Productivity Modeling Analysis
The Algenol Productivity Model (Legere, 2017; Chance and Roessler, 2019)
is used to analyze these indoor PBR experiment results, and determine if
a set of photosynthetic parameters can be developed to adequately
represent all experimental results. A representative model parameter set
for the productivity model is derived from the PI data sets with
[\(\alpha\), Ek, R0] = [0.061
fixed C/photon, 240 µE m-2 s-1, 0.1
µmol C mgChl.a-1 min-1] at 30 °C
providing a reasonable representation of the entire data base. The
R0 value at the reference 30 °C temperature was taken as
0.1 µmol C mgChl.a-1 min-1consistent with conclusions from outdoor experiments on a carbon basis
(Legere 2017; Chance and Roessler, 2019) and recognizing that
R0’ determinations from PI curves will show an
irradiance-related enhancement (Falkowski and Raven, 2007). To model
temperature effects, Ek is set as a function of
temperature (activation energy 60 kJ mol-1 which is
roughly Q10 = 2), and the respiration rate
(R0) was modeled as a function of temperature
(activation energy as 27 kJ mol-1), with the estimates
based on previous studies (Legere, 2017). Table 4 gives a summary of the
model parameter values. Comparison between the modeled and experimental
productivities are shown in Figure 7. The model results are in good in
agreement with experiment results for all cases considered here. Even at
35 °C, where clear changes in pigmentation are seen, the agreement is
quite good. For example, with temperature increased from 20 °C to 30 °C,
the biomass productivity increases by 28% (experimental) and 26%
(productivity model). At higher light intensities
>>Ek, an increase of 100%, or
Q10 = 2, would be expected. Good agreement between
biomass productivities for the small, L scale, experiments reported here
and the large, 24000 L scale, outdoor experiments (Chance and Roessler
2019) was noted earlier. This consistency can be extended to the PI
experiments (mL scale) where the derived photosynthetic parameters are
in good agreement with those deduced from model fits to the large scale
outdoor experiments (Chance and Roessler, 2019).