Oxidation

Article pubs.acs.org/EF

Determination and Comparison of CuO Reduction/Oxidation Kinetics in CLC Experiments with CO/Air by the Shrinking Core Model and Its Characterization Chiranjib Saha*,†,‡ and Sankar Bhattacharya† †

Faculty of Engineering, Department of Chemical Engineering, Monash University, Clayton Campus, Clayton, Victoria − 3800, Australia S Supporting Information *

ABSTRACT: The Shrinking Core Model (SCM) has been applied to determine and experimentally validate the reduction and reoxidation (redox) reaction kinetics of CuO in an in situ chemical looping combustion (CLC) process. This paper focuses on the determination of redox kinetics of CuO with one of the major coal gasification products i.e. carbon monoxide (CO) and air in a CLC process using a thermogravimetric analyzer (TGA). The comparison of the kinetic parameters of CuO obtained in CLC experiments, using CO and air as reducing and oxidizing atmosphere, respectively, with the predictions by the SCM model are also presented in this paper. The CuO particles are characterized in detail to obtain structural and elemental changes, due to their cyclic use in CLC experiments with CO/air, compared to the fresh particles. It has been observed that the reduction reaction control mechanism of SCM predicts very well the conversions of CuO during reduction in CO. However, the reoxidation of reduced CuO particles are governed by product layer diffusion controlled mechanism. Based on the kinetics data obtained from experiments, two generalized equations are formed for determination of reaction rate constant and effective diffusivity. A sensitivity analysis shows that both the reduction and reoxidation reactions respond equally to self-stabilize the system if the kinetic parameters are disturbed by ±5%. The CuO particles are characterized by scanning electron microscopy (SEM), energy dispersive X-rays (EDX), and X-rays photoelectron spectroscopy (XPS) to support the experimental results. A good agreement between predictions and the experimental values are achieved for all the cases studied. The maximum error percentage between predictions and experiments range within ∼(0.5−2)%. It is also interesting to notice that as the particle size increases, the reduction kinetic parameter error percentage increases, but (re)oxidation error percentage decreases. This supports the kinetic result that describes the reduction to be chemical reaction controlled and (re)oxidation to be diffusion controlled as an increase in the particle size generally moves the reactions toward diffusion controlled regime. Also the order of reaction, determined in this part, supports the assumption for application of SCM in this particular case. It is experimentally observed, by different solid characterization techniques, that the core of the CuO particles remain unreacted during a CLC process. Overall, it can be concluded that the shrinking core model is applicable to a CLC process. However, experimental validation with other oxygen carriers may be required.

1. INTRODUCTION The conversion of fuel based chemical energy into thermal energy in the traditional advanced thermal power systems such as combined cycle results in the largest irreversible losses related to the second laws of thermodynamics.1 A great amount of thermal and fuel NOX is released in this process. Also the separation of CO2 from conventional combustion products is highly energy intensive as it is diluted by N2 in air. To resolve these problems the reversibility of combustion2 is investigated, and a new concept called chemical looping combustion (CLC) is proposed3 where CO2 is separated inherently during combustion. The CLC system comprises of two reactors: an air reactor and a fuel reactor. In the fuel reactor, the fuel particles are combusted by the oxygen in oxides of some metal. The metal oxide particles, reduced during fuel conversion, are reoxidized in the air reactor. A schematic of the system is described by Saha and Bhattacharya.4 The reaction stoichiometry is defined by eqs 1 and 2. An advantage of this system is that a concentrated stream of nitrogen-free CO2 can be obtained from the fuel reactor after condensing the water vapor © 2014 American Chemical Society

(2n + m)M yOx + CnH 2m → (2n + m)M yOx − 1 + mH 2O + nCO2 M yOx − 1 + 1/2O2 → M yOx

(1) (2)

where M stands for metal, and MyOx represents metal oxide. Metal oxides play the most important role in CLC. Although several oxygen carriers are investigated for CLC, the focus of this work is application of CuO as it has certain advantages.5 It is exothermic in nature during combustion of fuel particles with high oxygen transport capability. It is also inexpensive. The reaction kinetics or conversion mechanism of metal oxides needs a detailed investigation to design, model, and scale up a CLC system. 1.1. Literature on Gas−Solid Reaction Models and Their Application to CLC. Some investigations were Received: February 2, 2014 Revised: March 31, 2014 Published: April 1, 2014 3495

dx.doi.org/10.1021/ef500306e | Energy Fuels 2014, 28, 3495−3510

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Figure 1. In situ CLC of coal.

investigated for iron based oxygen carriers. However, their oxidation kinetics in the presence of air was never examined. Although CuO based mixed oxygen carriers were investigated using different models, a comprehensive study of pure CuO is yet to perform with complete evidence of experimental validation during both reduction and reoxidation. A high operating temperature suitable for in situ CLC application is a most important parameter to determine the kinetics of reactions. Therefore, to address some of the issues with a reaction mechanism in a CLC process, CuO redox kinetics are determined in a CLC process using a TGA under CO/air as a reduction/(re)oxidation environment. A shrinking core model (SCM) is applied for the prediction of gas−solid reaction kinetics. CO is one of the major components of coal gasification products. The authors also investigated the in situ CLC of Victorian brown coals with CuO.18 During in situ CLC the gasified products (mainly CO and H2) react with the oxygen carrier in the bed directly. Therefore, such a model can enlighten a better understanding of the reaction kinetics in an in situ CLC process. The experimental validations of the SCM predictions are also performed, and the results are analyzed. The CuO particles are characterized by scanning electron microscopy (SEM), energy dispersive X-rays (EDX), and X-rays photoelectron spectroscopy (XPS) to support the experimental results. A detailed analysis of the experimental and characterization results is also performed to validate the assumptions of the SCM for further application in a CLC process. Finally fresh and combustion residues are characterized using SEM-EDX to provide morphological and surface elemental information on spent CuO.

performed to identify the reduction kinetics of metal oxides (mostly hematite and iron oxide containing minerals for their application in steel making6,7) with coal or other reducing gases such as CO and H2. Mathematical models (grain model, crackling core, or Ishida-Wen’s model) were used to predict the reduction kinetics of iron oxide particles7,8 in a noncatalytic gas−solid reactions process.9,10 However, none of these models were either developed or used to understand the reduction kinetics of iron oxides under CLC redox conditions. Ryu et al. first investigated the applicability of an unreacted core model for predicting the kinetics of reduction and oxidation of NiO/bentonite mixed oxygen carriers in a CLC process using CH4 as fuel in a thermogravimetric analyzer (TGA).11 Abad et al. applied a shrinking core model to determine the reaction kinetics for Cu, Fe, and Ni based metal oxides mixed with Al2O3 in CLC of gaseous fuels.12The authors used TGA for these purposes.13,14 However, these models used mixed metal oxide oxygen carriers, but the conversion mechanism of a pure metal oxide oxygen carrier may differ from a mixed one. Information on a pure metal oxide reaction mechanism is more necessary for a practical application point of view. Moreover, in these works rigorous experimental validation of the models was not performed. Son et al. applied a nucleation and growth process model to CuO impregnated by the Al2O3 oxygen carrier for chemical looping hydrogen (CLH) generation using a TGA.15 The model was described by Hancock and Sharp.16 However, this model was applicable to within a temperature range of 673− 923 K; it did not produce reasonable accuracy at higher temperatures. The intrinsic reduction kinetics of a CaSO4 based oxygen carrier with CO as fuel was also investigated using the shrinking unreacted core model by Xiao et al.17 However, the major focus was a metal oxide based oxygen carrier, not a nonmetallic oxygen carrier such as CaSO4. This is due to lack of lower heat transfer capability of nonmetallic oxygen carriers, and in CLC heat transport and balance between air and fuel reactors are also a very important aspect. Therefore, it can be observed from the above discussion that the reduction kinetics of solid−solid or gas−solid reactions was

2. REACTION MODEL 2.1. Basic Model − Unreacted Shrinking Core Model (SCM). In in situ CLC, the coal is first gasified, and the gaseous products then react with the metal oxides as shown in Figure 1. In general these gas−solid reactions follow different steps such as19 a) gas phase mass transfer of the gaseous reactant from the 3496


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bulk of the gas to the external surface of the solid particles, b) diffusion of the gaseous reactants through the pores in the solid, c) diffusion of the gaseous products through the pores out of the solid, d) gas phase mass transfer of the gaseous products from solid surface to bulk gas stream, and e) heat transfer from the gas stream to the solid particles. The reaction controlling steps in a gas−solid reaction19,20 clearly defines that for a smaller particle the reaction can be chemically controlled, whereas at high temperature it can be a combined effect of diffusion control and sintering effect. In between a zone exists where the reactions can be governed by mixed control mechanism. In CLC it is highly possible that during the redox operation the entire process can be dominated by both the control mechanisms as both the particle size and operating temperature are moderate in such a process. Therefore, the applicability of SCM to this particular problem of mixed control mechanisms has been investigated. The basic concept of this model is described in the literature.21 The concept of gas−solid reactions in this model assumes that the reactions proceed through a chemical reaction at the solid surface and diffusion of the gases through the product layer. The reactions first occur at the outer skin of the particle. The zone of reactions then move into the solid leaving behind completely converted solid material. As the time progresses during the reaction, the solid reactant inside the core of the particle shrinks in size. SCM is already applicable to burning of solid fuels such as coal and wood and seems to reasonably represent the reality. Therefore, we applied this model in this particular study with certain assumptions. 2.2. Assumptions. In this model the reactions are initiated at the external surface. As the reactions proceed with time the thickness of the product layer increases. Also the reacting surface separates the core of the reacting particles from outer product layer. In this way the solid inner core of the particles shrink but remain unreacted. Therefore, the concentration of the solid at the inner core is the same as the initial value. However, ideally it should be zero in the product layer. The gas−solid reactions in this instance can be represented by the reactions of CuO and CO as they are used as oxygen carrier and fuel during reduction of a CLC cycle. However, during reoxidation the reactions between reduced CuO and air represent the gas−solid reactions. The reactions stoichiometry is mentioned below CO(g) + CuO(s) = CO2 + Cu

(3)

1/2O2 (g) + Cu(s) = CuO

(4)

Therefore, with these assumptions the model is applied for kinetics determination and to find conversion over time. 2.3. Expression of Model for Kinetic Determination. Equations 3 and 4 can be generalized and written in the form of eq 5 as below a A(g) + b B(s) → Product

(5)

where a and b are the number of moles of gaseous and solid reactants, respectively. For diffusion through the product layer, the rate of reaction of A is given by the rate of diffusion to the reaction surface21 Therefore, −

dNA = 4πr 2Q A dt

(6)

where QA = flux of A through surface of any radius r. Then flux within the product layer can be expressed by Fick’s law and represented by eq 7 Q A = De

dCA dr

(7)

where De = effective diffusion coefficient. Combining eqs 6 and 7 and by integration from the radius of unreacted solid (R) to the radius of shrinking unreacted core solid (rc), time is obtained in terms of conversion (X) of solid. Equation 811 represents this time under a diffusion control mechanism. td =

C B0R p2 6bDeCA 0

[1 − 3(1 − X )2/3 + 2(1 − X )]

(8)

For a chemical reaction to occur −

a dNA b dNA =− = bkCA 0 4πrc 2 dt 4πrc 2 dt

(9)

Again by integration from radius of unreacted solid (R) to radius of shrinking unreacted core solid (rc), time is obtained in term of conversion (X) of solid. Equation 1011 represents this time under a chemical reaction control mechanism tc =

C B0R p bkCA0

[1 − (1 − X )1/3 ]

(10) 2

where De = diffusion coefficient (m /s), k = reaction rate constant (m/s), Rp = radius of metal oxide particles (m), CA0 = bulk concentration of gaseous reactant (kmol/m3), and CB0 = initial concentration of metal oxide particles (kmol/m3). CB0 can be calculated by the equation mentioned by Ryu et al.11 Therefore, the total conversion time can be expressed as tTotal = td + tc. The conversion prediction from this model is experimentally validated in this work under different conditions, and kinetic parameters including reaction rate constant k and diffusion coefficient De are calculated. 2.4. Logic of the Program Developed. Commercial package MATLAB Version 7 is used to develop a computer program based on the assumptions and expressions of the model. Different parameters varied in the model were the temperature, radius of the particle, gas concentration, and mass fractions in each of the cycles. The model can incorporate different physical and chemical parameters of the particles. The block diagram of the model developed is mentioned in Figure 2.

where g = gaseous reactant, and s = solid reactant. The following assumptions17,20 are mode, and the characterization analysis to support these assumptions will be presented later on in this paper. • The particles are spherical • The porosity of the particles are neglected • No influence of external mass transfer on the reaction • First order reaction−with respect to concentration of reactant gas • The reaction is isothermal • The sintering of the particles are not considered as the operating temperature was well below the fusion temperature of oxygen carrier • The side reactions are neglected as during experiments only CO was supplied as fuel during reduction and air during reoxidation with balance N2 3497


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Figure 2. Block diagram of the computer programming.

Table 1. Experimental Variables serial no.

variables

1

redox cycles

2 3 4 5

temperature (°C) gas flow rate (mL/min), CO−air corresponding gas concentrations to 3 (%), CO−air CuO particle size (μm)

values single cycle, 5 continuous cycles, 5 separate cycles using the same particle and the same experimental conditions 600, 700, 800, 850, 900, 950, 1000 3−8, 6−16, 9−24, 12−32 9−21, 16−34, 23−44, 28−51 average: 90, 95, 125, 200, 250

3.2. Chemical Looping Experiments. Tests were conducted using a Thermo gravimetric analyzer (TG-DTA/DSC, NETZSCH 449 F3) apparatus. The solid residue samples were collected carefully after each test for characterization and analysis. The details of the characterization processes are reported in section 3.3. The effect of consecutive reduction−oxidation cycles on the reactivity of CuO in CO−air was assessed in the TGA. During experiments the carrier particles were placed on alumina crucibles. In a single redox cycle, the particles were first heated to the operating temperature in an inert (N2) atmosphere followed by reduction in CO and reoxidation in air. In between the reduction and reoxidation step,

3. EXPERIMENTAL SECTION 3.1. Materials. The oxygen carrier used in this study is CuO powder with 99% purity. Different particle sizes, starting from 90 to 250 μm, are used in this study. The details are mentioned in Table 1. The bulk density of CuO is 1760 kg/m3, and the true or particle density is 6320 kg/m3. The same for Cu is 1620 kg/m3 and 8940 kg/ m3, respectively. During a cycle, the CuO was reduced by CO and reoxidized to its original state by air. The details of CLC experiments, different flow rates, and concentrations of CO and air used in experiments are described in section 3.2 and Table 1. 3498


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N2 was passed through TGA to minimize the chances of reaction between CO and air. In repeated and continuous cycle experiments, heating of the particles to the desired temperature followed the same procedure as mentioned above. However, once the operating temperature was reached, CO and air were passed through TGA alternatively with an inert gas (N2) step in between. The results of the repeated cycles are described in sections 4.1 and 4.2. Single redox cycle experiments are performed at different temperatures for determination of kinetic parameters, and these are compared with model’s prediction. The results are described in sections 4.3 and 4.4. For experimental validation of the predicted results, five such cycles were repeated to see the effect of variation of mass fraction in each cycle compared to the first cycle. Other different parameters varied for experimental validation were the concentration of reactant gases (CO during reduction and air during reoxidation) and CuO particle sizes. During a cycle, the sample was heated to an operating temperature at 10 °C/min heating rate and maintained at that condition for 1 h under reducing environment following which N2 was supplied for 10 min and after that air was introduced to oxidize the reduced particles for another hour. Experimental variables are summarized in Table 1. It should be noted here that 3 mL/min of CO flow or 9% CO concentration is used for CuO reduction kinetics determination in this part 1. The reason for this is the typical concentration of CO in brown coal gasification varies from 8 to 16% in the temperature range of 800−950 °C which is also our operational temperature range. This optimized flow rate of CO (and hence the concentration that is within the range mentioned above) chosen is based on the minimum requirements of protective and purge N2 (used as balance gas) flow in the TGA for this study. For experimental validation, we have used 16, 23, and 28% CO concentration for 6, 9, and 12 mL/min of CO flow, respectively. Similar CO fuel concentrations are used earlier12−14,17,23 in experiments with different oxygen carriers for practical applications where CO concentration in syngas may vary between 25 and 30% for different coals. This will help to predict experimental results in a practical system that may be designed for other CO constituents in syngas derived from different coals. All the experimental conditions are chosen based on bench or higher scale experiences for practical application. 3.3. Characterization of the Samples. The fresh and used particles under different experimental conditions are characterized by Scanning electron microscope (SEM), energy dispersive X-rays (EDX) and X-ray photoelectron spectroscopy (XPS). Different magnification ranges (e.g., 500×, 8000×, etc.) are used for SEM-EDX analysis. The EDX analysis is performed using Oxford Instrument’s INCA software. Spent CuO particles were also dipped into epoxy resin and then microtomed for characterization of the core of the used particles. It should be mentioned here that the polishing of the particles, before loading into microtomed instruments, was a very critical job. Many of the particles broke during polishing and trying to expose the central or core of the particles for analysis. However, we managed to get particles more than its half size.

Figure 3. Weight variation of CuO in a redox cycle at 800 °C in a TGA.

CO (9%) for 1 h of isothermal hold followed by flushing with N2 for 10 min and reoxidation of spent CuO in air (21%) for another hour. Once the first cyclic operation is over N2 is again passed through the TGA to make sure that air is not in contact with CO when the next reduction cycle starts. In this way the experiments are performed for five continuous cycles. The results are explained in Figure 4.

Figure 4. Weight variation of CuO in five continuous redox cycles at 800 and 950 °C.

Figure 4 shows the variation in weight of CuO in five continuous redox cycles operating at 800 and 950 °C. This two operating temperatures were chosen as it was observed in authors’ previous works18 in coal CLC with CuO that the best performance of CuO was achieved at 800 °C. The CuO particles after reduction in the presence of coal at 950 °C were not able to reoxidize to their original form. However, it can be seen from this figure that CuO loses a substantial amount of weight (8%) when initially heated to 950 °C under N2. This loss in weight is never regained in the rest of the cycles, although CuO was able to reoxidize almost up to 92% in each of the remaining 4 cycles. This loss in weight is because of the low sintering temperature of CuO that leads to structural changes at a relatively high operating temperature as described in an earlier work also.18 In addition to this, in the case of coal combustion, the mineral interaction with CuO affected its morphology severely, and the particles were never reoxidized.

4. RESULTS AND DISCUSSIONS The behavior of CuO during reduction in CO and reoxidation with air in five (5) continuous redox cycles at two different temperatures is discussed in this section. Also the kinetic parameters for CuO reduction and reoxidation, as investigated experimentally and predicted by SCM, are determined for single cycles at different temperatures. Both the single and multiple cycle experiments are repeated to ensure the reproducibility of the experimental data. 4.1. Weight Loss Comparison of CuO at Different Cycles and Temperatures. Figure 3 shows the weight variation of CuO (average size: 125 μm) in the first redox cycle of five continuous redox cycles. The operating temperature in this instance is 800 °C. Initially the oxygen carrier is heated to the operating temperature in N2 (100%) and then reduced in 3499


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also follows the same trend and cycle 1 is only plotted in Figure 5. It is clear that due to an increase in temperature the rate of conversion is relatively high in the case of 950 °C compared to 800 °C. The conversions during reoxidation are plotted in Figure 6. The oxidation conversion is defined by eq 12.

The weight loss of CuO due to the effect of CO in each cycle is around 16%. However, when CuO is heated in N2 up to 800 °C at the beginning of the first cycle very low weight loss is observed. At the first cycle the total weight loss during reduction of CuO in the presence of CO is 24%. During reoxidation in the first cycle the spent CuO is fully reoxidized to its original form. After that in progressive cycles the extent of reduction although remained very similar to the first cycle, the extent of reoxidation decreased and showed the same value to that of the operating temperature of 950 °C. This is because in the first cycle the temperature is ramped up from ambient to operating temperature and hence the structural changes in CuO are severe. Once these changes already occurred, the particles morphology is almost constant in remaining cycles as the operating temperatures are never changed in continuous cycle operating mode. Therefore, conversion of CuO in cycle 2 is less compared to cycle 1 but similar to cycles 3, 4, and 5. The loss in weight of CuO is 2% point more throughout all the cycles when operated at 800 °C compared to 950 °C. This leads to the conclusion that operating temperature does not play a severe role when CuO is used for syngas CLC but due to the presence of minerals in coals and interaction of CuO with these minerals at high temperature leads to deactivation of CuO particles as observed in an earlier work by the authors.18 4.2. Conversion Comparison of CuO at Different Cycles and Temperatures. Figure 5 shows the conversion

X = (W − WCu)/(WCuO − WCu)

Figure 6. Conversion of CuO during reoxidation at different redox cycles.

The notations in eq 12 are already mentioned above. The oxidation conversion of CuO during cycle one, when operated at 800 °C, is different from the other cycles. The conversion rate is relatively higher for this cycle. After that the conversion curves follow the similar trend for the remaining cycles. From Figure 4 it can be observed that the extent of reoxidation in cycle 1 is more, and after that it is almost stabilized in the remaining cycles. The structural changes in the first cycle, as mentioned above in section 4.1 and Figure 4, contribute to this effect. These observations are described in detail by identifying the rate of conversions. Similar to reduction conversion, the oxidation conversions in each cycle at 950 °C follow an almost similar trend. Also the rate of oxidation conversion at higher temperature is higher as expected. Figure 7 shows the rate of conversion of CuO at four different fractional conversions in different reduction cycles when operating at 800 °C. It can be seen from this figure that the rate of conversion is lowest for the second reduction cycle for all the fractional conversions. After that the rate of conversion is either increased by a little amount or is almost constant for all the four fractional conversions. It can be seen from Figure 4 that after the second cycle the extent of combustion and reoxidation decreases and this contributes to this low rate of conversion in the remaining cycles. The practical implementation of this result is that the solid inventory requirement in a CLC process will not always necessarily be determined based on the conditions at the start of a CLC operation. As described in this figure, the stability of operation is achieved after the second cycle, and hence the design criteria should not be fully based on initial conditions rather on the worst condition in a multiple cyclic process. But once the stability in cyclic operation is achieved, there is little variation in the rate of conversion as observed after the second cycle. Therefore, this information helps to determine

Figure 5. Conversion of CuO during reduction at different redox cycles.

of CuO during reduction with CO in five continuous cycles at 800 °C and in the first cycle at 950 °C. The reduction conversion, X, is defined as follows X = (WCuO − W )/(WCuO − WCu)

(12)

(11)

where WCuO = weight of CuO at the most oxidized state, WCu = weight of CuO in the most reduced state, and W = instantaneous weight of CuO as measured from TGA. It can be seen from Figure 5 that the reduction conversion and extent of conversion for all the cycles are similar at an operating temperature of 800 °C. This result supports the observation of Figure 4, where it is clearly seen that the extent of reduction in all the cycles follows a similar trend. Similarly, the extent of conversion of CuO at 950 °C for different cycles 3500


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Figure 7. The rate of conversion at 800 °C and different fractional conversions.

Figure 8. The prediction of temperature rise in the fuel reactor as a function of Δω.

the maximum deviation from an ideal operational case and hence is most important to engineering a design team for practical setup. It can also be noticed that the conversion rate decreases with the increase in conversion which is expected. A mass based conversion (ω) has already been defined in authors’ previous work and by Xiao et al.17 Based on this value of ω the solid circulation rate can be calculated using the following formula22

Assumption of this figure is an inlet temperature of CO to fuel reactor as 400 °C. Also the CuO temperature under reduction is 800 °C (as in experiments) and is used for this prediction. 4.3. Determination of Kinetic Data from Experiments and SCM. The effect of temperature on the reduction and reoxidation of the CuO oxygen carrier was investigated by varying the temperature as mentioned in Table 1. The concentration of CO and air during reduction and reoxidation were 9% and 21% by mass respectively with the remaining amount of N2. The experimental results were compared with the SCM predictions as described in section 2.3. Based on these experimental results and predictions, the kinetics of the reactions is established in this section, and these kinetic data are analyzed in section 4.4. It should be noted here that the starting points of the experimental conversions plotted for comparison with the predictions were those from which a significant conversion of the oxygen carrier started. Also the end points were those where the rate of conversions were almost stabilized. Figures 9 and 10 show the conversion comparison between experimental results and model’s prediction during reduction and reoxidation respectively at different temperatures in a single redox cycle.

mcir = (ωairm0)/Δω

(13)

where mcir = recirculation rate of oxygen carrier particles between air and fuel reactor, ωair = conversion in the air reactor, m0 = rate of oxygen transfer between two reactors, and Δω = extent of conversion between air and fuel reactor. Therefore, it can be concluded from eq 13 that the oxygen carrier recirculation between air and fuel reactor is inversely proportional to the extent of conversion between the two reactors. For a Δω value from 0.01 to 0.08, the mass circulation rate varies from 16 to 2 kg·MW−1.s−1. It is well-known that the reduction reaction of CuO is exothermic in a CLC fuel reactor. Also the sintering temperature of CuO is relatively low compared to other oxygen carriers. So a rapid increase in temperature in a fuel reactor can lead to severe sintering of the CuO particles. Thus, it is highly of interest to know the temperature rise in the fuel reactor to keep a control on oxygen carrier morphological changes. The temperature rise in the fuel reactor is dependent on the rate of circulation of bed material (mcir), which is connected to the mass conversion difference (Δω) of the oxygen carriers in between the two (air and fuel) reactors. The temperature change in the fuel reactor was calculated as a function of the change in mass-based conversion for CuO. The initial temperature of CuO in the fuel reactor is 800 °C, and the fuel inlet temperature is assumed to be 400 °C. This case is plotted in Figure 8. The practical implementation of this result is that as long as the difference in conversion between these two reactors can be kept at a minimum value, the rise in temperature in the fuel reactor will also be minimum. This will eventually mean that circulation between the two reactors should always be higher as much as possible. This figure is plotted for cycle one only. The results from other cycles also show very similar ΔT values.

Figure 9. Effect of temperature on oxygen carrier conversion during reduction. 3501


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Figure 10. Effect of temperature on oxygen carrier conversion during oxidation.

Figure 11. Arrhenius plot of reaction rate constants against temperature.

Figure 9 shows that the experimentally observed conversion of CuO during reduction in CO was best predicted by chemical reaction controlled SCM as described by eq 10. However, Figure 10 shows that the conversion of reduced CuO during reoxidation in fresh air was reasonably well predicted by the product layer diffusion control model described by eq 8. The symbols show the experimental results, and the lines indicate the predicted values by SCM in both Figures 9 and 10. Similar observations were reported by Ryu et al.11 in their work with NiO reduction by CH4. However, Xiao et al.17 reported that the reduction kinetics of CaSO4 is controlled by both the chemical reaction and the product layer diffusion mechanism. It can also be noted from Figures 9 and 10 that with an increase in temperatures more complete conversions are achieved with higher conversion rates. This is expected as higher temperature is expected to favor both the reduction and reoxidation reactions. The experimental results and predictions show this similar trend. However, due to limitation of CuO lower melting temperature the operating temperature of CuO is also limited, as described in section 4.1 and section 4.2. It should also be noted that the effect of temperature in reduction conversion is more pronounced compared to oxidation conversion. Moreover, for a particular temperature, the reduction conversion and conversion rate are higher compared to oxidation conversion as observed by both experimentally and model’s predictions. SCM predicted reduction and reoxidation conversions are based on chemical reaction control (eq 10) and diffusion control (eq 8) models, respectively. The Arrhenius plots of chemical reaction rate constants and effective diffusivity coefficients at different temperatures are shown in Figures 11 and 12, respectively. It can be observed from both of these figures that the k and De values at different temperatures during reduction and reoxidation, respectively, as predicted by a chemical reaction control model and a product layer diffusion control model, agree very well compared to the experimental results. The correlation coefficients of linear fittings (R2) are 0.989 and 0.986, respectively. The values of k and De are compared in Table 2. The experimental results and the predictions are used to express the chemical reaction rate constants and effective diffusivity coefficients in the following generalized forms.

Figure 12. Arrhenius plot of effective diffusion coefficients against temperature.

⎛ −4823.8 ⎞ ⎟m/s k = 3.50 × 10−2 exp⎜ ⎝ RT ⎠

⎛ −5137.9 ⎞ 2 ⎟m/s De = 2.73 × 10−7 exp⎜ ⎝ RT ⎠

R2 = 0.989

(14)

R2 = 0.986 (15)

The apparent activation energy (Ea) for chemical reaction and product layer diffusion were 4823.8 kcal/kmol and 5137.9 kcal/kmol, respectively. 4.4. Analysis of Kinetic Data from Experiments and SCM. The average error {[(XExp − XMod) × 100]/XExp, X is CuO conversion as defined in eqs 11 and 12, suffixes Exp and Mod denote experimental and SCM predicted values, respectively} in conversion between experimental values and model predictions for all the operating conditions are plotted in Figure 13 based on all the reactions kinetics determined in section 4.3. It can be observed that as the temperature increases the model predicts better the experimental results. Typically in a coal based CLC system, the operating temperatures of both the fuel and air reactor should be within 800 °C−1000 °C. Therefore, the practical implications of these models mean that it is very good to use these models to predict the kinetics within the temperature range of 800 °C−1000 °C with CuO when scaling up the CLC system. 3502


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Table 2. Kinetic Parameters at Different Temperatures Obtained from the SCM Model and Experimentsa reduction T (°C) 600 700 800 900 1000 a

CCO (kmol/m3) 1.27 1.14 1.03 9.44 8.70

× × × × ×

10−03 10−03 10−03 10−04 10−04

oxidation

k Exp (m/s) 2.10 3.00 3.70 4.20 5.20

× × × × ×

10−03 10−03 10−03 10−03 10−03

k Mod (m/s) 2.17 2.89 3.64 4.42 5.20

× × × × ×

T (°C)

10−03 10−03 10−03 10−03 10−03

600 700 800 900 1000

Cair (kmol/m3) 2.90 2.60 2.36 2.16 1.99

× × × × ×

10−03 10−03 10−03 10−03 10−03

De Exp (m2/s) 1.44 1.80 2.55 3.11 3.48

× × × × ×

10−08 10−08 10−08 10−08 10−08

DE Mod (m2/s) 1.41 1.91 2.45 3.01 3.58

× × × × ×

10−08 10−08 10−08 10−08 10−08

T = temperature, CCO = concentration of carbon monoxide, Cair = concentration of air, Exp = experimental values, Mod = modeling predictions.

However, for coal combustion in an earlier publication,18 the authors observed the best operating temperature for CuO is 800 °C, and hence experimental data of this temperature is only compared. Therefore, based on the comparisons made in this section, it can be concluded that the reduction reaction (eq 3) is controlled by chemical reaction resistances, and the oxidation reaction (eq 4) is controlled by product layer diffusion resistance. In order to determine the influence of the reaction control and product layer diffusion control mechanisms, a sensitivity analysis was performed for both the reduction (eq 3) and reoxidation (eq 4) reactions. The details of the sensitivity determination process are mentioned by Goldstein et al.23 Sensitivity values are determined for both these equations under five different operating temperatures. The reaction rate constants and the diffusivity coefficients are disturbed by ±5%. For each case the 90% conversion time (τ) is calculated, and sensitivity of reduction and reoxidation reactions (eqs 3 and 4, respectively) are calculated using the following two equations (SR = sensitivity of reduction, SO = sensitivity of oxidation). The results are plotted in Figure 15.

Figure 13. Average error between experimental and predicted values at each condition.

Xiao et al.17 in their work with CaSO4 concluded that the mechanism of reduction with CO is both controlled by the chemical reaction and product layer diffusion mechanisms. Therefore, the experimental conversion of CuO with CO is compared with the chemical reaction control mechanism (eq 10) and chemical reaction plus product layer diffusion control mechanisms (eqs 8+ 10). The result in the case of 800 °C is plotted in Figure 14. It can be seen from this figure that the predicted conversions deviate largely when product layer diffusion control is added with a chemical reaction control mechanism during the reduction process of CuO. The similar deviations can also be seen for other operating temperatures.

SR =

⎞ ⎛ ∂τ ⎞⎛ k ⎞ ⎛ τ1.05k − τ0.95k ⎞⎛ k ⎟⎜ ⎜ ⎟⎜ ⎟ = ⎜ ⎟ ⎠ ⎝ ∂k ⎠⎝ τ ⎠ ⎝ ⎝ 1.05k − 0.95k ⎠ τ (16)

⎛ ∂τ ⎞⎛ De ⎞ ⎛ τ1.05k − τ0.95k ⎞⎛ ⎞ De ⎟⎜ SO = ⎜ ⎟⎜ ⎟ = ⎜ ⎟ ⎠⎝ 1.05De − 0.95De ⎠ τ ⎝ ∂De ⎠⎝ τ ⎠ ⎝ (17)

It can be seen from Figure 15 that the sensitivity for both these reactions under all operating temperatures are negative

Figure 15. Oxygen carrier conversion sensitivity to ±5% change in reaction rate constants and diffusivity coefficients during reduction and reoxidations.

Figure 14. Experimental values of CuO reduction conversion vs two models. 3503


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for 90% conversion. A negative sensitivity implies that increasing the rate or diffusivity coefficient decreases the conversion time or vice versa. Therefore, to achieve 90% conversion during reduction, the optimum time for conversion can be achieved at higher reaction rates, but it can lead to a situation where the Δω (described in section 4.2) is higher between reduction and oxidation reactors leading to a higher temperature rise in fuel reactor. Therefore, it needs to be assured that both the reduction and oxidation reactions are equally sensitive at operating conditions to stabilize the thermal systems in a CLC reactor. From Figure 15, it can be concluded that the operating temperature of 800 °C can lead to such an operating regime where both the reduction and reoxidation reactions, with the kinetic parameters described in section 4.3, are equally sensitive to the external changes so as to having the potential of self-stabilizing the system. 4.5. Validation of the Model and Kinetic Parameters with Experiments. 4.5.1. Effect of CO and Air Concentration during Reduction and Reoxidation. Experiments are performed at three different sets of CO and air concentrations during three different single redox cycles operating at 800 °C. The CO and air concentrations are varied during reduction and reoxidation respectively as the following: Case 1−16% CO, 34% air Case 2−23% CO, 44% air Case 3−28% CO, 51% air In all the cases balance N2 is used. Figure 16A and B shows the oxygen carrier conversion with time at different concentrations during reduction and oxidation, respectively. The models used during reduction and oxidation are the chemical reaction control model and the product layer diffusion control model as identified and described in sections 4.3 and 4.4. It is clear from both the experiment and model’s prediction that increasing the concentrations increases the conversion and its rate significantly which is expected. However, as the CO and air concentrations during reduction and oxidation are increased from case 2 to case 3, an increase in conversion is relatively less compared to case 1 to case 2. In general both the reduction and oxidation models predict very well the experimental values with an error ranging from ∼1% to less than 0.5%. The error values are plotted in Figure 1 of the Supporting Information for all three cases. In order to obtain the reduction reaction order, the experimental conversion values of CuO in three different CO concentrations are used. The following relation between reaction rate (k) constant and CO concentration (CCO) is used for this purpose as described by Fermoso et al.24 k = k′ × C nCO

Figure 16. Oxygen carrier conversion comparison at different CO and air concentrations.

(18)

where k′ is a constant, and n is the order of reaction. The reaction rate can be calculated at a fixed temperature and different concentrations from experiments using the Fermoso et al.24 method. The slope of the log−log plot of eq 18 can give the order of reaction value. Figure 17 shows such kind of a plot with three different CO concentrations during reduction. The best fitted value of the curve gives a reaction order of 1.06 that indicates that the reduction reaction of CuO with CO (eq 3 of section 2.2) is of first order. Therefore, it is proved that the assumption of the first-order reaction, (4th assumption) mentioned in section 2.2 is correct. This calculated result is very similar to the result published by Xiao et al.17 where the order of reaction is 0.96.

Figure 17. ln k as a function of ln CCO to determine order of reaction.

4.5.2. Effect of Mass Fractions in a Series of Continuous cycles. Experiments are performed in five continuous cycles at 800 °C in 9% CO during reduction and 21% air supply during reoxidation. After each redox cycle the weight of the reoxidized CuO was noted down. A small amount of mass loss was observed in progressive cycles. Therefore, starting from cycle 2, the weight fraction or concentration of the CuO has been changed from its initial values. These weight fractions are calculated to be 0.96, 0.92, 0.91, and 0.90 for cycles 2, 3, 4, and 5, respectively. These are defined as follows: 3504


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Case 1 − mass fraction 0.96 (cycle 2) Case 2 − mass fraction 0.92 (cycle 3) Case 3 − mass fraction 0.90 (cycle 4) Case 4 − mass fraction 0.90 (cycle 5) The chemical reaction control and product layer diffusion control models (described in sections 4.3 and 4.4) during reduction and reoxidation are used to predict the conversion values with time and compared with experimental values when the weight fractions in every cycle changed as mentioned above. It can be seen from Figure 18 that the models predict the

4.5.3. Effect of Particle Size. Experiments are performed at 800 °C with 9% CO and 21% air supply during reduction and reoxidation, respectively, with four different particle sizes as mentioned in Table 1, section 3.2. The main idea of these experiments was to identify the limitation of the model’s prediction when particle sizes are varied. The four cases investigated here are as follows: Case 1 − average 90 μm CuO particle size Case 2 − average 95 μm CuO particle size Case 3 − average 200 μm CuO particle size Case 4 − average 250 μm CuO particle size It is expected that with an increase in particle size the conversions predicted by a model will deviate from the experimental values as the particle size plays a role in determining reaction mechanism control. The results of conversion between experiments and prediction are plotted in Figure 19A and B for reduction and reoxidation in CO and air, respectively.

Figure 18. Oxygen carrier conversion comparison at different initial weight fractions.

experimental results very well. Both the models’ and experimental values show that the conversion decreases by a small amount as the experimentally observed weight fractions decrease, but, in general the trends are very similar. The error percentages for different cases are mentioned in Figure 2 of the Supporting Information. A maximum error of 2% among all the cases examined can be observed. It should be noted here that to apply SCM the fresh particles of minimum porosity should be suitable at the first cycle, but as the particles are used in repeated cycles in a CLC process, the porosity will obviously increase due to structural changes. However, the results show that with repeated cycle operation also SCM predicts very well the conversion of CuO. Hence, it can be concluded that although the porosity of the fresh particles is at a minimum value, increasing porosity with repeated cycles does not increase the error of SCM prediction significantly.

Figure 19. Oxygen carrier conversion comparison at different particle size.

The models predict the experimental results well. However, it should be noted that the conversion decreases as the particle sizes increase for both the reduction and reoxidation. The error plots between experimental and predicted values are mentioned in Figure 3 of the Supporting Information. It is very interesting to note that as the particle size increases the reduction conversion error between the predicted and experimental value increases. 3505


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Figure 20. SEM images of fresh CuO particles at (A) 500× and (B) 8000× magnifications.

Figure 21. EDX spectra of fresh CuO particles.

which the authors claimed that reduction kinetics of a CaSO4 based oxygen carrier with CO as fuel are governed by both chemical reaction and product layer diffusion control. The behavior of CaSO4 (which is a nonmetallic oxygen carrier) may differ from a metal oxide oxygen carrier like CuO. Therefore, the heat and mass transfer to and from the core of the particle may vary between these oxygen carriers and that may contribute to the fact that CuO based reduction behavior is chemical reaction controlled, whereas the oxidation is diffusional controlled. However, in the case of the CaSO4 oxygen carrier, experimental evidence like behavior during oxidation or varying particle sizes are not provided in the literature,17 although it is thoroughly investigated in this work. Moreover, the operating temperature of Xiao et al.17 was 900 °C as opposed to our case of 800 °C which may also contribute to this difference in observation. 4.6. Characterization of Fresh and Used Oxygen Carrier Particles. Figure 20A and B shows the SEM images of fresh CuO at 500× and 8000×, respectively. It is clearly seen from this figure that the particles are in spherical shape, and almost no crack and fissures can be observed in the fresh particle. The higher magnification image also proves the fact that the porosity of the fresh particles is very low. Therefore, the first two assumptions, mentioned in section 2.2 to develop and apply the SCM, that the particles are spherical and porosity of the particles is neglected seem to be highly valid for applying this model. Figure 21 shows the EDX spectra of fresh CuO particles. It can be seen from this figure that only Cu and O are identified on the fresh particles as expected. The weight percentages of Cu and O are recorded to be 79.11% and 20.89%, respectively. These values are very similar to the theoretical values of Cu and O (Cu-79.88% and O-20.12%) in CuO.

The (re)oxidation conversion error between the predicted and experimental value decreases with increase in particle size. The predicted reduction reactions are governed by a chemical reaction controlled mechanism. However, we know that as the particle sizes increase, the reactions are more governed by diffusion controlled mechanisms. Therefore, this increase in error with increasing particle size in prediction of reduction conversion from experimental values is observed and expected. So it can be concluded that with particle sizes of more than 100 μm, reactions tend to be more product layer diffusion controlled during reduction reactions. The (re)oxidation predictions are based on a diffusion controlled mechanism, and also increasing particle sizes in experiments incline the resulting conversions more toward the diffusion controlled regime. Therefore, more interesting observations are noted when the error between experimental and prediction of oxidation conversion is analyzed. It can be seen from Figure 3 of the Supporting Information that as the particle size increases the error percentage decreases. It is obvious as the oxidation model is a product of a layer diffusion controlled model. As the particle size increases, the reactions are more diffusion controlled. So a good agreement between model and experimental results is observed. Therefore, it can be concluded that, as the particle size increases, both the reduction and reoxidation reactions of an oxygen carrier tend to be controlled by a product layer diffusion mechanism and justifies the importance of a diffusion control mechanism during oxidation. It is now experimentally proven and validated from the explanations of effect of particle sizes and Figure 14 in section 4.4 that CuO reduction in CO is clearly based on reaction control mechanism and its oxidation in air is a function of diffusion control mechanism. Although our results are different from the observations of Xiao et al.17 in 3506


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Figure 22. SEM images of used (A and B - 800 °C and C and D - 950 °C in five continuous cycles) CuO particles at (A) and (C) 500× and (B) and (D) 8000× magnifications, respectively.

Figure 23. EDX spectra of redox cycles used CuO particles at (A) 800 °C and (B) 950 °C.

and very less. Cracks and fissures have just started to develop as observed in Figure 22B with a circle. Figure 22D shows that the used particles have molten spots when operated at 950 °C for five continuous redox cycles. This is due to the fact that CuO particles have lower melting points,

Figure 22 shows the SEM images of CuO particles when used in five continuous redox cycles at 800 and 950 °C. It can be seen from this figure that the used particles also maintained their spherical shape. However, porosity of the particles used at 800 °C under redox experiments is similar to the fresh particles 3507


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Figure 24. SEM images of reduced CuO particle at 800 °C under CO (A) 500× and (B) 8000×.

Figure 25. EDX spectra of reduced CuO particles at 800 °C under CO.

Figure 26. EDX spectra of reduced CuO particles at 800 °C under CO − line scanning.

and for this reason an initial 8% loss in weight loss of CuO was observed as mentioned in section 4.1. Similar molten spots were observed by the authors in their previous works with CuO.4 Figure 23A and B shows the EDX spectrum of the same particles. It can be seen from Figure 23 that in the used particles only Cu and O are identified. However, it should be noted that in the 800 °C particles the percentage of weight of Cu and O

are 81.27% and 18.73%, respectively, which is little less compared to fresh particles. This is expected as in progressive cycles a very small amount of mass loss has been observed as discussed in section 4.5.2. and section 4.1. In case of 950 °C the O percentages dropped to 11.19% mainly because of initial mass loss as described in Figure 4, section 4.1. This similar value can also be seen in the author’s previous works.4 3508


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the same as explained by XPS results in Figure 4 of the Supporting Information.

Figures 24 and 25 show SEM images and EDX spectra for the reduced CuO particle under CO in one reduction cycle only. It can be seen from the SEM images that the cracks and fissures are more in the reduced particle compared to the fresh and redox particles described above in Figures 20 and 22. This result is expected as this oxygen carrier is never reoxidized, and therefore there was no chance for it to reach its original morphology. Hence the cracks are not filled up. However, the shape of the particle is spherical and it is well integrated. As this particle was not reoxidized, its oxygen weight percentage (O12.43%) has been reduced compared to the fresh and redox particles. This is reflected in Figure 25 when compared to Figures 21 and 23A. It can also be noted that as the particles are used in repeated cycles, the porosity changes and more cracks and fissures are developed. Therefore, a little amount of particles start to disintegrate. So small white granules are formed in the used particles as in Figures 22D and 24B compared to Figure 20. The line scanning result of the reduced particle at 800 °C under CO, after cutting the top surface and polishing it, is shown in Figure 26. The details of the particle preparation are described in section 3.3. The size of the particle was ∼100 μm. It should be mentioned here that the polishing of the particles was a very critical job, and many of the particles broke during polishing. However, we managed to get particles more than its half size, and this figure shows the line scan of half-length of the particle. It is very interesting to note down that in the reduced particle O element shows a peak at around 47 μm. This result is very significant from the point of view that at almost half of the particle length the CuO is not reacted and oxygen concentration shows a peak. That means the core of the particle from 42 to 51 μm remains unreacted. This result has been confirmed by examining at least five particles. Therefore, from a practical implementation viewpoint, it can be concluded that the shrinking core model can be highly applicable for predicting the oxygen carrier behavior in a CLC system as it is now proved that the center or core of the particles hardly reacts or remains unreacted. The XPS analysis results of fresh CuO, 800 °C redox particles and 800 °C reduced particles in CO also support the SEM-EDX analysis results. Figure 4 in the Supporting Information shows the XPS high resolution spectra for the three cases mentioned above. In all of the cases major elements identified are Cu and O. It should be noted here that for the fresh particles the CuO 2p3/2 area is 27.21% and the Cu 2p3/2 area is 27.51%. Therefore, the ratio is almost 1. In the case of redox particles, as mentioned in Figure 4B of the Supporting Information, the same ratio is 0.86 which is still close to the fresh CuO value. Therefore, it can be concluded that CuO is able to reoxidize to its original state and can be used in repeated cycles operation. This small amount of decrease is because of loss of oxygen in multiple cycles and supports the results described by EDX spectrum in Figure 23A. However, this ratio drops drastically to 0.21 in the case of reduced CuO particles under CO. Therefore, it is prominent that the oxygen has been released during CO oxidation from CuO. This result supports the SEM-EDX observations as described above where it is mentioned that the O percentage in reduced particles are less compared to fresh and redox particles. Also the porosity of the reduced particles are visibly more compared to the fresh and redox particles and the reason being

5. CONCLUSIONS The reduction and reoxidation of CuO in a CLC process using CO and air as reducing and oxidizing environment is tested experimentally using a thermogravimetric analyzer. The shrinking core model with certain assumptions has been used to predict the kinetics of CuO during single and multiple cycle redox operations with similar experimental conditions. The different parameters that have been varied for SCM predictions and experimental validation are temperature, CO/air concentration, mass fraction, and particle sizes of CuO in different redox cycles. CuO particles are also characterized in details for morphological information and to validate some of the assumptions which are used as the basis to develop and apply SCM in a CLC process. The following conclusions can be drawn based on this study. • It is observed that in the inert atmosphere if CuO is heated to an operating temperature of 950 °C, there is a weight loss of 8% which is never regained during reoxidation in the air process followed by reduction of CuO in CO. However, no such weight loss has been observed when the operating temperature was 800 °C. • The CuO particles after reduction in the presence of coal at 950 °C were not able to reoxidize to their original form as described in the author’s previous work. However, when used with CO, it still loses a substantial amount of weight, but it is able to reoxidize with contact in fresh air. Therefore, it can be concluded that when used with coal, mineral matters inherent in coal effects more in the performance of CuO at 950 °C. • Cycle 2 operates worst in terms of CuO conversion, but after that the CuO conversion is stabilized in progressive cycles. This information is important for engineering design to scaleup a practical system. • The difference in conversion between two reactors in CLC can be kept at a minimum value so that the rise in temperature in fuel reactor will also be minimum. This will not only help the CuO to be lower that its sintering temperature in an exothermic fuel reaction but also keep the CuO circulation between the two reactors as high as possible. • It is observed that the reaction control mechanism of SCM predicts very well the reduction kinetics of CuO. However, the reoxidation kinetics of reduced CuO particles are governed by product layer diffusion controlled mechanism. Overall, the predictions by the SCM model are within less than 1% error range as the operating temperature changes. • Two generalized equations are formed for determination of reaction rate constant and effective diffusivity for future applications. • A sensitivity analysis shows that at operating conditions of 800 °C both the reduction and reoxidation reactions respond equally to self-stabilize the system if the kinetic parameters of k and De are disturbed by ±5%. • A good agreement between predictions and the experimental values are achieved for all the cases studied. The maximum error percentage between predictions and experiments range within ∼(0.5−2)%. • One of the assumptions in SCM is the first order of reaction with respect to concentration of reactant gas. The order of reaction determined to be one in this study and validates the assumption in SCM for application in the CLC process. 3509


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• It is very interesting to note down that as the particle size increases the reduction conversion error between the predicted and experimental value increases, whereas the (re) conversion error decreases with an increase in particle sizes. As the particle size increases, the reactions are more diffusion controlled. It is obvious that a good agreement between model and experimental results of oxidation conversion will be achieved at higher particle sizes as the oxidation model is a product layer diffusion controlled model. • However, as the reduction reaction model is based on chemical reaction control, the error percentage increases with an increase in particle size. However, there is a limit of particle sizes until which the reduction reaction still shows chemical reaction controlled behavior, and for CuO this should be ≤100 μm average particle size. • The SEM images of the fresh CuO particles show that the particles are spherical in shape and have very less porosity. This information is highly relevant for application of SCM in CLC as this satisfies the two important assumptions of SCM. • The SEM images and line scanned EDX spectrum of core of the used CuO particles, that are polished and microtomed, showed that the O concentration is relatively high compared to Cu concentration in the central core of the particles. This indicates the fact that the core of the particle is unreacted. The basic of the shrinking core model is hence proved by this result. • XPS analysis quantifies the presence of O2 in fresh and used CuO, and it can be concluded that used CuO has less O2 compared to the fresh particles due to some loss in oxygen carrier in progressive cycles. This observation also strengthens the characterization results obtained by SEM-EDX analysis. • It can also be concluded that our experimental conditions are designed to represent the practical system so that a best judgment can be achieved between the two cases.

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(2) Ritcher, H. J.; Knoche, K. F. Reversibility of Combustion Processes, Efficiency and Costings; ACS Symposium Series 235: Washington, DC, 1983; pp 71−86. (3) Ishida, M.; Zheng, D.; Akehata, T. Energy Int. J. 1987, 12, 147− 154. (4) Saha, C.; Bhattacharya, S. Int. J. Hydrogen Energy 2011, 36, 12048−12057. (5) de Diego, L. F.; García-Labiano, F.; Gayán, P.; Celaya, J.; Palacios, J. M.; Adánez, J. Fuel 2007, 86, 1036−1045. (6) Sarkar, S. B.; Ray, H. S.; Chatterjee, I. J. Therm. Anal. 1989, 35, 2461−2469. (7) Bonalde, A.; Henriquez, A.; Manrique, M. ISIJ Int. 2005, 45, 1255−1260. (8) Valipour, M. S.; Hashemi, M. Y. M.; Saboohi, Y. Adv. Powder Technol. 2006, 17, 277−295. (9) Janowski, J.; Baranski, A.; Lagan, M.; Nedoma, J.; Sadowski, A. React. Solids 1990, 8, 103−114. (10) Kang, H. W.; Chung, W. S.; Muraymama, T.; Ono, Y. ISIJ Int. 1998, 38, 1304−1310. (11) Ryu, H.-J.; Bae, D.-H.; Han, K.-H.; Lee, S.-Y.; Jin, G.-T.; Choi, J.-H. Korean J. Chem. Eng. 2001, 18, 831−837. (12) Abad, A.; Adánez, J.; García-Labiano, F.; de Diego, L. F.; Celaya, P. G. J. Chem. Eng. Sci. 2007, 62, 533−549. (13) Abad, A.; García-Labiano, F.; de Diego, L. F.; Gayán, P.; Adanez, J. Energy Fuels 2007, 21, 1843−1853. (14) Adanez, J.; Cuadrat, A.; Abad, A.; Gayan, P.; de Diego, L. F.; ́ Garcýa-Labiano, F. Energy Fuels 2010, 24, 1402−1413. (15) Son, S. R.; Go, K. S.; Kim, S. D. Ind. Eng. Chem. Res. 2009, 48, 380−387. (16) Hancock, J. D.; Sharp, J. H. J. Am. Ceram. Soc. 1972, 55, 74−77. (17) Xiao, R.; Song, Q. Combust. Flame 2011, 158, 2524−2539. (18) Saha, C.; Zhang, S.; Xiao, R.; Bhattacharya, S. Fuel 2012, 96, 335−347. (19) Szekely, J.; Evans, J. W.; Sohn, H. Y. Gas-solid reactions; Academic Press: New York, 1976. (20) Marban, G.; Garcia-Calzada, M.; Fuertes, A. B. Chem. Eng. Sci. 1999, 54, 77−90. (21) Levenspiel, O. Chemical Reaction Engineering; Wiley: New York, 1999. (22) Mattission, T.; Jardnas, A.; Lyngfelt, A. Energy Fuels 2003, 17, 643−651. (23) Goldstein, E. A.; Mitchell, R. E. Proc. Combust. Inst. 2011, 33, 2803−2810. (24) Fermoso, J.; Arias, B.; Pevida, C.; Plaza, M. G.; Rubiera, F.; Pis, J. J. J. Therm. Anal. Calorim. 2008, 91 (3), 779−786.

ASSOCIATED CONTENT

S Supporting Information *

Figures 1−4. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 613-995-9805. Fax: 613-992-9335. E-mail: chiranjib. [email protected], [email protected]. Present Address ‡

Natural Resources Canada, CANMET Energy Technology Center (CETC) − Ottawa, Clean Electric Power Generation, 1 Haanel Drive, Ontario K1A 1M1, Canada. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the support of Brown Coal Innovation Australia (BCIA) for this work. Chiranjib Saha acknowledges Monash University for his scholarships. Teck Xin Seng is acknowledged for her help to develop the code. Also acknowledged is Dr. Ellen Lavoie of Monash center for Electron Microscopy (MCEM) for her support with the polished SEM-EDX samples preparation.

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REFERENCES

(1) Ishida, M.; Jin, H. Ind. Eng. Chem. Res. 1996, 35, 2469−2472. 3510


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