Techno-Economic
Modeling
of
AINABILIT
T
S
U
S
Y
Algal Processes with Aspen
Eric H. Dunlop
A. Kimi Coaldrake
E NER
G
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T
I
C
S
S
C
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M
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Some Lessons Learned
Pan Pacific Technologies Pty Ltd
Adelaide
Australia
[email protected]
EC
ABO Algal Biomass Summit, San Diego, October 2014
1
Lesson Learned 1: M&EB
• A fully converged mass and energy balance for
the total SYSTEM is essential.
• Without it, a model has limited use.
• It can be started on a spreadsheet, but cannot finish there.
ABO Algal Biomass Summit, San Diego, October 2014
2
Lesson Learned 2: Choice of Simulator
• Process simulation is essential.
• Using a full process simulator such as Aspen Plus is
a good investment in money and time, but it is not
enough.
• A range of simulators is available (see next slide) but we
found that AspenPlus and Comsol worked best for us.
ABO Algal Biomass Summit, San Diego, October 2014
3
Lesson Learned 2: Choice of Simulator
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 3: Reactive vs Proactive Simulation
Excellent when the process has been decided.
as needs a lot of information to be supplied.
Great for a mature process. Unable to make predictions about an immature process.
INPUT STREAMS DEFINED
REACTOR CONFIGURATION DEFINED
Provide:
Pond areas
Oil Production
Cell density at harvesting
Reaction kinetics etc etc
“NORMAL” ASPEN+ OPERATION
INPUT STREAMS DEFINED
OUTPUT STREAMS PREDICTED
GOOD FOR DEALING WITH THE PAST
REACTOR CONFIGURATION PREDICTED
Pond Areas predicted
Productivities predicted
Oil content of cell predicted
Identifies oportunites for improvement
Takes a lot of setting up
Slow to converge
OUTPUT STREAMS DEFINED
“REVERSE” ASPEN+ OPERATION
i.e. tell Aspen what you want and let it tell you how to get it.
Typically solves 50,000 simulataneous equations (and their derivatives)
so if what you ask for is unreasonable it will never converge.
GOOD FOR DEALING WITH THE FUTURE
Long convergence times can be a problem (3-4 hours)
ABO Algal Biomass Summit, San Diego, October 2014
5
What actually happens when Aspen runs?
• What happens?
Each unit operation , flow stream, physical properties of each reactant,
product and intermediate is run sequentially until convergence. Each
possible ion species is solved for, including precipitation etc.
• How many equations?
A separate insight is available in the “equation oriented mode” where
it can be seen that Aspen is solving 20,000 to 50,000 equations
simultaneously.
• What is the result?
If the problem is “well-posed” i.e. the number of degrees of freedom
is deliberately kept small, then the result is a rigorous imposition of a
carefully designed mass and energy balance.
ABO Algal Biomass Summit, San Diego, October 2014
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Selected Results from Proactive Use of Aspen
Selected Simulation Results
Full recycle, no lipid loss
Process operation
CO2 removal
Insolation (Corpus Christi, TX)
Evaporation (Corpus Christi, TX)
Carbon conversion to biodiesel (9 Methyl Esters)
Biodiesel produced (as 9 Methyl Esters )
7,884
1,000,000
4.2
1.27
95
46.4
2,630,450
hour/year (90 % )
tonne/year CO2
kWh/m2/day
m3/hr/hc
wt %
tonne/hr
barrel oil equivalent /yr
Total Area
Reaction Operation 1 Area for algae generation
Reaction Operation 2 Area for oil generation
20,033
484
19,549
hc
hc
hc
Oil productivity (based on total area)
Effective oil content
Genetic stability requirements
-Number of generations/year (binary fision)
5.6
57%
g/m2/day
wt%
43.6
ABO Algal Biomass Summit, San Diego, October 2014
7
Outputs from Aspen Model
The eighteen areas are:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18. Process Summary
Mass Balances
Energy Balance (1st Law)
Exergy (2nd Law)
Energy Analysis of Reactor 1
Energy Analysis of Reactor 2
Effluent/Bleed, Salinity, TDS
Water and Recycle
The Role of Evaporation
Control of Hazardous Precipitation of Inorganic Solids
Summary of Input and Output Streams
Photosynthetic Efficiency
Preliminary Check of Process Costs
Data Assembly for Flow Diagrams
Flow Diagrams :
(i)
Carbon
(ii) Water
(iii) Mass
(iv) Enthalpy (1st Law of Thermodynamics)
(v) Exergy (2nd Law of Thermodynamics)
(vi) Exergy and Lost Work (2nd Law of Thermodynamics)
Establishing the Value Chain
The Value of Adding Extra Glycerol
Full Financial Analysis
ABO Algal Biomass Summit, San Diego, October 2014
8
Carbon Flow (kg/hr)
A-100
CO2 Absorber
S-100
Flow Splitter
CO2 distribution
R-100
Reaction Operation/
Pond 1
R-200
Reaction Operation/
Pond 2
S-101
Flow Splitter
Algae Purge
S-102
Flow Splitter
Primary Cell Separation
Dissolved Air Flotation
17,979
36,166 Product
S-106
38,190
R-104
35,941
S-105
36,031
S-104
S-103
S-102
S-101
23,136
54,348
54,348
54,613
R-103
73,183
73,268
R-200
4,469
R-100
48,108
S-100
34,598
A-100
52,577
Methanol
2,249
90
18,570
Purge
681
Flue Gas In
18,667
S-103
Flow Splitter
Secondary Cell Separation
Decanter Centrifuge
R-103
Cell Lysis
Ball Mill
85
S-104
Flow Splitter
Debris Clarifier
265
S-105
Flow Splitter
Oil-Water Separation
18,317 new
R-104
Transesterification
Reactor
Glycerol recycle
2,024
S-106
Flow Splitter
Glycerol Separation
Carbon flowrate (kg/hr)
ABO Algal Biomass Summit, San Diego, October 2014
9
Lesson Learned 4: This is an Energy Project
-funded by the Department of Energy
• Biofuels projects rely heavily on the science of energy aka thermodynamics.
• Aspen and other data bases have been vastly expanded with new versions
but it is never enough.
• It is often necessary to calculate (because no-one has measured) ΔHformation
and ΔGformation to establish heats of reaction.
ΔHreaction=ΔHformation. products-ΔHformation. reactants)
• If running Aspen forwards ( reactive) you mainly only need the
thermodynamics of water.
• If running Aspen in reverse ( proactive) then you need good data.
ABO Algal Biomass Summit, San Diego, October 2014
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Reliable Thermodynamic Data is hard to find.
Not all databases are created equal. Need to establish much from 1st principles.
Components and Their Properties
Algae ("Nanno")
OC5
OC35
OC70
TAG9
Formula
C46.2859H87.9432O18.5143N3.8417S0.0787P0.0926
C47.1631H89.3564O17.9518N3.6496 S0.0748P0.0879
C52.4265H97.8357O14.5763N2.4971 S0.0511P0.0602
C58.5671H107.7282O10.6383N1.1525S0.0236P0.0278
C52.7316H96.0013O6.0000
ΔHf
kJ/mol
-12,907
-12,384
-9,250
-5,592
-2,030
S
kJ/mol-K
-6.04
-5.65
-3.32
-0.61
1.72
ΔGf
kJ/mol
-11,106
-10,699
-8,258
-5,411
-2,454
MW
1000.00
1000.00
1000.00
1000.00
826.12
Methyl Esters (FAME)
Type
Common Name
Aspen Name
CAS No.
C14:0
C16:0
C16:1
C18:0
C18:1n9
C18:2n6
C20:4n6
C20:5n3
C22:1
Methyl myristate
Methyl palmitate
Methyl palmitoleate
Methyl stearate
Methyl oleate
Methyl linoleate
Methyl arachidonate
Methyl eicosapentanoate
Methyl erucinate
METHY-01
METHY-02
METHY-05
METHY-03
METHY-04
METHY-06
METHY-07
METHY-08
METHY-09
124-10-7
112-39-0
1120-25-8
112-61-8
2462-84-2
112-63-0
2566-89-4
2734-47-6
1120-34-9
Formula
C15 H30 O2
C17 H34 O2
C17 H32 O2
C19 H38 O2
C19 H36 O2
C19 H34 O2
C21 H34 O2
C21 H32 O2
C23 H44 O2
MWt
242.3975
270.4507
268.4348
298.5038
296.4879
294.4721
318.4935
316.4776
352.5943
MJ/kmol
ΔHcomb
-9,430.70
-10,107.00
-10,547.90
-11,962.00
-11,832.40
-11,690.10
-13,263.00
-12,659.10
-14,451.50
MJ/kmol
ΔHform
-759.40
-1,441.80
-674.29
-945.60
-789.27
-604.88
-1,003.00
-186.90
-887.47
MJ/kmol
MJ/kmol
mol file
TS
ΔGform
available?
149
149
149
149
149
149
149
149
149
-908
-1,591
-823
-1,095
-938
-754
-1,152
-336
-1,037
NIST
NIST
NIST
NIST
NIST
NIST
NIST
PPT
NIST
MJ/kmol
MJ/kmol
Used in this study
wt%
2
36.5
37.5
2
9
3
2
7
1
100
Triglycerides
Common Name
Aspen Name
CAS No.
Trimyristin
Tripalmitin
Tripalmitolein
Tristearin
Triolein
Trilinolein
Triarachidonate
Trieicosapentanoate
Trierucin
TRIMY-01
555-45-3
TRIPA-01
555-44-2
TRIPA-02 118450-52-5
TRIST-01
555-43-1
TRIOL-01
122-32-7
TRILI-01
537-40-6
TRIAR-02 23314-57-0
TRAEI-01 99660-94-3
TRIEU-01
2752-99-0
Formula
C45 H86 O6
C51 H98 O6
C51 H92 O6
C57 H110 O6
C57 H104 O6
C57 H98 O6
C63 H98 O6
C63 H92 O6
C69 H128 O6
MWt
723.1925
807.3521
801.3044
891.5114
885.4637
879.4163
951.4805
945.4328
1053.7829
MJ/kmol
ΔHcomb
-27,643.70
-31,605.90
-31,179.20
-35,806.70
-35,099.60
-34,555.70
-39,467.70
-38,761.44
-42,802.30
MJ/kmol
ΔHform
-2,355.00
-2,468.70
-2,038.00
-2,344.00
-2,193.70
-1,880.10
-2,759.10
-1,890.80
-2,643.00
ABO Algal Biomass Summit, San Diego, October 2014
mol file
TS
ΔGform
available?
370.82
413.98
410.88
457.14
454.04
450.93
500.29
484.79
540.35
-2,725.82
-2,882.68
-2,448.88
-2,801.14
-2,647.74
-2,331.03
-3,259.39
-2,375.59
-3,183.35
NIST
NIST
PPT
NIST
NIST
NIST
PPT
CS
NIST
11
Calculated Thermodynamic Properties
of Algal Cells of Varying Oil Content
ABO Algal Biomass Summit, San Diego, October 2014
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Reactions with Corresponding Heats of Reaction
Table 5. Reactions with Corresponding Heats of Reaction
ΔH kJ/mol ΔG kJ/mol
of reaction of reaction
Reaction
Number
REACTION OPERATION 1
1
2
3
4
5
46.29 HCO3 + 3.84 NO3- + 0.079 SO4-- + 0.093 HPO4- + 46.02 H2O --> 1 Nanno + 64.05 O2 + 50.46 OH15.4286 Glycerol + 3.8417 NO3- + 0.0926 SO4-- + 0.0926 HPO4- + -->1 Nanno + 10.0502 O2 + 15.6971 H2O + 4.1842 OHDeb80 --> ALgDeb
0.0216048 NannoNew + 0.0074 NO3- 1.2708 O2 --> + 1 CO2- + 0.0017 SO4 + 0.002 HPO4- + 0.949 H2O + 0.0452 N2
0.1296292 NannoNew + 0.0444 NO3- 1.6248 O2 + 0.306 H2O --> + 1 SOLC- + 0.0102 SO4 + 0.012 HPO4- + 0.2712 N2
21,676 19,889
-3,888 -6,682
4,778
4,344
-21,676 -21,264
-1,148 -3,600
REACTION OPERATION 2
6
7
8
9
10
11
12
13
0.65 Nanno + 22.3292 HCO3- + 0.0 NO3-- + 20.4131 H2O --> 1 OC35 + 31.2653 O2 + 22.3292 OH- + 0.0 SO4- + 0.0 HPO4 + 22.3292 OH 19,126 15,710
0.3 Nanno + 44.6585 HCO3- + 40.8261 H2O --> OC70 + 62.5306 O2 + 0.0 NO3- + 0.0 SO4-- + 0.0 HPO4- + 44.6585 OH24,993 19,042
0.65 Nanno + 7.4431 Gly + +0.0 SO4-- + 0.0 HPO4- + 0.9942 H2O --> 1 OC35 + 9.3593 H2O + 5.2145 O2 + 0.0 NO31,973
1,209
0.3 Nanno + 14.88616 Gly- + 0.0 SO4-- + 0.0 HPO4 --> 1 OC70 + 18.7185 H2O + 10.4291 O2 + 0.0 NO39,132
5,487
0.95 Nanno + 0.0 HPO4- + 2.91615 H2O + 3.18989 HCO3 --> 1 OC5 + 4.4665 O2 + 0.0 SO4 + 0.0 NO3 + 3.18989 OH
2,276
1,740
0.95 Nanno + 1.063297 Gly + 0.0 SO4-- + 0.0 HPO4- -->1 OC5 + 0.7449 O2 + +1.3370 H2O+0.0 NO3234
146
4)
0.0216048 NannoNew + 0.0074 NO3- 1.2708 O2 --> + 1 CO2- + 0.0017 SO4 + 0.002 HPO4- + 0.949 H2O + 0.0452 N2 (duplicate of Reaction-21,676
-21,264
0.1296292 NannoNew + 0.0444 NO3- 1.6248 O2 + 0.306 H2O --> + 1 SOLC- + 0.0102 SO4 + 0.012 HPO4- + 0.2712 N2 (duplicate of Reaction-1,148
5)
-3,600
LYSIS OPERATION
14
15
16
OC35 + 0.187970 H2O --> 0.714286 Debris + 0.206767 O2 + 0.390977 TAG
1.4 OC70 + 0.4 H2O --> 0.4 Debris + 0.3 O2 + TAG
OC5 + 0.031328 H2O --> 1.285714 Debris + 0.31328 O2 + 0.065163 TAG
ABO Algal Biomass Summit, San Diego, October 2014
-3,344
-1,899
-6,131
-3,083
-1,768
-4,963
13
Lesson Learned 5: Handling Complex Stoichiometry
Balancing the stoichiometry of
these equations is a fast route to insanity.
ABO Algal Biomass Summit, San Diego, October 2014
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Stoichiometry Generator for Balancing Fractional Reactions
Table 3. Calculations using the Matrix Stoichiometry Generator
Example:
a OC70 +b Debris +c O2 +d H20 +e SO42-+f HPO42- → TAG9
INPUT MATRIX [A]
Raw materials
C
H
O
N
S
P
RESULT VECTOR [B]
Desired product
OC70
Debris
O2
H2O
SO42-
HPO42-
TAG9
58.5501
107.955
10.6368
1.1525
0.02368
0.02780
1
1.9
0.4
0.083
0.0017
0.002
0
0
2
0
0
0
0
2
1
0
0
0
0
0
4
0
1
0
0
1
4
0
0
1
63.83
116.21
7.263
0
0
0
b
c
d
e
f
a
SOLUTION VECTOR [X]
Stoichiometric coefficients
INVERSE MATRIX [A]-1
0.022389
-0.31089
0.399691
-0.91317
-1.7E-06
-6.5E-07
1.51E-21
1.01E-17
-0.25
0.5
-3.8E-19
-1.6E-19
0
0
0.5
0
0
0
-0.269
15.793
-1.420
-0.432
-0.020
-0.024
0
0
-2
0
1
0
3.5-17
-2.1E-15
-1.75
-0.50
4.3E-19
1
1.429097
-19.84379
0.091596
-0.183107
-0.00011
-4.13E-05
a
b
c
d
e
f
OC70
Debris
O2
H2O
SO4-HPO4--
[A][X]=[B] or [X]=[A]-1[B]
A positive coefficient in the solution vector denotes a reactant, while a negative coefficient denotes a product.
Thus the balanced equation for this example is :1.429097 OC70 +0.091596 O2 → TAG9 + 19.84379 Debris +0.183107 H2O+0.00011 SO42-+4.13E-05 HPO42-
ABO Algal Biomass Summit, San Diego, October 2014
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Total solar =-15,145 MW
Reaction Operation 1 Solar
-840 MW
Lost Work
660 MW
Reaction Operation 2 Solar -14,305 MW
-54,107 MW
-347 MW
-57 MW
≈-140 MW
-15 MW
-9 MW
-2 MW
-12 MW
The System
BioDiesel
Reactor
-33 MW
IN
Make-up Water In
Fluegas In
Make-up NaOH
Mechanical Work
pumps
Methanol
Chemwater
N, P, S
Product Biodiesel
16 MW
PR
-54,873 MW
OG
R
ES
S
Lesson Learned 6: 2nd Law and Lost Work
Reactor 1
-2,139 MW
W
OR
Work done on system
Solar 1
840 MW
Solar 2
14,305 MW
Shaft Work
140 MW
Total
15285 MW
Second Law Efficiency = 500/15,285
= 3.27%
Misc
lossses
17MW
K
Availablity in
-54,647 MW
Availability out
-54,147 MW
Availablity Increase
500 MW
Pipes & Pumps
15 MW
Lysis
16 MW
Absorber
loss
149 MW
Reactor R2
-38,235 MW
Recycle
658 MW
Bleed
969 MW
4% Photosynthetic Efficiency
3.3% Second Law Efficicncy
Exergy diagram for lost work analysis
Evap1
2,232 MW
Evap 2
37,996 MW
As this is a Second Law of Thermodynamics Analysis it
needs ΔG and hence ΔS. Entropy measurements are in
total disarray... if they even exist.
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 7: Pipes & Pumps
Get the pipes and pumps in early.
This is not normal practice, but with algae processes,
they consume so much energy and capex that the sooner
they are identified the better.
ABO Algal Biomass Summit, San Diego, October 2014
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Pipes & Pumps in Aspen Process Flow Diagram
The CO2 Absorber (Equilibium and Rate Based)
COMMERCIAL IN CONFIDENCE © Pan Pacific Technologies 2012
Chapter 1.4 Setting up Aspen
ABO Algal Biomass Summit, San Diego, October 2014
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18
Gas Pipe Line
Fluegas
Lay out Diagram.
Pipeline costs & pumping energies
Urea
Pipe 22
Reactor 1
Pipe 2
Coast
Pipe 1A
HCl
Pipe 5
Bleed
Pipe 19
Effluent
Treatment
Water recycle
Seawater Makeup
Pipe 3
Pipe 1C
Absorber
Split
Pipe 24
Pipe 4
NaOH
Pipe 6
Reactor 2
Pipe 21
Primary
Cell
Conc
Secondary
Cell
Conc
Pipe 7
Neutralisation
Pipe 18
Cell
Lysis
Pipe 9
Pipe 23
Pipe 17
Pipe 14
BioDiesel
Plant
Pipe 11
Pipe 13
MeOH
Pipe 15
Pipe 25
Pipe 10
Pipe 8
Debris
Clarifier
O/W
Sep
Pipe 20
Pipe 12
Pipe 16
Glycerol (OPG)
1000 m
Scale
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 8: Liquifaction
As we move away from the “simple” systems of biodiesel
and its components, we enter another difficult area.
Namely the hundreds of components that form, at
different temperatures and pressures, as the biomass is
liquified.
Multiple reaction schemes are being investigated.
How to deal with that in a TEA?
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 8: RGibbs
• RGibbs usually overlooked.
• All the others require knowledge of complex reaction stoichiometry and rate
constants, none of which we usually have.
• RGibbs treats all components, reactants and products, as potential products and
calculates the product mix that minimizes the Gibbs Free Energy. This is what nature
does anyway! But we do need the ΔH and ΔG as a function of temperature and
pressure, usually calculable.
“Einstein: God integrates empirically”. This must be the next best thing.
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 9: Number of Organism Generations
N(t)/N(0) = 2n
(binary fission)
≈ 40+ generations
An unexpected output from Aspen.
Sets the target for the stability of genetically modified
organisms that may subsequently be used.
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 10:Costing Accuracy comes at a (very) high cost
1
2
3
4
5
Study/Design
Probable Range
of Accuracy
Cost as % of
Project Expenditure
Preliminary Costing
Design Study
Preliminary Construction Estimate
Definitive Estimate
Detailed Estimate
30% to 50%
20% to30%
10% to 25%
5% to 15%
2% to 5%
0.05% to 0.1%
0.1% to 0.2%
0.4% to 0.8%
1% to 3%
5% to10%
Lesson 10a.
Beware of any TEA that does not come with error bars.
ABO Algal Biomass Summit, San Diego, October 2014
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Approach to Cost Financial Modeling
Costing Model —> Financial Model
Revenues & Costs—>Operating Margins
Cash Flow Analysis. How many plants, what size, when on line
Cost of capital, depreciation, taxation, tax shields, investment credits etc.
—>Range of cash flows
Financial Metrics (M)IRR, NPV etc.
Taxation has the potential to add significant cost OR tax shields may allow major cost
reductions
ABO Algal Biomass Summit, San Diego, October 2014
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Lesson Learned 11: All TEA “unit costs”are not the same.
ESTIMATED OPERATING MARGINS ($)
REVENUES
Selling Price per barrel ($120)
no. of new plants
total number of plants
% of CO2 market
oil production, barrels/year
% US consumption
Revenue
CASH FLOW ANALYSIS
Estimated cost of capital, 15%
PROJECT DEVELOPMENT PHASES
•Development Phase
•Plant Construction & Operating phase
-No. of new plants
-Capital cost of plant
-Cost of capital
-Start up costs, 5% of capital
-Maintenance, 5% of capital
-Working capital, 10% of capital
FINANCIAL METRICS
(M)IRR %
-Finance Rate
-Reinvest Rate
15.00%
10.0 %
NPV
$xxE+y
5.00%
@25.00% discount rate
discount
rate
NPV $
1.00%
.4E+11
EXPENSES
-VARIABLE COSTS (VC)
Raw materials cost/yr
Utility costs/yr (@ 10% raw material costs)
Labor costs (@ 10% raw material costs)
Workman's Comp & Unemployment Tax
(@10% of Labor Costs)
-FIXED COSTS (FC)
Supervisory/mgmt costs/yr
Cost of sales (6% of actual sales)
Insurance (5% of sales)
Plant overhead (5% of sales)
Total costs(VC+FC)
OPERATING MARGIN
-Salvage value, 5% of capital cost
2.00%
3.00%
Plant Depreciation (straight line 25 years)
4.00%
Total Operating costs
10.00%
5.00%
20.00%
Net operating cash flow
TAX SHIELDS; 35%tax
Depreciation (25 year straight line)
Investment Tax Credit (10%)
taxable income
TAX @ 35%
total tax shield
30.00%
P:E
10
Market cap.
Million $
Economy of scale over over 25 years
Total oil produced
1.3E+09 barrels
Total costs
$1.2E+x
Effective cost
$xx/bbl
$yy/gallon
±100%
AFTER TAX CASH FLOW
(revenue-costs)
FINANCIAL/INVESTMENT MODEL FLOWS
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Lesson Learned 12: Missing information can be found
by using other simulation tools
The Case for Comsol: Depth, Light Scattering & Non-steady State Models
t=0 hours.
mu=1.8 days-1
t=10 hours
mu=3.0 days-1
Close to maximum
growth rate.
t=14 hours.
mu=1.7 days-1
The complete
inversion point.
t=100 hours.
mu=0.24 days-1
Almost identical
with NMSU growth rate
at this point
mu=growth rate
Note the different scales, kept here to demonstrate the concept.
GROWTH RATE and its Distribution within the reactor vs Time
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Conclusions
• These are very personal observations on trying to do TechnoEconomic Analyses for algal processes & don’t suit everybody.
• Each lesson we learned contributes to an integrated systems
approach to algal biofuel production.
• When each lesson is fully implemented into a process simulation
such as AspenPlus, the result is more than the sum of the parts.
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Acknowledgements
The authors wish to acknowledge that this work
was prepared, in part, under the auspices of the U.S.
Department of Energy under :1. Contract DE-EE0003046 awarded to the National
Alliance for Advanced Biofuels and Bioproducts
and
2. Contract DE-EE0006316 awarded to the research
project, Realization of Algae Potential.
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