2lcombustion and reforming

eorg

Department of Chemical & Biomolecular Engi

a r t i c l e i n f o

Article history:

reserved.

oxygen carrier (OC) for chemical-looping combustion (CLC) [1].

Therefore, understanding of the intrinsic kinetics of NiO

reduction is crucial for designing efficient CLC systems.

on the gasesolid ki-

most studies are not

ed kinetics is limited

les, while in the CLC

environment the solid particles undergo repeated redox cy-

cles. Moreover, the experimental conditions, e.g., tempera-

ture, particle size and gas composition, are usually different

* Corresponding author. Department of Chemical & Biomolecular Engineering, University of Connecticut, 191 Auditorium Road Unit 3222,Storrs, CT 06269-3222, USA. Tel.: 1 860 486 4602.

E-mail address: george.bollas@uconn.edu (G.M. Bollas).

Available online at www.sciencedirect.com

ScienceDirect

w.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6Introduction

Research has shown that nickel oxide (NiO) is a promising

Although a breadth of literature exists

netics of NiO reduction and Ni oxidation,

applicable to CLC conditions. The publish

to the use of fresh or pre-treated particthe reaction kinetics of the oxygen carrier.

Copyright 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rightsReceived 22 January 2014

Received in revised form

11 March 2014

Accepted 22 March 2014

Available online 18 April 2014

Keywords:

Chemical-looping

Nickel oxidation

Nickel reduction

Oxygen carrier

Solid-state kineticshttp://dx.doi.org/10.1016/j.ijhydene.2014.03.10360-3199/Copyright 2014, Hydrogen Enere M. Bollas*

neering, University of Connecticut, Storrs, CT 06269-3222, USA

a b s t r a c t

Twenty solid-state kinetic models, including mechanisms dominated by reaction order,

geometrical contraction, diffusion, nucleation, random pore growth, as well as the more

flexible SestakeBerggren and ProuteTompkins models, are reviewed and applied to

describe the reduction of unsupported and supported NiO by H2 and the oxidation of

supported Ni by air. In an effort to address the inconsistencies in the literature regarding

the suitability of each mechanism to describe the kinetics of Ni-based oxygen carriers in

chemical-looping, all the models are compared against experimental data from the liter-

ature and with in-house experiments at conditions relevant to chemical-looping com-

bustion and reforming with Ni-based oxygen carriers. A statistical approach to compare

models of varying fidelity is employed, involving the Akaike Information Criterion and the

F-test. The effect of temperature on the selection of the best-suited model is investigated,

supplemented by experimental evidence from X-ray diffraction and scanning electron

microscopy analyses. This work reveals that unsupported and supported NiO reduction

can be described by nucleation and nuclei growth models. The oxidation kinetics of sup-

ported Ni is well-predicted by geometrical contraction models. Calcination and high-

temperature treatment during oxygen carrier synthesis are shown to significantly affectZhiquan Zhou, Lu Han, GKinetics of NiO reduction by Hconditions relevant to chemica

journal homepage: ww61gy Publications, LLC. Publand Ni oxidation at-looping

elsevier .com/locate/heished by Elsevier Ltd. All rights reserved.

Table 1 e Rate and integral expressions for different solid-state kinetic models.

No. Reaction model f(x) 1/k dx/dt g(x) kt nF1.5 Three-halves order (1x)3/2 2[(1x)(1/2) 1] 0.91F2 Second-order (1x)2 1/(1x) 1 0.83F3 Third-order (1x)3 (1/2)[(1x)2 1] 0.70R1 Zero-order (PolanyeWinger equation) 1 x 1.24

R2 Phase-boundary controlled reaction

(contracting area, i.e. bi-dimensional shape)

2(1x)1/2 1 (1x)(1/2) 1.11

R3 Phase-boundary controlled reaction

(contracting volume, i.e. tridimensional shape)

3(1x)2/3 1 (1x)(1/3) 1.07

D1 One-dimensional diffusion 1/(2x) x2 0.62

D2 Two-dimensional diffusion Valensi equation 1/[ln(1x)] (1x) ln(1x) x 0.57D3 Three-dimensional diffusion Jander equation 3(1x)(1/3)/[2(1x)(1/3) 1] [1 (1x)1/3]2 0.54D4 Three-dimensional diffusion GinstlingeBrounshtein 3/[2(1x)1/3 1] (1 2x/3) (1x)(2/3) 0.57AE1 First-order (Mampel) (F1) or AvramieErofeev (n 1) (1x) ln(1x) 1AE0.5 AvramieErofeev (n 0.5) (1/2)(1x)(ln(1x))1 (ln(1x))2 0.50AE1.5 AvramieErofeev (n 1.5) (3/2)(1x)[ln(1x)]1/3 (ln(1x))2/3 1.5AE2 AvramieErofeev (n 2) 2(1x)[ln(1x)]1/2 (ln(1x))1/2 2AE3 AvramieErofeev (n 3) 3(1x)[ln(1x)]2/3 (ln(1x))1/3 3AE4 AvramieErofeev (n 4) 4(1x)[ln(1x)](3/4) (ln(1x))1/4 4AEn AvramieErofeev n(1x)[ln(1x)](n1)/n (ln(1x))1/n nRPM Random pore model (1x)(1f ln(1x))1/2 x 1 exp[g(x)

(1 fg(x))]e

SB SestakeBerggren function xm(1x)n ePT ProuteTompkins x(1x) ln(x/(1x)) e

Table 2 e Summary of the NiO reduction and Ni oxidation kinetic studies in the literature.

Reference Support Reacting gas Temperature C Method Model

Jeangros et al. [17] Unsupported H2 300e600 ETEM AE

Jankovic et al. [11e14] Unsupported H2 Up to 500 TGA SB

Richardson et al. [15,16] Unsupported and

NiO/a-Al2O3

H2 175e900 In-situ XRD, TGA F1

Vogel et al. [78] Unsupported CO 1100 and 1400 Neutron diffraction, TGA R3

Plascencia and Utigard [18] Unsupported H2 350e1000 SEM, TGA R and D

Utigard et al. [19] Unsupported H2 550e1000 Optical micrograph, TGA R3

Chatterjee et al. [62] Unsupported H2 300e600 SEM, XRD, TGA R1

Syed-Hassan and Li [20,21] Unsupported and

NiO/SiO2

H2, CH4/C2H6 500e600 TGA e

Alizadeh et al. [79] Unsupported CH4 600e725 TGA R3

Rashed and Rao [80] Unsupported H2 230e425 SEM, TGA e

Lee and Kim [22] Unsupported H2 250e450 SEM, XRD e

Erri and Varma [23] Unsupported and

NiAl2O4

H2 300e600 TGA e

Rashidi et al. [81] Unsupported CH4 600e750 XRD, TG-MS RPM

Ryu et al. [26e28] Bentonite CH4, H2 650e1000 TGA R3

Air 850e1000 D4

Siriwardane et al. [82] Bentonite CO, CO2/H2 800 TGA, MS e

Ishida et al. [29e31] Al2O3, NiAl2O4,

MgO and YSZ

CO, H2, CH4 600e1000 TGA, SEM, XRD R3

YSZ Air 800e1000 R3 or D4

Dueso et al. [24,25] a-Al2O3 and g-Al2O3 CH4/H2O, H2, CO 950 TGA R3 or D4

Air 800e950 R3

Chen and Shiue [35] g-Al2O3 H2 600, 700 TGA R3

Abad et al. [36,63] a-Al2O3 Syngas 800e1000 TGA, SEM R3

Air 800e1000 R3

Moghtaderi and Song [34] Al2O3 CO, CH4 500e800 TGA R3

Mattisson et al. [32,33] MgAl2O4 CO, CH4 800e1000 TGA R3

Air 800e1000 R3

Hossain et al. [37e39] g-Al2O3, La-g-Al2O3,

a-Al2O3

H2, CH4 25e750 SEM, XRD, TPR, TPO AE1

Son and Kim [41] Bentonite,

TiO2, Al2O3

CH4 650e950 SEM, TGA AE1

Air 650e950 R3

Readman et al. [42] NiAl2O4 H2, CH4 800 In-situ XRD, TGA e

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68536

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8537than those required for CLC [2]. Finally, the kinetics of NiO

reduction andNi oxidation is often derived from free bulk NiO;

whereas, in CLC NiO is dispersed on a support, which in-

creases the active surface area andmechanical strength of the

oxygen carrier. Thus, limited information can be extracted

from the available reactivity data to determine NiO/Ni reac-

tivity with certainty at CLC-relevant conditions, which is a

necessary prerequisite for design purposes.

Generally, the detailed kineticmechanisms involve a group

of individual chemical steps, by which a reactant is converted

into a product. However, the information on chemical steps is

usually difficult to obtain. Therefore, in solid-state kinetics,

the kinetic mechanism study usually involves application and

verification of a reasonable model to the experimental data.

Ideally, the selection of a best-suited solid-state reaction

Fig. 1 e Scheme of (a) reaction order model; (b) geometrical cont

model applicable to a NiO crystallite.

Table 3 e Experimental data used in this work.

Reference [18] [19] [62] [

H2 vol.% composition 35 H2 in Ar 32/80 in Ar e 15 in

Gas flow ml/min 500 500 200 36

NiO type Tokyo Ni,

Sinter 75

Goro Ni NiO powder

and pellet

40wt

NiO/

Temperature (C) 800e1000 950 600 950model is supported by other complementary techniques, such

as microscopy, X-ray diffraction (XRD), scanning electron

microscopy (SEM), etc. [3]. Mechanisms for solid-state re-

actions include reaction-order models (F), geometrical

contraction models (R), diffusion-limited models (D), nucle-

ation models (AvramieErofeev, AE) [4e7], the random pore

model (RPM) [8], and the SestakeBerggren (SB) [9] and

ProuteTompkins (PT) [10] models. Table 1 presents all these

models, as they have been adopted in this work, along with

their naming and formulas. The principal assumptions and

physical meaning of these models are discussed briefly in

Section Solid-state kinetic model and mechanisms.

Table 2 summarizes the relevant kinetic studies on NiO

reduction and Ni oxidation in the literature. The most signif-

icant observations are discussed here to illustrate the lack of

racting model; (c) diffusion model and (d) AvramieErofeev

63] [35] [25] [30] This work

N2 e 15 in N2 e 4 in Ar

80 416 100 240

%

a-Al2O3

15wt%

NiO/g-Al2O3

18wt%

NiO/a-Al2O3

60wt%

NiO/YSZ

20wt% NiO/

g-Al2O3, a-Al2O3700 950 600 600, 800, 950

consistency in previous analyses. Jankovic et al. [11e14] used

the SB model to describe the reduction of NiO in a thermog-

ravimetric analyzer (TGA). Richardson et al. [15,16] used in-

situ hot-stage XRD and observed that the R3 model cannot

describe the reduction of NiO. By examining NiO powders in

an environmental transmission electron microscope (ETEM)

in the presence of H2, Jeangros et al. [17] proposed that the AE

model with the rate of nuclei growth as the rate determining

step describes the experimental data accurately. Plascencia

and Utigard [18] concluded that reduction kinetics of Sinter 75

and Tokyo NiO highly depends on the morphological features

and the R3 model is not suitable, based on SEM observations.

Instead, they proposed a mixed controlling mechanism with

the R and D models as more suitable. Optical microscopy ob-

servations led to the conclusion by Utigard et al. [19] that the

R3 model represents the kinetics of the reduction of Goro NiO.

By comparing the differences in the reduction behavior of bulk

NiO and silica-supported NiO, Syed-Hassan and Li [20,21]

related the incubation period of the Ni reduction with the

formation of radical species. Lee and Kim [22] studied the ki-

netics of H2 reduction of nanocrystalline NiO, concluding to

the dominance of reaction control at lower temperatures,

whereas diffusion control dominates at higher temperatures,

which was postulated to be the result of structural changes

with temperature [22]. Erri and Varma [23] published a kinetic

study of NiO and NiO/NiAl2O4 and concluded that the in-

consistencies in the literature value of the activation energy

are due to a lack of understanding and consideration of

diffusional effects.

Extensive studies on the reduction kinetics of supported

NiO have also been published (Table 2), among which studies

on Al2O3 (a- and g-Al2O3) are prominent. Dueso et al. [24,25]

Fig. 2 e Plot of the Hancock and Sharp method for the

Tokyo NiO reduction by H2 at 800, 900 and 950 C;experimental data from Plascencia and Utigard [18].

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68538Fig. 3 e Comparison between the experimental data (a, b, c) e x

Tokyo NiO reduction by H2 at 800 C (a, d), 900 C (b, e) and 950

[ f(t), (d, e, f) e dx/dt [ f(x) and model predictions for the

C (c, f); experimental data from Plascencia and Utigard [18].

Table 4 e Regression statistics and statistical comparison of reduction of Tokyo NiO; experimental data from Plascenciaand Utigard [18].

Method Model 800 C 900 C 950 C

RSS AICc RSS AICc RSS AICc

dxdtx F1.5 0.067884 223.7 0.089130 278.0 0.141363 145.9R3 0.011934 286.3 0.019412 346.6 0.024667 194.8D4 0.019973 267.8 0.030008 327.0 0.043327 179.0AE1.5 0.009895 293.1 0.015849 355.7 0.021718 198.4AEn 0.005164 314.2 0.006023 397.1 0.009001 220.7SB 0.004800 314.5 0.005635 397.8 0.008226 220.7

xt F1.5 0.182564 200.7 0.241917 245.6 0.179641 172.1R2 0.024517 277.0 0.039914 330.3 0.024786 241.3R3 0.025124 276.1 0.034118 337.6 0.019525 248.1D1 0.327074 178.6 0.324831 231.7 0.217014 165.8AE1.5 0.039004 259.4 0.038842 331.5 0.030443 232.2AEn 0.010447 308.0 0.008512 400.7 0.003586 309.4SB 0.018347 307.9 0.017247 365.2 0.004851 302.4

Temperature (C) Method Cases compared F-ratio Upper quantile Winner model

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8539800 dxdtx AEn/AE1.5SB/AE1.5

SB/AEn

xt AEn/R2SB/R2

SB/AEn

900 dxdtx AEn/AE1.5SB/AE1.5

SB/AEnselected the R3 and D4 models for Ni reduction and oxidation

kinetics of NiO and NiAl2O4 supported on Al2O3. Ryu et al.

[26e28] investigated Ni/bentonite oxidization and NiO/

bentonite reduction kinetics, concluding to the superiority of

R3 and D4 models for reduction and oxidation, respectively.

The R3 model was also found satisfactory in studies by Ishida

et al. [29e31], Mattisson et al. [32,33], Moghtaderi and Song

[34], Chen and Shiue [35] and Abad et al. [36] for the reduction

xt AEn/R3950 dxdtx AEn/AE1.5

SB/AE1.5

SB/AEn

xt AEn/R3

Fig. 4 e (Left) Plot of the Hancock and Sharp method; comparison

dt [ f(x) and model predictions for the Sinter 75 NiO reduction

Utigard [18].31.15 4.13 AEn (n 1.28)17.51 3.28 SB

2.50 4.14 AEn (n 1.28)50.13 4.11 AEn (n 1.26)26.99 3.27 SB

2.19 4.12 AEn (n 1.26)70.15 4.07 AEn (n 1.26)38.06 3.22 SB

2.89 4.07 AEn (n 1.26)of NiO on various supports. However, it was suggested by

Adanez [2] that the R3 model is not suitable for describing the

reduction of oxygen carriers of small size and high porosity.

Also, Hossain et al. [37e40] found that their experimental data

can be better matched by the nucleation and nuclei growth

model with a random nucleationmechanism (the AE1model).

The AE1 model was also selected by Son and Kim [41] to fit

their NiO reduction data. Readman et al. [42] used in-situ

135.37 4.06 AEn (n 1.28)36.73 4.23 AEn (n 1.26)20.50 3.39 SB

2.36 4.24 AEn (n 1.26)182.14 4.16 AEn (n 1.26)

between the experimental data: (middle) x[ f(t), (right) dx/

by H2 at 700 C; experimental data from Plascencia and

to systems where the rate is controlled by the transport of

Table 5 e Regression statistics and statistical comparison of the reduction of Sinter 75 NiO; experimental data fromPlascencia and Utigard [18].

Method Model RSS AICc Method Model RSS AICc

dxdtx F1.5 0.006442 241.8 xt F1.5 0.044887 208.1R3 0.011268 225.6 R3 0.024093 228.0D4 0.018207 211.7 D1 0.109718 179.5AE1 0.002557 268.6 AE1 0.002196 304.7AEn 0.001349 284.8 AEn 0.001596 312.6SB 0.001348 282.4 SB 0.001610 309.9

Temperature (C) Method Cases compared F-ratio Upper quantile Winner model

700 dxdtx AEn/AE1 24.19 4.21 AEnSB/AE1 13.91 3.37 SB

SB/AEn 2.39 4.23 AEn (n 0.93)xt AEn/AE1 11.27 4.17 AEn (n 0.95)

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68540powder XRD to study NiO supported on NiAl2O4, showing that

the NiAl2O4 support remained inert during the redox process.

In summary, the lack of agreement between the various

analyses is striking. The variability of the rate mechanisms

adopted could be a consequence of variations in the experi-

mental conditions or in the morphology of the Ni/NiO sam-

ples. Kruggel-Emden et al. proposed new empirical

correlations for the reduction reactions of CaSO4 and Mn3O4and oxidation reactions of Cu, Ni, and MnO, which achieved

better fits compared to the common kinetic models [43]. The

present work aims to analyze statistically and experimentally

solid-state models that describe the reduction and oxidation

of unsupported and supported NiO and Ni, which was not

studied as comprehensively by Kruggel-Emden et al. [43]. In

Sections Solid-state kinetic model and mechanisms and

Model discrimination and identification methods, the

methodologies used for modeling and statistical discrimina-

tion are briefly discussed. Section Reduction of unsupported

NiO statistically analyzes solid-state models applied to

describe H2 reduction of unsupported NiO of different origins

and morphology from the literature. In Section Reduction of

supportedNiO, reduction experiments of supportedNiO byH2from the literature and in-house TGA tests are examined. The

best solid-state reaction model is selected for each case

through statistical comparison of the capability of eachmodel

to match the experimental data. The effect of temperature inFig. 5 e (Left) Plot of the Hancock and Sharp method; comparison

dt [ f(x) and model predictions for Goro NiO reduction by H2 atreactants and products to and from the active interface [45].

Nucleation and nuclei growth models (AE) involve the for-

mation and growth of nuclei, where the reaction occurs. In

addition to the basicmodels of Fig. 1, the ProuteTompkins (PT)

model is an autocatalysis model, in which the nucleithe range of 600e950 C on selected kinetic models is inves-tigated, supported experimentally by scanning electron mi-

croscopy (SEM) and in-situ hot-stage XRD. Analysis of the

oxidation kinetics of supported Ni is presented in

Section Oxidation of supported NiO.

Solid-state kinetic model and mechanisms

Fig. 1 presents the schematic description of the models of

Table 1; commonly categorized as reaction-order (F),

geometrical contraction (R), diffusion (D), and nucleation

(AvramieErofeev, AE). The same ordering and categorization

was followed in Table 1. In summary, the reaction order-based

models (F) assume a homogeneous reaction process. Mecha-

nisms R2 and R3 of Table 1 are forms of the classic shrinking

core model with phase-boundary reaction control of different

geometries [44]. The R2 model represents two-dimensional

growth/shrinkage and the R3 model is for three-dimensional

growth/shrinkage. Diffusion models (D1eD4) can be appliedbetween the experimental data: (middle) x[ f(t), (right) dx/

950 C; experimental data from Utigard et al. [19].

complex crystallization processes involving partial nuclei

Table 6 e Regression statistics and statistical comparison of the reduction of Goro NiO; experimental data from Utigardet al. [19].

Method Model RSS AICc Method Model RSS AICc

dxdtx F1.5 0.022066 165.6 xt F1.5 0.004685 222.0R3 0.295076 103.4 R3 0.129212 135.7D4 0.457915 92.8 D2 0.046563 162.3AE1 0.073627 136.7 AE1 0.038590 167.2AEn 0.018390 167.6 AEn 0.003939 224.1SB 0.016435 167.7 SB 0.003556 224.2

Method Cases compared F-ratio Upper quantile Winner model

dxdtx AEn/F1.5 4.40 4.30 AEn (n 0.92)SB/F1.5 3.60 3.47 SB

SB/AEn 2.50 4.32 AEn (n 0.92)xt AEn/F1.5 4.55 4.26 AEn (n 0.87)

SB/F1.5 3.65 3.42 SB

SB/AEn 2.48 4.28 AEn (n 0.87)

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8541overlapping and growth steps [14]. Most of themodels of Table

1 contain one fitting parameter (the rate constant, k). How-

ever, some models are more elaborate and allow for better fit

of the data. Based on the number of independent variables,

thesemodels are grouped into one-parameter, two-parameter

and three-parameter models. In this work, only the Avrami-

eErofeev model (with the exponent n, AEn) and the random

pore model (RPM) contain two parameters. TheSestakeBerggren (SB)model contains three fitting parameters.

All the other models of Table 1 are one-parameter models.

Model discrimination and identification methods

Common approaches of evaluating the kinetic parameters of a

reaction include isoconversion and isothermal reactivityformation and chain branching rate constants are equal [46].

The random pore model proposed by Bhatia and Perlmutter

[8,47], was developed by taking into account pore growth

during the reaction, on the basis of a population balance

concept. Finally, the SB kinetic model is suitable for moreanalysis methods [14]. The former encompass the stationary

point method [48], Kissingers method [49], Friedman method

Fig. 6 e (Left) Plot of the Hancock and Sharp method; comparison

dt [ f(x) and model predictions for NiO pellet reduction by H2 aconversion, and (c) the fit quality of the transient time deriv-

ative of conversion.

Model-free method

Hancock and Sharp [56] proposed a simple method for the

identification of kinetic mechanisms of isothermal solid-state

reactions, on the basis of the nucleation model expressed as:

ln ln1 x ln a n ln t; (1)where x is the solid conversion, t is the reaction time, a is a

constant that relies on the frequency of nuclei formation and[50], FlynneWalleOzawa method [51,52], Kissin-

gerseAkahiraeSunose (KAS) method [53] and Maleks

method [54]. In this study, the isothermal method was chosen

because CLC is typically operated within a rather narrow

temperature range [2]. Additionally, as shown in previous

work, intraparticle heat gradients are negligible, and thus the

particle can be accurately approximated as isothermal [55].

Threemethods are used to compare several solid-state kinetic

models against isothermal experimental results: (a) the Han-

cock and Sharp method [56], (b) the fit quality of the transientrate of crystallite growth and n is the so-called Avrami-

eErofeev exponent [57].Their method includes plotting

between the experimental data: (middle) x[ f(t), (right) dx/

t 600 C; experimental data from Chatterjee et al. [62].

ln(ln(1x)) vs. ln(t), whereby the slope n reveals the most- Model-fitting method

Table 7 e Regression statistics and statistical comparison of the reduction of NiO pellet; experimental data from Chatterjeeet al. [62].

Method Model RSS AICc Method Model RSS AICc

dxdtx F1.5 0.041632 187.7 xt F1.5 0.319052 134.2R2 0.016688 214.2 R2 0.045019 192.9D4 0.018280 211.6 D1 0.267419 139.5AE1.5 0.013583 220.2 AE1.5 0.029748 205.3AEn 0.013032 219.1 AEn 0.029503 203.3SB 0.013346 215.9 SB 0.035615 195.2

Method Cases compared F-ratio Upper quantile Winner model

dxdtx AEn/AE1.5 1.14 4.21 AE1.5xt AEn/AE1.5 0.23 4.20 AE1.5

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68542suited kinetic model. Hancock and Sharp [56] proposed that

kinetic data which follow models other than nucleation also

give rise to approximately linear plots for ln(ln(1x)) vs. ln(t),if the range of conversion is limited to values from 0.15 to 0.5.

In this conversion range, the kinetics is not affected by the

experimental uncertainty and error at t 0 or by geometricalfactors [56]. According to Hancock and Sharp [56], the corre-

sponding values of n for models other than the AE are shown

in Table 1. These slopes can be used as a diagnostic tool to

preliminarily discriminate between the reaction mechanisms

of Table 1. For instance, when n < 1, the mechanism is

diffusion-controlled and when n z 1, the mechanism ap-

proaches phase-boundary control. However, sometimes it is

difficult to distinguish between models solely on the basis of

the Hancock and Sharp method. For instance, the four diffu-

sionmodels of varying order all exhibit slopes of 0.5e0.6. Also,

non-conclusive is often the Hancock and Sharp analysis when

n z 1, which indicates suitability of the R3, F1.5 and AE1

models. Therefore, this method cannot be used to discrimi-

nate a group of standard models with linear plots of similar

slopes, and it is preferable to find the best fit model by plotting

dxdt vs. x and x vs. t over a wider range of x and t (model-fitting

method) [56], as discussed in Section Model-fitting method.Fig. 7 e (Left) Plot of the Hancock and Sharp method; comparison

dt [ f(x) and model predictions for NiO/a-Al2O3 reduction by H2The rate of solid-state reactions may be controlled primarily

by the rate of transport to the reaction sites or by the rate of

reactions at the active site. In the latter case, the rate of a

solid-state reaction can be generally described by:

dxdt

A expEaRT

fx; (2)

where f(x) is the reaction model, and x is the solid conversion,

defined as:

x m0 mtm0 mN ; (3)

where m0 is the initial weight, m is the weight at time t, mN is

the final weight of the solid. Therefore, the integral of the re-

action model can be defined as:

gx Zx

0

dxfx (4)

The f(x) and g(x) expressions of the kinetic mechanisms

considered in this study for the solid-state reactions are

shown in Table 1. The basic procedure here is to utilize thebetween the experimental data: (middle) x[ f(t), (right) dx/

at 950 C; experimental data from Abad et al. [63].

Fisher distribution calculated for two models with different

Results and discussion

This study uses 20 different solid-state reactionmodels (Table

1) to match experimental data obtained from the literature

and this work. The models in Table 1 include 17 one-

parameter models, 2 two-parameter models (RPM and AEn)

and 1 three-parameter model (SB). Reduction data of 10 NiO

Table 8 e Regression statistics and statistical comparison of the reduction of NiO/a-Al2O3; experimental data from Abadet al. [63].

Method Model RSS AICc Method Model RSS AICc

dxdtx F1.5 6.258243 55.4 xt F1.5 0.021064 265.9R3 0.167432 178.5 R3 0.000074 469.4D4 0.219995 169.3 D1 0.142717 197.0AE1 1.495245 104.1 AE1 0.003864 326.9AEn 0.174896 174.8 AEn 0.000318 414.6SB 0.152248 177.1 SB 0.000315 412.5

Method Cases compared F-ratio Upper quantile Winner model

dxdtx SB/R3 1.55 3.30 R3

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8543number of fitting parameters. When the F-ratio of the two

models exceeds the upper quantile F0.05 (dF1 dF2, dF1), themore complicated model is accepted as statistically signifi-

cant. It is noted that the F-test and AICc do not always agree in

their choice of winner model [60].kinetic expressions of themodels reported in Table 1 tomatch

the experimental data in the form of dxdt vs. x and x vs. t

profiles and select the models with smallest residual sum of

squares (RSS) among candidatemodelswith the same number

of parameters.

Statistical analysis

The accuracy of fit for each model is quantified by the RSS.

Compared with the simpler one-parameter models, the more

complicatedmodels (AEn, RPM and SB) are expected to exhibit

better fits of the data (smaller RSS). To decide whether there is

a statistically substantial difference between models with

different number of parameters, in terms of the quality of fit to

the same experimental data, statistical analysis, including the

Akaike Information Criterion with correction (AICc) and the F-

test, is used. The AICc [58] is calculated for all the models

examined and the model with the smallest value of AICc is

selected. The F-test [59] is a commonmethod to determine the

statistically most significant model between versions of

varying complexity. The analysis procedure includes selecting

the model with the smallest RSS among all the models with

the same number of fitting parameters, and then comparing

the relative value of the F-ratio and the 95% upper quantile ofTable 9 e Regression statistics and statistical comparisonof the reduction of NiO/g-Al2O3; experimental data fromChen and Shiue [35].

Method Model RSS AICc

xt F3 0.004242 40.5R3 0.089305 22.2D3 0.002805 43.0AE0.5 0.000538 52.9AEn 0.000334 50.8SB 0.000233 42.9

Method Casescompared

F-ratio Upperquantile

Winnermodel

xt AEn/AE0.5 2.45 7.71 AE0.5SB/AE0.5 1.96 9.55 AE0.5materials (unsupported and supported) by H2 and oxidization

data of 4 Ni based oxygen carriers by air are analyzed. The

quality of fit (RSS) of all the models against the experimental

data and their corresponding AICc values are presented in

detail in the Supporting Information. The fit of the PTmodel is

always the most deviated from the experimental data. Addi-

tionally, among the two-parameters models (RPM and AEn),

the prediction of the RPM model is not as good as that of the

AEn model. Therefore, the model predictions of the RPM and

PT models are not shown in this paper, but in the Supporting

Information. The best model of each category (F, R, D, AE, AEn

and SB) is presented. The parameter estimation program was

solved using multivariable least-squares regression in

Matlab [61]. Due to the simplicity of the problem, no localsolutions were obtained in this study; therefore, local opti-

mization algorithms are sufficient.Fig. 8 e Comparison between the experimental data x[ f(t)

and model predictions for NiO/g-Al2O3 reduction by H2 at

700 C; experimental data from Chen and Shiue [35].

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68544Reduction of unsupported NiO

Limitedwork has been focused on the reduction of NiO at high

temperature (>500 C). Additionally, a large number of TGAstudies employ non-isothermal experiments. From the

studies summarized in Table 2, isothermal studies of unsup-

ported NiO include the reduction of Tokyo NiO and Sinter 75

NiO from Plascencia and Utigard [18], Goro NiO from Utigard

Fig. 9 e Comparison between the experimental data x[ f(t)

and model predictions for NiO/YSZ reduction by H2 at

600 C; experimental data from Ishida et al. [30].et al. [19] and pellet NiO fromChatterjee et al. [62]. Table 3 lists

the detailed experimental conditions of these analyses. The

models of Table 1 are used here with the objective to explore

whether the unsupported NiO reduction can be described by

one solid-state model, regardless of NiO origin, preparation

method, particle size and operating temperature. As shown in

Table 2, the modeling analyses performed in the past are

inconclusive; indicating R and D type models as better suited

for the description of the reduction kinetics of unsupported

NiO.

Table 10 e Regression statistics and statisticalcomparison of the reduction of NiO/YSZ; experimentaldata from Ishida et al. [30].

Method Model RSS AICc

xt F1.5 0.019935 26.5R2 0.002432 43.9D1 0.015984 32.6AE2 0.001200 48.1AEn 0.000055 66.6SB 0.000184 54.3

Method Casescompared

F-ratio Upperquantile

Winnermodel

xt AEn/AE2 83.88 7.71 AEn (n 1.60)Tokyo NiO, Plascencia and Utigard [18]Plascencia and Utigard [18] studied the reduction kinetics of

Tokyo NiO in TGA with 35 and 80 vol.% H2 in Ar. Their

reduction experiments with 35 vol.% H2 are chosen here

because the temperature range (800e950 C) is relevant to CLC.Fig. 2 shows the Hancock and Sharp plots for the Tokyo NiO

reduction at 800, 900 and 950 C. The calculated n value isrelatively consistent for this temperature range with values of

1.24e1.40, which identifies the R1 (n 1.24), R2 (n 1.11), R3(n 1.07), A1.5 (n 1.5) and AE1 (n 1) models as the mostsuitable kinetic mechanisms. Fig. 3 shows the comparison of

the prediction of the best models of each category with

experimental data at the three different temperatures. Similar

xt profiles can be observed from the experimental data,however, at 950 C the rate of reduction is significantly higher.Table 4 presents the regression statistics of the models

examined. From the dxdtx fit, the AE1.5 model exhibits thesmallest RSS among all the one-parameter models for all the

temperatures studied, while the R3 and R2models possess the

minimum RSS in the xt fit. From the statistical point of view,the AICc identifies AEn as the statistically more significant

model. The F-ratio of the AEn with respect to all other models

exceeds the 95% upper quantile of the Fisher distribution for

all the temperatures studied. In summary, the AEn model is

overall the best suited model to describe the reduction data of

Plascencia and Utigard [18] in the temperature range

800e950 C. The value for n is consistent for the three tem-peratures studied within 1.26e1.28.

Sinter 75 NiO, Plascencia and Utigard [18]In TGA experiments with high H2 flow rate to reduce diffu-

sional limitations, Plascencia and Utigard [18] observed that

the reduction rate of Sinter 75 NiO is not a strong function of

temperature in the range of 500e700 C. At temperatures900 C, they inspected that the slope of the reduction ratechanges after reaching 50% and 70% [18]. Therefore, these data

were not included in this work. Experiments of reduction in

35% H2eAr mixture at 700 C are chosen because of the CLC-relevant temperature. Application of the Hancock and Sharp

method on the experimental data at 700 C is shown inFig. 4(left), with the n value estimated at 1.23. Thus, possible

mechanisms are those of the R1 (n 1.24), the AE1 (n 1) andAE1.5 (n 1.5) models. Fig. 4(middle, right) presents thecomparison of the various models against the experimental

data at 700 C. As shown in Table 5, the AE1model exhibits thesmallest RSS of all the one-parametermodels. The AICc and F-

test show that the AEn (nz 0.94) model is the best model for

describing the reduction kinetics of Sinter 75 NiO at 700 C.Additionally, SEM micrographs by Plascencia and Utigard [18]

at different conversions showed that H2 does not start to react

homogeneously throughout the particle, but at specific active

sites, which implies the existence of nuclei.

Goro NiO, Utigard et al. [19]Utigard et al. [19] compared solid-state models against TGA

experiments of NiO with H2 and concluded that the reduction

can be described by the R3 model. This was also supported by

optical microscopic analysis in the temperature range400e600 C. However, the rate of reduction decreases as the

[ f

al.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8545temperature rises. One possible explanation supported by the

optical micrographs is the structure change of this material.

At 600e900 C, a Ni shell around the unreacted NiO core isformed, hindering H2 penetration into the NiO particle. The

experimental data at 950 C where the structure barrier dis-appeared is selected here for model discrimination. Fig. 5(left)

shows the Hancock and Sharp plot. The calculated value for n

is 0.93; thus, promising models can be the F1.5 (n 0.91) andAE1 (n 1). Comparison of the experimental data versusmodel predictions is shown in Fig. 5(middle, right). Table 6

presents the statistical analysis of the fits of the candidate

models from each category. F1.5 is shown to obtain the min-

imum RSS among the one-parameter models. The AICc and F-

test identify the AEnmodel as themost-suitedmodel from the

statistical viewpoint. The value of the exponent n of the AEn

model is estimated at 0.87e0.92.

NiO pellet, Chatterjee et al. [62]The reduction kinetics of NiO pellets (d 0.992 cm) were

Fig. 10 e Comparison between the experimental data: (left) x

reduction by H2 at 950 C; experimental data from Dueso etp

studied by Chatterjee et al. [62]. They concluded that the best

model to describe the experimental data at different temper-

ature is the R1. By applying the Hancock and Sharpmethod for

the experimental data of Chatterjee et al. [62] at 600 C, the

Table 11 e Regression statistics and statistical comparison of thet al. [25].

Method Model RSS AICc

dxdtx F1.5 0.007426 103.3R3 0.001327 127.4D1 0.019122 90.0D4 0.001272 128.0AE1 0.002948 116.2AE1.5 0.000885 133.0AEn 0.000623 135.2SB 0.000533 134.1

Method Cases compared F-ratio

dxdtx SB/AE1.5 3.63AEn/AE1.5 5.06

xt AEn/R3 45.35estimated value for n is w1.12 (Fig. 6(left)). Thus, the possible

kinetic mechanisms are these of the R3 (n 1.07), AE1.5(n 1.5) and AE1 (n 1) models. Further comparison betweenthemodel predictions against the experimental data is shown

in Fig. 6(middle, right). Two separate regimes are evident in

the dxdtx plot, which is likely due to the large size of thepellet. From the xt plots, it is evident that none of themodelsprovides satisfactory fits. For instance, the derivative of the

conversion behaves abnormally when the solid conversion is

w30%, which cannot be captured by any of the models

considered. However, the AE1.5 model predicts the overall

trend well, exhibiting better statistical indices (RSS, AICc and

F-test e Table 7). Overall, considering the aberrant dxdtxcurve of this set of experimental data, none of the models can

perfectly predict the whole range of data. Diffusional effects

are reasonably shadowing the kinetic observations.

Reduction of supported NiO

(t) (right) dx/dt[ f(x) and model predictions for NiO/a-Al2O3[25].Generally, in chemical-looping combustion, puremetal oxides

do not meet the process requirements of high reactivity and

stability. Reaction rates decrease quickly in a few cycles [31],

imposing a need for supported oxygen carriers. A porous

e reduction of NiO/a-Al2O3; experimental data from Dueso

Method Model RSS AICc

xt F1.5 0.073504 109.9R3 0.006872 157.3D1 0.118769 100.3D4 0.146153 96.2AE1 0.021640 134.4AE1.5 0.029612 128.1AEn 0.001953 180.0SB 0.003307 166.6

Upper quantile Winner model

3.98 AE1.5

4.75 AEn (n 1.23)4.41 AEn (n 1.20)

support provides higher surface area, while improving me-

chanical strength and attrition resistance. However, the

interaction between NiO and the support may affect the

thermodynamics and kinetics of the oxygen carrier. As dis-

cussed previously (Table 2), different models have been pro-

posed to describe the reduction behavior of NiO with various

supports. In this work, kinetic studies of interest are the

Table 12 e Properties of the fresh NiO-based oxygen-carriers prepared in this work.

Properties NiO/g-Al2O3 NiO/a-Al2O3

BET surface area (m2/g) 94 27

Pore size (A) 116 294

Porosity 0.45 0.40

Total NiO content (%) 32 24

Active NiO content (%) 21 19

XRD phases NiO, NiAl2O4,

g-Al2O3, SiO2

NiO, NiAl2O4,

a-Al2O3, SiO2Apparent density (kg/m3) 1195 1414

Particle size (mm) 50e150 50e150

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68546isothermal reduction experiments in TGAwith H2 at relatively

high temperatures. The relevant studies include the reduction

of NiO/a-Al2O3 fromAbad et al. [36,63], NiO/g-Al2O3 fromChen

and Shiue [35], NiO/YSZ from Ishida et al. [30] and NiO/a-Al2O3from Dueso et al. [25].

NiO/a-Al2O3 oxygen carrier, Abad et al. [36,63]Abad et al. [36,63] tested the reactivity of NiO/a-Al2O3 in 15%

H2eN2 mixture at 950 C. The oxygen carrier was preparedwith the freeze-granulation method followed by sintering at

1300 C for 4 h [64]. About 30% Ni was reported to exist in theform of NiAl2O4. The R3 model was selected to predict the

experimental data up to 100% reduction. The calculated

Hancock and Sharp value for n is 1.07, as shown in Fig. 7(left).

Thus, promising kineticmechanisms are the F1.5 (n 0.91), R2(n 1.11) and R3 (n 1.07), and AE1 (n 1). Further comparisonFig. 11 e XRD patterns of NiO/g-Al2O3 from in-situ oxidation

at 800 and 950 C.between themodels and experimental data in the range 0e0.7

(to minimize the contribution from NiAl2O4 reduction) is

shown in Fig. 7(middle, right). The R3 model exhibits good

agreement with the experimental data, validated by the small

RSS reported in Table 8. The calculated AICc and the F-test

(Table 8) identifies the R3 as the winner model. One of the

assumptions for applying the R3 model is that the size of

crystallite remains constant during the reaction [28,65,66].

Considering the very high calcination temperature (1300 C)used in the preparation of this oxygen carrier, the reactivity

test conducted at 950 C may not cause significant sintering.The surface of this material was studied by Cho et al. [67] with

SEM in six redox cycles, showing no particular differences in

surface texture. However, from Fig. 7(right), the comparison of

experimental with simulated dxdtx profiles concludes thatthe AEnwith n 1.07 is the onlymodel that captures the initialincubation step. The calculated AICc of the AEn model for the

dxdtx fit is only slightly larger than that of the R3 model(Table 8). Overall, considering the initial stage of reduction,

the AEn model (n 1.07) is also a good candidate. Analysis ofthis data set, in conjunction with data reported in the

following, indicates that calcination and other synthesis

methodologies and conditions have a significant effect on the

kinetics of supported NiO.

NiO/g-Al2O3 oxygen carrier, Chen and Shiue [35]Chen and Shiue [35] investigated the reactivity of NiO/g-Al2O3in H2 at 700 C. Approximately 64%Ni was found in the form ofNiO. The reduction rate is quite low, which is probably due to

the low H2 flow rate employed in their study. In this case, the

fit of dxdtx data was not used because of the limited numberof experimental points. The experimental data and model

predictions of the xt profiles are shown in Fig. 8. Shown in

Table 9, the AE0.5 model obtains the smallest AICc, and the F-

test also indicates that the AE0.5 model is the most-suited one

in describing the reduction kinetics of thismaterial. Moreover,

all the diffusion models (D1eD4) fit the experimental data

well. Considering the low flow rate used, it is possible that the

reactivity behavior of this material was affected by diffusional

effects.

NiO/YSZ oxygen carrier, Ishida et al. [30]Ishida et al. [30] studied the reduction of NiO/YSZ by H2 at

600 C in 8 redox cycles. The R3 model was chosen to matchNiO reduction conversion data up to 100%. The reactivity was

observed to increase with the number of cycles, but attain a

stable profile after cycle 7. Therefore, the data of cycle 8 are

selected in this study. Given that only 6 TGA points were

presented by Ishida et al. [30], the dxdtx profile was not usedhere. The comparison between the models and experimental

data is shown in Fig. 9, with the AE2 model exhibiting good

predictions. Table 10 shows the statistical analysis of the fits

of the best models from each category. The calculated AICc

and the F-test identify the AEnmodelwith n 1.60 as the best-suited model to match the kinetics of NiO/YSZ reduction data

by Ishida et al. [30].

NiO/a-Al2O3 oxygen carrier, Dueso et al. [25]

Dueso et al. [25] investigated the reactivity of NiO/a-Al2O3 with

15 vol.% H2 at 950 C. The experimental data of NiO/g-Al2O3

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8547from their work is not studied here because only 40% Ni was

reported as active NiO. They proposed that the relative

amount of NiO increasedwith the number of redox cycles and

about 80% Ni was in the form of free NiO in the fifth cycle. The

reactivity of NiO/a-Al2O3 of cycle 5 is used in this study. The

effects of different gas flow rates and sample weights were

studied in their work to minimize diffusional resistances.

Fig. 10 shows the comparison between the best fit of the

models of Table 1 against the experimental data. Comparison

of the RSS of all the one-parametermodels, shown in Table 11,

identifies the R3 model as the winner. Further statistical

analysis (AICc and F-test) shows that the AEn model with n

value of 1.20e1.23 is the better-suited model for these exper-

imental data.

NiO/g-Al2O3 oxygen carrier, this workWhen CLC is operated at high temperatures, the chemical

interaction between NiO and g-Al2O3, as well as sintering and

agglomeration of Ni, have to be accounted for. The properties

of NiO supported on g-Al2O3, such as the surface texture and

NiO active content, change after long treatment at high tem-

perature [68]. The objective of this section is to examine the

temperature effect on the most suitable solid-state model

used to describe the reduction kinetics of NiO/g-Al2O3 by H2.

Experimental. The incipient wetness impregnation methodwas used to prepare NiO/g-Al2O3 and NiO/a-Al2O3 oxygen

Fig. 12 e Comparison between the experimental data from this

predictions for NiO/g-Al2O3 reduction by H2 at 600 C (a, d), 800points at 600 and 800 C has been decreased for clarity.carriers. Themain properties of the freshmaterials are shown

in Table 12. Both materials were tested in TGA for multiple

redox cycles with air and 4 vol.% H2 in Ar. Details on the ox-

ygen carrier preparation method and TGA testing procedure

are provided in the Supporting Information. Various initial

sample weights and gas flow rates were used to investigate if

the external and interparticle diffusion is significant. The

extent of diffusion limitations in the TGA was also analyzed

by a heterogeneous TGA model in previous work [68] for the

experimental data at 950 C. The main driving force wasshown to be the kinetic term and the contribution of diffusion

(external, interparticle and intraparticle) was found minor.

Effect of temperature. Formation of NiAl2O4 cannot be avoidedwhen g-Al2O3 is used as the NiO support in high temperature

CLC. NiAl2O4 is also reducible by H2, but at slower rates and

potentially following different mechanisms. Therefore, it is

necessary to identify the range in which NiO reduction is

dominant. Also, the relative amount of NiAl2O4 usually

changes with temperature. Therefore, an in-situ hot-stage

XRD analysis was conducted to explore the effect of temper-

ature on the NiAl2O4 relative content. The detailed informa-

tion and operating procedure for the XRD analysis is given in

the Supporting Information. The XRD spectra of the NiO/g-

Al2O3 prepared in this work heated at 800 and 950 C areshown in Fig. 11. Comparison of the XRD patterns reveals a

decrease in the NiO intensity and an increase in NiAl2O4

work: (a, b, c) x [ f(t), (d, e, f) dx/dt [ f(x) and modelC (b, e) and 950 C (c, f); the number of experimental data

f the reduction of NiO/g-Al2O3 at 600, 800 and 950 C by 4%H2 in

800 C 950 C

RSS AICc RSS AICc

0.098382 907.7 0.002779 328.30.014031 1155.0 0.070674 215.10.021568 1100.4 0.050573 226.8

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68548Table 13e Regression statistics and statistical comparison oAr.

Method Model 600 C

RSS AICc

dxdtx F1.5 0.053196 3173.1R2 0.013153 3676.2R3 0.018560 3552.2D4 0.015724 3611.9AE1 0.031836 3358.0AE1.5 0.001716 4409.4AEn 0.001412 4477.5SB 0.001498 4459.2

xt F1.5 2.815494 1744.3R2 0.577300 2314.8R3 0.871679 2166.4D1 4.619451 1566.1AE1 1.585674 1951.0AE1.5 0.036869 3305.1AEn 0.033319 3339.5SB 0.033213 3338.7

Temperature C Method Cases compared

600 dxdtx AEn/AE1.5xt AEn/AE1.5

SB/AE1.5

SB/AEn

800 dxdtx AEn/AE1.5intensity. It is reasonable to postulate that NiO is converted to

NiAl2O4 when temperature rises to 950 C. The effect of tem-perature on the reduction rate of NiO/g-Al2O3 in H2 reduction

experiments is presented in Fig. 12. In the dxdt vs. x profile at

600 C, the dxdt decreases greatly when the Ni conversionreaches approximately 75% and the reduction of NiAl2O4 be-

comes dominant (Fig. 12(d)). However, the dxdt vs. x profiles at

800 and 950 C are quite smooth. This can be possiblyexplained by the relatively fast reduction rate of NiAl2O4 at

higher temperature, resulting in overlapping of the NiO and

NiAl2O4 reduction reactions. Since the oxygen carrier was

calcined at 800 C, it is appropriate to assume that the oxygencarrier at 800 C carries the same amount of NiO as the onereduced at 600 C. The same set of experimental data overredox cycles at 950 C was studied previously [68] and amethod of deconvolution was used to differentiate the con-

tributions of fast NiO reduction and slow NiAl2O4 reduction to

the overall rate profiles. The relative NiO content was pro-

posed to vary with the redox cycles and the fraction of NiO

over NiO plus NiAl2O4 was estimated to be 60%. Therefore, in

this study, the model discrimination was performed for NiO

conversions up to 0.75 at 600 and 800 C and up to 0.6 at 950 C,where the weight loss is primarily driven by the reduction of

NiO.

Table 13 compares the regression statistics of the models

examined against reduction experiments at 600, 800 and

SB/AE1.5

SB/AEn

xt AEn/AE1.5SB/AE1.5

SB/AEn

950 dxdtx SB/F1.5AEn/F1.5

xt AEn/F1.50.017750 1125.2 0.059272 221.20.047513 1000.1 0.020937 257.60.004711 1293.6 0.154017 187.80.002560 1369.0 0.002483 330.50.002529 1368.5 0.002733 324.20.695469 678.0 0.001122 337.40.089220 944.9 0.049853 212.20.157454 871.1 0.035076 223.81.275914 599.1 0.041691 218.10.344214 769.4 0.014428 253.10.018913 1146.6 0.201019 166.20.003213 1375.0 0.000542 359.20.003129 1376.3 0.014758 247.7F-ratio Upper quantile Winner model

77.00 3.87 AEn (n 1.52)38.14 3.87 AEn (n 1.52)19.65 3.02 SB

1.14 3.87 AEn (n 1.52)105.08 3.92 AEn (n 1.41)950 C. The AE1.5model produces the finest fit among the one-parameter models with the smallest RSS for the experiments

at 600 and 800 C. The F1.5 model obtains the smallest RSS forthe experiment at 950 C. The AICc of the AEn model and theSB model are the smallest. The F-test shows that the AEn

model is best suited to match the reduction experiments at

the three temperatures, with the exponent n 1.52 at 600 C,n 1.40e1.41 at 800 C and n 0.90e0.92 at 950 C. The nexponent of the AEn model decreases from 1.4 to 0.9 when

temperature increases from 800 to 950 C. Typically, theexponent n in AvramieErofeev model reflects the crystallite

size [45]. The Ni crystallite size is related to the active surface

for Ni nucleation process, and thus influences the overall re-

action rate [45]. For isothermal conditions, n 1.5 representsthe nuclei growth of small crystallites, while n 1 stands fornuclei growth in a considerably larger crystallite [69e71].

53.50 3.07 SB

1.49 3.92 AEn (n 1.41)625.56 3.92 AEn (n 1.40)320.37 3.07 SB

3.41 3.92 AEn (n 1.40)0.27 3.29 F1.5

4.56 4.14 AEn (n 0.90)33.17 4.16 AEn (n 0.92)

Table 14 e Crystallite size change of NiO/g-Al2O3 aftertreatment at 800 C and 950 C.

Component Crystallite sizeof sample

after treatmentat 800 C (A)

Crystallite sizeof sample aftertreatment at950 C (A)

NiO 101.0 161.0

NiAl2O4 90.9 160.9

Therefore, one possible explanation for the exponent n vary-

ing with temperature is that the Ni crystallite size changes

with temperature. The Ni crystallite at 950 C is of a larger sizethan at 600 C or 800 C. It is known that sintering occursmuchfaster at higher temperatures [72]. Therefore, it is reasonable

to postulate that NiO undergoes sintering at 950 C and formslarger crystallites. To address this hypothesis, SEM images of

the NiO/g-Al2O3 oxygen carrier after redox experiments in

TGA at 800 and 950 C were studied to examine the crystallitesize on the surface. Overall, the sphericity of the support

material is excellent and NiO is well dispersed on the surface

of the support, while some NiO agglomerates concentrate

around the cracks of the support (Fig. S23(a)). By comparing

the NiO crystallite size of the two cases (Fig. S23(c,d)), NiOwith

larger crystallite size can be observed on the surface of the

oxygen carriers after redox at 950 C, which is in agreementwith the statistical analysis of Table 13. To verify the effect of

temperature on the crystallite size, fresh materials are

examined by in-situ hot-stage powder XRD at 800 C and950 C (Fig. 11). The crystallite size of NiO and NiAl2O4 at thetwo temperatures is calculated from the XRD data, as shown

in Table 14. The size of both crystallites increases by 60% as

temperature rises to 950 C, which is in agreement with theSEM findings. Another interesting observation is that the n

value changes with the number of redox cycles. The variation

Fig. 13 e Effect of the number of cycles on the exponent n of

the AvramieErofeev model, estimated using experimental

data of NiO/g-Al2O3 reduction by H2 at 950 C.

Fig. 14 e Comparison between the experimental data (aeb) x [

models for NiO/g-Al2O3 reduction by H2 at 950 C for redox cycl

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8549f(t), (ced) dx/dt [ f(x) and predictions of the F2 and AEn

es 3 and 6; data from this work.

Table 15 e Regression statistics and statistical comparison of the reduction of NiO/g-Al2O3 after different number of redoxcycles at 950 C by 4% H2 in Ar.

Method Model Cycle 3 Cycle 4 Cycle 5 Cycle 6

RSS AICc RSS AICc RSS AICc RSS AICc

dxdtx F2 0.003212 333.6 0.001648 346.6 0.001311 354.6 0.005515 423.3R3 0.067937 223.7 0.064774 218.1 0.074160 213.4 0.069097 304.5D4 0.076174 219.6 0.073520 213.7 0.085864 208.2 0.081605 296.6AE1 0.037082 245.5 0.035991 238.7 0.037773 237.0 0.032127 340.5AEn 0.003201 331.4 0.000801 369.6 0.001050 360.1 0.001226 492.0SB 0.003006 331.3 0.000745 369.8 0.000994 359.7 0.001160 492.1

xt F2 0.000728 482.3 0.000715 375.8 0.001539 540.2 0.001404 544.9R3 0.057976 289.7 0.095023 204.7 0.097947 324.2 0.080166 334.6D1 0.035752 311.0 0.035208 239.4 0.035677 376.7 0.047861 361.4

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68550AE1 0.028316 321.2 0.046178AEn 0.000622 487.1 0.000544SB 0.000563 489.1 0.000482of n with the number of redox cycles at 950 C is illustrated inFig. 13. There is a slight decrease (w14%) in n with increasing

cycles of up to six at 950 C (n 0.86e1), which can beexplained by the crystallite size changing with time.

In previous studies [73e76], the F2 model was successfully

used to predict the CLC performance in fixed-bed and fluidized

bed reactors. Investigation of the suitability of the F2 model

Fig. 15 e Comparison between the experimental data (left) x[ f

reduction by H2 at 800 C; data from this work (the number of e

Table 16 e Regression statistics and statistical comparison of

Method Model RSS AICc

dxdtx F1.5 0.032232 667.5R2 0.009009 775.9D4 0.010862 760.0AE1.5 0.001148 951.0AEn 0.000900 969.6SB 0.000879 969.4

Method Cases compared F-ratio

dxdtx AEn/AE1.5 22.91SB/AE1.5 12.56

SB/AEn 1.95

xt AEn/AE1.5 5724.73229.9 0.044547 365.2 0.031403 383.3383.1 0.001194 551.2 0.000945 563.4385.0 0.001099 553.2 0.000890 564.2(commonly referred to as the volumetric model) to describe

the reduction of NiO/g-Al2O3 byH2, is performed by comparing

its prediction against the overall better model, the AEnmodel.

Fig. 14 shows the experimental data of two redox cycles (cy-

cles 3 and 6 to eliminate initial oxygen carrier restructuring

effects due to the high TGA temperature) and the predictions

of the F2 and AEn models, with n receiving its optimal value

(t), (right) dx/dt[ f(x) and model predictions for NiO/a-Al2O3xperimental data points has been decreased for clarity).

the reduction of NiO/a-Al2O3 at 800 C by 4% H2 in Ar.

Method Model RSS AICc

xt F1.5 0.849158 389.5R1 0.065319 607.5D1 1.208065 359.5AE1.5 0.011026 758.7AEn 0.006693 799.0SB 0.020310 702.5

Upper quantile Winner model

3.96 AEn (n 1.55)3.11 SB

3.96 AEn (n 1.55)3.96 AEn (n 1.56)

for each cycle. Table 15 shows the statistical comparison of

the best models of each category of Table 1 for each redox

cycle. Very good agreement between the predictions of both

models (F2 and AEn) and the four sets of redox data can be

of a-Al2O3 supported NiO is shown in Fig. 15. The reduction of

the oxygen carrier proceeds in two steps: in the first stage, the

reaction is fast, attributed to the reaction of NiO with the H2.

Then there is a slope change in the curves due to a decrease in

Fig. 16 e Comparison between the experimental curves (left) x[ f(t), (right) dx/dt[ f(x) and model predictions for Ni/a-Al2O3oxidation at 950 C; experimental data from Abad et al. [36,63].

f thfar

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8551observed. Thus the F2 model is also suitable for the reaction

kinetics of NiO reduction. Although the F2 model allows for a

good description of the data, the AEnmodel, with varying n as

a function of temperature or number of redox cycles, allows

for amore accurate representation of the reduction kinetics of

NiO supported on g-Al2O3.

NiO/a-Al2O3 oxygen carrier, this workThe main properties of the fresh NiO/a-Al2O3 are shown in

Table 12. The comparison between the reduction experiments

Table 17 e Regression statistics and statistical comparison oa-Al2O3 from Dueso et al. [25] at 950 C, Ni/MgAl2O4 from Za1000 C.Method Model Abad et al.950 C

Dueso e950

RSS AICc RSS

dxdtx F1.5 6.582791 10.0 0.007328R2 0.876840 40.3 0.000485R3 0.119356 70.2 0.001175D4 0.680705 44.1 0.000583AE1.5 1.341200 33.9 0.000919AEn 0.246227 56.6 0.000617SB 0.097637 67.3 0.000329

xt F1.5 0.084817 100.6 0.111351R2 0.007487 146.7 0.006151R3 0.001022 184.5 0.017215D1 0.064394 105.8 0.083015AE1.5 0.036842 116.4 0.009896AEn 0.001537 174.3 0.006645SB 0.012271 132.0 0.034274

Literature Method Cases compared

Abad et al. 950 C dxdtx SB/R3Dueso et al. 950 C dxdtx SB/R2Zafar et al. 950 C xt SB/AEn

AEn/R2

dxdtx SB/R2reactivity, corresponding to the reduction of NiAl2O4. Table 16

shows the regression statistics of the candidate models. The

AE1.5 model is shown to be capable of representing the

experimental conversion with best accuracy among all the

one-parameter models. The model with smallest AICc in xtfits is the AEn (n 1.55); while the dxdtx fits estimate similarAICc for the SB and AEn models with the AICc of SB slightly

smaller than that of AEn. The F-test indicates the AEn as the

winner model. The exponent n for the reduction of NiO/a-

Al2O3 at 800 C by H2 diluted in Ar is 1.55e1.56.

e oxidation of Ni/a-Al2O3 from Abad et al. [63] at 950 C, Ni/et al. [32] at 950 C, and Ni/YSZ from Ishida et al. [30] att al.C

Zafar et al.950 C

Ishida et al.1000 C

AICc RSS AICc RSS AICc

103.4 36.543133 15.7 e e141.5 2.001882 27.9129.1 3.623049 19.0138.9 2.520532 24.4132.5 3.829706 18.2135.3 2.820472 20.1140.8 1.742117 24.177.2 0.206803 72.7 0.010788 19.7123.5 0.005889 133.2 0.006727 21.6107.1 0.022753 110.2 0.000005 50.881.9 0.305027 66.1 0.020770 17.0115.9 0.006603 131.2 0.000046 41.5119.7 0.004999 133.4 0.000007 37.290.3 0.004581 131.9 0.012131 eF-ratio Upper quantile Winner model

1.33 3.89 R3

2.60 3.98 R2

1.27 4.60 AEn

2.67 4.54 R2

0.89 3.89 R2

[ f(t), (right) dx/dt [ f(x) and model predictions for Ni/a-Al2O3].

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68552Fig. 17 e Comparison between the experimental data (left) x

oxidation at 950 C; experimental data from Dueso et al. [25Oxidation of supported NiO

In CLC, the reduced oxygen carrier from the Reducer will be

transported to the Oxidizer for re-oxidation. As shown in

Table 2, mainly the R3 and D4 models have been used previ-

ously to describe the oxidation behavior of Ni-based oxygen

carriers. In this section, studies of interest include the oxida-

tion of Ni/a-Al2O3 from Abad et al. [36,63], Ni/a-Al2O3 from

Dueso et al. [25], Ni/MgAl2O4 fromZafar et al. [32], Ni/YSZ from

Ishida et al. [30], and Ni/g-Al2O3 oxidation data of this work.

The kinetic study of Ryu et al. [28] was not chosen because

diffusional effects were significant and the total oxidation

time was over 30 min.

NiO/a-Al2O3 oxygen carrier, Abad et al. [36,63]Abad et al. [36,63] oxidized previously reduced NiO/a-Al2O3 in

15 vol.% O2 at 950 C. The R3model was selected to predict theoxidation reactivity of this material in their study. Fig. 16

shows the comparison of the best models from each cate-

gory of Table 1. From the statistical analysis, the R3 model is

chosen as themost-suitedmodel (smallest RSS and AICc). The

Fig. 18 e Comparison between the experimental data (left) x[ f(t), (right) dx/dt[ f(x) and model predictions for Ni/MgAl2O4oxidation at 950 C; experimental data from Zafar et al. [32].

Fig. 19 e Comparison between the experimental data

x [ f(t) and model predictions for Ni/YSZ oxidation at

1000 C; experimental data from Ishida et al. [30].

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8553F-test also identifies the R3 model to be the winner, as shown

in Table 17.

NiO/a-Al2O3 oxygen carrier, Dueso et al. [25]Dueso et al. [25] studied the reactivity of reduced NiO/a-Al2O3oxygen carrier in diluted O2. The method of hot incipient wet

impregnation [77] was used to prepare this oxygen carrier

following calcination at 950 C for 1 h. Temperature(800 Ce950 C) was observed to have a minor effect on thereaction rate. The experimental data at 950 C is used in thisstudy for consistency with the rest of analyses of supported

NiO. The comparison of the best models of Table 1 with the

oxidation data is shown in Fig. 17. The statistical comparison,

shown in Table 17, indicates that the R2 model obtains the

smallest RSS among all the one-parameter models. The AICc

and F-test further validate that the R2model is the best-suited

model for describing this set of experimental data.

NiO/MgAl2O4 oxygen carrier, Zafar et al. [32]Zafar et al. [32] studied the oxidation kinetics of Ni/MgAl2O4with 10% O2. The material was prepared by freeze-

granulation, followed by calcination at 1400 C for 6 h [1].They observed that the reaction rate is not a function of

temperature when the temperature is higher than 900 C. TheR3 model was used in their study to match the oxidation data.

Fig. 20 e Comparison between the experimental data (a, b) x[ f(

oxidation at 800 and 950 C; data from this work.In this study, the reactivity data at 950 Cwere chosen and theperformance of the models of Table 1 is shown in Fig. 18.

t) and (c, d) dx/dt[ f(x) and model predictions for Ni/g-Al2O3

Table 18 e Regression statistics and statisticalcomparison of the oxidation of Ni/g-Al2O3 at 800 and950 C by Air.

Method Model 800 C 950 C

RSS AICc RSS AICc

dxdtx F1.5 0.945390 46.9 1.165369 43.3R2 0.041004 100.2 0.093635 86.2D4 0.042200 99.7 0.094625 86.0AE1.5 0.235517 70.5 0.327444 64.9AEn 0.213966 69.5 0.319619 62.7SB 0.036972 96.4 0.087907 81.7

xt F1.5 0.166254 93.6 0.154153 83.4R2 0.002269 179.5 0.002854 155.2D1 0.085329 106.9 0.069044 97.9AE1.5 0.023943 132.3 0.025190 116.0AEn 0.014088 140.5 0.017610 119.9SB 0.023630 127.3 0.033119 105.7

Temperature(C)

Method Casescompared

F-ratio Upperquantile

Winnermodel

800, this study dxdtx SB/R2 0.76 3.74 R2950, this study dxdtx SB/R2 0.46 3.74 R2

et

Supported NiO reduction

This work

-Al2

.55

Sint

700e

AEn

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 68554Supported by the statistical analysis, shown in Table 17, the R2

model is the best-suited model.

NiO/YSZ oxygen carrier, Ishida et al. [30]Ishida et al. [30] studied the oxidation kinetics of Ni/YSZ in

air at 1000 C for 8 redox cycles. The reactivity was observedto increase with the number of cycles, but material stability

was evidenced after cycle 7. Therefore, the experimental

data of cycle 8 are selected in this study. Again, the dxdtxprofile is not used, because of the lack of sufficient TGA data

points. Fig. 19 shows the comparison of the models of Table

1 against the experimental data. Overall, the R3 and AE1.5

models achieve better fit. Shown in Table 17, the AICc of the

R3 model is the smallest among all the models. It should be

noted that the AICc of the SB model was not calculated

because the sample size of this study is 4, which is smaller

Oxygen Carrier NiO/g-Al2O3 NiO/a

Temperature 600 800 950 800

Winner Model AEn (1.5) AEn (1.41) AEn (0.95) AEn (1

Unsupported NiO reduction

Ref. [18]

Bulk NiO type Tokyo NiO

Temperature 800e1000

Model AEn (1.25)

Supported Ni oxidation

This work Ref. [32]

Oxygen Carrier NiO/g-Al2O3 NiO/MgAl2O4Temperature 800 and 950 950

Model R2 R2Table 19 e Summary of the statistically most significant kinNiO by H2 and the oxidation of supported Ni by O2.than the smallest allowable sample size for the AICc calcu-

lation (5).

NiO/g-Al2O3 oxygen carrier, this workThe oxidation kinetics of reduced NiO supported on g-Al2O3was studied in air at 800 and 950 C. The total oxidation timewas controlled to avoid the excessive formation of NiAl2O4,

but long enough to fully oxidize the reduced sample. Fig. 20

presents the effectiveness of the models of Table 1 in

matching the oxidation experiments at 800 and 950 C. Thereaction rate profiles at these two temperatures are quite

similar and much faster than the reduction kinetics at the

same temperature. The prediction of the R2 model is in best

agreement with the experimental data for both temperatures,

with the smallest RSS (Table 18). Overall, the R2 model is the

winner model.

Conclusions

The summary of the selected solid-state kinetic models for

the reduction of supported and unsupported NiO by H2 andthe oxidation of supported NiO by air is shown in Table 19.

On the basis of the Hancock and Sharp analysis, as well as

model fitting methods the two-parameter AvramieErofeev

model with n ranging from 0.9 to 1.6 is shown to be the most

successful in representing reduction kinetics of supported

and unsupported NiO. Despite the remarkable inconsistency

in previous analyses, the statistical analysis preformed in

this work identifies that the reduction of NiO by H2, proceeds

via formation and growth of nuclei. It was shown that the

temperature and time of reduction (or number of redox cy-

cles) affect the kinetics of reduction, by altering the crys-

tallite size, which in turn is reflected as a change in the

AvramieErofeev exponent. This is an indication of hybrid

solid-state kinetic mechanisms involving partially homoge-

neous and phase-boundary reaction driving forces. The

geometrical contraction model was shown to better match

O3 NiO/a-Al2O3 NiO/YSZ NiO/a-Al2O3 NiO/g-Al2O3950 600 950 700

) R3 AEn (1.60) AEn (1.21) AE0.5

Ref. [19] Ref. [62]

er 75 Goro NiO NiO pellet

950 600 600

(1.0) AEn (1.3) AE1.5

Ref. [36,63] Ref. [25] Ref. [30]

NiO/a-Al2O3 NiO/a-Al2O3 NiO/YSZ

950 950 1000

R3 R2 R3ic models for the reduction of supported and unsupported

Ref. [36,63] Ref. [30] Ref. [25] Ref. [35]experiments of reduction of NiO/a-Al2O3 calcined at very

high temperature [63], which presumably is the result of

stabilization of relatively larger NiO crystallites that react via

a phase boundary mechanism. Analysis of unsupported NiO

reduction data further verifies the validity of the Avrami-

eErofeev model to represent NiO reduction. It is concluded

that the type of support, calcination temperature, and

temperature and time of reduction strongly affect the crys-

tallite size of NiO, which in turn results in altering the

dominance of nuclei formation, growth or overlapping, as

captured by the AvramieErofeev model. Ni oxidation solid-

state kinetics appears much simpler with the geometrical

contraction models (R2 and R3) shown to consistently

represent oxidation data.

Acknowledgments

This material is based upon work supported by the National

Science Foundation under Grant No. 1054718. Support byW.R.

Grace & Co. by providing the Al2O3/SiO2 matrices is gratefully

acknowledged.

catalysts. Appl Catal A Gen 2004;267:35e46.[17] Jeangros Q, Hansen TW, Wagner JB, Damsgaard CD, Dunin-

Borkowski RE, Hebert C, et al. Reduction of nickel oxide

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 6 8555particles by hydrogen studied in an environmental TEM. JMater Sci 2012;48:2893e907.

[18] Plascencia G, Utigard T. The reduction of Tokyo and Sinter 75nickel oxides with hydrogen. Chem Eng Sci 2009;64:3879e88.

[19] Utigard TA, Wu M, Plascencia G, Marin T. Reduction kineticsAppendix A. Supplementary data

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.ijhydene.2014.03.161.

r e f e r e n c e s

[1] Mattisson T, Johansson M, Lyngfelt A. The use of NiO as anoxygen carrier in chemical-looping combustion. Fuel2006;85:736e47.

[2] Adanez J, Abad A, Garca-Labiano F, Gayan P, de Diego LF.Progress in chemical-looping combustion and reformingtechnologies. Prog Energy Combust 2012;38:215e82.

[3] Khawam A, Flanagan DR. Solid-state kinetic models: basicsand mathematical fundamentals. J Phys Chem B2006;110:17315e28.

[4] Avrami M. Kinetics of phase change. I general theory. J ChemPhys 1939;7:1103.

[5] Avrami M. Kinetics of phase change. II transformation-timerelations for random distribution of nuclei. J Chem Phys1940;8:212.

[6] Avrami M. Granulation, phase change, and microstructurekinetics of phase change. III. J Chem Phys 1941;9:177.

[7] Erofeev BN. Generalized chemical kinetics equation and itsapplication to reaction with solid participants. Dokl AN SSSR1946;52:515.

[8] Bhatia SK, Perlmutter DD. A random pore model for fluid-solid reactions: I. Isothermal, kinetic control. AIChE J1980;26:379e86.

[9] Sestak J, Berggren G. Study of the kinetics of the mechanismof solid-state reactions at increasing temperatures.Thermochim Acta 1971;3:1e12.

[10] Prout EG, Tompkins FC. The thermal decomposition ofpotassium permanganate. Trans Faraday Soc1944;40:488e98.

[11] Jankovic B. Isothermal reduction kinetics of nickel oxideusing hydrogen: conventional and Weibull kinetic analysis. JPhys Chem Solids 2007;68:2233e46.

[12] AdnaCevic B, Jankovic B. Dispersive kinetic model for thenon-isothermal reduction of nickel oxide by hydrogen. PhysB Condens Matter 2008;403:4132e8.

[13] Jankovic B, AdnaCevic B, Mentus S. The kinetic analysis ofnon-isothermal nickel oxide reduction in hydrogenatmosphere using the invariant kinetic parameters method.Thermochim Acta 2007;456:48e55.

[14] Jankovic B, AdnaCevic B, Mentus S. The kinetic study oftemperature-programmed reduction of nickel oxide inhydrogen atmosphere. Chem Eng Sci 2008;63:567e75.

[15] Richardson J, Scates R, Twigg MV. X-ray diffraction study ofnickel oxide reduction by hydrogen. Appl Catal A Gen2003;246:137e50.

[16] Richardson J, Scates R, Twigg M. X-ray diffraction study ofthe hydrogen reduction of NiO/a-Al2O3 steam reformingof Goro nickel oxide using hydrogen. Chem Eng Sci2005;60:2061e8.[20] Syed-Hassan SSA, Li C-Z. NiO reduction with hydrogen andlight hydrocarbons: contrast between SiO2-supported andunsupported NiO nanoparticles. Appl Catal A Gen2011;398:187e94.

[21] Syed-Hassan SSA, Li C-Z. Effects of crystallite size on thekinetics and mechanism of NiO reduction with H2. Int JChem Kinet 2011;43:667e76.

[22] Lee J, Kim B. Synthesis and related kinetics ofnanocrystalline Ni by hydrogen reduction of NiO. MaterTrans 2001;42:1607e12.

[23] Erri P, Varma A. Diffusional effects in nickel oxide reductionkinetics. Ind Eng Chem Res 2009;48:4e6.

[24] Dueso C, Abad A, Garca-Labiano F, de Diego LF, Gayan P,Adanez J, et al. Reactivity of a NiO/Al2O3 oxygen carrierprepared by impregnation for chemical-looping combustion.Fuel 2010;89:3399e409.

[25] Dueso C, Ortiz M, Abad A, Garca-Labiano F, de Diego LF,Gayan P, et al. Reduction and oxidation kinetics of nickel-based oxygen-carriers for chemical-looping combustion andchemical-looping reforming. Chem Eng J 2012;188:142e54.

[26] Ryu H-J, Bae D-H, Jin G-T. Effect of temperature on reductionreactivity of oxygen carrier particles in a fixed bed chemical-looping combustor. Korean J Chem Eng 2003;20:960e6.

[27] Ryu H-J, Lim N-Y, Bae D-H, Jin G-T. Carbon depositioncharacteristics and regenerative ability of oxygen carrierparticles for chemical-looping combustion. Korean J ChemEng 2003;20:157e62.

[28] Ryu H-J, Bae D-H, Han K-H, Lee S-Y, Jin G-T, Choi J-H.Oxidation and reduction characteristics of oxygen carrierparticles and reaction kinetics by unreacted core model.Korean J Chem Eng 2001;18:831e7.

[29] Ishida M, Jin H, Okamoto T. Kinetic behavior of solid particlein chemical-looping combustion: suppressing carbondeposition in reduction. Energy Fuels 1998;12:223e9.

[30] Ishida M, Jin H, Okamoto T. A fundamental study of a newkind of medium material for chemical-looping combustion.Energy Fuels 1996;10:958e63.

[31] Jin H, Okamoto T, Ishida M. Development of a novelchemical-looping combustion: synthesis of a solid loopingmaterial of NiO/NiAl2O4. Ind Eng Chem Res1999;38:126e32.

[32] Zafar Q, Abad A, Mattisson T, Gevert B. Reaction kinetics offreeze-granulated NiO/MgAl2O4 oxygen carrier particles forchemical-looping combustion. Energy Fuels 2007;21:610e8.

[33] Mattisson T, Garca-Labiano F, Kronberger B, Lyngfelt A,Adanez J, Hofbauer H. Chemical-looping combustion usingsyngas as fuel. Int J Greenh Gas Control 2007;1:158e69.

[34] Moghtaderi B, Song H. Reduction properties of physicallymixed metallic oxide oxygen carriers in chemical loopingcombustion. Energy Fuels 2010;24:5359e68.

[35] Chen I, Shiue DW. Reduction of nickel-alumina catalysts. IndEng Chem Res 1988;27:429e34.

[36] Abad A, Garca-Labiano F, de Diego LF, Gayan P, Adanez J.Reduction kinetics of Cu-, Ni-, and Fe-based oxygen carriersusing syngas (CO H2) for chemical-looping combustion.Energy Fuels 2007;21:1843e53.

[37] Hossain MM, Quddus MR, de Lasa HI. Reduction kinetics ofLa modified NiO/La-Al2O3 oxygen carrier for chemical-looping combustion. Ind Eng Chem Res 2010;49:11009e17.

[38] Hossain MM, Lopez D, Herrera J, de Lasa HI. Nickel onlanthanum-modified g-Al2O3 oxygen carrier for CLC:reactivity and stability. Catal Today 2009;143:179e86.

[39] Hossain MM, de Lasa HI. Chemical-looping combustion (CLC)for inherent CO2 separations d a review. Chem Eng Sci2008;63:4433e51.

[40] Sedor KE, Hossain MM, de Lasa HI. Reactivity and stability ofNi/Al2O3 oxygen carrier for chemical-looping combustion

(CLC). Chem Eng Sci 2008;63:2994e3007.

[41] Son SR, Kim SD. Chemical-looping combustion with NiO andFe2O3 in a thermobalance and circulating fluidized bedreactor with double loops. Ind Eng Chem Res

Cu-, Fe-, and Ni-based oxygen carriers in chemical-loopingcombustion. Chem Eng Sci 2007;62:533e49.

[64] Cho P, Mattisson T, Lyngfelt A. Carbon formation on nickel

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 9 ( 2 0 1 4 ) 8 5 3 5e8 5 5 685562006;45:2689e96.[42] Readman JE, Olafsen A, Smith JB, Blom R. Chemical looping

combustion using NiO/NiAl2O4: mechanisms and kinetics ofreductioneoxidation (Red-Ox) reactions from in situ powderX-ray diffraction and thermogravimetry experiments. EnergyFuels 2006;20:1382e7.

[43] Kruggel-Emden H, Stepanek F, Munjiza A. A comparativestudy of reaction models applied for chemical loopingcombustion. Chem Eng Res Des 2011;89:2714e27.

[44] Evans JW, Song S, Leon-Sucre CE. The kinetics of nickel oxidereduction by hydrogen; measurements in a fluidized bed andin a gravimetric apparatus. Metall Trans B 1976;7:55e65.

[45] Galwey AK, Brown ME. Kinetic models for solid-statereactions. Amsterdam: Elsevier; 1999.

[46] Brown ME. The Prout-Tompkins rate equation in solid-statekinetics. Thermochim Acta 1997;300:93e106.

[47] Miura K, Silveston P. Analysis of gas-solid reactions by use ofa temperature-programmed reaction technique. EnergyFuels 1989;3:243e9.

[48] Kiado A. Kinetics of isothermal thermogravimetricaldegradation of PVC/ABS blends. J Therm Anal Calorim1995;45:1572e8943.

[49] Kissinger H. Reaction kinetics in differential thermalanalysis. Anal Chem; 1957:1702e6.

[50] Friedman HL. Kinetics of thermal degradation of char-forming plastics from thermogravimetry. Application to aphenolic plastic. J Polym Sci C Polym Symp 1964;6:183e95.

[51] Flynn JH, Wall LA. General treatment of thethermogravimetry of polymers. J Res Natl Bur Stand PhysChem 1966;70:487e523.

[52] Ozawa T. A new method of analyzing thermogravimetricdata. Bull Chem Soc Jpn 1965;38:1881e6.

[53] Akahira T, Sunose T. Joint convention of four electricalinstitutes. Res Rep Chiba Inst Technol 1971;16:22e31.

[54] Malek J. The kinetic analysis of non-isothermal data.Thermochim Acta 1992;200:257e69.

[55] Han L, Zhou Z, Bollas GM. Heterogeneous modeling ofchemical-looping combustion. Part 1: reactor model. ChemEng Sci 2013;104:233e49.

[56] Hancock JD, Sharp JH. Method of comparing solid-statekinetic data and its application to the decomposition ofkaolinite, brucite, and BaCO3. J Am Ceram Soc 1972;55:74e7.

[57] Son SR, Go KS, Kim SD. Thermogravimetric analysis ofcopper oxide for chemical-looping hydrogen generation. IndEng Chem Res 2009;48:380e7.

[58] Burnham KP, Anderson DR. Model selection andmulti-modelinference: a practical information-theoretic approach. 2nded. New York: Springer-Verlag; 2002.

[59] Mood AM. Introduction to the theory of statistics. 3rd ed.McGraw-Hill; 1974.

[60] Ludden T, Beal S, Sheiner L. Comparison of the Akaikeinformation criterion, the Schwarz criterion and the F test asguides to model selection. J Pharmacokinet Biopharm1994;22:431e45.

[61] Matlab. Matlab 2013a; 2013.[62] Chatterjee R, Banerjee S, Banerjee S, Ghosh D. Reduction of

nickel oxide powder and pellet by hydrogen. Trans IndianInst Met 2012;65:265e73.

[63] Abad A, Adanez J, Garca-Labiano F, de Diego LF, Gayan P,Celaya J. Mapping of the range of operational conditions forand iron oxide-containing oxygen carriers for chemical-looping combustion. Ind Eng Chem Res 2005;44:668e76.

[65] Sedor KE, Hossain MM, de Lasa HI. Reduction kinetics of afluidizable nickel-alumina oxygen carrier for chemicallooping combustion. Can J Chem Eng 2008;86:323e34.

[66] Szekely J, Evans JW, Sohn HY. Gas-solid reactions. New York:Academic Press, Inc.; 1976.

[67] Cho P, Mattisson T, Lyngfelt A. Comparison of iron-, nickel-,copper- and manganese-based oxygen carriers for chemical-looping combustion. Fuel 2004;83:1215e25.

[68] Han L, Zhou Z, Bollas G. Heterogeneous modeling ofchemical-looping combustion. Part 2: particle model. ChemEng Sci 2014. http://dx.doi.org/10.1016/j.ces.2014.03.030 [inpress].

[69] Malek J. The applicability of Johnson-Mehl-Avrami model inthe thermal analysis of the crystallization kinetics of glasses.Thermochim Acta 1995;267:61e73.

[70] Christian JW. Chapter 12 e formal theory of transformationkinetics. In: Christian JW, editor. The theory oftransformations in metals and alloys. Oxford: Pergamon;2002. pp. 529e52.

[71] Yan ZJ, He SR, Li JR, Zhou YH. On the crystallization kineticsof Zr60Al15Ni25 amorphous alloy. J Alloys Compd2004;368:175e9.

[72] Tanaka H, Yamamoto A, Shimoyama J, Ogino H, Kishio K.Strongly connected ex situ MgB2 polycrystalline bulksfabricated by solid-state self-sintering. Supercond SciTechnol 2012;25:115022.

[73] Zhou Z, Han L, Bollas GM. Model-based analysis of bench-scale fixed-bed units for chemical-looping combustion.Chem Eng J 2013;233:331e48.

[74] Iliuta I, Tahoces R, Patience GS, Rifflart S, Luck F. Chemical-looping combustion process: kinetics and mathematicalmodeling. AIChE J 2010;56:1063e79.

[75] Zhou Z, Han L, Bollas GM. Overview of chemical-loopingreduction in fixed bed and fluidized bed reactors focused onoxygen carrier utilization and reactor efficiency. Aerosol AirQual Res 2014;14:559e71.

[76] Han L, Zhou Z, Bollas GM. Chemical-looping combustion in areverse-flow fixed-bed reactor. Appl Energy; 2014 [in review].

[77] Gayan P, Dueso C, Abad A, Adanez J, de Diego LF, Garca-Labiano F. NiO/Al2O3 oxygen carriers for chemical-loopingcombustion prepared by impregnation anddepositioneprecipitation methods. Fuel 2009;88:1016e23.

[78] Vogel S, Ustundag E, Hanan J. In-situ investigation of thereduction of NiO by a neutron transmission method. MaterSci Eng A 2002;333:1e9.

[79] Alizadeh R, Jamshidi E, Ale Ebrahim H. Kinetic study of nickeloxide reduction by methane. Chem Eng Technol2007;30:1123e8.

[80] Rashed A, Rao Y. Kinetics of reduction of nickel oxide withhydrogen gas in the 230e452 C range. Chem Eng Commun1997;156:1e30.

[81] Rashidi H, Ale EbrahimH,Dabir B. Application of randomporemodel for synthesis gas production by nickel oxide reductionwith methane. Energy Convers Manag 2013;74:249e60.

[82] Siriwardane R, Poston J, Chaudhari K, Zinn A, Simonyi T,Robinson C. Chemical-looping combustion of simulatedsynthesis gas using nickel oxide oxygen carrier supported onbentonite. Energy Fuels; 2007:1582e91.

Kinetics of NiO reduction by H2 and Ni oxidation at conditions relevant to chemical-looping combustion and reformingIntroductionSolid-state kinetic model and mechanismsModel discrimination and identification methodsModel-free methodModel-fitting methodStatistical analysis

Results and discussionReduction of unsupported NiOTokyo NiO, Plascencia and Utigard [18]Sinter 75 NiO, Plascencia and Utigard [18]Goro NiO, Utigard et al. [19]NiO pellet, Chatterjee et al. [62]

Reduction of supported NiONiO/-Al2O3 oxygen carrier, Abad et al. [36,63]NiO/-Al2O3 oxygen carrier, Chen and Shiue [35]NiO/YSZ oxygen carrier, Ishida et al. [30]NiO/-Al2O3 oxygen carrier, Dueso et al. [25]NiO/-Al2O3 oxygen carrier, this workExperimentalEffect of temperature

NiO/-Al2O3 oxygen carrier, this work

Oxidation of supported NiONiO/-Al2O3 oxygen carrier, Abad et al. [36,63]NiO/-Al2O3 oxygen carrier, Dueso et al. [25]NiO/MgAl2O4 oxygen carrier, Zafar et al. [32]NiO/YSZ oxygen carrier, Ishida et al. [30]NiO/-Al2O3 oxygen carrier, this work

ConclusionsAcknowledgmentsAppendix A Supplementary dataReferences