Maximize Lipid output and biomass production of algae in open pond systems for biofuel synthesis
School: Las Cruces YWiC
Area of Science: Agronomy / Algology
Interim: Problem Definition : Fossil fuels, which contribute to anthropological climate change are not only harmful, but finite, and increasing crisis in the Middle East has raised still more questions regarding the wisdom of our fossil fuel dependence. In the interest of environmental protection, and national security,the development of renewable fuels is imperative. Biofuels derived from green microalgae are promising because of higher lipid yields than other oleaginous crops, and the unique ability to thrive on otherwise un-arable land.
In order to realize this venture, the lipid synthesis and biomass production within the system must be maximized, with respect to the overriding concern of cost. Algal fuels face the challenge of having to compete with traditional fossil fuels in order to achieve mainstream usage. Therefore, the commercial realization of this rosy picture is altogether dependent on the efficiency of the operation, and so optimization of algae growth and lipid production, (so as to maximize biofuel output) with respect to economic considerations is paramount. Our project intends to further investigate these important problems. By creating a simulation that takes in numerous applicable parameters (see appendix 1), we will work towards economically evaluating the system, in order to determine production optimal and commercially optimal scenarios through the analysis of simulation data..
Problem Solution : The basis for this simulation is a previously developed mathematical model of growth and neutral lipid synthesis in green microalgae (Packer, 2011). Within this model of differential equations are numerous parameters and state variables (Appendx 1). By changing our controllable parameters, we create different scenarios, called ponds. We intend to run our simulation and collect data regarding lipid synthesis and growth of green microalgae in these ponds. To accomplish this, the program makes use of numerical methods to evaluate the system of differential equations with respect to time. Data on these scenarios will be collected and analyzed. Through this analysis, we begin to investigate efficiency as it pertains to cost. For instance, what is the cost of maintaining optimal incident irradiance? Although this might be more productive than the constantly varying levels of natural light, does the additional production at least make up the cost? Through this type of scientific exploration through different runs of the simulation, coupled with corresponding economic analysis, we will be able to determine the most productive pond, and the most commercially optimal of those investigated.
Appendix 1 PARAMETERS AND STATE VARIABLES
State variables include algal biomass concentration, excluding the mass of neutral lipids, the neutral lipid concentration, the chlorophyll a content and the extracellular nitrogen concentration. All of these will be in constant flux within the system with respect to time.
Parameters include incident irradiance, light path, optical cross section of chl a, quantum efficiency, subsistence nitrogen quota, subsistence carbon quota, the maximum uptake rate of nitrogen, the half saturation coefficient, the maximum chl to N ratio, the max N-Limited growth rate and the maximum photosynthesis rate. These variables are all essential to determining characteristics of the algae growth and lipid production, our primary interests.
Progress to Date:
Following research with regards to the problem, we settled on a mathematical model and set out to implement it in Java. Presently, we have coded several numerical methods to be used to approximate the value of the series of differential equations described in the model. These include the Euler’s method, Euler’s midpoint method, and Heun’s method.
The simulation instantiates an algae “pond” object with parameters that are initially defined. The program then makes update calls to the model object, and each of those update calls updates the model to the next time slot based off of the step size, thereby allowing the approximation of the differential equation system, with respect to time in days.
Using these methods, and the set of differential equation from the mathematical model (Packer, 2011) we wrote a program that simulates algal biomass production in time. This model seems to be mostly working, with the exception of a constant that we’ve yet to define. Additionally, we created a framework that allows us to run algae pond simulations in parallel. This was accomplished by using a different thread for each simulation started and then writing a scheduler. Although the scheduler is still buggy, we are currently able to run the simulations in non-parallel.
Expected Results: Following the programming, subsequent edits, and tests of our computational model, this model could be used by those hoping to venture into the business of algae fuel production. Additionally, the data collected from this model has the potential to answer essential questions about the feasibility of algal fuel production in New Mexico, and the framework could easily be used for those in other areas by simply adjusting parameters. This simulation will serve as a basis for those looking to find their own optimums within their desired system, whether it be open pond, or closed bioreactor with only a change in analysis required on the standpoint of economic considerations.
Beal , C. M., Hebner, R. E., Webber, M. E., Ruoff, R. S., Seibert, A. F., & King, C. W. (2012). Comprehensive evaluation of algal biofuel production: Experimental and target results. Energies , 5(6), 1943-1981.
Chapra , S. (2011). Applied numerical method with matlab: For engineers and scientists . (3rd ed.). New York, NY: McGraw Hill Education.
Gao, Y., Gregor, C., Liang, Y., Tang, D., & Tweed, C. (2012). Algae biodiesel - a feasibility report.Chemistry Central Journal , 6(Suppl 1), S1. Retrieved from http://download.springer.com/static/pdf/339/art%3A10.1186%2F1752-153X-6-S1-S1.pdf?auth66=1386388101_4fcfdb90ad17ea648a9146b26d0c1ad0&ext=.pdf
Hannon, M., Gimpel, J., Tram , M., Rasala, B., & Mayfield, S. (2010). Biofuels from algae: Challenges and potential. Biofuels, 1(5), 763-784.
Packer, A. (2011). Growth and neutral lipid synthesis in green microalgae: A mathematical model. Bioresource Technology , 102(1), 111-117.
Sponsoring Teacher: Janie Chen
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