A harmonized stochastic comparison of renewable jet fuel costs of production

Abstract

Aviation currently contributes ~2% to anthropogenic GHG emissions. This contribution is expected to grow in the absence of mitigation measures, due in part to a projected annual industry growth (measured in revenue passenger kilometers) of approximately 5% out to 2034. Alternative jet fuels produced from biomass have received considerable attention from policy-makers and academia as a potential means for significantly reducing greenhouse gas emissions attributable to aviation. One of the main challenges for aviation biofuels is the economic feasibility of converting biomass into liquid fuel that meets current jet fuel specifications. Existing studies have estimated pathway-specific costs of production but no study exists, to date, that accounts for different feedstocks and pathways in a harmonized assessment framework that includes uncertainty in major parameters. This presentation compares the costs of production of alternative jet fuel pathways and the net present value of plant investment using harmonized assumptions. Uncertainty in revenues, fuel yield, and feedstock and other input costs is propagated through the analysis by means of Monte Carlo simulations. We evaluate six processes at varying stages of technical maturity, including hydroprocessed esters and fatty acids (HEFA), advanced fermentation (AF), conventional gasification and Fischer-Tropsch (F-T), aqueous phase processing (APP), hydrothermal liquefaction (HTL) and fast pyrolysis (FP). Each pathway produces drop-in, chemically-equivalent middle distillates—including both diesel- and jet-range hydrocarbons—from a variety of feedstocks, including woody biomass in the cases of APP and HTL, agricultural byproducts for AF and FP, municipal solid waste for FT, and vegetable oils and slaughtering byproducts for HEFA. A techno-economic assessment using a discounted cash flow rate of return method is employed to estimate the minimum selling price (MSP) and net present value (NPV) of each plant under harmonized financial, technical, and plant scale assumptions. The Monte Carlo simulation samples random results from probability distributions assigned to critical variables including capital costs, fuel yields, product slate prices, and costs for process inputs such as feedstocks, catalysts, makeup water, and hydrogen.