Supporting Information
Upgrading Lignocellulosic Products to Drop-In Biofuels Via
Dehydrogenative Cross-Coupling and Hydrodeoxygenation
Sequence
Sanil Sreekumar, Madhesan Balakrishnan, Konstantinos Goulas, Gorkem Gunbas, Amit
A. Gokhale, Lin Louie, Adam Grippo, Corinne D. Scown*, Alexis T. Bell* and
F. Dean Toste*
Energy Biosciences Institute, College of Chemistry and Energy Technologies Area,
Lawrence Berkeley National Laboratory, University of California, Berkeley, 94720. BP
North America Inc. United States
Reagents
All the chemicals were purchased from Sigma Aldrich, Alfa Aesar, Eastman, or Strem
Chemicals and used as received.
Reaction analysis
All the reactions were analyzed by gas chromatography using dodecane as internal
standard. Gas chromatography analysis was performed on a Varian CP-3800 instrument
with a FID detector and VF-5 MS column (5% phenyl and 95% methylpolysiloxane)
using helium as the carrier gas. 1H-NMR and 13C-NMR were recorded on a Bruker AVB,
AVQ-400 MHz NMR spectrometer in the indicated deuterated solvents. For 1H-NMR,
CDCl3 was set to 7.26 ppm (CDCl3 singlet) and for 13C-NMR, CDCl3 and C6D6 were set
to 77.16 ppm (CDCl3 center of triplet) and 128.06 ppm (C6D6 center of triplet)
respectively. All values for 1H-NMR and
13
C-NMR chemical shifts for deuterated
solvents were obtained from Cambridge Isotope Labs. Data are reported in the following
order: chemical shift in ppm () (multiplicity, which are indicated by br (broadened), s
(singlet), d (doublet), t (triplet), q (quartet), quint (quintet), m (multiplet)); assignment of
2nd order pattern, if applicable; coupling constants (J, Hz); integration. All
13
C-NMR
1
spectra were reported using the descriptor (o) and (e) referring to whether the peak is odd
or even, respectively, and correlate to an attached proton test (APT) experiment.
Representative procedure for Dehydrogenative Cross-Coupling
In a 12 mL Q-tube containing a stir bar, calcined hydrotalcite (0.4 g) was charged. To the
reaction mixture, butanol (0.074 g, 1 mmol), 2-furfural (0.192 g, 2 mmol), internal
standard (dodecane, known amount) and toluene (1 mL) were sequentially added. The Qtube was sealed and the reaction mixture was stirred for 20 hours at 150 °C in a preheated metal block. The reaction mixture was cooled to room temperature, diluted with
tetrahydrofuran and the GC analysis of the reaction mixture was carried out.
Table S1. Olefination of Furfural and 1-Octanol Using paraffin solvents
Entry
Solvent
Time
Alcohol
Base
Selectivity
Yield %
1
Nonane
20
octanol
HT
>99:1
65%
2
Tridecane
20
octanol
HT
>99:1
60%
(E)-2-(furan-2-ylmethylene)-6-hydroxyhexanal (3j).
1
H NMR (CDCl3, 400 MHz) δ 9.45 (s, 1H), 7.61 (d, J=1.6 Hz,
1H), 6.95 (s, 1H), 6.10 (d, J=3.5 Hz, 1H), 6.55 (dd, J=3.5 Hz,
J=1.8 Hz, 1H), 3.67 (t, J=6.4 Hz, 2H), 2.66 (t, J=7.8 Hz, 2H), 1.79 (br. s, 1H), 1.68-1.58
(m, 2H), 1.58-1.47 (m, 2H).
C NMR (CDCl3, 100 MHz) δ 194.56 (o), 151.33 (e), 145.66 (o), 139.47 (e), 135.53 (o),
13
117.07 (o), 112.80 (o), 62.64 (e), 32.63 (e), 24.61 (e), 24.38 (e).
ESI-HRMS calcd. for C13H23O2+: m/z 195.1016 ([M+H]+), found: m/z 195.1019
([M+H]+).
2
(E)-3-(furan-2-ylmethylene)cyclopent-1-ene-1-carbaldehyde.
1
H NMR (CDCl3, 400 MHz) δ 9.88 (s, 1H), 7.47 (d, J=1.3 Hz, 1H),
7.14-7.07 (m, 1H), 6.67-6.59 (m, 1H), 6.48 (dd, J=3.3 Hz, J=1.8
Hz, 1H), 6.41 (d, J=3.4 Hz, 1H), 2.99-2.91 (m, 2H), 2.84-2.76 (m, 2H).
C NMR (CDCl3, 100 MHz) δ 189.15 (o), 153.48 (e), 151.24 (o), 149.75 (e), 145.45 (e),
13
143.09 (o), 116.25 (o), 112.29 (o), 111.30 (o), 29.10 (e), 28.67 (e).
ESI-HRMS calcd. for C13H23O2+: m/z 175.0754, ([M+H]+), found: m/z 175.0754
([M+H]+).
(E)-2-(furan-2-ylmethylene)tetradecanal (3k).
1
H NMR (CDCl3, 400 MHz) δ 9.43 (s, 1H),
7.59-7.56 (m, 1H), 6.91 (s, 1H), 6.73 (d, J=3.4
Hz, 1H), 6.55-6.51 (m, 1H), 2.60 (t, J=7.6 Hz,
2H), 1.47-1.13 (m, 20H), 0.85 (t, J=6.7 Hz, 3H); 13C NMR (CDCl3, 100 MHz) δ 194.33
(o), 151.42 (e), 145.36 (o), 140.06 (e), 135.11 (o), 116.50 (o), 112.68 (o), 32.00 (e), 29.96
(e), 29.76 (e), 29.75 (e), 29.73 (e), 29.67 (e), 29.52 (e), 29.44 (e), 28.38 (e), 24.91 (e),
22.76 (e), 14.19 (o).
ESI-HRMS calcd. for C13H23O2+: m/z 291.2319 ([M+H]+), found: m/z 291.2326
([M+H]+).
Synthesis of 2wt% Pt/NbOPO4
A solution of chloroplatinic acid hexahydrate (0.16 g, 0.3 mmol) in nanopure
water (2 mL) was wet impregnated on 3 g of NbOPO4 (supplied by CBMM, Araxá,
Brazil). The catalyst was dried at 100 ⁰C overnight and then subjected to
3
reduction/calcination in the flow of hydrogen (50 mL/min) at 300 ⁰C for 4 h with a
temperature ramp of 2 C /min.
General procedure for hydrodeoxygenation:
The hydrodeoxygenation reactions were performed in a 4560 Mini Parr reactor. To a
solution of aldol adduct 3a (0.5 mmol, 0.103 g) in octane (8 mL) was added 2 wt%
Pt/NbOPO4 (0.5 mol%, 0.024 g) and internal standard (undecane/dodecane, known
amount) in a 25 mL Parr reactor. The reactor was then sealed and the dissolved gases in
the solution were purged (pressurize/depressurize) with nitrogen at 500 psi (3 times)
while stirring. The purging was repeated with hydrogen at 500 psi (3 times) before the
reactor was charged with hydrogen (500 psi). Then the reactor was heated to 250 C and
stirred for 4 h at the same temperature. The reactor was cooled to ambient temperature
and depressurized. Aliquot was drawn from the reactor and GC analysis of the reaction
mixture was carried out.
Catalyst stability test (3b representative example)
Reaction Conditions: Additional aldehyde 3b for subsequent cycle was added to the
reaction mixture containing Pt/NbOPO4 (0.5 mol%). Catalyst stability test was carried out
under H2 atmosphere (500 psi), 250 C, octane (8 mL), 4 hours internal standard
(dodecane, known amount) was added. Yield of 5b-8b were determined using the
response factor of alkanes by the GC-FID analysis of the crude sample.
4
Table S2. Recycling experiments for the Hydrodeoxygenation Reaction.
Conversion
Starting
Material (%)
100
Cycle 1
5b
(%)
32.6
6b
(%)
13.3
Yields
7b
(%)
11.7
8b
(%)
20.1
Total
(%)
77.7
Cycle 2
100
31.7
8.9
12.7
21.6
74.9
Cycle 3
100
28.2
8.9
14.4
18.5
70.0
5
(E)-2-(furan-2-ylmethylene)-6-hydroxyhexanal (3j).
6
(E)-3-(furan-2-ylmethylene)cyclopent-1-ene-1-carbaldehyde.
7
(E)-2-(furan-2-ylmethylene)tetradecanal (3k).
8
Life-cycle Assessment
9
We used life-cycle assessment (LCA) to quantify the net greenhouse gas (GHG) footprint
of the fuel production scheme presented in our paper, varying the source of higher
alcohols as a sensitivity analysis.
The feedstock used for analysis was Brazilian
sugarcane and conversion processes were modeled using a combination of existing
chemical process models, proprietary models, and simplifying assumptions where
necessary. Our model is run for a hypothetical facility that processes 5 million wet
tonnes of sugarcane annually, although we assume inputs scale approximately linearly
with size. Data sources for material and energy inputs as well as methodological choices
are outlined here.
Feedstock and cane milling
We assume the Brazilian sugarcane feedstock is cultivated in the southern part of Brazil,
in the states of São Paulo and Minas Gerais, where a large fraction of current sugarcane
cultivation and ethanol production is concentrated. We have also selected this region
because of the potential access to natural gas pipeline infrastructure, which is limited in
Brazil but is crucial for H2 production required by our production scheme. Our crop
input and yield data is largely drawn from national survey data reported in Seabra and
Macedo (2011).1 Process data for cane million, including sugar and bagasse yield are also
taken from Seabra et al. Table S3 shows the key data inputs.
Biorefining
Although the majority of biorefining inputs and outputs are drawn from either Seabra et
al. or the scheme documented in our paper, some addition inputs such as lime input for
flue gas desulfurization and sulfuric acid neutralization were calculated based on a
combination of proprietary Aspen Plus models and models released by the National
10
Renewable Energy Laboratory (NREL).2-3 The lime required for flue gas desulfurization
is correlated with the quantity of sulfuric acid utilized during pretreatment, using the
model documented in Humbird et al. (2011) as a base case.2 For wastewater treatment,
we utilize overliming as consistent with Aden et al. (2002), although we choose to use
CaCO3 as an input rather than hydrated lime.3 We assume that the quantity of lime
required for neutralization of sulfuric acid is linearly correlated with the input quantity of
sulfuric acid.
Hydrogen production
The most economical and technologically mature method for producing hydrogen is
steam reforming of natural gas. In this case, the resulting fossil GHG emissions are
estimated to be approximately 12 kg CO2e/kg H2 produced, with 25% of those emissions
coming from energy use and fugitive methane during natural gas extraction, processing,
and delivery, and the remaining 75% coming directly from the H2 production plant.4
Hydrogen can also be produced using renewable energy sources. A number of options
exist, including gasification of herbaceous biomass, electrolysis of water using renewable
electricity, aqueous-phase reforming of cane sugar, anaerobic digestion of sugars
followed by gasification, and steam reforming of ethanol produced via fermentation of
cane sugar. We selected steam reforming of ethanol because of its relative technical
feasibility in the short term. According to Haryanto et al (2005)5, selectivity to H2 is 90%
for steam reforming of ethanol. Table S3 provides yield details for both pathways to
hydrogen production.
11
Petroleum-derived higher alcohol production
LCA literature regarding petroleum-based butanol and higher alcohol production is
sparse. In an effort to quantify the key inputs and outputs of these processes, we rely
largely on stoichiometry. Petroleum-derived butanol originates from propene, which is
combined with syngas to produce butyraldehyde (Reaction 1). Butyraldehyde is then
reduced to form butanol (Reaction 2). In this process, we assume all syngas is formed
through steam reforming of methane. We assume a 10% H2 loss rate to account for the
need to maintain pressure in the reaction vessel.
Reaction 1: C3H6 + CO + H2  C4H8O
Reaction 2: C4H8O + H2  C4H9OH
Hexanol, octanol, and decanol are produced from ethylene via Oxo process, as shown in
Reactions 3 and 4. Triethylaluminium is ultimate converted to aluminium hydroxide,
which is a component of bauxite and can be sold for a variety of applications including
water purification. The resulting inputs and outputs for both petroleum-derived alcohol
production pathways are provided in Table S3.
Reaction 3: Al(C2H5)3 + 6C2H4  Al(C6H13)3
Reaction 4: Al(C6H13)3 + 1.5O2 + 3H2O  3HOC6H13 + Al(OH)3
Bio-derived alcohol production via Guerbet reaction
We approximate the yield of converting cane sugar-derived ethanol to higher alcohols
using results presented in Koda et al. (2009).6 In this case, approximately 90% of ethanol
is converted to higher alcohols. A 5% loss is assumed due to the need for an EtONa
catalyst for the process.
Because the limiting factor in alkane output is furfural
12
availability from C5 sugars in the bagasse, only 60% of ethanol is diverted to higher
alcohol production. The remaining 40% is sold directly as fuel.
Transportation
We assume that both alkanes and ethanol must be transported 700 km by tanker truck
before reaching terminals where they can be transferred to marine tankers. We then
assume all fuel is transported 1000 km by marine tanker to international markets.
Although these assumptions are fairly arbitrary, the results not sensitive to transportation
distances for the most part. Sugarcane transportation distances are taken from national
survey data in Seabra and Macedo (2011).1
13
Table S3. Data and assumptions for LCA calculations.
Process
Operating parameter
Unit
Data source
Sugarcane cultivation
Diesel input
115
MJ/wet tonne cane
Seabra & Macedo
(2011)1
Nitrogenous fertilizer
input
0.78
kg N/wet tonne
cane
P2O5 input
0.25
kg/wet tonne cane
Seabra & Macedo
(2011)1
Seabra & Macedo
(2011)1
Seabra & Macedo
(2011)1
K2O input
0.98
kg/wet tonne cane
Seabra & Macedo
(2011)1
CaCO3 input
Atrazine
5.2
kg/wet tonne cane
0.048
kg/wet tonne cane
GREET 1 2013
Seabra & Macedo
(2011)1
Cane transportation
Flatbed truck
21
km
Seabra & Macedo
(2011)1
Cane milling
Sucrose yield
140
kg/wet tonne cane
Seabra & Macedo
Bagasse yield
0.28
wet tonne/wet tonne
cane
(2011)1
Seabra & Macedo
(2011)1
Fermentation
Ethanol yield
Pretreatment
Sulfuric acid input
Furfural yield
1900
MJ/wet tonne cane
5
18
kg/wet tonne cane
Total generation
66
kWh/wet tonne
cane
Onsite use
21
kWh/wet tonne
cane
Calculated, based on
SupraYield process
Calculated based on
combustion of
cellulose, lignin, and
trash
Calculated based on
assumption of 50%
greater power use than
traditional sugarcane
biorefinery
Net export
45
kWh/wet tonne
cane
Calculated
kg/wet tonne cane
Calculated
kg H2/kg natural gas
Spath & Mann (2001)4
Furfural production
Power generation
kg/wet tonne cane
FGD lime input
H2 via steam-reforming of
natural gas
Calculated
Yield
5
0.28
14
H2 via steam-reforming of
ethanol
Yield
0.11
kg H2/kg sucrose
Calculated
Guerbet alcohols
production
Conversion efficiency
90%
--
Koda et al (2009)6
Petroleum-based butanol
production
Methane input
0.22
kg/kg butanol
Calculated
Propylene input
0.57
kg/kg butanol
Calculated
TEA input
0.37
kg/kg higher
alcohols
Calculated
Ethylene input
0.55
kg/kg higher
alcohols
Calculated
Aluminum hydroxide
co-product
0.25
kg/kg higher
alcohols
Calculated
10
mol/kg furfural
Calculated
0.0015
kg/MJ alkanes
Calculated
MJ/kg furfural
Calculated
kg/wet tonne cane
Calculated
Petroleum-based higher
alcohols production
Transfer hydrogenation &
aldol condensation
Alcohol input
Hydrodeoxygenation
Hydrogen input
Alkanes yield
110
Wastewater treatment
CaCO3 input
5
Liquid product
transportation
Tanker truck
700
km
Assumption
Marine tanker
1000
km
Assumption
Gasoline
3.56
MJ/km
Assumption
Diesel
3.29
MJ/km
Assumption
Sedan efficiency
Results
Table S4. Final fuel output for each scenario from a biorefinery processing 5 million wet tonnes of sugarcane per year.
Alcohols
MJ Ethanol
MJ Alkanes
Guerbet
5.78E+09
7.95E+09
Guerbet w/ onsite H2 production
4.42E+09
7.95E+09
Pet.-based higher alcohols
9.48E+09
7.95E+09
Higher alcohols w/ onsite H2 production
8.11E+09
7.95E+09
Pet.-based butanol
9.48E+09
7.95E+09
Butanol w/ onsite H2 production
8.11E+09
7.95E+09
15
Table S5. Greenhouse gas intensity reductions relative to petroleum-based gasoline and diesel.
Guerbet
alcohols
Fossil
Bio
H2
H2
Petroleum-based higher
alcohols
Fossil H2
Bio H2
Petroleum-based
butanol
Fossil
H2
Bio H2
-0.5%
0.6%
-0.4%
-0.4%
-0.4%
-0.4%
Upstream electricity use
Sugarcane cultivation &
harvesting
Other fossil fuel extraction &
processing
Natural gas extraction &
processing
0.7%
0.6%
1%
1%
0.7%
0.7%
8%
9%
6%
7%
6%
7%
1%
2%
6%
8%
6%
8%
0.8%
0.1%
0.8%
0.2%
0.8%
0.3%
Hydrogen production
12%
0%
10%
1%
10%
0%
4%
4%
3%
3%
3%
3%
0.3%
0.3%
0.6%
0.6%
0.4%
0.4%
Flatbedtruck transportation
1%
2%
1%
1%
1%
1%
Gas pipeline transportation
0.6%
0.1%
0.6%
0.2%
0.6%
0.2%
Tanker truck transportation
3%
3%
3%
3%
3%
3%
Direct
1%
1%
1%
1%
1%
1%
Combustion
0%
0%
13%
14%
9%
10%
Total excluding iLUC
31%
21%
47%
40%
41%
33%
Total including iLUC
52%
34%
57%
50%
50%
43%
Negative error bars (non-iLUC)
-6%
-6%
-5%
-5%
-5%
-3%
Positive error bars (non-iLUC)
3%
3%
2%
2%
2%
2%
Electricity offset credits
Chemicals and fertilizers
Other transportation
Heat and power needs and production
Brazilian sugarcane biorefineries burn bagasse to supply the process heat and electricity
required by the facility, with excess electricity exported to the grid. Seabra and Macedo1
report that the average surplus generation in 2008 was 10.7 kWh/tonne of cane input, and
the average for only facilities that sell power to the grid was 25 kWh/tonne cane.
Traditional sugarcane biorefineries require approximately 30 kWh/tonne cane to operate,
meaning that approximately half of total electricity production is exported. Two factors
are expected to drive net heat production and power exports in the coming decades: 1) the
phasing out of on-field trash burning and manual harvest in favor of mechanical
harvesting, in which the trash (mass equivalent to approximately half that of bagasse) will
16
be collected for use as additional fuel, and 2) the installation of high-pressure boilers in
new facilities, which will increase the efficiency of on-site energy production. Seabra
and Macedo1 estimate that these changes will result in greater than a factor of five
increase in total electricity exports, totaling 130 kWh/tonne cane where both bagasse and
trash are combusted, and substantially increased process heat supply. As indicated in
Table S3, total electricity production in our model is lower because we assume only
cellulose, lignin, and trash are routed to combined heat and power system.
Another important factor that impacts the importance of net electricity exports in Brazil is
the country’s grid mix. Estimates of Brazil’s electricity mix in the literature and popular
tools such as GREET and CA-GREET are remarkably inconsistent, ranging from 55% to
85% hydroelectricity. Furthermore, the marginal mix, which is meant to represent the
combination of power sources that ramp up to meet a small increase in power demand or
ramp down if demand decreases, is considered to be identical to the average mix in
GREET, while CA-GREET assumes that 100% of marginal power is supplied by natural
gas-fired plants. According to the January 2015 report from Brazil’s Ministério de Minas
e Energia, installed capacity is as follows: 66.6% hydro, 9.5% natural gas, 9.2% biomass,
6.8% fuel oil, 3.7% wind, 2.7% coal, 1.5% nuclear.7 During the wet season, hydro
supplies 76% Brazil’s total power.8 Hydro’s contribution drops to 67% of power during
the dry season, while gas and oil-fired electricity generation increases.9 Conversely,
sugarcane harvest and milling occurs during the dry season, when bagasse-fired power
supplies 6% of power, and this contribution drops by an order of magnitude during the
wet season, when sugarcane facilities are not operating.8-9
17
Because this article seeks to assess whether the drop-in fuels presented here will achieve
the required GHG emissions reductions under current regulations, we have chosen to use
a marginal electricity mix for Brazil that is consistent with GREET’s assumptions: 83%
hydroelectricity, 5% natural gas, 1.2% petroleum, 1.7% coal, 4.2% biomass, and 3.0%
nuclear.10 However, the data we present here shows that this is unlikely to be an accurate
representation of the electricity sources displaced by increasing biomass-fired power
exports. Instead, the marginal mix displaced by biorefinery power exports is more likely
to be comprised of gas and, secondarily, oil power plants, based on the reported seasonal
variations in power generation. If biorefineries choose to employ storage methods for
biomass, allowing them to generate and export power during the wet season, this may
alter the displaced power mix. Conservatively, we assume in this study that facility-wide
power demand is 50% greater per tonne of cane input than the power requirements for an
ethanol-only facility.
The heat and power needs for our modeled sugarcane biorefinery are uncertain; we hope
to improve these estimates in subsequent studies through a combination of additional
experiments and process modeling.
However, the already-abundant bagasse supply
(minus hemicellulose, which we assume is used for fuel production) plus the newlyavailable sugarcane trash and improved efficiency of high-pressure boilers are indications
that, unless the energy demands for our modeled biorefinery are greater than a five times
that of a traditional ethanol facility, the on-site biomass should serve as a sufficient
energy supply. For reference, using a lower heating value of 18 MJ/kg of dry biomass
and 185 kg of dry biomass yield (cellulose and lignin fractions of bagasse plus trash),
approximately 2600 MJ of thermal energy is available, minus the amount used for drying
18
the biomass and efficiency losses. In comparison, our model suggests that one tonne of
cane could yield up to 18 kg of furfural as intermediates. The modeled thermal energy
required to extract the furfural from solution is 4.1 MJ/kg, leading to a total thermal
energy requirement of approximately 72 MJ per tonne of cane. As is clear from these
simple calculations, the thermal energy needs are unlikely to exceed the energy that
residual biomass can provide.
Burning additional biomass for process heat beyond what is required in traditional
Brazilian sugarcane facilities will not appreciably impact the GHG balance because the
carbon released is biogenic. Furthermore, variations in power exports have a minor
impact on the overall GHG footprint of the fuels because the mix of power displaced is
largely carbon-neutral. However, as discussed earlier, the standard method of calculating
power-related carbon offsets is not necessarily reflective of actual power offsets.
Wastewater treatment
In conventional sugarcane ethanol facilities, wastewater treatment needs are minimal as
the vinasses, which contain valuable nutrients, are reapplied to the soil and the energy
needs of this system are already included in our model.
Converting bagasse-derived
hemicellulose creates an additional need for wastewater treatment, particularly because of
the H2SO4 acid catalyst required during pretreatment of bagasse and dehydration of C5
sugars to furfural. Our model includes CaCO3 requirements for overliming to neutralize
the acid during wastewater treatment, as shown in Table S3. Should a combination of
aerobic and anaerobic digestion be employed to reduce the chemical oxygen demand
(COD), the wastewater treatment unit would become a net energy producer, resulting in
biogas with high methane content that can be combusted alongside biomass in the boiler.
19
This factor contributes to the overall uncertainty associated with net heat and power
consumption/production at the facility, as discussed in the previous section and explored
in the following Uncertainty and sensitivity section.
Uncertainty and sensitivity
Although our analysis is based on empirical data and, in the case of sugarcane cultivation
and delivery, survey data from Brazil, there are a few factors that introduce substantial
uncertainty in our results. One important variable is the furfural yield from C5 sugars.
Although an HCl acid catalyst results in a higher yield (90%), we found that producing
HCl for use at a rural sugarcane facility is energy- and GHG-intensive. If H2SO4 can be
used to achieve a yield of 70%, that choice is preferable because the acid can, at least in
part, be reused from the pretreatment step and H2SO4 is less energy-intensive to produce.
Another important source of uncertainty is the assumed mix of electricity that is
displaced by increasing biorefinery power exports. We conduct a sensitivity analysis by
varying the amount of electricity exported from the biorefinery and the mix of grid
electricity that is offset. These ranges are reflected in the error bars for Figure 2 in the
main text.
In the “high” case, the biorefinery uses 2.5 times the electricity of a
comparably sized traditional ethanol facility and the net electricity usage is met entirely
by natural gas simple cycle power. In the “low” case, the biorefinery uses only 25%
more power than a traditional ethanol facility and net electricity exports offset entirely
natural gas simple cycle power. Our base case, for comparison, uses an electricity mix
that is primarily hydro and assumes that the biorefinery uses 50% more electricity than a
typical ethanol facility.
Indirect land use change
20
Indirect land use change (iLUC) refers to the emissions associated with bringing new
land into agricultural production as a result of increased biofuel demand.11 The GHG
emissions result from clearing and tilling of land that has presumably not been previously
cultivated. However, the GHG emissions are highly dependent on a variety of market
factors and the type of land brought into agricultural production (forest, grassland, etc.)
California’s Low Carbon Fuel Standard (LCFS) currently uses the highest iLUC factors
for sugarcane, although they are likely to be revised downward. The current factor, 46 g
CO2e/MJ sugarcane ethanol translates to 98 kg CO2e/wet tonne sugarcane.12 Adding the
maximum iLUC factor to the results does substantially increase the GHG footprint, with
the most dramatic impact on the Guerbet alcohols pathway because less fuel is sold per
tonne of sugarcane input. Still, GHG reductions exceed 60-65% for the Guerbet pathway,
and reach approximately 40-55% for pathways requiring petroleum-based alcohols (see
Figure 1).
21
Figure 1: GHG-intensity results, normalized by percent reduction relative to
petroleum-based diesel and gasoline, including iLUC factors
Discussion
22
A single fuel pathway can yield dramatically different results depending on the chosen
feedstock and location of production. We selected Brazilian sugarcane for sale on the
international market (to countries such as the United States) because it is a nearer-term
scenario than conversion of crop residues or other herbaceous feedstocks. However, our
results show that furfural derived from lignocellulosic feedstocks can be combined with
petroleum-derived alcohols, eliminating the need for sugar or starch and removing the
requirement that cellulose and hemicellulose be broken down and fermented to ethanol
(an expensive process). Selecting a Brazilian scenario is advantageous from a feedstock
availability perspective, but minimizes any electricity offset credits because Brazilian
electricity is primarily renewable – greater than 60% is satisfied by hydropower with the
remaining power supplied primarily by natural gas. In contrast, plants in the United
States would enjoy significant electricity offset credits if they cause carbon-intensive
coal-fired power plants to ramp down.
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