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the effect on hydrodynamics on photosynthetic efficiency
Microalgae cultures typically have a very high particle density which consequently causes also a high optical density. Due to mixing, cells are continuously advected between dark area and bright regions. Due to the dynamic nature of photosynthesis, this drastic changes in light intensity received by the cells can lead to significant effects on the growth rate. This mechanism has led several authors to analyze flow patterns and mixing by hydrodynamic models. Hydrodynamic modeling results are typically represented by time dependent velocity fields for local water flows (Euler approach) or trajectories of single (virtual) particles (Lagrangian Approach). Both results are equivalent, given the formulations have a comparable resolution. Velocity fields primarily allow for a macroscopic analysis of i.e. local dispersion or energy dissipation. Pruvost et al. [63] proposed an innovative toric PBR design while presenting an in-depth analysis of its hydrodynamic properties. By validation with Particle Image Velocimetry (PIV), they ended up with a trust-able model which allows for the comparison of different impeller models for mixing and the prediction of dispersion dynamics. On a macroscopic scale, these characteristics imply how much energy must be invested for mixing in order to provide good growth conditions. In a more recent work, Pruvost et al. [62] presented an approach for coupling hydrodynamics with a biological model using lagrangian particle trajectories using the toric reactor design. Similarly to Luo and Al-Dahhan [50], they found that the average light intensity which is calculated based on the trajectories is different from the average light intensity for the volume. While Luo and Al-Dahhan propose that this could be a measure to improve efficiency of photobioreactors, Pruvost et al. show that this effect is energetically inconsistent and that it indirectly results from the non-equally distributed resistance time of the simulated particles in the volume. Pruvost et al. consequently applied different strategies to correct this factor and finally define consistent light profiles with a static photosynthetic model. As they say, such a model does not account for flashing light effects. So finally they showed, that an influence on the growth rate by design of the hydrodynamic properties can only be estimated using models which account for cycling LD effects.
Algae, growth medium and pre-cultivation conditions
Laboratory experiments were conducted with the Chlorophyceae Dunaliella salina strain CCAP 18/19, from the Culture Collection of Algae and Protozoa (CCAP) in sterile filtered F/2 medium [34],
prepared from aged, Mediterranean Sea surface water (38 g L-1) collected at the permanent Point B SOMLIT station (4341,10’N and 718,94’E). The seawater was filtered through 1 m Durapore filter mounted in an housing for single cartridge to eliminate the majority of particles and stored in the dark at room temperature for 2 months. Before use, the seawater was filtered through 0.1 m Durapore filter, then autoclaved at 120°C for 30 min. After cooling and sterile addition of macro- and micronutrients [34], the medium was transferred to the photobioreactors through a 0.2 m sterile filter.
The strain was maintained and propagated in the same medium in polycarbonate flasks. Pre-cultures were grown for at least 10 generations at 300 E m-2 s-1 in the exponential phase in light-dark (12:12) incubator (SANYO MLR-351) at 27°C.
Experimental design
Growth experiments were performed in 8 aseptic, double-walled borosilicate glass photobioreactors (SCHOTT DURAN) with a capacity of 600 mL. The reactors were placed in two experimental chambers, each of them with two independent compartments. A closed water circuit with a cryastat inside the double walls maintained a constant temperature of 27°C in the reactors – a value which is reported to be optimal for the growth of this strain [28]. Magnetic stirrers were used to homogenize the cultures. Bubbling was provided with air filtered by a Whatman filter (0.2 m). pH was regulated at 8.3 using CO2 controller linked to pH sensors in each culture [6]. The same conditions were applied to both vessels in each compartment in order to duplicate the experiment.
Cell size and abundance
After inoculation, the changes in population density and mean diameter were monitored in triplicates, using a Coulter Counter Beckman (Multisizer 3) with a 50 m aperture tube. In parallel, cell abundance was also determined using a monitored liquid particle counter (Hiac 9703+). Daily averages of the cells abundances of turbidostat cultivations remained stable for consecutive days, which indicates the equilibrium state of the cultures regarding growth rate and dilution. In this setup, turbidostat cultivations in steady state has been stabilized to 2 105 cells mL-1.
Based on the dilution rate (d, day-1) and the apparent growth rate (a, day-1), the specific growth rate (, day-1) was calculated according to the following relation: = d + a.
Pigment extraction and analysis
Pigments were extracted and quantified from the algae starting with day 4 of the cultivation period. Samples of 3 mL cell suspension were taken from the reactors once per day and transferred to 10 mL tubes wrapped with aluminium paper to prevent photobleaching [47]. Extraction was done using 6 mL of 90% acetone per tube, thereafter stored during 1 hour at -20C. Cells were precipitated by centrifugation at 2000 rpm for 5 minutes. Absorbance was measured at 470 nm, 644.80 nm, 661.60 nm and 700 nm, corresponding to absorption maxima of carotenoids, chlorophyll a and chlorophyll b respectively [47], with a UV/Vis spectrophotometer (Perkin Elmer, Lambda 2). Pigment composition is presented as the temporal average SD of two replicates (four replicates for continuous light). Concentrations of chlorophyll a (Ca), chlorophyll b (Cb) and of total carotenoids (Cx+c) were calculated from the equations according to Lichtenthaler [47]: [Ca] = 11.24A661.6 – 2.04A644.8 (g mL-1 solution) [Cb] = 20.13A644.8 – 4.19A661.6 (g mL-1) [Cx+c] = (1000A470 – 1.90Ca – 63.14Cb)=214 (g mL-1).
Table of contents :
1 introduction
1.1 Applications of Bioreactors
1.2 Main factors affecting microalgae growth
1.3 Quantitative description of Photosynthesis
1.4 Dynamic Models for Photosynthesis
1.5 The effect on hydrodynamics on photosynthetic efficiency
1.6 Objective of this Thesis
2 material and methods
2.1 Experimental Methods
2.1.1 Algae, growth medium and pre-cultivation conditions
2.1.2 Experimental design
2.1.3 Culture conditions
2.1.4 Cell size and abundance
2.1.5 Pigment extraction and analysis
2.1.6 Elemental stoichiometry
2.1.7 Lipid analysis
2.2 Hydrodynamic Simulation of raceway systems with
3 long-term adaptive response to high-frequency light signals in the unicellular eukariote dunaliella salina
4 the effect of photosynthesis time scales on microalgae productivity
5 dynamic coupling of photoacclimation and photoinhibition in a model of microalgae growth
6 growth rate estimation of algae in raceway ponds: a novel approach
7 discussion
7.1 Yield of the photosynthetic apparatus as a response to LD frequency
7.2 Do we properly characterize the response with LD experiments
7.3 How much benefit can there be from Mixing?
7.4 Use and misuse of PI curves
7.5 Challenges for long term Lagrangian simulation of open raceways
7.6 Can a small scale raceway give information on the productivity of a large scale raceway?
8 conclusion
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