Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Univ. of Wisconsin, Madison, WI (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Arizona State Univ., Mesa, AZ (United States)
- Exponent, Inc., Irvine, CA (United States)
Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Moreover, algae-based biofuels are attractive for their large oil yield potential despite decreased land use and natural resource (e.g., water and nutrients) 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. Here, computational approaches allow for inexpensive predictions of algae growth kinetics for various bioreactor sizes and geometries without the need for multiple, expensive measurement systems. Parametric studies of algal species include serial experiments that use off-line monitoring of growth and lipid levels. Such approaches are time consuming and usually incomplete, and studies on the effect of the interaction between various parameters on algal growth are currently lacking. However, these are the necessary precursors for computational models, which currently lack the data necessary to accurately simulate and predict algae growth. In this work, we conduct a lab-scale parametric study of the marine alga Nannochloropsis salina and apply the findings to our physics-based computational algae growth model. We then compare results from the model with experiments conducted in a greenhouse tank and an outdoor, open-channel raceway pond. Results show that the computational model effectively predicts algae growth in systems across varying scale and identifies the causes for reductions in algal productivities. Applying the model facilitates optimization of pond designs and improvements in strain selection.
- Research Organization:
- Arizona State Univ., Mesa, AZ (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office; USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- EE0005996; AC04-94AL85000
- OSTI ID:
- 1785538
- Journal Information:
- Journal of Applied Phycology, Vol. 26, Issue 6; ISSN 0921-8971
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Pilot‐scale open‐channel raceways and flat‐panel photobioreactors maintain well‐mixed conditions under a wide range of mixing energy inputs
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journal | January 2020 |
Algal-based biofuel generation through flue gas and wastewater utilization: a sustainable prospective approach
|
journal | December 2019 |
Lipid Production from Nannochloropsis
|
journal | March 2016 |
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