Key points are not available for this paper at this time.
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 incident 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 respiration in the aphotic zone of the pond. The model is also applicable to photobioreactor cultures.
Huesemann et al. (Fri,) studied this question.