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.

DOE Algal Biofuels

Workshop

Philip T. Pienkos

NREL

December 9, 2008

National Algal Biofuels

Technology Roadmap 

Workshop

University of Maryland

NREL/PR-510-45622

Historical Overview of Algal 

Biofuel Technoeconomic Analyses
National Renewable Energy Laboratory Innovation for Our Energy Future

You Are Here

Patents approved; 

prepare to collect 

royalties.

Sell 

stake to 

oil 

company.

Low cost 

feedstock 

becomes 

available; begin 

process 

development. Algal biofuels 

make up 

significant 

proportion of 

renewable fuel 

portfolio..
National Renewable Energy Laboratory Innovation for Our Energy Future

Technoeconomic Modeling for 

Workshop

• Discussions began in August as part of workshop 

planning process (SNL/NREL/DOE)

• Work began in earnest with meeting at SNL in 

October (SNL/NREL/NMSU/CSU)

• Establish goal to capture and consolidate all publicly 

available algal biofuel models

• Use information to help guide roadmappping effort

– Current state of technology

– Identify known knowns, known unknowns, and unknown 

unknowns

– Provide focus on critical path elements

– Estimate time and cost to achieve technical milestones
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Cast of Characters

• NREL

– Al Darzins

– David Humbird

– Phil Pienkos

• NMSU

– Pete Lammers

– Meghan Starbuck

• CSU

– Bryan Willson

• SNL

– Katherine Dunphy-Guzman

– Ray Finley

– Geoff Klise

– Len Malczynski

– Ron Pate

– Amy Sun

– Cecelia Williams
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Acknowledgements

These people did most of the heavy lifting, 

consolidating information from a variety of 

models and providing key slides for this 

presentation:

–Amy Sun

–Katherine Dunphy-Guzman

–Cecelia Williams

–Ron Pate

•Finley
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Longer Term Purpose, Goals and Desired Outcomes for Algae R&D Program

• Assess algal biofuel production scale-up potential, constraints, consequences, preferred paths

- technical, economic, environmental, policy

- comparative tradeoffs of alternative technologies/systems/processes pathways 

• Understand and quantify impact(s) of proposed R&D strategies using key selected criteria 

or “objective function” metrics that can be represented as model parameters... use to 

inform and guide R&D investments and monitor performance of technology, process and 

applications development

• Project cost (& other performance metrics) of biofuel feedstock and/or biofuels production

• Project cost (& other performance metrics) of co-product feedstock or co-products production

• Inform policy decisions

Algal Biofuels TE Modeling & Analysis

Near Term Purpose, Goals & Plans for Algae Roadmap Workshop

• Updated Presentation on Current Status of Algae Biofuels Techno-Economics

• Formulate key questions for workshop breakouts to inform TE modeling & assessment

• Conduct evening session at workshop on Algae TE Modeling & Analysis

- present and elicit expert comment on strawman TE modeling / analysis purpose, goals, & approach 

- present and elicit expert feedback comments / suggestions on baseline systems/processes diagram

- present and elicit expert comment on strawman list of system & process evaluation criteria/metrics

- elicit initial expert evaluation of systems, processes, and pathways based on evaluation criteria/metrics 
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Elements and Issues for Techno-Economic Assessment

of Algae Biomass Feedstock, Fuels, & Co-Products
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Process Flow Diagram

1.0

Siting

2.1 Algal speci es

3.0 C ultivation

1.5.3 Water 

Treatment

4.0 H arvesting / D e-watering

5.0 Extraction & 

Separation

6.1 C onversion 

to

Biofuels

6.2 C onversion 

to

Co-products

10.0 Water 

Captur e 

3.4 Wast ewater 

Photoautotrophic & 

Heterotrophic;

Co-generati on

3.2 Open syst ems

3.1 Closed systems

3.3 H ybrid systems

1.2 CO2

1.5 Water

2.2 N utrients (NPK)

1.1 Land

1.3 F acilities

2.3 Pathogens, predators

1.4 Primar y Energ y

4.6 Wet algal or 

biological assi st 

derivati ve biomass

8.0 Algal 

biomass

10.1 Water 

disposal

4.7.1 Solar

4.7.2 FuelFired

4.7 Dr ying

4.4 C entrifuge

4.2 Airlift floccul ation

4.1 Flocc ulati on & 

settling

4.3 Filtering

9.0 R enewabl es

5.3 Protei ns

5.1 Lipids

5.2 C arbohydrates

5.1.1 TAG

5.1.2 Fatty 

acids

5.4 Other 

metabolites

6.1.3 Bi ochemical

6.1.1 Chemical

6.1.2 Thermochemical

6.2.3 Chemicals

6.2.1 Feed

6.2.2 Fertilizer

6.2.4 Materials

6.3.3 Avi ation

6.3.1 Bi odiesel

6.3.2 Green diesel

6.3.4 Gasoline-like

6.3.7 H ydrogen

6.3.5 Bi ogas/Methane

6.3.6 Ethanol

2.0 Algal speci es sel ection

3.5 Biological Assi st

- brine shri mp

- fish

- etc.

4.5 Biological Assi st 

Harvesting (shrimp, 

fish excrement, etc.)

1.5.1 Primar y water 

source 

(saline/br ackish/waste

water)

1.5.2 Fresh water 

source

OUTPUT

INPUT

PROCESS CAT EGORY

PROCESS
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Comparative TE analysis results depend on metrics used

• Minimize Capital Costs per unit of biofuel

• Minimize Operating Costs per unit of biofuel

• Maximize Biofuel Production Yield

• Minimize net GHG Footprint per unit of biofuel produced 

• Maximize net Energy Balance

• Minimize net Water Usage

• Minimize Land Footprint per unit of biofuel produced

• Minimize Time Required to reach desired production 

volume

• Minimize Investment Needed to reach desired prod. 

volume 

Evaluation Criteria & Objective Functions 

Total 

Production 

Cost $/gal
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Precedent for DOE: H2A

• President Bush launched the Hydrogen Fuel Initiative in 

February, 2003 to help ensure U.S. energy security and to 

reduce greenhouse gas and other harmful emission. 

• In response, DOE established the Hydrogen, Fuel Cells and 

Infrastructure Program

– Set research priorities and make other important program direction 

decisions informed by sound analysis 

– Evaluate costs, energy and environmental tradeoffs 

– Consider various pathways toward a hydrogen economy.

• A review of the public information available in this area led to 

these conclusions:

– Many excellent analyses had been conducted. 

– Many analyses of the same or similar routes to produce hydrogen 

appeared to yield different results. Principal discrepancies lie in the 

basis and assumptions used in the analysis. 
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H2A Objectives

• Establish a standard format and list of parameters for 

reporting analysis results for central production, 

distributed (forecourt) production, and delivery. 

• Seek better validation of public analyses through 

dialog with industry. 

• Enhance understanding of the differences among 

publicly available analyses and make these 

differences more transparent. 

• Establish a mechanism for facile dissemination of 

public analysis results. 

• Work to reach consensus on specific analysis 

parameters for production and delivery. 
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H2A Participants

Core Members

Daryl Brown: PNNL 

Jerry Gillette: ANL 

Brian James: Directed Technologies 

Steve Lasher: TIAX 

Johanna Levene: NREL 

Margaret Mann: NREL 

Dan Mears: Technology Insights 

Marianne Mintz: ANL

Joan Ogden: UC, Davis 

Marylynn Placet: PNNL 

Matt Ringer: NREL 

Mike Rutkowski: Parsons 

Harry Stone: Battelle 

Michael Wang: ANL 

Key Industry Collaborators

AEP

BOC

BP

Chevron

Eastman Chemical

Entergy

ExxonMobil

Ferco

Framatome

General Electric

Praxair

Stuart Energy

Thermochem
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H2A Analyses

• Original source(s) of all the data (i.e., report title, authors, etc.) 

• Basic process information (feedstock and energy inputs, size of plant, 

co-products produced, etc.) 

• Process flowsheet and stream summary (flowrate, temperature, 

pressure, composition of each stream) 

• Technology performance assumptions (e.g., process efficiency and 

hydrogen product conditions) 

• Economic assumptions (after tax internal rate of return, depreciation 

schedule, plant lifetime, income tax rate, capacity factor, etc.) 

• Calculation of the discounted cash flow (the calculation procedure is 

built into the standardized spreadsheet so that all technologies use the 

same methodology) 

• Results (plant-gate hydrogen selling price and cost contributions in $/kg 

H2, operating efficiency, total fuel and feedstock consumption, and 

emissions) 

• Sensitivity of the results to assumptions (e.g., feedstock cost, coproduct selling price, capital cost, operating costs, internal rate of 

return, conversion efficiencies, etc.) 

• Quantification of the level of uncertainty in the analysis
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H2A Production Technologies

• Central Production of Hydrogen 

– Coal Gasification: Hydrogen Production 

– Coal Gasification: Hydrogen and Electricity Production 

– Natural Gas Hydrogen Production 

– Biomass Gasification Hydrogen Production 

– Nuclear Energy Hydrogen Production 

– Wind Electrolysis Hydrogen Production 

• Forecourt Production of Hydrogen 

– Natural Gas Reforming 

– Electrolysis 

– Reforming of Ethanol sourced from biomass 

– Reforming of Methanol sourced from biomass 
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Not As Bad As We Thought

1.0

Siting

2.1 Algal speci es

3.0 C ultivation

1.5.3 Water 

Treatment

4.0 H arvesting / D e-watering

5.0 Extraction & 

Separation

6.1 C onversion 

to

Biofuels

6.2 C onversion 

to

Co-products

10.0 Water 

Captur e 

3.4 Wast ewater 

Photoautotrophic & 

Heterotrophic;

Co-generati on

3.2 Open syst ems

3.1 Closed systems

3.3 H ybrid systems

1.2 CO2

1.5 Water

2.2 N utrients (NPK)

1.1 Land

1.3 F acilities

2.3 Pathogens, predators

1.4 Primar y Energ y

4.6 Wet algal or 

biological assi st 

derivati ve biomass

8.0 Algal 

biomass

10.1 Water 

disposal

4.7.1 Solar

4.7.2 FuelFired

4.7 Dr ying

4.4 C entrifuge

4.2 Airlift floccul ation

4.1 Flocc ulati on & 

settling

4.3 Filtering

9.0 R enewabl es

5.3 Protei ns

5.1 Lipids

5.2 C arbohydrates

5.1.1 TAG

5.1.2 Fatty 

acids

5.4 Other 

metabolites

6.1.3 Bi ochemical

6.1.1 Chemical

6.1.2 Thermochemical

6.2.3 Chemicals

6.2.1 Feed

6.2.2 Fertilizer

6.2.4 Materials

6.3.3 Avi ation

6.3.1 Bi odiesel

6.3.2 Green diesel

6.3.4 Gasoline-like

6.3.7 H ydrogen

6.3.5 Bi ogas/Methane

6.3.6 Ethanol

2.0 Algal speci es sel ection

3.5 Biological Assi st

- brine shri mp

- fish

- etc.

4.5 Biological Assi st 

Harvesting (shrimp, 

fish excrement, etc.)

1.5.1 Primar y water 

source 

(saline/br ackish/waste

water)

1.5.2 Fresh water 

source

OUTPUT

INPUT

PROCESS CAT EGORY

PROCESS
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Source Material for TE Models

Source Authors Year Reference

Matt Ringer

Bob Wallace

Phil Pienkos

Meghan Starbuck

Pete Lammers

Solix Bryan Willson 2008 2nd Bundes-Algen-Stammtisch

Seambiotics Ami Ben-Amotz, Israel 2007-2008 Algae Biomass Summit

Sandia Ben Wu 2007 Analysis completed for this exercise

Bayer Ulrich Steiner 2008

European White Biotechnology 

Summit

General Atomics David Hazlebeck 2008 Algae Biomass Summit

California Polytechnic 

Institute Tryg Lundquist 2008 Algae Biomass Summit

E. Molina Grima

E. Belarbi

F. Fernandez

A. Medina

Y. Chisti

P. Tapie

A. Bernard

John Benemann

William Oswald 1996

Association pour la 

Recherche en Bioenergie 

NREL

NMSU

University of Almeria

University of California PETC Final Report

2008 Analysis completed for this exercise

2008 Analysis completed for this exercise

2003 Biotechnol. Adv. (2003) 20:491-515

1988 Biotech. Bioeng. (1988) 32:873-885
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Standardized Cost Comparison

•Average = $109 USD/gal

•Variability is wide, Std. Dev. = $301 USD/gal

PER GALLON Triglyceride Production Cost

$0

$10

$20

$30

$40

$50

$60

Benemann (1996), 30g/m2/dBenemann (1996), 60g/m2/d

NREL - current

NREL - aggressive

NREL - max

NMSU - 1ac, current

NMSU - 1ac, best

NMSU -2000ha,current

NMSU -2000ha,best

Solix - current

Solix -PI

Solix - PII

NBT Ltd., Israel

Seambiotic/IEC, Israel

Bayer AG, "WOS"

General Atomics-low

General Atomics-high

Cal Poly, Case1

Sandia - Raceway

Sandia - PBR

Molina Grima et al. (2003)

Tapie & Bernard (1987)Tapie & Bernard (1987)

USD/gal

NMSU

1 acre,

current

vs.

best NMSU

2,000 ha

current

vs.

best

NREL

current,

aggressive,

max yield

Solix:

current,

Phase I

Phase II Seambiotic/IEC:

waste-heat

coupling

Bayer:

"WOS"

PBR

General

Atomics:

low vs. high

$1,127/gal

Cal Poly:

WWT+

algal oil

$990/gal

Sandia

pond

vs.

PBR

France,

ground 

tubes 

vs.

double

tubes
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SCENARIO Reactor Type Lipid yield 

(wt% of dry 

mass)

Areal Dry 

Algae Mass 

Yield 

(g/m2/day)

Loan 

Period 

(yrs)

Benemann per ha basis open pond 50% 30 5

Benemann per ha basis open pond, max 50% 60 5

NREL Current Case open pond 25% 20 15

NREL Aggressive Case open pond 50% 40 15

NREL Maximum Case open pond 60% 60 15

NMSU current yield open pond 35% 35 20

NMSU highest yield open pond 60% 58 20

Solix Current hybrid 16% - 47% 0 - 24.5 unk

Solix Q2, 2009 hybrid 16% - 47% 30-40 unk

NBT, Israel Dunaliella open 35%* 2 unk

Seambiotic/IEC, Israel Best Yield open 35%* 20 unk

Sandia Raceway&PBR both 35% 30 20

Bayer Tech Services Germany PBR 33% 52 10

Bayer Tech Services El Paso, TX PBR 33% 110 10

General Atomics 100 acres open/hybrid unk unk unk

Molina-Grima et al. 26.2 metric ton/annum 75 0.8 m^3 outdoor 

T-PBRs 10% unk 10

Cal Poly, Case1 100 ha wastewater 

treatment + digester 25% 20 8

Tapie & Bernard 10 ha T-PBR 35%* 20 5

* Assumed quantity required to convert from weight-basis to oil-basis

Inherent Assumptions Vary Widely
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-$50 $0 $50 $100 $150 $200 $250 $300 $350 $400 $450 $500 $550

Co-Product credit Total

Capital-Indirect

Capital-LAND

Operating-Total

Capital-Facility

PER GALLON

Triglyceride

Average

Low

1-Std.Dev.

• Cost Uncertainties 

dominated by uncertainties 

in Facility and Operating 

cost estimation.

• Land cost is either not 

considered or small in most 

sources relative to Total 

Capital Cost.

• Co-product credit does not 

reduce the overall 

uncertainty in cost 

estimation.

Uncertainty by Cost Categories
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Cost Reductions (NREL)

NREL Cost Breakdown

-$2

$0

$2

$4

$6

$8

$10

$12

Current Case Aggressive Case Maximum Case

USD/gal

Co-Product credit Total

Operating-Total

Capital-Indirect

Capital-Facility

Capital-LAND

25% oil

20 g/m2/d

60% oil

60 g/m2/d

50% oil

40 g/m2/d
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Cost Reductions (Solix)

Slide used with permission of Dr. Bryan Willson
National Renewable Energy Laboratory Innovation for Our Energy Future

Conclusions

• Many things have changed since the last major push 

for algal biofuels

– The price of oil has gone up fluctuated wildly gone down

– Energy security is a real issue

– Climate change is widely recognized as a significant threat

– Real capital is being raised for algal biofuel commercialization

– Not many more known knowns but a few more known 

unknowns

• Technoeconomic modeling is a critical element to 

determine:

– Best estimate for current cost of algal biofuel production

– Fastest road forward to commercialization

• The current state of technoeconomic modeling

– Is more dependent upon assumptions than on data 

– Results in huge variations in cost estimates and uncertainty
National Renewable Energy Laboratory Innovation for Our Energy Future

Conclusions, continued

• Modeling for algal biofuel production is extremely 

complicated

– Alternative approaches to cultivation, harvest, extraction

– Different assumptions about input costs and byproduct values

– Availability of essential resources (sunlight, land, CO2, and water) 

vary significantly across the US and models must take these 

variations into account

• The H2A program for hydrogen production and 

storage can provide valuable insight and precedent for 

improved modeling

• The work initiated for this workshop is a step towards 

the development of a unified model that can be shared 

with all stakeholders to provide a common metric to 

measure progress towards the goal of 

commercialization of algal biofuels.