1 ASU Confidential Proprietary

Evaluation of ATP3 Unified Field Study Results to Identify the Primary Factors Affecting Pond

Productivity Braden Crowe , Valerie Harmon, John McGowen, Todd Lane, Eric Knoshaug, Thomas Dempster, Thomas Igou, Chris Withstandley, Marcela Saracco , Phil

Pienkos

2015 Algae Biomass Summit Washington D.C.

September 1, 2015

Motivation

2

What is a reasonable biomass yield?

5+ fold difference!

Huntley 2015

Motivation

3

What is a reasonable biomass yield?

2013 harmonized value, ~13 g/m2-

day

Huntley 2015

Test bed locations

4

Test bed locations

5 Venteris 2014

Test bed locations

6 Venteris 2014

Florida>Arizona>California>Georgia

Controlled variables

Controlled variables

•  pH •  Paddle wheel speed •  Strain •  Nutrient regime •  …

Year 1 of the UFS – What did it look like?

9

Year 1 of the UFS – What did it look like?

10 Productivity calculated as slope of the batch growth curve. Incident light intensity and pond water temperature averaged over this time interval

11

Daily Biomass Productivity, g AFDW/m2/day

Daily Productivity (g/m2-day) Based on change in total biomass in pond between AFDW data points (1-2 days typically) (area 4.2 m2, volume 1025L at 25 cm)

N. oceanica batch productivity

0

2

4

6

8

10

12

14

0 50 100 150 200 250 300 350

Bio

mas

s Pr

oduc

tivity

, g A

FDW

/m2-

day

Day of year

Southwest, desert Pacific, tropical Western, Coastal Southeast, Florida Southeast, GA

N. oceanica, annual average productivity

0

1

2

3

4

5

6

7

8

9

10

Bio

mas

s Pr

oduc

tivity

, g

AFD

W/m

2-da

y

Southeast, Florida Pacific, tropical Southwest, desert Western, Coastal Southeast, GA

N. oceanica, influence of temperature

0

2

4

6

8

10

12

14

5 10 15 20 25 30 Bio

mas

s Pr

oduc

tivity

, g A

FDW

/m2-

day

Average pond water temperature over batch phase, ˚C

Southwest, desert Pacific, tropical Western, Coastal

N. oceanica, influence of temperature

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

2

4

6

8

10

12

14

0 10 20 30 40

Spec

ific

grow

th ra

te, 1

/day

Bio

mas

s Pr

oduc

tivity

, g A

FDW

/m2-

day

Average pond water temperature over batch phase, ˚C

ATP3, all sites umax, Huesemann 2013

Average incident light intensity and productivity are linearly correlated

y = 0.0405x - 1.2961 R² = 0.8811

0

2

4

6

8

10

12

14

0 50 100 150 200 250 300 350 400 Bio

mas

s pr

oduc

tivity

, g A

FDW

/m2-

day

Average incident light intensity, W/m2 Southwest, desert Pacific, tropical Western, Coastal

Influence of dilution rate, Desert Southwest

0 2 4 6 8

10 12 14 16 18 20

0 50 100 150 200 250 300 350

Bio

mas

s pr

oduc

tivity

, g A

FDW

/m2-

day

Day of Year

Batch 0.21 0.32

Influence of dilution rate, Desert Southwest

0

2

4

6

8

10

12

14

16

18

20

Bio

mas

s pr

oduc

tivity

, g A

FDW

/m2-

day

0.21/day 0.32/day Batch

Influence of dilution rate, Desert Southwest

0

2

4

6

8

10

12

14

16

18

20

Bio

mas

s pr

oduc

tivity

, g A

FDW

/m2-

day

0.21/day 0.32/day Batch

~30 MT/ha-yr

Influence of dilution rate, Desert Southwest

0

200

400

600

800

1000

1200

0

5

10

15

20

25

30

35

40

6/30 0:00 7/1 0:00 7/2 0:00 7/3 0:00 7/4 0:00

Inci

dent

ligh

t int

ensi

ty, W

/m2

Pond

wat

er te

mpe

ratu

re, ˚

C

P1, water temperature P2, water temperature P3, water temperature P4, water temperature P5, water temperature P6, water temperature

•  Annual average productivity highest in Southeast Florida (~9.5 g AFDW/m2-day), although still low

•  Venteris et al. productivity maps reasonably accurate, although rain affects underestimated

•  Dilution rate doesn’t seem to matter, at least for N. oceanica

•  High temperature of feed water potentially skews dilution rate interpretation

Conclusions

Data available via Open EI

Acknowledgments Florida Algae Steven Schlosser Chris Withstandley Mary Riddle Nancy Pham Ho (FIT) UTEX Schonna Manning Jerry Brand Commercial Algae Management Albert Vitale Robert Vitale Harmon Consulting Valerie Harmon

Cellana Marcella Saracco Egan Rowe Emily Knurek Kate Evans Peter Prentiss Reyna Javar Kari Wolff Keao Bishop-Yuan Christina Boyko Charlie O’Kelley Sandia National Labs Ron Pate Todd Lane Patricia Gharagozloo Thomas Reichardt Pamela Lane Deanna Curtis Kunal Poorey Lucy Cui Jessica Drewry NREL Phil Pienkos Lieve Laurens Ed Wolfrum David Crocker Stefanie Van Wychen Eric Knoshaug Ryan Davis

ASU Gary Dirks John McGowen Thomas Dempster Milt Sommerfeld William Brandt Pete Lammers Jessica Cheng Jordan McAllister Sarah Arrowsmith David Cardello Theresa Rosov Mary Cuevas Jeffrey Prairie Richard Malloy Xuezhi Zhang Henri Gerken Pierre Wensel Linda Boedeker Sarah Mason Travis Johnson Sydney Lines Wyatt Western Mariah Patton Maria Bautista Carlos Luna Delaney De Hertogh Shaylin Mcghee Caden Offield

Cal Poly Tryg Lundquist Braden Crowe Garrett Murawsky Eric Nicolai Aydee Melgar Gulce Ozturk Kaitlyn Jones Michael Antoine Trung K Tran Jake Bender Heather Freed Daniel McBroom Michele Hendrickson Gerard Nguyen Deven Diliberto Jack Sunderland Dan Averbuj Ann Marie Sequeira Lauren Miller Michele Hendrickson Emily Wang Jack Sunderland Ann Marie Sequeira Soroush Aboutalebi Lauren Miller Samantha Lui Letty Thottathil …..and more

Georgia Tech Thomas Igou Yongsheng Chen Steven Van Ginkel Thomas Igou Zixuan Hu Fariha Hassan Jerry Duncan Frazier Woodruff Shusuke Doi Hao Fu Patricia Penalver-Argueso Allison Dunbar Allison Carr Sichoon Park Priya Pradeep Terry Snell Catherine Achukwu Christine Yi

Seasonal Variation in Yield Productivity

Nannochloropsis oceanica KA32 Chlorella vulgaris LRB-AZ-1201

•  Typically ~4-fold or less difference in yield productivity across the seasons at any given site.

•  GT experienced unusually wet and cold Winter season increasing the yield productivity differential.

•  Greater variation in seasonal productivity for KA32