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Chemical Looping Combustion Reactions and Systems Task 5 Topical Report, Utah Clean Coal Program

Reporting Period Start Date: October 2008

Report Period End Date: March 2011

Principal Authors: Adel F. Sarofim, JoAnn S. Lighty, Philip J. Smith, Kevin J. Whitty, Edward Eyring, Asad Sahir, Milo Alvarez, Michael Hradisky, Chris Clayton, Gabor Konya, Richard Baracki, and Kerry Kelly

Issue date: August 2011

DOE Award Number: DE-NT0005015 Project Officer: David Lang

University of Utah Institute for Clean & Secure Energy 380 INSCC 155 South, 1452 East Salt Lake City, UT 84112

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DISCLAIMER

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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ABSTRACT

Chemical Looping Combustion (CLC) is one promising fuel-combustion technology, which can facilitate economic CO2 capture in coal-fired power plants. It employs the oxidation/reduction characteristics of a metal, or oxygen carrier, and its oxide, the oxidizing gas (typically air) and the fuel source may be kept separate. This work focused on two classes of oxygen carrier, one that merely undergoes a change in oxidation state, such as Fe3O4/Fe2O3 and one that is converted from its higher to its lower oxidation state by the release of oxygen on heating, i.e., CuO/Cu2O.

This topical report discusses the results of four complementary efforts: (1) the development of process and economic models to optimize important design considerations, such as oxygen carrier circulation rate, temperature, residence time; (2) the development of high-performance simulation capabilities for fluidized beds and the collection, parameter identification, and preliminary verification/uncertainty quantification (3) the exploration of operating characteristics in the laboratory-scale bubbling bed reactor, with a focus on the oxygen carrier performance, including reactivity, oxygen carrying capacity, attrition resistance, resistance to deactivation, cost and availability (4) the identification of mechanisms and rates for the copper, cuprous oxide, and cupric oxide system using thermogravimetric analysis.

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TABLE OF CONTENTS

DISCLAIMER .................................................................................................................................. ii  ABSTRACT ................................................................................................................................... iii  LIST OF FIGURES ......................................................................................................................... vi  LIST OF Tables ............................................................................................................................... ix  LIST OF ABBREVIATIONS .......................................................................................................... x  EXECUTIVE SUMMARY ............................................................................................................. xi  INTRODUCTION ............................................................................................................................ 1  METHODS ....................................................................................................................................... 4  

Subtask 5.1 – Process Modeling and Economics ......................................................................... 4  Development of Preliminary Models ....................................................................................... 4  Integration with Subtask 5.4 .................................................................................................... 5  Development of ASPEN PLUS Simulations ........................................................................... 8  Formulation of Mathematical Model for the Carbon Burnout Process ................................... 9  

Effects of Equilibrium .......................................................................................................... 9  Study of Char Burnout and O2 Partial Pressure Profiles .................................................... 11  

ASPEN Custom Modeler ....................................................................................................... 13  Subtask 5.2 – LES-DQMOM Simulation of a Pilot-Scale Fluidized Bed ................................ 14  

DQMOM/LES Formulation in Fluidized Bed Systems ......................................................... 15  Data Collection, Parameter Identification and Validation/Uncertainty Quantification ......... 16  

Parameters of Numerical Relevance .................................................................................. 17  Parameters Relevant to the Operation ................................................................................ 17  Parameters Relevant to the Physical-Chemical Properties ................................................. 18  

Subtask 5.3 – Laboratory-Scale CLC Studies ............................................................................ 19  Oxygen Carriers ..................................................................................................................... 19  Iron ......................................................................................................................................... 19  Copper .................................................................................................................................... 20  Apparatus and Procedure ....................................................................................................... 21  Data Analysis and Interpretation ........................................................................................... 22  

Subtask 5.4 – CLC Kinetics ....................................................................................................... 25  RESULTS AND DISCUSSION ..................................................................................................... 28  

Subtask 5.1 – Process Modeling and Economics ....................................................................... 28  Determination of Optimum Circulation Rate ........................................................................ 28  Development of ASPEN PLUS Simulations ......................................................................... 30  Formulation of Mathematical Model for the Carbon Burnout Process ................................. 33  

Analysis of CLOU Experiments on Mexican Petcoke ....................................................... 33  Analysis of CLOU Experiments on German Lignite ......................................................... 35  

Subtask 5.2 – LES-DQMOM Simulation of a Pilot-Scale Fluidized Bed ................................ 38  Subtask 5.3 – Lab-Scale CLC Studies ....................................................................................... 42  

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Ilmenite .................................................................................................................................. 42  Unsupported CuO – 99.99% Pure .......................................................................................... 45  50% CuO with TiO2 support .................................................................................................. 45  13% CuO with Al2O3 support (Sigma-Aldrich material) ....................................................... 49  

Subtask 5.4 – CLC Kinetics ....................................................................................................... 50  CONCLUSIONS ............................................................................................................................ 58  REFERENCES ............................................................................................................................... 59  

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LIST OF FIGURES

Figure 1. Schematic of CLC. ......................................................................................................... 1  

Figure 2. Design of a CLC reactor using natural gas proposed by Lyngfelt. ................................... 2  

Figure 3. Schematic of a CLOU process (Mattisson et al. 2009a). .................................................. 3  

Figure 5. Preliminary material and energy balances using spreadsheet calculations. ...................... 5  

Figure 6. CuO reduction kinetics with coal char burnout kinetics at equilibrium partial pressure of oxygen conditions. ............................................................................................................... 9  

Figure 7. Process modeled for the case when the CuO particle is at equilibrium O2 partial pressure. ................................................................................................................................................ 10  

Figure 8. Bulk partial pressure of oxygen vs. λ. ............................................................................ 12  

Figure 9. Process modeled to study coal char burnout and O2 partial pressure profiles when the partial pressure of O2 at the CuO surface is initially at zero. ................................................. 13  

Figure 10. Schematic of the bubbling bed CLC reactor system at the University of Utah. .......... 21  

Figure 11. Dimensions of quartz reactor. ...................................................................................... 22  

Figure 12. Example of data acquired from the fluidized-bed system. ........................................... 23  

Figure 13. A simple method for deconvolution is achieved by subtracting a signal obtained while exposing an inert material to reaction gases and is denoted as the RTD signal (blue). ......... 24  

Figure 14. Comparison of TGA data (blue) and deconvoluted fluidized bed data (red). .............. 25  

Figure 15. The TA Q500 TGA instrument. .................................................................................... 26  

Figure 16. The TA Q600 TGA instrument. .................................................................................... 27  

Figure 17. Mass (kg) of copper circulated per MWt with variation in ΔXS of CuO. ..................... 29  

Figure 18. Mass of copper loading per MWt vs mole ratio of CuO at the exit of fuel reactor for different ΔXS values. .............................................................................................................. 30  

Figure 19. Schematic of an ASPEN PLUS simulation. .................................................................. 31  

Figure 20. Material and energy balance results of the ASPEN PLUS simulation with residence times and optimum recirculation rates. .................................................................................. 32  

Figure 21. Fractional char unburnt vs. time at various temperatures for Mexican petcoke modeled using global coal char kinetic data for Pocahontas coal. ........................................................ 33  

Figure 22. Reaction time for 95% burnout vs. temperature for Mexican petcoke modeled as a Pocahontas coal char. ............................................................................................................. 34  

Figure 23. Partial pressure of O2 vs. time for Mexican petcoke modeled as a Pocahontas coal char. ................................................................................................................................................ 34  

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Figure 24. Comparison of the carbon consumption profiles of Mexican petcoke simulated as a Pocahontas coal with the experimental data of Mattisson et al. (2009b). .............................. 35  

Figure 25. Fractional char unburnt vs. time at various temperatures for devolatilized German lignite using global coal char kinetics for Lower Wilcox coal char. ...................................... 36  

Figure 26. Reaction time for 95% burnout vs. temperature for a devolatilized German lignite modeled as a Lower Wilcox coal char. .................................................................................. 37  

Figure 27. Partial pressure of O2 vs. time for Devolatilized German Lignite modeled as a Lower Wilcox coal char. .................................................................................................................... 37  

Figure 28. Carbon consumption profile for CLOU experiments with German lignite coal and devolatilized German lignite (Leion et al. 2008) with the simulation results for Lower Wilcox coal. ............................................................................................................................ 38  

Figure 28. Pressure loss for viscosities of 1 and 0.1 m2/s and solids velocity of 1.03 m/s. ........... 39  

Figure 29. Void fraction profiles for viscosities of 1 and 0.1 m2/s and solids velocity of 1.03 m/s. ................................................................................................................................................ 40  

Figure 30. Pressure profiles for viscosities of 1 and 0.1 m2/s and velocity of 1.03 m/s. ................ 40  

Figure 31. Pressure loss for viscosity of 1 and 0.1 m2/s and solids velocity of 3.75 m/s. ............. 41  

Figure 32. Void fraction profiles for viscosities of 1 and 0.1 m2/s and velocity of 3.75 m/s. ....... 41  

Figure 33. Pressure profiles for viscosities of 1 and 0.1 m2/s and velocity of 3.75 m/s. ................ 42  

Figure 29. Ilmenite diluted to 20% by mass and tested in a bubbling fluidized bed at several different temperatures. .......................................................................................................... 43  

Figure 30. Evolution of reaction rates for 20 wt% ilmenite at various temperatures. ................... 44  

Figure 31. Arrhenius plot for oxidation of ilmenite. ..................................................................... 44  

Figure 32. Agglomeration of pure copper powder (90µm) at 600°C. ........................................... 45  

Figure 33. Oxygen consumption during oxidation of Cu2O for the 50 wt% CuO on TiO2 material. ................................................................................................................................................ 46  

Figure 34. Total oxygen consumption by Cu2O at various temperatures. ..................................... 46  

Figure 35. Arrhenius plot for oxidation of 50% CuO on TiO2. Activation energy = 55 kJ/mol. . 47  

Figure 36. Reduction of 50 wt% CuO on TiO2 under N2. ............................................................. 48  

Figure 37. Arrhenius plot for reduction of 50 wt% CuO on TiO2 between 650°C and 850°C. .... 48  

Figure 38. Release of gaseous oxygen (O2) versus time during reduction of 13 wt% CuO on Al2O3. ...................................................................................................................................... 49  

Figure 39. Arrhenius plot for reduction of 13 wt% CuO on Al2O3. .............................................. 50  

Figure 40. CuO/Cu2O/CuO sequence, at 850°C. ............................................................................ 51  

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Figure 41. CuO/Cu2O/CuO sequence, at 950°C. ............................................................................ 51  

Figure 42. Kinetics of the CuO/Cu2O/CuO systems. ..................................................................... 52  

Figure 43. The determined rate constants of the oxidation. ........................................................... 53  

Figure 44. The combined set of rate constants. .............................................................................. 54  

Figure 45. Oxidation of copper under isothermal conditions. ........................................................ 55  

Figure 46. Pressure dependence of the initial rate. ......................................................................... 56  

Figure 47. Non-isothermal oxidation of copper. ............................................................................ 56  

Figure 48. Non-isothermal oxidation of copper after blank subtraction. ....................................... 57  

Figure 49. Smoothed curve of data observed under 25 atm overlaid with the atmospheric result. 57  

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LIST OF TABLES

Table 1. Properties of Illinois#6 coal (Bartok and Sarofim1991). ................................................... 4  Table 2. Ultimate analysis for different coals. ............................................................................... 13  Table 3. Relevant parameters for uncertainty quantification. ........................................................ 16  Table 4. Ilmenite analysis. ............................................................................................................. 20  

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LIST OF ABBREVIATIONS

AR Air Reactor

ACM Aspen Custom Modeler

CLOU Chemical Looping with Oxygen Uncoupling

CFD Computational Fluid Dynamics

CLC Chemical Looping Combustion

DQMOM Direct Quadrature Method of Moments

FBR Fluidized Bed Reactor

FR Fuel Reactor

LES Large Eddy Simulation

RTD Residence Time Distribution

RSTOIC Stoichiometric Reactor Model used in ASPEN PLUS

SA Sigma-Aldrich

SLPM Standard Liters Per Minute

TGA Thermogravimetric Analyzer

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EXECUTIVE SUMMARY

CLC is a novel technology for producing electric power while producing a CO2-rich stream that can be suitable for sequestration with little additional processing. The typical CLC design is based on a dual fluidized-bed reactor (FBR) system. The oxygen carrier is oxidized in the air reactor and is subsequently reduced in a separate fuel reactor before being recycled (“looped”) back to the air reactor. By separating these two reactors and utilizing the oxidation/reduction characteristics of the oxygen carrier the oxygen in the air reactor is scavenged and then delivered as a metal oxide to the fuel reactor. By keeping the fuel and air separate a sequestration ready CO2 stream is created in the effluent of the fuel reactor, once gaseous H2O has been condensed and removed.

CLOU is a variant of CLC, which offers the promise of accelerating the rate of solid fuel combustion. The CLOU process also consists of two reactors － a fuel reactor and an air reactor. In the fuel reactor, solid carbonaceous fuel is burned by gaseous-phase oxygen released by the decomposition of cupric oxide, CuO. The reduced metal oxide is regenerated by reaction with atmospheric oxygen in the air reactor.

This topical report discusses the results of four complementary efforts: (1) the development of process and economic models to optimize important design considerations, such as oxygen carrier circulation rate, temperature, residence time, etc.; (2) the development of high-performance simulation capabilities for the fluidized beds; (3) the exploration of operating characteristics in the laboratory-scale bubbling bed reactor, with a focus on the oxygen carrier performance, including reactivity, oxygen carrying capacity, attrition resistance, resistance to deactivation, cost and availability; (4) the identification of mechanisms and rates for the copper, cuprous oxide, and cupric oxide system using thermogravimetric analysis.

Process and economic models. The first phase of the study was focused on the development of material and energy balance scenarios. Preliminary insights were provided by formulation of spreadsheet models and development of criteria for optimum recirculation rates. The results of this phase were later incorporated into a process model using ASPEN PLUS. The model consists of stoichiometric reactor models, which employ the results of the optimum conversion of the fuel and air reactors. In the formulation of the ASPEN PLUS model, the results from the kinetic studies conducted in thermogravametric analyzer (TGA) experiments (Subtask 5.4) were utilized to identify the optimum temperatures. Based on this kinetic data, a temperature of 950°C was selected for operation for the fuel reactor and 850°C for the air reactor. From the ASPEN PLUS simulation of a 100 kg/h of carbon feed to a CLOU system and an estimated optimum conversion of 60% CuO in fuel reactor and 64% conversion of Cu2O in the air reactor, 357 kW of energy could be obtained from the fuel reactor and 574 kW of energy could be obtained from the air reactor. A process based on CLOU offers a major advantage as reactions in both the air and fuel reactors are exothermic in nature. This contrasts with a CLC process where the circulation of oxygen carrier is influenced by the requirement of supplying energy for the endothermic reactions in the fuel reactor.

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In the second phase of the study, the process of carbon particle burnout in the fuel reactor in a CLOU system was considered by formulation of mathematical relationships. The equations take into account kinetic and mass-transfer effects. The results of these relationships were compared with the experimental data reported in literature for batch fluidized-bed CLOU experiments on Mexican petcoke and German lignite fuels. As coal char combustion kinetic data were not available in the literature for the fuels reported in aforementioned CLOU experiments, equivalent U.S. coal char combustion data was utilized for comparison. The results of the simulations using the derived relationships for oxygen partial pressure profiles and coal char burnout capture the trends reported in experiments and provide insights for future designs.

Our results show that the CuO/Cu2O system can be applied as the oxygen carrying material in CLOU. The reaction rates were determined, and the rate constants were calculated according to first-order kinetics. The 327 kJ/mol activation energy is in good agreement with literature data. The rate of the oxidation reaction exhibits a maximum at about 800°C. However, simple kinetic models cannot explain this observation.

Simulation of fluidized beds. This report discusses the development of a general formulation for multiphase flows in fluidized beds based on the population balance equation and the direct quadrature method of moments (DQMOM). This approach allows us to track different particle properties, which represent the physical behavior of the particles in the multiphase flow.

Identification of error bounds and uncertainties in the simulation approach is a critical issue when reporting the modeling results. Data collection/parameter identification has been carried out in the framework of the verification/uncertainty quantification methodology; this allows the performance of consistency analysis between experimental data and simulation results and identifying sensitive parameters.

Laboratory-scale studies. Oxygen carrier selection is critical to the development and design of a commercial CLC system. Characteristics such as oxygen carrying capacity, reactivity, durability, attrition resistance, deactivation resistance, cost and availability, and fluidization attributes are each important attributes to consider when selecting a suitable material as an oxygen carrier. Metals such as copper, nickel, iron, cobalt, manganese, and calcium have been identified as potential potentially suitable candidates for oxygen carriers.

In the study, iron and copper were selected for testing. Initially, pure metals were chosen to provide a baseline for more advanced metal-based carriers. However, the pure metals were found not to be suitable due to problems with sintering at higher temperatures. Consequently, the focus turned to supported metals. An iron ore, ilmenite, was selected to replace the pure iron. Alumina and titania were used as supports for copper.

Each of the carriers was tested in a TGA as well as a FBR. Porosity and surface area of each of the materials was determined by BET analysis. Composition of the materials was evaluated using

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scanning electron microscopy/x-ray defraction. Carrier capacity and kinetic parameters were determined from both FBR and TGA data.

Overall, ilmenite performed the best as an oxygen carrier. Ilmenite is inexpensive and has high attrition resistance. Each of the tested copper materials displayed characteristics unsuitable for CLC. The pure copper and the titania supported copper agglomerated at temperatures above 600°C. The alumina supported copper had very low reactivity due to the formation of copper aluminate.

Despite the undesirable performance of the copper-based materials tested, copper is attractive due to its ability to spontaneously release oxygen as O2 in the fuel reactor of a CLC system. This makes combustion of solid fuels possible, which is a tremendous advantage over conventional CLC systems. For that reason, the University of Utah elected to continue development and performance characterization of copper-based carriers.

CLC Kinetics. This subtask focused on reaction kinetics of the copper, cuprous oxide, and cupric oxide system using thermogravimetric analysis. The results revealed a lack of the expected pressure dependency for the oxidation of Cu in air at pressures up to 25 atm.

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INTRODUCTION

CLC is a novel energy production technology currently under development. The main attraction of CLC is its ability to inherently separate oxygen from air with little energy penalty, thus allowing production of a nearly pure CO2 stream suitable for sequestration with little additional processing. By utilizing the oxidation/reduction characteristics of a metal, or oxygen carrier, and its oxide, the oxidizing gas (typically air) and the fuel source may be kept separate. By scavenging the oxygen from the air and introducing only the metal oxide as the oxidizer to the fuel a nearly pure CO2 effluent may be achieved. A typical CLC process schematic is presented in Figure 1.

Figure 1. Schematic of CLC. A metal and its oxide are looped between the air reactor and the fuel reactor. By separating the air from the fuel a nearly pure CO2 stream may be

generated.

The key reactions for chemical looping, in general terms, are (Bergurand and Lyngfelt, 2009) reaction between the fuel and the metal oxide

(2n + m)MexOy + CnH2m → (2n + m)MexOy–1 + m H2O + n CO2

and the reaction between the air and the reduced metal

MexOy–1 + 0.5 O2 → MexOy

A schematic showing interconnection of two fluidized beds, proposed by Chalmers University in Sweden, is presented in Figure 2. The air reactor is designed as a transport reactor while the fuel reactor is designed as a bubbling fluidized bed.

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Figure 2. Design of a CLC reactor using natural gas proposed by Lyngfelt. Section 1: air reactor (AR)/riser. Section 2: cyclone to separate oxygen depleted air from the oxygen carrier. Section 3: fuel

reactor (FR). Sections 4: loop seals. (Bergurand and Lyngfelt, 2009).

In CLC, the combustion of solid carbonaceous fuels like coal and petcoke requires that the fuel has to be initially gasified. The products of the gasification reaction, namely CO and H2, directly react with the oxygen carrier in the fuel reactor. The reduced oxygen carrier is transported to the air reactor by reaction with atmospheric air. One particularly interesting variant of chemical looping is Chemical Looping with Oxygen Uncoupling (CLOU) process. In CLOU systems, thermodynamics of the oxygen carrier are such that gaseous oxygen (O2) is spontaneously released in the fuel reactor, which typically has low partial pressures of O2. This allows solid fuels such as coal to be used in the fuel reactor, which greatly simplifies the CLC process. With conventional CLC, the fuel must be in gaseous form, for example natural gas or synthesis gas from coal gasification, in order to react with the solid metal oxide. Lewis et al. (1951) reported the use of CuO as an oxygen carrier, which could be utilized in supplying the requisite gaseous-phase oxygen for combustion of a solid fuel. Independent investigations were carried out in the latter part of the past decade for combustion processes (Leion et al. 2008, Mattisson et al. 2009a, b) where oxygen dissociated from a metal oxide could be utilized in combusting solid fuels. The process was termed as CLOU. CLOU is a variant of CLC in which the participating oxygen carrier, a metal oxide is capable of releasing gaseous-phase oxygen through the process of decomposition in the fuel reactor (Mattisson et al. 2009a).

Figure 3 represents the steps by which the combustion of a solid fuel in CLOU and the subsequent regeneration of the metal oxide proceed. The oxygen required for the combustion of solid fuel (represented as CnH2m) is released by a metal oxide (represented as MexOy) in the fuel reactor. After

air

flue gas

fuel

1

3

2

CO2, H2O

4

4

1

3

supplying the O2, the reduced metal oxide (represented as MexOy-2) is circulated to the air reactor, where it is regenerated by reaction with atmospheric oxygen.

Figure 3. Schematic of a CLOU process (Mattisson et al. 2009a).

CLOU has the following advantages as compared to CLC:

• CLOU has the potential to reduce the time required to convert less-reactive solid carbonaceous fuels in the fuel reactor. In batch-reactor studies conducted for CLC on petroleum coke (Leion et al. 2007), the reaction of the coke with CO2 was impractically long, requiring 50 minutes to reach 95% conversion at 950°C for the injection of 0.2 g of coke in 20 g of fluidized carrier composed of 60% Fe2O3 and 40% MgAl2O4. These times observed in a CLC experiment could be compared with a time of 30 s required to react 0.1 g of petroleum coke completely in a CLOU experiment where 15 g of fluidized CuO/ZrO2 at 955°C was utilized for the reaction (Mattisson et al. 2009 b). It is expected that the reduced reaction time achievable in CLOU would help in reducing the amount of oxygen carrier used in the process and in reducing the size of the fuel reactor.

• In CLOU, the reactions in the fuel and air reactor are exothermic in nature. Hence the circulation rate of the oxygen carrier is governed by the need to supply requisite oxygen for combustion. In contrast, the oxygen carrier circulation rate in CLC is also governed by the requirement to transfer energy from the air reactor to fuel reactor to provide for the endothermic gasification reactions.

• CLOU also offers the potential for combusting high-sulfur (S) fuels. It would eliminate sulfide formation as S would be converted to SO2. The possibility of formation of CuSO4 is not thermodynamically favored, as unrealistically high concentrations of SO2 would be required (above 30000 ppm at 600°C), (Leion et al. 2008).

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Several different groups around the world are currently researching different aspects of CLC. Some groups are focusing on development of carrier materials suitable for use in a commercial-sized process, and using TGAs and other bench-scale type testing apparatuses. Others are working on pilot-scale testing facilities. The University of Utah has developed process and economic models, high-performance simulation tools, a bench-scale bubbling FBR for the testing of potential oxygen carriers, and is working to develop a bridge between the TGA and FBR in order to better understand the complexities in the scale-up from TGA testing to full-scale commercial employment of CLC.

METHODS

Subtask 5.1 – Process Modeling and Economics

Development of Preliminary Models using Spreadsheets and Literature sources

Preliminary material and energy balance calculations were performed using spreadsheets for a conceptual design for the CLOU combustion of coal. The required oxygen for combustion is supplied by Copper (II) Oxide via CLOU (Mattisson et al. 2009a).

Cu2O + ½ O2→ 2CuO (Occurring in Air Reactor)

2CuO → Cu2O + ½ O2 (Occurring in Fuel Reactor)

A coal flow rate of 1000 tonne/day of Illinois #6, having a net heating value of 13730 Btu/lb (or 31.987 MJ/kg determined on a mass ash free basis), was considered as shown in Table 1. The temperature of the air and the fuel reactor was determined based on assumed equilibrium O2 concentrations, as outlined in the design assumptions below.

Table 1. Properties of Illinois#6 coal (Bartok and Sarofim1991).

Coal Properties (mf basis) C H N O S Ash

Ultimate Analysis (wt. %) 70.2 4.6 1.4 8.9 2.9 12.0 mf: moisture free The major steps for the combustion process considered were the devolatilization and subsequent volatile oxidation of coal, char oxidation, and carbon burnout. The following design assumptions were taken into consideration for the initial spreadsheet calculations:

• Coal was modeled as a chemical compound with an empirical formula of CaHbOc .

• Ash was considered an inert and its effect on the energy balance of the process was considered by assuming the heating value of coal on a moisture-free basis.

• An exit O2 concentration of 2.1 mol% and a temperature of 916°C was used as a design basis for the air reactor.

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• A total of 40% of the coal burns in the volatile combustion section with a net heating value of coal on a moisture and ash-free basis. The outlet gas consisted of 10 mol% O2, and the fuel reactor temperature was 994°C.

• A total of 90% of the carbon burns in the char combustion section with a net heating value of coal on a moisture-free basis. The outlet gas consists of 10 mol% O2.

• The remaining carbon burns in the final carbon burnout section with a net heating value of coal on a moisture-free basis. The outlet gas consists of 10 mol% O2.

The results of preliminary material and energy balance calculations are presented in Figure 4. They emphasize the importance of the oxygen carrier circulation rate in the process. A significant contribution to the heat output in the process is due to the oxidation of the oxygen carrier in the air reactor.

Figure 4. Preliminary material and energy balances using spreadsheet calculations.

The conditions for these calculations were for scoping studies. They differ from those used later in the calculations presented in the Results and Discussion section.

Integration with Subtask 5.4

The results of the kinetic studies of the reactions 4CuO →2Cu2O + O2 and 2Cu2O + O2→4CuO determined from the TGA from Subtask 5.4 have been utilized to identify the appropriate temperatures and residence times for the air reactor and fuel reactor. The information has been utilized to determine the optimum circulation rate and in the development of ASPEN PLUS simulation as described in the subsequent sections.

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Determination of Optimum Circulation Rate

The oxygen carrier circulation rate, along with the total mass of oxygen-carrier, has been identified as an important variable in the economic design of a CLOU system. As discussed in the previous section, the circulation rate for CLOU is governed by the requirement to supply the fuel reactor with the oxygen needed to consume the fuel.

The process of devolatilization from coals is much faster than the char burnout process. Hence, the process of coal char burnout is expected to be the rate-determining step. Thus, it was decided to focus the study on solid carbonaceous fuel combustion. For simplicity of analysis, it was assumed that coal char would consist of pure carbon. The copper metal was chosen as the basis of determining the optimal recirculation rate as it is an invariant species in the process.

If is the molar flow rate of Cu metal circulating in the system and is the molar flow rate of

carbon feed introduced. are the moles of O2 required for combustion.

To simplify the calculations for the optimum recirculation rate, a mole ratio X is defined as:  

  (1)

As 4CuO → 2Cu2O + O2

(2)

(3)

(4)

The difference in the mole ratio XCuO at the exit of the air and fuel reactors is defined as

. The equation can be used to determine the proportionality between the energy in the fuel introduced to the fuel reactor (in megawatts of thermal energy) and the mass rate of Cu

circulation in the system,

(5)

7

where !! is the calorific value of carbon in MJ/kg.

To evaluate the mass loading of oxygen carrier, the mass flow rate introduced in the system must be multiplied by the sum of residence times in the air and fuel reactors. The residence time in the fuel reactor, assuming a plug-flow reactor, for the cupric oxide decomposition from an inlet concentration of XCuO,AR to an exit concentration of XCuO,FR is given by the relation:

(6)

where is a first-order decomposition rate constant (s-1) provided by TGA experiments (Subtask 5.4).

The residence time of a plug-flow reactor for oxidation from an inlet concentration of (1-XCuO, FR) to an exit concentration of (1-XCuO,AR) in the air reactor is given by:

(7)

where !!,!"!! is pseudo first-order oxidation rate constant (s-1) reported for oxidation in air provided by Subtask 5.4.

The pseudo-first order rate constant was adjusted from an inlet concentration of 21% O2 to a log mean concentration of O2. The log-mean concentration of O2 was calculated by taking into account the inlet O2 concentration of 21% and exit O2 concentration of 3%. In making this adjustment, the square root dependence on partial pressure was assumed, which is consistent with preliminary TGA oxidation results.

(8)

The mass of Cu metal required per MWt of carbonaceous fuel burnt in a CLOU system,  can be obtained from:

(9)

where the residence times are determined by Equations 6 and 7 as a function of XCuO,AR and XCuO,FR, or, alternatively XCuO,FR and ΔXS.

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Development of ASPEN PLUS Simulations

An ASPEN PLUS model was developed for the CLOU process based on the kinetic data determined in Subtask 5.4. The fuel and the air reactors have been modeled using RSTOIC reactors in ASPEN PLUS. The reactions occurring in the fuel reactor are CuO decomposition to yield gas-phase oxygen by the reaction 4CuO →2Cu2O + O2. The coal char particles are oxidized in the fuel reactor by the reaction C + O2 →CO2. The CuO is regenerated in the air reactor by the reaction: 2Cu2O + O2 → 4CuO. The effluent O2 concentration in the air reactor is set at 3%, which is consistent with that in coal-fired utility boilers.

The temperatures of the air and fuel reactor have been established based on the following principles: • The fuel reactor temperature was chosen to be 950°C, which offers the highest rate of reaction.

• To facilitate the mass transfer of oxygen from air to reduced metal oxide, the temperature of the air reactor was chosen to be 850°C. At this temperature the equilibrium partial pressure of gas-phase oxygen over the CuO corresponds to 0.55% O2, which facilitates a higher oxygen mass transfer rate from the air to the solid Cu2O.

These temperatures and excess air in the air reactor are the values used below and the in the case study presented in the Results and Discussion section.

Figure 5 represents the comparison of the residence time for a desired conversion of CuO (to be discussed in detail in the Results and Discussion section) with the coal char burnout at equilibrium oxygen partial pressure determined using the shrinking-sphere model with global coal char-oxidation kinetics for three coals – Pittsburgh#8 and Pocahontas (Hurt and Mitchell, 1992) and Australian brown coal (Hamor and Smith 1973). It shows that the time required for CuO conversion is larger than burnout times for Pittsburgh#8 and Australian brown coal. Only for the Pocahontas coal, the time of CuO reduction and coal char burnout at equilibrium partial pressure oxygen conditions were comparable. Hence the time for CuO reduction could be used as an estimate for determining the fuel reactor residence time.

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Figure 5. CuO reduction kinetics with coal char burnout kinetics at equilibrium partial pressure of

oxygen conditions.

Formulation of Mathematical Model for the Carbon Burnout Process

To model the fuel reactor in the CLOU process, the carbon burnout process was investigated in detail by considering the effect of equilibrium and taking into consideration global coal char kinetic models.

Effects of Equilibrium

A mathematical relationship accounting for the equilibrium oxygen concentration at the CuO surface, followed by the transfer of O2 through the boundary layer around the surface of the CuO and C particles and the subsequent consumption of O2 at the C surface was developed. Figure 6 illustrates the process modeled.

For the case when the release of O2 from the CuO is controlled by mass transfer, the molar rate of oxygen transferred from CuO particles in a fuel reactor is then given by:

(10)

10

Figure 6. Process modeled for the case when the CuO particle is at equilibrium O2 partial pressure.

The surface oxygen partial pressure on a carbon particle and the bulk oxygen partial pressure around the particle can be related by equating the mass rate of oxygen transfer from bulk environment to the surface of the carbon particle and the mass consumption of oxygen at the surface of the carbon particle,

(11)

Consequently, the molar rate of oxygen mass transfer to the surface of carbon particles in a fuel reactor can be expressed as:

(12)

The bulk oxygen concentration can be obtained by equating the molar rate of oxygen release from the CuO particles to the mass transfer to the carbon surface; that is, equating the right hand sides of Equations (10) and (12):

(13)

Equation (13) can be simplified to yield an expression in surface oxygen partial pressure,

11

(14)

where,

(15)

By equating the surface oxygen partial pressures which are obtained from Equations (11) and (13), Equation (16) is obtained:

(16)

This equation can be solved to obtain the bulk oxygen concentration as a function of two coefficients, λ,

which provides a measure of the ratio of loadings of carbon to CuO and which provides a measure of the ratio of reaction rate to the mass transfer coefficient for the carbon.

Figure 7 represents the bulk partial pressure of oxygen vs. λ for three coals – Pittsburgh#8, Pocahontas and Australian brown coal. Examining this figure yields the following important conclusions:

• As the reactivity of the coal increases, the bulk oxygen partial pressure decreases.

• As the diameter of the coal particle decreases (and with it the values of AC and λ, see Eq. 15), the bulk oxygen partial pressure approaches the equilibrium oxygen partial pressure conditions.

The parameter λ enables one to take into account reactivity, C and CuO mass-transfer coefficients, and the ratio of C to CuO loading, which could help in providing insights into some experimental studies in CLOU.

Study of Char Burnout and O2 Partial Pressure Profiles

The char combustion was modeled using a shrinking sphere combustion model where the kinetic constants were obtained from literature studies on global models of pulverized coal combustion (Pittsburgh#8 and Pocahontas - Hurt and Mitchell 1992; Australian brown coal – Hamor and Smith 1973).

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(17)

Figure 7. Bulk partial pressure of oxygen vs. λ.

A MATLAB program with a stiff solver, ode15s, was used for the computation of equation (17) with equations derived and modeled on similar lines as mentioned in the previous section. A schematic of the

scheme is represented in Figure 8. In this figure, the partial pressure of CuO at the surface is initially assumed to be zero. This situation contrasts with the calculations made in the previous section, as

equilibrium partial pressure of O2, is assumed on the CuO surface.

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Figure 8. Process modeled to study coal char burnout and O2 partial pressure profiles when the partial pressure of O2 at the CuO surface is initially at zero.

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. The studies of for global coal char combustion of equivalent Pocahontas and Lower Wilcox (Hurt and Mitchell 1992) were utilized to analyze CLOU experimental studies made on Mexican petcoke (Mattisson et al. 2009b) 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 were not available in literature.

Table 2 represents the ultimate analysis of the coals used in the study.

Table 2. 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 d.a.f: dry ash free. * Data for U.S. coals referenced from Smith et al. (1994).

ASPEN Custom Modeler

Attempts were made to incorporate the developed mathematical relationships with the ASPEN Custom Modeler (ACM). ACM can then be used for kinetic rates, heat transfer, etc.; in turn, this module can then integrate with ASPEN Plus and solve for the material and energy balances. While we have an

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understanding of the formation of an ACM module, the actual code development would take a longer-than-expected period of time since the code is a custom language. To stay on track of our other milestones, it was decided to modify our approach. The kinetics, heat transfer, etc. were developed within the MATLAB framework, and ASPEN was used for the material and energy balances alone. While the integration takes place, it is “manually” driven, not automatically as within ACM.

Subtask 5.2 – LES-DQMOM Simulation of a Pilot-Scale Fluidized Bed

For simulations of dense multiphase systems, such as those in the air and fuel reactors of a CLC system, one must account for convective and diffusive transport, mixing, mass transfer, chemical reactions, and inter-phasic interactions. Moreover, a proper averaging procedure to obtain macroscopic governing equations is required. While turbulence effects present a closure problem, particle size distributions add another dimension of complexity.

We have identified the multi-fluid model as a proper tool for averaging the microscopic transport equations for multiphase flows (Yeoh and Tu 2010, Drew 1983). It is a robust and widely used model that has been successfully applied to a variety of multiphase flow problems. The multi-fluid model assumes that the dense phase (gas or liquid) and the disperse phase (gas, liquid, or solid) are both continuous phases in an Eulerian framework. Furthermore, a proper averaging and filtering procedure is adopted to obtain the governing equation for Large Eddy Simulation (LES) (Selma et al. 2010).

This subtask originally planned to develop dense multiphase capabilities with ARCHES; however during the course of development, the investigators determined that Star-CCM+, a commercial computational fluid dynamics (CFD), would most effectively meet the task needs during the short term. Star-CCM+ was chosen because it offers a variety of desirable options, including:

• Scalability: Our own in-house assessment has shown that Star-CCM+ scales well up to 768 processors. This is a very desirable feature both for our university research and for industry.

• Complex Geometries and Meshing: Star-CCM+ provides advanced computer aided design features to handle complex geometries with a wide range of length scales. It also provides a unique set of meshing utilities for rapid mesh generation, processes that often require the majority of the time to set-up and complete a simulation.

• Extension: For this and many other applications, modeling and software extensions are required to fine-tune Star-CCM+ for the intended use of the simulation. For example, users need the flexibility to input their own models for source terms or chemical reactions.

• Collaboration: The Institute for Clean and Secure Energy has established a relationship with CD-adapco and are in contact with Star-CCM+ technical engineers. In addition, CD-adapco granted the University of Utah no-cost software licenses for this subtasktask.

We investigated the potential for Star-CCM+ to handle the type of simulations that are required for CLC and we found promising results.

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DQMOM/LES Formulation in Fluidized Bed Systems

The current formulation uses an Eulerian multi-fluid approach to treat the multiple phases in the fluidized bed. It assumes that the continuous phase (gas) and the dispersed phase (solids) are both 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 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 potentially 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 LES. 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 M! account for the interfacial mass and interfacial momentum exchanges, respectively. The interfacial momentum exchanges that are 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 in the solution of the population balance equation. The method of DQMOM solves the generalized population balance equation by using a quadrature approximation for the number density function. A number density function is a quantity that

16

represents the number of particles per unit volume and per unit of internal coordinate. The internal coordinates refer to characteristic properties 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 equation for the number density function tracks the evolution of a particular distribution of the number density function.

Here, is a source term and G! 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, DOE NETL and the Particle Simulation Research Institute released the experimental data set for circulating/bubbling fluidized beds in the framework of the “3rd Modeling Challenge in Granular Fluid Hydrodynamics.” This data collection allows us to identify the experimental error bounds needed in the consistency analysis for the Uncertainty Quantification. In this context 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 3. For these parameters, the investigators tried to identify reasonable ranges of variation based on the result obtained so far.

Table 3. 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

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Parameters of Numerical Relevance

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 obtain a 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 on the stability of the simulation.

Relaxation Factors: Developers at Star-CCM+ recommend keeping those factors as low as possible, ranging 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; instead, the mass flow rate is known from the experimental data.

Solver Settings: Some of the default solver settings have been modified based on the experience of the team members with related CFD simulations. The main modifications have been made to the AMG solver cycles providing more stability to the numerical simulations.

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 stability, second order discretization is activated.

Appropriate constants: Constants for the drag model have been appropriately identified according to Gibilaro (2001). Also a constant ranging from 100 – 600 has been identified for the solid pressure term, which causes the particles to reach a physically unreasonable 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 the Operation

Volume fraction of solids at the inlet: Not only is the velocity of the solids at the inlet unknown, but also the solid volume fraction. Typical values for this parameter could range between 0.1 (dilute system) to 0.6 (maximum packing limit). The current value is set to 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

18

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 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 21) to the terminal velocity of the particles (eq 22).

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 free-fall; i.e., 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, in some cases, this configuration was causing numerical instabilities in the velocity field. The current approach is to use the inlet directly on the wall; this reduces the impact of stagnation (or singularity 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 one.

Parameters Relevant to the Physical-Chemical Properties

Particle Distribution: Although only particles of one size have been tested, the current code has the possibility 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 why we intend to implement DQMOM in Star-CCM+, 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 this study, 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 physically unreasonable values for the viscosity as volume fraction approaches this value.

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Subtask 5.3 – Laboratory-Scale CLC Studies

Oxygen Carriers

During this project, experiments using the lab-scale fluidized bed at the University of Utah were focused on two base materials: iron and copper. Both metals have unique qualities that make them promising candidates for a commercial-scale CLC system. Several characteristics need to be considered when selecting oxygen carrier materials. These include oxygen carrying capacity, cost and availability, melting point temperature, durability, reactivity, deactivation resistance and resistance to attrition.

Copper and iron have different desirable characteristics. Iron is a more traditional CLC material and has a distinct cost advantage. Copper, however, can have better reactivity under certain conditions and is a candidate material for the more advanced CLOU process described previously. Both materials, along with most other metals, have the disadvantage of having relatively low melting points which may cause agglomeration and/or sintering problems.

Iron

The biggest advantage that iron has over other materials is its low cost and availability. Natural materials can be especially low cost. With this in mind, the University of Utah selected an iron-based ore called ilmenite. Ilmenite (FeTiO3) is a naturally occurring mineral that is used primarily in the production of titanium dioxide. Ilmenite is the most abundant form of all titanium materials and is therefore mined in large quantities (Leion et al. 2008). Due to its abundance and availability ilmenite is an economically feasible option as an oxygen carrier in a CLC system.

A number of studies have reported CLC testing using ilmenite as an oxygen carrier (Leion et al. 2008; Cuadrat et al. 2009). However, little data is available on oxidation/reduction kinetics of ilmenite. This may be due to the fact that the term ilmenite may also refer to various ores containing varying amounts of the mineral ilmenite. The variability that exists in the term ilmenite gives rise to difficulties in defining kinetic variables.

Depending on the temperature regime, ilmenite will be oxidized or reduced by different reactions. Below 800°C the ilmenite does not completely oxidize:

6 FeTiO3 + 3/2 O2 → 2 Fe2Ti3O9 + Fe2O3

Above 800°C two reactions occur:

4 FeTiO3 + O2 → 2 Fe2TiO5 + 2 TiO2

Fe2Ti3O9 → Fe2TiO5 + 2 TiO2

where TiO2 is rutile and Fe2TiO5 (pseudobrookite) is the most stable phase (Cuadrat et al. 2009).

20

The composition of ilmenite used in this study is presented in Table 4. It is mostly made up of hematite (Fe2O3) and titania (TiO2). The looping capabilities of ilmenite arise from the redox characteristics of the hematite. The theoretical mass change between the fully oxidized and fully reduced states of the ilmenite, corresponding to the change from FeO to Fe2O3, is about 10%.

Table 4. Ilmenite analysis. SPECIES Wt. % TiO2 63.90 Fe2O3 29.20 SiO2 0.56 Carbon 0.19 Phosphorus 0.04 Sulfur <0.001

Copper

Copper has been identified as a CLOU type material. CLOU materials have the ability to spontaneously release oxygen in the fuel reactor. Copper, while relatively costly, is especially suitable. Because of its CLOU capabilities, copper-based oxygen carriers may be utilized to directly combust coal without gasification either upstream or in-situ. Not only does gasification incur additional process complexities, but the kinetics of coal gasification are much slower than those of combustion. The rate of reduction of copper from CuO to Cu2O is faster than coal gasification rates at the same temperatures (Lewis et al. 1951), which makes copper-based oxygen carriers especially attractive.

In FBRs, particle sintering and agglomeration may pose serious problems. Even if the bed temperature is well controlled, local temperature spikes may cause particles to soften and agglomerate or even sinter. Formation of larger particles by sintering results in poor fluidization and eventual bed collapse.

For CLOU, looping of copper is between the CuO and Cu2O states. The following equations represent the redox reactions of CuO/Cu2O:

Reduction: 4 CuO → 2 Cu2O + O2 ΔH850 = 263 kJ/mol O2

C + O2 → CO2 ΔH850 = –379 kJ/mol O2

4 CuO + C → 2 Cu2O + CO2 ΔH850 = –133 kJ/mol O2

Oxidation: 2 Cu2O + O2 → 4 CuO ΔH850 = –263 kJ/mol O2

Even though the reduction of CuO to Cu2O is endothermic the combustion of carbon and oxygen is exothermic enough to generate an overall exothermic reaction.

21

Apparatus and Procedure

The schematic in Figure 9 represents the lab-scale reaction system built at the University of Utah. This system is similar to that used at other institutions such as Chalmers University in Sweden. Various gases can be mixed and fed through mass flow controllers into a small quartz FBR which is housed in a furnace. Most tests were performed using a 5 cm diameter reactor, although some were performed using a smaller 2.5-cm diameter reactor. In order to simulate a dual FBR system a single bubbling bed is utilized, and is switched between oxidizing and reducing gases.

Figure 9. Schematic of the bubbling bed CLC reactor system at the University of Utah.

Tests were performed at several temperatures ranging from 650°C to 950°C. In order to achieve fluidized conditions gas flow rates ranged from 1.5 SLPM to 5 SLPM. These flow rates correlated to U/Umf of 20-60 (where U is the superficial gas velocity and Umf is the theoretical minimum fluidization velocity). For each test, 100 to 200 grams of carrier material was placed into the reactor. Enough material was used to fill the reactor to one diameter bed height when not fluidized. The reactor was then placed within the furnace where the temperature was ramped at 5°C per minute to the desired operating temperature.

Reacting gases (air, N2, CH4, CO2) are supplied from laboratory gas cylinders. The flow of each gas is controlled by variable area flowmeters, and the gases are switched using solenoid valves. From the valves the gases flow through the furnace/reactor. The reactor is housed within a clamshell furnace. Two type K thermocouples are located within the furnace to measure the temperature of gases at the reactor entrance, below the distributor plate, and the center of the reacting bed. Upon exiting the reactor a copper cooling coil is used to cool the effluent and condense the moisture created in combustion. From the coil the gas enters a filter to capture any fractured carrier particles. The gases are then analyzed in a non-dispersive infrared/O2 analyzer which measures concentrations of CH4, CO2, CO, and O2. After analysis the gases are exhausted.

The reactor itself is made from quartz. The main reactor section includes a sintered quartz disc which acts as a gas distributor. Above the bed, the diameter expands to slow the gas velocity thereby

22

minimizing the possibility of particulate carryover. Dimensions of the larger quartz reactor used in these studies are shown in Figure 10.

Figure 10. Dimensions of quartz reactor.

The filter on the product gas line was weighed before and after each test to determine the degree of particle attrition.

Data Analysis and Interpretation

The fluidized-bed system is controlled, and output signals are measured through a central OPTO-22 based control system. Data was recorded in real-time once every second. Figure 11 displays an example of the raw data received by OPTO.

23

Figure 11. Example of data acquired from the fluidized-bed system. Reduction and oxidation of CuO/Cu2O on TiO2 using N2 and air at 700°C (left half) and 900°C (right half). Orange and blue lines

indicate temperatures below the distributor and within the bed, respectively. The purple line indicates O2 concentration.

The gas analyzer data does not directly indicate what is going on within the reactor. The data is convoluted due to the reaction gas residence time distribution, gas dispersion in the gas lines after the reactor and analyzer time delay. To account for this convolution of the actual data a deconvolution procedure was developed.

Several approaches for deconvolution of data are available. If the data set is a discrete set, then the set may be fit to a polynomial expression, which can then be subjected to a Laplace transform. The continuous Laplace transform method obeys the following relationship (Blair et al. 1977):

Where F denotes the collected data, A denotes the actual data and C represents the convolution of the data. The function C may be determined from residence time distribution tests, with data then fit to a polynomial in similar fashion to the transformation of F from a discrete array to a continuous function. The resulting transformed equations may be rearranged as follows:

24

The inverse Laplace transform generates the final d result of actual data as a function of time, or:

This process has proven useful and may be utilized, but the accuracy depends on the accuracy of the polynomial fit.

Another method used to deconvolute a data set is much simpler and more quickly employed. In this method the measured signal is subtracted from a second signal obtained by looping reaction gases over a bed of inert material (90 micron ceramic beads). The observed difference between the signal with inert material and a perfect step change represents the residence time distribution (RTD signal), or degree of data convolution. This method is displayed in Figure 12, which shows the result obtained when the measured signal is subtracted from the RTD signal.

Figure 12. A simple method for deconvolution is achieved by subtracting a signal obtained while exposing an inert material to reaction gases and is denoted as the RTD signal (blue). The measured signal

recorded by OPTO 22 (red) is then subtracted from the RTD signal and the deconvolved signal is resolved (green).

The reasonableness of using this method was evaluated by comparing the results obtained in the fluidized bed and deconvolved using this simple method against results obtained in a TGA using the same material.

Figure 13 shows a comparison between these methods. These tests were conducted at 800°C using air as the oxidizer and ilmenite as the oxygen carrier. While the signal lines do not line up exactly on top of each other, the two results agree very well. Due to the simplicity and reasonably good accuracy associated with this method, data analysis was conducted in this manner.

25

Figure 13. Comparison of TGA data (blue) and deconvoluted fluidized bed data (red).

Subtask 5.4 – CLC Kinetics

In the copper, cuprous oxide, cupric oxide system the chemical reactions are as follows:

2Cu(s) + O2 ⇌ 2CuO(s)

4Cu(s) + O2(g) ⇌ 2Cu2O(s)

2Cu2O(s) + O2(g) ⇌ 4CuO(s)

At elevated temperatures the copper is completely oxidized into cupric oxide.

The third reaction is the foundation of CLOU because it can be reversed by decreasing the partial pressure of the oxygen in the gas surrounding the solids. In air, the reaction proceeds to complete oxidation, simulating the air reactor of the CLC. Replacing the air with nitrogen, the CuO reduces to Cu2O, and O2 is released, simulating the behavior of the oxygen carrier material in the fuel reactor. The reaction was studied with thermogravimetric analysis (TGA).

The initial plan was the determination of the rate constants at different temperatures in the 850 – 950 °C range. An unexpected result for the oxidation reaction required the extension of the temperature range down to 650 °C.

The looping experiments were carried out using two TGA instruments: a TA Q500 and a TA Q600. The TA 500 is a vertical design, as shown in Figure 14. To protect the balance from hot gases the balance chamber was purged with 40 mL/min flow of nitrogen. The reacting gas – nitrogen or air – flow rate was

26

60 mL/min. This gas is introduced into the furnace close to the sample allowing the quick change of the atmosphere.

Figure 14. The TA Q500 TGA instrument.

The TA Q600 instrument is a horizontal design. Instead of separate purge and reactant gases it has one internal gas delivery channel. The gas can be selected from two connected sources, one at a time, and it flows through the entire instrument. The large volume of the balance compartment prevents the sudden replacement of the gas in the furnace. With a 100 mL/min flow rate the complete purge requires more than 8 minutes. This long delay was eliminated by introducing the 100 mL/min flow of air from the external gas delivery port of the instrument, the short tube between the balance beams in Figure 15, and adjusting the nitrogen flow through the instrument accordingly. The total flow rate was maintained at 200 mL/min.

Furnace

Balance

Sample

Counter  Balance

Purge  gas  InExhaust

Balance  gas  In

7.3  mm

Sample  Pan

27

Figure 15. The TA Q600 TGA instrument.

Looping experiments were executed using both a copper powder (Sargent-Welch Scientific Co., 150 mesh, reported average particle size 9 µm) and a cupric oxide powder (Johnson Matthey Chemicals, having a determined particle size range of 1-10 µm). As the initial results revealed no difference between the looping characteristics with respect to the starting material, the systematic study was carried out using the CuO powder.

The TA instruments operate under ambient (atmospheric) pressure. The pressure dependence of the oxidation of the copper was studied with a Cahn TherMax 500 TGA. This instrument is able to maintain a 1000 psi pressure and up to 1000 °C. However, it is not capable of switching the gas delivered to the reaction chamber. Therefore additional mass flow controllers were installed to allow the selection of nitrogen or air.

Due to the sintering of the solid materials at the desired high temperatures (Tammann temperatures: Cu 405 °C, Cu2O 481 °C, and CuO 526 °C) the experiments were executed using quartz replicas of the sample holders, made in the Glass Shop of the Department of Chemistry.

4  mm

Sample  Pan

Sample

Reference  Arm/Weight

Sample  ArmThermocouple

Furnace

Balance  Chamber

N2 in

Air  In

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RESULTS AND DISCUSSION

Subtask 5.1 – Process Modeling and Economics

The goal of this work is to identify and formulate the engineering relationships, which would be useful in comparing CLOU and CLC process on the basis of techno-economic parameters. The engineering analysis developed for CLOU using copper as an oxygen carrier consisted of the determination of optimum circulation rate, derivation of the relationships for char burnout and O2 partial pressure profiles and the development of ASPEN PLUS simulations. This work has been reported in a peer-reviewed journal (Eyring et al. 2011).

Determination of Optimum Circulation Rate

The optimum recirculation rate is an important variable, which provides insights to the economics of the CLOU process. Figure 16 provides a plot between the mass of copper circulated in the system per MWt of carbon introduced in the fuel reactor versus the difference in mole ratio ΔXS for different values of XCuO,FR from relation (5).

A value of 0.45 is assumed for ΔXS, which is similar to the value 0.2-0.4 recommended for a CLC process for low circulation rates and low solids inventories (Abad et al. 2007). From Figure 16, it can be observed that a circulation of approximately 1.8 kg/(s)(MWt) at a ΔXS of 0.45 is required on a support-free basis. The total rate of material circulated is equal to this value divided by the weight percent of CuO for supported CuO. For facilitating comparison with results reported in the literature, the data in the literature need to be converted to a support-free basis by multiplying the reported rates by the weight percent of active material on the supported oxygen carrier. A circulation rate of 1.8 kg/(s) (MWt) on a support free basis is comparable to values reported in the literature of 2 kg CuO/(s)(MWt) (at ΔXS = 0.4) for the combustion of petroleum coke using CLOU (Mattisson et al. 2009 b) and 1 kg CuO/(s)(MWt) for a fuel gas (Abad et al. 2007).

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Figure 16. Mass (kg) of copper circulated per MWt with variation in ΔXS of CuO.

Figure 17 shows the minimum carrier loading of the oxygen carrier estimated from relation (9) at a specific XCuO,FR for each value of ΔXS. The shape of each curve is determined by the increase in reaction time in the fuel reactor as the value of XCuO,FR decreases at a given ΔXS because of the first-order rate equation as represented by equation (6).

The reaction times for the air reactor by contrast increase with increasing XCuO,FR at a fixed ΔXS as can be noted from equation (7). A high value of ΔXS reduces the circulation rate of oxygen carrier, which also helps in minimizing the oxygen carrier loading.

A value of XCuO,FR = 0.3 and ΔXS of 0.45 have been chosen as a basis to perform an order of magnitude calculation, at which copper loading in the oxygen carrier is 135 kg CuO/MWt.

Calculations reported in the literature indicate a minimum oxygen carrier loading of approximately 25 kg CuO/ MWt for combustion of methane and a 10% Cu, aluminium support, given the same ΔXS (Garcia-Labiano et al. 2007). This is less than 1200 kg Fe2O3/MWt for combustion of petcoke with a 60% Fe2O3 / 40% MgAlO4 oxygen-carrier (Leion et al. 2007) and 48 to 80 kg CuO/ MWt for combustion of petroleum coke for a 40% CuO/ZrO2 oxygen carrier operated in a CLOU mode (Mattisson et al. 2009b).

30

Figure 17. Mass of copper loading per MWt vs. mole ratio of CuO at the exit of fuel reactor for different ΔXS values.

Development of ASPEN PLUS Simulations

An illustrative simulation in ASPEN PLUS (Figure 18 and Figure 19) has been developed for the conversions of CuO discussed above and the combustion of carbon using an optimum circulation rate of oxygen carrier (XCuO,FR = 0.3 and ΔXS of 0.45). This results in a conversion of 60% of CuO in the fuel reactor and 64% of Cu2O in the air reactor. From the kinetic data developed under Subtask 5.4, the expected residence time of the fuel reactor, which is the time for CuO reduction, would be approximately 41 seconds, and the Cu2O oxidation process would take 52 seconds.

The results discussed in the previous paragraph have been integrated in the ASPEN PLUS simulation employing RSTOIC reactor models. Combusting 100 kg/h (2.4 t/d) carbon requires an average flow of 5800 kg/h of total metal oxide (copper (I) oxide and copper (II) oxide) to facilitate the carbon conversion. The reactions in both reactors are exothermic, with 357 kW of energy recovered from the fuel reactor, and 574 kW energy recovered from the air reactor. Figure 18 and Figure 19 show the schematic of ASPEN PLUS simulation and the material and energy balance results, respectively, for the temperatures and excess air values described under Methods for Section 5.1.

31

Figure 18. Schematic of an ASPEN PLUS simulation.

32

Figure 19. Material and energy balance results of the ASPEN PLUS simulation with residence times and optimum recirculation rates.

The original statement of work discussed the use of ASPEN’s built-in economics package to estimate capital and operating costs for a chemical looping system, which will be facilitated by interactions with vendors. The current work and literature (Fan and Li 2010, Lyngfelt 2011) suggest that it is essential to investigate suitable oxygen carrier materials for the CLC process in the realm of reaction and process engineering. The pertinent reaction engineering aspects for metal oxide particles are: types and thermodynamic properties of metal oxides and support materials, oxygen transfer capacity, gas and solid conversions, rates in both reduction and oxidation reactions, heat capacity and heat of reactions, melting points, mechanical strength, long-term recyclability, ease in scale up, health and environmental effects, and particle cost (Fan and Li 2010). The process engineering aspects include intended products, reactor types, heat integration, and process intensification strategies, and overall process efficiency and economics (Fan and Li 2010). The adaption of the comprehensive experience from circulating fluidized-bed boilers in the design of suitable reactor systems has also been identified as an important objective in a recent literature study on CLC (Lyngfelt 2011). In this and recent work, it was decided to focus process engineering studies on identifying optimum recirculation rates of oxygen carrier and modeling the processes occurring in the fuel reactor for the CLOU process. The process models can then be used for comparative studies of materials and for CLC with CLOU.

33

Formulation of Mathematical Model for the Carbon Burnout Process

Analysis of CLOU Experiments on Mexican Petcoke

Figure 20 presents the fractional unburned char versus time for Mexican petcoke at different temperatures, and it shows that the time for burnout decreases with increasing temperature. As expected, an increase in temperature decreased char burnout time. The comparison of the experimental data at different temperatures for 95% burnout for a Pocahontas coal char has been made with the simulation values (Figure 21). As observed in Figure 21, the trend of the experimental data is similar to the simulation predictions. Figure 22 represents the plot of oxygen partial pressure for Mexican petcoke versus time at 955°C. Since petcoke has a slower reactivity and consumes oxygen relatively slowly, the oxygen concentrations at the surface of the copper oxide particle and coal particle show a more rapid increase to equilibrium. The oxygen concentration profile at the coal surface indicates a mass-transfer resistance. As the coal burns and the particle becomes smaller, the mass transfer resistance reduces. The phenomenon has also been observed in CLOU experiments on Mexican petcoke, as oxygen concentration approaching equilibrium is observed at the outlet of the reactor (Mattisson et al. 2009b).

Figure 20. Fractional char unburnt vs. time at various temperatures for Mexican petcoke modeled using global coal char kinetic data for Pocahontas coal.

34

Figure 21. Reaction time for 95% burnout vs. temperature for Mexican petcoke modeled as a Pocahontas coal char.

Figure 22. Partial pressure of O2 vs. time for Mexican petcoke modeled as a Pocahontas coal char.

35

Figure 23 represents the comparison of the model with the experimental data (Mattisson et al. 2009b) for the carbon consumption profiles at 985°C. The carbon consumption profiles were obtained by integrating the area under the curve for the products consumed, namely CH4 and CO2, for Mexican petcoke (Mattisson et al. 2009b).

The simulation captures the trend of experimental data adequately. In the mathematical model developed in this study the reaction is assumed to occur at isothermal conditions. The time for particle heat up has not been taken into account in the present mathematical model. An induction period of 5-6 seconds can be observed in the experimental data as compared to simulation results. A preliminary lumped parameter calculation was done which revealed that the time of particle heat up could possibly help to explain the difference in experimental and simulation results.

Figure 23. Comparison of the carbon consumption profiles of Mexican petcoke simulated as a Pocahontas coal with the experimental data of Mattisson et al. (2009b).

Analysis of CLOU Experiments on German Lignite

Figure 24 represents the fractional unburned char versus time for German lignite modeled by U.S. Lower Wilcox char at different temperatures. As in the previous case, an increase in temperature decreased the char burnout time. The decrease is significant as compared to Mexican petcoke. The comparison of the experimental data for devolatilized German lignite at different temperatures for 95% burnout was completed with the parameter values for Lower Wilcox char (Figure 25). As can be observed in Figure 25,

36

the trend of the experimental data is similar to the simulation predictions. The relationship between partial pressure of oxygen for devolatilized German lignite vs. time at 949°C can be observed in Figure 26. Lignite is a carbonaceous fuel possessing a higher reactivity and hence consumes oxygen at a relatively more rapid rate. As a consequence, the partial pressure of oxygen at the surface of the carbon particle has a slower approach to equilibrium conditions. The phenomenon has also been observed in CLOU experiments on devolatilized German lignite, where oxygen concentration is very low at the reactor outlet (Leion et al. 2008).

Figure 24. Fractional char unburnt vs. time at various temperatures for devolatilized German lignite using global coal char kinetics for Lower Wilcox coal char.

37

Figure 25. Reaction time for 95% burnout vs. temperature for a devolatilized German lignite modeled as a Lower Wilcox coal char.

Figure 26. Partial pressure of O2 vs. time for Devolatilized German Lignite modeled as a Lower Wilcox coal char.

38

Figure 27 represents the comparison of the model for the carbon consumption profiles at 949°C. The carbon consumption profiles were obtained by integrating the area under the curve, the product profiles of consumption, namely CO, CH4 and CO2 in the case of German lignite (Leion et al. 2008).

The experimental data of for the devolatilized German lignite (Leion et al. 2008) indicates rapid oxygen consumption in the initial stages. The oxygen released by the CuO particles is consumed in its entirety, leading to negligible oxygen concentration at the reactor outlet. Beyond the 80% char burnout, a significant time is required to achieve complete char burnout. On analyzing the comparison with experimental data for German lignite, it is likely that an oxygen deficiency occurred in the burnout process in the later stages, which led to slower reaction rates. This is supported by the formation of CO reported in experiments (Leion et al. 2008).

The formation of a long tail in the experimental results for the devolatilized char also indicates the disruption of the flow pattern in the batch fluidized bed, which led to improper mixing.

Figure 27. Carbon consumption profile for CLOU experiments with German lignite coal and devolatilized German lignite (Leion et al. 2008) with the simulation results for Lower Wilcox coal.

Subtask 5.2 – LES-DQMOM Simulation of a Pilot-Scale Fluidized Bed

The simulation results discussed in this section are compared to the NETL coldflow dataset. Figure 28 shows the pressure loss with respect to the height of the downcomer with Geldart type A particles (59 microns), for two different constant viscosities and a velocity of 1.03 m/s. The red and green dashed lines are the 95% confidence interval for the experimental data. The blue dots are the actual data and the solid blue lines are the simulation results. Figure 29 and Figure 30 represent the void fraction and pressure profiles from the simulation results. Figure 31, Figure 32, and Figure 33 represent the same information

39

as the previous figures but at a different solids velocity (3.75 m/s). The velocities reported are estimations of the solids velocity at the inlet into the bed. These cases show some qualitative agreement with the data, given the assumptions of the model and the uncertainty of the model and the uncertainty of the data. These assumptions rely on simple modeling approach to represent the flow of the gas and solids. This study follows modeling approximations made in previous studies (Bouillard et al. 1989) for fluidized bed simulation. These models will be further refined by accounting for the variability in uncertainty quantification parameters. Particles of Geldart type B have proved to be more difficult to simulate due to numerical instability. However, even when convergence is achieved, the results do not fit within the experimental uncertainty range.

Figure 28. Pressure loss for viscosities of 1 and 0.1 m2/s and solids velocity of 1.03 m/s.

40

Figure 29. Void fraction profiles for viscosities of 1 and 0.1 m2/s and solids velocity of 1.03 m/s.

Figure 30. Pressure profiles for viscosities of 1 and 0.1 m2/s and velocity of 1.03 m/s.

41

Figure 31. Pressure loss for viscosity of 1 and 0.1 m2/s and solids velocity of 3.75 m/s.

Figure 32. Void fraction profiles for viscosities of 1 and 0.1 m2/s and velocity of 3.75 m/s.

42

Figure 33. Pressure profiles for viscosities of 1 and 0.1 m2/s and velocity of 3.75 m/s.

Subtask 5.3 – Lab-Scale CLC Studies

Ilmenite

Initial testing of ilmenite indicated that the reaction progressed quickly enough to initially completely consume all oxygen introduced into the system. This created challenges for determining reaction rates. Because rate analysis required an excess of oxygen at all times, the concentration of ilmenite was reduced to 20 wt% by adding inert ceramic beads as diluent to the ilmenite mixture.

Leion et al. (2008) and Cuadrat et al. (2009) noted that ilmenite has an initial “activation” period during which reaction rates and carrying capacity are lower than normal. Cuadrat et al. (2009) determined that this “activation” period may be avoided by simple calcination of the ilmenite for 2 hours at 800°C under air. They report a substantial increase in overall performance after calcination of the ilmenite.

Ilmenite was tested at several different temperatures and looped for 3-4 cycles at each of these temperatures. One test was performed for 20 hours in order to get a better understanding of how ilmenite may behave in a commercial-type environment with continuous looping for extended periods of time.

Figure 34 displays the raw oxygen evolution profile for ilmenite at various temperatures. The reacting gas is air, therefore the effluent should be showing roughly 21% oxygen. However, the oxygen is being scavenged by the ilmenite. As temperature increases the amount of O2 in the effluent decreases at each respective time. This shows an increase in reaction rate with temperature.

43

Figure 34. Ilmenite diluted to 20% by mass and tested in a bubbling fluidized bed at several different temperatures. This figure shows the oxygen evolution profile in the effluent gases.

Figure 35 shows the calculated reaction rate versus time once the deconvolution procedure was applied. The reaction rates were normalized by the initial mass loading of ilmenite in the reactor. Each curve displays a maximum rate around 15 seconds. This 15 second delay is attributed in part to the time required to evacuate the particle bed of reduction gases while filling with oxidation gases.

44

Figure 35. Evolution of reaction rates for 20 wt% ilmenite at various temperatures. The reaction rate is normalized by the initial mass loading of ilmenite.

In order to determine the activation energy, an Arrhenius plot using the maximum rate was generated (Figure 36). Based on the slope of this curve, the activation energy of this reaction is determined to be 32 kJ/mol.

Figure 36. Arrhenius plot for oxidation of ilmenite. Activation energy = 32 kJ/mol.

45

As a test for attrition characteristics, the sample and fines filter were both weighed before and after testing. After more than 40 hours of testing and 32 complete cycles ilmenite lost only 0.01 grams of the 20 grams of initial load or about 0.05 wt%.

Unsupported CuO – 99.99% Pure

In order to establish a baseline for copper materials, initial testing was performed using pure unsupported CuO. Looping tests were performed between 600°C and 1000°C. All tests resulted in severe agglomeration. Figure 37 is a snapshot of agglomerated copper at only 600°C. Copper has a melting point of 1084°C and therefore becomes soft and may agglomerate at much lower temperatures. The conclusion of this testing is that pure copper is entirely unsuitable for a fluidized-bed system and that the carrier material must include a support material. Interestingly, despite the fact that copper agglomerated it still looped relatively well and therefore may be useful in a packed bed design of a CLC system.

Figure 37. Agglomeration of pure copper powder (90µm) at 600°C.

50% CuO with TiO2 support

For supported carriers, it is desirable to maximize the loading of the active metal complex so that the overall system remains as small as possible. After review of potential carrier support materials, it was determined that titanium dioxide may provide a good balance of low cost and acceptable performance. Material containing 50 wt% CuO on TiO2 was procured from the Institute for Chemical Processing of Coal in Poland. Pure unsupported copper has an oxygen carrying capacity of 11 wt% when looped between Cu2O and CuO. For this supported material the theoretical yield of oxygen carrying capacity was approximately 5.5 wt%.

46

Initial tests resulted in agglomeration at temperatures above 800°C. To help minimize this, zirconia silicate beads of similar fluidization characteristics were introduced as a diluent. This improved performance and allowed evaluation of the material at temperatures up to 800°C.

Figure 38 shows the volume fraction of O2 reacting with the Cu2O. The integration of this data is presented in Figure 39, which shows that by 50 seconds of reaction time most of the reaction has taken place. It is also important to note that as the operating temperature increases the amount of O2 scavenged also increases.

Figure 38. Oxygen consumption during oxidation of Cu2O for the 50 wt% CuO on TiO2 material.

Figure 39. Total oxygen consumption by Cu2O at various temperatures.

47

Figure 40 shows an Arrhenius plot, based on maximum rates, for this data. The resulting activation energy is 55 kJ/mole. Although the correlation coefficient of the data and the line is relatively low (R2 = 0.74), the resulting activation energy agrees well with the 55 kJ/mol reported by Chuang et al. (2010) for a mixture of co-precipitated Cu and Al2O3.

Figure 40. Arrhenius plot for oxidation of 50% CuO on TiO2. Activation energy = 55 kJ/mol.

Reduction of the 50% CuO material was carried out under two different atmospheres: N2 (for CLOU testing) and 5 vol% CH4. Under inert conditions the material did exhibit CLOU characteristics. Figure 41 displays the total mass of oxygen released by this material over the temperature range 650 to 800°C. The total amount of oxygen released increases as the temperature increases. At 800°C, 0.23 g oxygen was released, which corresponds to only 14% of total theoretical yield. Interestingly, the rates of reduction decreased with increasing temperature. A negative activation energy of –10 kJ/mol was observed (Figure 42). This behavior has been observed in TGA tests, as well.

48

Figure 41. Reduction of 50 wt% CuO on TiO2 under N2. The mass (grams) of oxygen released is shown vs. time.

Figure 42. Arrhenius plot for reduction of 50 wt% CuO on TiO2 between 650°C and 850°C. Activation energy = –10 kJ/mol.

Unfortunately, this material behaved poorly in the fluidized-bed environment. Test samples suffered a nearly 20 wt% loss over only 12 cycles and about 15 hours of operation. In addition to poor attrition resistance, this material also tended to agglomerate when undiluted at temperatures over 800°C. When

49

diluted with inert material, agglomeration tendencies decreased significantly but not completely. Even at low concentrations of carrier, agglomeration was still a problem.

13% CuO with Al2O3 support (Sigma-Aldrich material)

A copper supported material was purchased from Sigma-Aldrich (SA). The purchased material was approximately 1 mm in diameter and contained 13 wt% CuO on Al2O3. The 1 mm size was too big to fluidize at a reasonable flow rate. Therefore, the particles were crushed in a tumbler and sieved to an average size of 93 – 105 µm.

Initial TGA and FBR tests showed that the SA material had little to no reactivity. After a short activation period the SA material began to demonstrate some reactivity, but the theoretical limit of 1.3% weight gain was never achieved.

Due to the low reactivity and very fast reaction rate of the SA material it was difficult to extract any kinetic information from the oxidation data. However, reduction under an inert atmosphere has a slow enough reaction rate to be discussed. Figure 43 presents the actual oxygen released by the SA material normalized by the theoretical limit. Conversion of the SA material was far from the theoretical conversion due to the formation of copper aluminate, but due to the slower reaction rates of the reduction an activation energy may be determined. The Arrhenius curve, based on the maximum rate of oxygen release, is shown in Figure 44. The resulting activation energy for reduction of the SA material is 14 kJ/mole.

Figure 43. Release of gaseous oxygen (O2) versus time during reduction of 13 wt% CuO on Al2O3. Release is presented as a percentage of the theoretical oxygen carrying capacity based on carrier

chemistry.

50

Figure 44. Arrhenius plot for reduction of 13 wt% CuO on Al2O3. Activation energy = 14 kJ/mole.

Subtask 5.4 – CLC Kinetics

First the experiments were carried out using the TA Q500 TGA in the 850 – 950 °C temperature range under isothermal conditions. In these experiments the CuO sample was heated to the target temperature under air. After the temperature was equilibrated the sample gas was replaced with nitrogen, initiating the reduction of the CuO to Cu2O. When the reaction was completed as indicated by the stabilized weight the gas was switched back to air to oxidize the sample back to CuO. Figure 45 shows the reactions at 850°C, indicating that the reduction is slower than the oxidation. At 950°C in Figure 46 the reduction proceeds faster. The observed weight changes are in good agreement with the calculated 10.6 % for conversion of CuO to Cu2O.

51

Figure 45. CuO/Cu2O/CuO sequence, at 850°C.

Figure 46. CuO/Cu2O/CuO sequence, at 950°C.

Based on results reported in the literature, the rates were evaluated assuming first-order kinetics, according to the following model

)( 0)( ttkfift ewwww −−−+=

0 5 10 15 20 25 30 35 40 45 50 55 60 6589.0

90.0

91.0

92.0

93.0

94.0

95.0

96.0

97.0

98.0

99.0

100.0

101.0

102.0

Rel

. wei

ght (

%)

Time [min]

-15 0 15 30 45 60 75 90 105 120 13588.0

90.0

92.0

94.0

96.0

98.0

100.0

102.0

Rel

. Wei

ght (

%)

Time [min]

52

where wt is the observed mass at time t, and k is the rate constant of the reaction. The initial mass of the sample and the final mass after infinite time – wi and wf, respectively, – were fitted parameters. The t0 parameter adjusted for the time delay between the start of an experiment and the beginning of the reaction by isothermal holding periods, the time required to reach and stabilize the reaction temperature under air, and the time needed for the nitrogen or air to reach the sample. The t0 parameter was selected manually for each experimental segment. The function was fitted by the Levenberg-Marquardt algorithm provided in the Origin Pro program. The determined rate constants are presented in Figure 47.

While the first-order model was found to be a reasonable fit, the inspection of the residuals – the differences between the measured and the fitted values – revealed systematic deviations from the model. The data were reevaluated using the Avrami-Erofeev rate law:

nkte−−=1α

where α is the fractional conversion, and n is the index of the reaction. The index value was one of the fitted parameters. The rate constants obtained by applying the Avrami-Erofeev rate law are also plotted in Figure 47. This model greatly reduces the residuals; it is a closer fit. However, the trends are similar. Therefore the simple, first-order model was retained for convenience.

Figure 47. Kinetics of the CuO/Cu2O/CuO systems.

The 327 kJ/mol activation energy for the reduction was determined with the Arrhenius equation. This value is in good agreement with the 322 kJ/mol reported by Chadda et al. (1989), over the 760 – 910°C temperature range. Their oxidation rate results obtained at temperatures below 550 °C indicating faster reaction at increased temperatures are clearly different from the observations at the high temperatures.

825 850 875 900 925 9500.00

2.50x10-3

5.00x10-3

7.50x10-3

1.00x10-2

1.25x10-2

1.50x10-2

1.75x10-2

2.00x10-2

2.25x10-2

2.50x10-2

2.75x10-2

3.00x10-2

3.25x10-2

3.50x10-2

Reduction, Pseudo 1st order modelReduction, Avrami-Erofeev modelOxidation, Pseudo 1st order modelOxidation, Avrami-Erofeev model

Rat

e co

nsta

nt o

f α [s

ec-1]

Temperature [°C]

935°C

53

This discrepancy was addressed by investigating the oxidation of the Cu2O at lower temperatures as well (Figure 48). The determined rates of the reaction have a maximum at about 800 °C as a function of the temperature, indicating a dramatic change of the reaction mechanism.

Figure 48. The determined rate constants of the oxidation.

A further complication was identified by comparing the results obtained with the TA Q500 and TA Q600 instruments. The rate constants were found to be different depending on the TGA used to measure them. The raw rate constants are presented in Figure 48. Compensating the apparent rate constants for the different concentration of the air entering the TGA furnace is not sufficient to bring the rate constants into agreement. By considering the difference of between the sample holder pans of the TA Q500 and the cups used in the TA Q600, and normalizing the rate constants with the exposed surface area of the sample holder, the two sets of the experimental data were both brought into reasonable agreement as seen in Figure 49.

650 700 750 800 850 900 9500.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045 Q600 Q500

Rat

e of

Rea

ctio

n (d

Xox

/dt)

Temperature (oC)

54

Figure 49. The combined set of rate constants.

The oxidation of copper metal with air was investigated under elevated pressures. The rate of a “pure” chemical reaction is expected to depend on the amount of the not yet reacted copper metal and the concentration of the oxygen in the system as

2/12

}{}{OpCuk

dtCuOd

=

where k is the rate constant of the reaction, under the experimental conditions. A set of experiments was carried out under isothermal conditions and different pressures. The system was pressurized with nitrogen, then heated to 950°C. After the conditions stabilized the reactor gas was replaced with air. As shown in Figure 50, the reaction delayed after the gas was switched, and the time delay increased with increased pressure. The quick replacement of the reactor gas is not possible with the TherMax 500 instrument.

650 700 750 800 850 900 9500.00

0.01

0.02

0.03

0.04

0.05

Q600 Corrected Q500

Rat

e of

Rea

ctio

n (d

Xox

/dt)

Temperature (oC)

55

1000 2000 3000 4000 5000 6000 7000

100

105

110

115

120

125

1 atm 9 atm 16 atm 25 atm Theoretical yield

Mas

s [%

]

Time [s]

Figure 50. Oxidation of copper under isothermal conditions.

Figure 51 presents the pressure dependence of the reaction rate, using the initial slopes of the traces as a rate estimate. The experimental curve shows a slight decrease. The theoretical curve was calculated, based on the first point (atmospheric pressure) and the nominal concentration of oxygen, according to p1/2. These results are consistent with the TGA experiments reported by Garcia-Labiano et al. (2006). Their instrument was a TG-2151, an earlier model of the TherMax 500.

56

0 10 20 300.000.020.040.060.080.100.120.140.160.180.200.220.240.260.280.300.32

Initi

al ra

te [m

g/s]

Pressure [atm]

Experimental Theoretical

Figure 51. Pressure dependence of the initial rate.

0 200 400 600 80080

100

120

140

160

180

200

220

240 25 atm 16 atm 9 atm 1 atm blank 25 atm

Mas

s [%

]

Temperature [°C]

Figure 52. Non-isothermal oxidation of copper.

57

0 200 400 600 800 1000-10

-5

0

5

10

15

20

Mas

s ch

ange

[mg]

Temperature [°C]

Figure 53. Non-isothermal oxidation of copper after blank subtraction.

0 200 400 600 800 1000-10

-5

0

5

10

15

20

Mas

s ch

ange

[mg]

Temperature [°C]

Figure 54. Smoothed curve of data observed under 25 atm overlaid with the atmospheric result.

Another set of experiments was performed under non-isothermal conditions to elucidate the instrumental response function. In these experiments after the pressure was established with nitrogen, the reactor gas

58

was switched to air, and the system was purged for a long time (typically a day or more) at room temperature. This long purge was necessary to ensure that the reaction chamber was completely filled with air before the oxidation was carried out applying a linear heating program. The results are shown in Figure 52. The mass gains far exceed the theoretical 125% for full oxidation of copper. However, after subtracting the blank from the signal observed with the sample the result is reasonable, as indicated by the resulting – very noisy – curve in Figure 53. This curve shows the results of experiments at 25 atm pressure. The signal was smoothed with a percentile filter using the Origin Pro program. In Figure 54 the filtered data, under 25 atm, are overlaid by the corresponding curve obtained under atmospheric conditions. These experiments indicate the lack of pressure effect on the reaction.

CONCLUSIONS

The process modeling and economic subtask found that CLOU with a copper-based oxygen carrier holds promise for the combustion of solid carbonaceous fuels at possibly accelerated rates, which could aid in reducing the energy penalty for future coal power plants employing carbon capture. The possibility of energy recovery from both air and fuel reactors is an added advantage compared to a CLC. The estimates for operating conditions for the fuel and air reactors were calculated at a XCuO,FR = 0.3 and ΔXS of 0.45 using the developed criterion in conjunction with kinetic studies. An ASPEN model developed for carbon combustion and oxygen profiles in an idealized plug-flow reactor for equivalent U.S. Coal chars produced results that agree with literature findings on batch fluidized CLOU experiments on German lignite and Mexican petcoke.

The simulation subtask developed a general formulation to represent multiphase flows based on the population balance equation and DQMOM. This formulation can be used in CFD codes to represent the multiphase flows behavior in the context of LES. They gathered experimental data from the NETL-cold flow study, identified principal parameters that will be important for validation/uncertainty quantification, identified preliminary error bounds. The simulation results show qualitative agreement with experiments (NETL cold flow).

The lab-scale study subtask developed a novel method for deconvoluting product gas composition profiles from a lab-scale fluidized-bed chemical looping system and used this method to extract instantaneous reaction rates during oxidation and reduction of carrier materials. The rates and rate profiles compare favorably with rates measured in the TGA. This method was used to assess reaction rates for various carrier materials.

This subtask also evaluated one iron-based carrier (ilmenite) and three copper-based carriers for use in a commercial-scale CLC reaction system. The results indicated that the best of these for use in a fluidized-bed design of a conventional CLC type reactor is ilmenite. Ilmenite displayed better resistance to agglomeration and attrition than the 3 copper samples. Long-term testing (24 hours) not only revealed a resistance to deactivation but displayed enhanced reactivity after a few cycles or an initial activation period. However, ilmenite’s utility is limited only to gaseous fuels, since oxidation can only occur by

59

reducing the oxidized solid ilmenite. Direct processing of solid coal requires a carrier that will release gaseous O2 in the fuel reactor. The CuO/Cu2O complex will do this. However, pure copper is not an appropriate carrier material. Although reactivity is reasonable, copper agglomerates at very low temperatures in a fluidized-bed system. A supporting material such as porous titania or zirconia is necessary, but it is still desirable to maintain a high loading of CuO.

Both of the supported copper carriers showed some promise, but would not be suitable for a commercial system. The 50% CuO on TiO2 material was able to achieve high oxygen loadings, close to the theoretical limit, but suffered from significant attrition. The 13% CuO on Al2O3 material only displayed attrition problems during the first 2-3 loops (~2 hours of testing) after which the attrition rates decreased significantly. However, reactivity of this material was not anywhere near the theoretical limits because of the formation of copper aluminate making it an undesirable material. All the copper carriers did release gaseous oxygen under non-oxidizing conditions, as theory indicates they should.

The CLC kinetic studies of the CuO/Cu2O reactions identified the acceleration of the reduction with increasing temperature. The oxidation reaction rate was found to have a maximum at about 800 °C. This indicates a change in the rate-determining process above and below this temperature. Investigations of the oxidation of copper metal with air under elevated pressures revealed no evidence of the pressure influencing the rate of the reaction.

LIST OF PEER-REVIEWED PUBLICATIONS

Eyring, E.M. ; Konya, G.; Lighty, J.S.; Sahir, A.H.; Sarofim, A.F.; Whitty, K.J. Chemical Looping with Copper Oxide as Carrier and Coal as Fuel, Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, 2011,66,2, pp. 209-221

REFERENCES

Abad, A.; Adanez, J.; Garcia-Labiano, F.; de Diego, L. F.; Gayan, P.; Celaya, J. Mapping of the range of operational conditions for Cu-, Fe-, and Ni-based oxygen carriers in chemical-looping combustion, Chemical Engineering Science, 2007,62, 533 – 549.

Bartok, W.; Sarofim, A. (eds.), Fossil Fuel Combustion A Source Book, John Wiley and Sons, 1991.

Bergurand, N.; Lyngfelt, A. Chemical-Looping Combustion of Petroleum Coke Using Ilmenite in a 10 kWth Unit – High temperature operation. Energy & Fuels, 23:5257-5268 (2009).

Blair, D. W.; Wendt, J. O.; Bartok, W. Evolution of Nitrogen and Other Species During Controlled Pyrolysis of Coal. International Symposium (International) on Combustion (pp. 475-489). Elsevier 1977.

Bouillard, J.X.; Lyczkowsky, R. W.; Gidaspow, D. Porosity distribution in a fluidized bed with an inmersed obstacle. AICHE Journal 1989, 35 (6) 908-922.

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