1

Stochastic Techno-economic Evaluation of Cellulosic Biofuel Pathways

Xin Zhao, Purdue University

Dr. Tristan R. Brown, SUNY-ESF

Dr. Wallace E. Tyner, Purdue University

2

Cellulosic Biofuels

Cellulosic biofuels are produced from lignocellulosic biomass or woody

crops.

Compared with 1st-generation biofuels, cellulosic biofuels have

Lower life-cycle greenhouse gas (GHG) emissions

Use non-food feedstock

Smaller effects on food prices and land use change.

Cellulosic biofuels require more expensive technologies which may lead

to higher capital costs and lower yields.

3

Cellulosic biofuels production pathways

Renewable Diesel,

Gasoline, Jet Fuel

Acid or Enzyme Hydrolysis

Gasification

Pyrolysis

Hydrothermal Liquefaction

Saccharification Fermentation

Syngas

Bio Oil

Ethanol, Butanol,

Hydrocarbons

ETG via Catalysis

Syngas Fermentation

Fischer-Tropsch Catalysis

Bio-Gasoline, Renewable Diesel

Bio-Gasoline

Ethanol

CELLULOSIC BIOMASS

Source: Advanced biofuels Association

4

Techno-economic analysis

Extensive deterministic techno-economic analyses (TEA) have been

conducted on cellulosic biofuel productions (NREL, PNNL, etc.).

Direct comparisons cannot necessarily be made among the pathways

due to important differences in assumptions (e.g., different price

projections, technical and economic assumptions).

Uncertainties and risks were not modeled in most studies.

5

Investment decision making

Investors are risk-averse and intend to maximize expected utility.

Deterministic comparisons may be inadequate.

The deterministic breakeven price is the price for which there is a 50

percent probability of earning more or less than the stipulated rate of

return. It cannot represent the true investment threshold.

Stochastic dominance is a tool providing better information for risk-

averse investors.

6

Objective

To quantify the breakeven prices of cellulosic biofuel pathways under

technical and economic uncertainty in a manner permitting

comparisons among different pathways from the point of view of

investors.

7

Cellulosic biofuel pathways studiedPathways Abbreviations Feedstock Inputs ProductsHigh temperature gasification & Fischer–Tropsch synthesis HTG & FTS Corn Stover CH4, Electricity Gasoline, Diesel,

ElectricityLow temperature gasification & Fischer–Tropsch synthesis LTG & FTS Corn Stover CH4, Electricity Gasoline, Diesel,

Electricity

Fast pyrolysis & hydroprocessing FPH Corn Stover H2, Electricity Gasoline, Diesel, Electricity

Hydrothermal liquefaction HTL Hybrid Poplar CH4, Electricity Gasoline, Diesel,heavy oil, Electricity

Indirectly-heated gasification & acetic acid synthesis IHG & AAS Hybrid Poplar CO, H2, Electricity Ethanol, Electricity

Directly-heated gasification & acetic acid synthesis DHG & AAS Hybrid Poplar CH4, CO, H2,

Electricity Ethanol, Electricity

Enzymatic hydrolysis & fermentation EH Corn Stover Electricity Ethanol, Electricity

Gasification & methanol-to-gasoline MTG Hybrid Poplar Electricity Gasoline, Electricity,

LPG

8

Method

A financial analysis based TEA.

Important uncertain variables.

Monte Carlo simulation is employed

to account for the uncertainty in

techno-economic variables.

10,000 iterations.

9

Pathway modeling

Plants were designed in source studies.

Plant size of 2,000 dry metric tons per

day.

Base year: 2011

Production is assumed to begin in 2013

after a two-year construction period, with

20-year production life.

i.e. Fast pyrolysis and hydroprocessing

10

Uncertainty

Variables Mean  Distribution

Corn stover cost (2011 $/MT) 82.83 Pert

Hybrid poplar cost (2011 $/MT) 95.54 Pert

Hydrogen cost ($/Kg) 3.25 Pert

Capital investment($MM) Pert

Min, mode and max values are estimated.

5.0% 90.0% 5.0%

65.7 99.9

50

60

70

80

90

10

01

10

12

0

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Feedstock Cost ($/Ton)

Pert(55,83,110)

Minimum 55.000Maximum 110.000Mean 82.833Std Dev 10.393

@RISK Course VersionPurdue Univ

11

Conversion technology yield uncertainty

A Beta general distribution is

benchmarked for fuel yield for each

of the pathway scenarios based on

literature data. Beta general distribution has

advantage over Normal, Pert or

Triangular in considering Kurtosis

and skewness.

(GGE/ Mg of biomass)

12

Output fuel price uncertainty

Geometric Brownian Motion Annual growth rate: 0.27% in real

terms (EIA).

Historical volatility: $0.318/GGE

Diesel and LPG prices are projected

using historical price relationship

between diesel or LPG and gasoline.

13

Results: deterministic breakeven fuel price

Range: 3.11 – 4.93 $/GGE.

FPH is the lowest, 3.11 $/GGE. Capital cost, 0.94 $/GGE, feedstock cost 1.07

$/GGE, operating cost 1.35 $/GGE, electricity generation credit 0.25 $/GGE.

Breakeven fuel price distribution

15

Breakeven fuel price distribution

16

Stochastic Dominance

CDF based on return on investment Thus, risk averse investors prefer

to

HTG & FTS and FPH are not

comparable in terms of FSD or SSD. FPH 0.136-almost first-order

stochastically dominate HTG & FTS. Most decision makers would

prefer FPH to HTG & FTS.

17

Conclusion

With the current level of oil prices, none of the eight cellulosic biofuel

pathway scenarios could be profitable at expected values.

Most risk-averse decision makers would prefer FPH to other pathways

studied. The probability of loss is 59% for FPH.

Stochastic analysis provides more information on the measurements and

economic feasibility of a project. Risk-averse investors can make better

decisions base on these results.

18

Questions

19

Renewable fuel standardBi

llion

s of

Gal

lons

(BG

Y) E

than

ol

Equi

vale

nt

The targets for cellulosic advanced have never been achieved.

EPA revised the 2015 mandate to 106 million gallons.

Source: EISA, 2007; Tyner, 2013.

20100.1 BGY

202216 BGY

20153 BGY

20

Sensitivity Analysis

Future fuel prices play the most

important role in determining the

economic feasibility of a pathway.

Future cellulosic biofuels

development research should

concentrate on increasing technology

conversion yield and lowering capital

cost to reduce cost of cellulosic

biofuel.

21

Major Contribution

1. Financial stochastic techno-economic assessment (TEA) models were built in a manner permitting consistent comparisons among eight cellulosic biofuel production pathways.

2. Monte Carlo simulation was used to translate the uncertainty in inputs (conversion yield, capital cost, feedstock cost, and associated inputs) and output fuel prices into distributions of NPV and breakeven price.

3. The stochastic analysis permitted the comparison among pathways from the perspective of a risk-averse investor. According to stochastic dominance based on return on investment, most risk-averse investors would prefer the fast pyrolysis and hydroprocessing pathway.

22

Stochastic TEA and breakeven price distribution

23

Acknowledgement

We are pleased to acknowledge funding support from the Indiana Corn

Marketing Council and the Federal Aviation Administration for this research.

They are not responsible for the conclusions of the research.