Investigation and modeling of biomass decay rate in the dark and its potential influence on net productivity of solar photobioreactors for microalga Chlamydomonas reinhardtii and cyanobacterium Arthrospira platensis

Bioresource Technology 138 (2013) 271–276

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Investigation and modeling of biomass decay rate in the dark and its potential influence on net productivity of solar photobioreactors for microalga Chlamydomonas reinhardtii and cyanobacterium Arthrospira platensis François Le Borgne a,b,⇑, Jérémy Pruvost a a b

Université de Nantes, CNRS, GEPEA UMR-CNRS 6144, boulevard de l’Université, CRTT-BP 406, 44602 Saint-Nazaire Cedex, France AlgoSource Technologies, boulevard de l’Université, CRTT-BP 406, 44602 Saint-Nazaire Cedex, France

h i g h l i g h t s  Temperature influences greatly biomass consumption during night.  Microalga and cyanobacteria have different biomass consumption during night.  Biomass production in day–night cycles is highly influenced by night temperature.

a r t i c l e

i n f o

Article history: Received 6 September 2012 Received in revised form 1 March 2013 Accepted 9 March 2013 Available online 16 March 2013 Keywords: Respiration Nighttime Microalgae Cyanobacteria Temperature influence

a b s t r a c t Biomass decay rate (BDR) in the dark was investigated for Chlamydomonas reinhardtii (microalga) and Arthrospira platensis (cyanobacterium). A specific setup based on a torus photobioreactor with online gas analysis was validated, enabling us to follow the time course of the specific BDR using oxygen monitoring and mass balance. Various operating parameters that could limit respiration rates, such as culture temperature and oxygen deprivation, were then investigated. C. reinhardtii was found to present a higher BDR in the dark than A. platensis, illustrating here the difference between eukaryotic and prokaryotic cells. In both cases, temperature proved an influential parameter, and the Arrhenius law was found to efficiently relate specific BDR to culture temperature. The utility of decreasing temperature at night to increase biomass productivity in a solar photobioreactor is also illustrated. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Most processes using solar energy (e.g. photocatalysis and photovoltaics) have no output during the night. In the case of photobioreactors (PBRs), productivity can even be negative, because without light, photosynthetic microorganisms metabolize intracellular carbohydrate (starch, glycogen, etc.) as energy sources to sustain their metabolic activity (Ogbonna and Tanaka, 1996). Hence biomass productivity is influenced both in the day and at night, as shown by Pruvost et al. (2011), who simulated solar production in day–night cycles. In contrast to the numerous studies devoted to light use optimization during the day, little information is available on nighttime effects. However, we would expect that in appropriate ⇑ Corresponding author at: Université de Nantes, CNRS, GEPEA UMR-CNRS 6144, boulevard de l’Université, CRTT-BP 406, 44602 Saint-Nazaire Cedex, France. Tel.: +33 (0)2 40 17 26 41; fax: +33 (0)2 40 17 26 18. E-mail address: [email protected] (F. Le Borgne). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.03.056

culture conditions, biomass decay during the night could be reduced, thereby increasing the resulting productivity obtained on a 24-h basis. Overall, the literature reports a net fall in biomass concentration at the end of the day (Torzillo et al., 1991), or with respect to daytime production (Doucha and Lívansky´, 2006; Vonshak and Richmond, 1988). Several parameters have also been found to influence respiration rate during the night. Among these, the biochemical composition of cells (carbohydrates content), the biomass concentration, and the culture temperature are often mentioned (Grobbelaar and Soeder, 1985; Ogbonna and Tanaka, 1996; Torzillo et al., 1991; Vonshak, 1997). The culture conditions during photosynthetic growth are also important insofar as they influence the cell physiological state at the beginning of the night. Gibson (1975) has shown, for example, that the respiration rate at night and the carbohydrate content accumulated during the day are closely related. This carbohydrate content was found to depend not only on the irradiation conditions (Renaud et al., 1991), but also on the culture temperature

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(Oliveira et al., 1999; Torzillo et al., 1991) and the nutritional conditions (Degrenne et al., 2011). Among other parameters, temperature appears highly influential, as demonstrated by Grobbelaar and Soeder (1985) and Vonshak (1997). These authors show that the respiration rate of cyanobacteria and microalgae can be linked to the culture temperature using the Arrhenius law. This is of particular interest because it would allow the integration of the nighttime biomass decay rate into models designed to calculate solar PBR productivities (Pruvost et al., 2011) including culture temperature prediction (Goetz et al., 2011), for a general optimization of solar PBR productivity based on an optimal control strategy taking into account the specific features of both day- and nighttime. A study was accordingly carried out to investigate the behavior of photosynthetic microorganisms in the dark, so as to calculate the corresponding biomass decay rate (BDR). Operating parameters that could limit respiration rates, such as the culture temperature and oxygen deprivation, were investigated. The biomass concentration effect was also considered. It is stated in the literature that respiration rate can be increased by increasing biomass concentration (Richmond, 2004), although some results tend to show the opposite (Qiang et al., 1996; Torzillo et al., 1991). Because biomass concentration is found to range widely in practice, this point was investigated. To estimate the real impact of each parameter, photosynthetic microorganism cultures were performed in two steps. Photosynthetic growth was first performed in a chemostat with optimal growth conditions and continuous illumination. This enabled us to obtain a similar biochemical composition of the microalgal biomass at the beginning of each dark period studied. The BDR was then measured by placing cells in batch mode in the dark under different operating conditions. Two microorganisms were investigated: the cyanobacterium Arthrospira platensis and the microalga Chlamydomonas reinhardtii. This choice enabled us to compare the behavior of eukaryotic and prokaryotic microorganisms, known to have different responses in the dark. 2. Methods The experimental setup consisted of a torus PBR combined here with online gas analysis to monitor oxygen evolution and thus the respiratory activity of cells, so as to finally deduce the associated biomass loss and specific BDR. This enabled us to obtain a very accurate time course at a high frequency of the BDR during the dark period, without disturbing the culture system during the experiment (no biomass sampling). 2.1. Torus photobioreactor The flat panel torus PBR used for the experiments was made of stainless steel with a 1.5 L working volume and a specific illuminated area alight of 25 m1 (depth of culture 0.04 m). One advantage of the torus PBR is the high control of culture conditions (light, mixing, temperature and pH). Its detailed description can be found elsewhere (Fouchard et al., 2008; Pottier et al., 2005; Pruvost et al., 2006; Takache et al., 2010). During the growth stage, pH was monitored by a pH sensor (Mettler Toledo 3253SG/120/Pt10) and controlled by carbon dioxide injection. The system had a double jacket filled with water thermally regulated by an external cryothermostat (VWR 1146D). The PBR was illuminated with a white LED panel. The photon flux density (PFD) was set and measured as described in Takache et al. (2010). During the dark period, pH regulation was deactivated to increase the accuracy of the online gas analysis (no pH deviation was observed during the dark period).

2.2. Online gas analysis The BDR determination is based on an oxygen mass balance contained in the gaseous and liquid phases. This mass balance was obtained using the same method as that described in Fouchard et al. (2008) and Degrenne et al. (2011) for the investigation of microalgal hydrogen production (see these works for more details) by combining online measurements of flow-rates and gas composition. Two gas mass flowmeters (Bronkhorst EL-FLOW HIGH TECH) were placed on the inlet and outlet of the PBR respectively, and inlet and outlet gas compositions were analyzed every 10 s with a mass spectrometer (QMS 200 PFEIFFER VACUUM). Air was used as vector gas and was injected at a constant flow rate of 50 mL/min. 2.3. Strains Two strains were investigated, the eukaryotic microalga C. reinhardtii and the prokaryotic cyanobacterium A. platensis. The C. reinhardtii wild type strain (coded 137 AH, collection number CC124) was cultivated in the Sueoka autotrophic medium. The composition of the Sueoka medium was, per liter: 1.45 g NH4Cl; 0.28 g MgSO47H2O; 0.05 g CaCl2; 0.61 g KH2PO4 and 1.68 g NaHCO3. The cyanobacterium A. platensis (coded PCC 8005, from the Pasteur Institute) was grown in the medium described by Zarrouk (1966). For each medium, 1 ml of a trace element solution was added per liter: Hutner solution (Harris, 1989) for the Sueoka medium, and a solution corrected by Cogne et al. (2003) from Zarrouk (1966) for A. platensis cultures. Before applying dark periods, cells were photosynthetically grown in continuous mode at a PFD of 300 lmolhm m2 s1 and with optimal growth conditions (pH 7.5, temperature 25 °C for C. reinhardtii; pH 9.5, temperature 35 °C for A. platensis). Values of residence time (60 h for C. reinhardtii, 83 h for A. platensis) were chosen to obtain the light-limited regime characterized by the full absorption of incoming light. As discussed in Takache et al. (2012), this light-limited regime has to be promoted when operating a PBR (stable operating regime). It is also representative of operating conditions that could be obtained in solar exploitation at the end of a day period with decreasing irradiation conditions leading to the appearance of a dark zone (Pruvost et al., 2011). Steady-state was reached before application of a dark period for BDR measurements. In those conditions, biochemical composition was found constant, with biomass concentrations around 1.0 g/L for C. reinhardtii and 1.3 g/L for A. platensis. 2.4. Calculation of the specific BDR 2.4.1. Oxygen mass balance The biomass concentration CX change was deduced from the oxygen consumption volumetric rate hr O2 i in g/(L h) (or oxygen consumption rate hRO2 i, expressed in g/h). It was obtained from oxygen mass balance conducted on the PBR considering the inlet and outlet flux, and the produced, consumed or accumulated oxygen quantities in the PBR volume: in Qmout O2 ðtÞ  QmO2 ðtÞ ¼ hRO2 i  dO2 =dt

ð1Þ

The calculation of oxygen time variation in the PBR ðdO2 =dtÞ was deduced from total oxygen concentration in gaseous and liquid volumes:

O2 ðtÞ ¼ ½O2 Ldissolv ed ðtÞ  V L þ ½O2 G ðtÞ:V G

ð2Þ

where ½O2 Ldissolv ed is the dissolved oxygen concentration in the liquid phase, ½O2 G the concentration in the gaseous phase, V L the liquid volume and V G the gas volume.

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The concentration in the gaseous phase was obtained from the oxygen mass fraction measured by mass spectrometry:

½O2 G ¼

xO2 ðmolarÞ  M O2  P RT

ð3Þ

The dissolved oxygen concentration ½O2 Ldissolv ed was determined assuming gas–liquid equilibrium. This assumption was validated by direct monitoring of dissolved oxygen concentration (Mettler InPro 6800, data not shown). Gas–liquid equilibrium was explained by the slow time course of the gaseous phase composition and the high efficiency in terms of gas–liquid mass transfer of the torus PBR. The gas–liquid equilibrium assumption enabled us to relate the dissolved oxygen concentration to the oxygen mass fraction contained in the gaseous phase:

½O2 Ldissolv ed ¼ HO2  xO2  P

ð5Þ

where xi is the mass fraction of the i-component in the gas mixture. 2.4.2. Determination of the specific BDR The volumetric rate of oxygen consumption hRO2 iwas deduced from Eq. (1), knowing the time variation of oxygen concentration in the PBR ðdO2 =dt and oxygen flux in PBR inlet and outlet in Qmout O2 ðtÞ  QmO2 ðtÞ . The volumetric rate of oxygen consumption was then used to calculate the BDR hrX i in g/(L h) (or hRX i in g/h) by using the mass yield of oxygen to biomass consumption Y O2 =X:

hRX i ¼

hRO2 i Y O2 =X

ð6Þ

Biomass concentration time course was then deduced assuming the time step Dt low enough to consider hr X i as a constant:

C XðtÞ ¼ C XðtDtÞ þ hrX i  Dt

ð7Þ

This enabled us to calculate the instantaneous and time-averaged specific BDR over a period k (expressed in h1):

ðhr X i=C X Þt ¼

C XðtþDtÞ  C XðtÞ Dt  C XðtÞ

ðhr X i=C X Þt¼k ¼

k

t¼0

D  CX  V L  t

¼ 1:40  0:05

ð10Þ

CO2 þ 0:512  H2 O þ 0:0063  MgSO4 þ 0:181  NH4 Cl þ 0:0213  K2 HPO4 ! CH1:77 O0:472 N0:181 S0:0063 P0:0213 þ 1:0752  O2 Y O2 =X theoretical ¼ 1:0752 

M O2 ¼ 1:39 MX

ð11Þ ð12Þ

3. Results and discussion 3.1. Specific BDR time course at constant temperature All the trials were conducted over a 24-h period. This is obviously not representative of usual night duration, but covers almost all the potential geographical areas for microalgal cultures (24 h or less). The specific BDR time course over long periods could also provide interesting information for storing harvested biomass in the dark before further processing. Several temperatures were tested, from 283 to 308 K for C. reinhardtii, and from 293 to 308 K for A. platensis. The time course of the specific BDR was used to calculate average values, here obtained as a function of dark period duration (k). As shown in Fig. 1, temperature proved a major parameter, with a marked increase in the specific BDR with temperature. In the range of temperatures covered (283–308 K), a 10- and 4-fold increase was obtained for C. reinhardtii and A. platensis respectively. For a given temperature, the specific BDR time course with the applied dark period duration was also found almost constant. This was particularly so for C. reinhardtii (Fig. 1A). The averaged specific BDR remained constant throughout the 24-h period, except for the two highest temperatures (303 and 308 K), when a non-negligible variation during the first 16 h of darkness was observed. These temperatures are higher than the optimal growing value (about 298 K) and this overheating could lead to a specific biological response including cell degradation. This last hypothesis is supported by the specific BDR time course at 308 K, which rapidly decreased, indicating possible metabolism degradation. This BDR decrease was not observed in the case of the cyanobacterium A. platensis (Fig. 1B), whose optimal growing temperature is higher (308 K). For all the temperatures, a small variation in the specific BDR was observed in the first 16 h.

ð8Þ 3.2. Modeling of temperature influence on specific BDR

k X ðhr X i=C X Þt t¼0

¼

mO2 mX t¼end X R tþDt R tþDt  Qmout dt  Qmin dt O2 ðtÞ  t O2 ðtÞ  t

ð4Þ

where HO2 is Henry’s oxygen constant. As HO2 depends on the temperature and the salinity of the liquid phase, its value was corrected for each trial. Firstly, HO2 ;0 was calculated in pure water as a function of the temperature using Sander’s data (Sander, 1999), then the salinity medium influence has been added using the Weisenberger and Schumpe formula (1996). Oxygen mass flow rates are necessary to complete the mass balance. Inlet and outlet gas fractions measured by the mass spectrometer were combined to total mass flow rate Qm recorded by the mass flow meter to obtain the mass flow rate Qmi of each gas component:

Qmi ¼ xi  Qm

Y O2 =X experimental ¼

ð9Þ

Because Y O2 =X relates biomass and oxygen changes (Eq. (6)), this value is especially relevant. Its calculation can be obtained from (i) the direct measurement of the biomass production rate during the photosynthesis stage as deduced from dry-weight biomass concentration measurements (Eq. (10)), and (ii) from elementary analysis (Degrenne, 2009), which gives the oxygen biomass content (Eqs. (11) and (12)). Both methods gave the same Y O2 =Xvalue with less than 1% deviation (Y O2 =X ¼ 1:40  0:01). This result demonstrates the accuracy of the method proposed in this study to measure kinetics from oxygen mass balances.

The Arrhenius law is usually applied to relate reaction rates (such as respiration activity) to temperature: Ea hr X i=C X ¼ A  eð RT Þ

ð13Þ

The utility of the Arrhenius law has already been demonstrated by Vonshak (1997) for the cyanobacterium A. platensis and by Grobbelaar and Soeder (1985) for the dark respiration of the microalga Coelastrum sphaericum. In his doctoral work, Cornet (1992) also demonstrated that the specific BDR of A. platensis (PCC 8005) could be linked to the night temperature by the Arrhenius law. Fig. 2 presents the averaged specific BDR obtained over an 8-h dark period, corresponding to a typical summer night duration in France (given the small influence of dark period duration, the

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F. Le Borgne, J. Pruvost / Bioresource Technology 138 (2013) 271–276 1.2E-02

A 308 K

< rX >/CX (h-1)

1.0E-02

303 K

8.0E-03

293 K

6.0E-03 293 K

4.0E-03

288 K

2.0E-03 0.0E+00

283 K

0

1.4E-03

5

10

15

20

25

B

The Arrhenius law can be used to predict the corresponding biomass loss for a given night period. For example, the night biomass loss of C. reinhardtii culture will range between 4.2% and 6.4% if the night temperature culture is maintained at the optimal growth rate value (298 K) for a respective night duration of 8 and 12 h. By reducing night temperature to 10 °C, the loss in biomass will range between 1.1% and 1.7% for the same durations. In the case of A. platensis and for optimal growing temperature (308 K), the biomass loss will be 1.0% after 8 h of darkness (1.5% after 12 h). This biomass loss will fall to 0.5% for temperatures below 301 K. These results confirm the very different response of the two strains (around one order of magnitude). This corroborates the observations of Van Liere and Mur (1979), who concluded that the respiration rate of cyanobacteria was relatively low compared with microalgae. In our case, A. platensis BDR quickly fell to very low or negligible values.

1.2E-03

3.3. Biomass concentration effect

308 K

< rX >/CX (h-1)

1.0E-03 303 K

8.0E-04 6.0E-04

298 K

4.0E-04 293 K

2.0E-04 0.0E+00

0

5

10

15

20

25

Time (h) Fig. 1. Specific BDR time course of the microalga C. reinhardtii (A) and the cyanobacterium A. platensis (B) as a function of darkness duration. Confidence intervals have not been represented on the A. platensis section to make the graph easier to read. These confidence intervals are shown in Fig. 2.

1.8E-02 1.6E-02

< rX >/CX (h-1)

1.4E-02 1.2E-02 1.0E-02 8.0E-03 6.0E-03 4.0E-03 2.0E-03 0.0E+00 275

280

285

290

295

300

305

310

315

320

Biomass concentration is often evoked in the literature as a parameter influencing nighttime BDR, with a higher BDR for higher biomass concentration (Richmond, 2004). We therefore conducted experiments with higher biomass concentrations. For C. reinhardtii at 298 K after 8 h of darkness, the BDR was (5.27 ± 0.46)  103 h1 with the low biomass concentration (1.05 g/L), and (4.50 ± 0.26)  103 h1 with the high concentration (1.72 g/L). For A. platensis at 308 K, the specific BDR was (1.17 ± 0.39)  103 h1 with the low biomass concentration (1.42 g/L), and (1.24 ± 0.13)  103 h1 with the high biomass concentration (2.05 g/L). These results confirm some influence of the biomass concentration on the specific BDR, at least in the concentration range tested, which covers practical cases. However, this influence remains low, and can be considered negligible compared with that of temperature. This is particularly so for A. platensis at 308 K, where a difference of less than 6% was measured (included in the 95% confidence interval value). For C. reinhardtii, the observed difference was 15% (also included in the confidence interval). We also note that, in contrast to the literature data, the increase in biomass concentration reduced night biomass loss. Respiration being a catabolic process, the dependence of specific BDR on biomass concentration is difficult to explain. We note, however, that the variation in biomass concentration has an indirect influence on dissolved oxygen concentration, by reducing its value in the case of higher biomass concentrations, leading to lower respiration rate. This would, however, need to be confirmed with further experiments. During the trial with C. reinhardtii at 298 K with the high biomass concentration, the dissolved oxygen quantity was found, for example, to exceed 8 mg/L. This value cannot be considered as limiting in the first instance.

Temperature (K)

3.4. Influence of dissolved oxygen concentration on BDR Fig. 2. Representation in accordance with the Arrhenius law of the average specific BDR of the microalga C. reinhardtii (dotted line) and the cyanobacterium A. platensis (continuous line) as a function of temperature. On the A. platensis section, data from Cornet (1992) have been added (red square). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

following results and conclusion can be considered generic). The results of Cornet (1992) for A. platensis were added to our measurements. We note that the specific BDRs were very close. For both C. reinhardtii and A. platensis, the Arrhenius law was found to fit specific BDR change with temperature. Parameter identification gave an activation energy Ea of 91.6 kJ/mol and a preexponential factor A of 4.24  1012 h1 for A. platensis (62.69 kJ/ mol and 5.6  108 h1 for C. reinhardtii).

To test oxygen influence, further trials were conducted to evaluate the BDR in anoxic conditions (nitrogen was used as vector gas). The aim was to reduce the biomass respiration rate. Surprisingly, an important biomass concentration decrease was achieved, particularly with C. reinhardtii. This decrease was associated with a marked hydrogen release, as measured by the mass spectrometer (molar fraction reached 0.1%). This hydrogen production probably derived from biomass fermentation. The same phenomenon was observed with A. platensis, but with a lower hydrogen production. In both cases, cells were found highly degraded at the end of the dark period (high pigment and protein degradation). A long time period was necessary to recover healthy cells and initial biomass composition.

F. Le Borgne, J. Pruvost / Bioresource Technology 138 (2013) 271–276

3.5. Influence of BDR on solar photobioreactor net productivity Biomass production in solar cultivation systems is the overall result of both day and night periods, as emphasized in Pruvost et al. (2011, 2012), who modeled solar PBR running. This model was developed to predict the time course of biomass concentration in a solar rectangular PBR as a function of irradiation conditions (day– night cycles, season, Earth location, beam-diffuse light distribution, etc.), operating parameters (residence time in continuous mode and harvesting strategy) and engineering parameters (PBR inclination and orientation). It was applied for A. platensis, which was thus also retained in the present study. To illustrate the potential influence of the BDR on the solar photobioreactor net productivities, data obtained in the study were integrated in a first approximation into the model. Simulations were conducted for a horizontal PBR operated at a constant residence time (103 h), and for a typical day with ideal irradiation conditions (day-averaged irradiation of 1150 lmolhm m2 s1; see Pruvost et al. (2012) for more details). The same day–night cycle was repeated until a stable regime was achieved (same time course of biomass concentration over a 24 h period). Only specific BDR values were modified in the model. From the results obtained in this study, temperature was retained as the best operating parameter to adjust in order to decrease biomass consumption during the night, thereby increasing resulting biomass productivity on a 24 h basis. The Arrhenius law was introduced into the solar PBR model to calculate the specific BDR value used for the night period (during the day period, the kinetic growth model was kept unchanged). A typical result of the daytime time course of biomass concentration is shown in Fig. 3. As already observed in Pruvost et al. (2011), biomass concentration follows day–night cycles, with a biomass production during the day, and a biomass decrease during the night. As a result, biomass concentration oscillates by about 20% throughout the cycle. The BDR proved to influence the time course of biomass concentration directly. For A. platensis, increasing the temperature during the night from 288 to 318 K results in an increase in the specific BDR from 1  104 to 38  104 h1 respectively. The resulting biomass concentration (averaged value over the 24 h period) was found to decrease from 0.43 to 0.37 g/L. Because of the Arrhenius law, the influence of the night temperature was found to be nonlinear, as indicated by the simulation conducted for the intermediate temperature of 306 K (average biomass concentration 0.41 g/L). Biomass concentration enables us to calculate areal productivity (day-averaged value). As expected, a nonlinear time course was obtained. For night temperatures ranged from 278 to 295 K, areal productivity decreases from 10.3 to 10 g m2 day1 respectively (decrease of 3%). For higher temperatures, a prompt decrease

0.5

2000

0.45

1500

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in productivity was however achieved. For a temperature of 323 K, areal productivity was found to be decreased down to 7.5 g m2 day1 (35% compared with to one achieved at 278 K). Obviously, these results are purely theoretical and are to be considered only as a first estimate. The BDRs presented in this study were obtained in the particular condition of a one long dark period, which is not fully representative of the natural environment conditions. During consecutive day–night cycles, we know that a specific metabolism takes place: during the night, photosynthetic microorganisms continue to synthesize cellular constituents and may divide synchronically (nycthemeral cycles). Under an adequate photoperiod, phytoplankton can store sufficient carbon and energy to sustain a night metabolism. For example, Cuhel et al. (1984) report that during the night, total cell volume and Chl-a concentration in Dunaliella tertiolecta culture remained constant, while total carbon decreased by 18%. At the same time, they also observed that cells continued to divide at night and that both carbon and sulfur incorporation into protein continued at rates similar to those of daytime growth, indicating an active metabolism mainly oriented towards protein synthesis during the night. Asato (2003) also reported that photoautotrophic organisms such as Synechococcus PCC 6301 and Synechocystis PCC 6308, were able to produce energy by degradating intracellular carbohydrates accumulated during the day, but apparently in insufficient amount to support sustained cell cycles for a long dark period (longer that one night). Night metabolism is certainly determined by numerous environmental factors including prior light intensity history, nutrient status, temperature and the species itself (Cuhel et al., 1984). For example, Vítová et al. (2011a,b) have shown that the duration of the C. reinhardtii cell cycle is affected by both light intensity and temperature. Donnan et al. (1985) have also investigated the effect of temperature on the cell cycles of Chlamydomonas. Growth rate was found to be greatly influenced by temperature conditions, and transfer of cells from 20 to 30 °C before light exposure gave a growth rate similar to those maintained at 30 °C. Finally, Harding et al. (1981) also reported that cell division of the diatom Dutylum brightwellii during the night could be synchronized or not, depending on the physiological state of the cells and on an unknown endogenous component acting throughout the cycle, since photosynthesis generally declined before the end of the light period, and similarly increased before its start. All these results indicate that the cellular activities of photoautotrophic microorganisms in the dark are still not fully known (Asato, 2003). Given their potential influence on solar production systems, this lack of knowledge makes this work of strong interest in that it offers a method to investigate deep biological behavior during successive day–night cycles. Even so, this goal remains a major challenge, because light and temperature cycles can interact in the process, each with their own dynamics and history.

4. Conclusion

0.4

1000

0.35

0.3

500

0

6

12

18

0 24

Fig. 3. Influence of the biomass loss during the night on the daytime change in biomass concentration (Arthrospira platensis, horizontal solar photobioreactor, ideal irradiation conditions – see text for details).

The biomass decay rate of photosynthetic microorganisms during the night was investigated. Higher biomass consumption (around one order of magnitude) was found for microalga (C. reinhardtii) than for cyanobacterium (A. platensis). In both cases, temperature proved an influential parameter, highlighting the potential utility of lowering temperature during the night to increase biomass productivity. This was illustrated here by introducing in a previously described PBR model an Arrhenius law, which was found to efficiently represent temperature influence. Further studies will investigate effects of temperature over successive day–night cycles, to design temperature regulation operating strategies for optimizing solar production.

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