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ww
w.inl.gov
Determination of impact of feedstock
composition on fast pyrolysis oil yield
and quality using multiple linear
regression modeling
Tyler L. Westover (INL)
Rachel Emerson (INL)
Sergio Hernandez (INL)
Danny Carpenter (NREL)
Dan Howe (PNNL)
TCS2016
Nov. 1 – 4, 2016
Chapel Hill,
North Carolina
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Intra-lab collaboration (people)
Rachel Emerson
Sergio Hernandez
Chad Ryan
Tyler Westover
Luke Williams
Stuart Black
Daniel
Carpenter
Mark Davis
Steve Deutch
Abhijit Dutta
Robert Evans
Rick French
David Lee
Michele Myers
Kelly Orton
Scott Palmer
Kailee Potter
Josh Schaidle
Anne Starace
Eric Tan
Corinne Drennan
John Frye
Dan Howe
Sue Jones
Igor Kutnyakov
Teresa Lemmon
Richard Licke
Craig Lukins
Aye Meyer
Balakrishna Maddi
Tessa Oxford
Asanga
Padmaperuma
Ellen Panisko
Ken Rappe
Benjamin Roberts
Daniel Santosa
Lesley Snowden-
Swan
Huamin Wang
Thomas Wietsma
Alan Zacher
This work was supported by the Bioenergy Technologies Office (BETO) at the U.S. Department of Energy’s Office of Energy
Efficiency and Renewable Energy.
National Renewable Energy Laboratory is operated by The Alliance for Sustainable Energy, LLC under Contract no. DE-AC36-08-
GO28308.
Idaho National Laboratory is operated by Battelle under contract no. DE-AC07-05ID14517 with the Department of Energy Idaho
Operations Office.
Pacific Northwest National Laboratory is operated by Battelle under contract DEAC05-76RL01830.
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SyngasLiquid fuels
Indirect LiquefactionGasification (NREL)
Synthesis(NREL)
Liquid fuels
Feedstock
Bio-oil/ vapor
Raw BiomassSystems/Assembly
LogisticsCharacterization
(INL)
Direct Liquefaction(NREL/PNNL)
Pyrolysis/Bio-oil Catalytic Pyrolysis Hydrothermal Liq.
Hydroprocessing(PNNL)
Inte
rfac
e
Process specifications impact
feedstock development
Feedstock cost/properties impact
conversion R&D decisionsInterface
Bio-oil/vapor/syngas•Yield•Quality, composition•Contaminants
Upgrading• CFP, HDO
• Catalyst development
• Process conditions
• H2 consumption
Conversion• Feed handling
• Process conditions
(T, P, hot filtering,
condensation
systems)
TEA• Model
assumptions
• Correlations
• Wastewater
treatment
Impact
Intra-lab collaboration (approach)
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Overview and preview of conclusions
Preview of Conclusions
1. Conversion yields of blended materials
are ~ linear functions of components • Interactions between components appears small (< test uncertainty)
2. Must be very careful with multivariate analysis because of
correlation among variables. Many relationships are not causal.
• Background and motivation
• Blended vs. pure feedstocks
• Blended feedstocks as replications of pure feedstocks
• Methods: step-wise multilvariate linear regression
• Repeatability of results
• Influence of predictor variables
• Accuracy and precision of models
• Impacts/conclusions
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U.S. Resource Assessment
Goal of this effort: Facilitate the coordinated development of biomass
resources and conversion technologies by understanding the field-to-fuel
impact of feedstocks on thermochemical processes.
• Biomass feedstock = largest operating cost (~40%) and risk factor for biorefinery process developers
• Billion Ton 2016 Update (BT16) includes economic availability of biomass with county level estimates as well as sustainability considerations
• Technoeconomic analyses identify areas for process cost reduction
• Need to understand process sensitivities to adding low-cost feedstocks into the supply chain
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Biomass Supply/Cost Curves
Biomass supply projections for feedstock prices between $20
and $200/dry ton in 2022
Source: Williams, Westover, et al. BioEnergy Research, March 2016, Volume 9, Issue 1, pp 1-14.
For example…Per BT16, ~1,200 million dry
tons converted to 160 million
barrels gas equivalent or:
~1.5 billion barrels of
bio-crude oil
Comparison:In 2015, the U.S. consumed
~7 billion barrels of
crude oil
www.eia.gov/tools/faqs
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Importance of Blending−Woody Supply/Cost Curves
Woody feedstock supply as functions of
projected average cost
• Combining woody feedstocks in blend
accesses all 315 million tons for average
cost of $85/dry ton ($2011).
• Blending feedstocks increases supply
while reducing costs and risks
• ~315 million tons projected available in 2022
• Projections depend upon assumptions; − EPA estimated that 164 million tons of C&D
waste were generated in the U.S. in 2003 (EPA, 2011; Paper in Support of Final Rulemaking:
Identification of Nonhazardous Secondary Materials
That Are Solid Waste)
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Feedstocks and regression (predictor) variables
Pure feedstocks (10)1. Clean pine (CP) (5x)
2. Hybrid poplar (HP)
3. Tulip poplar (TP)
4. Piñion/juniper (PJ)
5. Oriented strand board (OSB)
6. Corn stover (CS)
7. Switchgrass (SG)
8. Construction & demo-lition waste (C&D-0.5mm)
9. Air classified forest residues (acFR-0.5mm)
10. Miscanthus (MS-0.5mm)
• CP-0.5mm
• SG-450oC
Blends (8)1. CP2HP1
2. CP1TP1SG1 (2x)
3. CP8SOB2
4. CP7OSB2SG1
5. CP4OSB2SG4
6. CP30acFR60HP10-0.5mm
7. CP45FR25C&D30-0.5mm
8. CP30FR35C&D25SG10-0.5mm
Response variables• Dry, ash-free pyrolysis oil yield
• Ash-free pyrolysis char yield
• Dry hydrotreating oil yield
Independent tests (pure feedstocks): 10
Effective replicates: 13
Predictor variables: 22• Ultimate (C, H, N, S, O, HHV, LLV)
• Proximate (Volatiles, Fixed C, Ash)
• Ash speciation (Al, Ca, K, Mg, Na, S, Si)
• Composition (H20 Ext, Protein, Arabinan, Glucan, Lignin)
Materials ground to 1 mm and temperature of pyrolysis reactor was 500oC except as noted
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Summary: FP and HT Performance Data vs. Feedstock
• FP oil yield (goil/gbiomass)ranged from CS (35%) to tulip poplar (60%)
• HT oil yield ranged from TP (40% to CS (57%)
• FP char yield ranged from C&D (6%) to CS (16%)
• Milling to 0.5 mm increased FP oil yield of CP from 51% to ~58%
• Decreasing reactor temperature to 4500C increased FP oil yield of switchgrass from 41% to 49%.
• FP: fast pyrolysis• HT: hydrotreating
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Meas. FP & HT oil yields of blends vs. expected yields
• Fast pyrolysis (FP), hydrotreating, (HT) oil organic oil yields and FP char yield appear linear with blend components
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Weighted-average process yields
FP oil yield and FP char yield of CP/OSB/SG
blends as functions of SG content.
Uses multivariate linear regression to determine weighted average process yields• For simple example of
switchgrass/woody blends, this is equivalent to finding the equation of a line for each response variable
• Linear combination assumption for blend yields appears good for fast pyrolysis organic oil yield
• Assumption not so good for fast pyrolysis char yields; however, as will be shown, char yields have lower repeatability
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Weighted average yields vs. measured values
• Process yields from blends considered as linear combinations of pure feedstocks to determine weighted average values using multivariate linear regression
• Estimates are close to individually measured values• Largest corrections are for hydrotreating: CP4OSB2SG4 (3%) & CP1TP1SG1 (2.5%)
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FP char yields
• FP char yield best predicted by Fixed C (R2 = 0.43)
• Also predicted by Water Ext., Arabinan, and Protein (R2 = 0.46)
• WATCH OUT! This model is may not be causal
• Water Ext. Arab., Prot. predict Fixed C• Adding Water Ext, Arab., or Prot. to
Fixed C model does not significantly improve the model
• Need to know predictor variables!
• Largest corrections are for hydrotreating: CP4OSB2SG4 (3%) & CP1TP1SG1 (2.5%)
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Water content of FP oil
• Water content of FP oil char best predicted by K+Na and P (R2 = 0.88)
• Also predicted by K+Na alone (R2 = 0.74)
• Again, due to correlation between variables, relationship may not be causal
• Extra sum of squares F statistic analysis indicates that P is significant in predicting water content (increase in F is 7 times F statistic of 3.5
• Adjusting water content of CP2HP1
brings value into agreement with measurements of CP and HP.
Model: K+Na, P
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Fast Pyrolysis Yield/Composition Correlations
• FP oil yield model with two parameters: K+Na and
VolMat (R2 = 0.76)
• FP oil yield model with 3 parameters: K+Na,
VolMat, and FixedC (R2 = 0.83)
• More tests still needed to fully deconvolve
correlations among variables
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Other Processes to Investigate...
• Catalytic fast pyrolysis (vapor phase upgrading)
– Bench-scale testing
– Hydrocarbon yield vs. feedstock
– Catalyst lifetime vs. feedstock
• Hydrothermal liquefaction
– Field-to-fuel tests using same feedstocks
– Upgraded oil yields vs. feedstock
– Potential 1-step upgrading (vs. 2)
• Gasification
– Integrated bench-scale gasification/fuel synthesis (sensitivity to feedstock)
– Inorganic byproducts, bed interactions, loading on syngas cleanup systems
Agglomerated bed material after feeding corn stover
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Conclusions
1. Conversion yields of blended materials
appear to be linear functions of
components • Interactions between components appears small
(< test uncertainty)
2. Must be very careful with multivariate
analysis because of correlation among
variables. Many relationships are not
causal.
3. Tentative: Each component in blends will
likely need to meet conversion
processability specifications (i.e. not all
specifications are equal)
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ACKNOWLEDEGMENTS
Rachel Emerson
Sergio Hernandez
Chad Ryan
Tyler Westover
Luke Williams
Stuart Black
Daniel
Carpenter
Mark Davis
Steve Deutch
Abhijit Dutta
Robert Evans
Rick French
David Lee
Michele Myers
Kelly Orton
Scott Palmer
Kailee Potter
Josh Schaidle
Anne Starace
Eric Tan
Corinne Drennan
John Frye
Dan Howe
Sue Jones
Igor Kutnyakov
Teresa Lemmon
Richard Licke
Craig Lukins
Aye Meyer
Balakrishna Maddi
Tessa Oxford
Asanga
Padmaperuma
Ellen Panisko
Ken Rappe
Benjamin Roberts
Daniel Santosa
Lesley Snowden-
Swan
Huamin Wang
Thomas Wietsma
Alan Zacher
This work was supported by the Bioenergy Technologies Office (BETO) at the U.S. Department of Energy’s Office of Energy
Efficiency and Renewable Energy.
National Renewable Energy Laboratory is operated by The Alliance for Sustainable Energy, LLC under Contract no. DE-AC36-08-
GO28308.
Idaho National Laboratory is operated by Battelle under contract no. DE-AC07-05ID14517 with the Department of Energy Idaho
Operations Office.
Pacific Northwest National Laboratory is operated by Battelle under contract DEAC05-76RL01830.
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Questions?
Thank you for listening
Publications so farCarpenter D, et al. Catalytic Hydroprocessing of Fast Pyrolysis Oils: Impact of Biomass
Feedstock on Process Efficiency, accepted for publication in Biomass and Bioenergy.Meyer et al.,
Howe D, et al. Field-to-Fuel Performance Testing of Lignocellulosic Feedstocks: An Integrated Study of the Fast Pyrolysis/Hydrotreating Pathway, Energy&Fuels 2015;
29: 3188-3197.
Email [email protected]
