Development of Integrated Screening, Cultivar Optimization, and Verification Research Michael Huesemann, Scott Edmundson, Song Gao Pacific Northwest National Laboratory Taraka Dale, Amanda Barry Los Alamos National Laboratory Lieve Laurens, Phil Pienkos, Eric Knoshaug National Renewable Energy Laboratory Todd Lane, Jeri Timlin, Kunal Poorey, Tom Reichardt Sandia National Laboratory John McGowen AzCATI, Arizona State University
Objective of the DISCOVR Consortium Project Reduce biofuel costs by increasing biomass productivity Challenge ➢ A major driver of algae biofuel costs is productivity , including culture resilience and biochemical composition . Project Goal ➢ Reduce total microalgae biofuels production costs by developing an integrated screening platform for the identification of high productivity strains with cellular composition suitable for biofuels and bioproducts for resilient, year-round outdoor cultivation . Outcomes ➢ Standardized identification, deep characterization, and delivery of robust, high productivity microalgae strains to the bioenergy and bioproducts communities, such as industry and BETO funded projects . ➢ Improved productivity and reduced costs via a streamlined approach to strain characterization and implementation in outdoor trials. 2
DISCOVR Project Overview and Work Flow Strains are tested and down-selected in pipeline consisting of 6 TIERs Objectives & Outcomes ➢ Standardized testing conditions for strain comparison ➢ Climate-simulated culturing to quantify winter and summer season biomass productivities ➢ Information on carbon storage and co-product potential ➢ Improvement in salinity tolerance and lipid/biomass accumulation ➢ Data on pest tolerance ➢ Outdoor validation and streamlined funneling of strains into the SOT 3
Approach: Overview DISCOVR pipeline accelerates identification of top producing strains Critical Success Factors Challenges ➢ Demonstrate high seasonal ➢ Unique state-of-the-art technical biomass productivities in new capabilities are employed at each and/or improved strains TIER. ➢ Optimize value of biomass via ➢ Complementary core identifying best strains and competencies of the consortium culture conditions labs and SOT testbed are applied together to make progress ➢ Prevent crop failures by towards BETO’s targets . deleterious agents via ➢ Effective communication and preventative and predictive methods cohesive decision-making across DISCOVR team. ➢ Demonstrate at least 10% per ➢ Strong partnership with outdoor year increase in SOT annual areal biomass productivity testbed. 4
Approach: TIER I Strain Characterization Temperature and salinity tolerance is measured in gradient incubators Objectives ➢ Identify the suitable growing season and approximate salinity for candidate DISCOVR strains ➢ Quantify maximum specific growth rate data for down-selection to LEAPS (Laboratory Environmental Algae Pond Simulator) testing. Approach ➢ PNNL Thermal Gradient Incubator (TGI) ➢ Measure maximum specific growth rates at saturating light intensities ➢ Temperature range from ~4 to 45 ˚C ➢ PNNL Salinity Gradient Incubator (SGI) ➢ Abbreviated salinity screen at 25 ˚C ➢ 5, 15, 35 parts per thousand (ppt) 5
Results: Typical TIER I Strain Characterization Data Each strain has a unique temperature and salinity tolerance range Salinity Tolerance Profile Temperature Tolerance Profile 6
Results: Salinity Tolerance of 41 TIER I Strains Optimum salinity determines choice of medium (brackish/seawater) Maximum Specific Growth Rate (day -1 ) 6 5 Chlorella sorokiniana DOE1412 Nannochloropsis oceanica CCAP849/10 Acutodesmus obliquus DOE0152.Z Nannochloropsis salina CCMP1776 Phormidium cf. autmnale CCMEE5034.1-3 4 Anabaena sp. ATCC33081 Arthrospira platensis ARS1 Leptolyngbya sp. CCMEE5010.3-1 Picochlorum soleocismus DOE101 Chlorella sp. DOE1044 Acutodesmus obliquus UTEX393 3 MONOR1 Scenedesmus acutus AZ0401 Stichococcus minor CCMP819 Chlorella sp. DOE1116 Industrial Strain AB1 2 Chlorella vulgaris LRB-AZ1201 Coelastrella sp. DOE0202 Monorahpidium minutum 26B-AM Nannochloris NREL39-A8 Scenedesmus sp. NREL46B-D3 1 Chlorococcum sp. UTEX117 Agmenellum quadriplicatum UTEX2268 Micractinium sp. NREL14-F2 Porphyridium cruentum CCMP675 0 Chlorella sp. NREL4-C12 Picochlorum oklahomensus CCMP2329 5 15 35 Salinity (ppt) Not all results shown due to space limitations 7
Results: Temperature Tolerance of 34 TIER I Strains Temperature tolerance range determines choice of cultivation season Maximum Specific Growth Rate (day -1 ) 8 7 6 5 Industrial Strain AB1 Agmenellum quadriplicatum UTEX2268 Picochlorum oklahomensis CCMP2329 Nannochloris sp. NREL39-A8 Stichococcus minor CCMP819 C. sorokiniana DOE1412 (Benchmark) 4 Chlorella sp. DOE1044 Acutodesmus obliquus UTEX393 Chlorella sp. DOE1116 Scenedesmus acutus AZ-0401 Chlorella sp. NREL4-C12 Coelastrella DOE0202 3 Micractinium sp. NREL14-F2 Acutodesmus obliquus DOE0152.Z Synechococcus elongatus UTEX2973 Chlorella vulgaris LRB-AZ1201 2 Picochlorum soleocismus DOE 101 Chlorococcum sp. UTEX BP7 Chlorococcum sp. UTEX117 Monorahpidium minutum 26B-AM MONOR1 1 Tisochrysis lutea CCMP1324 Arthrospira maxima CCALA27 Nannochloropsis sp. CCMP 531 0 Nannochloropsis oceanica CCAP849/10 Porphyridium cruentum CCMP675 Chloromonas reticulata CCALA870 4 10 16 26 29 36 41 48 Microchloropsis salina CCMP1776 = Benchmarks Temperature (˚C) Not all results shown due to space limitations 8
Results: Ranking TIER I Strains in Winter Season Top ranked TIER I strains are tested in LEAPS PBRs at TIER II 9
Results: Ranking TIER I Strains in Summer Season Top ranked TIER I strains are tested in LEAPS PBRs at TIER II 10
Approach: TIER II Strain Culturing in LEAPS Use unique pond simulator PBR to measure productivity (21 strains) Objective Quantify Arizona winter and summer season biomass productivity under identical climate-simulated culture conditions and identify best strains Approach ➢ The PNNL Laboratory Environmental Algae Pond Simulator (LEAPS) accurately simulates microalgae growth in outdoor ponds. ➢ The top winter and summer season TIER I strains were cultured in LEAPS using January 31 and July 1 light & temperature scripts for Mesa, Arizona (AzCATI). ➢ LEAPS cultures were grown first under nutrient-replete conditions (DISCOVR medium, 20 cm), then under nutrient-deplete conditions. ➢ Biomass composition was quantified by NREL. 11
Approach: LEAPS Light/Temp Scripts LEAPS photobioreactors simulate AzCATI ponds for January 31 PNNL Biomass Assessment Tool (BAT) generated light intensity and water temperature scripts for Mesa, AZ, January 31, error bars are for 30 year averages. 12
Approach: LEAPS Light/Temp Scripts LEAPS photobioreactors simulate AzCATI ponds for July 1 PNNL Biomass Assessment Tool (BAT) generated light intensity and water temperature scripts for Mesa, AZ, July 1, error bars are for 30 year averages. 13
Results: LEAPS Cultivation of Cold Season Strains Two top TIER II strains: Monoraphidium minutum & Micractinium NREL PAT PAT PAT PAT = Tested at the PNNL Algae Testbed Salinity in parts per thousand (ppt). Error bars are one stdev (n=4). = Benchmarks. 14
Results: LEAPS Cultivation of Warm Season Strains Two top TIER II strains: Nannochloris NREL + Scenedesmus obliquus 393 Nannochloris NREL: 28%-34% Better than Benchmarks! PAT PAT PAT PAT = Tested at the PNNL Algae Testbed Error bars are one stdev (n=4, with the exception of Nannochloris , n=20). = Benchmarks. 15
Results: TIER II Strains Show Strong Compositional Dynamics nutrients and replete and deplete winter/summer simulations experiments using Biomass collected from LEAPS Strain composition for LEAPS biomass measured & compared Stichococcus minor CCMP 819 - R Stichococcus minor CCMP 819 - D % Biomass * Note lack of full mass balance accounting will be addressed in FY19-FY20 Scenedesmus obliquus UTEX 393 - R 10 20 30 40 50 60 70 80 90 0 Scenedesmus obliquus UTEX 393 - D Scenedesmus obliquus DOE 0152 - R Protein Scenedesmus obliquus DOE 0152 - D FAME Chlorella vulgaris LRB-1201 - R Starch Chlorella sorokiniana UTEX BP15 DOE 1116 - R Chlorella vulgaris LRB-1201 - D Chlorella sorokiniana UTEX BP15 DOE 1116 - D Carbohydrates Biomass Composition Chlorella sorokiniana UTEX 1228 - D Chlorella sorokiniana UTEX 1228 - D Chlorella sorokiniana UTEX 1228 - BD2 - R Chlorella sorokiniana UTEX 1228 - BD2 - D Chlorella sorokiniana DOE 1412 - R Chlorella sorokiniana DOE 1412 - D 16
Results: Downselection based on Biomass Composition Preliminary valorization algorithm based on TEA being developed If after full TEA, cumulative “value” exceeds MBSP → profitable Example from previous work at NREL: $1,400 Lipid:Fuel Value ($/T) $1,213 $1,126 $600 $1,200 $1,107 $500 $923 $1,054 $1,091 y = 8.7x Biomass Value ($/ton)* $988 $400 $938 $1,000 $300 Carbohydrates:Succinic Acid Value $790 $200 $800 $800 $100 $700 $0 $600 $600 0 20 40 60 80 $500 y = 14.9x Lipid (% AFDW) $400 $400 $300 $200 $200 $100 $0 0 10 20 30 40 50 Protein:Bioplastics Value $0 Carbohydrates (% AFDW) $500 SD SD SD CZ CZ CZ NC NC NC For demonstration of $400 Early Mid Late Early Mid Late Early Mid Late $300 concept of biomass y = 9.4x Fuels Surfactants (ethoxylated sterols) $200 valorization only – $100 Polyol from Mannitol Polyols (PUFA upgrading) $0 preliminary analysis Succinic Acid Plastics (Algix) 0 10 20 30 40 50 Protein (% AFDW) 17
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