Bioreactor Technical Memorandum Report

Technical Memorandum

pg 1

Date: 10/5/15

To: Brian Thompson

Dr. Nielsen, Professor, CHE 451

From: Alfonso Figueroa, Team 1

Lab Section B

Subject: Aeration Rates and Agitation Rates Impact In a Bioreactor

Summary

Bioreactors are used for the fermentation of different materials to acquire desired goods including foods, antibiotics, and alcohols. This experimentation will go over the aeration and

agitation rates of a bioreactor in the presence and absence of bacteria culture. The volumetric mass transfer coefficient, 𝑘𝐿𝑎, will be determined through the slope of the linearized equation of

the buildup of oxygen in the liquid phase. The slopes gained for 𝑘𝐿𝑎 in static condition (no

bacteria present) in the reactor increase in value as agitation rate and aeration rate increase. The same theory holds true in dynamic condition (bacteria present) where the 𝑘𝐿𝑎 slopes increase in

value as the agitation rate and aeration rate increase. Errors are present in a ± 5% range where possible misreadings from the probe could have occurred.

Introduction

Fermentation has been performed for hundreds years all over the world to numerous delectables. These foods include yogurt, tempeh, beer, bread and much more. The science behind fermentation is that yeast and bacteria break down large molecules down to smaller sized

components. This results in waste products being cultivated from the cultures. Some of the waste that is accumulated emerge as alcohols; these comprise of ethyl alcohol, butyl alcohol, acetone,

and additional molecules.

Eventually in the 1900s, bioreactors were conceived for the up-scale manufacturing of

culture bacteria and yeast. Bioreactors used to mainly be used in the synthesis of alcohols which could be used to yield rubber for use in World War I. Another robust role the Bioreactor took

part in is the production of medicinal antibiotics. This started during World War II where the major antibiotic produced at the time was penicillin since fermentation was ideal in manufacturing the drug. The Bioreactor machines are a fine specimen of genius Chemical

Engineering design being applied to a new process.1

There are an abundant amount of parameters that are involved in the production of the

cells following pH, pressure, temperature, oxygen concentration, and more. The most important of the different parameters is oxygen concentration, or more commonly known as, aerobic

bioprocess, which will be the main focus of this paper. In the aerobic bioprocess, the oxygen transfer rate (OTR) describes oxygen transferring from the gas phase in the bubble into the liquid phase. The equation is as follows:


pg 2

(1)

𝑂𝑇𝑅 = 𝑘𝐿𝑎(𝐶∗ − 𝐶𝐿)

Where 𝐶𝐿 is the dissolved oxygen concentration in the bulk liquid, 𝐶 ∗ is the oxygen

saturation concentration in equilibrium between bulk liquid and bulk gas phase, 𝑘𝐿 is the liquid mass transfer coefficient, and 𝑎 is the gas-liquid interfacial area per unit of liquid volume. Since

defining 𝑘𝐿 and 𝑎 disjointedly is extremely hard, both variables in combination, called the

volumetric mass transfer coefficient, can have a value determined for the transport of a gas to a liquid. The jointed variables 𝑘𝐿𝑎 is important in determining aeration efficiency in the

bioreactor. A way to determine 𝑘𝐿𝑎 is by using the mass balance of dissolved oxygen in the

liquid phase.

(2)

𝑑𝐶

𝑑𝑡= 𝑂𝑇𝑅 − 𝑂𝑈𝑅

Where 𝑑𝐶

𝑑𝑡 is the buildup of oxygen in the liquid phase and OUR is the oxygen uptake rate

by the cells. The formula for OUR is as follows:

(3)

𝑂𝑈𝑅 = 𝑞𝑂2𝐶𝑋

Where 𝑞𝑂2 is the oxygen uptake rate of the cells and 𝐶𝑋 is the biological mass

concentration. The first part of this two part lab experiment will involve oxygen being inserted in a bioreactor with no yeast cultures. Aeration and agitation rates are to be determined to see how

they affect the volumetric mass transfer coefficient. Since no bacteria will be present in the bioreactor, equation (2) mass balance reduces as follows:

(4)

𝑑𝐶𝐿

𝑑𝑡= 𝑂𝑇𝑅 = 𝑘𝐿𝑎(𝐶 ∗ − 𝐶𝐿)

Based on equation (4), the hypothesis for experiment one is that with an increase of

oxygen flow rate and agitation, the bubbles will burst up quicker. This will then make 𝑎 smaller in value and thus increase the slope and decrease the time necessary to reach equilibrium. To

graph equation (4), the equation will be derived on both sides and then linearized. Also, the

dissolved oxygen (DO) will represent the ratio between 𝐶∗ and 𝐶𝐿 as so, 𝐶𝐿

𝐶 ∗, where 𝐶∗ can be

determined from Henry’s Law. The slope of the line is represented by 𝑡 =1

𝑘𝐿𝑎 .

(5)

ln(1 − 𝐷𝑂) = −𝑘𝐿𝑎 ∗ 𝑡


pg 3

For the second half of the experiment, oxygen will dissolve in a bioreactor containing a culture of yeast. Aeration and agitation rates are going to be determined once again on the

volumetric mass transfer coefficient. All of equation (2) will be used and the theory for this trial is that an increase in the aeration and agitation rates will transport the oxygen to the culture

faster, thus reaching equilibrium rapidly as well. The cell growth rate will also be inspected with comparison of OUR where the equation is as follows:

(6)

𝑑𝐶𝑋

𝑑𝑡= µ𝐶𝑋

Where µ is the specific cell growth rate, the slope comes out as −1

µ, representing oxygen

being consumed by the bacteria. The hypothesis is that with the rise in yeast growth rate, this

will decline the slope and therefore increase time of oxygen consumption. 2,3

Methodology

The schematic of the apparatus used for this laboratory is a small scale sized replica of a

bioreactor used in huge manufacturing plants. On the top of the bioreactor, there is a motor that connects to the stirrer inside of the glass vessel [1]. There is a double jacket that surrounds the

bowl for heating and cooling purposes [2]. There is a rotameter that controls the inlet flow rate of either air or Nitrogen gas into the reactor [3]. There is an opening on top of the container for any exhaust gases to escape from [4]. An instrumental probe is used for calibrating and measuring

oxygen concentration in the tank [5]. A computer screen that is connected to the probe is used to output oxygen concentrations in numerical values and display graphs [6].

Figure 1: Small scale bioreactor design4


pg 4

For the first experiment of this lab, fill the bioreactor with only distilled water close to the top of the vessel. The next step is to determine the specified oxygen flow rate and the agitation

RPMs for the upcoming trial runs. Now turn on Nitrogen gas to deoxygenate the system, leaving 0% concentration of oxygen in the water. Pick 3-5 oxygen flow rates, 3-5 agitation RPMs and a

grouping of 3 flows with 3 agitations. With the specified trials determined, record the DOC with

reverence to time, and then plot line graphs to then determine how 𝑘𝐿𝑎 is affected.

For the second experiment, fill the bioreactor with water and also yeast cultures to the brim of the bowl. As the bacteria consume the oxygen at a steady state, record the DOC with

respect to time and the optical density (OD). Using a spectrophotometer at 600 nm, the OD can then determine the cell growth in the system. Once enough oxygen has been consumed, pick 3-5

oxygen flow rates, 3-5 agitation RPMs and a grouping of 3 flows with 3 agitations. After trials are set, record the DOC and OD with condolences to time, and then plot the line graphs to see

how 𝑘𝐿𝑎 is affected.

Results & Discussions

For the first experiment, some graphs gathered from trial runs are observed for any trends

and errors that could have caused data to go haywire.

Figure 2: Oxygen concentration vs. time at 100 rpm

In figure 2, the lines show an ever increasing slope until each one reaches 100%

concentration of oxygen for 100 rpm agitation. The lowest flow rate, 2 scfh, takes around 24 minutes to reach a 100% oxygen level. The next flow rate, 3 scfh, takes around 16 minutes until

oxygen completely saturates the water system. The highest and last flowrate, 4 scfh, takes about 13 minutes to reach a 100% concentration of oxygen in the solution.

Based off the 100% oxygen completion rate for each line, a trend is observed. The higher the flow rates, the faster to completion the oxygen will fully saturate the system. The opposite

holds true as well, the lower the flow rate, the slower it takes for oxygen to reach a 100% concentration in the water. The error bars throughout the graph are in a ± 5% range where the

0

20

40

60

80

100

120

0 5 10 15 20 25 30

Oxy

gen

Co

nce

ctra

tio

n (

%)

time (min)

100 rpm 2 scfh

100 rpm 3 scfh

100 rpm 4 scfh


pg 5

error increases more as the slope increases. Reasons for errors happening can be because of inaccurate reading from the probe, unsteady rpm rates, and/or faulty readings from the computer.

Figure 3: Linearized equation (5) vs time at 100 rpm

Using the linearized equation (5), the slope values can be determined as follows in figure

3. At a flow rate of 2 scfh, the slope ended up coming out at a value of -0.2414. The next flow rate, 3 scfh, the slope results in a numerical value of -0.3304. The last and final flow rate, 4 scfh, the slope number comes out at a value of -0.4208. These values can be used to determine the

volumetric mass transfer coefficient, 𝑘𝐿𝑎.

Figure 4: Oxygen concentration vs. time at 200 rpm

-12

-10

-8

-6

-4

-2

0

0 5 10 15 20 25 30ln

(c*-

cL)

time (min)

100 rpm 2 scfh

100 rpm 3 scfh

100 rpm 4 scfh

0

20

40

60

80

100

120

0 2 4 6 8 10

Oxy

gen

Co

nce

ntr

atio

n (

%)

time (min)

200 rpm 3 scfh

200 rpm 4 scfh


pg 6

In figure 4, it is shown that similar trends are occurring just like in figure 2. Only two flow rates would be done though because of limited time in lab. The lowest flow rate, 3 scfh,

shows a 100% oxygen concentration in the system at around 9 minutes. The highest and last flow rate, 4 scfh, shows a complete saturation of oxygen in the water at around 7 minutes.

Comparing figures 2 and 4, the fastest completion time of the system to reach a 100% concentration of oxygen happens in figure 4. The reason for this has to nonetheless be the

agitation rate itself. What ends up being seen is that the higher the agitation rate, the faster the oxygen reaches completion in the system. The lower the agitation rate, the slower it takes for the

oxygen to reach 100% oxygen concentration in the water. Errors that could occur are the same as for figure 2 where there could be an inaccurate reading from the probe, unsteady rpm rates, and/or faulty reading from the computer.

Figure 5: Linearized equation (5) vs time at 200 rpm

Just like figure 3, the linearized equation (5) is used to determine the slopes for the flow

rates at an rpm of 200 in figure 5. The lowest flow rate, 3 scfh, gives off a slope of -0.0657. The final and highest flow rate, 4 scfh, outputs a slope of -0.6932. The graphs for an agitation of 300 rpm will not be displayed due to the fact that the graphs would just further prove the theories

confirmed from the other figures.

-14

-12

-10

-8

-6

-4

-2

0

0 2 4 6 8 10

ln(c

*-cL

)

time (min)

200 rpm 3 scfh

200 rpm 4 scfh


pg 7

Figure 6: Volumetric mass transfer coeff vs. aeration rate at various rpms

In figure 6, the volumetric mass transfer coefficient, 𝑘𝐿𝑎 , is shown in terms of agitation

rate and aeration rate. The lowest agitation rate, 100 rpm, starts at a 𝑘𝐿𝑎 of 0.2 at 2 scfh, and then

increases steadily to a value of 0.4 at 4 scfh. The next agitation rate, 200 rpm, starts at a 𝑘𝐿𝑎 of 0.6 at 3 scfh, and then inclines to a value of 0.7 at 4 scfh. The highest agitation rate, 300 rpm,

starts at a 𝑘𝐿𝑎 of 1.1 at 3 scfh, and then increases to 1.2 at 5 scfh.

After the following trends have been investigated in full detail, a trend ends up appearing from figure 6. The volumetric mass transfer coefficient increases in value as the agitation rate

and the aeration rate increase. The agitation rate though tends to increase 𝑘𝐿𝑎 much more than

the aeration rate. The error bars came out in a ± 5% range where error appeared to increase as the slopes inclined ever higher. The errors are the same yet again as for figures 2 and 4, where there could be an inaccurate reading from the probe, unsteady rpm rates, and/or faulty reading from

the computer.

Now transitioning to the second experiment of the lab, a few graphs will be observed for

any trends and errors that could have appeared.

Figure 7: Oxygen concentration vs. time in presence of yeast 1 and 2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 1 2 3 4 5 6

volu

me

tric

mas

s tr

ansf

er

coe

ff (

kLa)

Aeration O2 rate (scfh)

100 rpm

200 rpm

300 rpm

0

20

40

60

80

100

120

0 20 40 60 80

Oxy

gen

Co

nce

ntr

atio

n (

%)

time (min)

yeast 1: 200 rpm 4

scfh


pg 8

In figure 7, the slopes start to decline to a certain point and then exponentially increase until it starts to level off horizontally. At the lowest rate of 200 rpm and 4 scfh, the starting point

of 100% oxygen concentration occurs at the 5 minute mark where it declines ever so steadily to the 40 minute mark. The line then starts to increase exponentially until it starts to level off

horizontally at the 60 minute mark. At the highest flow rate of 300 rpm and 5 scfh, the 100% oxygen concentration happens at the 0 minute mark where it decreases constantly down to the 35 minute mark. It then increases exponentially to the 40 minute mark where it then levels off

horizontally at the 100% point for the rest of the time duration.

The reason for the following trends being observed is because of the varying flow rates. When the yeast 1 culture consumes all of the oxygen, the lower flow rate and agitation rate takes longer to resupply the system with oxygen. When the yeast 2 culture consumes all of the oxygen,

the higher flow rate and agitation rate takes a shorter amount of time to refill the water with 100% concentration of oxygen. These trends are similar to the figure 2 and 4 trends where higher

agitation rates and aeration rates supply the system with oxygen at a much faster rate. Errors that could have occurred can be from a misreading from the probe, uneven amount of cells put into the reactor for both runs, and/or misinterpreted data from the computer.

Figure 8: Volumetric mass transfer coeff vs. aeration rate in presence of yeast

Due to limited time in lab and low cell cultures to deal with, only two points could be

gained, but an analysis on figure 8 can still be done. At the lowest agitation rate and aeration rate

of 200 rpm and 4 scfh, the volumetric mass transfer coefficient, 𝑘𝐿𝑎, outputs a value of 0.2. At

the highest agitation rate and aeration rate of 300 rpm and 5 scfh, the volumetric mass transfer

coefficient, 𝑘𝐿𝑎, displays a number of 0.4. What can be observed yet again is that a higher 𝑘𝐿𝑎

numeral can be gained from higher agitation rates and aeration rates. The opposite holds true

where a lower value of 𝑘𝐿𝑎 can be gained from a lower agitation rate and aeration rate. Errors

that could have occurred are the same as for figure 7 where there could be a misreading from the

probe, uneven amount of cells was put into the reactor for both runs, and/or misinterpreted data

from the computer.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 1 2 3 4 5 6

volu

me

tric

mas

s tr

ansf

er

coe

ff

(kLa

)

Aeration O2 rate (scfh)

200 rpm

300 rpm


pg 9

Conclusion and Recommendation

After the two experiments were fully completed and the data was analyzed to its fullest

potential, important theories came about. The higher the agitation rate and aeration rate, the

faster the system can reach 100% oxygen concentration with or without bacteria present. The

higher the agitation rate and aeration rate, the bigger the volumetric mass transfer coefficient,

𝑘𝐿𝑎, is in value without the presence or with the presence of cell culture. A recommendation to

consider for future trial runs of this experiment is to provide more detail on how to use the

computer interface for graphing convenience.


pg 10

References

[1] Rice University. http://www.bioc.rice.edu/bios576/nih_bioreactor/NDL_Bioreactor%2

0Page.htm (accessed October 2, 2015)

[2] sadrabiotech. http://sadrabiotech.com/catalog/scale%20up%20bioreactor1.pdf

(accessed October 3, 2015)

[3] asu. https://myasucourses.asu.edu/bbcswebdav/pid-11466185-dt-content-rid-

60822354_1/courses/2015Fall-T-CHE451-76086-84043-70672-70673/CHE451%20-

%20B%20-%20Bioreactor%281%29.pdf (October 3, 2015)

[4] biomedcentral. http://www.biomedcentral.com/1753-6561/5/S8/P48/figure/F1

(accessed October 3, 2015)

[5] University of Minnesota. http://d.umn.edu/~rdavis/courses/che4601/notes/Bio

reactorDesignForChEs.pdf (accessed October 4, 2015)