Composition of Algal Biomass for Biofuels and Bioproducts

24 Pages·2013·3.27 MB·English

by  Carolyn Mathias

Composition of Algal Biomass for
Biofuels and Bioproducts:
High Impact Data and Method
Harmonization
Lieve Laurens

October 1st, 2013
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.
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
2
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
Fuel(cid:9) Yield(cid:9)
o Process Mass Balance
Lipids(cid:9)(kg/ton)(cid:9) 255(cid:9)±(cid:9)51(cid:9)

Green(cid:9)Diesel(cid:9)($/gallon)(cid:9) 12.1(cid:9)±(cid:9)2.4(cid:9)
*Davis et al. 2012 www.nrel.gov/docs/fy12osti/55431.pdf
3
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
4
2011 Method Uncertainties
Protein Lipids
• Goal: Quantify measurement
uncertainty
• Survey of routinely used
methods for proteins, lipids
and carbohydrate
measurements
• Comparison of yields, accuracy
and precision of each method Carbohydrates
• 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
5
2012 Sample Matrix Variability
Carbohydrates Proteins Lipids
CZ CZ CZ
NC NC NC
SD SD SD
0 0 0
5 5 5
0 0 0
4 4 4
A
S 0 B 0 B 0
s_P 3 i_ne 3 s_d 3
b to ip
ra rP iL
C
0 0 0
2 2 2
0 0 0
1 1 1
y = 0.69 x + 6.46 y = 0.93 x + 5.08 y = 0.86 x + 5.35
R2 = 0.77 R2 = 0.78 R2 = 0.94
0 0 0
0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50
Carbs_HPLC Protein_N Lipids_FAME
6
Biological vs Measurement Variability
Chlorella sp. Nannochloropsis sp. Scenedesmus sp.
0 0 0
5 5 5
0 0 Lipids 0
4 4 4
) ) )
W W W
D D D
( 0 ( 0 ( 0
s 3 s 3 s 3
s s s
a a Protein a
m m m
o o o
iB iB iB

% 0 % 0 % 0
2 2 2
Carbohydrates
0 0 0
1 1 1
Carbs_HPLC
Carbs_PSA
Lipids_FAME
Lipids_B
Protein_N
Starch
Protein_B
Starch_M
0 0 0
Starch_M_ASU
0 5 10 15 0 5 10 15 0 5 10
Time (days) Time (days) Time (days)
7
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
8
Standard Operating Procedures –
Harmonization
9
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
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