recent advances (and continuing challenges) in van... · recent advances (and continuing...

Laboratory for Chemical Technology, Ghent University

http://www.lct.UGent.be

RECENT ADVANCES (AND CONTINUING CHALLENGES) IN

BIOMASS FAST PYROLYSIS

Kevin M. Van Geem

1

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

8/66


• 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


9/66


• 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

10



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

12


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

13


chemical kinetics based on elementary steps

conservation laws, includingtransport phenomena

kineticLABORATORY data

14

Ab initio CALCULATIONS

Process and productDESIGN

LCT’s philosophy

15


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

18

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

19


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


Reaction network generation: Genesys

21


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

22


• 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

23


Andrew Dalke’s EuroQSAR 2008 Poster (http://www.dalkescientific.com/)

Multiple ligands alignment within 3D field potential from Qmol LLC.

Reaction Mechanism Generation

24


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

25


Kinetic group additivity

26


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

27


BDE (kcal mol-1)

Experimental setup

28


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

29


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

30


Model performance

31


FGVL = 1.67E-4 mol/s – FN2 = 1.67E-3 mol/s



institutes

32


Unique Biomass feedstocks

33


~50% cellulose

~25% hemicelluloses

~25% lignin

Wood

Lignin Lignin

Lignin pathway

34


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

35


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



institutes

36


Micropyrolyzer Experiments

37


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

38


• 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

39


Pilot plant

40




institutes

41


• 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

42


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)

43


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)

44


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

45


4s 4s 4s 4s 4s 4s 4s 4s

1st dimensionseparation

Modulation 2nd dimensionseparation

Detection

� Enhanced Resolution� Enhanced Signal/Noise Ratio

GC × GC data processing

46


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

47


Results – 1D vs 2D

48


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

49


GCxGC-FID chromatogram of Pine Wood Bio-oil

50


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

51


Someabundant

componentsin common

Derived frompine wood

Derived frompoplar wood

Different feedstockleads to different

components

Comparison crude with HDO bio-oils

52


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

53


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

54


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

55


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


-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

56

GC-MS: methodology and results

57


-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

58


-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

59


-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

60


- 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, …



institutes

61


Gas/Solid Fluidization Reactors

62


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

63


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

64


Real-time Video

65


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

66


70 micron particles (5000 FPS)

~1.6 mm particles (10000 FPS)

High-Speed Video

Gas-Solid Vortex Reactor Technology

67


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

68


• 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

69


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


VirginBiomass

ActiveBiomass

Tar (g)PyrolysisGases

Char (s) + Pyrolysis Gases

biomass

char

sand

Volume fraction

Hot flow unit

71


Top view schematics

Gravimetricfeeder

Air feeding lines

Exhaust

Scheme of the Proof-of-concept Unit

72


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)



institutes

73


Collaborations

74


Financial support

• Methusalem funding• Bioleum

75


Questions

76


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