chemical looping combustion and reactions

8/9/2019 Chemical Looping Combustion and Reactions

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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 Energy380 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 thatmerely undergoes a change in oxidation state, such as Fe3O4/Fe2O3and 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, andcupric 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 .......................................................................................................... 9Study of Char Burnout and O2Partial 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 TiO2support .................................................................................................. 45

13% CuO with Al2O3support (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 O2partial pressure.

................................................................................................................................................ 10

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

Figure 9. Process modeled to study coal char burnout and O2partial pressure profiles when the

partial pressure of O2at 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 MW twith variation in "XSof CuO. ..................... 29

Figure 18. Mass of copper loading per MWtvs mole ratio of CuO at the exit of fuel reactor for

different "XSvalues. .............................................................................................................. 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 aPocahontas 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 lignitemodeled 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 (90#m) at 600C. ........................................... 45

Figure 33. Oxygen consumption during oxidation of Cu2O for the 50 wt% CuO on TiO2material.

................................................................................................................................................ 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 TiO2under N2. ............................................................. 48

Figure 37. Arrhenius plot for reduction of 50 wt% CuO on TiO2between 650C and 850C. .... 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 850C. ............................................................................ 51


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Figure 41. CuO/Cu2O/CuO sequence, at 950C. ............................................................................ 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 ModelerCLOU 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

CO2stream 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 950C was

selected for operation for the fuel reactor and 850C 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 Cu 2O in the air reactor, 357 kW of energycould 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, equivalentU.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 800C. However, simple kinetic

models cannot explain this observation.

Simulation of fluidized beds. This report discusses the development ofa general formulation formultiphase 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

identifyingsensitive 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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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 CO2stream 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 (typicallyair) 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 CO 2effluent 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 CO2stream may be

generated.

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

(2n+ m)MexOy + CnH2m $ (2n+ m)MexOy1 + mH2O + nCO2

and the reaction between the air and the reduced metal

MexOy1 + 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 partialpressures 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


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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 CO 2was impractically long, requiring 50 minutes to reach

95% conversion at 950C for the injection of 0.2 g of coke in 20 g of fluidized carrier composedof 60% Fe2O3and 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/ZrO2at 955C 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 600C), (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 + %O2$2CuO (Occurring in Air Reactor)

2CuO $Cu2O + %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 O2concentrations, 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 916C 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% O 2, and the fuel

reactor temperature was 994C.

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 + O

2and 2Cu

2O + O

2$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 O2required 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 XCuOat 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 Cucirculation in the system,

(5)


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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,ARto an exit concentration of XCuO,FRis 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% O2to a log mean

concentration of O2. The log-mean concentration of O2was calculated by taking into account the inlet O 2

concentration of 21% and exit O2concentration 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 MW tof 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 X CuO,AR and XCuO,FR, or,alternatively XCuO,FR and "XS.


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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: 2Cu 2O + O2$4CuO. The effluent

O2concentration 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 950C, 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 850C. 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 studypresented 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 ofoxygen 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 O2through the boundary layer around the surface of the CuO and C particlesand the subsequent consumption of O2 at the C surface was developed. Figure 6 illustrates the process

modeled.

For the case when the release of O2from 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)


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Figure 6. Process modeled for the case when the CuO particle is at equilibrium O 2partial 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,


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(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, !,

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. !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 A Cand !, see Eq. 15), the

bulk oxygen partial pressure approaches the equilibrium oxygen partial pressure conditions.

The parameter !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 O2Partial 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. !.

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 O2partial pressure profiles when the partial

pressure of O2at 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 assumesthat 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 continuousphases 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 andM!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


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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 !!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 inGranular 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 solidsat the inlet

Particle surface area (reactive caseswith 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 anumerically 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 boundaryconditions 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 alsothe 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


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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 thepossibility 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 ofthe 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

800C the ilmenite does not completely oxidize:

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

Above 800C two reactions occur:

4 FeTiO3+ O2 $ 2 Fe2TiO5+ 2 TiO2

Fe2Ti3O9 $ Fe2TiO5+ 2 TiO2

where TiO2is rutile and Fe2TiO5(pseudobrookite) is the most stable phase (Cuadrat et al. 2009).


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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


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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 smaller2.5-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 650C to 950C. In order to achieve fluidized

conditions gas flow rates ranged from 1.5 SLPM to 5 SLPM. These flow rates correlated to U/Umfof

20-60 (where U is the superficial gas velocity and Umfis the theoretical minimum fluidization velocity).

For each test, 100 to 200 grams of carrier material was placed into the reactor. Enough material was usedto 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 5C 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/O2analyzer 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


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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.


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Figure 11. Example of data acquired from the fluidized-bed system. Reduction and oxidation of

CuO/Cu2O on TiO2using N2and air at 700C (left half) and 900C (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):

WhereFdenotes the collected data,Adenotes the actual data and Crepresents the convolution of the data.

The function Cmay be determined from residence time distribution tests, with data then fit to a

polynomial in similar fashion to the transformation ofFfrom a discrete array to a continuous function.

The resulting transformed equations may be rearranged as follows:


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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 800C 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.


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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 O2is

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 rangedown 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


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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.

!"#$%&'

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1"#2' 2%3 4$567%"3/

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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 m) and a cupric oxide powder (Johnson Matthey Chemicals,

having a determined particle size range of 1-10 m). 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.

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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 O2partial 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 MWtof

carbon introduced in the fuel reactor versus the difference in mole ratio "XS for different values of XCuO,FR

from relation (5).

A value of 0.45 is assumed for "XS, 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)(MW t) at a "XSof 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) (MW t) on a

support free basis is comparable to values reported in the literature of 2 kg CuO/(s)(MWt) (at "XS= 0.4)

for the combustion of petroleum coke using CLOU (Mattisson et al. 2009 b) and 1 kg CuO/(s)(MW t) for a

fuel gas (Abad et al. 2007).


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Figure 16. Mass (kg) of copper circulated per MW twith variation in "XSof 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 "XS. 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 "XSbecause of the first-order rate

equation as represented by equation (6).

The reaction times for the air reactor by contrast increase with increasing X CuO,FRat a fixed "XSas can be

noted from equation (7). A high value of "XS reduces the circulation rate of oxygen carrier, which also

helps in minimizing the oxygen carrier loading.

A value of XCuO,FR = 0.3 and "XSof 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/MW t.

Calculations reported in the literature indicate a minimum oxygen carrier loading of approximately 25 kg

CuO/ MWtfor combustion of methane and a 10% Cu, aluminium support, given the same "XS (Garcia-

Labiano et al. 2007). This is less than 1200 kg Fe2O3/MWtfor combustion of petcoke with a 60% Fe2O3

/ 40% MgAlO4oxygen-carrier (Leion et al. 2007) and 48 to 80 kg CuO/ MW t for combustion of

petroleum coke for a 40% CuO/ZrO2oxygen carrier operated in a CLOU mode (Mattisson et al. 2009b).


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Figure 17. Mass of copper loading per MWtvs. mole ratio of CuO at the exit of fuel reactor for different

"XSvalues.

Development of ASPEN PLUS Simulations

An illustrative simulation in ASPEN PLUS (Figure 18 and Figure 19) has been developed for theconversions of CuO discussed above and the combustion of carbon using an optimum circulation rate of

oxygen carrier (XCuO,FR = 0.3 and "XSof 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.


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Figure 18. Schematic of an ASPEN PLUS simulation.


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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 ASPENs built-in economics package to estimatecapital 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.


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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

955C. 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 usingglobal coal char kinetic data for Pocahontas coal.


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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.


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Figure 23 represents the comparison of the model with the experimental data (Mattisson et al. 2009b) for

the carbon consumption profiles at 985C. The carbon consumption profiles were obtained by integrating

the area under the curve for the products consumed, namely CH 4and CO2, for Mexican petcoke

(Mattisson et al. 2009b).

The simulation captures the trend of experimental data adequately. In the mathematical model developedin 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,


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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 949C can be observed in Figure 26.

Lignite is a carbonaceous fuel possessing a highe

chemical looping combustion and reactions

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