composition of algal biomass for biofuels and...
TRANSCRIPT
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Composition of Algal Biomass for Biofuels and Bioproducts:
High Impact Data and Method Harmonization
Lieve Laurens
October 1st, 2013
2
Outline:
• Compositional Analysis
o Productivity metrics and setting targets
o Understanding process chemistry
• Consequences of lack of standardization on data o Computational models (TEA/LCA) and data integration
• Common language on measurements?
o 2011 Baseline uncertainty quantification
o Biological and physiological measurement variability
o 2012-2013 ATP3 “Analytical Harmonization”
o Round robin experiments
o Conclusions and recommendations
3
Harmonization of Measurements
• Harmonized data and assumptions for LCA, TEA and RA models* after harmonization workshop, soliciting input from research and commercial community
• Consequence of lack of standardization is that measurement variability carries forward to computational models (TEA, LCA)
• Process and biomass improvement targets can only be set when measurement uncertainties are defined o Uncertainties in yield calculations
o Cost estimates
o Process Mass Balance
Fuel YieldLipids(kg/ton) 255±51
GreenDiesel($/gallon) 12.1±2.4
*Davis et al. 2012 www.nrel.gov/docs/fy12osti/55431.pdf
4
Compositional Analysis in Algae
• “How hard can it be?”
o Goal of 100% mass accounting
o Show between 60 and 120% mass balance closure
o Definitions of lipids, carbohydrates, protein
• Tracking metrics for target improvements in costs/productivity need better methods to robustly track progress at every step
• Empirical, historical methods utilized for algal biomass and process highly variable (12-18% RSD), often far from accurate, making comparison and modeling of data problematic
• Lack of standardized analytical procedures specifically tailored for algal biomass
5
2011 Method Uncertainties
• Goal: Quantify measurement uncertainty
• Survey of routinely used methods for proteins, lipids and carbohydrate measurements
• Comparison of yields, accuracy and precision of each method
• Design of Round Robin experiment o 8 analysts, 12 days, 9 methods, 5
replicates
o 1 biomass sample (Chlorella sp.)
Anal. Chem. (2012) 84(4):1879-87
Protein Lipids
Carbohydrates
6
Lipids Proteins
0 10 20 30 40 50
01
02
03
040
50
Carbs_HPLC
Ca
rbs_
PS
A
y = 0.69 x + 6.46
R2 = 0.77
CZ
NC
SD
0 10 20 30 40 50
01
02
03
040
50
Protein_N
Pro
tein
_B
y = 0.93 x + 5.08
R2 = 0.78
CZ
NC
SD
0 10 20 30 40 500
10
20
30
40
50
Lipids_FAME
Lip
ids_
B
y = 0.86 x + 5.35
R2 = 0.94
CZ
NC
SD
2012 Sample Matrix Variability
Carbohydrates
7
Biological vs Measurement Variability
Chlorella sp. Nannochloropsis sp. Scenedesmus sp.
0 5 10 15
01
02
03
04
05
0
Time (days)
% B
iom
ass (
DW
)
0 5 10 15
01
02
03
04
05
0
Time (days)
% B
iom
ass (
DW
)
0 5 10
01
02
03
04
05
0Time (days)
% B
iom
ass (
DW
)
Carbs_HPLCCarbs_PSALipids_FAMELipids_BProtein_NProtein_BStarch_MStarch_M_ASU
Lipids
Protein
Carbohydrates
Starch
8
Where we were a year ago…
• Significant bias between methods
o Underlying measurement chemistry differences
o Measurement interferences
• What are the best methods for compositional analysis of algae?
• What does the industry want to know?
• What data is needed to feed TEA/LCA models?
• Need for agreement on measurements for algal biomass constituents, and procedures for data collection
10
Harmonizing Productivity and Analytical Measurements for High Quality Data
Goal Develop and implement analytical pipeline for collection and dissemination of high-quality data to support computational models and experimental design
Specific Objectives • Distribution of harmonized
analytical procedures • Implementation and alignment
of methodology • Consensus of procedures and
data collection, QC and reporting • Interface with and population of
SDMS
11
Unified Field Studies – Harmonized Data
Goal Eliminate Measurement Variability from Unified Field Study experimental effectors
Objectives: • Need to lock down uncertainty
around measurements • Statistical analysis of UFS data
should not be influenced by analytical variability
• Unified biochemical data ingest to data management system
Productivity metrics: • Volumetric Dry Weight • Ash Free Dry Weight • Lipids • Carbohydrates • Protein
12
Harmonization Framework
Goal • Alignment and uncertainty quantification of measurements • Round Robin Experiment between participating laboratories
using standard biomass sample (reference material, Nannochloropsis sp.)
• Statistical interpretation and setting QC requirements of measurements for the duration of ATP3
Questions • Which methods should be used for compositional analysis? • What data format is used for data collection? • What are the checks/QC in place to make sure analytical
methods performed ok?
13
Harmonization Framework
AlgalBiomassMetricsHarmonizationFramework(Phase1)
ActivityID ActivityName
25-Feb
4-M
ar
11-M
ar
18-M
ar
25-M
ar
1-Apr
8-Apr
15-Apr
22-Apr
29-Apr
6-M
ay
13-M
ay
20-M
ay
27-M
ay
3-Jun
10-Jun
17-Jun
24-Jun
1-Jul
8-Jul
15-Jul
22-Jul
29-Jul
5-Aug
12-Aug
19-Aug
26-Aug
2-Sep
9-Sep
16-Sep
23-Sep
30-Sep
7-Oct
14-Oct
21-Oct
28-Oct
4-Nov
11-Nov
18-Nov
25-Nov
2-Dec
9-Dec
16-Dec
23-Dec
30-Dec
6-Jan
13-Jan
20-Jan
27-Jan
3-Feb
10-Feb
ProjectManagement
PM001 KickoffmeetingPM002 OrganizeandconductbiweeklyAnalytical/Datagroupmeetings
PM003 OrganizeandconductbiweeklyProduction/Datagroupmeetings
PM004 Finalharmonizationreportpublishing-DELIVERABLE
PM005 Phase1Go/NoGoReview
TaskA.1:Analyticalmethoddistributionandimplementation
FY13-A001 Compilationandstandardizationofformatofanalyticallaboratoryanalyticalprocedures(LAP)
FY13-A002 Distributionofmethodsamongpartners
FY13-A003 Feedbackandimprovementsofmethoddescriptions
FY13-A004 DistributionofQCbiomasssample
FY13-A005 Distributefirstdraftofstandardizedspreadsheetforanalyticaldatacollection
FY13-A006 Implementationof5methodsandintegrationofreplicatesonQCbiomass
FY13-A007 Reconveneandtroubleshootanalyticalprocedures
FY13-A008 UpdateLAPs-publishonNRELwebsite
FY13-A009 Updateandpublishanalyticaldatastandardizedspreadsheet-DELIVERABLE
TaskA.2:Analyticalmethodharmonizationinroundrobinexperiment
FY13-A001 RoundrobinexperimentationbetweenparticipatinglaboratoriesfollowingLAPs
FY13-A002 Collectdatainstandardizedspreadsheet
FY13-A003 Statisticaldataanalysisandintegration
FY13-A004 ReportandpublishharmonizeddataandrequiredQCrangebasedonmeasurements
FY13-A005 UpdateLAPswithQCrequirements-publishonNRELwebsite-DELIVERABLE
TaskA.3:BiomassProductivityAnalysismethoddistributionandImplementation
FY13-A001 Distributionofmethodsamongpartners
FY13-A002 Feedbackandimprovementsofmethoddescriptions
FY13-A003 DistributionofBiomassDW/AFDW/OD/Nutrientmethodverificationsamples
FY13-A004 Distributefirstdraftofproductiondataspreadsheet
FY13-A005 ImplementationofDW/AFDW/OD/Nutrientmethodverificationplan
FY13-A006 Reconveneandtroubleshootprocedures
FY13-A007 UpdateLAPs-publishonNRELwebsite
FY13-A008 Updateandpublishproductiondatastandardizedspreadsheet-DELIVERABLE
TaskA.4:BiomassProductionMethodAlignment(IndoorSeedCultureandMini-PondOperation)
FY13-A001 Distributionofmethodsamongpartners(IndoorSeedCultivation)
FY13-A002 Feedbackandimprovementsofmethoddescriptions
FY13-A003 Shipstarterseedculture1ststrainforUFS(NannochloropsisgranulataLRB-MP-0209)
FY13-A004 Scaleupseedcultureto800mlcolumns-maintaincolumnsmovingforward
FY13-A005 Initiate2x2trialrunsandverifyperformanceinmultiplerunswith1ststrain
FY13-A006 Collectdatainstandardizedspreadsheet
FY13-A007 Statisticaldataanalysisandintegration
FY13-A008 Shipstarterseedculture2ndUFSstrain(Freshwater-TBD)andscale-up
FY13-A009 Initialtrialsinmini-pondswithculture(1ststrainand<14daytrials)
FY13-A010 UpdateLAPswithQCrequirements-publishonNRELwebsite-DELIVERABLE
FY13-A011 PerforminitialbaselineUFSwithminipondexercisingfullsystem(freshandmarine)
FY13-A012 Statisticaldataanalysisandintegration
FY13-A013 FinalupdateofLAPsforPhase2UFSExperimentalPlan(Deliverable)
TaskA.5:Validationofcriticalproductivityandcompositionmetrics
FY13-A001 Establishmentofvalidationprotocolswithstandardquestionnaire
FY13-A002 Sitevisitsforvalidationofongoingexperimentaldatacollection
FY13-A003 FinalreportonValidationandsummaryoftestbedperformancerelativetoharmonization
FY13 FY14
Go/No Go Review Jan 2014
Analytical Method Implementation
Round Robin Experiment
Productivity Metrics Method Harmonization
Mini-pond operation and first Unified Field Study
Validation of productivity and composition measurements
14
Laboratory Analytical Procedures (LAP)
Goal: • Standardized procedures,
calculations and reporting • Integration of current best
practices and standardize data reporting
1. Determination of Total Solids and Ash in Algal Biomass
2. Determination of Total Lipid as Fatty Acid Methyl Esters
(FAME) by in situ Transesterification of Algal Biomass
3. Determination of Total Carbohydrate in Algal Biomass
4. Determination of Total Starch in Algal Biomass
5. Calculation of N-to-Protein Conversion Factor
15
Laboratory Analytical Procedures – Standard spreadsheet (v. 7)
Sample metadata
Protected calculations, only raw data to be entered
Interface with data management system
17
Method Implementation (pre-harmonization)
• Distribution of reference biomass sample Nannochloropsis sp.
• All laboratories were given methods to try for 2 months
• Goal to become familiar with procedures and spreadsheets and to downselect to final method best practice
• Feedback on inconsistencies or misinterpretation of the methods and standardized spreadsheet
• Improved methods and spreadsheet distributed before Round Robin start
Moisture Ash N Lipids_FAME Carbs_MBTH
Lab3 X X X ? ?
Lab1 X X X ? ?
Lab5 X X X ? X
Lab2 ? ? ? ? ?
Lab4 X X ? ? ?
18
Phase I Round Robin (July 2013)
Lab1 Lab2 Lab3 Lab4 Lab5
10
15
20
25
Ash
% O
DW
RSD = 6.3 %
Lab1 Lab2 Lab3 Lab4 Lab5
51
01
52
0
FAME
% O
DW
RSD = 22 %
Lab1 Lab2 Lab3 Lab4 Lab5
68
10
12
14
Carbohydrates%
OD
W
RSD = 13.5 %
Lab1 Lab2 Lab3 Lab4 Lab5
25
26
27
28
29
30
Protein
% O
DW
RSD = 2.6 %
0 20 40 60 80 100 120
10
15
20
25
Ash.ODW
Index
0 20 40 60 80 100 120
510
15
20
FAME.ODW
Index
0 20 40 60 80 100 120
68
10
14
Carbohydrates.ODW
Index
0 20 40 60 80 100 120
25
27
29
31
Protein.ODW
19
Phase II Round Robin (August 2013)
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6
10
15
20
25
Ash
% O
DW
RSD = 6.3 %
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6
51
01
52
0
FAME
% O
DW
RSD = 11.7 %
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6
68
10
12
14
Carbohydrates
% O
DW
RSD = 16.8 %
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6
25
26
27
28
29
30
Protein
% O
DW
RSD = 2.5 %
0 20 40 60 80 100 120
10
15
20
25
Ash.ODW
Index
0 20 40 60 80 100 120
510
15
20
FAME.ODW
Index
0 20 40 60 80 100 120
68
10
14
Carbohydrates.ODW
Index
0 20 40 60 80 100 120
25
27
29
31
Protein.ODW
20
• All 5 testbeds are using the same methods for analytical characterization
• Trained and troubleshoot laboratory procedures to address inconsistencies
• Continued monitoring of reference material and tracking improvements
• Goal to achieve < 10% RSD
• Implementation of methods for productivity metrics in Unified Field Studies (October 2013)
Conclusions
21
Acknowledgements
Philip Pienkos (NREL)
John McGowen (ASU)
Stefanie Van Wychen (NREL)
Jordan McAllister (ASU)
Sarah Arrowsmith (ASU)
Thomas Dempster (ASU)
Braden Crowe (CalPoly)
Eric Nicolai (CalPoly)
Tryg Lundquist (CalPoly)
Philip Lane (TRL)
Xueyan Liu (TRL)
Emily Knurek (Cellana)
Kate Evans (Cellana)
Steven Van Ginkel (GT)
Ed Wolfrum, David Crocker (NREL)
Questions?
Van Wychen, S. #134 “Accurately Determining Microalgal Carbohydrates with MBTH”
Christensen, E. #107 “Dynamic Lipid Profiling in Algae: Application of High-Resolution Mass Spectrometry and Discovery of Novel Components of Advanced Biofuels Intermediates”
Posters
ATP3 booth, discussion of methods and analytical harmonization: Tomorrow 10:45 am
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Relevant Publications
[1] Laurens, LML, Dempster, T, Jones, HDT, Wolfrum, E, Van Wychen, S, McAllister, J, Arrowsmith, S, Parchert, KJ, and Gloe, LM, "Algal Biomass Constituent Analysis: Method Uncertainties and Investigation of the Underlying Measuring Chemistries" (2012) Anal. Chem., 84(4): 1879
[2] Association of Official Analytical Chemists (AOAC). Official Fatty Acid determination method 991.39, 1995
[3] Laurens, LML, Quinn, M, Van Wychen, S, Templeton, DT, Wolfrum, EJ "Accurate and reliable quantification of total microalgal fuel potential as fatty acid methyl esters by in situ transesterification." (2012) Anal. Bioanal. Chem., 403(1):167-78
[4] Templeton, D. W.; Scarlata, C. J.; Sluiter, J. B.; Wolfrum, EJ "Compositional Analysis of Lignocellulosic Feedstocks. 2. Method Uncertainties" (2010) J. Agric. Food Chem. 58, 9054-9062.
[5] Megazyme. www.megazyme.com/downloads/en/data/K-TSTA. pdf, Megazyme International: Bray, County Wicklow, Ireland, 2011.
[6] Lourenço, SO, Barbarino, E, Lavin, PL, Marquez, UML, Aidar, E "Distribution of intracellular nitrogen in marine microalgae: Calculation of new nitrogen-to-protein conversion factors" (2004) Eur. J. Phyc., 39(1) 17-32
[7] Association of Official Analytical Chemists (AOAC). Official Amino Acid determination method 994.12, 1997
[8] MacDougall, KM, McNichol, J, McGinn, PJ, O’Leary, SJ, Melanson, JE "Triacylglycerol profiling of microalgae strains for biofuel feedstock by liquid chromatography–high-resolution mass spectrometry" (2011) Anal. Bioanal. Chem., 401(8):2609-16
[9] Templeton, DW, Quinn, M, Van Wychen, S, Hyman, D, Laurens, LML "Separation and quantification of microalgal carbohydrates" (2012) J. Chrom. A, 1270, 225:234