389968 Microbial Consortia for Algal-Based Biofuel: Modeling and Optimization

Wednesday, November 19, 2014: 1:48 PM
204 (Hilton Atlanta)
Sohrab Zendehboudi, Massachusetts Institute of Technology, Cambridge, MA, Kai Hoeffner, Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA and P.I. Barton, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Biofuels, as renewable fuels, are a promising alternative to fossil fuels in the near future in terms of source stability, availability and environmental prospective. Algal-based biofuels have received renewed interest in the recent years, since they have much greater productivity than terrestrial crops (Wigmosta et al., 2011), and do not compete with food crops for land (for example, by using seawater ponds in coastal desert areas). Yet, production of medium- and low-value products such as chemicals and fuels are currently not economically viable, since inexpensive open pond systems without protection often suffer from low biomass yield due to invasion and predation. In this case, resilience and stability are critical. An attractive solution to this problem is the design of synthetic consortia to fill ecological niches, which would otherwise be filled by invading species found at the production site. Such a design has been discussed in (Kazamia and Aldridge, 2012) at a qualitative level. This study presents a quantitative approach for synthesis of microbial consortia as a promising approach to low cost sustainable production of algal fuels and chemicals.

The developed mathematical tool is based on dynamic flux balance analysis (DFBA), which is the integration of genome-scale metabolic model and mass conservation law applied to extracellular environment (Mahadevan, 2002). A dynamic high-rate algae pond model (Buhr and Millar, 1983) is combined with synthetic community of primary producers, such as well-studied microalgae including cyanobacteria, and other microorganisms, such as non-photosynthetic bacteria and fungi. The model of the continuously operated raceway pond system is spatially distributed leading concentration gradients of the substrate and species distribution. Each species is modeled based on existing flux balance models, taken from literature, to yield predictions of growth rate and metabolic production, which are dependent on substrate concentrations and light intensity.

The interactions between the microorganisms vary by strength and directionality depending on time of the day (day-night cycles) and spatial distribution along raceway. The type of interactions is limited to indirect interaction through unidirectional or bidirectional exchange of external metabolites through the environment, since aquatic environment in high-rate open pond systems can be assumed to be well-mixed. These dynamic interactions are important for the stability of the consortia and are highly dependent on various process parameters.

A systematic parametric analysis is implemented to investigate impacts of important parameters such as types of algae, and other microorganisms in the consortia, and combination of various biological substrates on resilience culture and production of lipids and other industrially valuable metabolic products under dynamic conditions. The computational approach underlying this analysis is based on recently developed numerical methods (Höffner et al., 2013). This study showcases a process system engineering approach to design, control and optimize synthetic environments for biochemical/biological processes in energy and environment context that leads to greater efficiency in terms of technical, environmental and economical prospects.


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