This paper describes the assembly process and operation of a bench-scale photosynthetic bioreactor that can be used, in conjunction with other methods, to estimate pertinent kinetic growth parameters. This system continuously monitors the pH, light, and temperature using sensors, a data acquisition and control unit, and open-source data acquisition software.
The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software.
Due to growing concerns about global climate change and finite fossil fuel resources, governments have been developing policies to reduce fossil fuel consumption and to encourage the development of new, sustainable transportation fuels. The United States Environmental Protection Agency has developed the Renewable Fuel Standard (RFS), which requires that 36 of the annual 140 billion gallons of U.S. transportation fuel mix come from renewable fuel sources by 2022. Innovative and transformational technologies will be necessary to meet these and future renewable energy standards1.
The use of microalgae-based biofuels has the potential to help meet the national RFS while reducing greenhouse gas emissions2. Microalgae-based biofuels have several advantages compared to first-generation biofuels based on terrestrial food crops, such as corn and soybeans. Unlike first-generation biofuels, algae-based biofuels consume fewer land, water, and food-related resources, since algae can be cultivated year-round and on barren land using saltwater or wastewater. Microalgae have high growth rates compared terrestrial crops and can accumulate high levels of lipids, which can be readily converted to biodiesel3. Currently, no industrial-scale algae-to-biofuel plants exist due to the high costs of the energy-intensive production processes, which consist of algal cultivation, lipid separation, and lipid refining into biodiesel. More research is needed to make these processes more efficient and sustainable.
PBRs, which are optically clear, enclosed installations for the production of phototrophic microorganisms in an artificial environment, are considered one of the most promising cultivation methods3. However, current designs still lack the volumetric productivity necessary to make the algae-to-biofuel production process more efficient and economically attractive4. Powerful mathematical models that consider light irradiance and attenuation, the transport of nutrients and CO2, and the growth of the microalgae can greatly facilitate the optimization of PBR design and operation. Bench-scale growth experiments are required to determine species-specific growth parameters for these optimization models.
Kinetic tests require the careful monitoring and control of experimental setups to prevent unintended inhibitors of growth. Given the photosynthetic nature of algae (i.e., their consumption of CO2 and absorption of light), maintaining controlled conditions is especially difficult in bench-scale PBRs. As depicted in Equation 1, the amount of dissolved CO2 in the growth medium, commonly denoted as (Equation 2), will be, at minimum, a function of: 1) the CO2 partial pressure and Henry's equilibrium constant, which dictates the amount of gas that will dissolve in solution (Equation 3); 2) the initial chemical composition of the growth medium, which impacts the speciation and activity of the carbonate ions and pH (Equations 4 and 5); and 3) the temperature, which impacts Equations 3-55.
The various phases and the chemical speciation of carbon create a challenge for measuring and maintaining a consistent dissolved carbon concentration within a PBR while holding other conditions constant (e.g., the pH increases as the algae consume CO2, and increasing the dissolved CO2 substrate can possibly lead to an acidic environment that inhibits growth)6.
An additional layer of complexity for controlling conditions during algal kinetic tests involves the light intensity within the PBR. The average light intensity inside a PBR is a function of not only the incident light intensity, but also the design (e.g., material, shape, depth, and mixing), the absorbance of algal biomass components (particularly chlorophyll), and the light-scattering properties of the algal cells. As the algae grow, the average light intensity will decrease. This change in light intensity, whether caused by an increase in total cells and biomass, an increase in chlorophyll content per cell, or both, can eventually induce a metabolic response, such as an increase in chlorophyll production per cell or the use of carbohydrate and lipid storage products for energy7. Continuous monitoring of the light intensity from within the reactor provides invaluable information. This data can help to ensure that conditions stay within a specified range and can be used to help estimate algal growth and absorbance parameters if combined with other measurements (i.e., biomass, chlorophyll concentration, reactor depth, incident light, etc.).
Understanding how algae grow under a specified set of conditions requires that the pH, dissolved CO2, light intensity, and temperature be monitored in bench-scale kinetic experiments. Many algal growth setups are not equipped to monitor conditions to the extent required for calibrating kinetic models, making the modeling process extremely challenging8. Although many companies offer bench-scale PBRs with automation and control, these bench-scale setups can be extremely expensive (~$20,000) and might not accommodate all experimental considerations of a given research question.
The first step in setting up a control-feedback system for a batch experiment is live data acquisition. This paper aims to demonstrate how to construct and set up a bench-scale PBR equipped with continuous light, pH, and temperature monitoring. This real-time monitoring setup can help to ensure that the experimental conditions stay within desired ranges, at the researcher's discretion. While this protocol does not detail specific control mechanisms, these step-by-step instructions provide a basic foundation for the data acquisition framework required before more sophisticated control feedbacks can be implemented.
1. Construct the Bench-scale PBR Body and Lid
NOTE: For illustration purposes, Dunaliella sp., a ~10 µm halotolerant microalgae lacking a cell wall, was used as the model organism for the construction of this PBR.
Figure 1: Image of the Customized Bench-scale PBR Setup with Sensors and a Mixer. This setup shows a mixer, an electrode secured to the lid through a threaded port in the lid, and a light sensor attached to a specially designed lid. This lid design also includes the attachment of a 12 V DC mini-gear motor. Please click here to view a larger version of this figure.
2. Set up and Configure Sensors with the Data Acquisition and Control Unit
NOTE: Sensors translate changes in the physical world into a measurable analog signal, often voltage. Data acquisition units serve as an interface between the digital and physical world and can be used to read these analog signals and convert them into discrete values, as instructed by a computer. The data-acquiring unit described herein has an analog input resolution of 16 bits, can read up to 14 analog signals (±10 V), and can supply the power required by some sensors (up to 5 V). These instructions provide an overview on how to set up this data acquisition and control unit to convert an analog signal into more meaningful values for light, pH, and temperature within a PBR. These instructions do not detail important concepts (i.e., quantization, precision, response time, etc.) needed to fully interpret these measured values and to quantify uncertainty.
Figure 2: Sensor-to-data Acquisition and Control Unit Connection Diagram. This diagram shows how to set up pH, light, and temperature sensors to the data acquisition and control unit used for this protocol. Signal processing components for the pH and light sensor are shown. Please click here to view a larger version of this figure.
3. Set up the Live Data Acquisition and Experimental File
NOTE: The data acquisition and control software described here communicate with the data acquisition and control unit to monitor and log sensor data at user-specified time intervals. The instructions below explain how to set up a control file in this software to monitor and record pH, temperature, and light. These instructions are specific to the software and data acquisition and control unit listed in the materials section. Further instructions can be found in product user manuals.
Channel Name | Conversion Name | Equation | Notes |
Temperature | volts_to_celsius | (55.56 x value) + 255.37 – 273.15 | Manufacturer conversion equation to convert volts to celsius. |
Light | volts_to_PPFD | value x 500 | Manufacturer conversion factor to convert volts to photosynthetic photon flux density (µmol m-2s-1), manufacturer LED-correction not applied. |
pH | volts_to_pH | (-17.05 x value) + 6.93 | Calibration-dependent conversion equation (Figure 4b) to convert pH electrode voltage readings into pH values. Only apply conversion to pH channel after calibration. |
Table 1: Channel Conversion Table for the Data Acquisition File. Examples of how to input channel and conversion information for the sensors into the data acquisition software.
4. Calibrate the pH Probe
NOTE: pH calibration should be done before every experiment, at the intended temperature of experiment, and the pH channel conversions should be updated accordingly. pH electrode readings can drift during experiments; to determine the extent of this drift, repeat the calibration process after running the experimental setup and compare the readings. pH electrodes should be properly stored in the appropriate storage solution before and after experimentation, as directed by the manufacturer.
5. Set up the PBR for the Algal Experiment
NOTE: The steps below are specific to Dunaliella and the custom-made PBR shown in Figure 1. Moreover, these setup instructions are not in accordance with sterile protocols, as this system was not designed in such a way.
Figure 4: Wiring Diagram for the Mixer. This diagram shows how to set up a mixing device for a PBR using a mini-gear motor, a power supply, and a 3D-printed impeller and shaft. Please click here to view a larger version of this figure.
Figure 5: Reactor Experimental Setup Diagram. Visualization of a PBR experimental setup within a temperature-controlled incubator. This setup includes a grow lamp and a PBR, with sensors and a mixer secured within the PBR lid. Please click here to view a larger version of this figure.
Data from this real-time monitoring system show the dynamic culturing environment for algae within a bench-scale PBR and highlight the need for monitoring and controlling the system. The logged temperature data (Figure 6) demonstrates how light illumination, incubator air temperature, and energy dissipation associated with algal growth can change the temperature within the PBR and how the real-time data can be used to adjust incubator temperature controls, as needed.
The measured light over the course of the experiment further emphasizes the dynamic nature of this growing environment. As observed in Figure 7, the light sensor reading, measured as photosynthetic photon flux density (PPFD; µE-m−2s−1), was ~100 PPFD before algae was added and dropped immediately to 85 PPFD after inoculating the reactor with the algal culture. The light continued to drop to less than 5 PPFD on day 7. This decrease in light intensity is due to increasing biomass and cell counts, and/or to increasing absorption by increased chlorophyll content, showing that algae are active through day 7, despite low light levels. Additional biological measurements are required to make further inferences.
The continuously logged pH data show that, overall, the pH was adequately controlled during this experiment with the implemented pH control algorithm (Figure 8). This data, showing both minute-by-minute readings and hour-long averages, demonstrate a few key points about culturing algae and monitoring pH in real time. First, the pH increased above the desired set point of 7.6 immediately after inoculating the PBR with algae. This change was expected, as the culture seed that was added to the PBR had a pH value higher than the set point, since the flask used to grow the inoculum was not pH-controlled. Secondly, this live data highlights how sensitive pH electrodes are to external electrical noise. This sensitivity is noted by a drastic jump in the electrode values between day 1 and day 2. These sudden changes in pH values were likely created by electrical noise from a solenoid valve from an adjacent experimental setup. This electrical disturbance prematurely triggered the pH control algorithm to inject CO2 into the PBR. Consequently, the pH dropped below the desired set point. The sensitivity of the pH electrodes can lead to extreme outliers and can potentially disrupt control systems.
Figure 3: pH Response and Calibration Example Graphs. (a) Example response graph of the pH sensor (b) Example calibration graph of the pH sensor, with an equation to use for the conversion. Regression analysis shows a 95% confidence interval. Error bars are not visible (standard error less than 0.03%). These graphs show that the pH sensors was connected properly and that its signal was very steady. Please click here to view a larger version of this figure.
Figure 6: Temperature Measurements Within the PBR During a 7 day Experiment. Dark blue points represent 1-h averages of sensor data, and light blue points represent sensor readings acquired over 1 min (acquisition timing of 1 s, average length of 60) and converted to temperature using manufacturer-supplied conversion factors. Black arrows show when the incubator temperature setting was adjusted to maintain the culture temperature around 25 °C (this desired set point is designated with a red, dotted line). Fluctuations in temperature are due to algal growth and changes in incubator temperature. Please click here to view a larger version of this figure.
Figure 7: Light Measurements Within the PBR During a 7 day Experiment. Dark blue points represent 1 h averages of sensor data, and light blue points represent sensor readings acquired over 1 min (acquisition timing of 1 s, average length of 60) and converted into PPFD using default light-sensor factory calibration values. Please click here to view a larger version of this figure.
Figure 8: pH Measurements Within the PBR During a 7 day Experiment. Dark blue points represent 1-h averages of sensor data, and light blue points represent sensor readings logged every 1 min (acquisition timing of 0.1 s, average length of 600) and converted into pH using conversion equation established via calibration. The pH was maintained between 7.6 and 7.5 using a 99% CO2 gas injection. The red, dotted lines indicate the desired pH range. Please click here to view a larger version of this figure.
This PBR system offers the ability to monitor and control bench-scale algal kinetic growth experiments, allowing for more repeatable results from experimental assays used to quantify growth. However, an understanding of the limitations and uncertainties of sensor measurements is critical to ensure that the sensor readings accurately reflect reactor conditions. This understanding includes basic knowledge of the measurement principles involved with sensors, the process and frequency of calibration, the measurement uncertainty, and what the sensor can and cannot measure. For example, the electrical response for the light sensor described here is not equally distributed across the visible spectrum range, and certain correction factors may need to be applied to the sensor output, depending upon how this sensor data will be analyzed.
Temperature levels and variations are also extremely important, as changes in temperature can drastically influence the sensor response. Understanding potential interferences that can impact the sensor readings is also critically important; this interference can be ambient electrical noise from the building or could stem from the measurement environment (e.g., sodium ions can drastically impact pH readings at pH values over 10)12. Moreover, submerging multiple probes into a solution, especially a highly ionic and conductive salt solution, is also a potential source of interference. Electrodes that measure pH (or ionic strength, dissolved oxygen, dissolved CO2, etc.) are especially sensitive to ambient electrical noise and can be easily perturbed. Signal conditioning used for protecting the electrode signal cannot guarantee that other factors will not interfere with the probe readings. As part of quality control, other laboratory equipment, such as a hand-held pH probe, a hand-held spectrometer, and a thermometer, should be used to verify the sensor readings and to ensure that the system is set up and running properly.
Another limitation that must be addressed is the possible impact of the algae and/or culturing environment on the sensors. For example, if algal debris or bubbles cover the photodiode receptor of the light sensor, the readings will be affected. Similarly, pH electrodes are extremely sensitive and require extra care to ensure accurate readings. These electrodes work by measuring a voltage difference across an internal junction due to the buildup of H+ ions; a hydrated buffer layer within the probe is required to maintain accurate measurements12. Depending upon the conditions within the reactor, this layer will wear off, and the response of the sensor may change over the course of the experiment while the probe is submerged. In preliminary tests, the pH voltage output did not drift by more than ~0.2 pH units over the course of a 20-day experiment, but further assessments should be performed to characterize this change in sensor response and to establish maximum experimental run times, especially if fine pH adjustments/quantifications are needed.
Many current bench-scale PBR systems built to analyze algal growth do not monitor and control the internal culture environment as tightly as needed to discern how different factors impact algal growth, since setting up systems in this way can be challenging. This protocol can help facilitate more controlled experiments by giving step-by-step instructions for constructing a PBR with real-time monitoring. Moreover, this live data can be used not only to better control experimental conditions, but it can potentially be utilized to estimate growth kinetics (e.g., optical density readings as reference for general growth rates).
Controlled experimental systems can help to make algal research more reproducible. Bench-scale PBR setups that are monitored and controlled can increase experimental efficiency by minimizing unintended artifacts in experimental design and can help to advance efforts to make algal biofuels a sustainable, alternative fuel source.
The authors have nothing to disclose.
The authors acknowledge the National Science Foundation Emerging Frontiers in Research and Innovation (Award #1332341) for funding this research. The authors would also like to acknowledge Dr. Andrew Grieshop, as well as the LabJack and DAQFactory online support communities for their assistance and help offered throughout this process.
Cast acrylic sheets | McMaster Carr | 8560K244 | 7/8'' thick, 12×36'', optically-clear, the size of sheets purchased will depend on reactor dimensions. |
Acrylic cement | McMaster Carr | 7517A4 | Scigrip plastic pipe cement, #4SC nonwhitening for acrylic. Not needed if gaskets and screws are used for PBR assembly. |
Acrylic cement applicator needle | McMaster Carr | 75165A136 | Acrylic cement applicator needle, 25 Gauge, 1", Stainless steel, PTFE lined. |
Plastic dispensing bottle for acrylic cement | McMaster Carr | 7544A67 | Plastic dispensing bottle, 2-oz size, packs of 5. |
Viscous acrylic cement | McMaster Carr | 7515A11 | Scigrip plastic pipe cement. Medium-bodied acrylic cement to seal in any gaps within PBR body. |
PG-13.5 thread tap | McMaster Carr | 2485A14 | Can be used to help secure pH electrode to lid (if applicable). |
PBR and lid | NCSU Precision Machine Shop | Karam Algae 3.2L Reactor Revision E | This machine shop is open to public for business. Contact shop manager. |
pH sensor | Hamilton | 238643 | EasyFerm Plus 120, autoclavable, millivolt output. |
Light sensor | Apogee Instruments | SQ-225 | Amplified 0-5 volt electric calibration quantum sensor, water-proof. |
Temperature sensor | LabJack | EI1034 | Stainless steel, water-proof temperature sensor. |
pH transmitter wire with BNC end | Sigma-Aldrich | HAM355173-1EA | This wire will vary with type of pH probe. Make sure wire is compatible with pH probe and has BNC connector end. |
Unity gain pre-amplifier | Omega Engineering | PHTX-21 | Signal processing amplifier for pH electrode needed for high-impedance pH readings. |
Coaxial adapter, BNC female-to-binding post | Amazon | SMAKN B00NGD5K80 | For connecting pH signal from pre-amplifier to microcontroller. |
Capacitor (1000 uF) | Amazon | Nichicon BCBI4950 | For low-pass filter. |
Resistor (1000 ohm) | Radio Shack | 2711321 | For low-pass filter. |
Hookup wire | RadioShack | 2781222 | For making low-pass filters, connecting sensors to microcontroller, and wiring motor. |
Heat shrink tubing | RadioShack | 2781611 | For low-pass filter assembly. |
Data acquisition and control unit | LabJack | LabJack U6 | To process electrical signal from sensors and communicate with data acquisition and control software. |
DAQFactory data acquisition software | DAQFactory | DAQFactory Express Release 5.87c Build: 2050 | Free to download, for up to 10 channels. |
Mini DC-gearmotor | McMaster Carr | 6331K31 | Motor for mixer impeller. |
Impeller and shaft | N/A | N/A | Email authors for 3D files. |
Variable DC power supply | Amazon | Tekpower HY1803D | Variable DC power supply, 0-18V @ 0-3A. |
Grow Lamp | HydroGrow | SOL-1 | This exact model is no longer available. |
Incubator | Thermo Scientific | Precision Model 818 | This particular incubator can withstand an internal heat source since this unit's cooling compressors run non-stop regardless of temperature setting. |