BlueSens Report No.1
Transcription
BlueSens Report No.1
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, 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 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 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 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 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 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 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) 6 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!” 7 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 8 BlueSens Report No. 1 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 BlueSens.com Application Report 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, 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 Application Report 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. 10 BlueSens Report No. 1 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 300 l for interfacing with academic and industrial partners. www.bvt.bci.tu-dortmund.de BlueSens.com Application Report 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 BlueSens.com BlueSens Report No. 1 11 Application Report 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). 12 BlueSens Report No. 1 BlueSens.com Application Report 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 Application Report 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 14 BlueSens Report No. 1 BlueSens.com Application Report Figure 1: Course of off-gas measurement and gas balance values Figure 2: Course of O2-transfer rates during cultivation BlueSens.com BlueSens Report No. 1 15 Application Report 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 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-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 16 BlueSens Report No. 1 BlueSens.com Application Report 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 BlueSens.com BlueSens Report No. 1 17 Application Report 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 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 18 BlueSens Report No. 1 BlueSens.com Application Report 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 BlueSens.com BlueSens Report No. 1 19 Application Report 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 Report No. 1 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 BlueSens Report No. 1 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 BlueSens Report No. 1 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 TW 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 TW 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 pLM Accuracy [bar], O CV 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 CVout ) 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 28purpose 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 Report No. 1 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 BlueSens Report No. 1 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 Report No. 1 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 BlueSens Report No. 1 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 BlueSens Report No. 1 >> 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“ BlueSens Report No. 1 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 BlueSens Report No. 1 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 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. 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 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 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 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. 34 BlueSens Report No. 1 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: info@BlueSens.de Internet: www.BlueSens.de www.BlueSens.com