Report No. 1
BlueSens Report No. 1, October 21 21 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, marcus@riepe-im-inter.net 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 1 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 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 CO 2 and another one for the measurement of O 2. 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.com BlueSens Report No. 1 3
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 1 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 by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences 18 Automated Design of Experiments (DoE) in a multi-bioreactor system BIOSTAT Qplus 6 by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences 2 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 3 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 BlueSens.com BlueSens Report No. 1 5
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) Dr. Udo Schmale (R&D and Production) 6
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, 1 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! 7
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. 2), as well as for applications in white biotechnology (e.g. riboflavin (Marx et al. 28)). An overnight shake culture was used to inoculate a defined 2 l batch medium (as described in Maurer et al. 26) with 4 g glucose L-1 as sole carbon source, to a starting optical density (OD6) of 1.. The cultivation was carried out in a 5. l bioreactor (Minifors, Infors, Bottmingen-Basel, Switzerland; figure 1 B) with a tailored off gas analyser. This off gas analyser consists of a BCP-CO 2, a BCP-O 2 probe (BlueSens, Figure 1: B) bioreactor with off gas analyser Figure 1: A) self assembled off gas analyser 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. with addition of 25% ammonium hydroxide and the dissolved oxygen concentration was maintained above 2% saturation by controlling the 8 BlueSens Report No. 1 BlueSens.com
stirrer speed between 25 and 12 rpm and the air flow between 2. and 5. l min-1. Samples were taken frequently over the whole process and analysed as described below. Three aliquots of 1 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 15 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, i 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. BlueSens.com BlueSens Report No. 1 9
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 (specific glucose uptake rate qglucose=.44 g g-1 h-1) and forming ethanol with a rate of qp ethanol=.8 g g-1 h-1 as by product. After a first stationary phase the ethanol was utilised with a rate of qethanol =.4 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.5 during the ethanol utilization. The carbon utilisation was therefore balanced with a tolerance of 93-15%. 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 (2) Recombinant protein expression in Pichia pastoris. Mol Biotechnol, 16, 23-52. Marx, H., D. Mattanovich & M. Sauer (28) Overexpression of the riboflavin biosynthetic pathway in Pichia pastoris. Microb Cell Fact, 7, 23. Maurer, M., M. Kuehleitner, B. Gasser & D. Mattanovich (26) 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. i 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 3 l for interfacing with academic and industrial partners. www.bvt.bci.tu-dortmund.de 1 BlueSens Report No. 1 BlueSens.com
Figure 1: Stirred unit reactor with connected CO 2, O 2 and ethanol sensors The continuous bio-ethanol production by means of in sodium alginate entrapped Saccharomyces cerevisiae (ATCC 7752) was successfully carried out at 4 C in a stirred bioreactor with an operating volume of 6 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 CO 2 in the bioreactor and the oxygen content in the flue gas stream provided the expected results (see figure 2): the CO 2 concentration increased up to 9 Vol.-% and stabilized at that value as no ambient air could enter the bioreactor. The oxygen content stagnated after reaching its minimum (approx. Vol.-%), as only CO 2 and ethanol were discharged from the system. The ethanol concentration remained almost constant after the first 6 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 advantage 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 4 g glucose/l BlueSens.com BlueSens Report No. 1 11
Model based optimization of biogas plants Application Report 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 2 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). 12 BlueSens Report No. 1 BlueSens.com
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. i 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). BlueSens.com BlueSens Report No. 1 13
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-O 2 and BCP-CO 2 ). The sensors are placed in the offgas line of the fermenter, behind the off-gas filter. The signal for the molar fraction of oxygen xo 2 and carbon dioxide xco 2 are recorded and stored in the data acquisition system MFC- Swin. 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 xo 2 and xco 2 are shown. With an air aeration the incoming molar fractions are known (xogin = xoair =.294, xcgin = xcair =.3). So the oxygen supply rate QO 2, the carbon dioxide production rate QCO 2, the respiratory quotient RQ and the oxygen transfer capacity OTC can be calculated online. The dissolved oxygen tension po 2 is controlled via po 2 / agitation control at a setpoint of 25%. The regulation starts at t = 12 h, when the po 2 drops below the setpoint. During the fed batch phase QO 2 and QCO 2 are increasing Bioreactor for recombinant protein production research exponentially proportional to the volumetric cell growth rate. The RQ converges to a stationary endpoint of.9 at batch end. With reduced cell growth both rates drop down at the beginning of the fed batch phase, but increase exponentially again afterwards. In the production phase the cell activity is reduced again. This can be observed in a decreased QO 2 and QCO 2. The oxygen transfer capacity OTC is a valuable parameter for the characterization of a bioreactor plant and a capable scale up criteria. 14 BlueSens Report No. 1 BlueSens.com
Figure 1: Course of off-gas measurement and gas balance values Figure 2: Course of O 2 -transfer rates during cultivation BlueSens.com BlueSens Report No. 1 15
In figure 2 the online estimation of the OTC and the volumetric O 2 -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 po 2 -control (since t=12 h) the oxygen uptake is increasing exponential too. Therefore the OTC has to be increased. The po 2 -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 by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences 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-O 2 and BCP-CO 2 for the measurement of oxygen and carbon dioxide in the gas phase. For comparison an optical oxygen microsensor was also used. A recombinant Escherichia coli strain was cultivated. The experiments were conducted in a shaking flask cabinet at 2 rpm and 37 C. In figure 1 the course of the percentaged molar fraction of oxygen x O2 and carbon dioxide x CO2 is shown. The signal from the BlueSens O 2 -sensor (BS) is corresponding very well to the signal Figure 1: Course x O2 and x CO2 signals of shaking flask experiment of the optical sensor. The BlueSens signal however is much noiseless comparing to the other. In the beginning x O2 starts at a value around 21 % which is equal to the oxygen fraction of air (2.94 %). With increasing cell growth the oxygen demand is increasing proportional, so that the x O2 is decreasing to a value around 15.7 % at t = 6.5 h. The signal of x CO2 is contrary proportional to x O2. 16 BlueSens Report No. 1 BlueSens.com
Another experiment was conducted with additional measurement of the dissolved oxygen tension po 2 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 x O2 signals are corresponding still very well in this experiment. The po 2 is decreasing exponentially with increasing cell growth. After 4.2 hours oxygen limitation occurs. This can be seen also in the x O2 signal in a decreasing slope of the curve. At t = 6.5 h the substrate is exhausted and substrate limitation begins. The x O2 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 BlueSens.com BlueSens Report No. 1 17
Automated Design of Experiments (DoE) in a multi-bioreactor system BIOSTAT Qplus 6 by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences The bioreactor system BIO- STAT 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 equipped with probes for measurement of po 2, ph and foam. Two external gasmix stations with mass flow controllers are used for aeration up to 2 vvm. A pump station enables different substrate Figure 1: O 2 - and CO 2 -signals from one experiment showing all six vessels with batch phase followed by a fed batch phase limited fed-batch operations with reduced cell specific growth rates. With the BlueSens sensors BCP-O 2 for oxygen and the Multi-bioreactor system BIOSTAT Qplus 6 for the execution of DoE optimization experiments 18 BlueSens Report No. 1 BlueSens.com
BCP-CO 2 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 xo 2 was increased stepwise from 2.94 % (AIR) to 45 % (AIR/O 2 ) 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 xo 2 and xco 2, 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.18 h-1 in experiment 1 and.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 O 2 -balancing BlueSens.com BlueSens Report No. 1 19
Monitoring of baker s 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 (CO 2 and ethanol sensor) i 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 originated from our previous finding (Blank and Sauer, 24) 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 CO 2. As can be seen in figure 1, the new setup delivered fermentation 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., 29). 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 2 BlueSens Report No. 1 BlueSens.com
4 35 3 (a) CO 2 Biomass Ethanol 12 1 3 25 (b) CO 2 Ethanol 2. 1.6 CO 2 [Vol-%] 25 2 15 1 5 8 6 4 2 OD 6 [-], Ethanol [Vol-%] CO 2 [Vol-%] 2 15 1 5 1.2.8.4 Ethanol [Vol-%] 1 2 3 4 5 6 7 8 9 1 11 t [h]. 2 4 6 8 1 12 Biomass [OD 6 ] 14 12 (c) Glucose Ethanol Glycerol Acetate 14 12 14 12 (d) Glucose Ethanol Glycerol Acetate 14 12 Glucose and ethanol [mm] 1 8 6 4 1 8 6 4 Glycerol and acetate [mm] Glucose and ethanol [mm] 1 8 6 4 1 8 6 4 Glycerol and acetate [mm] 2 2 2 2 1 2 3 4 5 6 7 8 9 1 11 t [h] 2 4 6 8 1 12 Biomass [OD 6 ] Fig. 1. Fermentation course of S. cerevisiae during respiro-fermentative growth. (a) Figure 1. Fermentation course of S. cerevisiae during respiro-fermentative growth. (a) CO 2 and gaseous ethanol concentrations were monitored in the gas phase using infrared sensors. (b) Biomass plotted vs. CO 2 and gaseous ethanol concentrations. (c) Concentrations of glucose, ethanol, glycerol, and acetate were CO2 quantified and by UV-RI-HPLC. gaseous (d) Biomass ethanol plotted vs. concentrations of glucose, were ethanol, glycerol monitored and acetate. Lines in the represent gas a best phase fit of all experimental using data using an exponential growth model or by linear fit implemented in the Sigma Plot statistic module during exponential growth until 1 h. Linear fitting for gaseous CO 2 and Ethanol was only conducted until 9 h. 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 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, acetate. Microbiol. 24 Lines 15: 185-193 represent a best fit of all experimental data using an exponential Heyland growth J., J. Fu, model and L. M. Blank, or Correlation by linear between fit TCA implemented cycle flux and glucose uptake in the rate Sigma during respiro-fermentative Plot statistic growth of module Saccharomyces during cerevisiae, Microbiology, 29, 155: 3827-3837 exponential growth until 1 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. 24 15: 185-193 Heyland J., J. Fu, and L. M. Blank, Correlation between TCA cycle flux and glucose uptake rate during respiro-fermentative growth of Saccharomyces cerevisiae, Microbiology, 29, 155: 3827-3837 BlueSens.com BlueSens Report No. 1 21
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: O 2 and CO 2 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 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 2.957 Vol. % O 2 and.4 Vol. % CO 2. Both sensors contained a pre-installed internal noise filter to prevent high noise levels. The sensors have not been disconnected from the 22 BlueSens Report No. 1 BlueSens.com
in mg p M ( O V OUR 2 kg h R T W current supply during the entire study. A brief overview of.2h-1 the final viable cell concentrations at t = 132h the features for the used BlueSens sensors 4.48±.23 1 is depicted 6 cells/ml. 4.48±.23 1 reached Stimulus-response 4.48±.23 1 6 6 in cells/ml. experiments Stimulus-response cells/ml. in real fermentation experiments Stimulus-response mg were in performed real pfermentation M were ( p CO V in table 1. by manipulating the glutamine by experiments manipulating feed rate. in the real glutamine fermentation feed rate. CPR 2 were kgperformed h by R T W Sensor 4.48±.23 1 ID 1431 6 cells/ml. Stimulus-response 1426 manipulating the glutamine feed rate. OUR and CPR were calculated OUR experiments and as CPR follows: were calculated real as fermentation follows: were performed Gas O 2 CO 2 OUR and CPR were calculated as follows: by manipulating the glutamine feed rate. L in out in in out Measuring Zirconium Infrared: with in Cmg vol% p M (C C ) O V, V mg p M (C C ) O O V O O2 O 2 2 2 2 2 principle oxide Two wavelengths OUR OUR fac kg h fac R T W kg1, W = [kg], p = [bar], M O h 2 R T W 1 Concentration OUR and CPR.1-25 were Vol.% calculated -1 Vol.% as follows: gin in out range mg pm (C C ) CO V in in out mg pm (C C ) CO V L J CO CO M 2 2 2 2 CO 2 CO 2 CPRCO 2 44. CPR fac Resolution.1 Vol.%.1 Vol.% kg h, fac R T W kg 1 h,, V m 22.4 mol R T W, R 8.314 mol 1 mol in in out Accuracy <.2% MR ±3% Display <.2% MR ±3% Display with L C vol%, V L with, W C= [kg], vol% p =, V g g mg p M (C [bar], mg C ) O V O M, W O = 232. [kg], in p = [bar], O Measurement 35ml 35ml h 2 mol, out MO h 2 32., fac 1 mol and V 2 OUR V fac kg h 2 R T W chamber volume g L g J bar L MCO 2 44. mol, V m M 22.4 2 44. g1 L CO mol, R 8.314, V m. J bar L 22.4.8314 mol K mol K, mol, R 8.314 mol.8314, mol K mol K Table 1: Abstract from the data sheets for the O 2 und CO 2 BlueSens sensors mg in out fac 1 and V V The already installed quadrupole mass spectrometer g in mg in. in out fac 1 and V g out V mg pm (C C. ) CO V CO 2 CO 2 CPR fac kg h 3 2 R T WResults 1and Conclusions, (QMA 2, Balzers, Lichtenstein) was calibrated with 3. Results and Conclusions test gas (3 Vol.% CO 2, 97 Vol.% N 2 ). The gas 3 flow Results to the and Conclusions 3 Results and Conclusions 3.1 Stimulus-response to changing gas L mass with spectrometer C vol% in all, experiments V was 3.1 adjusted Stimulus-response to 3.1 compositions Stimulus-response g to changing gas co, W = [kg], p = [bar], 3.1 Mto Stimulus-response changing O gas compositions to changing gas compositions 2.1 l/h by means of a needle valve. h In 2 32., order The to responses increase of both volume fractions recorded of both mol during volume The responses of both volume of both fractions the volume measurements fractions recorded recorded fractions during are shown the during measurements recorded Fig. are dur show accuracy, the volume gfractions were 1. additionally L 1. the measurements J are shown in figure bar1. L MCO 2 44., V m 22.4 measured in the gas supply mol, R 8.314 line. For that purpose mol a 1..8314, mol K mol K 28 28 4 4 BlueSens BlueSens BlueSens BlueSens 2-way valve was installed to periodically 24 multiplex 24 MS MS 3.5 MS mg 3.5 MS in out fac 1 3 2 3 between input and output and and V V gauge gas measurements. 2. 28 4 2.5 BlueSens g 16 2.5 16 The volume fractions of the gases from both measurement devices (BlueSens & mass spectrometer) were 3 24 MS 2 3.5 2 12 12 1.5 1.5 8 8 2 1 1 recorded simultaneously in a Siemens SIMATIC PCS7 4 4.5 3 Results and Conclusions.5 2.5 16 system and used to calculate the oxygen uptake rate 2 4 6 8 2 4 6 8 1 12 14 16 1 2 12 4 14 616 2 4 6 8 1 12 14 16 8 1 12 14 16 2 Time [s] Time [s] Time [s] Time [s] (OUR) and carbon dioxide production rate (CPR). 12 Figure 1: Volume fractions Figure of O1: 2 and Volume CO 2 to fractions changing of gas O 2 and composition CO 2 to changing measured gas by composition MS and measured by M 3.1 Stimulus-response to changing gas 2. Experimental BlueSens (Gas 1: 3 vol% BlueSens compositions 1.5 CO 2, 97 vol% (Gas N1: 2 ; 3 Gas vol% 2: CO air, 2, Gas 97 vol% flow rate N 2 ; Gas through 2: air, BlueSens Gas flow rate through B 8 sensors: 3.9 L/min) sensors: 3.9 L/min) 1 Experiments The responses determining of the both response volume times at fractions the gas recorded during the measurements are shown in Fig. As can be clearly seen, the As mass can 4be spectrometer clearly seen, employed the mass here spectrometer reacts faster. employed The response here reacts times faster. The respo.5 flow rate used in fermentation (3.9 l/h) were performed 1. of the MS signals depend of on the the MS gas signals flow rates depend to the on the MS-inlet gas flow adjusted rates to by the the MS-inlet needle valve. adjusted by the needle with the above mentioned test gas and normal air. The 2 4 6 8 1 12 14 16 2 The higher this rate the The lower higher the response this rate times the lower and the vice response versa. In times animal and cell vice bioreactors, versa. In animal cell bio change of the volume fractions was recorded equidistant (1 s) to determine characteristic time constants Time [s] 28 however, the gas flow rates however, through 4 the gas the reactor flow rates is rather through low, the often reactor lower is rather than it low, would often be lower than it w BlueSens Figure 1: Volume fractions O 2 and BlueSens CO 2 to changing 2 24 MS 3.5 MS (Td, T95). The experiments were performed separately BlueSens (Gas 1: 3 vol% CO 2, 97 vol% N 2 ; G 3 for each measurement 2 device. sensors: 3.9 L/min) 2.5 For fermentation, 16 a serum-free suspension-cho-cell-line As can be clearly seen, the mass spectrometer employ 2 was used as the host cell system. The process was operated as a glutamine-limited fed-batch with a starting of the 1.5 MS signals depend on the gas flow rates to the 12 8 volume of.8 l and exponential feeding. Further details 1 of the process 4 conditions can be found in Aehle et al. The.5higher this rate the lower the response times an (21). The cultivations S687 and S693 were inoculated with 4.5 1 5 4 6 8 1 12 14 16 2 4 6 8 1 12 14 16 2 however, the gas flow rates through the reactor is r cells/ml whereas Time [s] S691 and S695 Time [s] were inoculated Figure 1: with Volume 5.4 1fractions 5 cells/ml, respectively. of O During exponential growth BlueSens with (Gas a specific 1: 3 growth vol% rate CO 2 Figure 1: Volume fractions of O 2 and CO 2 to changing gas composition 2 and CO 2 to changing gas composition measured by MS and BlueSens (Gas 1: 3 vol% CO measured by MS and 2, 97 vol% N 2 ; Gas 2: air, Gas of, 97 vol% flow rate Nthrough 2 ; Gas BlueSens 2: air, sensors: Gas 3.9 l/min) flow rate through BlueSens sensors: 3.9 L/min) O 2 [vol%] O 2 [vol%] OUR and CPR were calculated as follows: O 2 [vol%] O 2 [vol%] CO 2 [vol%] BlueSens.com As can be clearly seen, the mass spectrometer employed here reacts faster. BlueSens The Report response No. 1 times 23 CO 2 [vol%] CO 2 [vol%] CO 2 [vol%]
24 As can be clearly seen, the mass spectrometer employed smaller concentration differences that are present during cultivation as well. The maximum concentration dif- here reacts faster. The response times of the MS signals depend on the gas flow rates to the MS-inlet adjusted by ferences for O 2 and CO 2 at the end of the cultivation the needle valve. performed in this study are.75 Vol.% and.6 Vol.%, The higher this rate the lower the response times and respectively. At a second glance, such extreme concentration differences will not appear in real fermentations, vice versa. In animal cell bioreactors, however, the gas flow rates through the reactor is rather low, often lower as the gas sensors will be simply located in the exhaust than it would be desirable for a small time constant of line. Additionally, the gas flow rate to the sensors will not the off-gas measurement devices. The gas flow through be multiplexed in standard applications. Hence, those the measurement devices must always be lower than the T95 time constants will have no influence in practical aeration rate itself. Only then, an overpressure, necessary fermentation. for sterility purposes, can be maintained in the reactor. Corresponding 3.2 Fermentation stimulus-response experiments with the In the small-scale experiments reported the flow rate highest dynamic change of respiration rates expected into the mass spectrometer was fixed to 2.1 l/h. For will be demonstrated in chapter 3.3. 3.2.1 Oxygen uptake rate (OUR) systems that are operated at higher aeration rates anyway, such as microbial cultures, this problem does not 3.2.1 Oxygen uptake rate (OUR) 3.2 Fermentation play any role. Typical OUR-profiles simultaneously measured with the The BlueSens sensors react much slower on the same mass spectrometer and the BlueSens sensors are shown gas composition change. This is due to the slow gas the in figure trajectories. 2. What is easily to notice is that there is a throughputs through their relatively large measuring constant offset between the trajectories. chambers Corresponding of ca. 35ml. The stimulus-response smaller chambers of experiments ca. 6with the highest dynamic change of respiration Mass spectrometer 1 ml offered rates by expected BlueSens are will recommended be demonstrated to shorten in chapter 5 3.3. BlueSens the reaction time. The results of the time constants are 4 depicted in the following table. Device Mass spectrometer BlueSens Analyte O 2 CO 2 O 2 CO 2 Gas change Test gas-air T d = 9s T 95 = 17s T d = 8s T 95 = 56s T d = 4 T 95 = 225s T d = 4s T 95 = 34s Gas change Air-Test gas T d = 1s T 95 = 1s T d = 7s T 95 = 52s T d = 35s T 95 = 249s T d = 4s T 95 = 32s Table 2: Time constants (Td, T95) of the reaction characteristics for O 2 and CO 2 measured by mass spectrometry und BlueSens analyzers 7 Independent of the direction Mass spectrometer of the gas change there Figure 2: OUR of S687 und S691 without offset corre Mass spectrometer 5 BlueSens 6 BlueSens was no significant variation of the delay times for the 4 5 respective method. The BlueSens sensors have a 4.5 4 times higher 3 delay time compared to the MS. (O in 2 ) and outlet (O out 2 ) oxygen concentrations are i 3 For both methods, 2 the change of the O 2 signal is faster BlueSens sensors in chapter 1, this O in 2 2 value is ad than the CO 2 signal. At a first glance, the T95 time constants (i.e. the time needed to reach 95 % of the end S687 1 1 Thus, this value is fixed in the OUR equation durin S691 value) of both gas components measured with BlueSens the O 2 volume fraction was found to untypical mo -1 seemed to be critical. 2 Despite 4 of 6the use 8 of 1 a 3 Vol.% 12 14-1 2 4 6 8 1 12 14 CO 2 test gas resulting in a rather Process high time concentration [h] differences, Figure these T95 2: OUR values of are S687 assumed und to S691 be valid without at offset Figure correction 2: OUR of S687 und S691 without offset first 1 h. The courses Process of time the [h] volume fractions with correction BlueSens Report No. 1 3.2 Fermentation 3.2.1 Oxygen uptake rate (OUR) Corresponding stimulus-response experiments with rates expected will be demonstrated in chapter 3.3. Typical OUR-profiles simultaneously measured wit sensors are shown in Fig. 2. What is easily to notic OUR [mg/l/h] 3 2 Typical OUR-profiles simultaneously measured with the mass spectrometer and the BlueSens 1 sensors are shown in Fig. 2. What is easily to notice is that there is a constant offset between the trajectories. OUR [mg/l/h] 6-1 2 4 6 8 1 12 14 Process time [h] S687 BlueSens.com OUR [mg/l/h] 7 6 5 4 3 2 1-1 To explain this offset one must refer to the equation OUR [mg/l/h] 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 (O 2 in ) and outlet (O 2 out ) oxygen concentrations are incorporated. As already mentioned for the BlueSens sensors in chapter 1, this O 2 in value is adjusted before inoculation to 2.957 vol.%.
21 2.99 S687 21 2.99 S689 21.3 21.2 S691 2.98 2.97 2.96 2.95 2.94 2.98 2.97 2.96 2.95 2.94 21.1 21 2.99 2.98 2.97 2.96 2.93 2.93 2.95 O 2 [vol%] 2.92 5 1 15 2 25 2.98 2.97 2.96 S693 2.92 5 1 15 2 25 2.98 2.96 S695 2.94 5 1 15 2 25 Figure 3: BlueSens O 2 signals during the first 2h from 5 CHO fed-batch fermentations. The dashed line depicts the adjusted O 2 value prior to inoculation 2.95 2.94 2.94 2.93 2.92 2.92 2.9 2.91 2.9 2.88 2.89 5 1 15 2 25 2.86 5 1 15 2 25 Process time [h] Figure 3: BlueSens O 2 signals during the first 2h 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 O 2 in value dashed line depicts the adjusted O 2 value prior to inoculation the OUR calculation. There, the inlet (O 2 in) and outlet in the BlueSens-OUR equation. From (O 2 out) Fig. oxygen 3 it turns concentrations out that are the incorporated. measured OAs 2 volume If this fractions manual adjustment move immediately is made too early, to an higher offset or already mentioned for the BlueSens sensors in chapter between the OUR-values will remain. lower values from the adjusted O 2 concentration after inoculation. The consequence is a 1, this O 2 in value is adjusted before inoculation to Very good results were obtained for BlueSens-OUR compared with the which MS after appears appropriate randomly. correction. The We OUR are positive 2.957 Vol.%. or negative Thus, this value offset is fixed with in the respect OUR equation during cultivation. This could be a drawback as the to the MS-OUR currently investigating the reasons. O 2 volume fraction was found to untypical move away i from this initial value within the first 1 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 initially values adjusted was value determined (dashed black once line) are ca. depicted 1h after in inoculation. Dipl. Ing. Mathias This Aehle, difference Martin-Luther-University was then removed Halle- OUR figure3. by manually adjusting the O in Wittenberg, Institute of Biochemistry/Biotechnology Center for Bioprocess equation. Engineering 2 value in the BlueSens-OUR From figure 3 it turns out that the measured O 2 volume Central objective of the workgroup is the teaching and research in the area of biochemical engineering. In research fractions move immediately to higher or lower values If this manual adjustment is made too early, an offset the between emphasis is put the on OUR-values bioprocess engineering. will The remain. design from the adjusted O 2 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 Very to the good MS-OUR results which were appears obtained randomly. for We BlueSens-OUR diagnostic are currently investigating The OUR the reasons. trajectories determined by BlueSens compared applications, with are the in the MS focus after of the group. appropriate Development of improved process control strategies for industrial production correction. O 2 volume processes fractions development of stayed new methods for online characterization of fermentation processes within To remove the offset between the BlueSens-OUR and the the MS-OUR, MS-OUR the difference noise and in the are OUR thus values nearly was determined once ca. 1h after inoculation. This difference and production identical for (Fig.4). application The in process BlueSens-OUR control reflected the investigation of transfer processes in bioreactors in pilot scale. process dynamic accurately. BlueSens.com BlueSens Report No. 1 25
7 6 Mass spectrometer BlueSens 7 6 Mass spectrometer BlueSens 5 5 4 4 3 3 OUR [mg/l/h] 2 1 2 4 6 8 1 12 14 7 6 5 4 3 Mass spectrometer BlueSens S687 2 1 2 4 6 8 1 12 14 7 6 5 4 3 Mass spectrometer BlueSens S691 2 1 S693 2 1 S695 2 4 6 8 1 12 14 2 4 6 8 1 12 14 Process time [h] Figure 4: 4: Comparison of OUR from 4 of fermentations OUR from with offset 4 correction fermentations with offset correction 3.2.2 trajectories Carbon determined dioxide by BlueSens production O 2 volume fractions rate (CPR) when changing feed rates under tight glutamine limitation. Figure 6 shows the glutamine feed rate along with stayed within the MS-OUR noise and are thus nearly identical (figure4). The BlueSens-OUR reflected the process dynamic in chapter accurately. 3.2.1, were not identified. expected reactions to higher and lower feed pulses were the corresponding reaction of the OUR and CPR. The discussed 3.2.2 Carbon dioxide production rate (CPR) obtained. A delay time under these conditions compared Problems with a fixed CO 2 in-value for calculating the to the MS-signal was not identified. As this experiment CPR from BlueSens data, as already discussed in chapter 3.2.1, were not identified. cultivation the rather high T95 time constants from chapter described the fastest dynamic changes possible during The cell growth in form of its respiration rate CPR is 3.1 can be seen as not relevant any more. exactly determined by the BlueSens CO 2 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 measurement system analyzing the exhaust gas composition on- t=2 6 h) which resulted in very fast changes of the dissolved and consequently gaseous CO 2 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 fermentations. A well established mass spectrometer was used As indicated in chapter 3.1 stimulus-response experiments by changing the glutamine feed rate were performed to investigate the significance of response times. to lower aeration rates typically found for mammalian for direct comparison and evaluation of the signals. Due Clear responses in the respiration rates are expected cell culture processes, the independence of the Problems with a fixed CO 2 in -value for calculating the CPR from BlueSens data, as already The cell growth in form of its respiration rate CPR is exactly determined by the BlueSens CO 2 volume fractions. Even several ph- recalibrations (see S693, t=2 6 h) which resulted in very fast changes of the dissolved and consequently gaseous CO 2 volume fractions were 26 BlueSens Report No. 1 BlueSens.com
1 8 1 6 8 Mass spectrometer BlueSens Mass spectrometer BlueSens 1 8 1 6 8 Mass spectrometer BlueSens Mass spectrometer BlueSens CPR [mg/l/h] CPR [mg/l/h] 4 6 2 4 2 4 6 8 1 12S687 14 2 1 Mass spectrometer 2 BlueSens 4 6 8 1 12 14 8 1 6 8 4 6 2 Mass spectrometer BlueSens S687 S693 4 2 6 4 2 4 6 8 1 12 S691 14 2 1 Mass spectrometer BlueSens 2 4 6 8 1 12 14 8 1 6 4 8 6 2 Mass spectrometer BlueSens S691 S695 4 2 4 6 8 1 12S69314 2 Process time [h] Figure 5: Comparison of CPR from 4 fermentations. 2 4 6 8 1 12 14 3.2.3 Stimulus-response experiments Process time [h] Figure 5: Comparison of CPR from 4 fermentations. 5: Comparison of CPR from 4 fermentations. 4 2 4 6 8 1 12 S695 14 2 4 6 8 1 12 14 As indicated in chapter 3.1 stimulus-response experiments by changing the glutamine feed 3.2.3 rate were Stimulus-response performed to investigate experiments the significance of response times. Clear responses in the BlueSens sensors to the volumetric gas flow rate is quite tively. The maximal volume fraction differences at the As respiration advantageous. indicated rates Initial in chapter are concerns expected about 3.1 stimulus-response when the behavior changing at low feed experiments rates under end of the process by tight were changing glutamine.75 Vol. the limitation. % for glutamine O 2 and.6 Fig. feed Vol. rate 6 aeration shows were rates the performed and glutamine thus too to long feed investigate delay rate times along the were with significance relativized the corresponding % for of COresponse 2. Hence, reaction it should times. be of Clear explicitly the OUR responses stressed and that CPR. in the after the first fermentation. CO 2 measurement was always performed at the lowest respiration The expected rates reactions are expected to higher when and changing lower feed feed pulses rates under were obtained. tight glutamine A delay limitation. time under Fig. Despite of a low aeration rate (3.9 l/h) used in fermentation shows experiments, conditions the glutamine a compared rather low feed resolution to rate the along MS-signal of.1 with Vol. % the was corresponding the not process identified. dynamics reaction As without this experiment of any the problems. OUR described and CPR. measurement range. The signals, nevertheless, mirrored 6 these and the factory-made calibration in a wide concentration rate (-25 Vol.% for O 2 In first experiments, a constant offset between MS and The the fastest expected dynamic reactions changes to higher possible and during lower feed cultivation pulses the were rather obtained. high TA 95 delay time time constants under, -1 vol % for CO 2 ) the BlueSens derived OUR data was obtained. A first analysis these from chapter BlueSens conditions 3.1 sensors performed compared can be seen surprisingly to as the not good, MS-signal relevant any respec- was more. revealed not identified. the influence As of this fixing experiment the O 2 in value described which has the fastest dynamic changes possible during cultivation the rather high T 95 time constants 8 8 8 8 Mass spectrometer Mass spectrometer from 7chapter BlueSens 3.1 can be seen as not relevant 7 any more. 7 BlueSens 7 6 6 6 6 8 5 58 5 8 5 8 7 4 Mass spectrometer Mass spectrometer BlueSens 47 4 7 BlueSens 4 7 6 3 36 3 6 3 6 5 2 25 2 5 2 5 4 1 14 1 4 1 4 3 4 6 8 1 12 14 3 3 4 6 8 1 12 14 3 2 Process Time [h] 2 2 Process Time [h] 2 Figure 16: Comparison 6: Comparison of OUR and CPR of reactions OUR to glutamine and CPR feed rate reactions pulses 1 to glutamine 1 feed rate pulses 1 OUR [mg/l/h] OUR [mg/l/h] Glutamine Glutamine Feed Rate [g/h] Feed Rate [g/h] CPR [mg/l/h] CPR [mg/l/h] 4 6 8 1 12 14 4 6 8 1 12 14 6 Process Time [h] Process Time [h] BlueSens.com BlueSens Report No. 1 27 Figure 6: Comparison of OUR and CPR reactions to glutamine feed rate pulses 2 Glutamine Feed Rate [g/h] Glutamine Feed Rate [g/h]
to be adjusted prior to inoculation for calculating BlueSens-OUR. As the O 2 volume fraction arbitrary moved away from the fixed O 2 in value short after inoculation a constant positive or negative offset occurs resulting a time shift of the OUR profile. This effect of the O 2 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 O 2 in value excellent conformity to the MS-OUR was obtained. CPR data from CO 2 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: >> 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. 28 BlueSens Report No. 1 BlueSens.com
Information Sensors in PA housing with GL45 screwed connection on shake flask Flow adapter POM with GL45 and plug connections for hoses Aluminum housing with flow adapter stainless steel Aluminum housing with flow adapter stainless steel Tri-Clamp Tube with screwed connection 1 ¼ BlueSens.com BlueSens Report No. 1 29
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 Information coolers,pumps and valves are not needed to make the measurements. Sensor CO2 CH4 CO EtOH Measuring range 1 Vol. % 1 Vol. % 3 Vol. %.2 25 Vol. % 25 Vol. % 1 Vol. % 5 Vol. % 1 Measuring Principle Accuracy Long-term stability 2 Lifetime sensor element Infrared, dual wavelength < ±.2 % FS* ± 3% reading < ± 2% reading / year > 3 years Housing Aluminum, IP 65 Dimension (WxDxH) inch Weight lb 3.94 x 3.94 x 4.6 1.65 3.94 x 3.94 x 4.6 1.65 3.94 x 3.94 x 4.6 1.65 3.94 x 3.94 x 4.6 1.65 Housing PA6 Dimension (DxH) inch Weight lb 3.15 x 3.94.66 3.15 x 3.94.66 3.15 x 3.94.66 3.15 x 3.94.66 Disconnectable Measuring cap Connecting tolerance Material in contact with gas possible possible possible possible < ±.2 % FS* ± 3% reading Steel 1.4571 / Sapphire / Viton / PTFE Connection** G 1¼, GL 45, Tri-Clamp, hose connection 4-12mm etc. General Operating temperature max -25 55 C / -13 131 F ** Storage temperature Pressure range (absolute): Pressure dependence Operating humidity Power supply (max.) Output Maintenance once a month Maintenance yearly 6 C / 32 14 F / 75% RH non-condensing,8 1,3 bar / 11.6 18.85 psi** compensated: < ± 3 % reading (range)... 1% RF 12 or 24 VDC, 1 A RS 232, RS 485, 4 2 ma, Ethernet 1-point calibration with ambient air or nitrogen optional factory calibration with certified gases CE EN61326-1:1997 +A2:1998 1 2 accuracy < ±..5 % FS* ± 5% reading with monthly 1-point calibration *full scale ** others on request 3 BlueSens Report No. 1 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 1 Vol. %.1 25 Vol. % 1 Vol. % 1 Vol. % 3 Measuring range 1 5 Vol. % Infrared, dual wavelength ZrO 2 Galvanic cell Thermal conductivity Measuring Principle < ±.2 % FS* ± 3% reading Accuracy < ± 2% reading / year Long-term stability 2 > 3 years 15, hours Approximately 9 Vol. h operating hours > 3 years Lifetime sensor element 11,42 x 3,94 x 2,36 6.61 3.94 x 3.94 x 4.6 1.65 3.94 x 3.94 x 5.44 1.7 3.94 x 3.94 x 5.44 1.7 Housing Aluminum, IP 65 Dimension (WxDxH) inch Weight lb Not available 3.15 x 3.94.66 3.15 x 5.32.7 Not available Housing PA6 Dimension (DxH) inch Weight lb No possible possible No Disconnectable Measuring cap Connecting tolerance Steel 1.4571 / Sapphire / Viton / PTFE Steel 1.4571 / Viton / PTFE Stainless steel, Si, SiOxNy, gold,epoxy Acrylnitril-butadien-rubber, Viton Material in contact with gas Connector for 6mm hose and 8mm tube G 1¼, GL 45, Tri-Clamp, hose connection 4-12mm etc. max -25 55 C / -13 131 F ** Connection** General Operating temperature 6 C / 32 14 F / 75% RH non-condensing Storage temperature,8 1,3 bar / 11.6 18.85 psi** Pressure range (absolute): compensated: < ± 3 % reading (range) Pressure dependence... 1% RF Operating humidity 12 or 24 VDC, 1 A 24 VDC, 1 A Power supply (max.) RS 232, RS 485, 4 2 ma, Ethernet 1-point calibration with ambient air or nitrogen optional factory calibration with certified gases Output Maintenance once a month Maintenance yearly EN61326-1:1997 +A2:1998 CE 1 2 3 accuracy < ±..5 % FS* ± 5% reading with monthly 1-point calibration binary mixture *full scale ** others on request BlueSens.com BlueSens Report No. 1 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. >>Visualization of the process >>Increase of reliability and repeatability >>Dedicated process optimization without limitations (e.g. oxygen, nutrients etc.) >>Predictions for the scale up Yield Master Measure the gas yield and quality in every anaerobic process The unique structure of the CH 4 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 CH 4 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 (C 2 H 6 O), Hydrogen (H 2 ) 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 Report No. 1 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 CO 2 and O 2. 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 BlueSens Report No. 1 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. 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). 34 BlueSens Report No. 1 BlueSens.com
Questions? Please ask directly! Phone +49 2366 35 31 Or visit our homepage: www.bluesens.com
Konrad-Adenauer-Str. 9-13 D-45699 Herten (Germany) Phone +49 - ()2366-35-31 Fax +49 - ()2366-35-3 e-mail: info@bluesens.de Internet: www.bluesens.de www.bluesens.com