SUPPORTING INFORMATION LEGENDS
Table S1. Alternative biorefinery options with sequential extraction of proteins, lipids or fatty
acids (FA) and conversion of residual biomass to biocrude. Data show macroalgae projected
productivities (P, in metric t ha-1 yr-1) and values (V, in US$ ha-1 yr-1) of commodities generated
by marine (M) and freshwater (FW) macroalgae through different scenarios. Products prices are
derived from equivalent commodities prices (see Methods). Note that theoretical values (V) are
rounded to the nearest $100 for each scenario.
Table S2. Sensitivity analyses of marine Derbesia and Ulva and freshwater Oedogonium for
parameters influencing the value of feedstock (US$ ha-1 yr-1) for sequential extraction of protein
from biomass and hydrothermal liquefaction of the residual biomass to biocrude. Values (A);
Parameters (B); References (C); “Best Case” scenarios (D).
Table S3. Biocrude yield from several studies on hydrothermal liquefaction of macroalgae and
microalgae. M = marine origin, FW = freshwater origin, dw = dry weight, afdw = ash-free dry
weight.
Table S4. References cited in supporting information.
1
Table S1.
1.
Scenario
Commodity
Biodiesel Biocrude
Price (US$ t-1)
Species
Derbesia
Ulva
Chaetomorpha
Cladophora
Oedogonium
Cladophora
2.
3.
4.
5.
6.
7.
8.
9.
10.
Protein
Biocrude
- Protein
3+4
Lipid
Biocrude
- Lipid
6+7
Biocrude
- FA
1+9
941
682
432
682
1170
682
682
P
V
1.8
$1,700
7.1
$4,800
9.4
$4,100
5.4
$3,700
$7,800
4.5
$5,300
3.5
$2,400
$7,600
5.6
$3,800
$5,500
P
V
0.6
$600
4.6
$3,100
6.8
$2,900
3.4
$2,300
$5,200
0.8
$900
4.0
$2,700
$3,600
4.1
$2,800
$3,400
P
V
0.7
$700
4.0
$2,700
4.0
$1,700
3.3
$2,300
$4,000
1.2
$1,400
3.1
$2,100
$3,500
3.4
$2,300
$3,000
P
V
0.8
$700
4.3
$2,900
5.5
$2,400
3.3
$2,200
$4,600
1.4
$1,700
3.1
$2,100
$3,800
3.6
$2,500
$3,200
P
V
0.8
$800
3.3
$2,300
4.2
$1,800
2.5
$1,700
$3,500
1.8
$2,000
1.9
$1,300
$3,300
2.6
$1,800
$2,600
P
V
0.6
$600
2.0
$1,300
3.4
$1,400
1.4
$900
$2,300
0.7
$800
1.4
$1,000
$1,800
1.5
$1,000
$1,600
Source
M
M
M
M
FW
FW
2
Table S2.
A) Sensitivity Analysis Values
Species
Derbesia
Ulva
Oedogonium
Parameter
Biomass
productivity
Protein
content
Biocrude
yield
WTI crude
oil price
Soybean
meal price
Unit
US$ ha-1 yr-1
US$ ha-1 yr-1
US$ ha-1 yr-1
US$ ha-1 yr-1
US$ ha-1 yr-1
15,526
7,705
7,245
11,973
5,230
4,954
11,135
3,549
3,341
7,780
7,705
7,630
5,283
5,230
5,176
3,590
3,549
3,509
9,544
7,705
6,221
6,394
5,230
3,980
4,426
3,549
2,774
8,137
7,705
7,199
5,502
5,230
4,911
3,755
3,549
3,308
9,149
7,705
6,660
6,273
5,230
4,475
4,194
3,549
3,084
(P)
Biomass
productivity
(P1)
Protein
content
(P2)
Biocrude
yield
(P3)
WTI crude
oil price
(P4)
Soybean
meal price
(P5)
Unit
g m-2 d-1
wt%
wt%
US$ t-1
US$ t-1
24.0
11.9
11.2
26.1
11.4
10.8
16.0
5.1
4.8
22.0
21.6
21.2
16.6
16.3
16.0
23.0
22.5
22.0
18.5
12.3
7.3
12.2
8.1
3.7
20.6
13.7
7.6
763.3
682.5
587.9
763.3
682.5
587.9
763.3
682.5
587.9
585.8
431.9
320.7
585.8
431.9
320.7
585.8
431.9
320.7
Case
Favourable
Standard
Unfavourable
Favourable
Standard
Unfavourable
Favourable
Standard
Unfavourable
B) Sensitivity Analysis Parameters
Parameter
Species
Derbesia
Ulva
Oedogonium
Case
Favourable
Standard
Unfavourable
Favourable
Standard
Unfavourable
Favourable
Standard
Unfavourable
3
C) Sensitivity Analysis References
Case
Parameter
Biomass
productivity
Protein
content
Biocrude
yielda
WTI crude
oil priceb
Soybean
meal priceb
(P)
(P1)
(P2)
(P3)
(P4)
(P5)
This Study,
+1 SD
(P3) =
standard +
(standard *
0.5)
Maximum
price within
the last 2
years
Maximum
price within
the last 2
years
Species
Derbesia
Favourable
Magnussonc
d
Ulva
Bolton
Oedogonium
Colee
All species
This Study,
average
This Study,
average
(P3) =
0.80*WLIP +
0.15*WCARB
Average
price within
the last 2
years
Average
price within
the last 2
years
Unfavourable All species
This Study,
-1 SD
This Study,
-1 SD
(P3) =
0.55*WLIP +
0.06*WCARB
Minimum
price within
the last 2
years
Minimum
price within
the last 2
years
Standard
a
WLIP and WCARB are lipid and carbohydrate contents (wt%) of macroalgae respectively; conversion
factors from Biller & Ross, 2011
b
commodity prices from Indexmundi, http://www.indexmundi.com/australia/
c
Magnusson et al., submitted
d
Bolton et al., 2009
e
Cole, unpublished data
D) Sensitivity Analysis "Best Case"
All parameters are considered favourable
Value of feedstock (US$ ha-1 yr-1) = 3.65c * (P1) * [((P2) * (P5)) + ((P3) * (P4))] / 100%
Derbesia
US$23,660 ha-1 yr-1 = 3.65 * 24.0 * [(22.0 * 585.8) + (18.5 * 763.3)] / 100
Ulva
US$18,135 ha-1 yr-1 = 3.65 * 26.1 * [(16.6 * 585.8) + (12.2 * 763.3)] / 100
Oedogonium
US$17,051 ha-1 yr-1 = 3.65 * 16.0 * [(23.0 * 585.8) + (20.6 * 763.3)] / 100
c
multiplier derived from the conversion of productivity in g m-2 d-1 to productivity in t ha-1 y-1
4
Table S3.
Reference
Macroalgae
Zhou et al.
Anastasakis and Ross
Microalgaeb
Dote et al.
Minowa et al.
Yang et al.
Biller and Ross
Garcia Alba et al.
Vardon et al.
Ross et al.
Biller et al.
Zou et al. (a)
Zou et al. (b)
Brown et al.
Duan and Savage
Jena et al.
Valdez et al.
Yu et al.
Species
Enteromorpha prolifera
Laminaria saccharina
Botryococcus braunii
Dunaliella terciolecta
Microcystis viridis
Chlorella vulgaris
Nannochloropsis occulata
Porphyridium cruentum
Spirulina
Desmodesmus sp.
Spirulina
Chlorella vulgaris
Spirulina
Nannochloropsis occulata
Chlorella vulgaris
Dunaliella terciolecta
Dunaliella terciolecta
Nannochloropsis sp.
Nannochloropsis sp.
Spirulina platensis
Nannochloropsis sp.
Chlorella pyrenoidosa
Origin
T (oC)
Time (min)
Equationa
Mass of feed
Biocrude
yield (wt%)
Algae basis
M
M
300
350
30
15
23.0
19.3
algae + catalyst
algae + catalyst
dw
afdw
FW
M
FW
FW
M
M
FW
FW
FW
FW
FW
M
FW
M
M
M
M
FW
M
FW
300
300
340
350
350
350
350
375
300
350
350
350
350
360
360
350
350
350
350
280
60
5
30
60
60
60
60
5
30
60
60
60
60
50
30
60
60
60
60
120
64
43.8
33
36
35
27.1
29
49
32.6
27.3
20
34.3
38.9
25.8
36.9
43
57
39.9
39
39.4
algae
algae
cyanobacteria
algae
algae
algae + catalyst
cyanobacteria
algae
cyanobacteria
algae + catalyst
cyanobacteria + catalyst
algae + catalyst
algae + catalyst
algae
algae
algae
algae
cyanobacteria
algae
algae
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
afdw
dw
dw
dw
dw
dw
dw
dw
These biocrudes are composed of 68-75% carbon, 8-10% hydrogen, 9-19% oxygen and 4-8% nitrogen (dry weight)c
a
Biocrude yield (wt%) = Mass of biocrude (g) / Mass of feed (g) *100%
b
reproduced and modified from Lopez Barreiro et al., 2013
c
Frank et al., 2013
5
Table S4.
Anastasakis K, Ross AB (2011) Hydrothermal liquefaction of the brown macro-alga Laminaria saccharina: effect of reaction conditions on
product distribution and composition. Bioresource Technology, 102, 4876-4883.
Biller P, Ross AB (2011) Potential yields and properties of oil from the hydrothermal liquefaction of microalgae with different biochemical
content. Bioresource Technology, 102, 215-225.
Biller P, Riley R, Ross AB (2011) Catalytic hydrothermal processing of microalgae: decomposition and upgrading of lipids. Bioresource
Technology, 102, 4841-4848.
Bolton J, Robertson-Andersson D, Shuuluka D, Kandjengo L (2009) Growing Ulva (Chlorophyta) in integrated systems as a commercial crop
for abalone feed in South Africa: a SWOT analysis. Journal of Applied Phycology, 21, 575-583.
Brown TM, Duan P, Savage PE (2010) Hydrothermal liquefaction and gasification of Nannochloropsis sp. Energy & Fuels, 24, 3639-3646.
Dote Y, Sawayama S, Inoue S, Minowa T, Yokoyama S (1994) Recovery of liquid fuel from hydrocarbon-rich microalgae by thermochemical
liquefaction. Fuel, 73, 1855-1857.
Duan P, Savage PE (2011) Hydrothermal liquefaction of a microalgae with heterogeneous catalysts. Industrial & Engineering Chemistry
Research, 50, 52-61.
Frank ED, Elgowainy A, Han J, Wang Z (2013) Life cycle comparison of hydrothermal liquefaction and lipid extraction pathways to
renewable diesel from algae. Mitigation and Adaptation Strategies for Global Change, 18, 137-158.
Garcia Alba L, Torri C, Samorì C, Van Der Spek J, Fabbri D, Kersten SR, Brilman DW (2011) Hydrothermal treatment (HTT) of microalgae:
evaluation of the process as conversion method in an algae biorefinery concept. Energy & Fuels, 26, 642-657.
Jena U, Das K, Kastner J (2011) Effect of operating conditions of thermochemical liquefaction on biocrude production from Spirulina
platensis. Bioresource Technology, 102, 6221-6229.
Lopez Barreiro D, Prins W, Ronsse F, Brilman W (2013) Hydrothermal liquefaction (HTL) of microalgae for biofuel production: state of the
art review and future prospects. Biomass and Bioenergy, 53, 113-127.
Magnusson M, Mata L, de Nys R, Paul NA (in press) Biomass, lipid and fatty acid production in large-scale cultures of the marine macroalga
Derbesia tenuissima (Chlorophyta). Marine Biotechnology.
Minowa T, Yokohama S, Kishimoto M, Okakura T (1995) Oil production from algal cells of Dunaliella tertiolecta by direct thermochemical
liquefaction. Fuel, 74, 1735-1738.
6
Ross A, Biller P, Kubacki M, Li H, Lea-Langton A, Jones J (2010) Hydrothermal processing of microalgae using alkali and organic acids.
Fuel, 89, 2234-2243.
Valdez PJ, Dickinson JG, Savage PE (2011) Characterization of product fractions from hydrothermal liquefaction of Nannochloropsis sp. and
the influence of solvents. Energy & Fuels, 25, 3235-3243.
Vardon DR, Sharma BK, Scott J, et al.(2011) Chemical properties of biocrude oil from the hydrothermal liquefaction of Spirulina algae, swine
manure, and digested anaerobic sludge. Bioresource Technology, 102, 8295-8303.
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