Photobioreactor engineering for solar microalgae ...

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AlgoSource Technologies : F. Le Borgne, O.Lépine ..... To allow HVP and stable running in solar conditions, AlgoFilm technology included specific optimisations.
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Photobioreactor engineering for solar microalgae cultivation: methodology and applications

rX = ρφA − µs

J NADH M X K Kr r 2 X = ρφA − X Kr + G υ NADH − X Kr + G 2

GEPEA : J.Pruvost, B.Le Gouic, J.Legrand AlgoSource Technologies : F. Le Borgne, O.Lépine

Light-limited regime Limiting growth factors in microalgal cultivation systems • • • •

Light Dissolved carbon Chemical nutrients Physico-chemical condition (T, pH)



Physical (process) parameters

Bacterial contamination – biological drift

Biological parameters

An adequat engineering and control of the cultivation system as in PBR allows preventing from any limitation by nutrients and growth conditions (pH, T, bacterial contamination)

Productivity will then be limited by light: light-limited regime (which will guarantee by definition the maximum biomass production of the cultivation system) 2 major consequences • Except for very adverse culture conditions, light will always limit process productivity • In light-limited regime, the control of light (or its effect) will allow controlling process performances (the so-called « physical limitation » in chemical engineering) But light is a complex parameter

has to be considered with a special attention !!

Optimizing light attenuation conditions in PBR 0.8

Biomass productivity Px = Cx/τp

Fresh feeding medium (Flowrate Q)

0.7

0.6

Harvesting (Flowrate Q)

0.5

Biomass concentration Cx (kg.m-3)

0.4

Cxopt 0.3

An optimal biomass concentration exists, corresponding to an optimal dilution rate (or residence time)

0.2

0.1 0

50

100

150

Photobioreactor (Volume VR)

Residence time τpand dilution rate D

200

0.018

20

Biomass productivity Px = Cx/τp = Cx.D

0.016

VR 1 (= ) Q D

22

Residence time τp (hours)

Px max

τp =

18

Increasing biomass concentration decreases the light energy absorption rate

0.014

16 0.012

Rate of energy absorption (µmolehν.g-1.s-1)

Biomass 0.01 productivity Px -3 -1 (kg.m .h ) 0.008

12

0.006

10

0.004 0.002 0

14

8

τopt

50

100

150

Residence time τp (hours)

200

6 0

50

100

Residence time τp (hours)

150

200

Optimizing light attenuation conditions in PBR 0.8

0.02

Px max 0.6

Biomass concentration Cx (kg.m-3)

Biomass productivity Px = Cx/τp

Fresh feeding medium (Flowrate Q)

0.015

Harvesting (Flowrate Q)

Biomass productivity Px 0.01 (kg.m-3.h-1)

0.4

Cxopt 0.2

0 0

0.005

τopt

50

100

150

0 200

Residence time τp (hours)

Photobioreactor (Volume VR)

Residence time τp and dilution rate D

τp =

VR 1 (= ) Q D

0.02 0.018

Maximal productivity corresponds to an optimal biomass concentration, an optimal residence time (or dilution rate) and an optimal light energy absorption rate

0.016

Px max

0.014

Biomass 0.012 productivity Px (kg.m-3.h-1) 0.01 0.008 0.006 0.004 0.002 0 5

opt 10

15

Rate of energy absorption
(µmolehν.g-1.s-1)

20

Optimizing light attenuation conditions in PBR Takache et al., Biotechnology Progress, 2012.

The role of illuminated fraction γ on biomass PBR productivity (γ =illuminated to total volume ratio) Light transmission γ > 1 (Case C)

Full light absorption γ < 1 (Case A)

Luminostat regime γ = 1 (±10%) (Case B) 3

Maximal biomass productivity Px max 0.02

2

Biomass productivity Px (kg.m-3.day-1)

Biomass concentration Cx (kg.m-3)

0.01 1

Optimal biomass concentration Cxopt 0 0

20

40

60

Residence time τp (hours)

Optimal range of residence time

80

0 100

γ = 1 : a simple but relevant formula to optimize productivity

Optimizing light attenuation conditions in PBR Takache et al., Biotechnology Progress, 2012.

The role of illuminated fraction γ on biomass PBR productivity (γ =illuminated to total volume ratio)

Unstable !

Light transmission γ > 1 (Case C)

Stability !

Full light absorption γ < 1 (Case A)

Luminostat regime γ = 1 (±10%) (Case B) 3

Maximal biomass productivity Px max 0.02

2

Kinetic regime (light transmission, i.e. γ > 1)

Biomass productivity Px (kg.m-3.day-1)

Biomass concentration Cx (kg.m-3)

0.01 1

Physical limitation (full light absorption, i.e. γ ≤ 1)

Optimal biomass concentration Cxopt 0 0

20

40

60

Residence time τp (hours)

80

0 100

Optimal range of residence time

Chlorella v., PFD = 250µmoles/m².s

Solar production of photosynthetic microorganisms Photosynthesic conversion and thus photobioreactor running are fully dependent of the light supply •Transcient Solar radiation : very complex in nature

•Spatial dependence (location) •Spectral distribution •Beam and diffuse radiations •Moving source of illumination Clear day

Cloudy day

Examples of daily variation of total radiation

-2.s -1 -1 -2 -1.nm Photons (mm flux de flux photons .s .nm-1 )

6E+18

Photosynthetic Active Radiation (PAR) 400-700nm waveband (43% of total spectrum energy)

5E+18

4E+18

3E+18

Spectral distribution (photons flux)

2E+18

1E+18

0 250

500

750

1000

1250

1500

1750

2000

Wavelenght Wavelength (nm) nm

2250

2500

2750

3000

Direct (beam) and 7 diffuse radiations

Modeling light-limited photosynthetic growth Light (PFD q)

See Takache et al., Biotech progress, 2012 for full model description

Radiative transfer model Depth of Culture

Determination of the local rate of energy (photons) absorption A in µmolehν.kg biomass-1.s-1

A = f (VR)

Cells absorption and scattering (radiative properties)

Kinetic growth model Determination of local rate of biomass (or O2) production JO2 or rx in kg.m-3.s-1

Mass balance dC x 1 = rx (t ) − C x dt τP

Determination of biomass concentration evolution

Cx(t)

in kg

-3 biomass.m

JO2 = f (A, …) Determination of rate of biomass production < rX > =

< JO > CX M X 2

υO − X 2

< JO > = 2

1 VR

∫∫∫ J O VR

2

dV

Modeling solar production Pruvost et al., Biotech progress, 2013

Irradiation conditions (solar database – meteonorm.com)

Radiative transfer modelling Attenuation profile calculation (taking into account sunlight features : directdiffuse repartition, incident angle)

Prediction of biomass concentration (productivity) time course dC x 1 = rx (t ) − C x dt τ

Gcol 2 (1 + α ) exp[−δ col ( z − L)] − (1 − α ) exp[δ col ( z − L)] = q// cosθ (1 + α ) 2 exp[δ col L] − (1 − α ) 2 exp[−δ col L]

Gdif (1 + α ) exp[−δ dif ( z − L)] − (1 − α ) exp[δ dif ( z − L )] =4 q∩ (1 + α ) 2 exp[δ dif L] − (1 − α ) 2 exp[−δ dif L]

+ Photosynthetic growth model J O2 = ρ φO′ 2 A = ρ M

K φO′ A K +G 2

< JO > = 2

1 VR

Growth rate calculation < rX > =

< JO > CX M X 2

υO − X 2

∫∫∫ J O VR

dV 2

Evolution of light attenuation in solar conditions Pruvost et al., Biotech progress, 2013 Local rate of photosynthetic conversion JO2/< JO2 θ=0>

Conversion rate in culture volume

Diffuse light and non normal incident light creates high light gradient, reducing photosynthetic conversion

Depth of culture z/L

Loss of productivity 15%

1.5

Clear day – Normal incidence (0°) PFD 1500µmole/m²/s

1.5

1.5

Clear day – Oblique incidence (60°) PFD 1500µmole/m²/s

γ = 0.5

γ = 0.9

γ=1 1

Cloudy day – Normal incidence (0°) PFD 1500µmole/m²/s

Loss of productivity 60%

1

1

A /
Diffuse contribution 0.5

0.5

0.5

Diffuse contribution Beam contribution 0 0

0.2

Beam contribution

Beam contribution

Diffuse contribution 0.4

z/L

0.6

0.8

0 01

0.2

0.4

0.6

z/L

0.8

0 01

0.2

0.4

0.6

z/L

0.8

1

Optimisation of biomass production in solar PBR No steady-state and different time-scales: day-night cycles, seasons, clouds, biomass growth… Optimal conditions applied in the cultivation system can only be defined as a compromise. Non optimal light conversion: light transmission and oversaturating light

Annual production for two locations Daily evolution An in-depth understanding of light conversion in the cultivation system in solar use is necessary Pruvost et al., Biotech progress, 2013

Optimisation of biomass production in solar conditions is highly challenging !

AlgoFilm solar technology Technology for solar production with the aim to achieve High Volumetric Productivity (HVP concept – High Cell Density culture >10g/l in continuous culture) Photobioreactors (alight < 400m-1) Photobioreactors (alight < 50m-1) AlgoFilm operating range alight = 470m-1

Open systems

2 Patents (CNRS-Univ.Nantes) 2.1liter/m² of culture (150liter/m² for a raceway)

10-4

10-3

10-2

10-1

1

Biomass volumetric productivity (kg.h-1m-3)

To allow HVP and stable running in solar conditions, AlgoFilm technology included specific optimisations Tmc Tv

Tamb

Biofouling reduction (material optimisation)

Optimised thermal regulation using passive systems

Hydrodynamics optimisation (rheology of high-cell density culture)

AlgoFilm solar technology

CX( g.l-1)

30,00

7,0

25,00

6,0 5,0

20,00

4,0

Stable production (3 weeks in chemostat)

15,00 10,00 5,00

3,0 2,0 1,0

0,00 16/1/13

0,0 18/1/13

20/1/13

22/1/13

24/1/13

26/1/13

28/1/13

30/1/13

1/2/13

3/2/13

date CX

rX

Productivity target (equator location)

Maximal productivity achieved (15days cultivation) : 6.0 kg.m-3d-1

−3 < rX > max ≅ 2 − 10 kg.mliq .day −1

< s X > max ≅ 16 − 28.10 −3 kg.m -2 .day −1 < s X > max ≅ 60 − 100 t.ha -1.year −1

Expected from models (prior AlgoFilm development) : 5.5kg/m3.day

Models prediction: 11kg/m3.day

Volumetric productivity (kg.m-3.j-1)

Average radiation (µmolhn.m-2.s-1)

430

270

Increase of volumetric productivity (with Algofilm technology)

Raceway pond

~ 0,1

~ 0,7

~60-80

Conventional technology (alight=20m-1)

~ 0,3 - 0,5

~ 0,2 – 0,35

~ 20

Algofilm (experimental results)

~ 6,1

~ 5,7

Technology

rX (g.l-1.j-1)

Evolution of biomass concentration and volumetric productivity for D=0,013 h-1 (typical summer cycle, Saint-Nazaire)

Strain : C.vulgaris 211-19 Specific area : 470 m2.m-3 Culture thickness: 1,5 mm

AlgoFilm solar technology: investigation under day-night cycles with high PFD Biomass concentration and illuminated fraction fraction during a cycle (summer, St Nazaire) 1600

20,00

1400

−3 liq

< C X > = 19 kg.m .

1200

19,00 1000

CX(g.l-1)

18,00 17,00

800

Biomass loss during the night : 7-8 %

16,00 15,00

600 400

14,00 200

13,00 12,00 18:00

0 21:00

0:00

3:00

6:00

9:00

12:00

15:00

18:00

21:00

Global radiation (µmol.m-2.s-1)

21,00

1

0,5

Illuminated fraction γ

22,00

0

0:00

hour

q0= 430 µmolhν.m-2.s-1

Biomass concentration

−3 liq

< rX > = 6,1 kg.m .day

−1

global radiation

illuminated fraction

(in day-night cycles)

−3 < rX > max ≅ 11 kg.m liq .day−1 (in constant radiation)

Pigment content : 3,5%Biomass (5% in continuous light, same PFD)

Several phenomena tend to decrease biomass productivity in day-night cycles: optimal light absorption is never achieved, there is biomass loss during night

Maximization versus process stability… Maximal productivity Decrease of 15% in productivity

Maximal productivity is achieved for low residence time value (τpopt=0.7day), to avoid from too high biomass concentration promoting dark volumes with negative influence

Doubling residence time

β Periods with light excess: 50-60%

A compromise has to be found between productivity and process robustness (with regards to light)

Periods with light excess limited to 20% of the year

Conclusion Robust and validated engineering tools are today available to optimize PBR design and operating (a basis for PBR intensification as done with AlgoFilm© technology) A good engineering of the cultivation system is not sufficient: for any given system, optimizing internal light absorption conditions will have a major impact on biomass productivity

Efforts have still to be pursued, especially in the case of highly intensified and solar PBR. A major goal is to achieve robust and efficient technologies, but transient illumination conditions lead to specific optimization problems which have to consider that solar conditions have rather high uncontrolled variations, and that both PBR and algae have their own dynamics

Thank you for your attention Atlantique Ocean

http://www.gepea.fr/

La Baule

St Nazaire

GEPEA laboratory AlgoSource Technologies

POLYTECH Nantes campus Graduate School of Engineering Chemical Eng. and bioprocess dept

Biosolis experimental outdoor area

Outdoor R&D platform for PBR engineering and microalgae processing (available 2015)