Stochastic Techno-economic Evaluation of Cellulosic Biofuel Pathways Xin Zhao, Purdue University Dr. Tristan R. Brown, SUNY-ESF Dr. Wallace E. Tyner, Purdue University

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.

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 There are many cellulosic biofuel production pathways developed by engineers. Based on the pre-treatment, it consists hydrolysis, gasification, pyrolysis and hydrothermal liquefaction. In order to know, among all the cellulosic biofuel pathways, which ones are more technically and economically feasible, less risky, and has the lowest costs to investors. We studied 8 pathways that are expected to achieve commercialization some point in the future, includes. cellulose, hemicellulose and lignin

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. Many TEA have been conducted to measure the economic feasibility of those pathways. We cannot make comparisons based on their results because they used different technical and economic assumptions. Also, most of previous studies are deterministic analyses, which means uncertainties were not considered. The National Renewable Energy Laboratory (NREL) and the Pacific Northwest National Laboratory (PNNL)

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. As we know For example

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.

Cellulosic biofuel pathways studied Abbreviations Feedstock Inputs Products High temperature gasification & Fischer–Tropsch synthesis HTG & FTS Corn Stover CH4, Electricity Gasoline, Diesel, Electricity Low temperature gasification & Fischer–Tropsch synthesis LTG & FTS Fast pyrolysis & hydroprocessing FPH H2, Electricity Hydrothermal liquefaction HTL Hybrid Poplar Gasoline, Diesel, heavy oil, Electricity Indirectly-heated gasification & acetic acid synthesis IHG & AAS CO, H2, Electricity Ethanol, Electricity Directly-heated gasification & acetic acid synthesis DHG & AAS CH4, CO, H2, Electricity Enzymatic hydrolysis & fermentation EH Electricity Gasification & methanol-to-gasoline MTG Gasoline, Electricity, LPG I do not have time to go through all of them.

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.

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

Hybrid poplar cost (2011 $/MT) Uncertainty Variables Mean  Distribution Corn stover cost (2011 $/MT) 82.83 Pert Hybrid poplar cost (2011 $/MT) 95.54 Hydrogen cost ($/Kg) 3.25 Capital investment ($MM) We estimated a Pert distribution for Min, mode and max values are estimated.

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) We benchmarked a beta general distribution for each of the pathway

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.

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

Breakeven fuel price distribution CDF

Stochastic Dominance CDF based on return on investment HTG & FTS scenario second−order dominates (SSD) all other cases except FPH. Thus, risk averse investors prefer HTG & FTS to all other scenarios except FPH. 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. CDF based on return on investment

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.

Questions

Renewable fuel standard 2022 16 BGY 2015 3 BGY The targets for cellulosic advanced have never been achieved. EPA revised the 2015 mandate to 106 million gallons. 2010 0.1 BGY Billions of Gallons (BGY) Ethanol Equivalent This graph shows the RFS mandate set by EISA in 2007. The yellow bars are the mandates for first generation biofuels. And the blue bars are for cellulosic biofuels. The target for cellulosic biofuel production was set to increase from 0.1 BGY ethanol equivalent in 2010 to 16BGY in 2022. However, those targets have never been achieved. In 2015, the original target was 3 BGY, EPA just revised it down to 106 million gallons. Source: EISA, 2007; Tyner, 2013.

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. Policies aimed at reducing price risk could be more effective in promoting cellulosic biofuels investment and production

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

Stochastic TEA and breakeven price distribution

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.