BlueSens Report No.1

Transcription

Report No. 1
BlueSens Report No. 1, October 2010
© 2010 by BlueSens gas sensors GmbH, Herten, Germany, www.BlueSens.com
This report was worked out in co-operation with APZ Ruhr-Lippe, www.apz-rl.de
Page layout and cover design:
Marcus Riepe, Krefeld, Germany, [email protected]
Printing press:
Offset Company, Wuppertal, Germany, www.offset-company.de
Introduction
The first
BlueSens Report
You may wonder why we have published a
report with detailed product information and user data
only recently after almost 10 successful years and several
thousand sensors already sold?
The answer is quite simple – time.
It is only recently we have increased the human resources in our PR-department and in addition we have
received the support of the APZ (center for applications
Biotechnik Ruhr-Lippe) which has made it possible for
us to publish now.
Almost every new customer wanted this type of information
however until recently we referred them to our “reference
customers” (at this point we want to thank them sincerely).
Nevertheless, we knew that sooner or later we had to
produce a report with case studies and information
regarding our customer experience with our sensors.
Consequently we contacted our customers and asked
them for a case study where they describe how they use
our sensors.
Obviously, until recently many pharmaceutical companies
were unable to cooperate due to the confidential nature
of their work. However, as we do not require confidential
data about the microorganisms or the specified microbial strains, we only require the official statement: “Yes,
we use BlueSens sensors and they operate as specified.”
Stunning are also statements as follows: “BlueSens sensors? You don’t have to explain them to me, we solely
use them and no other sensor.” (stated by an anonymous customer during a call).
Where has this customer bought our sensors?
The answer is absolutely clear – of course we couldn’t
have achieved the global supply of our products without
our sales partners or OEM-distributors, like DASGIP AG,
Sartorius Stedim Biotech GmbH, Infors, Applikon, Bioengineering or many other plant developers. We also want
BlueSens.com
Dr. Holger Mueller
to thank them for the longtime and good cooperation.
Longtime double-digit growth rates give us the motivation
to continue to achieve these results in future. In order to
do this, we listen to our customers and respond quickly
to their needs. So our sensors are specified for each
customer’s application: temperature, respective gas flow
or different pressure ranges – we have a solution for their
requirements. Thanks to the PAT-initiative of the FDA,
which deals with the analysis of the process and not
only with the end product, our online-sensors are wellaccepted by our customers. Our sensors can be integrated with little effort directly in the process and so are
made for current requirements.
We also want to take a look to the future. For aerobic
fermentations, up to now, you had to connect one sensor
for the measurement of CO2 and another one for the
measurement of O2. Although that is not difficult, it would
be more convenient to receive all required measurement
data with one device.
We have listened to our customers and have reacted to
them:
BlueInOne. The most compact gas analyzer on the market
for the measurement of up to 4 gases with an automated
pressure and humidity compensation.
In this spirit I wish you to enjoy reading our report and
want to thank all of our customers and sales partners for
their confidence in us.
BlueSens Report No. 1
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Contents
6 BlueSens. Advanced information
Application Reports
8 Application of a self constructed off gas analyser in the education of bioengineers
Dr. Michael Maurer, FH Campus Wien
10 Continuous bio-ethanol production by means of yeast
Dr.-Ing. Eva Maria del Amor Villa, Technical University Dortmund
12 Model based optimization of biogas plants
Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert, Prof. Dr. Reiner Lohmüller,
University of Applied Sciences Emden/Leer
14 Online observation of oxygen uptake and carbon dioxide production and characterisation of oxygen
transfer capacity
by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences
16 The precultivation in shake flasks for the execution of bioreactor cultivations
18 Automated Design of Experiments (DoE) in a multi-bioreactor system BIOSTAT® Qplus 6
20 Monitoring of baker’s yeast fermentations
PD Dr.-Ing. Lars M. Blank, Technical University Dortmund
22 Application of BlueSens® Gas Analyzers in a Cell Culture Process
Mathias Aehle, Martin-Luther-University Halle-Wittenberg
Information
28 Connections for every application
30 BlueSens’ sensors overview
32 We help you understand, control and optimize your process!
BCpreFerm and YieldMaster
33 The freedom of software choice
FermVis and BACVis
34 Parallel systems · Measuring according to PAT
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For controlling biotechnological processes you primarily
depend on continuous information. BlueSens has
made it to its business to provide this information for
every customer by means of gas analysis directly in
the process. Reliable measurement engineering
makes the results available in highest measurement
density and in real time. So biotechnological processes
can be analyzed better and, as a result, of course
also optimized.
With this report we also want to give you advanced
information. Numerous examples of applications
show exactly how the products of BlueSens are used
under real conditions. With this booklet you can also
learn concretely how our sensors are connected and
readout.
Furthermore, you can inform yourself about the
accurate specifications of the particular sensors with
the help of a clearly arranged spreadsheet.
Bluesens: advanced information for your process,
advanced information about the products.
Nearly a decade after the foundation the dynamic
company is well-known in the world of biotechnology.
BlueSens stands for reasonably priced quality sensors – made in Germany. The strength of the company
is the personal contact to every single customer.
The Managers
Dr. Holger Mueller
(Sales and Marketing)
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Dr. Udo Schmale
(R&D and Production)
Every sensor a unique piece
During the calibration process each sensor is tested
and set up particularly. This process can take up to
one week. It involves a lot of time, but it‘s worth it. In
the detailed test procedure BlueSens solely uses certified
test gases. Depending on the gas component to be
measured, 10 to 18 different test gases are used. So
it is assured that the sensors provide best results for
each application the customer requires. Each sensor
so becomes a handmade piece and is individually
tested by BlueSens.
BlueSens is exclusively producing the sensors in Germany. We have the highest requirements regarding
the utilised components. So the company guarantees
long-lasting quality and reliability of the products.
Production goes hand in hand with research. By
short ways the results of our Research & Development department can be integrated quickly into the
production.
Keeping an eye on costs
BlueSens stands for sensors which are as uncomplicated as possible and therefore as competitive as
possible. Based on the ever latest developments
BlueSens would like to pass on its competitive edge
to its customers. With the measuring systems of
BlueSens the corresponding process parameters can
already be determined before the actual process
takes place. In the production range of active components, fermentation and also biogas generation, the
productivity of the raw material can thus be optimized
in preliminarily tests based on the gas measurement.
In research and development BlueSens sensors mean
that results are achieved faster and products can be
positioned in the market quicker. The use of BlueSens
sensors also means that production online can be
optimized when controlling industrial processes on
the spot, directly where the process takes place. This
saves both personnel and production capacities and
maximizes the outcome. The investment costs amortize very quickly (Return on investment).
Already installed bioreactors can also be upgraded
with the sensors of BlueSens with minimum effort.
Therefore older installations can be modernized costeffectively.
Many customers confirm ever gain:
BlueSens: “We cannot afford not to have it!”
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Application Report
Application of a self constructed off gas
analyser in the education of bioengineers
by DI Dr. Michael Maurer, FH Campus Wien – University of Applied Sciences, Bioengineering degree
programme
Our University of Applied Sciences, FH
Campus Wien, offers a degree program in ‘Bioengineering’.
In the course of this study a fermentation laboratory has
to be attended. The aim of this course is the design,
operation and analysis of a bioprocess experiment. The
students have to use their biological, mathematical and
technical skills to solve this exercise.
One of the experiments involved cultivation of the methylotrophic yeast Pichia pastoris (X33); a well known host
for recombinant protein expression (Cregg et al. 2000),
as well as for applications in white biotechnology (e.g.
riboflavin (Marx et al. 2008)). An overnight shake culture
was used to inoculate a defined 2 l batch medium (as
described in Maurer et al. 2006) with 40 g glucose L-1
as sole carbon source, to a starting optical density
(OD600) of 1.0.
The cultivation was carried out in a 5.0 l bioreactor
(Minifors, Infors, Bottmingen-Basel, Switzerland; figure 1
B) with a tailored off gas analyser. This off gas analyser
consists of a BCP-CO2, a BCP-O2 probe (BlueSens,
Figure 1: B) bioreactor with off gas analyser
Figure 1: A) self assembled off gas analyser
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Herten, Germany) and a mass flow controller (Vögtlin,
Aesch, Switzerland) with a power supply in a separate
control box (figure 1 A). The analogue signals were
directly led to an I/O input of the bioreactor and measured as control parameters in the monitoring software
(IRIS, Infors).
The fermentation temperature was controlled at 25°C,
pH was controlled at 5.0 with addition of 25% ammonium hydroxide and the dissolved oxygen concentration
was maintained above 20% saturation by controlling the
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stirrer speed between 250 and 1200 rpm and the air
flow between 2.0 and 5.0 l min-1.
Samples were taken frequently over the whole process
and analysed as described below. Three aliquots of
10 ml of culture broth were centrifuged and the supernatant saved for HPLC analysis. The pellets were washed
in distilled water and recentrifuged, transferred into
weighed beakers and dried at 105°C until a constant
weight was attained. The biomass concentration was
also monitored with an on-line probe (Fogale nanotech,
Nimes, France), which had previously been calibrated
with dry cell mass data (CDW).
Glucose and ethanol were analysed by HPLC (Shimadzu,
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DI Dr. Michael Maurer, FH Campus Wien – University of Applied Sciences, Bioengineering degree programme. The
University of Applied Sciences, FH Campus Wien, is an educational institution which offers a rich variety of academic studies. The bioengineering degree programme educates students for their work in the field of biotechnological
industry.
www.fh-campuswien.ac.at
Figure 2: A) Trends of measured cultivation parameters glucose - (squares), ethanol – (triangles) and bio mass concentration (crosses),
as well as the carbon balance (circles).
Figure 2: B) RQ trend read out of the P. pastoris batch cultivation.
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Japan) using an ion exchange column Aminex HPX-87H
(Bio Rad). The mobile phase was 15 mM sulphuric
acid.
The aim of the exercise was the calculation of typical
fermentation parameters such as biomass concentration,
substrate uptake rate, specific growth rate, and so on, as
well as the respiratory quotient (RQ) and the over all
carbon balance (OCB). Using the universal gas equation
and the recorded oxygen and carbon dioxide concentration
[%] and the air flow data. The students were able to
calculate the oxygen uptake rate (OUR), the carbon dioxide
evolution rate (CER) and hence the required RQ and
OCB.
Figure 2 A shows the diauxic behaviour of this yeast
strain, first using up glucose as preferred substrate (spe-
cific glucose uptake rate qGlucose= 0.44 g g-1 h-1) and
forming ethanol with a rate of qP ethanol= 0.08 g g-1
h-1 as by product. After a first stationary phase the ethanol
was utilised with a rate of qethanol = 0.04 g g-1 h-1.
The online measurement of the oxygen and carbon dioxide
concentrations enabled the simultaneous determination
of the shift based on the calculated RQ, which changed
from 1.2 during the aerobic glucose consumption to 0.5
during the ethanol utilization. The carbon utilisation was
therefore balanced with a tolerance of 93-105%. These
online measurements therefore serve as teaching vehicles
enabling the students to grasp application and value of
off-gas analysis.
Literature
Cregg, J., J. Cereghino, J. Shi & D. Higgins (2000) Recombinant protein expression in Pichia pastoris. Mol Biotechnol, 16, 23-52. | Marx, H., D. Mattanovich
& M. Sauer (2008) Overexpression of the riboflavin biosynthetic pathway in Pichia pastoris. Microb Cell Fact, 7, 23. | Maurer, M., M. Kuehleitner, B. Gasser &
D. Mattanovich (2006) Versatile modeling and optimization of fed batch processes for the production of secreted heterologous proteins with Pichia pastoris.
MICROBIAL CELL FACTORIES, 5, -.
Continuous bio-ethanol production
by means of yeast
by Dr.-Ing. Eva Maria del Amor Villa, Biochemical Engineering Laboratory, Biochemical and Chemical
Department, Technical University Dortmund
One example for applying the BlueSens
technology at the Biochemical Engineering Laboratory is
the gas online-monitoring for the continuous bio-ethanol
production in the field of the biotechnological production of alternative fuels (so-called biofuels). Yeast is able
to metabolize under anaerobic conditions several carbon sources (particularly sucrose and glucose) into carbon dioxide and ethanol, conventionally in a batch or
fed batch mode. However, if the ethanol concentration
exceeds the concentration threshold – ca. 115 g/l, depending on the strain – an inhibition of the metabolism
is initiated: ethanol becomes a toxic substance and the
maximum product concentration achieves a biological
limit. Keeping the product content under the tolerance
limit of the cells will allow increasing the bio-ethanol-yield
to its maximum.
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Dr.-Ing. Eva Maria del Amor Villa, Biochemical Engineering
Laboratory, Biochemical and Chemical Department, Technical University Dortmund. The Biochemical Engineering
Laboratory deals with research and teaching in the areas
of fermentation and sterilization technology, downstream
processing as well as biocatalysis (in aqueous and organic media). Pilot equipment for process scale up is available up to a fermentation capacity of 300 l for interfacing
with academic and industrial partners.
www.bvt.bci.tu-dortmund.de
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The continuous bio-ethanol production by means of in
sodium alginate entrapped Saccharomyces cerevisiae
(ATCC 7752) was successfully carried out at 40°C in a
stirred bioreactor with an operating volume of 600 ml by
continuous substrate feed over a period of five days.
The sensors were connected gastight, allowing quantitative online records on gases (carbon dioxide, ethanol
and oxygen) present in the headspace of the bioreactor
(see figure 1). By using a suitable calibrated ethanol
sensor a direct calculation of the ethanol content in the
liquid phase could be made based on the ethanol content in the gaseous phase; those results were validated
by comparative analysis using high performance liquid
chromatography.
The measurement of the unavoidable metabolite CO2 in
the bioreactor and the oxygen content in the flue gas
stream provided the expected results (see figure 2): the
CO2 concentration increased up to 90 Vol.-% and stabilized at that value as no ambient air could enter the
bioreactor. The oxygen content stagnated after reaching
its minimum (approx. 0 Vol.-%), as only CO2 and ethanol
were discharged from the system. The ethanol concentration remained almost constant after the first 60 operating hours. However, the tolerance limit for yeast with
respect to ethanol was by no means reached, as it was
solely intended to show that such a system could be
operated over a longer period of time.
The proposed measurement method offers the advan-
Figure 1: Stirred unit reactor with connected CO2, O2 and ethanol sensors
tage that the analysis is not influenced by further media
components and metabolites (e.g. organic acids). Strikingly, this demonstrates the potential that the arrangement used to determine online ethanol concentrations
can be applied to limit the ethanol content in the medium due to an adequate adjustment.
Actual works dealing with the continuous
production process of bio-butanol
(under anaerobic conditions) and biotensides (rhamnolipids) extent the
field of application of the BlueSens
technology for the gas online-monitoring in biotechnological processes.
Figure 2: Gas online-monitoring of the bio-ethanol production process by continuous feed of 40 g glucose/l
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Model based optimization
of biogas plants
by Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert, Prof. Dr. Reiner Lohmüller, University
of Applied Sciences Emden/Leer, EUTEC Institute
Motivation
Increasing amount of energy derived from biogas plants
will only be available if a wide variety of different substrates can be used. The feed to a biogas reactor will
change according to the fluctuating supply demand scenario for various substrates. The plant has to deliver
maximum gas yield and hence energy yield for various
substrates. This can only be achieved if the process
parameters are optimized continuously. The model
should be able to predict optimized process parameters
as well as energy yield for a given substrate mix. Therefore the model has to take biological processes into
consideration which takes place during anaerobic digestion.
The aim of our research at the Emder Institut für Umwelttechnik (EUTEC) is to develop a sophisticated process
model which is capable of predicting the behavior of an
industrial sized biogas plant. The model should
include:
>>Simulation of biogas production for different
substrate mixtures.
>>Adaptation of appropriate modeling approaches for
the simulation-based evaluation of complex substrates.
>>Design of a control concept for biogas plants.
Experiments
Following experimental facilities have been used:
Batch experiments in 1 liter flasks at 37°C for 2-3
weeks. Aim was to evaluate gas generation rate for various substrates continuous reactor in 20 liter scale.
Equipped with screw pumps and BlueSens analytics
system to count gas quantity and gas composition
(methane and carbon dioxide) in a continuous mode.
Simulation
Simulation studies have been performed using ADM1
model incorporated into Matlab/Simulink. Parameters
of ADM1 kinetic model have been regressed to experimental data.
Results
Figure 1 shows experimental results in comparison with
calculated results for the continous recator in semiindustrial scale. A very good agreement between both
data can be observed indicating that the model is
capable of describing the complex biological processes.
As input parameters only readily available data for the
substrates have been used.
In order to evaluate the capabilities of the model data
from the biogas plant in Wittmund (Germany) have been
Figure 1 Comparison of experimental (black line) and simulated data (red line) for manure (left diagram) and fat mud (right diagram).
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compared to results predicted by the model (figure 2).
Again just readily available parameters describing the
substrate and the biogas plant have been incorporated
into the process model.
As can be seen a very good agreement between experimental data and data from the biogas plant have been
achieved.
Further research will focus on incorporating a wide
variety of different substrates, to account for substrate
pre-treatment and for biogas purification.
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Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert,
Prof. Dr. Reiner Lohmüller, University of Applied Sciences
Emden/Leer, EUTEC Institute. Research and development
in the following areas:
>> Optimization of industrial processes with respect to
high level of sustainability
>> Technologies to reduce pollutants in soil water and air
>> Bioenergy
>> Renewable resources as new raw materials
www.technik-emden.de
Figure 2 Calculated (red line) and experimental data (black line) from industrial sized biogas plant in Wittmund (Germany).
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Online observation of oxygen uptake and
carbon dioxide production and characterisation
of oxygen transfer capacity
by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering
and Analytical Techniques Hamburg University of Applied Sciences
A 5 l stainless steel bioreactor
BIOSTAT® ED5 was used for the production of
the chemokine 1-8 del MCP-1, 1-3 del I-TAC,
vMIP-II as well as for potential Malaria vaccines with the yeast Pichia pastoris in HCDC.
The high instrumented reactor is equipped
with BlueSens sensors for the measurement
of oxygen and carbon dioxide (BCP-O2 and
BCP-CO2). The sensors are placed in the offgas line of the fermenter, behind the off-gas
filter.
The signal for the molar fraction of oxygen xO2
and carbon dioxide xCO2 are recorded and
stored in the data acquisition system MFCSwin. Different gas balance values are
calculated with the control system and stored
online.
The fermentation process starts with a batch
phase with unlimited growth on the substrate
glycerol. In the following glycerol fed batch
phase, limited cell growth is preparing the
cells for the production phase on controlled
methanol concentration.
In figure 1 the off-gas molar fractions xO2 and
xCO2 are shown. With an air aeration the inBioreactor for recombinant protein production research
coming molar fractions are known (xOGin =
exponentially proportional to the volumetric cell growth rate.
xOAIR = 0.2094, xCGin = xCAIR = 0.0003).
The RQ converges to a stationary endpoint of 0.9 at batch
So the oxygen supply rate QO2, the carbon dioxide proend. With reduced cell growth both rates drop down at
duction rate QCO2, the respiratory quotient RQ and the
the beginning of the fed batch phase, but increase expooxygen transfer capacity OTC can be calculated online.
nentially again afterwards. In the production phase the
The dissolved oxygen tension pO2 is controlled via pO2/
cell activity is reduced again. This can be observed in a
agitation control at a setpoint of 25%. The regulation
decreased QO2 and QCO2. The oxygen transfer capacity
starts at t = 12 h, when the pO2 drops below the setOTC is a valuable parameter for the characterization of a
point.
bioreactor plant and a capable scale up criteria.
During the fed batch phase QO2 and QCO2 are increasing
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Figure 1: Course of off-gas measurement and gas balance values
Figure 2: Course of O2-transfer rates during cultivation
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In figure 2 the online estimation of the OTC and the
volumetric O2-transfer coefficient kLa are shown together
with the influencing variables FnG (aeration rate) and
NSt (agitation speed).
Although FnG and NSt are constant in the beginning,
kLa and OTC are slightly decreasing. Due to the
exponential cell growth during pO2-control (since t=12 h)
the oxygen uptake is increasing exponential too. Therefore the OTC has to be increased. The pO2-controller rises
FnG and later on NSt to keep the kLa on track and therewith the OTC.
i
Prof. Dr.-Ing. Reiner Luttmann,
Prof. Dr. Gesine Cornelissen,
Dipl.-Ing. Ulrich Scheffler,
Dipl.-Ing. Hans-Peter Bertelsen.
Research and Transfer Center
of Bioprocess Engineering
and Analytical Techniques
Hamburg University of Applied Sciences. The institute is
engaged in advanced bioprocess engineering in fields
such as production of potential malaria vaccines, optimization of recombinant protein production (DoE), Process
Analytical Technology (PAT) and modeling and simulation
of bioprocesses.
The precultivation in shake flasks for the
execution of bioreactor cultivations
For the execution of bioreactor
cultivations the precultivation in shake flasks is
from great interest. The cells should be in good
condition to avoid a long adaption phase in the
beginning. For assuring vital cells in the preculture no substrate and no oxygen limitation should
occur during cultivation and cells should be in
exponential growth.
Shaking flask experiments have been carried out
for the optimization of preculture conditions.
Therefore a 1 l glass Erlenmeyer flask was
equipped with the BluSens Sensors BCP-O2 and
BCP-CO2 for the measurement of oxygen and car- Figure 1: Course x and x signals of shaking flask experiment
O2
CO2
bon dioxide in the gas phase. For comparison an
of the optical sensor. The BlueSens signal however is
optical oxygen microsensor was also used.
much noiseless comparing to the other.
A recombinant Escherichia coli strain was cultivated. The
In the beginning xO2 starts at a value around 21 % which
experiments were conducted in a shaking flask cabinet
is equal to the oxygen fraction of air (20.94 %). With
at 200 rpm and 37 °C.
increasing cell growth the oxygen demand is increasing
In figure 1 the course of the percentaged molar fraction
proportional, so that the xO2 is decreasing to a value
of oxygen xO2 and carbon dioxide xCO2 is shown. The
around 15.7 % at t = 6.5 h. The signal of xCO2 is contrary
signal from the BlueSens
proportional to xO2.
O2-sensor (BS) is corresponding very well to the signal
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Another experiment was conducted with
additional measurement of the dissolved oxygen tension pO2 in the liquid phase (figure 2).
This gives the opportunity for a better identification of oxygen limitation and verification of
the data from the gas phase.
The signals of the xO2 signals are corresponding
still very well in this experiment. The pO2 is
decreasing exponentially with increasing cell
growth. After 4.2 hours oxygen limitation
occurs. This can be seen also in the xO2 signal
in a decreasing slope of the curve. At t = 6.5
h the substrate is exhausted and substrate
limitation begins. The xO2 graph is at the lowest
point at this time.
As mentioned in the beginning, the cells
should be in exponential growth and limitations should be avoided. Therefore the duration of the preculture should not exceed 3.5
hours. With an optimized preculture consistent
initial conditions for bioreactor cultivations
can be realized. Thus a better reproducibility
and robust cultivation conditions can be
achieved.
Shaking flask experiments with BlueSens Sensors
Figure 2: oxygen measurement in gas and liquid phase of shaking flask
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Automated Design of Experiments (DoE) in
a multi-bioreactor system BIOSTAT®Qplus 6
The bioreactor system BIOSTAT® Qplus (Sartorius Stedim Biotech
GmbH, Göttingen) was established in the
Laboratory of Bioprocess Automation at
Hamburg University of Applied Sciences.
This multi-reactor system enables the execution of parallel experiments with independent measuring and control of process
parameters. Therefore it is a very powerful
solution for the execution of optimization
experiments following DoE.
The system consists of two supply towers,
a digital control unit DCU 4 and six autoclavable 1 l culture vessels. Each vessel is Figure 1: O2- and CO2-signals from one experiment showing all six vessels
with batch phase followed by a fed batch phase
equipped with probes for measurement of
pO2, pH and foam. Two external gasmix stalimited fed-batch operations with reduced cell specific
tions with mass flow controllers are used for aeration up
growth rates.
to 2 vvm. A pump station enables different substrate
With the BlueSens sensors BCP-O2 for oxygen and the
Multi-bioreactor system BIOSTAT® Qplus 6 for the execution of DoE optimization experiments
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BCP-CO2 sensors for carbon dioxide the measurement of
these gases in the off-gas of each vessel is possible. The
multiplexer unit BACCom transferring the off-gas values
to the process control system MFCSwin, where data are
recorded and further online calculations are carried
out.
Experiments for the optimization of the space-time-yield
of a recombinant fusion protein expressed in Escherichia
coli are conducted. The process starts with a glucose
batch, followed by a fed batch phase and the IPTG
induced production phase.
Figure 1 shows the course of the off-gas measurement
of all six vessels from the multi-reactor system. The initial conditions in every single reactor are the same. Also
for the batch part all parameters are identical. This can
be seen in an almost identical course of the six curves
in the batch phase and the very small variation of the
batch end time. In the fed batch phase the cell specific
growth rate µ and the liquid phase temperature JL are
changed to different values (see figure 1). Also the
incoming oxygen mole fraction xO2 was increased stepwise from 20.94 % (AIR) to 45 % (AIR/O2) to avoid oxygen limited cell growth.
The production phase of two different DoE experiments
is plotted in figure 2. For a better comparison of the two
experiments the timeline of the chart is standardized
onto the point of induction at the beginning of the production phase. The plot shows the observable cell specific growth rate , estimated online from the off-gas signals xO2 and xCO2, the fluorescence signal S48/53_sol
of the soluble fusion protein measured in relative fluorescence units (RFU) and the cell density cXL determined from cell dry mass. The setpoint of the cell specific growth rate µw, realized with an open loop controlled
glucose fed batch, was set to 0.18 h-1 in experiment
1 and 0.21 h-1 in experiment 2. After induction the
growth rate is decreasing due to the change in metabolism and a reduced liquid phase temperature in the production phase, but it is increasing afterwards and shows
an almost constant course.
The chosen parameters in experiment 1 yield in a much
higher target protein concentration compared to experiment 2.
Figure 2: -estimation with off-gas measurement and O2-balancing
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Monitoring of baker’s i
yeast fermentations
by PD Dr.-Ing. Lars M. Blank, Laboratory of Chemical
Biotechnology, Technical University Dortmund
The open question we addressed with the
new setup from BlueSens (CO2 and ethanol sensor)
originated from our previous finding (Blank and Sauer,
2004) that under aerobe and glucose excess conditions
ethanol production and the rate of TCA cycle operation
were dependent on the glucose uptake rate. As ethanol
generally cannot be quantified in shake flasks, the finding relied only on indirect observations from 13C-tracer
metabolic flux analyses. Here we aimed to directly quantify the TCA cycle flux by closing the carbon balance
using the BluesSens sensors for quantification of the
volatile fermentation products ethanol and CO2.
As can be seen in figure 1, the new setup delivered fer-
PD Dr.-Ing. Lars M. Blank, Laboratory of Chemical Biotechnology, Technical University Dortmund. The group Systems
Biotechnology characterizes, designs and constructs metabolic networks.
www.bci.tu-dortmund.de/bt
mentation data of very high quality (lines represent a
simultaneous fit of the experimental data using an exponential growth model). As contribution to the scientific
discussion, a strong negative correlation between glucose uptake rate and the rate of TCA cycle operation
could be communicated (Heyland et al., 2009). The
BlueSens setup was invaluable for the here presented
quantitative physiology project with baker’s yeast. Since
then, numerous co-workers used the setup and
experienced a tremendous increase in data amount and
more importantly in quality.
Shake flasks equipped with CO2, O2 and ethanol sensor in a waterbath shaker
20
BlueSens.com
Application Report
CO2
Biomass
Ethanol
35
12
30
10
25
(b)
2.0
CO2
Ethanol
1.6
20
6
15
4
20
CO 2 [Vol-%]
8
25
OD600 [-], Ethanol [Vol-%]
CO 2 [Vol-%]
30
1.2
15
0.8
10
10
5
0
0
3
4
5
t [h]
6
7
8
9
10
0
0
11
0.4
0.0
0
2
4
6
8
Biomass [OD600]
10
12
140
12
120
100
10
100
10
80
8
80
8
60
6
60
6
40
4
40
4
20
2
20
2
0
0
Glucose
Ethanol
Glycerol
Acetate
0
0
1
2
3
4
5
t [h]
6
7
8
9
10
11
Glucose and ethanol [mM]
14
(c)
120
Glucose and ethanol [mM]
2
5
Glycerol and acetate [mM]
140
1
2
Ethanol [Vol-%]
(a)
(d)
14
Glucose
Ethanol
Glycerol
Acetate
12
Glycerol and acetate [mM]
40
0
0
2
4
6
Biomass [OD600]
8
10
12
Fig. 1. Fermentation course of S. cerevisiae during respiro-fermentative growth. (a)
CO2 and gaseous ethanol concentrations were monitored in the gas phase using
infrared sensors. (b) Biomass plotted vs. CO2 and gaseous ethanol concentrations.
(c) Concentrations of glucose, ethanol, glycerol, and acetate were quantified by UVLiterature
RI-HPLC. (d) Biomass plotted vs. concentrations of glucose, ethanol, glycerol and
Blank, L. M. and U. Sauer, TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake
acetate.
Lines
represent a best fit of all experimental data using an exponential
rate,
Microbiol. 2004
150: 1085-1093
Heyland
J.,
J.
Fu,
and
L.
M.
Blank,
between
cycle flux and glucose uptake
rate Sigma
during respiro-fermentative
growth of module
Saccharomycesduring
cerevisiae,
growth model or Correlation
by linear
fitTCAimplemented
in the
Plot statistic
Microbiology, 2009, 155: 3827-3837
exponential growth until 10 h. Linear fitting for gaseous CO2 and Ethanol was only
conducted until 9 h.
Figure 1. Fermentation course of S. cerevisiae during respiro-fermentative growth. (a) CO2 and gaseous ethanol concentrations were monitored in the gas
phase using infrared sensors. (b) Biomass plotted vs. CO2 and gaseous ethanol concentrations. (c) Concentrations of glucose, ethanol, glycerol, and acetate
were quantified by UV-RI-HPLC. (d) Biomass plotted vs. concentrations of glucose, ethanol, glycerol and acetate. Lines represent a best fit of all experimental
data using an exponential growth model or by linear fit implemented in the Sigma Plot statistic module during exponential growth until 10 h. Linear fitting for
gaseous CO2 and Ethanol was only conducted until 9 h.
Literature
Blank, L. M. and U. Sauer, TCA cycle activity in Saccharomyces cerevisiae is a
function of the environmentally determined specific growth and glucose uptake rate,
Microbiol. 2004 150: 1085-1093
Heyland J., J. Fu, and L. M. Blank, Correlation between TCA cycle flux and glucose
uptake rate during respiro-fermentative growth of Saccharomyces cerevisiae,
BlueSens.com
Microbiology, 2009, 155: 3827-3837
21
Application Report
Application of BlueSens® Gas Analyzers
in a Cell Culture Process
by Mathias Aehle, Center for Bioprocess Engineering,
Martin-Luther-University Halle-Wittenberg, Halle (Saale)
1
2
3
4
5
6
1: Quadrupole mass spectrometer, 2: Needle valve to MS, 3: 2-way valve, 4: Inlet gas line, 5: Exhaust gas line, 6: O2 and CO2 BlueSens sensors
1. Machinery assembly
The cultivation system consisted of a fully equipped 2 l
Biostat B (Sartorius, Göttingen) bioreactor installed on a
balance. The BlueSens sensors were installed in series,
where the first one was directly connected to the
exhaust gas filter with a silicon tube. The gassing rate
22
through the measuring volumes was 3.9 l/h. The adjustment of the sensors was performed under process conditions, so that the initial volume fractions were 20.957
Vol. % O2 and 0.04 Vol. % CO2. Both sensors contained
a pre-installed internal noise filter to prevent high noise
levels. The sensors have not been disconnected from the
BlueSens.com
Application Report
OUR and CPR were calculated as follows:
 in (
 mg  p  M O2  V
OUR
  R TW 
 kg  h 
current supply during the entire study. A brief overview of
0.02h-1 the final viable cell concentrations at t = 132h
in
6
6
 were
6
(p
cells/mL.
Stimulus-response
experiments
real
cells/ml.
the features for the used BlueSens sensors
is depicted
reached
4.48±0.23·10
p fermentation
M CO2  V
Stimulus-response
in performed
4.48±0.23·10
cells/mL. 4.48±0.23·10
Stimulus-response
experiments
in real fermentation
mg were
CPR


by
manipulating
the
glutamine
feed
rate.


experiments
performed R
by  T  W
in table 1.
by manipulating the glutamine
feed rate. in real fermentation were
 kg  h 
6
manipulating
thecalculated
glutamine
feed rate.
Sensor4.48±0.23·10
ID
14031
14026
cells/mL. Stimulus-response
experiments
in
real asfermentation
were performed
OUR
and
were
follows:
OUR and CPR were calculated
as CPR
follows:
CO2
Gas
O2
OUR and CPR were calculated
 Las
 follows:
 (C
by manipulating
the glutamine
feed rate. with C  vol
 [kg],
(C pC =) [bar], M
% V,OUR
M =
V
V
 mg C
 )p,  W
Zirconium
Infrared:
O2 
 mg  p  M




 fac


OUR



fac
h
 kg  h 
oxide
Two wavelengths



kg
h
R

T

W
100
 100
R TW


Concentration
0.1-25
Vol.%
0-10
Vol.%
g
OUR and CPR were calculated as follows:
 J
  V (Cmg C p )M  V L(C   C )
p .M
 mg 44
range
,
,
R

8
.
314
M
0
V

22
.
4
,
CPR



fac
CO
m
,
CPR  2  

 mol
T mol
h 
R fac
 T  W
R 
W  kg 100
 mol 100
 kg  h 
Resolution
0.01 Vol.%
0.01 Vol.%
in (C in L C out )
vol
  O
 g 
< 0.2% MR ±3% Display < 0.2% MR±3%
Display  pLM
Accuracy
[bar],
 O CV
 pg =[bar],out
mg
mg
, W=32[kg],
with
%
 

,
W
=
[kg],
p=, V
,  M  32.0 mol  ,
with C 
vol% , V
  fac
M
.0 in



h

1000
V

V
OUR



fac
  and  mol 


h 
.
Measurement
35ml
35ml
 kg  h 
R  T  WL  g  g100
 bar  L 

 J  L 
chamber volume
 g 
 bar  L J 
Measuring
principle
O2
CO2
O
2
2
in
in
O
in
O2
2
out
O2
in
out
CO 2
CO 2
O2
CO2
2
in
in
O
in
2
out
O2
in
out
CO 2
CO 2
O2
, Vm  22.4  0, .08314
R  8.314
CO.24 44.0 , R 8
, Vm M22
,   0.08314 mol  K  ,
M CO2  44.0
 mol K
 
 mol  mol  .314
 mol
mol
 mol


 K  K 
 mol 
Table 1: Abstract from the data sheets for the O2 und CO2 BlueSens sensors
in
in
out
 mg   M
 in mg(Cand

out
CVout
)
 1000
V

 infac
V
 mg
fac
1000  p
 2V
CO 2.
.  g  CO 2
and V CO
g
CPR


 fac ,
 
mass spectrometer
The already installed quadrupole
T3 WResults
100and Conclusions
 kg  h with R 3.
Results and Conclusions
(QMA 200, Balzers, Lichtenstein) was calibrated
3 Results and Conclusions
3 flow
Results
to theand Conclusions
3.1 S
timulus-response to changing gas
test gas (3 Vol.% CO2, 97 Vol.% N2). The gas
L
 g 
3.1
Stimulus-response
to changing gas co

W3.1=
[kg],
pto = [bar],
,
C  vol
V    ,was
M
0 compositions
mass with
spectrometer
in%
all ,experiments
adjusted
3.1 compositions
Stimulus-response
to changing
gas compositions
O2  32.gas
Stimulus-response
to
changing
h
mol 


The
both
volume
fractions
recordedrecorded
during
theduring
measurements
are dur
show
2.1 l/h by means of a needle valve. In order
increase
The responses
responses
of both
volume
fractions
The to
responses
of both volume
fractionsofrecorded
thevolume
measurements
are shown
inrecorded
Fig.
The
responses
ofduring
both
fractions
J
bar
L
g
L









1.
accuracy,
volume
measurements are
in figure 1.
, Vm were
, R  8the
.314
.08314
M COthe
 44
.0 fractions
221..4additionally
 0shown
2





 mol  K  ,
1.
mol

K
mol
mol

4
 a
measured in the gas supply line. For that 28purpose
28
4
3.5
24
2-way valve was installed
3.5
24 multiplex
mg  to periodically
 in  V
 out
3
fac  1000 andand
V
3
20 28
4
20 .
between input and output
gauge
gas
measurements.
BlueSens
g
2.5
2.5
 
16
MS
3.5
24
2
The volume fractions of the gases from both16 measure2
12
12
1.5
1.5
3
ment devices (BlueSens & mass spectrometer) were
820
8
1
1
recorded3 simultaneously
a Siemens
SIMATIC
PCS7
4
0.5
2.5
4
Results inand
Conclusions
0.5
16
0
0
0 1000 1200 1400 1600
0
system and used to calculate the oxygen uptake
rate
0
200
400
600
800 1000 1200 1400 16
0
200
400
600
800
0
200
400
600
800 1000 1200 1400 1600
2
0
200
400
600
800 1000 1200 1400 1600
Time [s]
Time [s]
Time [s]
Time [s] 12
(OUR) and carbon dioxide production rateFigure
(CPR).
Figure
1:
Volume
fractions
of
O
and
CO
to
changing
gas
composition
measured
2 composition
2
1.5 by M
Volume
fractions ofgas
O2 andcompositions
CO2 to changing gas
measured by MS and
3.1 Stimulus-response to1: BlueSens
changing
BlueSens
(Gas
1:
3
vol%
CO
97
vol%
N
2:
air,
Gas
flow
rate
through B
2,Gas
2; Gas
(Gas
1:
3
vol%
CO
,
97
vol%
N
;
Gas
2:
air,
flow
rate
through
BlueSens
2
2
2. Experimental
8 sensors:
3.9 L/min)
sensors: 3.9 L/min)
1
The responses
volume
fractions
during the
measurements
are here
shown
in Fig.
Experiments
determining of
theboth
response
times at
the gas recorded
As mass
can4bespectrometer
clearly seen,employed
the mass here
spectrometer
employed
reacts
faster.0.5
The respo
As can be clearly seen, the
reacts faster.
The response
times
flow rate used in fermentation (3.9 l/h) were performed
ofon
thethe
MSgas
signals
depend
on the
gas flow
rates to
adjusted by the needle
of the MS signals depend
flow rates
to the
MS-inlet
adjusted
bythe
theMS-inlet
needle valve.
1.
0
0
with the above mentioned test gas and normal air. The
0
20
0
200
400
600
800 1000 1200 1400 1600
The
higher
this
rate
the
lower
the
response
times
and
vice
versa.
In
animal
cell bio
The higher this rate the lower the response times andTime
vice versa.
In animal cell bioreactors,
[s]
change of 28the volume fractions was recorded equidis4 the gas
however,
flow rates
through
theoften
reactor
is rather
often
however, the
gas flow rates
through
reactor
is rather
low,
lower
than itlow,
would
be lower than it w
BlueSens
Figure
1: the
Volume
fractions
of O
CO
tant (1 s) to determine characteristic time constants
2 andBlueSens
2 to changing
2
MS
3.5
MS
24
BlueSens (Gas 1: 3 vol% CO
2, 97 vol% N2; G
(Td, T95). The experiments were performed separately
3
sensors: 3.9 L/min)
20
for each measurement
device.
2.5
For fermentation,
a serum-free suspension-CHO-cell-line
16
As can
be clearly seen, the mass spectrometer employ
2
was used as the host cell system. The process was oper12
of the
1.5 MS signals depend on the gas flow rates to the
ated as a glutamine-limited fed-batch with a starting
8
1
volume of 0.8 l and exponential feeding. Further details
4
The0.5higher this rate the lower the response times an
of the process
conditions can be found in Aehle et al.
(2010) . The
0
0 cultivations S687 and S693 were inocuthe 400
gas 600
flow800rates
reactor is r
0
200
1000 through
1200 1400the
1600
0
200
400
600
800 1000 1200 1400 1600 however,
5
lated with 4.5·10 cells/ml whereas
Time [s]
Time [s]S691 and S695
5
Figure 1: Volume fractions of O2 and CO2 to changing gas composition meacells/ml, respectively.
were inoculated
5.4·10fractions
Figure 1:with
Volume
of O2 andDurCO2 tosured
changing
gas composition
byair,MS
by MS and BlueSens
(Gas 1: 3 vol% CO2,measured
97 vol% N2; Gas 2:
Gas and
ing exponential growth
with (Gas
a specific
flow rateN
through
BlueSens
sensors:
3.9
l/min)
BlueSens
1: 3growth
vol% rate
CO2of, 97 vol%
;
Gas
2:
air,
Gas
flow
rate
through
BlueSens
2

BlueSens
MS
CO2 [vol%]
CO2 [vol%]
O2 [vol%]
BlueSens
MS
BlueSens
MS
O2 [vol%]
CO2 [vol%]
O2 [vol%]
O2 [vol%]
BlueSens
MS
CO2 [vol%]

sensors: 3.9 L/min)
BlueSens
No. 1 times
As can be clearly seen, the mass spectrometer employed here reacts faster.
TheReport
response
23
BlueSens.com
Application Report
Analyte
Mass spectrometer
BlueSens
3.2.1 OOxygen
(OUR)
COuptake
Orate
CO2
2
2
2
Td = 8s
Td = 40
Td = 40s
Gas change
Td = 9s
Typical
OUR-profiles
simultaneously
T95 = 17s
T95 = 56s
T95 = 225s T95 = 340s
Test gas-Air
Gas change
Td = 10s
sensors
are
T95 = 10s
Air-Test gas
30
20
OUR [mg/L/h]
Device
OUR [mg/L/h]
As can be clearly seen, the mass spectrometer employed
smaller concentration differences that are present durhere reacts faster. The response times of the MS signals
ing cultivation as well. The maximum concentration difdepend on the gas flow rates to the MS-inlet adjusted by
ferences for O2 and CO2 at the end of the cultivation
performed in this study are 0.75 Vol.% and 0.6 Vol.%,
the needle valve.
respectively. At a second glance, such extreme concenThe higher this rate the lower the response times and
tration differences will not appear in real fermentations,
vice versa. In animal cell bioreactors, however, the gas
as the gas sensors will be simply located in the exhaust
flow rates through the reactor is rather low, often lower
line. Additionally, the stimulus-response
gas flow rate to the sensors
will not
than it would be desirable for a small time constant of
Corresponding
experiments
with
be multiplexed in standard applications. Hence, those
the off-gas measurement devices. The gas flow through
rates
expected
be demonstrated
in chapter 3.3.
T95 time
constantswill
will have
no influence in practical
the measurement devices must always be lower than the
fermentation.
aeration rate itself. Only then, an overpressure, necessary
Corresponding
stimulus-response experiments with the
for sterility purposes, can be maintained in the reactor.
3.2 Fermentation
highest dynamic change of respiration rates expected
In the small-scale experiments reported the flow rate
will be demonstrated in chapter 3.3.
into the mass spectrometer was fixed to 2.1 l/h. For
3.2.1
Oxygen uptake rate (OUR)
3.2 Fermentation
systems that are operated at higher aeration rates anyTypical
OUR-profiles
measured wit
3.2.1 Oxygen
uptake ratesimultaneously
(OUR)
way, such as microbial cultures, this problem does not
Typical OUR-profiles simultaneously measured with the
play any role.
sensors
are shown in Fig. 2. What is easily to notic
mass spectrometer and the BlueSens sensors are shown
The BlueSens sensors react much slower on the same
the
trajectories.
in figure
2. What is easily to notice is that there is a
gas composition change. This is due to the slow gas
constant offset between the trajectories.
throughputs through their relatively large measuring
Corresponding
stimulus-response
experiments
60with the highest dynamic change of respiration
chambers of ca. 35ml. The smaller chambers of ca.
70
Mass spectrometer
10 ml offered
BlueSens are
to shorten
ratesbyexpected
willrecommended
be demonstrated
in chapter503.3. BlueSens
60
the reaction time. The results of the time constants are
50
40
depicted in3.2
the following
table.
Fermentation
40
30
20
measured
10 with the mass spectrometer and the BlueSens
S687
10
Td = 7s in Fig.
Td = 35s
Td = 40s
shown
2. What
is easily to notice
is that there is a constant offset between
0
T95 = 52s
T95 = 249s
T95 = 320s
trajectories.
Table 2: Timethe
constants
(Td, T95) of the reaction characteristics for O2 and
CO2 measured by mass spectrometry und BlueSens analyzers
-10
60
0
20
40
60
80
100
Process time [h]
120
140
0
-10
0
70
OUR [mg/L/h]
OUR [mg/L/h]
Figure 2: OUR
of S687 und S691 without offset corre
Mass spectrometer
Independent of the direction
of the gas change there
Mass spectrometer
50
BlueSens
60
BlueSens
was no significant variation of the delay times for the
To 50explain this offset one must refer to the equation
40
respective method. The BlueSens sensors have a 4.5
(O240in) and outlet (O2out) oxygen concentrations are i
times higher 30
delay time compared to the MS.
30
For both methods,
the change of the O2 signal is faster
20
BlueSens
sensors in chapter 1, this O2in value is ad
20
than the CO210signal. At a first glance, the T95 time con10
S687
Thus,
this value is fixed in the OUR
equation durin
stants (i.e. the time needed to reach 95 % of the end
S691
0
0
value) of both gas components measured with BlueSens
the-10O2 volume fraction was found to untypical mo
-10
seemed to be
of60the use
a 3 Vol.%
0critical.
20 Despite
40
80 of100
120
140
0
20
40
60
80
100
120
140
Process
[h]
Process of
time
[h]volume fractions with
first 10 h. The courses
the
hightime
concentration
difCO2 test gas resulting in a rather
2: OUR
und to
S691
without
Figure correction
2: OUR of S687 und S691 without offset correction
ferences,Figure
these T95
values of
areS687
assumed
be valid
at offset
value (dashed black line) are depicted in Fig.3.
To explain this offset one must refer to the equation for the OUR calculation. There, the inlet
24
(O2in) and outlet (O2out) oxygen concentrations are incorporated. As already mentioned for the
BlueSens.com
BlueSens sensors in chapter 1, this O2in value is adjusted before inoculation to 20.957 vol.%.
Application Report
21
20.99
2
O [vol%]
21
S687
20.99
20.98
20.98
20.97
20.97
20.96
20.96
20.95
20.95
20.94
20.94
20.93
20.93
20.92
0
5
10
15
20
25
21
20.99
20.98
20.97
20.96
20.95
0
5
10
15
20
25
20.96
20.96
20.94
0
5
10
15
20
25
Figure 3: BlueSens O2 signals during the first
20h from 5 CHO fed-batch fermentations. The
dashed line depicts the adjusted O2 value prior
to inoculation
S695
S693
20.97
S691
21.02
21.01
20.98
20.98
20.94
20.95
20.94
20.92
20.93
20.9
20.92
20.91
20.88
20.9
20.89
20.92
21.03
S689
0
5
10
15
20
25
20.86
0
5
10
15
20
25
Process time [h]
Figure 3: BlueSens O2 signals during the first 20h from 5 CHO fed-batch fermentations. The
To explain this offset one must refer to the equation for
was then removed by manually adjusting the O2 in value
dashed line depicts the adjusted O2 value prior to inoculation
the OUR calculation. There, the inlet (O2in) and outlet
in the BlueSens-OUR equation.
concentrations
(O2out)
If this fractions
manual adjustment
is made too early,
offset or
From
Fig.oxygen
3 it turns
out that are
the incorporated.
measured OAs2 volume
move immediately
to an
higher
already mentioned for the BlueSens sensors in chapter
between the OUR-values will remain.
lower values from the adjusted O2 concentration after inoculation. The consequence is a
1, this O2in value is adjusted before inoculation to
Very good results were obtained for BlueSens-OUR compositive
or negative
offset
within the
respect
to the MS-OUR
20.957 Vol.%.
Thus, this value
is fixed
OUR equapared with thewhich
MS afterappears
appropriaterandomly.
correction. TheWe
OURare
tion during cultivation. This could be a drawback as the
currently
investigating the reasons.
O2 volume fraction was found to untypical move away
from this initial value within the first 10 h. The courses of
To remove the offset between the BlueSens-OUR and the MS-OUR, the difference in the
the volume fractions within the first hours and the iniDipl. Ing. Mathias
Martin-Luther-University
Halletiallyvalues
adjustedwas
valuedetermined
(dashed black once
line) are
in inoculation.
OUR
ca.depicted
10h after
ThisAehle,
difference
was then removed
Wittenberg, Institute of Biochemistry/Biotechnology Cenfigure3.
ter for Bioprocess
Engineering
equation.
by manually adjusting the O2in value in the BlueSens-OUR
Central objective of the workgroup is the teaching and reFrom figure 3 it turns out that the measured O2 volume
search in the area of biochemical engineering. In research
fractions move immediately to higher or lower values
the between
emphasis is put
bioprocess engineering.
design
If this
manual adjustment is made too early, an offset
theon OUR-values
willTheremain.
from the adjusted O2 concentration after inoculation. The
and optimization of the production processes for recombinant proteins, which are predominantly used for therapy or
consequence is a positive or negative offset with respect
diagnostic
applications,
in theMS
focusafter
of the group.
DeVery
good
results
obtained
forWeBlueSens-OUR
compared
witharethe
appropriate
to the
MS-OUR
whichwere
appears
randomly.
are curvelopment of improved process control strategies for industrial production
processes fractions
development of
new methrently investigating
the reasons.
stayed
within
correction.
The OUR
trajectories determined by BlueSens
O2 volume
ods for online characterization of fermentation processes
To remove the offset between the BlueSens-OUR and the
applicationThe
in process
control
theMS-OUR,
MS-OUR
noise and
identical for
(Fig.4).
BlueSens-OUR
reflected the
the difference
in theare
OURthus
valuesnearly
was deterinvestigation of transfer processes in bioreactors in pilot
and production scale.
mined once
ca. 10h
after inoculation. This difference
process
dynamic
accurately.
i
BlueSens.com
25
Application Report
70
70
Mass spectrometer
BlueSens
60
50
50
40
40
30
30
OUR [mg/L/h]
20
20
S687
10
0
0
70
20
40
60
80
100
120
140
0
50
40
40
30
30
20
S693
10
20
40
60
0
80
100
120
140
20
40
60
80
100
120
140
Mass spectrometer
BlueSens
60
50
0
S691
10
70
Mass spectrometer
BlueSens
60
0
Mass spectrometer
BlueSens
60
20
S695
10
0
0
20
40
60
80
100
120
140
Process time [h]
Figure 4:4:
Comparison
of OUR from 4of
fermentations
with offset4correction
Figure
Comparison
OUR from
fermentations with offset correction
trajectories
determined
by BlueSens
O2 volume fractions
when changing feed rates under tight glutamine limita3.2.2
Carbon
dioxide
production
rate (CPR)
stayed within the MS-OUR noise and are thus nearly
tion. Figure 6 shows the glutamine feed rate along with
for calculating
the CPR from BlueSens data, as already
Problems
with a fixed CO2in-value
identical (figure4). The BlueSens-OUR
reflected the prothe corresponding reaction of the OUR and CPR. The
cess dynamic
accurately.3.2.1, were not identified.
discussed
in chapter
expected reactions to higher and lower feed pulses were
3.2.2 Carbon dioxide production rate (CPR)
obtained. A delay time under these conditions compared
for calculatingrate
the CPR
with a in
fixed
CO2in-value
to the
MS-signaldetermined
was not identified.
As this
experimentCO2
TheProblems
cell growth
form
of its respiration
is exactly
by the
BlueSens
CPR from BlueSens data, as already discussed in chapdescribed the fastest dynamic changes possible during
volume
fractions.
Even
several
pHrecalibrations
(see S693,
– 60
whichfrom
resulted
ter 3.2.1, were not identified.
cultivation
the rathert=20
high T95
timeh)
constants
chapter in
The fast
cell growth
in form
its dissolved
respiration rate
CPRconsequently
is
3.1 can begaseous
seen as not
relevant
any more.
fractions were
very
changes
of ofthe
and
CO
2 volume
exactly determined by the BlueSens CO2 volume
3.3 Conclusions
recognized
without significant delay. Fig. 4 shows
the CPR profiles of MS and BlueSens.
fractions. Even several pH- recalibrations (see S693,
The BlueSens system is easy plug-and-play measuret=20 – 60 h) which resulted in very fast changes of the
ment system analyzing the exhaust gas composition ondissolved and consequently gaseous CO2 volume
line. The implementation to the already installed reactor
fractions were recognized without significant delay.
configuration and process control system was done
Figure 5 shows the CPR profiles of MS and BlueSens.
without serious problems.
3.2.3 Stimulus-response experiments
The sensors were used in several CHO fed-batch fermenAs indicated in chapter 3.1 stimulus-response experitations. A well established mass spectrometer was used
ments by changing the glutamine feed rate were perfor direct comparison and evaluation of the signals. Due
formed to investigate the significance of response times.
to lower aeration rates typically found for mammalian
Clear responses in the respiration rates are expected
cell culture processes, the independence of the
26
BlueSens.com
Application Report
100
100
Mass spectrometer
BlueSens
80
100
80
100
Mass spectrometer
BlueSens
60
80
80
40
CPR [mg/L/h]
CPR [mg/L/h]
Mass spectrometer
BlueSens
60
40
60
60
S687
20
S691
20
40
40
0
20
0
20
100
40
60
Mass spectrometer
BlueSens
20
40
60
0
0
80
80
100
80
100
0
120 S687
140
120
0
20
100
0
140
80
20
0
Mass spectrometer
BlueSens
60
80
60
80
120 S691
140
100
80
100
120
140
Mass spectrometer
BlueSens
60
80
40
40
S693
60
20
40
0
40
Mass spectrometer
BlueSens
20
40
60
100
100
S695
60
20
0
20
40
60
80
100
40
0
0
120S693
140
20
40
60
80
120 S695
140
100
20
Process time [h]
20
Figure 5: Comparison of CPR from 4 fermentations.
0
Mass spectrometer
BlueSens
0
20
40
60
80
100
120
0
140
0
3.2.3 Stimulus-response experiments
Process time [h]
20
40
60
80
100
120
140
Figure 5: Comparison of CPR from 4 fermentations.
Figure
5: Comparison
of CPR
from 4 fermentations.
As indicated
in chapter
3.1 stimulus-response
experiments by changing the glutamine feed
rate were
performed to investigate
the significance of response times. Clear responses in the
3.2.3
Stimulus-response
experiments
BlueSens sensors to the volumetric gas flow rate is quite
tively. The maximal volume fraction differences at the
respiration
rates
are
expected
when
changing
rates underby
tight
glutamine
limitation.
Fig.
As
indicated
in chapter
3.1 stimulus-response
experiments
changing
the
glutamine
Vol.
advantageous.
Initial
concerns
about
the behavior
at low feed
end of the process
were
0.75 Vol.
% for
O and 0.6feed
2
. Hence,reaction
it should
explicitly
stressed
% forofCOresponse
rates
and
thus too
delay
were
rela-the corresponding
6aeration
shows
the
glutamine
feed
rate times
along
with
ofClear
the OUR
and that
CPR.
rate
were
performed
tolong
investigate
the
significance
times.be
responses
in the
the
2
CO2 measurement was always performed at the lowest
tivized after the first fermentation.
The
expected
reactions
to higher
and
lower feed
were obtained.
A delay
time under
respiration
rates
are expected
when
changing
feedpulses
rates under
tight glutamine
limitation.
Fig.
measurement range. The signals, nevertheless, mirrored
Despite of a low aeration rate (3.9 l/h) used in fermenconditions
torate
thealong
MS-signal
wascorresponding
notprocess
identified.
As without
this experiment
6these
shows
the glutamine
feed
reaction
ofanythe
OURdescribed
and CPR.
the
dynamics
problems.
tation
experiments,
acompared
rather low
resolution
of 0.01with
Vol. %the
In first experiments,
a constant
offset
between
MS and
and fastest
the factory-made
calibration
in possible
a wide concentrathe
dynamic
changes
cultivation
high T
time
constants
95delay
The
expected
reactions
to higher
and during
lower feed
pulses the
wererather
obtained.
A
time
under
BlueSens derived OUR data was obtained. A first analysis
tion rate (0-25 Vol.% for O2, 0-10 vol % for CO2) the
from chapter
3.1 compared
can be seentoasthe
notMS-signal
relevant any
these
conditions
wasmore.
not identified. As this experiment described
BlueSens sensors performed surprisingly good, respec-
revealed the influence of fixing the O2in value which has
60
6
50
80
40
70
58
47
Mass spectrometer
BlueSens
30
60
20
50
36
25
10
40
300
14
0
1403
40
20
60
80
100
Process Time [h]
120
2
Mass spectrometer
BlueSens
7
60
CPR [mg/L/h]
CPR [mg/L/h]
OUR [mg/L/h]
OUR [mg/L/h]
70chapter
7 any more.
70
BlueSens
from
3.1 can be seen as not relevant
50
80
40
70
6
58
47
Mass spectrometer
BlueSens
30
60
20
50
36
25
10
40
0
30
14
0
140 3
40
20
60
80
100
Process Time [h]
120
2
Figure
of OUR and CPRof
reactions
to glutamine
feed rate
pulses
Figure
6: Comparison
OUR
and CPR
reactions
to glutamine
feed rate pulses
106: Comparison
1
10
0
40
BlueSens.com
60
80
100
Process Time [h]
120
0
140
Glutamine Feed Rate [g/h]
Glutamine Feed Rate [g/h]
Mass spectrometer
Feed Rate [g/h]
Glutamine Glutamine
Feed Rate [g/h]
the 80
fastest dynamic changes possible during
cultivation
the rather high T95 time constants
8
80
8
0
40
1
60
80
100
Process Time [h]
Figure 6: Comparison of OUR and CPR reactions to glutamine feed rate pulses
120
0
140
6
27
Application Report
to be adjusted prior to inoculation for calculating
BlueSens-OUR. As the O2 volume fraction arbitrary
moved away from the fixed O2in value short after inoculation a constant positive or negative offset occurs
resulting a time shift of the OUR profile. This effect of the
O2 values at start of the fermentation is not yet fully
understood. Further investigations should be made to
get consistent and reproducible OUR data. The usage of
additional BlueSens sensors at the inlet gas line or the
insertion of a multiplexing device to measure the incoming volume fractions as well would be helpful to overcome this problem. Nevertheless, after manual adaption
of the O2 in value excellent conformity to the MS-OUR
was obtained.
CPR data from CO2 volume fraction measurements
showed very good results as well.
Neither time delays nor significant loss of information
occurred during stimulus-response experiments in real
fed-batch fermentation.
To sum it up, the BlueSens system is suitable for exhaust
gas analysis under cell culture conditions. It is a cost
effective alternative to established mass spectrometers
often used for cell culture off-gas monitoring. The offset
problems could be more discussed when further application reports of cell culture processes are available.
Information
Connections for every application
The sensors of BlueSens dispose of universal possibilities
of installation. By its multifunctional connections each
sensor can be integrated in nearly every existent system.
So the measuring instrument can be installed easily and
cost-effectively. Existing installations can be upgraded
with the products of BlueSens without any problem. In
general you have the choice between the use of flow
adapters or to use already existing screwed connections.
Then the connection can be realized by the following
accesses:
28
>> any hose connector from 4-12 mm
>> GL45 screw thread
>> 1 ¼“ screw thread
>> Tri-Clamp
For the use of flow adapters you can make your choice
between the reasonably priced and robust POM-adapters
or the high-quality stainless steel adapters. Then the gas
flow to/in flow adapters is simply achieved via hose
connections.
BlueSens.com
Information
Sensors in PA housing with GL45 screwed connection on shake flask
Aluminum housing with flow adapter stainless steel
Tri-Clamp
BlueSens.com
Flow adapter POM with GL45 and plug connections for hoses
Aluminum housing with flow adapter stainless steel
Tube with screwed connection 1 ¼“
29
Information
Sensors overview
The BCP series’ exceedingly robust and reasonably
priced sensors can be easily integrated directly into the
gas lines independent of the gas flow. Additional gas
coolers,pumps and valves are not needed to make the
measurements.
Sensor
CO2
CH4
CO
EtOH
Measuring range
0 … 10 Vol. %
0 … 100 Vol. %
0 … 30 Vol. %
0.2 … 25 Vol. %
0 … 25 Vol. %
0 … 100 Vol. %
0 … 50 Vol. %1
Infrared, dual wavelength
Measuring Principle
< ± 0.2 % FS* ± 3% reading
Accuracy
< ± 2% reading / year
Long-term stability2
> 3 years
Lifetime sensor element
Housing Aluminum, IP 65
Dimension (WxDxH) inch
Weight lb
Housing PA6
Dimension (DxH) inch
Weight lb
Disconnectable
Measuring cap
3.94 x 3.94 x 4.06 1.65
3.94 x 3.94 x 4.06
1.65
3.94 x 3.94 x 4.06
1.65
3.94 x 3.94 x 4.06
1.65
3.15 x 3.94
0.66
3.15 x 3.94
0.66
3.15 x 3.94
0.66
3.15 x 3.94
0.66
possible
possible
possible
possible
< ± 0.2 % FS* ± 3% reading
Connecting tolerance
Steel 1.4571 / Sapphire / Viton / PTFE
Material in contact
with gas
G 1¼”, GL 45, Tri-Clamp, hose connection 4-12mm etc.
Connection**
General
max -25 – 55 °C / -13 – 131 °F **
Operating temperature
0 – 60 °C / 32 – 140 °F / 75% RH non-condensing
Storage temperature
0,8 – 1,3 bar / 11.6 – 18.85 psi**
Pressure range
(absolute):
compensated: < ± 3 % reading (range)
Pressure dependence
Operating humidity
0 ... 100% RF
Power supply (max.)
12 or 24 VDC, 1 A
RS 232, RS 485, 4 – 20 mA, Ethernet
Output
Maintenance once a
month
1-point calibration with ambient air or nitrogen
Maintenance yearly
optional factory calibration with certified gases
EN61326-1:1997 +A2:1998
CE
1
30
accuracy < ± .0.5 % FS* ± 5% reading
2
with monthly 1-point calibration
*full scale
** others on request
BlueSens.com
Information
The sensors measure at the point where things are
happening. Fast and reliable measurement data without
a lot of maintenance are the result. With the aid of
standard interfaces, the sensors can be connected to
any process control system or computer.
EtOH
O2
O2ec
H2
Sensor
0 … 1 Vol. %
0.1 … 25 Vol. %
0 … 100 Vol. %
0 … 100 Vol. %3
Measuring range
Galvanic cell
Thermal conductivity
Measuring Principle
1 … 50 Vol. %
Infrared, dual wavelength ZrO2
< ± 0.2 % FS* ± 3% reading
Accuracy
< ± 2% reading / year
Long-term stability2
15,000 hours
Approximately 900 000 Vol. > 3 years
h operating hours
11,42 x 3,94 x 2,36
6.61
3.94 x 3.94 x 4.06
1.65
3.94 x 3.94 x 5.44
1.70
3.94 x 3.94 x 5.44
1.70
Not available
3.15 x 3.94
0.66
3.15 x 5.32
0.70
Not available
No
possible
possible
No
Disconnectable
Measuring cap
–
–
–
–
Connecting tolerance
> 3 years
Steel 1.4571 /
Sapphire / Viton / PTFE
Steel 1.4571 / Viton / PTFE
Connector for 6mm hose
and 8mm tube
Lifetime sensor element
Housing Aluminum, IP 65
Dimension (WxDxH) inch
Weight lb
Housing PA6
Dimension (DxH) inch
Weight lb
Stainless steel, Si, SiOxNy, Material in contact
gold,epoxy
with gas
Acrylnitril-butadien-rubber,
Viton
G 1¼”, GL 45, Tri-Clamp, hose connection 4-12mm etc.
Connection**
General
max -25 – 55 °C / -13 – 131 °F **
Operating temperature
0 – 60 °C / 32 – 140 °F / 75% RH non-condensing
Storage temperature
0,8 – 1,3 bar / 11.6 – 18.85 psi**
Pressure range
(absolute):
compensated: < ± 3 % reading (range)
Pressure dependence
0 ... 100% RF
Operating humidity
12 or 24 VDC, 1 A
24 VDC, 1 A
RS 232, RS 485, 4 – 20 mA, Ethernet
Output
1-point calibration with ambient air or nitrogen
Maintenance once a
month
optional factory calibration with certified gases
Maintenance yearly
EN61326-1:1997 +A2:1998
1
accuracy < ± .0.5 % FS* ± 5% reading
BlueSens.com
Power supply (max.)
2
with monthly 1-point calibration
3
binary mixture
CE
*full scale
** others on request
31
Information
We help you understand, control and
optimize your process!
BC preFerm
Simple tool for process optimization
The same sensors are also used in the BCpreFerm
system, which is used for process optimization (scale
up) for flasks up to large-scale fermenters. The system
comprises up to 12 sensors that are linked to a computer via an electronic multiplexer. The related software
visualizes the results and can calculate
parameters such as the oxygen uptakerate (OUR), the carbon-dioxide emission
rate (CER) and the respiration quotients
(RQ) both on fermenters as well as on flasks.
Yield Master
>>Visualization of the process
>>Increase of reliability and repeatability
>>Dedicated process optimization without
limitations (e.g. oxygen, nutrients etc.)
>>Predictions for the scale up
Measure the gas yield and quality in every anaerobic process
The unique structure of the CH4 sensors from BlueSens
facilitate measuring methane concentrations in processes
that sometimes produce much, sometimes little gas. The
use of sample taking is impossible there, so conventional systems fail.
The CH4 sensors are simply screwed onto the fermentation container and measure the methane content directly
over the sample. Even at 55 °C (131° F) in water-saturated atmospheres. The accruing volumes are precisely
registered via a precision volumenometer (Milligascounter®*).
The data are registered online with the corresponding
software and visualized on the computer. Optionally,
BlueSens can provide everything as accessories; from
the stirrer through the incubator.
Additional sensors
To cover as many measurement parameters as possible,
BlueSens also offers sensors for Ethanol (C2H6O), Hydrogen (H2) and Carbon monoxide (CO).
* Registered trademark. The MilliGascounter was developed at the University of Applied Science Hamburg under the leadership of Prof. Dr. Paul Scherer.
32
BlueSens.com
Information
The freedom of software choice
BlueSens sensors can be used nearly
everywhere. Both screwed and clamped connections
and the standardized data transfer allow the integration
in nearly every biotechnical plant. You are also free in
the software choice for the process control.
FermVis
The use of the conductible FermVis software is obvious
for the parallel measurement of CO2 and O2. Oxygen or
substrate limitations can be detected along with metabolic transpositions.
Furthermore, a time specific analysis of the respective
products is made possible. For improved comparability,
the BCpreFerm measurement system can be used for
shake flasks and fermenters. FermVis calculates the
oxygen uptake rate (OUR), the carbon dioxide emission
rate (CER) and the respiratory quotient (RC) for fermenters
as well as for shake flasks.
BACVis
The software BacVis was made for data
recording of different sensors and gas flow
meters (Milligascounter®*). The sensors are
recognized automatically by means of their
identification number. Due to the easy
handling, BacVis is self-explanatory. As the
obtained data are recorded in the ASCIformat, you can process them without any
problems.
For sure you have also the option to use your own
software for your process control. We are pleased to
support you in finding the best solution for your plants.
* Registered trademark. The MilliGascounter was developed at the University of Applied Science Hamburg under the leadership of Prof. Dr. Paul Scherer.
BlueSens.com
33
Information
Parallel systems
Measuring according to PAT
The modern in-situ measurement on parallel
bioreactors offers various advantages compared to the
conventional method with just one central gas analyzer.
The parallel measurement of gas concentration directly
in every single fermenter saves the installation of complicated gas lines to a central analyzer and also the
complicated processing of the gases can be left out. The
identical test preparation in several fermenters reduces
the danger to work with incorrect results.
You rely not just on one analyzer, but on many, independently working sensors. Furthermore, contamination
between the particular bioreactors can nearly be excluded.
Acc. to PAT, every single fermenter disposes of an own
sensor which transfers continuous real time data to
control the process. The decisive process parameters
can be recognized and influenced in time. This is a real
advantage in bioprocessing.
34
Such a continuous data stream can‘t be produced by
means of the conventional measuring method.
The central analyzers are mostly extremely cost-intensive
to purchase and maintain. Often the entire production
process is on hold, if a component has to be changed or
maintained.
With the use of many, decentral sensors this problem
does mostly not come up. If a fermenter is turned off
due to maintenance, the remaining bioreactors can continue production without any problems.
With the use of parallel systems you mostly achieve
much faster results in research. Under identical terms of
cultivation, alternatives can be tested well-aimed in the
particular bioreactors and therefore the decisive factors
can be determined much faster (DOE).
BlueSens.com
Questions?
Please ask directly!
Phone
+49 2366 305 301
Or visit our homepage:
www.BlueSens.com
Konrad-Adenauer-Str. 9-13 • D-45699 Her ten (Germany)
Phone +49 - (0)2366 - 305-301 • Fax +49 - (0)2366 - 305-300
e-mail: [email protected]
Internet:
www.BlueSens.de
www.BlueSens.com

BlueSens Report No.1

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