A Systems Approach to Bio-Oil Stabilization FINAL

•Center for Sustainable Environmental Technologies

A Systems Approach to Bio-Oil Stabilization

February 17, 2011Bio-Oil Conditioning and Upgrading Platform

Robert BrownIowa State University


Goal Statement

• Develop practical, cost effective methods for stabilizing biomass derived fast pyrolysis oil for a minimum of six months of storage under ambient conditions– Reduce oxygen content of organic compounds– Remove carboxylic acid groups– Reduce charcoal content

2


Quad Chart Overview

• Project start date: 9/30/08• Project end date: 9/30/11• Percent complete: 75%

• Tt-E: Pyrolysis of Biomass

3

• Total funding: $2.1M‒ DOE share: $1.5M‒ Cost share: $0.6M

• FY09 funding: $1.3M‒ DOE share: $1.0M‒ Cost share: $0.3M

• FY10 funding: $0.8M‒ DOE share: $0.5M‒ Cost share: $0.3M

Timeline

Budget

Barriers

• ConocoPhillips CompanyPartners


Project Overview• Research includes stabilization techniques suitable for both

centralized and distributed processing facilities– Biomass pretreatments– Bio-oil vapor filtering– Fractionating condensation– Catalytic post treatment

• Stabilization techniques demonstrated individually and in combination

• DOE accelerated aging method used• Aged samples compared using both standard and alternative

characterization methodologies– Solids content– Total Acid Number (TAN) and Modified Acid Number (MAN)– Viscosity and molecular weight distribution

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Approach• Biomass pretreatment

– Water washing, dilute acid washing, and acid infusion

• Hot vapor filtering– Use moving bed filter to remove fine char particles

• Fractionating bio-oil recovery– Use ISU’s proprietary bio-oil recovery system to produce distinctive

fractions of oil and remove undesirable compounds

• Catalytic post-treatment– Develop heterogeneous catalysts capable of simultaneous hydrogenation

and esterification of post-production bio-oil

• Optimization and stability testing– Use laser diagnostics to optimize hot vapor filtration and bio-oil collection– Use computational fluid dynamics to understand transport processes– Characterize bio-oil stability using appropriate testing methodology

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Technical Accomplishments – Task 1

• Hydrochloric, nitric, sulfuric, phosphoric, acetic and formic acids were used for infusing switchgrass.

• Using the micropyrolyzer/GC/MS, it was found that phosphoric and sulfuric acids are very effective in reducing the yield of light oxygenates and increasing the yield of anhydrosugars.

• These pretreatments were down-selected for bench scale pyrolyzer trials.– Preliminary trials have generated so much levoglucosan

from carbohydrate that it cannot volatilize fast enough from reactor to avoid carbonizing

6Task 1 – Role of Biomass Pretreatments

Pretreatment of Biomass


Py/GC/MS Results of Acid Infused Feedstocks (2 wt% acid)

7

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

Light Oxygenates Anhydrosugars Furans Phenols

wt%

of b

iom

ass (

wet

bas

is) Switchgrass Control

Acetic AcidFormic AcidNitric AcidHydrochloric AcidPhosphoric AcidSulfuric Acid


Task 1 – Role of Biomass Pretreatments



8Task 2 – Hot Vapor Filtering of Particulate Matter

Moving Bed Granular Filter to Remove Char

• Experiments completed with a cold-flow moving bed granular filter (MBGF)

• A hot flow MBGF has been constructed and installed on the pyrolysis process development unit (PDU)

• Two preliminary filtration tests have been completed with the pyrolysis PDU/MBGF system Hot flow MBGF

(BioCentury Research Farm, ISU

MBGF Cross-Sectional View


Technical Accomplishments – Task 2• In cold-flow tests, the MBGF demonstrated >99% filtration efficiency• In preliminary hot-flow tests, the MBGF reduced particulate in bio-oil

fractions by as much as 75%. • Slight reduction in oil yield for SF 1 & 3; increase in yield for SF 5 probably

water associated with higher char yield.

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0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

SF1 SF2

Wei

ght P

erce

nt

Bio-oil Stage Fraction

Bio-oil Solids Content with and without filtration

(GR8, GR16: granular flow rates of 8 kg/h and 16 kg/h, respectively; PDU: unfiltered)

GR8

GR16

PDU

Task 2 – Hot Vapor Filtering of Particulate Matter

0

5

10

15

20

25

30

Char SF1 SF2a SF2b SF3 SF4 SF5 NCG

Perc

ent Y

ield

Bio-oil Stage Fraction

Fast Pyro Yield (%)

MBGF Yield (%)

Bio-oil yield with and without MBGF



• Recovers bio-oil as five distinctive fractions– Heavy ends (lignin oligomers and

oligosaccharides)– Light ends (acetic acid and other

carbohydrate decomposition products)

Fractionating Bio-Oil Recovery

Biomass Feeder

Pyrolysis Reactor

Particulate Filtration

Stage Fraction 1

Stage Fraction 2

Stage Fraction 3

Stage Fraction 4

Stage Fraction 5

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0

10

20

30

40

50

60

70

80

Red Oak

Mod

ified

Aci

d N

umbe

r (m

g KO

H/g

Bio

-Oil) SF5

SF4

SF3

SF2

SF1

Technical Accomplishments – Task 3• Modified acid number (MAN) – removes contribution of phenolics

to acidity of bio-oil (similar to SCAN used by petroleum industry)• MAN correlates with acetic acid content (b.p. 118° C) • Over 70% of acetic acid concentrated in SF 4 and 5• Corrosion in distillation units associated with high temperature

condensation of naphthenic acids (b.p. 200-400° C) • Acetic acid would remain vapor in distillation columns

11Task 3 – Fractionating Recovery of Vapors and Aerosols

Red Oak Pyrolyzed at 500°Cy = 8.9427x + 16.353

R² = 0.9616

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

0 5 10 15

MA

N (m

g K

OH

/gra

m o

f Bio

-Oil)

Acetic Acid Content (wt%)

MAN vs Acetic Acid

SF1

SF2

SF3

SF4

SF5

Linear (MAN vs Acetic Acid)

Generic naphthenic acid

Light blue indicates areas of naphthenic acid corrosion (http://www.aiche-chicago.org/symposium06/rechtien.pdf)


• Goals– Enable In situ assessment of aerosols, particulates, and vapors prior to and after hot

vapor filtration and bio-oil recovery– Provide information on filtration and condensation efficiency and speciation for

model validation and to improve pyrolysis reactor design• Approach

– In-situ laser-based measurements to detect and differentiate between aerosols and particulates in filters and condensers

• Unique Aspects– Diagnostics for in-situ detection of multiple phases (i.e, aerosols, particulates, and

vapors) in pyrolysis reactors currently not available– New diagnostic tools to be employed for hot vapor filtration and bio-oil recovery in a

pyrolysis reactor, as well as future related research– Results will help bridge gap between fundamental and applied models


Task 4 – Laser Diagnostics for Vapor Filtration and Bio-oil Recovery

Optimization: Laser Diagnostics of Transport Phenomena

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• Progress– Laser measurements used to capture aerosol and particle filtration as a

function of filter bed characteristics to guide model development– Relations developed for bed depth, filtration efficiency, and filtration

coefficient– Optical insertion rig and instrumentation to study hot vapor filtration in

pyrolysis reactor constructed• Significance

– Filtration mechanisms for predictive modeling require guidance and validation

– Results using 20 𝜇m monodisperse particulate display qualitative filtration efficiency trends as seen in the model

– Testing of multiple diagnostics (scattering and incandescence) and preparations with optical insertion rig will ease transition to in-situ measurements in the pyrolysis reactor (behind schedule for this milestone)


Task 4 – Laser Diagnostics for Vapor Filtration and Bio-oil Recovery 13


Filtration Efficiency vs. Time• 3 bed depths (11 mm, 20 mm,

27 mm)• Shows logarithmic progression


Task 4 – Laser Diagnostics for Vapor Filtration and Bio-oil Recovery

Filtration Efficiency vs. Bed Depth• Inset: filtration coefficient with linear fit

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• Progress– Elemental analysis of H-C-N-O gases using laser-induced breakdown

spectroscopy (LIBS) (right top/bottom plots) added to previous measurements of aerosols and particulates

– Optical insertion rig to study bio-oil recovery in pyrolysis reactor constructed (bottom pictures)

• Significance– In-situ measurement of H-C gases in presence of aerosols and particulates is

new– Measurement independent of signal intensity– Measurement precision of 5% per 1-second scan for real-time analysis– On schedule for milestone for in-situ studies of bio-oil recovery in pyrolysis

reactorCold plate

Optical insertion rig for studying bio-oil recovery

Cold Plate


Task 4 – Laser Diagnostics for Vapor Filtration and Bio-oil Recovery 15



Char Trapping Efficiency Model

• Direct Numerical Simulation

Computational Modeling of MGBF

• Purpose− Fluent-CFD needs a model for char

trapping by granules to represent char filtration

− Particle-tracking in a flow field obtained from granule-resolved direct numerical simulations (DNS)

• New model development using DNS is unique, and an improvement over past efforts (existing models for char trapping efficiency are not applicable and do not predict filtration accurately)

• Fluent-CFD model will be validated against experimental data from MBGF

Experimental Setup

Fluent-CFD Model Simulation

Task 5 – Computational Modeling of Vapor Filtration and Fractionating Condenser 16

Optimization: Modeling Transport Phenomena

•Center for Sustainable Environmental TechnologiesCh

ar a

ccum

ulat

ion

(g)

Time (min)

Task 5 – Computational Modeling of Vapor Filtration and Fractionating Condenser

Technical Accomplishments – Task 5MBGF Char Accumulation Over Time

Granule speed 0.00057 m/s, air flow rate 618 slpmGranule speed 0.00063 m/s, air flow rate 620 slpmGranule speed 0.00072 m/s, air flow rate 618 slpm

Char Contours in Cross-Section Passing Through the Filter Axis

After 1 min. After 30 min.

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Direct Numerical Simulation of Char Particle Filtration• Development of direct numerical simulation code for char

particle tracking through an array of spheres is complete• DNS char particle tracking code predicts exponential decay of

number density (concentration) that is reported by other researchers (Tien 1989)

• Multiple parameters affect filter efficiency– Particle inertia (Stokes number)– Volume fraction of the granules– Flow Reynolds number



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• The goal of the catalytic bio-oil stabilization task was to simultaneously convert the aldehydes and acids in bio-oil to more stable molecules

• Previous work has suggested that aldehydes and organic acids present in bio-oil contribute significantly to the instability of bio-oil– Aldehydes are highly reactive.– Organic acids make the bio-oil quite acidic.

• A bifunctional catalyst with both metal and strong acid sites was to be developed to react aldehydes and organic acids to esters via a combined hydrogenation/esterification reactions


Task 6 – Catalytic Stabilization of Bio-oils

Catalytic post-treatment


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• Optimized the synthesis of a bifunctional catalyst with 1 wt% Pt and arenesulfonic acid groups and tested for combined hydrogenation/esterification




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• The optimized catalyst Pt/SBA-15-ArSO3H doubled our previous best catalyst activity, but was still not sufficiently active

Cumulative turnover numbers for acetaldehyde and acetic acid with the catalysts having different sulfonic acid groups (150°C)

━━: Pt/SBA15-ArSO3H(RF); △_ _ _: Pt/SBA15-PrSO3H(RF).





• Viscosity evaluations are problematic in fractionated and aged bio-oil

• Good correlation between GPC and viscosity measurements

• Offers a fast alternative to longer, tedious viscosity testing

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GPC and Viscosity Correlation

y = 98.816x0.2585

R² = 0.9192

y = 53.64x0.5521

R² = 0.9526

y = 93.739x0.6

R² = 0.9797

-300

200

700

1200

1700

2200

0 100 200

Mol

ecul

ar W

eigh

t (Da

)

Viscosity(cP)

MnMwMzPower (Mn)Power (Mw)Power (Mz)

Task 7 – Bio-oil Characterization and Accelerated Aging Tests

Bio-Oil Characterization and Aging Tests



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0

5000

10000

15000

20000

25000

10 100 1000 10000

Area

(mAU

*min

/gm

)Mw (Da)

Molecular Weight Distribution of SF 3 Produced from Red Oak at 400 °C (via GPC)

0 HR

8 HR

16 HR

24 HR

Accelerated Aging (90°C)

Equivalent Ambient Aging

0 hours 0 months

8 hours 4 months

16 hours 8 months

24 hours 12 months

Task 3 – Fractionating Recovery of Vapors and Aerosols

• Fresh bio-oil shows peak in molecular weight around 100 Da corresponding to phenolic monomers

• Most stage fractions show dramatic increase in oligomers (>500 Da) after 8 hours of accelerated aging

• Slows down for longer times although monomers continue to disappear

• Phenolic-rich fractions particularly show rapid aging

• Polymerization models to be applied to data


Relevance

• Biomass pretreatments to reduce catalytic activity of naturally occurring alkali not only reduces unstable components in bio-oil but yields higher-value products (sugars instead of light oxygenates)

• Removal of particulate matter with a MBGF can improve quality of bio-oil without significant reduction in yield

• Fractionating bio-oil recovery removes acidity of bio-oil by 75% and allows blending of bio-oil fractions to desired feedstocks

• Use of bifunctional catalyst to remove carboxylic acids and aldehydes from bio-oil will improve stability and substitute more desirable bio-oil components (esters)

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Success Factors and Challenges

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Success Factors Challenges

Reduce acidity of bio-oil Evaluating relevance of TAN to bio-oil analysis; preventing condensation of acetic acid before last stage fraction in bio-oil recovery system; understanding transport processes that control bio-oil recovery.

Reduce particulate content of bio-oil

Exploiting advantages of hot-gas particulate removal without significant reduction in bio-oil yield; prediction of filter efficiency.

Reduce reactive compounds in bio-oil

Formulation of bifunctional catalyst able to convert aldehydes and organic acids in bio-oil into esters

Improve stability of bio-oil Accurate, direct measurement of bio-oil stability; intrinsic reactivity of lignin-derived components of bio-oil (phenolic compounds)


• Complete stability tests on bio-oil fractions obtained from bio-oil recovery (8 kg/h PDU)

• Perform stability tests on bio-oil obtained from acid-infused switchgrass• Perform stability tests on hot-vapor filtered bio-oil (using MBGF PDU)• Investigate additional possibilities for separating bio-oil by varying

condenser operating conditions• Laser diagnostic measurements of hot vapor filtration and bio-oil recovery

and model validation of hot vapor filtration• Model for char-trapping efficiency developed using high-fidelity DNS will

be validated against laser-based measurements of a small filter section; implement new model into Fluent

• Determine chemical identity of monomeric peaks observed in GPC chromatograms

• Continued development of GPC methodology and viscosity correlation

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Future Work


Summary• Infusion of phosphoric and sulfuric acid into biomass have proved effective in

increasing yield of anhydrosugars and decreasing yield of light oxygenates (acids and aldehydes);

• Tests to date indicate that a moving bed granular filter significantly reduces particulate matter in bio-oil, with only a slight reduction in bio-oil yield;

• CFD modeling has demonstrated the ability to simulate the performance of the moving bed filter;

• The fractionating bio-oil recovery system is able to segregate over 50% of the acetic acid in the bio-oil in the fifth stage fraction, an aqueous, low-solids fraction. Recent tests promise continued improvement in producing distinctive stage fractions that remove undesirable components and improve the quality of the remaining bio-oil;

• Laser diagnostics show promise in understanding the transport processes that control the performance of the bio-oil recovery system;

• The concept of bifunctional catalysts to promote conversion of acids and aldehydes to esters has been demonstrated but has not achieved desired levels of catalytic activity;

• Gel permeation chromatography represents a superior tool for monitoring stability.

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Additional Slides

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Publications and Presentations• Cecconi, M., Meyer, T., Brown, R., “Effect of bed depth on relative filtration

efficiency for aerosols and particulates,” in preparation (2011)• Cecconi, M., Meyer, T., Brown, R., “Laser diagnostics for gas-phase

speciation in aerosol- and particulate-laden flows,” in preparation (2011)• Kolakaluri, R., Subramaniam, S., “A model for efficiency of granular

filtration based on granule-resolved DNS of particle trapping,” in preparation (2011)

• Kuzhiyil, N., Dalluge, D., and Brown, R., 2010, “Biomass Pretreatments to Improve Bio-Oil Stability,” Oral Presentation, Annual Meeting of the American Institute of Chemical Engineers, Salt Lake City, Utah, Nov 8-12, 2010

• Kuzhiyil, N., Dalluge, D., and Brown, R., 2010, “Biomass Pretreatments to Improve Bio-Oil Stability,” Poster Presentation, TCS Conference, Ames, IA, September 21, 2010

• Murphy, E., Kolakaluri, R., Subramaniam, S., “A model for granular filtration of polydisperse particles,” in preparation (2011)

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Publications and Presentations (continued)• Olthoff, A.; Sadula, S.; Brown, R. “Fractionating Recovery for Bio-oil

Stabilization,” Poster Presentation, TCS Conference, September 21, 2010, Ames, IA

• Pollard, A.S., Rover, M.R., Brown, R.C., “Characterization of Bio-Oil Recovered as Stage Fractions with Unique Chemical and Physical Properties,” in preparation (2011)

• Qin, Z., “A flow intensification model for granular filter applications,” Advanced Powder Technology, 21, 180 (2010)

• Qin, Z., Fox, R. O., Subramaniam, S., Pletcher, R., Zhang, L., “On the apparent particle dispersion in granular media,” Advanced Powder Technology, article in press (2011)

• Tang, Y., Miao, S., Shanks, B.H., and Zheng, X., “Bifunctional Mesoporous Organic–Inorganic Hybrid Silica for Combined One-step Hydrogenation Esterification,” Appl. Catal A: Gen., 375, 310-317 (2010)

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• Tang, Y., Miao, S., Pham, H.N., Datye, A., Zheng, X., Shanks, B.H., “Enhancement of Pt Dispersion on Mesoporous Silica for the Hydrogenation of Aldehydes,” in preparation (2011)

• Tang, Y., Miao, S., Shanks, B.H., Mo, L., Zheng, X., “Synthesis of an Enhanced Performance Bifunctional Organic-Inorganic Hybrid Mesoporous Silica Catalyst for One-Step Hydrogenation/Esterification,” in preparation (2011)

• Whitmer, L., El-Hedok, I., Brown, R., “Gas Cleaning Systems for Syngas and Bio-oil Production”, Poster Presentation, TCS Conference, Ames, IA, September 21, 2010

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Publications and Presentations (continued)


Publications and Presentations

• “Combined One-Step Hydrogenation/Esterification over Bifunctional Mesoporous Organic–Inorganic Hybrid Silica: Model Reaction for Bio-Oil Upgrading,” with Tang, Y., Miao, S., Annual Meeting, American Institute of Chemical Engineers, Nashville, TN, November, 2009.

• “Effect of Pt Loading on Enhancing Aldehyde Hydrogenation for One-step Hydrogenation Esterification (OHE),” with Tang, Y., Miao, S., Pham, H., Datye, A., Zheng, X., Annual Meeting, American Institute of Chemical Engineers, Salt Lake City, UT, November, 2010.

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Development of CFD model for MBGF in Fluent• Pressure gradient calculated

from Fluent simulation (8200 Pa/m) was compared with the theoretical result of Ergun equation (with modified coefficients of MacDonald, et al.) (8002 Pa/m)

• Good comparison with theoretical results will give confidence in CFD model

References:1. MacDonald, I.F. et al., Flow

through porous media—the Ergun equation revisited. Ind. Eng. Chemistry and Fundamentals 18,199–207 (1979)

•Y=0.2m

•Y=0 m

Conditions• Flow rate 620 slpm

(30m/s)• Particle

concentration of 2.7X10^-06

• Granule volume fraction of 0.63

Gau

ge p

ress

ure

(Pas

cal)

Position(m)

Technical Accomplishments – Task 5Pressure Distribution Along the Filter Axis

Pressure Contours in the Cross-Section Passing Through the Filter Axis

Task 5 – Computational Modeling of Vapor Filtration and Fractionating Condenser 33


Flow PBCm=fine char particles

inmoutm

Steady Normalized Number Density Profile Along Flow Domain

' '

0

( )( ) 1

( ) ( )

out

int

out out

m ttm

m t m t dt

η = −

= ∫

( )tη



vs. Time Normalized With Flow Time Scale

Simulation Conditions•Granule volume fraction 0.1•Reynolds number 2.5•Stokes number 1.0

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DNS of Char Particle Filtration: Parametric Study• Filter efficiency increases with particle inertia and granule volume fraction• With the filter efficiency variation in hand, the char trapping model can be

developed as a function of the physical parametersReferences: Tien, C. Granular Filtration of Aerosols and Hydrosols (1989)


Filter Efficiency vs. Particle Stokes Number

Stea

dy S

tate

Tim

e Av

erag

ed F

ilter

Effi

cien

cy

Particle Stokes Number

Filter Efficiency vs. Granule Volume Fraction

Stea

dy S

tate

Tim

e Av

erag

ed F

ilter

Effi

cien

cy

Granule Volume Fraction

•Volume fraction 0.4•Reynolds number 1.0

•Stokes number 1.0•Reynolds number 1.0


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Validation of Fluent CFD Model for MBGF• Developed an analytical model for char trapping (Qin, 2010) • CFD model in Fluent is validated by comparing the char

accumulation rate with Task 2 experimental results (see references for models implemented in Fluent)

• Good comparison with experimental results gives confidence in CFD predictions of char particle concentration in MBGF

• Development and validation of CFD model concludes the first milestone of the computational modeling task

References:1. Jung, Y. W. et al., Aerosol Science and Technology, 11, 168 (1989)2. Zhaohui Qin, Advanced Powder Technology, 21, 180 (2010)



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• Levoglucosan captured in SF1-2 (50% and 40%)

• Acetic acid found in high concentrations in SF3-5

• Furans concentrated in SF3-4• SF3 and 4 designed to capture

phenols, yet higher boiling point phenols found in early SFs

• Benzenediol (hydroquinone) found in SF1-3

• Other GC/MS detected compounds are mostly water soluble ketones and aldehydes

• Fractions with low water content and decreased acidity can be produced

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Technical Accomplishments – Task 7Stage Fraction Distribution of Seven

Groups of Compounds in Bio-Oil

0.005.00

10.0015.0020.0025.0030.0035.0040.00

Wei

ght P

erce

nt SF1SF2SF3SF4SF5

Task 7 – Bio-oil Characterization and Accelerated Aging Tests

A Systems Approach to Bio-Oil Stabilization FINAL

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