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Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
Published on: October 1, 2013
In silico optimization for production of biomass and biofuel feedstocks from microalgae
1Centre for Sustainable Aquatic Research, Department of Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP UK.
Abstract:
Optimization of the production rate of biomass rich in N (e.g. for protein) or C (e.g. for biofuels) is key to making algae-based technology commercially viable. Creating the appropriate conditions to achieve this is a challenge; operational permutations are extensive, while geographical variations localise effective methods of cultivation when utilising natural illumination. As an aid to identifying suitable operational envelopes, a mechanistic acclimative model of microalgae growth is used for the first time to simulate production in virtual systems over a broad latitudinal range. Optimization of production is achieved through selection of strain characteristics, system optical depth, nutrient supply, and dilution regimes for different geographic and seasonal illumination profiles. Results reveal contrasting requirements for optimising biomass vs biofuels production. Trade-offs between maximising areal and volumetric production while conserving resources, plus hydrodynamic limits on reactor design, lead to quantifiable constraints for optimal operational permutations. Simulations show how selection of strains with a high maximum growth rate, U , remains the prime factor enabling high productivity. Use of an f/2 growth medium with a culture dilution rate set at ~25 % of U delivers sufficient nutrition for optimal biomass production. Further, sensitivity to the balance between areal and volumetric productivity leads to a well-defined critical depth at ~0.1 m at which areal biofuel production peaks with use of a low concentration f/4 growth medium combined with a dilution rate ~15 % of U . Such analyses, and developments thereof, will aid in developing a decision support tool to enable more productive methods of cultivation.
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