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CFD simulation of entrained-flow coal gasification: Coal particle density/sizefraction effects
Andrew Slezak a,b, John M. Kuhlman a,b,⁎, Lawrence J. Shadle a, James Spenik a,c, Shaoping Shi a,d
a National Energy Technology Laboratory, Morgantown, WV 26507-0880, United Statesb West Virginia University, Mechanical and Aerospace Eng. Dept. Morgantown, WV 26506-6106, United Statesc REM Engineering Services, Morgantown, WV 26505, United Statesd Ansys, Inc., Morgantown, WV 26505, United States
⁎ Corresponding author. National Energy Technology26507-0880, United States. Tel.: +1 304 293 3180; fax:
Computational Fluid Dynamics (CFD) simulation of commercial-scale two-stage upflow and single-stagedownflow entrained-flow gasifiers was conducted to study effects of simulating both the coal particledensity and size variations. A previously-developed gasification CFD model was modified to account for coalparticle density and size distributions as produced from a typical rod mill. Postprocessing tools weredeveloped for analysis of particle–wall impact properties.For the two-stage upflow gasifier, three different simulations are presented: two (Case 1 and Case 2) usedthe same devolatilization and char conversion models from the literature, while Case 3 used a differentdevolatilization model. The Case 1 and Case 3 solutions used average properties of a Pittsburgh #8 seam coal(d=108 μm, SG=1.373), while Case 2 was obtained by injecting and tracking all of the series of 28 differentcoal particle density and size mass fractions obtained by colleagues at PSU as a part of the current work, forthis same coal. Simulations using the two devolatilization models (Case 1 and Case 3) were generally inreasonable agreement. Differences were observed between the single-density solution and the density/sizepartitioned solution (Case 1 and Case 2). The density/size partitioned solution predicted nominally 10% lessCO and over 5% more H2 by volume in the product gas stream. Particle residence times and trajectoriesdiffered between these two solutions for the larger density/size fractions. Fixed carbon conversion was 4.3%higher for the partitioned solution. Particle–wall impact velocities did not vary greatly.Grid independence studies for the two-stage upflow gasifier geometry showed that the grid used in thecomparison studies was adequate for predicting exit gas composition and wall impact velocities. Validationstudies using experimental data for the Pittsburgh #8 coal from the SRI International pressurized coal flowreactor (PCFR) at 30 atmospheres indicated adequate agreement for gasification and combustion cases, butpoor agreement for a pyrolysis case. Simulation of a single-stage downflow gasifier yielded an exit gascomposition that was in reasonable agreement with published data.
The present study has been undertaken to demonstrate capabilityto predict the performance of entrained-flow gasifiers using currentstate-of-the-art Computational Fluid Dynamics (CFD) software, andmore specifically to predict differenceswithin the gasifier in behaviors(e.g., particle residence times, char conversion, and trajectories) ofvarious size and density fractions of the pulverized coal fuel producedfrom a typical rod mill. This effort is a part of a larger study, the CoalPartitioning Project, or CPP, which is being supported by the DOENational Energy Technology Laboratory (NETL). In the CPP project, aPittsburgh #8 seam coal from the Bailey mine in southwestern
Pennsylvania has been ground using a METSO rod mill to specifica-tions typical of slurry-fed entrained-flow gasifiers and separated intofour density fractions, and then each of these density fractions hasbeen further classified into seven diameter ranges [1,2]. The parentcoal and each of the 28 resulting density/size cuts have beencharacterized by an ultimate and proximate analysis, a maceralanalysis, and a mineral analysis of the ash from each cut [1,2]. While adensity separation is known to separate hydrogen-rich, liptinitemacerals in the lighter fractions from denser wood-derived vitrinite,and denser still mineral impregnated inertinite macerals, there waslittle evidence that this was the case in this vitrinite-rich coal. Thedensity separation however did produce a separation in the mineralmatter such that the lighter fractions consisted of fine clays imbeddedwithin the coal particles, the denser fractions had subsequently higherconcentrations of clays and heavier pyrite minerals, while the densestfraction was essentially individual minerals with some organic
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impurities [2]. The CPP project objectives are to evaluate the influenceof these compositional differences in coal particles on the partitioningof inorganics produced during gasification into flyash and slag. Assuch it is important to understand the differences in the trajectories ofthese various particles and their relative tendencies to strike the wall.
The main focus of the present CFD simulations is a commercial-scale, two-stage, upflow, slurry-fed, entrained-flow gasifier geometrypreviously studied by Shi et al. [3]. The Fluent user-defined function(UDF) developed by Shi to simulate gasification has been extendedherein to allow the feeding of (28) multiple coal fractions into thegasifier, each at the appropriate mass flow rate and chemicalcomposition. Results have been obtained at a typical operatingcondition using the devolatilization chemistry model of Kobayashiet al. [4] and the shrinking core char conversion models of Wen andChaung [5]. Results are compared for one solution for the parent coalat themeasured Sauter-mean diameter of 108 μmand average densityof 1373 kg/m3 for the parent coal, and for a second solution for theinjection of all 28 density/size cuts, each at the appropriate injectionmass flow rate, and each with its own volatile chemical compositionand ultimate yield as governed by the measured ultimate andproximate analysis for that size and density fraction, as given by theKobayashi et al. devolatilization model. Also, a solution using thedevolatilization model from PC Coal Lab6 has been compared with thefirst solution.
2. Background
Some previous CFD gasifier simulations have investigated thebehavior of a range of coal particle size fractions (e.g., [7–9]), but noneto date have included various density fractions. This is at least partly
Table 4Assumed model conditions for present solutions.
Case 1 Case 2 Case 3
Devolatilization Kobayashi Kobayashi PC Coal Laba
Char conversion Wen/Chaung Wen/Chaung Wen/ChaungCoal particle(s) d=108 μm 28 CPP size/ρ cuts d=108 μmCp equations Polynomial Polynomial Polynomialk equations Power law Power law Power law
a Recommended one point tuning of this model using experimental entrained-flowdata has not yet been completed.
Table 5Comparison of average temperatures, FC conversion, and exit gas compositions (byvol.).
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because commercial CFD software such as Fluent typically has thebuilt-in capability to allow use of a user-selected particle size pdfrepresented by several different diameter classes, and the individualparticle trajectories and burn out behaviors of each size class maythen be investigated individually. However, this built-in procedurerequires that all particles have a single diameter distribution, and alsohave the same density value and chemistry models. The study byCloke et al. [10] experimentally investigated the variation in coalproperties versus particle diameter for ten different world coals; thesecoals were selected to give a wide range of properties. The coals usedin this study were not density-separated. The experimental work byWu et al. [11] studied the composition of coarse and fine slags thatwere sampled for a Chinese commercial single-stage, downflow,slurry-fed entrained-flow gasifier unit. The coarse slag was collectedfrom the outlet of the lock hopper beneath the main gasifier unit,
Fig. 2. Computed gas phase temperature field: Fluent simu
while the fine slag was collected from the fine slag removal filter forthe water streams in the quench chamber and the downstream gasscrubber/convective cooler. Previous CFD simulation studies of coalgasification [3,7–9,12–27] are briefly summarized in Table 1; none ofthis previous work has simulated variations in particle densities.
Our present simulations have focused on the same gasifiergeometry, CFD mesh, and chemistry models as in Shi et al. [3], buthave utilized the ultimate and proximate analysis data for thePittsburgh #8 seam coal from the Bailey mine that is under study inthe current CPP, again at an operating pressure of 2.84 MPa. Theproximate and ultimate analysis data for the parent Bailey coal arepresented in Table 2. For the current simulations, the computedPittsburgh #8 coal Sauter-mean diameter of 108 μm and averagespecific gravity of 1.373 for the composite coal have been used; seeTable 3. For comparison purposes, the simulation by Shi et al. usingIllinois #6 coal assumed a specific gravity of 1.4 and an averageparticle diameter of 30 μm. (This average particle diameter is believedto be too small to represent conditions in a slurry-fed entrained-flowgasifier.)
For the present simulations using the Bailey coal, the same total coalmass flow rate as used by Shi et al. [3] has been used in both of thepresent simulations (28.04 kg/s), with the same split between the firstand second stages (78%/22%). However, because of the higher carboncontent of this Pittsburgh #8 coal relative to the Illinois #6 (86.08%versus 80.51%), the stage 1 inlet carrier gas (consisting of 94.4% O2) wasscaled up from 21.61 kg/s to 23.17 kg/s, and the slurry water flow rateswere scaled in the sameproportion (from11.62 kg/s to 12.31 kg/s). Thiswas done to maintain the same (O/C) ratio of 1.44, and (steam/C) ratioof 0.34 as was used by Shi et al. for Illinois #6 coal. Further details of thepresent case setup conditions and solution process have been given inthe thesis by Slezak [28].
3. Results
Details of the present Fluent CFD solutions using the DiscretePhase Method (DPM) for the commercial-scale two-stage upflowentrained-flow gasifier geometry previously studied by Shi et al. [3] atan operating pressure of 2.84 MPa are summarized and comparedherein. Additional comparisons have been presented by Slezak [28].Shi et al. [3] used the devolatilization chemistrymodel of Kobayashi etal. [4] and the char conversion models of Wen and Chuang [5]. TheCase 1 simulation is for the parent coal at a Sauter-mean diameter of108 μm and the mean density of 1373 kg/m3 (Table 3), while the Case2 simulation is for the injection of all 28 density/size cuts, each at the
lation of upflow entrained-flow gasifier; p=2.84 MPa.
Fig. 3. Computed gas velocity magnitude: Fluent simulation of upflow entrained-flow gasifier; p=2.84 MPa.
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appropriate injection mass flow rate (Table 3), and each with its ownvolatile chemical composition and proximate analysis [1,2]. A thirdsimulation (Case 3) has also been obtained using a devolatilizationmodel developed using secondary pyrolysis results from the PC CoalLab software developed by Niksa [6], and the char conversion modelsof Wen and Chaung. The gasifier geometry and the grid used in thesimulations are shown in Fig. 1, while the assumed model conditionsfor the Case 1, Case 2, and Case 3 runs are summarized in Table 4.
The computed average gas temperature in the first stage for Case 2is about 50 K hotter than results for Case 1 (Table 5), while the Case 2second stage average temperature is about 12 K cooler than for Case 1;these are modest differences. However, the computed exit gas speciesvolume fractions for these two solutions differ (Table 5). In particular,the exit CO volume percentage is 10% lower for the partitioned Case 2solution than for Case 1, while the exit H2 volume fraction increasedby over 5%. Computed exit volume fraction of CH4 increased by about0.5%, while the exit CO2 volume percent increased by about 4%. Theother gas species volume percentages are quite low, and are similarfor both solutions. The Case 3 solution (PC Coal Lab devolatilizationmodel) has exit gas volume fractions that compare more closely with
Fig. 4. Computed CO species mole fraction: Fluent simula
the Case 1 solution (Kobayashi devolatilization model, as used in Shiet al. [3]). However, the average temperatures differ dramaticallyfrom those of Case 1, being approximately 250 K hotter in stage 1 and215 K cooler in stage 2. This is believed to be due to the differentassumed volatile composition for the two models, where PC Coal Labpredicts that the predominant secondary pyrolysis product is soot,which was not produced at all in the Kobayashi model. Soot wasallowed to react in the gas phase with oxygen and rapidly consumesany available O2 in the first stage leading to higher temperatures, andgasifies with the available steam in the second stage. The overallpredicted fixed carbon conversion is 85.5% for Case 1, 90% for Case 2,and 83.1% for Case 3.
Average temperature contours for Cases 1 and 3 appear nearlysymmetric in the first stage, while for Case 2 there is an asymmetry inthe temperature distribution in the first stage of the gasifier (Fig. 2B).
This may be because the heaviest particles become trapped in thegasifier first stage. Mean velocity contours (Fig. 3) for all three caseslook more nearly the same than do the temperature contours,although asymmetry in the velocity distribution is visible in the firststage for Case 2 in Fig. 3B. The COmole fraction distributions for Case 1
tion of upflow entrained-flow gasifier; p=2.84 MPa.
Fig. 5. Computed H2 species mole fraction: Fluent simulation of upflow entrained-flow gasifier; p=2.84 MPa.
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and Case 3 show higher concentrations in stage 2 as well as in thevicinity of the slag tap relative to levels for Case 2; compare Fig. 4A andC to Fig. 4B. This is consistent with the CO exit volume fractions shownin Table 5. Again, asymmetry is observed in the spatial contours in
Fig. 6. Fixed carbon conver
stage 1 for the Case 2 solution (Fig. 4B). Conversely, the H2 molefraction is higher in stage 2 for the Case 2 solution; see Fig. 5.
Predicted overall fixed carbon conversion is compared for the Case1 and Case 2 solutions for each particle size, for the SG1, SG2, and SG3
sion for Cases 1 and 2.
Fig. 7. Average particle residence time for Cases 1 and 2.
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density cuts in Fig. 6. Trends are quite similar for the two solutions,with nearly 100% conversion predicted for the three smallest particlediameter ranges (PS7, PS6, and PS5, or 50, 90, and 128 μm), and lowercarbon conversion predicted for the larger particle sizes. For example,between 60% (for SG3) and 40% (for SG1) conversion is predicted forthe largest particles (PS1, or 800 μm). Case 1 conversion levels werealways lower than those using the individual density fractions (Case
Fig. 8. Impact velocity normal to the wall versus axial location in second stage f
2) which is consistent with the lower temperatures for the averageddensity fractions used for Case 1.
The computed average particle residence times are compared forthese two solutions in Fig. 7. Residence times for the three smallestparticle size classes (up to 128 μm diameter) are approximately 3s,with residence times increasing for the larger particles, especially forthe Case 2 solution. There are significant differences in the predicted
or density class SG1 for Cases 1 and 2. Particles injected from stage 2 inlet.
Fig. 9. Impact velocity normal to the wall versus axial location in second stage for density class SG4 for Cases 1 and 2. Particles injected from stage 2 inlet.
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residence times, with Case 2 consistently predicting larger values.Including accurate density variations, and the individual inclusion andtreatment of the larger size fractions (especially PS1, PS2, and PS3)while iterating the DPM solution for Case 2 results in the longerpredicted residence times, which are captured in the Case 1 solutiononly for the highest density and largest particle size fraction (SG4PS1;see Fig. 7D).
The computed average wall impact velocity distributions are quitesimilar for the Case 1 and Case 2 solutions; see Figs. 8 and 9 forexamples for particle injection from the stage 2 injection locationonly. Additional examples have been given by Slezak [28]. (Slurryinjection velocities were set to the same value of 50 m/s as used by Shiet al. [3] at all three injection locations.) Also, the wall impact velocityis largest near the stage 2 injection location, and then decreasessignificantly (to between 1 and 0.5 m/s). There also is an increase inimpact velocity near the exit, due to the contraction just prior to theexit. Impact velocities near the second stage injector are largest for thelarger particles (PS1; 800 μm). There is a trend for the heavierparticles to have higher wall impact velocities (SG4; Fig. 9). Also,impact velocities for second stage injection tend to be higher than forparticles that were injected into the gasifier first stage [28].
An example ofmeasured particle–wall impact probability results isshown in Fig. 10 along the length of the second stage. This depicts theprobability of particle–wall collision for injection of all 28 density/sizefractions into both the first and second stages assuming that all
Fig. 10. Impact probability versus second stage distance for 28 size/density fractions.Particles injected from both stage 1 and stage 2.
particles stick to the wall at their first impact with the wall. Impactprobability is highest near the second stage injection location. TheCase 1 solution predicts 10% of all particles never impact a wall, whileCase 2 predicts only 5% do not impact the wall. Again, further detailshave been given by Slezak [28].
Fig. 11 displays contours of computed impact particle mass flux forparticles injected from both stage 1 and stage 2 at the Sauter-meandiameter and composite average density of the Bailey coal. If oneassumes that 100% of the particles impact the wall and 80% of thecarbon has been converted prior to impact, then the averagemass fluximpacting the second stage wall from this rough estimate isapproximately equal to the area-averaged value obtained from thedata shown in Fig. 11. The mass flux is highest almost directly acrossfrom the second stage slurry injection port, as one would expect.
Significant differences have also been observed for the computedparticle trajectories, for different particle initial density and diameter.The dramatic differences in particle trajectories for the extreme endsof the particle size-density spectrum are highlighted in Fig. 12, wherethe computed particle trajectories until the initial particle impact withthe gasifier wall are shown for second stage injections of the lightest,smallest cut (SG1PS7) and the heaviest, largest cut (SG4PS1). Impactsfor the SG1PS7 cut appear to be distributed over the entire gasifiersecond stage wall, while the SG4PS1 particles all impact the wallalmost directly opposite the injection location. Also, the particle–wall
Fig. 11. Contours of mass flux (kg/m2s) for Sauter-mean diameter and compositeaverage density. Particles impact once and then stick.
Fig. 12. Particle trajectories until first wall impact for second stage injection of: A) cut SG1PS7 (SG=1.2, d=50 μm), and B) cut SG4PS1 (SG=3.3, d=800 μm).
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impact velocities are much larger for the SG4PS1 cut, being as large as10–14 m/s, as compared to average values of 1–3 m/s for the SG1PS7cut; see Figs. 8B and 9A. These trajectory and wall impact differenceswould significantly influence the location and rate of slag formation,which then would feed back into a secondary influence on the localgas phase properties (temperature; gas composition).
Grid independence checks for the present geometry have beenreported in the thesis by Slezak [28]. Simulation results for the setupsfor Case 1 and Case 2 were run to 10,000 iterations for increasing griddensities of 12,256 cells (the present mesh), 20,061 cells, 28,264 cells,and 40,112 cells. Centerplane contours of gas species mole fractions,average gas temperatures, and wall temperatures were presented bySlezak [28], along with the second stage particle–wall impact normalvelocities, and some localized visible differences were observed insome regions. However, no clear trends could be discerned for thesevariations versus the grid density. As an overview, the computed exitgas mole fractions are compared in Fig. 13 for Case 1 and in Fig. 14 forCase 2. Similar plots are given by Slezak [28] for the gasifier stage 1and stage 2 average temperatures. Only modest differences areobserved in these major gas species and average temperatures, so thepresent mesh (12,256 cells) is judged to be adequate to predict theseoverall gasifier characteristics. Also, particle–wall impact velocitiesgenerally did not change significantly as the grid density wasincreased. However, variations were observed in the predicted spatial
Fig. 13. Case 1 exit gas compositions for 12,256, 20,061, 28,264, and 40,112 gridelements.
contours of gas specie volume fraction, for some species and in somelocalized regions. So, it is concluded that the finer grid should be usedto obtain the most realistic overall simulations. However, this wouldresult in significant run time increases and was not deemed necessaryhere when looking at the particle–wall collisions.
Preliminary validation efforts have been summarized in the thesisby Slezak [28]. Radiant coal flow reactor (RCFR) experimentsconducted by SRI International as a part of the CPP for a pyrolysisrun, a combustion run, and a gasification run for a Bailey coal sample[29] were simulated in Fluent. Wen and Chaung's shrinking core charconversion model and the devolatilization model as implemented byShi et al. [3] were used, with the same chemical kinetic rateparameters as have been used in the present commercial-scalegasifier simulations. The computational model of the SRI RCFR isshown in Fig. 15. The SG2PS5 fraction was chosen as the coal density/size fraction for the validation study, because this fraction was thecenter point of the SRI test matrix.
The computational grid used (Fig. 15) contains 91,584 hexahedralcells; no grid sensitivity studies were conducted for this SRI PCFRgeometry The reactor is 1.2 m long, with a 12 mm ID. The coal wasinjected via the core flow inlet in Ar carrier gas, and any oxygen wasintroduced via the outer sheath flow. Steam was injected through theconical inlet shown in Fig. 15. Wall temperature boundary conditionswere chosen to match the experimental setup at SRI as closely aspossible. The inlet velocity of the coal particles was set at 24 cm/s. The
Fig. 14. Case 2 exit gas compositions for 12,256 and 40,112 elements.
Fig. 15. Computational model of SRI facility.
Fig. 17. Comparison of exit gas yields for SRI run 80.
Fig. 18. Comparison of exit gas yields for SRI run 81.
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operating conditions for the three SRI runs chosen for this validationstudy are summarized in Table 6. Run 49 was a pyrolysis case, run 81was a combustion run, and run 80 was a gasification run at themaximum steam and O2 levels. The computed centerline gastemperatures for all three runs are shown in Fig. 16.
Results for the CO2, H2, and CO exit gas yields obtained from theCFD validation runs are compared to the SRI International experi-mental data for runs 80, and 81 in Figs. 17 and 18, respectively.
The CFD predictions of the CO and H2 yields for the pyrolysis case(run 49; not presented) did not agree with the experimental data.These discrepancies are either due to an experimental O2 leak thatwas discovered after the run was completed, or to the fact that tar andsoot were not included as pyrolysis species in the present devolati-lization model. It is noted that the volatile distribution predicted inthe CFD model [3] assumes that the product gases resulting frompyrolysis consist primarily of CH4, which is not found in the SRIpyrolysis results. The CO and H2 predictions are realistic for runs 80and 81, but CO2 is underpredicted for both of these cases (by 40% ofthe measured value for run 80 and 34% for run 81). Fixed carbonconversion predicted by Fluent for the gasification case (run 80) wasapproximately 72%, versus 88.3% measured by SRI. For run 81 thecomputed fixed carbon conversion was 47%, versus a measured levelof 72%.
Additional Fluent CFD simulations have been obtained for acommercial-scale single-stage downflow entrained-flow gasifier,
Table 6Operating conditions for SRI SG2PS5 density/size cut experimental runs [29].
Fig. 16. Computed centerline gas temperature for SRI validation runs.Fig. 19. Commercial-scale, single-stage, downflow, slurry-fed, entrained-flow gasifiergeometry.
Table 7Proximate and ultimate analyses of Kentucky #9 coal.
Fig. 20. Comparison of exit gas composition for commercial-scale downflow gasifier.
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using the same chemistry models [4,5] and gasification UDF [3,28] aswere used to simulate the two-stage upflow gasifier. The geometryand 7058-cell axisymmetric grid used are shown in Fig. 19. Thepresent simulations were for a Kentucky #9 coal, to enablecomparison with published performance data for the commercialunit [30]. Proximate and ultimate analysis data for this coal are givenin Table 7.
Coal slurry flow rate (27.8 kg/s coal and 11.0 kg/s H2O), gas flowrate (22.5 kg/s of 96% O2), and operating pressure (2.69 MPa) wereselected to correspond to results given by Hornick and McDaniel [30].Coal particle average diameter and specific gravity were set tod=231 μm, SG=1.373, with a Rosin–Rammler size distributionbetween 25 μm and 850 μm.
The computed exit gas composition is compared with data fromHornick and McDaniel [30] in Fig. 20. Agreement between the CFDpredictions and measured data are reasonable, except for under-prediction of CO and overprediction of CO2 volume fractions. Thecomputed fixed carbon conversion for this simulation is 95%,
Fig. 21. Particle trajectories for injection of: A) cut SG1PS7 (SG=
compared to a measured value of 91%. Examples of the computedparticle trajectories are shown in Fig. 21 for the lightest, smallestparticles (SG1PS7; SG=1.2; d=50 μm) and the heaviest, largestparticles (SG4PS1; SG=3.3, d=800 μm). The lighter, smaller parti-cles tend to follow the gas flow and are recirculated in the gasifier,while the heavier, larger particles impact the wall nearer to the exitand do not recirculate. This is shown quantitatively in Fig. 22, wherethe percent of particles striking the wall is shown versus distancealong the gasifier for the two particle size/density cuts.
4. Conclusions
Fluent CFD simulations using DPM particle tracking have beenobtained for commercial-scale two-stage upflow and single-stagedownflow entrained-flow gasifier geometries, and the solutions havebeen used to examine variability in the computed gas phase averageexit conditions (temperature and species volume fractions), gas phaseflow field contours, particle char conversion, particle residence time,and particle–wall impact velocity for the 28 varying particle densityand size fractions under study in the current Coal Partitioning Project.For these density/size fractions, the density ranges between SG=1.2and SG=3.3, while particle diameter ranges between d=50 μm andd=800 μm.
For the two-stage upflow gasifier, the computed gas phase flowfield, computed particle char conversion, and computed residencetimes for some coal fractions, each differ significantly between thesimulation that used the parent coal properties (Case 1) and thesolution that used properties for each of the 28 different Bailey coaldensity/size fractions that have been separated and characterized inthe CPP project (Case 2). This indicates that in order to mostaccurately determine both the actual trajectories of coal particles foreach density and size fraction and the gasifier flow field, it is necessaryto also model the injections of each of these density/size cuts in theiterated DPM simulation. Significant differences have also beenobserved for the computed particle trajectories, as the particle initialdensity and diameter are varied. Solutions that were obtained usingtwo different devolatilization models generally compared reasonablywell.
Grid independence studies showed the grid used in the compar-ison studies was adequate for predicting exit gas composition andwall impact velocities. Preliminary validation studies using experi-mental data for the Bailey coal by SRI International in theirpressurized coal flow reactor at 30 atmospheres [29] indicatedadequate agreement for gasification and combustion runs, but pooragreement for a pyrolysis case. Additional simulation of a single-stagedownflow gasifier yielded an exit gas composition that was inreasonable agreement with published data.
1.2, d=50 μm), and B) cut SG4PS1 (SG=3.3, d=800 μm).
Fig. 22. Particle–wall impacts for lightest, smallest particles (SG1PS7; SG=1.2, d=50 μm) and heaviest, largest particles (SG4PS1; SG=3.3, d=800 μm).
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Acknowledgements
This work has been supported under the DOE GasificationTechnology Program, funded by the National Energy TechnologyLaboratory in Morgantown, WV, as part of the University ResearchInitiative, through the “Collaboratory for Multiphase Flow Research,”RDS Contracts No. 41817M2318 and 41817M2100.
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