Abstract
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. 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Journal of Applied Phycology |
| DOIs | |
| State | Accepted/In press - 2014 |
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Keywords
- Algae growth model
- CO
- Computational fluid dynamics
- Greenhouse
- Light
- Limitation
- Nannochloropsis
- Open-channel raceway
- Salinity
- Temperature
ASJC Scopus subject areas
- Plant Science
- Aquatic Science
Cite this
Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments. / Gharagozloo, Patricia E.; Drewry, Jessica L.; Collins, Aaron M.; Dempster, Thomas; Choi, Christopher Y.; James, Scott C.
In: Journal of Applied Phycology, 2014, p. 1-12.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments
AU - Gharagozloo, Patricia E.
AU - Drewry, Jessica L.
AU - Collins, Aaron M.
AU - Dempster, Thomas
AU - Choi, Christopher Y.
AU - James, Scott C.
PY - 2014
Y1 - 2014
N2 - 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. 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.
AB - 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. 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.
KW - Algae growth model
KW - CO
KW - Computational fluid dynamics
KW - Greenhouse
KW - Light
KW - Limitation
KW - Nannochloropsis
KW - Open-channel raceway
KW - Salinity
KW - Temperature
UR - http://www.scopus.com/inward/record.url?scp=84893676414&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893676414&partnerID=8YFLogxK
U2 - 10.1007/s10811-014-0257-y
DO - 10.1007/s10811-014-0257-y
M3 - Article
SP - 1
EP - 12
JO - Journal of Applied Phycology
JF - Journal of Applied Phycology
SN - 0921-8971
ER -