Guolin YaoMark D. StaplesRobert Malina

Wallace E. Tyner

Stochastic Techno-Economic Analysis of Alcohol-to-Jet Fuel Production

Outline

• Introduction

• Pathway and Feedstock Description

• Methodology

• Results

• Conclusions

Introduction

• Aviation biofuels can be used to meet the Renewable Fuel Standard (RFS).

• US FAA has a short-term goal of 1 billion gallons of alternative aviation fuel consumption by 2018 for military and commercial applications.

• Unlike ground transportation which can use ethanol, CNG, or electricity, aviation requires the use of energy dense, non-oxygenate, hydro-carbon, liquid fuels.

• Four major aviation biofuel technologies are currently technically feasible: Fischer-Tropsch (F-T), hydroprocessed renewable esters and fatty acids (HEFA), sugar conversion (fermentation, thermochemical), and direct liquefaction (pyrolysis).

• More than twenty airlines have already used petroleum-derived jet fuel blended with aviation biofuels on thousands of passenger flights.

Introduction

• Three contributions of this research:– Breakeven price distributions in addition to NPV and IRR– Link technical efficiency with input/output quantities through

econometric methods– Time-series price estimation based on historical prices

Pathway and Feedstock Description

FeedstockFermentation

EthanolDehydration

OligomerizationFuels

Alcohol to Jet Pathway

Pathway and Feedstock DescriptionAlcohol to Jet Pathway

• Three feedstocks: Corn grain, sugarcane, switchgrass

• Two procedures: feedstock to ethanol & ethanol to fuels

• Four basic categories of stochastic variables: Two conversion factors

Feedstock prices

Natural gas prices

Output fuel prices

• Other variables are linked to the four basic stochastic variables

MethodologyLinkage of Stochastic Variables within the Model

MethodologyConversion Efficiency Assumptions

*=

Min Mode Max Mean

Feedstock to EtOH (kg feedstock per kg

EtOH)

Corn Grain 3.29 3.56 3.90 3.57

Sugarcane 11.38 13.19 14.38 13.09

Switchgrass 4.00 4.82 8.22 5.25

EtOH to Fuel (kg EtOH per MJ Fuel)

Corn Grain Sugarcane

Swtichgrass0.03 0.04 0.07 0.04

𝑘𝑔 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘𝑘𝑔 h𝑒𝑡 𝑎𝑛𝑜𝑙

∗𝑘𝑔 h𝑒𝑡 𝑎𝑛𝑜𝑙𝑀𝐽 𝑓𝑢𝑒𝑙

=𝑘𝑔 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘

𝑀𝐽 𝑓𝑢𝑒𝑙

𝑚𝑒𝑎𝑛=𝑚𝑖𝑛+4𝑚𝑜𝑑𝑒+𝑚𝑎𝑥

6

PERT Distribution:

MethodologyCapital Costs• Consist of

– Feedstock Preprocessing & Fermentation• A function of feedstock input quantity.• Estimated from Staples et al. (2014) on the basis of

dollars-per-unit-mass of feedstock processing capacity

• Feedstock quantity is dependent on two conversion factors which determine overall feedstock to jet

conversion efficiency.– Dehydration, Oligomerization and

Hydrotreating• Fixed, as a function of facility size

Feedstock Processing Capacity Min Mode Max

Corn Grain 55 75 95

Sugarcane 20 25 30

Switchgrass 115 165 215

MethodologyCorn and Sugarcane Prices

Pt=μ+b1 ε t −1+b2 εt −2+ε t

(1) is the corn grain or sugar prices in time t

(2) and is the volatility parameter

(3) ), and , are the moving average coefficients.

Parameters µ σ b1 b2 ε0 ε-1

Corn Grain($/bushel)

4.8 0.66 0.87 0.46 0.25 1.81

Sugar (cents/lb) 19.5 3.24 0.91 0.42 -1.85 1.54

MA2• Time-series estimation effectively

captures the uniqueness of the motion processes of each product market, based on historical prices.

• Corn grain and sugarcane are commodities with mature markets.

• Annual historical prices from 1980 to 2014 are available from USDA.

• The second-order moving average process (MA2) results in the best fit of historical data to project future corn grain and sugarcane prices according to the Akaike information criterion (AIC).

MethodologyException: Switchgrass Price

Yield Units  MeanStd Dev

Coefficient of Variation

Upland 1000kg/ha 8.70 4.20 0.483

Lowland 1000kg/ha 12.90 5.90 0.457

Mean 1000kg/ha 10.80 5.08 0.470

• CV=SD/mean CV(mean)=[CV(upland)+CV(lowland)]/2 SD(mean)=mean(mean)*CV(mean)• Yield (1000kg/ha): min=mean-2SD=10.8-2*5.08=0.65; mean=10.08; max=mean-

2SD=10.8+2*5.08=20.95• Average switchgrass cost=$116.50/1000kg• Farmer Payment ($/ha)=Average Switchgrass Cost ($/kg)* Mean Yields (kg/ha)=$1258.2/ha• Switchgrass Cost ($/kg) =Farmer Payment ($/ha)/ Yield Distribution (kg/ha)

MethodologyNatural Gas and Other Input Prices

𝑃 𝑡=𝜇+𝑏1𝜀𝑡− 1+𝜀𝑡

(1) is the natural gas prices in time t

(2) and is the volatility parameter

(3) ), and is the moving average coefficient

MA1

Parameters µ σ b1 ε0

Natural Gas($/thousand cubic feet)

6.9 1.3 0.5 -2.1

The operating costs for water, power and

other (enzymes, yeast, chemicals) inputs are

less than 0.01%, 0.1%, and 1% of the total

costs for each feedstock case, respectively, and

their variations do not exert significant

influence on the calculated NPV and

breakeven price distributions. Therefore, we

treat their prices as exogenous and

deterministic.

MethodologyOutput Prices ARMA11

(1) is the diesel prices in time t

(2) and is the volatility parameter

(3) , is the autoregressive coefficient, and is the moving

average coefficient

Parameters µ σ a1 b1 ε0

Diesel ($/gal) 2.72 0.44 0.94 -0.59 0.47

=0.996

=()*

MethodologyBreakeven Jet Price Distribution

All sets of simulated values for all the uncertain variables are saved.

1000 sets of randomly simulated values are plugged in the model,

respectively, to generate 1000 corresponding breakeven prices.

The resulting distribution of breakeven jet prices are derived.

Probability and cumulative density distributions are generated and

fitted to the closest standard distributions.

The breakeven price at each percentile is reported.

MethodologyOther Basic Assumptions Assumptions Values

Facility Size (bpd) 4,000

Land Cost Factor (% of Capital Investment Each Year)

4%

Equity 20%Loan Interest 5.5%Loan Term (Years) 10Type of Depreciation VDBDepreciation Period (Years) 10Construction Period (Years) 3% Spent in Year 1 8%% Spent in Year 2 60%% Spent in Year 3 32%Nominal Discount Rate 15%Income Tax Rate 16.9%Operating Hours per Year 8,400Cost Year for Analysis 2012

20% equity and 80% of capital investment, financed through loans at a 5.5% interest rate for 10 years, based on the original assumption by Staples et al. (2014).

Capital investment has a fixed component and a stochastic component. The fixed capital investment is 99.83 for all feedstocks. The mode of the total capital costs are $300, $347, and $697 million for corn grain, sugarcane, and switchgrass respectively.

Working capital is calculated as 20% of first year (4th project year) operating costs.

ResultsNet Present Value (NPV)

Statistics (Million $) Corn Grain Sugarcane Switchgrass

Mean (203) (167) (579)Std Dev 123 144 239Minimum (610) (829) (1,665)Maximum 198 320 69 Probability of Loss 95% 88% 100%

ResultsNet Present Value (NPV)

ResultsNet Present Value (NPV)

Sugarcane FSD (SSD) Corn Grain FSD (SSD) Switchgrass

ResultsBreakeven Price Distributions

• Swtichgrass FSD (SSD) Corn Grain and Sugarcane• Corn Grain SSD Sugarcane but not FSD Sugarcane• Corn Grain is profitable at higher feedstock prices due

to higher DDGS revenue

Feedstocks Corn Sugarcane Switchgrass

Distribution Normal BetaGeneral Gamma

Minimum −∞ 0.64 (2.42) 0.84 (3.17)

Maximum ∞ 1.56 (5.91) ∞ Mean 1.01 (3.84) 0.97 (3.68) 1.41 (5.32)

Mode 1.01 (3.84) 0.95 (3.59) 1.32 (4.99)

Median 1.01 (3.84) 0.96 (3.65) 1.38 (5.21)

Std Dev 0.08 (0.31) 0.12 (0.44) 0.22 (0.84)

1% 0.83 (3.13) 0.74 (2.81) 1.02 (3.85) 5% 0.88 (3.34) 0.79 (3.00) 1.10 (4.15) 15% 0.93 (3.53) 0.85 (3.21) 1.18 (4.48) 25% 0.96 (3.64) 0.89 (3.36) 1.24 (4.71) 50% 1.01 (3.84) 0.96 (3.65) 1.38 (5.21)

75% 1.07 (4.05) 1.05 (3.97) 1.53 (5.81)

95% 1.15 (4.35) 1.17 (4.44) 1.81 (6.87) 99% 1.20 (4.56) 1.25 (4.75) 1.25 (7.75)

Note: Values in parenthesis are measured in $/gallon. 1

ResultsSensitivity Analysis

Capital Cost

Feedstock to EtOH

EtOH to Fuel

-350 -300 -250 -200 -150 -100 -50 0

-229

-263

-337

-176

-150

-86

Corn Grain

NPV (Million $)

Base: -202

Capital Cost

Feedstock to EtOH

EtOH to Fuel

-350 -300 -250 -200 -150 -100 -50 0

-191

-230

-320

-142

-85

-37Sugarcane

NPV (Million $)

Base: -167

Capital Cost

EtOH to Fuel

Feedstock to EtOH

-1100 -1000 -900 -800 -700 -600 -500 -400 -300

-669

-774

-1052

-489

-412

-378

Switchgrass

NPV (Million $)

Base: -579

• Price uncertainty is not included here because there is a stochastic price variable each year for each price, which cannot be simply aggregated to a single range.

• Technical uncertainties insert greater impacts on NPV variations.

• Corn grain and sugarcane ATJ is more sensitive to ethanol-to-fuel efficiency.

• Switchgrass ATJ is more sensitive to feedstock-to-ethanol efficiency.

Conclusions• Sugarcane ATJ is not only the least expensive pathway of the three considered, but also the

least risky.

• Even for sugarcane ATJ, we find that there is an 88% probability that investors will not break even without price supports.

• ATJ fuel can qualify under the US Renewable Fuels Standard, and thus it would qualify for RINs.

• RIN values are approximately $0.20/liter ($0.75/gallon) of fuel, thereby reducing the required mean breakeven price of sugarcane, corn grain and switchgrass ATJ fuel to around $0.77/liter ($2.90/gallon), $0.82/liter ($3.09/gallon) and $1.18/liter ($4.46/gallon), respectively (OPIS Ethanol & Biodiesel Information Service, 2015).

• Technical efficiency is a major contributor to uncertainty within the ATJ pathway, and highly impacts expected NPV and required breakeven prices. It suggests that investment to achieve higher conversion efficiencies could significantly increase the economic viability of the ATJ pathway.

Conclusions• In this research, we use the three methodological innovations to calculate probabilistic

distributions of NPV and breakeven prices.

• The NPV results show that the ATJ processes considered are not economic under the projected economic and technical conditions.

• The cumulative breakeven price distributions provide a breakeven price for each probability level, and the distribution of breakeven prices provides useful information for public and private investors irrespective of their specific risk-profile.

• From a policy-perspective, risk profiles as those developed in this paper can also be used to assess the impact of alternative policies such as loan guarantees, tax credits, crop insurance, end user off-take agreements, reverse auction based on off-take contract and capital subsidy on reducing project risk (Tyner and Van Fossen, 2014)

Acknowledgement

We are pleased to acknowledge funding support from the Federal Aviation Administration for this research. This work was partially funded by the US Federal Aviation Administration (FAA) Office of Environment and Energy as a part of ASCENT Project 107208 under FAA Award Number 13-C-AJFE-PU.  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA or other ASCENT Sponsors.

Thank You!Questions?

Appendix

AppendixCategory Units Process Sub-Procedure Function Form

Elestricity kWh per MJ Fuel

Feedstock-EtOH

Preprocessing 𝛽0 + 𝛽1𝐶𝑓𝑠−𝑒𝑡 + 𝛽2𝐶𝑒𝑡−𝑓; + 𝛽3𝐶𝑓𝑠−𝑒𝑡𝐶𝑒𝑡−𝑓𝑙 Saccharification 𝛽0 + 𝛽1𝐶𝑓𝑠−𝑒𝑡 + 𝛽2𝐶𝑒𝑡−𝑓; + 𝛽3𝐶𝑓𝑠−𝑒𝑡3 𝐶𝑒𝑡−𝑓𝑙 Fermentation 𝛽0 + 𝛽1𝐶𝑓𝑠−𝑒𝑡 + 𝛽2𝐶𝑒𝑡−𝑓; + 𝛽3𝐶𝑓𝑠−𝑒𝑡𝐶𝑒𝑡−𝑓𝑙

EtOH-Fuel

Separation 0

Postprocessing 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

Heat MJ NG per MJ Fuel

Feedstock-EtOH

Preprocessing 𝛽0 + 𝛽1𝐶𝑓𝑠−𝑒𝑡 + 𝛽2𝐶𝑒𝑡−𝑓; + 𝛽3𝐶𝑓𝑠−𝑒𝑡3 𝐶𝑒𝑡−𝑓𝑙 Saccharification 0

Ferment 0

EtOH-Fuel

Separation 0

Postprocessing 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

Postprocessing H2 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

Water L water consumed per MJ Fuel

Feedstock-EtOH

Preprocessing 𝛽0 + 𝛽1𝐶𝑓𝑠−𝑒𝑡 + 𝛽2𝐶𝑒𝑡−𝑓; + 𝛽3𝐶𝑓𝑠−𝑒𝑡𝐶𝑒𝑡−𝑓𝑙 Saccharification 0

Fermentation 0

EtOH-Fuel

Separation 0

Postprocessing 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

Co-Products

kg per kg feedstock (50% moisture content)

DDGS co-prod (Corn Only)

Biomass co-prod (Sugarcane Only)

Fuel Products Percentage

MJ fuel per MJ total fuel

% heavy oil 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

% fuel propane 0

% fuel LPG 0

% fuel naphtha 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

% fuel diesel 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

% fuel jet 𝛾0 + 𝛾1𝐶𝑒𝑡−𝑓𝑙 + 𝛾2𝐶𝑒𝑡−𝑓𝑙2

1

AppendixWelch’s t test

Corn grain

Sugarcane Corn grain

Switchgrass Sugarcane Switchgrass

Mean 1.02 0.97 1.02 1.41 0.97 1.41 Variance 0.01 0.01 0.01 0.05 0.01 0.05

Observations 1000 1000 1000 1000 1000 1000 Hypothesized

Mean Difference 0.00 0.00 0.00

df 1791.00 1262.00 1505.00

t Stat 9.91 (52.22) (54.90)

P(T<=t) one-tail 0.00 0.00 0.00

t Critical one-tail 1.65 1.65 1.65

P(T<=t) two-tail 0.00 0.00 0.00

t Critical two-tail 1.96 1.96 1.96

1 Corn Grain> Sugarcane Corn Grain< Switchgrass Sugarcane<Switchgrass

Switchgrass> Corn Grain> Sugarcane

AppendixStochastic Dominance

Let F and G be the two cumulative distribution functions defined over (-∞,∞).• First-order Stochastic Dominance (FSD): F FSD G if for all x

and F≠G. (F is more likely to have larger values than G)• Second-order Stochastic Dominance(SSD): F SSD G if for all

x and F≠G. (F have smaller size under the F(x) curve)