A Comprehensive Model of Single Particle Pulverized Coal Combustion Extended to Oxy-coal Conditions Troy Holland and Thomas H. Fletcher* 350 CB Chemical Engineering Department Brigham Young University Provo, UT, USA 84602 *Corresponding author, email: [email protected], phone: 1-801-422-6236
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A Comprehensive Model of Single Particle Pulverized Coal Combustion Extended to Oxy-coal
Ac The preexponential factor for computing the CO/CO2 ratio Ad The preexponential factor in the thermal annealing reaction Ap Particle area (m2) AR,1-AR,8 The preexponential factor for 8 reactions and 2 reverse reactions. These come
largely from correlations, and can be adjusted for specific data. c0 The number of stable bridges cHR An NMR structure based swelling parameter cj The jth coefficient of the NMR correlations Cp The per mass heat capacity of the char particle J/kg/K dp,0 The initial particle diameter, in microns EA The activation energy in the annealing reaction Ec The activation energy for computing the CO/CO2 ratio in cal/mol ER,1-ER,8 The activation energy for 8 reactions and 2 reverse reactions. These come
largely from correlations, and can be adjusted for specific data. fi The fraction of active sites in bin “i" in the thermal annealing model HHR Higher Heating Rate HR The initial heating rate of the raw coal particle (K/s) ki The rate constant of reaction “i” in the particle mp The mass of the char particle (kg) Mδ The average molecular weight of the side chains in a coal “monomer” Mcl The average molecular weight of an aromatic cluster in a coal “monomer” N The intrinsic order of R2 (formation of CO2 by combustion). This defaults to
unity, but can be adjusted to explore other kinetic regimes. p0 The fraction of intact bridges Pi The partial pressure of reactive gas “i" at the surface of the particle rp,i The rate of reaction “i” in the particle smin A proximate analysis based swelling parameter svar An NMR structure based swelling parameter Tg The gas temperature (K) Tp The particle temperature (K) Ts The temperature of the surroundings for radiative heat transfer (K) Xc The percentage carbon from the ultimate analysis α The mode of burning parameter ΔHrxn,i The enthalpy of reaction “i" in the particle εp Particle emissivity η The effectiveness factor Φi The Thiele modulus ψ A random pore model parameter. This value has some uncertainty, and defaults
to 4.6.
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σ Stefan-Boltzman constant or a parameter in the log normal distribution σ+1 The coordination number τ/f A random pore model parameter. This value has some uncertainty, and defaults
to 12. Ω The swelling coefficient (dp/dp,0)
1. Introduction
Coal-fired power plants have provided a substantial percentage of global electricity for decades, and
current outlooks indicate that they will continue to do so for the foreseeable future. The high proportion
of electrical power generation is matched by a correspondingly high proportion of CO2 emissions. In
order to meet regulatory targets for reduced emissions, carbon capture and sequestration techniques must
be employed, and oxycoal combustion is a promising potential solution.
Oxycoal combustion has been reviewed thoroughly elsewhere,1, 2 but in essence it consists of injecting
high purity O2 with the pulverized coal rather than the conventional air-fired method. To reduce the
boiler temperatures to manageable levels, the flue gas is typically recycled, producing a combustion
environment with high concentrations of CO2, O2, and (potentially) H2O. The flue gas then contains very
high concentrations of CO2, and the CO2 is thus relatively easy to capture.
While an oxycoal system simplifies carbon capture, it also radically changes the environment the coal
particles experience. The new environment changes the O2 diffusion rate, may cool the char particle via
endothermic gasification, and may alter the overall char consumption rate due to gasification reactions.3
These effects and others such as reduced flame temperature, delayed ignition, decreased acid gases, and
increased gas emissivity can largely be ascribed to differences between CO2 and N2 (the respective
diluents in oxycoal and air-fired pulverized coal systems).1 The change in diluent gas induces several
interrelated effects that alter the burnout time and radiative behavior of the system, so accurate CFD
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predictions of oxycoal combustion require models that describe these phenomena. This work supports
computational fluid dynamics (CFD) modeling of oxy-coal boilers either for the retrofit of existing
boilers or the construction of new oxy-coal fired power plants by providing a detailed code that can
predict the temperature and burnout profiles of coal particles in a hot, oxidative environment. The
detailed model could also be used to train low computational cost, reduced-order models to accurately
describe a specific scenario.
2. Experimental
To conduct a relevant comparison, the model was executed at conditions related to real-world
application. Here, the most applicable conditions are the oxy-coal combustion environment, so
experimental data from the literature were chosen for comparison at useful conditions. The experimental
data also allowed the kinetic parameters to be calibrated. The model was then compared both to the
calibration data and similar data not used in the calibration. The experimental data referenced here were
collected by Shaddix and Molina4 and Geier et al.5 The reactor consists of a burner-stabilized flat flame,
a quartz chimney for gas and particles to flow through, and a coal particle inlet in the center of the
burner. The particle temperatures were measured with a 2-color pyrometry system and the diameters
were measured by imaging of the particle emission. No burnout data from probe measurements were
available from this data set. The coal particle flow rate was sufficiently low that particles did not affect
each other or the bulk gas composition. The data were for two subbituminous coals (Black Thunder and
North Antelope) and two high volatile bituminous coals (Utah Skyline and Pittsburgh seam (Bailey))
which were subjected to conditions of 14 or 16% H2O, 12, 24, or 36% O2, and the balance CO2, at gas
temperatures ranging from approximately 1400-1700 K. The proximate and ultimate analyses of the
coals and a summary of experimental conditions are given in Table 1 and Table 2. The char particles
were in the reactor for up to approximately 0.2 seconds (post devolatilization), and on the order of 1,000
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particle data triplets of temperature, location, and diameter were collected for each condition. These data
were used in a related sensitivity analysis of the Carbon Conversion Kinetics (CCK) model6 to
determine which model parameters were most sensitive at oxy-fuel conditions, and to target model
updates and refinements. These updates were implemented, but it should be noted that the updates did
not detract from the ability of the CCK code to predict char behavior in conventional oxidation and
combustion scenarios. Instead, it extended the submodels to also capture intense oxy-fuel conditions.
Table 1 - Proximate and Ultimate Analysis of Coal Particles between 76 and 105 microns
The literature data and the results of fitting the CCK/oxy model to data imply that a CFD simulation
should take into account two distributions to accurately capture coal char particles. The first distribution
is the diameter distribution of the raw coal particles that form the char. The diameter heavily impacts
burnout predictions, and the mean, variance, and distribution form may propagate that impact on to the
CFD simulation. In general, pulverized coal particle diameter distributions follow a Rosin-Rammler
distribution, and it is this distribution that should be used in simulating industrial pulverized coal
systems. For the experimental literature data used here, the full distribution is expected to be
approximately normal after sieving, swelling, and fragmentation, but the observed data are likely to be a
truncated normal distribution. This is because the small diameter and/or rapidly oxidizable portion of the
distribution quickly becomes undetectable due to the small radiative emission from these particles. The
exact truncation point in the normal diameter distribution is unknown, and it is not consistent between
burner heights, coal type, or O2 condition. To fully describe the true distribution of a data set, a
correlation between particle detectability, temperature, and diameter would be devised. Accurate
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parameters for such a correlation would assume a char emissivity, require an assumed distribution for
the raw coal, and incorporate knowledge of the optical limits of the detecting system. These
assumptions, in conjunction with the coal swelling model, would predict the post-devolatilization
diameter of a char particle, and the partially burned diameter at a given height, and appropriate
correlation parameters would reconstruct the entire raw coal input diameter distribution.
The second distribution of interest is the change in particle combustion behavior due to maceral
character and ash content. For a given particle diameter, the combustion temperatures (from the
literature data referenced in this work) vary by approximately ±150 K. If these values are simply used as
error bars, the high accuracy of the CCK/oxy model is effectively useless. Instead, this variation should
not be treated as error, but as actual variation in any given cohort of particles. The data shown here
imply that a normal distribution with a mean of the CCK/oxy temperature prediction and a variance of
approximately 75 K may be appropriate to capture the particle-to-particle variation.
An appropriate CFD application to combine accuracy and computational efficiency is needed. One
potential method would be to first determine the initial particle diameter distribution. Given an
approximation of that distribution, CCK/oxy can be executed using “n” diameters that cover the
distribution in sufficient detail. Bin values separated by 10 microns are likely adequate. For each bin,
CCK/oxy should be executed with a gamut of gas temperature and composition profiles, and the output
vectors recorded. Finally, the output vectors for a given bin size would be used to train a surrogate
function that depends on gas composition, temperature, and the peak temperature in the burnout history
of the particle. Such a function would execute very rapidly but potentially capture the majority of the
information of the CCK/oxy model. Implementation into a CFD simulation would appropriately weight
the available particle diameters and temperature variation within each diameter according to the two
distributions described above.
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6. Conclusions
A comprehensive coal char conversion model (Carbon Conversion Kinetics) was extended to
function at the extremes of oxy-coal combustion environments. These extensions included both
numerical stability and submodel accuracy, including improved submodels for coal devolatilization,
the mode of burning parameter, coal particle swelling, and the thermal annealing model. These
improvements are thought to be valid in any char combustion regime, rather than being limited to
oxy-coal combustion specifically. The specific submodel improvements given here were previously
indicated to be the most sensitive submodels in a comprehensive, global sensitivity analysis.7 Model
improvements were implemented and the model was subsequently validated and explored by
optimizing the model oxidation and gasification parameters to match a selection of the highly limited
oxy-coal data from the literature. The validation revealed:
1. The CCK/oxy model matched the available data extremely well, with enormous
improvement over past attempts using the CCK model.7, 8 The CCK/oxy model was able to
simultaneously fit all O2 conditions for a given coal with a single set of kinetic parameters.
This was largely due to improvements in the devolatilization, swelling, and mode of burning
models, as well as more exacting numerical solutions. The thermal annealing model is also
exceptionally sensitive, but it is so tightly coupled to the kinetic preexponential factor that
the submodel has minimal impact when optimizing the kinetic parameters of a single coal in
a narrow range of heating rates and peak temperatures. Instead, the annealing model is vitally
important to any attempt to create coal-general kinetic correlations or in exploring widely
varying heating rate and peak temperature regimes with a given coal.
2. The CCK/oxy model, when optimized to the 12% O2 oxy-coal data only, made reasonable
extrapolations to 24 and 36% O2 conditions.
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3. The CCK/oxy model, when optimized to the 12% O2 conventional fired condition, made
reasonable extrapolations to all levels of oxy-coal firing in two of three cases. These results
are inconclusive, but imply that data collected in conventional firing conditions may be
useful in determining kinetic parameters relevant to oxy-coal scenarios.
4. In oxy-fuel conditions, several competing effects complicate the combustion regime. These
effects are mainly due to high concentrations of gasification reactants (especially CO2), high
temperatures that accompany enhanced O2 levels, and a balance between endothermic and
exothermic reactions. The CCK/oxy model predictions are as anticipated: 1) that O2
combustion is by far the dominant reaction pathway, 2) that gasification becomes relatively
less important at more intense oxygen conditions, and 3) that gasification becomes relatively
more important at high temperature. The last two effects are in competition, and the second
effect proved dominant here.
Finally, as the present work was intended to support predictive boiler design via computational fluid
dynamics simulation, a brief suggestion for CFD application was outlined. In this work, it was observed
that both particle diameter distributions and particle reactivity distributions are vitally important to
accurate model predictions. As CFD work ideally models the entirety of both distributions, accurate
descriptions of both distributions must be estimated as closely as possible. This estimation is
problematic when data are collected via radiant particle detection, because certain subsections of the
activity and size distribution fall below the lower temperature and size limit of detectability.
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Acknowledgements
This material is based upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002375.
Funding for this work was also provided by the Department of Energy through the Carbon Capture Simulation Initiative. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Disclaimer
This publication was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Release Number:
This document is authorized for public release (LA-UR-16-29489).
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