Increasing Annual Microalgae Biomass Productivity
Through Crop Rotation:
Characterization and Modeling of Winter and Summer Strains
Michael Huesemann*, Scott Edmundson*, Mark Wigmosta**, and Louis Brown***
*Pacific Northwest National Laboratory, Marine Sciences Laboratory, Sequim, WA
**Pacific Northwest National Laboratory, Hydrology Group, Richland, WA
***Texas A&M AgriLife Research, Pecos, TX
October 7, 2015
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Overview
  Hypothesis: Annual biomass productivity can be improved by strain rotation
  Strains: Chlorella sorokiniana (warm season) & Kirchneriella cornuta (cold season)
  Strain Characterization: µmax(T,I), µdark(T,I), light attenuation coefficient (ksa)
  Biomass Growth Modeling: Predict monthly biomass productivity for each strain
at two locations (Fort Myers, FL & Mesa, AZ) at three pond depths (10, 20, 30 cm)
  Crop Rotation Strategy: For each month, select strain with highest predicted
biomass productivity. Identify gain in annual productivity relative to single cropping.
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Biomass Growth Modeling
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Model Assumptions and Input Parameters
  Model Assumptions
  Light and temperature are the main determinants of biomass growth and
productivity
  There are no other growth-limiting factors (N, P, CO2, mixing)
  Physical Input Parameters
 
 
 
 
Incident light intensity (Io) as a function of time (1 min)
Water temperature (T) as a function of time (5 min)
Culture depth (d)
Dilution rate (D) for continuous or semi-continuous cultures
  Biological Species-Specific Input Parameters
  Maximum specific growth rate (µ) as a function of T and I
  Rate of biomass loss in the dark (µdark) as a function of T & Iavg
  Scatter-corrected biomass light absorption coefficient (ksa)
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Maximum Specific Growth Rate (µmax)
as a Function of Temperature
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Photosynthetic Oxygen Production
as a Function of Light Intensity (PI Curve)
Chlorella sorokiniana
Kirchneriella cornuta
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Percent Biomass Lost During Dark Period
Chlorella sorokiniana
Kirchneriella cornuta
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Light Attenuation as a Function of Depth
Chlorella sorokiniana
Kirchneriella cornuta
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Model Validation in Outdoor Ponds in Pecos, TX
Kichneriella cornuta
Water Temperature Script
Measured Biomass as a Function of Time
Sunlight Intensity Script
Feb. 2015
Pond depth = 10 cm
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Model Validation in Outdoor Ponds in Pecos, TX
Kichneriella cornuta
Predicted Biomass as a Function of Time
Measured vs. Predicted Productivity
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Fort Myers, Florida
Light Intensities and Water Temperatures*
*Generated with PNNL’s Biomass Assessment Tool using 30 year average meteorological data
and a pond energy balance model.
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Fort Myers, Florida
Monthly Biomass Productivities
Pond depth = 10 cm
Pond depth = 20 cm
Pond depth = 30 cm
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Mesa, Arizona
Light Intensities and Water Temperatures*
*Generated with PNNL’s Biomass Assessment Tool using 30 year average meteorological data
and a pond energy balance model.
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Mesa, Arizona
Monthly Biomass Productivities
Pond depth = 10 cm
Pond depth = 20 cm
Pond depth = 30 cm
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Benefit of Strain Rotation
Comparison of Annual Productivities
Fort Myers, Florida
Mesa, Arizona
October 7, 2015
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Summary and Conclusions
  Difference in warm and cold season strain characteristics:
  Temperature optimum: C. sorokiniana (36 °C) versus K. cornuta (27 °C)
  Dark biomass loss increases with temperature and light exposure during day
  Light attenuation coefficients
  Model was successfully validated for C. sorokiniana and K. cornuta
  Culture depth can be used for thermal management of ponds
  Annual biomass productivity increases with decreasing culture depth
  Shallow ponds heat up more during day (µ↑) & cool down more at night (µdark↓)
  The deeper the pond, the greater the aphotic zone and dark biomass losses
  Strain rotation increases annual biomass productivity, relative to single
cropping, by 8 to 25%, depending on location and culture depth
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Additional Viewgraphs
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Model Validation in Outdoor Ponds in Arizona
Chlorella sorokiniana
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Kirchneriella cornuta
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