Dissolved Oxygen Level Control and its Application in
Waste Water Treatment Plants
Jeffrey Shore
Paul Martorano
CEE 453
5/12/03
Table of Contents
Abstract _____________________________________________________________________ 3
Introduction _________________________________________________________________ 3
Objectives ___________________________________________________________________ 4
Materials and Methods ________________________________________________________ 4
Results______________________________________________________________________ 7
Discussion__________________________________________________________________ 13
Appendices _________________________________________________________________ 17
2
Abstract
Proper aeration in a CSTR wastewater treatment plant is essential for optimal degradation of
waste and survival of bacteria inoculants. Current methods of adjusting airflow delivery results
in large oscillation in the dissolved oxygen (DO) which will lower transfer efficiency and
increase operational costs. An empirical model based on oxygen consumption and oxygen
transfer into solution was derived in order to better moderate the influent airflow. Transferring
this model into a LabVIEW programming environment and incorporating it with a current NRP
software program, DO levels much closer to the target level of 2 mg/L were able to be achieved.
Monitoring the DO levels over a period of 4 hours during varying time intervals between airflow
calculations, it was found that an interval of 120 seconds achieved the best results. Overall,
results show that when using a 120 second time interval, an average DO of 2.003 mg/L was
maintained and the DO was within 0.1 mg/L of the target 98% of the time.
Introduction
Cornell University has recently built a brand new, state of the art waste water treatment plant
facility in order to control all the waste water that is produced from university owned buildings.
Computers running LabVIEW software and a few humans are used to control and monitor all
aspects of the running plant. The plant consists of several processes, including control of
influent waste, control of aeration in degradation tanks, control of settling of biosludge, and
control of clean effluent from the settling tank. Biosludge is constantly being recycled and
reused throughout all processes of the plant.
All aspects of the plant are currently computer run, with some minor human interaction, except
the aeration stage. Currently, aeration is controlled by an employee, Jeff Shore, a recent
graduate of the BEE department at Cornell, who monitors the dissolved oxygen levels (DO) in
the tank. The two current control schemes are to either keep the air flowing permanently at a
selected aeration rate, or turn the air on or off depending on whether the desired level is currently
met. Keeping the air flowing at a constant rate will result in values of dissolved oxygen that are
too high (as well as wasted money), while using a single on/off control creates (often large)
oscillations because only one airflow rate can be used.
Jeff has recently gotten very frustrated with his job after not being able to find better work with
his BEE degree, and feels that there are much better things that he could do with his life than sit
around and monitor oxygen levels. Jeff realizes that he must come up with an automated
solution that will be able to recognize and deliver a specific airflow rate to keep the system
balanced for him. In order to come up with a solution, he wants to propose a small scale
experiment composed of a single, multistage reactor that is run entirely by LabVIEW software.
The new design is based on an empirical aeration model which monitors current transfer rates to
calculate current consumption, and in turn determine how much air must currently be supplied to
the tank to maintain a specific DO.
3
Objectives
Our hypothesis is that an empirical model could provide better control over the dissolved oxygen
level than the currently implemented state control. Current setups used in the lab either remain
on all the time, or switch on/off with only one aeration rate; our model will allow an infinite
range of flows (within the physical constraints of the plant), which will continually be adjusted
based on the current oxygen demand, and changes in the demand over time. This should provide
finer control, and especially in periods of low oxygen demand, be able to provide a smoother DO
level (since it can deliver low aeration rates if the situation requires). Our research will be
performed in four stages, as follows:
1. Research the current models and physical parameters, and come up with a usable model
2. Implement this model into actual LabVIEW code which will be fully automated and
capable of logging our results
3. Monitor the new DO behavior in conjunction with the software, and analyze any new
effects
4. Adjust parameters in the model if necessary.
Materials and Methods
The plant is set up as a batch process in a Completely Stirred Tank Reactor (CSTR) with all
stages taking place with in the same tank. The process begins with settled bacteria already in the
tank in minimal amounts of water (total volume 2 Liters). An additional 2 liters of water are
added to the tank, and then 100 mL of 20x waste is added to the system. The final volume of the
filled tank will be approximately 4 liters (volume of the waste is assumed to be negligible). The
shut off control for the addition of water is a pressure sensor located at the bottom of the tank,
while the waste is set on a timer (~60 seconds). Because the pressure sensor will have some
error associated, it is best to measure small volumes by the amount of time it is pumped. After
the “fill with waste” and “fill with water” stages are complete, it enters “aeration stage” for 4
hours. During this time a stir bar constantly mixes the tank while the diffusion stone aerates the
water. This is the stage in which most of the research will be concerned; it will be discussed in
further detail later. After the aeration stage is complete, it enters the “settle stage” in which no
external forces act on the tank, except gravity. The solids are allowed to settle out for 2 hours,
after which point all visible solids have reached the bottom, and the water is completely clear.
The plant then enters the “drain stage” during which the top layer of water (which is now clean
and solids-free) exits to the stream, leaving a volume of 2 liters in the tank. This volume
remaining in the tank is again made up predominantly of bacteria. The plant is back to its initial
condition and is again ready to enter the fill stages. There are no “recycle” lines in the system, as
the inflow and outflow lines are completely separate; however the bacteria stay in the tank and
are reused every cycle. A full cycle takes between 6 and 7 hours to complete. Between 3 and 4
cycles complete a day, and therefore 300-400 mL of 20x strength waste may be cleaned every
day.
4
Figure 1. Plant setup
Figure 1 shows the basic layout of the treatment cycle. Most of these stages are of little concern
for our research, and we will focus future discussion around the processes of the aeration stage.
In order to predict how much air must be pumped into the tank, we need to evaluate how much
oxygen is being removed.
Our model uses the following basic governing equation (at time i):

 D 

  kconsumptioni  kv,li Di
 t i
or rearranged
kconsumption i

 D 

  kv ,li Di
 t i
where:
kconsumption = rate of consumption of oxygen by the bacteria [mg/L-s]

k v , li =
volumetric gas transfer coefficient [1/s]
Oxygen Deficit, defined as C*-C, where C* is the oxygen saturation level at the
current temperature, and C is the current level of dissolved oxygen as
measured by an dissolved oxygen probe [mg/L]
D  Di 1
 D 
where Di-1 is the observed deficit

 = rate of change of the deficit, defined by i
t
 t i
at the last interval, and Δt is the time interval, which can be changed to improve the
efficiency [mg/L-s]
Di =
That is, the rate of change in oxygen deficit is equal to the rate of bacterial consumption, minus
the re-aeration rate. When the consumption rate exceeds the re-aeration rate, the deficit change
will be positive, and the DO concentration decreases. Likewise, if the aeration term exceeds
consumption, DO will rise and result in a negative change in deficit.
We can determine what the rate of consumption is at this point from the rearranged equation, and
make the assumption that the value of kconsumption does not change between this interval and the
previous. Consumption rate will change over time, though with a small time step (1-2 minutes),
5
it is close enough to allow successful analysis. If we assume consumption remains the same, and
apply our general equations to the next time step, time = i+1:
kconsumptioni 1

 D 

  kv,li 1 Di 1
 t i 1

OR
kv ,li 1
 D 
kconsumption  

t i1


Di1
where:
k consumptioni  k consumptioni 1
(assume consumption has not changed from the last interval)
Di 1 = the target deficit level, = C* – (target DO concentration)
D  Di
 D 

 = target rate of change for the next interval, equal to i 1
t
 t i 1
With these equations, we can solve for the new kvl value. After we determine kvl we finally want


the n so that we can use it to adjust our flow. The relationship of n vs kvl follows a logarithmic
curve when tested in the lab1 and so may be approximated by a Michaelis-Menton type equation:


kv ,l  kv ,lmin  kv ,lmax

nairi


n airi  n half
kv ,lmin  kv ,l n half

OR
n airi 
kv ,l  kv ,lmin  kv ,lmax
where


n h a lf = average of the maximum and minimum possible n [μmol/s]

n airi = target air flow [μmol/s]
k v ,lmin , k v ,lmax = minimum and maximum possible kvl values (limits based on physical constraints)

- for our model, we used kvl min =0.00099 s-1, kvl max =0.0426s-1, and n half = 13,511 μmol/s
A new desired flow rate is calculated from these equations, and this makes up the basis of the
model. These are the basic calculations that will be performed by the computer, once every
chosen time period. Once it determines the appropriate airflow, it sends this information to other
previously developed components of the LabVIEW software.
1
See Shore, J & P Martorano, 453 Lab, Oxygen Measurements, 2003
6
The length of the period is determined by the user, and will be the same as the Δt. At the end of
every cycle, the Di becomes the Di-1 for the next cycle, and kvl i+1 will become the kvl I for the next
cycle.
Results
Prior to implementation of the empirical aeration model, the plant was not very successful at
keeping a constant DO level of 2 mg/L. Analyzing Figure 2 graphically, the plant spends the
majority of its time above the target dissolved oxygen levels of 2 mg/L. The high fluctuations
are in part due to the aeration being always on at the set level of 2000 μmol/s when the plant falls
below the target.
6
5
DO (mg/L)
4
3
2
1
0
11.4
11.8
12.2
12.7
13.1
13.5
13.9
14.3
14.8
Time (hr)
Figure 2. Plot of dissolved oxygen levels prior to changes in aeration model. Values were
taken during an aeration stage on April 25, 2003.
Analytically, this method is able to produce an average dissolved oxygen concentration of 2.43
mg/L and a standard error of 0.5623. Accepting an error in the range of 2 ± 0.25 mg/L, the
current controls still only stay in this range about 31% of the time it is in aerate mode.
Once the empirical model was implemented into the program, dissolved oxygen readings were
much closer to the target level of 2 mg/L. As can be seen in Figures 3, 4 and 5, after the initial
phases of high oxygen consumption where the bacteria were readily degrading the waste,
dissolved oxygen readings were easily able to stay close to the target level of 2 mg/L.
7
10
9
8
7
DO (mg/L)
6
5
30 second Δt
4
30 second Δt
60 second Δt
120 second Δt
3
2
1
0
5.2
6.2
7.3
8.3
9.3 10.3 11.3 12.4 13.4 14.4 15.4 16.6 17.6 18.6 19.7 20.7 21.7 22.7 23.7 0.8
1.8
Time
Figure 3. Dissolved oxygen readings over varied times intervals using empirical model.
3.5
3.5
3
3
2.5
Dissolved oxygen (mg/l)
Dissolved oxygen (mg/l)
2.5
2
1.5
2
1.5
1
1
0.5
0.5
0
0
0
2000
4000
6000
8000
10000
12000
14000
Time (s)
Figure 4. Dissolved oxygen readings
during aeration stage, with 120 second
time interval.
16000
0
5000
10000
15000
20000
25000
30000
35000
40000
Time (s)
Figure 5. Dissolved oxygen readings
during multiple aeration stages, with 60
second time intervals.
The only time when dissolved oxygen overshot the target by a large amount was immediately
following the initial consumption phase. During this initial phase, airflow was set to the
maximum value allowable by the program (2000 μmol/s), and once it reached the target, the kvl
is reduced in stages, which takes a few minutes to level out the first time. However, once the DO
was within range of 2 mg/L, the 60s interval model rarely goes higher than 2.2 mg/L (or lower
than 1.5 mg/L) and the 120s interval model almost never deviates more than 0.1mg/L from the
8
intended target. Compared to the levels obtained prior to implementing the empirical model
(Figure 1), this is much more effective in moderating the airflow.
3.5
3.5
3
3
2.5
2.5
Dissolved oxygen (mg/l)
Dissolved oxygen (mg/l)
While the 60s and 120s time intervals have similar results, things deviate significantly once the
time interval becomes small enough. Comparing the results in Figures 3, 4 and 5 with Figures 6
and 7 below, the choice of time interval drastically alters the effectiveness of the model.
2
1.5
2
1.5
1
1
0.5
0.5
0
0
0
0
2000
4000
6000
8000
10000
5000
12000
10000
15000
20000
25000
30000
35000
Time (s)
Time (s)
Figure 6. Dissolved oxygen readings
during 10 second time interval.
Figure 7. Dissolved oxygen readings
during 15 second time interval.
Small time intervals (10 and 15 seconds) were not as effective at achieving the target results. As
can been seen in Figures 6 and 7, the DO readings were very jumpy, and had an average DO
readings of 2.17 mg/L and 2.21 mg/L, respectively, with much higher standards of error. In
comparison, higher time intervals of 30, 60 and 120 seconds were able to achieve average DO
readings of 2.04 mg/L, 2.01 mg/L and 2.00 mg/L, respectively.
A summary of all calculated values from each time interval of the empirical model and from
before the model was implemented, can be found in Table 1, and demonstrates the true success
of this model.
Table 1
Data Summary
Average DO *Standard Error
(mg/L)
(mg/L)
Prior to empirical implementation
10s interval
15s interval
30s interval
60s interval
120s interval
* Found using
2.430
2.169
2.204
2.038
2.013
2.003
0.5623
0.2385
0.3274
0.0858
0.0998
0.0461
Percent of aeration time in range, %
1.9 - 2.1
1.8 - 2.2
1.75 - 2.25
mg/L
mg/L
mg/L
13.2
25.5
31.1
40.0
65.0
73.6
33.5
56.1
63.3
78.1
96.7
98.9
70.0
96.9
98.4
98.2
100
100
1 n
 y  yi 2

n  1 i 0
9
Our results can reveal far more about the system than just plotting the DO level. We can observe
how the actual physical parameters are behaving, and use this information to further verify our
model is reacting the way we hypothesized.
A good parameter to first analyze is the rate of oxygen consumption by the bacteria, modeled
earlier under the variable kconsumption. This was used as an intermediate calculation to reach our
desired airflow rate, but we can now look at its pattern over the course of a full cycle.
Kconsumption vs time (120s interval)
0.07
0.06
kconsumption (mg/L-s)
0.05
0.04
0.03
0.02
0.01
0
59000
61000
63000
65000
67000
69000
71000
73000
75000
time (s)
Figure 8. Kconsumption over a 4 hour period for the 120 second time interval.
This graph shows the rate of consumption dropping off dramatically after the first few minutes of
aeration. The rate of consumption will be based on the wastes concentration and as
concentration decreases, so does the rate of degradation. This makes sense, and follows the
Michaelis-Menton equation approximately:2
dS
kXS

dt Ks  S 
2
Lion, LW. CEE 351 Course notes, Cornell University. p. 91. 2003
10
This verifies that our calculations and logging technique are close, and that the bacteria are
behaving as we predicted.
Another parameter that can be plotted to gauge the success of our work is the mass flow of air,

represented as n in our model. If we look at our earlier graph of DO levels at a 120s time

interval, now with our n values also plotted(Figure 9), we see:
DO and mass flow vs time (for 120 s intervals)
3.5
400
350
300
2.5
250
2
200
1.5
150
1
Mass flow rate of air (μmol/s)
Dissolved Oxygen Concentration (mg/L)
3
DO
n
100
0.5
50
0
0
0
20
40
60
80
100
120
140
160
180
200
220
time (minutes)
Figure 9. Dissolved oxygen and mass flow versus time for the 120 second time interval.
This graph provides much more insight into how our algorithms are behaving. Our DO trend
line has been oscillating (very little, but it is still there), and it is difficult to tell whether the flow
rates are smooth, or simply going from zero to the maximum to zero again. This data proves
otherwise. While at first the mass flow rate is at 2000 μmol/s, and then quickly drops back down
to zero, after this point the flow very rarely shuts off (n = 0). There is a degree of oscillation
during the next 3 hours, but in general it tries to stay at a steady value around 50 to 75 μmol/s.
Thus, when the DO is above the target slightly, air does not shut off completely but rather
maintains a low rate to consistently replace the oxygen used by the bacteria and stay practically
at 2 mg/L. The small variations can best be explained by the imprecision of the accumulator
system, which does not handle low flow rates very accurately. It is difficult to even calibrate the
current setup for values this low. Since it cannot consistently deliver the airflow rate the
software would like, the algorithm compensates for the difference in the next interval. This
graph clearly demonstrates that the software is far more advanced than a simple on/off system,
and given ideal conditions would likely produce a smooth curve.
11
The results for smaller time intervals are less encouraging, but demonstrate the effectiveness of
using large time intervals. As Figure 10 shows, small intervals lead to very choppy extremes for
the air mass flow rate.
DO and mass flow vs time (10 s intervals)
3.5
2000
1800
3
2.5
1400
1200
2
1000
1.5
800
Mass flow rate of air (μmol/s)
Dissolved Oxygen Concentration (mg/L)
1600
DO
n
600
1
400
0.5
200
0
0
2000
4000
6000
8000
10000
0
12000
time (s)
Figure 10. Dissolved oxygen and mass flow versus time for the 10 second time interval.
These lines do not level out, but rather spike up and then jump back down to zero. In this form,
the algorithms do little more than the original on/off switch. Fortunately, longer time intervals
produce more favorable results, and demonstrate the importance of giving the system enough
time to adjust.
The influent has BOD levels of 323 mg/L at single strength. We were using 20x solution, which
had levels of 6,460 mg/L. We introduced 100 mL each time, or 646 mg of BOD each cycle.
Distributed over 4 liters, this becomes 160 mg/L in the initial reactor. It is assumed that over 4
hours the BOD becomes basically 0. With an even distribution, this would be 40 mg/L, however
common sense and our research both encourage a skewed graph, with a majority of the
consumption occurring in the first several minutes. If our kconsumption data collection was slightly
off, and we assume no consumption is occurring once the rate reaches 0.005 mg/L-s, then the
rate of consumption appears to be 10 times greater at the beginning of the process. The area
under the curve is very close to 0.6 (take each value and subtract by 0.005, then sum). The area
from the first 1000 seconds is 0.25, and so within the first 15 minutes, over 40% of the total
degradation appears to take place. The peak, 0.06, represents close to 10% of the total
consumption, or 64 mg/L-s. This very quickly becomes 0.01, or 11 mg/L-s.
12
Discussion
Analysis of all time ranges tests shows that the 120 second time interval was the best at keeping
the DO close to 2 mg/L. Over the course of the four hour aeration, the DO was within an
interval of 2 ± 0.1 mg/L for 98.2 % of the time. Considering that prior to implementation of our
model the program was only able to keep it within that range about 13.2 % of the time, our
model is much better. It was also noted that when sharp declines occurred over the course of
certain time intervals, the program foresaw this and turned on the airflow prior to the DO falling
below 2 mg/L. Turning the airflow on prior to the drop below the target enabled the program to
better situate the DO at the desired level, instead of it allowing it to drop too low and having to
turn the air on at a much higher volume. Overall our model was a complete success.
Analysis of Figures 8 and 9 shows that kv,l values followed a similar trend line as the kconsumption.
Comparing this to our model formulas, as the change in the oxygen deficit in the plant tends
toward 0 (reach our target DO level of 2), the oxygen consumption level (kconsumption) will be
equal to the oxygen transfer rate (kv,l*D). If the plant is actually able to keep the oxygen deficit
constant, then we will find that kconsumption ~ kv,l ~ airflow rate. This agrees with the trends seen in
the graphs.
Large fluctuations in the DO were recorded during very small time intervals, 10 seconds being
the most extreme. These oscillations can be explained by looking at the logic behind the
algorithms. When Δt is very small, the computer essentially tries to make up the difference from
the target in a very small time period. This results in large necessary airflows. If the time
interval is very high (such as 120s) the plant allows itself much more time to reach the target,
and so delivers less air over a longer period of time. Also, as the kvl value would come back to
zero in stages, it would take longer for the n to adjust to a reasonable level, and once if finally
does come down, the DO is so high that is sets the air flow to zero for some time. Another
reason is that the kvl will continue to increase as long as the DO level is not met. As it gets close,
the increases get smaller, until it eventually stays constant. This creates a problem when you do
the calculations too many times in a short time period. The kvl is recalculated many times with a
10s interval, and becomes large far too quickly compared with the more controlled 120s version.
This is why the flow rate will reach the maximum more frequently. This small failure gives
further insight into our work, and fortunately was easily overcome.
Our model worked very well, and we did not have too many unforeseen problems in our design.
One initial problem came from rapidly increasing kvl values during the heavy oxygen
consumption period. Kvl
There were still some fluctuations in the DO level (though significantly smaller than in the
original design) which can not be solved by just altering the software. One such hardware
constraint is the aeration delivery system, which uses an accumulator and 2 solenoids to regulate
the airflow. Each time the target airflow rate changed, there was an observable amount of time
during which the actual airflow delivered adjusted (delivering inaccurate flows) until it reached
its correct level. Even after reaching the mark, there were fluctuations in the airflow. This was
not such a problem during high oxygen demand periods, where the demand is already far from
being met, and small variations would not have a large impact. However, later in the aeration
cycle when oxygen demand is low, low flow rates are required to keep the DO level constant,
and this is where the hardware runs into problems. When the accumulator begins pumping air, it
13
initially has a higher flow which almost immediately pumps too much air into the tank.
Additionally, small variations in the airflow rate (ie when the target is as low as 40 μmol/s) can
easily exceed the desired rate several fold, and make it difficult to have exact control over the
DO level. Also, if the software changes the flow rate to 0 μmol/s under the current system, air
still bubbles through the diffuser stone as the pressure in the accumulator lowers. All of these
help contribute to some of the observable spikes in the DO. An easy change to the current setup
would be to add a release valve from the accumulator, allowing built up pressure to exit
immediately rather than exit through the diffuser stone where it adds additional air (this extra air
could be used to generate electricity as well - see release valve design from Station 1, with their
5-gallon accumulator). Fortunately a lot of these problems are reduced when the time interval is
increased to 120s. The flow rate rarely shuts off completely (n=0), eliminating problems
associated with start-up/shut-down of the accumulator.
There are also other problems with the current aeration delivery system. With this system, we
cannot calibrate it to handle very low flows (~50 μmol/s) and then later on pump flow rates of
5000 μmol/s. With our current design and arrangement of hardware, the DO can be kept
constant during low oxygen demand periods, but the maximum aeration rate is extremely
insufficient during the rapid feeding stage. The system should be able to handle very low flows
(for low-feeding periods) but also deliver oxygen very quickly at the beginning of a cycle.
Either a new system for selecting flows must be used which can handle a wider range without
any constant hardware adjustment, or an additional diffuser stone needs to be added which would
be activated in emergency situations (like when the DO is below 1 mg/L). This would be
connected before the accumulator and allow maximum aeration when needed (somewhere in the
range of 2,000-10,000 μmol/s. With this set-up, the plant could handle both high and low
demands with greater precision.
One error in the analysis might be the selected kvl values used for calibration to n. Kvl is based
on the properties of the liquid, and the two liters of solids will probably result in some physical
properties which differ from regular water, oxygen transfer rate potentially being one of them.
The kvl values could be tested by using a tank of the bacteria which is not currently feeding.3
Another problem is the lag that exists because the tank is not an ideal CSTR. Addition of air
does not instantly change the DO in the entire tank uniformly. This is observable in the original
on/off control, in which it takes times for the DO to start increasing again once it falls below 2
mg/L. If it was a true CSTR, the DO would begin climbing again as soon it passes this line and
the aeration resumes. Clearly this does not happen, and is a limitation to the control. When the
software instructs aeration to cease, the DO will still continue to rise for a time (since the DO
probe doesn’t instantly measure the true DO) and leads to a peak higher than the software
intended to reach. This might prove difficult to improve, though a long time interval will
minimize these effects.
One change that could be made to the model to avoid large oscillations (a problem at small time
intervals) would be to direct the plant to aim for a DO level between the desired and current DO,
such as the middle. This would help prevent overshooting past the mark, but has certain
drawbacks as well. When the desired DO is close to being met, arriving at the target DO in steps
would give a smoother, more gradual approach which might stick closer to the target better, but
3
Bisogni, J, L. Lion & M. Weber-Shirk, Laboratory Research in Environmental Engineering, Cornell University.
p. 32-34. 2003.
14
has significant drawbacks when there is a sudden or initial large change in DO. Once the DO
passes the target, the airflow rate decreases in steps, and so it will take it significantly longer to
get back down to the correct level once it has passed too high. This major drawback outweighs
the small benefit over the current method, and unless designed in conjunction with other
algorithms, would not be useful.
There are also other potential sources of error which we must speculate about. Other projects
were being developed simultaneously, which were affecting the characteristics of the bacterial
population. Their food received excessive salting which was intended to kill off certain types of
bugs and encourage others to grow. If these different varieties had different feeding behavior
(such as rate) our plant would be reacting differently as time went on. To minimize this source
of error, any data used for comparing with the original simple model was taken only from the
previous two weeks.
Another assumption that was made in the model was that the rate of consumption remained the
same from the previous interval. As observed in Figure 8, this does not hold true for the
beginning of the process. This is most likely to be an issue for long time intervals such as 120s.
This can be solved by taking the last 2-3 kconsumption values and extrapolating the current predicted
value.
If we wanted to further improve the plant to include Nitrogen removal, there are several possible
options. Nitrogen removal requires a two-stage process, Nitrification and Denitrification.
During Nitrification, nitrosomonas convert ammonia to nitrite, and then nitrobacter convert
nitrite to nitrate. During denitrification, the nitrate (NO3-) is converted to N2 gas and bubbled
out. If the residence time of the sludge is left at 15-20 days, and a correct pH is maintained, the
bacteria will carry out the process of nitrification. Therefore, some of our waste might have been
degrading nitrogen, however further conditions must be met. We can separate the nitrification
and denitrification stages but first allowing the bacteria to carry out nitrification in an aerated
tank for 6 to 8 hours. After, we separate the sludge and the waste, and add methanol and new
sludge to the waste to initiate denitrification. After this occurs for 4 hours, it is aerated again to
force the N2 gas to diffuse into the air bubbles and out of the waste. There are additional, more
sophisticated methods for removal (that involve further separation) however in our lab this is
likely to be the most complex and efficient way to remove nitrogen.
Conclusion
Our results conclusively show that using a model of dissolved oxygen consumption and reaeration are far more effective than simple on/off controls. While there are limitations on the
current hardware, it is possible to develop very precise algorithms to predict and react to changes
in demand, and keep a constant level of concentration. By altering the time interval we can
make the software even more precise, until we have an error of less than 0.1 mg/L as high as

98% of the time. Other trend lines of kconsumption and n confirm that our work can accurately
calculate the current demands and necessary compensation. This new technology should allow
Cornell to fully automate its waste water treatment plant, and not rely on primitive operations or
humans to run the plant. So in the end, as happens so often in the world, Jeff did such a good job
of solving Cornell’s problem that he was no longer needed; as a reward he is now jobless again
and joining the rest of his classmates in pursuing a Master of Engineering degree.
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*All suggestions and comments regarding the project (besides the suggestion that you use our
work in next year’s software!) were included in our end of the semester evaluation.
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Appendices
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Set AirFlow VI
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Sub VIs
Calculate saturation
concentration
Calculate kv,l+1
Calculate kconsumption
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Create datalog
Calculate n
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