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.