Mul$%scalesimulaonoffluidized bedbiomassgasificaon* · Mul$%scalesimulaonoffluidized...

Mul$-‐scale simula$on of fluidized bed biomass gasifica$on

Ahmed F. Ghoniem Christos Altantzis, Addison K. Stark, Richard B. Bates

Akhilesh Bakshi, Rajesh Sridhar

Department of Mechanical Engineering MassachuseHs Ins$tute of Technology

NETL Workshop of Mul$phase Simula$ons

August 12-‐13, 2015

Project support by BP

Biomass to liquids pathways

•  Gasifica$on offers improved feedstock u$liza$on versus exis$ng 1st genera$on routes •  Gasifica$on allows produc$on of drop-‐in fuels (e.g. FT Diesel) as well as chemicals

Cellulosic biomass

Syngas

Pyrolysis Bio-oils

Acid Hydrolysis Aqueous products

FT Synthesis

Catalysis

Catalysis

Fermenta$on Sugar monomers

Alkanes

Liquid fuels

Fuels and chemicals

Ethanol

Thermochemical routes

Biochemical route

Fluidized bed gasi6ication of biomass

Raw biomass characteris/cs •  High moisture content •  Expensive par$cle size reduc$on

Entrained flow gasifier: Very dilute par$cle-‐gas mul$phase flow •  High opera$ng temperatures •  High levels of carbon conversion with low tar content •  Requires very fine par$cles (<mm)

Not suitable for raw biomass, unless it is pre-‐torrefied, see Bates and Ghoniem, Bioresource Tech

3

Fluidized bed gasi6ication of biomass

Raw biomass characteris/cs •  High moisture content •  Expensive par$cle size reduc$on

Fluidized bed: Widely used paradigm for dense par$cle-‐gas mul$phase flows •  High surface area contact between fluid and solid per unit bed volume •  High levels of intermixing •  Suitable for coarse par$cles with large residence $mes •  Lower levels of carbon conversion with considerable tar content

Appropriate for biomass gasifica$on, poses opera$onal and modeling challenges

4

Multi-‐scale nature of biomass gasi6ication

Mul$-‐scale process: •  Gas-‐phase chemistry •  Surface chemistry •  Single-‐par$cle modeling •  Hydrodynamics •  Mixing -‐ Segrega$on

Fluidized bed biomass gasi6ication Modeling

Challenges •  Complex interplay between:

•  Mul$phase fluid dynamics •  Massive chemical reac$on networks

•  Exis$ng chemical kine$c mechanisms are too computa$onally demanding

•  Ranzi et al. [2] 460 species, 16000 reac$ons •  Reliable chemistry reduc$on strategies are needed

•  Simula$ng industrial scale reactors requires the development of advanced closure sub-‐models for the physics descrip$on

Typical structure of sodwood lignin (Faravelli et al., 2010 [2])

Multiscale simulations & technical challenges

2. Undesirable tar compounds

Technical challenges in fluidized bed gasifica/on

Demonstra/on scale d=2.5m

Commercial-‐scale d=5m

Lab scale d=0.25m

1.  Expensive scale-‐up

3. Complex gas-‐solid mixing and hydrodynamics

Multiscale-‐multi physics simulations

2. Undesirable tar compounds

Technical challenges in fluidized bed gasifica/on

MIT’s modeling work 1.  Bakshi et al. Efficient computa$onal schemes for

cylindrical FB reactors with CFD, Powder Tech. 2014. [3]

2.  Altantzis et al., Sensi$vity of solids circula$on to walls, par$cle, flow characteris$cs using CFD. , Powder Tech., 2015 [4]

3.  Bakshi, et al., Eulerian-‐Eulerian simula$on of dense solid-‐gas cylindrical fluidized beds; wall boundary condi$on and its impact on fluidiza$on. Powder Tech., 277 (2015) 47-‐62 [5]

4.  Stark etal., Predic$on and valida$on of syngas and tar species from a reactor network model of fluidized bed biomass gasifica$on, Energy & Fuels, 2015 [6]

5.  Bates et al, Char combus$on, gasifica$on and aHri$on in bubbling fluidized beds. Energy & Fuels 2015 [7]

6.  Stark, A., Comprehensive par$cle and homogeneous chemistry modeling, PhD Thesis 2015 [8]

7.  Sridhar, Reactor Network Modeling of biomass gasifica$on with detailed chemistry, Master Thesis 2015. 8

1.  Expensive scale-‐up

3. Complex gas-‐solid mixing and hydrodynamics

CFD

Reactor network modeling Par/cle scale modeling

Computa/onal efficiency and scalability

Valida/on and metrics

Modeling length scales (m)

Internal + External Mass Transfer

Primary Pyrolysis Reaction zone

Gas Phase Secondary Pyrolysis Reactions, Tar Reformation and Oxidation Reactions

Heterogeneous Reactions

Heat Transfer (conduction, convection, radiation)

Abrasion causes attrition

Devola/liza/on

10-‐6-‐10-‐2 10-‐3-‐100 10-‐2-‐101

Network model Comprehensive

chemical kine/cs

Gasifica/on

Towards Multi-‐scale simulation of biomass gasi6ication

Approach Gas Phase Solid Phase Scale

Discrete Bubble Model

Lagrangian Eulerian Industrial (10 m)

Two Fluid Model

Eulerian Eulerian Engineering (1 m)

Discrete Element Model

Eulerian (unresolved)

Lagrangian Laboratory (0.1 m)

Discrete Particle Model

Eulerian (resolved)

Lagrangian Laboratory (0.1 m)

Molecular Dynamics

Lagrangian Lagrangian Mesoscopic (<0.001 m)

10

Two-Fluid Model (TFM) balances computational efficiency with

modeling fidelity making it suitable to industrial scale but challenges remain!

Accuracy

Compu

ta/o

nal efficien

cy

Computational approaches to 6luidization

Two Fluid Model

•  Par$cle-‐Par$cle interac$ons –  Kine$c theory of granular flows –  Inter-‐par$cle drag law

•  Segrega$on slope coefficient, Cs •  Fric$on coefficient, Cf

•  Par$cle-‐gas interac$ons –  Drag laws

•  Par$cle-‐wall interac$ons –  Res$tu$on coefficient, e –  Specularity coefficient, ϕ

Closure models involve parameters whose influence on hydrodynamics need inves$ga$on by means of: •  Detailed experiments •  Valida$on strategies •  Use of detailed numerical methodologies in smaller

scales

•  Ascending bubbles drive solids motion

•  Bubbles act as

pathways for gas flow

Importance of bubble physics during 6luidization

Gas phase mo/on Solid phase mo/on

13

Bubble statistics Dense phase statistics

Time mean solids holdup

Size vs height Velocity vs Height

Original CFD data f(x,y,z,t)

Circulation flux

Metrics for 6luidization hydrodynamics

0.0

0.3

0.6

3D bubble statistics tool Bakshi at.al,

Bubble geometric characteristics •  Surface area •  Volume •  Cord length (axial/radial) •  Aspect ratios

Statistical analysis of bubble size and velocity distributions

3D bubble analysis for: •  Accurate detection of the

number of bubbles in the bed (only a fraction is captured with 2D analysis)

•  Azimuthal motion of bubbles

Dense phase mixing metric

•  Circulation fluxes & circulation times are sensitive to the parameters in sub-models •  Useful quantities for rigorous quantitative validation with novel experimental

measurements employing particle velocimetry •  Strongly coupled with the bubbling behavior in the bed

16

100

cm

50 cm

Lab-Scale Model Delgado et al. 2013 [9]

•  Lack of experimental data on φw => φw is a fitting parameter

φw tuned to 2D simulations is not appropriate for 3D simulations

•  Thin rectangular beds extensively used in experimental studies employing non-intrusive measurements techniques

•  Cylindrical beds more realistic geometries for scale-up and different hydrodynamics compared to pseudo-2D beds

•  Variation of specularity coefficient to evaluate impact on simulation of pilot-scale model

100

cm

14.5 cm

Lab-Scale Model Rudisuli et al. , 2012 [11]

100

cm

10 cm

Lab-Scale Model Verma et al. , 2014 [10]

Parameter estimation and validation strategy Example: specularity coef6icient

100cm

15cm

100cm

30cm

Rudisuli et al. , 2012 (Op$cal probe measurements

for bubble dynamics)

Pilot scale reactor

100cm

50cm Delgado et al. 2013 [9] (Digital Image Analysis for solids mo$on)

Parametric analyses and valida$on •  Parametric analyses with respect to the specularity coefficient •  Valida$on based on experimental studies for sand-‐like par$cles in different bed geometries

•  Thin rectangular beds are extensively used

in experimental studies employing non-‐intrusive measurements techniques

•  Significant influence of the wall presence especially in the spanwise direc$on

•  NOT suitable for comparison with 2D simula$ons

100cm

50cm Delgado et al. 2013

(Digital Image Analysis for solids mo$on)

Parametric analyses and valida$on •  Parametric analyses with respect to the specularity coefficient •  Valida$on based on experimental studies for sand-‐like par$cles in different bed geometries

19

Decreasing φ results in: •  Larger bubble sizes

•  Slugging fluidization

•  Gas bypassing effects

•  Higher bubble velocities

Uin = 2.5Umf φ = 0.0005

Negligible hindrance

Uin = 2.5Umf φ = 0.5

Significant hindrance

Simulation Results

Bubble hydrodynamics and solids motion significantly influenced by wall boundary condition

Thin rectangular bed

1×10-4 1×10-3 1×10-2 1×10-1 1×100

Specularity coefficient

0

2

4

6

8

Tim

e [s

]

tc+

tc-

tc

1×10-4 1×10-3 1×10-2 1×10-1 1×100

Specularity coefficient

0

2

4

6

8

Tim

e [s

]

tc+

tc-

tc

Case A Case D

Φ = 0.05 Φ = 0.4

Experiment Experiment

Circ

ulat

ion

time

[s]

Circ

ulat

ion

time

[s]

Uin=2.5Umf Uin=1.75Umf

20

•  Circulation time ↑ when

•  Uin ↓ - closer to Umf and less bubbling

•  φ ↑ - wall resistance increases ⇒ less solid motion close to walls and smaller bubbles

•  Appropriate φ ↓ as Uin ↑. For the range, 1.5Umf – 2.5Umf, Φ ∈ [0.05,0.5]

Choosing Φ

Thin rectangular bed

100cm

15cm

100cm

30cm

Rudisuli et al. , 2012 [11] (Op$cal probe measurements

for bubble dynamics)

Pilot scale reactor

100cm

50cm Delgado et al. 2013

(Digital Image Analysis for solids mo$on)

Parametric analyses and valida$on •  Parametric analyses with respect to φ and the drag model •  Valida$on based on experimental studies for sand-‐like par$cles in different bed geometries

•  Realis$c geometries of gasifiers •  Effect of boundary condi$ons for

different bed sizes

•  Scaling-‐up simula$on towards industrially relevant sizes

22

φ = 0.01

Mechanism

0.2

0.4

0.6

0.8

Influence of specularity coefficient in a 3D cylindrical bed

solids distribu/on (circula/on flux / /me) bubble dynamics (Bubble dia/count)

Glass 2500 kg/m3, 1.1 mm Bed Dia = 10 cm U=2.0 Umf

Alumina 1040 kg/m3, 1.0 mm Bed Dia = 10 cm U=3.0 Umf

Suitable ϕ ?

•  Range – 1.25-6.80 Umf, 860-2500 kg/m3, 0.289-1.1 mm •  Bubble diameter comparisons show higher values of ϕ (0.01-0.3) more suitable for dense flows •  Low sensitivity of metrics (solids / bubbles) for suitable ϕ •  Variable ϕ model (Li&Benyahia [12]) matches the results of the suitable ϕ

Valida$on

2.3 Umf 6.8 Umf •  Gidaspow model = pressure

drop data from packed + homogeneous fluidization experiments

•  Syamlal-O’Brien model = terminal velocity correlation of particles from liquid-solid beds

Takeaways - 1.  Drag model has a strong

impact on fluidization

2.  Gidaspow model more suited for low velocities (2-4 Umf)

Effect on Pilot-‐Scale Bed

Influence of drag model

CFD











Devola/liza/on

10-‐6-‐10-‐2 10-‐3-‐100 10-‐2-‐101

Network model

Towards Multi-‐scale simulation of biomass gasi6ication

Comprehensive chemical kine/cs

Gasifica/on

Thermochemical Conversion of Biomass in FBBG

Gas-‐Phase Conversion Pathway

Solids Conversion Pathway

Solids -‐> Gas conversion: Complex interplay between Heat transfer and Chemistry

Gas-‐Phase Reac$ons: Mul$ple species and pathways

High char conversion crucial to op$mal efficiency and opera$on

Approach: Integrated par$cle/reactor network models to predict gasifier outputs

Reactor network model (Homogeneous reac=ons)

Char conversion par$cle model (Heterogeneous reac=ons)

Gasifica$on, aHri$on rates

Gas-‐phase concentra$ons Iterate un$l gas phase composi$on converges







Devola$liza$on par$cle-‐scale model Inputs: Reactor temperature, pressure, feed par$cle size composi$on

C+H2O = CO+H2 C+CO2 = 2CO C+0.5O2= CO

Transport limita/ons during par/cle-‐Scale devola/liza/on

28

}  Par$cle-‐phenomena directly influence overall conversion

}  Interplay between }  External convec$ve heat transfer

from the bed to the par$cle }  Internal conduc$ve heat transfer }  Primary pyrolysis reac$on kine$cs

Devolatilization Model Development

1-‐D par/cle model Devola/liza/on Mechanism

29

•  Base of model is integration of heat transfer and chemical conversion:

–  1-D heat equation coupled with reactions

–  Mechanism of Ranzi et al. [1]

•  Boundary Conditions: –  Convective and radiative heat transfer at

surface from particle trajectory history •  Can come from CFD!

•  System of equations are integrated with respect to time.

–  Requires a stiff ODE integrator. –  Simulation cost: 100 cpu-seconds

per second.

Ranzi et al. [1]

Single Particle Model Predictions

Resolves internal gradients and reac/on dynamics

Cellulose, Hemicellulose and Lignin devola$lize sequen$ally, yielding a triple pyrolysis wave

30

Single Particle Model Predictions

Predic/on of Devola/liza/on Species Rate of produc$on of tar precursors are of par$cular interest. Physical parameters have a strong effect. 20 gaseous species and char

31

Par/cle devola/liza/on /me versus par/cle size

0.005 0.01 0.015 0.02 0.025 0.030

50

100

150

200

250

300

Particle Diameter [m]

Con

vers

ion

Tim

e [s

]

500C

900C

“Tar+water” yields versus reactor temperature

Par$cle scale devola$liza$on model valida$on

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

500 600 700 800 900

Tar yield (kg/kg)

Reactor temperature ○C

Exp. 6mm

Lines: Best-‐fit by Gaston et al. 2011 [13] Points: Model predic$ons

32














Role of char conversion during FBG

•  Gomez-‐Barea & Leckner 2013 [14]: •  “The conversion of char is the most

decisive factor in FBG, because the main loss of efficiency is due to unconverted carbon in the ashes.”

•  “...results show that value assigned for char conversion has a major effect on the temperature, gas hea=ng value, and therefore on other parameters like gas composi=on and cold gas efficiency”

•  LHV ranges 5.4-‐7.75 MJ/Nm3 depending on assigned char conversion.

•  Char conversion needs to be predicted accurately to reduce uncertainty in major output variables Calcula$ons for air-‐blown pilot scale wood pellet

FBG by Gomez-‐Barea & Leckner 2013 [14]:

Air/fuel equivalence ra$o

Char Particle Conversion Model

Devola$liza$on + primary fragmenta$on+

shrinkage

AHri$on pr

oduces

elutriable fi

nes

CO+H2

2CO

CO2

+H2O +CO2 +O2

Assump/on Jus/fica/on

Fines produced by aHri$on are assumed to be elutriated as soon as they are produced

Elutria$on $me scale is fast compared to aHri$on $me scale

Intrapar$cle diffusion limita$ons negligible during gasifica$on , dominant during combus$on

Thiele modulus <<1 for gasifica$on Thiele modulus >> 1 during combus$on

Isothermal par$cle Bi<1 35

Modi6ied attrition kinetics rate expression

Exis$ng models do not account for the reduc$on in density/hardness during conversion to affect the aHri$on rate

Current model aHempts to accounts for this

Fi\ed model results: Spruce wood pellet char gasifica$on in 60%vol CO2 800C

•  Proposed structural model able to accurately fit results for aHri$on rate and overall conversion.

•  ~25%wt of ini$al char is aHrited over the course of 180 minutes

0.E+00 1.E-06 2.E-06 3.E-06 4.E-06 5.E-06 6.E-06 7.E-06 8.E-06

0 50 100 150

Elut

riatio

n ra

te (k

g/m

in)

Time (minutes)

Expt. (Ammendola et al., 2013) Model

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 50 100 150

Car

bon

Con

vers

ion

(Xg)

Time (min)

Model Experiment (wood pellet)

“Shrinking core model” for char combustion; Internal and external diffusion limitations

Valida$on of combus$on/aHri$on par$cle model

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 5 10

Carbon

Con

version (X)

Time (min)

Ammendola wood chip Shrinking core model

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 10 20 30

Carbon

Con

version (X)

Time (min)

Ammendola 2013 spruce pellet Shrinking core model

Predic/ons are sensi/ve to: -‐Par$cle geometry/aspect ra$o -‐External mass transfer coefficient -‐Char par$cle density and diffusivity

Condi/ons 4.5% Oxygen at 800C 95.5% Nitrogen

Char gasifica$on/combus$on par$cle model

•  Gasifica$on assisted aHri$on well represented by new model –  Sensi$ve to assumed gasifica$on kine$cs

•  Combus$on for different feedstocks is well represented by effec$veness factor model –  Rate is sensi$ve to par$cle shape and size –  Sensi$ve to external mass transfer coefficient model

•  Can be incorporated in a RNM. •  In CFD it will be part of a Lagrangian descrip$on














Importance of tar yields during biomass gasifica$on

Tar classifica$on based on, 1.  Water solubility 2.  Condensa$on temperatures

Downstream problems: 1.  Condensa$on at low

temperatures might result in fouling or clogging of the gas pipelines

2.  High solubility results in toxic wastewater which would require expensive disposal systems downstream

[16] van Paasen and Kiel, 2004

Reactor network model of fluidized biomass gasifier

•  Fluidized bed is well mixed at rate faster than $mescale of devola$liza$on

•  Devola$liza$on is uniform through bed

•  Gas-‐phase reac$ons in emulsion can be modeled as a CSTR (con$nuously s$rred tank reactor)

•  In freeboard few solids present thus liHle axial mixing

•  Gas-‐phase reac$ons in freeboard can be modeled as a PFR (plug flow reactor)

43

Reactor network model of fluidized biomass gasifier

Mechanism: CRECK/Ranzi ~460 Species ~16000 Reac$ons

44

CRECK mechanism for primary pyrolysis and secondary gas phase

reac$ons

• 18 Reac$ons

• 33 Species • Diffusion Modeling

Biomass • 16,000 Reac$ons

• 460 Species • Homogeneous Gas-‐Phase

Primary Pyrolysis Products

Secondary Pyrolysis and Gasifica$on Products

45

Levoglucosan

Phenol

Coumaryl Alcohol

C=O

H2

Benzene

Toluene

Naphthalene

Experimental data available for valida$on: Air-‐blown bubbling fluidized bed gasifiers.

(Van Paasen and Kiel, 2004 [16]) •  Biomass feed rate: 1kg/hr •  Biomass feedstock: Beech •  Air-‐Fuel Equivalence Ra$o: 0.25 •  Fluidizing medium: Sand, 270 μm micron •  Bed diameter= 7.4 cm

(Narvaez et al., 1996 [17]) •  Biomass feed rate: 0.5kg/hr •  Biomass feedstock: Pine sawdust •  Air-‐Fuel Equivalence Ra$o: 0.25-‐0.4 •  Fluidizing medium: Sand, 360 μm •  Bed diameter=6 cm

(Kurkela & Stahlberg, 1992 [18]) •  Biomass feed rate: 40kg/hr •  Biomass feedstock: Pine sawdust •  Air and Steam employed •  Air-‐Fuel Equivalence Ra$o: 0.25-‐0.4 •  Fluidizing medium: Sand, 600 μm •  Bed diameter=15 cm •  At pressure (4 bar) •  Secondary Air injec$on.

46

Freeboard

Bubbling bed

Product Gas

Hk

Biomass

Air/Steam/O2

Hb

Db

Air/Steam/O2

RNM valida$on: Effects of temperature on major species, effects of CO and Water Gas

Shid Kine$cs.

Comparison of full mechanism with experimental data

•  Full mechanism and reactor network model able to accurately represent the destruc$on of tars at higher temperatures

Tar Class predic$ons

49

We are under-‐predic$ng the

forma$on of PAH compounds… by orders

of magnitude

Well S$rred bed possibly over-‐predicts availability of oxygen everywhere, impeding

PAH forma$on… Transport likely playing

a role!

Well-‐s$rred Bed Zone is a strong assump$on

50

Increasing Gas velocity => Faster bubble growth and faster bubble flow Larger bubbles => Less gas exchange between emulsion and bubble

51

Well-‐s$rred Bed Zone is a strong assump$on

XN2 Xtari Rdevol Voidage •  Bubbles carry majority

of excess fluidiza$on gas

•  Devola$liza$on occurs in emulsion

•  Full mixing occurs at splash zone

•  Emulsion rela$vely rich…

RNM needs to capture this to predict PAH forma$on.

Improved RNM Formula$on

Improved RNM Formula$on

Devol. Gases

Air

Freebo

ard

(PFR)

Bubb

le

(PFR)

Emulsio

n (C

STR)

x 1-‐x

y 1-‐y

Devol. Gases

Air

Freebo

ard

(PFR)

Bubb

le

(PFR)

Emulsio

n (C

STR)

x 1-‐x

y 1-‐y

Impact of bubble-‐phase on gas-‐phase conversion

Impact of bubble-‐phase on gas-‐phase conversion

Devol. Gases

Air

Freebo

ard

(PFR)

Bubb

le

(PFR)

Emulsio

n (C

STR)

x 1-‐x

y 1-‐y

CFD











Devola/liza/on

10-‐6-‐10-‐2 10-‐3-‐100 10-‐2-‐101

Network model

Current work: Coupling CFD, particle thermochemistry and RNM

Comprehensive chemical kine/cs

Gasifica/on

Bubble phase and emulsion proper$es

Concluding remarks •  Mul$-‐scale simula$on necessary to tackle the complexity of fluidized bed

biomass gasifica$on

•  Eulerian Eulerian approach for fluidiza$on offers computa$onal scalability but requires closures and valida$on –  Contribu$ons in appropriate metrics, robust parametriza$on, computa$onal

efficiency

•  Par$cle scale modeling: –  FBG dominated by par$cle scale processes of devola$liza$on, gasifica$on and

combus$on –  Dominant physical/chemical processes must be iden$fied

•  Impossible to model “all” processes

•  Reactor network modeling –  Flexible, incorporates comprehensive chemistry and par$cle models –  Can benefit from CFD results (gas distribu$on, exchange … )

References [1] E. Ranzi, M. CorbeHa, F. Manen$, S. Pierucci, “Kine$c modeling of the thermal degrada$on and combus$on of biomass”, Chemical Engineering Science, 2014, 110:2-‐12 [2] T. Faravelli, A. Frassolda$, G. Migliavacca, E. Ranzi, “Detailed kine$c modeling of the thermal degrada$on of lignins” Biomass and Bioenergy, 2010,34(3):290–301 [3] A. Bakshi, C. Altantzis, A.F. Ghoniem, “Towards accurate three-‐dimensional simula$on of dense mul$-‐phase flows using cylindrical coordinates”, 2014, Powder Technology, 264:242-‐255 [4] C. Altantzis, R.B. Bates, A.F. Ghoniem, “3D Eulerian modeling of thin rectangular gas–solid fluidized beds: es$ma$on of the specularity coefficient and its effects on bubbling dynamics and circula$on $mes”, Powder Technology, 2015, 270(A):256-‐270 [5] A. Bakshi, C. Altantzis, R.B. Bates, A.F. Ghoniem, “Eulerian–Eulerian simula$on of dense solid–gas cylindrical fluidized beds: Impact of wall boundary condi$on and drag model on fluidiza$on”, Powder Technology, 2015, 277:47-‐62 [6] A.K. Stark, R.B. Bates, Z. Zhao, A.F. Ghoniem, “Predic$on and Valida$on of Major Gas and Tar Species from a Reactor Network Model of Air-‐Blown Fluidized Bed Biomass Gasifica$on”, Energy & Fuels, 2015, 29(4):2437-‐2452

References [7] R.B. Bates, C. Altantzis, A.F. Ghoniem, “Modeling of biomass char gasifica$on, combus$on and aHri$on kine$cs in fluidized beds”, Energy & Fuels, 2015, submiHed for publica$on [8] Addison Stark, “Mul$-‐Scale Chemistry Modeling of the Thermochemical Conversion of Biomass in a Fluidized Bed Gasifier”, MassachuseHs Ins$tute of Technology, PhD Thesis, 2015 [9] S. Sánchez-‐Delgado, C. Marugán-‐Cruz, A. Soria-‐Verdugo, D. Santana, “Es$ma$on ans experimental validaiton of the circula$on $me in a 2D gas-‐solid fluidized beds”, Powder Technology, 2013, 235:669-‐676 [10] V. Verma, J. T. Padding, N.G. Deen, J.A.M. (Hans) Kuipers, F. Barthel, M. Bieberle, M. Wagner, U. Hampel, “Bubble dynamics in a 3-‐d gas–solid fluidized bed using ultrafast electron beam X-‐ray tomography and two-‐fluid model”, AIChE Journal, 2014, 60(5):1632-‐1644 [11] M. Rüdisüli, T.J. Schildhauer, S.M.A. Biollaz, A. Wokaun, J.R. van Ommen, “Comparison of bubble growth obtained from pressure fluctua$on measurements to op$cal probing and literature correla$ons”, Chemical Engineering Science, 2012, 74(0):266-‐275 [12] T. Li, S. Benyahia, “Revisi$ng Johnson and Jackson boundary condi$ons for granular flows”, AIChE Journal, 2012, 58(7):2058-‐2068

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