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Welcome to
Industrial Bioprocessing and
Bioremediation 2014 !
(Environmental Biotechnology)
Unit coordinator background:
http://profiles.murdoch.edu.au/myprofile/ralf-cord-ruwisch/
This unit is different to your other biotechnology units as it
focusses on the TECHNOLOGY part (engineering).
This requires being able to analyse processes, solve
problems, predict outcomes, carry out mass balances, etc.
2
Welcome to
Industrial Bioprocessing and
Bioremediation 2014 !
(Environmental Biotechnology)
Example processes: How to:
• make renewable biogas from organic wastes.
• remove polluting nutrients from wastewater
• breed microalgae for food or energy production
• make beer, yoghurt,
• mine ores by using bacteria (bioleaching)
• understand microbial processes in ocean, soil and
bioreactors
3
Fundamentals taught
Bioprocesses convert S to P
How much S to P?
Mass balance
How much hydrogen gas can we make as fuel
from fermenting sugars ?
How much oxygen is needed for respiration?
How much electricity can be formed from sugar ?
4
Lecture content over the next weeks
Wee
k
1
Day
Time
2
Mon
Jul 28
Mon
Jul
28
Tues
Jul 29
Wed
Jul 30
Mon
9.30-10.30
3
Venue
Topic
Lectur
er
Mon 9.30-10.30 AMEN 1. Introduction, Diffusion, Bioreactor RCR
Jul 21
2.023
Mon 10.30-11.30 LB
RCR
Jul
3.032
21
Tues 8.30-9.30
AMEN 1.2 Oxygen solubility and transfer
RCR
Jul 22
2.023
Wed 12.30-1.30 AMEN 1.3 Oxygen mass transfer coefficient
RCR
Jul 23
2.023
kLa
AMEN 2.1 Microbial oxygen uptake
2.023
10.30-11-30 LB
Help with BioProSim1
3.032
8.30-9.30
12.30-1.30
9.30-10.30
RCR
RCR
AMEN 2.2 Oxygen steady state calculations
RCR
2.023
AMEN 2.3 Online OUR monitoring
RCR
2.023
5
AMEN 3.1 Fundamentals of microbial growth RCR
Lab schedule over the next weeks
Week Venue
1
LB 3.032
Small Comp
Lab
2
BS 2.050
Topic
Virtual Lab 1- BioProSim1
Time
1.30-5.30pm
Oxygen Transfer - RCR
1.30-5.30pm
3
BS 2.050
Oxygen Uptake - RCR
1.30-5.30pm
4
BS 2.050
1.30-5.30pm
5
BS 2.050
Penicillin Production as a secondary
metabolite demonstration – RCR
Algal Biotechnology- NM
6
7
Study Break
BS 2.050
Chemostat Project (or week 9)
1.30-5.30pm*
8
BS 2.050
Chemostat Project (or week 10)
All Week *
9
BS 2.050
Chemostat Project (or week 7)
1.30-5.30pm*
10
BS 2.050
Chemostat Project (or week 8)
All Week*
1.30-5.30pm
6
Assessed activities over the next weeks
Type
CBLA1)
CBLA1)
CBLA1)
Group
Topic
Oxygen solubility (OTR1)
Oxygen transfer (OTR2)
Oxygen uptake (OTR3)
Instant Lab Report on oxygen
transfer
Group Instant Lab Report on oxygen
uptake
Individ. BioProSim 1 mass transfer
simulation
CBLA1) Microbial growth principles (GRO1)
CBLA1) Microbial growth kinetics (GRO2)
CBLA1) Microbial cell cultivation (GRO3)
Individ. Lab Report on Algal Biotechnology
Group Penicillin a secondary metabolite?
CBLA1) Bio-reaction oxidation states
(OXS)
Individ. BioProSim 2 chemostat simulation
Due
Marks Due
Week Day
1
1
1
1
1
1
2
2
Fri
Fri
Fri
Thurs
1
3
Thurs
2
2
Fri
1
1
1
4
2
1
3
3
3
5
7
7
Mon
Wed
Fri
Thurs
Thurs
Fri
7
8
8
Mon
Type
Topic
Marks
Due
Week
Due Day
CBLA1)
CBLA1)
CBLA1)
Group
Group
Individ.
CBLA1)
CBLA1)
CBLA1)
Individ.
Group
CBLA1)
Individ.
Oxygen solubility (OTR1)
Oxygen transfer (OTR2)
Oxygen uptake (OTR3)
Instant Lab Report on oxygen transfer
Instant Lab Report on oxygen uptake
BioProSim 1 mass transfer simulation
Microbial growth principles (GRO1)
Microbial growth kinetics (GRO2)
Microbial cell cultivation (GRO3)
Lab Report on Algal Biotechnology
Penicillin a secondary metabolite?
Bio-reaction oxidation states (OXS)
BioProSim 2 chemostat simulation
1
1
1
1
1
2
1
1
1
4
2
1
8
1
1
2
2
3
2
3
3
3
5
7
7
8
Fri
Fri
Fri
Thurs
Thurs
Fri
Mon
Wed
Fri
Thurs
Thurs
Fri
Mon
Exam 2)
Group
Exam 2)
Mid-semester Exam
Chemostat Group Report
End-semester Exam
Total
25
10
40
100
7
10 /12
Thurs
8
Type
Topic
Due
Marks Due
Week Day
Instant Lab Report on oxygen
transfer
Group Instant Lab Report on oxygen
uptake
Individ. BioProSim 1 mass transfer
simulation
Individ. Lab Report on Algal Biotechnology
Group Penicillin a secondary metabolite?
Individ. BioProSim 2 chemostat simulation
1
2
Thurs
1
3
Thurs
2
2
Fri
4
2
8
5
7
8
Thurs
Thurs
Mon
Exam 2) Mid-semester Exam
Group Chemostat Group Report
Exam 2) End-semester Exam
Total
25
10
40
7
10 /12 Thurs
Group
9
Type
Topic
Due
Marks Due
Week Day
Instant Lab Report on oxygen
transfer
Group Instant Lab Report on oxygen
uptake
Individ. BioProSim 1 mass transfer
simulation
Individ. Lab Report on Algal Biotechnology
Group Penicillin a secondary metabolite?
Individ. BioProSim 2 chemostat simulation
1
2
Thurs
1
3
Thurs
2
2
Fri
4
2
8
5
7
8
Thurs
Thurs
Mon
Exam 2) Mid-semester Exam
Group Chemostat Group Report
Exam 2) End-semester Exam
Total
25
10
40
7
10 /12 Thurs
Group
10
Fundamentals taught
Bioprocesses convert S to P
Sugar to energy (biogas, ethanol, hydrogen, electricity)
Pollutants to harmless substances (dechlorination,
degradation, dirty water to clean water
Need to be able to predict (modelling)
Quantify (rates)
Understanding (driving force, equilibrium)
11
Fundamentals taught
Bioprocesses questions:
Why ?
How ?
How fast?
What mechanism ?
Modelling
12
Learning tools used
Bioprocess analysis (theory) Computer simulation
Bioprocess execution and analysis (Chemostat project)
Spreadsheets for data processing and analysis
Peer reviewed websites of scientific analysis of literature
Using computer learning activities
Industry trip
Focussed scientific writing.
13
Link between Research and Teaching
A number of undergraduates  honours  PhD  Senior
Engineers (Water Corporation, Design companies,
Bioprocess Operators)
Teaching topics drawn from own research projects and
publications.
Input from current researchers into the project
Link to industry.
So, on many of the topics you talk to science experts, not
just “teachters that obtained their knowledge from books”
14
Bioprocessing – Biotechnology:
Make money from bioprocesses
Inputs are of lower value than outputs (products)
Computer based learning activities (CBLA) are on
http://sphinx.murdoch.edu.au/
units/extern/BIO301/teach/index.htm
15
16
17
18
19
20
21
Lecture overview L1-3
Lecture 1: Intro, study guide, what is a bioreactor,
Learning by interacting
Lecture 2: What is diffusion, how can we predict
the behaviour of a randomly moving molecule?
moving dots, entropy, driving force, equilibrium,
rate of diffusion, first order kinetics, kLa
Lecture 3: oxygen transfer rate, kLa value.
Graphical method of determining the kLa.
Mathematical (2 point) determination of kLA
Calculation and prediction of oxygen transfer as
function of DO. Oxygen transfer efficiency.
Bacterial OUR, DO. steady state
22
Molecular diffusion relies on random movement
resulting in uniform distribution of molecules
23
Oxygen Transfer Rate (OTR)
Overview
Diffusion, how does it work, how can we predict it?
Diffusion is random ….
and yet predictable.
A simple model simulation can show that although the diffusion movement is random,
it can be precisely predicted for large number of molecules (e.g. Fick’s law of diffusion)
24
Oxygen Transfer Rate (OTR)
Low
OTR
(diffusion, convection)
High
OTR
Transfer by diffusion is extremely slow
and depends on surface area
Wind
Air In
•
•
•
• • • •
•
• •
Oxygen transfer by convection
(turbulences) is more efficient
• Bioreactors combine maximum convection
with maximum diffusion
• Course bubbles cause more convection,
fine bubbles more diffusion
25
How soluble is oxygen?
Oxygen solubility (cS)
The net transfer of oxygen from
•gas phase to solution reaches a
dynamic equilibrium
•O2 input = O2 output
•equilibrium results in defined
saturation concentration (cs).
•The saturation concentration is
also the oxygen solubility
•How soluble is oxygen?
26
Oxygen solubility (cS)
Oxygen is not very polar  poorly soluble in water.
Oxygen Solubility is described by Henry’s Law
which applies to all gases
p = k*cS
p = partial pressure of oxygen
k = constant depending on gas type, solution
and temperature
cS = concentration of oxygen dissolved in water
•
Meaning: The amount of oxygen which dissolves in water is
proportional to the amount of oxygen molecules present per
volume of the gas phase.
•
Partial pressure ~ number of O2 molecules per volume of gas
increases with O2 concentration in gas
increases with total gas pressure
How to calculate partial pressure? (refer to CBLA)
27
Oxygen solubility (cS)
Examples of using the proportionality between
partial pressure of oxygen in the atmosphere and
the saturation concentration cS:
p = k*cS
•
•
p = partial pressure of oxygen
k = constant depending on gas type, solution
and temperature
cS = concentration of oxygen dissolved in water
If the reactor is operated under 2 times atmospheric pressure
(200kPa instead of 100 kPa air pressure), the new saturation
concentration will be abou 16 mg/L instead of 8 mg/L.
If air (partial pressure = 0.21* 100 kPa) is replaced by pure
oxygen atmosphere (partial pressure 100kPa) the oxygen
saturation concentration is about 40 mg/L (more precise 8*100/21)
instead of 8 mg/L.
How to calculate partial pressure? (refer to CBLA)
28
Oxygen solubility (cS)
Oxygen Saturation
Concentration cs (mg/L)
Effect of temperature
15
cs =
10
468
(31.6 + T)
5
0
0
20
40
60
Temperature (°C)
Oxygen solubility decreases with increasing temperature.
Overall: oxygen is poorly soluble (8mg/: at room temp.)
More important than solubility is oxygen supply rate (oxygen
transfer rate OTR).
29
Oxygen Transfer Rate (OTR)
(gradient, driving force)
Question: What is the driving force for oxygen dissolution?
OTR
At oxygen saturation concentration (cs):
dynamic equilibrium exists between
oxygen transferred from the air to water
and vice versa.  No driving force
Answer: The difference between cS and the actual dissolved
oxygen concentration (cL) is the driving force. OTR is
proportional to the that difference. Thus:
OTR ~ (cS – cL)
Need to determine the proportionality factor
30
OTR – depends on DO (cL)
Significance of OTR: critical to know and to control for
all aerobic bioreactors
1. Deoxygenation (N2, sulfite + Co catalyst)
2. Aeration and monitoring dissolved oxygen concentration
(D.O. or cL) as function of time
3. OTR = slope of the aeration curve (mg/L.h or ppm/h)
Air On
cL (ppm)
8
0
5
Time (min)
10
31
OTR – depends on DO (cL)
4. Observation: OTR decreases over time (and with incr. cL)
5. OTR is not a good measure of aeration capacity of a bioreactor
6. OTR is highest at cL = zero (Standard OTR)
7. OTR is zero at oxygen saturation concentrations (cs)
8. OTR is negatively correlated to cL
9. OTR is correlated to the saturation deficit (cs - cL),
which is the driving force for oxygen transfer
9. The factor of correlation is the volumetric mass transfer
coefficient kLa
OTR = kLa (cs - cL)
Mg/L/h
h-1
mg/L
32
OTR –Significance of gradient
First: steep step in oxygen
(top layer saturated, next
layer oxygen free)
Then: buildup of a gradient
of many layers.
Each layer is only slightly
different from the next 
Transfer from layer to layer
has little driving force.
Gradient build-up inhibits fast
diffusion
33
10. OTR is not a useful parameter for the assessment of the
aeration capacity of a bioreactor. This is because it is
dependent on the oxygen concentration (cL)
11. The kLa value is a suitable parameter as it divides
OTR by saturation deficit:
kLa =
OTR
(cs - cL)
12. kLa = the key parameter oxygen transfer capacity.
How to determine it?
34
Lec 2 summary:
Oxygen is poorly soluble depending mainly on
•partial pressure in headspace
•Temperature
OTR is driven and proportional to driving force (cS-cL)
kLa is the proportionality factor (first order kinetics)
kLa describes the performance of a bioreactor to provide
Oxygen to microbes
Next lecture: quantify kLA
35
Lec 3 outlook:
•Aeration curve
•Quantify OTR at a given point of an aeration curve
•Quick estimate of kLa
•Graphical determination of kLa
•Mathematical determination of kLa
•Run computer simulation to obtain data
•Oxygen transfer efficiency (OTE)
•OTR proportional to cs-cL
•OTR inverse proportional to cL
36
OTR – Quick estimate of kLA
cL (mg/L)
Example: determine OTR at 6 mg/L
8
Air On
6
5 mg/L
4.5 min
0
5
Time (min)
10
OTR is the slope of the tangent for each oxygen concentration
OTR = ∆ cL/ ∆ t
= 5 mg/L/ 4.5 min
= 1.1 mg/L/min
= 66 mg/L/h
37
OTR – quick estimate of kLA
kLa =
=
OTR
(cs-cL)
66 ppm / h
(8 ppm – 6 ppm)
= 3.3 h-1
Q: Problem with this
method?
DO
A: based on one single
OTR slope measurement
and unreliable to obtain
from real data.
Time
38
OTR – Graphical determination of kLa
1. Monitor aeration curve
2. Determine graphically the OTR at various oxygen
concentrations (cL)
cL (ppm)
8
At 6 ppm: OTR = 25 mg/L/h
6
At 4 ppm: OTR = 50 mg/L/h
At 3 ppm: OTR = 60 mg/L/h
4
2
0
5
10
At 0.5 ppm: OTR = 30 ppm/h
Time (min)
3. Tabulate OTR and corresponding cL values
cL (mg/L)
0.5
3.0
4.0
6.0
8.0
Cs - cL (mg/L)
7.5
5.0
4.0
2.0
0.0
OTR (mg/L/h)
30
60
50
25
0
39
OTR – Graphical determination of kLa
OTR (mg/L/h)
4. Plot OTR values as a function of cs - cL.
Standard OTR
100
cs kLa =
50
70 mg/L/h
6 mg/L
= 12 h-1
0
0
2
4
6
cs- cL (mg/L)
8
5. A linear correlation exists between kLa and the saturation
deficit (cs - cL) which is the driving force of the reaction.
6. The slope of the plot OTR versus cs - cL is the kLa value.
7. The standard OTR (max OTR) can be read from the intercept with
the cs line. (Standard OTR = 96 ppm/h)
40
Mathematical Determination of kLa
1. OTR is a change of cL over time, thus = dcL/dt
2. kLa = dcL/dt
Integration gives
cs - co
3. kLa = ln c - c
s
i
ti - to
(
)
Dissolved Oxygen
Concentration (mg/L)
(cs- cL)
cs
•
ci = 6
co = 3
•
to
8 - 3 ppm
kLa = ln
8 - 6 ppm
10.5 - 6.1 min
(
)
ti
Time (min)
= ln 2.5 = 0.21 min-1 = 12.5 h-1
4.4 min
41
4. This method should be carried out for
3 to 4 different intervals. By aver
5. Once the kLa is known it allows to calculate the OTR
at any given oxygen concentration:
OTR = kLa (cs - cL)
42
Factors Affecting the Oxygen Transfer
Coefficient kLa
kLa consists of:
[Oxygen]
• kL = resistance or thickness
of boundary film
• a = surface area
Bubble
Bulk
Liquid
Cell
Distance
Main boundary layer = steepest gradient
43
→ rate controlling, driving force
Effect of Fluid Composition on OTR
The transfer across this boundary layer increases with:
1) ↓ thickness of the film, thus ↑ degree of shearing (turbulence)
2) ↑ surface area
3) ↓ surface tension
4) ↓ viscosity (best in pure water)
5) ↓ salinity
6) ↓ concentration of chemicals or particles
7) detergents?
8) ↑ emulsifiers, oils, “oxygen vectors”
44
Oxygen Transfer Efficiency (OTE)
oxygen transferred
OTE =
oxygen supplied
Significance of OTE: economical, evaporation
Calculation of OTE (%):
oxygen transferred (mol/L.h)
X 100
% OTE =
oxygen supplied (mol/L.h)
Why do students find this type calculation difficult?
Units are disregarded. Molecular weights are misused.
45
Oxygen Transfer Efficiency (OTE)
A bioreactor ( 3 m3) is aerated with 200 L/min airflow. If the OTR is
constant (100 mg/L/h) determine the %OTE.
1. Convert the airflow into an oxygen flow in mmol/L/h
200 L air /min = 12000 L air/h
(x 60)
= 2520 L O2/h
(x 21%)
= 102.9 mol O2/h
(÷ 24.5 L/mol)
= 34.3 mmol O2/L.h
(÷ 3000 L)
2. OTR
100 mg/L.h = 3.1 mmol O2/L.h
% OTE =
3.1 (mmol/L.h)
34.3 (mol/L.h)
= 9%
(÷ 32 g/mol)
X 100
46
Oxygen Transfer Efficiency (OTE)
OTE is dependent upon the cL in the same way than OTR
% OTE
OTE decreases with increasing airflow
(more oxygen is wasted)
10
5
Airflow
47
Engineering Parameters Influencing OTR
Increase depth vessel
Deeper vessel  bubbles rise a long way  ↑ OTR, OTE
but more pressure required  ↑ $$
Decrease bubble size
 Larger surface area  ↑ OTR, OTE
smaller bubbles rise slower  more gas hold up
 ↑ OTR, OTE
Increase air flow rate
 ↑ Number of bubbles  ↑ OTR but ↓ OTE
Increase stirring rate
 ↑ turbulence  ↓ thickness of boundary layer  ↑ OTR,
OTE
48
 ↓ Bubble size  ↑ OTR, OTE
OTR – from aeration curve to kLa summary
Dissolved Oxygen
(first order kinetics)
(cs)
Air on
Aeration Curve
Time
OTR (mg/L.h)
max
OTR
Slope = kLa
Rate is proportional
to concentration 
First order kinetics
Dissolved oxygen [mg/L]
OTR = kLa (O2 saturation (cS) – O2 concentration (cL))
49
OTR – Aeration curve from CBLA
During aeration of oxygen free water, the dissolved
oxygen increases in a characteristic way
50
OTR – aeration curve from CBLA
•Highest Rate at lowest
dissolved oxygen
concentration
•Rate of zero when DO
reaches saturation
concentration
Can the relationship between rate and DO be expressed
mathematically?
51