Life-Cycle Fossil Energy Consumption and Greenhouse Gas

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LIFE-CYCLE FOSSIL ENERGY CONSUMPTION AND GREENHOUSE GAS EMISSIONS OF BIODERIVED CHEMICALS AND THEIR CONVENTIONAL COUNTERPARTS Felix Adom, Jennifer B. Dunn, Jeongwoo Han, and Norm Sather Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es503766e • Publication Date (Web): 07 Nov 2014 Downloaded from http://pubs.acs.org on November 19, 2014

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Environmental Science & Technology

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LIFE-CYCLE FOSSIL ENERGY CONSUMPTION AND GREENHOUSE GAS

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EMISSIONS OF BIODERIVED CHEMICALS AND THEIR CONVENTIONAL

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COUNTERPARTS

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Felix Adom§, Jennifer B. Dunn§,*, Jeongwoo Han§, and Norm Sather§

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9700 S. Cass Avenue,

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Argonne, Illinois 60439, United States

§

Argonne National Laboratory

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*

Corresponding Author

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Phone Number: 01-630-252-9984

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Fax Number: 01-630-252-3443

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Email Address:

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[email protected]

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*

[email protected]

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[email protected]

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[email protected]

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Key words:

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Bioproducts, Greenhouse gases, Fossil energy consumption, Algal glycerol, Corn stover

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1 ACS Paragon Plus Environment


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ABSTRACT

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Biomass-derived chemical products may offer reduced environmental impacts compared to their

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fossil-derived counterparts and could improve profit margins at biorefineries when co-produced

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with higher-volume, lower-profit margin biofuels. It is important to assess on a life-cycle basis

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the energy and environmental impacts of these bioproducts as compared to conventional, fossil-

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derived products. We undertook a life-cycle analysis of eight bioproducts produced from either

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algal-derived glycerol or corn stover-derived sugars.

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readiness and market potential, the bioproducts are: propylene glycol, 1,3-propanediol, 3-

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hydroxypropionic acid, acrylic acid, polyethylene, succinic acid, isobutanol, and 1,4-butanediol.

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We developed process simulations to obtain energy and material flows in the production of each

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bioproduct and examined sensitivity of these flows to process design assumptions. Conversion

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process data for fossil-derived products were based on the literature. Conversion process data

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were combined with upstream parameters in the Greenhouse gases, Regulated Emissions, and

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Energy use in Transportation (GREETTM) model to generate life-cycle greenhouse gas (GHG)

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emissions and fossil energy consumption (FEC) for each bioproduct and its corresponding

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petroleum-derived product.

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compared to their fossil counterparts ranging from 39-86% on a cradle-to-grave basis. Similarly,

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FEC was lower for bioproducts than for conventional products.

Selected on the basis of technology

The bioproducts uniformly offer GHG emissions reductions

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INTRODUCTION

43 1–3

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One possible route to improve the challenging process economics

of producing hydrocarbon

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fuels directly from lignocellulosic feedstocks is to produce bioproducts alongside fuels as value-

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added biorefinery outputs. Another motivation to produce bioproducts is that they may be less

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energy- and emissions-intensive than their conventional, fossil-derived counterparts.

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potential benefits are important to assess holistically on a life-cycle basis.

These

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Several academic researchers, largely in Europe, have examined the question of the relative

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energy and environmental performance of bioproducts as compared to their conventional

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counterparts on a life-cycle basis. We summarize recent studies here with an emphasis on the

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life-cycle greenhouse gas (GHG) emissions they reported, although several of the studies also

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considered other metrics. Hermann and coworkers developed estimates of the life-cycle energy

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consumption and greenhouse gas (GHG) emissions of 12 bioproducts produced from sugars

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sourced from corn, sugarcane, or corn stover.4 Adopting process design heuristics to estimate

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energy consumed in different conversion process unit operations, they concluded that, when corn

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stover is the feedstock, bioproducts can be 25% to nearly 100% less GHG-intensive than their

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conventional counterparts. GHG savings for bioproducts produced from other feedstocks could

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also be quite high although the authors indicated that producing adipic acid and acetic acid from

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starch with today’s technology could be more GHG-intensive than producing these compounds

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via conventional routes. Lammens et al.5 assessed the life-cycle impacts of producing four

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compounds [N-methylpyrrolidone (NMP), N-vinylpyrrolidone (NVP), acrylonitrile (ACN), and

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succinonitrile (SCN)] from glutamic acid, which could be isolated from biorefinery by-products

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including vinasse or distiller’s grains with solubles. Biobased NMP and NVP had the potential 3 ACS Paragon Plus Environment


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to be approximately 50% and 35% less GHG-intensive than their conventional counterparts,

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respectively. On the other hand, bio-derived ACN and SCN were more GHG-intensive than

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conventional products unless Lammens et al. altered their process models to reflect more

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optimistic assumptions. Urban and Bakshi6 examined the effect of different system boundaries

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and included reliance on ecological resources in their life-cycle evaluation of 1,3-propanediol

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(PDO) produced from fossil sources and corn. These authors concluded that biobased 1,3-PDO

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was 46-71% less GHG-intensive than fossil-derived 1,3-PDO depending on the analysis

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approach (process versus hybrid) and co-product allocation technique. Finally, Cok et al.7

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examined succinic acid production from corn-derived dextrose by three different technologies,

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estimating cradle-to-factory gate non-renewable energy use and GHG emissions . These authors

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estimated that producing succinic acid from dextrose was about 90% less GHG-intensive than

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producing it from petroleum-derived feedstocks.

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Based on a high-level market analysis we conducted8, we selected eight bioproducts produced in

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a U.S. context either from algal glycerol or from corn stover-derived sugars and developed life-

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cycle GHG emissions and FEC results for these products and their fossil-derived counterparts.

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For the production of each bioproduct, we developed detailed process models in Aspen Plus.8

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Additionally, we adopted Argonne National Laboratory’s Greenhouse gases, Regulated

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Emissions, and Energy use in Transportation (GREETTM) model as the life cycle analysis (LCA)

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framework (see Figure S1). Our aims in conducting this analysis were, first, to assess whether

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bioproducts derived from biomass offer lower FEC and GHG emissions compared to their fossil-

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derived counterparts. Second, we sought to understand the drivers of the LCA results for these

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bioproducts. This study is intended to provide a consistent, detailed, and transparent evaluation


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of life-cycle impacts of a broad suite of bioproducts produced from emerging feedstocks in a

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U.S. context.

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METHODOLOGY

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In this section, we describe the methodology we adopted for selecting the bioproducts and

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feedstocks. Further, we explain our use of process modeling and the literature to develop

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material and energy flows for bioproduct and conventional production processes, respectively.

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The material and energy flows from both feedstock production and conversion were incorporated

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into GREET to generate bioproduct and conventional product LCA results. As such, the results

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rely on GREET background data for key process inputs such as natural gas, electricity, and

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agricultural inputs.9–12

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Bioproduct Selection Process

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Two landmark U.S. Department of Energy (DOE) bioproducts studies identified a number of

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promising biomass-derived chemicals considering technology advancements and market trends.

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These reports13,14 guided our selection of bioproducts. To select a subset of the compounds

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included in those reports for analysis, a market-based set of selection criteria was adopted. First,

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we assessed the number of companies currently active in developing technology to produce the

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bioproduct as an indicator of market readiness. Next, we evaluated the market flexibility of each

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bioproduct, counting the end-use products that could be produced from it. Finally, we collected

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market potential information, such as the current sales and projected sales growth rate for either

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the bioproduct or an end product obtainable from it. A separate report details the findings of this

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evaluation, which concluded with the selection of the bioproducts considered herein.8 5 ACS Paragon Plus Environment


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Feedstock

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For this analysis, we considered corn stover, which is an agricultural residue, and algae as

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feedstocks because they have minimal land-use impacts and are not subject to food versus fuel

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concerns. It is certainly possible that other cellulosic feedstocks (e.g., energy grasses, short

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rotation woody crops, purpose-grown trees, or municipal solid waste) could be used in place of

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corn stover. Similarly, glycerol could be produced from soybeans and oil crops. The GREET

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bioproducts module relies on existing GREET data for corn stover and algae feedstocks.12,15,16

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Figure 1 diagrams the production pathway for each selected bioproduct.

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Material and Energy Flows in the Production of Bioproducts

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Information about emerging technologies to convert glycerol and sugars to bioproducts is not

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generally publicly available, in part because these technologies are not yet widely used. As a

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result, we generated process models in Aspen Plus® that allowed us to estimate the material and

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energy intensity of each conversion pathway depicted in Figure 1. The key parameters used in

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these simulations (yield, reaction temperature, consumption of materials) were based on journal

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articles, technical reports and patents and supplemented with additional assumptions and

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engineering judgment. Process simulation parameters and results are documented in a recent

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Argonne technical report8 and summarized in the supporting information (SI) (Table S1).

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Conventional Products Process Data

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Material and energy flow data for conventional products were drawn from the literature. Data

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sources included journal articles, technical reports, and one industry report.17–22 For propylene


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oxide and adipic acid, we relied upon process simulation results from journal articles.19,20,23–25 In

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the case of propylene glycol, we were unable to find material and energy flow data and

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developed a process simulation to estimate them.

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methodology, assumptions, and GREET input data for each of the conventional products in a

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separate Argonne technical report8 but summarize key information in Table S2.

We detail data sources, calculation

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System Boundary Considerations

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FEC and GHG emissions are both reported on a cradle-to-grave basis, encompassing feedstock

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production, conversion, and end-of-life stages. A detailed description of the system boundary is

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provided in the SI (see Figures S2 and S3). We assume at end-of-life, both conventional and

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biobased products are disposed of in a landfill; no combustion for energy recovery occurs. Thus,

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we consider that the fossil energy embedded in the products is consumed. On the other hand,

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carbon within the products could be released as CO2 or CH4. The amount of CH4 emissions from

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landfilled products via anaerobic digestion, however, is uncertain and influenced by climate and

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landfill composition, among other factors. We therefore exclude CH4 emissions from landfilled

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products, and assume that all carbon within the product is released as CO2 eventually.

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It is possible that some products like polyethylene (PE) may take a very long time to degrade. If

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the time horizon for the analysis is less than this time to complete PE degradation, only a portion

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of the carbon within this product (bio- or fossil-derived) may reach the atmosphere. In this case,

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a portion of the carbon will remain stored in the products, and the bioproduct will essentially be

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credited with storing some of the biogenic carbon it contains, improving its GHG reduction as

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compared to the fossil-derived bioproduct.

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products as we have done therefore yields the worst-case result for bioproduct life-cycle GHG

Assuming complete degradation of landfilled



Pawelzik et al.17 discuss alternatives for characterizing bioproduct end-of-life CO2

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emissions.

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emissions and the impact of these assumptions on bioproduct life-cycle GHG emissions. The

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approach we have taken in our analysis, which gives bioproducts no credit for storing carbon, is

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similar to the GHG Protocol Initiative and ISO 14067 methodologies that Pawelzik et al.

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describe.

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RESULTS

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Material and energy flow data for the biomass-based and fossil-based conversion processes for

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the considered system boundaries are summarized in the SI (Tables S3-S6). In the subsequent

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sections, we present results for cradle-to-grave GHG emissions (Figures 2-5) for each bioproduct

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and its counterpart conventional product. Additionally, we summarize FEC results from our

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analysis in this section and provide further details in the SI (Figure S4-S7).

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Algal Glycerol to Bioproducts

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Figure 2 reports the life-cycle GHG results for the three bioproducts produced from algae as a

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feedstock. These figures show the contribution of each input to the bioproduct’s life cycle. In

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the case of PG and 1,3-PDO, we compare results for bio- and fossil-derived compounds; we did

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not examine production of 3-HP from fossil-derived feedstocks because there is no

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commercially-viable production route.26 Life-cycle GHG emissions of bio-derived PG and 1,3-

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PDO were more than 60% less than that of their fossil-derived counterparts (Figure 2). In the

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case of bio-derived PG, the net GHG emissions are attributable to consumption of algal glycerol

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(59%), NG (21%) and hydrogen (17%) needed for the hydrogenation reaction. The key drivers

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of the 1,3-PDO GHG emissions are also algal glycerol (47%), NG (21%) and dipotassium 8 ACS Paragon Plus Environment

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phosphate (29%) consumption in the conversion stage. The key contributors to GHG emissions

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for 3-HP are algal glycerol (75%), NG (16%) and dipotassium phosphate (8%), consumed during

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conversion.

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FEC results are summarized in the SI (Figure S4). We estimated that from cradle-to-grave, bio-

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derived PG and 1,3-PDO consume 62% and 60% less fossil energy, respectively, than their

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fossil-derived counterparts. Key drivers of FEC emissions are similar to those for GHG.

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3-HP to Bioproducts

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Approximately 39% and 53% reductions in GHG emissions were estimated for biobased 1,3-

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PDO and AA respectively, when compared to their fossil-derived counterparts (Figure 3). We

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estimated that 49% and 39% of 1,3-PDO life-cycle GHG emissions derive from consumption of

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3-HP and NG, respectively. The latter contribution stems from energy consumption during

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hydrogenation, a high-temperature process. Consumption of 3-HP (98%) and electricity (2%)

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are the key drivers of bio-derived AA life-cycle GHG emissions.

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Compared to its fossil fuel counterpart, bio-derived 1,3-PDO and AA consumes 24% and 58%

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less fossil energy on a life-cycle basis (Figure S5). Dominant contributors to GHG emissions are

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similar to those reported for FEC.

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Clean Sugars to Bioproducts

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All the bioproducts produced from clean sugars demonstrated a life-cycle GHG reduction as

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compared to their fossil counterparts (Figure 4). Adipic acid (ADP), a feasible substitute for

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succinic acid (SA), was chosen as the fossil-based product against which to evaluate biobased

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SA.

Approximately 84% of the GHG emissions of PE is attributable to consumption of 9 ACS Paragon Plus Environment


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bioethylene (62%) and NG (22%). Consumption of NG (61%) and clean sugars (23%) most

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strongly influence biobased SA GHG emissions.

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isobutanol’s life-cycle GHG emissions are about 47% lower than fossil-derived isobutanol. Key

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drivers for biobased isobutanol GHG emissions are consumption of NG (53%) and clean sugars

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(39%). These results reflect modeling the SA process with liquid liquid extraction (LLE) as the

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separations technology. In a sensitivity analysis (below), we explore the influence of separation

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technology choice on SA FEC and GHG emissions results.

Additionally, we estimated biobased

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The FEC for biobased PE,SA and isobutanol were 60, 76 and 26% less than that of their fossil-

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derived counterpart, respectively (Figure S6). Additionally, FEC trends (Figure S6) mirror GHG

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emissions trends (Figure 4). One key observation about the GHG results is that ADP production

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emits N2O, which contributes 34% of the life-cycle GHG emissions of this fossil-derived

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compound.

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Succinic Acid to 1,4-Butanediol

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Life-cycle GHG emissions of biobased 1,4-BDO, driven by consumption of SA (92%), are about

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52% less than that of fossil-based 1,4-BDO (Figure 5). Consumption of other inputs such as

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electricity and NG contribute the remaining 8% of life-cycle GHG emissions.

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Figures S7 show approximately a 46% reduction for FEC for bio-derived 1,4-BDO compared to

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production of this chemical from fossil sources.

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predominant contributor to FEC for this pathway is the consumption of biobased SA (91%).

Similar to GHG emissions trend, the

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Sensitivity Analysis 10 ACS Paragon Plus Environment

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We conducted sensitivity analyses to examine how yield, plant capacity, and choice of

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separations technology influence LCA results for the bioproducts. Table 1 summarizes the

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various scenarios investigated; the corresponding results are displayed in Figure 6. Previously

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reported results for the baseline scenarios (Figures 2-5 and Figures S4-S7) are included in Figure

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10 for comparison. Production of fossil-derived isobutanol consumes syngas, which can be

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produced from a number of sources. In a sensitivity analysis, we estimated the FEC and GHG

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emissions of fossil-derived isobutanol when syngas is produced from coal rather than NG, the

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syngas feedstock in the baseline scenario. The life-cycle FEC and GHG emissions of fossil-

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derived isobutanol are relatively insensitive to syngas feedstock, increasing by just 2 MJ/kg and

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0.4 kg CO2e/kg, respectively, when the syngas feedstock is carbon-intensive coal. The selection

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of plant capacity can influence conversion stage energy consumption and therefore LCA results.

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We examined the influence of our assumption of 3-HP throughput in our simulations of biobased

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AA production. The base and sensitivity case 3-HP throughputs (30 wt% 3-HP) were 69,400

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and 1,000 kg/hr respectively. FEC and GHG emissions were modestly lower at the higher 3-HP

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throughput.

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Another key assumption made in developing simulations of biobased processes was the choice of

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separation technology. We examined the influence of this assumption in simulations of 3-HP

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production. The baseline simulation uses distillation. In the sensitivity case, we exchanged

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distillation for electrodeionization (EDI). FEC consumption and GHG emissions were two to

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three times higher in the latter case. More detailed information about the energy consumed in

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EDI for specific processes will help improve simulations that adopt this technology.



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We conducted detailed sensitivity analyses of the simulations we developed for production of

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1,4-BDO, exploring the impact of yield, plant capacity and two different process design

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configurations. The two 1,4-BDO yields considered were (27 and 70 wt%).27,28 Figures S8 and

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S9 (in the SI) display the unit operations in the 1,4-BDO process under PC-1 and PC-2,

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highlighting the three key differences between them. In PC-1, a single three-phase separator

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removes moisture prior to product separation. In PC-2, two two-phase separators were used.

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The second difference is the number of distillation columns used. Three distillation columns are

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incorporated in PC-1; four are used in PC-2. Finally, residual glycerol after the hydrogenation

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reaction was crystallized out of the product stream and subsequently subjected to distillation to

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recover 1,4-BDO and gamma-butyrolactone (GBL) in PC-1 (Figure S8). In contrast, in PC-2,

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the product stream was distilled to recover GBL before crystallization to recover residual

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succinic acid crystals and 1,4-BDO (Figure S9). On average, PC-1 minimizes total energy

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consumption in the reboilers of the distillation columns by more than 80%, reducing energy

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consumption. Additionally, the 1,4-BDO scenario with the highest yield resulted in the highest

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GHG and FEC savings. These results demonstrate that yield and efficient process design are

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critical factors that can influence the FEC and GHG impacts of bioproducts production.

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The sensitivity analysis for SA production also examined the influence of separations

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technology. The baseline technology was LLE. In the sensitivity case, we used EDI. This

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change increased life-cycle FEC and GHG emissions by 38% and 47%, respectively.

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DISCUSSION

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Bioproducts, compared to their fossil-based counterparts, exhibited cradle-to-grave GHG

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emissions reductions ranging from 39% (1,3-PDO from 3-HP) to 86% (succinic acid) in the

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baseline scenarios we considered. Conversion process NG consumption emerged as a significant

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factor in cradle-to-grave GHG emissions for bioproducts, constituting between 16-61% of the

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life-cycle GHG emissions for six of the bioproducts (PG, 3-HP, SA, isobutanol, PE and 1,3-

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PDO). Comparing the conversion technology and LCA results for 1,3-PDO production from

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glycerol and 3-HP exemplifies the significance of conversion process NG consumption. As a

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result of the relatively harsh hydrogenation conditions, the 3-HP-based pathway to 1,3-PDO

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consumes three times more NG than the algal glycerol-based pathway. Both FEC and GHG

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emissions are approximately two times lower for the latter pathway. Reduction of process

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energy consumption is then one key opportunity to reduce energy and environmental impacts of

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bioproducts. It is important to note that, while our process simulations include heat integration,

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actual facilities may be able to enhance heat integration and further drive down NG

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consumption. Feedstock consumption was the key contributor to life-cycle GHG emissions for

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algae-derived compounds (PG, 1,3-PDO, AA). Reducing the energy and emissions burdens of

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algae production29 and increasing yields would be effective routes towards reducing the energy

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and environmental burdens of these products.

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emissions for all chemical compounds analyzed in this study are summarized in Table S7.

Results for cradle-to-grave FEC and GHG

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Published LCA results for bioproducts in the public literature are sparse. Results for the same

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compounds we considered were identified in the literature and compared with our results (Table

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2). Although the feedstocks and modeling tools in the benchmark studies are different from

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those we used, the results are comparable between this and existing studies. The key highlight

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from Table 2 is that bioproducts made from corn stover and algae feedstocks, second and third 13 ACS Paragon Plus Environment


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generation feedstocks, respectively, have the potential to match the fossil energy and GHG

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reduction potential of bioproducts made from first generation feedstocks, such as corn and

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soybeans despite less mature conversion technologies for these emerging feedstocks.

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Comparing the relative impact of 3-HP and clean sugars as raw materials (platform chemicals in

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Figure 1) reveals that 3-HP contributes about twice as much to life-cycle FEC and GHG

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emissions of the final bioproduct. Two factors are responsible for this trend. First, the clean

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sugars-to-bioproducts pathways generally have relatively higher conversion process yields.

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Second, clean sugars are less FEC- and GHG-intensive than 3-HP (Tables S7). Biorefineries

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producing clean sugars can combust residual lignin, generate power, and displace grid electricity.

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The clean sugars then receive a displacement credit based on the FEC and GHG intensity of the

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U.S. average grid (Table S8). 3-HP receives no such credit.

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Key Sources of Uncertainty

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We tapped the best public information available to us to obtain the material and energy flow data

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that underpin the calculations in our analysis. It is important to remember, however, that the

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current state of technology for bioproducts may be different than the public literature suggests.

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Additionally, data for conventional products based on process simulations or engineering

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estimates provide approximations of energy and environmental burdens for producing these

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compounds, but it should be a priority to continue to seek out or build more refined analyses and

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data.

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fermentation microorganisms, which are currently excluded. It is suspected, however, that the

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contribution of the production of these fermenting organisms may be minimal based on analyses

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of corn and cellulosic ethanol life-cycle impacts.30,31 LCA of catalysts is an emerging area of

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research, but some biofuel LCAs suggest that catalyst consumption is a minor contributor to

The analysis could also be expanded to include process inputs of catalysts and


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results32. Finally, the metrics considered could be expanded beyond FEC and GHG emissions to

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air emissions and water consumption, for example.

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Overall, our analysis illustrates that, for the most part, production of key compounds from

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biomass offers FEC and GHG emissions savings compared to conventional, fossil-based

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production of these same compounds.

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depending on the bioproduct pathway. One important observation was the significant impact of

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NG use on the GHG emissions (21-61%) and FEC (26-67%) in a majority of bioproducts

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pathways. Finally, bioproducts made from algal glycerol and corn stover-derived sugars could

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offer reduced FEC and GHG emissions compared to those made from first generation feedstocks

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such as corn grain and sugarcane.

Key drivers for FEC and GHG emissions varied

338 339

Using GREET as a consistent, transparent platform for the analysis of this suite of bioproducts

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and their conventional counterparts enables the identification of important trends in the results

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that are not a result of methodology or data source differences. The bioproducts module in

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GREET is publicly available and allows users to edit existing bioproduct pathways and analyze

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new pathways.

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As the technology to convert second and third generation feedstocks to bioproducts emerges, it is

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essential to continue to update and refine the material and energy flow data for production of

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bioproducts and conventional products that underpin LCA of these compounds. One example of

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a potential model refinement is the incorporation of waste water treatment into the process

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models. Moreover, it is important to apply carbon accounting consistently and transparently to

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bioproducts and fossil products to which they are compared. In this analysis, we used a uniform 15 ACS Paragon Plus Environment


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assumption that all carbon would be emitted at end of life as a worst-case scenario. This

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assumption is helpful because some of the bioproducts we examined (e.g., PE) are final products

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with relatively well-defined end uses and life spans whereas other products are intermediates en

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route to an uncertain final use. Alternative choices, such as complete or partial sequestration of

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biogenic carbon in bioproducts could improve comparative LCA results with fossil-derived

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products.

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ASSOCIATED CONTENT

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Supporting Information

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Detailed methodology for process simulation development as well as material and energy flow

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data for bioproducts and conventional products are presented in the supporting information.

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AUTHOR INFORMATION

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Corresponding Author

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* Phone: 630.252.4667; fax: 630-252-3443, email: [email protected]

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Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGEMENTS

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This work was supported by the Bioenergy Technologies Office (BETO) of the Office of Energy

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Efficiency and Renewable Energy of the United States Department of Energy, under contract

371

DE-AC02-06CH11357.

372

concerning the selection of bioproducts of interest and John Molburg of Argonne for assistance

373

with Aspen Plus modeling. Additionally, Travis Tempel, Alicia Lindauer, Kristen Johnson,

We acknowledge Gene Petersen of NREL for helpful discussions


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Michael Wang and Seth Snyder have provided support and guidance. This information is

375

available free of charge via the internet at http://pubs.acs.org/

376



377

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463

List of Figures

464

Figure 1: Platform chemicals and bioproducts selected for analysis (3-HP and Succinic Acid are

465

both considered platform chemicals and bioproducts in our analysis)

466 467

Figure 2: GHG emissions for glycerol to bioproducts pathways

468 469

Figure 3: GHG emissions for 3-HP to bioproducts pathways

470 471

Figure 4: GHG emissions for clean sugars to bioproducts pathways

472 473

Figure 5: GHG emissions for succinic acid to bioproducts pathway

474 475

Figure 6: Cradle-to-grave FEC and GHG emissions obtained from various sensitivity analyses

476


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477 478

Figure 1: Platform chemicals and bioproducts selected for analysis (3-HP and Succinic Acid are

479

both considered platform chemicals and bioproducts in our analysis)

480 481



482 483

Figure 2: GHG emissions for glycerol to bioproducts pathways

484 485


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Page 23 of 30


486 487

Figure 3: GHG emissions for 3-HP to bioproducts pathways

488



489 490

Figure 4: GHG emissions for clean sugars to bioproducts pathways

491


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492 493

Figure 5: GHG emissions for succinic acid to bioproducts pathway

494



495 496

Figure 6: Cradle-to-gate FEC and cradle-to-grave GHG emissions obtained from various

497

sensitivity analyses

498


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499


List of Tables

500 501

Table 1: Sensitivity analysis scenarios (PC: Process design Configuration)

502 503

Table 2: Comparison of LCA results from the literature and from this study

504 505



Table 1: Sensitivity analysis scenarios

506 507

Bioproduct

508

Scenario Bioproduct baseline Fossil baseline Isobutanol Fossil-S1 Bioproduct baseline Bioproduct-S1 AA Fossil baseline Bioproduct baseline 3-HP Bioproduct-S1 Bioproduct baseline Bioproduct-S1 Bioproduct-S2 1,4-BDO Bioproduct-S3 Fossil- baseline Bioproduct baseline Bioproduct-S1 SA Fossil- baseline a. PC: process configuration

Comments Bio-derived isobutanol NG-derived syngas Coal-derived syngas 69,400 kg 3-HP/hr. 1,000 kg 3-HP/hr Fossil-derived acrylic acid Distillation Electrodeionization (EDI) 6,500 kg SA/hr; 70 wt% yield, PCa-1 6,500 kg SA/hr; 27 wt% yield, PC-1 1,000 kg SA/hr; 27 wt% yield, PC-1 6,500 kg SA/hr; 27 wt% yield, PC-2 Fossil-derived 1,4-BDO LLE EDI Fossil-derived adipic acid

509


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Table 2: Comparison of LCA results from the literature and from this study

510 511

FEC : Cradle-to-Grave Bioproduct

Propylene Glycol

Feedstock

GHG Emissions: Cradle-to-Grave

MJ/kg

% Reductiona

kgCO2e/ kg

% Reduction

-

-

3.2

61%

35

62%

1.1

66%

Corn

41-50

55-68%

2.7-3.5

46-71%

Corn

22-58

37-55%

1.2-2.9

37-55%

Sugar Cane

-6.6

78-103%

-1.8

62-115%

Glycerol 3-HP

35 120

60% 24%

2.7 5.3

64% 39%

Soybean & Canola Glycerol

References

33

This study 6

4

1,3-PDO

4

This study This study 4

Corn starch

34

34%

2

43%

Corn stover

18

66%

1.2

66%

Sugar cane

4.4

91%

0.7

80%

3-HP

49

58%

4.1

53%

33

74%

0.88

90%

49

60%

1.7

81%

45

64%

1.5

83%

4

Acrylic Acid

4

This study 7

Corn starch Succinic Acid

512

7

7

Corn 4 Stover 20-42 44-74% 1.3-2.4 64-81% Clean Sugars 28 76% 1.9 86% This study a % Reduction as reported in individual studies assuming bioproduct ends in a landfill

513 514



ACS Paragon Plus Environment

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Life-Cycle Fossil Energy Consumption and Greenhouse Gas

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