Composition of Algal Biomass for Biofuels and...

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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

9

Standard Operating Procedures – Harmonization

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-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-A003 Shipstarterseedculture1ststrainforUFS(NannochloropsisgranulataLRB-MP-0209)

FY13-A004 Scaleupseedcultureto800mlcolumns-maintaincolumnsmovingforward

FY13-A005 Initiate2x2trialrunsandverifyperformanceinmultiplerunswith1ststrain

FY13-A006 Collectdatainstandardizedspreadsheet


FY13-A008 Shipstarterseedculture2ndUFSstrain(Freshwater-TBD)andscale-up

FY13-A009 Initialtrialsinmini-pondswithculture(1ststrainand<14daytrials)

FY13-A010 UpdateLAPswithQCrequirements-publishonNRELwebsite-DELIVERABLE

FY13-A011 PerforminitialbaselineUFSwithminipondexercisingfullsystem(freshandmarine)


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

16

Laboratory Analytical Procedures – Standard spreadsheet (v. 7)

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 %


51

01

52

0

FAME

% O

DW

RSD = 22 %


68

10

12

14

Carbohydrates%

OD

W

RSD = 13.5 %


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 %


51

01

52

0

FAME

% O

DW

RSD = 11.7 %


68

10

12

14

Carbohydrates

% O

DW

RSD = 16.8 %


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?

[email protected]

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

Composition of Algal Biomass for Biofuels and...

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