427331 Scaling-up Microalgae Production Systems: Inferring Biomass Productivity in Raceway Ponds Using Numerical Simulation

Wednesday, November 11, 2015: 10:10 AM
257B (Salt Palace Convention Center)
Andreas Nikolaou and Benoit Chachuat, Centre for Process Systems Engineering, Imperial College London, London, United Kingdom

Microalgae have long been identified as promising candidates for biofuel production. In comparison to conventional oil crops, they are independent from arable land and fresh water and can accumulate an array of useful by-products for the food, cosmetics or pharmaceutical industry. In addition, lab- and pilot-scale experiments have shown that many microalgae species can grow orders of magnitude faster than conventional oil crops. The main commercial applications of microalgae so far have been concerned with high-value products, including carotenoids and omega-3 and omega-6 polyunsaturated fatty acids [4]. In contrast, the commercial viability of using microalgae for biofuel production is still uncertain and calls for the development of large-scale outdoor raceway ponds to bring down production costs [6].

Scaling up microalgae production systems is particularly challenging due to the presence of light- and nutrient-dependent processes that are competing for growth [1]. In contrast to lab-scale experiments, full-scale production systems can exhibit a dramatic loss of productivity due to imperfect mixing or light distribution, contamination or lack of adequate monitoring and control [3].

The focus of this paper is on quantifying the effects of imperfect light distribution on growth. More specifically, we use a dynamic model of the photosynthetic activity that is capable of quantitative prediction of light-limited growth at the cell level [2,5], in combination with light-attenuation models describing the vertical light distribution in a raceway pond and computational fluid dynamics (CFD) models describing the flow conditions. A large number of Lagrangian trajectories representing the motion of elementary fluid volumes are first extracted from the CFD simulation results in ANSYS Fluent® (Figure 1). In a subsequent step, the photosynthesis model is integrated along all the trajectories, reconstructing the light-exposure history experienced by the cells in a given section based on the light attenuation model. We compare the resulting microalgae productivity in the full-scale raceway pond with that of a lab-scale, perfectly-mixed photobioreactor in order to assess the scale-up effects. Moreover, we analyze the effects of various parameters on microalgae productivity.

Figure 1: Two Lagrangian trajectories in a raceway pond.
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  1. O. Bernard, F. Mairet, and B. Chachuat. Modelling of microalgae culture systems with applications to control and optimization. Advances in Biochemical Engineering/Biotechnology, in press (doi: 10.1007/10_2014_287), 2015.
  2. A. Bernardi, A. Nikolaou, A. Meneghesso, B. Chachuat, M. Tomas, and F. Bezzo. A framework for the dynamic modelling of PI curves in microalgae. In Proc. 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, Copenhagen, Denmark, 2015.
  3. L. Brennan and P. Owende. Biofuels from microalgae--a review of technologies for production, processing, and extractions of biofuels and co-products. Renewable and Sustainable Energy Reviews, 14(2):557-577, 2010.
  4. S. Leu and S. Boussiba. Advances in the production of high-value products by microalgae. Industrial Biotechnology, 10(3):169-183, 2014.
  5. A. Nikolaou, A. Bernardi, A. Meneghesso, F. Bezzo, T. Morosinotto, and B. Chachuat. A model of chlorophyll fluorescence in microalgae integrating photoproduction, photoinhibition and photoregulation. Journal of Biotechnology, 194:91-99, 2015.
  6. R. H. Wijffels and M. J. Barbosa. An outlook on microalgal biofuels. Science, 329(5993):796-799, 2010.

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See more of this Session: Advances in Algal Biorefineries I
See more of this Group/Topical: Sustainable Engineering Forum