RECENT ADVANCES (AND CONTINUING CHALLENGES) IN Van... · RECENT ADVANCES (AND CONTINUING...
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Laboratory for Chemical Technology, Ghent University
http://www.lct.UGent.be
RECENT ADVANCES (AND CONTINUING CHALLENGES) IN
BIOMASS FAST PYROLYSIS
Kevin M. Van Geem
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Ghent University : LCT
15/03/2016
•assistant professors: 3
•Visiting/senior scientists: 7
•Post-docs: 10
•PhD students: 65
•Technical staff: 11
•Administrative staff: 3
Gent or Ghent or Gand
315/03/2016
•Pilot plant set-up: 1
•Lab-scale set-ups: 10
•High-throughput kinetics: 2
•TAP: 1
•Computing resources: TIER1 high performance computer100 TeraFLOPS, appr. 10000 cores, 1014 s-1 floating point calc.
•GCxGC (on line): 3
4
•Cold flow set-ups: 2
INFRASTRUCTURE
STEAM CRACKING PILOT PLANT
515/03/2016
Furnace + Reactor
Online Analysis Section
Control Room
Pipeline strategy: circular economy
Biomass fast pyrolysis
Fast pyrolysis and hydrotreatment
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
• Thermochemical conversion process• High heating rates• Fast condensation• High yields towards liquids (up to 75%)
depending on feed
• Energy carrier, lower energy density (stationary boiler or furnaces )• Complex mixture • High amounts of reactive oxygenates (50wt% O), reactive & unstable
1 atm500°C
Bridgwater, A.V., Review of fast pyrolysis of biomass and product upgrading. Biomass and bioenergy, 2012. 38: p. 68-94.
Stabilisation of bio-oil
Fast pyrolysis and hydrotreatment
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
• Gasoline • Diesel • Jet fuel
Less than 1 wt% O remaining
Howe, D.T., et al., Field-to-Fuel Performance Testing of Lignocellulosic Feedstocks: An Integrated Study of the Fast Pyrolysis/Hydrotreating Pathway. Energy & Fuels, 2015.
• To stage reactor ( to bypass reactivity of oil)• Ru & Co/Mo
100 bars220-400°C
Overall process• H2 consumption ±0.07kg/kgdryfeed
:
• Yields ± 25% ( on dry feed)• C-recovery ± (50wt% of dry feed)
Results are highly influenced by starting material
Commercialization status
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Fast pyrolysis and hydrotreatment
Meier, D., et al., State-of-the-art of fast pyrolysis in IEA bioenergy member countries. Renewable and Sustainable Energy Reviews, 2013. 20: p. 619-641.
Capacity: 35000 ton/yr of waste wood (hardwood pellet rejects, dried to 5 a 6 wt% of water using belt dryer)Production of approx. 22500 ton/yr of bio-oilGas & Char: burnt for process heat and cogeneration (4.5 GWh & 80000 ton steam/yr)
Opening EMPYRO plant on 21/05
The main challenges
• Hydrogen consumption
• Separation
• Reactor design
• Optimization
• Biomass selection
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
chemical kinetics based on elementary steps
conservation laws, includingtransport phenomena
kineticLABORATORY data
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Ab initio CALCULATIONS
Process and productDESIGN
LCT’s philosophy
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
1
2
3
Design new units
Optimize existing units
Real-time process control
from feed to productFundamental modeling strategy
Spin-off: started in 2015
1615/03/2016
Computational
Product Design
Lengthscale, m
Computational
Chemistry
Computational
Chemistry
10-12 10-810-10 10-410-6 10010-2 102
Computational
Thermodynamics
Computational
Thermodynamics
Reactor, DevicesMaterial Structure
Surface/Solid-Ph. Transport
Elementary Kinetics
Fluid dynamics
Turbulent Transport
Models to relate phenomena at smaller length scales
to properties and behavior at larger scale
Models to relate phenomena at smaller length scales
to properties and behavior at larger scale
Diffusion
Material Properties
Computational
Process Engineering
Computational
Fluid Dynamics
Computational
Fluid Dynamics
Process System
Modeling
Process System
Modeling
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Model
compound
intrinsic kinetic lab scale tests
reaction network generation
validation
analysisintegration ODE
quantum chemistry
reactor
operating conditions
reactor model
microkinetic model
kinetics & thermodynamics
product yields
Feedstock
MODELING
EXPERIMENTAL
Kinetic models grow larger and larger
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
T.F. Lu, C.K. Law / Progress in Energy and Combustion Science 35 (2009)
192–215
2015-3-2016
IN SILICO PROCESS MODELINGIn Silico proces modeling
FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Reaction network generation: Genesys
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
k6
k15
k1 k2
k3
k4
k5
k7
k8
k9
k10k11
k12
k13
k14
Molecules Unique Representation
Thermo-dynamics
Reactionfamilies
Reaction Identification
Kinetic Parameters
Reactionrules
Kinetic model enlargement
Termination criterion
1. N.M. Vandewiele, K.M. Van Geem, M.-F. Reyniers, G.B. Marin, Genesys: Kinetic model construction using chemo-informatics, Chemical Engineering Journal, 207 (2012) 526-538.
Graph representation of molecules
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
• Graph theory and algorithms can be applied: main methods for chemistry apps:
• Sub-graph matching ~ functional group query• Equivalence test ~ structure database query
InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)
Defining sub-molecular patterns:SMARTS (SMILES Arbitrary Target Specification)
CHCH
CH
H
CH3
CH3
Application of chemoinformatics
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Andrew Dalke’s EuroQSAR 2008 Poster (http://www.dalkescientific.com/)
Multiple ligands alignment within 3D field potential from Qmol LLC.
Reaction Mechanism Generation
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
mechanism enlargmentSusnow et al. (1996)
E. g. Reaction families:– H-abstraction by radicals
(inter- / intramolecular) – Radical addition to double / triple
bonds(inter- / intramolecular)
– Radical recombination (inter- / intramolecular)
Thermodynamic data
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Kinetic group additivity
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
M.K. Sabbe, Ph.D Thesis
Saeys 2004, AIChE J.
GAV Library
* Ci-(H)2(C) -1.161 -3.6
* Ci-(C)2(H) -0.884 -8.3
* Ci-(C)3 -0.342 -14.7
* Ci,d-(H) +0.407 -10.6
* Ci,d-(C) -0.009 -14.6
* ...
SMARTS C1 log(A) Ea
GAV Library
* Si-(H) +0.101 -20.2
* Si-(Cd) -0.273 +26.6
* Si-(CS) -0.788 +22.0
* Si-(Cd) +0.566 +35.1
* Si-(Cb) +0.406 +24.3
* ...
SMARTS S1 log(A) Ea
GVL example
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
BDE (kcal mol-1)
Experimental setup
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Most important aspects :• L =1.5m , D = 6mm , Incoloy 800 HT
• Analysis equipment :
Refinery Gas Analyser
(permanent gasses, C4- HC)
Light Oxygenates Analyser
(CH2O, CH3OH, H2O)
GCxGC-FID/(TOF-MS)
Model development
Primary mechanism CBS-QB3 by Hans-Heinrich Carstensen:• Hydrogen abstraction by H. – CH3. – C2H3. – OH. • C5H8O2 PES (unimolecular chemistry)• C5H7O2 PES (GVL-radicals)• C5H9O2 PES (H-addition)
Secondary mechanism• Missing small oxygenates• Aromatic formation
NO oxidation chemistry
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Experimental conditions investigated• FGVL = 8.35E-5 mol/s to 3.24E-4 mol/s @ 913 & 993K• FGVL = 1.67E-4 mol/s – FN2 = 1.67E-3 mol/s• FGVL = 8.32E-5 mol/s – FN2 = 4.16E-3 mol/s• FGVL = 8.32E-5 mol/s – Ftoluene = 4.17E-4 mol/s – FN2 = 3.75E-3 mol/s
Model performance
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Model performance
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
FGVL = 1.67E-4 mol/s – FN2 = 1.67E-3 mol/s
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Unique Biomass feedstocks
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
~50% cellulose
~25% hemicelluloses
~25% lignin
Wood
Lignin Lignin
Lignin pathway
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
G-units S-units
Three transgenic groups:
• CCoAOMT (416 and 429) • CAD T21• COMT ASB (2B and 10B)
Less lignin
X
More aldehydes
X
No S, more G-units
XX
X X
X
Biomass feedstocks
• 16 samples
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
1 10B COMT-ASB10B2 10B COMT-ASB10B3 WT WT-Biological4 WT WT-Biological5 CAD21 CAD T216 CAD21 CAD T217 2B COMT ASB2B-28 2B COMT ASB2B-29 2CoA-416 CCoAOMT-416
10 2CoA-416 CCoAOMT-41611 CCOA-429 CCoAOMT-42912 CCoA-429 CCoAOMT-42913 WT WT-Technical14 WT WT-Technical15 WT WT-Technical16 WT WT-Biological
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Micropyrolyzer Experiments
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
TOF-MS Micro-pyrolyzer
GC ×GC
Trace GC 1310
� Fast pyrolysis of genetically modified biomass samples
Micropyrolyzer Experiments
• Detailed characterization of pyrolysis vapor
composition :
� GC×GC-TOF-MS for qualitative analysis
� GC×GC-FID for quantitative analysis
• Time-resolved experiments:
� Determination of intrinsic pyrolysis kinetics at different
pyrolysis temperatures
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
• Relevance for Bioleum project:
� Small amounts, i.e. micrograms are sufficient for experiments
� Full quantification of products (mass closure)
� Intrinsic pyrolysis kinetics as a function of:
� Pyrolysis temperature
� Biomass composition
tube reactor
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Pilot plant
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
• Complex mixture of several hundred compounds • Not miscible with conventional petroleum fractions• Chemically unstable; instability increases with heating • Ageing of the liquid, causes unusual time-dependent behaviour • Viscosity increases with time
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Improvement of these characteristics? Upgrading
Characteristics
Bio-oil characteristics and upgrading
• Oxygen containing components are converted into aliphatic and aromatic components
• Consumption of H2
• Heteregeneous catalyst• Instability is mainly caused by presence of reactive
ketones and aldehydes (Venderbosch, 2012)• Alcohols are much more stable and have good
combustion properties
Hydrodeoxygenation (HDO)
GC × GC (1)
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
DetectorDetector
(7)
1. Injector• Split/Splitless injector• Cold-on column injector• PTV injector• Online Split/Splitless injector
2. 1st dimension column• Apolar column (normal phase)• Polar column (reverse phase)
3. 2nd dimension column• Medium polar column
(normal phase)• Apolar or medium polar column
(reverse phase)
GC × GC (2)
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
DetectorDetector
(7)
4. Cryo valves for modulator
5. Modulator
6. Piece of deactivated column
7. Detector• Flame ionization detector (FID)
= universal and quantitative
• Sulfur chemiluminescence detector (SCD)= Sulfur selective and quantitative
• Nitrogen chemiluminescence detector (NCD)= Nitrogen selective and quantitative
• TOF-MS= universal and mainly qualitative but also
quantitative analysis are possible (but difficult)
GC × GC modulation
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
4s 4s 4s 4s 4s 4s 4s 4s
1st dimensionseparation
Modulation 2nd dimensionseparation
Detection
� Enhanced Resolution� Enhanced Signal/Noise Ratio
GC × GC data processing
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Dallüge et al., J. Chrom. A 2003
GC×GC analyses methodology
• Quantitative characterization via GC×GC-NCD/SCD with
internal standards
• Internal standards (IS):
– Not being a part of the sample itself
– Not overlapping with other N- or S-compounds present in the
pyrolysis oil
– S-compounds quantification: 3-chlorothiophene
– N-compounds quantification: 2-chloropyridine
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Results – 1D vs 2D
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
0 5 10 15 20 25 30 35 40 45 50 55 60
0
100000
200000
300000
1st dimension retention time (min)
1,2-benzenediol
Acetic acid
GC-FID chromatogram of Pine Wood Bio-oil
Results – 1D vs 2D
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
GCxGC-FID chromatogram of Pine Wood Bio-oil
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
2D Gas chromatography for bio-oils
Two independent separation
mechanisms (based on BP and polarity)
Enhanced resolutioncompared to 1D-GC
Two detectors
TOF-MS: peak identification (qualitative results)FID: Quantitative results
2nd d
imen
sion
ret
entio
n tim
e (s
)
0
7
090
1st dimension retention time (min)
45
6
5
4
3
2
1
Acetic acid
THF (solvent)
Butanedial
2-Methoxy-phenol
2-methoxy-4-(1-propenyl)-phenol
4-Ethyl-2-methoxy-phenol
2-Methoxy-4-vinyl-phenol
Fluoranthene (IS)Benzenediols
N-butyl ether (IS)acids
1-Hydroxy-2-butanone
Cyclopentanones
15 30 60 75
Phenol 1,2-Benzenediol
2-Furanmethanol
105
Butyrolactone
D-Allose1,2,3- Butanetriol
2,6-Dimethoxy-4-(2-propen-1-yl)-phenol
1-(2,5-Dimethoxyphenyl)-ethanone
2,6-Dimethoxy-phenol
C16H22O4
Results: comparison crude oils
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Someabundant
componentsin common
Derived frompine wood
Derived frompoplar wood
Different feedstockleads to different
components
Comparison crude with HDO bio-oils
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Crude bio-oil
HDO bio-oil
sugars
acids
Phenolic components
Effect different catalyst and/or different process conditions on bio-oil composition is traceble with the GCxGC technique
Detailed composition
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Detailed composition of Pine Wood Bio-oilCompound Name wt%
Formaldehyde 3.056
Acetaldehyde 0.728
Acetone 1.777
Propanal 1.045
2-Propanone, 1-hydroxy- 0.792
Acetic acid 2.857
Propanoic acid 0.670
1-hydroxy-2-butanone 0.086
1,2-etanediol, monoacetate 0.552
2-methyl propanoic acid 0.068
acetic acid, 2-ethylbuthyl ester 0.037
2,3-dihydroxy-propanal, (S)- 0.841
butanoic acid 0.162
3-furaldehyde 0.042
3-butenoic acid 0.057
2-cyclopenten-1-one 0.202
furfural 0.296
1,3-propanediol, diacetate 0.065
1-(acetyloxy)-2-propanone 0.075
2(5H)-Furanone 0.589
Glutaraldehyde 0.022
2-cyclopenten-1-one, 3-methyl- 0.062
1-(2-furanyl)-ethanone 0.030
1,3-butadiene-1-carboxylic acid 0.0302,5-Furandione, dihydro-3-methylene-
0.053
2-Cyclopenten-1-one, 2-hydroxy- 0.411
Compound Name wt%
2(5H)-Furanone, 5-methyl- 0.050
2H-Pyran-2-one 0.033
3,4-dimethyl-2-cyclopenten-1-one 0.077
1-(acetyloxy)-2-butanone 0.0572-Furancarboxaldehyde, 5-methyl-
0.108
2(5H)-Furanone, 3-methyl- 0.090
4H-Pyran-4-one 0.057
Phenol 0.1112,5-dihydro-3,5-dimethyl-2-furanone
0.032
3,4-dihydro-6-methyl-2H-pyran-2-one
0.082
4-methyl-5H-furan-2-one 0.106
1,2-Cyclopentanedione, 3-methyl- 0.364
Hydroxyacetaldehyde 3.571
2-(1-methylpropyl)-1,3-dioxolane 0.129
2,3-dimethyl-2-cyclopenten-1-one 0.035
2-hydroxy-benzaldehyde 0.022
Phenol, 2-methyl- 0.120
2(5H)-Furanone, 5-ethyl- 0.029
C8H14O 0.021
Acetophenone 0.053
Cyclopropyl carbinol 0.084
Phenol, 4-methyl- 0.110
Phenol, 2-methoxy- 0.062Maltol 0.1162-ethyl-phenol 0.014
Compound Name wt%
Phenol, 2,4-dimethyl- 0.137
2-hydroxy-6-methyl-benzaldehyde 0.067
2,3-dihydroxybenzaldehyde 0.031
Phenol, 2-ethyl- 0.042
1,4:3,6-Dianhydro-a-d-glucopyranose 0.233
2-methoxy-4-methyl-phenol 0.050
1,2-benzenediol 1.045
2-Deoxy-D-galactose 0.557
2-Furancarboxaldehyde, 5-(hydroxymethyl-
0.529
3-ethyl-5-methyl-phenol 0.019
2-ethyl-6-methyl-phenol 0.054
4-(2-propenyl)-phenol 0.019
1,2-benzenediol, 3-methyl- 0.150
Hydroquinone 0.073
2,3-dihydro-1H-Inden-1-one 0.020
4-ethyl-2-methoxyphenol 0.017
1,2-benzenediol, 4-methyl- 1.108
5-Acetoxymethyl-2-furaldehyde 0.053
3-hydroxy-benzaldehyde 0.025
2,5-diethylphenol 0.012
2-allyl-4-methyl phenol 0.025
2-methyl-6-propylphenol 0.013
3,5-dihydroxytoluene 0.042
4-ethyl-1,3-benzendiol 0.051
1,6-dihydro-a-d-talopyranose 0.102
2,5-dimethyl-1,4-benzenediol 0.049
Results - Reproducibility
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Pine Wood Bio-oil Hydrotreated Bio-oil
0
2
4
6
8
10
12
wt,
%
0
0.5
1
1.5
2
2.5
3
Micropyrolysis: set-up & methodology
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Methodology • Identification of the 41 most abundant components• (1) Comparison of applying the normalised or the non-
normalised data for PCA• (2) Based on (1): developing of 3 models, each includes
the comparison of one of the different transgenic groups and WT
Set-up
Micropyrolysis: results
FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
-8 -6 -4 -2 0 2 4 6
-6-4
-20
24
6
pc1
pc2
S5_CAD
S6_CAD
S10_OMT416
S9_OMT416S11_OMT429
S12_OMT429
S1_10B
S2_10B
S7_2B
S8_2B
S3_WT
S4_WT
S14_WT
S16_WT
• Grouping of L-S10 and L-S6 (syringaldehyde and sinapaldehyde) => higher present in CAD
• Grouping G units, strong neg, contribution on PC2 => more present in COMT-2B.
• Grouping other S units => less present in COMT lines
Score plot • 3 clusters (CAD, COMT & rest)
• CAD separated of WT by PC2• COMT separated of WT by
PC1 and PC2 => strong deviation between COMT-2B & COMT-10B
Loading plot
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GC-MS: methodology and results
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
-1 0 -5 0 5
-4-2
02
p c 1
pc2
S 5 _ C A D
S 6 _ C A D
S 1 0 _ O M T 4 1 6
S 9 _ O M T 4 1 6
S 1 1 _ O M T 4 2 9
S 1 2 _ O M T 4 2 9
S 8 _ 2 B
S 7 _ 2 B
S 1 _ 1 0 BS 2 _ 1 0 B
S 1 3 _ W T
S 1 4 _ W T
S 1 5 _ W T
S 1 6 _ W T
S 4 _ W T
Clear shift of COMT lines compared to wild type (WT)
No shifts of CAD and CCoAOMT lines compared to WT
Furtherinvestigationnecessary!
- Identification of 25 most abundant peaks- Normalisation of the peak surface by the total peak surface of each sample => Elimination of the problem of the unknown THF/bio-oil ratio
Methodology
Score plot
Comparison of COMT vs. WT
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
-8 -6 -4 -2 0 2 4
-3-2
-10
1
pc1
pc2
S8_2B
S7_2B
S1_10B
S2_10B
S13_WT
S14_WT
S15_WT
S16_WT
S4_WT
Score plot - COMT and WT clusters
Loadingplot
• Distinctive grouping of G and S units
• Contribution to PC1 of G and S units is about equal in size
• All S units: pos. contribution• All G units: neg. contribution
- S4 and S7 separated
- Separation by PC1
Comparison of CAD vs WT
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
-6 -4 -2 0 2 4
-3-2
-10
12
3
pc1
pc2
S5_CAD
S6_CAD
S13_WT
S14_WT
S15_WT
S16_WT
S4_WT
Score plot • Clustering of CAD and WT (except S4)
• Separation of the clusters by PC2
• Large deviation in WT
Loadingplot
• Some grouping of S units• Biggest pos. contribution
on PC2 is due to 4 components encircled, incl. syringaldehyde andsinapaldehyde
For CCoAOMT: No natural clustering visible on the different score plots
GCxGC-MS/FID: methodology and results
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
- Identification and quantification of over 100 componentsMethodology- Input data for the PCA: 1) Calculated weight percentages
2) Normalised corrected peak volumes2a) All identified components2b) Exclusion of non-lignin components
- Comparison between WT and each transgenic group with only the lignin derived components
-10 -5 0 5
-6-4
-20
24
6
pc1
pc2
S1_10B
S2_10B
S7_2B
S8_2B
S5_CAD
S6_CAD
S9_OMT416
S10_OMT416
S11_OMT429
S12_OMT429S4_WT
S13_WT
S14_WTS15_WTS16_WT
Score plot Results
- Clear shift of COMT vs. WT- Subtle shift of CAD vs. WT- No shift of CCoAOMT vs. WT
! Regarding components causing these shifts ≠ results GC-MS !Reason(s): used libraries foridentification, quantificationmethod, …
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
61
FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
Gas/Solid Fluidization Reactors
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FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
gravitational technologies centrifugal technologies
ConventionalFluidized Bed1
Riser/Circulating Fluidized Bed2
Conventional Rotating Fluidized Bed3
Gas/Solid Vortex Reactor
1. van Hoef et al., Ann. Rev. Fluid Mech. 40 (2008) 47-702. http://www.fluidcodes.co.uk/fbed.html3. adapted from Watano et al., Powder Tech.131 (2003) 250-255
Gas/Solid Fluidization Reactors
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Gas
/Sol
id S
lip V
eloc
ity
Solid Volume Fraction
Terminal velocity
Fluidized
Beds
Risers &
Circulating
Fluidized Beds
GSVR &
Rotating
Fluidized
Bed
Reactors
Improved gas/solid mass transferPotential for intensification
Images from: Watano et al., Powder Tech.131 (2003) 250-255
Gravitational technologiesLonger gas/solid contact time
Centrifugal technologiesShorter gas/solid contact time
Experimental GSVR Set-up
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Real-time Video
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Polyvinylidene fluoride particles (dp= 0.9 mm, ρ = 1800 kg/m3) ~5 kg bed mass
~1 kg/s air flow
0.54 m
0.15 m
0.07 m
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70 micron particles (5000 FPS)
~1.6 mm particles (10000 FPS)
High-Speed Video
Gas-Solid Vortex Reactor Technology
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LCT Cold flow unit
LCT Hot flow unit
Bed behavior during a cold flow test
J.Z. Kovacevic et al. Chemical Engineering Science123(2015)220–230
CFD simulation results for the volume fraction of biomass during fast pyrolysis in a GSVR
R.W. Ashcraft et al. Chemical Engineering Journal 207–208 (2012) 195–208
Computational Fluid Dynamics
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• Eulerian/Eulerian two-fluid model, granular solid phase• Gidaspow drag model 1
Model geometries tested:
1. Gidaspow, D. (1994). Multiphase flow and fluidization: Continuum and kinetic theory description. New York: Academic Press.
2D 3D
(a) (b)
CFD Example Movies
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3D, 0.74 kg/s air, 3250 g bed (iso-surfaces = 0.40 and 0.01 solids volume fraction)
Pyrolysis Modeling in a GSVR
701. Xue, Heindel, and Fox, Chem. Eng. Sci. 66 (2011) 2440
• 10-reaction network with pseudo-components 1
• Continuous feeding of biomass
• Cellulose, hemicellulose, and lignin
• Different rates for each biomass component
• 4-phase Eulerian multiphase simulation
Gas, biomass, char, and sand
� Sand and biomass retained in reactor
� Char leaves with gas flow due to lower density
� Complete biomass conversion
FUTURE FUELS WORKSHOP, KAUST, KSA, 07/03/2016
VirginBiomass
ActiveBiomass
Tar (g)PyrolysisGases
Char (s) + Pyrolysis Gases
biomass
char
sand
Volume fraction
Hot flow unit
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Top view schematics
Gravimetricfeeder
Air feeding lines
Exhaust
Scheme of the Proof-of-concept Unit
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GSVR
FeedingBiomass Feeding
heatingGas
heating
separationChar
separation
condensationBio-oil
condensation
Biomass1.4x10-4– 1.1x10-3 kg/s
(0.5 - 4 kg/h)
Gas0.005 – 0.0125 kg/s
(18 - 45 kg/h)
How do we approach this?
• Applying LCT’s modeling methodology• Using unique material• Unique infrastructure• Detailed experimental data• Scale-up and design• Collaboration with top groups and
institutes
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Collaborations
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Financial support
• Methusalem funding• Bioleum
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Questions
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