Environmental, Economic, and Scalability Considerations and Trends

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Environmental, economic, and scalability considerations and trends of selected fuel economy-enhancing biomass-derived blendstocks Jennifer B. Dunn, Mary J. Biddy, Susanne B. Jones, Hao Cai, Thathiana Benavides, Jennifer Markham, Ling Tao, Eric C. D. Tan, Christopher Kinchin, Ryan Davis, Abhijit Dutta, Mark Bearden, Christopher Clayton, Steven Phillips, Kenneth G. Rappe, and Patrick Lamers ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b02871 • Publication Date (Web): 30 Oct 2017 Downloaded from http://pubs.acs.org on November 8, 2017

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ACS Sustainable Chemistry & Engineering

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Environmental, economic, and scalability

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considerations and trends of selected fuel economy-

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enhancing biomass-derived blendstocks

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Jennifer B. Dunn,1,* Mary Biddy,2,† Susanne Jones,3, ‡ Hao Cai,1 Pahola Thathiana Benavides,1

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Jennifer Markham,2 Ling Tao,2 Eric Tan,2 Christopher Kinchin,2 Ryan Davis,2 Abhijit Dutta,2

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Mark Bearden,3 Christopher Clayton,3 Steven Phillips,3 Kenneth Rappé3, Patrick Lamers4

1. Systems Assessment Group, Energy Systems Division, Argonne National Laboratory,

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9700 S. Cass Avenue, Argonne, IL 60439

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2. National Bioenergy Center, National Renewable Energy Laboratory, 15013 Denver West

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Parkway, Golden, Colorado 80401, United States

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3. Energy Processes and Materials Division, Pacific Northwest National Laboratory, 902

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Battelle Boulevard, Richland, WA 99352

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4. Bioenergy Technologies Group, Idaho National Laboratory, 2525 N Fremont Ave, Idaho

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Falls, ID 83415, United States

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Corresponding authors

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*Email: [email protected]

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†

Email: [email protected] 1 ACS Paragon Plus Environment


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‡

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Keywords: techno-economic analysis, life-cycle analysis, biofuels

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Abstract

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24 biomass-derived compounds and mixtures, identified based on their physical properties,

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which could be blended into fuels to improve spark ignition engine fuel economy were assessed

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for their economic, technology readiness, and environmental viability. These bio-blendstocks

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were modeled to be produced biochemically, thermochemically, or through hybrid processes. To

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carry out the assessment, 17 metrics were developed for which each bio-blendstock was

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determined to be favorable, neutral, or unfavorable. Cellulosic ethanol was included as a

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reference case. Overall economic and, to some extent, environmental viability is driven by

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projected yields for each of these processes. The metrics used in this analysis methodology

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highlight the near-term potential to achieve these targeted yield estimates when considering data

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quality and current technical readiness for these conversion strategies. Key knowledge gaps

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included the degree of purity needed for use as a bio-blendstock. Less stringent purification

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requirements for fuels could cut processing costs and environmental impacts. Additionally, more

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information is needed on the blending behavior of many of these bio-blendstocks with gasoline

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to support the technology readiness evaluation. Overall, the technology to produce many of these

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blendstocks from biomass is emerging and as it matures, these assessments must be revisited.

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Importantly, considering economic, environmental, and technology readiness factors, in addition

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to physical properties of blendstocks that could be used to boost engine efficiency and fuel

Email: [email protected]

2 ACS Paragon Plus Environment

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economy, in the early stages of project research and development can help spotlight those most

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likely to be viable in the near term.

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Introduction

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Fuel properties influence engine efficiency.1,2 The primary focus of the Co-Optima initiative, a

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collaborative effort among nine U.S. Department of Energy National Laboratories, is to identify

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the fuel properties that will enable enhanced fuel economy, blended fuels with these properties,

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and engines that will work with these fuels towards increased efficiency and fuel economy. To

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date, the project has investigated potential biomass-derived blendstocks that could be blended

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with gasoline and used in spark ignition engines to reduce the energy consumption and emissions

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associated with the transportation sector. While many of the blendstocks considered could be

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produced from petroleum or natural gas feedstocks, this analysis has focused on biomass-derived

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blendstocks which may offer numerous technical, societal, and environmental benefits. Within

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Co-Optima, fuel properties, including boiling and freezing points, heat of vaporization, research

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octane number, solubility, ignition quality, corrosivity, toxicity, and heteroatom concentration,

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among other properties, of 400 biomass-derived potential blendstocks were evaluated. After this

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assessment, about 40 biomass-derived blendstocks exhibited favorable fuel properties.3

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Not all of these 40, however, could be produced in the near term (~15 years) economically and at

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scale. Further analysis was therefore needed to evaluate the economic and market viability and

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environmental impact of these bio-blendstocks. In the analysis herein, 24 of the bio-blendstocks

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– selected from the 40 to achieve diversity in chemical class, representativeness in conversion

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route (fermentation, thermochemical, and hybrid (with both biochemical and thermochemical

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attributes)), and sufficiently well-characterized conversion routes to enable a high-level techno-

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economic analysis (TEA) – were evaluated for these factors. We included cellulosic ethanol


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performance as a benchmark. Table S1 catalogues key physical property information of the 24

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bio-blendstocks and ethanol.

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The 24 selected bio-blendstocks listed in Table S1 include alcohols (8), esters (4),

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ketones (4), hydrocarbon mixtures (6), an alkane, and a furan blend. All Co-Optima bio-

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blendstocks have research octane numbers (RON) exceeding 98, a key enabler of

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enhanced fuel economy for spark ignition engines.2 Ethanol, a biomass-derived octane

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enhancer, blended at a 10% volume in most gasoline in the United States, has been

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included as a reference case. While most of the ethanol in the market today is made from

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corn starch, this analysis considers cellulosic ethanol from municipal or agricultural

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waste, among other feedstocks, which offers additional benefits and is in the early stages

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of commercial production. It is important to note that other compounds derived from

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biomass or other feedstocks that could offer desirable fuel properties. The 24 compounds

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and mixtures selected as case studies for the Co-Optima initiative are referred to herein as

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“Co-Optima bio-blendstocks.”

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In this paper, we evaluate these 24 Co-Optima bio-blendstocks for economic viability,

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scalability, and energy and environmental impact. While the influence of high-level ethanol

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blends on engine efficiency and the costs and environmental impacts of producing high-level

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ethanol blends have been investigated previously,1,2 this is the first systematic study of other

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potential biomass-derived blendstocks that may improve fuel economy. This study does not

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recommend a specific blendstock be pursued or be included in gasoline at a specific blending

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level with or without ethanol. Rather, the aim is to identify whether these Co-Optima bio-



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blendstocks are viable to enter the market in a near-term timeframe and to identify potential

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roadblocks to their commercialization and whether these can be overcome.

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Methodology

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While some of the Co-Optima bio-blendstocks (Table S1) are on the path to commercialization,

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many are just emerging or are still undergoing R&D at a range of scales. For these latter

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biomass-derived blendstocks, insights into how they may be produced are available only through

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the literature (academic and patent). For biomass-derived blendstocks in the commercialization

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pipeline (e.g., methanol-to-gasoline), more information may be available through company

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literature and presentations in addition to the literature. This nascent state of the industry

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translates into some uncertainty in establishing, for example, production costs based on process

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modeling. Therefore the evaluation of these potential biomass-derived blendstocks is qualitative

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and based on thresholds. We developed 17 metrics (Tables 1–3) in the categories of economic

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viability, technology readiness (i.e., scalability), and environmental impact based on prior

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experience with cost, scalability, and environmental drivers of biomass conversion processes.4-9

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For each metric, we established three categories, or bins, into which each blendstock fell. When

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possible, these targets were based on regulatory thresholds (e.g., Renewable Fuel Standard

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requirements for GHG reductions) or previous analyses of mature (e.g., corn ethanol, gasoline)

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or emerging fuels. For example, the categories for the carbon efficiency metric were based on

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analyses of pyrolysis and gasification.8,9 The metrics and bins developed were vetted with Co-

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Optima stakeholders including the project’s External Advisory Board.


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Notably, we considered two production cases for each potential blendstock. The first, called the

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state of technology (SOT) case, reflects the current performance of the conversion process. SOT

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key parameters, such as yield and selectivity, are lower than they would be when the technology

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is more mature. The second production case considered is called the target case, which is

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forward-looking and considers the potential of the technology at full scale. A process model for

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each case informed bio-blendstock cost estimates. (Detailed TEA and life-cycle analysis [LCA]

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assumptions are in the SI.) These process models generally were modifications of existing

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models.4–9 The integration of these process models and economic evaluations, also described in

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previous studies,4–9 produces an estimate of the cost per gasoline gallon equivalent (GGE) to

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produce the biomass-derived blendstocks. The overall designs are based on fully integrated,

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standalone facilities and include all supporting utilities and equipment required to operate the

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biorefinery. The financial assumptions align with recent process designs developed by both the

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National Renewable Energy Laboratory and Pacific Northwest National Laboratory.4–9

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Technology readiness metrics (Table 1) were evaluated based on the SOT case. Given the

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emerging nature of the production of many of these compounds, production cost estimates

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should be viewed as the best understanding we can gain given the information that is available.

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For this reason, we did not rate the Co-Optima bio-blendstocks based on their absolute cost of

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production estimate, but rather on how their cost of production compared to other biomass-

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derived blendstocks in this analysis. A separate metric was included to reflect the state of

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knowledge regarding the production route considered and the source/quality of the baseline data.

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An unfavorable rating was assigned when process information was largely notional due to

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limited data availability.



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We considered producing biomass-derived blendstocks from either a herbaceous biomass blend

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for the biochemical production routes or from a woody biomass blend for the thermochemical

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production routes (see SI). Within the technology readiness metrics, biomass feedstock

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influences were considered, including the state of knowledge regarding the effect of feedstock

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type and specifications on product yield and quality. The final technology readiness metric that

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was evaluated was the degree of biomass-derived product blending behavior with conventional

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gasoline given the information currently available. Importantly, the Co-Optima R&D projects are

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currently assessing the influence of blending levels on fuel properties3 and working towards

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understanding the optimal blending levels for each Co-Optima biomass-derived blendstock.

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Table 1. Technology Readiness Metrics Metric

Favorable (+)

Neutral (0)

Unfavorable (-)

Approach

Co-Optima bioblendstock production SOT cost

Falls in cluster of lowest cost pathways

Falls in cluster of moderate cost pathways

Falls in cluster of high cost pathways

Cost will be adopted from established target cases, published TEA data, and newly developed analysis. Costs compared on a $/GGE basis to take into account differing energy densities.

State of knowledge regarding conversion process and its technology readiness level (TRL)

Demonstration-scale (or larger) data available

Bench-scale data available

Notional, partly literature based

Conduct review of existing research and analyses, literature, and discussions with national laboratory researchers.

Number of viable routes to produce fuel or blendstock

>3

2–3

1

Evaluate literature and discuss with national laboratory researchers. New viable routes are through a different conversion pathway (such as biochemical versus thermochemical designs) and are not changes to one particular design (such as a new catalyst).

Data quality regarding feedstock assumptions in process modeling

Experimental data only from real feedstocks

Experimental data are a combination of real feedstocks and mock feedstocks

Experimental data from mock feedstocks

Evaluate feedstock source in experiments used to inform process modeling.

Production process sensitivity to feedstock type

Feedstock changes result in minor variations in fuel yield/quality

Feedstock changes result in some variations in fuel yield/quality

Feedstock changes can cause significant variations in fuel yield/quality

When experimental data unavailable, use highlevel mass balance to estimate.

Robustness of process to feedstocks of different specs

Changes in feedstock specifications minimally influences yield/quality

Changes in feedstock specifications moderately influences yield/quality

Changes in feedstock specifications greatly influences yield/quality

Examine experimental data for information on influence of specification changes (e.g., ash, hydrocarbon content) on yield and quality.

Blending behavior of blendstock with current fuels for use in vehicles

Current quality good enough for replacement

Current quality good enough for blend

Current quality in blend not good or unknown

Consider level of information regarding fuel quality of biomass-derived blendstock, such as whether it is known that it can be directly blended versus there is some limited knowledge about blending properties such as wide boiling range bulk properties.



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Economic viability metrics (Table 2) take into account the target case and consider various

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aspects of process economics. First, we ranked the target cost for each blendstock through the

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ratio of the SOT cost to the target cost. This is a critical metric that assesses the amount of

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research and development required to cut processing costs. Furthermore, we considered the

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extent to which economic viability of the Co-Optima bio-blendstock depended upon the co-

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production of electricity (e.g., through lignin combustion), chemicals, or a co-produced

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blendstock (e.g., diesel). This was a minor effect given that only the n-butanol case has a

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chemical co-product. Nonetheless, this metric was included because a process heavily dependent

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upon co-products for viability may not be desirable. Swings in the market value of the chemical

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could prompt a producer to stop producing it along with the co-produced bio-blendstock.

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Additionally, if the bio-blendstock were produced from, or is itself, a valuable chemical

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intermediate, market factors could pull it to other uses. Most commodity chemicals have higher

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profit margins than fuel blendstocks and production of biomass-derived fuel blendstocks could

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be challenged if competing with commodity chemicals. Finally, feedstock cost, an important

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process economics driver, was included as a metric.

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Table 2. Economic Viability Metrics Metric

Favorable (+)

Neutral (0)

Unfavorable (-)

Approach

Fuel production target cost

Falls in cluster of lowest cost pathways

Falls in cluster of moderate cost pathways

Falls in cluster of high cost pathways

Cost adopted from established target cases, published TEA data, and newly-developed analysis. Costs compared on a $/GGE basis to take into account differing energy densities.

Ratio of SOT-totarget cost

4

See above approaches for developing SOT and target costs.

Percentage of product price dependent on coproducts (i.e., chemicals, electricity, other blendstocks/fuels produced as coproduct to CoOptima fuel)

50%

Evaluate with process models and technoeconomic analysis.

Competition for the biomassderived blendstock or its predecessor

Blendstock is not produced from, nor is itself, a valuable chemical intermediate

Blendstock is produced from, or is itself, a raw chemical intermediate

Blendstock is produced from, or is itself, a valuable chemical intermediate

Evaluate market size for biomass-derived blendstocks and their predecessors in the production process (i.e., intermediates between feedstock and final blendstock product).a

Cost of feedstock (in US$2014)

Cost at or below target of $84/dry ton delivered at reactor throat10,11

Some uncertainty as to whether feedstock cost will be at or below $84/dry ton (e.g., < $100 ton10,11)

Feedstock cost at delivery to reactor throat likely to exceed $120/dry ton

Feedstock prices are based upon Idaho National Laboratory’s feedstock cost analyses.10,11

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a.

It may be possible that competition for the bio-blendstock for multiple end uses could lead to more stable financing for biorefineries that would produce the bio-blendstock, but our primary focus in this analysis is availability as a bio-blendstock fuel, which could be compromised if there were competition with the chemicals market.

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(Table 3). This part of the analysis aims to understand the impact that targeted bio-blendstocks

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could have on the reduction of greenhouse gas (GHG) emissions, water consumption and air

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pollution when compared to traditional fossil fuels production. To evaluate life-cycle energy and

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environmental impacts of the biomass-derived blendstocks, we incorporated material and energy

The final group of metrics considered reflects the environmental impacts of the bio-blendstocks



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flows from process models into Argonne National Laboratory’s Greenhouse gases, Regulated

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Emissions, and Energy use in Transportation (GREET®) model (2015 release) and carried out an

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LCA of each biomass-derived blendstock. Additional data sources included feedstock processing

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and logistics from Idaho National Laboratory’s analyses10,11 and feedstock production data from

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GREET.12

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Table 3. Environmental Impact Metrics Metric

Favorable (+)

Neutral (0)

Unfavorable (-)

Approach

Efficiency of input carbon (fossil and biomass-derived) to Co-Optima bioblendstock for the target casea

>40%

30–40%

98

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RON) portion of an overall fuel stream produced in a gasification process and this portion of the

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total energy product output of the process is heavily dependent on the majority of fuel product

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that has a RON below 98. 1-butanol is co-produced with other alcohols. Feedstock costs again

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are uniformly favorable across bio-blendstocks because we assumed a single feedstock cost.

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Examining the environmental metrics, although the target case carbon efficiency for many of the

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Co-Optima bio-blendstocks was low, life-cycle GHG emissions tended to be favorable or

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neutral.

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Overall, methanol and methanol-to-gasoline—subject to commercialization efforts—received the

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most favorable ratings of the various thermochemically derived bio-blendstocks. Most

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unfavorable ratings for these pathways fall in the technology readiness metric group and overall

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target case carbon efficiency tends to be unfavorable. Thermochemical ethanol serves as a point

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of comparison in Figure 2, but this route is not fully commercialized and has an unfavorably

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rated carbon efficiency and many neutral ratings. It should be noted that the baseline gasification

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designs are energy self-sufficient and burn biomass rather than relying on imports of natural gas


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or electricity for these designs. Carbon efficiency could be boosted if such imports were

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assumed; however, further analysis would be required to understand the impact on sustainability

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metrics.

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The aromatic/olefinic gasoline blendstock via pyrolysis-derived sugars/upgrading had the

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greatest number of unfavorable ratings in Figure 2. Limited information was available regarding

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the SOT for sugars recovery and upgrading. Very preliminary experimental work suggested that

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hydrotreating the mostly lignin fraction may be difficult.21 Furthermore, feedstock quality (such

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as high ash or low lignin fraction) has a significant effect on yield. Ash also poses challenges to

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catalyst maintenance.

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Figure 2. Thermochemically produced bio-blendstocks screening results. Blue, green, and brown

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circles represent favorable, neutral, and unfavorable categorization as defined in Tables 1–3.

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Gray circles reflect a lack of information to categorize a given bio-blendstock for a certain

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metric. Cellulosic ethanol is included as a benchmark. Italicized bio-blendstocks are produced 21 ACS Paragon Plus Environment


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via pyrolysis. Other bio-blendstocks are produced via indirect liquefaction. *Carbon efficiency

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and target yields are for the Co-Optima blendstock for the target case.

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Five bio-blendstock candidates were evaluated based on process models that incorporated

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biochemical and thermochemical elements (Figure 3). For example, routes to the furan mixture,

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ester mixture, and gasoline produced via the catalytic conversion of sugars considered in this

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analysis first employ an enzymatic hydrolysis step followed by a thermochemical step. The route

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to isooctene begins with fermentation that produces isobutanol, which is subsequently

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catalytically converted to isooctene. Production of butyl acetate proceeds through biological

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conversion in two separate fermentation trains. One train produces ethanol; the other produces

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isobutanol. Subsequent conversion and catalysis steps produce butyl acetate. Thermochemical

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gasification produces syngas, which is first biologically upgraded to produce a 2,3-butandiol

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intermediate, then dehydrated to yield methyl ethyl ketone.

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For each of these hybrid routes, the SOT cost of production received a neutral rating. Production

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information for these compounds tended to be from the literature for relevant feedstock types.

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The multiple conversion routes to each of these bio-blendstocks tended to be robust regarding

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feedstock types and specifications. Whereas gasoline produced from sugar catalytic conversion

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would be blendable with gasoline, and isooctene should be similarly blendable as long as

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impurities are low, the blending behavior of other bio-blendstocks is not clear at this point.

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Target costs for these bio-blendstocks could be high, but these were paired with somewhat high

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SOT costs yielding relatively favorable ratios of SOT-to-target costs. For these particular


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blendstocks, this latter metric may not yield the most important insight compared to the

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individual target costs. Overall, co-product dependency for these bio-blendstocks was low.

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Notably, the pathway to methyl ethyl ketone that we considered goes through 2,3-butanediol,

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which is a potentially valuable intermediate. For this reason, we assigned a neutral rating to the

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market competition metric for methyl ethyl ketone. The butyl acetate pathway proceeds through

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valuable intermediates ethanol and isobutanol.

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Environmental metrics aside from life-cycle fossil energy consumption were mostly neutral. The

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carbon efficiencies of isooctene and butyl acetate pathways, however, were relatively low and

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the life-cycle GHG emissions of gasoline from catalytic sugar conversion and butyl acetate were

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high.

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Overall, all bio-blendstocks produced via hybrid biochemical and thermochemical technologies

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had one or more unfavorable ratings. Many were in the environmental metric category. One

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reason for high GHG emissions in the gasoline from the catalytic conversion of sugar pathway is

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the use of significant quantities of hydrogen that was assumed in process modeling to be sourced

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from natural gas steam-methane reforming. Alternative design options could allow for internally

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sourced hydrogen, although Co-Optima bio-blendstock yield would decline and the production

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cost would likely rise as hydrogen was purchased from a vendor.5 On the other hand, the high

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GHG emissions associated with butyl acetate are driven by the relatively low yield of this

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compound in the process modeling. If these compounds (or any included in this analysis that can

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be produced by multiple technologies) were produced through an alternative route, the analysis



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results might be different. For example, the furan mixture could be produced through pyrolytic

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pathways.

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Figure 3. Screening results for bio-blendstocks produced via hybrid biochemical-thermochemical

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routes. Blue, green, and brown circles represent favorable, neutral, and unfavorable

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categorization as defined in Tables 1–3. Gray circles reflect a lack of information to categorize a

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given bio-blendstock for a certain metric. *Carbon efficiency is for the Co-Optima blendstock

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for the target case. **CC denotes catalytic conversion.

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This analysis highlighted several key overarching themes. First, based on our current

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understanding of these pathways, feedstock considerations are not insignificant but are also not

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roadblocks provided feedstocks are available at sufficient levels and reasonable cost. Secondly,

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yields of bio-blendstocks in biochemical, sugar-based routes may be relatively lower than bio-

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blendstock yields in thermochemical processes because, in biochemical routes, the lignin fraction

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of the feed is not available for bio-blendstock production. On the other hand, thermochemical

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routes tend to mimic those that would be used if the candidate were a chemical, which consist of


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many steps. If the impurity level were known for various thermochemically produced bio-

395

blendstocks, then the carbon usage could be optimized and economic and environmental metrics

396

may improve. Additionally, the quality of these fuel mixtures is uncertain regardless of the

397

conversion pathway. The impact that the composition of these streams has on the fuel properties

398

(including octane) for these further-looking target cases is also uncertain. Also, the results

399

suggest that new synthesis routes that focus on fuel rather than chemical grade production are

400

needed. Routes that proceed through an ethanol intermediate could produce a high-octane

401

component. Such bolt-on technologies for converting ethanol to a different high-octane bio-

402

blendstock is motivated in part by the infrastructure challenges ethanol faces and current

403

blending limits, which may be altered in the future.

404

Another key issue identified in this analysis is the uncertainty about the blending level of many

405

of these bio-blendstocks. Any oxygenate that had not been certified (isobutanol) or tested (e.g.,

406

methanol underwent limited testing) was noted as having unknown blending behavior. Higher

407

alcohols were expected to behave at least as well as ethanol, but if no testing had been

408

performed, a bio-blendstock received an unknown rating. Ongoing work within the Co-Optima

409

initiative will address these data gaps.

410

This analysis presented several challenges. First, the emerging nature, or the limited public

411

information, of the technology precluded a robust quantitative evaluation of the Co-Optima bio-

412

blendstocks. With time, technology maturation, and increased information disclosure, it will

413

become viable to increase the robustness and quantitative nature of this type of analysis. For

414

example, corn ethanol plants routinely participate in surveys that publish information regarding



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Page 26 of 32

415

yield and energy consumption18 and this may become the norm for other biorefineries over time.

416

Second, balancing the importance of technology readiness, economic, and environmental metrics

417

is a challenge, although some have developed methodologies to handle this balancing

418

quantitatively.19 The qualitative approach we adopted makes possible the identification of

419

options that are not likely viable, at least in the near term. For example, 2-methyl butanol

420

exhibited unfavorable SOT cost, ratio of target-to-SOT cost, target case Co-Optima bio-

421

blendstock carbon efficiency, and GHG emissions. This bio-blendstock is an inadvisable choice

422

for targeted efforts towards development in the near term. This method can also flag Co-Optima

423

bio-blendstocks with few barriers towards deployment, such as the methanol-to-gasoline route.

424

Future work will refine several of the process modeling, TEAs, and LCAs involved in this

425

screening process and consider alternative screening techniques. The current screening analysis,

426

however, was instrumental as a supplement to a physical-property-based screening of Co-Optima

427

bio-blendstocks, allowing the initiative to check for roadblocks that could arise in even the most

428

promising of blendstocks if only properties were considered. The current harmonized assessment

429

between fuel and engine developers and analysts yields a robust approach to identify/develop a

430

renewable transportation fuel that can potentially decrease the overall fossil energy consumption

431

and improve the environmental impact and economic viability of the transportation sector.

432

Acknowledgements

433

The research reported in this paper was sponsored by the U.S. Department of Energy (DOE)

434

Bioenergy Technologies Office (BETO) and Vehicle Technologies Office (VTO) under the DOE

435

Co-Optimization of Fuels and Engines Initiative. The authors gratefully acknowledge the support


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436

and direction of Alicia Lindauer at BETO, Kevin Stork at VTO, and the Co-Optima leadership

437

team. Furthermore, the authors acknowledge helpful discussions with Kristi Moriarty, Teresa

438

Alleman, and Bob McCormick of the National Renewable Energy Laboratory.

439

Abbreviations

440

BETO, Bioenergy Technologies Office; CC, catalytic conversion; DOE, U.S. Department of

441

Energy; EPA, U.S. Environmental Protection Agency; GGE, gasoline gallon equivalent; GHG,

442

greenhouse gas; GREET, Greenhouse gases, Regulated Emissions, and Energy use in

443

Transportation; LC, life cycle; LCA, life-cycle analysis; MTG, methanol-to-gatsoline; RON,

444

research octane number; SOT, state of technology; TEA, techno-economic analysis; TRL,

445

technology readiness level; VTO, Vehicle Technologies Office

446

447

Supplementary Materials

448

Fuel properties of bio-blendstocks considered in this study, biomass feedstock assumptions,

449

high-level process information for production of bio-blendstocks

450

References

451

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TOC/Abstract Graphic and Synopsis. Bio-blendstocks with physical properties indicative of an

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ability to increase the fuel economy of spark ignition engines were evaluated for their technology

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readiness and economic and environmental viability.

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Environmental, Economic, and Scalability Considerations and Trends

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