La ricerca e l’innovazione tecnologica per il Servizio Idrico Integrato
BESTFinalReport
1. April 20th, 2016
Dr. Aydin K. Sunol
University of South Florida
Department of Chemical and Biomedical Engineering
4202 E. Fowler Ave
Tampa, FL, 34620
Subject: Submission of the Hillsborough County Wastewater BEST Group report on “The
Optimization of an Existing Wastewater Treatment Plant”.
Dear Dr. Sunol,
We are writing to notify you that our team, the Hillsborough County Wastewater BEST Group, is
submitting a detailed report on the modeling and optimization of the Valrico Advanced
Wastewater Treatment Facility. This facility was built in the 1990s and has a current capacity of
12 MGD. Although this capacity has made several upgrades possible throughout the years, the
plant still has various limiting factors that constrain the staff’s control of the processes and
therefore the quality of the plant’s final products. This is due to the lack of optimization of
several processes, a limitation our team is confident we can address.
This modeling and optimization of the wastewater plant was developed by one of the finest
groups of Chemical Engineers working in the Bulls Engineering Success training (BEST)
program. Our project began by taking several water samples from various units throughout the
plant. Then, the team performed laboratory tests to determine the quality and composition of the
water samples. Once new data was collected, our team developed three plant models: Biowin,
Ansys, and Neural Nets.
Through the application of these models and our studies of optimization, our engineering team
is confident that the Valrico Advanced Wastewater Treatment Facility will be able to integrate
the study of modeling and optimization analysis to surpass environmental regulations, while
simultaneously keeping with the most cost-effective methods.
Please contact LaNiece O’Steen with an questions or concerns at lanieceo@mail.usf.edu.
Sincerely,
The Hillsborough County Wastewater BEST Group Letter of Transmittal
2. Cover Page
Optimization of an Existing Wastewater
Treatment Plant
Edin Veladzic, Brian Norman, Christine Smith,
LaNiece O’Steen, Claudia Giraldo, Matthew Azarian,
Doantrang Tran, Tito Pino, Evan Crall, Mahdi Hamdan
Report Due: April 20th, 2016
Table of Contents
Letter of Transmittal
3. Cover Page
Table of Contents
List of Figures/Tables
Executive Summary
Introduction
Results
Process Equipment Flow Sheet
Material Balance and Stream Summary
Process Description
Design Premise
Equipment List and specification
Energy Balance and Utility Summary
Summary of Economics
Feasibility Analysis
Environmental Impact Analysis
Safety and Operability Considerations
Discussion of economic, technical, environmental, and economic performance of your
design and its performance
Conclusions and Recommendations
Acknowledgements
Bibliography/References
Appendix for the Group
Nomenclature
List of Technical Assumptions
List of Economic Assumptions
8. List of Tables
Table 1: Flow and Carbonaceous Biological Oxygen Demand. 14
Table 2: Volatile and Total Suspended Solids. 15
Table 3: Total Kejeldahl Nitrogen and Total Phosphorus. 16
Table 4: Headworks Equipment List and Costing Information. 18
Table 5: Anoxic Basin Equipment List. 19
Table 6: Oxidation Ditch Equipment List. 23
Table 7: Clarifiers Equipment List. 25
Table 8: Filter Equipment List. 27
Table 9: UV unit Equipment List. 28
Table 10: Common Parameters. 55
Table 11: Ammonia Oxidizing Bacteria. 55
Table 12: Nitrite Oxidizing Bacteria. 56
Table 13: Anaerobic-Anoxic-Oxic Parameters. 57
Table 14: Ordinary Heterotrophic Organisms. 58
Table 15: Phosphate Accumulating Organisms. 58
Table 16: Dissolved Oxygen Mirror Image and Model Comparison. 70
Table 17: Dissolved Oxygen Results at Varying Depths. 71
Table 18: Aerator Technical Specifications. 110
Table 19: List of Computer Files Submitted. 11
9. Executive Summary
The The Hillsborough County Wastewater Bulls Engineering Success Training (BEST)
Group has made large advancements in the modelling of the Valrico Advanced Wastewater
Treatment Facility using three major software tools. Through the use of the BioWin, an accurate
model of the plant process was developed including the bug reaction kinetics. This model was
tested and tuned using experimental influent composition and flow data and checked against
desired effluent composition data. The influent analysis was completed by the BEST project
team by collecting influent samples at the plant and running tests in a laboratory at USF. (At this
point in time, the BioWin model is nearly optimal and it is producing confident results.)
Besides the main BioWin model, there are two other models that were vital to the
development of the BioWin model: Ansys and Neural Nets. Ansys is a useful tool for
determining the behavior of the water within the oxidation ditches. Not only can Ansys model the
biological process kinetics and water composition, but it also incorporates the implications of the
flow patterns within the ditch generated by the aerators. At the current stage, the Ansys model’s
primary purpose is to ensure that the kinetic model of the oxidation ditches in BioWin closely
mimic the more advanced Ansys simulation.
The Neural Net is an important practical tool for the real-world application of the BioWin
model. The Neural Net aims to smooth the noisy influent flow data from the SCADA
(Supervisory Control and Data Acquisition) system in order to be able to make a useful model of
the flow over time. Then, the net is trained using the smoothed flow data to learn the various
trends and patterns seen in the plant. Once trained, the net is capable of making useful
predictions of future flow patterns based on the current plant situation. This is extremely useful
for the application of the BioWin model by plant operators because it enables them to look at the
current flow, ask the net to predict the next few hours, and feed this scenario into BioWin to help
10. them make the most accurate control decisions. This should enable operators to work more
economically because they can now make more precise decisions regarding when to increase
aerator speed and hopefully save money on energy consumption as a result.
In order to gauge how the BioWin model affects energy consumption, a correlation
between aerator speed and power consumption needed to be developed. This was
accomplished using the TECO Energy Analyzer, operator logs, and TECO energy bills. This
allowed for a direct correlation between the BioWin model and the power consumption of the
aerators. This is extremely important for the application of the BioWin model as now operators
can make control decisions with energy consumption in mind.
At this point in the project, the BioWin model is ready for the next stage of testing and
application. In its current state, it is capable of accurately mimicking the response of the plant,
allowing for plant operators and engineers to test various new control strategies without
compromising the actual plant operation and risk becoming noncompliant. This meets and
exceeds the goals of the project set forth by the Hillsborough County Public Utilities
Department, and will allow them to continue to optimize the operation of the Valrico plant.
11. Introduction
The Valrico Advanced Wastewater Treatment Facility is one of Hillsborough County’s
most dynamic and challenging wastewater plants due to the large variations in plant influent.
For engineers, the hardest details to model in any process are the variations from steady state
where many equations can no longer be simplified and variables are no longer constant. In
many chemical processes and plants, the inputs to the process have known parameters (such
as pressure, temperature, flow rate, etc.) which allows for process design while holding those
variables near constant. When it comes to wastewater treatment, the input parameters can vary
drastically due to a variety of factors. This poses a significant challenge because not only does
the process need to be designed and optimized to meet strict regulations, but it also needs to be
able to respond to changes in influent parameters and environmental disturbances while
maintaining effluent compliance.
While it is important for the plant to meet and exceed all environmental regulations, it is
equally important to do so in the most cost effective way possible. This BEST project is
designed to address this problem through the development of three plant models: Biowin,
Ansys, and Neural Nets. Each software model is looking at a different aspect of the plant model,
so that together they provide a thorough picture of what is happening within the process.
BioWin is a dynamic wastewater treatment process modeling simulation and
optimization software in which biological, chemical, and physical process models can be
combined to provide insight into a wastewater treatment plant. BioWin simulations can help
engineers and operators make decisions that reduce both capital and operating costs while
ensuring treatment objectives are met. BioWin can be used to:
12. ● Select optimal treatment processes
● Reduce capital investment
● Explore strategies for reducing wastewater treatment plant energy consumption and
operating costs
● Evaluate expansion of existing treatment plants
● Make daily decisions about plant operation
● Teach students and operators fundamental wastewater treatment concepts
● Build model extensions and conduct research into emerging technologies
This project will focus on using a BioWin model to match the optimal treatment of the
Valrico AWTF’s wide-ranging influent to the key process variables of the oxidation ditch while
reducing energy consumption and operating costs. Although a BioWin model of the Valrico
AWTF has already been completed by a previous BEST group, we will adjust parameters using
the n-Factorial approach in effort to tune the model. By doing so we will be able to see how
various factors such as aeration levels, which influences DO levels, and influent flow in MGD
affect the process and to what extent.
ANSYS is a complete software suite used to model any aspect of physics,
encompassing its entire range. This software provides access to a virtual engineering
logarithmic database. With this database, any technical simulation needed for any design
process is made possible. ANSYS’s value is derived from its ability to deliver efficiency, drive
innovation, and reduce any physical constraints of the process. This allows for simulated tests
that would not be achievable otherwise. Accurately predicting and controlling fluid flow is a
critical aspect in the optimization and efficiency of the AWTF process. The ANSYS CFD solution
may perhaps enable us to model and simulate the fluid flow process within the oxidation ditch.
13. This includes the fluid-structure and its metaphysical interactions, which will allow us the prime
optimization.
Neural Nets is a mathematical tool that finds correlations between dynamic inputs and
outputs. The utilization and implication is to feed outputs of the neural nets into BIOWIN for
accurate process simulation. Neural Nets will be used for two purposes: to smooth the existing
noisy flow data and to make future predictions of flow patterns. Smoothing the data is necessary
to aid in the discovery of any trends in data. A scattered group of data points is difficult to
analyze, whereas a curve fit of the data allows for better analysis of the data. Once data is
smoothed and free of unnecessary noise, the NARX net can be used to make future predictions
of flow patterns. Having an accurate way to determine the rate of influent flow is important for
the tuning of the BioWin model as it enables the model to be tweaked towards those flow
patterns.
Results
Process Flow Sheet
Valrico Advanced Wastewater Treatment Facility is capable of treating wastewater for a
long period of time due to extended aeration, with a mean residence time of 24 days. In turn,
this produces higher quality reclaimed water returned to customers and less probability of EPA
fines when surface discharging. The figure below is a two-dimensional overview of the plant as
seen from the SCADA system. It not only provides a basic outline of the plant and the major
units within but also allows plant operators to visualize and assess plant performance without
being on-site.
14. Figure 1: Two-Dimensional SCADA Overview of Valrico AWTF
Process Description
The Valrico AWTF treatment process is considered a BioP process which treats sewage
in three different stages. The block diagram below shows the treatment process from influent to
effluent.
15. Figure 2: Block Diagram of Valrico AWTF
The primary stage of the BioP process includes the removal of large trash through a bar
screen and grit through a vortex chamber in the headworks.
In the secondary stage or micro-organism phase activated sludge is combined with the
screened influent and fed to the anaerobic often referred to as anox basins and oxidation
ditches. During this stage, “bugs” like microbes, ciliates, and rotifers use the dissolved oxygen
produced by the aerators and the chemical process of nitrification and denitrification to reduce
the concentration of nutrients (NH3, NO3, NO2, and Phosphorous) in the system. Alum is mixed
with the flow and split evenly between four operational clarifiers which act as large settlers.
Here, suspended solids fall to the bottom of the tank to form sludge which is pumped out as
RAS or WAS and either recycled or dewatered and sent to a landfill while the water on top of
the clarifier is sent to the final stage of the process.
The tertiary phase is the disinfection stage where water is sand filtered and disinfected
by UV before being stored in reclaimed water tanks for customer use, sent to one of the seven
spray fields, or surface released to Turkey Creek.
16. Material Balance and Stream Summary
The material balance for the Valrico plant is done by the BioWin model. It has been a
key goal of the project to develop a working computer model of the plant for the operators to use
in effort to safely test operational changes to the plant. Because of the compliance requirements
and steep penalties for exceeding both compliance and energy consumption, the accuracy and
applicability of the computer model must be of the highest quality.
The main concern with the previous BioWin model is that the influent flow was not
properly reflecting the flow pattern expected at the Valrico plant. After completing a second
round of influent testing and collecting flow data from the plant, we were able to select a 24-hour
period of “Typical-Flow” conditions and feed that to BioWin in a loop. A snapshot of the influent
flow is shown below:
Figure 3: Corrected Influent flow rates for “Typical Flow” pattern.
17. The model was then simulated for a period of time to allow it to come to steady-state and
the stream summary results were then captured for a 24 hour period starting and beginning at
6:00 AM. The three streams shown in the tables below are the influent to the plant, treated
effluent water, and cake removed. Due to the required influent specifications in BioWin, the
following parameters can be traced in the system: flow rate, carbonaceous biological oxygen
demand, volatile suspended solids, total suspended solids, total Kejeldahl nitrogen, and total
phosphorus. These summaries can provide a useful quantitative perspective on what is entering
and leaving the plant throughout the day.
Table 1: Flow and Carbonaceous Biological Oxygen Demand
19. Table 3: Total Kejeldahl Nitrogen and Total Phosphorus
Design Premise
The primary objectives of the Bulls Engineering Success Training (BEST) Hillsborough
County Wastewater Group project is to provide a viable Biowin model to HCPUD which
accurately represent the Valrico Advanced Wastewater Treatment Facility using Ansys and
20. Neural Net models to improve accuracy. These simulations aid in the process of finding
potential control schemes and points of interest for optimization of the plant. An economic
analysis of potential control schemes will highlight trends in utility costs and plantwide energy
distribution. A basic economic and technical feasibility analysis will identify the profitability of the
plant and an environmental impact analysis will ensure that they are meeting strict EPA
guidelines.
Equipment List and specification
Although an overall complex process, the treatment of wastewater is easily broken down
into multiple blocks as seen on Figure 2, composed primarily of the Headworks, Anox, Oxidation
Ditches, Clarifiers, Filters, and finally UV chambers for disinfection. Overall, the Valrico
Wastewater Treatment plant is a $96 Million facility with over 1,800 unique assets. The following
sections break down the various costs for each of the six major units and their assets as well as
a brief description of their duties in the treatment process.
Headworks
Figure 4 : Headworks station at Valrico Water Plant
21. Headworks is the initial stage of the wastewater treatment process. Here, the level of
solid pollutants in the incoming domestic and industrial wastewater which allows the treated
wastewater or effluent to be discharged into a stream, river or lake. The untreated water is
pumped to headworks. During the headworks station, the grit is removed. The grit consists of a
variety of particles including sand, gravel, and other heavy discrete inorganic materials found in
the domestic sewage.
Grit chambers and separators supply a basin or channel that reduces flow velocity,
allowing inert grit particles to be hydraulically removed or settled out. The velocity plays an
important role in efficiency of grit removal.
22. Table 4: Headworks Equipment List and Costing Information
Anoxic Basin
After the nitrification process has been completed in the oxidation ditch, the next process
is the digestion of the organics in the wastewater which is called denitrification. It is very
important to control the nitrogen otherwise, the ecological effects and human health harm;
furthermore, large quantities of nitrogen in the effluent water, produces algae overgrowth in the
rivers which would decompose and kill aquatic lives.
2 𝑁𝑁2
−
+ 𝑁2 → 2 𝑁𝑁3
−
Figure 5 : Anoxic Basin Valrico Water Plant.
23. Table 5: Anoxic Basin Equipment List.
Oxidation Ditch
An oxidation ditch is a modified activated sludge biological treatment process that uses
SRT (solid retention times) to remove the biodegradable organics.
Nitrification is the biological oxidation of ammonia to nitrite by Ammonia Oxidizing
bacteria (AOB) which are prokaryotic cells that accept oxygen as a terminal electron.The
nitrification process is composed of two steps. First the ammonia is converted to nitrite (the
latter happens in the anoxic basins).
2 𝑁𝐻4
+
+ 3 𝑂2 → 2 𝑁𝑂2
−
+ 2𝐻2 𝑂
24. Figure 6 : Oxidation Ditch flow with aerators turned on Valrico Water Plant.
The oxidation ditch has a rectangular geometry whose ends have an oval shape. There
are four aerators located at the four extremes of the ditch as illustrated below. The aerators
have a minimum and maximum rotation capacity.
Figure 7 : Oxidation Ditch Geometry specifications.
Aerator Specification:
25. Figure 8: Aerator Detail
The aerator is the heart of the oxidation ditch. The aerator provides oxygen transfer,
mixing and recirculation of the mixed liquor. There are different types of aerators used in the
industry such as turbine aerators, jet aerators, surface aerators and brush aerators.
Valrico uses the carousel process which means that the vertical shaft mechanical
aerators are positioned in the oxidation ditch channel at the two ends of the track configuration.
The rotating action of the aerators provides oxygen transfer and mixed liquor recirculation
/mixing.
26. Table 6: Oxidation Ditch Equipment List.
Clarifier
The clarifier is a settling tank that is used to remove suspended solids and separate the
sludge from the liquid. It mainly uses gravity to settle the heavier particles. The mechanism that
uses is a long arm that travel around the base of the tank and along the surface of the water.
27. The sweep mechanism takes the returned activated sludge off the floor and pumps it back to
the oxidation ditches to maintain the bacteria population ratio.
Figure 9: Clarifiers at Valrico Water Plant.
28. Table 7: Clarifiers Equipment List.
Filters
The filtering process removes the remaining suspended solids that may be in the water.
This finishing process produces a higher quality effluent.
The filtrate percolates through each of the multi-media filter cells and then into the area
below the filter nozzle plates. From there, the filtered wastewater flows through the backwash
piping, the backwash pumps, and into the clearwell tank. The filtered water in the clearwell will
29. then overflow an effluent weir trough and exit the tertiary filter system. Then the backwashing
takes place.
The rising wastewater level activates the air scouring and backwash cycles. The
backwash cycle will use filtrate from the clearwell to backwash and dislodge the solids
entrapped in the media. The media will be automatically air scoured and backwashed as air
and clean filtrate water is pumped through the filter media from the bottom up, dislodging the
retained solids.
Figure 10: Image of a Filtration Unit.
As shown in Figure 10, the rising backwash water overflows into the surge (backwash
return) chamber. The surge chamber collects the backwash water and, over a several hour
period, will return it back to the head of the wastewater treatment system.
30. Table 8: Filter Equipment List.
Ultra Violet Lights
The UV disinfection unit damages bacterial nucleic acid as the wavelength goes through
the water. This prevents the reproduction of microorganisms within the treated water. The
process adds nothing to the water but UV light, and therefore, has no impact on the chemical
composition or the dissolved oxygen content of the water. UV is the only cost-effective
disinfection alternative that does not have the potential to create or release carcinogenic by-
31. products into the environment. In addition, UV is an effective disinfectant for chlorine-resistant
protozoa like Cryptosporidium and Giardia. The specific range of UV light is between 200 to 300
nanometers
Figure 11: Ultraviolet Disinfection at Varico Water Plant.
33. Technical Modeling and Design
BioWin
The next big step to be taken with the BioWin model is to ensure that for a proper
influent flow pattern with accurate species concentrations, we can predict the Valrico plant
performance in removal of nutrients. The main strategy in perfecting the BioWin model is to:
a) Specify influent characteristics that are typical to the plant
b) Collect accurate effluent data from the plant that will give us absolute ranges for
concentrations of effluent species (To test model performance).
c) Obtain typical plant control scenarios from operator logs or operators themselves
to replicate control actions taken in the actual plant
d) Tune the “bug” kinetic parameters in the BioWin model so that the BioWin results
match the effluent data obtained in part b).
New influent testing performed on April 4th, 2016 allowed us to complete objective a).
This was implemented and presented to Hillsborough county on April 19th, 2016. Effluent data
is relatively abundant, as the plant has to maintain a level of compliance in their discharge. A full
year’s worth of effluent data was provided to us which gave general ranges for the important
species the plant is tracking, completing the objective b).
Next in the strategy is to implement a control scheme that is consistent with the plant.
This begins with providing BioWin with the capability of controlling the aerators by regulating
power uptake (effectively aerator speed). Aerator power uptake was taken from TECO Energy
Analyzer for each of the aerators in the oxidation ditches. From this data, we were able to obtain
a maximum power uptake for each of the aerators. We noticed that Aerators 1A and 2A have
34. the same maximum (76 kW), Aerators 1B and 2B also have the same maximum (50 kW). The
aerators in ditches 3 and 4 are larger and therefore draw more power, the A and B aerators
draw 101 kW and 68 kW respectively. In the model, the plant is modeled as one large oxidation
ditch with an aerator at each end. To properly model the aerators, the A aerator power uptake
was summed together to obtain the total A power uptake (336 kW), and the same was done for
the B aerators (224 kW). Typically the A aerators are run from 95%-100% at all times, so the
input to the surface aerator block is 336 kW. The BioWin simulation with Surface Aerators is
shown below:
The power uptake in Aerator 1B is regulated by a BioWin controller, which is shown
below:
35. The controller samples effluent ammonia and manipulates the Total power uptake in
Aerator 1B. There are 7 settings or steps that the controller has to work with. The Multi-Step
controller operates by thresholds. For example, if effluent ammonia increases and past 0.08
mg/L, the controller will move from step 2 to 3 (From 134.5 kW to 145.7 kW which correspond to
50% and 60% aerator speed). Similarly, if the effluent ammonia is decreasing and falls below
0.08 mg/L, the controller is more likely to fall from step 3 to step 2 (the reverse of the previous
example). The “Hysteresis” option allows a positive input value that is subtracted from each of
the values in the “For decreasing” column which effectively keeps the aerator at the higher
setting for a longer period of time.
The first step in this direction was taken by making an attempt at developing a
correlation between recorded aerator speed and it’s power consumption. A preliminary guess
was obtained using the fluid velocities simulated by the ANSYS model and completing a
mechanical energy balance to determine the shaft work needed to move the fluid in the
oxidation ditch. A better estimate of the aerator power consumption came from the TECO
Energy Analyzer data, which was compared to operator logs to match the recorded speed to
instantaneous power consumption. After a plot of consumption versus aerator speed was
generated using this information, it was observed that the relationship between the two is linear.
This information allowed us to estimate the power consumption for each aerator for any control
scheme.
Having completed this procedure, we have a feel for the aeration speed that BioWin
currently needs to meet the effluent requirements. This also shows us which species are out of
range and identifies which of the “bugs” need further tuning. The next step is to obtain actual
operator control actions from those at the plant and input that data to BioWin. At this point, we
will have accurate values for the influent, effluent, and control scheme, which narrows down the
source of any errors in the BioWin simulation to “bug” kinetic parameters (objective d).
36. ANSYS
ANSYS is now being employed instead of COMSOL to help model the dynamics of the Oxidation
Ditch by combining thermodynamics and hydrodynamics. One of the goals that need to be achieved in
the oxidation ditch is to increase the bacteria growth rate and increase the rate of reaction in
which the organics are consumed in the fluid.
Thereare two carrousel Oxidation ditches at theValrico Wastewatertreatmentplant.Only one was
chosen for this simulation. The larger oxidation ditches which is 3 and 4 have the exactly the same
specifications. The one that was simulated has a capacity of 2.5 MG. There are several sets of
submerged impellers in the reactor (OD). A set of differential equations describing the physics of
the flow, boundary and initial conditions, and mesh points.
The flow of the fluid is described by the following equation after some simplification.
37. Where:
● P is the pressure.
● V is the velocity component.
● X and y are the coordinate component.
Figure 12: Oxidation Ditch velocity pathlines
Thepicture aboveshows pathlinesof velocity which are colored by magnitude(mixture) in unitsof
meter per second.
The next stage in the ANSYS model needed the addition of kinetic parameters to be able to
determine the effects of the aerator speeds in the kinetics. Based on an european study performed by
Lettinga Associates Foundation, Wageningen University [6], it is apparent that at high horizontal velocities,
therate of nitrate removal is lower than the rate of ammonia removal, due to increase in the volume of the
aerated zones.
38. Kinetics Model in Oxidation Ditch
The Volumes of the Oxidation Ditch
𝑉𝑉𝑉𝑉𝑉𝑉 = 2 ∗ (𝑉𝑉𝑉𝑉𝑉𝑉 ∗ 𝑉𝑉𝑉𝑉𝑉 ∗ 𝑉𝑉𝑉𝑉𝑉𝑉) + 𝑉 ∗ 𝑉2
∗ 𝑉𝑉𝑉𝑉𝑉𝑉
Where:
● Length = 273 ft
● Width = R
● R = 33ft 10 in
● Height = 14 ft
𝑁𝑁𝑁𝑁𝑁𝑁 = 2 ∗ (273 𝑁𝑁∗ 33 𝑁𝑁 ∗ 14 𝑁𝑁) + 𝑁 ∗ 𝑁𝑁𝑁𝑁𝑁𝑁2
∗ 14 𝑁𝑁
Reactions:
The first step is nitration, which is carried out by ammonia oxidizing bacteria (AOB) and
ammonia-oxidizing Archaea (Eq. 1):
39. 2 𝑉𝑉4
+
+ 3 𝑉2 → 2 𝑉𝑉2
−
+ 2𝑉2 𝑉
The second step is the oxidation of nitrite to nitrate, which is carried out by nitrite
oxidizing bacteria (NOB) (Eq. 2):
2 𝑁𝑁2
−
+ 𝑁2 → 2 𝑁𝑁3
−
The overall reaction, if biosynthesis is included, can be shown as (Ergas and Aponte-
Morales,2013):
𝑁𝑁4
+
+ 1.86 𝑁2 + 0.098 𝑁𝑁2
→ 0.0196 𝑁5 𝑁7 𝑁2 𝑁 + 0.094𝑁2 𝑁 + 1.92𝑁2 𝑁𝑁3 + 0.98𝑁𝑁3
−
+ 1.98𝑁+
Bacteria Growth Rate:
𝑁𝑁
𝑁𝑁
=
𝑁 𝑁𝑁𝑁 𝑁
𝑁 𝑁 + 𝑁
Where:
●
𝑁𝑁
𝑁𝑁
is the growth of ammonia oxidizing bacteria in the oxidation ditch.
● 𝑁 𝑁𝑁𝑁 is the maximum bacterial growth rate
● 𝑁 is the concentration of the substrate in (mg / L)
● Km is the saturation coefficient
● If S>>Ks we can assumethatthebacteria growth = 𝑁 𝑁𝑁𝑁
40. The following coefficients were taken from (reference- pending) which emphasized its studies in the
analysis of a California plant whose influent had similar characteristics with Valrico Wastewater plant
in Florida.
➢ Half saturation coefficient for ammonia = 0.25 mg O2/m3
➢ Half saturation coefficient for ammonia = 1 mg N/ m3
➢ Half saturation coefficient for oxygen = 0.2 mg O2/ m3
Growth rate of bacteria:
𝑉 = 𝑉𝑉𝑉𝑉 ∗
[𝑉𝑉𝑉]
[𝑉𝑉𝑉] + 𝑉𝑉 𝑉2
Where
● [𝑁𝑁𝑁] is the chemical oxygen demand concentration.
● 𝑁𝑁 𝑁2 is the oxygen half saturation constant.
Oxygen concentration is a function of time and a function of the aerators revolutions.
Maximum Growth rate of bacteria:
41. The growth of bacteria is directly related to the temperature and Ph of the water. Also, it
is related to the total amount of dissolved oxygen present; however, in order to simplify the
model, the Ph and DO were neglected. Only temperature was considered.
The following graph was obtained:
Figure 13: Maximum bacterial growth rate
𝑁 𝑁𝑁𝑁 is a function of temperaturewheremiu aut= 𝑁 𝑁𝑁𝑁whichis the maximum growth rate of
bacteria
𝑉𝑉𝑉𝑉 = 0.77 𝑉0.098 (𝑉−20)
Responseof ammonia:
Theresponse forammonia was obtained based on theexperimental data gatheredin the laboratory.
The effluent behavior of ammonia helps us describe how much ammonia was consumed by the bacteria
(AOB- Ammonia Oxidizing Bacteria) during the residence time.
42. Figure 14: Effluent Ammonia vs Time
Effluent Nitrite
The response for Nitrite was obtained based on the experimental data gathered in the laboratory.
The effluent behavior of Nitrite helps us describe how much ammonia was converted to Nitrite by AOB.
Figure 15: Effluent Nitrite vs Time
Effluent COD
43. COD or Chemical Oxygen Demand is the total measurement of all chemicals (organics
& inorganics) in the water / wastewater. Higher COD levels mean a greater amount of oxidizable
organic material in the sample, which will reduce dissolved oxygen (DO) levels. A reduction in
DO can lead to anaerobic conditions
Figure 16: Effluent COD vs Time
Ammonia response
𝑉𝑉𝑉4
𝑉𝑉
= 𝑉 ∗
𝑉𝑉𝑉𝑉
𝑉𝑉𝑉𝑉
Where:
● 𝑁𝑁𝑁𝑁 is the nitrifier yield coefficient
● 𝑁𝑁𝑁𝑁 is
44. 𝑉𝑉𝑉𝑉
𝑉𝑉𝑉𝑉
= 𝑉 ∗
𝑉 (𝑉𝑉𝑉𝑉𝑉𝑉 − 𝑉𝑉𝑉𝑉𝑉𝑉𝑉) ∗ 𝑉
(1 + 𝑉𝑉𝑉𝑉 + 𝑉)
Where:
● 𝑁 is the SRT (solid retention time) of the oxidation ditch.
● 𝑁𝑁𝑁𝑁𝑁𝑁 is the concentration of the nitrite effluent.
● 𝑁𝑁𝑁𝑁𝑁𝑁𝑁 is the concentration of the nitrite influent.
● 𝑁 𝑁𝑁𝑁 is the bacteria decay rate.
Ammonia Response Calculation Assumptions:
● We will assume the SRT of the plant is 10 days.
● The influent of the ammonia of the oxidation ditch is 35.25 L/day.
𝑉 = 𝑉𝑉𝑉𝑉 ∗
[𝑉2]
[𝑉2] + 𝑉𝑉𝑉2
Where:
● [𝑁2]is the oxygen concentration
● 𝑁 𝑁𝑁2 is the oxygen half concentration coefficient
𝑉𝑉𝑉𝑉
𝑉𝑉𝑉𝑉
= 𝑉𝑉𝑉𝑉 ∗
[𝑉2]
[ 𝑉2] + 𝑉𝑉𝑉2
∗
𝑉 (𝑉𝑉𝑉𝑉𝑉𝑉 − 𝑉𝑉𝑉𝑉𝑉𝑉𝑉) ∗ 𝑉
(1 + 𝑉𝑉𝑉𝑉 + 𝑉) ∗ 𝑉𝑉𝑉𝑉𝑉𝑉
45. 1. 𝑁𝑁𝑁𝑁𝑁𝑁 = -0.8189x6
+206202x5
- 2E+10x4
+1E+15x3
- 4E+19x2
+ 6E+23x - 4E+27, Where ‘x’
are days
2. 𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = 0. There is no influent of nitrite. See assumptions.
3. 𝑁 𝑁𝑁2 = 0.2
4. 𝑁 𝑁𝑁𝑁 = 𝑁 𝑁𝑁𝑁𝑁𝑁 = 0.17 ∗ 1.029(𝑁−20)
5. 𝑁𝑁𝑁𝑁𝑁𝑁 = 2 ∗ (𝑁𝑁𝑁𝑁𝑁𝑁∗ 𝑁𝑁𝑁𝑁𝑁∗ 𝑁𝑁𝑁𝑁𝑁𝑁)+ 𝑁∗ 𝑁2
∗ 𝑁𝑁𝑁𝑁𝑁𝑁
𝑁𝑁𝑁𝑁𝑁𝑁 = 𝑁𝑁𝑁𝑁𝑁𝑁= 2 ∗ (273 𝑁𝑁∗ 33 𝑁𝑁 ∗ 14 𝑁𝑁)+ 𝑁 ∗ (33𝑁𝑁)2 ∗ 14 𝑁𝑁
6. 𝑁 = 35.25 𝑁/𝑁𝑁𝑁
7. [𝑁2] = 65.521x6
- 2E+07x5
+2E+12x4
- 1E+17x3
+3E+21x2
- 5E+25x + 4E+29. Where ‘x’ are
days
Dissolved Oxygen
The oxygen concentration distribution along the ditch is one of the main keys that
determines the design of the ditch. The objective is to obtain the parameters that affect the
distribution of the oxygen and to get the distribution in the aerobic and anoxic zones.
The more oxygen the ditch has, the more bacteria is encouraged to grow; therefore,
more ammonia is consumed, more nitrite is created, and the nitrification process is completed
more effectively; however, since the creation of oxygen in the ditch is created by the rotation of
the aerators, there is a high energy input that has to be fed, hence increasing the utility costs of
the plant.
The end result is to obtain an optimal distribution along the ditch at the minimum aerator
rate, so the aerators are not operating at high speeds for long periods of time.
46. In order to model the DO concentration in the ditch, all the sources and sinks of the oxygen
have to be considered.
Since the ditch is open to the atmosphere, it has to be known that there is constant
oxygen coming in and leaving the ditch through phase change in the interphase,. Also, the
oxygen created through the bubbles that the aerators rotation. For the purposes of our
analyses, the bubble dynamics will be neglected.
Figure 17 : Oxygen Solubility (mg/L) in fresh water as a function of Temperature (C)
The dissolution of oxygen in fresh water provides the amount of oxygen that dissolves in
the water from the atmosphere; however, the oxygen entering the water through surface air-
water interface does not have the enough quantity to complete. The graph above provides the
dissolution of oxygen in water as a function of temperature in Celsius.
The creation of oxygen and consumption of oxygen were modeled based on Biowin
data. The aerators air creation was changed to observe the amount of dissolved oxygen,
47. oxygen uptake rate, and oxygen transfer rate. Then models were created assuming linear
behavior.
It is understood that the real models do not have linear behavior, but for our initial guess,
it is acceptable to obtain an skeleton design and then start building upon it.
After modeling a linear behavior for the consumption of oxygen, the following equation
was obtained.
Where
Y consumption: is the amount of oxygen consumed by the bacteria.
X: is the amount of air injected by the aerators.
Furthermore, a model was created for the creation of oxygen in the ditch based on the
air injected by the aerators.
Where
Y consumption: is the amount of oxygen consumed by the bacteria.
X: is the amount of air injected by the aerators.
The equations above describe the creation and the consumption of oxygen in the ditch
solely based on the air injected by the aerators. As stated before, the bubble dynamics and the
transfer behavior that follows will not taken in consideration in this preliminary study intended to
be used in ANSYS.
48. The only concentration taken in consideration of oxygen in the tank besides the aerators
is the concentration from the interphase which was assumed to be in equilibrium with the
atmosphere; therefore, Henry’s law was used.
Where
[𝑉 2]: Is the concentration of Oxygen in the gas-liquid interphase.
𝑉𝑉: Is the partial pressure of Oxygen in water.
𝑉𝑉 𝑉: Is the tabulated Henry’s constant.
The kinetic models, dissolved oxygen models are next to be incorporated with the
hydrodynamic model to observe the effects of the velocity profile variation on the reaction in the
ditch. The Models will be discussed in the future once the proper tuning and optimization is
performed.
Neural Nets
The objective of Neural Net is to add predictability to a working BioWin model. By adding
predictability, the BioWin model can then be compared to the Valrico plant in real time. The
implication of a working BioWin model that matches the plant in real time is the operators can
test minimum control strategies for the aerators and feel confident the discharge compositions
will stay compliant. This section will give a brief introduction to the Neural Nets and then it will
go into the results and finish with discussion.
49. Neural Nets is an application of MATLAB. There are several versions including Fitting,
Pattern Recognition, Clustering and Time Series App. The relevant version is Time Series App,
and more specifically the Nonlinear Autoregressive with External Input (NARX) selection.
The input generally defines the period of interest for the net. For example, in this
experiment data was available every 5 minutes between midnight on June 6th 2015 thru 8:40pm
January 5th, 2016. Therefore, the goal is to create a matrix of numbers that represent the date
and time of data set.
The past values of output are integral to training the net. It becomes the “Target” for
training. Just as an Olympic runner strives for gold during training, the neural net strives for the
target. For the date range of the data set several past values plant of performance are known.
The ones focused on here are influent flow (Million Gallons per Day), oxidation ditch Ammonia,
and oxidation ditch Nitrate composition (mg/L).
When you zoom in on the NARX Net box in the above figure there are two subsequent
boxes.
50. By using the input and the past values of the desired output or targets, the NARX net
trains itself through backpropagation. Backpropagation is simply the algorithm of generating a
random signal and tuning that signal to meet a target signal through the adjustment of weight.
Finally the loop is considered open because the hypothesis are not being fed back to the target.
Once the termination criteria are met, the net accepts the weights as final and the user
can re initialize the net with values that it has not seen before. In this section, about 19K points
were used as inputs and targets to train the net. Next, the net was re initialized with inputs
representing about 48 hours (as opposed to about 2 months for training) and about 6 points
spanning about 30 minutes. By using the weights at termination, hypothesis values are
51. generated for 30 minutes and then the same hypothesis values are treated by the weights
generating theoretical values in a close loop.
The 4 experiments were run with the NARX net. This project was continued from a
previous group and the work of University of South Florida Graduate Student Faculty, Aaron
Driscoll under Dr. Sunol. The base case provided that the input into the nets was:
● Input=[1x19000] of timestamps in the form of Date +Time
○ [736225.163, 736225.167,...736294.649]
■ [18-Sep-2015 03:55:00, 18-Sep-2015 04:00:00,...26-Nov-2015 15:35:00]
■ The timestamps represent 19K values in 5 min increments from
September 18, 2015 3:55 am until November 26, 2015 at 7:35 pm.
● Targets= [3x19000] or 19K values of Influent, Ammonia, and Nitrate.
The below figure represents the results of the base vase.
52. Black= Actual Future Values; Red= Predicted Values, Green=Black-Red
A hypothesis was made that by coding days into the input for nets, the program would be able
to propagate weights uniquely for different days. It was preferential to code days in such a way
that no day had greater magnitude than any other day, and that each day remains unique. The
solution was a function called ‘dummyvar’ in MATLAB that arranges 0’s and 1’s for placeholders
representing days. Also, an objective was to represent time.
● Input=[8x19000] of timestamps in the form of [Day (7x1900); Time (1x19000)]
○ Day= [7x19000]
■ The length seven refers to 7 days in the week and 19000 are the 5 minute
timesteps through the day
■ [Monday (1:288)=1; Tuesday(288+1:N*288);...Friday(N-1*288+1:N*388)]
53. Time= [1x19000]
■ The timesteps in this case do not incorporate the date but only the time in
288 time step=24hr in 5 minute increments.
■ [(1/288), (2/288)...(287/288), 0, (1/288)...]
● Target=Same for all experiment
The below figure represents the results of the first tuning experiment case.
Black= Actual Future Values; Red= Predicted Values, Green=Black-Red
It seemed like a natural progression to try and incorporate back the date+time value as opposed
to just the time value.
54. ● Input=[8x19000] of timestamps in the form of [Day (7x1900); Date+Time (1x19000)]
○ Day= [7x19000]
■ The length seven refers to 7 days in the week and 19000 are the 5 minute
timesteps through the day
■ [Monday (1:288)=1; Tueday(288+1:N*288);...Friday(N-1*288+1:N*388)]
○ Input=[1x19000] of timestamps in the form of Date +Time
■ [736225.163, 736225.167,...736294.649]
● Targets=Same for all experiment
The results can be seen in the figure below.
Black= Actual Future Values; Red= Predicted Values, Green=Black-Red
vv
Lastly, the third and final manipulation of the control or base case was the conceptually the most
different.The file that contains the target information, SCADA data from Valrico, also contains
the aeration speeds as set by the operators. The operating staff at Valrico made it clear that a
key decision variable for aeration speeds is the total nitrogen content. And as demonstrated in
the energy balance section, that is quite accurate. This an extremely strong visual correlation
between the total nitrogen content and the aeration speed. Generally, it is understood that the
aerator speed is not an independent variable like is time, but is dependent on various factors.
However, by using aeration speed as an input (independent variable)- it is generally going to
help MATLAB correlate the erratic behavior of nitrogenous species to known aeration trends.
● Input=[9x19000] or [Day (7x1900); Time (1x19000); Aerator Speed (1x19000)]
○ Day= [7x19000]
■ The length seven refers to 7 days in the week and 19000 are the 5 minute
timesteps through the day
■ [Monday (1:288)=1; Tuesday(288+1:N*288);...Friday(N-1*288+1:N*388)]
55. ○ Time= [1x19000]
■ The timesteps in this case do not incorporate the date but only the time in
288 time step=24hr in 5 minute increments.
■ [(1/288), (2/288)...(287/288), 0, (1/288)...]
○ Aerator Speed % =[1x19000]/100;
■ The aerator speeds for the correct date and time interval were selected
The results can be seen in the figure below.
Black= Actual Future Values; Red= Predicted Values, Green=Black-Red
56. The black dots represent values that have never been ‘touched’ by the net and provide a
test for the accuracy of the predictive data. Performance was analyzed on the error plotted in
the graphs and visual trends of the predictions were audited. A discussion of the results
elucidates that the control base can qualitatively and quantitatively be improved positively by
the addition of the coded day values. Beyond that, it was shown that date values are introducing
error as opposed to when they are left out. Finally, by using aerator speeds, the influent flow is
57. not necessarily improved upon, but the oxidation ditch and ammonia predictions are greatly
enhanced. This is again attributed to actions of the operators on the aerator speed in response
to total effluent nitrogen which naturally is a function of oxidation ditch nitrogen. Summary of
results below.
In conclusion, the Neural Net tuning program has met the goal of prediction for the
influent flow and has made great strides in the prediction of nitrogenous compositions in the
process. Further experiments include testing different input values, using predictive nets, and
trying to correlate nitrogenous compositions to that of aerator speed alone.
Experimental Results
One major goal for our BioWin model was to ensure that it accurately simulates and
represents the operational conditions of the plant. To do so the following experiments were
completed:
● Sensitivity analysis of bug kinetics using n-Factorial design
○ AOB, NOB, Common, PAO, OHO
● Two rounds of influent testing
58. ○ TSS, VSS, COD, rbCOD, VFAs, Cations, Anions, Total P, Total N, BOD, TKN
● Dissolved oxygen testing and comparisons
● ChemScan accuracy testing
Results and analysis from these experiments can be found in the sections below.
N-Factorial Sensitivity Analysis
In order to tune the BioWin model to match the actual plant as accurately as possible, a
number of parameters in the software can be modified. As most of the uncertainty in the
physical, chemical, and biological processes stems from the oxidation basins and oxidation
ditches, a sensitivity study was performed to evaluate the various kinetic model parameters
which control the simulation of the bugs in the ditches. The goal of this study was to determine
which parameters produce noticeable changes in plant effluent, which is required to meet the
United States Environmental Protection Agency’s specifications for wastewater discharge.
The kinetic parameters in BioWin that are able to be manipulated are broken down into
several tabs, each corresponding to a certain group of organisms, based on their function. For
example, the AOB tab contains parameters relating to the growth and activity of ammonia
oxidizing bacteria, which convert ammonia to nitrite. The following tabs were included in our
analysis:
● Common Parameters
● AOB (Ammonia Oxidizing Bacteria)
● NOB (Nitrite Oxidizing Bacteria)
● AAO (Anerobic-Anoxic-Oxic Parameters)
● OHO (Ordinary Heterotrophic Organisms)
59. ● PAO (Phosphate Accumulating Organisms)
Since each group of bugs performs different tasks, the changes we encountered in the
effluent varied. To again use ammonia oxidizing bacteria as an example, increasing the growth
rates of this group of organism should produce a decrease in effluent ammonia and an increase
in effluent nitrite. Several members of our team were appointed to completing this analysis, and
formed teams of two. Each member of the team analyzed two of the six tabs of interest
independently, and compared results with their partner, to reduce any human error in
interpreting the simulation results.
The sensitivity analysis was performed using an n-factorial approach, to ensure that
testing was completed systematically. For each kinetic parameter available, the numerical value
was increased by 10%, and a two week simulation was run, to ensure a new constant trend was
reached. The results of this simulation were compared to a base case scenario, in which all of
the parameters were kept at their original values. Next, the value was decreased by 10%, and
the comparison process was repeated. Since this analysis is qualitative rather than quantitative
by nature, a simple plus/minus system was used for reporting results. A slight increase in an
effluent water quality trend was designated with one plus sign (+), and a large increase was
designated with two plus signs (++). The same applies to decreasing trends, with minus (-)
signs.
Graphical representations of the base case scenario produced by BioWin are available
in the Computer Outputs section of the appendices. The following tables illustrate the results of
the analysis. Some parameters were already optimized in the model, and were not tested.
60. Untested parameters are marked in red. Parameters identified as producing significant effluent
change are highlighted in yellow.
65. Influent Testing
A major factor in the simulated results of BioWin is influent specifications; the model
must first know what is coming into the plant before being able to determine how to process it.
In order to determine the typical characteristics of the plant influent, two rounds of influent
testing was completed and tested for 13 different data concentrations including TSS, VSS,
COD, rbCOD, VFAs, Cations, Anions, Total P, Total N, BOD, and TKN. Samples were taken on
November 23, 2015 and April 4th, 2016 at various time points in the day as shown on the graph
below.
66. Figure 33 : Influent Sampling Times
For these samples, our team worked tirelessly in the lab to determine both their quality
and composition. The results and their analysis can be seen in the following graphs:
74. Figure 32: Influent Total Nitrogen
The first series of graphs show results from Ion-Chromatography analysis of the fall and
spring samples. The results for Ammonium through Chloride are in pretty good agreement
between the fall and spring testing. Deviations can be seen for Nitrite, Phosphate, and Sulfate.
The Phosphate results for both semesters were high and did not agree with the Total
Phosphorus Test. The Total Phosphorus test is taken to be more accurate as it agrees with data
from the plant. Influent Nitrate only displays results from the fall because the spring results
indicated there was an undetectable amount of Nitrate present in the influent.
For both semesters, we conducted tests for Total Carbonaceous Oxygen Demand
(tCOD) and Readily Biodegradable Carbonaceous Oxygen Demand (rbCOD). The rbCOD
results were much more tight with only one outlier which is easily identified. The tCOD results
varied greatly for each round of testing and the fall results were generally lower than the spring
results.
75. The total phosphorus results have some variation but generally agree with values we
obtain from the plant. TKN is only shown for spring because we did not obtain valid results in
the fall. Dissolved Oxygen was only tested in the lab for the fall round of testing. Total Nitrogen
is shown for both rounds of testing, but the values for fall were much too low.
Combining both sets of data and choosing the concentrations most similar to what the
plant experiences on a regular basis, we were able to tune our model and compare the
simulated results with concentrations of effluent species mined from SCADA, WIM, and ASPEN
files to test model performance.
Dissolved Oxygen Testing
Dissolved oxygen is an extremely important factor to the BioWin model since it dictates
much of the bug kinetics that occur within the oxidation ditch. To ensure accurate modeling of
plant conditions in both BioWin and Ansys, our team completed dissolved oxygen testing in both
ditch one and two.
76. Figure 34: Oxidation Ditch Dissolved Oxygen Sampling Locations
We tested the DO in the oxidation ditches at various points along the outside closest to
the aerators and along the center concrete catwalk every 25 ft as depicted in the figure above.
Our first step was to compare the concentration of DO in both ditches to determine if one
was out-performing the other but our mirror image comparisons showed both were on par with
one another as seen in Table 16. We then compared our experimental data with the DO data
from BioWin to get a maximum percent error of 178% at location 3 closest to Aerator A.This
large error is due to both the speed and turbidity of the water closest to the largest aerator in the
ditch. Although location 3 has the highest percent error, location 2 has the lowest with no error.
77. Table 16: Dissolved Oxygen Mirror Image and Model Comparison
Mirror Image Comparison Model Comparison
Sample #s Conc. OXD 2 Conc. OXD1 Difference BioWin Conc, % Error
1,37 2.58 1.82 0.76 1.9997 9%
2,35 2.4 1.6 0.8 1.9997 0%
3,34 0.85 0.59 0.26 1.9997 178%
4,33 1.07 1.31 0.24 1.9997 68%
5,32 1.19 1.33 0.14 1.00031 21%
6,31 0.84 0.57 0.27 0.00092 100%
7,30 0.71 0.58 0.13 0.007831 99%
8,29 0.69 0.63 0.06 0.014742 98%
9,28 0.71 0.58 0.13 0.101321 84%
10,27 0.7 0.78 0.08 0.1879 75%
11,26 0.71 0.63 0.08 0.527155 21%
12,25 0.89 0.8 0.09 0.86641 3%
13,24 0.97 0.96 0.01 0.866205 10%
14,23 1.18 1.15 0.03 0.866 26%
15,22 2.12 2.49 0.37 0.866 62%
16,21 0.73 0.65 0.08 0.866 26%
17,20 1.26 1.22 0.04 0.866 30%
18,19 1.11 1.22 0.11 0.866 26%
Additional DO measurements were taken along the inner wall closest to the catwalk to
determine if there was a difference in DO at varying depths. As we expected, the largest
difference in depths are closest to the aerators. At location 25 nearest aerator B, there was a
0.1 mg/L difference in surface DO versus the DO at 3 feet and 0.2 mg/L difference in surface
78. and 5 feet but very little change from concentrations at 5 feet and 7 feet. From the table below,
you can see that this trend continues with very little change in concentrations at the 5 feet and 7
feet marks. Potential “dead zones” may exists in these depths preventing very little flow and
thus keeping concentrations stable while the reverse is true closest to the aerators.
Comparisons to the dissolved oxygen and flow vector Ansys models should be considered in
the future.
Note: No sample was taken at location 30 at 7 feet due to drains in the oxidation ditch
preventing access.
Table 17: Dissolved Oxygen Results at Varying Depths
Sample # Surface Conc. 3 Ft Conc. 5 Ft Conc. 7 Ft Conc.
25 0.8 0.7 0.59 0.58
26 0.63 0.58 0.57 0.58
27 0.78 0.61 0.58 0.57
28 0.58 0.55 0.55 0.54
29 0.63 0.56 0.55 0.54
30 0.58 0.55 0.54
31 0.57 .55/.61 0.54 0.54
ChemScan Testing
ChemScan accuracy has long been a major complaint of plant operators at Valrico and
while ideally a BioWin control scheme would be based off the nutrients within the oxidation ditch
our team chose not to do so due to the inconsistency of SCADA and ChemScan with
79. experimental numbers. The graph below shows the overall trend in the difference between the
combined ChemScan data for NH3 and NO3 in both ditch one and two and the experimental
data found by plant operators.
Figure 35: Distribution in Difference Between Experimental and SCADA Data
Based on this information, if we assume the experimental lab data from plant operators
to be the true concentration of nutrients within the ditch, ignoring possible human error, we are
provided with interesting data. According to the graph, ChemScan on ditch 1 reports an average
of 0.1 to 0.2 mg/L lower nutrient level than is found in the lab while the ChemScan on ditch 2 is
within 0 to 0.1 mg/L.
80. Additional analysis was completed to determine if ChemScan error varied based on a
correlation between time or influent flow. Although no correlation was found, we were able to
determine the general trend of both NH3 and NO3 in the ditch over a 24 hour period as shown
by the following graphs.
Figure 36: General Trend of NH3 Concentration in Oxidation Ditches Over 24 Hrs
Figure 37: General Trend of NO3 Concentration in Oxidation Ditches Over 24 Hrs
From these graphs, we can see that NO3 and NH3 follow similar daily patterns reaching
their maximum concentrations somewhere around 11pm to 1am. It is quite possible that these
peaks are due to the typical reduction of Aerator B during these timeframes.
81. Energy Balance and Utility Summary
The energy balance below is around oxidation ditch number 3 aerator A. The results of
this section should show how the mechanical energy balance can provide a rough estimate of
aerators power usage. Moreover, with a correlation between aeration speed and power the
BioWin model can be assessed on the merits of minimum cost versus compliance only.
Valrico plant operates 24 hrs 7 days a week, and one of the key control elements is the
Aeration speed. Aeration speed varies because the key nutrients, nitrogen and phosphorous,
grow high and high aerations speeds help the plant to stay in compliance.
High flow rates naturally mean there will be more nutrients in the oxidation ditch,
however the nutrients are not strictly dependent of mass flow but of aeration speed also. The
relationship between nitrogen and flow of a typical day can be visualized below.
Figure 38: Total Nitrogen and Flow
82. The available data for nitrogenous components are nitrate, nitrite, and ammonia. The
above figure is the sum of these three components.
Aeration speed is manually controlled by the Valrico operating staff based on the values
of the nitrogenous components. “A” aerators are kept constant near 99% while the back or “B”
aerators remain in control. The below figure can show how the aerator 3B’s speed varies in
response to the nitrogen content of the tank. Looking at 5 days shows clearly how the operators
respond to nitrogen. When nitrogen is decreasing in the tank the aerators speed tend to be
small and visa versa.
Figure 39: Total Nitrogen and Aeration vs Time
ANSYS modeling team has provided that the maximum rpm of the Aerators are 22.
Also,that at this power the water moves at a maximum of 2 m/s. For the A aerators. The
mechanical energy balance, with a few key assumptions, provides that the maximum kW is 160.
83. Please see appendix energy section for detailed assumptions, energy balance, and
intermediate results.
On April 7th 2016, the best team visited the valrico site and learned the relationship
between power percentage and kilowatts from the TECO energy analyzer. The results of energy
consumption for 100% power are provided below.
By fitting the velocity around oxidation ditch 3A, the best fit is 1.7 m/s. This value is on
the correct order of magnitude and may very well represent the average velocity around the
aerator much better than the 2 m/s. In fact, on ANSYS models the maximum speed pictorially
takes up a very small volume around the aerator
In conclusion, by using the above informations of 100% power consumption and the
theoretical model of the aerator energy- each aerator was fit with a scaling factor in order to
accurately estimate the electric bill. The results are below and will be used exclusively to
calculate kW from % power.
84. Figure 40: Kilowatts vs % Power
Summary of Economics-The Electric Bill
TECO energy is the energy provider for Valrico plant. The need to calculate an energy
bill as a function of aerator power is pivotal. By providing an economic objective function the
BEST group will be able to evaluate plant control on two dimensions. The first dimension is
compliance feasibility. That is, can the controller manage the plant within EPA regulation. At the
second quarterly meeting the Valrico board stressed the importance of remaining compliant and
explained the fines are such because it is unlawful to operate at sub par effluent compositions of
key nutrients.
The second dimension is economic feasibility. Presently, the plant does well with
compliance. However, energy bill are about $50K per month. The optimal plant will operate
within compliance while minimizing the aerator cost.
Calculating the bill is somewhat trivial once the energy balance and kW vs. power
relationship is established. For a full list of assumptions and bill assumptions, please see the
economic and energy appendix. However, the most pertinent key assumptions will be listed
here.
Key Assumption
● Aerator 3B varies per scada data for 5 full months, and is known
● % Power for all B aerators are the same
● 100% kW is unique between 1-2 and 3-4
○ kW Aerator 1A=kW Aerator 2A
○ kW Aerator 3A=kW Aerator 4A
85. ○ kW Aerator 1B=kW Aerator 2B
○ kW Aerator 3B=kW Aerator 4B
● Aerator A’s (1 thru 4) are set at 99.99%
By using the TECO bill summary and the theoretical aerator energy and back calculating
plant energy the below figure was created.
Figure 41: Teco Bill Summary
The implications of this figure are varied. The ratios presented are in the units of energy.
For example, once such information that can be extrapolated is the relationship between
aerators and the total bill. That is, the ratio of aerator energy to total energy for a given month
range from about 50%-60%. This is a big chunk of energy and exemplifies one of the most
important concepts in this paper. The cost of energy can be reduced by managing the aerators
more efficiently.
The next implication of the figure above is how to analyze the dollar per kilowatt hour
number figure. For example, August used less energy than October but cost more per kilowatt
86. hour. How can this be? TECO charges their customers on a peak and nonpeak basis. It can be
concluded then that the plant was operating on peak times more often in August than in
October. These ratios can provide good constraints for the economic objective function. Beside
minimizing net cost, we can also work on minimizing and making more constant the dollar to
kWh ratio.
Finally, the figure provides assumptions that the BEST team can use to take the
economic analysis to the next level. In skeleton design the demand charge was left out and only
the cost due to the aerators was calculated. However, after being supplied with actual bills, the
energy energy due to the rest of the plant could be calculated. This provided information as to
what is the total energy used in any given 5 minute interval. Not only that, the total energy
calculation on the time intervals was unique to each month making the demand charge and total
bill closer to reality.
The performance of the calculated energy bill is good. In a mix of theory and actual
figures, an estimate was made for 6 full months and the results are within 10% of the actual bill.
For example in July, the actual bill is $50K and the calculated bill is $45K.
As pointed out by senior member, Dr. Sunol, the lack of fluctuations in the graph show
systematic error. Systematic error of 10% means that when the bill is recalculated for theoretical
controller situations, there will be an absolute error. Although there is absolute error, the error is
small compared to the total bill. Not only that, but by demonstrating economic feasibility in the
theoretical bill still means the cost will be reduced in reality. Just scaled down by a small
amount.
87. Figure 42: July-December Calculated vs Actual Teco Bills
Economic Case Study
Available aerator 3B speeds are from June 6, 2015 through January 5, 2016. By
applying these pricing rules. The following figures were found, for aerator 3B.
Also provided was a list of standard operating procedure suggested by Carollo in 2012
can be summarized as follows.
88. By replacing the actual power of Aerator 3B with the suggested values, and keeping all
other assumptions the following economic results were found for the low range of suggested
speeds. The potential is roughly $4K annually considering only aerator 3B. This exemplifies how
a standard operating procedure can economically improve the process. Further studies include
a extending the operating procedure to the A aerators and also including demand.
If the plant is ran at the high range the economic summary is:
89. However, if the plant is operating at the high range of the standard operating
procedures, the control leads to a loss of $3K annually.
This study shows a sensitivity of cost to the aeration speed of the B motors. It can
provide good control strategy for biowin economically as potential savings are around $20K.
Finally, it has ties to all three aspects of the project. It will take BioWin to test if the control
strategy can operate the plant in compliance, ANSYS to make an initial projection of power
consumption, and Neural Nets for further studies to see if nitrogen is in fact is correlated to the
aeration speed.
Environmental Impact Analysis
War Algorithm
The WAR algorithm is a general theory was developed to evaluate environmental impact of a
chemical operation. This process also includes potential environment impact as a purpose to
reduce the total waste formed by the process. An impact that a chemical have on the
atmosphere if it was release to the environment of a chemical is also known as Potential
Environment Impact or PEI.
Main Screen of WAR algorithm
90. The way this program is used for a wastewater treatment plant after setting up a case study. Is
first add the chemicals that are used in the process which are Aluminum sulfate, ammonia,
phosphorus, nitrogen dioxide, nitrogen trioxide, methane and hydrogen into the displayed case.
Then add the streams that are considered in this process which in this case are one Aluminum
sulfate inlet/influent, one gas byproducts outlet, effluent product, and cake product
By using the optimization Software and assuming an average 7.04 MGD influent and 6.28 MGD
effluent, four streams are considered.
1. Alum Dose (influent stream)
Alum Dosage: 79.55 lb/day =0.86 Kg/hr
2. Gas byproducts (outlet waste stream)
Methane generated: 10.29 lb/day =0.194478 Kg/hr
91. Hydrogen generated: 4381.37 lb/day = 82.807 Kg/hr
3. Plant effluent (product stream)
Effluent Ammonia: 7.04 MGD * 0.07 mg/L = 0.069336 Kg/hr
Effluent Nitrate: 7.04 MGD * 1.13 mg/L = 1.11928 Kg/hr
Effluent Nitrite: 7.04 MGD * 0.03 mg/L = 0.029715 Kg/hr
Effluent Phosphorus: 7.04 MGD * 0.18 mg/L = 0.178292 Kg/hr
4. Cake (product stream)
Cake Ammonia: negligible
Cake Nitrate: 0.09 lb/day = 1.7E-3 Kg/hr
Cake Nitrite: negligible
Cake Phosphorus: 501.12 lb/day = 9.47 Kg/hr
Assuming an average of 600,000 kWH is used at the plant per month, and that the average
month has 730 hours, the plant uses 2.959 MJ/hr. Power is assumed to be coal fired.
WAR algorithm includes 8 categories as shown on the graph below
1. Human Toxicity Potential by Ingestion or HTPI
2. Human Toxicity Potential by Inhalation or HTPE
3. Ozone Depletion Potential or ODP
4. Global Warming Potential or GWP
5. Photochemical Oxidation Potential or PCOP
6. Acidification Potential or AP
7. Aquatic Toxicity Potential or ATP
8. Terrestrial Toxicity Potential or TTP
Inputting these parameters into the WAR Algorithm Software yields the following PEI estimate:
92. Figure 47: Output rate of PEI
Nutrient pollution in the United States is one of the most expensive and extremely
challenging environmental problem. The root of this environmental problem is the excess of
nitrogen and phosphorous in the air and water according to the EPA. Even that nitrogen and
phosphorous are indispensable for the growth of most living organism elevated amounts of
those compounds in receiving waters can have a detrimental ecological impact in lakes, rivers,
and oceans.
Like was explained above natural amounts of phosphorous concentrations are present in
various forms in surface waters, which is extremely essential for living organisms. Naturally, the
levels of phosphorous are in homeostasis with the ecosystem. Nevertheless, when
phosphorous concentration exceed the amount the population of living organisms can
assimilate in receiving waters; normally leads to excessive algal grow a phenomena
93. scientifically known as eutrophication. Controlling the release of phosphorous in industrial and
municipal wastewater treatment plants is a key element to avoid eutrophication of surface
waters. Also, it is important to mention that phosphorus does not have a significant adverse
health effects for humans, but levels greater than 1.0 mg/L could disturb the coagulation
process in wastewater treatment plants.
Nitrogen is important in the wastewater treatment, but can have detrimental effects to
the ecosystem, and human health. If the amounts of organic and inorganic nitrogen release to
the environment is above10 mg/L, and can cause eutrophication as well in lakes freshwater,
estuaries, and coastal waters. Once nitrogen is present in the environment ammonia is oxidized
to nitrate creating a big oxygen demand, and have the effect of low dissolved oxygen in surface
waters. Also, high levels of nitrate affect infants’ health, but do not possess an eminent treat to
older children and adults. Methemoglobinemia is the most substantial health problem related to
nitrate in water. The way that nitrite has an effect in the human body is that blood contains an
iron-based compound called hemoglobin that transports oxygen, but when nitrite is exist in the
blood hemoglobin is converted to methemoglobin, which fail on transporting oxygen in the
body. Equally important, nitrogen in the form of ammonia is extremely toxic to fish and exerts an
oxygen demand on receiving water by nitrifiers [11].
Safety and Operability Considerations (HAZOP)
The following table is a safety and operability analysis provided by our team
94. Headworks
Deviation Causes Consequences Protection Actions/
recommendations
More solids Stationary Filter High Debris Level Preventative
maintenance
Clean
Low Flow Clog High Pressure
Broken Vortex
Preventative
maintenance
Cleaning
More grit Broken Vortex High Turbidity Sampling Replace
More Odor Low Chemicals High Smell Chemical Level
Sensor/Control
Refill Chemicals
Annox
Deviation Causes Consequences Protection Actions/
recommendations
High Separation Broken Mixer Solid buildup
Dead bugs
Ensure integrity of
mixer component
Regular maintenance and
oil changes
Low Mixing Dead bugs Measure speed of
mixer
Speed up mixer
High mixing Bugs not starved Measure speed of
mixer
Slow Down
Oxidation Ditches
Deviation Causes Consequences Protection Actions/
recommendations
No Flow Pump Failure/ Broken
Pipe
Back Flow into
Headworks
Check Valve No Additional Action
Necessary
95. Low Flow Pipe Leak/Inefficient
Pump
Back Flow into
Headworks
Periodic Checkup No Additional Action
Necessary
High Flow Holiday/Weekend/Sum
mer
Possible
Incomplete
Treatment
Increase Retention
Time & Aeration Rate
No Additional Action
Necessary
No Additional Action
Necessary
Impurity Broken Aerators Violation of EPA
Standards
Scheduled
Maintenance
No Additional Action
Necessary
Empty Plant Shutdown No Water
Treatment
Meet EPA
Regulations
No Additional Action
Necessary
Low Level Pipe Leak/Inefficient
Pump
Back Flow into
Headworks
Periodic Checkup No Additional Action
Necessary
No Agitation Broken Aerator No Treatment Replace Aerator No Additional Action
Necessary
Poor Mixing Worn Aerator Poor Treatment Replace Aerator No Additional Action
Necessary
Excessive
Mixing
Control Malfunction No Denitrification Emergency Control
Kill Switch
Electrical System
Maintenance
Irregular
Mixing
Inconsistent Motor Improper
Treatment of
Mixed Liquor
Readily Accessible
Spare Motor
No Additional Action
Necessary
Foaming General Operation Slight Cavitation
of Aerator
Scheduled
Maintenance
No Additional Action
Necessary
No Reaction No DO concentration No Treatment Turn On Aerators No Additional Action
Necessary
Slow Reaction Low D.O.
Concentration
Improper
Treatment of
Mixed Liquor
Turn On Aerators No Additional Action
Necessary
Partial
Reaction
Abnormal DO
Concentration or Low
Bug Concentration
Improper
Treatment of
Mixed Liquor
Increase/Decrease
Aeration Rate or
Increase/Decrease
SRT
No Additional Action
Necessary
Side Reaction Heavy metals in
influent
Destruction of
Bacteria
Population
Testing for Metals
Before Plant Influent
No Additional Action
Necessary
Note: Oxidation Ditch HAZOP table extracted from BEST project group 2015 (canvas site).
96. Clarifiers
Deviation Causes Consequences Protection Actions/
recommendations
Excess foam Poor solids capture
from a belt press or a
centrifuge or from
digester supernatant
return that contains
excess solids.
High PH influent
to UV disinfection
unit
Improve the solids
capture in the sludge
processing scheme.
Improve the solids
capture in the sludge
processing scheme.
Dark Brown,
Thick, Scummy
Foam
Growth of Nocardia
and Microthrix
parvicella,
Higher
temperature
conditions UV
disinfection unit
Higher N and P
concentrations may
be necessary
Removal of foam from
the system before going
to filter
Poor Settling Excessive Old Sludge High turbidity Increase recycle of
effluent sludge from
oxidation ditch.
Increase recycle of
effluent sludge from
oxidation ditch.
Low DO Bulking Food-to-
microorganism
ratio increases
Increase recycle of
effluent sludge from
oxidation ditch.
Watch potential
denitrification problems
Growth of fungi Low pH PH does not meet
specs to go to UV
disinfection unit
Decreasing any
nitrification that is
occurring, since
nitrification tends to
depress aeration tank
pH
Increase the pH by
adding either a caustic
solution or a buffer
solution to increase the
alkalinity
Filters
Deviation Causes Consequences Protection Actions/
recommendations
97. Low suspended
solids removal
efficiency
Short Filter runs Air dissolves in the
water and become
trapped in the filter
Wash filter regularly
or perform Filter
backwash.
The filter must be
backwashed more
frequently.
Plugged nozzles Low effluent quality Filters may need to
be washed if the
aerator is shut off for
extended periods or
they are clogged by
grease, soap, residue
or solids
Install alarm that
indicates filter
plugging
Algae and
bacterial growth
High phosphorus
concentration
Implement new
higher priced
algaecides that
contain chelated
copper or Sodium
Percarbonate
Implement new higher
priced algaecides that
contain chelated
copper or Sodium
Percarbonate
Ultraviolet Disinfection Light
Deviation Causes Consequences Protection Actions/
recommendations
Low water
quality
Low UV radiation Low water quality Periodic lamp
maintenance
Periodic lamp
maintenance
Increase in
organic matter
present in the
water
Water effluent
specifications not met.
Periodic lamp
maintenance
Improve the influent
water quality to the
disinfection
High flow rate
into the UV
section
Insufficient UV dose for
effective disinfection,
Keep proper steady
state flow to maintain
high UV disinfection
efficiency
Keep proper steady
state flow to maintain
high UV disinfection
efficiency
No flow Stagnant water being Flush solids deposited No further
98. heated by the lamp
which results in
increased precipitation
of water components.
in the bottom with
water.
recommendations
High amount of
total
suspended
solids
Low quality of
filter's effluent
Accumulation of
solids in the bottom of
the UV
Flush solids deposited
in the bottom with
water.
Improve the influent
water quality to the
disinfection
99. Conclusions and Recommendations
In conclusion, a great amount of progress has been made towards the completion of the
Wastewater project. Improvements have been made on every front, including BioWin, Neural
Nets, ANSYS, Cost Estimation, and Influent testing. Two rounds of influent tests were
completed which showed general agreement as to the kind of flow expected in the plant.
Progress in Neural Nets has been sufficient enough to show promise in the predictability of the
influent flow pattern mainly, in addition to preliminary estimates of some select nitrogenous
species. An ANSYS model was developed to really nail down the dynamics and interplay
between flow, dissolved oxygen, and the “bug” kinetics in the ditch. Cost estimation was done to
find the objective cost function that related Aerator speed to the energy bill cost obtained from
TECO. This was done by calculating the energy requirements of the aerators and relating that to
the rest of the plant. Once the power usage of the plant was found, the electric company’s rates
were applied for on-peak and off-peak hours as they pertain to fuel, energy, and demand
charges. This estimate was found to be accurate in predicting the costs of the plant to within
about 10% of the actual TECO bills. And finally, improvements were made to the BioWin model
to more accurately model the plant and its characteristics. Afew corrections were made in the
flowsheet and associated sizing of equipment, influent was specified more accurately, and a
preliminary controller was developed for the process that can be used to model how the
operators actually run the Valrico Plant.
Future directions of the project are:
1. Further development of the ANSYS model
2. Improvement of the Neural Nets to provide better estimates for nitrogenous species
100. 3. Tuning the “bug” kinetic parameters of BioWin to match plant performance once a
controller is in place.
4. Correlation between energy cost and aeration control used to test the feasibility of
different control schemes.
Acknowledgements
We’d especially like to thank Dr. Gita Iranipour and Adam Hunsberger along with the rest of the
Hillsborough County Valrico Shift Managers and Operators for providing assistance and plant
access throughout the duration of this project. For the support and advice, we would like to
thank our profes Dr. Aydin Sunol and Dr. Sarina Ergas for assembling and leading the BEST
Project Team, also to the following graduate students for their assistance with learning the
modeling software and for their instruction in the laboratory: Aaron Driscoll, Kyle Cogswell,
Ahmet Manisali and Annie Sager. Last but not least is the special thanks to Dr. Tolga Pirasaci
for assisting with our ANSYS Model Design.
101. Bibliography/References
[1] (n.d.). Retrieved March 09, 2016, from https://en.wikipedia.org/wiki/Polyphosphate-
accumulating_organisms
[2] Randall, C. W.,Barnard, J. L., & Stensel, H. D. (1992). Design and retrofit of wastewater
treatment plants for biological nutrient removal. Lancaster, PA: Technomic Pub.
[3] Butler, E., Hung, Y., Ahmad, M. S., Yeh, R. Y., Liu, R. L., & Fu, Y. (2015). Oxidation pond for
municipal wastewater treatment. Appl Water Sci Applied Water Science. doi:10.1007/s13201-
015-0285-z
[4] Wastewater Nitrification: How it works - ECOS. (2013). Retrieved April 13, 2016, from
http://www.ecos.ie/wastewater-nitrification-how-it-works
[5] Muller, E., Stouthamer, A., & Verseveld, H. V. (1995). A novel method to determine maximal
nitrification rates by sewage sludge at a non-inhibitory nitrite concentration applied to determine
maximal rates as a function of the nitrogen load. Water Research, 29(4), 1191-1197.
doi:10.1016/0043-1354(94)00268-c
[6]Yu, L. (2011). Effect of SRT on Nitrogen and Phosphorus Removal in Modified Carrousel
Oxidation Ditch Process. AMR Advanced Materials Research, 396-398, 1995-2001.
doi:10.4028/www.scientific.net/amr.396-398.1995
102. [7] Yang, Y., Yang, J., Zuo, J., Li, Y., He, S., Yang, X., & Zhang, K. (2011). Study on two
operating conditions of a full-scale oxidation ditch for optimization of energy consumption and
effluent quality by using CFD model. Water Research, 45(11), 3439-3452.
doi:10.1016/j.watres.2011.04.007
[8] Water Science. (n.d.). Retrieved April 20, 2016, from http://www.epa.gov/science-and-
technology/water-science
[9] Alaya, S. B., Haouech, L., Cherif, H., & Shayeb, H. (2010). Aeration management in an
oxidation ditch. Desalination, 252(1-3), 172-178. doi:10.1016/j.desal.2009.11.001
[10] Liu, Y., Shi, H., Shi, H., & Wang, Z. (2010). Study on a discrete-time dynamic control model
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wastewater-treatment-plant/
[13] Grossman, A., Kucharski, B., & Kusznik, W. (1980). Problems Of Choice Or
Sorptive Filters On The Basis Of Biot Number Quantities. Physicochemical Methods
103. for Water and Wastewater Treatment, 211-219. doi:10.1016/b978-0-08-024013-
8.50025-9
104. Appendix
List of Acronyms
Symbol Meaning
SCADA Supervisory Control And Data Acquisition
WAS Waste Activated Sludge
RAS Return Activated Sludge
HCPUD Hillsborough County Public Utilities Department
TSS Total Suspended Solids
AOB Ammonia Oxidizing Bacteria
NOB Nitrite Oxidizing Bacteria
VSS Volatile Suspended Solids
SRT Solid Retention Time
MTZ Material Transfer Zone
105. MLSS Mixed-Liquor Suspended Solids
MLVSS Mixed-Liquor Volatile Suspended Solids
COD Chemical Oxygen Demand
BOD Biochemical Oxygen Demand
OD Oxidation Ditch
HTPI Human Toxicity Potential by Ingestion
HTPE Human Toxicity Potential by Inhalation
ODP Ozone Depletion Potential
GWP Global Warming Potential
PCOP Photochemical Oxidation Potential
AP Acidification Potential
ATP Aquatic Toxicity Potential
TTP Terrestrial Toxicity Potential
TDS Total Dissolved Solids
TKN Total Kjeldahl Nitrogen
WWTP Waste Water Treatment Plant
107. A. List of Technical Assumptions
Kinetics Model in Oxidation Ditch:
● Oxidation ditch is assumed to be a CSTR.
● Well mixed.
● Continuous flow in and out.
● Steady state.
● Even though the ditch is open to the atmosphere. We will assume the
temperature of the ditch Isothermal.
● The volume of the flow treated is assumed to be constant. Volume =
2*[L*W*H]+pi*r^2*H.
● Generation of Nitrite is equal to the consumption of ammonia.
● Ammonia is the limiting reactant.
● No other sources of ammonia or nitrite in the oxidation ditch.
● Effluent ammonia + effluent nitrite = Initial ammonia concentration. This
assumption was made since we do not have information for the influent of
ammonia.
● PH constant.
108. B. List of Energy and Economic Assumptions
Energy Calculations
Assumptions
● Isothermal- internal energy goes to zero
● Constant Volume- boundary work goes to zero
● Oxidation ditch is level- potential energy goes to zero
● Energy Input into the system is shaft work (Ws)
● Ignore Skin friction as the model will be fit to actual data anyway
● Energy Output from the system is energy loss (Q)
● Adiabatic- energy loss is negligible
● d/dt(kinetic energy) approximately equal to the kinetic energy entering the
system- the kinetic energy leaving the system
● Velocity of water entering is system is zero; for Aerator A.
● Provided from Ansys- v2=max velocity=2 m/s
● m= mass/ second
Now the mass flow rate will be calculated using the cross sectional area, velocity and density of
water.
109. • Length=23ft
• Width=radius=33 ft (10/12)ft
• Height=13ft
It would be advantageous to correlate the aeration speed in rpm to the actual power
consumption. Towler (pg. 665-666) has a ‘neat’ formula relating the Power number defined as
Np to System Properties such as aerator blade length, revolutions per second and water density
and viscosity.
Assumptions:
110. ● K=constant
● B=1 if in laminar region (assumption, b=1, c=0)
● C=0 if Reynolds number high (max order on chart is 10e5)
● Revolutions per minute=22rpm
● Control Volume
o By considering 2 rectangles 10 feet into the ditch from a cylindrical tank
o Volume=2*(Length*Width*Height)+ π*r^2*height
o Dimension Defined above
o Volume=43,607ft^3=1235 m^3
Diameter of Agitator= 5ft 9 in=3.6m
Density=1000kg/m^3
Viscosity= 8.83E-04 Pa*s
Highly Turbulent Justifies C=0, B assumed=1
Because the kW was calculated and we know the volume we can calculate the Np for the
system to be:
Lastly, because the Reynolds number is known, equation 3 can be solved to find a K value.
𝐾 = 𝑁𝑝/𝑅𝑒 = 2.41377𝐸 − 08
111. After this analysis was performed theoretically for the 3A aerator, the BEST project
visited the Valrico plant and exported data from the TECO Energy analyzer. The data specified
the power output of all the aerators as a function of time. By using this data and operator logs
the maximum power output for all the aerators was recorded and can be summarized as:
At thispointthe velocityof the watercanbe bestfitto make the maximumkWmatch any
aerator at the plant.Lastly,andmost importantly,we now have anrpmor % powerrelationshipwith
kW for all the aerators.A table foraerator forpowervs.kW is below.Thisspecificdataisfor calculated
and the actual fitpowerforaerator 3A.The difference inactual andcalculatedforaerator3A isshown
belowthe table.
112. See energy discussion for graphs and results.
Economic Calculations
The energy bill assumptions were provided by Dr. Sunol, and are summarized in the lsit below.
Assumptions
● Use the correlations from the energy balance for kW vs. % Power
● The aerator 3B varies per scada data for 5 full months
● % Power for all B aerators are the same
● However, 100% kW is unique between 1-2 and 3-4
○ kW Aerator 1A=kW Aerator 2A
○ kW Aerator 3A=kW Aerator 4A
○ kW Aerator 1B=kW Aerator 2B
○ kW Aerator 3B=kW Aerator 4B
● Aerator A’s (1 thru 4) are set at 99.99%
Energy Consumption
Because 1kWh= 1hr*kW and we are working in 5 minute intervals
Lastly, the energy bill can be calculated with this and logical if statements to incorporate
the time and therefore peak and non-peak hours.
113. It was a big goal of the economic analysis to be able to capture the demand charges. By
using total Teco Bill reported kWh and subtracting the calculated aerator kWh the rest of the
plant was able to be solved in a backward way. This allowed us to create a ratio between
Aerator and “Plant” power, that allowed us to calculate total power in an given minute interval
and therefore maximum consumption in a given month. Similar logical statements were used to
incorporate time for peak and nonpeak demand charge.For the figure and discussion see
economic analysis.
Teco Bill Summary
118. File Name Contents
BioWin_ImprovedAerators.bwc Updated Influent Flow; Anox Volume; Aerator Type,
Influent Composition
UserDefinedController_multistep.bcf BioWin Controller multistep based on flow
UserDefinedController_continuous.bc
f
BioWin Controller continuous based on nitrate
Cost_rigorous.xlsx Calculated Energy and Calculated Bill
Valrico3.m NARX net for Experiment 3
vawtp10 Data from Full File and for NARX net
FullFile2015.xlsx SCADA data for three months, influent, aerator
speed and nutrient composition in oxidation
ditches, other
ox_ras.xlsx Oxidation ditches and ras pump energy information
from TECO energy analyzer
BEST Results Spring 2016.xlsx Experimental Lab Results from Spring 2016
BEST Results Fall 2015.xlsx Experimental Lab Results from Fall 2015
DO Sampling DATA_Spring
2016.xlsx
ChemScan Operator Log
