Modelling and Optimization of Nannochloropsis and Chlorella Growth for Various Locations and Seasons

Abstract:

Efficient production of algal biofuels could reduce dependence on foreign oil providing domestic renewable energy. Algae-based biofuels are attractive for their large oil yield potential despite decreased land use and natural-resource requirements compared to terrestrial energy crops. Important factors controlling algal-lipid productivity include temperature, nutrient availability, salinity, pH, and the light-to-biomass conversion rate. Computational approaches allow for inexpensive predictions of algae-growth kinetics for various bioreactor sizes and geometries without multiple, expensive measurement systems.

In this work, we parameterize our physics-based computational algae growth model for the marine Nannochloropsis oceanica and freshwater Chlorella species. We then compare modelling results with experiments conducted in identical raceway ponds at six geographical locations in the United States (Hawaii, California, Arizona, Ohio, Georgia, and Florida) and three seasons through the Algae Testbed Public Private Partnership – Unified Field Studies. Results show that the computational model effectively predicts algae growth in systems across varying environments and identifies the causes for reductions in algal productivities. The model is then used to identify improvements to the cultivation system to produce higher biomass yields.

This model could be used to study the effects of scale-up including the effects of predation, depth-decay of light (light extinction), and optimized nutrient and CO₂ delivery. As more multifactorial data are accumulated for a variety of algal strains, the model could be used to select appropriate algal species for various geographic and climatic locations and seasons. Applying the model facilitates optimization of pond designs based on location and season.