A PILOT SCALE-STUDY AT THE NINE SPRINGS WASTEWATER TREATMENT
PLANT: SEASONAL COD AND F/M RATIO TRENDS AND THEIR APPLICATION
TO MODELING TREATMENT PROCESSES
By
Gustavo Adolfo Uribe Santos
A thesis submitted in partial fulfillment of
the requirements for the degree of
Master of Science in
Civil & Environmental Engineering
at the
UNIVERSITY OF WISCONSIN-MADISON
2021
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TABLE OF CONTENTS
TABLE OF CONTENTS........................................................................................................................................ 3
LIST OF ABREVIATIONS .................................................................................................................................... 4
LIST OF FIGURES............................................................................................................................................... 7
LIST OF TABLES ................................................................................................................................................ 9
ABSTRACT ...................................................................................................................................................... 13
INTRODUCTION ............................................................................................................................................. 15
CHAPTER 1: PILOT PLANT DESCRIPTION AND OPERATION ............................................................................. 18
1.1
1.2
1.3
1.4
UNIVERSITY OF CAPE TOWN (UCT)-TYPE PILOT PLANT .................................................................................... 18
AO-TYPE PILOT PLANT ............................................................................................................................... 20
LOW DO CONDITIONS ............................................................................................................................... 22
PILOT PLANT OPERATION ........................................................................................................................... 25
CHAPTER 2: CHARACTERIZATION OF ORGANIC MATTER IN INFLUENT VIA COD ANALYSES ............................ 28
2.1
2.2
2.3
The COD Concept ........................................................................................................................................ 28
COD materials, sampling, and measurement ............................................................................................. 30
COD sampling results and discussion .......................................................................................................... 38
CHAPTER 3: FOOD MASS RATIO CALCULATION AND COMPARISON ............................................................... 60
3.1
3.2
3.3
3.4
3.5
3.1
Food Mass Ratio concept and the settling process ................................................................................ 60
Temperature .......................................................................................................................................... 64
F/M relativity ......................................................................................................................................... 66
Food mass ratio and COD ....................................................................................................................... 67
Food mass ratio results and discussion .................................................................................................. 68
Additional Variable Correlation Analysis ................................................................................................ 72
CHAPTER 4: PILOT PLANT MODELING ............................................................................................................ 77
4.1
4.2
4.3
4.4
4.5
Biowin modeling software ................................................................................................................ 77
Model calibration process ................................................................................................................. 78
Influent Specifier tool ........................................................................................................................ 80
Biowin controller tool ....................................................................................................................... 82
Modeling results and discussion ....................................................................................................... 86
CHAPTER 5: RECOMMENDATIONS ............................................................................................................... 103
CHAPTER 6: CONCLUSIONS .......................................................................................................................... 106
REFERENCES................................................................................................................................................. 107
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LIST OF ABREVIATIONS
ABAC
Ammonia basis Aeration Control
AO
Anoxic Oxic
AOB
Ammonia Oxidizing Bacteria
BOD
Biological Oxygen Demand
BOD5
Biological Oxygen Demand 5
COD
Chemical Oxygen Demand
DO
Dissolved Oxygen
DOSP
Dissolved Oxygen Set Point
EBPR
Enhanced Biological Phosphorus Removal
Eff
Effluent
F/M
Food to Mass Ratio
fBS
Fraction of Readily Biodegradable
FFCOD
Flocculated-Filtered Chemical Oxygen Demand
fUP
Fraction of Unbiodegradable Particulate
fUS
Fraction of Unbiodegradable Soluble
GFCOD
Glass Filter Chemical Oxygen Demand
HR COD
High Range Chemical Oxygen Demand
HRT
Hydraulic Retention Time
ICZ
Initial Contact Zone
Inf
Influent
IR
Internal Recycle
LR COD
Low Range Chemical Oxygen Demand
MLVSS
Mixed Liquor Volatile Suspended Solids
MMSD
Madison Metropolitan Sewerage District
N
Nitrogen
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NOB
Nitrite Oxidizing Bacteria
NOM
Natural Organic Matter
OFC
Oxygen Flow Controller
P
Phosphorus
PAO
Polyphosphate Accumulating Organism
PD
Predicted Data
PHB
Poly hydroxy butyrate
PI
Proportional-Integrative
PID
Proportional-Integrative-Derivative
RAS
Return Activated Sludge
RBCOD
Readily Biodegradable Chemical Oxygen Demand
RD
Real Data
SBS
Readily Biodegradable
SBSC
Readily Biodegradable Complex
SCFA
Short Chain Fatty Acid
SCOD
Soluble Chemical Oxygen Demand
SND
Simultaneous Nitrification Denitrification
SRT
Solids Retention Time
SUS
Unbiodegradable Soluble
SVI
Sludge Volume Index
TCOD
Total Chemical Oxygen Demand
TSS
Total Suspended Solids
UCT
University of Cape Town
VFA
Volatile Fatty Acids
WAS
Waste Activated Sludge
WWTP
Wastewater Treatment Plant
XS
Slowly Biodegradable
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XSC
Slowly Biodegradable Colloidal
XSP
Slowly Biodegradable Particulate
XUP
Unbiodegradable Particulate
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LIST OF FIGURES
FIGURE 1.1 UCT-TYPE PILOT PLANT. ............................................................................................................... 18
FIGURE 1.2 AO TYPE PILOT PLANT. ................................................................................................................. 21
FIGURE 2.1 WASTEWATER COMPONENTS ..................................................................................................... 29
FIGURE 2.2 CALIBRATION CURVE FOR LR COD. .............................................................................................. 33
FIGURE 2.3 CALIBRATION CURVE FOR LR COD ............................................................................................... 34
FIGURE 2.4 TCOD CONCENTRATION TRENDS IN INFLUENT WASTEWATER AND BOTH EFFLUENTS ................. 56
FIGURE 2.5 SCOD CONCENTRATION TRENDS. ................................................................................................ 58
FIGURE 2.6 RBCOD FRACTION (FBS) CONCENTRATION IN INFLUENT WASTEWATER. ..................................... 59
FIGURE 3.1 UCT PLANT BIOWIN SCHEME. ...................................................................................................... 63
FIGURE 3.2 AO PLANT BIOWIN SCHEME......................................................................................................... 63
FIGURE 3.3. SEASONAL TEMPERATURE IN WATER AT THE MMSD ................................................................. 65
FIGURE 3.4 UCT PLANT F/M RATIO THROUGH THE TIME. .............................................................................. 70
FIGURE 3.5 AO PLANT F/M RATIO THROUGH THE TIME. ................................................................................ 71
FIGURE 4.1 BIOWIN MODEL. .......................................................................................................................... 78
FIGURE 4.2 MODEL CALIBRATION PROCESS FLOW DIAGRAM. ....................................................................... 79
FIGURE 4.3 INFLUENT SPECIFIER SCREEN ....................................................................................................... 80
FIGURE 4.4 INFLUENT SPECIFIER FRACTIONS AND COMPONENTS RESULTS. .................................................. 81
FIGURE 4.5 EDITING INFLUENT SCREEN IN BIOWIN. ....................................................................................... 82
FIGURE 4.6 UCT CONTROLLER SCREENSHOT. ................................................................................................. 84
FIGURE 4.7 AO CONTROLLER SCREENSHOT. ................................................................................................... 85
FIGURE 4.8 BIOWIN CONFIGURATION OF UCT TYPE PILOT PLANT.................................................................. 87
FIGURE 4.9 BIOWIN CONFIGURATION OF AO TYPE PILOT PLANT. .................................................................. 87
FIGURE 4.10 AMMONIA CONCENTRATION IN TANK LD4. ............................................................................... 88
FIGURE 4.11 DO CONCENTRATION IN TANK LD3 ............................................................................................ 89
FIGURE 4.12 DO CONCENTRATION IN TANK LD4 ............................................................................................ 89
FIGURE 4.13 DO CONCENTRATION IN LD5...................................................................................................... 90
FIGURE 4.14 AMMONIA CONCENTRATION IN AO4 ........................................................................................ 93
FIGURE 4.15 NITRATE CONCENTRATION IN TANK AO4. ................................................................................. 94
FIGURE 4.16 NITRITE CONCENTRATION. ........................................................................................................ 94
FIGURE 4.17 DO CONCENTRATIONS IN TANK AO2 ......................................................................................... 95
FIGURE 4.18 REPRESENTS THE DO CONCENTRATION IN TANK AO3. .............................................................. 96
FIGURE 4.19 DO CONCENTRATIONS IN TANK AO4 ......................................................................................... 96
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FIGURE 4.20 DO CONCENTRATION IN TANK AO5 ........................................................................................... 97
FIGURE 4.21 HYDRAULIC RETENTION TIME DURING JANUARY 2021. ........................................................... 100
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LIST OF TABLES
TABLE 1.1 MAIN REACTIONS IN WASTEWATER TREATMENT PROCESSES. ...................................................... 24
TABLE 1.2 OPERATIONAL CONDITIONS IN BOTH PLANTS. .............................................................................. 26
TABLE 2.1. COD MEASUREMENT DEFINITIONS. .............................................................................................. 29
TABLE 2.2 CONCENTRATIONS AND ABSORBANCE OF KHP STANDARDS USED FOR LR COD TESTS .................. 32
TABLE 2.3 CONCENTRATIONS AND ABSORBANCE OF KHP STANDARDS USED FOR HR COD TESTS ................. 33
TABLE 2.4 COD FRACTION EQUATIONS. ......................................................................................................... 36
TABLE 2.5 COD COMPONENT EQUATIONS. .................................................................................................... 37
TABLE 2.6 TCOD RESULTS FROM JANUARY TO APRIL 2021. ............................................................................ 38
TABLE 2.7 AVERAGE TCOD VALUES. ............................................................................................................... 40
TABLE 2.8 SCOD RESULTS FROM JANUARY TO APRIL 2021. ............................................................................ 40
TABLE 2.9 AVERAGE SCOD VALUES. ............................................................................................................... 42
TABLE 2.10 GF COD RESULTS FROM JANUARY TO APRIL 2021. ....................................................................... 43
TABLE 2.11 AVERAGE GFCOD VALUES ............................................................................................................ 45
TABLE 2.12 FFCOD RESULTS FROM JANUARY TO APRIL 2021. ........................................................................ 45
TABLE 2.13 AVERAGE FFCOD VALUES............................................................................................................. 47
TABLE 2.14 VFA RESULTS FROM JANUARY TO APRIL 2021 ............................................................................. 47
TABLE 2.15 AVERAGE VFA VALUES. ................................................................................................................ 49
TABLE 2.16 COD FRACTIONS AND COMPONENTS. ......................................................................................... 51
TABLE 2.17. AVERAGE COD FRACTIONS. ........................................................................................................ 54
TABLE 2.18 AVERAGE COD COMPONENTS. .................................................................................................... 54
TABLE 2.19 COD COMPONENTS IN PERCENTAGE. .......................................................................................... 55
TABLE 3.1 PRIOR ART CONTINUOUS FLOW EXPERIENCE WITH BULKING SLUDGE CONTROL CONCEPTS......... 61
TABLE 3.2 F/M RATIO RESULTS FOR PAST AND CURRENT UCT CONFIGURATION. .......................................... 68
TABLE 3.3 F/M RATIO RESULTS FOR PAST AND CURRENT AO CONFIGURATION. ........................................... 68
TABLE 3.4 CORRELATION BETWEEN PROCESS VARIABLES IN THE AO PLANT. ................................................. 73
TABLE 3.5 CORRELATION BETWEEN PROCESS VARIABLES IN THE UCT PLANT. ............................................... 74
TABLE 3.6 CORRELATION BETWEEN TEMPERATURE AND TCOD. .................................................................... 74
TABLE 3.7 CONTROL PARAMETERS SUMMARY FOR UCT TYPE PILOT PLANT .................................................. 84
TABLE 3.8 CURRENT CONTROLLER PARAMETERS FOR AO. ............................................................................. 85
TABLE 3.9 OPERATIONAL CONDITIONS IN REAL PLANTS AND MODELS .......................................................... 86
TABLE 3.10 TOLERANCE LEVEL AND MAGNITUDES DEFINITION ..................................................................... 87
TABLE 3.11 REAL DATA AVERAGE CALCULATED ............................................................................................. 90
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TABLE 3.12 MODEL DATA AVERAGE VALUES .................................................................................................. 91
TABLE 3.13 CALCULATED ERRORS IN AMMONIA AND DO CONCENTRATIONS................................................ 91
TABLE 3.14 RD DATA COLLECTED FROM JANUARY 2021. ............................................................................... 98
TABLE 3.15 PD DATA FROM JANUARY 2021. .................................................................................................. 98
TABLE 3.16 ERRORS CALCULATED BETWEEN THE RD AND PD DATA SETS. ..................................................... 99
TABLE 3.17 HRT VALUES FOR PD AND RD DATA SETS. .................................................................................. 101
TABLE 3.18 INFLUENT FLOW RATE CALCULATED. METRICS FOR UCT AND AO PLANT. .................................. 102
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ACKNOWLEDGMENTS
I would have liked to personally thank everyone who collaborated with me in this
project, but the current pandemic conditions have changed our normal interactions. Thanks
God, this has been one of the most challenging and joyful stages of my engineering career,
giving me the chance to grow personally and professionally while studying and working with
such incredible and talented people.
Coming back to school from industry was very challenging. If I were not supported by
Dr. Noguera and Dr. Matt Seib this project would not have been possible. I really want to thank
Dr. Noguera for his essential support. His incredible intellect combined with his willingness to
teach, allowed him to answer all my questions despite his busy schedule.
Dr. Matt Seib has been a fundamental part of my learning process, always willing to
share his knowledge and show different ways to approach engineering problems with sharp
intelligence. I want to thank Lab Manager Jackie Cooper for all her support during my time in
the environmental engineering laboratory at Engineering Hall, always making sure that her
students have everything they need to pursuit new knowledge and achieve new goals.
In addition, I want to thank all my classmates and workmates during this process:
Trenton Weiss, Rachel Stewart, Carly Amstadt, Sara Neufcourt, Michael Liu, James Alvin,
Lucas LoBreglio and Kailey DeVault. All of you have been part of this project and it was a
pleasure to learn and share my knowledge with you.
A big thank you goes to the Laboratory Team of the Madison Metropolitan Sewerage
District: Jessica, Bill, Josh, Karol and all who participated somehow in this project. Your
support was fundamental to running the pilots successfully.
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I would like to thank all my professors at the Civil & Environmental Engineering
Department: Dr. Greg Harrington, Dr. Matt Ginder-Vogel, Dr. Jim Park, Dr. Trina McMahon,
and Dr. Dan Noguera. All my interactions with each of you taught me many different abilities
that are related not only to technical knowledge. Emotional intelligence and good manners have
been part of all this educational package.
I want to thank all Noguera Laboratory Group members. Abel Ingle was the first
member I met; thank you for helping me at the beginning of this journey. Coty and Diana-thank you for being cool, I hope to see you guys around.
I want to thank my wife Alejandra, who taught me how to handle difficult times with
patience and peace. In addition, I want to thank my family. My dad Augusto, my mom Isidora,
my older brother Augusto, my younger sister Dora, and my dear niece Mariana. Having the
opportunity to grow around you made me the person I am today. I hope you feel happy and
proud of me.
Finally, I want to thank my aunt Laura, who right now is fighting for her life against
Covid 19. She encouraged and supported me during bad times. I hope we can get together and
talk about this great experience later.
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ABSTRACT
Wastewater treatment is a complex process that involves the simultaneous application
of diverse sciences such as chemistry, biology, and engineering. Chemistry allows to describe
the composition of wastewater through the Total Chemical Oxygen Demand (TCOD), which
is an essential parameter in wastewater treatment. TCOD influences sludge production, oxygen
demand, anoxic denitrification, phosphorus removal, and effluent quality.
Typically, the TCOD concentration in domestic wastewater fluctuates within the range
of 250 to 800 mgCOD/L. TCOD value is dependent of the day and the time when the
measurement is done, as well as on the influent wastewater flowrate, which changes the
Hydraulic Retention Time (HRT). Greater flowrates decrease HRT, increasing the dilution of
the wastewater. Decreasing HRT consequently increases the oxygen required to treat the
wastewater due to more remaining COD and ammonia loads in the process. As a result of that,
COD has become an interesting parameter to study in wastewater treatment. The
characterization of COD into different components has become an essential set of data to model
different treatment systems.
Activated sludge modeling has grown to be an important part of the wastewater
treatment industry. Software developers have built powerful tools to predict and understand
current and future plant facilities. Two Biowin 6.0 models were built in this study, using the
Influent Specifier tool and the Biowin Controller tool, seeking to increase the models’ accuracy
in predictions related to treatment plant configurations.
In this project, 11 variables were analyzed with Biowin models. The successful
calibration of 7 out of 11 of the analyzed variables was possible. More work on the calibration
should be done to identify possible sources of error and identify the most sensitive parameters
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that influence the calibration of these models. The results allowed the identification of HRT as
a parameter that influences oxygen demand and the Solids Retention Time (SRT) as a
parameter that influences nutrient removal and microbial concentration.
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INTRODUCTION
Urban sanitation in the US has evolved from the mid-1800s practice of sewage
collection but little treatment, to partial treatment during the early to mid-1900s, to the Clean
Water Act era of federal regulations requiring secondary or greater treatment following the
Act’s passage in 1972 (NACWA, WERF, & WEF, 2013). Throughout time, improvements in
wastewater treatment technology had to respond to population increase, a wide range of climate
conditions, and changes in the wastewater characteristics. These factors have influenced how
wastewater treatment plants (WWTP) are designed, built, and operated.
Typically, WWTPs have well-defined treatment trains that comprise four different
stages (Tchobanoglous, Burton, Stensel, & David, 2003). The first stage is called pretreatment,
where the wastewater is screened to remove large objects and rapid settling is used to remove
sand. The second stage is called primary treatment, where settling is used to remove particulate
organic material. The third stage is the secondary treatment, which uses different types of
microorganisms to remove soluble organic and inorganic contaminants. Finally, the fourth
stage, called tertiary treatment, is primarily used for disinfecting the treated water before
discharging it into the environment. UV light or chlorine are typical disinfectants used during
wastewater treatment.
The performance of every WWTP depends upon many factors such as wastewater
composition, temperature, weather, influent flow rate, etc. One of the most important
parameters of the wastewater is the chemical oxygen demand (COD). This parameter
influences biomass concentration and solids production. The COD is the amount of oxygen
required to oxidize the natural organic matter (NOM) present in the water using a strong
oxidizer, and thus, it represents the concentration of NOM in the water. Influent wastewater
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COD values in municipal wastewater fluctuate between 250 and 800 mgCOD/L
(Tchobanoglous et al., 2003).
Another parameter that relates to the concentration of organic matter in the influent is
the biochemical oxygen demand 5 (BOD5), which is the oxygen required to oxidize in 5 days,
using microorganisms, the biodegradable organic matter present in the water (Boyles, 1997).
These two parameters are essential in secondary treatment processes and are determinant
factors during WWTP design. Therefore, the characterization of composition and strength of
the wastewater over time, as well as the contributions to COD from industrial and commercial
activities, will determine the final WWTP design (Davis, 2010).
This study focuses on measuring COD in the influent and effluent of two pilot plants
located at the Nine Springs WWTP, operated by the Madison Metropolitan Sewerage District
(MMSD) in Madison, WI, USA. The main objectives of this study are to evaluate COD
variations over time, calculate COD fractions and components, and employ those results in the
modeling of two wastewater treatment pilot plants using the Biowin Software
(EnviroSimAssociates, 2014). This study covers a period of 4 months, from January to April
2021. This time frame includes the coldest temperatures of the year.
Additionally, the COD data is used to calculate the Food to Mass Ratio (F/M)
parameter. The F/M ratio compares the load of the organic material entering a treatment process
per unit time to the concentration of microorganisms that decompose the organic material
(Szelag, Bartosz, Studziński, & Jan, 2017). This parameter might be useful to understand
variations in plant performance in terms of nitrogen (N) and phosphorus (P) removal, sludge
production, total suspended solids (TSS) concentrations, and sludge settleability.
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Two Biowin models were constructed to simulate the configurations of two pilot-scale
plants. The COD data collected and analyzed in this study served as an input in the software’s
influent specifier tool. In addition, the Biowin’s controller tool was used to recreate the
ammonia-based aeration control (ABAC) loops used on each treatment train to control
dissolved oxygen (DO) levels under intermittent and constant aeration, respectively.
This document has 6 chapters. In Chapter 1, a description of the pilot plants and their
operation, including key parameters, process configuration, volumes and control systems are
provided. In Chapter 2, the COD measurement procedure is described, including the COD
concept, materials, methods, and results. In Chapter 3, the F/M ratio is described, including its
relationship with some key variables, and results are analyzed. In Chapter 4, the Biowin
modeling procedure is described, including influent specifier and controller tools. Models and
results are discussed in this chapter. Chapter 5 summarizes the recommendations about actions
to take to improve the models’ results. Lastly, in Chapter 6, conclusions related to all
experimentation and modeling results are presented.
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CHAPTER 1: PILOT PLANT DESCRIPTION AND OPERATION
1.1 University of Cape Town (UCT)-type pilot plant
The UCT-type pilot plant is configured as a University of Cape Town (UCT) process
without internal recycle of nitrate (Figure 1.1). The UCT-type pilot plant has three distinct
zones. The first is the anaerobic zone, where no oxygen is present inside of the tanks and no
additional oxygen resource is provided for microbial activity (tanks LD1a, LD1b and LD1c in
Figure 1.1). Thus, there is no external electron acceptor present. The second is the anoxic zone,
where no air is pumped into the tank and nitrate is the external electron acceptor available (tank
LD2). The third zone is the aerobic zone where air is pumped directly to the tanks and oxygen
serves as the external electron acceptor (tanks LD3, LD4). The last tank (LD5) is a polishing
aerobic zone, where air is pumped into the tank permanently.
Figure 1.1 UCT-type pilot plant. Control system scheme detailing tanks, flows (solid lines),
pumps, controllers, and control signals (dash lines). Nomenclature: P1=influent pump, P2=
internal recycle (IR) pump, P3=Return activated sludge (RAS) pump, P4=Waste activated
sludge (WAS) pump, V1=Air valve in tank LD3, V2= Air valve in tank LD4, V3=Air valve in
tank LD5.
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The plant has a total volume of 773 gal (2926 L) including the clarifier unit. This
volume is distributed as follows: 111 gal (420 L) correspond to the anaerobic zone, 37 gal (140
L) is the anoxic zone volume, and 375 gal (1419 L) is the aerobic zone volume. The total
volume of the 3 zones is 523 gal (1979 L) and the clarifier volume is 250 gal (946 L). The
anaerobic zone is composed by 3 tanks of 37 gal (140 L) each, labeled as LD1a, LD1b and
LD1c. The anoxic zone is only one tank of 37 gal (140 L) labeled as LD2. The aerobic zone is
composed by 3 tanks of 125 gal (473 L) each, labeled as LD3, LD4, LD5, respectively. Each
tank has its own mixer system. Mixing was conducted intermittently (every 30 mins) in the
anaerobic and anoxic tanks, and constantly in the aerobic tanks.
The plant has 4 pumps (P1, P2, P3 and P4). Pump P1 controls the flow rate of primary
effluent into the treatment train (influent to the pilot plant). This pump is controlled by an
external control system to mimic the daily variations of full-scale influent. Pump P3 sends
return activated sludge (RAS) from the bottom of the clarifier to thank LD2, P4 pushes the
waste activated sludge (WAS), and P2 drives the internal recycle flow (IR) from tank LD2 to
tank LD1a. Pumps P2, P3, and P4 are manually adjusted according to daily requirements.
Additionally, the plant has an ammonia-based aeration control (ABAC) system to
control the air pumped into the aeration tanks (LD3, LD4, LD5) according to the measured
concentration of ammonia in tank LD4, measured with an ammonia sensor (AmmoLyt Plus
700 IQ, YSI, Yellow Springs, OH). The system uses a proportional integrative (PI) controller
with the following criteria to set the valve position controlling air flow into the tanks: the
ammonia set point (ASP) in tank LD4 must be 5 mgNH3-N/L all time, and this ASP should be
maintained using a maximum DO concentration of 0.35 mgO2/L and a minimum DO of 0.1
mgO2/L. To perform this control action, the ammonia sensor senses periodically (every minute)
the concentration of ammonia in tank LD4. Additionally, the DO concentrations in tanks LD3,
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LD4 and LD5, are measured using DO sensors (IQ SensorNet FDO 701, YSI, Yellow Springs,
OH).
The control system used in this plant is a cascade control type, where the ammonia
controllers and oxygen flow controllers (OFC) are linked, giving continuous aeration but at
different rates to tanks LD3, LD4 and LD5. The control system has one ammonia sensor and
three DO sensors. The ammonia signal readout is used to compare its value with the ASP,
meanwhile the DO read signals in each of the tanks, are used to compare the current DO values
with the instantaneous DO set point (DOSP) values in each tank. The resulting DO signals
calculated by the OFC are feed to the ammonia controller, which finally will make the required
adjustment in the air flow rate adjusting the valves (V1, V2, V3) opening percentage, according
to the ammonia concentration value in LD4 as well as the measured DO values for each tank.
1.2 AO-type pilot plant
The Anoxic/oxic (AO)-type plant is a secondary treatment train with three different
zones. The plant scheme is shown in Figure 1.2. In the AO plant, the first stage is the anaerobic
zone, where no air is pumped into the tanks and no other oxygen resource is available. The
second is a double function zone, where aerobic and anoxic conditions are intermittently
achieved according to measured ammonia concentrations. The last tank (AO5) is a polishing
aerobic zone, where air is pumped into the tank permanently. The plant has three pumps (P5,
P6 and P7) for influent, RAS, and WAS flows, respectively. The anaerobic zone is split in four
tanks of 37 gal (140 L) each, labeled as AO1a, AO1b, AO1c and AO1d in Figure 1.2. The
aerobic/anoxic zone is split in three tanks of 125 gal (473 L) of capacity each. The last zone is
the aerobic zone, composed by one tank of 50 gal (189 L). The tanks are labeled as AO2, AO3,
AO4 and AO5, respectively. The total volume of all the zones is 573 gal (2169 L). The plant
has a total volume of 893 gal (3380 L) including the clarifier. The anaerobic zone volume is
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148 gal (560 L) and the aerobic/anoxic zone is 375 gal (1419 L), and the aerobic zone is 50 gal
(189 L). The volume of the clarifier is 320 gal (1211 L).
Figure 1.2 AO type pilot plant. Control system scheme detailing tanks, flows (solid lines),
pumps, controllers, and control signals (dash lines). Nomenclature: P5=Influent pump,
P6=RAS pump, P7=WAS pump, V4=Air valve in tank AO2, V5=Air valve in tank AO3,
V6=Air valve in tank AO4, V7=Air valve in tank AO5.
The AO plant has an ABAC cascade control system, which works measuring the
ammonia concentration in tank AO4 as well as the DO concentrations in AO2, AO3, AO4 and
AO5, giving intermittent aeration to tanks AO2, AO3 and AO4 according to the ammonia
concentration in tank AO4. Intermittent aeration is the main characteristic of the AO-type
plant as well as the main difference with the UCT-type pilot plant, which performs continuous
aeration.
The readout DO concentrations are compared in the OFC with the instantaneous DO
set point, then the ammonia concentration is used by the ABAC to compare it with the ammonia
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set point. Then, the DO values calculated in the OFC are used to feed the ABAC controller and
adjust the air flow rate through the air valves. The main goal of this control system is to
maintain a cycling ammonia concentration between 2 and 5 mgNH3-N/L through the
manipulation of the DO concentration in tanks AO2, AO3 and AO4 using the air flow valves
(V4, V5 and V6). The tanks have one independent mixing system each, which periodically
mixes the anaerobic tanks using 30 mins periods, and constantly mixes the aerobic/anoxic
tanks.
As mentioned previously, this control system uses intermittent aeration, where the air
flow is completely stopped periodically. A minimum DO value of 0 mgO2/L and a maximum
DO value of 0.5 mgO2/L is set for tank AO2. A maximum value of DO concentration of 0.7
mgO2/L is set for tanks AO3 and AO4, and a minimum of 0 mgO2/L is set for both tanks.
Finally, a DO of 1.5 mgO2/L in tank AO5 is achieved constantly, independent from ammonia
concentration in tank AO4. In summary, the intermittent aeration in the aerobic/anoxic zone,
changes the air flow rate in tanks AO2, AO3 and AO4, depending upon the concentration of
ammonia in tank AO4.
1.3 Low DO conditions
A secondary process within a WWTP removes organic matter, nitrogen, and
phosphorus from the wastewater. To achieve all these goals, the WWTP uses different
bioprocesses such as nitrification, denitrification, and enhanced biological phosphorus removal
(EBPR). In principle, at DO conditions below 0.5 mgO2/L, nitrification and denitrification can
be simultaneously achieved in a WWTP. This bioprocess is called simultaneous nitrificationdenitrification (SND). This is possible if the aeration system can create an oxygen gradient
through the tanks as well as through the activated sludge flocs.
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The SND process has two different steps. The first is biological nitrification (BN),
where ammonia or ammonium (NH4-N) are oxidized and converted to nitrite (NO2-N) by
ammonia-oxidizing bacteria (AOB). Then, NO2-N is oxidized to nitrate (NO3--N) by nitriteoxidizing bacteria (NOB). After biological nitrification, limiting DO concentration results in
biological denitrification (BD) where (NO3--N) is reduced to nitrogen (N2) gas.
Phosphorus is removed from the wastewater through the EBPR process. Polyphosphate
accumulating organisms (PAOs) consume phosphorus and internally accumulate large
amounts of polyphosphate (Dold & Conidi, 2019). In the anaerobic zone, fermenting organisms
produce acetate (C2H3O2-) through the fermentation of readily biodegradable COD (RBCOD).
Subsequently, the PAOs assimilate the acetate, producing poly hydroxybutyrate (PHB), which
is an internally stored organic polymer (Tchobanoglous et al., 2003). The energy needed for
acetate assimilation and PHB production comes from the breakdown of polyphosphate and
glycogen polymers inside the organisms. As a result of this metabolism, the concentration of
PHB inside the cell increases, while the polyphosphate concentration decreases, and phosphate
is released from the cells. Subsequently in the aerobic zone, the stored PHB is oxidized
producing energy for new biomass growth. Some of that energy is used to form polyphosphate
bonds inside the cells, so the orthophosphate is taken up from the water and then incorporated
into polyphosphate inside the cells. Finally, some part of the RAS is wasted as WAS, which
goes to anaerobic digestion containing PAOs with high content of phosphorus.
Some chemical and environmental factors affect COD removal, SND and EBPR. For
COD removal the most adequate pH value is 7, but the process still works well within values
of 6 to 9. The aeration regime is critical to obtaining SND by creating an oxygen gradient
through the tanks of the aeration zone, creating an oxygen gradient within the flocs, or setting
certain zones with greater DO in some specific parts of the tank and the floc.
23
The reactions involved in COD removal, SND, and EBPR are listed in the Table 1.1
(Tchobanoglous et al., 2003).
Table 1.1 Main reactions in wastewater treatment processes.
Typically, the DO concentration employed in a WWTP to stimulate aerobic conditions
is 2.0 mgO2/L (Keene et al., 2017). This DO concentration can vary through the aerobic zone
according to local parameters, influent wastewater characteristics, weather and pluviosity in
each WWTP. Maintaining an adequate DO concentration and oxygen gradient through the
tanks is essential to achieve SND and EBPR, but the energy required for aeration is a big
concern in terms of environmental and economic evaluation due to the high amounts of energy
and money required to pump air and maintain certain DO concentration through the whole
treatment.
Hence, many scientists have been researching different forms to reduce energy and cost
during aeration while maintaining good nutrient removal through SND and EBPR. One
interesting alternative is the aeration under low DO conditions, where the concentration of
oxygen might be decreased below 0.3 mgO2/L (Keene et al., 2017), maintaining SND and
EBPR and achieving 80 to 90% of N and P removal, respectively. Running a WWTP under
24
low DO conditions can reduce energy demand by 25% (Keene et al., 2017). Additionally, it is
well known that the energy demand for aeration in every WWTP represents between 50 to 60%
of the total energy demand required for the whole treatment operation (Bayer, 2018). The Low
DO conditions are part of a long-term strategy which looks for energy self-sufficiency in
WWTP operation.
Furthermore, two important parameters are the solids retention time (SRT) and the
hydraulic retention time (HRT), which are adjusted according to the changes in the seasonal
temperature and kinetics of the organisms participating in each activated sludge (AS) process.
The SRT is the average time that a cell spends inside of the whole treatment train and it is
measured in days. The HRT is the average time that a drop of water spends inside of the whole
treatment train and it is measured in hours. SRT influences microbial concentrations and
nutrient removal, whereas HRT influences wastewater dilution and oxygen demand through
the process.
1.4 Pilot plant operation
An optimum reduced aeration strategy plus an optimized anaerobic digestion process
with combined heat and power units (CHPU) is the current goal for many WWTP around the
world (Wan, Gu, Zhao, & Liu, 2016). In this study, both pilot plants are working under Low
DO conditions, using the parameters summarized in Table 1.2.
25
Table 1.2 Operational conditions in both plants.
Nomenclature: INF=influent, IR= internal recycle, RAS=return activated sludge, SRT=solids
retention time, HRT=hydraulic retention time, DO=dissolved oxygen concentration.
Both configurations have differences in DO concentrations and flows. In the UCT-type
pilot plant, the internal recycle IR brings mixed liquor to increase the mixed liquor volatile
suspended solids (MLVSS) concentration in the anaerobic zone. In contrast, the AO
configuration does not have an IR flow. The AO plant directly uses the RAS flow to increase
the MLVSS in the anaerobic zone. The aeration in the UCT is continuous and in the AO is
intermittent. Additionally, the DO concentrations in the UCT are half of the concentrations in
tanks LD3, LD4 compared to tanks AO3 and AO4. Other important difference in the AO
configuration is that it has one additional tank in the aeration zone, tank AO2 where the
concentration of oxygen is 0.5 mgO2/L. Moreover, the DO concentrations in the polishing tanks
are different. In tank LD5 the DO is 1 mgO2/L while in AO5 the DO concentration is 1.5
mgO2/L. L
All these differences determine different behaviors throughout the treatment trains in
terms of biological and chemical processes. As a result, the physical process of settling is
affected. The main parameter to measure the sludge settling is the sludge volume index (SVI)
26
which measures how fast the sludge settle in a 2-liter cylinder during a 30 min period. Other
sludge features such us bulking and foaming might be evaluated to understand different process
behaviors.
The average SRT and HRT in both plants are very similar. In this case study, the influent
wastewater coming to both plants is pushed by two different pumps controlled by a flow
controller that simulates the increments and decrements in influent flow rate according to full
scale influent flow. This controller was placed to mimic influent flow behavior in the full-scale
plant and to provide variable loads of COD, N and P to the plants. This flow controller was set
up to understand more accurately how these factors change plant´s behavior in terms of SND
and EBPR performance. All factors will be touched upon further in this study. COD plays an
essential role in all these bioprocesses and it is described with more detail in the next chapter.
27
CHAPTER 2: CHARACTERIZATION OF ORGANIC MATTER IN INFLUENT VIA
COD ANALYSES
2.1 The COD Concept
COD is one of the most important parameters in WWTP operations. The COD concept
is defined as a measurement of the oxygen required to oxidize the organic matter present in the
water using a strong chemical oxidizer, but this definition is limited. COD is a bigger concept,
where its different fractions play an important role in the wastewater treatment process.
Detailed data about organic fractions of influent wastewater is indispensable to
understand the performance of a treatment process and develop detailed modeling scenarios
(Pasztor, Thury, & Pulai, 2009). Influent COD fractions can substantially influence the results
of simulation-based design such as reactor volumes, SRT, effluent quality, oxygen demand,
and sludge production (Pasztor et al., 2009). It is important to measure this parameter and
quantify the different COD fractions and components to characterize the influent wastewater
more accurately. The COD in wastewater has three main components: the biomass, the
biodegradable COD, and the unbiodegradable COD.
The biodegradable COD is divided in two different components, the readily
biodegradable RBCOD (𝑆𝐵𝑆 ), which is divided in complex COD (𝑆𝐵𝑆𝐶 ) and the short chain
fatty acid COD (SCFA)(𝑆𝐵𝑆𝐴 ). The slowly biodegradable COD, SBCOD (𝑋𝑆 ), is composed by
colloidal COD (𝑋𝑆𝐶 ) and particulate COD (𝑋𝑆𝑃 ). In addition, the unbiodegradable COD has
two components, the soluble unbiodegradable COD (𝑆𝑈𝑆 ) and the particulate unbiodegradable
COD (𝑋𝑈𝑃 ). Figure 2.1 shows the relationship between these components:
28
Figure 2.1 Wastewater components. Biomass, biodegradable and unbiodegradable COD
(EnviroSimAssociates, 2016).
Using the wastewater components described in Figure 2.1, definitions in Table 2.1 were
used to link the field measurements with the wastewater components.
Table 2.1. COD measurement definitions.
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡
𝑁𝑜𝑚𝑒𝑛𝑐𝑙𝑎𝑡𝑢𝑟𝑒
𝐷𝑒𝑓𝑖𝑛𝑖𝑡𝑖𝑜𝑛
𝑇𝑜𝑡𝑎𝑙 𝐶𝑂𝐷
𝑇𝐶𝑂𝐷
𝑇𝐶𝑂𝐷 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 (𝐼𝑛𝑓 & 𝐸𝑓𝑓)
𝑆𝑜𝑙𝑢𝑏𝑙𝑒 𝐶𝑂𝐷
𝑆𝐶𝑂𝐷
0.45 𝑚𝑖𝑐𝑟𝑜𝑛 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 (𝐼𝑛𝑓 & 𝐸𝑓𝑓)
𝐺𝑙𝑎𝑠𝑠 𝐹𝑖𝑙𝑡𝑒𝑟 𝐶𝑂𝐷
𝐺𝐹𝐶𝑂𝐷
1.2 𝑚𝑖𝑐𝑟𝑜𝑛 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 (𝐼𝑛𝑓 & 𝐸𝑓𝑓)
𝐹𝑙𝑜𝑐 & 𝐹𝑖𝑙𝑡𝑒𝑟 𝐶𝑂𝐷
𝐹𝐹𝐶𝑂𝐷
𝐶𝑜𝑎𝑔𝑢𝑙𝑎𝑡𝑒𝑑 + 0.45 𝑚𝑖𝑐𝑟𝑜𝑛 (𝐼𝑛𝑓&𝐸𝑓𝑓)
𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑒 𝐹𝑎𝑡𝑡𝑦 𝐴𝑐𝑖𝑑𝑠
𝑉𝐹𝐴
𝑉𝐹𝐴 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 (𝐼𝑛𝑓)
29
The COD fractions and components mentioned in Figure 2.1 provide information to
evaluate different aspects of the treatment process, such as:
•
Sludge production: Inert particulate COD
•
Oxygen demand: Total biodegradable COD
•
Anoxic denitrification and anaerobic phosphorus release: Readily biodegradable COD
•
Effluent COD: Inert soluble COD
In this study, the pilot plants received as influent wastewater the primary effluent (PE)
from the full-scale process at the MMSD.
Another important parameter to understand WWTP and their modeling procedure is the
weather. Since the city of Madison, Wisconsin, has four seasons during the year, it is essential
to consider temperature and pluviosity to comprehend how those conditions affect the WWTP
performance in terms of effluent quality, air demand, and energy consumption.
2.2 COD materials, sampling, and measurement
2.2.1
Materials
To perform the COD measurement, a 5 mL pipette (Eppendorf Research Plus) was used
to collect and prepare samples. The pipette used 5 mL pipette tips (Thermo ScientificTM
Finntip). A vacuum pump (Gast DOA-P704-AA Diaphragm Vacuum/Pressure Pump, Plastic,
115V, 60 Hz) was used to perform some steps in the filtration of the GFCOD samples collected
from the pilot plants. For manual filtration of SCOD and VFA, 20 mL syringes (Fisherbrand™
Sterile Syringes for Single Use) were used as well as two different filters, syringe filters (PES
Syringe Filters, 0.45 µm, 25 mm, Luer-Lok/Luer Slip) and GF filters (Cytiva Whatman Grade
30
GF/C Glass Microfiber 1.2 µm Filters). Sample collection used two different types of tubes, 50
mL sampling tubes (FalconTM 50mL Conical Centrifuge Tubes) for raw samples, and 15 mL
sampling tubes (Falcon™ 15 mL Conical Centrifuge Tubes) for filtered samples.
The sampling used 1.1-liter sampling bottles (Thermo Scientific™ HDPE Cylinder
Round Bottles) for collection of raw samples. The COD test used the High Range (HR) COD
kits (range 20-1500 mgCOD/L, HachTM COD Digestion Vials, High Range, pk/150) for all
influent samples, and the Low Range (LR) COD kits (3-150 mgCOD/L, HACH COD Digestion
Vials, Low Range, pk/150) for effluent samples. Potassium hydrogen phthalate (KHP BioXtra,
≥99.95%, Millipore Sigma) was used to prepare standard solutions for COD tests; a KHP
solution of 8.5g KHP/L was used for HR COD tests and a KHP solution of 0.85 g KHP/L for
LR COD tests.
To perform coagulation, an aluminum sulfate solution was prepared using 10 g of
aluminum sulfate (98%; Aldrich Chem Company) per liter of deionized water. Digestion of
COD vials used a Block digester (Corning LSE Digital Dry Bath, range up to 150°C). Finally,
to read the vials after digestion, a visible light spectrophotometer (Thermo Scientific™
GENESYS™ 20) was used.
2.2.2
Sampling procedure
The sampling procedure used to determine the COD fractionation and components was
a combination of practical measurements of COD, and theoretical calculations based upon the
assumption of all the effluent COD is unbiodegradable. The experimental procedure is based
on the procedure of Wentzel, Mbewe, Lakay, and Ekama (2000). The procedure is shown
below:
31
➢ Before starting the measurement, a calibration curve was set using a KHP solution. The
concentration of the solution was 8.5 g KHP/L for HR COD, and 0.85 g KHP/L for LR
COD. Each of these solutions were used to prepare different KHP dilutions to create
two calibrations curves. Table 2.2 and Figure 2.2 show the calibration for LR COD
calibration whereas Table 2.3 and Figure 2.3 show the HR COD calibration.
Table 2.2 Concentrations and absorbance of KHP standards used for LR COD tests
Standards for LR COD (Feb 8/2021)
mg COD/L
Absorbance
0.00
0.484
9.37
0.429
18.75
0.424
37.50
0.356
75.00
0.242
150.00
0.000
32
Calibration Curve for LR COD
0.600
0.500
Absorbance
ABS
0.400
y = -0.0032x + 0.4757
R² = 0.9973
0.300
Linear
(Absorbance)
0.200
0.100
0.000
0.000
50.000
100.000
150.000
200.000
mgCOD/L
Figure 2.2 Calibration curve for LR COD.
Table 2.3 Concentrations and absorbance of KHP standards used for HR COD tests
Standards for HR COD (Feb 8/2021)
mg COD/L
Absorbance
0.00
0.000
50.00
0.027
150.00
0.070
250.00
0.117
500.00
0.240
750.00
0.352
1500.00
0.692
33
Calibration Curve for HR COD
0.800
0.700
y = 0.00046x + 0.00311
R² = 0.99979
0.600
ABS
0.500
0.400
Absorbance
0.300
Linear (Absorbance)
0.200
0.100
0.000
0.000
500.000
1000.000
1500.000
2000.000
mgCOD/L
Figure 2.3 Calibration curve for LR COD
➢ The sampling took place from January 14th, 2021 to April 30th, 2021, sampling 4 days
a week (Tuesday, Thursday, Friday, and Saturday), and sampling between 8 a.m. to 9
a.m. each day.
➢ Samples of 1.1 liter were collected from the influent (Inf), and the effluent of the AOtype plant and the effluent of the UCT-type plant.
➢ From each of the 1.1-liter samples, aliquots were taken to prepare the additional
samples as follows:
•
3 samples of 15 mL for TCOD
•
3 samples of 15 mL for SCOD
•
3 samples of 15 mL for GFCOD
•
3 samples of 15 mL for FFCOD
•
3 samples of 15 mL for volatile fatty acids (VFA). These samples were tested by
the MMSD laboratory personnel.
➢ The TCOD samples were stored without any treatment or filtration.
34
➢ The SCOD samples were filtered using a 0.45-micron syringe filter.
➢ The GFCOD samples were filtered using a 1.2-micron glass fiber filter during vacuum
filtration.
➢ The FFCOD samples were flocculated using a solution of aluminum sulfate (10 g/L) in
deionized water, using 10 mL of the solution in each of the initial 1-liter samples of
influent (Inf), AO effluent (AO Eff) and UCT effluent (UCT Eff). Then, each sample
was well mixed at 100 rpm for 2 min. Then, samples were settled for 30 min, and 15
mL of the supernatant were taken from each sample. Lastly, the supernatant samples
were filtered with syringe 0.45-micron filters.
The COD measurement procedure is described in the steps below:
➢ Two mL of each of the influent samples, Inf, were added to one HR COD test tube. For
both effluent flows, AO Eff and UCT Eff, the LR COD Hach kit was used, where 2 mL
of each sample were added to each individual Hach kit test tube.
➢ After adding the 2 mL of each sample to each corresponding tube, and sealing the tubes,
the tubes were mixed manually for 5 sec, then the vials were placed on a block digester
for 2 hours at 150°C degrees.
➢ Once digestion was completed the tubes could cool at room temperature for
approximately 20-25 min.
➢ In the final reading step for HR COD, the wavelength used in the spectrophotometer
was 600 nm, and for LR COD the wavelength was 420 nm. Then the result was recorded
and related to the calibration curve using the linear equation generated based in the
calibration result and shown in each corresponding figure.
Once the resulting absorbance measurements were determined, they were used as input to
calculate the COD fractions and COD components using the equations shown in Table 2.4 and
35
Table 2.5, respectively. The definitions for terms used in these equations are included in Table
2.1 (EnviroSimAssociates, 2016):
Table 2.4 COD fraction equations.
Formulas used in
Formulas used in
AO plant
UCT plant
𝐹𝐹𝐶𝑂𝐷 𝐴𝑂𝐸𝑓𝑓
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
𝐹𝐹𝐶𝑂𝐷 𝑈𝐶𝑇𝐸𝑓𝑓
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
̅̅̅̅
𝑆
𝐵𝑆
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
̅̅̅̅
𝑆
𝐵𝑆
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
𝑋𝑈𝑃(𝐴𝑂) (𝑋𝐼 𝑖𝑛𝑒𝑟𝑡)
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
𝑋𝑈𝑃(𝑈𝐶𝑇) (𝑋𝐼 𝑖𝑛𝑒𝑟𝑡)
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
Equation
𝑓𝑈𝑆
(𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑢𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑠𝑜𝑙𝑢𝑏𝑙𝑒)
𝑓𝐵𝑆
(𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑟𝑒𝑎𝑑𝑖𝑙𝑦 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒)
𝑓𝑈𝑃
(𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑢𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑝𝑎𝑟𝑡𝑖𝑐𝑢𝑙𝑎𝑡𝑒)
Note that the readily biodegradable 𝑆𝐵𝑆 composition used to calculate the 𝑓𝐵𝑆 is the average
within the AO and the UCT configuration values. This was done to obtain a single value for
fBS that represented the influent characteristics, instead of having two different values, one from
each plant. Equation 2.1 shows the calculation done:
Equation 2.1 Average readily biodegradable 𝑺𝑩𝑺 calculation.
̅̅̅̅
𝑆
𝐵𝑆 =
𝑆𝐵𝑆(𝐴𝑂) + 𝑆𝐵𝑆(𝑈𝐶𝑇)
2
36
Table 2.5 COD component equations.
Formulas used in
Formulas used in
AO plant
UCT plant
𝐺𝐹𝐶𝑂𝐷𝐼𝑛𝑓 − 𝐹𝐹𝐶𝑂𝐷𝐼𝑛𝑓
𝐺𝐹𝐶𝑂𝐷𝐼𝑛𝑓 − 𝐹𝐹𝐶𝑂𝐷𝐼𝑛𝑓
𝑓𝑈𝑆(𝐴𝑂) ∗ 𝑇𝐶𝑂𝐷 𝐼𝑛𝑓
𝑓𝑈𝑆(𝑈𝐶𝑇) ∗ 𝑇𝐶𝑂𝐷 𝐼𝑛𝑓
Equation
𝑋𝑆𝐶
(𝑆𝑙𝑜𝑤𝑙𝑦 𝑐𝑜𝑙𝑙𝑜𝑖𝑑𝑎𝑙 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒)
𝑆𝑈𝑆
(𝑈𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑠𝑜𝑙𝑢𝑏𝑙𝑒)
𝑋𝑈𝑃 (𝑋𝐼 𝑖𝑛𝑒𝑟𝑡)
𝑇𝐶𝑂𝐷 𝑈𝐶𝑇𝐸𝑓𝑓
𝑇𝐶𝑂𝐷 𝐴𝑂𝐸𝑓𝑓 − 𝑆𝑈𝑆(𝐴𝑂)
(𝑈𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑃𝑎𝑟𝑡𝑖𝑐𝑢𝑙𝑎𝑡𝑒)
𝑆𝐵𝑆
− 𝑆𝑈𝑆(𝑈𝐶𝑇)
𝐹𝐹𝐶𝑂𝐷 𝐼𝑛𝑓 − 𝑆𝑈𝑆(𝐴𝑂)
𝐹𝐹𝐶𝑂𝐷 𝐼𝑛𝑓 − 𝑆𝑈𝑆(𝑈𝐶𝑇)
𝑉𝐹𝐴𝐼𝑛𝑓
𝑉𝐹𝐴𝐼𝑛𝑓
𝑆𝐵𝑆(𝐴𝑂) − 𝑆𝐵𝑆𝐴
𝑆𝐵𝑆(𝑈𝐶𝑇) − 𝑆𝐵𝑆𝐴
(𝑅𝑒𝑎𝑑𝑖𝑙𝑦 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒)
𝑆𝐵𝑆𝐴
(𝑆ℎ𝑜𝑟𝑡 𝑐ℎ𝑎𝑖𝑛 𝑓𝑎𝑡𝑡𝑦 𝑎𝑐𝑖𝑑)
𝑆𝐵𝑆𝐶
(𝑅𝑒𝑎𝑑𝑖𝑙𝑦 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑐𝑜𝑚𝑝𝑙𝑒𝑥)
𝑇𝐶𝑂𝐷𝐼𝑛𝑓 − 𝑆𝐵𝑆(𝐴𝑂) − 𝑋𝑈𝑃(𝐴𝑂)
𝑇𝐶𝑂𝐷𝐼𝑛𝑓 − 𝑆𝐵𝑆(𝑈𝐶𝑇) − 𝑋𝑈𝑃(𝑈𝐶𝑇)
𝑋𝑆𝑃 (𝑆𝑆𝑃 )
− 𝑆𝑈𝑆(𝐴𝑂)
− 𝑆𝑈𝑆(𝑈𝐶𝑇)
− 𝑋𝑆𝐶(𝐴𝑂)
− 𝑋𝑆𝐶(𝑈𝐶𝑇)
(𝑆𝑙𝑜𝑤𝑙𝑦 𝑝𝑎𝑟𝑡𝑖𝑐𝑢𝑙𝑎𝑡𝑒 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒)
𝑋𝑆
𝑋𝑆𝑃(𝐴𝑂) + 𝑋𝑆𝐶
𝑋𝑆𝑃(𝑈𝐶𝑇) + 𝑋𝑆𝐶
∑ 𝐴𝑙𝑙 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 𝑇𝑎𝑏𝑙𝑒 6
∑ 𝐴𝑙𝑙 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 𝑇𝑎𝑏𝑙𝑒 6
(𝑆𝑙𝑜𝑤𝑙𝑦 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒)
𝑇𝐶𝑂𝐷𝐼𝑛𝑓
(𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑡 𝐶𝑂𝐷)
37
2.3 COD sampling results and discussion
The results obtained from the measurements of Table 2.1 for Total COD in Inf, AO Eff
and UCT Eff COD concentration are show in Table 2.6 below:
Table 2.6 TCOD results from January to April 2021.
Date
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
TCOD (mgCOD/L)
Inf
AO Eff
428.02
441.07
447.59
493.24
451.93
445.41
434.54
475.85
451.93
423.67
499.76
423.67
460.63
434.54
395.41
423.67
528.02
525.85
391.07
373.67
678.02
473.67
369.33
428.02
791.07
495.41
504.11
410.63
454.11
545.41
543.24
412.80
495.41
8.66
28.34
13.03
12.09
2.72
13.03
11.47
21.78
23.97
14.91
26.47
14.91
18.03
8.66
10.22
7.41
30.53
30.53
61.78
21.16
33.34
30.22
45.22
39.28
35.53
54.28
23.34
26.16
32.09
37.09
68.03
27.09
87.09
38
UCT Eff
6.47
31.78
22.09
26.16
12.72
27.41
73.34
18.03
10.53
35.84
35.22
35.84
23.03
17.72
27.41
53.03
28.97
29.91
49.28
14.91
43.97
36.47
30.53
98.34
31.78
37.72
41.47
23.66
58.34
57.72
102.09
30.22
64.28
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
460.63
499.76
391.07
417.15
386.72
484.54
517.15
482.37
343.24
341.07
423.67
382.37
467.15
447.59
388.89
417.15
384.54
380.20
471.50
493.24
391.07
430.20
347.59
536.72
499.76
406.28
367.15
306.28
436.72
536.72
480.20
473.67
332.37
419.33
399.76
382.37
367.15
378.02
338.89
491.07
458.46
404.11
536.72
469.33
31.16
27.41
24.91
35.84
43.97
33.66
38.34
25.84
28.34
36.78
36.47
19.59
27.72
23.34
61.16
19.59
28.34
16.47
49.91
42.72
36.16
22.72
19.91
33.34
25.53
43.34
33.97
21.16
32.09
43.66
36.16
34.91
36.16
25.22
34.91
26.47
29.28
42.41
48.34
48.66
53.66
23.34
39.59
42.72
39
39.28
26.47
38.03
36.78
42.41
39.59
37.09
40.53
27.41
31.16
34.91
21.47
24.28
18.34
83.97
19.59
34.28
26.78
36.78
40.22
37.41
75.53
31.47
32.09
18.97
28.66
33.66
22.72
30.84
40.22
35.84
40.53
42.41
16.16
39.59
38.03
32.41
32.72
43.03
78.97
53.34
22.72
39.28
53.97
Table 2.7 Average TCOD values.
TCOD (mg/L)
Flow
Average
SD
Max
Min
Inf
445.75
53.25
791.07
306.28
AO Eff
31.26
10.81
87.09
2.72
UCT Eff
37.07
12.15
102.09
6.47
According to the results in Table 2.7, the TCOD value in the influent water is 445±53
mgCOD/L reaching a maximum value of 791.07 mgCOD/L and a minimum value of 306.28
mgCOD/L. These results allow for the conclusion that the wastewater treated at the MMSD
has medium strength, according to Tchobanoglous (2003). This is mostly due to the low
contaminant load received from routine domestic activities and the lack of large contributions
from industrial sources.
Table 2.8 SCOD results from January to April 2021.
Date
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
SCOD (mgCOD/L)
Inf
AO Eff
251.93
6.47
332.37
21.16
295.41
1.78
332.37
6.47
332.37
1.78
321.50
6.16
267.15
15.53
328.02
12.09
451.93
23.97
264.98
0.22
349.76
22.09
264.98
4.91
308.46
15.22
238.89
5.84
40
UCT Eff
8.34
17.41
3.66
2.09
1.47
10.84
4.91
2.72
10.53
1.16
4.28
2.09
12.72
1.78
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
286.72
249.76
323.67
293.24
297.59
217.15
551.93
258.46
236.72
236.72
264.98
286.72
299.76
234.54
247.59
317.15
280.20
264.98
328.02
297.59
325.85
243.24
245.41
228.02
291.07
369.33
367.15
243.24
184.54
288.89
243.24
234.54
275.85
219.33
293.24
264.98
275.85
304.11
247.59
234.54
291.07
206.28
345.41
367.15
41
11.78
8.66
0.22
9.59
5.53
3.34
23.03
22.72
28.34
22.09
28.34
31.78
21.16
13.03
18.34
15.22
19.59
19.91
30.22
28.34
24.28
18.34
18.34
35.84
20.22
32.41
1.78
20.84
17.72
36.78
18.97
29.59
12.09
11.78
16.78
24.91
21.16
36.16
30.53
33.03
21.16
14.59
30.84
25.53
14.28
1.16
1.47
9.91
3.97
0.22
22.72
21.16
29.91
33.03
27.09
33.03
23.03
19.28
18.97
26.78
19.59
58.97
32.72
25.84
20.53
18.66
11.16
37.41
15.53
31.78
26.16
19.91
25.53
33.34
24.59
16.16
28.97
15.53
17.41
22.41
13.97
36.78
32.09
28.03
29.28
17.41
26.47
13.03
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
367.15
225.85
195.41
282.37
388.89
336.72
345.41
191.07
278.02
264.98
258.46
208.46
197.59
158.46
321.50
306.28
260.63
321.50
293.24
22.09
27.72
20.53
27.41
33.66
28.03
28.34
21.78
19.59
33.97
28.66
19.28
28.03
32.41
28.97
46.78
19.59
20.84
31.47
13.03
27.41
22.41
26.16
30.22
28.66
26.78
23.97
21.78
30.53
36.16
30.53
14.28
32.41
42.41
43.66
0.53
33.03
32.09
Table 2.9 Average SCOD values.
SCOD (mg/L)
Flow
Average
SD
Max
Min
Inf
284.52
44.45
551.93
158.46
AO Eff
20.58
7.94
46.78
0.22
UCT Eff
20.65
9.85
58.97
0.22
According to Table 2.9, the average SCOD in the wastewater of Madison is around 284
±44 mgCOD/L, reaching a maximum of 551.93 mgCOD/L and a minimum of 158.46
mgCOD/L. This result is due to the high presence of soluble components in the water.
42
Table 2.10 GF COD results from January to April 2021.
Date
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
GFCOD (mgCOD/L)
Inf
AO Eff
330.19
14.28
330.20
14.28
338.89
7.72
351.93
3.03
312.80
11.78
423.67
21.47
336.72
12.09
378.02
10.53
234.54
1.16
275.85
1.78
469.33
22.09
275.85
1.78
351.93
20.22
308.46
1.47
325.85
21.47
306.28
14.59
360.63
3.34
319.33
7.72
330.20
14.59
275.85
26.47
393.24
44.59
291.07
27.41
280.20
33.03
293.24
34.59
308.46
37.09
460.63
31.16
328.02
30.22
264.98
33.03
306.28
23.97
345.41
28.97
299.76
23.66
297.59
30.53
367.15
32.72
321.50
28.34
328.02
30.22
258.46
27.09
367.15
25.53
249.76
34.59
295.41
26.78
378.02
38.03
43
UCT Eff
6.16
6.16
9.91
2.72
1.16
9.28
6.78
11.47
2.41
0.53
9.59
0.53
16.78
1.16
9.91
0.53
8.66
13.03
0.84
99.59
27.09
22.72
32.41
29.91
21.78
33.34
24.28
34.28
19.28
25.53
24.28
25.84
35.53
25.84
35.53
35.53
26.47
42.09
22.41
38.34
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
384.54
256.28
204.11
293.24
254.11
267.15
334.54
230.20
280.20
271.50
304.11
334.54
271.50
269.33
317.15
228.02
349.76
371.50
280.20
236.72
201.93
299.76
399.76
345.41
367.15
206.28
308.46
288.89
267.15
254.11
325.85
167.15
345.41
406.28
278.02
419.33
312.80
44
26.78
25.53
23.97
35.22
23.34
19.91
14.59
18.03
63.66
31.47
23.34
38.34
32.09
32.09
29.91
19.59
37.41
20.53
31.47
33.34
28.03
32.72
41.16
34.28
37.41
31.16
32.72
37.72
35.22
29.59
36.47
26.78
38.34
38.66
31.47
33.97
36.78
32.41
31.47
28.97
33.34
26.47
28.66
15.53
14.59
23.97
21.47
18.34
37.72
38.03
30.84
19.59
22.09
34.59
17.72
27.41
34.59
28.97
28.66
43.03
34.59
33.97
24.28
23.03
33.03
38.34
28.66
22.09
28.66
37.72
44.28
23.97
39.59
38.97
Table 2.11 Average GFCOD values
GFCOD (mg/L)
Flow
Average
SD
Max
Min
Inf
311.44
44.61
469.33
167.15
AO Eff
26.21
8.85
63.66
1.16
UCT Eff
24.67
10.61
99.59
0.53
Given the results in Table 2.11, the average GFCOD is 311±40 mgCOD/L. The
maximum is 469.33 mgCOD/L and the minimum is 167.15 mgCOD/L.
Table 2.12 FFCOD results from January to April 2021.
Date
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
FFCOD (mgCOD/L)
Inf
AO Eff
186.72
8.34
260.63
23.66
245.41
0.22
280.20
0.22
280.20
2.41
273.67
3.66
230.20
0.53
293.24
3.66
312.80
3.97
195.41
4.91
330.20
4.91
195.41
1.78
280.20
7.09
212.80
0.84
254.11
8.97
236.72
0.53
299.76
8.66
254.11
25.53
221.50
18.03
208.46
9.59
45
UCT Eff
5.84
23.34
0.84
0.84
3.03
25.53
3.34
3.66
7.09
0.22
9.91
0.22
6.16
0.84
11.16
0.22
7.72
23.34
29.28
0.53
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
306.28
208.46
208.46
201.93
206.28
258.46
254.11
197.59
221.50
271.50
31.78
180.20
258.46
212.80
273.67
149.76
201.93
171.50
219.33
280.20
286.72
169.33
134.54
219.33
180.20
193.24
214.98
182.37
210.63
204.11
199.76
284.54
199.76
173.67
201.93
158.46
286.72
282.37
210.63
188.89
123.67
208.46
317.15
271.50
46
25.53
21.16
22.41
28.66
17.09
28.66
17.72
21.78
17.41
18.66
14.59
19.59
27.41
15.53
25.84
20.22
13.03
25.22
19.91
30.84
23.34
20.84
18.66
24.28
14.59
18.34
18.03
5.84
15.53
19.59
14.28
32.09
22.72
18.97
20.53
17.41
23.97
9.28
21.47
25.22
18.66
23.03
27.41
29.91
23.66
16.78
24.59
26.78
15.84
19.91
21.78
23.03
18.34
26.78
14.59
24.28
27.41
16.09
12.72
24.28
14.59
27.09
20.22
28.34
26.47
9.28
9.28
24.28
16.78
13.66
17.41
9.91
6.78
18.34
13.97
32.41
20.53
25.22
26.78
21.47
19.28
13.66
24.59
30.22
15.84
26.78
29.28
23.66
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
280.20
121.50
230.20
201.93
195.41
138.89
184.54
97.59
260.63
328.02
186.72
197.59
230.20
23.34
19.91
23.66
24.59
24.59
21.47
32.09
15.53
28.66
38.34
20.22
27.72
26.78
22.09
19.28
15.22
22.09
23.66
27.41
25.22
18.03
36.16
39.28
17.72
21.78
40.22
Table 2.13 Average FFCOD values.
FFCOD (mg/L)
Flow
Average
SD
Max
Min
Inf
222.50
43.14
330.20
31.78
AO Eff
18.02
7.25
38.34
0.22
UCT Eff
18.24
7.91
40.22
0.22
Given the results from Table 2.13, the average FFCOD is 222±43, the maximum is
330 mgCOD/L and the minimum value is 31 mgCOD/L.
Table 2.14 VFA results from January to April 2021
Date
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
VFA (mg/L)
Inf AO Eff
36.60 1.00
47.60 1.00
37.50 1.00
30.50 1.00
6.50
1.00
35.10 1.00
47
UCT Eff
1.00
1.00
1.00
1.00
1.00
1.00
23-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
35.10
49.60
36.00
36.00
49.60
35.00
40.00
45.00
36.00
48.00
40.00
10.0
36.00
48.00
40.00
50.00
40.00
30.00
33.3
34.00
70.5
53.4
53.5
34.00
44.4
50.00
35.00
58.1
36.00
40.00
32.3
39.00
63.6
36.00
40.00
40.00
26.7
35.00
41.7
52.1
35.00
33.1
49.8
46.8
48
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
29.00
70.3
36.00
35.00
35.00
40.00
35.00
40.00
50.00
50.00
36.00
36.00
36.00
36.00
40.00
35.00
40.00
36.00
40.00
36.00
50.00
50.00
50.00
50.00
36.00
36.00
36.00
36.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Table 2.15 Average VFA values.
VFA (mg/L)
Flow
Average
SD
Max
Min
Inf
38.82
5.11
50.00
6.50
AO Eff
1.00
0.00
1.00
1.00
UCT Eff
1.00
0.00
1.00
1.00
49
Table 2.15 shows that the average VFA is 38 ±.5 mg/L. The maximum value is 50
mg/L and the minimum value is 6.5 mg/L. This result suggests that part of the fermentation
process in the wastewater is done during the transportation of the sewage to the treatment plant,
making easier the phosphorus removal process in the plants.
Table 2.16 shows the COD fraction and components results from January to April 2021.
Table 2.17 and Table 2.18 show the average concentration of COD fractions and components
from January to April 2021. Given Table 2.17, the average unbiodegradable fraction (fUS) is
0.05 ±0.02, reaching a maximum of 0.09 and a minimum of 0. The readily biodegradable
fraction is in average (fBS) is 0.46 ±0.09, the maximum is 0.64 and the minimum is 0.23. The
unbiodegradable particulate fraction (fUP) is in average 0.04 ±0.03, the maximum is 0.18 and
the minimum is 0. Scientific literature suggest that readily biodegradable fraction (fBS) is in
average 0.14-0.57 in settle water (Pasztor et al., 2009), which coincide with results obtained.
Given Table 2.18, the components of the wastewater in percentage are shown in Table 2.19:
50
Table 2.16 COD fractions and components.
Date
fUS
fBS
fUP
XSC
SUS
XUP(XI)
SBS
SBSA
SBSC
XSP(SSP)
XS
14-Jan-21
15-Jan-21
19-Jan-21
20-Jan-21
21-Jan-21
22-Jan-21
23-Jan-21
25-Jan-21
26-Jan-21
27-Jan-21
28-Jan-21
29-Jan-21
30-Jan-21
1-Feb-21
2-Feb-21
3-Feb-21
4-Feb-21
5-Feb-21
6-Feb-21
7-Feb-21
8-Feb-21
9-Feb-21
10-Feb-21
11-Feb-21
12-Feb-21
0.02
0.06
0.00
0.00
0.01
0.04
0.00
0.01
0.03
0.01
0.02
0.00
0.02
0.00
0.03
0.00
0.02
0.05
0.07
0.01
0.04
0.04
0.07
0.07
0.02
0.42
0.53
0.55
0.57
0.61
0.58
0.52
0.61
0.40
0.45
0.64
0.46
0.59
0.49
0.62
0.56
0.55
0.43
0.50
0.54
0.41
0.40
0.50
0.40
0.24
0.00
0.02
0.04
0.04
0.01
0.01
0.10
0.04
0.01
0.06
0.05
0.06
0.03
0.03
0.02
0.08
0.04
0.01
0.09
0.04
0.02
0.03
0.04
0.10
0.02
132.00
64.00
86.00
66.00
30.00
138.00
98.00
78.00
108.00
74.00
128.00
74.00
66.00
88.00
66.00
64.00
56.00
60.00
100.00
62.00
80.00
76.00
66.00
84.00
94.00
7.09
23.50
0.53
0.53
2.72
14.59
1.94
3.66
13.66
2.56
7.41
1.00
6.62
0.84
10.06
0.38
8.19
24.44
23.66
5.06
24.59
18.97
23.50
27.72
16.47
0.47
6.56
17.03
18.59
5.00
5.63
40.47
16.25
3.59
22.81
23.44
24.38
13.91
12.34
8.75
29.84
21.56
5.78
31.88
12.97
14.06
14.38
14.38
41.09
17.19
164.71
216.30
225.27
257.27
255.08
237.21
209.86
266.14
166.14
177.24
296.39
178.80
251.18
194.96
223.74
217.43
267.61
209.36
180.14
186.74
257.21
172.83
168.30
158.08
173.33
36.60
47.60
37.50
30.50
6.50
35.10
35.10
49.60
35.10
36.00
49.60
1.00
40.00
45.00
36.00
48.00
40.00
10.10
36.00
48.00
40.00
50.00
40.00
30.00
33.40
128.11
168.70
187.77
226.77
248.58
202.11
174.76
216.54
131.04
141.24
246.79
177.80
211.18
149.96
187.74
169.43
227.61
199.26
144.14
138.74
217.21
122.83
128.30
128.08
139.93
89.53
95.44
82.97
111.41
123.00
14.38
49.53
73.75
124.41
113.19
4.56
111.63
86.09
103.66
55.25
78.16
132.44
184.22
24.13
77.03
247.94
153.63
67.63
82.91
426.81
221.53
159.44
168.97
177.41
153.00
152.38
147.53
151.75
232.41
187.19
132.56
185.63
152.09
191.66
121.25
142.16
188.44
244.22
124.13
139.03
327.94
229.63
133.63
166.91
520.81
51
13-Feb-21
16-Feb-21
17-Feb-21
18-Feb-21
19-Feb-21
23-Feb-21
24-Feb-21
25-Feb-21
26-Feb-21
27-Feb-21
1-Mar-21
2-Mar-21
3-Mar-21
4-Mar-21
5-Mar-21
6-Mar-21
8-Mar-21
9-Mar-21
10-Mar-21
11-Mar-21
12-Mar-21
15-Mar-21
16-Mar-21
18-Mar-21
20-Mar-21
22-Mar-21
23-Mar-21
25-Mar-21
26-Mar-21
0.05
0.04
0.06
0.04
0.05
0.05
0.06
0.06
0.04
0.04
0.06
0.04
0.07
0.05
0.06
0.06
0.05
0.04
0.06
0.04
0.04
0.04
0.02
0.03
0.05
0.04
0.07
0.05
0.06
0.47
0.46
0.42
0.45
0.45
0.36
0.38
0.46
0.42
0.51
0.32
0.45
0.37
0.41
0.48
0.54
0.45
0.35
0.46
0.43
0.38
0.44
0.45
0.48
0.48
0.49
0.53
0.36
0.38
0.05
0.03
0.01
0.07
0.05
0.12
0.02
0.11
0.05
0.02
0.03
0.06
0.05
0.04
0.02
0.02
0.04
0.06
0.03
0.01
0.02
0.01
0.18
0.02
0.03
0.02
0.03
0.04
0.04
186.00
68.00
62.00
78.00
68.00
72.00
108.00
100.00
100.00
50.00
100.00
152.00
72.00
70.00
90.00
90.00
80.00
64.00
68.00
68.00
68.00
110.00
44.00
64.00
62.00
96.00
46.00
66.00
88.00
24.28
19.75
22.41
17.88
22.72
23.19
21.94
26.94
15.81
19.28
22.25
13.81
26.16
20.06
29.59
24.91
15.06
13.97
24.28
15.69
16.00
17.72
7.88
11.16
18.97
14.13
32.25
21.63
22.09
21.72
12.66
2.50
27.34
24.69
61.88
6.72
48.75
19.41
7.66
9.22
22.50
17.03
16.56
8.13
8.28
8.39
20.00
11.41
4.84
10.00
3.12
64.69
8.44
12.34
7.50
11.09
19.84
14.69
52
213.52
214.05
159.39
185.93
227.08
180.61
143.86
210.86
179.99
232.52
115.55
171.99
131.64
181.74
228.21
238.89
12.81
109.83
177.52
150.11
161.80
180.08
159.93
182.64
168.83
169.68
229.55
162.18
137.71
35.00
70.60
53.40
53.50
35.00
44.50
50.00
35.00
58.20
36.00
40.00
32.40
40.00
63.70
36.00
40.00
140.74
26.80
177.52
41.80
52.00
36.00
33.00
49.90
46.90
30.00
70.40
36.00
35.00
178.52
143.45
105.99
132.43
192.08
136.11
93.86
175.86
121.79
196.52
75.55
139.59
91.64
118.04
192.21
198.89
40.00
83.03
141.52
109.41
109.80
144.08
126.93
132.74
121.93
139.68
159.15
126.18
102.71
10.28
149.34
131.50
108.66
159.31
162.13
99.28
69.25
108.59
179.76
112.78
23.50
108.97
157.44
119.88
81.72
100.74
106.00
108.59
113.16
174.00
100.88
81.31
117.56
91.66
62.50
114.91
184.16
97.31
196.28
217.34
193.50
186.66
227.31
234.13
207.28
169.25
208.59
150.34
112.78
175.50
108.97
227.44
209.88
171.72
67.19
170.00
104.16
181.16
242.00
210.88
125.31
181.56
153.66
158.50
160.91
250.16
185.31
29-Mar-21
30-Mar-21
31-Mar-21
1-Apr-21
2-Apr-21
3-Apr-21
5-Apr-21
6-Apr-21
7-Apr-21
8-Apr-21
10-Apr-21
12-Apr-21
15-Apr-21
16-Apr-21
17-Apr-21
19-Apr-21
20-Apr-21
22-Apr-21
23-Apr-21
24-Apr-21
26-Apr-21
27-Apr-21
29-Apr-21
30-Apr-21
0.06
0.06
0.04
0.02
0.06
0.08
0.06
0.06
0.06
0.06
0.05
0.06
0.05
0.06
0.07
0.07
0.08
0.05
0.07
0.09
0.05
0.05
0.08
0.07
0.41
0.40
0.49
0.54
0.46
0.44
0.34
0.42
0.53
0.50
0.54
0.30
0.50
0.44
0.44
0.31
0.41
0.23
0.46
0.62
0.41
0.32
0.41
0.45
0.06
0.02
0.02
0.02
0.03
0.02
0.02
0.02
0.03
0.02
0.03
0.06
0.00
0.04
0.02
0.02
0.03
0.09
0.07
0.03
0.01
0.03
0.03
0.02
106.00
64.00
58.00
82.00
64.00
44.00
72.00
84.00
76.00
68.00
80.00
78.00
72.00
80.00
66.00
106.00
130.00
64.00
39.05
72.00
84.00
204.00
76.00
52.00
23.66
19.44
21.63
11.47
21.47
27.72
17.25
24.91
28.34
26.78
22.72
19.59
19.44
23.34
24.13
24.44
28.66
15.53
78.00
38.81
18.97
24.75
33.50
27.25
25.47
6.25
11.09
10.78
12.97
6.09
4.69
6.56
13.59
9.22
15.00
19.69
1.25
13.91
8.13
6.41
8.91
28.91
32.41
14.69
4.06
14.69
14.84
7.66
53
162.14
126.36
242.18
248.33
170.77
146.08
51.01
166.89
263.46
223.02
235.08
92.21
192.36
162.46
155.68
103.36
141.14
73.02
31.41
262.99
152.83
157.05
178.30
186.55
35.00
40.00
35.00
40.00
50.00
50.00
36.00
36.00
36.00
36.00
40.00
35.00
40.00
36.00
40.00
36.00
50.00
50.00
207.39
50.00
36.00
36.00
36.00
36.00
127.14
86.36
207.18
208.33
120.77
96.08
60.55
130.89
227.46
187.02
195.08
57.21
152.36
126.46
115.68
67.36
91.14
23.02
50.00
212.99
116.83
121.05
142.30
150.55
78.53
103.75
160.91
107.22
103.03
113.91
91.31
119.44
112.41
114.78
83.00
96.31
100.75
88.09
97.88
97.59
39.09
129.09
102.59
33.31
111.94
93.31
129.16
138.34
184.53
167.75
218.91
189.22
167.03
157.91
163.31
203.44
188.41
182.78
163.00
174.31
172.75
168.09
163.88
203.59
169.09
193.09
180.59
105.31
195.94
297.31
205.16
190.34
Table 2.17. Average COD Fractions.
COD Fractions (m/m)
Fraction
fUS
(Unbiodegradable soluble)
fBS
(Readily biodegradable)
Average
SD
Max
Min
0.05
0.02
0.09
0.00
0.46
0.09
0.64
0.23
fUP
(Unbiodegradable
particulate)
0.04
0.03
0.18
0.00
Table 2.18 Average COD components.
COD components (mgCOD/L)
Component
Xsc
(slowly
colloidal
biodegradable)
Sus
(Unbiodegradable
soluble)
Xup=XI
(Unbiodegradable
particulate)
SBS
(Readily
biodegradable)
SBSA
(Short chain
fatty acid)
SBSC
(Readily
biodegradable
complex)
XSP=SSP
(Slowly
particulate
biodegradable)
XS
(Slowly
biodegradable)
Average
81.40
18.89
15.78
182.75
45.11
145.10
107.62
183.91
SD
29.18
11.07
12.20
54.05
28.89
49.80
55.37
56.71
Max
204.00
78.00
64.69
296.39
207.39
248.58
426.81
520.81
Min
30.00
0.38
0.47
12.81
1.00
23.02
4.56
67.19
54
Table 2.19 COD components in percentage.
COD components (%)
Component
Percentage
(%)
Xsc
Sus
Xup=XI
SBS
(slowly
(Unbiodegradable (Unbiodegradable
(Readily
colloidal
soluble)
particulate)
biodegradable)
biodegradable)
10%
2%
2%
23%
55
SBSA
(Short chain
fatty acid)
SBSC
(Readily
biodegradable
complex)
6%
19%
XSP=SSP
XS
(Slowly
(Slowly
particulate
biodegradable)
biodegradable)
14%
24%
Table 2.19 shows the percentage of COD components, according to that this wastewater
has nearly 90% of biodegradable COD, the remaining 10% is mostly unbiodegradable COD
and part of the short chain fatty acid.
The COD experimentation procedure allowed for the calculation of all COD fractions
and components, but this analysis is focused in the TCOD, SCOD and readily biodegradable
COD (𝑓𝐵𝑆 ). Figure 2.4 shows the trending (using a 4-period moving average) of the TCOD
content in the PE wastewater (Influent) which comes into both pilot plants:
Figure 2.4 TCOD concentration trends in influent wastewater and both effluents. The
yellow dash line represents the temperature. The blue dash line represents the TCOD in the
influent wastewater. The orange and grey lines represent both effluent COD concentration
trends in AO and UCT, respectively.
56
The yellow trend shows the temperature variation since January 14th to April 30th of
2021. The blue trend represents the TCOD concentration in the influent wastewater. The orange
and the grey lines represent the TCOD effluent in AO and UCT, respectively. According to the
temperature trend and the TCOD trend, is interesting to see that while temperature decreases
from mid-January to mid-February, the TCOD increases. In contrast, when the temperature
increases from mid-February to mid-April, the TCOD decreases. This linear correlation is
described as an inversely proportional relationship. This correlation is probably due to the
decrease of the microbial activity during the colder days and the increase of microbial activity
during warmer days. This temperature might affect the fermentation process through the whole
piping system, decreasing the TCOD concentration in the wastewater which comes to the
MMSD plant. In addition, during drier months such as January and February, the amount of
water infiltrated to the piping system is smaller than the amount of water during wet months.
This weather factor contributes to the behavior described priorly, diminishing dilution during
drier months, increasing TCOD concentration. In contrast, decreasing TCOD concentration
during wet months.
This correlation was analyzed using a 2 Tail T-Student test probe, where the results
gave a critical value of 2.01 and the statistical probe was 2.21 with a confidence of 95%. Since
the statistical probe is greater than the critical value, the probe allowed to conclude that this
inversely proportional correlation between temperature and TCOD concentration can exist with
95% of confidence.
57
The SCOD trend between January and May of 2021 is shown in Figure 2.5:
Figure 2.5 SCOD concentration trends. Influent wastewater and both effluent.
In this graph the yellow line represents the temperature during January 14th and April
30th of 2021. At the same time, the blue line represents the concentration of SCOD in the
influent wastewater during the same period. According to the graph and the T-Student probe
the critical value was 2.01 and the statistical probe was 0.29 with a 95% of confidence. Since
the statistical probe is smaller than the critical value, is possible to conclude that the SCOD
has no relation with the temperature with 95% of confidence, since both trends are almost flat
during all period without showing any clear linear correlation.
58
The RBCOD is described in Figure 2.6:
Figure 2.6 RBCOD fraction (fBS) concentration in influent wastewater.
In figure 2.6, the line in blue is the RBCOD trendline. The yellow line represents the
temperature throughout the period of January 14th and April 30th of 2021. According to both
trendlines graphs, there is no clear linear relationship within the variables. Corresponding to
the T-Student probe the critical value calculated was 2.01 and the statistical probe was 0.67
with 95% of confidence. Since the statistical probe is smaller than the critical value, is possible
to conclude that there is no linear correlation between the variable with 95% of confidence.
59
CHAPTER 3: FOOD MASS RATIO CALCULATION AND COMPARISON
3.1 Food Mass Ratio concept and the settling process
There are many parameters that can affect the nutrient and solids removal of wastewater
during secondary treatment (Mishoe, 1999). Some of these parameters are controllable such
as DO, RAS, and WAS. In contrast, other parameters such as BOD or TCOD in influent
wastewater, current volumes inside of the plant, and the weather conditions are uncontrollable.
One important controllable parameter is the F/M ratio which can be controlled using the WAS
flow or changing the volumes of some tanks in the plant configuration, which was the decision
made in the MMSD pilot plants before the beginning of this study phase.
The F/M is the relationship between the BOD or the COD coming into the plant
(primary effluent) and the biomass available in the plant to consume it. The purpose of
estimating this parameter is based upon previous evidence since 1959 (Albertson & Orris,
1991), suggesting that within certain low F/M ratios, filament organisms responsible of
foaming and bulking, such as Michrotrix parvicella and Nocardia are favored to growth,
diminishing the speed of the settling process during secondary clarification as well as
increasing endogenous respiration, growing the potential loss of biomass (Szelag et al., 2017).
In contrast, higher F/M ratios can improve the secondary settling process increasing the specific
gravity of the flocs, enhancing the proportion of food available for some specific amount of
biomass. Most of the previous studies about tackling bulking and foaming during secondary
clarification have studied the influence of setting an Initial Contact Zone (ICZ) (Albertson &
Orris, 1991) or selector, looking to increase the F/M ratio, giving a higher shock of food to the
biomass, avoiding the formation of filamentous bacteria through the increase of food quantity
respect to the amount of biomass.
60
Since 1959, many different authors have been researching about setting the ICZ or
selector zones. Different configurations of selectors zones, using anaerobic, anoxic, and
anaerobic conditions as well as continuous and discontinuous feeding, have been tested before.
Some of those tests and results are shown in Table 3.1 (Albertson & Orris, 1991):
Table 3.1 Prior art continuous flow experience with bulking sludge control concepts
Selector
Author
DO
F/M
SVI
(mg/L)
(mgCOD/mgVSS*day)
(mL/g)
0.0
1.0
34
Continuous
0.0
˃2.5
˂120
Continuous
unknown
0.8
˂75
Continuous
0.0
˃2.0
˂100
Continuous
≤0.5
≥2.5
˂100
0.0/0.0
˃5.0/∞
˂100/40
Feed Mode
Mode
Continuous
Davidson, 1959
Anaerobic
Aerobic
Bhatia, 1969
Low DO
British, 1969
Aerobic
Aerobic
Milbury, 1971
Low DO
Aerobic
Chudoba, 1973
Low DO
Heide & Pasveer,
Anaerobic
Continuous
1973
/Anoxic
and batch
Rensink, 1974
Aerobic
Continuous
0.0
3.6
˂100
Tomlinson, 1976
Aerobic
Continuous
unknown
˃2.0
≤100
Spector, 1977
Anaerobic
Continuous
≤0.7
˃3.0
˂100
Chudoba &
Aerobic
Continuous
≈1.0
12.0
˂50
Wanner, 1988
High DO
61
According to Table 3.1, F/M values greater than 2 in most of the cases improved the
settling process. Since the plants studied in this research have set an anaerobic selector, the
research done by Spector (1977) is useful as reference for this specific case. On Spector’s study,
an F/M ratio greater than 3 helps to maintain a sludge volume index (SVI) smaller than 100
mL/g (Albertson & Orris, 1991). Usually, according to previous field experience, an SVI value
smaller than 150 mL/g guarantees low concentration of total suspended solids (TSS) in the
effluent as well as good secondary clarification. In this report, both plants were set with two
anaerobic selectors of 37 gal each as ICZ.
Additionally to the F/M ratio -mentioned in the next list-, there are four main
operational conditions that stimulate the filament bacteria growth (Jenkins, Richard, &
Daigger, 2004) and need to be controlled such as:
1. Low DO associated with Michrotrix parvicella organism.
2. Low F/M associated with Michrotrix parvicella organism.
3. Complete mix reactor conditions associated with Nocardia organism.
4. Septicity wastewater associated with Thiathrix organism.
5. Nutrient deficiency and Low pH associated with Fungi.
Since both plants are already exposed to conditions 1 (Low DO), 2 (Low F/M) and 3
(Complete mix reactor conditions), and conditions 1 and 3 are already under control, the main
interest is analyzing the current behavior of the F/M in the existing plant configurations and
comparing it with previous configurations, where the ICZ volume were bigger. In the previous
UCT configuration the ICZ or selector zone was an anaerobic tank of 75 gal (284 L) and in the
previous AO configuration the ICZ was an anaerobic tank of 125 gal (473 L). Both selectors
were replaced by one anaerobic tank of 37 gal (140 L) in each plant.
62
It is important to clarify that in the UCT plant not only the first tank was changed,
besides, the whole anaerobic zone was changed from 75 gal (284 L) to 111 gal (420 L), and
the anoxic zone was change from 125 gal (473 L) to 37 gal (140 L). Alternatively, in the AO
plant the anaerobic zone went from 125 gal (473 L) to 148 gal (560 L), no additional changes
were done in the AO plant. The changes done in the UCT plant are described in Figure 3.1, and
modifications in the AO plant are described in Figure 3.2 which are the Biowin diagrams of
the previous and current configuration. The yellow arrows identify the ICZ, the red arrows
identify the remaining anaerobic tanks, and the green arrows identify the anoxic zone:
Figure 3.1 UCT plant Biowin scheme. Modification in anaerobic and anoxic zone volumes.
Top diagram shows previous configuration. The bottom diagram shows current configuration.
Figure 3.2 AO plant Biowin Scheme. Modification in anaerobic zone volume. Top diagram
shows previous configuration. The bottom diagram shows current configuration.
63
3.2 Temperature
Another condition which affects the secondary treatment process and settling is the
temperature. Temperature plays an important role influencing biological process, allowing
them to be classified according to the temperature operation range as follows (Tchobanoglous
et al., 2003):
1. Psychrophilic with a range within 10-30 °C (Optimum range 12-18°C)
2. Mesophilic with a range within 20-50°C (Optimum range 25-40°C)
3. Thermophilic with a range within 35-75°C (Optimum range 55-65°C)
It is important to keep temperature tracked during wastewater treatment due to its
influence in the metabolism and kinetics of the microbiology implied in the process, increasing
microbial activity during summer (high temperatures), and decreasing it during the winter
(lower temperatures). Temperature is more important in places where the weather cycle goes
through extreme conditions. Madison is the perfect example, where the temperature can
achieve 32°C during the summer and can achieve temperature about -22°C during the winter.
Although this lower temperature might be reached in certain point of the year, in previous
studies at the MMSD and the UW-Madison the lowest temperature reached in the water was
12°C degrees, which is a low temperature in terms of biological processes conditions. The
temperature trend from July 2020 to April 2021 is shown in Figure 3.3:
64
Figure 3.3. Seasonal temperature in water at the MMSD. August 2020 to April 2021.
Figure 3.3 shows the trend of temperature through the time since August 2020 until
April 2021. It is very clear that the highest temperature value is 25°C degrees and the lowest
temperature value is 12° C degrees. The original period of data for this COD study was set
from August 2020 to April 2021, but due to problems related to Covid 19 pandemic, the range
of useful COD data during this study was reduced from January to April 2021. This lack of
data reduced the possibility of having a longer reliable data base to establish the base of the
models, although previous studies such as Yang et al. (2019) indicate that in a period of 1 year
20 samples taken during normal operation of the facility are enough to correctly characterize
the wastewater without making excessive measurements. This study covers 78 samples during
period of 4 months.
65
3.3 F/M relativity
The concept of F/M is subject to the criteria of the user. It might be calculated for the
whole treatment train or only for certain tank or zone in the WWTP. Moreover, there are 2
different forms to calculate the food mass ratio relationship, which are the sludge age method
(SAM) and the mean cells residence time (MCRT). In this research, only the F/M ratio was
calculated and followed up through the period of January to April 2021. Calculated for the first
anaerobic tank in both plants, seeking to understand F/M variation through the time as well as
the difference between the current and the previous configurations. The formula used to
calculate the F/M is the Equation 3.1 (Tchobanoglous et al., 2003):
Equation 3.1 Food-Mass ratio equation using BOD.
𝐹 𝑄 ∗ 𝑆0
𝑔𝑏𝑠𝐶𝑂𝐷
=
[=]
𝑀 𝑉∗𝑋
𝑔𝑉𝑆𝑆 ∗ 𝑑𝑎𝑦
Where:
•
[=]= nomenclature to express “units of”
•
Q= Influent flow in cubic meter per day [𝑑𝑎𝑦 ]
•
S0= BOD in g per cubic meter [𝑚3 ]
•
V= Aeration basin volume in cubic meters [𝑚3 ]
•
X= Mixed liquor concentration g per cubic meter [𝑚3 ]
𝑚 3
𝑔
𝑔
A similar equation than Equation 3.1 allowed to estimate the F/M ratio for both current
pilot plants. Based upon those results, a comparison between the F/M of the current
configurations and the F/M of previous configurations was done, seeking to understand how
changes in ICZ can affect the F/M ratio result.
66
3.4 Food mass ratio and COD
The F/M ratio is directly proportional to the TCOD concentration. In several cases the
F/M is calculated using the BOD5 which is the most common parameter utilized in many
WWTP with a 24-hour composite samples to measure biological oxygen demand (BOD). This
experiment takes longer than the COD test and is most common in domestic WWTP due the
lower strength of the wastewater treated.
In this research, the BOD5 concentration was replaced in Equation 3.1 by the calculated
RBCOD based upon the TCOD measured in the field, assuming that all effluent COD was
unbiodegradable. The resultant equation is the Equation 3.2:
Equation 3.2 F/M ratio equation using COD as RBCOD.
𝐹
𝑄 ∗ 𝑆𝑅𝐵
𝑚𝑔𝑅𝐵𝐶𝑂𝐷
=
[=]
𝑀 𝑉 ∗ 𝑀𝐿𝑉𝑆𝑆
𝑚𝑔𝑉𝑆𝑆 ∗ 𝑑𝑎𝑦
Where:
•
[=]= Nomenclature to express “units of”
•
Q= Influent flow in gallons per day [𝐺𝑃𝐷]
•
SRB= RBCOD in mg of RBCOD per liter [
•
V= Aeration basin volume in gallons [𝐺𝑎𝑙]
•
X= Mixed liquor concentration mg of VSS per liter [
𝑚𝑔𝑅𝐵𝐶𝑂𝐷
𝐿
]
𝑚𝑔𝑉𝑆𝑆
67
𝐿
]
3.5 Food mass ratio results and discussion
The F/M ratio calculation in this research was done using Equation 3.2. The average
results for UCT previous and current configuration are shown in Table 3.2. In contrast, the
results for the AO previous and current configuration are shown in Table 3.3:
Table 3.2 F/M ratio results for past and current UCT configuration.
UCT (F/M) Old
Average
SD
Max
Min
(mgCOD/mgVSS*day)
1.28
0.39
2.40
0.53
UCT (F/M) New
Average
SD
Max
Min
(mgCOD/mgVSS*day)
2.59
0.79
4.87
1.07
Giving Table 3.2, the average F/M ratio in the New UCT plant is almost 2 times bigger
than the previous configuration. Even the SD is 2 times greater, the new plant reaches greater
maximum and minimum values, confirming a substantial increase in the amount of food
available for microbial activity comparing both current and previous configurations.
Table 3.3 F/M ratio results for past and current AO configuration.
AO (F/M) Old
Average
SD
Max
Min
(mgCOD/mgVSS*day)
0.58
0.14
1.21
0.18
AO (F/M) New
Average
SD
Max
Min
(mgCOD/mgVSS*day)
1.94
0.49
4.09
0.61
According to Table 3.3, the average F/M ratio in the New AO configuration is almost
3 times greater than the previous configuration. Although it has greater standard deviation
(SD), it reaches greater maximum and smaller minimum values. This confirmed a substantial
68
increment of food available to be consumed by the biomass between both new and past
configurations.
The UCT-type plant shows greater average F/M ratio than the AO plant. This can occur
due to the differences in the aeration process within both plants. Since the UCT-type plant has
continuous aeration system, this condition might produce a more stable microbial activity.
Which eventually can create a more stable microbial population, avoiding the growth of
filamentous bacteria such as Nocardia and Michrotrix parvicella bacteria. On the other hand,
the intermittent aeration system in the AO plant can lead more instability due the constant
changes in the aeration regime. Trending to produce changes in the microbial metabolism,
impacting the growth and decay rate of all the microbes in the activated sludge culture due to
their possibly slow adaptability to these extreme oxygen changes.
Figures 3.4 and 3.5 show the F/M ratio behavior from January to April 2021 in both
plants, comparing current and previous configurations:
69
Figure 3.4 UCT plant F/M ratio through the time. Current and old configuration.
According to the trends in Figure 3.4, the new UCT configuration F/M represented by
the orange line shows greater values than the previous configuration; this is expected due to
the change of the volume of the first tank caused by the volume changes. The new UCT
configuration reaches F/M values greater than 4 mgCOD/mgVSS*day while the previous
configuration reaches only 2 mgCOD/mgVSS*day. Furthermore, the peaks in the orange line
are higher than the peaks in the green line, showing greater shocks of food to the microbes in
the volume of the new configuration during January to mid-February as well as early April to
mid-April. As result greater F/M ratio values are achieved more often in the current
configuration. In addition, the F/M in both UCT plant configurations follow a trend pattern
with the temperature. F/M increases directly when the temperature increases, as well as
decreasing its value when temperature decreases.
70
Figure 3.5 shows the behavior of the F/M ratio in the AO plant. In this case the change
in volume produces higher peaks of food. These peaks are very noticeable in the current AO
configuration represented by the blue line comparing it with the green line which represents
the previous AO configuration during the periods of January to mid-February and early April
to mid-April. The maximum F/M value reached by the new anaerobic zone is 3.2
mgCOD/mgVSS*day, in contrast, the maximum value achieved by the old anaerobic zone is
0.9 mgCOD/mgVSS*day. These values can demonstrate that reductions in the volume of the
ICZ or selector will lead to better F/M relations, possibly impacting the settling process in a
positive manner.
Figure 3.5 AO plant F/M ratio through the time. Current and old configuration
71
3.1 Additional Variable Correlation Analysis
The wastewater treatment process implies many variables. Those parameters influence the
process in different manners. Variables such as SRT, HRT and DO are essential to maintain a
well control during the treatment process. During this study, many variables were measured
within January and April of 2021. Some correlations between SRT, SVI, F/M, temp, etc. are
show in Tables 3.4, 3.5 and 3.6:
72
Table 3.4 Correlation between process variables in the AO plant.
Test Number
Statistic of data
CORRELATIONS BETWEEN PROCESS VARIABLES
Plant
AO
AO
AO
Variables
SRT
TCOD
MLVSS
SVI
F/M
F/M
Pearson
Directly
Inversely
Coefficient Proportional Proportional
(R)
Relationship Relationship
-0.34
0.59
-0.57
X
X
X
Degrees
Significance
of
Level
Freedom
Critical
value
t
n
DF(n-2)
α (5%)
t(α/2,n-2)
-2.20
4.37
-5.66
38.00
38.00
70.00
36.00
36.00
68.00
0.05
0.05
0.05
2.03
2.03
2.00
73
Decision and Conclusion
If the absolute value of t
is greater than t(α/2,n-2)
the lineal correlation
exist with a 95% of
confidence
YES
YES
YES
Table 3.5 Correlation between process variables in the UCT plant.
Test Number Significance
Statistic of data
Level
CORRELATIONS BETWEEN PROCESS VARIABLES
Plant
UCT
UCT
UCT
UCT
UCT
Pearson
Coefficient (R)
Variables
TCOD
Temp
Temp
MLVSS
SRT
F/M
F/M
MLVSS
F/M
MLVSS
Directly
Proportional
Relationship
0.45
0.42
-0.60
-0.56
0.57
Inversely
Proportional
Relationship
X
X
X
X
X
Critical
value
t
n
α (5%)
t(α/2,n-2)
3.03
3.75
-6.07
-5.51
4.10
39.00
69.00
69.00
69.00
37.00
0.05
0.05
0.05
0.05
0.05
2.03
2.00
2.00
2.00
2.03
Decision and Conclusion
If the absolute value of t
is greater than t(α/2,n2) the lineal correlation
exist with a 95% of
confidence
YES
YES
YES
YES
YES
Table 3.6 Correlation between temperature and TCOD.
Test Number
Statistic of data
CORRELATIONS BETWEEN PROCESS VARIABLES
Plant
Influent
Variables
Temp
TCOD
Pearson
Directly
Inversely
Coefficient Proportional Proportional
(R)
Relationship Relationship
-0.23
X
Degrees
Significance
of
Level
Freedom
Critical
value
t
n
DF(n-2)
α (5%)
t(α/2,n-2)
-1.93
70.00
68.00
0.05
2.00
74
Decision and Conclusion
If the absolute value of t
is greater than t(α/2,n-2)
the lineal correlation
exists with a 95% of
confidence
YES
In accordance with data in Table 3.4 and using a Two Tails T-Student statistical probe,
it is possible to conclude with 95% of confidence that: (1) In the AO plant the pair of variables
such as (SRT, SVI) have an inversely proportional relation. This can be due to the intermittent
aeration, which changes the floc specific gravity causing different behavior in terms of settling
process. (2) In contrast, the pair (TCOD, F/M) have a directly proportional relation, which is
expected due to the relation described in Equation 3.2 where both variables are directly
proportional related. And (3) the pair (MLVSS, F/M) have an inversely proportional relation
as well, which is also expected due to the relation described in Equation 3.2 where the variables
are inversely related.
In addition, in the UCT plant, the same statistical probe can be used with 95% of
confidence. Table 3.5 shows that in the UCT plant, the pair variables (TCOD, F/M) have a
directly proportional relation, which is expected due to Equation 3.2. The pair variables (Temp,
F/M) have directly proportional relation. This can be explained due to the more stable aeration
process which favor a more stable relation of F/M with temperature. The variables (SRT,
MLVSS) have a directly proportional relationship, which is expected due to the SRT equation
where both variables are directly proportional related. In contrast, the pair (Temp, MLVSS)
have an inversely proportional relation, which is expected due to multiple factors which affect
the MLVSS such as the WAS and the SRT variables. The pair (MLVSS, F/M) have an inversely
proportional relation, which is expected due to their relation in Equation 3.2.
Additionally, in Table 3.6 the pair of variables (Temp, TCOD) have an inversely
proportional relationship. This is interesting in conjunction with the hypothesis, which suggest
this behavior is due to the increase of microbial activity during warmer weather producing
degradation of the COD through the piping system around the treatment plant as well as the
75
dilution due to the infiltration of the melted and storm water through the pipes of the collections
system. However, more data points need to be collected to probe this behavior.
It is interesting to see how according to the results of this study, the UCT plant is
impacted more strongly by the temperature, influencing the F/M and the MLVSS. This result
might probe more sensitivity of the UCT process to changes in temperature comparing it to the
AO plant. Perhaps the AO plant have developed a more adaptable and tolerant temperature
microbial culture, using the intermittent aeration instead of constant.
During this period, changes were done to the SRT manually, decreasing their value
from 13 to 10 days gradually from February 23rd to March 10th. This SRT decrease might affect
the normal behavior of the SRT in both plants. During the same period of March and April,
sludge from the top of the clarifier was dumped outside of both systems. This could decrease
the MLVSS concentration in an unexpected way increasing the noise in these results. A more
stable and longer data collection can be useful in further investigations.
76
CHAPTER 4: PILOT PLANT MODELING
4.1 Biowin modeling software
WWTP simulation software is a useful tool to design, analyze, and retrofit WWTPs.
Most of the existing WWTP simulation software is based on the activated sludge models
developed by the International Water Association (IWA) (Hauduc et al., 2013; Hauduca et al.,
2009). The Biowin software is a WWTP simulation tool commonly used by the wastewater
treatment industry. In this project, Biowin models were built to represent the configuration of
the two pilot-scale WWTPs. The new collected influent COD data (Chapter 2) was used to
modify the default Biowin influent parameters. In addition, a prior Biowin model of the pilot
plants, developed by Bayer (2018), was used for kinetic parameters that deviated from
Biowin’s default parameters. A Biowin model might be used as a black box full of all chemical,
physical, and biological equations (Figure 4.1), where input data about wastewater
characteristics produces output data that represents the water quality and other parameters after
treatment.
Biowin offers different ways to create models for specific applications. It includes
complementing tools such as the “Influent Specifier” and the “Biowin Controller”. These two
tools were used during the development of the new pilot plant models. All measured COD data
was used as an input to the Influent Specifier, which provides an output the different COD
fractions that are used by the model. As an example, the readily biodegradable fraction (𝑓𝐵𝑆 )
in the MMSD settled water (primary effluent) was calculated by the Influent Specifier tool to
be 𝑓𝐵𝑆 (𝐵𝑖𝑜𝑤𝑖𝑛) = 0.48, whereas the calculation of this parameter using the equations
described in Chapter 2 was 𝑓𝐵𝑆 (𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠) = 0.46. According to the literature on wastewater
treatment, readily biodegradable substrate ranges between 0.14–0.57 in settled wastewater
77
(Pasztor et al., 2009), which is in accordance to the readily biodegradable value calculated in
this study.
Figure 4.1 Biowin model. The “Black Box” which ties together chemical, physical, and
biological equations. The input data are the variables in the influent, such as COD, total N, and
total P, and the output data can be the same variables in the effluent.
The Biowin Controller tool was used to simulate the control systems that regulate DO
in the different tanks as a function of ammonia concentrations in one of the aerated tanks. The
limits, ranges, and values of all these parameters are discussed in the following sections.
4.2 Model calibration process
The calibration of wastewater treatment models requires the fit of the model to a
specific data set, which can come from a full-scale or pilot-scale plant. In general, a process
for model calibration includes three different stages: (1) wastewater characterization in terms
of composition and flow, (2) compilation of treatment plant dimensions, and (3) use of selected
operational conditions to run the model, evaluate results and change parameters as needed, and
iterate until realistic results are obtained and the model output fits experimental results of
treatment plant performance. Specific criteria are defined to evaluate the accuracy of the model.
The approach used in this project for model calibration is shown in Figure 4.2. This
method is similar to calibration processes described by others (Sin, Hulle, Pauw, Griensven, &
78
Vanrolleghem, 2005). However, the number of iterations to obtain a well calibrated model were
not implemented. Although the most sensitive parameters for calibration are generally the
kinetic and stoichiometric constants (Liwarska, Bizukojc, & Biernacki, 2010), in this research
most of these variables were kept as Biowin default values. The only kinetic parameters that
deviated from the default values were the half saturation constant (𝐾𝐷𝑂 ) for ammoniaoxidizing bacteria (AOB), which changed from 0.25 to 0.30 mgO2/L, and the half saturation
constant for nitrite-oxidizing bacteria (NOB), which was changed from 0.5 to 0.1 mgO2/L.
These two changes were implemented following the recommendations made by Bayer (2018),
which calibrated a Biowin model for pilot plants operating using low-DO conditions.
Figure 4.2 Model calibration process flow diagram. Step by step procedure.
79
4.3 Influent Specifier tool
The Influent Specifier is a complementary tool in Biowin. This tool is useful to compile
the information related to wastewater characterization based on COD measurements. The
average TCOD, SCOD, GFCOD and FFCOD (Chapter 2) were used as input to the Influent
Specifier (Figure 4.3), which calculated the different COD fractions used in Biowin simulations
(Figure 4.4). A comparison of Biowin default values and the values obtained with the Influent
Specifier are shown in Figures 4.4 and 4.5.
Figure 4.3 Influent specifier screen. Red circles show influent TCOD and their main
components such as GFCOD and FFCOD. The BOD is calculated according to the
measurements of COD.
80
Figure 4.4 Influent specifier fractions and components results. The fourth and fifth
columns, in the red circle, show the default and the calculated COD influent fractions.
81
Figure 4.5 Editing influent screen in Biowin. The red circle highlights the default values and
the values obtained with the Influent Specifier. The values in red font show the parameters that
deviate from default values.
4.4 Biowin controller tool
The pilot plants use two different feedback controller ABAC loops (Smith, 2002). In
the UCT type plant the aeration is continuous, keeping the air diffusers on all the time, but
regulating the air flow rate based on an ammonia concentration set point of 5 mgN/L in tank
LD4 where the ammonia sensor is located. If the ammonia concentration goes above the set
point, the air flow rate increases, and if the ammonia concentration goes below the set point,
82
the air flow decreases. Under this control condition, air flow to the treatment plant is always
on.
In contrast, in the AO type plant the aeration is not continuous. The controller turns
on and off the air flow based on the ammonia concentration in the tank AO4 where the
ammonia sensor is located. The control system seeks to maintain the concentration of ammonia
within the range of 2 to 5 mgN/L in the tank. If the ammonia concentration goes about 5 mg
N/L, aeration is turned on. If ammonia concentration goes below 2 mg N/L, aeration is turned
off.
The Biowin Controller tool allows to simulate different types of controllers. Figure 4.6
shows the controller that was built for the UCT type pilot plant, and Table 3.7 shows the UCT
control parameters summary. Figure 4.7 shows the controller built for the AO type pilot plant,
and Table 3.8 shows the parameters used. In this case, the controller requires 2 criteria to
maintain the ammonia concentration in the desired values due to the On/Off air flow condition.
83
Figure 4.6 UCT controller screenshot. The controller type is a PI control.
Table 3.7 Control parameters summary for UCT type pilot plant
Parameter
Max Dissolved
Oxygen (mg/L)
Min Dissolved
Oxygen (mg/L)
Ammonia
concentration Set
point (mgN/L)
Tank LD3
Tank LD4
Tank LD5
0.35
0.35
1.00
0.10
0.10
1.00
-
5.0
-
84
Figure 4.7 AO controller screenshot. This controller uses an On/Off type control for air flow
rate.
Table 3.8 Current controller Parameters for AO.
Parameter
Max Dissolved
Oxygen (mg/L)
Min Dissolved
Oxygen (mg/L)
Ammonia
concentration
range (mgN/L)
Tank AO2
Tank AO3
Tank AO4
Tank AO5
0.45
0.70
0.70
1.5
0.00
0.00
0.00
1.5
-
-
2.0-5.0
-
In addition to the control parameters shown in Tables 3.7 and 3.8, other operational
parameters were defined, as shown in Table 3.9. Note that in the simulations the SRT and HRT
were kept constant due the limited capacity of Biowin to simulate SRT and HRT dynamically.
Whereas in the pilot plants SRT was adjusted based on water temperature, and HRT fluctuated
to represent the variations in flows observed in the full-scale WWTP.
85
Table 3.9 Operational conditions in real plants and models
System
UCT real
UCT model
AO real
AO model
Flows (L/day)
SRT (days)
HRT (Hours)
IR=2*INF
10 - 16
8 - 26
10
10
RAS=2*INF
10 - 16
8 - 26
RAS=2*INF
10
11
RAS=2*INF
IR=2*INF
RAS=2*INF
4.5 Modeling results and discussion
Simulations were intended to be performed using 365 days of data, January 2020 to January
2021. However, the simulations were only done for a period of 31 days, corresponding to
January 2021. This reduced amount of time is used due to the longer time required to run longer
simulations in non-steady state using the Biowin controller tool. Additionally, the size of the
resulting file made it difficult to export the data from Biowin to Excel due to the format and
file size issues. Other alternatives such as text html can be used to export Biowin results, with
longer time of data organization.
Figure 4.8 shows the configuration of the UCT-type pilot plant implemented in Biowin, and
Figure 4.9 shows the AO-type configuration. These figures detail pumps, influent, effluent, and
clarifier.
86
Figure 4.8 Biowin configuration of UCT type pilot plant.
Figure 4.9 Biowin configuration of AO type pilot plant.
As shown in Figures 4.8 and 4.9, the main physical difference between both
configurations are the receptor tank of the RAS and the additional IR recycle used in the UCTtype plant. In the UCT-type plant, the RAS is returned to the anoxic tank (LD2). The anoxic
tank moves liquid to the first anaerobic tank (LD1a). In contrast, the AO plant returns the RAS
flow to the first aerobic tank (AO1a).
The model results were compared to experimental data as shown below. The observed
difference between model results and experimental data was evaluated based on the tolerance
levels established by IWA as Good Modeling Practice parameters. According to the Guidelines
for Using Activated Sludge models of Rieger et al. (2013) a model could be consider calibrated
if parameters achieve the tolerance levels shown in Table 3.10.
Table 3.10 Tolerance level and magnitudes definition
Type of magnitude
Definition
Tolerance
Significant
Values greater than 1
5-15%
87
Non-significant
Values smaller than 1
10-100%
Figure 4.10 shows a comparison of experimental data (RD) and predicted data (PD) for
the UCT-type pilot plant, through the month of January 2021.
Figure 4.10 Ammonia concentration in tank LD4.
Additionally, Figures 4.11, 4.12 and 4.13 show DO concentrations in tanks LD3, LD4
and LD5.
88
Figure 4.11 DO concentration in tank LD3.
Figure 4.12 DO concentration in tank LD4.
89
Figure 4.13 DO concentration in LD5.
Tables 3.11 and 3.12 present a comparison of experimental and modeled values for the
data presented in Figures 4.10 to 4.13.
Table 3.11 Real Data Average calculated
LD3 DO
LD4 DO
LD5 DO
LD4 (NH4)
(mgO2/L)
(mgO2/L)
(mgO2/L)
(mgN/L)
Average
0.41
0.40
1.21
5.08
SD
0.12
0.14
0.57
0.51
Max
1.14
1.04
3.60
8.71
Min
0.18
0.12
0.36
3.03
Real Data
90
Table 3.12 Model Data average values
LD3 DO
LD4 DO
LD5 DO
LD4 (NH4)
(mgO2/L)
(mgO2/L)
(mgO2/L)
(mgN/L)
Average
0.30
0.30
1.00
2.58
SD
0.01
0.00
0.00
0.56
Max
0.32
0.31
1.02
4.12
Min
0.15
0.18
0.95
1.79
Model Data
Using Table 3.11 and 3.12, the errors between both set of data were calculated. Table
3.13 shows the results.
Table 3.13 Calculated errors in ammonia and DO concentrations.
LD3 DO
LD4 DO
LD5 DO
LD4 (NH4)
(mgO2/L)
(mgO2/L)
(mgO2/L)
(mgN/L)
Average
28%
24%
17%
49%
SD
95%
97%
99%
-10%
Max
72%
70%
72%
53%
Min
18%
-54%
-164%
41%
Errors
Green-highlighted cells indicate variables for which the average was within the range
of tolerance defined in Table 3.10. Red-highlighted cells indicate parameters for which the
average was outside the tolerance range when compared to the experimental data. In tanks LD3
and LD4 the deviation from experimental results for DO has values of 28 and 24%,
respectively, which is less than the acceptable100% deviation for magnitudes below 1, the
upper boundary of the tolerance set by the IWA GMP for non-significance variables.
91
Consequently, since the deviation was within tolerance for DO concentrations, no attempts to
calibrate the model for these parameters were made, even though the figures show very
different responses in terms of oscillation. This oscillation in the real data might be produced
by many different parameters: (1) the ultimate gain (𝐾𝐷 ) of the controller which is the ratio
between the manipulated signal and the response of the controlled variable. In the model, this
parameter is the default value of 0 (m3/h /mg/L) which is going to reduce the oscillation of the
system to zero. The determination of the ultimate gain in any controller requires a tuning
process, which were not performed during this study. (2) The model is working with a constant
flow instead of the variable flow used to operate the pilot plants. This factor makes a difference
in terms of nutrients and COD load to the model compared to the pilot plant system. Variable
flow may change the demand of oxygen.
For the DO concentration in tank LD5, the error calculated was 17%, which is 2 units
above the tolerance criteria for a significance variable, defined as those variables having
magnitudes greater than 1. Due to inability to run additional simulations to evaluate how to
bring the experimental values within the acceptable range of tolerance, the model was not
further improved with respect to this variable.
As shown in Table 3.13, the average ammonia concentration in tank LD4 had an error
of 49%. This amount of error is greater than the upper boundary established by the IWA GMP
for significance value. This error is a concern since the ammonia concentration is the main
control loop in the UCT plant. Figure 4.10 shows the large difference between the ammonia
RD and PD data. No more simulations were performed to improve the predictions in ammonia
concentrations.
This large difference can be due to many parameters, which may not be adjusted
correctly in the model, such as (1) the ultimate gain in the model controller, (2) the DO
92
concentrations obtained through the current model controller, which are not matching the
observed value of 0.3 mgO2/L. Even though the controller is set to maintain DO at 0.3 mgO2/L,
it is not reaching it.
Figures 4.15 to 4.21 show the collected and predicted data for the for ammonia, nitrate,
and nitrite concentrations in the AO pilot plant.
Figure 4.14 Ammonia concentration in AO4.
93
Figure 4.15 Nitrate concentration in tank AO4.
Figure 4.16 Nitrite concentration.
94
Figure 4.18 to 4.21 show the data collected and predicted for the DO concentrations in
the AO type pilot plant.
Figure 4.17 DO concentrations in tank AO2.
95
Figure 4.18 Represents the DO concentration in tank AO3.
Figure 4.19 Do concentrations in tank AO4.
96
Figure 4.20 DO concentration in tank AO5.
Using all compiled information about ammonia, nitrate, nitrite and DO concentrations
shown in Figures 4.15 to 4.21, Table 3.14 and 3.15 summarize the RD and PD for the AO pilot
plant.
97
Table 3.14 RD data collected from January 2021.
Real
Data
AO2 DO
AO3 DO
AO4 DO
AO4
AO4
AO4
NH4
NO2
NO3
AO5 DO
(mgO2/L) (mgO2/L) (mgO2/L) (mgO2/L)
(mgN/L) (mgN/L) (mgN/L)
Average
0.28
0.74
0.32
2.08
3.30
0.37
7.39
SD
0.20
0.71
0.26
0.52
0.93
0.22
3.98
Max
1.55
5.93
1.56
4.66
5.61
1.26
21.99
Min
0.03
0.00
0.00
0.29
1.59
0.12
0.10
AO4
AO4
AO4
NH4
NO2
NO3
Table 3.15 PD data from January 2021.
Model
Data
AO2 DO
AO3 DO
AO4 DO
AO5 DO
(mgO2/L) (mgO2/L) (mgO2/L) (mgO2/L)
(mgN/L) (mgN/L) (mgN/L)
Average
0.23
0.28
0.37
2.94
3.02
0.35
4.27
SD
0.22
0.22
0.23
0.97
0.82
0.08
1.32
Max
0.59
0.65
0.81
3.96
5.04
0.52
12.77
Min
0.00
0.00
0.00
0.00
2.00
0.06
1.17
98
Table 3.16 Errors calculated between the RD and PD data sets.
AO2 DO
AO3 DO
AO4 DO
AO4
AO4
AO4
NH4
NO2
NO3
AO5 DO
Errors
(mgO2/L) (mgO2/L) (mgO2/L) (mgO2/L)
(mgN/L) (mgN/L) (mgN/L)
Average
18%
62%
-15%
-41%
8%
5%
42%
SD
-10%
69%
9%
-86%
11%
66%
67%
Max
62%
89%
48%
15%
10%
58%
42%
Min
98%
-
-
100%
-26%
48%
-1063%
Table 3.16 shows the errors calculated for all parameters evaluated. Green-highlighted
cells are used to show parameters for which the deviation of values from simulations compared
to experimental observations are below the tolerance threshold already calibrated variables.
Green-highlighted cells point out calibrated variables, which are he DO concentration in tanks
AO2, AO3, AO4 as well as ammonia and nitrate in AO4. The first three variables are nonsignificant values, so the maximum tolerance of errors according to IWA GMP 2013 is 100%.
Giving the results of 18, 62 and -15%, I did not attempt to improve the model predictions with
respect to these variables.
Table 3.16 shows the average error calculated for DO concentration in tank AO5. This
is a significant variable, which agreeing with the IWA GMP 2013 establish a maximum error
tolerance of 15%. In this variable the error is -41% which means that the model is over aerating
the tank with 41% more air in average. An error of 41% is not acceptable, so it follows that DO
in tank AO5 is not calibrated. In Figure 4.19 the orange line drops to zero many times during
the simulated interval. This can be related to a bad set up of the controller, making the controller
shut down valve 7, instead of keeping it open permanently. This can be corrected in the Biowin
99
controller through iteration process until find the correct air flowrate; however, no attempt was
made to make this correction.
The average concentration error of ammonia, nitrate and nitrite is shown in Table 3.16.
Ammonia and nitrate are significance values while nitrite is non-significance value. For
ammonia and nitrate the errors are 8 and 42%. These results indicate that ammonia
concentrations are within the tolerance levels, but nitrate concentrations are not and would need
to be improved. No additional simulations were performed to attempt to bring these errors
within tolerance levels. The nitrite concentration in tank AO4 shows an error of 5% in Table
3.16. This is within the range of tolerance.
Figure 4.20 shows the HRT variation from January 2021.
Figure 4.21 Hydraulic retention time during January 2021.
100
HRT has been mentioned during the analysis of all the variables studied on this
research. Table 3.17 show the average HRT calculated for the RD and PD sets.
Table 3.17 HRT values for PD and RD data sets.
HRT (hr)
Predicted Data
Real Data
Average
11.00
12.99
SD
0.00
3.66
Max
11.00
27.84
Min
11.00
7.82
Matching to Table 3.17, the HRT for the predicted data is 11 hours, which remains
constant during the simulation. This is a big difference between the PD and the RD sets. The
simulation uses a constant influent flow rate of 1300 gal/day (3.4 L/min), while according to
Table 3.18, the UCT-type plant used an average flow rate of 1422±5062 gal/day (3.7±13.3
L/min) and the AO-type plant used an average flow rate 1650±5849 gal/day (4.3±15.3 L/min)
12.99±3.66 gal/day (0.03 L/min). Occasionally during real operation, a max HRT value of
27.84 hours and min value of 7.82 hours are reached. This fluctuation is related to the influent
flow variation of the full-scale plant which is linked to the weather and rate of consumption of
the population showing HRT peak values every 24 hours. Decreasing the wastewater influent
coming to the plant since 12:00 am until 6:00 am approximately. Reaching it highest HRT peak
at 9:00am, then decreasing until 12:00pm, to finally stabilize after 12:00pm.
101
Table 3.18 Influent flow rate calculated. Metrics for UCT and AO plant.
Statistics
Average
SD
Max
Min
Flow rate UCT
(L/min)
3.76
13.31
1.75
6.24
Flow rate AO
(L/min)
4.34
15.38
2.02
7.20
102
% Difference
13%
13%
13%
13%
CHAPTER 5: RECOMMENDATIONS
For future investigations it might be useful to have two different pilots with the same
volume. This physical difference could become a source of error during the collection of new
data and the training process of new research staff. In addition, using identical clarifiers in
terms of volume could allow for easier analysis of the settling process. This difference in the
clarifier volumes might produce some confusion during measurements and experimentations.
The current operation uses two different systems of control. Both are FEEDBACK
control types. This means the controllers measure the control variable downstream to take
actions downstream. It might be interesting to use FEEDFORWARD systems of control, where
the controller plays a more proactive role and reacts before the control variable is disturbed,
measuring variables upstream to prevent disruption in the control variable downstream.
A larger COD dataset is desirable. In this research, only 78 data points were collected,
representing 78 days. Even though other authors suggest that a campaign of 20 data points
during a year is enough to characterize wastewater, measuring COD variation during the whole
year could give broader information about the behavior of the COD concentration related to
pluviosity and temperature. Furthermore, a more detailed analysis of the F/M ratio with a
dataset encompassing a longer period could be helpful to understand the real impact of it in the
settling process. Even though this research collected 78 days of data, a longer period would be
useful to evaluate the relationship between F/M and SVI.
The modeling procedure requires more work. Although two different models were built,
the influent specifier tool was used and the Biowin controller tool was employed, inaccuracies
and many sources of errors remain in the models. For example: (1) The influent data uses a
constant influent flow rate that is not an adequate representation of the variable flow rate, and
103
the changes the HRT and air flow demand in the pilot plants. (2) The clarifiers used in the
models are specified as ideal, which means default parameters are used to run it and no
accumulation of suspended solids occurs inside the clarifier. Since the pilot plant operation has
problems with settling and accumulates 10 to 30 gallons of sludge in the top of each of the
clarifiers, the current model is not considering this physical phenomenon; (3) the DO controller
in tank AO5 drops the DO concentration to 0 mgO2/L, a drop that is not occurring in the pilot
plant controller. This problem might be solved by tuning the model controller so that it more
accurately reflects the pilot plant operation. (4) Although the aeration and kinetic parameters
used were mostly the default values, tuning these parameters to better reflect pilot plant
operation would be useful. (5) Running non steady state simulations that capture the SRT
changes during winter and summer will be useful to better simulate how the microbes
performed SND during the different seasons at different SRT conditions. (6) Calculating the
ultimate gains of both controllers is important to give more accuracy to the model and
reconciliate data easier. (7) The water temperature in the model remains constant during the
simulation at 20 °C. This is not consistent with the changes in water temperature during the
year. A tool in Biowin called “Edit Global Temperature” allows one to set the temperature of
the water to either a constant value or a pattern. This would help increase the accuracy of the
model output.
Additionally, some simulations modeled 365 days of plant operation, but due to the
large size of the output file, I was not able to download it. The model takes 3 complete days to
finish a full non-steady cycle of simulation. Because of this, simulations of only 30 days were
done for both plants. Therefore, it is important to learn how to use and download the large data
files that result from long simulations.
104
A deeper economic analysis would be useful. Biowin offers the option “Cost/Energy” in the
project option button.
105
CHAPTER 6: CONCLUSIONS
This research allowed the building of two different models that can be used in future
studies to predict DO, ammonia, nitrate, and nitrite concentrations in tanks LD4 and AO4 of
the pilot plants with more realistic operation parameters, due to the implementation of the
influent specifier and Biowin controllers. In addition to the COD characterization done during
this project, the range of HRT fluctuation in the plant was quantified. Understand how this
HRT fluctuation can affect the oxygen demand and dilution process during treatment process
is important to comprehend the plant operation. General conclusions of this research are listed
below:
•
The TCOD average concentration in the wastewater treated at the MMSD is 445±53
mgCOD/L.
•
The SCOD average concentration in the wastewater treated at the MMSD is 284±44
mgCOD/L.
•
The GFCOD average concentration in this wastewater is 311±44 mgCOD/L.
•
The FFCOD average concentration in this wastewater is 222±43 mgCOD/L.
•
The VFA average concentration in this wastewater is 38±5 mgCOD/L.
•
The COD average fractions in this wastewater are 𝑓𝑈𝑆 (𝑢𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑠𝑜𝑙𝑢𝑏𝑙𝑒) =
0.05,
the
𝑓𝐵𝑆 (𝑟𝑒𝑎𝑑𝑖𝑙𝑦 𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒) = 0.46,
and
the
𝑓𝑈𝑃 (𝑢𝑛𝑏𝑖𝑜𝑑𝑒𝑔𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑝𝑎𝑟𝑡𝑖𝑐𝑢𝑙𝑎𝑡𝑒) = 0.04.
•
After the modeling process 7 out of 11 variables analyzed were calibrated. Calibrated
variables are DO concentration in tanks LD3, LD4, AO3, AO4 and ammonia and nitrite
concentration in tank AO4.
•
Modeling is a very sensitive process, which requires large amounts of time. The
iterative process is long and requires permanent comparison of real and model data.
106
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