Numerical  Simulation  of  Biomass  Gasi3ication  in  a  Steam-­‐Blown  

Bubbling  Fluidized  Bed    

 Christos  Altantzis,  Addison  Stark,  Richard  Bates,    Akhilesh  Bakshi,  Rajesh  Sridhar,  Ahmed  Ghoniem  

 Department  of  Mechanical  Engineering  Massachuse5s  Ins7tute  of  Technology  

 NETL  Workshop  of  Mul7phase  Simula7ons,  August  12-­‐13,  2015  

 Project  support  by  BP  

 

Steam-­‐blown  3luidized  bed  gasi3iers  Fluidized  bed:  Favorable  reactor  technology  for  biomass  gasifica7on  with  minimum  pretreatment    Advantages:  •       High  levels  of  intermixing  •       Suitable  for  coarse  par7cles  with  large  residence  7mes  Disadvantages:        •       Lower  levels  of  carbon  conversion  with  considerable  tar  content  

Steam  as  a  gasifica2on  agent:  Advantages:  •       Reduced  cost,  no  air  separa7on  is  needed  •       In  the  absence  of  oxida7on,  hot  zones  are  avoided  in        the  bed  Disadvantages:        •       Biomass  devola7liza7on  and  char  gasifica7on  are  endothermic  •       External  hea7ng  is  needed  for  controlling  the  process      

Key  phenomena  in  a  3luidized  bed  gasi3ier  Mul7-­‐scale  process:    •   Gas-­‐phase  chemistry  •   Surface  chemistry  •   Single-­‐par7cle  modeling  •   Hydrodynamics    

•  Development   of   a  mul7scale   CFD  methodology   for   the   reac7ve  mul7phase  simula7ons  to  assist  the  design  and  op7miza7on  of  gasifica7on  processes  by  reducing  the  cost,  compared  with  experiments,  and  offering  informa7on  for  integra7on  in  ROM  

Goal  

Modeling  Challenges:  Initial  char  loading  •  Challenge:  Steady  state  char  inventory  takes  hours  to  reach  for  

gasifica7on  condi7ons  –  Ini7al  transient  too  long  for  CFD  simula7on  

•  Solu2on:  Standalone  MATLAB  steady  state  char  conversion  model  computes  char  inventory  for  CFD  ini7al  condi7on  –  Char  gasifica7on  and  combus7on  –  Gasifica7on  assisted  a5ri7on  due  to  hardness  reduc7on  

Steady  state  char  inventory  for  steam  blown  gasi3ier  

dreactor=10.2cmmchar=138g

Vchar =2029cm3

T=700  ○C

Hchar=24.8cm

Transient  model:System  of  ordinary  

differential  equations  

Steady  state  model:  Iterative  solver  for  

average  residence  time

Char  conversion  model

Outputs:

Inputs:

Average  char  inventory  (kg)Average  char  resident  time  (sec)Average  char  conversion  (-­‐)

Gas  composition  (XCO2,XH2O…)Reactor  temperature,  pressure,Initial  biomass  particle  size

��#$,&'( 𝑘𝑔 𝑠𝑒𝑐⁄  steady  biomass  feed  rate𝑌12'&[𝑘𝑔 𝑘𝑔⁄ ]  char  devolatilization yield

Computational  cost<1  sec

CFD  ini2al  condi2ons  :  -­‐char  inventory  -­‐par7cle  size  

Modeling  Challenges:  Chemistry  description  

•  Challenge:  Number  of  species/reac7ons    prohibi7vely  large  for  use  in  CFD  •  Solu7on:  Use  of  Global  models  for  pyrolysis    and  tar  cracking  [1]  

•  Devola7liza7on  dynamics  are  strongly  influenced  by  par7cle  radius.  

•  Shrinking  Core  Model  Implemented  for  Eulerian  Modeling  Framework:  

•  Trade-­‐off  of  devola7liza7on  7me  and  mixing  7me  important  for  well-­‐s7rred  assump7on.    

Global  devolatilization  model    

keff =1

1kkin

+ akcond

•  Raw  biomass  injected  as  par7cles  and  removed  as  char  •  Biomass  typically  <2-­‐3%  of  bed  mass  •  Bed  temperature  600-­‐1000°C  •  Very  rapid  heat  up  •  Mixing  and  par2cle  residence  2mes  are  very  important  to  product  

composi7on  and  conversion  

t2 t3 t4t1

Vol

atile

rele

ase

time

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUEL t=t0

OXIDANT

FUEL

CHARCOMBUSTION

CHARGASIFICATION

BIOMASSPYROLYSIS

OXIDANT

FUEL

BIOMASSPYROLYSIS

CHARGASIFICATION

OXIDATIONOF CHAR AND

VOLATILES

TA

R C

RA

CK

ING

OXIDANT

FUELPYROLYSIS

CHARGASIFICATION

+OXIDATION

OF CHAR AND VOLATILES

+TAR

CRACKING

+

(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1

Xb

Xb,avg

z

XbXb

(b.3) Dapy,V>> 1(a.3) Dapy,V<< c.3) Dapy,V~ 1

t2 t3 t4t1 t2 t3 t4t1

Vol

atile

rele

ase

time

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUEL t=t0

FUELFUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUELFUEL t=t0

OXIDANT

FUEL

CHARCOMBUSTION

CHARCOMBUSTION

CHARGASIFICATION

BIOMASSPYROLYSIS

OXIDANT

FUEL

BIOMASSPYROLYSIS

CHARGASIFICATION

OXIDATIONOF CHAR AND

VOLATILES

TA

R C

RA

CK

ING

TA

R C

RA

CK

ING

OXIDANT

FUELPYROLYSIS

CHARGASIFICATION

+OXIDATION

OF CHAR AND VOLATILES

+TAR

CRACKING

+

(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1

Xb

Xb,avg

z

XbXb

(b.3) Dapy,V>> 1(a.3) Dapy,V<< c.3) Dapy,V~ 1(b.3) Dapy,V>> 1(a.3) Dapy,V<<

1 (

1 (

1 (1 (

1 (1 (c.3) Dapy,V~ 1

OXIDANT OXIDANT OXIDANT

Fig. 6. (a.1)–(c.1): Possible situations according to Damkohler number Dapy,V in a bubbling FB. (a.2)–(c.2) Models of the limiting situations (a) (b) and general case (c). (a.3)–(c.3)Models of distribution of fuel in the three cases.

A. Gomez-Barea, B. Leckner / Progress in Energy and Combustion Science 36 (2010) 444–509458

Importance*of*accurate*predic$on*of*mixing*dynamics*

•  Devola$liza$on* $me* scales* are* similar* with* mixing* $me* scales,* so* accurate*predic$on*of*the*posi$on*of*raw*biomass*is*important*

•  Low* density* and* small* size* of* char* par$cles* will* result* in* segrega$on* during*gasifica$on.* Accumula$on* of* char* to* the* top* of* the* bed* causes* lower* heat*release*within*the*bed,*reducing*the*reac$vity**

t2 t3 t4t1

Vol

atile

rele

ase

time

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUEL t=t0

OXIDANT

FUEL

CHARCOMBUSTION

CHARGASIFICATION

BIOMASSPYROLYSIS

OXIDANT

FUEL

BIOMASSPYROLYSIS

CHARGASIFICATION

OXIDATIONOF CHAR AND

VOLATILES

TA

R C

RA

CK

ING

OXIDANT

FUELPYROLYSIS

CHARGASIFICATION

+OXIDATION

OF CHAR AND VOLATILES

+TAR

CRACKING

+

(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1

Xb

Xb,avg

z

XbXb

(b.3) Dapy,V>> 1(a.3) Dapy,V<< c.3) Dapy,V~ 1

t2 t3 t4t1 t2 t3 t4t1

Vol

atile

rele

ase

time

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL

t4

t1 t3t2

t=t0

(b.1) Dapy,V>> 1

FUEL FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

t1

t=t0

t2

t3

t t4

(c.1) Dapy,V~ 1

FUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUEL t=t0

FUELFUEL

FUELPARTICLE

D

L

t1

t=t0

t

(a.1) Dapy,V<< 1

FUELFUEL t=t0

OXIDANT

FUEL

CHARCOMBUSTION

CHARCOMBUSTION

CHARGASIFICATION

BIOMASSPYROLYSIS

OXIDANT

FUEL

BIOMASSPYROLYSIS

CHARGASIFICATION

OXIDATIONOF CHAR AND

VOLATILES

TA

R C

RA

CK

ING

TA

R C

RA

CK

ING

OXIDANT

FUELPYROLYSIS

CHARGASIFICATION

+OXIDATION

OF CHAR AND VOLATILES

+TAR

CRACKING

+

(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1(b.2) Dapy,V>> 1(a.2) Dapy,V<< c.2) Dapy,V~ 1

Xb

Xb,avg

z

XbXb

(b.3) Dapy,V>> 1(a.3) Dapy,V<< c.3) Dapy,V~ 1(b.3) Dapy,V>> 1(a.3) Dapy,V<<

1 (

1 (

1 (1 (

1 (1 (c.3) Dapy,V~ 1

OXIDANT OXIDANT OXIDANT

Fig. 6. (a.1)–(c.1): Possible situations according to Damkohler number Dapy,V in a bubbling FB. (a.2)–(c.2) Models of the limiting situations (a) (b) and general case (c). (a.3)–(c.3)Models of distribution of fuel in the three cases.

A. Gomez-Barea, B. Leckner / Progress in Energy and Combustion Science 36 (2010) 444–509458

Devola7liza7on   7me   scales  are   similar   with   mixing   7me  scales,   so   accurate   predic7on  of  the  posi7on  of  raw  biomass  is  important  

Modeling  Challenges:  Hydrodynamics  

CFD  modeling  strategy  for  gas-­‐par7cle  flows  Two  Fluid  Model  

Gas  Phase                  à  Eulerian  Framework  Par7cle  Phase    à  Eulerian  Framework  

Both  phases  are  considered  as  fully  interpenetra7ng  con7nua  

ADVANTAGE:    High  computa7onal  efficiency  makes  this  method  more  a5rac7ve  since  parametric  and  design  studies  of  large-­‐scale  systems  are  feasible    DISADVANTAGE:    Closures  are  required  for  the  modeling  of:  

•   Interphase  momentum  exchange  •   Par7cle-­‐par7cle  interac7on  

Numerical  tool:  MFIX  (DOE-­‐NETL)  [2,3]  

Experimental  Parameter   Value  

Bed  material                      Size,  dp,                      Density  ρp  

Olivine  (Mg,Fe)2SiO4  

270  μm    3300  kg/m3  

Minimum  Fluidiza2on  umf   ~0.037m/s  

Superficial  velocity,  ,u0   ~0.12  m/s  

Bed  diameter,  dbed    Bed  Height  

4’’(0.106  m),    0.13m  

Bed  temperature,  Tbed    (heated  walls)    

750,  800,  850  ○C,    

Inlet  gas  composi2on  Xfeed   100%  H2O(%vol)  <2%  Helium  

Input  biomass  feed  rate  bio,0   800g/hour  

Biomass  mean  diameter  dbio,0   1  mm  

Steam  flow  rate   800g/hour  

NREL  gasi3ier  

130

cm

10.6 cm

•  2D  simula7ons  of  a  steam  blown  gasifier    (Both  reac7ng  and  non-­‐reac7ng)  •  3  solid  phases  considered  

•  Biomass  :  Rho=600kg/m3,  d=1mm  •  Char  :  Rho=170kg/m3,  d=0.38mm  •  Sand  :  Rho=3300kg/m3,  d=0.27mm  

•  Drag  model:  Gidaspow  •  Inter-­‐par7cle  drag  model:  Gera  et  al.  2004  [4]  

•  Fric7on  coefficient,  Cf  =  0.1  •  Segrega7on  slope  coefficient,  Cs  =  0.1  

•  Par7al  slip  BC  for  solids  •  Specularity  coefficient,  φ  =  0.05  [5,6]  •  Dirichlet  BC  for  temperature  (1023  K)  along  walls  and  inflow  •  Ini7al  Condi7on  

•  Sta7c  bed  height:  24.4  cm  •  εs,sand  =  0.4582  •  Εs,char  =  0.1218  

Simulation  setup  

•  Resolu7on:  40X400  cells  

Chemical  mechanism  •  Drying  

•  Devola7liza7on  (Compe7ng  pathways  following  Gronli  2000  [7])  bio  -­‐-­‐>  7.7872H2  +  4.7274CO  +  4.3016CO2  +  1.7109CH4  +  6.9712H2O    bio  -­‐-­‐>  6.2792tar1    bio  -­‐-­‐>  40.8361FC1  

•  Tar  cracking  (Details  in  following  slides)  

•  Water  gas  shix  (Fast  kine7cs  Biba  1978  [8])  CO  +  H2O  -­‐-­‐>  H2  +  CO2  H2  +  CO2  -­‐-­‐>  CO  +  H2O  

•  Char  gasifica7on  (Hobbs  1992  [9])  C  +  CO2  -­‐-­‐>  2CO  C  +  H2O  -­‐-­‐>  CO  +  H2      

Chemistry  description  

Tar1 à 1.5709CO+0.197CO2 + 0.4304CH4 + 0.6704H2+ 0.22Tari  

Component  mass  frac2on  

Seebauer  1999    

Light  gases   0.78  

Tarinert   0.22  

Tar  cracking  reaction  

•  Both  tar1  and  tari  are  considered  to  be  benzene    •  The  global  reac7on  is  developed  for  biomass  pyrolysis  in  inert  

environment  (N2)  at  moderate  temperatures    

Tar1  à  light  gases  +  inert  tar      

Comparison  with  NREL  experiments,  1023K    Steam  blown  bed  

Comparison  with  NREL  experiments,  1023K    Steam  blown  bed  

The  tar  cracking  mechanism  of  Seebauer  1999  over-­‐predicts  the  produced  inert  tars  by  two  orders  of  magnitude        

•  Levoglucosan  iden7fied  as  a  the  major  tar  species  present  axer  devola7liza7on  [10]  •  Global  tar  cracking  mechanism  in  a  steam  environment,  based  on  leveglucosan  cracking  •  Benzene  assumed  as  the  major  inert  tar  species    

Identi3ication  of  major  tars  after  devolatilization  

Tar  cracking  mechanism  was  modified  using  a  reactor  network  model  employing  the  Ranzi  mechanism  to  represent  the  LVG  decomposi7on  pa5ern  in  the  absence  of  oxygen    Tar1(LVG)  à  3.27CO  +  0.2CO2  +  0.65CH4  +  1.1H2  +  2.68H2O  +  0.01038Tari(Benzene)  

Comparison  with  NREL  experiments,  1023K    Steam  blown  bed  

Comparison  with  NREL  experiments,  1023K    Steam  blown  bed  

•  Tar  produc7on  is  predicted  accurately  •  Discrepancies  for  CO2,  H2  and  H2O  

Reac7ng  vs  non-­‐reac7ng  hydrodynamics  

Summary  and  future  work  

•  Ongoing  work  towards  valida7on  of  a  reac7ng  CFD  methodology  for  biomass  gasifica7on  in  a  steam  environment  

•  Use  of  tools  employing  detailed  chemistry  to  extract  informa7on  about  the  global  chemical  mechanisms  implemented  in  CFD  

•  Revisit  the  devola7liza7on  mechanism  by  implemen7ng  more  detailed  schemes  

•  Poten7al  effect  of  the  emulsion  phase  on  the  water  gas  shix  reac7on  

•  Enhancement  of  reactor  network  models  employing  detailed  chemistry  by  feeding  back  informa7on  about  gas-­‐solids  mixing  obtained  from  CFD    

References  [1]  C.  Di  Blassi,  “Modeling  chemical  and  physical  processes  of  wood  and  biomass  pyrolysis”,    Progress  in  Energy  and  Combus>on  Science,  2008,  34(1):47-­‐90  [2]  M.  Syamlal,  W.  Rogers,  T.J.  O'Brien,  “Mfix  documenta7on  theory  guide”,    Technical  Report,  U.S.  Department  of  Energy,  Na>onal  Energy  Technology  Laboratory,  1993  [3]  M.  Syamlal,  W.  Rogers,  T.J.  O'Brien,  “Mfix  documenta7on  numerical  technique”,    Technical  Report,  U.S.  Department  of  Energy,  Na>onal  Energy  Technology  Laboratory,  1998  [4]  D.  Gera,  M.  Syamlal,  T.  O’Brien,  “Hydrodynamics  of  par7cle  segrega7on  in  fluidized  beds”,    Interna>onal  Journal  of  Mul>phase  Flow,  2004,  30:419-­‐428  [5]  C.  Altantzis,  R.B.  Bates,  A.F.  Ghoniem,  “3D  Eulerian  modeling  of  thin  rectangular  gas–solid  fluidized  beds:  es7ma7on  of  the  specularity  coefficient  and  its  effects  on  bubbling  dynamics  and  circula7on  7mes”,    Powder  Technology,  2015,  270(A):256-­‐270  [6]  A.  Bakshi,  C.  Altantzis,  R.B.  Bates,  A.F.  Ghoniem,  “Eulerian–Eulerian  simula7on  of  dense  solid–gas  cylindrical  fluidized  beds:  Impact  of  wall  boundary  condi7on  and  drag  model  on  fluidiza7on”,    Powder  Technology,  2015,  277:47-­‐62  [7]  M.G.  Grønli,  M.C.  Melaaen,  “Mathema7cal  model  for  wood  pyrolysis-­‐comparison  of  experimental  measurements  with  model  predic7ons”,  Energy  Fuels,  2000,  14:791–800  [8]  V.  Biba,  J.  Macak,  E.  Klose,  J.  Malecha,  “Mathema7cal  model  for  the  gasifica7on  of  coal  under  pressure”,  Industrial  &  Engineering  Chemical  Process  Design  and  Development,  1978,  17:92  [9]  M.L.  Hobbs,  P.T.  Radulovic,  L.D.  Smoot,  “Modeling  fixed-­‐bed  coal  gasifiers”,    AIChE  Journal,  1992,  38(5):681–702  [10]  Addison  Stark,  “Mul7-­‐Scale  Chemistry  Modeling  of  the  Thermochemical  Conversion  of  Biomass  in  a  Fluidized  Bed  Gasifier”,  Massachuse5s  Ins7tute  of  Technology,  PhD  Thesis,  2015