The Future of Drop-In Fuels

Arpad Horvath, Corinne Scown, Michael Taptich, Kate Piscopo

University of California, Berkeley

December 19th, 2016

Project Team Arpad Horvath, PI

Professor Department of Civil and Environmental Engineering

University of California, Berkeley

Corinne Scown Research Scientist

Energy Technologies Area Lawrence Berkeley National Laboratory

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Michael Taptich Graduate Student Researcher

Department of Civil and Environmental Engineering University of California, Berkeley

Kate Piscopo

Graduate Student Researcher Department of Civil and Environmental Engineering

University of California, Berkeley

Project Objectives 1) Review the literature to gather existing information related to

renewable drop-in fuels. » Establish if data are available for life-cycle assessments of various technology

pathways and their related costs and environmental impacts.

2) Analyze technology, feasibility, costs, and environmental impacts at both demonstration and commercial scale.

» Estimate where facilities could potentially be located in order to maximize production while minimizing environmental impacts.

3) Identify additional areas of research to facilitate the growing need for data related to technological advancement, costs, and environmental impacts.

4) Identify barriers to the success of these technologies, and where applicable, strategies to overcome these barriers.

5) Develop a strategy to monitor and track progress of these technologies, as well as supplies and costs.

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TASK 1: REVIEW OF LITERATURE AND ONGOING LABORATORY RESEARCH

The Future of Drop-In Fuels

Petroleum & Alternative Fuel Use in California

5 2014, Higher Heating Value, Source: EIA 2014

Review: Fuels of Interest

Diesel (size ~ C12-20) » 75% saturated hydrocarbons – paraffins, etc. » 25% aromatic hydrocarbons

Gasoline (size ~ C4-12) » 55% paraffins » 25% aromatic hydrocarbons » 10% each of cycloparaffins & olefins

Jet fuel (Jet A or Jet A-1) (size ~ C8-16) » 80% saturated hydrocarbons – paraffins, etc. » 20% aromatic hydrocarbons

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General Findings on Drop-in Fuels

Two types of drop-in fuels: » Bio-based crude that can be processed alongside

conventional crude in a petroleum refinery (preferred) » Finished product that is compatible with existing infrastructure

and engines The term “drop-in” is used liberally, even when fuel can

only be blended at limited fraction Just because a process results in hydrocarbons doesn’t

mean it is a 1:1 replacement for a fuel Some fuels may be drop-in if supplemented with the

necessary additives Challenge: new fuels are typically produced at volumes

too small (mL) for required properties and engine testing 7

What is Drop-In Diesel?

Fuel production researchers tend to focus on C number

Important characteristics: » Cetane number » Energy content » Density » Lubricity » Cold-flow properties » Sulfur content » Stability

Need fuel testing to confirm whether fuel is potentially “drop-in”

8 Source: Chevron http://www.chevronwithtechron.ca/products/documents/Diesel_Fuel_Tech_Review.pdf

What is Drop-In Gasoline?

Fuel production researchers focus on C number

Important characteristics: » Octane number » Stability » Energy content » Density » Sulfur content » Vapor pressure

9 Source: Chevron http://www.chevronwithtechron.ca/products/documents/69083_MotorGas_Tech_Review.pdf

Variation in Feedstock Composition

C6 polysaccharides

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C5 polysaccharides Polyaromatic Polysaccharide-to-lignin ratio generally higher for herbaceous biomass

Importance of Composition

Ratio of polysaccharides to lignin should play into pathway choice for feedstocks

Old industry saying: “You can make anything from lignin except money”

Majority of synthetic vanillin used to be made from lignin (black liquor), but market share has fallen

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Short-term: Harsh processes convert at least some lignin to useable fuel

Long-term: Targeted breaking of bonds via biological or catalytic routes offers more flexibility, better yields

Dauenhauer video of lignin pyrolysis: https://www.youtube.com/watch?v=E1pq2lg-lkI

Overview of Pathway Types

Biological: Pathways that begin with sugars, sourced from either sugar, starch, or biomass feedstocks, and utilize host microbes to produce final fuels.

Hybrid biological/chemical: Pathways that begin with sugars, sourced from either sugar, starch, or biomass feedstocks, and utilize host microbes to produce fuel precursors that are converted through catalytic processes to final fuel products.

Chemical: Pathways that begin with sugars, sourced from either sugar, starch, or biomass feedstocks, or lipids, and utilize purely chemical routes to producing fuels. Furan pathways that convert five-carbon sugars to furfural, and ultimately to fuels are an example, as are renewable diesel pathways.

Thermochemical: Pathways that use high-temperature processes such as pyrolysis or gasification to produce fuel mixtures.

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or no additives

More options for diesel

Biological pathways are useful jumping-off point

Focus on Thermochemical Pathways

Although yields and blending limits may vary, thermochemical pathways are generally closer to commercialization

Biological and hybrid biological-catalytic routes offer more precision/control and may be a better long-term solution

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Pathway: Pyrolysis

Fast pyrolysis of biomass to bio-oil » Rapid heating of biomass in the absence of oxygen

to temperatures of 400 - 600°C to thermally decompose the biomass

» Products include light gaseous hydrocarbons, solid char, and a mixture of oxygenated hydrocarbons referred to as pyrolysis oil, or bio-oil.

Hydrotreating the bio-oil to drop-in gasoline and diesel

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Pyrolysis

Data source: PNNL (2013)

Pathway: Gasification/F-T

Relatively mature process, but less so for biomass applications

Gasification of biomass to CO and H2 » Occurs in presence of oxygen and higher

temperatures than pyrolysis: 800°C Water-gas shift reaction used to adjust H2:CO

ratio to optimal point for F-T reaction (2.1:1 ) F-T reaction grows diesel chains:

» CO + 2.1H2 → --(CH2)-- + H2O 17

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Gasification/Fischer-Tropsch

Pathway: Gasification/MTG

Also relatively mature process, demonstrated at commercial scale in New Zealand, several plants under construction in U.S.

Scrubbed syngas enters steam reforming step, (800 - 900°C), adjusts H2:CO ratio to 2:1

Methanol synthesis reactor Methanol partially dehydrated to dimethyl ether

(DME), then DME converted to olefins, and then aromatics and paraffins

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Gasification/MTG

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TASK 3: SCALE-UP SCENARIO METHODS

The Future of Drop-In Fuels

Scale-Up Scenario Objectives & Constraints

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Components of the California Drop-In (CAdi) Fuel Logistics Model

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Distribution of CA

Biomass

• Forest residues: 44% of CA total biomass

• Forest residues & primary mill wastes concentrated in NCM

• Crop residues in the CV region

• Urban wood & secondary mill wastes in the CCS

• Herbaceous: 26%, Woody biomass: 74% of total

Potential Biorefinery Locations

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Red: new development for drop-in fuels Black: petroleum refinery Orange: biodiesel refinery Green: ethanol refinery

Bulk Fuel Terminals & Fuel Demand Service Areas

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• Terminal: storage facility used primarily for petroleum products with total bulk storage capacity >= 50,000 barrels

• In absence of storage capacity data, terminal storage based on county-level retail fuel sales, allocated to tracts based on population, reallocated to terminals based on minimum travel distances by truck.

TASK 2: LIFE-CYCLE COST AND ENVIRONMENTAL ASSESSMENT DATA GAP ANALYSIS

The Future of Drop-In Fuels

Pathway Yields

28 Values rounded to two significant figures, MT = metric ton, GGE = gallon of gasoline equivalent

Energy Demand by Pathway

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Values rounded to 2 significant digits, GGE = gallon of gasoline equivalent, NG = natural gas, off-gases = noncondensable light hydrocarbons emitted from unit processes

Nth Plant Assumptions

30 *MACRS = Modified Accelerated Cost Recovery System

Cost Assessment Summary

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Based on 2000 metric tons/day plant size Notable inconsistencies in literature:

» Construction period: Pyrolysis and the FT pathways assumed a 3-year construction period. MTG assumes a 2.5-year construction period.

» Plant life: MTG and FT assume a 20-year plant life while the pyrolysis pathway costs were calculated assuming a 30-year plant life

All based on Aspen modeling, so cannot verify these assumption until more commercial-scale facilities are built and operated

Values rounded to two significant digits. GGE = gallon of gasoline equivalent. All values are in 2014 dollars.

GHG Emission Factors

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GHG Emissions Comparison

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Yield & Emissions by Pathway

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Not including electricity credits FT and MTG Pathways have 0 GHG emissions due to steam cycles and combustion of biogenic carbon. Error bars represent the range in yields found in the literature. GGE = Gallon of gasoline equivalent, MT = metric ton

GHG Emissions Using System Expansion

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Negative values for FT and MTG pathways are due to offset credits for net electricity exports, using system expansion. Base case assumes system expansion to CAMX grid, lower bound of error bars assumes system expansion to WECC grid, and upper bound assumes system expansion to offset straight natural gas.

CAP Results:

Pyrolysis

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CAP Results: Fischer-Tropsch

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CAP Results:

MTG

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Pathway CAP Results

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Error bars represent different energy offset for system expansion allocation, in addition to variations in yield and emission factors: lower bound = WECC, upper bound = natural gas, baseline value = CAMX

Pathway Water Results

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TASK 3 RESULTS The Future of Drop-In Fuels

Scenario 1A: Scale-Up Scenario Modeling Results

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Scenario 1B: Maximize Diesel

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Scenario 2: Maximize Fuel Output

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Scenario 3: Incentivize Only New Growth

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Scenario 4: Incentivize Only Co-Location

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Scenario 5: Incentivize Distributed Growth

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Scenario 6: Require Equal Blending

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Annual Metric ton-km by Supply-Chain Segment and Transport Mode

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Changes in Emissions from 2015 Baseline

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Well-to-Pump Emissions

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Well-to-Pump Emission Factors

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TASKS 4 & 5: IDENTIFICATION OF RESEARCH NEEDS AND IDENTIFICATION OF POTENTIAL BARRIERS

The Future of Drop-In Fuels

Key Issues

Fuel yield & hydrogen requirements Sugar utilization Clean sugar stream requirements Co-products Biocrude Compatibility with Petroleum Refineries Engine and System Compatibility Well-to-Wheel Criteria Air Pollutant Emissions Potential Production Scale

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TASK 6: DEVELOPING A STRATEGY TO MONITOR AND TRACK PROGRESS WITH DROP-IN FUELS

The Future of Drop-In Fuels

Sharing Data and Insights

We have documented our scenarios analyses, including three thermochemical pathways, in a wiki, freely available through github, where anyone can access the model and alter parameters to generate new results. This platform provides an opportunity for feedback and suggested changes.

As these pathways develop, yields, emission factors, and other inputs can be changed to generate up-to-date results. The URL is: https://github.com/mtaptich/California-Drop-In-CAdi-Fuel-Model/tree/master/docs

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Contact Information: Arpad Horvath

Professor Department of Civil and Environmental Engineering

University of California, Berkeley horvath@ce.berkeley.edu

Tel.: 510-642-7300

Corinne Scown Research Scientist

Energy Technologies Area Lawrence Berkeley National Laboratory/Joint BioEnergy Institute

cdscown@lbl.gov Tel.: 510-486-4507

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Questions?

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Backup Slides

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California Feedstock Availability

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Region North Coast & Mountain Central Valley Central Coast & Southern Label cropres = crop residue forestres = forest residue primmill = primary mill waste secmill = secondary mill waste

Data sources: CBC 2015, NREL 2014

Municipal Solid Waste Makeup

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Backup: Model Assumptions

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• Drop-in fuels are perfect substitutes for conventional gasoline, diesel, and marine fuels. (e.g., there is no “blend wall.”)

• Drop-in fuels displace only fuels sold for use in California. • Demand for fuel is fixed, estimated at an annual level, and proportional to population density. • Intermodal terminal exchanges and fuel storage activities have negligible impacts on optimizing transport and

shipment of fuels. • The locations of bulk fuel terminals (total bulk storage capacity of 50,000 barrels or more) are fixed and no additional

pipelines are constructed to move drop-in fuel around the state. Therefore, isolated biorefineries would need trucks and/or trains to move fuel to regional bulk terminals or send their fuels to refineries to ship to terminals via pipelines. The directionality of pipelines is not considered.

• Information regarding the local connections between petroleum product pipelines and bulk terminals is limited in availability. We assume that pipeline-terminal transfers are carried out using 8” pipelines and are situated such that the transport distances are minimized.

• Biorefinery capacity can be designed across a continuum, such that the optimization could be solved as the relaxation of a mixed integer facility location problem.

• Emissions from feedstock collection, transport, and fuel production scale linearly with biomass quantities. • As a means of reducing the dimensionality of our optimization model, which is discussed in a later section, we

combine the mill and urban wood wastes into a single category deemed scrapwood. • Facilities are assumed to support more than one biomass-to-biofuel pathway (e.g., by building two different

biorefineries next to each other in close proximity). • Emissions associated with storage are ignored (e.g., initial biomass handling, evaporative emissions from refined

products, etc.).

Backup: Model

Variables

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Emission Factors for Biomass Handling

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• Corn stover used as proxy for all crop residues. • Wood wastes: biomass is ground into course material using a

hammermill grinder, which requires a direct electricity input of 77 kWh per dry ton

Well-to-Wheel Emission Factors for Freight Modes

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• Heavy-duty truck EFs based on CA In-State Class-8 truck (model year: 2012) using California Air Resources Board’s EMission FACtors (EMFAC) model

• 20.5 MJ/km operational fuel used to calculate the well-to-pump emissions associated with this mode using CA-GREET

• Average of 24.1 metric tons of payload, 50% of the kilometers driven are empty.

• Locomotives achieve the fleet-average fuel economy of 1,132 gross metric ton-km per gallon, hauls an average of 3,500 metric tons/train

Pipeline Emission Factors

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• Assumed the average pipeline diameter across the distribution network was 8”

• Energy demand: 71 kJ/tkm (electricity) • GIS data for the petroleum product pipeline network data from the

U.S. Energy Information Administration used to determine distances traveled

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CAP Assumptions

Mode-Shares by Segment

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