Modeling of Particle Fates in gIsothermal Plug Flow Reactor

Chiara Galletti1, Gianluca Caposciutti1, Giovanni Coraggio2, Leonardo Tognotti1

1 Department of Civil and Industrial Engineering, University of Pisap C g g, U y2 International Flame Research Foundation

Agendag Motivations

The IFRF Isothermal Plug Flow Reactor

Procedures for heterogeneous kinetics and UQ Procedures for heterogeneous kinetics and UQ

Modeling the particle fate in IPFR

Conclusions

Motivation: predicting solid fuel combustionp g

Improving existing technologies and/or developing new ones for lid f lsolid fuels:

biomass coal”clean coal” technologies coal clean coal technologies

Oxy-coal combustion is emerging technological solution for: CCS (Carbon Capture and Sequestration); CCS (Carbon Capture and Sequestration); Abatement of NOx and SOx emissions; Retrofitting of existing coal-fired power and industrial plants.

Motivation: solid fuel combustion pyrolysis or devolatilization reactions of the solid fuel particles;

heterogeneous reactions of the residual char;

secondary gas-phase reactions of the released gases

Brown R C Iowa State Press Ames IA

4

Brown, R. C, Iowa State Press, Ames, IA,

2003.

from IFRF R&D Agenda – 2007-2013Fuel characterisationFuel characterisation

• Develop/test methodologies for fuel characterisation; establish

t l / lifi ti d (UQ)protocols/qualification procedures (UQ)

• Characterise solid (and liquid) fuels to agreed protocols– to agreed protocols

– to fill data gaps for sub-model validation & application

– includes fuels that are environmentallyincludes fuels that are environmentally and economically significant

• Biomass, Wastes, Blends with coalsI t h th t fl t• In atmospheres that reflect O2/RFG approach, temperatures and pressures of current interest to members and other sponsors

Produce and maintain DATABASES (IFRF Solid Fuel Database- http://sfdb.ifrf.net

LAB/PILOT SCALELAB/PILOT SCALE SEMISEMI--INDUSTRIAL INDUSTRIAL SCALESCALE

FULL SCALEFULL SCALESCALESCALE

WH

AT MOLECULAR PROCESSUNIT PROBLEMS

COUPLED PROBLEMS COMPLETE SYSTEM

WUNIT PROBLEMS SYSTEM

OM

ENA

TIG

ATED

CHEM.KINETICSDEVOLATILISATION

HETEROG. REACTIONSMIN MATTER TRANSF

FLOW FIELDFLAME STABILITY

TURBULENCE-KINETICS INTERACTIONS

SYSTEM FLOW FIELDAND TEMPERATURE

DISTRIBUTION,HEAT TRANSFER,

PHEN

OIN

VEST

MIN.MATTER TRANSF. INTERACTIONS,POLLUTANT GENERATION

HEAT TRANSFER,BURNER INTERACTIONS, FATE OF POLLUTANTS

L VALIDATION OF SUB «REDUCED» SUB-MODELS COMPREHENSIVE

PUTA

TIO

NA

LH

OD

S A

ND

SC

OPE

S

MODELS AND UNCERTAINTIES

QUANTIFICATION (UQ)

Kinetics parameters/

AND COUPLING METHOD.(COMBUSTION MODELS)

VALIDATION/UQ

Design optimisation of

MODELLINGVALIDATION/UQ

boiler/furnace overallperformances

CO

MP

MET

H S Kinetics parameters/Particles properties

variation

Design, optimisation of component performances

(i.e. burners) –

performancesdesign/optimisation/

Control

D «AD HOC» EXPERIMENTS Design Of Experiment (DOE) DESIGN OF FULL-

ERIM

ENTA

L R

OA

CH

AN

DSC

OPE

S CFD-AIDED EXPERIMENTS: QUALIFICATION OF PROCEDURES AT

LAB/PILOT SCALE: UQ

DEVELOPMENT/TESTING OF MEASUREMENT

PROBES/DIAGNOSTICS (UQ) for industrial application

SCALE CAMPAIGNS

FULL SCALE DIGNOSTICS

OPTIMISATION

EXPE

APP

R S LAB/PILOT SCALE: UQ for industrial application OPTIMISATION

Motivation: reliable devolatilization models Devolatilization is the foundation of

comprehensive solid fuel combustion CFD is crucial for the

development of cost-effective comprehensive solid fuel combustion models

Flame characteristics (e.g. flame front position and stability, combustion

development of cost-effective oxy-coal technologies

Coupling of detailed chemistryand CFD is prohibitive for position and stability, combustion

efficiency) are partially governed by volatile release

and CFD is prohibitive for industrial applications

We need simple, reduced & reliable DEVOLATILIZATION MODELS to be

7

used in CFD codes

Devolatilization kinetics change substantially when varying the heating ratewhen varying the heating rate

EXPERIMENTS in facilities emulating industrial conditions:industrial conditions:

ENTRAINED FLOW REACTORS (EFRs)

N2 , CO2, O2, Air

Isothermal Plug Flow Reactor L=4.5 m, ID=15 cm t = 5-1500 ms HR = 103 105 K/s HR = 103-105 K/s Tmax = 1673 K Devolatilization tests

in oxy-fuel environment

From IJmuiden to Livorno:the Isothermal Plug Flow Reactor

8

!the Isothermal Plug Flow Reactor

40%

50%

sion

[-]

Devo and char conversion kinetics

IPFR experimentsIPFR experiments

0%

10%

20%

30%

Coa

l con

vers

Laboratory analyses

0%0.00 0.05 0.10 0.15 0.20 0.25 0.30

Residence time [s]900 1100

Elemental release rate

IPFR experiments:collecting samples along the reactor (different residence times) at fixed operating

50%

75%

100%

enta

l con

v. [.

]conditions (T, gas composition) and analyse the samples

3 test procedures 290 f d l tili ti

0%

25%

0% 25% 50% 75% 100%

elem

e

coal conv. [-]C H N S

290 for devolatilization, 523 for char oxidation

35 for nitrogen/ elements partitioning 9 oxy-devo17 oxy-char coal conv. [ ]C H N S

100

]

Particle size evolution

y

1 0

ash

25

50

75

d 50

[µm

]

100

1001

1

0

ashashX [%,daf]

00% 25% 50% 75% 100%

char conversion [-]air oxy

IPRF “Qualification” Issues: experiments + analysisUncertainties Quantification

heterogeneous nature of the fuel

Ash tracer method validation

IPRF “Qualification” Issues: experiments + CFD simulations: Uncertainties Quantification

heterogeneous nature of the fuel

gaps from isothermal conditions in the reactor

thermal history of particles and nominal temperature are

PSD is important for evaluating the effective nominal temperature are

different the effective

trajectories and thermal histories

adhesion of particles: mass never balances

segregation of particles (depending on size)

CFD modelling as a

diagnostic tool

solid collection depends on the efficiency of separation units

measurement of gas concentration is difficult (deposition of tar)

separation units

Specific objectives: kinetics from EFRsp j EFRs often used to derive solid fuel conversions in specific

ti ditioperating conditions

Estimation of kinetics requires knowledge of particle temperature sophisticated diagnostics usually the particle heating up is neglected TP = TR

Objectives:

Analyse procedure to derive devolatilization kinetics from EFRs

I ti t ibl f t i ti Investigate possible sources of uncertainties

Can we improve the reliability of simple models by revising the d f th i d i ti ?procedure for their derivation?

CFD modelStep 1 Step 2 Step 3

POST-PROCESSING with Matlab® routines

Experiments

13

CFD settings

Simulation cases• single-phase runsthermal field• injection of inert particles thermal particle history• injection of reactive particles optimized A, E

Coal type

particle size: 65 – 90 mm VM 40.30 %; FC 47.95 %; ASH 11.75 % (dry

b i )• SebukuCoal type basis) Tin=293 K

Domain • 3D symmetric half IPFR, 1M cells

Sebuku

Temperature • 1173 K, 1373 K, 1573 K

Gas composition• At inlet: CO2 9.45 %; H2O 7.73 %; N2 79.93%; O2 2.89 % (mass

fraction)Gas composition fraction)• CO2 carrier gas for coal particles

Solvers• ANSYS Fluent, steady, second order discretization scheme• SIMPLE algorithms for the pressure-velocity couplingSolvers SIMPLE algorithms for the pressure velocity coupling• Two way coupled lagrangian tracking

Turbulence • Std k-, RNG Std k-SST kTurbulence - chemistry • Eddy Dissipation ModelTurbulence - chemistry • Eddy Dissipation Model

Radiation • P1 model coupled with WSGGDevolatilization • SFOR, SFOR-HR, CPD 14

Single-phase thermal fieldg p

velocitytemperature

15

Injection of inert particlesj p

Coefficient of restitution

R=R⁄⁄=R

16

Injection of inert particlesj pParticle thermal histories

17

Mean particle residence time is different from expected one Ranges of residence times t and particle temperature TP exist at the same

sampling location y

Reactive simulations: devolatilization models

(Semi-empirical) models computationally cheap but poor di ti f l til i ld Constant-rate model

Standard/modified model Two-step model: Kobayashi et al (1977)

predictions of volatile yield

Two-step model: Kobayashi et al. (1977), … Multi-step kinetic model: Sommariva et al. (2009)

Phenomenological models t b t t ti ll Phenomenological models CPD FLASHCHAIN

accurate but computationally expensive

FG-DVC

Single First Order Reaction Model SimplicitySingle First Order Reaction Model • Simplicity • Easily included in CFD codes• Not generally accepted• Inaccurate for T above the Tprox

Reactive simulations: post-processingp p g

Post-processing to emulate experiments in which the position of th li b i i dthe sampling probe is varied.

19

CFD: kinetics from reactive simulationsKinetics from CFD+expNovel procedureStandard procedure

(X ) t T

EXPERIMENT EXPERIMENT

(X y) at T(X, y) at TR

(X, y) at TR

SFOR-HRhp: TP=T0+HR t

A l ti l i t ti

SFORhp: TP=TR

SFORhp: TP=TR

(X, y) at TR

V/UQ

Analytical integration over particle thermal

history

p P RArrhenius plot

p P RArrhenius plot

A E

t t, HRA, E

CFDA E A, E

Iterative procedure: 2-3 iterations needed to get fi l (A E) t

A, E

final (A, E) parameters

Kinetics from CFD+exp.

SFOR

p

SFOR-HR

Minimize the following function Minimize the following function

Arrhenius plot A,E

SFOR model: conversions

TR=1173K TR=1373K TR=1573KR R TR 1573K

Strong underestimation of conversion Experimental error bars:

horizontal: uncertainty due to sampling probe position vertical: uncertainty due to ash tracer method

i Modeling error bars: horizontal: uncertainty on residence time vertical: uncertainty on conversion

SFOR-HR model: conversions

TR=1173K TR=1373K T =1573KTR 1173K TR 1373K TR=1573K

Good predictivity

Uncertainty on kinetic parametersy p

Uncertainty on kinetic parameters

Performance of CPD model

CPD model leads to an overestimation of particle

i t l dconversions at low and medium temperature with a constant final conversion value for all temperaturesp

Model-form uncertainty: lack of accuracy in the evaluation of ultimate yield at a givenultimate yield at a given temperature

Conclusions (1)( ) Need to characterise solid fuels for practical applications:

“Fuel specific” sub models: Simple, Reduced & Reliable Kinetic Mechanisms for devolatilisation , char oxidation and gasificationgas ca o

EFRs/DTs used to derive heterogeneous kinetics in specific operating conditionsoperating conditions

Need to to qualifyqualify the EFRs/DTs procedures

Uncertainties quantification : Experimental/analysis uncertainties CFD modeling as a diagnostic tool (particle thermal histories)

Conclusions (2)( ) CFD analysis of solid fuel particles injected into a pilot-scale EFR:

particles experience different paths and thermal histories particle have a temperature lower than the reactor one at most of the

sampling positions

Iterative (2-3 cycles) joint CFD-experimental procedure to derive kinetics: Residence times from CFD calculations Particle temperature estimated with an average heating rate from

CFD model (analytical integration of the volatile release equation)

Large improvement of the agreement between experimental and predicted conversion data, even with simple SFOR model

Si ifi t d ti ti f th ki ti t ith ti l Significant underestimation of the kinetic rates with conventional assumption of constant particle temperature

Analysis of cloud of particles allows estimating uncertainty on kinetic Analysis of cloud of particles allows estimating uncertainty on kinetic parameters

References – IFRF archiveIFRF Reports at http://www.research.ifrf.net/research/new.html

IFRF Members’ Conferences and TOTeMs athttp://www ifrf net/page/conference notes/index conferenceshttp://www.ifrf.net/page/conference-notes/index-conferences

Iavarone, S., Caposciutti, G., Galletti, C., Tognotti, L., Contino, F., Parente, A.Iavarone, S., Caposciutti, G., Galletti, C., Tognotti, L., Contino, F., Parente, A.Adaptive Kinetic model for coal devolatilization in oxy-coal combustion conditions(2015) 18th IFRF Members’ Conference – Flexible and clean fuel conversion to industry Freising.

Federica Barontini, Enrico Biagini, Leonardo TognottiCharacterization of the devolatilization products of selected second generation biofuelsCharacterization of the devolatilization products of selected second generation biofuels18th IFRF Members’ Conference – Flexible and clean fuel conversion to industry, Freising, 1-3 June, 2015

Galletti, C., Tarquini, S., Bruschi, R., Giammartini, S., Coraggio, G., Tognotti, L.Ignition delay of coal particle clouds in oxy-fuel conditions(2012) 17th Members' Conference, Mafflier, France 2012

References - Journal PaperspC Galletti, G Caposciutti, L Tognotti Evaluation of scenario uncertainties in entrained flow reactor tests through CFD modelling: devolatilizationEnergy & Fuels 2016Energy & Fuels, 2016

S.Iavarone, C.Galletti, F. Contino, L.Tognotti, P.J.Smith, A.ParenteCFD-aided benchmark assessment of coal devolatilization one-step models in oxy-coal combustion conditionsFuel Processing Technology, in press 2016

Li, J., Bonvicini, G., Biagini, E., Yang, W., Tognotti, L.Characterization of high-temperature rapid char oxidation of raw and torrefied biomass fuels(2015) Fuel 143 pp 492 498(2015) Fuel, 143, pp. 492-498.

Li, J., Bonvicini, G., Tognotti, L., Yang, W., Blasiak, W.High-temperature rapid devolatilization of biomasses with varying degrees of torrefaction(2014) Fuel, 122, pp. 261-269.

Biagini, E., Tognotti, L.A generalized correlation for coal devolatilization kinetics at high temperature(2014) F l P i T h l 126 513 520(2014) Fuel Processing Technology, 126, pp. 513-520.

Galletti, C., Giacomazzi, E., Giammartini, S., Coraggio, G., Tognotti, L.Analysis of coal combustion in oxy-fuel conditions through pulsed feeding experiments in an entrained flow reactor(2013) Energy and Fuels, 27 (5), pp. 2732-2740.

Karlström, O., Brink, A., Biagini, E., Hupa, M., Tognotti, L.Comparing reaction orders of anthracite chars with bituminous coal chars at high temperature oxidation conditionsp g g p(2013) Proceedings of the Combustion Institute, 34 (2), pp. 2427-2434.

Biagini, E., Simone, M., Barontini, F., Tognotti, L.A comprehensive approach to the characterization of second generation biofuels(2013) Chemical Engineering Transactions, 32, pp. 853-858.

Karlstrom O; Brink A; Hercog J; Hupa M; Tognotti LOne-Parameter Model for the Oxidation of Pulverized Bituminous Coal Chars.One Parameter Model for the Oxidation of Pulverized Bituminous Coal Chars. ENERGY & FUELS - vol. 26 (2), 2012

Karlström, O., Brink, A., Hupa, M., Tognotti, L.Multivariable optimization of reaction order and kinetic parameters for high temperature oxidation of 10 bituminous coal chars(2011) Combustion and Flame, 158 (10), pp. 2056-2063.

Simone, M., Biagini, E., Galletti, C., Tognotti, L.Evaluation of global biomass devolatilization kinetics in a drop tube reactor with CFD aided experimentsEvaluation of global biomass devolatilization kinetics in a drop tube reactor with CFD aided experiments(2009) Fuel, 88 (10), pp. 1818-1827.