Biofuels via Fast Pyrolysis of Perennial Grasses: A Life Cycle

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Environmental Science & Technology

Biofuels via fast pyrolysis of perennial grasses: A life cycle evaluation of energy consumption and greenhouse gas emissions George G. Zaimesa, Kullapa Soratanab, c, Cheyenne L. Hardenb, Amy E. Landisb, and Vikas Khanna*, a a

Swanson School of Engineering, Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261 b School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona 85287 *

Corresponding Author Email: [email protected] Address: 742 Benedum Hall 3700 O’Hara Street Pittsburgh, PA 15261 Phone Number: 412-624-9870 Fax Number: 412-624-0135 Abstract: A Well-to-Wheel (WTW) life cycle assessment (LCA) model is developed to evaluate the environmental profile of producing liquid transportation fuels via fast pyrolysis of perennial grasses: switchgrass and miscanthus. The framework established in this study consists of: (1) an agricultural model used to determine biomass growth rates, agrochemical application rates, and other key parameters in the production of miscanthus and switchgrass biofeedstock; (2) an ASPEN model utilized to simulate thermochemical conversion via fast pyrolysis and catalytic upgrading of bio-oil to renewable transportation fuel. Monte Carlo analysis is performed to determine statistical bounds for key sustainability and performance measures including life cycle greenhouse gas (GHG) emissions and Energy Return on Investment (EROI). The results of this work reveal that the EROI and GHG emissions (gCO2e/MJ-fuel) for fast pyrolysis derived fuels range from 1.52 to 2.56 and 22.5 to 61.0 respectively, over the host of scenarios evaluated. Further analysis reveals that the energetic performance and GHG reduction potential of fast pyrolysis-derived fuels are highly sensitive to the choice of coproduct scenario and LCA allocation scheme, and in select cases can change the life cycle carbon balance from meeting to exceeding the renewable fuel standard emissions reduction threshold for cellulosic biofuels.

c

Kullapa Soratana is also a member of the faculty at School of Logistics and Supply Chain, Naresuan University, Phitsanulok 65000, Thailand

ACS Paragon Plus Environment


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Introduction

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global challenges facing the U.S. and the world. Fuels derived from biomass resources

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represent a promising option for mitigating anthropogenic carbon emissions and related

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climate change impacts, while concurrently providing a potential large-scale domestic

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source of renewable transportation fuel and energy1. Subsequently, this has prompted the

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development of U.S. regulatory programs designed to increase domestic energy

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independence and security as well as mitigate greenhouse gas (GHG) emissions from the

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transportation sector via establishing mandatory volumetric production targets and carbon

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reduction criteria for transportation fuels derived from biomass. In 2007 the U.S.

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congress passed the Energy Independence and Security Act (EISA), this legislation

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mandates the production of 36 billion gallons of renewable fuels by the year 2022, and

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stipulates that a percentage must be derived from conventional, cellulosic biofuel,

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biomass-based diesel, and advanced biofuels2. Additionally, EISA sets minimum life

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cycle GHG reduction standards for biofuels relative to baseline-petroleum fuels.

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Currently corn based ethanol and soybean biodiesel are the most widely produced and

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utilized biofuels in the US3. However, critical concerns have been raised about the

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potential of corn ethanol and other first generation biofuels in mitigating climate change

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and reducing dependence on fossil fuels. Previous work has reported that the direct and

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indirect land use change (LUC) effects may possibly negate the carbon dioxide reduction

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potential of first generation biofuels 4-6. Accordingly, the production of liquid fuels

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derived from non-food biofeedstocks such as forest and agricultural residues, industrial

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wastes, herbaceous crops, and microalgae7-10 has garnered widespread attention11. Fast

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growing perennial grasses such as miscanthus and switchgrass are considered an ideal

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candidate for conversion to biofuel/bioenergy due to their high growth rates over a

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variety of soil conditions/types, ability to be cultivated and harvested utilizing

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conventional farming practices, and minimal nutrient requirements relative to other

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leading biofeedstocks12, 13.

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Substantial research and development has been invested in developing next generation

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hydrocarbon bio-refineries capable of converting lignocellulosic biomass into

The threat of climate change is considered to be one of the most critical and far-reaching


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infrastructure-compatible liquid transportation fuels, i.e. biomass derived fuels that can

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act as drop-in replacements for petroleum-based gasoline, diesel, and jet fuels14.

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Recently, fast pyrolysis has emerged as a promising technological route for converting

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cellulosic biomass15 into liquid transportation fuel and value-added products16-18. Fast

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pyrolysis is the process of thermal decomposition of organic matter in the absence of

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oxygen, at elevated temperatures19 and short residence times20. Fast pyrolysis converts

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biomass into three main intermediate products: bio-oil, biochar, and non-condensable

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gases (NCGs). Bio-oil generated via fast pyrolysis can be upgraded using established

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refining technologies into renewable transportation fuel(s)21 that are compatible with

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current fuel infrastructure and vehicle fleets22; while biochar can be co-generated via

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combined heat and power (CHP) to produce heat and electricity to be utilized internally

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in the fuel conversion system, or exported and used as a soil amendment for agricultural

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lands. As such, renewable fuel(s) derived via the fast pyrolysis of perennial grasses may

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be a favorable option for achieving policy mandated renewable energy/fuel targets

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focused on mitigating anthropogenic GHG emissions including the U.S. Renewable Fuels

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Standard (RFS2)23, California Low Carbon Fuel Standard (LCFS)24, and the European

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Renewable Energy Directive (RED)25.

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Prior work has investigated various sustainability aspects of fast pyrolysis of

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lignocellulosic biomass. Techno-economic analysis has shown that fast pyrolysis is

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competitive with other lignocellulosic fuel conversion platforms, and may be

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economically viable at a commercial scale16. Several recent studies have evaluated the

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environmental impacts of producing liquid fuels by fast pyrolysis of microalgae26, corn

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stover15, 27, and woody biomass18, 28-30 using life cycle assessment (LCA), and have

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shown that fast pyrolysis derived fuels may provide life cycle GHG reductions relative to

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petroleum fuels. However, little emphasis has been placed on environmental systems

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analysis of drop-in replacement biofuels derived via fast pyrolysis of perennial grasses.

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Additionally, the effect of different coproduct scenarios, allocation schemes, as well as

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uncertainty in the LCA results, is widely understudied in the literature.

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This study performs a comparative life cycle assessment of renewable fuels derived via

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fast pyrolysis of perennial grasses switchgrass and miscanthus. Data for the agricultural



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production of perennial grasses is based on peer-reviewed literature and technical reports

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from field trails. Additionally, this work develops an ASPEN model to simulate the

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thermochemical conversion of lignocellulosic biomass to fuel and utilizes Monte-Carlo

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simulation to account for uncertainty and variability in key LCA metrics. The main

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objectives of this study are to (1) quantify the Energy Return on Investment (EROI) and

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life cycle GHG emissions for renewable fuels derived via fast pyrolysis of miscanthus

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and switchgrass biofeedstock and catalytic upgrading of resultant bio-oil; (2) investigate

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the sensitivity of the results to various LCA allocation schemes as well as several

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coproduct scenarios for biochar including: direct application as a soil amendment and co-

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generation to produce heat and electricity; (3) identify combinations of feedstock and

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biofuel pathways that meet the life cycle GHG reduction threshold as set by the RFS2;

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(4) identify areas for process improvement in the biomass-to-fuel supply chain; (5)

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compare the environmental performance of fast pyrolysis derived fuels to the production

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of other leading biofuels. These insights are pivotal for guiding the sustainable adoption

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of biofeedstocks for fuel conversion, and for benchmarking the performance and

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environmental sustainability of emerging fast-pyrolysis thermochemical fuel platforms.

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Process Description, Data, and Methods Process inventories for the production of miscanthus and switchgrass feedstock were

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developed based on peer-reviewed literature; while data for fast pyrolysis and fuel

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upgrading were constructed based on a combination of detailed ASPEN simulation,

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experimental data, and best available engineering knowledge. An overview of the

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biomass-to-fuel process is provided in Figure 1.

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1

Scenario 1 (Bioenergy pathway): Biochar is sent to a combined heat and power unit for conversion to heat and electricity 2 Scenario 2 (Soil Amendment pathway): Biochar is sent to a local farm to be applied as a soil amendment

Figure 1. Simplified Block Diagram: Fast Pyrolysis Biofuel Production Chain Agricultural model Production of miscanthus and switchgrass feedstock is modeled assuming a 10 and 15-

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year stand life31, respectively. Perennial grasses require an initial establishment year, and

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approximately 3 years until optimal crops yields are achieved32. In the initial

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establishment period, several land/farm preparations are performed including plowing,

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harrowing, rolling, herbicide application, soil compactification, and rhizome/seedling

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planting. For miscanthus feedstock, it is assumed that rhizomes from mature perennial

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grasses are planted directly in the field32, thus avoiding the high energetic costs of

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producing plantlets via greenhouses. Herbicides are applied during the first and second

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year of establishment to control and mitigate the growth of invasive plant species33.

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Herbicides are not required post third year, as weed interference is effectively suppressed



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once the perennial grass achieves maturity. Additionally, fertilizers are not applied during

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the first two years of establishment as they are subject to large losses/high runoff rates

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and encourage the growth of other unwanted weeds and grasses. Furthermore, irrigation

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is not required due to the low economic value of the produced biomass34. Post-3rd year of

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establishment, it is assumed that fertilizers are applied annually (once per harvest cycle)

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to mitigate soil nutrient depletion and maintain overall soil quality31. However, it is

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important to note that numerous field trails have shown that perennial grasses have

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limited to no yield response to heighten N-fertilizer application35-38; and it is possible that

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these crops require marginal synthetic fertilizer for growth. As such, the assumptions in

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this study represent a conservative estimate for fertilizers required for biofuel production.

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Miscanthus and switchgrass feedstock(s) are harvested once per year during the late fall

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via a windrower and baler, each with an average field efficiency of 80%39. Late (delayed)

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harvest lowers total dry matter yield relative to peak harvest; however, it substantially

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decreases the moisture content (MC) of the harvested biomass40, 41. This is favorable as it

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reduces the amount of energy required for feedstock drying prior to fuel conversion. Post

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harvesting, baled biomass is transported to a local biorefinery via lorries. Densification

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and/or long-storage of biomass is not considered in the present work, but due to logistic

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and geographic constraints may be required for commercial biofuel production. The

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average round trip distance from farm to refinery is assumed to be 100 km, with 2500

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metric tons of biomass delivered to the biorefinery at a moisture content of 20%

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weight/weight (w/w)42.

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Probability distribution functions (PDFs) for switchgrass yields were developed based on

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the Global Switchgrass database43, which contains over 1190 observations from 39 field

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trials conducted across the United States44-59. Distributions for miscanthus yields were

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estimated based on values reported in peer-reviewed studies and field trials23, 33, 35-38, 60-62.

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Average switchgrass and miscanthus yield is estimated at 11.04 dry metric tons per

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hectare (t/ha) and 15.29 t/ha, respectively. PDFs for direct volatilization of N fertilizer

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(N2O conversion rates) were estimated based on 59 trials conducted on various

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agricultural lands. Indirect N volatilization is developed based on estimates of soil

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nitrogen leaching and run-off rates as well as conversion rates of soil N to N2O as


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reported by the Intergovernmental Panel for Climate Change (IPCC)63. PDFs for diesel

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use in farming practices including: plowing, soil compactification, harrowing, rolling,

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stubble conversion, spreading fertilizer, and weed harrowing were constructed based on a

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survey of published literature reported in Dalgaard et al64. Point estimates were used for

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diesel use in rhizome/seedling planting, windrower harvesting, baling, stacking, and

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handling. Summary information for distribution types as well as a detailed summary of

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all inventory data is provided in the supporting information (SI).

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Fast Pyrolysis and Fuel Upgrading model An ASPEN process model is developed using Aspen Plus version V8.6 software to

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simulate the thermochemical conversion of cellulosic biomass to bio-oil, and catalytic

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upgrading of resultant bio-oil to renewable transportation fuel. The developed ASPEN

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model provides a scientifically rigorous basis for determining the utility requirements as

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well as material and energy flows for conversion of cellulosic biomass to liquid

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transportation fuel. Furthermore, the fuel upgrading and conversion model provides

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important details such as the liquid carbon yield, product distribution, and hydrogen

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consumption for fast pyrolysis conversion of cellulosic feedstock to renewable

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transportation fuel. The primary stages in the fuel conversion and upgrading model

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consist of pretreatment, thermochemical conversion of biomass via fast pyrolysis,

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hydroprocessing of bio-oil to renewable fuel, coproduct integration/use, and

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fuel/coproduct transportation.

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This work models a theoretical stand-alone biorefinery operating at a throughput of 2000

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dry metric ton-biomass/day, consistent with the design case provided in Jones et al.65 and

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Wright et al.16 Biomass received by the refinery is dried to a moisture content of 15%

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w/w and subsequently chopped/ground via a hammer mill and screened until the exiting

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mass has a particle size diameter of 3mm66. Reduction in particle size increases total heat

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transfer area, resulting in high heat transfers rates. Energy requirements for

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chopping/grinding of switchgrass and miscanthus were developed based on empirical

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correlations provided in Miao et al.67 After chopping/grinding, biomass is dried to a

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moisture content of 7% w/w and sent to a fast pyrolysis reactor operating at 1 atmosphere



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(atm) and 500 oC. Product distribution for fast pyrolysis was constructed based on

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experimental results for pure (dry, ash-free) hemicellulose, cellulose, and lignin provided

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in Patwardhan68, 69. Feedstock specific product characterization is obtained via weighting

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the product distribution based on the fractional cellulose, hemicellulose, and lignin

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content of the input biofeedstock. It is important to note that trace minerals in biomass

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can act as catalysts during thermochemical conversion of biomass to fuel, and thus alter

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the resulting product distribution. However, the effect of differing mineral concentrations

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on the product distribution of fast pyrolysis derived fuels is beyond the scope of this

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work, and thus is not considered. The study by Patwardhan captured/identified 84.3-

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92.9% of the input feed mass in their spectrometric analysis of the products of fast

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pyrolysis, and speculated that the unaccounted mass is primarily in the form of water

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vapor and other light molecular compounds68, 69. In this work, to satisfy overall mass and

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elemental balances, an optimization routine is performed to determine the composition of

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the unaccounted mass via minimizing the Gibbs free energy of formation of several light

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hydrocarbons as well as gaseous compounds including: CO, CO2, H2O, H2, C2H6, C3H6,

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and C3H8. Analysis indicates that the product distribution for fast pyrolysis ranges from

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44.56-53.1% bio-oil (dry basis), 12.0-17.5% biochar and ash, 24.4-27.2% non-

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condensable gases, and 10.5-10.8% water on a mass basis for the two feedstocks

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considered in this work. The resultant product stream is sent to a cyclone separator to

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separate biochar/ash from pyrolysis vapors and NCGs.

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Two coproduct scenarios were considered for biochar formed via fast pyrolysis, denoted

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as Bioenergy (BE) and Soil Amendment (SA) pathways. In the first scenario biochar is

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sent to a combined heat and power unit for conversion to electricity and heat. Coproduct

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heat generated via CHP is used to offset processing heating requirements, while excess

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heat is discarded. Coproduct electricity is utilized internally in the fuel conversion

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system, while any surplus electricity is exported to the grid. The higher heating value

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(HHV) of biochar/ash is estimated based off of its fractional element composition (%

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C,H,O,N,S, & Ash) via correlations provided in Channiwala and Parikh70, and is

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estimated to be 23.81 MJ/kg for miscanthus and 24.22 MJ/kg-biochar for switchgrass.

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CHP conversion efficiencies for electricity and heat were assumed to range from 20% to


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35% and 44% to 52%, respectively71. In the second scenario, biochar is transported to a

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local farm to be applied as a soil amendment72. The use of biochar as a soil amendment

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may provide many benefits to local farms/agricultural land73, 74 including: reduction of

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N2O emissions and leaching of nitrogen into groundwater75, improved soil fertility,

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moderating soil pH76, heightened retention of soil nutrients and water77, and increased

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number of beneficial soil microbes78. Due to lack of data regarding the bioavailability of

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the nutrient content of the biochar it is assumed that the application of biochar does not

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displace synthetic fertilizer (N, P, K) requirements. Additionally, this works assumes that

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up to 20% of the carbon content of the biochar is released to the atmosphere as carbon

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dioxide79. Energy use for machinery required to spread/apply biochar to farmland is

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estimated based on diesel use for spreading and loading manure.

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Post cyclone separation pyrolysis vapors are cooled and separated from NCGs via a

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quench, while NCGs are recycled back to the pyrolysis reactor to act as a fluidizing agent

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and are flared and vented onsite. Bio-oil is highly oxygenated and must be hydrotreated

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prior to use in commercial vehicle engines. As such, bio-oil produced via fast pyrolysis is

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sent to a hydrotreating unit utilizing a catalyst bed operating at 400 oC and 2000 psig80, 81,

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wherein it is catalytically hydrodeoxygenated (HDO)82. Hydrogen requirements for HDO

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were constructed based on stoichiometry, i.e. the amount of hydrogen necessary for

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complete removal of oxygen from bio-oil compounds and hydrogen required for

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saturation of carbon compounds. Furthermore, it is assumed that all hydrogen is

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externally sourced and produced via steam methane reforming. The high efficiency and

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low cost of fossil hydrogen production via steam methane reforming relative to other

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renewable hydrogen producing platforms such as hydrogen from steam reforming

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biomass or electrolyzing water using wind or solar energy sources, make it attractive as a

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commercial source of hydrogen for biofuel production. However, future work should

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investigate the economic and environmental tradeoffs of catalytic steam reforming the

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aqueous fraction of fast pyrolysis bio-oil to produce hydrogen for subsequent use in

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downstream conversion of bio-oil to renewable fuel. The mass of hydrogen consumed

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relative to input bio-oil feed is approximately 8.16 and 8.23% on a dry basis or 6.57 and

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6.87% on a wet basis for switchgrass and miscanthus respectively.



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After hydrotreating, light hydrocarbons (C1-C4 molecular species) are separated from the

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fuel pool via distillation and are sent to an onsite CHP unit to be combusted to produce

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heat and electricity. Higher carbon chain molecular species (C5+) are then separated from

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water via a decanter centrifuge, and transported to a regional fuel facility assuming an

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average round trip distance of 100 km. Combustion of biofuel and bioenergy products

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was modeled based on stoichiometry, under the assumption that that all carbon is

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converted to CO2. Final renewable fuel is comprised of carbon compounds ranging from

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C5 to C11 in carbon number, and is chemically similar to petroleum gasoline. In the

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absence of biofuel loss due to coking, the fraction of carbon in final fuel and coproducts

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relative to carbon in the input biomass feed ranges from 37.0 to 44.1% in the liquid

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transportation fuel, 9.39 to 9.42% in light hydrocarbons (C1-C4 molecular species), 18.0

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to 24.7% in ash and biochar, and 28.6 to 28.8% in NCGs for the different biofeedstocks.

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The ASPEN energy analyzer module is utilized to optimize heat integration for the fuel

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conversion/upgrading system, and estimate net heating and cooling requirements. The

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results show that after heat integration, the heating requirements for fuel conversion and

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upgrading range from 1.22 to 1.25 MJ heat/kg-dry biomass. Total electricity requirements

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for pretreatment, fuel conversion, and upgrading including: chopping/grinding,

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compression, and pumping requirements are approximately ~ 0.63 MJ electricity/kg-dry

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biomass for either switchgrass or miscanthus. PDFs were developed for several key

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parameters in the fuel conversion and upgrading module including: catalyst lifetime, CHP

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heat and electricity conversion efficiency, biochar carbon sequestration, loss of biofuel

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due to coking, weight hourly space velocity (WHSV), and transportation distance.

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Summary information for distribution types as well as a detailed summary of all

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inventory data is provided in the supporting information (SI).

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Methodology: The scope of the LCA is well-to-wheel and the functional unit is chosen as 1 MJ of

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renewable fuel. As such, carbon dioxide absorption (i.e. Biogenic CO2) and combustion

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emissions are considered in the analysis. It is important to note that the functional unit

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considered in this work does not account for vehicle fuel use efficiency, and was selected


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so that the results are comparable to prior published literature83. The developed LCA

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model translates process data including material, energy, emissions, and heat flows into

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specific impacts on energy and the environment. In this work the Intergovernmental

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Panel for Climate Change (IPCC) 100-year Global Warming Potential (GWP)

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characterization factors84 are utilized to quantify the life cycle GHG emissions for biofuel

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production while the Cumulative Energy Demand (CED) methodology85 is utilized to

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determine primary energy consumption. Life cycle data is obtained from the ecoinvent

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database86. Infrastructure related environmental impacts, as well as direct and indirect

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land use change impacts, are not considered in this study. Thus, changes in the above and

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below-ground soil organic carbon (SOC) dynamics and carbon balances are beyond the

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scope of this work and thus not considered in the analysis. However, past research has

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shown that the LUC impacts can play an important role in the carbon balance for the

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production of biofuels87.

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In this study, probability distribution functions (PDFs) are developed for key parameters

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in the cultivation, harvesting, and transportation of switchgrass and miscanthus feedstock

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as well as the upgrading and conversion of bio-oil to renewable transportation fuel.

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Anderson-Darling statistical tests are performed to determine the distribution type and

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parameters that best fit the sample data. All physically relevant and/or possible families

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of distributions are considered including: normal, triangular, lognormal, weibull, gamma,

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extreme value, exponential, and loglogistic. These probability distribution functions are

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randomly sampled via Monte Carlo simulation (10,000 trials) to capture uncertainty in

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the life cycle inventory (LCI). Uncertainty in the life cycle impact assessment (LCIA) is

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captured via the use of Monte Carlo simulation (10,000 trials) to randomly sample from

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statistical distributions for environmental impact factors obtained from the Ecoinvent

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database. Addressing uncertainty in a stochastic manner, at both the process and life

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cycle-scale, allows for a broader understanding of the expected range for key life cycle

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sustainability and performance metrics. Summary information for distribution types as

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well as a detailed summary of all inventory data is provided in the supporting information

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(SI).

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Two LCA schemes were considered for dealing with coproducts in this work: (i) energy-

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based allocation and (ii) displacement method (Disp.). In energy-based allocation,

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environmental burdens are partitioned between final renewable fuel and coproducts

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(bioelectricity, useable heat) based on their fraction of total energy flow. For the

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displacement method, coproducts generated via a process/system are assumed to displace

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an existing product; the system is then credited with the avoided life cycle energy use and

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environmental burdens of the displaced product(s). In this work it is assumed that heat

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generated via CHP would displace heat derived via combustion of natural gas, while

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electricity generated via CHP displaces the U.S. average electricity mix. Only useable

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heat (i.e. heat used to meet system-wide heat duty requirements) generated via CHP are

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considered in energy allocation and displacement calculations. For soil amendment (SA)

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scenarios, carbon sequestered via biochar is subtracted from total life cycle GHG

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emissions. It is important to note that energy based allocation is the preferred method of

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the EU in determining fuels eligible for the RED, while displacement is the favored

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method of the U.S. Environmental Protection Agency (EPA) for determining fuels

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eligible for the renewable fuel standard (RFS2). Additionally, economic- and/or exergy-

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based allocation provide a method for adjusting for the difference in quality of energy

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and material resources and thus overcome many of the limitations of traditional energy

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allocation. However, economic and exergy-based allocation are beyond the scope and

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goal of the current work, but should be considered in future analysis.

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Sustainability Metrics Energy return on investment and life cycle GHG emissions have been extensively used to

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quantify and compare the energy intensity and GHG reduction potential of producing

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transportation fuel(s) derived from biomass feedstocks8, 88, 89. Net life cycle GHG

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emissions are calculated based on the quantity of life cycle GHGs emitted throughout the

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biomass supply chain as well as carbon credit and/or impacts allocated to coproducts, and

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is contingent on the choice of LCA scheme utilized for dealing with coproducts. This

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work identifies biofuel pathways that meet the US EPA Renewable Fuel Standard for

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cellulosic biofuels, i.e. 60% reduction in overall life cycle GHG emissions relative to

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baseline petroleum fuels (~92 gCO2e/MJ-fuel).


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EROI quantifies the amount of primary energy required to produce a functional unit of

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transportation fuel energy evaluated over the entire supply chain. As the primary

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motivation for biofuel production is its potential to displace fossil derived fuels, a fossil

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energy return on investment metric is chosen to assess the sustainability of biofuel

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production. In this work, EROI is defined as the ratio of output biofuel energy to the

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embodied primary fossil energy required for its production, and is presented in equation

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1. Output biofuel energy is calculated based on the mass flow of fuel compounds

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(pentane, hexane, etc.) and their respective lower heating value (LHV).

392 =

∑( )∗()

393

(1)

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EROI values greater than 1 are desirable, as more energy is provided by the biofuel as

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compared to the primary fossil energy required for its production. Recent studies have

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proposed that a liquid fuel must have a minimum EROI of 3 in order to support the U.S.

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transportation system90; and, failure to meet such criteria could have significant economic

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and societal implications91, 92. As such, it is critical to identify biofuel pathways that

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achieve life cycle GHG reductions targets as set by the RFS2 and have favorable EROI

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profiles, i.e. comparable to petroleum fuels and leading first generation biofuels.

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ℎ !" #$ %

Results and Discussion Life Cycle Energy Analysis Figure 2 plots the median EROI for producing renewable fuels from miscanthus and

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switchgrass biofeedstocks, error bars represent the 10th and 90th percentile. Median values

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are presented instead of average values, as the distributions for EROI are highly skewed

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and non-symmetric. For each feedstock two allocation scenarios (displacement and

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energy allocation) as well as two biochar coproduct scenarios (use as a soil amendment or

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cogeneration to produce bioenergy) were evaluated. The results reveal that the median

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EROI for fast pyrolysis derived fuels range from 1.52 to 2.56 over the host of pathways

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evaluated, with similar trends found for switchgrass and miscanthus. For BE scenarios,

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the EROI using displacement is significantly higher as compared to energy allocation and

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is a consequence of the avoided high upstream impacts of electricity generation—



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producing a large coproduct credit in displacement scenarios. As shown in Figure 2, the

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combination of coproduct scenario and allocation scheme can have a dramatic impact on

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the environmental performance of fast pyrolysis derived fuels. Additionally, the results

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reveal that the EROI is greater than unity for all examined biofuel pathways, indicating

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that these systems are net energy positive. However, none of the examined pathways

422

meet the minimum EROI criteria of 3 as advocated by Hall and co-workers90. 7

425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445

Energy Return on Investment

424

(MJ Fuel / MJ Primary Fossil Energy Invested)

423 6 Switchgrass 5

Miscanthus Energy Break Even

4

3

2

1

0 Displacement

Energy Allocation

Coproduct: Soil Amendment

Displacement

Energy Allocation

Coproduct: Bioenergy

Figure 2 – Energy Return on Investment for producing renewable fuels from miscanthus and switchgrass biofeedsock. Results are shown for displacement and energy-based allocation, as well as soil amendment and bioenergy coproduct scenarios. Median values (i.e. 50th percentile) are shown; error bars represent the 10th and 90th percentile. Figure 3 presents the average fossil life cycle energy consumed by process input,

446

normalized per MJ renewable fuel output. Results are shown utilizing the displacement

447

methodology; negative values represent primary fossil energy credit(s) received from

448

coproducts. The results from Figure 3 indicate that hydrogen and electricity requirements

449

constitute the largest energy burdens in the biomass-to-fuel supply chain. Primary energy

450

consumption for hydrogen is ~0.38 MJ primary fossil energy/MJ-Fuel for either

451

biofeedstock; while electricity requirements account for 0.16 to 0.20 MJ primary fossil


Page 15 of 28


452

energy/MJ-Fuel for miscanthus and switchgrass respectively. Additionally, the

453

combustion of light hydrocarbons and/or biochar to produce heat and bioelectricity

454

results in a significant coproduct credit, up to -0.79 MJ primary energy/MJ-Fuel for

455

switchgrass bioenergy scenarios. Further analysis reveals that fuel conversion and

456

upgrading stages constitute over 75% of total fossil life cycle energy consumption in the

457

biomass to fuel supply chain. As such, minimizing the quantity of hydrogen and

458

electricity required for fuel conversion will be crucial for increasing the energetic

459

performance of fast pyrolysis derived biofuels. The findings of the present work compare

460

favorably with those reported in prior studies28, 93. Net = 0.34

Miscanthus Bioenergy Coproduct Credit = -0.54

Primary Fossil Energy Consumption = 0.88

Net Primary Energy Use

Electricity

Miscanthus Soil Amendment

Heat

Coproduct Credit = -0.33


Diesel

Net = 0.26

Agrochemicals Transport

Switchgrass Bioenergy

Catalyst Primary Fossil Energy Consumption = 1.04


Net = 0.66

Bioelectricity Bioheat

Switchgrass Soil Amendment Coproduct Credit = -0.39

-1

-0.75

-0.5

-0.25

Material & Energy Inputs Coproducts

Hydrogen

Net = 0.56


0

0.25

0.5

0.75

1

1.25

Average Primary Fossil Life Cycle Energy Consumption

461 462 463 464 465 466 467 468 469 470 471

(MJ Primary Fossil Energy / MJ-Fuel)

Figure 3 – Life cycle fossil energy analysis. Average primary energy impacts for process inputs and coproduct are normalized to 1 MJ of renewable fuel. Results for displacement methodology are provided. Net primary energy use represents the difference between primary fossil energy consumed in the supply chain and coproduct primary fossil energy credit. Life Cycle GHG Analysis Figure 4 presents the median life cycle GHG emissions for producing renewable fuels from miscanthus and switchgrass biofeedstocks. The results reveal that the life cycle



Page 16 of 28

472

GHG emissions for fast pyrolysis derived fuels range from 22.5 to 61.0 gCO2e/MJ-fuel.

473

The choice of displacement or energy allocation has a significant impact on the LCA

474

results; with miscanthus and switchgrass meeting the RFS2 GHG reduction threshold

475

under soil amendment (SA) pathways, but exceeding the threshold for bioenergy (BE)

476

pathways. These results are significant as they indicate that the choice of biofeedstock,

477

coproduct scenario, and allocation scheme can change the life cycle GHG emission

478

profile of fuels derived via perennial grasses from meeting to exceeding the GHG

479

reduction threshold as established by the RFS2.

480 90

70

Switchgrass Miscanthus RFS2 60% GHG Reduction Threshold

(gCO2e / MJ-Fuel)

Life Cycle GHG Emissions

80

60 50 40 30 20 10 0 Displacement

481 482 483 484 485 486 487

Energy Allocation

Coproduct: Soil Amendment

Displacement

Energy Allocation

Coproduct: Bioenergy

Figure 4– Life cycle GHG emissions for producing renewable fuel from miscanthus and switchgrass biofeedsock. Results are shown for displacement and energy-based allocation, as well as soil amendment and bioenergy coproduct scenarios. Median values (i.e. 50th percentile) are shown; error bars represent the 10th and 90th percentile. The average life cycle GHG emissions by process input normalized per unit fuel energy

488

output are presented in Figure 5. Results for displacement methodology are shown,

489

negative values represent life cycle GHG credit(s) received from coproducts. The results

490

from Figure 5 indicate that hydrogen, agrochemicals, and electricity requirements have

491

the largest global warming potential impacts in the biomass-to-fuel supply chain. Figure


Page 17 of 28


492

5 reveals that hydrogen impacts account for approximately 38.7 gCO2e/MJ-fuel for

493

switchgrass and 38.2 gCO2e/MJ-fuel for miscanthus feedstocks, agrochemical impacts

494

account for over 26.5 gCO2e/MJ-fuel for switchgrass and 11.9 gCO2e/MJ-fuel for

495

miscanthus feedstocks, while electricity requirements account for 16.8 gCO2e/MJ-fuel for

496

switchgrass and 13.9 gCO2e/MJ-fuel for miscanthus feedstock. Additionally, the results

497

from Figure 5 show that biochar sequesters, on average, 49.0 gCO2e/MJ-fuel for

498

switchgrass and 29.7 gCO2e/MJ-fuel for miscanthus. The GHG reductions for biochar are

499

significantly higher for switchgrass as compared to miscanthus, due to increased rate of

500

biochar formation as a result of higher lignin content. Net = 39.70

Miscanthus Bioenergy Coproduct Credit = -41.58

Net GHG Emissions

Life Cycle GHG emissions = 81.28

Agrochemicals

Miscanthus Soil Amendment

Electricity Heat



Diesel Net = 43.49

Transport

Switchgrass Bioenergy

Catalyst Biochar



Bioelectricity Bioheat

Net = 27.95

Material & Energy Inputs Coproducts

Hydrogen Net = 28.54

Switchgrass Soil Amendment Life Cycle GHG emissions = 105.26


-100

-75

-50

-25

0

25

50

75

100

125

Average Life Cycle GHG Emissions

501 502 503 504 505 506 507 508 509 510

(gCO2e / MJ-Fuel)

Figure 5 – Life cycle carbon footprint analysis. Greenhouse gas emissions for material and energy inputs as well as coproduct credits are normalized to 1 MJ of renewable fuel. Results for displacement are provided. Net life cycle GHG emissions represent the difference between GHGs (direct and indirect) emitted throughout the life cycle and coproduct GHG credit. EROI vs Life cycle GHG emissions: Figure 6 presents a comparison of EROI vs life cycle GHG emissions for fast pyrolysis-



511

derived biofuels against select first generation, second-generation (i.e. cellulosic ethanol),

512

and petroleum fuels. As shown in Figure 6, 1st generation biofuels such as corn and

513

sugarcane ethanol are found to have higher GHG emissions relative to 2nd generation fuel

514

derived from corn stover, miscanthus, or switchgrass. Additionally, the results reveal that

515

sugarcane ethanol has a high EROI (~4.3) exceeding the EROI performance threshold of

516

3, while corn ethanol has a marginally positive energy balance. Figure 6 reveals a weak

517

clustering of 2nd generation bioethanol derived from corn stover, switchgrass, and

518

miscanthus, and shows that bioethanol generated via 2nd generation feedstocks are near

519

carbon neutral and have a correspondingly high EROI (i.e. greater than 3). Fast pyrolysis

520

derived gasoline is found to have significantly lower EROI and higher overall life cycle

521

GHG emissions as compared to 2nd generation bioethanol derived via miscanthus or

522

switchgrass. Fossil hydrogen consumption and process electricity requirements are

523

primarily responsible for the low environmental performance of pyrolysis gasoline

524

relative to other 1st generation and 2nd generation biofuels. It is important to note that

525

bioethanol must be blended with existing transportation fuel prior to use in vehicle

526

engines, while pyrolysis gasoline can be used as a drop-in replacement for existing

527

hydrocarbon fuels. Petroleum diesel is found to have a high EROI (~4.8), yet have

528

correspondingly high fossil GHG emissions (~92 gCO2e/MJ-Fuel). These results

529

highlight the challenge of producing liquid transportation fuel from biomass that is

530

simultaneously carbon neutral over its life cycle, chemically similar to traditional

531

hydrocarbon fuels, and energetically competitive with existing petroleum resources.

532 533

Direct and indirect land use change induced global warming impacts are not considered

534

in this work. Estimates of LUC impacts for miscanthus and switchgrass derived ethanol

535

range from -10 to -2.1 gCO2e/MJ-miscanthus ethanol and 2.7 to 19 gCO2e/MJ-

536

switchgrass ethanol depending on the choice of domestic/international emissions

537

modeling scenarios and model parameter(s) settings94, 95. Negative values for LUC

538

impacts are due to changes in soil organic carbon (SOC) dynamics. This suggests that

539

while LUC impacts may increase the carbon footprint of switchgrass fuels, it has the

540

potential to lower that of miscanthus derived fuels; and thus merits further investigation.

541


Page 18 of 28


100 Conventional Biofuel Not Sustainable

Petroleum Diesel

RFS2 GHG Reduction Threshold: Conventional Biofuel

Petroleum Diesel (GREET)

(gCO2e / MJ Fuel)


80

EROI Performance Threshold

Energy Break Even

Page 19 of 28

1st Generation Bioethanol (Wang et al. 2012)

60 Cellulosic Biofuel Not Sustainable

Fast Pyrolysis Gasoline (This study)

Corn Ethanol MSC: BE

40

2nd Generation Bioethanol (Wang et al. 2012)

SWG: BE Sugarcane Ethanol

MSC: SA 20

RFS2 GHG Reduction Threshold: Cellulosic Biofuel

SWG: SA

Corn Stover Ethanol

SWG Ethanol

MSC Ethanol

0 0

1

2

3

4

5

6

7

Energy Return on Investment -20

542 543 544 545 546 547 548 549 550 551 552 553


Values for 1st & 2nd generation bioethanol were obtained from Wang et al. 201283 and do not consider direct or indirect LUC impacts Values for petroleum diesel were adapted based on data obtained from the 2014 GREET model96 For fast pyrolysis derived gasoline results using the displacement method are shown SWG: Switchgrass; MSC: Miscanthus; SA: Soil amendment; BE: Bioenergy

Figure 6 – Comparison of EROI vs Life cycle GHG emissions for select 1st generation and 2nd generation biofuels. Values for EROI and GHG emissions of fast pyrolysis fuels using the displacement method are shown. Further work is needed at the process level, such as determining ideal catalyst(s) for fuel

554

conversion, effect of bio-oil on catalyst coking, and catalyst lifetime. High-level

555

biomass/biofuel logistical and geospatial modeling97 as well as economic evaluation

556

should be concurrently considered to ensure that biofuel production is technically feasible

557

and economically viable. The results of this work indicate that single-stage fast pyrolysis

558

and hydroprocessing system have several limitations including: high hydrogen

559

consumption, high production of light hydrocarbons, and low liquid carbon yield. A one-

560

at-a-time (OAT) sensitivity analysis was performed to show how variations in individual

561

parameters affect the LCA results (see section S16 in the S.I.). Analysis reveals that the

562

EROI and GHG emissions for single-stage fast pyrolysis systems are highly sensitive to

563

bio-oil yield as well as hydrogen consumption. Alternative reactor configurations,



564

process options, and chemical conversion pathways have the potential to reduce the

565

hydrogen requirements for fuel conversion and upgrading while simultaneously

566

increasing the liquid carbon yield, and thus increase the environmental sustainability of

567

emerging thermochemical fuel platforms98, 99, and should be considered in future

568

environmental sustainability analysis of pyrolysis derived biofuels. Furthermore,

569

additional research is required to determine the far-reaching environmental implications

570

and feasibility of commercial scale biofuel production and related coproduct scenarios.

571

For example, while biochar scenarios show merit for reducing life cycle GHG emissions

572

for fuel production, past research has suggested that the application of biochar could have

573

severe environmental ramifications, as a fraction of biochar may be lost to wind and

574

seepage and thus pose an environmental hazard to local waterways and groundwater

575

aquifers100. Accordingly, while energy return on investment and life cycle GHG

576

emissions are important criteria for screening potential biofuel pathways for commercial

577

use, other environmental criteria such as impacts on water and air quality, biodiversity,

578

etc. must also be considered so that biofuel production does not generate adverse impacts

579

on ecological and human welfare101, 102.

580 581 582 583

Supporting Information Available More information regarding data sources and the modeling approach are provided via the

584

Supporting Information accompanying this document. This material is available free of

585

charge via the Internet at http://pubs.acs.org.

586 587 588 589

This work is supported by the National Institute of Food and Agriculture, United States

590

Department of Agriculture (USDA) (Agriculture and Food Research Initiative

591

Competitive Grant No. 2012-67009-19717). Any opinions, findings and conclusions or

592

recommendations expressed in this material are those of the authors and do not

593

necessarily reflect the views of USDA. This material is based upon work supported by

594

the National Science Foundation Graduate Research Fellowship under Grant No. (DGE-

595

1247842). Any opinion, findings, and conclusions or recommendations expressed in this

Acknowledgement


Page 20 of 28

Page 21 of 28


596

material are those of the authors(s) and do not necessarily reflect the views of the

597

National Science Foundation.

598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639

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Table of Content (TOC) Art 100 Conventional Biofuel Not Sustainable

Petroleum Diesel

RFS2 GHG Reduction Threshold: Conventional Biofuel

Petroleum Diesel (GREET)

(gCO2e / MJ Fuel)


80

EROI Performance Threshold

Energy Break Even

912 913 914 915 916 917 918 919 920 921 922 923 924 925 926

1st Generation Bioethanol (Wang et al. 2012)

60 Cellulosic Biofuel Not Sustainable

Fast Pyrolysis Gasoline (This study)

Corn Ethanol MSC: BE

40

2nd Generation Bioethanol (Wang et al. 2012)

SWG: BE Sugarcane Ethanol

MSC: SA 20

RFS2 GHG Reduction Threshold: Cellulosic Biofuel

SWG: SA

Corn Stover Ethanol

SWG Ethanol

MSC Ethanol

0 0

1

2

3

4

5

6

7

Energy Return on Investment -20


927 928


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Biofuels via Fast Pyrolysis of Perennial Grasses: A Life Cycle

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