Transcript DOE Biomass RDD Review Template

Pond-to-wheels algae biodiesel
life cycle assessment (LCA)
Dunaliella bardawil production plant. Nature Beta Technologies Ltd., Eilat, Israel. Courtesy Dr. Ami Ben-Amotz
8 April 2011, Algae Platform Review
Howard Passell
Sandia National Labs
This presentation does not contain any proprietary, confidential, or otherwise restricted information
•Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation,
a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National
Nuclear Security Administration under contract DE-AC04-94AL85000.
Goal Statement
• Pond-to-wheels life cycle assessment of CO2, N2O and
CH4 emissions for light and heavy duty vehicles across
cultivation, harvesting, extraction, separation, conversion
and combustion.
• Provide a model that can be shared with other
institutions.
• Provide data on algae cultivation processes to GREET
• Publish results in peer-reviewed literature
• Provide Israeli partners with structure for pond flow
dynamics optimization model (Dr. Scott James)
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Quad Chart Overview
Barriers
Timeline
• Feedstock Integration (Ft-B):
Sustainable Production
• Integrated Biorefineries (Im-C): Lack
of understanding of environmental
and energy tradeoffs
• Sustainability (St-D): Indicators and
Budget
methodology for evaluating
• Total project funding
sustainability
• Oct. 2009
• Sept. 2011
• ~ 80% complete
– $500K
– Contractor share
• FY09 -- $250K
• FY10 -- $250K
• No ARRA Funding
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Partners
NREL
Seambiotic, Inc.
SRS, Inc.
OPB Project Management
Joanne Morello
Project Overview
• 2 deliverables: 1) Algae Biodiesel LCA, pond to wheels,
and 2) pond flow optimization model.
• Institutional partners include Seambiotic, Inc. (Israel),
and Solution Recovery Systems, Inc. (US).
• Model is built using SimaPro 7.2
• Process and impact databases include Ecoinvent and
GREET
• SNL staff and management include Marissa Reno, Ben
Wu, Scott James, and Stephanie Kuzio, Blake Simmons.
Consultants include Lise Laurin and Harnoor Dhaliwal of
Earthshift, Inc.
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Algae
and
water
Algae
paste
(15-20%)
Methanol
Electricity
LCA approach -Base Case (Seambiotic/SRS/
GREET)
Electricity
Solvent
Chemical A
Electricity
Electricity
Inoculant
Waste CO2
Wastewater
Built in Simapro 7.2
Nannochloris sp. and nannochloropsis sp. modeled
Assumes 10-20% lipid content /dry biomass (avg. 15%)
Wastewater and waste CO2 come from co-located power plant
Secondary data (electricity, methanol, solvent, other chemicals) from
Ecoinvent 2.2
Seambiotic produces algae for high value food additives, not fuel
TAG’s
Biodiesel
Burned in
light and
heavy
duty
vehicles
No treatment of wastewater, returned to
power plant wastewater stream
No drying of residual biomass in model.
Wastewater is treated after separation using Ecoinvent
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wastewater treatment process (in acccord with European
standard).
N2O
CH4
CO2
Glycerin
Wastewater
Biomass
Low-value
Hydrocarbons
GREET
GREET
SRS
SEAMBIOTIC
Wastewater
SEAMBIOTIC
LCA Technical Accomplishments/ Progress/Results
Base case (950 m2), pond to wheels, light duty vehicle
120
110
100
90
80
%
70
60
50
40
30
20
10
0
GHGs
VOCs
Particulate matter
formation
Water depletion
Fossil depletion
NOx
SOx
Pond to wheels: algae biodiesel
Well to wheels: Conventional and Low Sulfur Diesel
Field to Wheels: Soy biodiesel
NER (MJ in/MJ out) Algae: 24.7; Petroleum Diesel: 0.18; Soy: 0.80
Comparing 1000 MJ worth of fuel in each category. Algae based on 15% oil/dry biomass. No infrastructure
processes included. Petroeum diesel and soy are 100% from GREET, algae pond to biodiesel is from
Ecoinvent, biodiesel to wheels is from GREET (integrated into SimaPro). The Econinvent data is richer and so
may be making the results show more impact relative to the GREET results. No water impacts appear for
petroleum diesel and soy diesel because GREET does not evaluate water depletion.
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LCA Technical Accomplishments/ Progress/Results
Base case and future projection assumptions
Base Case
Future Projection
Cultivation area/plant
950 m2
100,000 m2 -- 50 ponds at 2000 m2
each
Energy required/m2
18.6 kW/950 m2
5.30 kW/1000 m2
Algae yields
11,570 kg/ha/yr (~3g/m2/da)
91,000 kg/ha/yr (25g/m2/da)
Electricity mix
Current US grid
Current German grid (higher
renewables)
Oil content/dry algae
biomass
15 kg/100 kg
50 kg/100 kg
Volatilized solvent
0.33 kg solvent/kg oil content
0.0033 kg solvent/kg oil content
Allocation (energy)
Fish feed & glycerin
Fish feed & glycerine
Future projection assumes that the base case production is used as the inoculant ponds for a 100,000 m2
cultivation facility (actually, forty-nine 2000 m2 ponds and one 1000 m2 pond, plus the one ~1000 m2
inoculant pond from the base case). Current ratio of inoculant ponds area to total pond area is about 1:10.
This future projection is 1:100. Future projection also asumes that paddlewheel energy scales from 7.5 kw in
base case to 1.125 kW/2000 m2 pond. It assumes that pump and blower energy (both at 2.2 kW each) is
99x base case. It assumes autoflocculation that reduces water volume for centrifugation by a factor of 20, so
if 1 centrifuge is required for every 1000 m2, we would need 100 for 100,000 m2; 100,000/20 = 5000,
requiring 5 centrifuges (4 kw each) . We assume that there is no more scaling efficiency gained by adding
more ponds, More ponds would be added in units of 100,000 m2, including inoculant ponds, with energy
use being additive. Future projection also assumes increased solvent recovery/scrubber capabilities.
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LCA Technical Accomplishments/ Progress/Results
Algae future projection (100,000 m2), pond to wheels, light duty vehicle
120
110
100
90
80
%
70
60
50
40
30
20
10
0
GHGs
VOCs
Particulate matter
formation
Water depletion
Fossil depletion
NOx
SOx
Pond to wheels: algae biodiesel
Well to wheels: Conventional and Low Sulfur Diesel
Field to Wheels: Soy biodiesel
NER (MJ in/MJ out) Algae: 0.77; Petroleum Diesel: 0.18; Soy: 0.80
Comparing 1000 MJ worth of fuel in each category. Algae based on 15% oil/dry biomass. No infrastructure
processes included. Petroeum diesel and soy are 100% from GREET, algae pond to biodiesel is from
Ecoinvent, biodiesel to wheels is from GREET (integrated into SimaPro). The Econinvent data is richer and so
may be making the results show more impact relative to the GREET results. Considering uncertainty in data
and results, these results might be quite comparable. No water impacts appear for petroleum diesel and soy
diesel because GREET does not evaluate water depletion.
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GHG emissions per 1000 MJ, pond/well/field to Wheels
with combustion in ‘light duty vehicle’
Impact
category
GHG Total
CO2
CH4
N2 O
Unit
kg CO2 eq
kg CO2
kg CH4
kg N2O
Algae
Biodiesel
Future
Algae Biodiesel Projection Conventional
Base Case
(100,000 m2, and Low
(950 m2)
50 ponds)
Sulfur Diesel Soy Biodiesel
1568
1502
0.0554
0.0487
137
130
0.0228
0.00386
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93.6
90.4
0.1020
0.00240
22.3
18.1
0.0329
0.0113
LCA contribution analysis, base case (950 m2)
120
110
100
90
80
%
70
60
50
40
30
20
10
0
Climate
change
VOCs
Particulate matter
formation
Algae cultivation and harvesting
Water depletion
Extraction and separation
Fossil depletion
Conversion
NOx
SOx
Combustion
Analyzing 1000 MJ Combustion / Excluding infrastructure processes
Electricity consumption in cultivation and harvesting is the
primary driver
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LCA contribution analysis, future case, 100,000 m2
120
110
100
90
80
%
70
60
50
40
30
20
10
0
Climate
VOCs
Particulate matter Water depletion Fossil depletion
NOx
change
formation
Algae cultivation and harvesting
Extraction and separation
Conversion
SOx
Combustion
Improvements must be made in cultivation, harvesting, extraction and separation
Analyzing 1000 MJ Combustion/GREET Specific Impact Assessment Method V1.00/Characterization/Excluding infrastructure processes
120
120
110
100
100
80
90
60
80
40
%
%
70
60
20
50
0
40
-20
30
-40
20
-60
10
0
-80
Climate change
VOCs
Top
Particulate matte
r formation
Oil production
Fossil depletion
Diesel produciton
NOx
Climate change
SOx
Particulate matte
r formation
Oil production
Fossil depletion
Biodiesel produciton
NOx
SOx
Combustion
Analysing 1 MJ '_Well to Wheels: Soy Biodiesel (LDT)';
Method: GREET specific Impact Assessment Methos V1.00 / World ReCiPe H / Characterisation / Excluding infrastructure processes
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Analysing 1 MJ '_Well to Wheels: Conventional and Low Sulfur Diesel (LDT)';
Method: GREET specific Impact Assessment Methos V1.00 / World ReCiPe H / Characterisation / Excluding infrastructure processes
Petroleum diesel, present
VOCs
Top
Combustion
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Soy biodiesel, present
LCA relevance
Barriers addressed:
Feedstock Integration (Ft-B): Sustainable Production
Integrated Biorefineries (Im-C): Lack of understanding of
environmental and energy tradeoffs
Sustainability (St-D): Indicators and methodology for evaluating
sustainability
Algae biodiesel LCA addresses those barriers by providing:
•
•
•
understanding of GHG emissions, water use and net energy ratios
associated with algae biodiesel production and combustion in
comparison with other fuels using current commercial technology
Understanding of scale-up issues associated future production
targets
indicators and methodology for evaluating sustainability of algae
biofuels.
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LCA Milestones
•
•
Milestones completed
• Data Gathering
• Model Development
• Model Verification
• Model Sensitivity Analysis
Milestones to be completed by Sept. 30, 2011
• Final analysis and interpretation
• Final report
• Peer reviewed publication submission
Critical success factors
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•
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Project is a narrow, technological snapshot, i.e., other technologies
and approaches exist
Co-products projection and therefore impact allocation is imperfect
(i.e. what will future fish food and glycerine markets be?)
Scaling efficiencies are hypothetical
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LCA summary
•
Base case results using Seambiotic ‘pilot-scale’ system (950 m2) for net
energy ratio (NER), GHG emissions and other results exceed those for
soy biodiesel and petroleum diesel by roughly an order of magnitude,
– Petroleum biodiesel impacts do not include oil wars
– Algae modeling crucial assumptions:
• full co-location of resources and processes.
• 1:100 innoculant area to production area, instead of current 1:10
– ‘Pilot-scale’ algae production and commercial/industrial petroleum or soy production
are not comparable
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•
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Scaling up to 100,000 m2, with some improved technologies and
efficiencies, produce algae results comparable to current soy biodiesel
and petroleum diesel
Electricity consumption in algae cultivation and harvesting creates the
high impact
Overall results supports premise that algae biodiesel can be viable in
the future
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Project Deliverable 2:
Approach, Pond Flow Optimization
DS-INTL
• Solves for the 3D flow field, with heat transfer, subject to
atmospheric conditions (solar radiation, rain, evaporation,
cloud cover, temperature, wind speed, etc.)
• Adjust the algae growth parameters so that algae
concentrations agree with the Seambiotic data
• ModelUS-Israel
will help
guide data gathering efforts to build
Collaboration, Seambiotic 7
Assumptions:
optimzation model .
20 cm/s avg. velocity
DS-INTL
1 mm roughness
Depth (m)
.244
DS-INTL
[Time 181.583]
.255
DS-INTL
US-Israel Collaboration, Seambiotic 7
Velocities
-.005
[Time 181.583]
.005
1.00 (m/s)
Layer: 15
1
5
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Vertical
velocities:
helical flow
around
corners
cause
upward and
downward
flows.
Technical Accomplishments/ Progress/Results (cont’d) Pond
Flow Optimization
• 3d computational grid developed (3 layers deep)
• Boundary conditions applied (roughness, forcing functions, recirculation)
• Flow calculated
• Preliminary algae calculations are underway (example output below)
Benchmark the progress versus previously reported results (if applicable) Benchmark the accomplishments against the technical targets (if
applicable) Please limit your slides to the time you have available
1
6
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Better growth in Jan.
because of temperature,
consistent with Seambiotic
data
Pond Flow Optimization
Relevance
•
•
Provides analytical structure for understanding how to reduce
energy requirements while increasing productivity in algae
cultivation
Provides potential improvement to LCA and sustainability
indicators, and so assists in overcoming barriers identified in LCA
Critical success factors
•
•
Model provides a structure for Seambiotic pond optimization, but is
incomplete without more data from Seambiotic
Model can be useful for all algae producers
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Milestones, Pond Flow Optimization
• Milestones completed
– Physics model development
– Physics model validation
• Milestones pending
– Data gathering (Seambiotic must deliver data)
– Model calibration (dependent upon data)
– Results and report
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Pond flow optimization summary
• Model accurately captures horizontal and vertical pond
flow dynamics and is poised for inclusion of data specific
to particular ponds in particular geographic locations
• Model can provide guidance to help lower energy costs
and increase productivities in algae production
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Additional Slides
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Environmental Fluid Dynamics Code
(EFDC)
• 3-D hydrodynamic solver
• Solves the hydrostatic, free
surface, Reynolds-averaged
Navier-Stokes equations
• Hydrodynamic flow fields
required for CE-QUAL are
generated by EFDC
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•Cedar Lake, Indiana
CE-QUAL
(ACE WQ Code)
• Water-quality code
• Code developed for
eutrophication study of
Chesapeake Bay
• 23 independently-activated state
variables (e.g., nutrients, CO2,…)
•C. F. Cerco and T. Cole, (1995) User’s Guide to the CEQUAL-ICM Three-Dimensional Eutrophication Model, Release
Version 1.0, U.S. Army Corps of Engineers
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Growth Equation

 

B  x , t    P  B M  PR  W S
 B  x, t 
t
z 

•B(x,t) is the spatio-temporal algal biomass (gm Carbon/m3)
•P is the production rate (1/day)
•BM is the basal metabolism rate (1/day)
•PR is the predation rate (1/day)
•WS is the settling velocity (m/day)
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Growth Limiters
P  PM f
 N  g  I  h T 
•P is the production rate (1/day)
•PM is the production under optimal conditions (1/day)
•f(N) is the effect of non-optimal nutrient concentration (0 ≤ f(N) ≤ 1)
•g(I) is the effect of non-optimal illumination (0 ≤ g(I) ≤ 1)
•h(T) is the effect of non-optimal temperature (0 ≤ h(T) ≤ 1)
•In the present analysis, nutrients are assumed to be in excess; f(N) = 1
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Sub-optimal illumination
f I  
I
1
e
I
Is
,
Is
I is the illumination rate
Is is the optimal illumination
•Geider et al. (1985)
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Non-optimal temperature
f T   e

 K T  T opt

2
,
•T is the water temperature
•Topt is the temperature for
optimal growth
•K is a constant index
•Montagnes and Franklin (2001)
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DS-INTL
DS-INTL
DS-INTL
US-IsraelSeambiotic
Collaboration,
US-Israel Collaboration,
7 Sea
DS-INTL
Model grid
US-Israel Collaboration, Seambiotic 7
Depth (
Depth (m)
• Curvilinear orthogonal grid
.244
[Time 181.583]
.244
.255
• 45-m-long, 8-m-wide, and 0.25-m-deep
closed-circuit algal raceway (370 m2)
• The 189-cell grid comprises 3
x-coordinate cells, 63 y-coordinate cells
and 3 z-coordinate levels
• The time step was 0.2 s to ensure
model stability and accuracy as
stipulated by the CFL criteria
• A withdraw/return boundary condition
replicates the operation of a N
N
paddlewheel flowing water at 20 cm/s
N
DS-INTL
7.555
5m/s
Meters
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DS-INTL
[Time 181.
Initial conditions
• Flow rate: 225 L/s
• Initial algae concentration: 0.6 g/m3
• Growth media has excess nutrients
• Log-law roughness height: 0.001 m (cement-lined
channel)
• Initial water temperature: 25°C
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Atmospheric forcing
• Atmospheric data from Palm Springs, CA during
January and July, 2005, modified for Ashkelon
• Parameters incorporated (hourly observations):
- Temperatures (wet/dry) - Evaporation
- Incident radiation
- Precipitation
- Cloud cover
- Relative humidity
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Applications for the Future
• Evaluate the impacts of various system conditions without
having to risk the algal colony under study
• Determine the feasibility and potential benefits of scaling up
• Quantify the benefits of integrating algae culture ponds with
waste treatment plants and fossil-fuel-based power plants
• Optimize system parameters to improve efficiency
- new raceway designs - optimal harvest times
• Correlation of flow rate and channel depth with solar energy
conversion efficiency
• Impact of climate change on growth rates and biomass
productivity
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