understanding bioprocesses Report No. 3 Introduction Understanding Bioprocesses. BlueSens Report No. 3, January 2014 © 2014 by BlueSens gas sensors GmbH, Herten, Germany, www.BlueSens.com Page layout, artwork and cover design: Marcus Riepe, ebene1 Kommunikation GmbH, Düsseldorf, www.ebene1-kommunikation.de We are pleased to present the third BlueSens Report. Nearly two years after Report No.2, we can introduce to you the new report with interesting application reports about our gas sensors for bioprocesses. We always receive very good feedback for the BlueSens Report from our customers and prospects, and have now brought together many meaningful contributions for the third time. Many universities have used the BlueSens analysis technology for their research and present their results in the individual project reports. Like its predecessor report, in the third edition the contributions also come from our global science competition, the BlueCompetition. A special thank you goes to the independent jury which assessed the individual contributions. In 2013, we were also able to again win Prof. Dr. Gesine Cornelissen from the HAW Hamburg, Prof. Dr. Lars Blank, RWTH Aachen, and Reps. Prof. Dr. Frank Eiden, W-HS Gelsenkirchen, for the evaluation team. We also thank all participants in the BlueCompetition 2013, who again demonstrated pioneering and novel applications for the BlueSens measurement solutions with their contributions. We would especially like to draw attention to the innovative winning entry from Clemson University. The group comprised of Matthew Pepper, Li Wang, Ajay Padmakumar, Assoc. Prof. Timothy C. Burg, Assoc. Prof. Sarah W. Harcum and Assoc. Prof. Richard E. Groff from the Institute of Electrical and Computer Engineering, as well as the Institute of Bioengineering, developed a realtime bioprocess control for aerobic metabolic processes. The runners-up from West Chester University were also BlueSens.com Dr. Holger Mueller persuasive with their contribution and took a close second. The scientists employed photobioreactors to produce biogas sustainably using algae. In conclusion it can be stated that the BlueCompetition was a huge success in 2013 and that we gained great many new friends in our work with the science groups. Since Report No.2, BlueSens has achieved a great deal. In 2012, our company moved to larger premises, and our products and measurement solutions are also constantly evolving. With BlueVis we introduced bioprocessing software which automatically carries out many important calculations for the user, such as OUR, CER, and RQ calculations. The software shows the direction in which BlueSens is to develop in the coming years: we will not only continue to offer our highly successful and reliable sensors for gas analysis but also provide complete solutions for measurement tasks in fermentations, biofuels production, and the life sciences, in order to help you to fully understand your bioprocess. BlueSens Report No. 3 3 Contents understanding bioprocesses 3 BlueSens. Introduction - The BlueSens Report No. 3 Application Reports 6 A Real-time Adaptive Oxygen Transfer Rate Estimator for Metabolism Tracking in Escherichia coli cultures By Matthew Pepper, Li Wang, Ajay Padmakumar, Timothy C. Burg, Sarah W. Harcum, Richard E. Groff 12 Assessing biodegradation of oil with marine bacteria by monitoring O and CO concentrations 2 2 online in a closed loop Fr A.G. Valladares Juárez, N. M. Enwena, M. Schedler, R. Müller Institute of Technical Biocatalysis, Hamburg University of Technology, Germany 17 Bio-hydrogen production by fermentative bacteria using crude glycerol as substrate in experimental testsystems Hydrogen and Gas Sensors Laboratory Institute of Solid State Physics, University of Latvia 20 Application of BlueSens® H sensor in a gas separation study 2 P. Bakonyi, N. Nemestóthy, K. Bélafi-Bakó Research Institute on Bioengineering, Membrane Technology and Energetics University of Pannonia, Egyetem street 10, Veszprém, Hungary 24 Enhanced waste to fuel conversion with a bioelectrochemically controlled autotrophic bioreactor John M. Pisciotta, Joe Mossman, Zehra Zaybak and William Schultz. Department of Biology, West Chester University. Information 28 BlueVis: Start QbD with BlueVis. BlueInOne-Series: The combined gas analyzers for CO2 and O2: The BlueInOne series 29 BCP-Series: In-situ gas analysis for p­ rocess controlled fermentations 30 BlueSens’ sensors overview 32 Analyzer systems BCpreFerm and YieldMaster BlueSens.com BlueSens Report No. 3 5 Application Report A Real-time Adaptive Oxygen Transfer Rate Estimator for Metabolism Tracking in Escherichia coli cultures Application Report , (3) By Matthew Pepper, Li Wang, Ajay Padmakumar, Timothy C. Burg, Sarah W. Harcum, Richard E. Groff, Department of Electrical and Computer Engineering/Department of Bioengineering, Clemson University. where Mf is the mass flow rate. The concentration b2 is sampled by the BlueInOne sensor, and the sensor output, b3, is modeled as a first order linear system with time constant 2, Estimator Mathematics The adaptive estimator is based on a structure from [6]. The mathematics for the estimator will be briefly summarized. The estimator is based on the sensor and transport dynamics, Eqns (3) to (5), rewritten as a state space model in observable canonical form, . (4) Introduction Oxygen and carbon dioxide off-gas measurement enables the application of advanced estimation and control methods for aerobic bioprocesses. BlueSens, GmbH (Herten, Germany) offers an exhaust gas sensor capable of measuring mole ratios of both oxygen and carbon dioxide with accuracy comparable to mass spectrometry, the benchmark standard, but at a fraction of the cost [1]. The reduced cost makes it feasible to dedicate a sensor to each bioreactor to enable more sophisticated real-time estimation and control of bioprocesses. Direct computation of the oxygen transfer rate (OTR) from any type of off-gas measurement is problematic for use in control, since transport dynamics and sensor dynamics cause the computed OTR to be an attenuated and delayed version of the true OTR signal. The primary objective of this report is to describe a state estimator that combines exhaust gas, stir speed, and dissolved oxygen measurements to predict the true OTR in real time without attenuation or delay. The predicted OTR can be used for a variety of controls and estimation purposes, such as determining when the culture is in oxidative or overflow metabolism. Background: OTR Dynamics In a stirred-tank bioreactor, the input gas passes through two stages before being measured by the exhaust gas sensor. Figure 1 shows a bioreactor and attached offgas sensor. The input gas contains a known concentration of oxygen, b0. The gas enters from the bottom of the vessel and a rotating impeller breaks up the bubbles, facilitating the absorption of oxygen into the culture. The difference in oxygen concentration between the entering gas, b0, and the exiting gas, b1, represents the amount of 6 BlueSens Report No. 3 (6) Clearly, computing OTR directly from the concentration b3 would be significantly different from the true value of OTR computed from concentration b1. Figure 1 – Oxygen concentration in a stirred-tank bioreactor, the input gas concentration is b0, the concentration exiting the liquid, b1, the concentration of the head-space b2, and b3 the concentration reported by the sensor. oxygen transferred into the liquid V1. Assuming the mass inflow is equal to the outflow, this difference can be used to calculate the OTR in the liquid, (1) where Mf is the mass flow, 04 is the density of oxygen at 25ºC, and V1 is the volume of the liquid. OTR can also be modeled using the stir speed and dissolved oxygen concentration, (2) where kLa is the oxygen transfer coefficient, C* is the maximum oxygen carrying capacity of the liquid, C is the current dissolved oxygen concentration, and N is stir speed. The dependence of kLa on stir speed N is modeled as linear about stir speed N0. The gas leaving V1 mixes with the gas in the headspace volume V2, resulting in the oxygen concentration b1. This process is a standard mixing model, BlueSens.com Estimator Design A state estimator predicts the unmeasurable state variables of a system by using the measureable outputs and inputs in combination with the known dynamics of the system. When an adaptive state observer is used, the system dynamics are known except for some unknown or time-varying parameters. In the bioreactor system presented in the previous section, the oxygen concentration dynamics are represented by the state variables b2 and b3, which follow the dynamics given by Eqns (3) and (4). The input to the system is concentration b1, which cannot be measured directly, but can be computed from stir speed using Eqns (1) and (2), The adaptive estimator simultaneously estimates the state variables b2 and b3 and the parameters a0 and a1. The estimator is driven by known variables b3, C, N, V1, and V2. The adaptive estimator is given by . (7) . (5) All parameters in the model may be reliably characterized in advance, with the exception of the parameters a0 and a1 which relate stir speed to kLa. These parameters are notoriously hard to find and tend to vary slowly over the length of the culture [2,4]. The estimator described in this report uses an adaptive law based on measurements from the BlueSens sensor to identify the possibly-timevarying parameters a0 and a1 in real time. With good estimates of these parameters, OTR can be accurately predicted from stir speed and dissolved oxygen concentration using Eqn (2). BlueSens.com where v0 and v1 are auxiliary variables. The adaptive law for the two unknown parameters are dewhere 0 and 1 signed as are observer gains. Other variables are designed as Note that the argument s denotes the differential operator d/dt . BlueSens Report No. 3 7 Application Report Table 1 contains all the parameters for the oxygen dynamics model. kLa is known to slowly change over the course of a fermentation and is influenced by multiple factors such as anti-foam addition and liquid viscosity changes; therefore, the values for the linear model seen in Eqn. (2) are time-varying and can only be defined approximately. Parameter Name Value Mass Flow rate 3 L/min Culture volume 1.67 L Headspace volume 5.03 L BlueSens time constant 55 s Oxygen density 1.331 g/L Initial value 0.0035 1/s Initial value 0.00022 1/(s RPM) Input Oxygen Volume 20.97% Maximum Oxygen carrying capacity 6.71 mg/L Initial Stir speed 200 RPM Experiments Bioreactor System The bioreactor system is a Sartorius Biostat B 5-L glass vessel and digital control unit (DCU). The DCU connects to probes for pH (Hamilton Company, Reno, NV), temperature (Sartorius AG), and dissolved oxygen (Hamilton). The data is sampled every 15 seconds using the OPC protocol in a Simulink model running on Matlab 2012a (Mathworks Inc. Natick, MA). The model also samples the relative humidity, temperature, pressure, and oxygen and carbon dioxide volumetric percentages coming from the BlueSens BlueInOne exhaust gas analyzer every 10 seconds. The mass flow of the sparged gas is measured with a mass flow controller (Omega Engineering Inc, Stamford, CT) and sampled every 5 seconds. Lastly, two balances (Ohaus Corp, Parsippany, NJ) keep track of the amount of glucose and base dispensed and report weight every 5 seconds. Table 1: Adaptive Estimator Parameters Application Report Sensor Characterization In order to implement the adaptive OTR estimator on the system, the behavior of the BlueInOne sensor was analyzed in several characterization experiments. The first experiment was a study of the delay and response time of the sensor, 4, Previous publications have numbers for response time and measurement delay, but for a different model and at a much smaller flow rate (0.040 vs. 2 L/min (lpm)). The input gas, bo, was connected directly to the input of the BlueInOne sensor, bypassing the stirred-tank vessel; this allowed for measurement of only the sensor dynamics. In this experiment, the composition and flow of the input gas was varied. The input gas was switched from nitrogen to air in 5 minute increments. The gas would cycle twice, and then the flow rate would increase by 1 lpm, from 2 to 8 lpm. The delay (15 seconds) represents the time it took the sensor to respond once the gas composition had changed. The response time (55 seconds) is the time constant for the sensor, i.e., the time for the sensor to reach 63% of its final value. The data showed that the response time did not change with increasing gas flow, indicating the measuring chamber in the BlueInOne was being flushed well above maximum requirements. The second experiment explored the accuracy and stability of the sensor measurements to changes in pressure, ranging from 1.02 to 1.67 bar. This experiment determined the usability of the sensor in experiments requiring pressurizing of the headspace. The Biostat-B vessel has a maximum pressure limit of 1.63 bar. The manufacturer’s certified, accurate operational pressure range for the BlueInOne sensor is between 0.8 and 1.3 bar, with a maximum of 2 bar. The experiment began by performing a one-point calibration on the BlueInOne; the input gas of air had a constant assumed oxygen volume of 20.97%. As in the previous experiment, the input gas was connected directly to the input port of the BlueInOne sensor. The mass flow rate was varied between 1 and 9 lpm in steps of 1 lpm. The steps in mass flow caused the internal pressure of the sensor to rise from 1 to 1.67 bar (Table 2). Mass Flow (lpm) Pressure (bar) Pressure Step (bar) O2 Volume (%) O2 Spike (%) O2 Spike Duration (s) 1 1.02 2 1.06 0.045 20.97 1.35 35 3 1.12 0.055 20.97 1.85 45 20.97 4 1.18 0.064 20.96 2.41 75 5 1.27 0.081 20.97 3.17 55 6 1.35 0.085 20.98 3.53 85 7 1.45 0.095 21.01 3.78 55 8 1.56 0.105 21.04 3.57 75 9 1.67 0.110 21.10 3.49 85 Table 2: Measurement Stability Tests Figure 3 – Measurement Stability Results, the pressure compensation algorithm in the BlueInOne quickly corrected any error caused by the step changes in pressure. The final corrected value changed by less than 1% over the tested pressure range. After each step of the flow rate and pressure, the reading would spike due to the increased amount of oxygen in the chamber. The compensation software acted quickly to correct the reported measurement, and on average only 64 (±19) seconds of data was affected. The reported oxygen volume drifted from 20.97% to 21.1% over this range of pressure, representing <1% error. As expected, the base reading began to drift above 1.3 bar. The pressure compensation software produced accurate measurements even outside its certified range, indicating the sensor should yield accurate stable readings under conditions of constant or slowly varying pressure. Figure 2 – The Biostat-B bioreactor system, shown with 5 L vessel and motor, DCU, BlueSens BlueInOne sensor, and Balances. 8 BlueSens Report No. 3 BlueSens.com BlueSens.com BlueSens Report No. 3 9 Application Report Bioreactor Conditions Two experiments were performed to test the accuracy of the OTR estimator. In the first experiment, the OTR estimator was implemented on a bioreactor simulator. The delays on input and output data characteristics mirror that of the Sartorius Biostat-B, the BlueInOne sensor, and the two balances. The simulated behavior of the Escherichia coli (E. coli) is based on the work of 1999 Xu [8]. In the second experiment, E. coli MG1655 pTV1GFP [5] was cultured for 12 hours in minimal media. The bioreactor was inoculated at an optical density (measured by spectrophotometer) of 0.5 OD. In both experiments, the growth rate was kept at 0.25 h-1. The volume of the culture was 1.67 L. The mass flow of the input gas was 3 liters per minutes. Validation of the OTR Estimator A feed rate disturbance in the form of a pulse was used to test the capability of the OTR estimator to track E. coli metabolism. The pulse increased the feed rate to four times the current feed rate. This pulse was designed to increase the glucose concentration such that the E. coli enter overflow metabolism. While overflow metabolism is not desirable for E. coli, the feed rate pulse is short and any negative byproduct is quickly consumed. Tracking the E. coli metabolism transition from oxidative to overflow will help determine a feed rate to keep the culture on the verge of overflow, maximizing oxidative metabolism. In Figure 4, the experimental results (A, B) show the OTR and OUR rise sharply as the E. coli metabolism increased in response to the glucose pulse. This behavior duplicates the behavior seen in simulation, Figure 4 D and E, indicating that experimental results are reasonable. Notice the response of the OTR formed from the BlueInOne measurements, OTRsens. The filtering effects are seen in the attenuation of the maximum height of OTRsens versus the OTRest and the delay between both their respective peaks. The response of the OTRest is almost immediate while the peak in OTRsens does not occur until after the 5 minute pulse is over. The plateau effect seen in OTRest indicates that the culture entered overflow metabolism. In overflow metabolism, the oxidative metabolism becomes saturated with glucose and any excess glucose absorbed by the cells is anaerobically converted to acetate, which can 10 BlueSens Report No. 3 Application Report values. The kLa for the actual fermentation was plotted in Figure 5. The distribution of the data points indicates the kLa varied linearly with stir speed, validating our linear model. A linear model is appropriate since the short culture length did not allow the cell density to affect the viscosity of the media. Figure 5 – Experimental Oxygen Transfer Coefficient, the estimates for the kLa calculated by the OTR estimator. The kLa and stir speed maintained an approximately linear relationship during the 12 hour fermentation. This result validates the kLa model. Figure 4 – Glucose Pulse Experiments, a pulse (C) in the feed rate was implemented to test tracking of the OTR estimator on actual (A, B) and simulated (D, E) bioreactor systems. In A, the OTR estimator was able to accurately track the OTR and provided a better indication of E. coli metabolism than the OTR using the BlueInOne measurements. The attenuation and delay effects of the system are clearly seen (A, B). inhibit growth at high concentrations (2 g/L). The profile of OTRest indicates that the oxidative metabolism could process glucose at a higher rate, thus the feed rate could be increased significantly. A much longer pulse would be required to obtain the same data from OTRsens, resulting in significantly more acetate production. The behavior of the E. coli in actual and simulated experiments was very similar, validating the tuning of the OTR estimator gains (Figure 4 A, B, D, and E). In the simulated experiments (Figure 4 D, E), the OTRest tracks the actual OTR very well. This indicates that the estimate of the kLa parameters a0 and a1 converged to their true BlueSens.com Conclusions The OTR estimator provided a more accurate OTR profile by removing the influences of the headspace and sensor dynamics on the exhaust gas oxygen volume measurement. The BlueInOne measurements provided both accurate and stable measurements, allowing the adaptive algorithm to estimate both the unknown kLa and OTR values. The OTR estimator accurately tracked the E. coli metabolism and will be an integral part in the development of maximizing controllers for oxidative metabolism and improving biomass yield. Researchers have been using exhaust gas sensors to develop advanced estimator and control algorithms for over 20 years. The implementation of these advanced estimator and control algorithms in an industrial context has been very slow, much to the detriment of the industry [7]. Ranging from neural networks to model predictive control, the BlueInOne sensor enables academic and industrial researchers to explore and implement the next level of bioprocess control techniques. BlueSens.com References [1] M . Aehle, R. Simutis, and A. Lubbert, “Comparison of viable cell concentration estimation methods for a mammalian cell cultivation process,” Cytotechnology, vol. 62, no. 5, pp. 413–422, Oct 2010. [2] M . Akesson, P. Hagander, and J. P. Axelsson, “A pulse technique for control of fed-batch fermentations,” in Proceedings of the 1997 IEEE International Conference on Control Applications, pp. 139–144. [3] V. Babaeipour, S. A. Shojaosadati, S. M. Robatjazi, R. Khalilzadeh, and N. Maghsoudi, “Over-production of human interferon by hcdc of recombinant Escherichia coli,” Process Biochemistry, vol. 42, no. 1, pp. 112–117, Jan 2007. [4] G . Bastin and D. Dochain, On-line Estimation and Adaptive Control of Bioreactors. Amsterdam, Netherlands: Elsevier Science, 1990. [5] E . Garcia-Fruitos et al., “Aggregation as bacterial inclusion bodies does not imply inactivation of enzymes and fluorescent proteins,” Miocrob. Cell Fact. vol. 4, no. 27. [6] K . S. Narendra, A. M. Annaswamy, Stable Adaptive Systems, Chp 4, Prentice Hall, Englewood Cliffs, NJ, 1989. [7] U . S. Food and D. A. (USFDA), “Guidance for industry process analytical technology (PAT) - a framework for innovative pharmaceutical development, manufacturing, and quality assurance,” Sep 2004. [8] B . Xu, M. Jahic, and S. O. Enfors, “Modeling of overflow metabolismin batch and fed-batch cultures of Escherichia coli,” Biotechnology progress, vol. 15, no. 1, pp. 81–90, Jan-Feb 1999. i The Cell-Culture and Optimization Lab is part of the Department of Bioengineering (BioE) at Clemson University under the direction of Sarah Harcum (harcum@ clemson.edu). The work presented is in collaboration the Bio-Inspired Systems Lab in the Electrical and Computer Engineering (ECE) Department at Clemson University under the direction of Richard E. Groff (regroff@clemson. edu). The goal of this project is to develop advanced estimation and control strategies for the fermentation and culture of bacteria and mammalian cells. The research is supported by National Institute of Health (NIH) Centers of Biomedical Research Excellence (COBRE) under grant # P20GM103444. Project Team Matthew E. Pepper (right) Li Wang (center) Ajay Padmakumar (left) Dr. Timothy C. Burg Dr. Sarah W. Harcum Dr. Richard E. Groff PhD Student, ECE MS Student, ECE MS Student, ECE Associate Professor, ECE Associate Professor, BioE Associate Professor, ECE BlueSens Report No. 3 11 Application Report Assessing biodegradation of oil with marine bacteria by monitoring O2 and CO2 concentrations online in a closed loop Because of the hydrophobic nature of the oil, at least­ two immiscible phases appear when mixed with seawater and bacteria, making it impossible to sample the system representatively. Moreover the low molecular weight components of the oil are volatized during sampling and that affects the experiment. Currently, we have been quantifying the degradation of oil indirectly by offline analysis of bacterial concentrations or 12 BlueSens Report No. 3 surement of oxygen or carbon dioxide changes is not possible. Therefore, a closed loop reactor was designed in order to monitor cumulative oxygen consumption and carbon dioxide production. by sacrificing one reactor for gas chromatography analysis per time point, which is labour intensive. In order to quantify the extent of oil biodegradation in real time, it is necessary to measure one or more of these parameters: bacterial, oxygen, carbon dioxide or oil concentration online. A continuous measurement of oxygen and carbon dioxide can be correlated to the disappearance of the oil and to the growth of the bacteria. Setup description One of the low-pressure reactors was adapted with airtight connections and was connected to the BlueInOne Cell analyzer via flexible tubing (Figure 1). To transport the gas in closed-loop modus through the analyzer, a peristaltic pump recirculated the air at 3 rpm. The reactor was stirred with a magnetic stirrer. In the same way an airtight 3-port-Schott flask was connected to the analyser. Two ports were used for the gas recirculation; the third port was used for taking samples for determining the concentration of bacteria (Figure 2). A general scheme of the setup is shown in Figure 3. Research objective The objective of this research was to monitor the rate of oil biodegradation by newly isolated marine bacteria using the oxygen and carbon dioxide BlueInOne analyzer (BlueSens gas sensor GmbH, Herten, Germany). Since the biodegradation is a rather slow process a direct mea- Materials and methods The oil-degrading bacterial strains used in this study were kindly provided by Prof. Joe Lepo and Prof. Wade Jeffrey (University of West Florida). They were isolated from sea samples taken in expeditions to the Gulf of Mexico before (reference strains) and after (test strains) Fr A.G. Valladares Juárez, N. M. Enwena, M. Schedler, R. Müller Institute of Technical Biocatalysis, Hamburg University of Technology, Germany Introduction In April 2010, the Deep-Water-Horizon (DWH) well explosion at the Gulf of Mexico caused the worst oil spill in the deep sea to date. About 5 million oil barrels flowed during 86 d into the sea1. The effects on the marine habitat and the fate of the oil are not well understood. Bacteria play a major role in the degradation of petroleum in marine spills. The high pressure, high salinity and low temperatures in that environment make it difficult to study the oil-degradation mechanisms in situ. It is therefore important to study the mechanisms of the biodegradation of oil in the laboratory. For that purpose, we have built high-pressure reactors (400 bar) and low-pressure reactors (1 bar) where it is possible to compare the biodegradation rates of mineral oil at room condition (1 bar, 20°C) vs. those in the deep sea around the DWH well (150 bar, 4°C). Application Report Figure 1: Closed-loop setup used for monitoring of the CO2 and O2 concentration during the incubation of marine bacteria with oil BlueSens.com Figure 3: Diagram of the reactor setup with the BlueInOne analyzer and pump connected in a closed recirculation loop in the gas phase. A liquid sampling port as used in the setup of Figure 2 is depicted the DWH explosion. The strains were used as inocula for the experiments. The fermentations were carried out in batch mode. In order to overcome oxygen limitation, the reactor was partially filled up to 1/4th of its volume with minimal medium and the rest with air. Sweet Louisiana crude oil or some of its main compounds: naphthalene, xylene, toluene and hexadecane were used as the only carbon source in 0.03% to 0.1% (v/v) concentrations. Finally 10% of bacterial inoculum was added. The O2 and CO2 values were recorded with the BlueVis software. Samples were collected at the start and end points or in case of the setup with the sampling port, throughout the experiment in order to determine the number of bacteria. The BlueInOne analyzer was calibrated with air, when the analyzer requested it and the calibration values were automatically saved. Data storage was reliable and Figure 2: Closed-loop setup used for monitoring of the CO2 and O2 and the bacterial concentration during the user-friendly. incubation of marine bacteria with oil BlueSens.com BlueSens Report No. 3 13 Application Report 14 Application Report Figure 4: CO2 ( ), O2 ( ) and bacterial ( ) concentration of the i­ncubation of strain DWHO6A in minimal medium with 0.1% (v/v) oil Figure 6: CO2 ( ), O2 ( ) and bacterial ( ) concentration of the ­incubation of strain DF8 in minimal medium with 1 mM hexadecane Figure 8: CO2 ( ), O2 ( ) and bacterial ( ) concentration of the i­ncubation of strain DWHO6B in minimal medium with 1 mM toluene Figure 5: CO2 ( ), O2 ( ) and bacterial ( ) concentration of the i­ncubation of strain GM2 in minimal medium with 1 mM hexadecane Figure 7: CO2 ( ), O2 ( ) and bacterial ( ) concentration of the ­incubation of strain DS4P5 in minimal medium with 0.1% (v/v) Louisiana oil Figure 9: CO2 ( ), O2 ( ) concentration of the incubation of strain SY1 in minimal medium with 1.8 mM naphthalene Results Growth of oil-degrading bacteria in oil and oil-compounds The strain DWHO6A could degrade Louisiana oil, hexadecane, xylene and toluene on agar. In liquid medium the strain DWHO6A was able to biodegrade Louisiana oil in mineral medium as seen from the bacterial cell number of Figure 4. The system was leaking through the silicon hoses, leading to diffusing of air into the reactor. This can be observed by the increase in the oxygen concentration to its initial value of 21% after 35 h. For the next experiments the setup was air-tight, the hoses were changed to PVC material and with this, the leak was sealed. The strain GM2 is milky coloured in appearance when grown on agar plates. GM2 showed good growth in minimal medium with hexadecane (Figure 5), oil and moder- ate growth with naphthalene. GM2 reached the highest bacterial cell numbers during this investigation. The strain DF8 grew well on selective mineral medium agar supplemented with Louisiana oil and hexadecane. The results of the incubation of strain DF8 in liquid medium containing 1 mM hexadecane are shown in Figure 6. The strain DS4P5 could not degrade any of the oil components and showed residual growth on agar plates. Using Louisiana oil as carbon source this strain did not grow and the oxygen and carbon dioxide values remained constant (Figure 7). The BlueInOne analyzer helped us to corroborate the information from the bacterial density. The strain DWHO6B is orange when grown on naphthalene agar plates and can degrade Louisiana oil, naphthalene, toluene and xylene. This strain was the only one that could degrade naphthalene in a medium supple- mented with 3% NaCl, which is the concentration of salt in the open ocean. This strain can degrade toluene in liquid mineral medium as shown in Figure 8 and it can also grow well at 145 bar. Both pressure regimes gave similar bacterial cell values, but the O2 and CO2 cannot be analysed at the high pressure. It would be interesting to know if the rates of degradation of toluene are similar. The fermentation shown in Figure 8 was not recorded completely due to a communication error between the analyzer and the computer, but this strain could grow to 1x108 with toluene as only carbon source. The degradation capabilities of this interesting strain should be further studied. The strain SY1 (Figure 9) was able to degrade naphthalene without any lag phase, but consumes less oxygen than strains GM2, DF8 and DWHO6b do with hexadecane and toluene. BlueSens Report No. 3 BlueSens.com BlueSens.com BlueSens Report No. 3 15 Application Report Conclusion Spilled oil can be degraded by marine bacteria. Oxygen is a key substrate for aerobic microbial growth and biodegradation. Carbon dioxide and biomass are the major products of the biodegradation and can be used to estimate the amount of oil that has been degraded. The degradation capabilities of some deep-sea and surface-isolated bacteria could be tested and some promising strains were found. Sampling in this multiphase system was impossible and this was circumvented by the measurements with the BlueInOne analyzer. This accelerated the screening of the new strains. The use of the BlueInOne analyzer provided valuable, online and real-time information of the biodegradation of oil. Moreover we proved that the BlueInOne analyzer can be used to measure batch fermentations with rather low metabolic rates by recirculation of the exhaust flow into the fermenter. This new application is attractive for laboratory research because the analyzer can be connected to any type of reactor or fermenter via a couple of hoses. Its application is high versatile, exact and a cost effective solution for monitoring oxygen and carbon dioxide. Outlook Further experiments, changing various parameters like temperature and type of strains can be conducted using the BlueSens system efficiently. This BlueInOne analyzer would also be highly useful for conducting long-term biodegradation studies. A further useful application of the BlueInOne analyzer would be to take samples from the gas in the pressurized reactors, decompress it, and supply it to the analyzer. These data would be extremely useful for comparing the metabolism of the bacteria between atmospheric and deep-sea pressures. References 1. Marcia K. McNutt, Rich Camilli, Timothy J. Crone, George D. Guthrie, Paul A. Hsieh, B. Ryerson, Omer Savas and Frank Shaffer. Review of flow rate estimates of the Deepwater Horizon oil spill. PNAS 2012 109 (50) 20260-20267 Application Report Bio-hydrogen production by fermentative bacteria using crude glycerol as substrate in experimental testsystems Hydrogen and Gas Sensors Laboratory Institute of Solid State Physics, University of Latvia Introduction Our scientist group in the Institute of Solid State Physics and Microbiology department of Faculty of Biology is working on bio-hydrogen and biogas production, harvesting, storage and usage technology research. Our goal is to explore possibilities for bio-hydrogen and biogas production using alternative local resources – industrial and agricultural wastes as well as byproducts of food industry. Industrial and agricultural organic waste used as feedstock for bacteria is a perspective way for alternative energy production and it noticeably decreases the raw material cost. During the conversion of organic wastes, in anaerobic environment, hydrogen or methane gas is produced as by-products. One of the substrates that can be effectively used for microbial hydrogen and methane production is glycerol, which is a by-product from the process of biodiesel production. Because of large quantities available of crude glycerol and the highly reduced nature of carbon in glycerol per se, microbial conversion of it seems to be economically Experimental set-up i The main focus of the group environmental biotechnology led by Prof. Müller of the Institute of Technical biocatalysis lies in the elucidation of novel pathways in the biological degradation of environmental pollutants. For this purpose new bacteria with the ability to degrade problem-causing substances are isolated from environmental samples and the intermediates in the degradation pathways are determined. For interesting new reaction steps the corresponding enzymes are purified, cloned, sequenced and characterized. Biodegradation is the key to understand the environmental fate of chemicals released into the environment either intentionally (e.g. pesticides) or by accidents (e.g. oil spills). For many chemicals the biological degradation pathways are not known yet. For others the degradation is only known under certain conditions. Therefore, research is conducted to close these gaps in our knowledge, and to find safe solutions for environmental problems. Figure 1 16 BlueSens Report No. 3 BlueSens.com BlueSens.com BlueSens Report No. 3 17 Application Report and environmentally viable possibility, especially because, over the last several years, the demand and production of biodiesel has remarkably increased [1]. Methods ans materials Growth media, cultivation and experimental set up Different anaerobic and facultatively anaerobic bacteria were used for hydrogen production measurements from Microbial Strain Collection of Latvia (MSCL) - Clostridium sporogenes MSCL 764, Enterobacter asburiae MSCL 839, Enterobacter cloacae MSCL 778, Enterobacter aerogenes 758 MSCL. Experiments were continued with the best hydrogen producer using crude glycerol as substrate - Enterobacter aerogenes 758 MSCL. Bacterial cultures were inoculated in 200 ml flasks containing AB medium (2,5 g/L yeast extract, 1g/L tryptone, 12,5g/L glycerol), adapted from Tolvanen et al. (2011) [2]. Strains were cultivated overnight aerobically in shaken flask at 37°C for 12 hours at 150 rpm using a multishaker PSU-20 (BioSan, Latvia). Optical density (OD) calibration curve was used to find out number of cells in 1mL of culture [3]. The overnight culture in AB liquid medium was put in a measurement flask sterilized for measurements. The measurement flask was kept in a termostat (Precisterm 2-110, 2L), in order to maintain temperature around 37±2 ˚C. Analytical glycerol (AG) (97%) and crude glycerol (CG) (40% wt/wt, determined with HPLC analysis) from biodisel fuel production was used as substrate, final concentration of glycerol used was 135 mM. Glycerol was autoclaved for 60 min at 121 oC. Argon gas (99.99 % purity) bubbling through the media was used to sustain anaerobic environment. BlueSens gas sensors were used for the exhaust gas analyses the bacterial test-system (H2, CO2 and CH4 sensors). Sensors were calibrated in AB medium using pure hydrogen, carbon dioxide and methane and air and argon gas for zero measurements. The evolved gas was also subsequently injected in the mass-spectrometer RGAPro-100 (HyEnergy, Setaram, France) for hydrogen gas measurement. All cultivation experiments were carried out in three independent repeats. H2 production from crude glycerol H2 concentration, mmol/l 4.50 4.00 3.50 3.00 E. aerogenes I 2.50 Cl. sporogenes I 2.00 1.50 E. cloacea I 1.00 E. asburiae I 0.50 0.00 0 20 40 60 80 100 120 Time, h Application Report Substrate Bacteria max, CG AG Enterobacter aerogenes CG Clostridium sporogenes AG CG AG CG AG Enterobacter cloacea Enterobacter asburiae BlueSens Report No. 3 BlueSens.com mmol/L/h H2 production rate average, mmol/l/h 1.700 1.063 0.831 0.736 0.019 0.014 1.084 0.546 0.938 0.260 1.223 0.620 0.009 0.006 0.013 0.011 References 1 – Adhikari, S., Fernando, S.D., Haryanto, A. 2009. Hydrogen production from glycerol: An update. Energy Conversion and Management, 50, 2600–2604. 2 - Tolvanen, K.S., Mangayil, R.K. Karp, M.T., Santala, V.P. 2011. Simple Enrichment System for Hydrogen Producers. - Appl Environ Microbiol, 77(12): 4246–4248 3 – Widdel, F. 2007. Theory and measurement of Bacterial growth, Grundpraktikum Mikrobiologie, 4. Sem. (B.Sc.). Bremen: Universität Bremen. 4 - Sarma, S.J., Brar, S.K., Sydeny, E.B., Bihan, Y., Buelna, G., Soccol, C.R. 2012. Microbial hydrogen production by bioconversion of crude glycerol: A review. – International Journal of hydrogen energy, 37: 6473-6490 Table 1 Results Representative results from gas production experiments of each type are shown in Table 1 and Figure2. There were no CH4 gas production in the samples and CO2 gas was measured only for determination of fermentation process and results are not displayed in the report. When comparing analytical glycerol and crude glycerol as hydrogen fermentation substrates in liquid phase with different anaerobic and facultatively anaerobic bacteria, substantial differences were observed. The highest rates were gained using crude glucose as substrate (H2 production rate max - 1.700 mmol/L/h) with Enterobacter aerogenes. Increased rate using crude glycerol (concentration 40%, comparably analytic glycerol concentration was 97%) must be regarded to different impurities in crude glycerol as biodisel by-product that increase rude glycerol’s energy value- 19 MJ/kg (analytical glycerol), 25,3 MJ/kg (crude glycerol) [4]. Acknowledgements We acknowledge BlueSens Company for providing the gas sensing equipment. Figure 2 18 H2 production rate BlueSens.com i by Dr. phys. Janis Kleperis, MSc. Ilze Dimanta, BSc. Arturs Gruduls, MSc. Laimonis Jekabsons, Hydrogen and Gas Sensors Laboratory, Institute of Solid State Physics, University of Latvia Hydrogen and Gas Sensors Laboratory: Research and development of new materials and devices for hydrogen production via electrolysis, using direct current and short voltage pulses; Development of technology for bio-hydrogen production in anaerobic fermentation process; elaboration and research of hydrogen separation membranes; Development of new materials for hydrogen storage in metal hydrides and composites; Development and research of proton exchange membranes and electrodes for fuel cells, rechargeable MH/Ni and Lithium batteries and their applications in electric – hydrogen vehicles; Research on gas sensors and sensor arrays with application for specific tasks (searching the rapid diagnostic methods to detect lung diseases for patients and wheat diseases). BlueSens Report No. 3 19 Application Report Application of BlueSens® H2 sensor in a gas separation study P. Bakonyi, N. Nemestóthy, K. Bélafi-Bakó Research Institute on Bioengineering, Membrane Technology and Energetics University of Pannonia, Egyetem street 10, Veszprém, Hungary Introduction Moreover, evaluating the influences of notable process Biohydrogen is an emerging alternative energy carrier variables, namely separation temperature and recovery with high potential for sustainable development. It can factor was also aimed. be utilized in fuel cells or internal combustion engines. Materials and Methods However, purified hydrogen should be provided for these Hydrogen purification experiments using BlueSens® end-use applications thus its enrichment – through H2 sensor getting rid of gaseous side-products especially CO2 generated in the fermentative bioreaction – is very In the current experiments a membrane module important. containing polyimide hollow-fibers (UBE Industries Ltd., A wide range of absorptive, adsorptive, cryogenic NM-B01A) was investigated. This polymer is considered techniques and alternatives relying on membranes may as a highly promising material for membrane gas be employed to concentrate biologically produced separation including H2 purification, as well. The measurements aiming the concentration of hydrogen hydrogen. In recent decades as a result of advancements were carried out in a laboratory-scale device made in the field of membrane technology it is now schemiatically presented in in Fig. 1. accounted among the efficient, energy-saving and ecological-friendly opportunities. Membranes for gas separation are mature, commercially available nowadays and mostly fabricated from high-performance polymers such as polyimides. Membranes for H2 purification, however, are featured with challenges. On one hand, membranes possess sufficiently high permeability and selectivity should be developed. On the other hand, revealing the membranes real potential under practical separation conditions e.g. with mixed gases is still of interest but such studies are not frequently conducted despite their importance. Therefore, in this work we wanted to test a lab-scale polyimide memFig. 1 – Experimental membrane testing set-up brane module for fermentative hy- 1; gas cylinder containing binary H2/CO2 mixture, 2; pressure controller, 3; feed valve, 4; feed pressure drogen recovery applying binary H2/ indicator, 5; module thermostat, 6; membrane module, 7; differential pressure indicator, 8; permeate pressure indicator, 9,10; permeate valves, 11,15; digital gasflow meter, 12,16; BlueSens® H2 sensor, CO2 gaseous mixtures. 13; permeate fraction, 14; retentate valve, 17; retentate fraction 20 BlueSens Report No. 3 BlueSens.com Application Report Results and discussion Statistical analysis (ANOVA) was performed to evaluate the experimental design (Table 1). Based on ANOVA results, feed H2 concentration, temperature and recovery factor were all identified as significant process parameters (p<0.05). During the experiments, the hydrogen concentrations of the feed, permeate and retentate streams were continuously followed by using on-line BlueSens® H2 analyzers (BlueSens Gas Sensor GmbH). The equipment is able to measure hydrogen in the range of 0-100 vol% with a quick response time of 20 seconds. The additional characteristics can be accessed by visiting the manufacturer’s homepage (www.bluesens.com). The feed gas pressures during the experiments were adjusted as low as approximately 3 bar since considering practicality, a high compression degree of biologically produced gases comprising hydrogen would consume a noticeable portion of its energy content and hence making the process economically unattractive. The permeate side of the module was kept under ambient conditions. The influences of three process variables (temperature, feed hydrogen content and retentate/feed flow ratio referred to as recovery factor) were determined by experimental design approach using binary H2/CO2 mixtures. To estimate the mixed gas selectivities (ai/j) according to Eq. 1, only the data recorded at steady-state – when no change in the retentate and permeate flows and in their compositions occured – were considered: ai/j = (xiP/xjP)/(yiF/yjF) The response plots displaying the impacts of the independent variables mentioned can be seen in Fig. 2-Fig. 4. Impact of gas composition The composition of the feed gas is apparently a factor that determines the performance of the membrane module. However, the separation efficiencies of the membranes are more often than not sought only in single gas experiments. This ideal value can be notably different to those obtained under practical (mixed gas) separation circumstances. The theoretical and real selectivities would expectedly be similar only when the constituents of a gas mixture do not strongly interact with the membrane material. In the case of CO2 and H2 separation, however, the carbon-dioxide is easily dissolved in membrane materials such as polyimide. This phenomenon can remarkably affect the permeation of the other gases e.g. hydrogen and thus the efficiency of the overall separation process. From the tentative results, we found that higher hydrogen concentration could be attained in the permeate fraction by feeding H2/CO2 mixtures with higher initial hydrogen concentration. Based on the experiments it was (1) where x iP and x jP are the volumetric fractions of compound „i” and „j” in the permeate, meanwhile yiF and yjF are the their volumetric fractions in the feed mixture. ANOVA – Dependent Variable: Mixed gas selectivity; R2: 0.98 Factor SS df MS F p Separation Temperature 0.007200 1 0.007200 11.2941 0.02828 Feed H2 content 0.068450 1 0.068450 107.3725 0.00049 Recovery factor 0.051200 1 0.051200 80.3137 0.00086 Error 0.002550 4 0.000637 Total SS 0.129400 7 SS: Sum of squares; df: Degree of freedom; MS: Main square; F: F-value, p: Significance value Table 1 – ANOVA table showing the significance of the variables studied BlueSens.com BlueSens Report No. 3 21 Application Report concluded that the more amount of carbon-dioxide in the feed mixture represented a limiting factor to achieve better selectivities. This might be explained by the fact that carbon dioxide likely influences the migration of hydrogen across the membrane capillaries. As CO2 concentration has decreased, improved separation efficiency could be observed. Fig. 2 – Influence of separation temperature and feed composition on mixed gas selectivity Influence of recovery factor Membranes, in general, can enrich the slowly permeating compounds in the concentrate stream, however, only when low recoveries are applied. In our investigation, almost pure (>99 vol%) CO2 could be obtained in the retentate at certain, extremely low recoveries when the vast majority of the gas introduced to the membrane module was taken at the permeate side (Table 2). CO2 Permeate (vol%) CO2 Retentae (vol%) Recovery value Mixed Gas Selectivity 67.17 99.61 0.05 1.12 65.42 83.8 0.17 1.21 63.82 76.13 0.38 1.3 62.8 71.74 0.69 1.36 Table 2 – Results of separating H2/CO2 gas mixture (Separation temperature: 37 oC; initial H2 content: 30 vol%) Fig. 3 – Influence of feed composition and recovery factor on mixed gas selectivity It was also observed that the separation of the faster permeating H2 is determined by the membrane’s selectivity, since H2 concentration in the permeate was only slightly increased when recovery factor was increased. Hence, if hydrogen is to be concentrated in the permeate fraction, membranes reflecting higher selectivities need to be developed or multi-step, cascade applications should be employed. Furthermore, the change in the recovery value also changes the amount of permeate which together with the total feed flow and the surface velocities should not be neglected to avoid concentration polarization. Basically, maintaining a high recovery value is favorable so as to get a more sufficient membrane performance. This might be the reason for the increment of selectivity with increased recoveries. Application Report Separation temperature effect It is quite obvious from the research outcomes that the mixed gas selectivity has increased at higher temperatures. It is understandable if one keeps in mind that increasing temperature has contradictory impacts on the solubility and diffusivity of the permeating gases. In general, the former decreases, whilst the latter increases with raising temperatures. Furthermore, hydrogen and carbon dioxide have distinct characteristics from diffusion and solubility points of views in rigid, glassy polymers such as polyimide. CO2 usually expresses a relatively higher solubility in comparison to hydrogen which is in turn more rapid and possesses faster diffusion coefficient. These properties of the gases are in correlation with molecular size, their affinity to polymers and the developing interactions with the membrane material during permeation. Polyimide, as mentioned, is a glassy polymer which basically achieves selectivity mainly on diffusivity-difference basis. Since CO2 is a larger molecule than hydrogen, the increment in its diffusivity is less pronounced than that of hydrogen at elevated temperatures. This was assumed to be the reason for the increasing separation efficiency of H2/ CO2 with increasing temperature. Conclusions In our research work we established that the polyimide membrane module had a potential to enrich hydrogen from gaseous mixtures. The results demonstrated that process variables such as gas composition, temperature and the recovery factor had remarkable influence on hydrogen separation and could affect the achievable separation efficiency. Thus, these parameters are to be considered when designing a suitable enrichment process. It has been proven that higher H2/CO2 mixed gas selectivity could be accomplished by increasing the feed hydrogen concentration as well as the temperature and the recovery value within the studied design boundaries. Fig. 4 – Influence of separation temperature and recovery factor on mixed gas selectivity 22 BlueSens Report No. 3 BlueSens.com BlueSens.com i DipI. Eng Péter Bakonyi, University of Pannonia Research Institute on Bioengineering , Membrane Technology and Energetics Ph.D. Nándor Nemestóthy, University of Pannonia Research Institute on Bioengineering , Membrane Technology and Energetics Prof. KataIin Bélafi-Bakó, University of Pannonia Research Institute on Bioengineering , Membrane Technology and Energetics The main tasks of the Research Institute on Bioengineering , Membrane Technology and Energetics are: >> to teach and train students to get BSc In bioengineering >> to introduce them to a wide range of biochemical and biotechnological processes >> to introduce them to the world of membranes and their applications Application possibilities of various membrane processes, improvements of bioprocesses and manufacture of renewable , “green” energy sources are studied at the institute involving the PhD students of the Doctoral School of Chemical Engineering and Material Sciences (University of Pannonia). BlueSens Report No. 3 23 Application Report Enhanced waste to fuel conversion with a bioelectrochemically controlled autotrophic bioreactor John M. Pisciotta, Joe Mossman, Zehra Zaybak and William Schultz. Department of Biology, West Chester University. 1. Introduction Photobioreactors (PBRs) are increasingly being used to grow algae for production of biofuel and other products. Light and CO2 are two major limiting resources that can restrict algal growth in PBRs. Atmospheric CO2 is only 0.04% and to overcome this limitation researchers have started to couple biogas released from the anaerobic digestion (AD) of organic waste with PBRs as biogas consists of mostly CO2 and CH4. The CO2 component of biogas is normally considered a nuisance and its abiotic removal can be expensive. Unfortunately, the conventional mesophilic AD is a slow process. Recently, bioelectrochemical systems (BES) have Fig 1.: Simplified schematic overview of BES biogas fed PBR fitted with in-line BlueSens O2, CO2 and been described that accelerate the an- CH4 gas sensors. aerobic breakdown of organic waste erated biogas via potentially enhanced growth of photointo biogas (Booth, 2009, Cheng et al., 2009). BESs autotrophs. This gas sensor integrated arrangement accomplish this via anaerobic cultures of red pigmentshould provide real time characterization of the overall ed, exoeletrogenic Geobacter species grown on the anmetabolic activity inside a hybrid BES-PBR linked sysode with poorly understood electroactive methanogens tem. Two PBR systems were built and tested head-tothat grow in association with biocathodes (Cheng et al, head to study Chlorella vulgaris growth in BG-11 media 2009, Pisciotta et al, 2012, Zaybak et al., 2013). on standard AD derived biogas versus BES derived 2. Aim of Investigation In this report, BlueSens CO2, CH4 and O2 Gas Sensors were integrated into a new type of BES-fed PBR built to test if bioelectrochemically produced biogas can be used to grow algae. If successful, this could provide a method for more rapidly treating organic waste streams while biologically processing bioelectrochemically-gen- 24 BlueSens Report No. 3 Application Report 0.5 cm holes were drilled into both ends of the glass covers using diamond tipped drill bits. Gas incurrent and out-current silicone tubing lines were passed through these holes and sealed to prevent gas leaks. The input gas line was connected to an airstone affixed to the bottom of each tank. This provided for even gas dispersion and bubble mediated mixing of the media. Gas bubbling up into the headspace then exited PBR via the out-current gas line connected to an air pump sealed inside a hollow plastic reservoir. The pump set up provides for constant gas flow through the closed loop PBR systems. Prior to reentering the PBRs gas was pumped through in-line O2, CO2 and CH4 sensors via gas line tubing. For each PBR, a biogas delivery line tied into the circulating gas just upstream of the pump and was fitted with a one way valve. The one way valves prevent oxygen from entering the BES or AD chambers and inhibiting anaerobes therein. Log grown C. vulgaris was inoculated into 5 L of illuminated (12hr light/12hr dark) BG11 per PBR at an initial density of OD 0.025 (Fig 2). biogas (Fig 1). 3. Materials and Methods 3.1 Photobioreactors Two rectangular 18.9 L glass aquaria were converted into biogas-fed, closed-loop photobioreactors. Each aquarium was sealed with a custom cut sheet of glass epoxyed into place over the open top of each tank. Next, BlueSens.com Fig 2.: Side-view photograph detailing Chlorella containing PBRs fed by either BES (left) or AD biogas (right). Components of the system including pumps and sensor indicated by numbered arrows on side bar legend. BlueSens.com BlueSens Report No. 3 25 Application Report 3.2. Anaerobic Digesters and Bioelectrochemical Cells. Triplicate gas-linked anaerobic digesters were constructed from 100 ml serum vials containing 75 mls of synthetic wastewater; ie 50 mM Phosphate Buffered Saline (PBS) amended to contain 1 g/l sodium acetate plus Wolfe’s vitamins and minerals. All vials were degassed with nitrogen and crimp sealed prior to injection of 0.5 mls of starter culture from a 5 week old, acetate fed, biogas producing digester. Headspace of the three anaerobic digesters was connected by needles passing through the rubbers stoppers and connected by tubing. Passive pressurization of biogas formation forced the biogas through a one way valve just prior to biogas entry into the recirculated PBR gas stream. Triplicate BESs were similarly produced by inserting two 10 cm electrodes into each BES vial and a potential of 0.7 V was applied using a Biologic MPG-2 that also monitored current over time; an indicator of bioelectrochemical activity and acetate consumption. Cell density was periodically measured at OD 600nm via spectrophotometer. 4. Results and Conclusions In-line BlueSens CH4 Sensors revealed a considerably more rapid accumulation of methane in the BES-PBR headspace compared to the conventional control AD reactor (Fig 3A). Potentiostat measured current peaked within four days for all three BESs indicating rapid catabolism of acetate (Fig 3B). The optical cell density of both C. vulgaris cultures increased from the initial inoculum baseline level (0.025) during the course of the incubation indicating algal growth in BG-11 media in both PBRs (Fig 4A). The control AD linked PBR increased more rapidly but the BES-PBR system reached a higher final cell density. Oxygen sensor also indicated higher algal photosynthesis in the late stage BES-PBR (Fig 4B). For both reactors CO2 remained near baseline levels of detection; likely a function of the relatively high headspace volume and photosynthetic demand of the cultures. Increasing the volume of the BESs and ADs relative to the PBR culture volume could further improve productivity. These results suggest integration of 26 BlueSens Report No. 3 BlueSens Sensors with photobioreactors can enable optimized biofuel production from renewable resources like sunlight and bioelectochemically treated organic waste. Application Report References Booth B. 2009. Electromethanogenesis: the direct bioconversion of current to methane. Environ Sci Technol. 43(13):4619. Cheng S, Xing D, Call DF, Logan BE. 2009. Direct biological conversion of electrical current into methane by electromethanogenesis. Environ Sci Technol. 43(10):3953-8. Pisciotta JM, Zaybak Z, Call DF, Nam JY, Logan BE. 2012. Enrichment of microbial electrolysis cell (MEC) biocathodes from sediment microbial fuel cell (sMFC) bioanodes. Appl Environ Microbiol. 2012 Aug;78(15):5212-9. Zaybak Z, Pisciotta JM, Tokash JC, Logan BE. 2013. Enhanced start-up of anaerobic facultatively autotrophic biocathodes in bioelectrochemical systems. J Biotechnol. In press. doi:pii: S0168-1656(13)00425-2. Acknowledgments We would like to thank BlueSens for use of the sensors and the West Chester University College of Arts and Sciences for providing funding to build the photobioreactors. i Fig 3.: Methane accumulation and CO2 consumption in PBRs (A). Current generation in triplicate BESs attached to the BES-PBR (B). The Pisciotta Laboratory at West Chester University develops ­microbial systems for conversion of wastes into biofuels and value-added products. Current research focus is directed at electrical enhancement of CO2 fixation by microorganism using biocathodes and novel systems like the MEC-linked photobio­ reactor described. Laboratory instruments and methods used include potentiostats, bioelectrochemical systems, optical and light microscopy, HPLC, GC, mass spectrometry, spectrophoto­ metry, molecular biology and experimental culture techniques. Other projects include research and development of microfluidic diagnostics to combat infectious diseases like malaria and low cost water sanitation tools for developing nations. Project team pictured from left: Students William Schultz, Zehra Zaybak, Joseph Mossman and Assistant Professor Dr. John M. Pisciotta. Dana Charitonchick carried out stock culture maintenance and logistical support for this project. Fig 4.: C. vulgaris optical cell density in PBRs (A) and sustained O2 detected in PBRs (B). BlueSens.com BlueSens.com BlueSens Report No. 3 27 Information Information BCP- Series In-situ gas Blue analysis for p ­ rocess controlled fermentations Start QbD with BlueVis. The BlueVis software is the most convenient solution to operate the BlueInOne gas analyzers. It logs and visualizes the data and is a maintenance-tool for the monthly calibration as well. Now you can easily connect up to 12 analyzers to one interface of your PC. This Bioprocessing software calculates the oxygen uptake rate (OUR), carbon dioxide emission rate (CER) and respiratory quotient (RQ) automatically by the collected data of the BlueInOne analyzer and the given process parameters. No additional corrections of the data or manual calculations will be needed. The BlueVis software is easy to use and to install. The BlueVis software comes with an integrated OPC server and can easily communicate with process lead systems. BlueVis will make QdB as easy and as convenient as possible. Data 24V DC ID=1 Gas in Data Bus RS 485 Off gas 24V DC ID=2 Gas in Process 1 USB-adaptor Off gas Data Off gas Data Data Terminating resistor 24V DC ID=3 etc. Gas in Process 2 Process 3 etc. The combined gas analyzers for CO2 and O2: The BlueInOne series The state-of-the-art BlueInOne gas analyzers combine the parallel measurement of CO2 and O2 in one space-saving analyzer. The BlueInOne Ferm is the modern tool for monitoring fermentations and the BlueInOne Cell is the perfect device for the measurement of cell growth processes. The BlueInOne analyzers will help you to monitor and understand your bioprocess in real-time. Advantages >> Parallel measurement of O2 and CO2 >> Compact stainless steel housing (IP65) >> QbD and PAT >> PAT conform in-situ-measurement >> Auto compensated humidity and pressure >> No gas cooler, pumps or valves needed >> Connectable to any hose/tube or pipe >> RS485 Modbus, RS232, USB, 2x4-20mA Output, Modbus OPC server available BlueSens Report No. 3 Data Your advantages: >> Run up to 12 parallel processes >> Gain all the process data >> Visualization of the measurements >> Automatic CER, OUR und RQ calculation >> Easy to use >> Overview of all current measuring results >> Log all data >> OPC server included >> Real RQ calculation 28 nection New con The BCP sensors are reliable devices for the laboratory and the industrial use. You can choose between a robust version with an aluminum housing (IP 65) and a compact version with a PA-plastics housing. Whatever you decide for – BCP series optimizes bioprocessing easily, efficiently and precisely. So far we offer sensors of the BCP series for ethanol, CH4, O2, CO2, H2 and CO. The sensors are already used in large numbers for the Biotechnologies, Life Science, the production of Biofuels like Biogas and Bioethanol, the pharmaceutical industry and numerous other areas of application. You can order various optional accessories like a ball valve (e.g. for the industrial scale Biofuels production). There a lot of optional features like an external pressure sensor and a display and buttons for the on-site operation. The field of application is large – whether in the bioreactor or in the fermenter, in the laboratory or on the industrial large scale. The sensors are reasonably priced, long-term stable and already often used in parallel bioreactors. BCP series works independently of the gas flow quantity with an established dual wavelength infrared measurement technology. The maintenance is more than easy and the operation costs are very low. Due to its low weight of 150g, the compact sensor in a PA housing can be screwed sterile directly at the shake flask. BlueSens sensors are always measuring on the spot, where the process takes place. Thanks to its universal connections, BCP sensors can be easily integrated in existing gas lines. Wherever your bioprocess takes place – in a shake flask, the fermenter or of course also for the use of disposals – the BCP series is always the right choice. Sampling, gas cooling or additional complicated gas lines are no longer needed. Thanks to the standard interfaces the data can be readout in real time by any electronic process control system. Get the sensors in you process: Easy installation: > > Direct installation in the exhaust gas line > > No sampling, no danger of contamination >> No exhaust gas treatment (exhaust gas cooler) necessary > > Minimum maintenance without test gases > > Suitable for every fermenter and easy to integrate Advantages: > > Easiest process control > > Increase of reliability and repeatability > > Log all data > > Metabolic flux analysis and mass-balance calculations BlueSens.com BlueSens.com Gas tight and sterilizable installation on shake flask Optional display and button Tri-Camp-installation Biofuels-Production in the lab Biogas-application with ball valve and external pressure sensor BlueSens Report No. 3 29 Information Everything at a glance: product overview Information All the BlueSens’ gas analyzers can be easily integrated directly into the gas lines independent of the gas flow. Additional gas coolers, pumps and valves are not needed 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. to make the measurements. The sensors measure at the point where things are happening. Fast and reliable measurement data without Sensor CO2 CH4 CO EtOH Measuring range 0 … 10 Vol. % 0 … 100 Vol. % 0 … 30 Vol. % 0.3 … 25 Vol. % 0 … 25 Vol. % 6 0 … 100 Vol. % BlueInOne Ferm BlueInOne Cell O2 O2ec H2 Sensor 0 … 10 Vol. % CO2, 0.1 … 25 Vol. % O2 0 … 10 Vol. % CO2, 0 … 100 Vol. % O2 0.1 … 25 Vol. % 0 … 100 Vol. % 0 … 100 Vol. % 0 … 25 Vol. % CO2, 0.1 … 25 Vol. % O2 0 … 25 Vol. % CO2, 0 … 100 Vol. % O2 1 … 50 Vol. % Infrared, dual wavelengths (CO2) / Galvanic cell (O2) ZrO2 Galvanic cell Thermal conductivity 7 Measuring range 0 … 10 Vol. % CO2, 1 … 50 Vol. % O2 0 … 50 Vol. %1 0 … 25 Vol. % CO2, 1 … 50 Vol. % O2 Infrared, dual wavelengths Measuring principle Accuracy Long-term stability Infrared, dual wavelengths (CO2) / ZrO2 (O2) < ± 0.2 % FS* ± 3% reading < ± 0.2 % FS* ± 3% reading < ± 2% reading / year < ± 2% reading / year 2 > 3 years Lifetime sensor element Accuracy Long-term stability2 Approx. 3 years (CO2), 15,000 operating hours (O2) Approx. 3 years (CO2), approx. 15,000 operating hours 900 000 Vol. % h operating hours at 1 bar (14.5 psi) (O2) Approx. 900.000 Vol.% h operating hours at 1 bar (14.5 psi) > 3 years Lifetime sensor element Housing Aluminum, IP 65 Dimension (WxDxH) mm Dimension (WxDxH) inch Weight 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 3000 g (6.61 lb) 170 x 150 x 1205 6.69 x 5.91 x 4.725 4000 g (8.82 lb) 170 x 150 x 1205 6.69 x 5.91 x 4.725 4000 g (8.82 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) 100 x 131 x 118 3.94 x 5.16 x 4.64 900 g (1.98 lb) Housing Aluminum, IP 65 Dimension (WxDxH) mm Dimension (WxDxH) inch Weight Housing PA6 Dimension (DxH) mm Dimension (DxH) inch Weight 80 x 130 3.15 x 5.12 350 g (0.77 lb) 80 x 130 3.15 x 5.12 350 g (0.77 lb) 80 x 130 3.15 x 5.12 350 g (0.77 lb) 80 x 130 3.15 x 5.12 350 g (0.77 lb) Not available Not available 80 x 130 3.15 x 5.12 350 g (0.77 lb) 80 x 130 3.15 x 5.12 350 g (0.77 lb) 80 x 130 3.15 x 5.12 350 g (0.77 lb) Housing PA6 Dimension (DxH) mm Dimension (DxH) inch Weight Steel 1.4404 / Sapphire / Viton / PTFE Stainless steel / Viton / Sapphire / PTFE / Polymer H.L. / Nitrile Stainless steel / Viton / Sapphire / PTFE / Polymer H.L. / Nitrile G 1¼”, GL 45, Tri-Clamp, hose connection 4-12mm etc. ¼“ – 1 ¼“6, hose connection 4-12mm etc. ¼“ – 1 ¼“6, hose connection 4-12mm etc. Material in contact with gas Connection6 Steel 1.4404 / Viton / PTFE Steel 1.4404 / Viton G 1¼”, GL 45, Tri-Clamp, hose connection 4-12mm etc. General Max. -25 – 55 °C / -13 – 131 °F6 Operating temperature Pressure range (absolute): 15 - 40 °C / 59 - 104 °F 0.8 – 1.3 bar / 11.6 – 18.85 psi absolute pressure 0.8 – 1.3 bar / 11.6 – 18.85 psi absolute pressure 75% RH noncondensing 0 ... 100% RH Operating humidity 3 12 or 24 VDC, 1-2 A Power supply (max.) RS232, 4-20mA, USB, Ethernet Output EN61326-1:1997 +A2:1998 accuracy < ± .0.5 % FS* ± 5% reading BlueSens Report No. 3 2 with monthly 1-point calibration 3 not compensated 4 Storage temperature Pressure range (absolute): > 75% RH noncondensing 5...100% RH noncondensing > 75% RH noncondensing 5...100% RH noncondensing 75% RH noncondensing Storage humidity 0…100% RH 5...100% RH noncondensing 0 ... 100% RH 5...100% RH noncondensing 0 ... 100% RH Operating humidity Integrated humidity compensation Integrated humidity compensation 24 VDC, 1 A 24 VDC, 1 A 3 12 or 24 VDC, 1-2 A 24 VDC, 1-2 A RS232, RS485 Modbus, 2x 4-20mA, USB, Modbus OPC Server RS232, 4-20mA, USB, Ethernet One point calibration with ambient air once a month (other conditions on request) One point calibration with ambient air once a month (other conditions on request) 1-point calibration with ambient air or nitrogen (other conditions on request) EN61326-1:2006 / FCC 15:2009 Subpart 107/109, ICES - 001:2006 EN61326-1:2006 / FCC 15:2009 Subpart 107/109, ICES - 001:2006 Optional factory calibration with certified gases Maintenance yearly CE Operating temperature 6 RS232, RS485 Modbus, 2x 4-20mA, USB, Modbus OPC Server 4 1-point calibration with ambient air or nitrogen (other conditions on request) Maintenance once a month Max. -25 – 55 °C / -13 – 131 °F6 0 – 60 °C / 32 – 140 °F 6 Storage humidity 1 15 - 40 °C / 59 - 104 °F 0 – 60 °C / 32 – 140 °F Storage temperature *full scale Material in contact with gas Connection6 General 30 Measuring principle 4 Optional factory calibration with certified gases Ethernet with BACCom 5 BlueSens.com BlueSens.com 6 others on request Power supply (max.) Output Maintenance once a month Maintenance yearly EN61326-1:1997 +A2:1998 stainless steel housing, dimensions depends on flow adapter 3 7 CE binary mixture BlueSens Report No. 3 31 Information Notes Analyzer systems Yield Master Measure the gas yield and quality in every anaerobic process The unique structure of the CH4 devices from BlueSens facilitate measuring methane concentrations in processes that diversify in gas production. The use of sample taking is impossible there, so conventional systems fail. The CH4 sensors are easily screwed onto the fermentation container and obtain the methane content directly over the sample in the gas phase. Even at 55 °C (131 °F) in water-saturated atmospheres. The accruing volumes are precisely registered via a precision volumenometer (Milligascounter®*). The data is registered online with the corresponding software and visualized on the computer. Optionally, BlueSens can provide everything as accessories; from the stirrer to 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. BC preFerm BlueSens Report No. 3 ab tig s 32 Ga The same sensors are also integrated in the BCpreFerm system, which is designed for process optimization (scale up) from 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 calculates vital parameters such as the oxygen uptake-rate (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 your scale up le w connection e N Simple tool for process optimization h t & s t e r il i z BlueSens.com BlueSens.com BlueSens Report No. 3 33 Notes 34 BlueSens Report No. 3 BlueSens.com Notes BlueSens.com BlueSens Report No. 3 35 Questions? Please ask directly! Phone +49 2366 4995 500 Or visit our homepage: www.BlueSens.com BlueSens gas sensor GmbH Snirgelskamp 25 • D-45699 Herten (Germany) Phone +49 2366 4995-500 • Fax +49 2366 4995-599 e-mail: info@BlueSens.de BlueSens.com