Biophysical Model of Microalgal Productivity in...

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!