• Energy Research

AbstractWe estimate the profitability of producing biochar from crop residue (corn stover) for two scenarios. The first employs slow pyrolysis to generate biochar and pyrolysis gas and has the advantage of high yields of char (as much as 40 wt‐%) but the disadvantage of producing a relatively low‐value energy product (pyrolysis gas of modest heating value). The second scenario employs fast pyrolysis to maximize production of bio‐oil with biochar and pyrolysis gas as lower‐yielding coproducts. The fast pyrolysis scenario produces a substantially higher value energy product than slow pyrolysis but at the cost of higher capital investment.We calculate the internal rate of return (IRR) for each scenario as functions of cost of feedstock and projected revenues for the pyrolysis facility. The assumed price range for delivered biomass feedstock is $0 to $83 per metric ton. The assumed carbon offset value for biochar ranges from $20 per metric ton of biochar in 2015 to $60 in 2030.The slow pyrolysis scenario in 2015 is not profitable at an assumed feedstock cost of $83 per metric ton. The fast pyrolysis scenario in 2015 yields 15% IRR with the same feedstock cost because gasoline refined from the bio‐oil provides revenues of $2.96 per gallon gasoline equivalent. By 2030, the value of biochar as a carbon offset is projected to increase to $60 per metric ton and the price of gasoline is expected to reach $3.70 per gallon, which would provide investors with an IRR of 26%. © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd

Negative emission technologies (NETs) are of growing importance for society to meet the atmospheric carbon levels required to maintain global temperatures under sustainable limits. A wide range of NETs have been proposed, but there are limited NET assessments that integrate life cycle analysis (LCA) and techno‐economic analysis (TEA). This Review gathers NET TEA/LCA findings and compares their costs and greenhouse gas emissions. Eight different NET‐producing transportation fuels and power‐generation technologies are considered: anaerobic digestion (lignocellulosic), fermentation, torrefaction, combustion, fast pyrolysis, gasification, and hydrothermal liquefaction. Most of these technologies sequester carbon either as carbon dioxide or as biochar. Some are carbon negative by virtue of avoided or displaced emissions. Overall, results indicate that NET energy production costs range between $20 and $80 GJ−1 and have emissions from −400 to 100 kg CO2,eq GJ−1. These results suggest that there are potential tradeoffs to consider when developing NETs. These results also show that future TEA/LCA studies of NETs are needed to decrease their uncertainty and improve their technology readiness level.

AbstractIndustry statistics indicate that technology‐learning rates can dramatically reduce both feedstock and biofuel production costs. Both the Brazilian sugarcane ethanol and the United States corn ethanol industries exhibit drastic historical cost reductions that can be attributed to learning factors. Thus, the purpose of this paper is to estimate the potential impact of industry learning rates on the emerging advanced biofuel industry in the United States. Results from this study indicate that increasing biorefinery capital and feedstock learning rates could significantly reduce the optimal size and production costs of biorefineries. This analysis compares predictions of learning‐based economies of scale, S‐Curve, and Stanford‐B models. The Stanford‐B model predicts biofuel cost reductions of 55 to 73% compared to base case estimates. For example, optimal costs for Fischer‐Tropsch diesel decrease from $4.42/gallon to $2.00/gallon. The optimal capacities range from small‐scale (grain ethanol and fast pyrolysis) producing 16 million gallons per year to large‐scale gasification facilities with 210 million gallons per year capacity. Sensitivity analysis shows that improving capital and feedstock delivery learning rates has a stronger impact on reducing costs than increasing industry experience suggesting that there is an economic incentive to invest in strategies that increase the learning rate for advanced biofuel production. © 2014 Society of Chemical Industry and John Wiley & Sons, Ltd

Abstract Microalgae have been proposed as potentially promising feedstock for the production of renewable transportation fuels. The plants are intriguing for their capacity to serve both as a source of renewable carbon fuels and as a powerful tool for carbon sequestration. Microalgae remnant, a low-cost by-product of microalgae lipid extraction, is a particularly appealing candidate for these processes. Through catalytic pyrolysis, microalgae remnant is capable of producing aromatic hydrocarbons that could be used for the production of drop-in biofuels. One of the most challenging barriers to this promising pathway is the high moisture content of harvested microalgae. The goal of this study is to compare the economics of two catalytic pyrolysis pathways for the production of drop-in biofuels, each pathway employing its own distinct method of feedstock dewatering: thermal drying or partial mechanical dewatering. The study presents chemical process models, capital expense and operating cost estimates, and sensitivity analyses of both scenarios. Results indicate that thermal drying prior to catalytic pyrolysis (TDCP) incurs capital costs similar to those incurred in partial mechanical dewatering prior to catalytic pyrolysis (MDCP) ($346 million vs. $409 million). TDCP and MDCP yield minimum fuel-selling prices (MFSPs) of $1.80/l and $1.49/l, respectively. Energy analysis shows that TDCP achieves 16.8% energy efficiency and MDCP achieves 26.7% efficiency.

This study evaluates the techno-economic uncertainty in cost estimates for two emerging technologies for biofuel production: in situ and ex situ catalytic pyrolysis. The probability distributions for the minimum fuel-selling price (MFSP) indicate that in situ catalytic pyrolysis has an expected MFSP of $1.11 per liter with a standard deviation of 0.29, while the ex situ catalytic pyrolysis has a similar MFSP with a smaller deviation ($1.13 per liter and 0.21 respectively). These results suggest that a biorefinery based on ex situ catalytic pyrolysis could have a lower techno-economic uncertainty than in situ pyrolysis compensating for a slightly higher MFSP cost estimate. Analysis of how each parameter affects the NPV indicates that internal rate of return, feedstock price, total project investment, electricity price, biochar yield and bio-oil yield are parameters which have substantial impact on the MFSP for both in situ and ex situ catalytic pyrolysis.

This paper incorporates pathway-specific financial assumptions into techno-economic analyses of cellulosic biofuel pathways under price uncertainty. Five cellulosic biofuel pathway scenarios are developed in a discounted cash flow rate of return spreadsheet to determine pathway-specific costs of debt. The cost of equity for the scenarios is calculated based on the financial characteristics of the US biorenewable industrial sector. A 20-year net present value (NPV) and probability of default for each scenario are stochastically calculated. Mean NPVs vary from a low of –$774 million to a high of –$135 million. Probabilities of default range from a high of 100% to a low of 80.5%. Sensitivity analyses find that the use of pathway-neutral financial assumptions overestimates NPV and underestimates probability of default.

Levoglucosan has significant potential in commercial applications for the synthesis of polymers, solvents and pharmaceuticals.

AbstractThis study evaluates uncertainties in the techno‐economic analysis of transportation fuel production from biomass gasification and mixed alcohol synthesis. Two scenarios are considered: a state‐of‐technology scenario and a target scenario with projected technological advances. Uncertainties of more than 10 parameters are investigated. The probability distributions of these parameters are estimated based on historical price data and experimental data. Data samples generated from the corresponding distribution are then utilized to run a Monte Carlo simulation. The results yield minimum fuel‐selling prices of $ 1.85 L−1 with a standard deviation of 0.13 for the state‐of‐technology scenario and $ 1.14 L−1 with a standard deviation of 0.11 for the target scenario, respectively. The feedstock price and internal rate of return (IRR) have significant impacts on the minimum fuel‐selling price in both scenarios. These findings are indicative of the reduction in biofuel cost and uncertainty achievable with increasing technology maturity.