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Title: A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures

Journal Article · · Algal Research

A microalgae growth model was developed for predicting biomass productivity in outdoor ponds under nutrient- replete conditions and diurnally fluctuating light intensities and water temperatures. The model was validated for three different species (Chlorella sorokiniana, Nannochloropsis salina, Picochlorum sp.), successfully predicting biomass growth and productivity in all three cases in raceway pond cultures. The model can be run in batch and continuous culture mode at different culture depths and, in addition to inci- dent sunlight and water temperature data, requires the following experimentally determined strain-specific input parameters: growth rate as a function of light intensity and temperature, biomass loss rate in the dark as a function of temperature and light intensity during the preceding light period, and the scatter-corrected biomass light absorption coefficient. Light attenuation due to biomass was estimated on the basis of a scatter-corrected Beer–Lambert law in a culture theoretically divided into discrete volume layers which receive decreasing amounts of light with depth. Sensitivity of model predictions to deviations in input parameters was moderate. To increase the predictive power of this and other microalgae biomass growth models, a better understanding is needed of the effects of mixing-induced rapid light–dark cycles on photo-inhibition and short-term biomass losses due to dark respira- tion in the aphotic zone of the pond. The model is also applicable to photobioreactor cultures.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Arizona, Tucson, AZ (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
Grant/Contract Number:
EE0003046; AC05-76RL01830; ee0006269
OSTI ID:
1351647
Alternate ID(s):
OSTI ID: 1233757; OSTI ID: 1592760
Report Number(s):
PNNL-SA-111707; S2211926415300989; PII: S2211926415300989
Journal Information:
Algal Research, Journal Name: Algal Research Vol. 13 Journal Issue: C; ISSN 2211-9264
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 120 works
Citation information provided by
Web of Science

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