Kara G. Cafferty

Process Design and Economics for the Conversion of Lignocellulosic Biomass to Hydrocarbon Fuels: Fast Pyrolysis and Hydrotreating Bio-oil Pathway

This report describes a proposed thermochemical process for converting biomass into liquid transportation fuels via fast pyrolysis followed by hydroprocessing of the condensed pyrolysis oil. As such, the analysis does not reflect the current state of commercially-available technology but includes advancements that are likely, and targeted to be achieved by 2017. The purpose of this study is to quantify the economic impact of individual conversion targets to allow a focused effort towards achieving cost reductions.

Techno-economic analysis of decentralized biomass processing depots

Decentralized biomass processing facilities, known as biomass depots, may be necessary to achieve feedstock cost, quantity, and quality required to grow the future U.S. bioeconomy. In this paper, we assess three distinct depot configurations for technical difference and economic performance. The depot designs were chosen to compare and contrast a suite of capabilities that a depot could perform ranging from conventional pelleting to sophisticated pretreatment technologies. Our economic analyses indicate that depot processing costs are likely to range from ∼US

Blending municipal solid waste with corn stover for sugar production using ionic liquid process

30 to US

A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

63 per dry metric tonne (Mg), depending upon the specific technology implemented and the energy consumption for processing equipment such as grinders and dryers. We conclude that the benefits of integrating depots into the overall biomass feedstock supply chain will outweigh depot processing costs and that incorporation of this technology should be aggressively pursued.

Logistics, Costs, and GHG Impacts of Utility Scale Cofiring with 20% Biomass

Municipal solid waste (MSW) represents an attractive cellulosic resource for sustainable fuel production. However, its heterogeneity is the major barrier to efficient conversion to biofuels. MSW paper mix was generated and blended with corn stover (CS). It has been shown that both of them can be efficiently pretreated in certain ionic liquids (ILs) with high yields of fermentable sugars. After pretreatment in 1-ethyl-3-methylimidazolium acetate ([C2C1Im][OAc]), over 80% glucose has been released with enzymatic saccharification. We have also applied an enzyme-free process by adding mineral acid and water directly into the IL/biomass slurry to induce hydrolysis. With the acidolysis process in 1-ethyl-3-methylimidazolium chloride ([C2C1Im]Cl), up to 80% glucose and 90% xylose are released. There is a correlation between the viscosity profile and hydrolysis efficiency; low viscosity of the hydrolysate generally corresponds to high sugar yields. Overall, the results indicate the feasibility of incorporating MSW as a robust blending agent for biorefineries.

Feedstock Supply System Design and Economics for Conversion of Lignocellulosic Biomass to Hydrocarbon Fuels Conversion Pathway: Fast Pyrolysis and Hydrotreating Bio-Oil Pathway "The 2017 Design Case"

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the

Lignocellulosic crop supply chains (eg, Miscanthus, switchgrass, reed canary grass, rye, giant reed, etc.)

\hbox {CO}_{2}

Strategic supply system design – a holistic evaluation of operational and production cost for a biorefinery supply chain

CO2 emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented