computational study on biomass fast pyrolysis: … as typical lab-scale example •key steps:...

ORNL is managed by UT-Battelle for the US Department of Energy

Computational study on biomass fast pyrolysis: Hydrodynamic effects on the performance of a laboratory-scale fluidized bed reactor

Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan).

This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725

Presented at the 2018—AIChE Spring Meeting8th World Congress on Particle TechnologyTransport Phenomena and Reactor Performance IIOrlando, Florida, April 26, 2018

Emilio Ramirez1,2, Tingwen Li 3, MehrdadShahnam3, C. Stuart Daw1

1 Oak Ridge National Laboratory, Oak Ridge TN 37831 USA2 University of Tennessee, Knoxville TN 37996 USA3 National Energy Technology Laboratory, Morgantown, WV 26507 USA

2Computational study on biomass fast pyrolysis: Hydrodynamic effects on the performance of a laboratory-scale fluidized bed reactor

Background and Motivation (1)

• High yield and composition of raw oil are key, so commercial risk and economics depend on accurate performance predictions.

• Most available basic lab data are from bubbling fluidized bed reactors (FBRs).

• Good physics-based models are necessary for interpreting, scaling up lab experiments.

Thermochemical conversion of biomass based on fast pyrolysis

Char & RecycleBed Solids

Raw BiomassRaw Bio-Oil Vapor

(Tar)

StableBio-OilVapor

Non-condensableGas

AqueousPhase

HydrocarbonFuel Feedstock

Hydrogen

Biomass Feed Preprocessing

Fast Pyrolysis

Vapor Phase Catalytic

UpgradingCondensation

Liquid Phase Catalytic

UpgradingSo

urce

: Hea

t-Its

Rol

e in

Wild

land

Fire

by C

live

M. C

ount

rym

an

Unburned woodPyrolysis Zone

CharAsh

PyrolysisVapor Phase

Upgrading (VPU)


Background and Motivation (2)How should lab FBR data be interpreted/analyzed?

Bubble dynamics• Bubbling regime

– gas/solid particle mixing intensity increases with fluidizing gas mass flow

• Slugging regime– Gas and solid particles not mixed

well– Gas bypassing through bubbles

• Bubbling to Slugging Transition– Mixture of bubbling and slugging

Note: Bubble boundary depicted where void fraction > 0.65

FB Hydrodynamics directly impact:

1. Particle residence time2. Gas residence time3. Particle heating rate4. Particle attrition/fragmentation5. Particle and ash elutriation6. Particle segregation

All the above significantly impact raw oil yield and composition.

E. Ramirez, C.E.A. Finney, S. Pannala, C.S. Daw, J. Halow, Q. Xiong, Computational study of the bubbling-to-slugging transition in a laboratory-scale fluidized bed, Chemical Engineering Journal 308 (2017) 544-556. http://dx.doi.org/10.1016/j.cej.2016.08.113

http://dx.doi.org/10.1016/j.cej.2016.08.113


Approach (1): MFIX simulations of FBR pyrolysis

– Eulerian-Eulerian – Kinetic theory of granular flow– 3D cylindrical mesh– DLSODA ODEPACK chemistry solver

• First order irreversible Arrhenius rates• Liden 1988 biomass pyrolysis kinetics

Gas

Parti

cle

Vsv

Char Particle elutriation

Particle Segregation

Tar (

oils

)

Bed

of s

and

parti

cles

Pyrolysis reactor physics Two-Fluid CFD Model

Targas + char

k1

k2

k3 gaswood


Approach (2):Interpret MFIX Results with Low-Order Models

– Quantify impact of bubbles and bed solids circulation on biomass solids and py vapor RTDs

– Identify major reaction/mixing zones needed to understand/approximate performance trends

– Relate solids and gas RTDs to predict trends for how biomass particle properties and reaction chemistry impact overall yields

– Utilize low-order models for rapid studies of operating/design parameter sweeps

Gas

Vsv

Mixing and residence

time

Tar (

oils

)

Bed

of s

and

parti

cles

Use simplified reactor models to ‘compress’ essential hydrodynamic information from MFIX and combine it with pyrolysis chemistry


Approach (3): Compare MFIX predictions for lab-scale FB pyrolysis reactor with literature and experiments

10 cmdiameter

40.1 cm

Free

boar

d

Expanded Bed

Static BedHo=16.3 cm

ṁinlet

Pout = 101 kPa • Target: Select NREL lab-scale pyrolysis experiment as typical lab-scale example

• Key steps:• Simulate expected particle and gas

RTDs with MFIX including segregation and elutriation

• Are MFIX mixing patterns consistent with the literature?

• Can existing FB correlations capture MFIX predicted RTD trends?

• When chemistry is added, do predicted bio-oil yields agree with experiments?

• Are MFIX improvements needed?

RTD studyBerruti 1988

11.5 cmdiameter

50 cm

Free

boar

d

Expanded Bed

Static BedHo=15.92 cm

ṁinlet

Pout = 101 kPa

Mixing biomass charPark and Choi 2013

H.C. Park, H.S. Choi, The segregation characteristics of char in a fluidized bed with varying column shapes, Powder Technology 246 (2013) 561-571.

F. Berruti, A.G. Liden, D.S. Scott, Measuring and modelling residence time distribution of low density solids in a fluidized bed reactor of sand particles, Chemical Engineering Science 43 (1988) 739-748.

5 cm

43 cm

Free

boar

dExpanded Bed

Static BedHo=10 cm

ṁinlet

Pout = 101 kPa

NREL Pyrolysis Experiment


Approach (4): NREL lab pyrolyzer details• Target: Fluidized bed particle studies used to verify model• Key steps:

• Reproduce exp. particle residence time distribution (RTDs)• Relate impact of char elutriation and mixing on RTDs• Reproduce impact of solids segregation on mixing

H.C. Park, H.S. Choi, The segregation characteristics of char in a fluidized bed with varying column shapes, Powder Technology 246 (2013) 561-571.

Property UnitsMixing Study

Park & Choi 2013RTD Study

Berruti 1988 NREL Exper.

Particle diameter (Sand) m 387 × 10-6

710 × 10-6

500 × 10-6

Particle density (Sand) kg/m3

2383 2470 2500

Particle diameter (Styrofoam/char) m 957 × 10-6

450 × 10-6

278 × 10-6

Particle density(styrofoam) kg/m3

- 82 -

Particle density(Char) kg/m3

391 - 80

Temperature K 300 300 773

Pressure (inlet) kPa 101 101 133

Fluidizing N2 (range) m/s 0.14 - 0.19 0.554 0.13 - 0.47

Minimum fluidization m/s 0.14 0.30 0.0565

Coefficient of restitution - 0.9 0.9 0.9

Angle of repose ° 30 30 30

Friction coefficient - 0.1 0.1 0.1

F. Berruti, A.G. Liden, D.S. Scott, Measuring and modelling residence time distribution of low density solids in a fluidized bed reactor of sand particles, Chemical Engineering Science 43 (1988) 739-748.


Preliminary MFIX Results(1): Biomass Particle RTD

Axial slice of 3D bubbling bed simulation Residence time distribution (RTD) study

Comparison of simulation and experiment RTD (Berruti 1988)


36

-70

0

102

0

Cha

r den

sity

g/c

m3

Cha

r vel

ocity

cm

/s

• Bubbles are the main mixing mechanism

• More bubbles, more char/sand mixing

• Char layer decreases with gas flow

• Simulated tracers track char/gas mixing and RTD’s

Comparison of simulation and experiment char mixing (Park and Choi 2013)

Axial slice of 3D bubbling bed simulation at 1.34 Umf

Preliminary MFIX Results(2): Biomass Particle Segregation


Preliminary MFIX Results (3): Biomass Particle Elutriation

Particle size and density must be selected carefully such that elutriation will occur

Biomass particle densityBiomass particle size

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

CDFnormalized

F-curve[C(t)]

time (sec)

RTD whole reactor, biomass particles

40um58um100um278um344um426um543um

0

0.2

0.4

0.6

0.8

1

0 10 20 30

CDFnormalized

F-curve[C(t)]

time (sec)

Biomass RTD in reactor bed and freeboard

0.8rho0.9rho1.1rho1.3rho1.4rho1.6rho


Preliminary MFIX Results (4): Biomass Particle ElutriationAs inlet gas flow increases, biomass particle RTD

converges to a limit

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

CDFnormalized

F-curve[C(t)]

time (sec)

RTD whole reactor, biomass particles

2umf3umf4umf5umf6umf7umf


Preliminary MFIX Results (5): Yield Convergence with Chemistry

Liden lumped kinetics in MFiX reactor and low order reactor model predict tar experiment yields

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 2 4 6 8 10 12 14 16 18

yield(C/Co)

Time (s)

producttargascharwood

low order tarlow order gaslow order charlow order wood


Preliminary Low-Order Results: Chemistry + MFiXHydrodynamics (Possible ‘Hybrid’ Modeling Approach)

2. Use zone model + Liden kinetics to predict yields

1. Use MFiX gas and biomass RTDs to create zone reactor

model approximation

0 1 2 3Stage Number

0

0.2

0.4

0.6

0.8

1

Exit

Mas

s Fr

actio

n (D

ry B

asis

)

WoodTarLight GasChar

E. Ramirez, Tingwen Li, Mehrdad Shahnam, C. Stuart Daw, Computational study on biomass fast pyrolysis: Hydrodynamic effects on the performance of a laboratory-scale fluidized bed reactor, Manuscript in preparation.

0 5 10 15Time (s)

0

0.5

1

Cum

ulat

ive

Prob

abili

ty

gas CSTR+PFRgas MFiXchar CSTR+PFRchar MFiX


How do the models compare with experimental data?

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Experiment MFiX Low Order Hybrid

targascharwood


Concluding remarks

• Quantifying the combined effects of hydrodynamics and chemistry is essential in utilizing lab-scale biomass pyrolysis reactor data for scale up

• Biomass particle properties and fluidization intensity have major impacts on product yields

• A key question remains: Is there a single combination of biomass feed particle size and fluidization intensity where tar yield is maximized?

• Two-fluid codes like MFIX can yield useful details about pyrolyzer hydrodynamics and gas and solid RTDs but improvements to the physics are still needed

• Combining MFIX hydrodynamics with low-order chemistry models appears to offer potential benefits


AcknowledgementsThis research was supported by the U.S. Department of Energy Bioenergy Technologies Office (BETO) as part of ChemCatBio, an Energy Material Network, and the Consortium for Computational Physics and Chemistry. The authors would like to thank BETO sponsors Jeremy Leong, Cynthia Tyler, and Kevin Craig for their guidance and support.

NREL –Brennan Pecha, Kristiina Ilsa, Kristin Smith, Katherine Gaston, Jessica, Olstadt, Thomas Foust, Rick French, Danny Carpenter, Peter Ciesielski

NETL – William Rogers, Justin Weber, Dirk VanEssendelft

ORNL – Gavin Wiggins, James Parks, John Turner, Maggie Connatser, Charles Finney

SABIC – Sreekanth Pannala

University of Tennessee – Nourredine Abdoulmoumine and Biomass convErsion And Modeling (BEAM) team, Bredesen Center for Interdisciplinary Research and Graduate Education

cpcbiomass.org

www.chemcatbio.org


Questions?

Emilio Ramirez – [email protected]://www.ornl.gov/staff-profile/emilio-ramirez

Consortium forComputationalPhysics andChemistryhttp://cpcbiomass.org

mailto:[email protected]

https://www.ornl.gov/staff-profile/emilio-ramirez

computational study on biomass fast pyrolysis: … as typical lab-scale example •key steps:...

Documents