Microorganisms, such as microalgae, have recently been a popular research field because of their promise as a biofuel feedstock with advantages over first generation biomass sources, such as wood, corn, and sugarcane. Such advantages include algae’s ability to be grown on low value land and to be grown using low quality, brackish, or even salt water. Algae also produce much more oil per acre than traditional energy crops. Currently, however, the production cost of microalgae biofuels can be uneconomical, which impedes its wide scale implementation as a feedstock. One way to improve the economics of algae-based fuels is to maximize the amount of algae that can be grown within a given bioreactor. This would have the effect of not only reducing the number of reactors needed to produce a given amount of algae, but would also reduce the land area needed for the algae growth process.
One method of maximizing algal biomass production is to develop better bioreactors, with reactor geometry and flow conditions optimized for maximal algae production. Computational fluid dynamics (CFD) models excel at capturing the important hydrodynamics of this problem, including turbulence and nutrient and carbon dioxide distribution within the growth substrate. These models can be employed along with surrogate modeling and optimization solvers to determine the reactor conditions for maximum algae growth [1,2]. Unfortunately, these methods can be very computationally expensive. To this end, I have developed several alternative methods to respond to these challenges by reducing the computational expense associated with individual simulations, reducing the number of simulations necessary, and reducing the costs in the “downstream” algae supply chain, such as transportation. I have developed a heuristic for simulation of bioreactors which reduces the computational expense of applying CFD in testing of many different growth conditions. Additionally, I have conducted a linear stability analysis of an algae bioreactor and have determined hydrodynamic conditions which would lead to maximum algae growth. Finally, I have also developed a pair of optimization directed sampling algorithms which allow for better selection of sample points when developing a surrogate model involving CFD studies. These algorithms have been used to investigate algae growth, and all show promise in assisting with maximizing the amount of algae produced. In this poster session, I will present my work to this point as well as continuing work in this subject area.
1. Smith, Justin, Cremaschi, Selen and Crunkleton, Daniel W. (2012), "CFD Based Optimization of a Flooded Bed Bioreactor for Algae Production," Computer Aided Chemical Engineering 31, pp. 910-914
2. Smith, Justin D., Neto, Amadeu, Cremaschi, Selen and Crunkleton, Daniel W. (2013), "CFD-Based Optimization of a Flooded Bed Algae Bioreactor," Industrial & Engineering Chemistry Research 52(22), pp. 7181-7188, doi:10.1021/ie302478d
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