Technoeconomic analysis of corn stover conversion by decentralized pyrolysis and electrocatalysis

Abstract

Maximizing fossil fuel displacement and limiting atmospheric carbon dioxide levels require a high efficiency of carbon incorporation in bioenergy systems. The availability of biomass carbon is a constraint globally, and strategies to increase the efficiency of bioenergy production and biogenic carbon use are needed. Previous studies have shown that “energy upgrading” of biomass by coupling with renewable electricity through electrocatalytic hydrogenation offers a potential pathway to near full petroleum fuel displacement in the U.S., even when annual U.S. biomass production is limited to 1.2 billion dry tonnes. Commercialization of such technology requires economic feasibility. A technoeconomic model of decentralized, depot-based pyrolysis with electrocatalytic hydrogenation and centralized upgrading (Py-ECH), producing liquid hydrocarbon fuel is presented and compared to a cellulosic ethanol pathway using consistent assumptions. Using a discounted cash flow approach, a minimum fuel selling price (MFSP) of $3.62 per gallon gasoline equivalent (GGE) or $0.96 per gasoline liter equivalent (GLE) is estimated for Py-ECH fuel derived from corn stover, considering n^th plant economics and a fixed internal rate of return of 10%. This is comparable to the MFSP for cellulosic ethanol from fermentation with the same feedstock ($3.71 per GGE or $0.98 per GLE) and is in the range of gasoline prices over the last 20 years of $1 per GGE ($0.26 per GLE) to $4.44 per GGE ($1.17 per GLE) in 2018. Optimization studies on depot sizing identified a trade-off between transportation and economies-of-scale costs, with an optimum size of 500 tpd. Sensitivity analyses showed that electricity cost, raw material costs, bio-oil yields, and cell efficiencies are the key parameters that affect the Py-ECH MFSP. With system improvements, a pathway to less than $3 per GGE or $0.79 per GLE is articulated for liquid hydrocarbon fuel from corn stover using Py-ECH.