The overall goal of the following protocol is to show that on off gas control can improve micro algal growth on flu gas via computational simulations, and to demonstrate procedures for algal cultivation and scale up. This is achieved by first using a kinetic model to simulate micro algal growth conditions under different mass transfer rates on off gas control modes and carbon dioxide concentration profiles shown here are the model equations. Next, the procedures to grow algae and scale up lgal cultivation are demonstrated.
Subsequently, the algae are grown under flu gas in a small photo bioreactor. The flu gas is generated by natural combustion and its flow rates are controlled by a mass flow control system before being introduced into the lgal system. Results from the model simulations show that on off gas control can reduce substrate inhibition and promote micro algal growth with flu.
Gas on off gas control was also shown to reduce the usage of flu gas and improve the energy efficiency. For the bio process, We are demonstrating the model guided algal cultivation with flu gas algal processes can be useful in the bioenergy and bioremediation fields, such as reduction of CO2 emissions from industrial flu, gas, biofuel, or biochemical production and removal of toxic metal ions. Generally bioprocess engineers new to model simulations will struggle because they have to test many conditions by time consuming experiments.
For example, many factors may influence algal growth and it is difficult to examine all conditions. With the computer simulation, we can understand and analyze microalgae cultures under CO2 light and mixing conditions so that we can design optimal flu gas treatment. Demonstrating the procedure will be Leon Hu Amelia Chen, Yyou, ARU var, DNE, and Larry Page from the Tang Laboratory.
To begin this procedure, prepare the culture medium. Following the recipe in the accompanying manuscript, adjust medium pH to seven to eight. Sterilize the culture medium using the autoclave.
Next inoculate chlorella spirulina from a single colony on a fresh agar plate into a shaking flask containing 50 milliliters medium with a sterile inoculating loop culture algae at 150 RPM and 30 degrees Celsius for six days. Monitor cell density by a spectrophotometer when cells are in middle. Log growth phase with OD 730 greater than one.
Transfer 50 milliliters of the algal culture into a two liter glass flask containing approximately one liter of sterilized culture. Medium incubate for five days pumping flu gas from natural gas combustion into the culture during the incubation. After five days, transfer the one liter algal culture into a 20 liter glass carboy containing 15 liters of non sterilized culture.
Medium culture algae under the same conditions as indicated earlier for six days. Six days later, placed 15 liters of fresh algal culture and 85 liters of non sterilized medium into a flat plate photo bioreactor. In the university's advanced coal and energy research facility, the photo bioreactor is equipped with a heating and cooling system.
Light emitting diodes, a computer controller, a gas mixture and analyzers for cell optical density, pH dissolved oxygen temperature and dissolved carbon dioxide. Pump the flue gas air mixture into the bioreactor, harvest the biomass at OD 730 greater than 20. After centrifuging the cultures, collect the algal biomass and pour it into a tray for further drying.
After the harvest, disassemble and thoroughly dry clean the photo bioreactor using 70%ethanol. Start this demonstration by inoculating algal cultures at an initial OD 730 of about 0.3 in glass bottles containing 200 milliliters of medium per bottle, burn natural gas and pump the flue gas through a funnel, a condenser tube, and a 0.5 liter wash bottle containing a water limestone slurry. The flue gas flow into the algal culture is controlled by the mass flow controllers.
Flue gas pulses include two modes, flu, gas on and flu gas off the latter mode pumps air instead. Previous studies have indicated that continuous flu gas exposure at adversely affects chlorella growth while decreasing carbon dioxide exposure. Time alleviates this inhibition.
A kinetic model was developed to test three methods for avoiding growth inhibition by gas. One, keeping a low flow rate into the culture to reduce the mass transfer condition. Two on off pulses of flu gas into the culture.
Three, controlling the inflow carbon dioxide compositions at the optimal level. The assumptions, equations, and parameters involved in this model will not be presented here, but are discussed in the accompanying protocol text. To begin, construct a Simulink file for the model simulation.
Choose file new model on the MATLAB interface to create a Simulink model and open library browser. Choose subsystem block in the library browser to create the subsystems for equation one and two. Drag one subsystem block to the Simulink model file.
Change its name to equation one and then repeat the same steps for equation two. Create appropriate blocks and parameters in each subsystem. Double click the equation one block.
Choose appropriate blocks from the library browser and connect them with arrows that denote the calculation sequence. Double click the blocks to set up the parameters and repeat these steps for the other subsystem. Link the two subsystems to represent model equations one and two.
Connect the output of one subsystem to the input of the other subsystem by arrow if necessary. For example, the dissolved carbon dioxide concentration is the output in the equation two subsystem and also the input of the equation one subsystem. Use pulse generator block as the input for equation two to simulate the on off carbon dioxide pulses.
Use constant block as the surface light input value. Double click the blocks to change the parameters such as the period, time, and amplitude. Choose M block in the library browser.
Connect all the outputs to mux and then connect it to two workspace block that stores the simulated results. Define the simulation Stop time on the top toolbar. Click the arrow button to start the simulation and the results will be stored in the MATLAB workspace.
Lastly, apply the MATLAB program in supporting material two to perform a dynamic optimization approach to profile optimal carbon dioxide concentrations. In this study to better understand the relationship between flu, gas inflow and algal growth, an empirical model was developed to simulate biomass growth in the presence of flu gas. The parameters used in this model are shown in this table.
Flu gas is assumed to contain 15%carbon dioxide. Firstly, the influence of the mass transfer rate of carbon dioxide on algal growth was tested. The resulting graph of final biomass concentration at day 12 as a function of mass transfer rate under continuous flu gas treatment indicates that an optimal mass transfer rate of 0.17 to 0.18 per hour is able to reduce the flue gas inhibition of algal growth.
If the mass transfer rate is lower or higher than the optimal value, the algal growth will be reduced. Shown here is equation four of the kinetic model where PG over V is the power consumption of the aerated system in the bioreactor UGS is the superficial velocity of the gas flow through the bioreactor and alpha, beta and gamma are constants related to mixing conditions. This equation suggests the decrease of aeration and gas flow through the culture can reduce the mass transfer coefficient.
This table shows biomass growth with 15%flu gas at day 12 under different superficial gas flow velocities or flow rates. Generally, a low flow rate reduces flu gas usage and mass transfer rate That prevents carbon dioxide inhibition of lgal growth. However, further reducing the flow rate through the bioreactor will cause the mass transfer coefficient to become too small to provide enough carbon dioxide for lgal growth.
This is shown by the blue line in this graph, which compares biomass growth with different mass transfer rates. Under continuous flu gas treatment, the blue line represents a KLA of 0.017 per hour. The yellow line represents a near optimal KLA of 0.17 per hour, and the black line represents a KLA of 17 per hour.
Next, an on off flu gas pulse mode was introduced to overcome growth inhibition if the flu gas mass transfer rate were high in the photo bioreactor, for example, 17 per hour to optimize the flu gas pulse mode, different on off frequencies were tested. The simulation shows that high frequency flu gas pulses are able to promote algal growth. This table that was shown earlier also indicates that the on off control mode uses less flu gas than continuous feeding of flu into the bioreactor.
Thirdly, carbon dioxide concentration profiles for maximal algal growth were calculated using model parameters listed here. The dynamic optimization approach shows that the optimal carbon dioxide concentrations in the gas phase should be continuously increased during lgal growth. Model simulation also shows that both the on off carbon dioxide pulses and the control of optimal carbon dioxide input are equally good methods to promote the lgal growth with flu gas.
This graph shows a comparison of biomass growth under an optimal carbon dioxide profile represented by the yellow line. The on off frequency of 10 seconds gas on five minutes gas off represented by the red line on off control at a frequency of 10 seconds gas on seven minutes gas off represented by the green line on off control at a frequency of one minute gas on 29 minutes gas off represented by the black line and the continuous treatment with flu gas containing 15%carbon dioxide represented by the blue line. This final figure from a previous study shows an experimental comparison of biomass growth using different approaches.
A is 10 seconds gas on seven minutes gas off B is 30 minutes, gas on 30 minutes gas off C is five hours gas on seven hours gas off and D is cultivation in shaking flasks. Compared to a continuous flu gas treatment or a shaking flask culture, higher biomass production is achieved using the on off flu gas pulses. When attempting flu gas-based algo bioprocess, it's important to use computer modeling to design optimal growth operations.
Following this procedure, experimental tests can be performed to validate the model predictions. After watching this video, you should have a good understanding of how to build up a mass transfer and bio reaction model to predict biomass growth with CO2 and to optimize growth conditions. Keep in mind the model simulation is only for a simplified homogenous system.
The model can be improved by adding more sophisticated fluid mechanics in the future.
