ZSM-5 catalyst

ZSM-5 catalyst

Energy Conversion and Management 192 (2019) 269–281 Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www...

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Energy Conversion and Management 192 (2019) 269–281

Contents lists available at ScienceDirect

Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

General dynamic modeling of monolith catalytic reactors: Microkinetics of dimethyl ether oxidation on Pt/ZSM-5 catalyst


Gonzalo A. Almeida Pazmiñoa,b, Seunghun Junga,


Department of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería Mecánica y Ciencias de la Producción, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador




Keywords: Monolith reactor Dimethyl ether Catalytic oxidation Modeling Kinetics

A general dynamic model of monolith catalytic reactors is developed to elucidate the intricacy of the physicochemical phenomena that occur in it. The dynamic model includes microkinetic reactions on a washcoat catalyst, which is incorporated in a heat- and mass-transfer submodels based on the quasi-2D analysis of both gasand solid-phases. To support the derived general dynamic model, a dimethyl ether (DME) catalytic reactor that uses Pt/ZSM-5 catalyst is developed and simulated. The model prediction is validated using experimental data under nearly stoichiometric, lean, and rich conditions. The results demonstrate the catalytic conversion and selectivity of DME, distribution of the wall temperature, distribution of the species concentrations in steadystate, and transient response of the catalytic reaction. We found that the time response of the lowest inlet-gas temperature can be yielded ∼6 min, but the DME conversion is limited under the low-temperature condition.

1. Introduction The application of reactors with monolith channels has been expanding to chemical process and refining industries, catalytic combustion, ozone reduction, etc. [1]. The advantageous features of monolith reactors over traditional technologies have encouraged researchers to investigate and improve the performance of monolith reactors for other applications such as catalytic combustion [2–5], catalytic reduction [6,7], catalytic oxidation [8–10], hydrogenation or dehydrogenation [11,12], and methanation [13,14]. To further understand the complex physicochemical phenomena that occur in monolith reactors, a computational approach must be considered. Chen et al. [15] published a comprehensive review of mathematical modeling studies and simulation works of monolith catalytic reactors used in various gas-phase reactions applied to numerous fields. Important approaches were presented in this review; for example, one-dimensional modeling might be necessary to elaborate the heat- and mass-transfer models in bulk flow, together with those in catalytically porous microstructures and support materials. Subsequently, accurate estimation of the heat- and mass-transfer coefficients is necessary. Moreover, chemical kinetics play an important role and may be critical in some cases; detailed and elementary step-based kinetics are required to realize accurate simulation results. Another approach is to consider the monolith reactor performance as a function of ⁎

various inlet conditions. Therefore, to simulate the dynamic changes, a transient model is used. The dynamic response time primarily depends on the thermal inertia: a low thermal inertia (metallic) leads to a faster dynamic response than that observed for higher thermal inertia (ceramic). Small channel size, square channel shape, high inlet temperature and pressure, and low gas velocities also generate faster dynamic responses. Furthermore, periodic switching between lean- and rich-fuel conditions can greatly improve the overall catalytic conversion in a monolith catalytic reactor. Studying the transient behavior is also critical when irregular operations are implied, such as under coldstart, light-off, or feed-oscillation conditions. Catalytic reactors in automotive applications are typically operated under dynamic conditions such as a cold startup, wherein the cold catalytic reactor is directly exposed to hot inlet gas that induces an abrupt fluctuation in the gas temperature. In contrast, cold inlet gas can be instantly introduced into a catalytic reactor that has been under a high-temperature condition. Furthermore, knowing the time constant when the catalytic reaction is stabilized under a dynamic condition is important, and is critical in automotive applications. For example, the apparatus efficiency during the warm-up interval is a notable feature in the design of catalytic converters. Consequently, comprehending the manner in which heat is produced and scattered in catalytic monoliths through exothermic chemical reactions under transient conditions is essential.

Corresponding author. E-mail addresses: [email protected]ac.kr, [email protected] (S. Jung).

https://doi.org/10.1016/j.enconman.2019.04.034 Received 31 December 2018; Received in revised form 24 March 2019; Accepted 9 April 2019 Available online 18 April 2019 0196-8904/ © 2019 Elsevier Ltd. All rights reserved.

Energy Conversion and Management 192 (2019) 269–281

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T S u¯ x

List of symbols stoichiometric coefficient of species in reactions pre-exponential factor cross-sectional area, m2 specific heat, J kg−1 K−1 concentration, (moles of species) (total moles in gas mixture)−1 [CO2] CO2 concentration, (moles of species) (total moles in gas mixture)−1 D diffusion coefficient, m2 s−1 DH hydraulic diameter, m [DME] DME concentration, (moles of species) (total moles in gas mixture)−1 Ea activation energy, J mol−1 fCO2 CO2 flowrate, ml min−1 fDME DME flowrate, ml min−1 h heat transfer coefficient, W m−2 K−1 hm mass transfer coefficient, m s−1 enthalpy of reaction, J mol−1 H k thermal conductivity, W m−1 K−1 rate constant for surface reaction, mol g−1 s−1 k K rate constant for adsorption of DME and O2, m3 mol−1 L perimeter, m M molecular weight, kg mol−1 N1, N2, N3, N4 constants P pressure, Pa Pt active site for chemisorption on the solid catalyst [Pt ] vacant surface concentration of Pt, mol mol−1 [Pt]0 total site density of Pt, mol m−2 r reaction rate, mol m−3 s−1 Ru universal constant of gases, 8.314 J mol−1 K−1 t time, s

a A Ac cp C

x X y

temperature, K DME selectivity, % uniform average velocity, m s−1 length coordinate, m volume element length, m DME conversion rate, % molar fraction

Greek letters



µ λ ρ

specific catalyst area (catalytically active area per washcoat volume), m2 m−3 washcoat thickness, m porosity Lennard–Jones energy constant, J fraction of adsorbed dimethyl ether fraction of adsorbed oxygen Boltzmann gas constant, J K−1 viscosity, Pa s stoichiometric ratio density, kg m−3 Lennard–Jones length constant, m function of molecular weights and viscosities of the mixture components

Superscripts and subscripts g i in j k out s 0

Dimethyl ether (DME) is a synthesized fuel that possesses numerous features, namely, it can be easily synthesized via renewable routes, is relatively inert, noncorrosive, and chemically benign, resembles liquefied petroleum gas (LPG) in its physical properties [16], has a high energy density, and has a low reforming temperature [17]. The DME production technique includes methanol dehydration or direct syngas conversion [18,19]. Therefore, DME is considered a promising alternative fuel [20]. Owing to its eco-friendly features (low NOx, HC, and CO emissions) and energy security, potential applicability of DME in compression ignition engines as an alternative to conventional diesel fuel or as a fuel mixture [21–23], as an alternative fuel to LPG in domestic appliances [24], and as a DME-methanol fuel mixture in directmethanol fuel cells [25], is under study, showing promising results. Compared to homogeneous DME combustion, which has been widely researched, DME catalytic combustion has been scarcely reported. As catalytic combustion has been proven to effectively enhance the combustion efficiency and reduce pollutant emission [26], catalytic combustors of selected hydrocarbon fuels are under development [27–30]. Since the catalytic oxidation of DME occurs at relatively low temperatures, it is currently being investigated [16,31–34] as a heat source in the fuel reforming process to generate hydrogen in fuel-cell systems [17,35]. Solymosi et al. [36] reported the following order of catalyst activity in DME oxidation: Ru > Pt > Ir > Pd > Rh. However, such reactivity trends were measured for clusters of different dispersions [Ru (0.06), Pt (0.41), Ir (0.76), Pd (0.23), and Rh (0.46)]. Among noble metals, supported platinum is preferred for the oxidation of hydrocarbons. Deng et al. [31] conducted a kinetic analysis on the deep

gas phase number (integer) inlet number (integer) number (integer) outlet solid phase standard condition

oxidation of DME over Pt/ZSM-5 below 423 K, employing the powerlaw and Langmuir–Hinshelwood (L–H) models to describe the reaction kinetics. The purpose of this paper is to present a general method of performing dynamic modeling and simulation of a monolith catalytic reactor by focusing on the heat- and mass-transfer, and chemical microkinetics on a washcoat catalyst. To accomplish this objective, a computational quasi-2D model of DME catalytic oxidation over Pt/ ZSM-5 was developed using MATLAB/Simulink, based on experimental mechanistic kinetics, to comprehend the intricacy of interactions in the various physicochemical phenomena that occur in a monolith catalytic reactor. 2. Mathematical model This study considers a ceramic-based catalytic reactor consisting of several monolith channels, as shown in Fig. 1a. We assume that every monolith channel is identical. Therefore, the proposed model focuses on a one-dimensional, fully developed flow of the gas mixture through a single square monolith channel in the catalytic reactor. The gas mixture is assumed to have an incompressible laminar flow following the idealgas law, and the pressure drop through the monolith is ignored. Additional assumptions for this model are as follows: (i) Uniform distribution of catalyst loading. (ii) The single square channel is insulated; hence, heat loss is negligible. (iii) Uniform average velocity. 270

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Fig. 1. Schematic representation of catalytic monolith reactor: (a) 3D view, (b) cross-sectional view of a single square channel.

(iv) Volume change of gas mixture during the reaction is negligible.

dCi,g dt

Since the radial gradients of the temperature and concentration are insignificant under the standard condition [37], a quasi-2D heterogeneous model simplifies the complexity of the radial interphase transport using the gas–solid heat- and mass-transfer coefficients, as shown in Fig. 1b and c.




u¯ (Ci,g,in x

4h m.i (Ci,g DH

Ci,g )

g dCi,s g 4h m.i = (Ci,g Mg dt Mg D H



Ci,s ) +

(ai,k r k ) k


Accordingly, the heat balances are expressed as follows:

dTg dt

2.1. Heat and mass balances


The present model considers the convection in the gas-phase, heatand mass-transfer processes between the gas and solid phases, and heterogeneous chemical reactions in the washcoat. The heat- and masstransfer processes in the catalytic reactors have been discussed for various applications [38,39]. The heat- and mass-transfer coefficients are calculated from Nusselt (Nu) and Sherwood (Sh) numbers, respectively. The monolith reactor is spatially discretized in the axial direction, as shown in Fig. 1a, and the heat and mass balances in the gas phase and washcoat are numerically solved for each volume element. Fig. 1b and c show that the mass balance for component i in the gas phase and washcoat are expressed by the following equations:


u¯ (Tg,in x

) s cp,s

Tg )

1 4h (Tg g c p,g D H

dTs 4h = (Tg dt DH


(r k Hk0)

Ts) + k

(3) (4)

The followings are considered as boundary and initial conditions: dT Inlet (x = 0) Ci = Ci,0 , Tg = Tg,0 , Ts = Ts,0 , dts = 0 0 Initial (t = 0) Ci,s = 0 , Ts = T . Ci,g,in and Ti,g,in represent the inlet-species concentration and inletgas temperature of each computational node, respectively, and become the outlet parameters from the previous computational node. The rate of the kth reaction taking place on the surface of the catalyst is denoted as rk in the above equations. Pattas et al. [40] demonstrated that the left-hand side terms in Eqs. 271

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(2) and (4) present considerable difficulties for the numerical solution of the system of equations using implicit solvers. Hence, the temperature of the washcoat and the surface coverages are treated in a fully transient manner, whereas the gas-phase concentrations and temperature are assumed to be in a steady state for the numerical solution. The resulting system of differential algebraic equations is numerically integrated using MATLAB/Simulink in this study. The Reynolds (Re), Nu, and Sh numbers for an internal flow are based on the hydraulic diameter DH = 4A c / L . For a laminar flow (Re < 2300), Nu and Sh values are set to 3.608, which is the average of the fully developed flow values for square cylinders under constant wall-temperature and constant wall-flux conditions [41]. The heat- and mass-transfer coefficients are defined as


1 n

Di =





DAB = 0.001858T 3/2




cp,g =

P P0






1 yi


1 yi


is the collision integral

µg = i



Pr =


i,j yj



i,j yj



1/4 2

( ) {8 (1 + ) }


and oAB = ( oA oA )1/2 where T = oAB The calculation for the binary diffusion coefficient, under different pressure and temperature conditions, is described as follows: Tg

yi c p,i i




In addition, the thermophysical properties of the gas phase, including the constant-pressure specific heat, thermal conductivity, dynamic viscosity, and Prandtl number, are evaluated based on the properties of the individual species according to the following equations:



yi Mi i





1.06036 0.19300 1.03587 1.76474 + + + (T )0.15610 exp(0.47635T ) exp(1.52996T ) exp(3.89411T )


PMg Ru Tg

kg =

where fD is the second-order corrector and [43] that is defined as D


where Mg is determined using the equation:

Different theoretical models are available for computing the diffusion coefficient of a binary-gas mixture DAB. For non-polar molecules, the Lennard-Jones potential offers a basis for obtaining the diffusion coefficients of binary-gas mixtures. The mutual diffusion coefficient of species A and B is defined as [42]



By considering small temperature and pressure gradients along the volume element, the density of the mixture is calculated based on an ideal-mixture state equation at the working temperature and pressure, as follows:

Mg =



i=1 j 1


hm =





Mi Mj

Mi Mj



µg c p,g kg



2.2. Catalytic kinetics


Catalytic kinetics are studied from two different perspectives: (i) microkinetics, which involves detailed surface reactions, and (ii) global kinetics, which consists of a simplified explicit rate expression for the

Owing to the presence of multispecies in the gas mixture, the diffusion coefficient for the species i in the mixture is related to the binary diffusion coefficients, as expressed by Blanc’s Law [43]:

Fig. 2. Schematic of the catalytic oxidation mechanism in the developed DME catalytic reactor model. 272

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overall reaction. Microkinetic modeling contributes important information regarding the mechanistic side. Recently, some reports on the catalytic oxidation of DME, presenting a high thermal efficiency of the DME engine and reduced greenhouse-gas emissions, have been published [44,45]. Additionally, DME oxidation is an exothermic reaction that can be thermally combined with endothermic steam reforming to optimize the reactor size and increase energy efficiency. Jung et al. [17] proposed a compact combined DME reforming system that includes both DME catalytic combustor and DME catalytic reformer for hydrogen production. To investigate its feasibility, a three-dimensional computational model of the integrated system, using a computational fluid dynamics approach was constructed. The effects of the reactant flowrate, thermal conductivity of the substrate, flow direction, and porosity of the catalyst layers on the hydrogen production efficiency were analyzed at steadystate. In this work, elaborate surface microkinetics that includes species adsorption, species desorption, and surface reaction is employed to study the time-dependent DME catalytic oxidation on a Pt catalyst supported on ZSM-5 in a low inlet-gas temperature range. Earlier studies of DME oxidation on Pt catalysts [16,31] reported that the powerlaw and Langmuir–Hinshelwood (L–H) models could appropriately describe the kinetics of the catalytic oxidation. Therefore, the L–H model is selected for the microkinetics of the deep oxidation of DME over a Pt/ZSM-5 catalyst in this study. As presented in Fig. 2, a heterogeneous catalytic reaction involves the following: i) adsorption of reactants from a fluid phase onto a solid surface, ii) surface reaction of the adsorbed species, and iii) desorption of the products into the fluid phase. The necessary first step in a heterogeneous catalytic reaction includes the activation of the reactant molecules by adsorption onto a catalyst surface. The activation step implies that a strong chemical bond is formed with the catalyst surface (chemisorption). The following assumptions for a heterogeneous catalytic reaction are adopted in this work:

be determined by

rDME = rO2 =



( H0 =

1, 460.4 kJ mol - 1)


O2 + 2Pt (CH3) 2 O

(CH3)2 O

K O2

Pt + O

2O Pt

(Surface reaction)

k [Pt]0




d O = K O2 [O2 ]s dt where v = 1

the DME

2 v


2 O






KDME [DME]s 1 + KDME [DME]s +


K O2 [O2 ]s

K O2 [O2 ]s 1 + KDME [DME]s +


K O2 [O2 ]s

k [Pt]0

KDME [DME]s K O2 [O2 ]s (1 + KDME [DME]s +

K O2 [O2 ]s ) 2



HDME/(Ru Ts)]


HO2/(Ru Ts)]


The Arrhenius form of the reaction rate constant is an empirical relationship. Deng et al. [31] determined the kinetic parameters for the L–H model according to the method introduced by Tseng et al. [47]. 2.3. Model setup The catalytic monolith reactor considered in this study possesses the general characteristics shown in Fig. 1. It consists of flow channels, catalyst layers, and a substrate. As shown, the monoliths are stacked in a repeating pattern; therefore, a square single-reactor unit is chosen for the numerical study to simplify the modeling task. To solve the general governing equations, which are represented as partial differential equations, the reactor is spatially discretized into multiple equally positioned nodes, and the equations are converted into a group of ordinary differential equations. The node spacing is kept constant throughout the catalytic reactor length. The model is divided into 1 oblique node × 16 longitudinal nodes for the monolith catalyst reactor. The streamflow and fluid–solid convective mass transfer directions are considered as the axial and radial directions, respectively. Fig. 1c shows an expanded single node. The values of the dimensions of the reactor and chemical properties are taken from reference experimental studies, and are listed in Tables 1 and 2, respectively. The developed computational model is incorporated into MATLAB/ Simulink, as shown in Figs. S1–S3. Fig. S1 shows the model structure of a partial catalyst reactor. The system incorporates subsystems that are



where each of the fractional coverages DME and O can be expressed in the form of a Langmuir isotherm for the competitive adsorption of DME and O2, as shown in Eqs. (21) and (22).



k = A exp[ Ea/(Ru Ts )]




K O2 = AO2 exp[

The simplified rate expression for this bimolecular reaction is expressed as follows:


d O dt

KDME = ADME exp[

Pt (Adsorption of DME)



The temperature dependence of rate coefficients KDME , K O2 , and k is described by the following Arrhenius expressions:

Pt (Adsorption of O2) k



A rate expression for the reaction of DME and O2 to form products can be developed by assuming an irreversible, rate-determining, and bimolecular surface reaction, as follows:

(CH3) 2 O+ Pt



Therefore, the overall rate of the reaction of DME and O2 can be expressed from Eqn. (20) as:

The overall DME oxidation can be expressed as shown in Eqn. (18).

3H2 O+ 2CO2



For a steady-state equilibrium relationship (rDME = rO2 = 0 ), Eqs. (21) and (22) become DME = KDME [DME ]s v and O2 = K O2 [O2]s v2 , respectively. Combining θDME and θ0 with v , the Langmuir adsorption isotherms can be calculated using the following equations:

i) Reactants are present in a single fluid phase (i.e., either liquid or gas). ii) All surface sites have the same energetics characteristics for adsorption. iii) The adsorbed molecules do not interact with one another [46].

(CH3) 2 O+ 3O2


Table 1 Geometric parameters of the monolith reactor models [17].

(21) (22)

fraction of vacant sites is determined using O . Then, the net rates of dissociative adsorption can 273





Channel length Channel width Channel height Washcoat catalyst layer thickness Substrate thickness

Lchn Wchn Hchn

50.0 1.0 1.0 80.0 0.5

mm mm mm μm mm


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G.A. Almeida Pazmiño and S. Jung

of the DME conversion rate under lean (λ = 4.21), stoichiometric (λ = 1), and rich (λ = 0.8) conditions of the feeding rate of DME and air at different particular gas-hourly space velocity (GHSV) values were compared with the experimental results, as shown in Fig. 3a. The following DME conversion rate ‘X’ proposed by Deng et al. [31], where fCO2,out and fDME,in are the outlet flow rate of CO2 and inlet flow rate of DME measured in ml min−1, respectively, is adopted to evaluate the performance of the DME catalytic combustion in the present study.

Table 2 Values of chemical properties used in the monolith reactor models [31]. Description Site density Catalyst specific area Catalyst density Catalyst porosity Gas hour space velocity Pressure DME inlet concentration O2 inlet concentration N2 inlet concentration


[Pt]0 γ s

GHSV P CDME,in CO2,in CN2,in


Unit −5

2.72 × 10 1.5 × 106 2000 0.5 125,000 101.3 × 103 15,000 205,000 780,000

mol m−2 m2 m−3 kg m−3 – h−1 Pa ppm ppm ppm


2fCO2 ,out fDME,in

× 100 (%)


Generally, the DME conversion is defined as the ratio between the overall DME consumption rate and the DME inlet flowrate. However, since it is difficult to precisely measure the DME consumption in the low-temperature regime, it is often replaced with the equivalent CO2 generation at the DME conversion rate. On employing Eq. (31) to obtain the simulated DME conversion under the three conditions, we observed that in all the cases, the DME conversion rate gradually increased according to the inlet-gas temperature because DME catalytic combustion is quite an exothermic reaction with a high energy content ( H 0 = 1, 460 kJ mol - 1). Under the stoichiometric condition, although the GHSV is higher than that in lean feeding, the DME conversion also appears to be higher at 365–385 K. On the other hand, it appears to be much lower for the fuel-rich intake. The conversion response of the kinetic model under λ = 0.8 is inferior to those under other conditions because the GHSV is almost four times higher, leading to an insufficient residence time for the catalytic reaction. Additionally, it is expected that the higher oxygen concentration would improve the reactor performance. Although the experimental reference [31] did not present DME conversion values higher than 8%, the developed model, which adopted microkinetics, predicted that the DME monolith catalytic combustor would achieve reaction temperatures T10 and T90 of ∼392 and ∼437 K, respectively, when the reactor is operated under lean conditions (λ = 4.21) and at GHSV = 125,000 h−1. Calculating the oxygen selectivity in the DME oxidation process is also important to predict its selective reactivity in the presence of DME, N2, and the products; therefore, we use Eqn. (32) to obtain the O2 selectivity percentage. The results are shown in Fig. 3b.

each denoted as “Catalyst”. The primary outputs of the whole system are the outlet concentrations of the reactants and products, the heat generated in all the nodes in the system, the exhaust gas temperature, and the wall-temperature values. Fig. S2 shows the components in Fig. S1 corresponding to four subsystems that are each named as “Node;” each subsystem represents one reactor node. Fig. S3 shows the heatand mass-transport model of every node, which is embedded using the mean gas-mixture temperature, wall temperature, and heat-generation signals. For each of these subsystems, the mathematical models are simultaneously computed, and represent the physicochemical phenomena that occur in a real catalytic monolith reactor. Table 2 lists the set of parameters employed in the DME catalytic reactor modeling. For this reactor, the parametric sensitivity of the system is investigated by systematically varying the key operating parameter to obtain a better insight into the transient system performance. 3. Results and discussion To simulate the detailed surface kinetics involving the adsorption, desorption, and surface reactions of the DME catalytic oxidation on the Pt catalyst, a microkinetic reaction scheme consisting of the adsorption and desorption of DME and O2 is used. It also includes the surface reactions of the adsorbed species, namely, (CH3)2O–Pt and O–Pt. The form of the reaction rates and the corresponding kinetic constants are acquired from the report of Deng et al. [31] and are listed in Table 3. The adsorption constant of DME has been reported to be higher than that of O2, which indicates that DME is more strongly adsorbed on the catalyst. In addition, when the catalyst surface is saturated with a high content of DME, the reaction is significantly promoted upon increasing the O2 concentration of the inlet gas. To evaluate the performance of a catalyst, the catalytic activity can be confirmed by measuring the reactant conversion. Cheng et al. [34] presented an experimental research on DME catalytic combustion over α-MnO2 catalyst with different morphologies (nano-rod, ultra-long nano-wire, and micro-sphere), and reported that the α-MnO2 nano-rods exhibited a higher catalytic activity than those of other morphologies at reaction temperatures T10 (DME conversion = 10%) and T90 (DME conversion = 90%) of 443 and 511 K, respectively. This demonstrates that the morphologies of catalysts have a significant impact on catalytic combustion. Through catalyst characterization, it was revealed that αMnO2 nano-rods possess a larger specific surface area, higher average oxidation state of Mn, more abundant surface lattice oxygen, and higher reducibility than those of other morphologies. Sun et al. [32] studied catalysts of transition metals (Fe, Co, Ni, Cu, and Cr) doped with cryptomelane-type manganese oxide (M−OMS−2), which has a nanorod morphology, to investigate DME catalytic combustion. They found that the Cu-doped OMS-2 catalyst exhibited the best activity, achieving a T10 and T90 of 444 and 453 K, respectively. This result was attributed to a higher average oxidation state, reducibility, and oxygen mobility, which were proven via additional physicochemical characterizations. To check the fidelity of the developed model, the simulation results


3(fDME,in (fO2 ,in

fDME,out ) fO2 ,out )

× 100 (%)


Fig. 3b shows the variation in the O2 selectivity with the inlet-gas temperature ranges for the lean and stoichiometric intakes. For the lean condition, a discrete change in the selectivity was observed at 368–373 K and 378–388 K intervals. Hence, the characteristics are similar, although the value of the O2 selectivity at Tg,inlet = 388 K is the highest (100%). On the other hand, in the range between 373 and 378 K, the variation in the O2 selectivity, which is ∼10%, is prominent. A different tendency was observed for the selectivity curve under the stoichiometric condition (λ = 1); the selectivity remains constant at 100% for all the ranges of the inlet-gas temperature. Overall, the O2 selectivity is high (> 80%) even at a low inlet-gas temperature and under the lean condition. It was predicted that a higher inlet-gas temperature further would improve the O2 selectivity owing to the Table 3 Kinetics parameters of the DME monolith reactor model [31]. Description Pre-exponential factor Pre-exponential factor Pre-exponential factor Activation Energy Activation Energy Activation Energy






Unit 9

(7.17 ± 0.36) × 10 (6.00 ± 0.12) × 10−6 (1.05 ± 0.02) × 10−2 109.30 ± 3.21 −33.18 ± 1.80 −6.05 ± 0.28

mol g−1 s−1 m3 mol−1 m3 mol−1 kJ mol−1 kJ mol−1 kJ mol−1

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conversion: a higher DME conversion corresponds to a higher selectivity. Fig. 4 shows the species concentration distribution in the catalytic reactor according to the inlet-gas temperature when the GHSV is 125,000 h−1. The inlet gas is composed of DME (15,000 ppm) and air (O2 = 205,000 ppm and N2 = 780,000 ppm). It was observed that as the DME concentration gradually decreased, the CO2 concentration increases along the channel direction for the stoichiometric relationship (DME:CO2 = 1:2). The inlet temperature had a strong influence on the reaction strength; hence, at an elevated inlet-gas temperature, greater DME oxidation, resulting in a large amount of CO2 emission, was observed. The heterogeneous DME oxidation on the active Pt catalyst surface results in the consumption of DME by liberating the energy in the form of heat. For instance, the change in the DME concentration at an inlet-gas temperature of 368 K is 56.7 ppm; however, it is 215.9 ppm at 388 K. The Pt catalyst becomes more active under elevated temperature conditions, which results in the decreases in DME concentration accompanied with heat release from the reactor. In addition, a proportional growth (1:1) in the product (CO2 and H2O) concentration is shown under the same reaction behavior. Fig. 5 shows the influence of the inlet-gas temperature on the wall temperature along the catalytic reactor for a lean mixture (λ = 4.21) with the GHSV of 125,000 h−1. The reaction rate is a function of the wall temperature, which implies that heat generation is also affected. A part of this heat increases the wall temperature, and the other part increases the exhaust gas temperature through convection. Fig. 5 shows that the wall temperature also increases in the DME catalytic reactor because the inlet-gas temperature is higher (being the highest at 388 K). In addition, all the curves confirm the idea that catalytic oxidation at a low inlet-gas temperature is possible using the Pt/ZSM-5 catalyst, which can be useful for operations with less energy input. The effects of the gas flowrate (GHSV) on the exhaust gas temperature and DME conversion rate are shown in Fig. 6a and 6b, respectively. Fig. 6a can be divided into two segments for every inlet-gas temperature profile, which we have designated as the “low-velocity regime” and “high-velocity regime”. In the low-velocity regime (< 40,000 h−1), a slight increase in the exhaust gas temperature occurs up to a maximum value that differs with the inlet-gas temperature values. For example, at Tg,in = 368 K , the highest exhaust gas temperature is ∼428 K at GHSV = ∼28,000 h−1. However, at Tg,in = 388 K , the peak exhaust gas temperature is ∼435 K at GHSV = ∼40,000 h−1. On the other hand, in the high-velocity regime, the exhaust gas temperature considerably decreases with an increase in the GHSV, especially at the lowest inlet-gas temperature. These physical behaviors can be explained by simultaneously solving Eqs. (1)–(4), for which Ci,s and Ts are determined as follows:

Fig. 3. Steady-state simulation result of (a) DME conversion rate, and (b) O2 selectivity according to the inlet-gas temperature.

Ci,s =

increased surface coverage of O on the Pt particles, which plays an important role in the selectivity process. Prior to catalytic combustion, DME occupies nearly the entire catalyst surface area until the temperature reaches the adequately elevated level required to induce combustion. When the catalyst temperature reaches an elevated level, DME can initiate the desorbing process from the catalyst surface while it allows the molecular absorption of O2, and the oxidation reaction between the adsorbed DME and O occurs. An augmentation in the vacant site concentration occurs when O gets released from the catalyst surface at highly elevated temperatures [48] (see Fig. 2). Therefore, the O2 selectivity will eventually diminish under highly elevated temperatures beyond the temperature range shown in Fig. 3b. Furthermore, it is worth mentioning that the reaction selectivity depends on the kind of oxidative gas and the preparation methods of the catalyst [49]. A comparative analysis of Fig. 3a and b shows an evident relationship between the DME selectivity and DME

Ts =

1 dCi,g N1 dt

1 dTg N3 dt

u¯ (Ci,g,in N2

u¯ (Tg,in N4

Ci,g ) + Ci,g

Tg ) + Tg

(33) (34)

The terms N1, N2, N3, and N4 in the above equations are summarized as constants. The values of the terms dCi,g/ dt and dTg /dt increase with the gas velocity. Therefore, the species concentration on the catalyst, the surface reaction, and the heat generation increase in the low-velocity regime. On the contrary, the second terms in the right-hand side of Eqs. (33) and (34) increase in value in the high-velocity regime, which leads to a reduction in the species concentration on the catalyst surface, poorer reaction, and lower heat generation. Fig. 6b shows a behavior similar to that shown in Fig. 6a, which implies that two segments of a curve are present, and their GHSV values almost match when the DME conversion always decreases. Evidently, the DME conversion rate improves under a lower flowrate condition in the simulation because the catalytic reaction on the surface of the washcoat layer requires some time for reactant absorption and product 275

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Fig. 4. Steady-state simulation results of (a) DME, (b) O2, (c) CO2, and (d) H2O concentration along the reactor channel direction (GHSV = 125,000 h−1; λ = 4.21).

release. Cheng et al. [34] experimentally investigated the effect of the weight hourly space velocity (WHSV) on the catalytic activity, and demonstrated that the DME conversion increases with a drop in the WHSV at the same reaction temperature due to the lengthened residence time between the DME and the catalyst surface. Complete combustion inside a monolith reactor depends on the ratio between the flow residence time and the chemical reaction time (the Damköhler number, Da). An augmentation of the flowrate corresponds with a smaller residence time, which adversely affects the complete reaction [30]. However, maintaining a very low flow rate cannot be justified in a reactor, because both the efficiency and performance of the processing fuel are important for practical applications. Fig. 6b shows that in the low-velocity regime, the variation or decrease in the conversion is extremely significant until the GHSV peak (< 40,000 h−1), which is a minor rate in the high-velocity regime although it continually decreases. For example, at Tg,in = 388 K , a GHSV increment from 15,000 to 40,000 h−1 shrinks the DME conversion from 66.17% to 24.72%, whereas an increase from 40,000 to 125,000 h−1 decreases the DME conversion from 24.72% to 8%. The transient behavior of a reactor is important for its quick startup or shutdown. Figs. 7 and 8 show the startup characteristics of the DME catalytic reactor initially under the room-temperature condition (298 K). The time response at the lowest inlet-gas temperature was

Fig. 5. Steady-state simulation results of the reactor wall temperature according to the inlet-gas temperature (GHSV = 125,000 h−1; λ = 4.21).


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Fig. 7. Transient simulation results of the exhaust-gas temperature in the DME catalytic reactor (GHSV = 125,000 h−1; λ = 4.21).

100 s, the rising rate of the exhaust gas temperature appears to be the same for all the five curves differentiated over time. Fig. 8 shows that the stoichiometric ratio of DME and CO2 must be kept at 1:2 during the entire reaction time, and in both cases, the time response appears to have the same value. Furthermore, the CO2 and H2O production are proportional at 1:1. Since catalytic monolith reactors usually run under abrupt fluctuating inlet transient conditions, we wanted to analyze the dynamic response of the DME catalytic reactor model under oscillating fuel feeding. Fig. 9b and c show the transient variations in both the DME and CO2 concentrations and exhaust-gas temperature, respectively, when the feeding flowrate (GHSV) changes from 120,000 to 20,000 h−1 in the period of 300 – 600 s, as shown in Fig. 9a. As noted, the same time delay (∼100 s) occurs in both the concentration curves when the GHSV starts changing at 300 s, i.e., the concentration signals respond to the GHSV variation after ∼400 s. However, when the GHSV reaches a constant value of 20,000 h−1, an immediate response is visible in the concentration curves. This behavior can be explained with regard to the exhaust-gas temperature curve. Initially, the exhaust-gas temperature sharply increases and rapidly surpasses 380 K in 300 s because of the large flowrate into the reactor. However, the molecular coverage of DME and O2 on the catalyst surface is low because the molecular adsorption process is weak under this high-flowrate condition, which results in a low reaction rate and poor DME conversion. This tendency of the concentration curve in maintained until ∼400 s, at which the GHSV and exhaust-gas temperature reach ∼95,000 h−1 and ∼392 K, respectively. When the molar concentration of the reactants on the catalyst surface and the catalytic activity become adequately high, the concentration curves bend, showing considerable DME oxidation within a short time (∼200 s). When the GHSV completely shifts to 20,000 h−1 at t = 600 s, the DME and CO2 concentration curves approach saturation. On the other hand, the reactor model was dynamically simulated when the gas flowrate suddenly increased. Fig. 9e and f represent the concentration curves of both DME and CO2 and the exhaust-gas temperatures when the flowrate changes from 20,000 to 120,000 h−1, as shown in Fig. 9d. Considering Fig. 9d and f, it is notable that there is an initial resting period (∼50 s) wherein both the GHSV and exhaust-gas temperature values apparently remain constant (20,000 h−1 and 298 K, respectively). This may be associated with the convective heat transfer from the gas mixture to the catalyst layer when the gas mixture flows

Fig. 6. Steady-state simulation results of (a) exhaust-gas temperature and (b) DME conversion rate according to the inlet flowrate (λ = 4.21).

predicted to be ∼5.98 min, but at the steady-state condition, the conversion of the DME fuel was poor (the CO2 concentration was ∼141.38 ppm). Additionally, as the inlet-gas temperature increased, the time response also increased, reaching a value of ∼16.65 min for the highest inlet-gas temperature at a CO2 steady-state concentration of 537.95 ppm. All these results imply that with an augmentation in the inlet-gas temperature of 20 K, the time response increases to ∼10.67 min and the CO2 concentration growth is ∼396.57 ppm, which represents only 8% of the DME conversion under the very high GHSV condition (125,000 h−1). This slow time response of the catalytic reaction for the elevated inlet-gas temperature could be attributed to the large thermal inertia (I = ks s cp,s ) of the reactor during transient operation because the specific heat of the catalyst strongly increased with the temperature, eventually improving the DME conversion [50]. Fig. 7 shows the transient behavior of the exhaust gas temperature of the DME catalytic reactor, which exhibits different demeanors. Until 277

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Fig. 8. Transient simulation result of (a) DME, (b) O2, (c) CO2, and (d) H2O concentration in the exhaust gas according to the inlet-gas temperature (GHSV = 125,000 h−1; λ = 4.21).

along the reactor, such that the gas temperature completely decreases to the approximate room temperature, at the monolith output. During this time, as the wall temperature slowly increases, the catalyst shows a slight elevation in its activity, which is denoted in the concentration curves of Fig. 8e. After t = 50 s, the exhaust-gas temperature quickly reaches ∼355 K in a relatively short period (50–300 s). Although the GHSV is only 20,000 h−1, which implies that the molar concentration of the reactants on the Pt/ZSM-5 catalyst surface might be adequate, the catalytic activity is still negligible at this temperature, and hence the faint DME conversion is more related to the elevated surface coverages θDME and θO. Similarly, Fig. 8e shows an equal time delay (∼30 s) in both the DME and CO2 concentration curves when the GHSV suddenly shifts at t = 300 s. During the time delay, the change in velocity is negligible, the surface coverages are still suitable, and the catalytic activity briefly increases with the temperature; therefore, the DME combustion continues increasing, and both the DME and CO2 concentration curves maintain a growth trend. Nevertheless, from ∼330 to 500 s, the velocity quickly increases, causing not only a decrease in the adsorption of DME and O2 on the catalyst surface, but also an increase in the concentration of DME in the gas phase with the augmented feeding molar flowrate; the desorption of CO2 from the solid phase is relatively negligible. An additional observation was that although the velocity continued growing after t = 500 s, the DME oxidation started to increase slightly, causing a slight decline in the DME concentration and augmentation of CO2 in the gas mixture, indicating that the catalytic activity was reasonably high at ∼375 K, and enhanced the reaction rate.

An analysis of Fig. 9a and c at t = 400 s, as well as Fig. 9d and f at t = 500 s, shows that, overall, there exists a coincidence in the GHSV values (∼95,000 h−1), and an approximation in the exhaust-gas temperature values (392–375 K) for both the acceleration and de-acceleration of the velocity, i.e., the states in which the catalytic activity is initially suitable. When the GHSV reaches a constant value of 120,000 h−1, the exhaust gas temperature increases by ∼13 K, further enhancing the catalytic activity and improving the DME oxidation. It is evident that the dynamic response of the DME catalytic reactor model using Pt/ZSM-5 is directly proportional to the temperature and inversely proportional to the velocity. The augmentation of the temperature improves the catalytic activity for the adsorption, desorption, and surface reactions, whereas the low-velocity regimes allow an increase in the molecular concentration over the catalyst surface where the reactions take place. Additionally, it is notable that the catalytic activity has much more influence on the rapid dynamic responses than the low velocity, because although the GHSV value is not quite low, the time responses are immediate at higher temperatures. For a quick startup of our DME monolithic reactor, the conditions of velocity and temperature should be appropriate; i.e., within the range of temperatures considered in our study, a long startup period was predicted, which was not suitable for practical applications. Several studies about catalyst physical features that contribute to an improvement in the catalytic activity have been reported. Antolini [51] presented a review article to study the effect of the particle size, interparticle distance, and metal loading on the catalytic activity, concluding that these represent the main influence factors for supported 278

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Fig. 9. Dynamic response of the DME catalytic reactor according to the flowrate ramp signal (λ = 4.21; Tg,in = 388 K): (a) de-acceleration profile of GHSV, (b) DME and CO2 concentrations in the exhaust gas during de-acceleration, (c) exhaust-gas temperature profile during de-acceleration, (d) acceleration profile of GHSV, (e) DME and CO2 concentrations in the exhaust gas during acceleration, (f) exhaust-gas temperature profile during acceleration.

catalysts. In fact, the metal loading affects the thickness of the catalyst layer. Thiele [52] argued that with an adequately large particle size, a quick reaction rate is possible such that the diffusion forces are only able to remove the product away from the surface of the catalyst particle; i.e., the reaction takes place only on the catalyst surface. However, Antolini [51] suggested that an excessive particle size may lead to the blockage of the required active sites, negatively affecting the molecular adsorption. Additionally, it has been reported that a decrease in the catalytic activity is generally proportional to a reduction in the

inter-particle distance due to a reduction in the number of molecules adsorbed, although the catalyst preparation techniques have a big influence on this behavior. Finally, the augmentation of molecular adsorption is generally related to an increase in the catalyst metal loading, which stimulates high reaction rates. Nevertheless, the catalytic selectivity is an additional parameter to be considered in metal-loading studies because a large coverage of fuel molecules on the catalyst might not allow the adsorption of oxidizers. The kinetic model reported by Deng et al. [31] was developed using the Pt/ZSM-5 catalyst with a Pt 279

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loading of 5 wt%, which was prepared via the impregnation method. Experimental characterization indicated that the mean diameter of the Pt particles was 3.96 nm. Additionally, in our simulation, we assumed a porosity ε = 0.5. This type of catalytic combustor is currently used as a heat source for additional devices or processes such as fuel reforming to produce hydrogen on-board automobiles. The supply of hydrogen in a fuel cell electric vehicle depends on the driving cycles wherein the current load varies randomly, i.e., the heat demand fluctuates with the current load. Our DME monolith reactor shows the capacity to provide backup energy for this kind of application, especially when employing the Pt/ ZSM-5 catalyst at a low temperature. Further investigation into a combined DME combustor and reformer system might confirm an outstanding performance of this equipment as a part of a fuel-cell system.

4. Conclusions In this work, a general dynamic model of a monolith catalytic reactor was developed and verified by the simulation of microkinetics oxidation of DME over a Pt/ZSM-5 catalyst, to study the complexity of the physicochemical phenomena that prevails in a monolith catalytic reactor model based on the quasi-2D analysis of both the gas- and solidphases. The L–H model was applied to describe the reaction kinetics. Through this study, the following outcomes were achieved.

• The DME conversion rate of the catalytic reactor model was vali-

dated using the available experimental data, which showed a good agreement. Under the three fuel conditions (lean, stoichiometric, and rich), the DME conversion rate gradually increased according to the inlet-gas temperature due to the high energy content of DME catalytic combustion. Additionally, it was predicted that the DME monolith catalytic combustor might achieve reaction temperatures T10 and T90 of ∼392 and ∼437 K, respectively, running under lean conditions and GHSV = 125,000 h−1, with a high catalytic activity. The O2 selectivity was predicted to reach ∼100% when Tg,in = 388 K under the fuel-lean condition. For the stoichiometric intake rate, the O2 selectivity was kept constant and always 100%. The values of the O2 selectivity were always high, even at a low inlet-gas temperature and under the lean condition. Furthermore, at higher inlet-gas temperatures, higher O2 selectivity was achieved due to an increase in the surface coverage of O on the Pt particles. There exists a relationship between the DME selectivity and DME conversion; a higher DME conversion corresponds to a higher selectivity. We found that catalytic oxidation is possible at a low temperature when using the Pt/ZSM-5 catalyst. However, the DME conversion rate appeared to be low under this low-temperature condition, which resulted in a long startup time of the reactor. The inlet temperature had a strong influence on the reaction strength; a higher inlet-gas temperature resulted in higher DME oxidation, facilitating a larger emission of CO2 because the gas reaction was stimulated by catalytic surface activity. The wall temperature of the reactor strongly influenced the reaction rate, and in turn, the heat generation. The wall temperature increased in the DME catalytic reactor because the inlet-gas temperature was higher (being the highest at 388 K). It was proven that catalytic oxidation at a low inlet-gas temperature is possible using the Pt/ZSM-5 catalyst, which can be useful for operations with less energy input. Moreover, the higher inlet temperature demands the smallest catalytic combustor dimension to accomplish the oxidation process. The proposed model manifested the effect of flow rate on the performance of the catalytic reactor. In the low-velocity regime, augmentation of the exhaust gas temperature occurred, whereas the exhaust gas temperature decreased in the high-velocity regime. The reason is that the fuel concentration inside the reactor was high in

the low-velocity regime, which resulted in an improved reaction rate and large heat generation. In contrast, in the high-velocity regime, less surface coverage led to a lower reaction rate and lower heat generation. Complete combustion inside a monolith reactor depends on the ratio between the flow residence time and the chemical reaction time. A transient simulation was conducted to investigate the startup performance of the catalytic reactor. We observed that a long time was required to reach steady-state with an elevated inlet temperature because of the high thermal inertia of the reactor due to an increase in the specific heat capacity. Higher temperatures and lower velocities had a positive influence on the dynamic response of the DME reactor model using Pt/ZSM-5, because of an improvement in the catalytic activity for the adsorption, desorption and surface reactions, and the augmentation of the molecular concentration on the catalyst surface where the reactions take place. Furthermore, the catalytic activity dominantly influenced the rapid dynamic response, even more than the gas velocity, because, at higher temperatures, the time responses were immediate even though the velocities were not low. The metal particle size, inter-particle distance, and metal loading are physical characteristics that generally influence on positively on the catalytic activity of a supported catalyst. For an adequate and quick startup of our DME monolithic reactor, the conditions of velocity and temperature should be appropriate; in the range of temperatures considered in our study, a long startup period was predicted, which is not suitable for practical applications. The DME monolith reactor could be used as a heat supplier to additional processes such as fuel reforming to produce hydrogen onboard automobiles. This DME monolith reactor shows the capacity to provide backup energy for this kind of application, especially when employing the Pt/ZSM-5 catalyst at a low temperature.

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