Mary Silver images
Biophysical Model of Microalgal Productivity in Ponds
John J. Cullen1
1Department of Oceanography, Dalhousie UniversityHalifax, Nova Scotia, Canada B3H 4J1
Richard F. Davis1, Robert R. Bidgare2
Zackary I. Johnson2, Mark E. Huntley2
2 University of Hawaii
ASLO Aquatic Sciences Meeting — Nice30 January 2009
Supported by Cellana
ASLO 2009: John Cullen et al.
Marine AlgaeCompelling Advantages
• Saline water• Non-arable land• Algae consume a major greenhouse
gas: CO2• Higher productivity (~15x)• New, additional fuel feedstock• New, additional animal feedstock
Bigelow Laboratory Phytopia
ASLO 2009: John Cullen et al.
Not a new idea
ASLO 2009: John Cullen et al.
Studied for years
ASLO 2009: John Cullen et al.
Open Ponds
Advantages Economical Relatively simple High rates of production possible
Disadvantages Potential for contamination (competitors, invaders) Less control on conditions (e.g., pH, Temp)
www.seambiotic.com
ASLO 2009: John Cullen et al.
Photobioreactors
Advantages Controlled, optimized conditions Contamination can be minimized High rates of production
Disadvantages Expensive
http://www.algaelink.com/algae-cultivation.htm
ASLO 2009: John Cullen et al.
PHOTO-BIOREACTORS
Continuous Nutrient sufficient High yield Small area
Cellana two-stage cultivation
ASLO 2009: John Cullen et al.
PHOTO-BIOREACTORS
Continuous Nutrient sufficient High yield Small area
Cellana two-stage cultivationOPEN PONDS
Batch Short residence time Large area
ASLO 2009: John Cullen et al.
Exploiting Algal Physiology
NUTRIENT SUFFICENT
Nile red stained culture
ASLO 2009: John Cullen et al.
Exploiting Algal Physiology
NUTRIENT SUFFICENT
NUTRIENT STRESSED
Nile red stained culture
ASLO 2009: John Cullen et al.
Perennial Goal: Optimizing Production
Huesemann et al., 2008, Appl Biochem Biotechnol
ASLO 2009: John Cullen et al.
Sustained Production Rates (P) at Large Scale
SpeciesP
(g DW m-2 d-1)Period(days) Reference
Tetraselmis suecica 62 24 Laws et al. 1986
Skeletonema costatum 61.3 240 Kitto et al. 1999
Phaeodactylum tricornutum 81-96** 150 Acién-Fernandez et al. 1998
All in outdoor reactor systems, 5,000 to 50,000 L
** Monthly average
Longstanding question: What are the limits to production?
ASLO 2009: John Cullen et al.
Many good models: each has its own sets of assumptions and most use different sets of variables (e.g., dry weight, energy units, etc.)
Models are essential
ASLO 2009: John Cullen et al.Mary Silver images
Photosynthesis Optimization Model for Production of Useful Substances
(POMPoUS)
PZ,T (g C m-2d-1) = [P(z,t)z=0
bottom
∫t=0
T
∫ − R] ⋅dz ⋅dt
LZ,T (g lipid m-2d-1) = PZ,T ⋅ (CL
CTot
) ⋅ ( g lipidg lipid C
) / (PQ)L
Our model:
ASLO 2009: John Cullen et al.
Motivation:
ASLO 2009: John Cullen et al.
Motivation:
New model withdefinitive answers
ASLO 2009: John Cullen et al.
Motivation:
New model withdefinitive answers
ASLO 2009: John Cullen et al.
Motivation:
ASLO 2009: John Cullen et al.
Evaluate the influences of environmental factors and physiological properties on production without having to “mix and
match” results of different models
Motivation:
ASLO 2009: John Cullen et al.
A systematic approach
ASLO 2009: John Cullen et al.
A systematic approach
1) Develop a quantitative framework to predict the production of algal biomass (e.g., lipid, protein, carbohydrate) in ponds.
ASLO 2009: John Cullen et al.
A systematic approach
1) Develop a quantitative framework to predict the production of algal biomass (e.g., lipid, protein, carbohydrate) in ponds.
ASLO 2009: John Cullen et al.
A systematic approach
1) Develop a quantitative framework to predict the production of algal biomass (e.g., lipid, protein, carbohydrate) in ponds.
2) Optimize production potential through strain selection and manipulation of growth conditions, guided by the model.
then…
ASLO 2009: John Cullen et al.
A systematic approach
1) Develop a quantitative framework to predict the production of algal biomass (e.g., lipid, protein, carbohydrate) in ponds.
2) Optimize production potential through strain selection and manipulation of growth conditions, guided by the model.
then…
ASLO 2009: John Cullen et al.
A systematic approach
1) Develop a quantitative framework to predict the production of algal biomass (e.g., lipid, protein, carbohydrate) in ponds.
2) Optimize production potential through strain selection and manipulation of growth conditions, guided by the model.
then…
3) Validate and improve the model through comparison with measurements from ponds.
ASLO 2009: John Cullen et al.
Objectives of the modelingPredict the production of algal biomass (e.g., lipid, protein, carbohydrate):As influenced by environmental factors: Pond depth Irradiance (including daylength and clouds) Concentration of algae Nitrogen source Growth mode (dilution)
and physiological factors Maximum quantum yield Light saturation Respiration Light absorption characteristics Inhibition of photosynthesis
Using explicit, transparent and ultimately testable application of assumptions.
ASLO 2009: John Cullen et al.
Many choices were made Options (choice in bold) Described as a function of Input variable Irradiance Energy, Quant a Shortwave, PAR, spectral, absorbed PU R [Daily integral], Date, Latitude, Total daily, time resolv e d [Fixed], Latitude, Date, Cloudiness,
[measured], prognostic model Incident, depth resolved, Eulerian,
Lagrangian (particle tracking) Algal biomass, Algal optical properties, bottom reflection
Temperature Constant, variable Prescribed (e.g., from climatology), measured, calculated
Biomass Chlorophyll, dry weight, carbon, nitrogen, energy
[Fixed], time [specified], prognostic model
Growth mode [NA], batch, dilution (continuous, semi-continuous)
Properties Spectral absorption per unit chlorophyll Prescribed (from cell size), [measured], modeled
Photosynthesis vs irradiance
Quantum yield (mol C / mol photons absorbed)
Nitrogen source, end products (lipid, protein, carbohydrate), NPQ
Saturation irradiance (PAR, PU R ) Temperature, surface irradiance, average irradiance, cellular optical properties
Photorespiration, alternate electron sinks, NPQ, “photoinhibition”
Specified only through assumed infleunces on P vs E, modelled directly
Repiration (d-1) Fixed, variable, basal plus variable Irradiance (mean, maximum), photosynthetic capacity (e.g., PBmax), Daily gross photosynthesis, specified (species), modelled directly
Output variables Carbon, Dry weight, Nitrogen, Oxygen, Growth rate, Lipid, Protein, Carbohydrate
Parameter
Modeling the process:
Modeling the process:
All day and night, top to bottom:
PZ,T (gC m-2d-1) = [P(z,t)z=0
bottom
∫t=0
T
∫ − R] ⋅dz ⋅dt
Modeling the process:
Fully spectral with physiological parameters:
P(z,t) = Chl ⋅PmaxB ⋅ (1− e−(φmax ⋅aφ
* ⋅EPUR (z,t )/PmaxB ) )
(Photosynthesis is a function of absorbed radiation)
All day and night, top to bottom:
PZ,T (gC m-2d-1) = [P(z,t)z=0
bottom
∫t=0
T
∫ − R] ⋅dz ⋅dt
Modeling the process:
Fully spectral with physiological parameters:
P(z,t) = Chl ⋅PmaxB ⋅ (1− e−(φmax ⋅aφ
* ⋅EPUR (z,t )/PmaxB ) )
(Photosynthesis is a function of absorbed radiation)
All day and night, top to bottom:
PZ,T (gC m-2d-1) = [P(z,t)z=0
bottom
∫t=0
T
∫ − R] ⋅dz ⋅dt
Respiration includes light independent and light dependent terms:
R = RB
⋅Chl; where
RB
= RoB + FR ( PB(z,t) ⋅dz ⋅dt∫∫ )
Modeling the process:
The result is primary carbon with the reduction state of carbohydrate
Fully spectral with physiological parameters:
P(z,t) = Chl ⋅PmaxB ⋅ (1− e−(φmax ⋅aφ
* ⋅EPUR (z,t )/PmaxB ) )
(Photosynthesis is a function of absorbed radiation)
All day and night, top to bottom:
PZ,T (gC m-2d-1) = [P(z,t)z=0
bottom
∫t=0
T
∫ − R] ⋅dz ⋅dt
Respiration includes light independent and light dependent terms:
R = RB
⋅Chl; where
RB
= RoB + FR ( PB(z,t) ⋅dz ⋅dt∫∫ )
ASLO 2009: John Cullen et al.
Photosynthetic quotients and dry weight:C ratios are applied to calculate yields
gC g DW-1 PQ nitrate PQ NH4g DW per g Primary C
NO3
g DW per g Primary C
NH4Carbohydrate 0.4 1.00 1.00 2.50 2.50
Protein 0.53 1.57 1.01 1.20 1.87Lipids 0.76 1.50 1.50 0.88 0.88
other than phospho-glycerides
Photosynthetic quotients from Williams and Robertson, 1991 (J. Plankton Res.)g C g DW-1 from Geider and LaRoche 2002 (Eur.J. Phycol.)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield Maximum rate normalized to Chl (tied to saturation irradiance)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield Maximum rate normalized to Chl (tied to saturation irradiance) Respiration function (basal plus variable)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield Maximum rate normalized to Chl (tied to saturation irradiance) Respiration function (basal plus variable) Photosynthetic quotient (function of N-source)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield Maximum rate normalized to Chl (tied to saturation irradiance) Respiration function (basal plus variable) Photosynthetic quotient (function of N-source) End products (lipid, carbohydrate, protein)
ASLO 2009: John Cullen et al.
Tunable variables and parameters
Solar irradiance (including daylength and cloud factor) Bottom depth + reflectivity (pond liner) Chlorophyll concentration Absorption spectrum normalized to chlorophyll (packaging) Photosynthetic quantum yield Maximum rate normalized to Chl (tied to saturation irradiance) Respiration function (basal plus variable) Photosynthetic quotient (function of N-source) End products (lipid, carbohydrate, protein)
Initial runs (> 1.9 million) relevant to Hawaii;subset explored here
ASLO 2009: John Cullen et al.
Results: Optimizing pond depth and chlorophyll concentration
Interaction of biomass concentration and pond depth as influenced by respiration
0
5
10
15
20
25
30
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
Prod
uctio
n (g
C m
-2)
Bottom Depth (m)
24-h Net Production
1000
Chl = 500 mg m-3
5000
2000
1500
30004000
'Chl'>0 and'Zmax'>0 and
'Pbmax'=25 and'Phimax'=.083 and
'Rb'=.1 and'Ro'=5 and
'CloudFact'=1 and 'Refl'=.5
ASLO 2009: John Cullen et al.
Assessing losses to saturation of photosynthesis
0
5
10
15
20
25
30
35
40
0 200 400 600 800 1000
Ek(PUR) [µmol m-2 s-1]
24 h
Pro
duct
ion
(gC
m-2
)Mitra and Melis (2008) Optics Express
ASLO 2009: John Cullen et al.
Assessing losses to saturation of photosynthesis
56% increase in saturated rate corresponds to 22% increase in daily production
0
5
10
15
20
25
30
35
40
0 200 400 600 800 1000
Ek(PUR) [µmol m-2 s-1]
24 h
Pro
duct
ion
(gC
m-2
)Mitra and Melis (2008) Optics Express
22%
56%
ASLO 2009: John Cullen et al.
24 h
Net
Pro
duct
ion
(g C
m-2
d-1
)
Daytime average PUR (µmol m-2 s-1)
Net Production vs Average Daily Utilizable Radiation(5 depths X 6 concentrations of chl)
ASLO 2009: John Cullen et al.
24 h
Net
Pro
duct
ion
(g C
m-2
d-1
)
Daytime average PUR (µmol m-2 s-1)
Thin culture: less light absorbed, higher
average PUR
Net Production vs Average Daily Utilizable Radiation(5 depths X 6 concentrations of chl)
ASLO 2009: John Cullen et al.
Net Production vs Average Daily Utilizable Radiation(5 depths X 6 concentrations of chl)
Light regime set by [Chl] and bottom depth: optimal combinations correspond to modest average irradiance. (Left side - too much light absorbed near surface; right side - culture too thin and
light is not absorbed.)
24 h
Net
Pro
duct
ion
(g C
m-2
d-1
)
Daytime average PUR (µmol m-2 s-1)
Culture too thick: respiration of shaded
algae
ASLO 2009: John Cullen et al.
Maximum production at modest biomass-normalized rates(<< maximum growth rates)
Maximum production corresponds to roughly 50 g C gChl-1 d-1. This implies relatively low growth rates of perhaps 0.7 d-1 or less.
24 h PB (g C g Chl-1 d-1)
24 h
Net
Pro
duct
ion
(g C
m-2
d-1
)
0 50 100 150 200
ASLO 2009: John Cullen et al.
Loss processes will take a toll
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized?
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized? Photorespiration
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized? Photorespiration Alternate electron sinks
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized? Photorespiration Alternate electron sinks “Photoinhibition”
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized? Photorespiration Alternate electron sinks “Photoinhibition”
Downregulation / damage of PSII
ASLO 2009: John Cullen et al.
Loss processes will take a toll Respiration
How should it be parameterized? Photorespiration Alternate electron sinks “Photoinhibition”
Downregulation / damage of PSII
Optimization requires minimizing these losses
ASLO 2009: John Cullen et al.
Further steps
ASLO 2009: John Cullen et al.
Further steps Parameterize physiological functions
e.g., f (temperature), f (light history)
ASLO 2009: John Cullen et al.
Further steps Parameterize physiological functions
e.g., f (temperature), f (light history) Adapt for range of locations
ASLO 2009: John Cullen et al.
Further steps Parameterize physiological functions
e.g., f (temperature), f (light history) Adapt for range of locations Test model predictions vs observations
Production as well as rate processes
ASLO 2009: John Cullen et al.
Summary and Conclusions
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process Established and possibly new insights can be examined in a
comprehensive, quantitative framework
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process Established and possibly new insights can be examined in a
comprehensive, quantitative framework
Maximum yields can be calculated and efforts can focus on minimizing inevitable losses
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process Established and possibly new insights can be examined in a
comprehensive, quantitative framework
Maximum yields can be calculated and efforts can focus on minimizing inevitable losses
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process Established and possibly new insights can be examined in a
comprehensive, quantitative framework
Maximum yields can be calculated and efforts can focus on minimizing inevitable losses
Assumptions are transparent and can be changed
ASLO 2009: John Cullen et al.
Summary and Conclusions
A quantitative model accounts for many of the factors that influence production of algal biomass in ponds Based on absorption of light, photochemical conversion efficiency and
chemical composition of nutrients and end-products
It is a useful tool for sensitivity analysis leading to optimization of the production process Established and possibly new insights can be examined in a
comprehensive, quantitative framework
Maximum yields can be calculated and efforts can focus on minimizing inevitable losses
Assumptions are transparent and can be changed Through testing, results will become increasingly realistic
ASLO 2009: John Cullen et al.Mary Silver images
Thank you!