NETL 2014 Multiphase Conference

8/9/2019 NETL 2014 Multiphase Conference

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Non-intrusive Uncertainty Quantification for

Reacting Multiphase Flows in Coal Gasifiers

Performance Measures x.x, x.x, and x.x

Aytekin Gel1! Mehr"a" #hahna$1

Arun %& #u'ra$aniyan( )or"an Musser 1

)ean-Fran*ois +ietiker 1,

1. National /nergy 0echnology a'oratory Morgantown 23 U&A&

!. A4/M5 Consulting C 4hoeni6 A7

(. G/ Glo'al Research Center

N8 ,. 2est 3irginia University Research Corporation 23

!91, N/0 Multiphase Flow #cience Conference

Morgantown 23

August :-; !91,


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Outline

Motivation and Objective Brief review of Gasification

Overview of Uncertainty

Quantification Frameworks Used

Preliminary Findings from Nonintrusive UQ !nalysis"

• #$emically %eacting case

Observations and #oncluding

%emarks


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Motivation an"


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Gasification

Gasification is t$e &rocess w$ere asolid fuel- suc$ as coal reacts wit$

steam- carbon dio+ide or $ydrogen

in a $ig$ &ressure- $ig$ tem&erature

reactor to &roduce a fuel gas- or

synt$esis gas '12- #O- #O2 *

(team is added to t$e fuel gas andsent t$roug$ a watergas s$iftreactor- w$ere #O and steam areconverted to 12 and #O2

!fter removal of #O2- $ydrogen ric$syngas can be utili3ed in a gasturbine or steam turbine for&roducing electricity or used togenerate c$emicals http://www.netl.doe.gov/File%20Library/Research/Coal/energy

%20systems/gasification/gasifipedia/


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Uncertaininputs

Quick Overview of

Uncertainty Quantification (UQ) Methods Employed

.ntrusive UQ

(everal !vailable Met$ods"

Polynomial #$aos )+&ansions

'P#)*

(toc$astic )+&ansion

Pro"

Quick &rediction

#on"

(urgery in t$e code and long

develo&ment time

Non.ntrusive UQ

(everal !vailable Met$ods"

(urrogate Model 4 Monte #arlo

Polynomial #$aos )+&ansions

Bayesian 5ec$ni/ues

Pro"

($ort develo&ment time

#on"

(am&ling error

Stochastic simulation(UQ embedded in the model)

UncertaintyinformationModel

Uncertaininputs

UQ Toolbox

Model

UQ achieved by samplingmany deterministic simulations

(ource" !n .ntroduction to Uncertainty Quantification Met$odologies and Met$ods- #, 5ong '2672* 8 #om&aring Uncertainty Quantification Met$ods Under

Practical .ndustry %e/uirements- 9ang '2672*


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0emonstration of a&&licability of UQ met$ods in

answering /uestions t$roug$ re&resentative &roblems"

#ase !" Nonreacting :0 5ransient Fluidi3ed Bed %iser

(imulation1 • #irculating Fluidi3ed Bed riser at N)5; wit$ e+&erimental data

from 2676 N)5;? Che$ically Reacting 0ransient Flui"i@e"

>e" Gasifier #i$ulation 'work in &rogress*•

)+&erimental data available for labscale setu&,• 20 8 :0 reacting multi&$ase flow simulation

• Bayesian #alibration for reaction rates wit$ available

e+&erimental data,

Non-5ntrusive UQ Metho"ology

0est 4ro'le$s

1 Gel et al& 3ali"ation an" Uncertainty Quantification of a Multiphase CF+ Mo"elB& 5n"ustrial /ngineering Che$istry Research !91(. :!((.

pp 11,!,-11,(: +


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Schematic diagram of the lab-scale fluidized-bed gasifier used for experiments

Coalinlet

Outlet

Air inlet

Uncertainty Quantification

#tu"y 4roperties?5nput para$eters with Uncertainty

$in-$a6range? '7* #oal Flow %ate 'g6 @ C66A

':* 12O < O2 ratio " >6,C @ 7,6A

Quantities of 5nterest"'7*#arbon #onversion 'D*'2*Gas Eield 'D*':*Gasification )fficiency 'D*

'*12


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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)

&hysical E'periments

Reference:(1) Shayan Karimipour, Regan Gerspacher, RajenderGupta, Raymond J. Spiteri, “Study of factors affectingsyngas quality and their interactions in fluidized bedgasification of lignite coal”, Fuel, Vol. 103, January 2013,Pages 308-320, ISSN 0016-2361,http://dx.doi.org/10.1016/j.fuel.2012.06.052.(http://www.sciencedirect.com/science/article/pii/S0016236112004723)


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#F0 simulations &erformed wit$

!N(E( F;U)N5 for same set of in&ut&arameters,

#oal &yrolysis- combustion- steam 8#O2 gasification along wit$ 12- #O

and #1 combustion are modeledusing 77 c$emical reactions,

5otal of :: trans&ort e/uations aresimultaneously solved for trans&ort of27 s&ecies and multi&le &$ases,

#om&utational cost &er simulation"

• 20 " 2: weeks on 7? cores

• :0 " H weeks on ? cores


Computational "luid ynamics $imulations

3D CFD Model ofFluidized Bed Gasifier

Coal inlet

Air inlet

Outlet

Contour plot ofcoal volume fraction


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0ue to in$erently com&le+ nature oftransient reacting multi&$ase flows and t$e

e+&ensive com&utational cost- several

different strategies were investigated, 20

and :0 simulations at multi&le gridresolutions 'coarse- medium 8 fine* were

initiated,

0ifferent sam&ling strategies were

em&loyed"

• O&timal ;atin 1y&ercube (am&ling

'e,g, :6 sam&les for 20 runs*

• #entral #om&osite 0esign '2C sam&les*


Computational "luid ynamics $imulations

Contour plot ofCO mole fraction


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llustration of e'periment and C" samplin% in the parameter space

Scatter plot of the sampling locations in the parameter space for the physicalexperiments (14 samples based on Central Composite Design) and CFDsimulations (30 samples based on Optimal Latin Hypercube sampling)


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Computational "luid ynamics $imulations* +eview of initial results

Individual comparison of initial Fluent simulation result with the correspondingreplicated experiment data (Runs 8-13) show good agreement for that sampleHowever, review of the full picture with scatter plot matrix tells a different story…

Discrepancy < 1 %

Comparison of Fluent simulation (Run # 1) with the corresponding experiments (Run # 8-13)


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Computational "luid ynamics $imulations* +eview of initial results

ExperimentsInitial Fluent 2D Simulations (v.1)

Scatter Plot Comparison of Secondary Quantities of Interest

Opposite trendsobservedbetween

experiments andsimulations

triggered furtherinquiry andrevisions inseveral aspectsof the model

such as reactions

Individual CO mole fractioncompared in the previous slide


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Computational "luid ynamics $imulations , +eview of results v-.

Experiments

New Fluent 2D Simulations (v.2)

Same trendsobservedbetween

experiments andnew 2Dsimulations

Scatter Plot Comparison of Secondary Quantities of Interest


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$urro%ate model for /. mole fraction at the e'it monitor location

3D plot of the surrogate model for

H2 mole fraction

2D plot of H2 mole fraction surrogate modelat Coal Flow Rate = 0.05 g/s

Cross-validation errors to assess quality ofthe surrogate model

I PSUADE UQ toolbox from LLNL employed in

surrogate model construction.I Several surrogate models tested with theavailable simulation data (e.g., 1st, 2nd and 3rd order polynomial, MARS, etc.)

I Gaussian Process Model (GPM) provided thebest fitted surrogate model for H2 molefraction at the exit monitor location as shown.


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terative &rocess to Construct the Best $urro%ate model for each Qo

3D plot of the surrogate model for

CO mole fraction

2D plot of CO mole fraction surrogate modelat Coal Flow Rate = 0.05 g/s

Cross-validation errors to assess quality ofthe surrogate model

I PSUADE UQ toolbox from LLNL employed in

surrogate model construction.I Several surrogate models tested with theavailable simulation data (e.g., 1st, 2nd and 3rd order polynomial, MARS, etc.)

I Gaussian Process Model (GPM) provided thebest fitted surrogate model for CO molefraction at the exit monitor location as shown.

Surrogatemodel isperforming

poorly for COmole fraction> 0.14


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nput uncertainty forward propa%ation for /. , Mi'ed Uncertainty

Forward propagation of input uncertaintiesI Deciding on the proper treatment of uncertainties with adequate characterization is quitechallenging.

I For demonstration purposes, some of the input parameters treated as epistemicuncertainty and the rest as aleatory.

I Coal flow rate treated as epistemic uncertainty between interval of [3.47e-2,6.56e-2]

Enlarged view of the region marked with circle:

0.795

0.761

76 % < Prob (H2 mole fraction ≤ 0.14) < 80 %

Prob (H2 mole fraction ≤ 0.14) ≈ 78 %


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• !nalysis of t$e simulation and e+&erimental results wit$ Bayesian

framework &erformed J Global sensitivity analysis for #O mole fraction

Case B !ransient "luidi#ed Bed Gasifier $imulation

Glo0al $ensitivity 1nalysis with Bayesian "ramework


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framework &erformed J Global sensitivity analysis for 12 mole fraction


Glo0al $ensitivity 1nalysis with Bayesian "ramework (continued)


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framework &erformed J Global sensitivity analysis for gasification efficiency


Glo0al $ensitivity 1nalysis with Bayesian "ramework


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/. mole fraction surro%ate model with discrepancy ad2ustment

Predictions of ExperimentSample # 4

+

=

P r e d i c t i o

n o f

t h e e m u l a

t o r

c o n s t r u c t

e d f r o m

b o t h s i m u l a t i

o n &

e x p e r i m e

n t s

Gaussian processmodel based modeldiscrepancy

Model discrepancy corrected emulator prediction of # 4


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CO mole fraction surro%ate model with discrepancy ad2ustment

Predictions of ExperimentSample # 14

+

=


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#o$e o'servations an" conclu"ing re$arks

Our goal continues to be e+&loring different nonintrusive UQ tec$ni/ues to identify t$ose t$at are best

suited for reacting multi&$ase flows,

;arge &art of t$e effort is s&ent on constructing

ade/uate surrogate models, Bayesian met$ods a&&ear to offer various favorable

features suc$ as /uantification of model discre&ancy

and inclusion of &rior information- w$ic$ can be used

effectively to alleviate lack of data,


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"uture 3ork

Bayesian calibration for t$e most

uncertain model &arameter"

KJ kinetic reaction rates


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NETL 2014 Multiphase Conference

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