quarterly progress report phase ii: clean and secure...
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Quarterly Progress Report Phase II: Clean and Secure Energy from Coal
University of Utah DE-NT0005015 January 31, 2011
Philip J. Smith (PI) Project Period
October 1, 2010 to December 31, 2010 EXECUTIVE SUMMARY The University of Utah is pursuing research to utilize the vast energy stored in our domestic coal resources and to do so in a manner that will capture CO2 from combustion from stationary power generation. The research is organized around the theme of validation and uncertainty quantification through tightly coupled simulation and experimental designs and through the integration of legal, environment, economics and policy issues. The results of the research will be embodied in the computer simulation tools which predict performance with quantified uncertainty; thus transferring the results of the research to practitioners to predict the effect of energy alternatives using these technologies for their specific future application. A summary of highlights from the last quarter follows.
The Oxycoal simulation team focused on addressing several issues that had been observed with the current LES and DQMOM approach to simulate oxy-coal flames this quarter, including minimizing the thermodynamic table size. This required non-uniform discretization; however, the third-party software used to read the table didn’t handle non-uniform discretization correctly, creating inaccuracy and instability in the code, so it was preferable to put all simulations on hold. This has been recently resolved so LES simulations should resume soon. As discussed in the previous report, we had concluded that prediction of flame ignition and stand-off distance wasn’t sensitive to the concentration of oxygen, which contradicts the experiments. A sensitivity study was then performed and suggests that both wall temperature and heterogeneous reaction may be responsible for short stand-off distances in the cases with high oxygen concentrations in the primary feed. The last issue concerned the ability of DQMOM to predict particle dispersion. Previous results have indeed showed that particle dispersion was underpredicted. Further investigation was conducted and proved that DQMOM is a valid approach to model particle dispersion.
During this quarter, the PIV experiments were completed on the lab-scale burner and the pilot-scale oxyfuel combustion (OFC) for some of the combustion conditions that had been performed previously. The newly completed coal feeder for the lab-scale experiments reduced variability in the PIV measurements. The results of lab-scale experiments demonstrated the effect of coal particle size and equivalence ratio (f) on the dynamics and velocity fields in the pulverized coal flames. In the pilot-scale OFC, the PIV results show a decrease in velocity far from the burner due to turbulent jet decay and an increase in the velocity fluctuations far from the burner due to an increase in turbulent eddies. In addition, the recently modified oxy-fired pilot-scale circulating fluidized bed (CFB) was used to study operational impacts of variations in oxygen concentration. Finally, the Oxycoal team completed the installation of a new flue gas recycle (FGR) system and developed new burners equipped with a pure oxygen, which will enable experiments to explore the effect of oxygen injection on the flame stability. The FGR system included a baghouse to remove particulates, and a scrubber to remove sulfur oxides and moisture, or a condenser to remove moisture from the flue gas.
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This quarter, the Gasification Team selected validation parameters in consultation with several faculty members, both experimentalists and theorists. First, it was decided that the verification procedure would be performed separately from the validation, rather than including a verification parameter like grid resolution in the validation. The four parameters were A2 and E2, the pre-exponential factor and activation energy for the second reaction of the two-step Kobayashi devolatilization model; average particle size and wall temperature (for the gasification case) centerline gas temperature profile and particle mass flowrate (for the laminar reacting particle case). The radiation task focused on verification, early development of a parallel model, and scaling analyses. The RMCRT code has been shown to give results of accuracy to within 1% of benchmark values. In addition, the entrained flow gasifier (EFG) was operated for two campaigns, both of which focused on evaluating performance of the system. System pressure and throughput were successively increased during each campaign, and the system was finally brought to the maximum pressure and throughput that can be achieved with the on-site oxygen system available. Characterization and improvement of the injector continued during the quarter. A new non-adjustable injector was fabricated and tested and showed much improved stability but unexceptional performance. Minor modifications were made to the system to improve safety and operability.
The CLC investigated the fractional char burnout and the oxygen partial pressure profiles in the fuel reactor of a Chemical Looping with Oxygen Uncoupling (CLOU) system. The simulation results were compared with published experimental data for the combustion of Mexican Petcoke and German Lignite respectively in a CLOU system. The mathematical approach developed captures the observed experimental trends and thus are promising for further study. The Team also focused on data collection, parameter identification and a preliminary analysis in verification/uncertainty quantification, for a fast fluidization technology simulation using STARCCM+.
The experimental CLC work included the investigation of a supported oxygen carrier – 50% CuO deposited on titania. The material performed well in the TGA experiments. It has provided good yields and exhibited a remarkable longevity during extended looping under nitrogen and air. The titania supported CuO may be a viable candidate for CLC. During this quarter, lab-scale CLC efforts focused on the identifying and obtaining copper-based carriers. One of the materials acquired contains 50% CuO by mass, but the physical integrity of this material is poor. It rapidly disintegrated in the small lab-scale fluidized bed, suggesting that it would be completely unsuitable for operation in an industrial-scale system.
During this quarter, the UCTT Team readied a small fixed-bed reactor that is a component of a new high-pressure TGA system for coal pyrolysis studies under in-situ thermal treatment conditions. Upgrades to the reactor control system are complete, and initial data should be collected in the first quarter of 2011. Design of a new high-pressure coal block pyrolysis reactor that will simulate heating in a coal bed has begun. They also studied the operational parameters for underground coal thermal treatment (UCTT) and developed recommendations for design matrix of these processes. Progress continued on the UCTT simulation tasks. We implemented a new algorithm for representing coal particles inside a computational domain. The new algorithm has two main advantages over the previous method: it helps preserve the particle local coordinate system, which is important for implementation of directional properties of coal, and it produces smooth edges for a proper computational mesh. This enabled the development of a thermal profile for a more complicated geometry.
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RESULTS AND DISCUSSION Task 1.0 – Project Management
During this quarter, the Project Team submitted the fourth quarterly report and continued to work with the Program Manager to ensure that the tasks and subtasks are on target. The Task 7 topical report was completed, and two of the three Task 8 topical reports were prepared.
Task 2.0 – Technology Transfer and Outreach
This task focuses on industry, academic and public outreach and education efforts, as well as implementing the External Advisory Board (EAB) recommendations. The final EAB recommendations approved by EAB members last quarter were circulated to ICSE faculty and staff. Preparations also were begun this quarter for the 2011 EAB meeting, which will be held in the fall of 2011. Preparations were also begun for the 2011 Energy Forum, at which former Governor Dave Freudenthal has agreed to be a speaker and which will be held in September 2011.
Task 3.0 – Power Generation “Retrofit”: Oxy-Coal
Subtask 3.1 – Oxy-Coal Combustion Large Eddy Simulations
Predicting particle dispersion. Particle dispersion is a key phenomenon in coal flames that the LES and DQMOM approach should be able to capture. LES simulations of non-reacting coaxial particle-laden jets were performed and compared to experimental results (Budilarto 2003) for three different velocity ratios and two particle sizes (see previous reports). The gas and particle velocities were predicted with a satisfying agreement, but the particle dispersion was underpredicted as shown on Figure 1.
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Figure 1. Comparison with experimental data. Radial profiles of particle number density taken at x = 15d 25 µm particles (blue) and 70 µm particles (green).
Simulations predict that the particles won’t disperse much and remain near the centerline. Moreover, these simulations were run using an upwind scheme, which is only first-order accurate and therefore introduces numerical diffusion. It is therefore useful to rerun these cases with a second-order scheme to assess the ability of DQMOM to model particles.
Simulations were first run with one quadrature node to represent the particle number density function (NDF), i.e. all particles have the same size (25 or 70 microns) and velocity. This approach proved to be overly simplistic and almost no dispersion was predicted.
In his experiments, Budilarto (2003) reported that particles are not monosized and follow a size distribution. It was also observed that large particles (70 um) are injected with various initial velocities and keep their momentum, which results in the spreading of the particle jet. Consequently, it was chosen to represent the NDF by five quadrature nodes.
Small particles don’t have a large momentum and would quickly reach equilibrium with the gas-phase velocity. However, their size distribution ranges from a few microns to 40 microns, so it was important to represent this wide size range accurately. Therefore, the NDF was modeled with 5 quadrature nodes, each one representing a different particle size:
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W/Wc
Node Diameter (um) W (#particles/m3)
0 7 1.31e11
1 14 2.46e10
2 25 7.2e9
3 34 2.9e9
4 39 1.7e9
As shown in
Figure 2, the simulation results show good agreement with experimental data. The smaller particles (7um) are responsible for most of the dispersion, whereas larges ones (25 um and more) do not disperse much.
Figure 2: Comparison with experimental data, VR = 1.0, 25 µm particles. Radial profiles of particle number density taken at x = 15d.
Large particles on the contrary don’t have a wide range of size distribution, but their initial velocity will play a much more important role for predicting the jet spreading. So, for large particles, the NDF was modeled with 5 quadrature nodes, each one associated with a different velocity vector. The weights and abscissas were chosen assuming a 2D normal distribution and accordingly to the measure particle velocity fluctuations at the inlet. All particles have a 70 um diameter.
r/d
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Node Ux (m/s) Uy (m/s) Uz (m/s) W (#particles/m3)
0 9.94 0 0 3.8e8 1 9.55 -0.58 0 2.3e8 2 9.55 0.58 0 2.3e8 3 9.55 0 -0.58 2.3e8 4 9.55 0 0.58 2.3e8
Figure 3 shows good agreement between simulation results and experimental data. Particles with initial radial velocity account for a large part in the particle dispersion and it is essential to have an initial distribution on particle velocity to predict the dispersion correctly.
Figure 3. Comparison with experimental data, VR = 1.0, 70 µm particles. Radial profiles of particle number density taken at x = 15d.
Subtask 3.2 – Near-Field Aerodynamics of Oxy-Coal Flames with Directed Oxygen and Minimum Flue Gas Recycle PIV, IR experiment and the results. During the second half of the October, the experiments were conducted to apply the PIV and IR technology to explore more details of the flame (i.e., temperature and velocity fields). The original CMOS camera was used in conjunction with these measurements in order to compare these new methods to our existing data. The PIV results are discussed in the Subtask 3.3 section, and the IR results are currently being processed.
Design of New Burners for Direct Oxygen Injection. A burner is being designed and constructed to permit coal to be transported by pure CO2 (Figure 4). The location of the oxygen injector will play a very
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important role in the combustion process. The initial design concept was to locate the pure oxygen injector on the axis, and the initial flow of oxygen would be injected axially into the center of the coal jet. This design would unburned coal particles to shield the walls from excessive heat fluxes. A secondary axial flow of CO2 and O2 will surround the two interior jets and will be adjusted to allow for minimum CO2 entry into the furnace, consistent with survival of the cooled furnace walls.
Figure 4. Burner equiped with oxygen lance. However, after reviewing some of the research on the L1500 at the University of Utah, the investigators found that a burner with oxygen lance around the coal stream generates a more stable flame. The coal stream is located in the center of the annular burner. The pure oxygen jet is an annulus around the coal stream jet, and the secondary stream is the second annulus located around the pure oxygen stream (oxygen lance). Figure 5 illustrates this design.
The velocity of the primary stream (or the coal stream) and the oxygen lance are equal. The ratio of the secondary stream velocity to the primary and lance stream is approximately 2.4. One of the advantages of the equal velocity for primary stream and the oxygen lance is less mixing. This limited mixing at the beginning of the burner will lower the combustion temperature, which results in lower NOx emissions. The other advantage of this design is the location of the oxygen stream closer to the secondary stream, which leads to more mixing of the secondary stream and the oxygen. This new mixture will be introduced to the coal jet, which causes lower jet temperatures and reduces NOx emissions. Also the higher velocity of the secondary stream will keep the entrainment of the coal jet flow.
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Figure 5. The schematic of the burner and the location of the streams. In order to investigate the effect of the oxygen lance on the coal ignition and flame stability, the velocities will be matched with previous work. The approach is to reduce the amount of oxygen from the secondary stream and eventually increase the amount of the oxygen in the oxygen lance while the total oxygen concentration is constant and is equal to 40% overall. By this method, differences in flame stability associated with increases in oxygen in the primary stream and the oxygen lance can be compared. Also, this study allows us to explore the flame shape, stand-off distance, and the heat flux from the flame.
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A Fluent simulation study was applied to study the expected shape of the flow in the near-burner region. In this simulation, combustion is not considered because the goal is to explore the near-burner mixing.
The results of this simulation are shown in Figure 6.
Figure 6. Simulation of the velocity flow field in the OFC
Another simulation was been conducted that shows the turbulent mixing of the oxygen in the burner. This simulation does not include combustion; however, it describes the fluid mechanic phenomena before the devolatilization of the coal in which the turbulent mixing of oxygen is very important.
Figure 7 shows the distribution of the oxygen. The simulation only produces realistic results for areas very close to the tip of the burner.
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Figure 7. Simulation of the O2 mixing in the OFC (NO Combustion)
Figure 8 shows the same simulation for CO2 concentrations.
Figure 8. Simulation of the CO2 mixing in the OFC (NO Combustion).
Table 1 presents the velocity and mass flow rate values in the annular burner stream. These values are calculated based on the overall oxygen of 40%. The velocities of primary stream and Lance stream were maintained at 6.3 (m/s). Also the velocity of the secondary is close to 15.7 (m/s) in most of the cases.
Table 1. Combustion operating conditions for the pure oxygen injection.
Percent Velocity
(m/s) Mass Flow
Rate (Ib/hr) Velocity
Ratio
O2 % Lance Pri Lance Sec Pri
CO2 Lance O2 Sec O2
Sec CO2
Pri / Lance
Sec / Lance
Sec / Pri
0% 6.37 0 15.7 15.2 0 25.9 31.44
10% 6.37 5.9 15 15.2 2.6 23.3 31.44 1.08 2.54 2.35
14% 6.37 8.3 14.6 15.2 3.64 22.3 31.44 0.77 1.76 2.29
20% 6.37 5.5 15.6 15.2 5.2 20.7 31.44 1.16 2.84 2.45
23% 6.37 6.4 15.3 15.2 5.98 20.0 31.44 1.00 2.39 2.40
35% 6.37 5.8 14.7 15.2 9.1 16.9 31.44 1.10 2.53 2.31
50% 6.37 6 15.5 15.2 13 13 31.44 1.06 2.58 2.43
65% 6.37 6.1 14.9 15.2 16.9 9.1 31.44 1.04 2.44 2.34
76% 6.37 6.4 15.5 15.2 19.75 6.24 31.44 1.00 2.42 2.43
82% 6.37 6.2 14.2 15.2 21.31 4.68 31.44 1.03 2.29 2.23
100% 6.37 6.3 15.5 15.2 25.99 0 31.44 1.01 2.46 2.43
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Each case will be performed, and images will be captured from the flame. Images will be analyzed which can give us information regarding stand-off distance and the stability of the flame. In addition, simultaneously, spatially and temporally resolved data from more advanced optical diagnostics will be collected.
Subtask 3.3 – Advanced Diagnostics for Oxy-Coal Combustion
To minimize the effect of flame luminosity an interference filter centered at the 532 ± 5 nm (Edmunds Scientific) was used and mounted in the camera lens (Tamron). Also a camera shutter (Lavision) was mounted in the camera lens to shorten the exposure time of the 2nd frame taken by the camera from 67 ms to 3 ms. Without this shutter it is impossible to get PIV information as the 2nd frame is always over exposed with the luminosity of the flame due to the long time of the exposure time of the 2nd frame.
Figure 9 shows examples of instantaneous Mie scattered images of pulverized coal flames and the corresponding velocity fields at 3 different heights of the flame with f = 0.92 and using coal particles of sizes < 38 mm. It is to be noted that each image is one of twin frames taken at two different times, t and t + dt (dt = 50 ms). From these twin frames one velocity map is created. The inner cone in the flame is formed due to the premixed coal-air stream fed through the central tube of the burner. In the images the burner head is seen at the bottom where the outer diameter of the central part is 7.2 mm. As can be seen in Figure 9, the velocity fields of pulverized coal flames show that the lower parts of the flame are more stable than the upper parts and as such have lower axial velocities than upper parts. To show the dynamic nature of the coal flame as we move vertically up in the flame, the average instantaneous velocity inside a small area in the velocity field on the flame axis are compared at three different heights (Figure 10 left ). It is apparent that time-average mean velocity and the fluctuation in the instantaneous velocity during the 1 minute run increase vertically. Another important consequence of this increased fluctuation in the upper parts of the flame is that it happens that the flame may move perpendicular to the laser sheet during the run and therefore no coal particles are present to scatter the laser and the image becomes blank. This results in some zero velocities in the middle and upper flame parts- heights 5 and 8.5 cm. This increase in velocity is a result of the acceleration of the flow vertically due to air entrainment. This fluctuation looks random as no specific periodicity was observed in the power spectrum as shown in the right plot of Figure 10. Also the standard deviation was found to increase slightly with height above the burner head.
Pulverized coal particles of different size bins were prepared in order to investigate the effect of coal particle size on the velocity field and the dynamics of the combustion of pulverized coal. Additionally, experiments were carried out under three different equivalence ratios (f = 0.25, 0.44 and 0.92). Figure 11 shows that as the equivalence ratio of the pulverized coal flame increases, the height of the inner cone in the pulverized coal flames decreases. In these experiments increasing the equivalence ratio is made by decreasing the mass flow rate of the secondary air (air fed through the annular ring). This reduction in the air flow rates results in less air entrainment in the inner cone of the flame and hence a reduction in the cone height. Furthermore, inspecting the instantaneous images in Figure 12 it is clear that with high excess air (very lean fuel-air mixture, f= 0.25) burning coal particles can be identified more easily than with higher f. For all coal particle sizes it was found that increasing f results in an increase in the axial velocity. It is evident from Figure 12 that flames with coarser coal particles have, in general, less axial velocity than those with smaller coal particle sizes. Also the rate of the reduction in the axial velocity
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increases as the equivalence ratio decreases as mentioned before, although the trend varies somewhat as stoichiometric conditions are approached. Although there is some scatter observed in the data, there may be trade-offs between hydrodynamic effects at low vs. higher equivalence ratios, and temperature effects due to particle ignition, which may be impacted by both local oxygen concentration and particle size.
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Figure 9. Image maps (left), and the corresponding velocity fields (right) of pulverized coal flame.
Viewing the flame from burner head to a height 3.5 cm (bottom,), 3.5-7 cm (middle), and 7-10.5 cm (top). φ = 0.92 and coal particle sizes < 38 µm.
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Figure 10. Fluctuation in axial velocity at 3 different heights (left plot) and corresponding power spectrum
(right plot). Coal particle size < 38 µm and φ = 0.25. σ is the standard deviation.
Φ= 0.25 Φ= 0.44 Φ=0.92
Figure 11. PIV Images of pulverized coal flames at different φ. Coal particle size is < 38 µm.
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Figure 12. Each data point is the average of 200 instantaneous velocity values at a height of 3 cm in the flame (75 images in the non-combusting case). Particle Image Velocimetry (PIV) of Pulverized Coal flames in the 100 KW OFC. After completing and demonstrating the applicability and success of using PIV in investigating pulverized coal flames in the lab-scale burner, the investigators moved on to the 100 KW OFC. Figure 13 shows the experimental setup for the 100 KW OFC. PIV experiments were carried out at different experimental conditions, and the concentration of the primary O2 was also changed to study examples of attached and detached flames in the combustor. The 100 KW OFC is a down-flow burner and velocity fields are studied at two different locations in the flame, one is at the top window, 7.6 cm from burner tip, and the other at its bottom, 30.1 cm from the burner tip. Figure 14 depicts some examples of the PIV at two different Primary (Pr) O2 concentrations, namely 18 % for an attached flame and 0% for a detached flame. Figure 15 shows the PIV at both the near-burner and the far-burner regions. It is evident that the instantaneous velocity is larger in the near-burner region than the far burner region, as expected due to the decay of a turbulent jet. Investigating the instantaneous velocity filed for all Pr O2 concentrations for the top and bottom regions show that the even though the top region has higher velocity than the bottom, the fluctuation in the velocity is larger at the bottom than in the top near the burner tip. The increase in velocity fluctuations is due to an increase in turbulence eddies further from the burner tip. In addition, the fluctuations in the axial velocity increase with an increase in the Pr O2 concentration.
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(a)
Top
Bottom
Figure 13. Experimental setup for the 100 KW OFC showing the PIV camera and the combustor with locations of near and far regions in the combustor where PIV was carried out.
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(b)
Figure 14. Two examples of PIV taken at two locations in the 100 KW OFC, (a) top and (b) bottom, as
shown in Figure 13. Primary O2 percentages, 18 % for detached flame and 0% for attached flame.
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Subtask 3.4 – Oxy-Coal Combustion in Circulating Fluidized Beds
A pilot-scale testing campaign was carried out during this past quarter to finish the oxycoal dataset that was interrupted due to operational problems encountered in the previous quarter. Both air- and oxy-firing conditions were to be explored with and without limestone addition, and the measurements to be taken as a function of operating parameters included: temperature; species concentration including O2, CO2, CO, NO, SO2, at the furnace exit and also axial profiles at 5 locations for select conditions, SO3 concentrations under select conditions, solid samples at exits and also at 5 axial locations for select conditions.
Gaseous emissions profiles are shown in Figure 16 for both air and oxy-firing conditions. The trends for both conditions are similar. CO starts out high as the fuel burns in the bed and is reduced after the secondary oxidant is introduced. The CO then goes back up as the entrained fuel is burned in the upper sections of the CFB before dropping back off at the exit.
0 10 200
5
10
15
0 10 200
5
10
15
0 10 200
5
10
15
0 10 200
5
10
15
Time, s
Inst
anta
neou
s ax
ial v
eloc
ity, m
/s
0 10 200
5
10
15
0 10 200
5
10
15
Pr O25.5 %
Pr O210 %
Pr O221 %
Pr O218 %
Pr O215 %
Pr O20 %
Figure 15. Instantaneous axial velocity in the 100 KW OFC at the top and bottom locations.
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Figure 16. Emissions profiles measured under air- and oxy-fired conditions in the 330KW circulating fluidized bed combustor.
Limestone addition in the bed showed some reduction of SO2 under both air- and oxy-fired conditions, Figure 17, but there is a lot of scatter in the data. Some of this scatter can be explained by variations in bed temperature. Although not shown, the SO2 concentrations are significantly higher for oxy-firing vs. air-firing conditions. In terms of normalized mass emissions from the CFB, however, the emission levels are similar as shown in Figure 17. Figure 18 shows the dependence of SO2 mass emissions as a function of bed temperature.
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Figure 17. SO2 emissions while varying limestone addition in the 330 KW circulating fluidized bed.
Figure 18. Bed temperature effect on SO2 emissions in the 330 KW circulating Fluidized Bed. The impact of limestone addition on SO3 emissions for both air- and oxy-fired conditions was evaluated using the controlled condensation method of sampling. The SO3 concentration is significantly higher under oxy-fired conditions; however, on a normalized mass basis, the emission levels are similar for air- and oxy-firing as shown in Figure 19.
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Figure 19. Effect of limestone addition on SO3 emissions in the 330 KW circulating fluidized bed.
Subtask 3.5 – Single-Particle Oxy-CO2 Combustion
The investigators have recruited a student to work on the single-particle kinetics in oxy-CO2 combustion portion of the task. The student will be receiving a stipend from Sandia National Laboratories to pursue this opportunity.
Single-particle CFB studies. Previous experiments on sulfur removal by limestone in the presence of N2/O2/SO2 and CO2/O2/SO2 showed significantly different sulfation mechanisms. The sulfation mechanism in N2/O2/SO2 seems to be an indirect mechanism, while the sulfation of limestone in CO2/O2/SO2 seems to be a direct mechanism. Generally speaking, the two basic mechanisms depend on whether calcination of the limestone occurs. The direct sulfation reaction takes place in an uncalcined condition. And the indirect sulfation reaction happens in a calcined state. Hence, the investigation of calcination in the presence of N2 or CO2 is definitely important.
The calcination process, as shown by the reaction below, is endothermic:
, 182.1kJH molΔ =
The calcination and re-carbonation of limestone were carried out by using thermal gravimetric analysis (TGA SDT Q600) at atmospheric pressure in the presence of various gases. A 20±0.5 mg sample in a platinum pan was heated up to the desired temperature at a specific rate. The details of the experimental conditions are shown in Table 2.
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Table 2. Experimental conditions. Experimental Parameters CO2 or N2 concentration (vol.%) 100% Diameter of limestone (mm) 0.6-0.9 Limestone weight (mg) 20 Total gas Flow rate (liter/min) 0.1
A continuous N2 stream was used to evacuate the CO2 produced during the calcination tests. This was to ensure there was no CO2 that could re-carbonate the limestone even at low temperatures.
The calcination process in N2 is shown in Figure 20. Figure 20 shows, the temperature was increased from 520℃ to 700℃ at a rate of 3℃/min and then ramped from 700℃ to 920℃ at a rate of 10℃/min. The
calcination happened at 650℃. As the calcination curve shows, the rate of calcination at low temperature
is slow, while it was much faster at higher temperatures. The sample weight decreased from 93.87% to 88.9% during the temperature range of 650℃-700℃, and it took 16 minutes. The relatively lower
calcination rate results from two possible reasons: large particle size and relatively low reaction temperature. In the same manner, the calcination process was also investigated at higher temperatures. The sample weight decreased from 88.9% to 57.38% during the temperature range of 700℃-850℃, and it took
15 minutes. In this range, the rate of calcination was much faster compared to lower temperatures because the rate of calcination is a strong function of temperature. After the calcination process was complete, the temperature was reduced. The sample weight remained stable at temperatures lower than 500℃ showing
that no re-carbonation took place in the presence of N2.
The above discussion shows the initial calcination of limestone happened at around 650℃ and the rate of
calcination is strongly dependent on the temperature. Moreover, re-carbonation cannot occur in the presence of N2 even at very low temperature and higher than 650℃ it is highly probable that indirect
sulfation is the dominant mechanism.
Figure 20. Calcination of limestone in presence of N2
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Re-carbonation is the reverse of the calcination process. Thus, the CO2 concentration has a significant effect on the calcination reaction. Generally speaking, re-carbonation and calcination are competitive reactions in a high concentration CO2 atmosphere while the limestone particle is heating up.
Calcination and re-carbonation were examined by TGA. First, the sample was heated to 870℃ and held for 20 min. in order to evaluate whether calcination happens. Then the temperature was increased from 870℃ to 900℃ at a rate of 1℃/min, and held for 20 min. to examine whether calcination took place in this range of temperature. The temperature was then ramped 0.20 °C/min to 920.00 °C and held for 20 minutes. The final heat ramp was 1 °C/min to 930°C. Finally, in order to investigate the re-carbonation of calcined limestone, the temperature was decreased from 930°C to 870 °C and held for 20 minutes.
As Figure 21 shows, calcination in the presence of high concentrations of CO2 doesn’t occur to any significant extent at temperatures lower 907°C. The sample weight decreased from 83.25% to 77.48% from 907°C to 915°C. Due to competition with re-carbonation, the rate of calcination was low at lower temperatures. Sample weight decreased to 68.8% from 915°C to 920°C, then to 53.67% at 930°C, and the calcination was complete.
The re-carbonation process was studied as the temperature dropped from 930°C to 870°C at a rate of 5°C/min. It took 25 minutes to increase sample weight from 53.67% to 72.02%. As the curve shows re-carbonation is favored over calcination at 870 °C, so the rate of re-carbonation was much faster.
As the above discussion shows, calcination of limestone happens above 907°C in the presence of 100% CO2 while re-carbonation dominates at lower temperatures (below 900°C). At the intermediate temperatures (900-920°C), both re-carbonation and calcination were competitive reactions.
Figure 21. Calcination / re-carbonation of limestone in presence of CO2
Subtask 3.6 – Ash Partitioning Mechanisms for Oxy-Coal Combustion with Varied Amounts of Flue Gas Recycle
Ash partitioning mechanisms in lab-scale oxy-coal combustion.
Elemental analysis of Utah Skyline and PRB coals. Elemental size distributions of particulate matter generated by burning Utah Skyline and PRB coals at 1500 K in O2/N2 and O2/CO2 atmosphere are shown in Figure 22 and Figure 23. Elemental distribution is presented as the weight percentage of the major
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element oxides as a function of particle size. Eleven main elements oxides were analyzed by SEM and EDS for the 11 ash samples collected by BLPI, including major elements, Na, Mg, Al, Si, P, S, K, Ca, Ti, Mn, and Fe (reported as oxides). Both Utah Skyline and PRB data showed that the contents of MgO, SO2 and Fe2O3 decreased with increasing of particle size, while Al2O3, SiO2 and CaO showed the opposite trends. Na2O, P2O5, K2O, TiO2 and Mn fluctuated in a certain range of value.
Ultrafine particles formed in Utah Skyline O2/CO2 combustion had lower contents of all elements compared with O2/N2 combustion, shown in Table 3. The oxides of Al2O3, Si2O, and Fe2O3 presented lower contents in their O2/CO2 combustion than those formed from O2/N2 combustion. These results support the hypothesis that high concentrations CO2 leads to less generation of refractory oxides in oxy-fuel combustion.
Oxygen concentration in O2/CO2 combustion was also shown to have an impact on the elemental size distribution and was much greater than that in O2/N2 combustion. For example, increasing the oxygen concentration of the O2/CO2 mixture from 21% to 31.5% significantly increased the contents of Al2O and SiO2, while the same extent of oxygen concentration increase in the O2/N2 mixture only slightly changed the contents of these oxides. When burning in O2/CO2 mixture with a high CO2 concentration, the CO2 to CO ratio increases, decreasing the vaporization of the refractory element components. From the elemental analysis of Utah Skyline, its CO2 effect was clearly presented on its SiO2 and Al2O3 contents.
Table 3. Utah Skyline ash element weight percent of PSDs with three modes.
Mode Na2O MgO Al2O3 SiO2 P2O5 SO3 K2O CaO TiO2 MnO Fe2O3 Ash Collected
21%
O2/N2
1 0.013 0.011 0.012 0.040 0.002 0.014 0.008 0.025 0.004 0.004 0.064
100 2 0.931 0.610 3.162 5.153 0.138 0.091 0.524 1.981 0.598 0.512 1.433
3 4.186 3.334 15.538 32.387 0.506 0.515 3.196 13.308 2.219 2.347 7.133
21%
O2/CO2
1 0.004 0.003 0.010 0.005 0.002 0.004 0.004 0.010 0.003 0.005 0.012
100 2 1.064 0.672 2.490 4.039 0.209 0.096 0.553 2.466 0.624 0.579 1.861
3 4.629 4.090 16.243 30.021 0.239 0.368 3.383 15.457 1.983 1.559 7.307
31.5%
O2/N2
1 0.157 0.113 0.155 0.661 0.010 0.103 0.081 0.268 0.041 0.029 0.591
100 2 1.321 0.384 4.029 5.886 0.106 0.036 0.579 1.644 0.441 0.299 1.414
3 4.433 2.354 18.866 35.526 0.451 0.308 3.921 7.494 1.850 1.619 4.838
31.5% O2/CO2
1 0.049 0.029 0.033 0.118 0.007 0.047 0.024 0.084 0.012 0.014 0.199
100 2 0.881 0.475 2.900 5.247 0.175 0.077 0.299 2.041 0.491 0.237 1.380
3 4.979 2.759 19.803 36.018 0.724 1.145 2.116 8.606 2.064 1.234 5.711
Note that mode 1 is ultrafine mode, mode 2 is fine mode and mode 3 is cores mode.
25
(a)
(b)
(c)
26
(d)
Figure 22. The element distributions of Utah Skyline combusted under 1500 K.
Similar to the Utah Skyline data, ultrafine particles formed in PRB O2/CO2 combustion had lower contents of all elements compared with O2/N2 combustion, except for SO3 and K2O, as shown in Table 4. The oxides of Al2O3, CaO, and TiO2 were present in lower contents in O2/CO2 combustions as compared to those of O2/N2 combustions. These results also support the CO2 hypothesis as stated for Utah Skyline.
As with Utah Skyline, the increase in O2 concentration in O2/CO2 combustion has an impact on the elemental distribution of ultrafine and fine ash particles in PRB data with greater changes for the CO2 environment. For example, increasing the oxygen concentration of the O2/CO2 mixture from 21% to 31.5% significantly increased the content of CaO, while the same extent of oxygen concentration increase in the O2/N2 mixture only slightly changed the contents of these oxides. PRB has a high content of CaO. When burning in an O2/CO2 mixture with a high CO2 concentration, the CaO vaporization reduced and condensed to fine particles with a lower CaO content.
Table 4. PRB ash element weight percent of PSDs with three modes
Mode Na2O MgO Al2O3 SiO2 P2O5 SO3 K2O CaO TiO2 MnO Fe2O3
Ash Collected
21%
O2/N2
1 0.018 0.268 0.035 0.028 0.020 0.012 0.006 0.217 0.017 0.016 0.393
100 2 0.236 0.944 2.395 1.814 0.087 0.022 0.091 10.814 0.379 0.191 1.071
3 1.380 3.220 14.685 13.356 0.296 0.126 0.758 40.641 1.688 0.871 3.906
21%
O2/CO2
1 0.012 0.117 0.025 0.020 0.011 0.013 0.007 0.097 0.009 0.005 0.174
100 2 0.184 1.033 1.797 3.183 0.239 0.026 0.075 7.263 0.306 0.195 1.312
3 1.308 4.190 14.203 19.123 0.504 0.244 0.827 34.085 2.067 0.781 6.610
31.5%
O2/N2
1 0.024 0.519 0.065 0.135 0.070 0.053 0.010 0.526 0.040 0.028 0.595
100 2 0.253 0.940 4.050 2.889 0.164 0.136 0.106 12.445 0.660 0.332 0.986
3 1.513 2.626 13.923 14.684 0.614 0.443 0.635 32.919 2.274 1.608 3.731
31.5% O2/CO2
1 0.011 0.159 0.025 0.020 0.011 0.010 0.007 0.109 0.010 0.006 0.220
100 2 0.406 1.149 2.147 1.582 0.158 0.072 0.171 8.448 0.449 0.427 0.924
3 3.183 3.801 15.634 18.099 0.789 0.552 1.486 31.928 2.351 0.866 4.792
Note that mode 1 is ultrafine mode, mode 2 is fine mode and mode 3 is cores mode.
27
(a)
(b)
(c)
28
(d) Figure 23. The element distributions of PRB combusted under 1500 K.
The elements discussed above generally fall into two categories. One group are refractory metallic oxides, including MgO, Al2O3, SiO2, CaO, TiO2, MnO, and Fe2O3, which vaporize at a high temperature, and then nucleate to form ash particulates. The other group includes Na2O, K2O, SO3 and P2O5, where these components easily vaporize and then condense on the surface of existing particle during the cooling process. It was observed that in the O2/CO2 combustion and O2/N2 combustion these elements show no change on their size distributions.
The differences in the results with O2 concentration are likely to be caused by an increase in particle temperature. We are currently developing a simple particle temperature model to verify this increase.
Ash partitioning mechanisms in pilot-scale oxy-coal combustion.
Description of FGR configurations. The same three coals, i.e. Utah Skyline bituminous coal, Illinois bituminous coal, and PRB sub-bituminous coal, were tested with FGR. Since these coals have different properties, different FGR configurations were applied so that the impacts of varying flue gas composition on ash partitioning could be investigated. Each coal was tested under two different FGR configurations. One was the configuration that used FGR without any flue gas cleaning devices (denoted as “Dirty FGR”). The other configuration was different for different coals. For the Utah coal, only a baghouse was used to remove particulates in the flue gas, as shown in Figure 24(a). For the Illinois coal, both the baghouse and a scrubber were used to remove particulates, moisture and sulfur oxides (Figure 24(b)). As for the PRB coal, both the baghouse and a condenser were used to remove particulates and moisture in the flue gas (Figure 24(c)). This configuration was denoted as “Cleaned FGR”. For each coal, the same firing rate was used for different FGR configurations, so that the impacts of different FGR on ash chemistry can be compared.
29
(a) Cleaned FGR for the Utah coal
(b) Cleaned FGR for the Illinois coal
(c) Cleaned FGR for the PRB coal
Figure 24. The configuration of Cleaned FGR for tested coals.
A schematic of the overall process is shown in Figure 25. Recycle loops around each blower were utilized in order to help balance pressures throughout the system. While this does add complexity, it was critical for stable operation of the system with minimal air in-leakage. The valve on the recycle loop for the high-pressure blower was automated to provide control of the pressure of the blower, while the valve on the low temperature blower recycle loop was a manual valve kept fully open. The isolation valve on the inlet of the large blower was automated and used to provide the necessary pressure drop to meter the flow of the flue gas drawn out of the furnace. Initially, the valve on the recycle loop of the large low pressure valve had been automated, but testing proved that the controller should be moved to the blower inlet for better control.
The condenser and scrubber unit could be filled with a slurry of lime and water to provide sulfur removal, or just a water bath, to provide the necessary cooling to condense much of the water vapor in the flue gas.
30
This system could also be bypassed, and the bags removed from the bag house to provide an untreated gas stream for flue gas recycle. In these instances, a hydrometer was used to record the percent moisture content of the wet recycled flue gas. Due to space and temperature constraints, the blowers and the accompanying valves were located outside of the research building as shown in Figure 26.
Figure 25. Schematic of flue gas recycle system.
Figure 26. Blower configuration.
31
Preliminary results on ash partitioning. The BLPI described in previous reports was used to classify and collect fly ash particles <15.7 µm. All the size-segregated ash samples >0.16 µm were subjected to Energy Dispersive X-Ray Spectroscopy (EDS) analysis. The analyzed major and minor oxides included Na2O, MgO, Al2O3, SiO2, P2O5, SO3, K2O, CaO, TiO2, and Fe2O3. The size distributions of the oxides in the ash samples of the Utah coal, the Illinois coal, and the PRB coal are illustrated in Figure 27, Figure 28, and Figure 29, respectively.
For the Utah coal, in the studied size range, it seems that varying flue gas composition has insignificant impacts on the size distributions of Al2O3 (Figure 27(c)) and SiO2 (Figure 27(d)), but does have effects on the size distributions of other oxides, though definite conclusions cannot be made at this point. The interesting results are the size distributions of SO3 (Figure 27(f)). For both cases, the content of SO3 increases with decreasing particle size. This is consistent with the vaporization and heterogeneous condensation/reaction theory. Compared to the “Cleaned FGR” case, the fly ash from the “Dirty FGR” case contains higher contents of SO3, especially in the small size range. It is likely because the fly ash from the “Dirty FGR” case contains a larger fraction of fine particulates that can absorb sulfur oxides.
(a) (b)
(c) (d)
32
(e) (f)
(g) (h)
(i) (j)
Figure 27. Size distributions of the oxides in the Utah coal ash. For the Illinois coal, the size distributions of Al2O3, SiO2, and SO3 for the “Cleaned FGR” case are not significantly different from those for the “Dirty FGR” case, while differences in other oxide size distributions can be observed (Figure 28). The underlying mechanisms need further clarification.
33
(a) (b)
(c) (d)
(e) (f)
34
(g) (h)
(i) (j)
Figure 28. Size distributions of the oxides in the Illinois coal ash. In contrast to the Utah and Illinois coals, the PRB coal ash composition seems not to be so significantly affected by varying flue gas composition (Figure 29). It indicates that the impacts of FGR on ash chemistry may also be dependent on coal type. However, further verification is necessary.
(a) (b)
(c) (d)
35
(e) (f)
(g) (h)
(i) (j)
Figure 29. Size distributions of the oxides in the PRB coal ash. Task 4.0 - Power Generation “Retrofit”: Gasification
Subtask 4.1 – Entrained-Flow Gasifier Simulation and Modeling
Given the structure of the gasification validation hierarchy, the original validation procedure proposed was to validate the laminar reacting particle case (bench scale) separately from the gasification case (pilot scale). However, there has been interest in exploring a technique by which validation is performed across scales. If a single validation were performed that encompassed both scales, the computer model used
36
would be consistent across scales, as opposed to being consistent at the bench scale and consistent at the pilot scale but not necessarily at scales in between. The difficulty with this, however, lies in the scenario parameters. There must be at least one scenario parameter shared between the two cases (for example, the same coal composition, or the same mass flowrates, etc.). Because the validation hierarchy being used is utilizing two disparate experiments, this cannot be done. However, a limited shared validation can be performed by looking at the model parameters only.
For this reason, two model parameters and two scenario parameters were selected for both the gasification case and the laminar reacting particle case. For both of these cases, the model parameters were shared. The devolatilization parameters have the largest sensitivity of all parameters (Smith , 1990), and for this reason both the pre-exponential and the activation energy of the second devolatilization reaction (the one that dominates at higher temperature) were agreed upon as critical parameters. However, each case had different scenario parameters (or at least disparate ranges of scenario parameters).
The scenario parameters selected for the gasification case were average particle size and wall temperature. It was determined that using the average particle size as a scenario parameter would explore uncertainty about which particles were going where. Additionally, uncertainty in wall temperature has been shown to have a strong affect on heatup and ignition of particles in combustion environments, and it is anticipated that it will have a similarly important affect in a gasifier. For the laminar reacting particle case, the centerline gas temperature profile was identified in the experiments as a source of significant uncertainty (50-100 K) [Molina and Shaddix 2007], and plays a similar role to wall temperature in the gasification case. Additionally, the particle mass flowrate plays an important role for the temperature and velocity profiles in the reactor, and was therefore selected as the second scenario parameter.
Subtask 4.2 – Subgrid Mixing and Reaction Modeling
The modeling technique considered for this study is a variant of the One-Dimensional Turbulence (ODT) formulated in Eulerian reference frame. ODT is an outgrowth of the Linear Eddy model that solves unfiltered governing equations in one spatial dimension with a stochastic model for turbulence. The stochastic process consists of a sequence of events, each of which involves transformation of the fields evolving in the flow. These events may be interpreted as the model analogue of individual turbulent eddies which are referred to as “eddy events” or simply “eddies” punctuate the continuously evolving gas phase. Each eddy event in ODT is characterized by a length and time scale. Model is successfully applied to turbulent reacting flows. For the dispersed phase (particles) a Lagrangian tracking model is implemented and when a particle encounters an eddy, along with the drag exerted by the gas phase velocity, its motion is also affected by an additional eddy velocity. The additional velocity, represents turbulent mixing effects in the flow, influence the particle dispersion and the dispersion depends on the size of the particles. There have been some recent efforts in modeling the particle laden turbulent flows and coal combustion process using a similar kind of model (Lagrangian ODT), details of different ODT variants can be found in [Sutherland and Punati 2010]. Particles with different sizes and properties will be considered for this study and verification will be done for particle laden co-axial jets [Budilarto 2003].
37
Particle Transport. The motion of a single particle in gas-solid flows can be described by using Newton’s second law
(2)
where i denotes the it h
direction, mp , ui , p , gi , F f p , and Fc are mass of single particle, particle velocity, gravity acceleration, force generated by fluid-particle interaction, and force generated by
particle-particle interaction. For this study particle-particle interaction is neglected so Fc= 0 . In a gas-solid flow, the particle motion is affected by the drag force, which can be described by the Stokes drag law. Now the particle momentum equation can be expressed by an ordinary differential equation.
where f d is the coefficient of the drag force,
which has a close relationship with particle Reynolds number
where Re p , Dp , µg are the particle Reynolds number, particle diameter and gas dynamic viscosity
respectively. In Equations (3) and (4), τ p is the particle relaxation time
(5)
Now the particle position equation can be defined as
(6)
where xi , p is particle position in it h
direction.
ODT model’s capability in accurately representing the multiphase flows depends on how well particle-eddy interaction will be modeled. Carrier phase and dispersed phase both evolve continuously in time, however in ODT carrier phase evolution will be interrupted by the stochastic eddy events. Figure 30 describes a scenario where selected eddy occupies 6 fluid elements. Due to the application of (1) fluid
mpd ui , pd t
= mp gi F f p F c
d upd t
=f dτ pu− u p
gi ρ p− ρgρ p
, (3)
d vpd t
=f dτ p
v− vpg i ρp− ρg
ρ p, (4)
f d= {1 Re p 1
1 0.15 Re p0 .687 1 Rep 1000
0.0183Re p Re p 1000
Re p=ρ p d p∣u p− ug∣
µg
τ p=ρ p d p
2
18 µg.
d xi , pd t
= ui , p ,
38
parcels will be instantaneously rearranged, where as particles of different sizes should move differently relative to gas phase. For the scenario described here three particle of different sizes occupy fluid element
5. During the implementation of (1), the fluid parcel occupied by the eddy region ( y0 - y0 ℓ ) will be subjected to a certain displacement ( h ). If a tracer particle (zero inertia) occupies the same region as the
fluid parcel it should follow the fluid parcel and displaced by the same distance h , on the other hand particles with finite inertia should not follow the fluid parcels but should be displaced according to drag exerted on them. Solving only particle transport equations and applying (1) for the particles will not satisfy the scenario described for the particles of different sizes. Thus there is a need for modeling the particle-eddy interaction which will satisfy tracer particle, ballistic particle limit and also the size range between them.
Figure 30. Triplet map - different size particles.
Particle interaction with a triplet map requires a novel approach, albeit in the spirit of existing particle-eddy interaction models [Schmidt, 2004]. Particle-eddy interaction occurs when the particle and triplet map occupy the same space-time. Although eddy event implementation is instantaneous, they are
characterized by time scale [Sutherland and Punati, 2010] which is described as eddy life time ( τ e ). Thus particle-eddy interaction can be described using space-time diagram, where space being the eddy size and time being the eddy life time. Figure 31 describes the space-time diagram for the particle
transport during particle-eddy interaction. Here t0 refers to the time of eddy occurrence. Based on different amounts of drag the particles feel they will be moved to different locations.
Figure 31. Space-time particles.
39
Based on the fluid parcel displacement and eddy life time, eddy velocity can be defined as, ve=hτ e .
There are two fundamentally different ways of applying this additional velocity particle momentum equation to accurately model the particle transport. Model I is proposed by Schmidt Schmidt [2004] and the new approach Model II being the one formulated for this work. The details of these models are described below.
Table 5. Particle-eddy interaction models. Model I Model II Instantaneous rearrangement of the fluid parcels
at t0
Instantaneous rearrangement of the fluid parcels
at t0 Separate interaction time coordinate ( T ) is defined based on eddy life time
No such definitions
Solve the following particle momentum equation for the pseudo time scale ( T ) using the eddy velocity for additional drag
(7)
Eddy velocity generates an additional drag and will act as a source term for the particle momentum equation as defined below
(8)
Flow solution will not be advanced during the execution of above step and new particle position and velocity are the instantaneous displacement resulted from the eddy event implementation
Both gas and dispersed phase will evolve continuously in time and particles feel additional drag, generated by eddy velocity, for the entire duration of eddy life time
Both the approaches discussed above should satisfy the following conditions
1. Particle occupies the eddy region for the entire duration of the eddy life time from t0 to t0 τ e in Figure 31.
2. Particle enters the space-time diagram from sides (between t0 and t0 τ e ).
3. Particle leaves the eddy region before the eddy life time elapses and reenters the same eddy region again.
4. In some situations multiple eddies can occupy the same region and time, influencing the particle transport simultaneously, in such cases one should account for the cumulative effect of these eddies.
Two-way coupling. In the context of multiphase flow simulations different levels of coupling will considered between phases. One-way, two-way and four-way coupling are the commonly formulated approaches depending the type of flow being simulated Balachander and Eaton [2010]. In the one-way coupling only the effects of gas phase on dispersed are considered through drag term in the particle momentum equation. The presence of dispersed phase significantly alters the gas phase behavior and to account for such effects two-way coupling will be considered where source terms for the gas phase are constructed. For four-way coupling particle-particle interaction will also be considered. For the proposed work two-way coupling is considered. For the non-reacting flows, dispersed phase alters only gas phase
d v pd t
=f dτ p
v− v pgi ρ p− ρg
ρ pf dτ p
ve− vpd v pd T
=f dτ p
ve− vp
40
momentum where as for reacting flows it alters mass, momentum and energy of the gas phase. In ODT both initialization and two-way coupling have an interesting dependency on cell control volume size. Same control volume size will be used in both places to replicate the experimental conditions. Simulation details
1. Velocity ratio (VR) = 0.0 case with Re=11000 (based on maximum inlet velocity) is simulated. More details can be found in [Budilarto, 2003]
2. ODT model parameter values are C= 10 , Z = 200 , β= 1.0 γ= 1.0 , α= 0. 5 , here γ is the coefficient to scale eddy life time for particle-eddy interaction.
3. Spatial ( d y ) and temporal reslolutions ( d t ) are 10 0 µm and 60 µ s respectively.
4. Three different simulations are performed - single phase, 25 micron and 70 micron.
5. For particle laden jets solid loading = 0.5 and number density ( N ) for 25 and 70 micron
cases are 3. 0573e 10 and 1.3927 e 9 respectively..
6. Based on experimental conditions, # of particles for 25 microns - 10 0 ,00 0 (assuming control
volume as pancake with d y∗ A as the volume, where A= d j∗ d j )
7. Based on experimental conditions, # of particles for 70 microns - 4500 (assuming control
volume as pancake with d y∗ A as the volume)
8. For 25 micron case having 10 0 ,00 0 particles is too expensive, so adjusted A value and
simulation has only 10 ,000 particles.
9. For 70 micron particles, in two-way coupling implementation the control volume size will be d y∗ A , where A= d j∗ d j .
10. For 25 micron particles, in two-way coupling implementation the control volume size will be
d y∗ A , where A=d j∗ d j10 .
11. Statistics gathered over 150 realizations for each simulation. Since there are three cases (single
phase, 25, 70) total # of runs are 450 .
Results. Figure 32 - Figure 34 show the comparison of gas centerline velocity evolution for three different simulations with experimental data. The comparison shows that model can qualitatively represent the gas phase evolution for particle laden jets.
41
Figure 32. Gas center line velocity decay for single phase.
Figure 33. Gas center line velocity decay for flow with 25 micron particles.
42
Figure 34. Gas center line velocity decay for flow with 70 micron particles
Figure 35 and Figure 36 show the comparison between experimental and simulation for the particle center line velocity evolution for 25 and 70 micron cases respectively. From the comparison it can be deduced that model can accurately describe the dispersed phase behaviour.
Figure 35. 25 micron particle center line velocity decay.
43
Figure 36. 70 micron particle center line velocity decay.
Subtask 4.3 – Radiation Modeling
Debugging of the RMCRT algorithm led to an order of magnitude decrease in the Root Mean Squared (RMS) error. Examination of Figure 37 demonstrates the agreement between the radiative flux as calculated by RMCRT compared to that given by an exact solution. RMS error is on the order of 2X10^-3.
44
Figure 37. Flux divergence for a 41 cubed domain using 770 rays per cell (red) compared to the exact solution for the Burns benchmark case (blue).
Figure 38. Root mean square error of the flux divergence at increasing resolution. There is a general decrease in error as the resolution increases.
Subtask 4.4 – Char and Soot Kinetics and Mechanisms
During this quarter, the investigators focused on the development of coal swelling models during pyrolysis, which strongly influences subsequent heterogeneous reaction rates. The diameter swelling ratio is a convenient measure of swelling for modeling applications. None of the current models predict
45
observed decreases in swelling at 104-105 K/s. New experimental swelling data confirms and clarifies this previously observed trend. An empirical swelling model has been developed that accounts for effects of coal rank and heating rate. The model correctly predicts swelling ratios from the new experiments and previously published data over a wide range of heating rates at atmospheric pressure. The use of the model with the pressure term allows more meaningful comparison of pressurized swelling data sets obtained at different heating rates. Clarifications have been made regarding the proper calculation of heating rate for this model. Guidelines for application of the model in large codes have been developed. This modeling study has been documented and is being prepared for submission to Energy & Fuels.
CO2 gasification experiments have also continued for three coals during this quarter. Analysis of these experiments by ICP elemental tracer techniques is underway. Analysis of previous experiments was conducted to produce the topical report for this contract.
In addition, in order to gain a better understanding of the behavior of tar/soot formation in our pyrolysis experiments, we evaluated a number of model compounds. One of the major challenges was securing sufficient quantities of very costly model compounds with coal related functional groups. We compromised and selected biphenyl as the surrogate. On a previous project we had previously carried out a detailed study of biphenyl as a surrogate under a different pyrolysis environment. An extensive amount of analytical (NMR and very sophisticated GC/MS) data were obtained together with reaction pathways for formation of most of the tar components that were present (see Winans et al. 2007). Tar/Soot spectra made from biphenyl at 1484 K and collected at 2” above the burner are shown in Figure 39. Two other biphenyl soot samples were collected at 1405 K and 2” above the burner were analyzed by GC/MS. The analysis of the biphenyl tar/soot samples by GC/MS revealed several pyrolytic products. The products identified by the GC/MS techniques described below were terphenyl (C18H14), 2-phenylnaphthalene (C16H12), fluoranthene (C16H10) and/or pyrene (C16H10) and either benzo[a]anthracene (C18H12), chrysene (C18H12), or triphenylene(C18H12). All of these compounds were observed in the large array of polyaromatic compounds identified in the tar/soot materials reported in Winans et al. 2007.
Biphenyl is a completely aromatic molecule but, in the case of the 1484 K sample, a few aromatic rings must have opened during pyrolysis as a small amount (~1 %) of aliphatic carbons are visible around 35 ppm in the short contact time spectrum that identifies primarily protonated carbons. The 5 ms contact time spectrum also shown in Figure 39 represents all carbons. Notice how the small aliphatic signal is almost lost in the wings from the large aromatic signal when the non-protonated carbons are also present. When a sample is highly carbonized, wings from the main aromatic signal extend into the aliphatic region of the spectrum and also into the carbon/carboxyl region of the spectrum. Structural parameters depending on chemical shifts can no longer be calculated for these highly carbonized materials as they can be for samples produced at lower temperature. This aromatic signal still has a shoulder at about 139 ppm that represents the substituted carbons connecting aromatic rings with zero mass bridges. This shoulder represents the connecting carbons as biphenyl polymerizes to form longer chains of three, four or more rings connected by biaryl linkages (see Winans et al. 2007). As the carbonization proceeds further, these biaryl linkages will disappear as acetylene present in the gas phase adds to the structure to make it fully condensed and it then becomes very conductive as noted for the sample discussed below.
46
Figure 39. Two spectra of a tar/soot made from biphenyl at 1484 K. The (bottom) spectrum shows all carbon types and the (top) short contact time spectrum shows mostly carbons types that are protonated.
Sample was collected at 2” HAB and 5 atmosphere pressure. Subtask 4.5 – Slag Formation and Slag-Wall Interactions
During the past quarter the investigators continued to try to organize a workshop with Albany. We contacted higher level management, who agreed with the lower level people that we should meet, and scheduled a meeting for mid December. However, the people who were to attend the meeting contacted us and indicated they were again unable to meet. We will continue to try to organize this workshop in the next quarter. Subtask 4.6 – Acquisition of Validation Data in an Entrained-Flow Gasifier
Two experimental campaigns were performed this quarter. Each campaign was approximately two weeks long, with one week devoted to warmup and shutdown and one week devoted to the actual testing. The main objectives of the tests were:
• Test and characterize new slurry feed system • Systematically increase system pressure and overall throughput from day to day • Evaluate performance gasifier injector (burner) • Evaluate system performance as a function of oxygen/fuel ratio and pressure • Perform balance around system when at steady state
Overall, these objectives were met during this quarter. The experience with each is described in the paragraphs that follow:
47
Test and characterize new slurry feed system. The main component of the new feed system is a large, high-pressure progressive cavity (Moyno) pump, which is responsible for transporting the coal slurry from the mixing tank to the gasifier injector. The pump is designed to pump 42 gallons per hour of slurry from atmospheric pressure to 500 psi, which is a bit in excess of the gasifier pressure rating of 450 psi. This quarter we successfully fed as much as 23 gal/hr coal slurry at 260 psi. Pump operation was repeatable and reliable, and the pump had no problem handling this load. The other component of the coal-slurry feed system is an ultrasonic flow meter, which clamps to the outside of the slurry line. This worked reasonably well, although resolution and sensitivity at low flow rates is poor. Overall, the new feed system works well. Some modifications will be made to the slurry preparation and mixing tanks to improve operability. Systematically increase system pressure and throughput. This objective was met. During this quarter, we were able to achieve the maximum pressure that the system currently allows. Although the slurry pump is able to operate at 500 psi pressure, system operating pressure is currently limited by the oxygen supply pressure. The Industrial Combustion and Gasification Research Facility has a 20-ton liquid oxygen tank to serve needs of both the oxy-fuel and gasification systems. A “Trifecta” unit is used to boost oxygen pressure to that needed for gasification, but even that system has a maximum output pressure of 320 psi. Once pressure drops through the system and across the injector are taken into account, the maximum practical operating pressure becomes roughly 250 psi. As we gained confidence with the system, we pushed to higher pressures, eventually achieving approximately 260 psi pressure during one run. The system is operated such that the residence time in the reactor is maintained at 5-6 seconds. This means that as pressure increases, more fuel and oxygen can be introduced. Consequently, the system operates more stably at higher pressures since the higher thermal load results in a lower percentage heat loss. Evaluate performance of the gasifier injector. This is an ongoing task, and there will always be room for improvement of injector performance. Development to this point has resulted in an injector that has reasonable performance and good reliability. Those two aspects—performance and reliability—often run counter to one another; performance can be improved by increasing velocities and pressure drops across the injector, but this involves reducing the size of the openings in the injector, which increases the risk for plugging. One aspect that has been recognized as important in the Utah gasifier is consistency from hour to hour and day to day. The prototype injector used in early campaigns did have the nice feature that it was adjustable. This allowed the oxygen pressure drop (responsible for atomization) to be varied during the run, but it would drift on its own once set, resulting in unstable operation. During this quarter we constructed and tested a pair of non-adjustable injectors with the thought that stability of the system would improve. The results were mixed. Stability did improve. However, due in part to challenges machining small gaps at this scale, oxygen pressure drop was lower than desired. This resulted in poor performance. The interior of the injector also corroded very quickly, which was attributed to poor sealing of the oxygen fitting inside the injector. Evaluate system performance as a function of oxygen/fuel ratio and pressure. This has been studied in each of the campaigns performed under this program, and continued during this quarter with data at the higher pressures we were able to achieve. An example of the change in pressure and syngas composition
48
is depicted in the figures below. For this particular afternoon, the objective was to scope feed conditions necessary to maintain a stable temperature in the reactor over a range of pressures.
Figure 40. Stepwise pressure increases versus time on 12/15/2010.
Temperature in the reactor was relatively constant, as seen in the figure below. TC2 (thermocouple at position 2 in the reactor) broke early in this run. TC5 had been broken but came back to life around 15:40, although that measured temperature was artificially low. Such problems with thermocouples are common in high-temperature coal gasifiers. We have learned to repair the thermocouples in our system, which is good because they are very expensive and don’t last very long.
Figure 41. Temperature profile of the EFG versus time.
The syngas trend indicated below is typical for operation. Note that the time scale matches that of the above figures. As pressure is increased, the O2/slurry ratio can be decreased since heat loss through the reactor becomes a lower fraction of the overall heat input. The decrease in ratio means that less combustion is taking place, so CO2 decreases while H2 and CO increase.
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Figure 42. Syngas composition (left axis) and relative oxygen-fuel ratio (right axis) versus time.
Perform balance around the system when at steady state. It is important to be able to perform a mass balance around the gasifier. This involves measuring the flows of material entering and exiting the system. We can measure oxygen flow (lb/hr) very accurately with the coriolis-style flowmeter/totalizer. Slurry flow is measured with the ultrasonic flow meter and then compared against the loss in slurry height in the feed tank over an hour or more. The slurry is regularly tested for density and coal solids content so it is possible to determine the mass flow rates of slurry water and coal. Syngas flow rate is measured with a v-cone (orifice-type) flowmeter, and composition is measured and recorded with a gas chromatograph (GC). Water discharge from the quench bath can be determined by monitoring quench bath height and discharge frequency. The operation data log and GC data were used to perform material balances around the gasifier during periods of steady state operation, generally about an hour. Three different periods were chosen for operation at 200 psi, based on the stability of the data. System closure was defined as follows:
Closure for the system during the three periods ranged from 118% to 144%. This is quite poor. Not only does it appear that more material is exiting the system than is entering (obviously impossible), but the range of values is unacceptably far from 100%. After scrutinizing the data, it was concluded that the measurement of syngas flow rate is contributes most to poor system closure. That flowmeter is relatively low-tech and old, and it is susceptible to fouling by soot and other particulate in the gas. Although it is cleaned regularly, any fouling of the flowmeter will result in a higher pressure drop, which is what is measured to calculate flow. So, the observed flow rate becomes higher than the actual flow rate. The length of time used for balancing may also contribute to the wide range of values. Ultimately, several hours of operation should be used.
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Task 5.0 – Chemical Looping Combustion Reactions and Systems
Subtask 5.1 – Process Modeling and Economics
In this quarter, the investigators worked on developing the following mathematical relationships to incorporate the chemical kinetics and mass transfer effects for coal char combustion in a CLOU system using CuO as an oxygen carrier.
• Conversion of CuO to release O2 as based on the kinetics found in Task 5.4. • Mass transfer of O2 between the CuO and coal char particles (assumed to consist of pure C for
simplicity of analysis) • Consumption of O2 at the carbon surface and the consequent conversion of coal char particles
governed by a shrinking sphere model.
The fuel reactor is assumed to have a plug-flow reactor configuration, which reasonably presents the batch fluidized-bed experimental setup used by researchers at Chalmers for solid fuel combustion studies (Mattisson et al. 2009; Leion et al. 2008). The studies of Hurt and Mitchell (1992) for global coal char combustion equivalent to Pocahontas and Lower Wilcox coals were utilized to analyze experimental studies performed on Mexican Petcoke (Mattisson et al. 2009) and German Lignite (Leion et al. 2008), respectively. The rationale of using equivalent U.S. coal char combustion data was that kinetic data for combustion of Mexican Petcoke and German Lignite was not available in literature. Table 6 represents the ultimate analysis of the coals used in the study.
Table 6. Ultimate Analysis for Different Coals. Coal
C(wt% d.a.f)
H (wt% d.a.f)
O(wt% d.a.f)
N(wt% d.a.f)
S(wt% d.a.f)
Cl(wt % d.a.f)
Heating Value (MJ/kg)-as recd.
German Lignite
69.9 5.4 23.1 0.6 1.0 - 20.9
Lower Wilcox
72.34 5.21 20.11 1.35 0.94 0.07 16.4
Mexican Petcoke
88.8 3.1 0.5 1.0 6.6 - 30.9
Pocahontas 91.48 4.38 2.30 1.10 0.69 0.06 33.4
Figure 43 and Figure 44 represent the fractional char burnout profiles for Mexican Petcoke and German Lignite, respectively. Figure 45 represents the comparison of the experimental data obtained for 95% burnout of a Mexican Petcoke compared with the simulation results for an equivalent Pocahontas coal char. The trend of the experimental data and simulations are similar. The experimental results for 95% burnout for a devolatilized German Lignite were compared with the model simulations for the above referenced Lower Wilcox coal char. The results of the comparison are shown in Figure 46. As seen in these figures, the trend of the data and the simulation are comparable indicating that the assumptions of the model appear valid.
Figure 47 represents the plot of partial pressure of oxygen for Mexican Petcoke vs. time at 955°C. It can be observed that, since petcoke has a slower reactivity and consumes oxygen relatively slowly, the partial
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pressure of oxygen at the surface of the coal char particle approaches near-equilibrium conditions rapidly. The phenomena has also been observed in CLOU experiments on Mexican Petcoke in the studies of Mattison et al. (2009) , as an oxygen concentration approaching equilibrium is observed at the outlet of the reactor.
Figure 48 represents the plot of partial pressure of oxygen for German Lignite vs. time at 949°C. Lignite has a higher reactivity and thus consumes oxygen at a relatively more rapid rate; therefore, the partial pressure of oxygen at the surface of the carbon particle has a slower approach to equilibrium conditions. The phenomena has also been observed in CLOU experiments on German Lignite in the studies of Leion et al. (2008), where oxygen concentration is very low at the reactor outlet.
Integration with data from Subtasks 5.3 and 5.4. The results of the kinetic data of the reactions 4CuO →2Cu2O + O2 and 2Cu2O + O2→4CuO from the TGA from Subtask 5.4 have been utilized to conduct an engineering analysis which has been reported submitted to a peer-reviewed journal (Eyring et al. in press). In addition, Task 5.4 has shown that the TGA results and the fluidized bed results are similar.
As detailed above, the suitability of the developed analysis to analyze the experimental results obtained in batch, fluidized bed experiments on CLOU with coal conducted by researchers at Chalmers University (Mattisson et al. 2009; Leion et al. 2008) were completed. To date, the University of Utah fluidized bed (Task 5.4) has not been utilized for CLOU with coal. For this reason, the integration efforts will continue with current focus on the analysis of data obtained from coal experiments in a fluidized-bed configuration by researchers at the University of Cambridge.
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Figure 43. Plot of fractional char unburnt for Mexican Petcoke vs. time at different temperatures (modeled using Hurt and Mitchell’s kinetic constants for combustion of a Pocahontas Coal Char).
Figure 44. Plot of fractional char unburnt for German Lignite vs. time at different temperatures (modeled using Hurt and Mitchell’s kinetic constants for combustion of a Lower Wilcox Coal Char).
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Figure 45. Comparison of the experimental data for CLOU for combustion of a Mexican Petcoke with simulation results for combustion of an equivalent U.S. Pocahontas Coal Char.
Figure 46. Comparison of the experimental data for CLOU for combustion of a German Lignite with simulation results for combustion of an equivalent U.S. Lower Wilcox Coal char.
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Figure 47. Plot of partial pressure of oxygen for Mexican Petcoke vs. time at 955°C (modeled using Hurt and Mitchell’s kinetic constants for combustion of a Pocahontas Coal Char).
Figure 48. Plot of partial pressure of oxygen for German Lignite vs. time at 949°C (modeled using Hurt and Mitchell’s kinetic constants for combustion of a Lower Wilcox coal).
Subtask 5.2 – LES-DQMOM simulation of a pilot-scale fluidized bed
The current formulation uses a multi-fluid approach for treating the multiple phases in the fluidized bed. The multi-fluid model assumes that the continuous phase (gas) and the dispersed phase (solids) are both
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continuous phases in an Eulerian framework. Previous work using this model has demonstrated its robustness and generality.
The multi-fluid model assumes that both phases are continuous and that the conservation equations for single phases are readily extensible to each of the phases as if it were the only phase present in a given control volume. The representative control volume must be larger than the size of the individual phases, but small enough to ensure the smoothness of the derivatives of flow properties (Brennen, 2005).
Single-phase conservation equations (phase k) may not be continuous over the entire range of the domain and thus could potential represent discontinuity in the domain. To overcome this problem it is necessary to use the concept of the phase indicator function (Drew, 1983),
This function allows one to track the different phases across their interface. Interfacial interactions are accounted for by using an averaging procedure to recover the macro-scale instantaneous description of the multiphase configuration. This averaging process applied over the phase indicator function will give raise to the concept of volume fraction that describes the amount of residence time of one phase in a given region of the domain.
Once the conservation equations are properly averaged, it is necessary to apply a filtering operation in the context of the Large Eddy Simulation. In this framework a filtering operation is performed to separate the large and small-scale features of the flow field. The idea is to fully resolve the larger scales and to model the small scales. The averaging and filtering processes will yield the following mass and momentum equations:
where stand for volume fraction of phase k. The subgrid stress tensor is modeled using eddy viscosity models suitably extended for multiphase flows, with the proper turbulent viscosity coefficient. The quantities and account for the interfacial mass and interfacial momentum exchanges, respectively. The interfacial momentum exchanges that represented in this formulation are the drag force, lift force and added mass force.
Some of the most important characteristics (size, composition, temperature, etc.) of the solid phase in the context of the Eulerian two-fluid model will be accounted for the solution of the population balance equation. The direct quadrature of moments (DQMOM) solves the generalized population balance equation by using a quadrature approximation for the number density function. A number density function is one that represents the number of particles per unit volume and per unit of internal coordinate. The internal coordinates refer to characteristic random variables for the particle phase. The number density function as a whole contains all possible information about the particle phase as it provides an unequivocal description of the particle properties distribution and subsequently its evolution. A transport
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equation for the number density function tracks the evolution of a particular distribution of the number density function.
Here, is a source term and is the velocity of the number density function in the phase space.
Knowledge of the local particle distribution is used to compute local concentrations of the solid phase which couple back to the multi-fluid model through the volume fraction.
Data Collection, Parameter Identification and Validation/Uncertainty Quantification. In December 2010 NETL and Particle Simulation Research Institute (PSRI) released the experimental data set for circulating/bubbling fluidized beds in the framework of the “3rd Modeling Challenge in Granular fluid Hydrodynamics.” These data collection allow us to identify the experimental error bounds needed in the consistency analysis for the Uncertainty Quantification. In a previous report we recognized some relevant parameters that could possibly affect the quality of the prediction of the numerical results. We also categorized those parameters into three main groups, as shown in Table 7.
Table 7. Relevant Parameters for Uncertainty Quantification Parameters of Numerical Relevance
Parameters Relevant to the operation
Parameters Relevant to the Physical-Chemical Properties
Mesh Quality Volume fraction of solids at the inlet
Particle distribution
Time Step for Unsteady solvers
Mass flow rate/velocity of solids at the inlet
Particle surface area (reactive cases with coal)
Relaxation Factors Geometric configurations at inlets and outlets
Chemical composition of particles (for reactive cases with coal) Humidity in air
Boundary Conditions Maximum packing limit Pre-exponential factors and activation energies (for the reactive cases)
Solver Settings Viscosity of solids (depending on which kind of model we are going to work with)
Discretization Order Heat of formation of coal particles (for the reactive case)
Appropriate constants for the different models (Drag models, solids stress tensor model, turbulence models)
Particle temperature at the inlet
For these parameters, we tried to identify reasonable ranges of variation based in the result obtained so far. Parameters of numerical relevance include:
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Mesh Quality: Meshes ranging from 800,000 cells to 2,500,000 cells have been tested since the project started. Refinement in areas such as the solids inlet and the wall are needed in order to get numerically stable simulation.
Time steps for Unsteady Solvers: Time steps ranging from 0.001s to 0.005s have been tested. Further decreasing the time step has been considered to estimate the effect in the stability of the simulation.
Relaxation Factors: Experts developers at STAR-CCM+ recommended keeping those factors as low as possible, they range from 0.2-0.4 for the velocity field, pressure field and volume fraction.
Boundary conditions: We are using non-slip boundary conditions for walls inside the bed, velocity inlet for the inlets (solids and gas) and outflow for the outlets. No differences have been detected between pressure outlet and outflow for the outlets. It is desirable to have mass flow inlet type boundary conditions for the solids inlet but the software capabilities are limited to velocity inlet as a boundary condition. This adds a new source of uncertainty because the inlet velocity of the solids is unknown but instead the mass flow rate is know from the experimental data.
Solver Settings: Some of the default solver settings have been modified based on the experience of the team members with other CFD simulations. The main modifications have been made to the AMG solver cycles providing more stability to the numerical simulations. As long as other numerical parameters are more sensitive than others (mesh quality, for example), we won’t further intent to change or optimize the values for this parameter.
Discretization Order: Second order discretization is currently being used for the convection terms in the momentum equations. Although the simulations start using first order discretization, once they reach some degree of stability, second order discretization is activated.
Appropriate constants: Constants for the drag model have been appropriately identified according to Gibilaro (Gibilaro, 2001). Also a constant ranging from 100 – 600 has been identified for the solid pressure term, which causes the particles to reach an unphysical void fraction (close to 1). The currently used value is 200. Appropriate constants for turbulent model (turbulent intensity, turbulent length scale) have not yet been identified.
Parameters relevant to operation include:
Volume Fraction of Solids at the Inlet: Not only the velocity of the solids at the inlet is unknown but also the solids volume fraction. Typical values for this parameter could range between 0.1 (dilute system) to 0.6 (maximum packing limit). The current used value is 0.3.
Mass solid flow rate at the inlet: As was pointed out previously the software capabilities are limited regarding the application of this boundary condition. Currently there is no direct conversion from mass flow rate values to velocity values for multiphase flows. This is in part because such conversion should depend on volume fraction at the inlet, which it is also not available. As there is uncertainty in the velocity inlet, an interval of possible values was defined ranging from a defined averaged velocity (eq 5) to the terminal velocity of the particles (eq 6).
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The criterion of the terminal velocity was chosen because as the particles come down through the bed downcomer to reach the inlet and complete the loop, they are almost in a free-fall; this velocity of falling is the terminal velocity. This means that, on average, the particles cannot travel faster than their terminal velocity.
Geometric Configurations at inlets and outlets: Previously, a tilted and protruded inlet was used in the riser geometry. However this configuration was causing numerical instabilities in the velocity field in some of the cases. The current approach is to use the inlet directly on the wall; this reduces the impact of stagnation points on the edges of the inlet.
Maximum packing limit: The current value used is 0.623 and is valid for packed spheres. The system of particles is represented in the code as spheres with a size equal to the particle diameter. In this particular case the approximation is valid since the sphericity of the actual particles is close to 1.
Parameters relevant to the physical-chemical properties include:
Particle Distribution: Although only particles of one size have been tested, the current code has the capability to work with more than one particle diameter. It requires the definition of one solid phase for each different particle diameter. That would make the computations more expensive and the boundary conditions more difficult to define. This is one of the reasons to implement DQMOM in the code, to account not only for different particles sizes but also for their change and the change in other different characteristics as well.
Viscosity of Solids: In the previous work, constant values of viscosity were used; they ranged between 0.01 – 1. The current approach is to represent the viscosity of the mixture with the Graham model (Graham, 1981). This model manages of the viscosity variation of the mixture with the volume fraction and accounts for the maximum packing limit as parameters preventing unphysical values for the viscosity as volume fraction approaches this value.
The analysis of the reactive cases has not been yet considered.
Some preliminary results (Figure 49 and Figure 50) show the pressure loss with the height of one of the simulated cases with particles Geldart type A (59 microns), for different constant viscosities and different velocity values. The red and green dashed lines are the 95% confidence interval for this experiment. The blue dots are the actual data, and the solid blue lines are the simulation results. These cases present some qualitative agreement with the data given the assumptions of the model. These models will be further improved according to the guidelines described for the Uncertainty Quantification parameters. In most of the cases particles Geldart type B have proved being difficult to simulate, with numerically unstable simulations. However, even when convergence is achieved, the results do not fit within the experimental range.
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Figure 49. Pressure loss for viscosity of 1 and 0.1 m2/s and solids velocity of 1.03 m/s
Figure 50. Pressure loss for viscosity of 1 and 0.1 m2/s and solids velocity of 3.75 m/s.
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Subtask 5.3 – Laboratory-Scale CLC Studies
This quarter has been dedicated to testing various copper-based carriers in the fluidized bed reactor that was described in previous reports. Two materials that represent extremes of copper-based carriers were used. Experience with these materials is described below.
High CuO engineered material. One was the very high loading (50 wt% CuO) material acquired from the Institute for Chemical Processing of Coal in Poland that was described in the previous quarterly report. This material uses titania as a support, and was engineered specifically for these tests. The particle size distribution of the material is shown in Figure 51. The volume average particle size distribution is 74 microns and about 37% of the material (by volume) is above 100 microns. Material less than 62 microns was sieved out for our tests.
Figure 51. Particle size distribution of engineered carrier material. In terms of carrier performance, the engineered carrier did display the expected oxygen capacity (11% by weight), and it maintained its physical properties well at temperatures below 800°C. At or above that temperature, however, the material showed signs of sintering, which affected fluidization performance. The material also displayed much higher rates of attrition than other iron- and copper-based carriers that have been tested, as evidenced by particle carryover onto the filter downstream of the bed.
Evaluation of this material will continue. Testing under less severe conditions, with lower concentrations of reducing and oxidizing gases, will be employed. Tests involving mixing with an inert material will also be performed, with the thought being that the more dilute carrier will be less prone to agglomeration. This is not a suitable long-term strategy, but it may shed light on mechanisms responsible for sintering at higher temperatures.
Stock CuO catalyst material. The other material tested during this quarter is a relatively low-cost copper-on-alumina material offered by Sigma Aldrich. The material is targeted for catalytic studies, but because of its low cost and appropriate physical form (beads), it was decided to use this for comparison to the engineered material described above. This material is a CuO/Al2O3 product with a copper oxide loading of 13 wt %.
Unfortunately, it is only available from Sigma Aldrich in a 14 mesh (1410 micron) size. This is much larger than desired for fluidized bed operation. Initial testing resulted in no fluidization. Minimum fluidization calculations put the minimum fluidization velocity (umf) of the material at about 141 cm/s,
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and bubbling beds are typically operated at 5-10 times that minimum. The flow rate required for this velocity is much higher than the lab-scale fluidized bed is able to achieve.
In order to be able to fluidize this material in the lab-scale reactor, it was necessary to crush and then sieve the beads. This was unfortunate since the beads initially were near-perfect spheres, which fluidized very nicely. Once pulverized, the material was no longer spherical which, unfortunately, reduces fluidizing performance.
Once pulverized the material was then separated using sieves to a particle range of 88-107µm. This particle range was then tested in the fluidized bed. It was observed that after a relatively short time, a large portion of the sample was discovered in the fines filter downstream of the bed. Clearly, the material used in the bed has poor attrition resistance. It is unclear whether this is inherent in the material or if the crushing procedure somehow compromised the integrity of the particles.
Future work will involve further evaluation of these materials and completion of the chemical characterization, even in the face of high attrition rates. We shall also perform post-mortem analysis of the material to evaluate what is responsible for the physical breakdown.
Subtask 5.4 – CLC Kinetics
During this quarter, the CLC team began the study of supported oxygen carriers. A material consisting of 50% CuO deposited on TiO2 was created explicitly for the University of Utah, to be examined in the fluidized-bed reactor. This material was investigated using the TA Q600 TGA instrument. The material was heated to 935°C under a 1:1 mixture of nitrogen and air using the external gas delivery system. After the thermal equilibrium was established, the gas mixture was replaced with pure nitrogen. Once the spontaneous oxygen decoupling completed, the sample was exposed to the gas mixture, and oxidized again. The data observed for the
2Cu2O(s) + O2(g) ⇌ 4CuO(s)
reaction is presented in Figure 52. The yield was 4.77%. The theoretical yield for the above reaction is 10.06%, therefore the measured yield is quite good for the material having only 50% oxygen carrier deposited on the inert titania support. The longevity of the material was probed by executing a long looping, consisting of 68 cycles. The last cycle was carried out as described above, but the other 67 cycles were done by using the internal gas switch, delivering pure nitrogen and air, respectively. After a small drift during the initial cycles, the material looped well, as shown in Figure 53. The last cycles – revealing 4.9% change – are enlarged in Figure 54.
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94
96
98
100
102
Wei
ght (
%)
0 500 1000 1500 2000 2500 3000
Time (min) Universal V4.7A TA Instruments
Figure 53. 68 loops recorded with 50% CuO deposited on titania.
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96
98
100
102
Wei
ght (
%)
0 10 20 30 40 50 60 70
Time (min) Universal V4.2E TA Instruments
Figure 52. Spontaneous oxygen decoupling and oxidation cycles with copper supported on titania.
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A “fully reduced” derivative of this material was prepared for us by Chris Clayton in the fluidized bed reactor using methane. The TGA trace recorded at 935°C and presented in Figure 55 shows 6.95% weight increase and 4.26% decrease, respectively. The extent of oxidation is larger than the transition from Cu(I) to Cu(II) suggesting that in the presence of fuel the
2Cu(s) + O2(g) ⇌ 2CuO(s) reaction has to be considered. Based on Cu the theoretical yield for this reaction is 25.18% percent.
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100
102
104
106
108
Wei
ght (
%)
0 10 20 30 40 50
Time (min) Universal V4.7A TA Instruments
Figure 55. The reduced material.
94
96
98
100
Wei
ght (
%)
2600 2650 2700 2750
Time (min) Universal V4.2E TA Instruments
Figure 54. The last cycles of the longevity test.
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Task 6.0 – In-Situ Fuel Production: SNG from Deep Coal
Subtask 6.1 – Bench-Scale RF Thermal Treatment
The performed activities for Q4 of 2010 primarily consisted of an overhaul of the control system on the fixed-bed reactor system from an antiquated system to a more modern one. Basic design parameters for a new coal block pyrolysis reactor were also settled so that a new high-pressure reactor can be constructed. It was previously reported that the FBR system was delayed due to repairs and upgrades that were needed to ready the system after years of not being used. The instrumentation control has been completely changed over to an OPTO-22 system and appears to now work as expected. Several pieces of hardware needed to be repaired including a broken heater element, but all those problems appear to have been resolved. Prof. Kevin Whitty is currently vetting the system by running samples in the fixed bed for an unrelated project, but we expect time will be available to begin testing coal samples during Q1 of 2011. We have also begun to consider the design of a new coal block reactor that will better simulate in situ thermal treatment conditions. Our current prototype reactor allows single 5-kg blocks of coal to be electrically heated under an inert atmosphere, however the reactor cannot operate at elevated pressures. With input from the modeling team, we have laid out the basic design parameters for a second generation block pyrolysis reactor. Our preferred design will be capable of:
• Accommodating a bed of solid coal chunks up to several inches in size • Coal temperatures up to 600oC • Heating rates up to 10oC/hr • Pressures of up to 1500 psi • Injection of multiple atmospheres including N2, H2O and CO2. • Adequate void fraction between coal chunks to generate internal convective heating patterns • Sampling of gases for product determination
Based upon these design parameters, we are considering a “reactor-within-a-vessel” approach to the reactor design. To achieve high pressures, we believe it will be easiest to house a smaller bed reactor with heating elements within a larger pressure vessel. Previous experience indicates that this is the most efficient way to allow high-P operation while giving enough flexibility to redesign the actual reactor as needed. Also, the thermal mass of the high-pressure reactor, due to the thickness of the walls and flanges, makes it very difficult to heat to desired operating temperatures. Also, the combination of high-temperature and high-pressure greatly adds to the costs (and thickness) of the reactor. With the reactor within a vessel, the outer pressure vessel will not see temperatures as high as the insulated and heated inner reactor.
We have conceived the coal bed reactor as being a horizontal cylinder housing 3 heating elements in the lower part of the cylinder. It will be filled with a broken bed of coal chunks that will achieve good contact with the heaters and still allow a decent amount of void space for convective flow development, especially in the vertical direction. The inner coal bed reactor will likely have to be wrapped with an external heat source and extensive insulation to counteract large heat losses to the outside. The coal bed
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reactor will be situated inside a larger pressure vessel which will be equipped with the necessary ports (thermocouples, gases, sampling, electrical wiring) for communicating with the inside reactor. A preliminary design for this reactor and pressure vessel combination is shown in Figure 56.
Figure 56. Schematic and cross-section view of pressure vessel with internal pyrolysis reactor.
Subtask 6.2 – In-Well Heater Design Alternatives
During this quarter, the investigators developed recommendations for design matrix of these processes.
Targeted Coal Rank: Preferably high volatile bituminous coals. Conversion Approach: Conduction heating using an externally generated hot gas; or convection heating using an externally generated hot gas; or a combined approach. Well Orientation: Parallel to the targeted coal seam Rubblization: Not necessary Presence of Oxygen: Not necessary, and preferred not. Temperature: The selection depends on what we want in the product stream.
a. Maximum at 600-700 C since the process reaches asymptotic volatile yield;
b. Coal-bed methane, less than 300 C; c. Liquid Products, less than 390 C; d. Moisture, less than 400 C; e. Methane, 400-500 C; f. Hydrogen, above 500 C; g. Tar, above 400 C and peak at 550 C; h. Tar gasification, above 550 C; i. Syngas (H2+CO), 450-700 C.
Pressure: The selection depends on what we want in the product stream. a. Liquid, low pressure; b. Gas, high pressure; c. Maximum at the lithostatic pressure; d. A pressure at 500 to thousands of psi is not uncommon.
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Heating Sources: a. Fossil fuel is the major source currently; b. In the future, solar, wind and renewable; c. Using process gas makes it possible for energy input self-sufficient; d. Aggressive heat management, such as waste heat in coal ash.
Coal Properties. The investigators reviewed the chemical, physical and geological properties of various coal and oil shale samples available in the literature. The most relevant properties are coal rank, coal seam depth and thickness, and coal permeability. Coal composition presents various challenges towards production of coal liquids in an economical and environmentally friendly way using underground thermal treatment. Moisture is an energy barrier to UCTT as water has a heat capacity four times that of dry coal. Typically lignite and sub-bituminous coals contain high moisture content, over 20%. A 20% moist coal sample wastes 50% of energy input in heating up water molecules. Oxygen content correlates closely with pollutant formation and CO2 emissions. A low oxygen content is preferred, which typically found in bituminous and anthracite coals. Hydrogen content determines the quality of UCTT products, since higher hydrogen corresponds to lighter syncrude and less carbon pollution. Usually anthracite coals have 40% lower hydrogen compared to other coal ranks. Therefore, to be cost competitive, high-volatile bituminous coals are target resources for UCTT technology.
The US record of the thickness of a single coal layer was discovered in the Wasatch Formation near Lake DeSmet on the western edge of the Powder River Basin. The thickest coal layer was estimated to be 250 feet. However, the coal is buried deep and currently not extractable. A coal seam usually consists of many single coal layers with rock layers in between. In comparison, shale oil is more uniformly distributed in the rock without separating into different layers. Therefore, when a heating and extraction piping system is designed for UCTT, the pipe orientation should consider the structure of the coal seam. To avoid wasting energy in the rock layers between the coal layers, pipes parallel to the coal layer make most sense. It is noted that many single coal layers are very thin and separated by rock layers. It might not be economically feasible to convert these coal resources using UCTT techniques.
Rubblization is usually needed for underground oil shale thermal treatment due to the lower permeability and porosity in oil shale formation. In contrast, rubblization was determined not necessary. In Shell’s UCTT patent (Wellington et al., 2001), high permeability and porosity in coal seams will develop itself by simply applying heat.
Adsorped water, methane and light hydrocarbons are first removed by physical processes. The water vapor will further catalyze the removal of hydrocarbons from pores. Starting with the removal of water, the permeability in coal seams develops. The temperature during heating stage will pass a steam plateau where the moisture in the coal escapes (Westmoreland and Dickerson, 1980) and then experience a steep temperature gradient versus time.
When the temperature is above 270 C, hydrocarbons start to evolve by chemical reactions. As the pyrolysis process continues, more labile phase compounds are removed, and the coal permeability grows rapidly. The extent of growth in permeability is remarkable. When the temperature reaches 390 C, the permeability of the coal formation can increase by a factor of 1000 (Wellington et al., 2001).
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Coal Permeability and Process Selection. Coal permeability is also determined by the way it is heated up. Wall conduction using hot pipes leads to very uniform thermal heating, which indicates a uniform distribution of porosity and a higher permeability. When the coal formation is heated by the combustion of gases, channeling is usually observed with a non-uniform distribution (Wellington et al., 2001). Therefore, wall conduction heating has an advantage over heating method using the combustion of injected gases.
Design Matrix Based on Reaction Engineering. There is abundant literature on the coal conversion yield as a function of temperature and pressure. In general, the volatile yield increases with temperature and decreases with pressure. In general, coal conversion increases with temperature. Farage and coworkers (1987 determined that an asymptotic volatile yield is approached at temperatures above 700 C, at which temperature less than 10% of the ultimate volatile yield remains.
Depending on what products the process will produce, the retorting temperature of individual process can be very different for underground coal thermal treatment. In general, the retorting temperature should not be higher than 600-700 C as most carbonaceous materials have been volatilized under this temperature. Coal bed methane is desorbed at temperatures below 300 C. By 400 C, hydrocarbons, methane and hydrogen are produced. Moisture is also extracted under 400 C. Between 400 and 500 C, methane is continuously released and above 500 C, hydrogen is produced in large quantity. Above 400 C the process enters tar-production regime, and the rate peaks at 550 C, after which secondary reactions take control.
The operation pressure cannot exceed the lithostatic pressure applied by the overlying formation. The operation pressure for oil shale production ranges from 30-500 psi in Shell’s ICP to 2400 psi in ExxonMobil’s Electrofrac. A pressure gradient is usually applied to underground retort by drilling suction wells. The volatile materials will flow from the released sites to the extraction sites drive by pressure and gravity. Like the temperature, operation pressure is also influenced by the product specification. High pressure leads to lower yields of total volatile, a lower oil yield and a high yield in gaseous species. The effect of pressure on yield from a bituminous coal was reported by Suuberg et al. (1979) (Figure 57).
For example, in one experiment, the oil yield was about 80% of the Fischer Assay (FA) yield if the ICP sample was operated under the atmospheric pressure, and reduced to 60% of the FA yield if the pressure was elevated (Brandt, 2008). Since underground processing is usually at higher than atmospheric pressures, the oil yield should always be lower than its FA yield limit; and the opposite is true for the gas yield. For example, the Shell ICP product distribution between oil and gas is close to 2:1, and the oil yield is about 70% of the FA yield (Brandt, 2008). Western Resource Institute claimed their oil yield can be as high as 88.7% of the FA yield (Shurtleff and Doyle, 2008).
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Figure 57. Tar and methane yields from bituminous coal pyrolysis. Design Matrix Based on Expense/Profit Analysis. Conduction heating leads to very uniform and high permeability with the average particle size in the retorted coal seam at the order of millimeters. This high permeability reduces the pressure drop for steam injection and provides more contact surface for gas-surface reactions. It also reduces the number of extraction wells needed, which can be located at the end of the gas passage through the seam. Therefore, conduction heating has an advantage in terms of cost compared to heating by the combustion of coal itself using an injected gas. Also conduction heating offers more control of the process that may lead to pyrolysis products of higher quality. In comparison, hydrocarbon from coal is usually degraded in approaches using internal combustion because the process is difficult to control. Wall conduction using electricity commands a very high energy input with 33-45% energy conversion efficiency. Therefore, using an externally generated gas or a volumetric heating are recommended for underground coal thermal treatment in order to achieve competitive in price. Volumetric heating provides a unique advantage for its very short waiting period between process initiation and production. Within 1-2 months, coal liquid can start flow out the extraction wells. Volumetric heating also provided an even temperature profile so that the coal conversion rate is uniform and the process is more controlled.
Electrical heating was used in Shell’s ICP. The electricity can be generated near the retorting facilities using coal, gas, nuclear power and renewable energy sources. Around the oil shale rich Green River Formation in Colorado, Utah, and Wyoming, coal is the cheapest fuel for electricity production as coal deposit layers are often in the vicinity of oil shale formations. Coal-fired plants, however, have socio-political and environmental consequences as they generate a significant amount of greenhouse gases and air pollutants. The producer gas generated from underground oil shale thermal treatment is a natural source of fuel for a gas-fired power-plant. Nuclear energy is also considered to be an option in France.
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Waste gases generated during the coal conversion can be heated and injected into the coal seam as a heat carrier. This stream of gases with hydrogen-rich content is also recommended to facilitate the coal conversion even if an electrical heating method is selected. The recycled gas can diffuse into the formation and be drawn to surface after exchanging heat with coal. Besides recycle the gas as a heat carrier, it can be used to fire a gas turbine to generate electricity. Therefore, no gas needs to be imported to the production site.
Recently, the solar and wind technologies have shown great promise for future energy needs. Chinese solar companies using photovoltaic technology were bidding a state contract for only 10 cents per kilowatts (Chemical & Engineering News, 2010). Land-based wind turbines are able to generate electricity at a comparable cost as coal-fired power stations (Businessweek, 2010). Therefore, besides the onsite generation of energy sources, wind and solar show a promising future in coal and oil shale underground thermal treatment.
Evaluation of Available UCTT Technologies. Companies have tested internal combustion approaches, which use a fire front moving through the coal formation. The combustion of coal generates energy to break down macromolecular hydrocarbon clusters into syncrude and gases. Internal combustion used in coal conversion is generally known as underground coal gasification (UCG). There are significant efforts ongoing on UCG which, while encouraging, are beyond the scope of the resent project.
Underground thermal treatment using heated pipes has been explored for oil shale conversion. This technique has later been adapted to coal deposits. Underground coal thermal treatment using a wall conduction heating was proposed by Shell Oil Company (Wellington et al., 2001). In their process, the coal was heated to 525 C and gas and oil of very high quality were produced. The major products include a coal liquid with a API gravity over 30.
The coal seam can also been treated using externally generated hot gases. Calderon and Laubis (2010) proposed an approach of in situ coal pyrolysis using a heated hydrogen-rich recycle gas to extract syncrude and syngas, and subsequently to convert in situ CO2 to CO, SO2 to S, NOx to N2 using the residual coal char with a gas stream consisting of CO2 and air.
Covell and coworkers at Western Research Institute (1984) developed an approach of coal and oil shale in situ co-processing. In many locations around the world, particularly in Wyoming, coal and oil shale layers are in the vicinity of each other. The approach, Total Resource Energy Extraction (TREE), uses the heat generated from an underground coal conversion (combustion or gasification) process to heat and produce hydrocarbon gases and oils from oil shale.
This project focuses on underground coal thermal treatment (UCTT). There is very little literature on UCTT. A large number of underground retorting techniques relevant to UCTT have been explored for underground oil shale thermal treatment (Bartis et al., 2005; Crawford et al., 2008; Liu et al., 2009; Qian and Wang, 2006; United States Department of Energy, 2007). The existing shale conversion technologies provide a starting point in developing an underground coal thermal treatment approach. The shale conversion concepts can be divided into four major categories according to the heating techniques: internal combustion, wall conduction, externally generated hot gas, and volumetric heating. During the previous and current quarter, we have examined the pros and cons of these approaches. The analysis is presented in Table 8.
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Table 8. Advantages and drawbacks of various hydrocarbon underground conversion approaches. Company/Tech Site Heating Fracturing T,Fa P,psic Efficiency
Occidental Petroleum CO Internal combustion Rubblized
Geokinetics UT Internal Combustion Rubblized
Shell ICPb CO, Jordan
Conduction; electrical; 2-4 y
650-750
30-500
Electrofrac Conductant; planar conduction, 7-8 y
Hydraulically 750 2400 50-80% extraction rate
Geothermic Fuel Cell Fuel cell Yes, Raised T
Taiyuan Tech U CN Hot Alkane Convection Yes 750-1300
Chevron CRUSH CO2 Convection Yes
EGL CCR Hot Alkane Convection Yes 750 2000
Petroprobe, omnishale Hot Air => Shale Gas Convection
Yes
Mountain West Energy In situ Vapor Extraction
Natural Gas Convection; 2-4 years
Yes 750 1300 extraction efficiency can be up to 90%
Radio Frequency Radiation, 1-2 months low
Microwave Radiation
Total Resource Energy Extraction
Coal gasification
Flue gas
Yes 1340
a Kerogen decomposition rate depends on temperature: 90% decomposition occurs within 5000 minutes at 700F and within 2 minutes at 930F. b Frozen wall is 3.1m thick; freeze-wall wells are 2.5m apart from each other; refrigerant at -40C; stabilized within 1.5-2 years; maintained for 6.5-8 years. Heater wells are 7.8m apart; the heating rate is 0.5C per day; Heat loss to overburden is relatively small; At atmospheric pressure and ICP heating rate, 80% of FA oil yield can be reached; at higher pressure, 60% of FA oil yield was reported; Shell reported a 66% of the FA oil yield from test plots. Hydrocarbons travel to production well in vapor form, then was pumped to the surface as liquid at ~200C. After production ceased, water is flushed to the production wells for 20 times of pore volumes to recover mobile HCs. c Pore pressure cannot exceed the lithostatic pressure that applied to pore space from the overlying formation.
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Company/Tech Producta Quality Water/oil productionb
Depth, 1000ft
Emission Energy Bal. Out:In
Occidental Petroleum
Geokinetics
Shell ICP 2/3 liquid + 1/3 syngas
> 30 API
3 to 1 1-2 21%-47% more C than petroleum
1.2-3.5:1
Electrofrac Gas + Liquid
Geothermic Fuel Cell Electricity, gas, oil 18:1
Taiyuan Tech U Oil, Gas and Water
Chevron CRUSH
EGL CCR
Petroprobe, omnishale Hydrogen; 1000 BTU methane; condensate; and water
45 API 3
Mountain West Energy In situ Vapor Extraction
Gas only
Radio Frequency Oil and gas Twice as other processes
Microwave
Total Resource Energy Extraction
Oil yield 88.7% of assay
a For a 26.7 gal/ton shale, the Fischer Assay yield is 84% oil, 6% gas, and 10% char. The FA involves heating the shale to 500C at 12C per minute and holding at that temperature for 10 minutes. Higher pressure, lower temperature, and slower heating leads to lower oil yield and higher gas yield. Synthetic crude has a high H:C ratio of 1.9:1. b Underground water that fills the porosity and fractures needs to be removed as much as possible because the heat capacity of water is 4 times that of shale. After all drainable water has been removed, water will occupy ~7% of shale bulk volume.
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Company/Tech Advantages Drawback
Occidental Petroleum
Utilizing more of the heat and chemical values; capture sulfur dioxide. IRR 20% ó $23-35/barrel
Geokinetics
Shell ICP Lower up-front cost; low pollution; lower reclamation cost; available tested technology
Complex configuration; low thermal efficiency; excessive electricity consumption; low extraction rate
Electrofrac Fracturing to increase permeability; by-product of NA2CO3; higher thermal efficiency of planar heating; reduced surface footprint
No mention of ground water protection; excessive electricity consumption;
Geothermic Fuel Cell
Even heating; self-sustainable; producing electricity; $14/barrel; lower air emission (SOX, NOX, particle, toxic); minimal water usage; minimal surface footprint
Taiyuan Tech U Fracturing to increase permeability; low demand for water; even heating
Chevron CRUSH High water usage; pollution
EGL CCR High thermal efficiency; low pollution; self-sustainable
Petroprobe, omnishale
Fracturing to increase permeability; Low Pollution; self-sustaining; minimal surface footprint
MWE’s In situ Vapor Extraction
Natural gas is soluble in shale; even heating; some control in product distribution; fewer wells
Heating efficiency is unknown
Schlumberger and LLNL’s Radio Frequency
Short time heating; neutral carbon footprint; tunable process; targeted products
excessive electricity consumption to generate RF
Global Resource Corp’s Microwave
Short time heating; volumetric heating; selective heating; low pollution; very high extraction rate
Total Resource Energy Extraction
Using coal syngas for heating; sulfur reduction by 60%; product quality can be altered
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Subtask 6.3 – LES in Reacting Porous Media
Discrete Element Method (DEM) models recently added to the newest version of Star-CCM+ have led to further development of the coal-bed geometry. Investigation of the new model and capabilities led to the creation of a sixty-particle geometry consisting of spherically grouped elements to form a hexahedral geometric particle. Using CCM+ for the DEM simulation led to the idea of keeping the shapes in the form shown in Figure 58.
Figure 58. DEM shape formed from the grouping of spheres in Star-CCM+. This is beneficial in two ways: first, the representations of each coal particle can maintain distinct identities in the simulation, with distinct properties if desired; and secondly, the geometry will contain no intersections, decreasing the difficulty of meshing. As reported in the previous quarterly report, sharp edges on each piece of coal particle led us to consider multiple round-off techniques that would increase the complexity of the mesh and reduce number of highly skewed elements. Using surface wrapping methods, the spherical characteristics of the surface may be smoothed. In order to demonstrate the new DEM capabilities, geometry was created which contains sixty-two particles. These particles are uniform in size, and are distributed evenly throughout the inlet of the domain. After the DEM simulation was completed, a process of taking the data from the simulation and creating a computer-aided-design (CAD) of the geometry was done using Matlab and CCM+. The new geometry was meshed in CCM+ using the surface wrapping model and a polyhedral volume mesh. The surface mesh generated contains 3.5 million cells and is shown in Figure 59. Because of the rounded edges on each particle, the meshing was a straight-forward process with no extra time required to produce a proper simulation mesh.
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Figure 59. Image of a mesh created for Coal-Bed in CCM+.
A simulation of the geometry was performed using laminar model. Figure 60 and Figure 61 show the velocity vectors in a plane within the geometry and the temperature profile in the coal and fluid regions at 22.5 minutes of simulation time. Results appear as expected, yielding an upward buoyant plume of hot air, interacting with the geometry and mixing throughout the domain. The pieces of coal in the pathway of the convective current of hot air show increased heating.
Further work has included the DEM simulation of a 2000+ particle geometry shown in Figure 62. DEM simulation in CCM+ has shown good success and ability to handle large amounts of particles. However, creation of geometries with large numbers of particles in DEM simulations has been an issue due to high memory demands in CCM+. Further development of the creation of the geometry is under way.
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Figure 60. Velocity vectors ion a plane inside the domain for the laminar simulation.
Figure 61. Temperature profile in both coal and fluid regions in a plane in the laminar simulation.
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Figure 62. 2000+ particle geometry created for coal-bed simulation.
Subtask 6.4 – CO2 Sequestration Chemistry The Geochemists Workbench (GWB) is a chemical-reactor type module based software that simulates chemical reactions under both equilibrium and kinetic conditions. It contains a set of software tools for manipulating chemical reactions, calculating stability diagrams and the equilibrium states of natural waters, tracing reaction processes, modeling reactive transport and plotting the results of these calculations. GWB package has been developed at the Department of Geology of the University of Illinois at Urbana-Champaign under the guidance of Craig Bethke.
GWB can be used for equilibrium, path of reaction and kinetic modeling of CO2-brine- mineral reactions. Equilibrium modeling can be used to determine the ultimate fate of CO2 in the aquifer. Kinetic modeling calculates the pace of the reactions based on the appropriate kinetic parameters i.e., reactive surface areas and kinetic rate constants. It also calculates the time it takes to approach dynamic equilibrium.
GWB requires CO2 pressure be input as fugacity. GWB has an internal thermodynamic database and as mentioned earlier requires kinetic data as user input. It does not take into account the flow in the aquifer. Such batch analysis under complete no-flow conditions yields valuable knowledge on the important parameters controlling the geological interactions such as the effect of pressure, composition of the mineral matrix, temperature, pH and the kinetic parameters on the reaction products.
The rate equation adopted in the modeling was give by Lasaga (1995):
€
Rate =dnidt
=KAmin exp−Ea
RT⎛
⎝ ⎜
⎞
⎠ ⎟ QKeq
−1⎛
⎝ ⎜ ⎜
⎞
⎠ ⎟ ⎟
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Where K is the rate constant (mol/cm2s), Amin is the reactive surface area (cm2), Ea is the activation energy (J/mol), R is the gas constant (J/Kmol), T is the absolute temperature (K), Q is the activity product and Keq is the equilibrium constant. The rate constants used in this study are from various literature sources, based on laboratory experiments. However the problem is that these rates can be several orders of magnitude greater than rates of weathering measured in field. To check the validity of the model and the kinetic parameters used in this model the results were compared to the experimental results in previous sections. It was observed that the rate constants for the same mineral varied greatly depending on the literature source. Hence kinetic parameters from different sources were used and the results were compared with each other and also with the experimental results. Initial brine chemistry is given in Table 9. Based on this the initial conditions of the models for both the cases (CO2 and CO2+SO2) are described in Table 10 – Table 14.
Table 9. Initial brine chemistry.
Na (mg/l) Mg (mg/l) K (mg/l) Ca (mg/l) Al (ug/l) Fe (ug/l) Cl (mg/l) 23032 1 6 3 172 54 58525
Table 10. Kinetic parameters used.
Mineral Surface area Kinetic rate constants cm2/g mol/cm2sec Calcite 0.711 3.16E-14 Dolomite 0.635 4.17E-12 Quartz 6.6 1.86E-16 Chlorite 0.1130 2.34E-16 Microcline 7.2 1.60E-13 Andesine 6.7 1.80E-13
Brine chemistry (concentrations of ions) was used as the comparison parameter as the quantification of the minerals precipitated in the experiments was impossible. Both the cases (CO2 and CO2 + SO2) were compared with the experimental data. The kinetic parameters adopted from different sources in the literature were screened using the parameter sensitivity analysis and a realistic set of kinetic parameters was adopted as inputs for the geochemical model. The principal contributors for uncertainty in the model were the reactive surface areas used for the minerals. After a thorough study of the geochemical behavior of all the participating minerals in the reactions and also determination of the rate controlling reactions from amongst the complex set of geochemical interactions in the repository, the active reactive surface areas adopted were selected for each mineral.
Table 11. Conditions experimental and modeling. Temperature 100 C Pressure 2000 psi Feed Gas Pure CO2 Brine composition 3 wt% Rock composition Equal wt% Reaction period for CO2 experiments 137 days Reaction period for CO2+SO2 experiments 37 days
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Table 12. Initial conditions for CO2 as feed gas. H20 0.02 Free kg Ca++ 3 mg/l CO2 (g) 113.08 fugacity (bars) H+ 6 pH Na+ 23032 mg/l Cl- 58525 mg/l SiO2(aq) 0.4 mg/l Mg++ 1 mg/l Al+++ 0.172 mg/l K+ 5 mg/l Fe++ 0.024 mg/l
Table 13. Initial conditions for CO2 as feed gas.
H20 0.02 Free kg Ca++ 3 mg/l CO2 (g) 113.08 fugacity (bars) H+ 6 pH Na+ 23032 mg/l Cl- 58525 mg/l SiO2(aq) 0.4 mg/l Mg++ 1 mg/l Al+++ 0.172 mg/l K+ 5 mg/l Fe++ 0.024 mg/l
Table 14. Initial conditions for CO2+SO2 as feed gas.
H20 0.02 Free kg Ca++ 3 mg/l CO2 (g) 113.08 fugacity (bars) H+ 2.9 pH Na+ 23032 mg/l Cl- 58525 mg/l SiO2(aq) 0.4 mg/l Mg++ 1 mg/l Al+++ 0.172 mg/l K+ 5 mg/l Fe++ 0.024 mg/l
Case 1 Experiments with pure CO2. In the case of calcium ion the model captured the initial increase in the Ca ion which occurs due to the dissolution of the primary carbonate minerals calcite and dolomite and also the silicate dissolution (plagioclase feldspar) (Figure 63). The Ca ion concentration was found to decrease in the latter stages of the reaction as seen in the experiments, which led to the precipitation of calcite. The Mg ion concentration followed a similar trend to that of Ca ion indicating precipitation of Mg
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bearing phases such as magnesite and dolomite in the latter stages of the experiment. It should also be noted that the dissolution rate of dolomite is significantly higher (two orders of magnitude greater than calcite), which could have led to immediate dissolution of dolomite and re-precipitation in other Mg bearing phases in the latter stages. K ion concentration increases and continues to increase because of the dissolution of potassium feldspar (microcline). Al concentration decreases initially and increases in the latter stages of the experiment. In the experiment Al bearing minerals like kaolinite were found in traces in the SEM analyses. Fe ion concentration increases at the beginning of the experiment because of the dissolution of chlorite. But it decreases in the latter stages because of the precipitation of iron bearing phases like Ankerite or siderite (which were not found in the experiment). The qualitative match of the modeling results with those of experiments gives us a good understanding of the reaction mechanisms leading to the precipitation of the carbonates in the repository. All the experimental measurements were taken after degassing the reactor and at atmospheric conditions. Hence the scale on each of the plots comparing modeling and experimental results differs.
Figure 63. Comparison of CO2+SO2 modeling with experimental results.
Reactive surface areas were calculated from geometric approximations and also adopted from laboratory measurements from the literature. For geometric approximations, spherical geometry of the grains was assumed and an average grain size of 100µm was assumed. For a spherical grain the specific surface area is given by A*ν/V*MW, where A is the sphere area, ν is the molar volume, V is the sphere volume and MW s the molecular weight. For the clay minerals like clinochlore and average grain diameter of 2µm is taken which is the coarsest clay size. Interaction with the minerals is generally expected to occur only at selective sited at the surface and the difference between total surface area ad the reactive surface area can
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be between 1 to 3 orders of magnitude (White and Peterson 1990). These surface areas and kinetic rate constants used for modeling are described in Table 10.
Activity coefficients were calculated using the B-dot equation. The reason for not choosing the virial equations which is better suited to high ionic strengths is explained by Zerai et al 2006.
Brine chemistry (concentrations of ions) was used as the comparison parameter as the quantification of the minerals precipitated in the experiments was impossible. Both the cases (CO2 and CO2 + SO2) were compared with the experimental data. The kinetic parameters adopted from different sources in the literature were screened using the parameter sensitivity analysis and a realistic set of kinetic parameters was adopted as inputs for the geochemical model. The principal contributors for uncertainty in the model were the reactive surface areas used for the minerals. After a thorough study of the geochemical behavior of all the participating minerals in the reactions and also determination of the rate controlling reactions from amongst the complex set of geochemical interactions in the repository, the active reactive surface areas adopted were selected for each mineral.
Case 2: Experiments with pure CO2+ SO2. Ca ion concentration increases due to the dissolution of the primary carbonate minerals calcite and dolomite and also the silicate dissolution (plagioclase feldspar) (Figure 64). In this case the dissolution rate is fast due to the acidic nature of the brine (presence of SO2 in the gas stream). There is a steep fall in the rate of Ca ion increase in the latter stages of the experiment because of the precipitation of Ca bearing phases (anhydrite , gypsum) . The Mg ion concentration increased in the experiment and this increase is greater than that of Ca ion. This supports the mechanism “dolomotization of calcite” by Rosenbauer et al. (2005), brines with high sulfate concentrations. K ion concentration increases and continues to increase because of the dissolution of potassium feldspar (microcline). In the experiment Al bearing minerals like kaolinite were found in traces in the SEM analyses. Fe concentration decreases and is consistent with the experimental observations. Its decrease can be attributed to of the precipitation of iron bearing phases like Ankerite or siderite (which were not found in the experiment). SO4 ion concentration increases in the initial stages of the experiment because SO2 dissolves in the brine. In the latter stages of the experiment SO4 ion decreases (similar to Ca ion), precipitating anhydrite, gypsum or bassanite. These observations are consistent with the experimental results.
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Figure 64. Comparison of CO2+SO2 modeling with experimental results
Task 7.0 – Mercury Control
This task is complete.
Task 8.0 – Strategies for Coal Utilization in the National Energy Portfolio The Task 8.0 researchers finalized the empirical survey of industry views on CCS regulatory models and completed the necessary internal University of Utah review and approval phases for deployment of the survey. During this quarter, the Task 8.0 researchers continued their review of the database of CCS industry players already developed during this project in order to ensure completeness and accuracy of the survey recipients’ contact information. Researchers have continued work on the conceptual and theoretical portions of the draft Topical Report on CCS regulation. Despite administrative delays in deploying the survey, data-gathering from the survey, as well as other research relevant to the Topical Report for this task, is expected to be completed by March 2011. Due to internal delays associated with the survey, as well as the loss of a post-doctoral legal fellow working on this project, completion of the Topical Report will be delayed from March 2011 to June 2011.
Additional research and drafting this quarter focused on completion of two Topical Reports presenting research completed as part of Task 8. This research was presented in two reports rather than one due to length. These Topical Reports, entitled, “Federal Control of Geologic Carbon Sequestration” and “State
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and Regional Control of Geologic Carbon Sequestration” were reviewed and finalized for submission to DOE-NETL.
CONCLUSIONS
During this quarter the Oxycoal team demonstrated that DQMOM has the ability to predict both gas and particles velocities as well as where the particles are and how they disperse. Experimental studies of selected pilot-scale CFB conditions with limestone addition showed some SO2 reduction under both air- and oxy-firing tests. Limestone addition did not appear to affect SO3 emissions for either air- or oxy-fired conditions. Preliminary calcination and re-carbonation measurements using TGA revealed significant difference in the calcination temperature in the presence of N2 or CO2. In addition, varying recycled flue gas composition in the OFC can have effects on ash partitioning behavior, but the extent of the effects may depend on coal type as well. The laboratory-scale studies revealed that elemental size distributions of ash particulates showed significant CO2 effects on SiO2 for Utah Skyline coal and CaO of PRB coal. The reduction in elemental oxides was found in ultrafine and fine particles under O2/CO2 combustion conditions compared to O2/N2 combustion conditions. Moreover, changing the O2 concentration under O2/CO2 combustion conditions had a large effect on the fine particulates formation compared to O2/N2 combustion.
The Gasification Team successfully ran the EFG at 200 psig system pressure for several (non-continuous) days. Injector development continues, but it was confirmed that a fixed (non-adjustable) injector nozzle provided a much more steady oxygen pressure drop across the injector. System balances were performed at 200 psi, but closure was worse than desired, most likely due to inaccuracies with the syngas flow rate measurements.
During this quarter, the CLC Team compared their process modeling results for equivalent U.S. coal chars with experimental studies with Mexican Petcoke and German Lignite using Chemical Looping with Oxygen Uncoupling (CLOU). The results indicate the suitability of the analysis to explain the phenomena occurring in the fuel reactor and in experimental batch fluidized-bed reactor configurations. CLC laboratory-scale work focused on CuO supported on titania, but the material that we tested suffered from high attrition rates.
The UCTT Team performed DEM simulations in Star-CCM+ and demonstrated the ability to create complex geometries involving thousands of pieces of coal. Especially significant is the new method of creating the rubblized coal geometry, which allows us to create rubblized coal particles which do not present many difficulties for proper meshing of the computational domain. A 60-piece coal particle simulation has shown capability in modeling the heat transfer phenomena that occurs in a heated coal bed using laminar model.
MILESTONE STATUS
Critical path milestones. Table 15 summarizes the critical-path milestones. During the past quarter, the Project Team had no critical-path milestones due.
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Table 15. Phase II Milestone status.
Milestone Planned Completion Date Actual Completion Date
Notes
Project management plan October 2009 October 2009 Oxy-fuel furnace modifications for PIV studies
June 2010 September 2010
Characterization of one metal-based carrier
September 2010 September 2010
In addition, the investigators completed the following milestones:
• Subtask 3.4. The pilot-scale oxy-coal CFB dataset for one coal. • Subtask 3.3. PIV dataset in the 100 kW OFC. • Subtask 5.1. The integration of results from subtasks 5.3 and 5.3. The available data has been
integrated. Data from Subtask 5.4 has been utilized to conduct an engineering analysis, which has been reported in a peer-reviewed journal. The data from Task 5.3 will be integrated when it becomes available. In addition, the investigators are identifying other sources of data, such as those obtained from CLOU experiments in fluidized beds by researchers at the University of Cambridge.
• Subtask 5.2. Fluidized-bed DQMOM formulation for ARCHES. • Subtask 6.2. Design matrix analysis. • Subtask 6.3. Formulation of algorithm for extending ARCHES to porous media application. The
development of the Star-CCM+ capabilities appear to be serving the short-term needs of the UCTT task. While certain areas are still to be addressed, it appears most prudent to focus attention on the development of the Star-CCM+ UCTT algorithm. As a result, the ARCHES development will receive less attention in the near term. We do not anticipate that the longer term ARCHES development will occur in a time frame allowing for full simulation of the UCTT configuration during the lifetime of this project. Thus, we intend to satisfy all simulation requirements for this task using the Star-CCM+ algorithm.
Delays/Problems with upcoming milestones and deliverables
• Subtask 3.1. Because of the issues with the thermodynamic table, LES oxycoal simulation and validation should be ready to start beginning of March 2011. Currently, the heterogeneous reaction model and mass coupling need to be implemented in the code before running simulations. Those tasks should be done by the end of February and simulations should start thereafter. So, first results may be obtained by March 2011, but completion of the above milestones may be delayed until May 2011.
• Subtask 6.3. The development of the Star-CCM+ capabilities appear to be serving the short-term needs of the UCTT task. While certain areas are still to be addressed, it appears most prudent to focus attention on the development of the Star-CCM+ UCTT algorithm. As a result, the ARCHES development will receive less attention in the near term. We do not anticipate that the longer term ARCHES development will occur in a time frame allowing for full simulation of the UCTT configuration during the lifetime of this project. Thus, we intend to satisfy all simulation requirements for this task using the Star-CCM+ algorithm.
• Task 8. Due to internal delays associated with the survey as well as the loss of a post-doctoral legal fellow working on this project, completion of the Topical Report will be delayed from March 2011 to June 2011.
ACCOMPLISHMENTS
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During this quarter, the oxycoal team designed burners for pure oxygen injection and successfully applied PIV and IR camera to the OFC reactor. The Gasification Team achieved sustained operation of the EFG at 250 psig. Finally, a new OPTO-22 control system has been installed on the fixed-bed reactor (UCTT task). A new high-pressure pyrolysis reactor has been preliminarily designed to allow heating of large beds of fractured coal. COST PLAN
The cost plan can be found in the attached appendix.
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RECENT AND UPCOMING PRESENTATIONS/PUBLICATIONS J. Ahn, L. Wang, D. Overacker, R. Okerlund and E.G. Eddings, “Fate of Sulfur Trioxide Under Oxy-Coal
Combustion Conditions,” presentation at the 2010 AIChE Annual Meeting, Salt Lake City, UT, Nov. 8-12, 2010.
C. Clayton, K. Whitty, Copper as An Oxygen Carrier in a Chemical Looping Combustion System: Reaction Kinetics and Fluidized Bed Performance. AIChE Annual Meeting, Salt Lake City, UT (Nov. 7-12, 2010).
E. M. Eyring, G. Konya, J. S. Lighty, A. H. Sahir, A. F. Sarofim, Kevin Whitty, “Chemical Looping with Copper Oxide as Carrier and Coal as Fuel”, Oil & Gas Science and Technology, (in press)
W. J. Morris, D. Yu, J.O.L. Wendt. “A Comparison of Particle Size Distribution, Composition, and Combustion Efficiency as a Function of Coal Composition”. 2010 AIChE Annual Meeting, Salt Lake City, UT, November 7-12, 2010.
William J. Morris, Dunxi Yu, Jost O.L. Wendt, “Effect of Flue Gas Recycle Composition on Coal Ash Aerosol Chemistry in Oxy-fired Combustion”, Submitted to 2nd Oxyfuel Combustion Conference, Queensland,
T. Ring, J. Zhang, H. el Gendy, J.O.L. Wendt, and E.G. Eddings, Particle Image Velocimetry of Pulverized Oxy-Coal Flames, 2010 AIChE Annual Meeting, which will be held in Salt Lake City, Utah in November 2010.
1. Sahir, A.H. , Lighty, J.S., Sarofim, A.F., " Determination of burnout profiles for coal chars in a Chemical Looping with Oxygen Uncoupling process", Abstract accepted for oral presentation at the 7th U.S. National Combustion Meeting, Georgia Institute of Technology , Atlanta, March 20-23, 2011.
R. Shurtz, S. Goodrich, G. Sorensen, and T. H. Fletcher, “Pressurized Coal Pyrolysis and CO2 Gasification at High Initial Heating Rates,” to be presented at the presented at the AICHE National Conference. Topical E: High Temperature Environmentally Sustainable Energy Processes; Session: Advances in Gasification Research I, Salt Lake City, UT (Nov. 7-12, 2010).
L. Wang and E.G. Eddings, “Sulfur Release and Sulfur Capture in N2/O2 and CO2/O2 under Fluidized Bed Combustion Conditions,” presentation at the 2010 AIChE Annual Meeting, Salt Lake City, UT, Nov. 8-12, 2010.
Dunxi Yu, William J. Morris, Raphael Erickson, Jost O. L. Wendt, Andrew Fryc, Constance L. Senior, “Ash and Deposit Formation from Oxy-coal Combustion in a 100kW Test Furnace”, Submitted to the International Journal of Greenhouse Gas Control, 2011.
Dunxi Yu, William J. Morris, Raphael Erickson, Michael Newton, Jost O. L. Wendt, Andrew Fry, “Impacts of oxy-fuel combustion on coal ash and deposits”, Submitted to 2nd Oxyfuel Combustion Conference, Queensland, Australia, September 12-16, 2011.
Q1 Total Q2 Total Q3 Total Q4 TotalBaseline Cost PlanFederal Share 415,658 415,658 415,658 831,316 619,934 1,451,250 571,291 2,022,541Non-Federal Share 103,322 103,322 103,322 206,644 154,984 361,628 144,007 505,635Total Planned 518,980 518,980 518,980 1,037,960 774,918 1,812,878 715,298 2,528,176Actual Incurred CostFederal Share 311,433 311,433 434,008 745,441 703,910 1,449,351 839,302 2,288,653Non-Federal Share 27,194 27,194 154,851 182,045 243,213 425,258 146,905 572,163Total Incurred Costs 338,627 338,627 588,859 927,486 947,123 1,874,609 986,207 2,860,816VarianceFederal Share -104,225 -104,225 18,350 -85,875 83,976 -1,899 268,011 266,112Non-Federal Share -76,128 -76,128 51,529 -24,599 88,229 63,630 2,898 66,528Total Variance -180,353 -180,353 69,879 -110,474 172,205 61,731 270,909 332,640
Q5 Total Q6 Total Q7 Total Q8 Total Q8 Total Q8 TotalBaseline Cost PlanFederal Share 617,000 2,639,541 617,000 3,256,541 617,001 3,873,542 617,005 4,490,547 411,334 4,901,881 411,329 5,313,210Non-Federal Share 154,250 659,885 154,250 814,135 154,250 968,385 154,250 1,122,635 102,832 1,225,467 102,835 1,328,302Total Planned 771,250 3,299,426 771,250 4,070,676 771,251 4,841,927 771,255 5,613,182 514,166 6,127,348 514,164 6,641,512Actual Incurred CostFederal Share 485,132 2,773,785 474,758 3,248,543 738,959 3,987,502 547,950 4,535,452 553,781 5,089,233 5,089,233Non-Federal Share 28,730 600,893 205,794 806,687 233,267 1,039,954 165,402 1,205,356 66,627 1,271,983 1,271,983Total Incurred Costs 513,862 3,374,678 680,552 4,055,230 972,226 5,027,456 713,352 5,740,808 620,408 6,361,216 0 6,361,216VarianceFederal Share -131,868 134,244 -142,242 7,998 121,958 -113,960 -69,055 -44,905 142,447 -187,352 0 223,977Non-Federal Share -125,520 -58,992 51,544 7,448 79,017 -71,569 11,152 -82,721 -36,205 -46,516 0 56,319Total Variance -257,388 75,252 -90,698 15,446 200,975 -185,529 -57,903 -127,626 106,242 -233,868 0 280,296
Q3 Q47/1/08 - 12/31/08 1/1/09 - 3/31/09 4/1/09 - 6/30/09 7/1/09 - 9/30/09
Q6 Q7 Q8 Q9 Q10
COST PLAN/STATUS
Baseline Reporting Quarter
BP1Q1 Q2
1/1/11 - 3/31/11Baseline Reporting Quarter
10/1/09 - 12/31/09 1/1/10 - 3/31/10 4/1/10 - 6/30/10 7/1/10 - 9/30/10 10/1/10 - 12/31/10
BP2 - Yr. 1 BP2 - Yr. 2Q5