Stypka (1998). Ekama et al. (1997) classified the sedimentation models

according to their spatial resolution from 0 to 3 dimensions (0D to 3D). The most common

models are the one-dimensional models (1D), which describe both dynamic processes of

liquid-solids separation and solids accumulation in the clarifier. One-dimensional models

are usually adequate for training purposes, because these models can be calibrated with

actual plant data (Ekama et al., 1997). The IWA one-dimensional model considers a layer

approach based on the continuity equation, the solids flux theory and in mass balances.

In one-dimensional models, the settler is divided into a number of layers of equal

thickness (usually from 10 to 100, depending on the accuracy required and the aim of

modeling). A mass balance is then performed around each layer, providing for the

simulation of the solids profile throughout the settling column under steady-state and

dynamic simulations. There are five different groups of layers, depending on their position

relative to the feed point, as depicted in Figure 5.4. At the inlet section, the inflow and

sludge are homogeneously spread over the horizontal cross section. The flow is divided

into a downward flow towards the bottom and an upward flow towards the effluent exit at

the top.

Figure 5.4 | Solids balance around the settler layers (adopted from Hydromantis, 2006)

45

It is assumed that in settlers the profiles of horizontal velocities are uniform and that

horizontal gradients in concentration are negligible. Only the processes in the vertical

dimension are modeled. The traditional solids flux theory is used to analyze the solids flux

due to bulk movement or sedimentation.

Solids flux theory

The theory assumes that the thickening capacity of the secondary clarifier is limited by the

values of the mixed liquor (MLSS), the return sludge concentration and the sludge settling

characteristics. At a sufficiently high solids load, the capacity is limited by the minimum

solids flux. The total solids flux (jT) is the sum of the solids flux due to settling (jS) and the

water flux (downward or upward) due to bulk movement (jB). The total solids flux in a

continuous settler at any level between the sludge–supernatant interface and the bottom

of the settler can be calculated as:

;� = ; + ;< = ! ∙ V + ! ∙ V (19)

where: ! = suspended solids concentration [g TSS/m3]; V = settling velocity of the sludge [m/d]; V = vertical bulk velocity [m/d].

According to the layer approach, the bulk and the settling fluxes out of any layer � or ; are

always related to the concentration !L or !� in the respective layer. For continuity reasons,

the fluxes must be identical with those of the neighbouring layers through the common

boundary. The inlet layer, the top layer where the effluent exit the tank and the bottom

layer where the recycling to the aeration tank occurs, are subject to special treatment as

described in more detail in Ekama et al. (1997).

Settling velocity models

Many settling velocity models can be found in the literature, such as the Vesilind or the

Takács models (Stypka, 1998; Dochain & Vanrolleghem, 2001). In the IWA simulator, a

double exponential settling function, described by Takács et al. (1991), is adopted to

specify the solids flux due to sedimentation:

V = V��*ui��∙(1u1��) − V��*ui��∙(1u1��) (20)

where: V = settling velocity of the layer [m/d]; V�� = maximum Vesilind settling velocity [m/d];

46

GKLI = hindered zone settling parameter [m3/g TSS]; GMNOP = flocculant zone settling parameter [m3/g TSS]; ! = suspended solids concentration of the layer [g TSS/m3]; !�LI = minimum attainable suspended solids concentration [g TSS/m3].

In Equation (20), sedimentation is represented by the first exponential term and

clarification by the second exponential term. The settling velocity model of Takács is

shown in Figure 5.5, where four different regions are depicted (Hydromantis, 2006):

I. The settling velocity equals to zero, as the TSS reach the minimum attainable

concentration, !�LI;

II. The settling velocity is specially influenced by the flocculating nature of the

particles; thus the settling velocity depends mainly on the parameter GMNOP;

III. Settling velocity has become independent of TSS concentration; it is admitted that

particles have reached their maximum size and settle at maximum velocity, V��′; IV. Settling velocity is dominated by hindering (GKLI), which is why the model reduces

to the Vesilind equation (the first term of Equation (24)).

Figure 5.5 | Graphical representation of the settling velocity model of Takács (adopted from Hydromantis, 2006)

5.4. MODEL CALIBRATION AND VALIDATION

One of the final steps in the development of an WWTP model is its calibration and

validation (Olsson & Newell, 1999; Dochain & Vanrolleghem, 2001; quoted by Ferreira,

2006). Model calibration is understood as a sequence of steps that have to be taken for

the model to fit a certain set of information obtained from the full-scale WWTP under

study. To this end and concerning the calibration of ASMs, four different systematic

calibration protocols are available in the current literature (Hulsbeek et al., 2002;

47

Vanrolleghem et al., 2003; Melcer et al., 2003; Langergraber et al., 2004). These

protocols differ from the range of its applicability and complexity, the technical limitation of

available tools for data collection, design of measurement campaigns,

knowledge/experience of the modeler and use of mathematical approaches for sensitivity

analysis/parameter selection. Sin et al. (2005) performed a SWOT analysis (Strengths,

Weaknesses, Opportunities, Threats) of these protocols and addressed that although all

of them highlight the very important point of standardization of calibration efforts, the

complex calibration practice of ASMs needs to be further improved. Moreover, the degree

of detail and structure of both WWTP model and calibration procedure are constrained by

time, budget and definition of a goal. The calibration of ASMs includes a measurement

campaign, which consists of sampling and measuring some characteristics of (all) the

flows of the WWTP and lab-scale experiments for the determination of

kinetic/stoichiometric parameters of the biological process ongoing in the WWTP, namely

based on respirometric assays.

Model validation consists of the comparison of the results obtained with the model and an

independent set of experimental data that was not used during calibration. The validation

process establishes the credibility of the model by demonstrating its ability to replicate the

WWTP behavior.

These are therefore essential steps when the WWTP models are developed with the

purpose of process optimization and control, as mentioned in Chapter 5.1

6. CASE STUDY

6.1. OVERVIEW OF THE WORK PERFORMED

The experimental work included both measurements in the field and in the laboratory, as

follows:

♦ Field monitoring campaigns of quality parameters at seven different sections of the

wastewater treatment plant;

♦ Measurement of dissolved oxygen in the oxidation ditches;

♦ Laboratory determination of the oxygen uptake rate of activated sludge.

During the model application step, after some attempts to translate the treatment

processes of Valhelhas WWTP into a model, it was verified that the simulation results

were not similar to the measured data, possibly due to all the problems and constraints

referred in Chapter 6.4.1. This task became rather difficult and time-consuming, and it was

48

eventually recognized that modeling the WWTP as it was in operation during the

campaigns was not possible. Consequently, analyzing the treatment process efficiency

relative to some historical events became crucial for understanding the process.

Nevertheless, a simplified simulation of the case study was carried out for learning

purposes and is presented as an example of model application.

Because the results obtained from the laboratory experimental work could not be

implemented in a model calibration step, it was decided to present and discuss separately

each phase of the work (respirometric assays, measuring campaigns and dynamic

simulation of the wastewater treatment process), including the procedures followed and

the results obtained.

6.2. CHARACTERIZATION OF THE WASTEWATER TREATMENT S YSTEM

The activated sludge WWTP of Valhelhas is located in the district of Guarda and is part of

the multimunicipality system of Águas do Zêzere e Côa (AdZC). It serves six civil parishes

(Famalicão, Sameiro, Santa Maria, São Pedro, Vale da Amoreira and Valhelhas) as

presented in Appendix A.6. The connected drainage system was designed as a separate

sewer system, but it is believed to behave rather as a partially separated one. This WWTP

was designed in 2004 for a design capacity of 15700 population equivalent (p.e.) and an

average flow-rate of 2000 m3/d wastewater having a contribution up to 50% from industrial

sources. The start-up of the WWTP was in April 2007. The treatment scheme of Valhelhas

is illustrated in Figure 6.1 and the plant operation presented in Appendix A.7.

The wastewater influent is first submitted to a pretreatment step for grit, sand and grease

removal. After this pretreatment the influent is divided over two parallel oxidation ditches

(with a volume of 1047 m3 each) for the biological activated sludge treatment, where it is

mixed with recycle sludge. The mixed liquor from both lines is mixed (in-pipe) and flows to

two secondary clarifiers, each having a diameter of 9 m and a volume of 235.4 m3. The

final effluent is discharged into a nearby stream after a disinfection step. The underflows

from both clarifiers are mixed in a recirculation chamber and flow back to the oxidation

ditches. Excess sludge is thickened prior to dewatering. A more detailed physical

characterization of the oxidation ditches and clarifiers is provided in Table 6.1.

49

Figure 6.1 | Flow diagram of the liquid and solid phases of the Valhelhas WWTP

Table 6.1 | Physical characteristics of the most relevant treatment units of Valhelhas WWTP

Component Unit Value

Oxidation ditches

Number of oxidation ditches - 2 Length m 33.3 Width m 5.65 Depth m 3 Unitary area m2 348.9 Unitary volume m3 1047 Number of mechanical aerators per ditch - 2 Unitary power of each mechanical aerator kW 22

Secondary clarifiers

Number of clarifiers - 2 Intern diameter m 9 Depth m 3.7 Unitary area m2 63.6 Unitary volume m3 235.4

By-pass

Water body

Sand/Grease removal chamber

Bar screen and pump

Wastewater influent

Screenings

Sand

Grease

Sidestreams

Return activated sludge

Secondary clarifiers

Oxidation ditches

UV disinfection channel

Sand filter

Effluent

Excess sludge

Dewatered sludge

Polyelectrolyte Belt press

Thickener

Liquid phase Solid phase

50

The aeration in the ditches is automatically controlled by a DO sensor positioned at the

surface of the mixed liquor. Each ditch has two surface mechanical brushes (responsible

for aeration and some mixing), which work alternatively and for a minimum period of 5

minutes, stopping for 10 minutes. The oxygen concentration in the mixed liquor is kept

between 0.5 and 2 g O2/m3. The recirculation flow of RAS, pumped from the bottom layers

of the clarifiers, is defined to achieve about 100% of the average influent daily flow from

the previous four days. Excess sludge is usually removed from the system once per week,

depending on the sludge volume index value.

The effluent discharged by Valhelhas WWTP must meet the legal requirements indicated

in Table 2.1 and a microbiological quality between 100 and 2000 MPN/100 mL for fecal

coliforms (in accordance with Law nº 236/98).

Operation data analysis: Flows and analytical results

Table 6.2 summarizes the average daily flows of wastewater influent and RAS registered

during a period of almost a year. The historical wastewater influent and discharged

effluent compositions are summarized in Table 6.3, where the data is relative to 24h

composed samples of monthly control analysis performed by Valhelhas WWTP, as

required by law.

Table 6.2 | Average daily flows of wastewater influent and return activated sludge (RAS) registered from June 2008 to April 2009

Date Average flow [m3/d]

Year Month Qinf QRAS

2008 June 1164 1271

July 1318 1223

August 1138 1145

September 852 868

October 1089 1076

November 1109 1221

December 1102 1099

2009 January 1621 1143

February 1080 904

March 1573 1386

April 1018 580

Global average 1187 1083

By the time this study was carried out it was noticed that all the surrounding industries

(olive mill, textile cleaning and painting), considered when the treatment plant was

designed, had already gone out of business. This has influenced the influent flows and

their composition as it can be seen from Table 6.2 and Table 6.3. Comparing the global

51

average value of wastewater influent and its design value of 2000 m3/d, the difference is

quite significant. However, when it comes to the influent composition the average values

of each parameter are close to the design values, except for BOD5 and total nitrogen,

which are considerably higher (Table 6.3). The reported concentration values of the

discharged effluent are under the limit values, except for total nitrogen. Apparently, the

nitrogen removal is somewhat inefficient; yet the WWTP is not required to meet the limit

value for total nitrogen. Also, very few samples were analyzed for Ntotal during the relevant

period and the collection might not be representative.

Table 6.3 | Summary of historical wastewater influent and final effluent analytical composition (data related to the period from June 2008 to December 2009); full data is reported in Table A.8.1 in Appendix A.8; q.l.:

quantification limit of the method

INFLUENT EFFLUENT

REMOVAL EFFIENCY

Parameter Unit Nr. of

samples Design value

Avg. Min. Max. Limit value

Avg. Min. Max. Avg.

T ºC 19 20 20 16 23 - 20 15 23 - pH - 19 - 7 5.2 7 6.0-9.0 6 5.4 7 -

BOD5 g/m3 19 467 917 140 5400 25 19 8 40 95.9% COD g/m3 19 1231 1323 203 7800 125 59 15 120 91.4% TSS g/m3 19 621 593 40 2300 35 16 2 46 93.4% Ntotal g/m3 4 36 85 37 155 15 23 8 45 53.6% Ptotal g/m3 4 10 8 3 12 2 2 <2 (q.l.) 4 55.0%

6.3. RESPIROMETRIC ASSAYS

6.3.1 General considerations

Respirometric approaches have recently gained increasing attention for the interpretation

of wastewater characteristics and activated sludge behavior, as in Copp et al. (2002).

OUR profiles have been interpreted to identify different COD fractions (Ginestet et al.,

2002) and to determine rate coefficients (Vanrolleghem et al., 1999; Koch et al., 2000;

Avcioglu et al., 2003; Schwarz et al., 2003; Mhlanga et al., 2009), as well as to assess

new processes such as biological storage (Karahan-Gül et al., 2002).

In this context, the objective of this part of the study was to estimate kinetic and

stoichiometric coefficients of activated sludge. The experimental setup was carried out

using samples of influent wastewater (source of substrate) and return activated sludge

from the clarifier.

The protocol used in these respirometric experiments was adopted from Ros (1993),

considering the measurement conditions previously described in Chapter 4.2.1.

52

6.3.2 Materials and methods

6.3.2.1 Sampling and storage

Samples of raw wastewater and return activated sludge (RAS) were collected at

Valhelhas WWTP and immediately transported to the laboratory in a portable cool box. At

the laboratory, a part of the samples of raw wastewater was subject to analysis, while the

remaining was stored at 4ºC. The samples of RAS were aerated for 24h or 48h at a

constant temperature of about 20 ºC.

6.3.2.2 Experimental procedure

Inoculum

Prior to the OUR assay, RAS (source of biomass) was subject to a cycle of settling,

removal of supernatant, wash with chlorine-free water (so that most of the residual organic

carbon could be removed) and aeration for 24h. Half of the samples were aerated for 24h

(R1-1 and R1-2) and the other half were subject to a second cycle and thereby aerated for

48h (R2-1 and R2-2). Solutions of micronutrients were added to each sample according to

the quantities reported in Table 6.4 (details on solutions composition are presented in

Ferreira (2004)). In the experiments, pH was kept in the range of 6.5-7.5, suitable for

biological activity, by using a phosphate buffer solution. The settled biomass was then

transferred to the respirometer. The volume of biomass to extract had to be determined

each time in order to attain a low S0/X0 ratio (< 4 mg COD/mg VSS), as suggested in

Chudoba et al. (1992). Ratios below that value must be maintained in order to get

estimates of the kinetics parameters representative of the conditions in real scale

activated sludge systems.

Substrate addition

A volume of 20 mL of raw wastewater was injected in the respirometer as source of

substrate. Micronutrients solutions were also added to promote growth of biomass. The

composition and quantity of each solution used is presented in Table 6.4. The phosphate

buffer solution was used to control pH.

53

Table 6.4 | Composition and used volumes of the mineral solutions

Reagents Composition Volume used in the respirometer (mL)

Volume used in the washing phase of RAS

(mL)

Calcium chloride solution CaCl2�2H2O 5 1

Magnesium sulfate solution MgSO4�7H2O 5 1

Ferric chloride solution FeCl3�6H2O 5 1

Oligoelements solution

MnSO4�4H2O

5 1 H3BO3 ZnSO4�7H2O (NH4)6MO7O24�4H2O C10H12FeN2NaO8�3H2O

Phosphate buffer solution

KH2PO4

20 5 K2HPO4 Na2HPO4�7H2O NH4Cl

Formula 2533 nitrification inhibitor (Hach Co.)

2-chloro-6-(trichloromethyl) pyridine

(0.5g/1000mL)

Experimental set-up

The experiments were carried out in a 1 L bottle bioreactor (respirometer) according to the

LFS principle (liquid static, flowing gas) described in Table A.1.1 and Table A.1.2. The

respirometer was placed on an L-33 stirrer unit (Labinco BV, Netherlands) and a magnetic

stirrer was used to homogenize continuously the sample (at 100 to 200 rpm). Aeration

was provided through an aquarium-type diffusion stone, by a TetraTec AP150 air pump

with regular flow (maximum capacity: 150 L/h).

Figure 6.2 | Schematic layout of the respirometer

54

Dissolved oxygen, temperature and pH were continually measured through a CellOx 325

and a SenTix 41 probes, connected to a Multi 340i meter (WTW, Germany). The sensor

was connected to a data acquisition system which transmitted on-line data to a computer,

every 5 seconds. The temperature in the laboratory temperature was kept at 20 ± 0.2 ºC.

A scheme of the respirometer is shown in Figure 6.2 and a view of the equipment is

presented in Figure 6.3.

Figure 6.3 | Respirometer device for measurement of OUR

Four assays were carried out using RAS as a source of biomass and influent wastewater

as substrate. A 1 L respirometer was filled with 100 mL of biomass (previously aerated for

24h or 48h), 5 mL of each micronutrient solution, 10 mL of buffer solution, 0.5 g of

nitrification inhibitor (allylthiourea) and fulfilled with chlorine-free water. The experimental

procedure followed the one indicated by Rozich & Gaudy (1992) and Spanjers et al.

(1998) and adopted in the studies of Ferreira (2004); it just developed the phases I to IV of

Figure 4.1. The mixture was aerated for a minimum of 5 minutes until a range of DO

between 7.5 and 8 g O2/m3 was achieved – the DO saturation level. After interrupting the

aeration, DO was measured until dropped below 30% of its concentration of saturation

(Ferreira, 2004). The mixture was reaerated up to the DO saturation level and substrate

(20 mL of raw wastewater) was added. The substrate was stored at 4 ºC for 24h and 48h,

for assays R1-1, R1-2 and R2-1, R2-2, respectively. Immediately after addition of

substrate, a sample of the mixture was collected to determine the following parameters:

COD, BOD5, NH4-N, NO2-N, NO3-N, TSS and VSS. The aeration was interrupted after

substrate addition and the DO values were measured continuously until the OUR reached

the endogenous respiration level.

55

The samples of raw wastewater and the mixture of RAS/raw wastewater were analyzed

for the same set of parameters previously referred. COD concentrations were determined,

according to the standard DIN 38409-4, using a CADAS 50 spectrophotometer UV-Vis

(LANGE, Germany) and the cuvette-tests LCK 314 (15-150 g/m3), LCK 414 (5-60 g/m3)

and LCK 514 (100-2000 g/m3). The concentrations of NH4-N, NO2-N and NO3-N were

obtained using the same spectrophotometer and the cuvette-tests LCK 302 (47-130 g

NH4-N/m3), LCK 303 (2-47 g NH4-N/m3), LCK 304 (0.015-2 g NH4-N/m3), LCK 341 (0.015-

0.6 g NO2-N/m3), LCK 342 (0.6-6 g NO2-N/m3), LCK 339 (0.23-13.5 g NO3-N/m3) and LCK

340 (5-35 g NO3-N/m3), in accordance with standards DIN 38406-E 5-1 (NH4-N), DIN

38405 D10 (NO2-N) and DIN 38405-9 (NO3-N). For concentrations above the limit of the

cuvette-tests, the samples were previously diluted. VSS and TSS were determined

according to Standard Methods (APHA-AWWA-WEF, 1999). CBO5 concentrations were

determined using a manometric equipment, OxiTop®C (WTW, Germany).

6.3.3 Results of the respirometric experiments

The physical-chemical characterization of the influent wastewater (injected substrate) and

the volume of the respirometric cell after substrate injection is provided in Table 6.5 for the

four respirometric experiments. These result are relative to discrete samples, therefore the

differences between each set of parameters (for substrate) may reflect the influence of a

small industrial wastewater discharge. However, the biodegradability indicates that

generally the wastewater is biodegradable by selected microorganisms; only one sample

had low readily biodegradable substrate (R1-1).

Figure 6.4 presents the respirograms obtained of sludge samples with addition of

wastewater influent. Dissolved oxygen measurements were registered one time per

minute and the values were later converted into hours in order to facilitate comparisons

with other reported values. Comparing the respirograms R1-1 and R1-2 given in Figure

6.4, a peak of the OUR of R1-1 can be observed, which is related to a higher

concentration of biomass and S0 (corresponding to the concentrations of VSS and CODS

in Table 6.5 after injection, respectively). Although the soluble fraction (CODS) could be

more quickly biodegraded, microorganisms continued to hydrolyze the particulate fraction,

with the consequent consumption of dissolved oxygen.

56

Table 6.5 | Characterization of the influent wastewater (substrate) and the volume of the respirometric cell after substrate injection

R1-1 R1-2 R2-1 R2-2

(10-12-2009) (16-12-2009) (15-01-2010) (15-01-2010)

Parameter Unit Substrate After

injection Substrate

After injection

Substrate After

injection Substrate

After injection

CODt g/m3 560 1256 720 504 400 778 400 608

CODs g/m3 45.4 31.5 172 36.5 181 23.9 181 24.8

CODs/CODt - 0.081 0.025 0.239 0.072 0.453 0.031 0.453 0.041

BOD5 g/m3 45.1 - 434 73.2 203 - 203 -

N-NH4 g/m3 25.8 - 0.935 8.57 18.1 20.15 18.1 31.0

N-NO3 g /m3 0.06 - 2.23 1.47 - - - -

N-NO2 g /m3 >6 - 0.014 <0,6 - - - -

pH - 7.32 7.25 6.79 7.24 - 7.12 - 7.19

DO g/m3 4.45 7.4 1.2 7.2 - 6.67 - 7.57

TSS g/m3 70 1020 210 700 160 870 160 870

VSS g/m3 65 710 210 620 140 560 140 610

Biodegradability g BOD5/g CODt 0.081 - 0.603 0.145 0.508 - 0.508 -

S0/X0 g COD/g VSS 8.615 1.769 3.429 0.813 2.857 1.389 2.857 0.997

Figure 6.4 | Oxygen uptake rate evolution over time for all the respirometric tests (1 minute measurements)

0

3

6

9

12

15

0 2 4 6 8 10

OU

R [

g O

2 / m

3 .h

]

T [h]

a) R1-1

0

3

6

9

12

15

0 2 4 6 8 10

OU

R [

g O

2 / m

3 .h

]

T [h]

b) R1-2

0

10

20

30

40

50

0 3 6 9 12 15

OU

R [

g O

2 / m

3 .h

]

T [h]

c) R2-1

0

3

6

9

12

15

0 2 4 6 8 10

OU

R [

g O

2 / m

3 .h

]

T [h]

d) R2-2

57

On the contrary, in the batch respirometric tests R2 (c and d in Figure 6.4), where the

biomass was previously aerated for 48h, the concentrations of CODS and biomass after

substrate addition are lower than in the tests R1. This may have influenced the fact that in

respirometric tests R2 the endogenous phase (at the end of the OUR curve) took more

time to be achieved, especially for R2-1, as it can be seen in the figure. In other words, in

the tests R2 the biomass took more time to remove the soluble organic fraction under

aerobic conditions. Because the available DO in a batch test is low, a slight variation in

the concentration of CODS may cause a significant change in the OUR; this would not

happen if the respirometer was continuously aerated. These aspects had been mentioned

by Grady Jr. et al. (1999), when discussing the importance of the initial S0/X0 ratio.

On the other hand, the same biomass was used in respirograms R2 while biomass with

different characteristics was used in respirograms R1. The substrate samples used in

tests R2 could have particulate organic matter more difficult to biodegrade (e.g. due to

higher concentrations of fats, oils or hydrocarbons). Consequently, the biomass used in

R2 needed more time to adapt to this substrate, when injected in the respirometer. The

analyses performed to characterize the substrate used in each respirometric assay were

not able to indicate which compounds more difficult to biodegrade were in the influent

wastewater.

The stoichiometric and kinetic parameters obtained for each of the respirometric assay

are presented in Table 6.6. The parameters GHIJ, ��, �� and �� were calculated using

Equations (16), (17), (18) and (20), respectively. The coefficient �� was calculated as

described in Appendix A.3.

Table 6.6 | Stoichiometric and kinetic parameters obtained from the respirometric assays

Parameter Unit R1-1 R1-2 R2-1 R2-2 �� d-1 2.88 4.8 0.19 0.94 �� d-1 4.32 2.88 4.30 3.40 �� d-1 6.4 6.4 6.4 6.4 �� g CODVSS/g COD 0.7 0.7 0.7 0.7 � g COD/m3 523 523 523 523 �� g O2/m3�d 0.084 0.02 0.14 0.29 �� h-1 46 30 21 39

The parameters �� and were obtained by curve-fitting Equation (1) to the set of

data of �� (Table 6.6) and � (concentration of CODt after injection in Table 6.5) of the four

respirometric assays. This estimation is illustrated in Figure 6.5. As can be depicted from

58

the figure, for a value of of 523 g COD/m3 the parameter �� equals to ��/2, which

means that in the experiments substrate was not limiting.

Figure 6.5 | Estimation of �� and based on the data presented in Table 6.6

In general, reasonable results for ��, �� and �� were obtained in comparison with

typical values for these parameters (Table 4.1). Concerning the decay rate, ��, only the

results obtained for R2-1 and R2-2 were consistent with the literature.

Model based interpretation

Since the major purpose of this work was to obtain kinetic and stoichiometric parameters

from respirometry to calibrate the WWTP simulation model more accurately, a model

based interpretation of the respirograms was carried out. Much research in recent years

has focused on the calibration of ASM3 with experimental respirometric data (Koch et al.,

2000; Karahan-Gül et al., 2002; Avcioglu et al., 2003; Schwarz et al., 2003; Mhlanga et

al., 2009). Hence, the dynamic biological model used was adapted from Avcioglu et al.

(2003) and consists in a simplified version of ASM3 for aerobic processes. The simplified

model fits empirical equations to the respirogram data considering three phases

(endogenous, storage and growth); in each phase different kinetic/stoichiometric

coefficients should be obtained. The process equations relative to the simplified model are

presented in Appendix A.4. For a set of parameters and initial conditions ( ), the model

was fitted to the experimental OUR data and the error (¡) was calculated by a least

squares method, as given in Equation (21), comparing simulated and experimental

respirograms (Mathieu & Etienne, 2000).

0

1

2

3

4

5

0 504 608 778 1256

µ H[d

-1]

S [g COD/m3]

Estimation of µHmax and KS

Experimental

Simulated

KSKS

µHmax/2

59

¡( ) = ¢ £¤¢ ¥#��H�¦( , (L) − #��QL�((L)§aUL¨� © ªmL,� − 1«¬n

�¨� (21)

Where: #��H�¦ = experimental heterotrophic oxygen uptake rate [h-1]; #��QL� = simulated heterotrophic oxygen uptake rate [h-1]; ( = time [h]; ; = number of phases constituting the model; � = number of measurements on each phase; mL,� = number of total measurements performed at each phase.

A simple optimization procedure was then used in order to minimize this function and

select optimized values for stoichiometric and/or kinetic coefficients.

Different methods for parameter estimation were compared and it was decided, as an

alternative approach, that the combination of the methods was more suitable. The

advantage was the ability to estimate more model coefficients as accurately as possible.

As an example of this approach, Table 6.7 summarizes the results obtained for the model

based interpretation of R1-2. In the table, five sets of parameters are presented

corresponding to five different combinations of parameter estimation methods.

The parameters �� , !� , )� and � were held constant and equal to ASM3 default

values for all trials. The inert fraction of soluble COD (+,) was considered to correspond to

the ratio of soluble COD measured at the beginning (substrate) and at the end of the

respirometric assay. � and !� (in Set 1) were obtained from the application of the

TSSCOD model of Influent Advisor, an auxiliary GPS-X tool for the characterization of

wastewater influent. In Set 5, the calculated parameters ��, ��, �� and presented in

Table 6.6 were simultaneously used, yet the error was higher than in the other cases.

Other parameters were obtained through model simulation.

60

Table 6.7 | Comparison of several parameter set obtained through model based interpretation and empiric calculation of respirogram R1-2; Legend:

Parameter Unit Default ASM3

Set 1 Set 2 Set 3 Set 4 Set 5 YH g CODVSS/g COD 0.63 0.63 0.67 0.60 0.70 0.70 YSTO g CODXSTO/g CODSS 0.85 1.076 1.048 1.112 1.419 0.80 µH1 d-1 2.0 31.03 31.10 30.82 30.51 2.88 µH2 d-1 2.0 5.05 6.27 3.86 8.29 4.32 bH d-1 0.2 0.20 0.20 0.20 0.94 2.20 bSTO d-1 0.2 0.20 0.20 0.20 0.20 0.20 XH g COD/m3 30 109.60 113.98 95.46 24.25 10.36 XSTO g COD/m3 1.0 1.0 1.0 1.0 1.0 1.0 kSTO g CODSS/g CODXH�d 5.0 5.0 5.0 5.0 5.0 5.0 KS g COD/m3 2.0 1.06 1.06 1.06 523 523 KSTO g CODXSTO/g CODXH 1.0 1.0 1.0 1.0 1.0 1.0 SS g COD/m3 60 115.17 115.17 115.17 115.17 115.17 +, - 0.2 0.33 0.33 0.33 0.33 0.33

ER

RO

R

[mg/

(L�h

)] Endogenous phase 0.456 0.454 0.495 0.454 0.454

Storage phase

0.770 0.770 0.770 0.770 5.697

Growth phase

1.357 1.357 1.357 1.357 20.900

TOTAL

2.584 2.581 2.622 2.581 27.051

In general, some results were within plausible ranges in comparison with reported values

from literature. In Schwarz et al. (2003), �� was 0.7 g CODVSS/g COD and �� varied

between 6 to 20 d-1, against 2.88 to 31.10 d-1 obtained in this work. Other sources (Koch

et al., 2000) reported a value of 3.0 d-1 for ��, in agreement with the lower values of ��

obtained in Sets 3 and 5. The calculated aerobic heterotrophic decay rate (��) of 0.94 d-1

is slightly little higher than the reported value of 0.3 d-1 (Koch et al., 2000). The simulated

value of �� in Sets 1-3 are in agreement with the reported value of 0.96 g CODXSTO/g

CODSS (Karahan-Gül et al., 2002).

Figure

However, it should be noted that the model is not completely adj

measurements (as

Table

be correlated with how the heterotrophic growth is simulated in

model (

accurate development and investigation of the simplified model was needed, it was not

within the scope of this study. Nevertheless, reasonable results were obtained in a

despite the str

combination

6.4.

6.4.1

The m

attempted to perform m

November 2009) was cancelled due to

♦

♦

In addition to th

WWTP

equipment failure were observed

Figure

owever, it should be noted that the model is not completely adj

measurements (as

Table


model (



despite the str

combination

6.4.

6.4.1

The m



a

conditions in Oxidation 1;

a

result of the deactivation of

In addition to th

WWTP


Figure


measurements (as

Table 6


model (



despite the str

combination

M

6.4.1

The monitor



technical failure in Aerator 2 (


discharge of dry sludge (a


In addition to th

WWTP,


Figure 6.6


measurements (as

6.7) especially for the growth phase having the highest associated error. This


model (using two different values



despite the str

combination

MONITORING

General considerations and constrain

onitor







In addition to th

, permanent problems (since the plant was activated)


| H


measurements (as

) especially for the growth phase having the highest associated error. This


sing two different values



despite the str

combination of

ONITORING


onitoring







In addition to th

permanent problems (since the plant was activated)


Heterotrophic


measurements (as






despite the striking difference between the values of

of different methods to estimate the other parameters in each set.

ONITORING


ing







In addition to th



eterotrophic


measurements (as






iking difference between the values of

different methods to estimate the other parameters in each set.

ONITORING


campaigns were carried out in December 2009. This was the second







In addition to the



eterotrophic


measurements (as can be








ONITORING









ese



eterotrophic OUR vari


can be








ONITORING CAMPAIGNS









events



OUR vari


can be








CAMPAIGNS



attempted to perform monitoring






events



OUR vari


can be seen








CAMPAIGNS



onitoring






events,



OUR variation over


seen








CAMPAIGNS



onitoring



discharge of dry sludge (about 70

result of the deactivation of old

, which



ation over


seen from



sing two different values for





CAMPAIGNS



onitoring campaigns in Valhelhas WWTP. The first attempted (in



bout 70

old Manteigas

which


equipment failure were observed:

ation over


from



for





CAMPAIGNS



campaigns in Valhelhas WWTP. The first attempted (in


technical failure in Aerator 2 (Figure

bout 70

Manteigas

which resulted in


ation over time


from Figure



for ��) t








November 2009) was cancelled due to:

Figure

bout 70-80 m

Manteigas

resulted in


time


Figure



) to fit each phase of the curve.








Figure

80 m

Manteigas

resulted in


of R


Figure



o fit each phase of the curve.








Figure 6.11

80 m3)

Manteigas WWTP

resulted in


R1-2


Figure 6.











11) compromising the aeration and sti

) upstream of Valhelhas treatment plant as a

WWTP

resulted in


2 (OUR simulated considering Set 4 of


.6











) compromising the aeration and sti

upstream of Valhelhas treatment plant as a

WWTP

the complete malfunction


(OUR simulated considering Set 4 of


and






iking difference between the values of different methods to estimate the other parameters in each set.

General considerations and constrain ts





WWTP.





and from the total error presented in





within the scope of this study. Nevertheless, reasonable results were obtained in a.










from the total error presented in






This difference
















This difference
















This difference







permanent problems (since the plant was activated) concerning operation and









This difference







concerning operation and





the original simplified




This difference













o fit each phase of the curve. Although a more



This difference is a









owever, it should be noted that the model is not completely adjusted to the




Although a more



is a









usted to the




Although a more



is a result






of Valhelhas


(OUR simulated considering Set 4 of Table

usted to the




Although a more



result





of Valhelhas


Table

usted to the




Although a more


within the scope of this study. Nevertheless, reasonable results were obtained in all sets

result o





of Valhelhas


Table 6.7

usted to the


) especially for the growth phase having the highest associated error. This might


Although a more


ll sets

of the



) compromising the aeration and stirring


of Valhelhas


61

7)

usted to the


might


Although a more


ll sets

f the



rring


of Valhelhas


61

usted to the


might


Although a more


ll sets

f the



rring


of Valhelhas


62

♦ there is settling of sludge in the oxidation ditches when the aerators are turned off, due

to an inefficient stirring system. Foaming sludge (see Chapter 3.4.2.3) can be also

observed at the surface of the mixed liquor, as shown in Figure 6.7;

♦ the sensors responsible for the control of DO in the oxidation ditches float at the

surface of the mixed liquor. This influences the DO measurements and consequently

the aeration cycles; higher values are measured and do not represent the

environmental conditions at the bottom of the ditch;

♦ the sludge in one of the clarifiers tends to rise (see also Figure 6.7);

♦ the removal of excess sludge from the bottom of the clarifiers is controlled by the SVI

(as described in Chapter 3.4.2.3). However, this control is not correctly applied (it

depends on the experience and opinion of the operator) and high solids retention times

may occur;

♦ there was lack of flow measurement infrastructures, which were necessary to realize

accurate mass balances to the process units.

Figure 6.7 | Foaming sludge in the oxidation ditch (left) and rising of sludge in the clarifier (right)

Furthermore, in October and November 2008 effluents from an olive mill were discharged

into the drainage system, resulting in higher concentrations of total nitrogen in the

wastewater influent (see Table A.8.1 in Appendix A.8).

Finally, during the campaign of December 2009 and due to unknown reasons, the

Oxidation Ditch 2 was operating under continuous aeration, having both aerators working

simultaneously. Moreover, as a consequence of the discharge of sludge in November

2009:

♦ the sand filters clogged and the disinfection step had to be interrupted; since that time

and until the end of the campaign of December, the effluent was being discharged after

clarification per by-pass;

63

♦ the collection tank of sidestreams (drainages and liquid removed by sludge dewatering

processes) accumulated a great amount of sediments, generating the establishment of

a thick layer of solids enmeshed in sludge at the liquid surface, as depicted in Figure

6.8. As a result the pumping control was changed into manual because the level

sensors became stuck in that layer and could not work properly, and in the sludge

treatment building occurred floods since there is a connection to that tank in the ground

(see Appendix A.7).

6.4.2 Description and methods

The monitoring campaigns of December included a 1-day campaign (in order to obtain

data to calibrate the WWTP model) and, after a one-day break, a 2-day campaign (aiming

to collect information for model validation). During the 1-day campaign samples were

collected every 3 hours in several locations of the WWTP as indicated in Appendix A.7

and illustrated in Figure 6.8. On the 2-days campaign, samples were collected only twice

per day in the exact same locations.

Discrete samples were carefully manually collected, stored in refrigerated boxes and

transported to the laboratory, twice per campaign. The influent samples were analyzed for

the following parameters: TSS, VSS, COD, BOD5, Ntotal, NH3-N, NH4-N, NO2-N, NO3-N,

Ptotal and fecal coliforms. The effluent samples were analyzed for TSS, VSS, COD, BOD5,

Ntotal, Ptotal and fecal coliforms. Samples taken from intermediate sections of the WWTP

were analyzed for TSS, VSS and COD. In Table 6.8 the analytical methods used in the

measurement of those parameters are indicated. Influent and recirculation flow data were

obtained from the treatment plant operation logbooks.

Table 6.8 | Analytical methods used for physical-chemical and microbiological measurements during the campaign at Valhelhas WWTP

Parameter Analytical method

BOD5 Manometric with pressure sensor (OxiTop®C system)

COD Digestion using potassium dichromate TSS/VSS Filtration, drying at 105 ºC and ignition at 550 ºC Ntotal, nitrite, nitrate, ammonium and Ptotal Ultraviolet spectrophotometry Fecal coliforms Multiple tubes: Most Probable Number (MPN)

64

A1 – Wastewater influent (arrival chamber)

A2 – Mixture of wastewater influent, RAS and

sidestreams

A3 – Effluent of oxidation ditch 1

A4 – Effluent of oxidation ditch 2

A5 – Return activated sludge

A6 – Collection tank of sidestreams (drainages and liquid

removed by sludge dewatering processes)

A7 – Treated effluent

Figure 6.8 | View from the sampling locations in Valhelhas WWTP

65

6.4.3 Results of the measuring campaigns

6.4.3.1 Flows

In order to obtain flow data useful for model simulation, two sets of data concerning the

wastewater influent and return activated sludge flows were analyzed: average hourly flows

registered during the campaign of 14/15 December and during the period from 2 to 17

December. It was noticed that:

♦ Because a pumping station is located upstream from the treatment plant, the data

reported for the campaign revealed sudden and steep changes in the pattern of the

influent flow throughout the day and was not representative. Furthermore, the program

used for model simulation appeared to be sensible to these changes, so the data could

not be directly used.

♦ Considering the average hourly flow values for one day, for the period from 2 to 17

December, an average influent flow of 1408 m3/d was obtained; this value is

significantly higher than the operational historical value of 1187 m3/d, presented in

Table 6.2.

Therefore, the second set of data was chosen for model application, but using a correction

factor of 0.85 (obtained by the ratio of both averages) to calculate the average hourly

flows for one day, as presented in Table 6.9 and illustrated in Figure 6.9.

Table 6.9 | Average hourly flows for wastewater influent and RAS from 2 to 17 December

Hour Average flow [m3/h] Hour (cont.) Average flow [m3/h]

Initial Final Qinf QRAS Initial Final Qinf QRAS

0.00 1.00 43.31±25.33 35.83±17.95 12.00 13.00 47.67±34.56 42.88±21.06 1.00 2.00 43.09±37.67 36.72±16.36 13.00 14.00 68.38±34.03 38.87±20.20 2.00 3.00 44.49±24.24 36.24±16.44 14.00 15.00 71.14±37.75 38.35±20.15 3.00 4.00 45.58±32.24 37.97±19.29 15.00 16.00 73.53±35.63 37.90±18.63 4.00 5.00 36.37±36.36 36.88±17.15 16.00 17.00 63.64±32.45 37.81±20.90 5.00 6.00 39.10±27.43 36.82±17.02 17.00 18.00 61.27±38.74 41.83±25.93 6.00 7.00 33.07±31.19 34.83±15.30 18.00 19.00 59.87±34.63 40.88±25.50 7.00 8.00 35.32±34.07 37.35±18.00 19.00 20.00 52.95±33.68 39.70±23.38 8.00 9.00 37.07±24.85 39.91±17.16 20.00 21.00 51.12±32.38 38.83±22.56 9.00 10.00 45.71±29.87 39.95±17.58 21.00 22.00 54.30±36.81 39.21±22.56

10.00 11.00 45.96±27.81 40.51±16.71 22.00 23.00 42.65±29.29 41.04±24.91 11.00 12.00 52.91±28.54 40.03±16.87 23.00 24.00 48.68±28.44 41.81±26.49

Qinf QRAS

Global average [m3/h] 49.88±31.99 38.84±19.92

Global average [m3/d] 1206.77±767.68 931.02±478.11

66

6.4.3.2 Influent wastewater characterization

Figure 6.9 and Figure 6.10 show the analytical results (measured and interpolated values)

of wastewater obtained during the first campaign. Moreover, the influent flow values given

in Table 6.9 are also presented in Figure 6.9. As can be seen from the figure, the period

with higher inflow is from 13:00 to 18:00. This may be due to the fact that the treatment

plant serves mainly a rural population and has no industrial contribution.

Concerning the wastewater composition, it was noticed that the proportion between BOD5,

COD and TSS was rather atypical in comparison with usual values of raw wastewater

presented in Table 3.1. This is consistent with historical analytical results provided in

Table A.8.1 in Appendix A.8, where the concentrations of BOD5 and TSS are considerably

low, while the concentration of COD is high. During the period from 22:00 to 10:00, the

wastewater was observed to be hard biodegradable, i.e. with BOD5/COD lower than 0.2. It

is believed that the readily biodegradable organic matter is consumed during conveyance

of wastewater in sewers, as a result of microbial activity. Though, the drainage system

connected to Valhelhas WWTP is over 16 Km long, which means that the conveyance of

sewage spends about 3-4.5 h at an average constant velocity of 1 m/s.

Figure 6.9 | Concentrations of influent wastewater components and average influent flow during the campaign of 14/15 December, 2009 (fulfilled points: measured values; unfulfilled points: estimated values)

It was also noticed that the concentration of total nitrogen throughout the day was not in a

good proportion with the remaining parameters (Figure 6.9) and the concentration of

nitrate was relatively high (Figure 6.10). In this case, high concentrations of nitrogen and

reduced forms of nitrogen in the wastewater influent may be associated to discharges

from olive mills (or even from the cleaning of its facilities), to the utilization of excrements

8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00

0

10

20

30

40

50

60

70

80

0

100

200

300

400

500

Ave

rag

e in

flu

ent

flo

w [

m3 /

h]

Co

nce

ntr

atio

n [

g/m

3 ]

T [h:min]

Average influent flow

BOD5

COD

TSS

VSS

N total

P total2.00 4.00 6.00

67

as fertilizer and to animal urine coming from the wash of courtyards. Yet, no olive mill was

officially working when the campaign was carried out, nor in the previous months.

Figure 6.10 | Concentrations of nitrogen compounds in the influent wastewater during the campaign of 14/15 December, 2009 (fulfilled points: measured values; unfulfilled points: estimated values)

The data presented in Figure 6.9 and Figure 6.10 was used as input data for modeling.

Table 6.10 shows the average values of wastewater composition relative to the campaign

of 16/17 December (full data results are present in Table A.8.3 of Appendix A.8). The

average concentrations presented in Table 6.10 are generally lower than the

concentrations relative to the campaign of 14/15 December, due to a rain event prior to

the campaign of 16/17 December.

Table 6.10 | Average concentrations of influent wastewater components (average values) during the campaign of 16/17 December, 2009

BOD5 COD TSS VSS Ntotal Ptotal Fecal

coliforms pH Temp. N-NO2 N-NO3 N-NH4 N-NH3

[g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [MPN/100 mL] - [ºC] [g/m3] [g/m3] [g/m3] [g/m3]

57.8 225.8 40.3 29.1 23.5 2.3 7.3E+07 6.5 11.9 0.2 1.5 14.9 0.01

6.4.3.3 Measurements of dissolved oxygen in the oxi dation ditches

Considering what was stressed in Chapter 6.4.1, assessing the performance of the

aeration system in the biological reactors seemed to be of great relevance. The aeration

system in each ditch (consisting of two mechanical surface aerators) was designed to

ensure good aeration and stirring conditions of the mixed liquor; yet it has been incapable

of doing so. As soon as the air-off period begins, the sludge starts to settle. This problem

has been acknowledged by the plant operators and was observed in Oxidation Ditch 1

(Figure 6.11) during the campaign. Even so, the problem has not yet been resolved.

0

10

20

30

40

50

60

70

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

8.00 12.00 16.00 20.00 24.00 28.00

Co

nce

ntr

atio

n o

f T

KN

an

d N

-N

H4+

[m

g/L

]

Co

nce

ntr

atio

n o

f N

-NO

2 ,

N-

NO

3an

d N

-N

H3

[mg

/L]

T [h:min]

N-NO2

N-NO3

N-NH3

TKN

N-NH4+

4.00 7.00

68

Therefore, it was reasonable to suppose that the sludge settling could

establishment of anoxic and even anaerobic conditions, especially in the bottom of the

ditch

In an attempt to

measurement

the two campaigns

different sections of each oxidation ditch (as illustrated in

different depths (corresponding to surface, medium depth and bottom), for different

temperatures and/or aeration conditions. The results of these measurements are reported

in

Figure

The


rotor.

respec

in

relative to 11

correspond to measurements just after 20 min. of aeration, which explains higher DO

concentrations. Th

none

is not completely clear; measurements of the reduction

should be carried out

the aquatic env

68



ditch

In an attempt to

measurement

the two campaigns




in Table

Figure

The problem stated above


rotor.

respec

in Table

relative to 11


concentrations. Th

none



the aquatic env



ditch, which would impact

In an attempt to

measurement

the two campaigns




Table

Figure

problem stated above


rotor. S

respectively, maintaining a DO setpoint control

Table

relative to 11


concentrations. Th

none. Although the results suggest that anaerobic conditi



the aquatic env



, which would impact

In an attempt to

measurement

the two campaigns




Table 6.

Figure 6.11



Since then the aeration cycle was changed into 20/30 min. aeration on/off,

tively, maintaining a DO setpoint control

Table 6.

relative to 11


concentrations. Th

Although the results suggest that anaerobic conditi



the aquatic env




In an attempt to

measurement

the two campaigns




.11

11 | Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to



ince then the aeration cycle was changed into 20/30 min. aeration on/off,


.11

relative to 11


concentrations. Th




the aquatic env




In an attempt to

measurements of diss

the two campaigns




11.

Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to





11 relative to

relative to 11 December were measured during the air


concentrations. Th




the aquatic environment.




In an attempt to

of diss

the two campaigns









relative to

December were measured during the air


concentrations. Th




ironment.




In an attempt to investigate

of diss

the two campaigns. U









relative to



concentrations. The DO concentration at 13 ºC was very low and in most cases near to




ironment.




investigate

of dissolved oxygen (DO) w

Using an Oxi 330 sensor (WTW, Germany)




Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to Table





relative to



e DO concentration at 13 ºC was very low and in most cases near to



to identify which redox reactions (as listed in

ironment.



, which would impact the

investigate

olved oxygen (DO) w

sing an Oxi 330 sensor (WTW, Germany)




Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to Table





17 December were measured during the air







ironment.



the

investigate

olved oxygen (DO) w





Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to Table 6.11

became

technical failure in Aerator 2 (Figure












the overall process efficiency.

investigate the aeration conditions inside

olved oxygen (DO) w





Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to 11 (the arrows indicate the direction of the flow)

became

Figure












overall process efficiency.

the aeration conditions inside

olved oxygen (DO) w





Sections of measurement of dissolved oxygen in the oxidations ditches 1 and 2, according to (the arrows indicate the direction of the flow)

became

Figure














olved oxygen (DO) w






eve

Figure 6.














olved oxygen (DO) w






even more pertinent

.11














olved oxygen (DO) w






n more pertinent

11) Oxidation Ditch 1














olved oxygen (DO) were






n more pertinent

Oxidation Ditch 1














ere carried out before and during the period of






n more pertinent

Oxidation Ditch 1


tively, maintaining a DO setpoint control of 2












carried out before and during the period of






n more pertinent

Oxidation Ditch 1


of 2


















n more pertinent

Oxidation Ditch 1


of 2 g O


















n more pertinent when

Oxidation Ditch 1


g O2


December were measured during the air-off pe















when

Oxidation Ditch 1


2/m


off pe



Although the results suggest that anaerobic conditions m

is not completely clear; measurements of the reduction-oxidation











when in November 2009

Oxidation Ditch 1 began


/m3. The reported values shown


off period; the values of section G



ons m

oxidation







different sections of each oxidation ditch (as illustrated in Figure




in November 2009

began


The reported values shown


riod; the values of section G



ons might

oxidation




the biological reactors,


sing an Oxi 330 sensor (WTW, Germany), DO was measured at

Figure




in November 2009

began operating with only one



17 December were measured during the air-




ight occur in the ditch, this

oxidation

to identify which redox reactions (as listed in Table





DO was measured at

Figure 6




in November 2009

operating with only one



-on p




occur in the ditch, this

oxidation (or redox)

Table





DO was measured at

6.11




in November 2009




on period while those





(or redox)

Table 3





DO was measured at

11) and at thr




in November 2009




eriod while those





(or redox)

3.2

Therefore, it was reasonable to suppose that the sludge settling could enhan




DO was measured at

) and at thr




in November 2009




eriod while those





(or redox)

2) occur within

enhan




DO was measured at

) and at thr




in November 2009, due to




eriod while those





(or redox) potential

) occur within

enhance




DO was measured at

) and at thr




, due to




eriod while those





potential

) occur within

e the




DO was measured at

) and at thr




, due to




eriod while those





potential

) occur within

the




DO was measured at

) and at three




, due to a




eriod while those





potential

) occur within

the



DO was measured at

ee



a




eriod while those





potential

) occur within

69

It was also observed that during the 1-day campaign that Oxidation Ditch 2 was operating

under continuous aeration conditions having both aerators working simultaneously, as can

be depicted from Table 6.11 (for 15 December). The aeration control is automatic but the

aerators did not respond as programmed to DO control, the concentration of which

(approx. 8.5 g O2/m3) was substantially above the setpoint (2 g O2/m

3). Consequently,

environmental anoxic conditions could not be established and denitrification might not

have occurred; this may have caused an increase of nitrate in the final effluent.

Measurements relative to the 17 December correspond to the air-on period under normal

aeration conditions.

Table 6.11 | Results of the measured dissolved oxygen in the oxidation ditches during the campaign and in accordance with the sections of measurement as indicated in Figure 6.11 (n.a.: not assessed)

OXIDATION DITCH 1 OXIDATION DITCH 2

Depth (from surface of

water)

Section of measurement

Dissolved Oxygen

Section of measurement

Dissolved Oxygen

11-12-2009 (T=13 ºC)

Aeration off

17-12-2009 (T=11.1ºC) Aeration on

15-12-2009 (T=11.1 ºC)

Aeration always on

17-12-2009 (T=10.9 ºC) Aeration on

[m]

[g O2/m3] [g O2/m3]

[g O2/m3] [g O2/m3]

0.2 A

1.400 3.750 I

9.055 2.275 2.0 0.700 2.550 8.675 1.800 3.0 0.430 2.950 8.590 1.415

0.2 B

0.090 4.650 J

8.930 9.050 2.0 0.060 3.630 8.450 1.950 3.0 0.050 3.590 n.a. 2.075

0.2 C

0.080 4.615 K

8.290 2.810 2.0 0.060 4.360 8.355 2.890 3.0 0.060 4.415 8.365 2.910

0.2 D

0.100 2.075 L

8.620 3.135 2.0 0.060 2.050 8.400 3.280 3.0 0.050 2.585 8.370 2.695

0.2 E

0.090 2.980 M

8.585 2.650 2.0 0.050 2.930 8.280 2.715 3.0 0.040 3.250 8.355 2.830

0.2 F

0.080 3.625 N

9.055 1.970 2.0 0.060 3.685 8.420 1.635 3.0 0.050 3.750 8.475 1.660

0.2 G

1.310 4.410 O

8.500 2.250 2.0 1.305 4.325 8.325 2.275 3.0 1.185 4.560 8.305 2.310

0.2 H

0.070 2.680 P

8.675 1.935 2.0 0.055 2.735 8.400 2.105 3.0 0.045 2.835 n.a. 2.160

Despite that some particles of the mixed liquor might have been close to the membrane of

the sensor, thus affecting a correct measurement, the results reported in Table 6.11 are

considered to be reliable. Furthermore, considering the issues discussed above

measurements of redox potential under normal operation conditions would be necessary

70

in order for a full characterization of the aerobic/anoxic/anaerobic dynamics in the

biological reactors to be possible.

6.4.3.4 Analysis of the treatment process efficienc y

Table 6.12 summarizes the monitoring results of wastewater influent and final effluent

relative to the campaign of 14/15 December; more detailed data is reported in Table A.8.2

in Appendix A.8. It also shows the percentage of component removal of the system.

However, it should be stressed that the percentages were calculated without

consideration of the hydraulic retention time in the system (i.e. the concentrations of each

component in the raw influent and final effluent were compared at the same instant t).

Hence, these results allow for a characterization on the whole, but their individual analysis

care for some criticism.

In general, the average concentration values of COD, BOD5, TSS, Ntotal, Ptotal and fecal

coliforms in the effluent significantly exceed the limit values for emission presented in

Table 2.1. Also, the maximum percentage of component removal is considerably lower

than the legal requirements, except in the case of BOD5 which percentage of removal fits

the range of 70-90%. The concentration of fecal coliforms in the effluent was high above

the legal requirements. However, this can be explained by the fact that although the

WWTP has a filtration/UV disinfection step (as shown in Figure 6.1), it was deactivated

during the campaign due to clogging of the sand filters.

From Table 6.12 it is evident that the efficiency of TSS removal is very low, which is

consistent with the problem of rising of sludge in the clarifiers (see Figure 6.7).

The values relative to the percentage of total nitrogen removal have to be analyzed with

caution. Comparing detailed results of total nitrogen concentrations in the wastewater

influent (A1) and final effluent (A7) (see Table A.8.2 in Appendix A.8) shows that during

the day the removal of the component lies between 19% and 68%. Yet, the effluent

samples collected at 23:00 and 2:00 revealed very high concentrations of total nitrogen. It

is believed that this is due to a measurement error, since no significant alterations were

observed on the other components.

Table 6.13 displays the measurement results of wastewater influent and final effluent

relative to the campaign of 16/17 December; more detailed data is reported in Table A.8.3

in Appendix A.8.

71

Table 6.12 | Summary of measurements of wastewater influent and final effluent carried out during the campaign of 14/15 December at Valhelhas WWTP and the percentage of component removal

PARAMETER pH BOD5 COD TSS VSS Ntotal Ptotal TKN N-NO2 N-NO3 N-NH4 N-NH3 Fecal

coliforms

UNITS → - g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 MPN/100 mL

WASTEWATER INFLUENT (A1)

Average 6.8 69.3 285.7 72.6 66.7 33.4 4.2 31.4 0.3 1.8 28.7 0.0 4.9E+07

St.Dev. 0.3 38.0 103.7 47.3 44.3 13.6 2.1 12.9 0.1 0.8 14.9 0.0 3.7E+07

Maximum 7.2 133.3 477.0 176.0 163.5 65.2 8.7 61.8 0.4 3.1 64.4 0.1 9.2E+07

Minimum 6.5 24.3 167.0 21.0 17.5 23.8 2.3 22.6 0.2 1.0 18.9 0.0 2.1E+06

Nr. Obser. 7 7 7 7 7 7 7 7 7 7 7 7 3

EFFLUENT (A7)

Average 6.8 28.6 165.3 49.1 43.6 56.1 2.6 - - - - - 1.1E+07

St.Dev. 0.2 9.7 27.3 9.8 9.3 62.9 0.9 - - - - - 6.9E+06

Maximum 7.0 40.7 212.0 67.0 62.5 186.0 4.4 - - - - - 1.6E+07

Minimum 6.5 11.7 139.0 37.5 31.5 13.3 2.0 - - - - - 1.3E+06

Nr. Obser. 7 7 7 7 7 7 7 0 0 0 0 0 3

PERCENTAGE OF COMPONENT REMOVAL

Average - 53% 32% 8% 9% -108% 30% - - - - - 64%

St.Dev. - 14% 31% 54% 57% 254% 21% - - - - - 19%

Maximum - 74% 68% 62% 62% 68% 57% - - - - - 83%

Minimum - 30% -20% -117% -123% -632% 2% - - - - - 38%

Nr. Obser. - 7 7 7 7 7 7 0 0 0 0 0 3

Table 6.13 | Summary of measurements of wastewater influent and final effluent carried out during the campaign of 16/17 December at Valhelhas WWTP

BOD5 COD TSS Ntotal Ptotal Fecal coliforms

[g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [MPN/100 mL]

WASTEWATER INFLUENT (A1)

Average 57.8 225.8 40.3 23.5 2.3 7.3E+07

St.Dev. 12.9 49.3 12.0 7.2 0.4 1.9E+07

Maximum 78.7 297.0 57.5 35.0 2.8 9.2E+07

Minimum 47.0 161.0 26.5 15.0 1.9 5.4E+07

Nr. Observations 4 4 4 4 4 2

EFFLUENT (A7)

Average 169.6 837.0 902.5 58.1 12.4 1.6E+07

St.Dev. 54.8 215.6 422.6 18.1 5.7 0.0E+00

Maximum 259.3 1126.0 1382.0 88.1 22.2 1.6E+07

Minimum 113.0 530.0 471.0 39.4 8.3 1.6E+07

Nr. Observations 4 4 4 4 4 2

Very high concentrations (especially of COD and TSS) were observed in the final effluent

as the consequence of pumping effluent from the collection tank of sidestreams (see

Appendix A.7) to upstream the oxidation tanks, in the morning of 16 December and in the

72

afternoon of 17 December. Subsequently, during this campaign the WWTP was not

performing as expected. In fact, Figure 6.12 shows how the sample from effluent is clearly

much more concentrated than the sample from influent (also, there was a rain event

during the night). Given these results, the data concerning the campaign of 16/17

December was not used for simulation.

Figure 6.12 | Samples collected in 16 December

The results obtained during the campaigns relatively to the wastewater influent and final

effluent concentrations have shown no similarity with the historical data present in Table

A.8.1 in Appendix A.8. No reason was found to explain this difference.

In Table 6.14 a comparison of the design data and update values is presented. Currently,

the concentration of BOD5 in the influent and the flow is relatively different from the design

data, considering that the value of BOD5 concentration was calculated based on updated

flows and population, and yet is almost four times higher the observed value during the

campaigns.

Table 6.14 | Comparison between dimension values adopted for design and historical operation values relative to 2008/2009 from Valhelhas WWTP

Parameter Unit Design data Updated values

Population hab 4538 5136*

Flow per capita L/(hab�d) 130 145

Daily production of BOD5 per capita g/(hab�d) 60 60

Total flow (average) m3/d 2167 1100 Domestic flow (average) m3/d 1050 770 Infiltration flow (average) m3/d 0.00 330 Peak flow (average) m3/d 3439 2119 Concentration of BOD5 in the influent wastewater (20 ºC) g/m3 432 290**

*Data reported for 2001, adopted from INE (2002)

**Calculated value, based on updated values of flows and population

73

Furthermore, a comparison between the number and volume of the biological reactor of

Valhelhas treatment plant for different scenarios (different concentrations of BOD5 in the

influent wastewater) was performed and is presented in Table 6.15. For instance, if the

characteristics of the influent as presented in Table 6.14 were considered and only one

line was operating, the removal of BOD5 could be maintained within the requirements and

the volume of the oxidation tank would need to have a smaller volume. In addition,

operational costs relative to equipment functioning could decrease.

Table 6.15 | Comparison between the volume of the aeration tank of Valhelhas WWTP relative to design parameters and different operation scenarios

Parameter Unit Reference Set A Set B Set C Set D

Total flow (average) m3/d 2167 1100 1100 1100 1100

Concentration of BOD5 in the influent wastewater (20 ºC)

g/m3 432 290 145 72 72

Percentage of BOD5 removal % 94% 91% 83% 66% 66%

Number of oxidation ditches - 2 1 2 2 1 Total volume m3 2093 706 322 149 161

Globally, the studied treatment system appears to be extremely instable, which influences

the overall process efficiency. Moreover, it seems that the treatment plant has been

operating with approximately half the flow it was designed for and with lower influent loads

(about 6 times less of what was designed for), indicating that it is over dimensioned.

Regardless of all the discussed aspects concerning the process efficiency, the WWTP

continues to be operated according to the methodology used during its design.

Consequently, the results obtained in this case study were very poor and are by far much

different from what was expected.

6.5. DYNAMIC SIMULATION OF VALHELHAS WWTP

For the purpose of modeling Valhelhas WWTP, the GPS-X simulator developed by

Hydromantis was used. GPS-X is a modular, multi-purpose modeling environment for the

simulation of municipal and industrial wastewater treatment plants (Hydromantis, Inc.,

2006). The program allows assessing the operation efficiency, process unit capacity,

costs of operation and control strategies or different scenarios, whether during

dimensioning or operation phases. GPS-X incorporates several simulation models

according to the treatment unit, including the biological and sedimentation models

previously described in Chapter 5. In GPS-X a set of basic wastewater components is

grouped into libraries, e.g. Carbon - Nitrogen (CNLIB) or Carbon – Nitrogen - Phosphorus

74

(CNPLIB), depending on the characteristics of the wastewater or the type of treatment

process.

6.5.1 General considerations

For the reasons discussed in Chapters 6.4.1 and 6.4.3, it was not possible to model the

Valhelhas treatment process under the conditions and mode of operation which were

verified during and prior to the campaign. Regardless of the alternatives implemented

during the model construction step (e.g. simulation of the biological reactor as an

oxidation ditch versus an aeration tank, different number of layers in the clarifier, different

aeration controls, different physical coefficients inherent to the biological reactor), the

model was not able to reproduce the actual process with a minimum level of accuracy,

especially with regard to the simulated effluent composition. Moreover, one restriction of

the model is the constant temperature at which the system must operate. However,

according to the staff of Valhelhas WWTP, the variation of the air temperature throughout

the day is significantly high due to the geographical location of the treatment plant, which

likely influenced the results. Consequently, the final steps of model calibration and its

validation using the kinetic/stoichiometric obtained from the respirometric experiments

could not be performed. Instead and in order to demonstrate the potential of wastewater

treatment modeling, an alternative academic approach was performed. Therefore,

Valhelhas WWTP was modeled considering:

♦ only one line in operation, in order to simulate the process efficiency in case of

maintenance of one oxidation ditch;

♦ the characteristics of the wastewater influent (depicted in Figure 6.9) as typical and

representative;

♦ the performance of the biological reactor as a complete mixed tank with a DO setpoint

control of 2 g O2/m3;

♦ the recirculation of RAS and the remove of excess sludge at constant rates of 60% and

2% of wastewater influent, respectively;

♦ dry weather conditions and liquid temperature of 10 ºC;

♦ period of simulation of 1 day;

♦ default values of the ASM3 for kinetic and stoichiometric coefficients.

6.5.2 Model construction

The layout of Valhelhas WWTP (shown in Figure 6.13) corresponds to a simplified layout,

where only one biological reactor and one clarifier were modeled using the physical

dimensions of each unit and the characteristics of the equipment as presented in Table

6.1.

75

Figure 6.13 | Simplified layout of Valhelhas WWTP used for modeling

The layout is comprised of the following simulation models for each treatment unit:

♦ Influent wastewater: influent flow was described by a BODbased model, to which

input data consists on values of BOD5, TSS and TKN; other parameters (e.g. COD, �

and !2) are calculated according to stoichiometric coefficients given by the modeler.

♦ Biological reactor: ASM3 (described in detail in Chapter 5.2.1) was used to model the

biological reactions, based upon the CN library (CNLIB) which allows the simulation of

carbon and nitrogen removal.

♦ Secondary clarifier: the clarifier was modeled by simple1d, which consists in a multi-

layer model (as described in Chapter 5.3) associated to a double exponential settling

function (Takács et al., 1991) to specify the solids flux due to sedimentation (Equation

(24)).

6.5.3 Simulation results

The results obtained from the dynamic model ASM3 are presented in Figure 6.14,

respectively to operation flows, concentrations of TSS and DO in the biological reactor,

solids retention time and the global concentrations in the final effluent.

These results are intended to represent an example of model application, considering

however real influent data and design parameters as previously addressed in Chapter

3.4.2.2 (e.g. recirculation and removal of excess sludge rates, concentration of total

suspended solids of the mixed liquor). Moreover, they consist of an attempt to understand

the consequences, in terms of process efficiency, of operation with only one line. In

general, given the considerations stated in Chapter 6.5.1, the discharge of the final

effluent would be in compliance with the legal requirements relatively to component

concentrations and/or component removal. However, the concentrations of total nitrogen

in the effluent seemed to be higher than what was expected, which may be due to the

atypical characteristics of the wastewater influent used as input data.

76

Figure 6.14 | Example of application: Results of the dynamic simulation with ASM3 (T=10 ºC), considering: 1 line; Qras/Qinf= 0.6; Qes/Qinf≈0.02

After several attempts of simulation of different operation scenarios, it was possible to

conclude that the global quality of the final effluent could theoretically be improved and the

operation costs minimized if only one treatment line was used.

0

500

1000

1500

2000

8.00 12.00 16.00 20.00 24.00 28.00

Q [

m3 /

d]

T [h.min]

Flows

Qinf Qras Qes Qeff

4.00

0

1

2

3

4

4000

4500

5000

5500

6000

8.00 12.00 16.00 20.00 24.00 28.00

DO

[g

O2 /m

3 ]

TS

S [

g/m

3 ]

T [h:min]

TSS and DO in the Mixed Liquor

TSS DO

4.00

10

15

20

25

30

35

8.00 12.00 16.00 20.00 24.00 28.00

SR

T [

d]

T [h:min]

Solids Retention Time

4.00

0

20

40

60

80

100

8.00 12.00 16.00 20.00 24.00 28.00

Co

nce

ntr

atio

n [

g/m

3 ]

T [h:min]

Concentrations in the final effluent

COD TSS BOD5 N total

4.00

77

7. CONCLUSIONS Within the last two decades, one of the most significant advances in wastewater treatment

has been the development of dynamic mathematical models capable of describing the

physical, chemical and biological removal pathways that occur in a wastewater treatment

processes. Specifically, the Activated Sludge Model Nº3 (Gujer et al., 2000) was proposed

in order to correct some deficiencies of the former ASM1 (Henze et al., 1987) and become

the new standard model. ASM3 has introduced the concepts of endogenous decay and

biochemical storage (as the primary mechanism of substrate utilization), and

consequently, a new set of kinetic and stoichiometric coefficients (Gujer et al., 2000).

However, the default values of these coefficients suggested with the model were not

validated with experimental work, which has instigated much research by using

respirometric procedures for the assessment of ASM3 parameters.

The activated sludge models are worldwidely recognized and used as powerful tools for

design, control and optimization of wastewater treatment processes. However, in Portugal

little attention has been given to their potential. Considering the general precarious

situation of treatment plants, in regard to process efficiency and compliance with

discharge legal requirements, and the objectives set out in PEAASAR II for the sector, the

development of WWTP modeling seems to be necessary on a national level.

The main goal of this study was to contribute to understanding of the activated sludge

process, simulation of organic carbon removal based on ASM3 and the use of

respirometric assays in order to obtain kinetic and stoichiometric coefficients for model

calibration.

Respirometric experiments were carried out using raw wastewater (as substrate source)

and return activated sludge (as biomass source) from the WWTP and the parameters ��, ��, ��, �� and were estimated.

In the field, two monitoring campaigns were carried out to characterize the composition

effluents from seven different sections of the wastewater treatment plant. Depending on

the section of the WWTP, the following parameters were analyzed: BOD5, COD, TSS,

VSS, Ntotal, NH3-N, NH4-N, NO2-N, NO3-N, Ptotal and fecal coliforms. Concerning the

wastewater composition, it was noticed that the proportion between BOD5, COD and TSS

was rather atypical in comparison with usual values of raw wastewater. Regarding the

process efficiency, the average concentration values of COD, BOD5, TSS, Ntotal, Ptotal and

fecal coliforms in the effluent significantly exceed the limit values for discharge. Also, the

maximum percentage of component removal was considerably lower than the legal

requirements, except in the case of BOD5, of which the percentage of removal fitted the

78

range of 70-90%. The concentration of fecal coliforms in the effluent was high above the

legal requirements. In addition, measurements of dissolved oxygen were performed to

investigate the aeration conditions inside the biological reactors.

Apparently, these results of the monitoring campaigns were strongly related to several

events and operational problems that occurred in the WWTP before and during the

campaigns. Examples of these problems are: the discharge of dry sludge; deficient

aeration and stirring conditions in the biological reactors resulting in the settlement of

sludge; resuspension of sludge on the secondary clarifier.

The dynamic simulation of the Valhelhas WWTP was confronted with several limitations:

a) scarcity of good analytical and flows information, and lack of flow measurement

infrastructures, which were necessary for the accuracy of mass balances to the process

units; b) over dimensioning of the system regarding to the influent characteristics (flows

and loads); and c) deficient operation. The desired stability for modeling was not verified

and consequently, the calibration and validation of the model of this WWTP as it was in

operation was not possible. Alternatively, an academic approach was carried out as an

attempt to understand the consequences, in terms of process efficiency, considering

different operation methodologies and a simplified layout. As a result, the global quality of

the final effluent could theoretically be improved and the operation costs minimized if only

one treatment line was used.

Despite the difficulties encountered throughout this project, which constrained the

development of the work, the goals of the study were accomplished. Moreover, it is

believed that this is the first study to integrate two different fields, namely wastewater

treatment modeling and respirometry, into a Portuguese case study.

Topics for future research

In the course of the work presented in this thesis, several types of problems and questions

that deserve future attention were encountered. In relation to the results that have been

presented, some important issues that need to be focused upon are:

1) Application of the wastewater treatment modeling to systems that have a more

stringent monitoring and better performances, in order to facilitate the calibration

and validation steps for learning purposes;

2) Development of the comprehension of the respirometric experiments for calibration

of activated sludge models;

3) Promotion of a dynamic international cooperation within this field of research to

increase the use of modeling in control and optimization of processes.

80

REFERENCES Abusam, A., Keesman, K., Spanjers, H., & van Straten, G. a. (2002). Effect of oxidation

ditch horizontal velocity on the nitrogen removal process. European Water Management

Online , European Water Association.

Almeida, M. C. (2000). Pollutant transformation processes in sewers under aerobic dry

weather flow conditions. PhD. Collection Teses e Programas de Investigação LNEC 17.

LNEC, Lisbon.

APHA-AWWA-WEF (1999). Standard methods for the examination of water and

wastewater (20th ed.). American Public Health Association, Washington DC, USA.

Avcioglu, E., Karahan-Gül, Ö., & Orhon, D. (2003). Estimation of stoichiometric and kinetic

coefficients of ASM3 under aerobic and anoxic conditions via respirometry. Wat. Sci.

Tech.,48 (8), 185-194.

Chudoba, P., Capdeville, B., & Chudoba, J. (1992). Explanation of biological meaning of

the So/Xo ratio in batch cultivation. Wat. Sci. Tech., 26 (3/4), 743-751.

Commission of the European Communities (2009). 5th Commission Summary on the

Implementation of the Urban Waste Water Treatment Directive. Brussels: Commission of

the European Communities.

Copp, J., Spanjers, H., & Vanrolleghem, P. (2002). Respirometry in control of the

activated sludge process: Benchmarking control strategies. IWA Scientific and Technical

Report Nº 11. IWA Publishing, London, UK.

Dang, J., Harvey, D., & Grady Jr., C. (1989). Evaluation of biodegradation kinetic with

respirometric data. Research Journal of the Water Pollution Control Federation, 61

(11/12), 1711-1721.

Dayton and Knight (2001). Optimization of the oxidation ditch process through automated

ORP control of aerobic/anoxic operation. Dayton and Knight LDA, Canada.

Dochain, D., & Vanrolleghem, P. (2001). Dynamical modelling and estimation in

wastewater treatment processes. IWA Publishing, London, UK.

Dold, P. (1980). A general model for the activated sludge process. Prog. Wat. Tech., 12

(6), 47-77.

Domingos, I. J. (1999). Contribuição para o estudo da biodegradação aeróbia de ácido

abiético e terpineol. PhD. UNL-FCT, Lisbon.

81

Drolka, M., Plazl, I., & Koloini, T. (2001). The results of mathematical model and pilot plant

research of wastewater treatment. Chemical Biochemical, 15 (2), 71-74.

Droste, R. (1997). Theory and practice of water and wastewater treatment. John Willey

and Sons, Inc.

Ekama, G., Barnard, J., Günthert, F., Krebs, P., MCorquodale, J., Parker, D., et al. (1997).

Secondary settling tanks: Theory, modeling, design and operation. IAWQ Scientific and

Technical Report Nº 6. IAWQ, London, England.

Ekama, G., Dold, P., & Marais, G. (1986). Procedures for determining influent COD

fractions and the maximum specific growth rate of heterotrophs in activated sludge

systems. Wat. Sci.Tech., 18 (6), 91-114.

EPA (2000). Wastewater Technology Fact Sheet: Oxidation ditches. Environmental

Protection Agency, Washington, D.C., U.S.A..

ERSAR (2009). Relatório Anual do Sector de Águas e Residuos em Portugal (2008).

Volume 3 - Avaliação da qualidade do serviço prestado aos utilizadores. Entidade

Reguladora dos Serviços de Águas e Resíduos.

Ferreira, E. (2002). Aplicação da respirometria na caracterização do esgoto doméstico

afluente a uma ETE por processo de lodos ativados. Faculdade de Tecnologia,

Departamento de Engenharia Civil e Ambiental. Universidade de Brasília, Brasília.

Ferreira, F. (2006). Modelação e gestão integrada de sistemas de águas residuais. PhD.

UTL, Instituto Superior Técnico, Departamento de Engenharia Civil, Lisboa.

Ferreira, M. d. (2004). Estudo da cinética da biodegradação de águas residuais por

respirometria. Departamento de Engenharia Civil e de Arquitectura. Universidade da

Beira Interior, Covilhã.

Ginestet, P., Maisonnier, A., & Spérandio, M. (2002). Wastewater COD characterization:

biodegradablity of physico-chemical fractions. Wat. Sci. Tech., 45 (6), 89-97.

Grady Jr., C. L., Smets, B. F., & Barbeau, D. (1996). Variability in kinetic parameter

estimates: A review of possible causes and a proposed terminology. Wat. Res., 30 (3),

742-748.

Grady Jr., C., Daigger, G., & Lim, H. (1999). Biological wastewater treatment (2nd ed.),

1076. Marcel Dekker, Inc., New York.

Gray, N. (2004). Biology of wastewater treatment (2nd ed.). Imperial College Press,

London.

82

Gujer, W., Henze, M., Mino, T., & van Loosdrecht, M. (2000). Activated Sludge Model Nº

3. Scientific and Technical Report Nº9. IWA, London.

Henze, M., Grady, C. J., Gujer, W., Marais, G., & Matsuo, T. (1987). Activated Sludge

Model Nº 1. Scientific and Technical Report Nº1. IAWPRC, London.

Henze, M., Gujer, W., Mino, T., Matsuo, T., Wentzel, M., & Marais, G. (1995). Activated

Sludge Model Nº2. IAWQ Scientific and Technical Report Nº3. IAWQ, London.

Henze, M., Gujer, W., Mino, T., Wentzel, M., Marais, G., & van Loosdrecht, M. (1999).

Activated Sludge Model Nº 2d. Wat. Sci. Technol., 39 (1), 165-182.

Hulsbeek, J., Kruit, J., Roeleveld, P., & van Loosdrecht, M. (2002). A pratical protocol for

dynamic modeling of activated sludge systems. Wat. Sci. Tech., 45 (6), 127-136.

Hydromantis, Inc. (2006). GPS-X technical reference: Version 5.0. Hydromantis, Inc.

IGAOT (2006). Temática - Avaliação do nível de cumprimento da Directiva do Conselho

91/271/CEE, de 21 de Maio. Decree-Lei nº 152/97, de 19 de Junho - Tratamento de

águas residuais urbanas índice geral. IGAOT, Lisboa.

INAG (2002). Aplicação da Directiva 91/271/CEE do Conselho, de 21 de Maio de 1991,

relativa ao tratamento de águas residuais urbanas, alterada pela Directiva 98/15/CE da

Comissão, de 27 de Fevereiro de 1998: Relatório da situação em Dezembro de 2000

Portugal. Instituto da Água, MAOTDR.

INAG (2007). Retrieved from Inventário Nacional de Sistemas de Abastecimento de Água

e de Águas Residuais: URL: http://insaar.inag.pt/index2_noflash.htm in December 2009.

INAG (2009). Relatório do estado do abastecimento de água e drenagem e do tratamento

de águas residuais, Sistemas públicos urbanos:INSAAR 2008. Instituto da Água.

INE (2002). Instituto Nacional de Estatística. Retrieved from http://www.ine.pt/ in

December 2009.

INE (2009). Estatísticas do Ambiente 2008. Instituto Nacional de Estatística, Lisboa.

Insel, G., Orhon, D., Vanrolleghem, P., & Henze, M. (2002). Important limitations in the

modeling of activated sludge - Biased calibration of the hydrolysis process. Wat.

Sci.Tech., 45 (12), 23-36.

IRAR. (2009). Annual report on water and waste services in Portugal 2007: Executive

summary. Institute for the Regulation of Water and Solid Waste.

Jeppsson, U. (1996). Modelling aspects of wastewater treatment processes. PhD. Lund

University, Lund Institute of Technology, Lund.

83

Karahan-Gül, Ö., Artan, N., Orhon, D., Henze, M., & van Loosdrecht, M. (2002).

Respirometric assessment of storage yield for different substrates. Wat. Sci. Tech., 46 (1),

345-352.

Koch, G., Kuhni, M., Gujer, W., & Siegrist, H. (2000). Calibration and validation of

activated sludge model Nº 3 for Swiss municipal wastewater. Water Res., 34, 3580-3590.

Langergraber, G., Rieger, L., Winkler, S., Alex, J., Wiese, J., Owerdieck, C., et al. (2004).

A guideline for simulation studies of wastewater treatment plants. Water Sci. Tech., 50 (7),

131-138.

MAOTDR (2007). PEAASAR II: Plano Estratégico de Abastecimento de Água e de

Saneamento de Águas Residuais 2007-2013. Ministério do Ambiente, do Ordenamento

do Território e do Desenvolvimento Regional.

Mathieu, S., & Etienne, P. (2000). Estimation of wastewater biodegradable COD fractions

by combining respirometric experiments in various S0/X0 ratios. Wat. Res., 34 (4), 1233-

1246.

Melcer, H., Dold, P. L., Jones, R. M., Bye, C. M., Takács, I., Stensel, H. D., et al. (2003).

Methods for wastewater characterization in activated sludge modeling. Water

Environment Research Foundation, Alexandria.

Metcalf & Eddy (1991). Wastewater engineering: treatment, disposal and reuse (3rd ed.).

McGraw-Hill.

Mhlanga, F., Brouckaert, C., Foxon, K., Fennemore, C., Mzulwini, D., & Buckley, C.

(2009). Simulation of a wastewater treatment plant receiving industrial effluents. Water

SA, 35 (4), 447-454.

Nelson, D. (1984). Biological treatment process control: Oxidation ditches. Student

manual. Linn-Benton Community College, Oregon.

Olsson, G., & Newell, B. (1999). Wastewater treatment systems: Modelling, diagnosis and

control. IWA Publishing, London.

Pattarkine, V.M., & Randall, C.W. (1999). The requirement of metal cations for enhanced

biological phosphorous removal by activated sludge. Water Sci. Tech., 40 (2),159-165.

Petersen, B. (2000). Calibration, identifiability and optimal experimental design for

activated sludge models. PhD., Universiteit Gent, Gent.

Ros, M. (1993). Respirometry of activated sludge. Technomic Publishing Company, Inc..

Lancaster, U.S.A..

84

Rozich, A. F., & Gaudy, A. F. (1992). Design and operation of activated sludge processes

using respirometry. Lewis Publishers, U.S.A..

Schwarz, C. A., Eilersen, A., Henze, M., Wichern, M., & Wilderer, P. (2003). Modeling of

storage and endogenous respiration with Activated Sludge Model Nº3. Environmental

Informatics Archive, 1, 357-365.

Sin, G., van Hulle, S. W., De Pauw, D. J., van Griensven, A., & Vanrolleghem, P. A.

(2005). A critical comparison of systematic calibration protocols for activated sludge

models: A SWOT analysis. Water Res,39, 2459-2474.

Sozen, S., Çokgor, E. U., Orhon, D., & Henze, M. (1998). Respirometric analysis of

activated sludge behaviour - Heterotrophic growth under aerobic and anoxic conditions.

Wat. Res., 32 (2), 476-488.

Spanjers, H., Vanrolleghem, P., Olsson, G., & Dold, P. (1998). Respirometry in control of

the activated sludge process: Principles. IAWQ Scientific and Technical Report Nº 7.

IAWQ, London.

Stypka, A. (1998). Factors influencing sludge settling parameters and solids flux in the

activated sludge process: A literature review. Advanced Wastewater Treatment Report

Series. Royal Institute of Technology, Stockholm.

Takács, I., Patry, G., & Nolasco, D. (1991). A dynamic model of the clarification thickening

process. Water Research, 25 (10), 1263-1271.

Vanrolleghem, P.A., Insel, G., Petersen, B., Sin, G., De Pauw, D., Nopens, I., Weijers, S.,

Gernaey, K. (2003). A comprehensive model calibration procedure for activated sludge

models. In: Proceedings: WEFTEC 2003, 76th Annual Technical Exhibition and

Conference.

Vanrolleghem, P., Spanjers, H., Petersen, B., Ginestet, P., & Takács, I. (1999). Estimating

(combinations of) activated sludge model Nº1 parameters and components by

respirometry. Water Sci. Tech., 39 (1), 195-214.

A1

APPENDICES

A3

Appendix A.1 ― RESPIROMETER CLASSIFICATION

In Table A.1.1 and Table A.1.2 the classification and a brief description of respirometers

according to Spanjers et al. (1998) is presented, respectively, considering oxygen

measuring phase, regime, mass balance and diagram. The gas phase includes oxygen

dispersed as bubbles from the liquid phase.

Table A.1.1 | Respirometer classification (adapted from Spanjers et al., 1998)

Type Phase Flows Mass balance Diagram

LSS Liquid Static gas, static liquid

�"#�( = −GT

LFS Liquid Flowing gas, static liquid

�"#�( = ��("# − "#) − GT

LSF Liquid Static gas,

flowing liquid

�"#�( = FLI"#LIW� − FORT"#W� − GT

LFF Liquid Flowing gas, flowing liquid

�"#�( = FLI"#LIW� − FORT"#W� + ��("# − "#) − GT

GSS Gas Static gas, static liquid

�"#�( = ��("# − "#) − GT �(WX#a)�( = −W��("# − "#)

GFS Gas Flowing gas, static liquid

�"#�( = ��("# − "#) − GT �(WX#a)�( = �LI#a,LI − �ORT#a − W��("# − "#)

GSF Gas Static gas,

flowing liquid

�"#�( = FLI"#LIW� − FORT"#W� + ��("# − "#) − GT

�(WX#a)�( = −W��("# − "#)

GFF Gas Flowing gas, flowing liquid

�"#�( = FLI"#LIW� − FORT"#W� + ��("# − "#) − GT

�(WX#a)�( = �LI#a,LI − �ORT#a − W��("# − "#)

DO

DO

DO

DO

DO

DO

DO

DO

DO

DO

DO

DO

A4

Table A.1.2 | Respirometer description (adapted from Spanjers et al., 1998)

Type Description Observations

LSS

It measures the decrease of DO as a function of time due to respiration, without liquid flow and oxygen mass transfer. To prevent transfer of oxygen across the gas/liquid interface, the gas phase may be absent.

DO may be exhausted after some time and therefore reaeration is needed to increase the DO concentration. DO and substrate are limiting the respiration; when their concentration is too low, it causes a nonlinear DO decrease and complicates the calcule.

LFS

It measures the decrease of DO as a function of time due to respiration, without liquid flow but with continuously aeration of biomass. �� and DO saturation must be known or determined.

�� and DO saturation must be determined regularly, depending on temperature, pressure and liquid properties. They can be determined by using look-up tables, separate reaeration tables or by applying parameters estimation techniques. rO can be measured at a nearly constant DO concentration, eliminating the dependency of rt on the DO, if DO concentration ≥ 0 g/m3. This principle can be implemented in a separate respirometer or directly in a batch aeration tank.

LSF

It measures the decrease of DO as a function of time due to respiration, without gas phase. �� and DO saturation do not need to be determined, because the continuously flowing liquid has a high DO concentration. DO and DOin must be measured continuously; Qin and VL are instrument constants that are know or calibrated.

This respirometer is sensitive to the effect of substrate and to DO limitation, which can be eliminated by the continuously supply of substrate and DO. This principle is applicable to a plug flow system type cell. However, the exact respiration rate cannot be obtained because of the spatial distribution of rt and DO along the plug flow cell; it can be calculated from the DO concentration in the liquid entering the cell and that in the liquid leaving the cell, resulting in a measurement delay equal to the hydraulic residence time of the cell.

LFF It measures the decrease of DO as a function of time due to respiration. Flow rates and the inlet oxygen concentration must be measured.

�� and DO saturation must be assessed, for example by estimating these from the dynamics of the DO concentration.

GSS

There are no inputs and outputs. DO must be known, therefore O2 must be measured, for example by a gasometric method (based on the variation of volume or pressure).

When the O2 becomes exhausted it must be replenished.

GFS

Biomass is continuously aerated with air or pure oxygen so that the presence of sufficient oxygen is ensured. Gas flow rates (Fin and Fout), and the oxygen concentrations in the input and output streams (O2,in and O2) must be known in addition to the variables from the previous technique.

O2 is measured, for example, by the paramagnetic method, while the other parameters are set or known.

GSF The variation of oxygen concentration in the liquid phase must be determined in addition to the oxygen measurement in the gas phase.

No applications of this type of respirometer have been found in the literature.

GFF

The assumption on proportionality between DO and O2 becomes more critical because also the liquid outflow term depends on it. Additional measurements of DO should be made to determinate the respiration rate.

It can be applied to a full-scale aeration tank.

A5

Appendix A.2 ― RESPIRATION RATE OF SUBSTRATE OXIDATION

Figure A.2.1 illustrates the DO curve pattern corresponding to an operation cycle of a

respirometer based on the LFS principle (see Table A.1.1, Appendix A.1). The four

phases indicated in Figure A.2.1 are in accordance to the description of Figure 4.1 in

Chapter 4.2.2.

The respiration rate GQRS corresponds to the oxygen consumed for substrate oxidation and

can be obtained from the area A in Figure A.2.1, by interpolating the DO values.

Figure A.2.1 | DO curve of a LFS respirometer test (illustration of ��)

A7

Appendix A.3 ― DETERMINATION OF THE OXYGEN MASS TRANSFER

COEFFICIENT ( � )

The oxygen mass transfer coefficient (��) is considerably influenced by factors such as:

type of respirometer, stirring, temperature of the liquid, components of the wastewater,

gas flow and aeration conditions. In this work, �� was determined during the aeration

curve (phase III of Figure 4.1) at 20 ºC, as given by Equation 22 (Ferreira, 2004): {"#{( = �� ∙ ("#Q − "#) (22)

where: "#T = concentration of dissolved oxygen measured in the liquid phase [g O2/m3]; "#Q = saturation dissolved oxygen concentration [g O2/m

3]; ( = time [h].

Given the observed "#Q value of 7.72 mg/L and the variation of "# concentration

measured in function of time, as presented in Table A.3.1, after linearization of Equation

(26) the curve ¶m("#Q − "#) versus time was plotted (Figure A.3.1); �� value

corresponds to the slope of the curve.

Table A.3.1 | Measured DO concentration values of the respirometric experiment R1-14

Time [h] DO [g O2/m3] DOS-DO [g O2/m3] Ln(DOS-DO)

0.000 1.99 5.73 1.75 0.017 4.75 2.97 1.09 0.033 6.50 1.22 0.20

Figure A.3.1 | Decay curve of Ln(DOS-DO) in function of time

As a result, the �� value determined was 46.4 h-1.

y = -46.406x + 1.7845R² = 0.9925

0

0.5

1

1.5

2

2.5

0 0.01 0.02 0.03 0.04

Ln

[DO

s-D

O]

Time [h]

Estimation of KLa

A9

Appendix A.4 ― SIMPLIFIED ASM3 PROCESS EQUATIONS

The dynamic biological model used for the interpretation of the respirograms presented in

Chapter 6.3.3 was adapted from the work of Avcioglu et al. (2003) and consists in a

simplified version of ASM3 for aerobic processes. The simplified model fits to the

respirogram data considering three phases: endogenous, storage and growth, according

to Equations (26), (27) and (28), respectively, and as illustrated in Figure A.4.1 (Avcioglu

et al., 2003).

#��·IJO¸HIORQ ¦K�QH = ¥∆º ∆T §ºHP�] (23)

#��TO. ¦K�QH = ¥∆º ∆T §ºHP�] + ¥∆º ∆T §TOi�¸H + ¥∆º ∆T §XiO»TK (0) + ¥∆º ∆T §¼HQ¦.10½¾ (24)

#��XiO»TK ¦K�QH = ¥∆º ∆T §ºHP�] + ¥∆º ∆T §XiO»TK (10½¾) + ¥∆º ∆T §¼HQ¦. 10½¾ (25)

Figure A.4.1 | Simplified model for aerobic conditions (adapted from Avcioglu et al. (2003))

Each phase is associated to different mechanisms of oxygen utilization, namely

endogenous decay, storage, growth and respiration of storage products given by

Equations (29), (30), (31) and (32), respectively:

¥∆º ∆T §ºHP�] = ª1 − +1,« ∙ �� ∙ !� (26)

¥∆º ∆T §TOi�¸H = (1 − �� ) ∙ )� ∙ 0(¿0r0) ∙ !� (27)

¥∆º ∆T §XiO»TK = (�uÀÁ)ÀÁ ∙ �� ∙ ¥ 10½¾ 1Á⁄¿0½¾r¿0½¾ 1Á⁄ § ∙ !� (28)

A10

¥∆º ∆T §¼HQ¦. 10½¾ = �� ∙ !� (29)

where:

!� = heterotrophic biomass [g COD/mn]; !� = organics stored by heterotrophs [g COD/mn]; �� = aerobic endogenous respiration rate of !� [du�]; �� = heterotrophic growth rate (i = �, !� ) [du�]; �� = aerobic yield of !� [g COD7@ g⁄ COD7>DE]; �� = aerobic yield of stored product per � [g COD7>DE g⁄ COD=>]; = saturation constant for � [g COD=> mn⁄ ]; � = saturation constant for !� [g COD7>DE g⁄ COD7@]; )� = storage rate constant [g COD=> g⁄ COD7@ ∙ d]; � = readily biodegradable substrates [g COD/mn]; +1, = production of !2 in endogenous respiration [g COD78 g⁄ COD79:].

According to Avcioglu et al. (2003), in the simplified model it is assumed that the

concentration of storage products is much lower than that of heterotrophic biomass (!�

<< !�) at the start of substrate addition (t=0). In addition, two different growth rates were

used, corresponding to simultaneous storage and direct growth on readily biodegradable

substrate, followed by growth on stored products. More details about this simplified

version of ASM3 can be found in Avcioglu et al. (2003).

A11

Appendix A.5 ― ASM3 MODEL: MATRIX OF PETERSEN, TYPICAL

VALUES AND COMPONENTS

Typical values of kinetic and stoichiometric parameters for ASM3 are presented in Table

A.5.1 and Table A.5.2, respectively. Table A.5.3 assembles the stoichiometric matrix of

Petersen, the composition matrix and the kinetic rate equations for ASM3.

Table A.5.1 | Typical values of kinetic parameters for ASM3 (adopted from Gujer et al.,2000)

Symbol Characterization Temperature

Units 10 ºC 20 ºC

HYDROLYSIS )� Hydrolysis rate constant 2.0 3.0 - .#"10 -⁄ .#"1Á ∙ { 1 Hydrolysis saturation constant 1.0 1.0 - .#"10 -⁄ .#"1Á

HETEROTROPHIC ORGANISMS, AEROBIC AND DENITRIFYING ACTIVITY )� Storage rate constant 2.5 5.0 - .#"0 -⁄ .#"1Á ∙ { YU 1 Anoxic reduction factor 0.6 0.6 — % Saturation constant for �U % 0.2 0.2 - #a �n⁄ U 1 Saturation constant for �U 1 0.5 0.5 - b#nu − b/�n Saturation constant for � 2.0 2.0 - .#"0 �n⁄ � Saturation constant for !� 1.0 1.0 - .#"10½¾ -⁄ .#"1Á �� Heterotrophic max. growth rate 1.0 2.0 {u� U�� Saturation constant for ammonium, �U�� 0.01 0.01 - b �n⁄ ��¿ Saturation constant for alkalinity for !� 0.1 0.1 �p|* �.#nu/�n ��, % Aerobic endogenous respiration rate of !� 0.1 0.2 {u� ��,U 1 Anoxic endogenous respiration rate of !� 0.05 0.1 {u� �� , % Aerobic respiration rate for !� 0.1 0.2 {u� �� ,U 1 Anoxic respiration rate for !� 0.05 0.1 {u�

AUTOTROPHIC ORGANISMS, NITRIFYING ACTIVITY �� Autotrophic max. growth rate of !� 0.35 1.0 {u� �,U�� Ammonium substrate saturation for !� 1.0 1.0 - b �n⁄ �, % Oxygen saturation for nitrifiers 0.5 0.5 - #a �n⁄ �,��¿ Bicarbonate saturation for nitrifiers 0.5 0.5 �p|* �.#nu/�n ��, % Aerobic endogenous respiration rate of !� 0.05 0.15 {u� ��,U 1 Anoxic endogenous respiration rate of !� 0.02 0.05 {u�

Table A.5.2 | Typical stoichiometric and composition parameters for ASM3 (Source: Gujer et al. (2000))

Symbol Characterization Value Units �� , % Aerobic yield of stored product per � 0.85 - .#"10½¾ -⁄ .#"0 �� ,U 1 Anoxic yield of stored product per � 0.80 - .#"10½¾ -⁄ .#"0 ��, % Aerobic yield of heterotrophic biomass 0.63 - .#"1Á -⁄ .#"10½¾��,U 1 Anoxic yield of heterotrophic biomass 0.54 - .#"1Á -⁄ .#"10½¾�� Yield of autotrophic biomass per NOnu − N 0.24 - .#"1Ä -⁄ bÅ¾Æ +, Production of �2 in hydrolysis 0 - .#", -⁄ .#"10 +1, Production of !2 in endogenous respiration 0.20 - .#"1, -⁄ .#"1ÇÈ �U,, N content of �2 0.01 - b -⁄ .#", �U,0 N content of � 0.03 - b -⁄ .#"0 �U,1, N content of !2 0.02 - b -⁄ .#"1, �U,10 N content of !� 0.04 - b -⁄ .#"10 �U,<� N content of biomass, !�and !� 0.07 - b -⁄ .#"1ÇÈ �,1, SS to COD ratio for !2 0.75 - �� -⁄ .#"1, �,10 SS to COD ratio for !� 0.75 - �� -⁄ .#"10 �,<� SS to COD ratio for biomass, !�and !� 0.90 - �� -⁄ .#"1ÇÈ

A12

Table A.5.3 | Stoichiometric matrix ��,� , composition matrix ��,� and kinetic rate expressions �� for ASM3 (adopted from Gujer et al., 2000) P

roce

ss (j)

Component (i) → 1 2 3 4 5 6 7 8 9 10 11 12 13

Process rate equations, �� % �2 � �U�� U% �U 1 ��¿ !2 ! !� !� !� !

Expressed as → [gOa/mn] [gCOD/mn] [gCOD/mn] [gN/mn] [gN/mn] [gN/mn] [mole] [gCOD/mn] [gCOD/mn] [gCOD/mn] [gCOD/mn] [gCOD/mn] [gSS/m3] 1 Hydrolysis +, �� e� h� −1 −�1 )� ∙ ! !�⁄1 + (! !�)⁄ ∙ !� HETEROTROPHIC ORGANISMS, AEROBIC AND DENITRIFYING ACTIVITY

2 Aerobic storage of � �a −1 ea ha �� , % (a )� ∙ � % % + � % ∙ � + � ∙ !� 3 Anoxic storage of � −1 en −�n �n hn �� ,U 1 (n )� ∙ YU 1 ∙ % % + � % ∙ �U 1U 1 + �U 1 ∙ � + � ∙ !� 4 Aerobic growth of !� �q eq hq 1 −1�� , % (q �� ∙ � % % + � % ∙ �U��U�� + �U�� ∙ ��¿��¿ + ��¿ ∙ !� !�⁄� + (!� !�)⁄ ∙ !� 5

Anoxic growth (denitrif.)

e_ −�_ �_ h_ 1 −1�� ,U 1 (_ �� ∙ YU 1 ∙ % % + � % ∙ �U 1U 1 + �U 1 ∙ �U��U�� + �U�� ∙ ��¿��¿ + ��¿∙ !� !�⁄� + (!� !�)⁄ ∙ !� 6

Aerobic endog. respiration

�Î eÎ hÎ +2 −1 (Î ��, % ∙ � % % + � % ∙ !� 7

Anoxic endog. respiration

e` −�` �` h` +2 −1 (` ��,U 1 ∙ % % + � % ∙ �U 1U 1 + �U 1 ∙ !� 8

Aerobic respiration of !�

�Ï hÏ −1 (Ï �� , % ∙ � % % + � % ∙ !� 9

Anoxic respiration of !�

−�Ð �Ð hÐ −1 (Ð �� ,U 1 ∙ % % + � % ∙ �U 1U 1 + �U 1 ∙ !� AUTOTROPHIC ORGANISMS, NITRIFYING ACTIVITY 10 Aerobic growth of !� ��\ e�\ 1�� h�\ 1 (�\ �� ∙ � %�, % + � % ∙ �U��,U�� + �U�� ∙ ��¿�,��¿ + ��¿ ∙ !� 11

Aerobic endog. respiration

�� e�� h�� +2 −1 (�� , % ∙ � %�, % + � % ∙ !� 12

Anoxic endog. respiration

e�a −��a ��a h�a +2 −1 (�a ��,U 1 ∙ %�, % + � % ∙ �U 1�,U 1 + �U 1 ∙ !� COMPOSITION MATRIX ��,Ñ k Conservatives 1 ThOD −1 1 1 −1.71 −4.57 1 1 1 1 1 2 Nitrogen �U,, �U,0 1 1 1 �U,1, �U,10 �U,<� �U,<� 3 Ionic charge 1 14⁄ −1 14⁄ −1 Observables

4 SS �,1, �,10 �,<� 0.6 �,<�

A13

Appendix A.6 ― MAP OF VALHELHAS WASTEWATER DRAINAGE SYSTEM

A15

Appendix A.7 ― PLANT OF OPERATION OF VALHELHAS WASTEWATER

TREATMENT PLANT

A17

Appendix A.8 ― DETAILED MEASUREMENTS CARRIED OUT AT

VALHELHAS WASTEWATER TREATMENT PLANT

In Table A.8.1 the historical wastewater influent and final effluent compositions are

presented. The data is relative to the monthly controls performed in Valhelhas WWTP.

Table A.8.1 | Historical wastewater influent and final effluent compositions; q.l.: quantification limit of the method

Date Temp.

[ºC] pH [-]

BOD5 [g/m3]

COD [g/m3]

TSS [g/m3]

Ntotal [g/m3]

Ptotal [g/m3]

Year Month Qinf Qeff Qinf Qeff Qinf Qeff Qinf Qeff Qinf Qeff Qinf Qeff Qinf Qeff

2008 June 21 21.5 6.6 6.5 500 9 757 37 604 2 - - - -

July 21 21.2 7.0 5.6 140 20 203 57 40 12 - - - -

August 21 21 6.7 6.4 500 22 880 70 93 10 - - - -

September 20 20 6.9 6.4 200 20 310 52 132 11 - 18 - 4

October 20 20 6.7 6.7 380 18 540 53 452 21 - 45 - 2

November 16 15 7.3 6.7 400 8 590 38 500 5 - 20 - <2 (q.l.)

December 16 16 6.6 6.5 5400 20 7800 67 2300 46 - 8 - <2 (q.l.)

2009 January 16 16 6.4 6.3 1150 10 1663 38 1560 15 46 - 5 - February 16 16 7.1 6.7 350 12 500 33 105 15 37 - 3 - March 17 17 6.5 6.0 760 8 1085 15 356 11 101 - 12 - April 20 20 5.2 6.0 1550 15 2200 34 208 6 155 - 12 - May 23 23 6.4 6.6 1050 23 1500 75 706 22 - - - - June 21 21 6.7 6.8 240 10 330 32 218 5 - - - - July 22 22 6.5 6.6 1300 14 1851 35 1190 6 - - - - August 21 21 6.0 6.8 1300 35 1780 120 980 24 - - - - September 21 21 6.6 6.7 210 18 295 54 98 20 - - - - October 22 22 5.9 5.9 480 40 690 120 240 26 - - - - November 22 22 6.0 6.3 950 35 1360 120 980 20 - - - - December 20 20 6.4 5.4 560 20 810 71 500 28 - - - -

Table A.8.2 and Table A.8.3 report in detail the results of the measuring campaigns of

14/15 and 16/17 December, respectively.

A18

Table A.8.2 | Results of measurements carried out during the campaign of 14/15 December at Valhelhas WWTP

Time BOD5 COD TSS VSS Ntotal Ptotal Fecal

coliforms pH

Temp.

N-NO2 N-NO3 N-NH4+ N-NH3

[h:min] [g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [MPN/100mL] - [ºC] [g/m3] [g/m3] [g/m3] [g/m3]

A1 - WASTEWATER INFLUENT

8:00 24.3 222.0 21.0 17.5 23.8 2.3 - 6.8 2.7 0.15 1.10 24.0 0.01

11:00 46.3 168.0 62.5 57.0 30.6 3.1 2.1E+06 7.2 4.0 0.15 1.10 29.3 0.05

14:00 36.0 167.0 42.5 40.5 26.4 2.3 - 7.0 7.0 0.15 1.00 21.7 0.03 17:00 117.0 293.0 176.0 163.5 65.2 8.7 - 7.1 8.2 0.40 3.05 64.4 0.12 21:00 133.3 477.0 97.0 90.0 36.8 4.8 - 6.6 13.6 0.40 2.90 23.5 0.02 23:00 61.7 364.0 59.5 51.0 25.4 3.3 - 6.5 11.0 0.30 1.90 18.9 0.01 2:00 66.7 309.0 50.0 47.5 25.8 4.7 - 6.5 14.2 0.25 1.70 19.2 0.01

A2 - MIXTURE OF WASTEWATER INFLUENT, RAS AND SIDESTREAMS

8:00 - 410.0 274.5 248.5 - - - 7.0 4.0 - - - - 11:00 - 1151.0 3758.0 3120.5 - - - 6.9 6.0 - - - -

14:00 - 577.0 2176.0 1872.0 - - - 6.9 7.1 - - - -

17:00 - 566.0 2607.0 2091.0 - - - 7.0 11.8 - - - - 21:00 - 8244.0 4444.0 3316.0 - - - 6.3 13.6 - - - -

23:00 - 4935.0 1352.0 1156.0 - - - 6.7 11.4 - - - -

2:00 - 4868.0 5112.0 4430.0 - - - 6.4 14.3 - - - -

A3 - ACTIVATED SLUDGE EFFLUENT OF OXIDATION DITCH 1

8:00 - 6180.0 4453.5 3960.5 - - - 6.7 2.4 - - - - 11:00 - 7280.0 3775.5 3259.5 - - - 6.8 6.8 - - - - 14:00 - 4068.0 3802.0 3285.0 - - - 6.7 7.0 - - - - 17:00 - 1175.0 4198.0 3640.5 - - - 6.5 12.0 - - - - 21:00 - 6705.0 3002.5 2589.5 - - - 6.5 13.7 - - - - 23:00 - 5985.0 1668.0 1250.0 - - - 6.5 13.2 - - - - 2:00 - 5082.0 3186.0 3036.0 - - - 6.3 14.2 - - - -

A4 - ACTIVATED SLUDGE EFFLUENT OF OXIDATION DITCH 2

8:00 - 5695.0 3965.5 3510.0 - - - 6.5 2.9 - - - -

11:00 - 5905.0 5763.5 5087.5 - - - 6.7 5.8 - - - -

14:00 - 4795.0 3894.0 3174.0 - - - 7.0 6.4 - - - - 17:00 - 1187.0 3678.5 3188.0 - - - 6.4 9.6 - - - - 21:00 - 6394.0 3028.5 2684.0 - - - 6.6 14.3 - - - - 23:00 - 1255.0 4080.5 3668.0 - - - 6.4 13.2 - - - - 2:00 - 4545.0 3060.0 2786.0 - - - 6.5 14.6 - - - -

A5 - RETURN ACTIVATED SLUDGE (RAS)

8:00 - 9935.0 6466.0 5535.5 - - - 6.7 4.0 - - - - 11:00 - 1060.0 115.5 107.5 - - - 6.8 5.5 - - - - 14:00 - 9800.0 7311.0 6407.0 - - - 6.8 7.2 - - - - 17:00 - 3177.0 2911.5 2664.5 - - - 6.9 11.1 - - - - 21:00 - 11245.0 3510.5 2737.5 - - - 6.3 13.2 - - - - 23:00 - 9922.0 6200.0 5770.0 - - - 6.4 13.8 - - - - 2:00 - 9654.0 6582.0 5674.0 - - - 6.4 14.1 - - - -

A6 - SIDESTREAMS

11:00 - 1450.0 6497.5 5798.5 - - - 6.8 5.9 - - - - 14:00 - 1077.0 2889.0 2740.5 - - - 6.7 8.1 - - - - 17:00 - 2274.0 6898.5 6142.5 - - - 6.8 10.7 - - - -

A7 - EFFLUENT

8:00 11.7 212.0 45.5 39.0 14.4 2.3 - 6.9 3.6 - - - -

11:00 32.5 202.0 39.5 38.0 13.3 2.1 1.3E+06 6.9 5.0 - - - -

14:00 16.3 145.0 37.5 31.5 21.5 2.0 - 7.0 6.1 - - - - 17:00 30.7 139.0 67.0 62.5 21.0 3.7 - 7.0 11.0 - - - - 21:00 40.7 153.0 54.5 47.5 20.5 4.4 - 6.6 14.0 - - - - 23:00 34.3 161.0 56.5 47.5 186.0 2.0 - 6.6 13.7 - - - - 2:00 34.0 145.0 43.5 39.0 116.0 2.1 - 6.5 14.6 - - - -

A19

Table A.8.3 | Results of measurements carried out during the campaign of 16/17 of December at Valhelhas WWTP

Time BOD5 COD TSS VSS Ntotal Ptotal Fecal

coliforms pH Temp. N-NO2 N-NO3 N-NH4+ N-NH3

[Date] [h:min]

[g/m3] [g/m3] [g/m3] [g/m3] [g/m3] [g/m3]

[MPN/100 mL] - [ºC] [g/m3] [g/m3] [g/m3] [g/m3]

A1 - WASTEWATER INFLUENT 16-12-09

9:00 58.3 237.0 26.5 20.5 35.0 2.6 - 6.7 10.6 0.15 1.20 23.10 0.02

16-12-09 13:00

47.3 208.0 45.0 36.0 22.4 1.9 5.4E+07 6.4 11.1 0.15 1.45 15.60 0.01

17-12-09 12:00

47.0 161.0 32.0 25.5 15.0 1.9 - 6.4 12.9 0.15 1.70 9.20 0.01

17-12-09 17:30

78.7 297.0 57.5 34.5 21.7 2.8 9.2E+07 6.5 13.0 0.25 1.65 11.80 0.01

A2 - MIXTURE OF WASTEWATER INFLUENT, RAS AND SIDESTREAMS 16-12-09

9:00 - - - - - - - - - - - - -

16-12-09 13:00

- 3214 1856 1668 - - - 6.4 10.8 - - - -

17-12-09 12:00

- 1902 1104 998 - - - 6.4 12.9 - - - -

17-12-09 17:30

- 3537 1858 1616 - - - 6.3 13.5 - - - -

A3 - ACTIVATED SLUDGE EFFLUENT OF OXIDATION DITCH 1 16-12-09

9:00 - 3672 2670 2489 - - - 6.4 10.8 - - - -

16-12-09 13:00

- 3666 2210 1964 - - - 6.4 11.0 - - - -

17-12-09 12:00

- 2726 1358 1208 - - - 6.6 12.5 - - - -

17-12-09 17:30

- 2909 1960 1728 - - - 5.9 12.7 - - - -

A4 - ACTIVATED SLUDGE EFFLUENT OF OXIDATION DITCH 2 16-12-09

9:00 - 3788 2234 1906 - - - 6.3 10.9 - - - -

16-12-09 13:00

- 3397 2078 1844 - - - 6.5 11.6 - - - -

17-12-09 12:00

- 2811 1858 1714 - - - 6.5 11.8 - - - -

17-12-09 17:30

- 3049 2214 1700 - - - 6.3 13.3 - - - -

A5 - RETURN ACTIVATED SLUDGE (RAS) 16-12-09

9:00 - 5509 2831 2472 - - - 6.4 10.3 - - - -

16-12-09 13:00

- 6296 4050 3552 - - - 6.3 11.8 - - - -

17-12-09 12:00

- 4941 2722 2384 - - - 6.0 12.2 - - - -

17-12-09 17:30

- 5521 4396 3586 - - - 6.0 13.1 - - - -

A7 - EFFLUENT 16-12-09

9:00 113.0 783 1382 - 52.1 8.3 - 6.6 10.1 - - - -

16-12-09 13:00

143.0 909 471 - 52.7 8.7 1.6E+07 6.5 11.3 - - - -

17-12-09 12:00

163.0 530 493 - 39.4 10.3 - 6.7 13.9 - - - -

17-12-09 17:30

259.3 1126 1264 - 88.1 22.2 1.6E+07 6.5 14.4 - - - -

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