Achieving deep reductions in CO2 emissions from today’s transportation system presents major challenges, given the dominant role played by crude-oil derived fuels. Advanced biofuels, produced sustainably, provide one potential path for deep emissions reductions. A clear understanding of the prospective economics of advanced biofuels is thus important to support analysis aimed at informing public- and private-sector decision making on biofuels. Many techno-economic studies of advanced biofuels have been published, but individual studies are often difficult for decision makers to evaluate because of differences in analytical methodologies, input-data uncertainties, scope and battery limits of the analysis, and key assumptions. Using both literature and data from demonstration projects, we address the following questions for advanced thermochemical cellulosic biofuels: What will first-of-a-kind (FOAK) cellulosic biofuels cost, and What cost levels can be expected in the near-term?

1. 1
Making Sense of Cost and
Performance Estimates for
Thermochemical Biofuel Plants
Ilkka Hannula1 Johannes C. Meerman2 Eric D. Larson2 Chris Greig3
1VTT Technical Research Centre of Finland
2Andlinger Center for Energy and the Environment,
Princeton University, USA
3Dow Centre for Sustainable Engineering Innovation,
The University of Queensland, Australia

2. Motivation
• Most scenarios for meeting Paris goals rely on
cellulosic biofuels for important contributions.
• Understanding costs is important for designing
effective policies to drive such futures.
• Here, we address cost questions for advanced
thermochemical cellulosic biofuels:
• What will first-of-a-kind (FOAK)
cellulosic biofuels cost?
• What cost levels can be expected in the
near-term?
Source: IEA ETP 2017,
2DS scenario
• ”Difficult-to-electrify”
sectors: 64 EJ/yr
• About 60 % of this
should be low-carbon
by 2050
• 1 EJ/yr 500 BTL
plants à 1000 bpd

3. Haarlemmer, et al. (2014) conclusion: Demonstration plants and pre-commercial units will be needed to obtain a
clearer picture. BtL plants are situated at the beginning of the learning curve.
ProductionCost(€2012/liter)
Capex (million € 2012)
BtL
CtL
400 MWth BTL (“mature technology costs”)
3.0
2.5
2.0
1.5
1.0
0.5
0
0 140012001000800600400200
Haarlemmer, et al. (2014)

4. Towards more harmonized Nth-of-a-kind estimates
• We derive Bare Erected Cost
(BEC) from literature.
• We harmonise for currencies &
cost years, and use same
assumption for EPCM,
contingency, WACC, feedstock
cost and refinery margin.
• NOAK break-even oil price
(BEOP) estimates: 60 – 110 $/bbl
WACC: 6 %
Feedstock: $60/tonne
Electricity: $60/MWh
Heat: $20/MWh
Availability: 8000 h/yr

5. 5
COST EXPECTATIONS FOR NEW TECHNOLOGY – WHERE ARE THERMOCHEMICAL BIOFUELS ON THIS MOUNTAIN ?
($/output)

6. 6
Nth plant
N-1th plant
N-2th plant
1st-of-its-kind($/output)
The ”Mountain of Fog”
COST EXPECTATIONS FOR NEW TECHNOLOGY – WHERE ARE THERMOCHEMICAL BIOFUELS ON THIS MOUNTAIN ?

7. RAND Pioneer Plants Study (1978-1981)
• Analysis of actual versus
estimated costs for 44 FOAK
chemical process plants, built in
the US or Canada between 1966
and 1981.
• Developed an empirical formula
for forecasting growth in the
cost estimates.

8. “World’s first plant producing
synthetic biomethane via gasification”
Location: Gothenburg, Sweden
Start-up: 2013
Biomass input: 30 MW
Methane output: 20 MW
Cost estimate 1: 888 MSEK
Cost estimate 2: 1075 MSEK (+ 21%)
Final as-built cost: 1480 MSEK ( +38%)
Application of the RAND method to GoBiGas

9. GoBiGas analysis Est. 1
Complexity 10
PctNew 38
Impurities 5
Inclusiveness 100
Project definition 4.25
Technology level R&D
CapEx multiplier 1.59
Low end 1.39
High end 1.85 0
200
400
600
800
1000
1200
1400
1600
1800
Actual
costs
Early
estimate
RAND Later
estimate
RAND
Investmentcostestimate(MSEK)
Application of the RAND method to GoBiGas
Estimate 1 Estimate 2

10. GoBiGas analysis Est. 1 Est. 2
Complexity 10 10
PctNew 38 38
Impurities 5 5
Inclusiveness 100 100
Project definition 4.25 2
Technology level R&D R&D
CapEx multiplier 1.59 1.29
Low end 1.39 1.16
High end 1.85 1.46 0
200
400
600
800
1000
1200
1400
1600
1800
Actual
costs
Early
estimate
RAND Later
estimate
RAND
Investmentcostestimate(MSEK)
Application of the RAND method to GoBiGas
Estimate 1 Estimate 2

11. Application of RAND to scoping level studies
RAND analysis Typical scoping study
Complexity 10
PctNew 50 (40 – 60)
Impurities 5
Inclusiveness 0
Project definition 8
Technology level R&D
CapEx multiplier 3.8
Low end 2.8
High end 5.8

12. Application of RAND to scoping level studies
RAND analysis Typical scoping study
Complexity 10
PctNew 50 (40 – 60)
Impurities 5
Inclusiveness 0
Project definition 8
Technology level R&D
CapEx multiplier 3.8
Low end 2.8
High end 5.8
WACC: 6 %
Feedstock: $60/tonne
Electricity: $60/MWh
Heat: $20/MWh
Availability: 8000 h/yr

13. Deriving estimates from a demo plant
• Medium-scale FOAK plant
• 1000 bpd (~132 MWbiomass (LHV))
• Total Capital Investment range:
417 - 512 M$
• Large-scale FOAK plant
• 3000 bpd (~395 MWbiomass (LHV))
• Total Capital Investment range:
771 - 1105 M$

14. • Operational problems not
usually anticipated in the
techno-economic literature.
• Too idealistic assumptions can
significantly misrepresent the
financial outcome.
Impact of FOAK operational problems on costs
Source: Hess, W. (1985).

15. • We assume 20 % for year 1,
ramping linearly to 8000 h/yr
over four years.
• Resulting levelised availability
6756 h/yr (WACC 6 %)
• Resulting increase in BEOP:
• 15 % for WACC 6 %
• 24 % for WACC 12 %
Impact of FOAK operational problems on costs
Results for the 1000 bpd plant

16. Production cost outlook for 10 000 bpd deployment
• Our demo plant –derived FOAK
estimates:
• $220/bbl for the 1000 bpd plant
• $150/bbl for the 3000 bpd plant
• These plants represent two potential
starting points for a deployment path.
• Centralised (3000 bpd FOAK)
Large scale, high investment, high risk,
competitiveness through scale
• Distributed (1000 bpd FOAK)
Smaller scale, modest investment, limited
risk, competitiveness through replication
1000bpd
3000bpd
WACC: 6 %
Feedstock: $60/tonne
Heat/Steam: $20/MWh
Lev. availability: 6756 h/yr

17. • Three learning rates (LRs) per
doubling of cumulative capacity
• 11 % (electricity from biomass)1
• 20 % (Brazilian ethanol)2
• 26 % (organic chemicals)3
• At fixed deployment, the distributed
path benefits more from learning
(more doublings).
• At high LRs, the distributed path
catches and even undercuts the costs
of the centralised path.
Production cost outlook for 10 000 bpd deployment
1000bpd
3000bpd
1 Rubin et al. (2015)
2 van den Wall Bake, J. et al. (2008)
3 Merrow, E. (1989)
WACC: 6 %
Feedstock: $60/tonne
Heat/Steam: $20/MWh
Lev. availability: 6756 h/yr

18. HVO 75 – 145 $/bbl
Production cost outlook for 10 000 bpd deployment
• Three learning rates (LRs) per
doubling of cumulative capacity
• 11 % (electricity from biomass)1
• 20 % (Brazilian ethanol)2
• 26 % (organic chemicals)3
• At fixed deployment, the distributed
path benefits more from learning
(more doublings).
• At high LRs, the distributed path
catches and even undercuts the costs
of the centralised path.
1 Rubin et al. (2015)
2 van den Wall Bake, J. et al. (2008)
3 Merrow, E. (1989)
WACC: 6 %
Feedstock: $60/tonne
Heat/Steam: $20/MWh
Lev. availability: 6756 h/yr

19. Learning investment analysis for 10 000 bpd deployment
WACC: 6 %
Feedstock: $60/tonne
Heat/Steam: $20/MWh
Lev. availability: 6756 h/yr
• We set the market value of synthetic
biodiesel to $110/bbl (average HVO
production cost).
• The Net Present Value (NPV) of subsidy
is
• 461 M$ for 1000 bpd FOAK plant
• 502 M$ for 3000 bpd FOAK plant
• The cumulative NPV of subsidy is
• 0.7 – 2.7 B$ for distributed path
• 0.5 – 1.2 B$ for centralised path

20. • We set the market value of synthetic
biodiesel to $110/bbl (average HVO
production cost).
• The Net Present Value (NPV) of subsidy
is
• 461 M$ for 1000 bpd FOAK plant
• 502 M$ for 3000 bpd FOAK plant
• The cumulative NPV of subsidy is
• 0.7 – 2.7 B$ for distributed path
• 0.5 – 1.2 B$ for centralised path
Learning investment analysis for 10 000 bpd deployment
WACC: 6 %
Feedstock: $60/tonne
Heat/Steam: $20/MWh
Lev. availability: 6756 h/yr

21. Take-home messages 1/2
• Divergence of cost estimates in the literature partly explained by lack
of common standards in applying economic parameters.
• The RAND method seems useful for estimating cost growth in
estimates for FOAK synthetic biofuel projects.
• RAND method not intended to replace engineering.
• High CapEx multiplier -> do more engineering before taking FID.
• Helps to have more realistic understanding of costs earlier.
• Policy implication 1: Getting a grant should not be a beauty contest
that rewards rushing to a project with minimal FEED and due
diligence.

22. Take-home messages 2/2
• Choosing the ”optimum” scale for a FOAK plant complicated.
• Medium-scale and large-scale paths can both lead to reasonable
BEOPs in the medium-term if high learning rates can be realised.
• Better understanding on the economics of scale, and early-stage learning
needed.
• Most policies in the past have promoted the centralised, large-scale
approach.
• Problematic due to lack of investment appetite for large-scale high-risk bets.
• Policy implication 2: Learning, not initial cost effectiveness ($/bbl),
should be an important goal of early-stage technology promotion.

23. Bibliography 1/2
• Haarlemmer, G., Boissonnet, G., Peduzzi, E. and Setier, P., Investment and production costs of synthetic fuels
– A literature survey, In Energy, Volume 66, 2014, Pages 667-676, ISSN 0360-5442
• Greig, C., Kreutz, T., Larson, E., Meerman, J. and Williams, R. Lignite-plus-Biomass to Synthetic Jet Fuel with
CO2 Capture and Storage Design, Cost, and Greenhouse Gas Emissions Analysis for a Near-Term First-of-a-
Kind Demonstration Project and Prospective Future Commercial Plants. Final Report to The National Energy
Technology Laboratory U.S. Department of Energy (contract number DE-FE0023697). 2017.
• Hannula I. and Kurkela E. Liquid transportation fuels via large-scale fluidised-bed gasification of
lignocellulosic biomass. VTT Technology 91, Technical Research Centre of Finland. 2013.
• Hess, R. Review of cost improvement literature with emphasis on synthetic fuel facilities and the petroleum
and chemical process industries. N-2273-SFC. Rand. 1985.
• IEA, 2017. Energy Technology Perspectives 2017. International Energy Agency, Paris, France.
• Kreutz, T., Larson, E., Liu, G. and Williams, R. Fischer-Tropsch Fuels from Coal and Biomass. Prepared for 25th
Annual International Pittsburgh Coal Conference. Princeton Environmental Institute, Princeton University.
2008.
• Larson, E., Jin, H. and Celik, F. (2009), Large-scale gasification-based coproduction of fuels and electricity
from switchgrass. Biofuels, Bioprod. Bioref., 3: 174–194. doi:10.1002/bbb.137

24. Bibliography 2/2
• Liu, G., Larson, E., Williams, R., Kreutz, T. and Guo, X. Making Fischer-Tropsch fuels and electricity from coal
and biomass: performance and cost analysis, Energy & Fuels 25(1). doi:10.1021/ef101184e.
• Merrow, E., Phillips, K. and Myers, C. Understanding cost growth and performance shortfalls in pioneer
process plants, Tech. Rep. RAND/R-2569-DOE, RAND Corporation, Santa Monica, USA. 1981.
• Merrow, E. An analysis of cost improvement in chemical process technologies. R-3357-DOE. RAND
Corporation, Santa Monica, USA. 1989.
• Rubin, E., Azevedo, I., Jaramillo, P. and Yeh, S. A review of learning rates for electricity supply technologies,
Energy Policy, Volume 86, 2015, Pages 198 – 218.
• Swanson, R., Satrio, J., Brown, R., Platon, A. and Hsu, D. Techno-Economic Analysis of Biofuels Production
Based on Gasification. NREL/TP-6A20-46587. National Renewable Energy Laboratory. 2010.
• van den Wall Bake, J., Junginger, M., Faaij, A., Poot, T. and Walter, A. Explaining the experience curve: Cost
reductions of Brazilian ethanol from sugarcane, Biomass and Bioenergy, Volume 33, Issue 4, 2009, Pages
644-658, ISSN 0961-9534,
• Zhu, Y., Tjokro Rahardjo, S., Valkenburg, C., Snowden-Swan, L., Jones, S., and Machinal, M. Techno-economic
Analysis for the Thermochemical Conversion of Biomass to Liquid Fuels. PNNL-19009. Pacific Northwest
National Laboratory. 2011.

25. 25
Thank you for your
attention!
Please send your feedback to:
ilkka.hannula@vtt.fi