Methanol steam reforming for hydrogen generation: A comparative modeling study between silica and Pd-based membrane reactors by CFD method

Fuel Processing Technology 199 (2020) 106273

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Methanol steam reforming for hydrogen generation: A comparative modeling study between silica and Pd-based membrane reactors by CFD method

T

K. Ghasemzadeha, , J.N. Harasia, T.Y. Amirib, A. Basilec, A. Iulianellic ⁎

a

Faculty of Chemical Engineering, Urmia University of Technology, Urmia, Iran Department of Chemical Engineering, Faculty of Engineering, University of Zanjan, Iran c Institute on Membrane Technology of the National Research Council (CNR-ITM), via P. Bucci 17/C, 87036 Rende, CS, Italy b

ARTICLE INFO

ABSTRACT

Keywords: Silica membrane reactor Pd-Ag membrane reactor Hydrogen production CFD model

Pd-based membranes are the most studied in applications of membrane reactors in the field of high grade hydrogen production. The main issues of Pd-membranes such as high cost and relatively low hydrogen permeability limit their wide development at larger scale, favoring other inorganic materials such as silica to be used as membrane for hydrogen generation/purification. Therefore, this theoretical study aims to evaluate the performance of silica (4 mm of thickness and 5 cm of active length) and PdeAg (50 μm thick and 5 cm of active length) membrane reactors exercised at the same operating conditions and using the same reaction kinetics to produce hydrogen from methanol steam reforming reaction. Furthermore, an equivalent traditional reactor is studied for comparison. A computational fluid dynamics model was developed, firstly validating the former with experimental literature data. The effects of reaction pressure and temperature on the reactors performance in terms of hydrogen yield, methanol conversion and CO selectivity were hence studied and discussed. The simulations via CFD method indicated that the silica membrane reactor results to be the best solution over the PdeAg MR and the TR as well, presenting the best simulation results at 513 K, 10 bar, sweep-factor = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1 with CO selectivity equal to 0.04%, methanol conversion and hydrogen yield > 90%.

1. Introduction One of the main obstacles to the widespread use of hydrogen as energy source in portable applications is represented by its not safe transport and distribution. To overcome this issue, the utilization of liquid fuels to be transformed into hydrogen via fuel processing may constitute a viable option [1]. Among a number of several feedstocks useful for their catalytic conversion into hydrogen, methanol seems to be an attractive choice due to its indubitable advantages such as: large availability as oil, natural gas and coal, well-established production market, absence of CeC bonds in its chemical structure, mild operating conditions required for its catalytic transformation into hydrogen (steam reforming), high hydrogen to carbon ratio, inexpensive and safe handling. Nevertheless, the main benefit not included in the previous list is related to the possibility that methanol may be produced from renewable sources [2]. Indeed, the former could play an important role in the fuel processing to produce hydrogen, widely considered as the

⁎

energy carrier of the future [3]. Meanwhile, the growing attention on the so-called hydrogen economy is involving a special interest toward the membrane engineering and the use of hydrogen permeable membranes for hydrogen production and separation [4,5]. Therefore, combining the fuel processing of a renewable feedstock, such as methanol, with the adoption of the membrane reactor (MR) technology makes innovative and intensified the approach to produce and simultaneously purify hydrogen in only one stage, resulting particularly advantageous with respect to the conventional multi-steps processes [6–9]. Indeed, in a MR the selective removal of hydrogen from the reaction side toward the permeate side makes the reduction of process stages and cost of downstream separation units possible, enhancing the conversion and hydrogen yield in comparison with the conventional reformers exercised at the same operating conditions [10,11]. The characteristics required for such a membrane material to be used in hydrogen generation/purification processes are high hydrogen perm-selectivity and permeability, high mechanical, thermal and chemical stability, and low

Corresponding authors. E-mail addresses: [email protected] (K. Ghasemzadeh), [email protected] (A. Iulianelli).

https://doi.org/10.1016/j.fuproc.2019.106273 Received 13 September 2019; Received in revised form 4 November 2019; Accepted 5 November 2019 0378-3820/ © 2019 Elsevier B.V. All rights reserved.

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cost [12]. Both polymeric and inorganic membranes, the latter subdivided into metallic such as palladium or its alloys and ceramic such as silica, carbon etc., were successfully adopted for high grade hydrogen purification [13–16]. Nevertheless, polymeric membranes show a restricted operating temperature range, which limits their utilization in high temperature processes such as reforming of hydrocarbons for hydrogen generation. On the other hand, an extensive literature is addressed on unsupported and supported Pd-based membranes for applications in MRs to produce high grade hydrogen [17–20] due to their high hydrogen perm-selectivity with respect to all the other gases. In particular, methanol steam reforming (MSR) reaction to produce hydrogen via MR technology was the main subject of a large number of scientific studies, both from an experimental and a theoretical point of view [21–27]. To the best of our knowledge, in the last two decades, there was not a large literature dealing with the theoretical investigation of MSR reaction in MRs [28] and, besides, most of the modeling studies about MSR reaction involved the adoption of conventional reactors. On the other hand, the theoretical approach followed by several authors to study MSR reaction in MRs was oriented toward the modeling of Pd-based MRs utilization [23,24,29–37]. Nevertheless, it is well-known that Pd-based membranes are costly and some further drawbacks such as low H2 permeance, limited operating temperature range and mechanical resistance, are the main cause of their restricted application also at larger scale. Consequently, in the last decades different solutions were considered for purifying hydrogen and, among other inorganic materials, the adoption of silica and carbon membranes resulted to be a cheaper, more robust and viable option to Pd-based membranes, although their hydrogen perm-selectivity is lower than the former [14,15]. In particular, silica membranes attracted a growing scientific interest because they are cheap and possess high hydrogen permeability. On the contrary, the most critical issue is related to their limited hydrogen perm-selectivity. However, it is generally accepted that the theoretical approach followed for simulating such a process is of a great importance as it allows the cost saving due to the reduction of the experimental tests to be realized. Furthermore, it is also useful for optimizing the choice of the most adequate operating conditions to be adopted. Regarding the modeling of silica-based MRs, a limited number of papers is addressed in literature [38], dealing with black box, artificial neural network (ANN) and molecular dynamic (MD) models, even though most of them are based on 1-D model [15]. In particular, silica MRs were modeled in a few theoretical works dealing with steam and dry reforming of methane [39–42], water gas shift (WGS) [41,43] and decomposition/dehydrogenation reactions [44,45]. MSR reaction was previously studied in silica MRs from a theoretical point of view, using 1-D model to analyze the performance in terms of conversion and hydrogen recovery by operating at relatively low temperature (473 K) or by approaching with the a ANN model as well as carrying out a HAZOP (Hazard and Operability) analysis, useful to avoid the economic and safety loss during the reaction process [46–49]. Furthermore, in our previous work the computational fluid dynamics (CFD) method was used to model MSR reaction in different flow patterns of a silica MR. In particular, cocurrent and counter-current flow patterns were analyzed as well as a configuration based on the presence of a water gas shift (WGS) catalyst in the permeate side of the silica MR in order to transform the CO permeated through the membrane into further hydrogen [50]. The simulations pointed out that the former seems to be the best modality to utilize a silica MR during MSR reaction and it was hence adopted in this new work. Furthermore, CFD method was also used to theoretically analyze and compare MSR reaction and the autothermal methanol reforming reaction carried out in a PdeAg MR, evaluating the performance in terms of methanol conversion and hydrogen recovery [51]. Then, according to the ever more pressing requests of cost reduction for Pd-based MRs scale-up [52–56] as also function of the trade-off between hydrogen permeability and its purity [56], the aim of this

work is to theoretically compare a not fully hydrogen perm-selective but high permeable silica based MR (it was chosen a reference silica membrane with ~4 mm of thickness and 5 cm of active length, showing a H2/N2 ideal perm-selectivity ~30 at 473 K [57]) with a fully hydrogen perm-selective but low permeable PdeAg MR (among a number of unsupported Pd-based membranes in literature [58–61], it was chosen a reference unsupported membrane 50 μm thick and with 5 cm of active length and infinite H2/N2 ideal perm-selectivity [51]), by analyzing their modeling performance in terms of hydrogen yield, methanol conversion and CO selectivity during MSR reaction and discuss about the benefits and drawbacks of the silica MR over the PdeAg MR at particular set operating conditions. Furthermore, a comparison among the MRs and an equivalent traditional reactor (TR) was also performed and discussed. 2. CFD model development and MR geometry A CFD model was developed by using the software COMSOL Multiphysics 5.3 to simulate the performance of the two MRs housing a single tubular silica and an unsupported PdeAg membrane, respectively, by following the general assumptions reported below:

• Both the silica and PdeAg MRs work at steady state and under isothermal conditions. • Retentate and permeate bulk streams follow the ideal gas law. • The physical properties, such as gas density and viscosity, are constant with temperature. • Mass transfer resistance at gas-membrane interface is negligible. • Transport resistance in the bulk fluid is negligible. • In the reaction side, the catalyst surface and bulk fluid conditions are the same. • Pseudo-homogenous conditions are considered in the reaction zone. • PdeAg membrane is permeable only to hydrogen. • Silica membrane is permeable to all components. • MSR reaction takes place in the annulus of both the silica and PdeAg MRs. • For the PdeAg MR, no reactions take place in the permeate side. For both the MRs, the reactants are feed into the shell (reaction side), in which a MSR catalyst is packed. The MSR reaction is developed in the annulus of the MRs, while the products permeating through the membrane are removed by a sweep gas, Fig. 1. The permeate side is packed with a WGS reaction catalyst in the case of the silica MR because the membrane is not fully H2 perm-selective and the permeated CO may be converted into further H2 and CO2, while it is considered empty in the case of the PdeAg MR because the membrane is fully H2 permselective and no CO to be converted is present in the permeate side. In the latter case, nitrogen is used as sweep gas, whereas in the silica MR the sweep gas is a mixture of nitrogen and steam. Further details about the experimental setup may be found elsewhere [50]. 2.1. Governing equations The CFD model was developed taking into account some fundamental equations including continuity equation (Eq. (1)), momentum equation (Eq. (2)) and mass transport equation (Eq. (3)):

( f . . u) = Si ( f . u. u) =

( f . ui . ) =

(1)

p

u+

+

( f Di, e x i ) + (1

f

g

(2)

) Mi

vij rj + Si j

(3)

in which ρf is the fluid density, ε the void fraction of the catalytic bed, Si the sink/source terms indicating the permeating flux of the icomponent through the membrane, β the friction coefficient, xi the 2

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Table 1 Pe,i and Ea,i values for each i-species involved during MSR reaction in both silica and PdeAg MRs included in the CFD model [51,57]. Gas species, i

Pe0,

Pd-Ag

H2 H2 CO2 CO N2 H2O CH3OH

0.54 0.764 0.00387 0.011 0.00862 0.00763 0.00121

Silica

*Unit of Pe0,

i

mass fraction of component i, ρ the catalyst density, Mi and Di,e the molar weight and the diffusion coefficient of the i-component, respectively, rj the rate of reaction j, vij the stoichiometric coefficient of icomponent in reaction j. CFD model required also the utilization of secondary equations to calculate some parameters present in the Eqs. (1)–(3) as described in the following. β was calculated by Ergun equation (Eq. (4)):

)2

3d 2 p

+

1.75 (1 3d p

)

f

|u|

Ea, i (pH0.52, retentate RT

k SR K rSR = 1+K

A Ji Mi V

(4)

rMD = 1+K

+K

eq T CT (p3H pCO2/KSR pCH3OH pH2O ))CS1 S1a 2

1 1 2 +K 2 pCO2 pH pH O/pH 2 2 HCOO(1) OH(1) 2

1

1

1

2 1 + K 2 (1a) pH

2

H

T T (p2H2 pCO /Keq MDpCH3OH ))CS2 CS2a

CH3O(2)

pCH3OH /pH2 2 (1

CH3O(2)

pCH3OH /pH2 2 + K

1

1

OH(2)

pH2O pH2 2

1

1

1 + K 2 (2a) pH2 2 H

Water Gas Shift reaction: CO + H2O ↔ CO2 + H2

rWGS kWGSK = 1+K

(6)

1

OH(1)

pCO pH2O /pH2 2 (1 1

CH3O(1)

pCH3OH /pH2 2 + K

2

T (pH2 pCO2 /Keq WGS pCO pH2O ))CS1 1 2

p p HCOO(1) CO2 H2

+K

1 2

p /p OH (1) H2O H2

2

(11)

Pe

Ea,i RT

(8)

(10)

where Pei = i is the permeance of i-species (in which δ is the membrane thickness and Pei the permeability coefficient of i-species). The permeability of i-species, Pei may be calculated as an Arrhenius-like equation (Eq. (7)):

Pei = Pe 0,i exp

pH0.52, permeate )

1 2 (1 pCH3OH /pH 2 CH3 O(1)

1 2 pCH3OH /pH 2 CH3O(1)

kMDK

where A is the membrane surface, V the computational cell volume and Ji is the permeation flux of i-component, calculated by Eq. (6):

pi,permeate)

10.58 7.18 −2.25 4.94 3.07 2.92 2.13

(9)

(5)

Ji = Pe i (pi,retentate

Ea,i (kJ.mol−1)

Methanol decomposition reaction: CH3OH ↔ CO + 2H2

The term Si takes into account the permeating flux of each i-component permeating through the membrane (Eq. (5)) and is considered zero for computational cells a part from the case of cells adjacent to the membrane, i.e. in the boundary cells between permeate and retentate zones.

Si =

∗

In the case of silica MR, being the silica membrane not fully hydrogen perm-selective, all species may permeate through the membrane and each component shows own Si value, and the correspondent permeating flux is described by Eq. (6), with n-value equal to 1. Furthermore, all the kinetics and parameters for Cu-based catalysts during MSR reaction were adopted from Peppley et al. [62,63]. Each reaction rate, rj, was determined on the basis of the reaction kinetics, assuming that, for both the PdeAg and silica MRs, the reactions considered included MSR, methanol decomposition and WGS. Methanol steam reforming reaction: CH3OH + H2O ↔ CO2 + 3H2

Fig. 1. Scheme of the membrane reactors used for carrying out MSR reaction: a) PdeAg MR; b) silica MR.

150 µ f (1

i

in PdeAg membrane is mol.m−2.s−1.bar-0.5.

∗

JH 2 = Pe0, i exp

=

(mol.m−2.s−1.bar−1)⁎

Membrane type

S reported above, in the silica MR, the WGS reaction takes place in the permeate side and its reaction rate is calculated using the Eq. (11). On the contrary, in the case of the PdeAg MR there is no reaction in the permeate side and hence the correspondent rj values are equal to zero.

(7)

2.2. Boundary condition

where Pe0,i is a constant coefficient and Ea,i the transport activation energy of each i-species, R the universal gas constant and T the temperature. The values of Pei and Ea,i were assumed constant for each ispecies in both MRs cases, according to the experimental data reported in Table 1. In the case of PdeAg MR modeling, as it is assumed that only hydrogen permeates through membrane (being the PdeAg membrane fully hydrogen perm-selective), Si is considered equal to zero for all species except for hydrogen. Consequently, the hydrogen partial pressure exponent in Eq. (6) is assumed to be equal to 0.5, becoming the Sieverts-Fick law (Eq. (8)).

Table 2 summarizes the boundary conditions used to develop the CFD model in both retentate and permeate sides of the MRs. In particular, a boundary condition in axial direction is considered, i.e. z direction, and two boundary conditions in radial direction. Pressure and input mass flow (or concentration and velocity) are known for all components in the inlet of both permeate and retentate sides, which were chosen as boundary condition at z = 0. Due to symmetric assumption in r direction, at r = 0 the radial derivative of all components is zero. As shown in Fig. 2, at r = R1 and r = R2, which respectively denote the radius of permeate side and the inner radius of retentate 3

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Table 2 Boundary conditions used in retentate and permeate sides for the CFD model in silica MR and PdeAg MR. Position

Retentate side

Permeate side

In inlet (Z = 0) In inlet (Z = 0) In inlet (Z = 0) In inlet (Z = 0) r=0 r = R1 (Excluding Hydrogen in PdeAg MR) r = R1 (only hydrogen in PdeAg MR and all species in silica MR) r = R2: (only hydrogen in PdeAg MR and all species in silica MR) r = R2 (Excluding Hydrogen in PdeAg MR) r = R3

F1(Methanol flow rate) F2 (Steam flow rate) F3 (N2 flow rate as carrier gas) PU (Feed pressure) Concentration derivative Concentration derivative Equality of Fick law diffusion rate and permeation rate through the membrane

F4 (Steam flow rate as sweep gas, only for silica MR) F5 (Nitrogen flow rate as sweep gas) PL (Sweep gas pressure)

Equality of Fick law diffusion rate and permeation rate through the membrane Concentration derivative Concentration derivative

2.4. Mesh independency To obtain accurate modeling of whatever process, first it is important to find a mesh number range in which the simulation results are not dependent on the mesh number itself. On the other hand, in the viewpoint of solving time and calculation volume, the benchmark of the lowest mesh number above which a further increase does not affect the simulation results represents the optimum. For this purpose, CFD modeling of both PdeAg and silica MRs as well as the equivalent traditional tubular reactor (i.e. a tubular conventional reactor having the same reaction volume and catalyst mass as in the MRs analyzed in this study) was carried out with different mesh numbers. The simulations were carried out setting the pressure at 1.5 bar, the reaction temperature at 513 K, the gas hourly space velocity (GHSV) at 6000 h−1, the steam/methanol molar ratio = 3/1 and the SF equal to 6. Fig. 3 illustrates the effect of grid number on the simulated hydrogen yield by CFD model during MSR reaction in both the MRs and the TR. As shown, by increasing the mesh number up to ~50,000, the hydrogen yield for all the reactors changes remarkably, whereas above the latter mesh number the simulated hydrogen yield shows a substantial constant trend, with a variation lower than 1.0% for all the reactors for mesh numbers between ~50,000 and ~105,000. Therefore, mesh number equal to 50,000 was considered the optimal value in all the simulations of this work, meaning that further mesh number increase had not significant effects on the solution results, leading only to a significant increase of the calculations volume without enhancing the solution precision.

Fig. 2. The simplified MR module scheme as a 2D-axisymmetric geometry.

side, for each species permeating through the membrane the diffusion rates are equal to the permeating flux determined by Eq. (6) and, for the species non-permeated, the radial derivative of concentration is zero. At r = R3, the outer radius of retentate side, due to the absence of diffusion, the radial derivative of concentration for all components is zero. Nitrogen and hydrogen are the components present in the permeate side of the PdeAg MR, whereas all components may be present in the permeate side of the silica MR. 2.3. Solving strategy The governing equations developed by CFD model for both the MRs were solved using a finite-element method. COMSOL Multiphysics 5.3 software was applied to simulate the MRs performance on the basis of CFD model. In the case of PdeAg MR, a very low value of hydrogen composition was considered at the MR inlet to avoid numerical calculation problems because a partial pressure of hydrogen equal to zero at the denominator of reaction rates (Eqs. (9)–(11)) could be responsible for the failure of the numerical solving. The MRs and TR performances were evaluated in terms of methanol conversion (Eq. (12)), hydrogen yield (Eq. (13)), and CO selectivity (Eq. (14)), which are defined below:

Methanol conversion (%) =

Total hydrogen yield (%) = CO

CH3 OHin CH3 OH out × 100 CH3 OHin

H2,permeate + H2,retentate 3 × CH3 OHin

× 100

(12) (13)

Selectivity (%)

= (CO outlet molar stream/Total molar outlet streams) × 100

(14)

where CH3OHin and CH3OHout represent the inlet and outlet methanol molar flow rates, respectively, and H2-retentate and H2-permeate represent the hydrogen molar flow rates in the retentate and permeate streams, respectively. However, CO outlet molar stream indicates the sum of both permeate and retentate CO molar streams in the case of the silica membrane, whereas only the retentate CO molar stream in the case of the PdeAg membrane. Meanwhile, Eq. (15) describes the sweep factor as the ratio between the molar flow rate of sweep gas and the inlet molar flow rate of methanol.

Sweep Factor (SF) =

Molar flow rate of sweep gas Molar flow rate of CH3 OHin

Fig. 3. Evaluation of grid independency: effect of grid number on the simulated hydrogen yield by CFD model during MSR reaction in the PdeAg and silica MRs and the TR as well. Operating conditions: 1.5 bar, 513 K, GHSV = 6000 h−1, SF = 6, H2O/MeOH = 3/1.

(15) 4

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Fig. 5. Methanol conversion vs reaction pressure during MSR reaction in silica MR, PdeAg MR and TR at T = 513 K, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1.

MSR reaction proceeds with an increase of moles number and an increase of pressure doesn't favor the conversion. On the contrary, in both the MRs an increase of pressure determines an improvement of the hydrogen permeation driving force, favoring a larger removal of hydrogen that permeates through the membranes from the reaction toward the permeate side, shifting MSR reaction toward the products with a consequent enhancement of methanol conversion. In particular, the former is improved from ~85% at 1.5 bar to ~94% at 10 bar in the silica MR, confirming that the effect of hydrogen removal for permeation through the silica membrane is dominant over the thermodynamic effect. Another important result is related to the comparison between the methanol conversion of the two MRs, resulting lower for the PdeAg MR in all the pressure range investigated in this modeling work. Indeed, the hydrogen permeance of the silica membrane is higher than that of the PdeAg one (0.762 mol·m−2·s−1·bar−1 vs 0.54 mol·m−2·s−1·bar-0.5, Table 1). Consequently, the better hydrogen permeation characteristics of the silica membrane leads to a larger hydrogen removal and to an emphasized shift effect on MSR reaction, determining higher methanol conversions in the silica MR than in the PdeAg MR. Fig. 6 illustrates the effect of reaction pressure on CO selectivity for the TR and the MRs, revealing that the silica MR shows the lowest CO concentration in the reactor effluent. Generally, the hydrogen removal from the reaction zone in the MRs favors WGS reaction toward higher hydrogen production, which is responsible for a larger CO consume. This favorable phenomenon in silica MR is more effective due to the higher hydrogen permeating flux of silica membrane than the PdeAg one. Furthermore, the CO stream passing in the permeate side for

Fig. 4. Methanol conversion and hydrogen yield versus feed molar ratio: (a) comparison between CFD modeling results and experimental data coming from [51] for the PdeAg MR (T = 553 K, p = 2.0 bar and GHSV = 1800 h−1); (b) comparison between CFD modeling results and experimental data coming from [57] for the silica MR (T = 573 K, p = 1.5 bar and GHSV = 6000 h−1).

3. Results and discussion 3.1. Model validation Before evaluating the reactors performance on the basis of the CFD model proposed in this work, its validation was carried out by comparing the modeling results with experimental data obtained in our previous studies [51,57]. Fig. 4 sketches that, for both the MRs, the simulated hydrogen yield and methanol conversion at set operating conditions matched quite well the experimental values. In particular, the operating conditions were T = 553 K, 2.0 bar and GHSV = 1800 h−1 for the PdeAg MR, whereas 573 K, 1.5 bar and 6000 h−1 for the silica MR at different steam/methanol molar ratio. Consequently, the validity of CFD model was confirmed and, then, it was used for evaluating the MRs and TR performance at different conditions. 3.2. Parametric study on MR performances In this section, the performance of the silica and PdeAg MRs, and the TR as well were evaluated in terms of simulated hydrogen yield, methanol conversion and CO selectivity using the CFD model, and the effects of temperature and pressure variation were investigated and discussed. 3.2.1. Evaluation of reaction pressure The effect of the reaction pressure on the methanol conversion was theoretically evaluated at T = 513 K, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1, by comparing the modeling results among the TR, silica and PdeAg MRs, Fig. 5. Methanol conversion decreased with temperature in the TR because, from a thermodynamic point of view,

Fig. 6. CO-selectivity vs reaction pressure during MSR reaction in silica MR, PdeAg MR and TR at 513 K, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1. 5

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Fig. 7. Hydrogen yield vs reaction pressure during MSR reaction in Silica MR, PdeAg MR and TR at 513 K, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1.

Fig. 8. Methanol conversion vs reaction temperature during MSR reaction in silica MR, PdeAg MR and TR at p = 1.5 bar, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1.

permeation through the silica membrane is further transformed via WGS reaction carried out in the permeate side, leading to a further reduction of CO selectivity. Furthermore, the latter was reduced as the reaction pressure raised and the differences among the CO selectivities of the reactors increased at higher pressures. For example, in the TR and silica MR it was 0.73 and 0.31%, respectively, at 1.5 bar (the TR value was 2.3 times larger than the silica MR), while it was 0.56 and 0.06%, respectively, at 10 bar (the TR value was 9.3 times larger than the silica MR). Fig. 7 highlights how the silica MR shows the best performance in terms of hydrogen yield. As described above, the silica MR possess simultaneously the highest methanol conversion with the lowest CO selectivity as a consequence of the shift effect on MSR reaction (favoring an increase of hydrogen production and, consequently, a higher hydrogen yield) and of the WGS reaction in its permeate side, which are responsible for the highest hydrogen yield among the TR and the MRs. The best simulated hydrogen yield is reached in both the MRs at around 5.0 bar, with values of ~95% and ~87% for the silica and PdeAg MRs, respectively. This result indicates also that higher pressures are not required to achieve better results due to the constant trend of hydrogen yield in both the MRs. On the contrary, for the TR the decreasing trend is related to the analogous decreasing trend of methanol conversion, as a consequence of a lower hydrogen production at higher pressures.

Fig. 9. CO-selectivity vs reaction temperature during MSR reaction in silica MR, PdeAg MR and TR at p = 1.5 bar, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1.

permeate side of the PdeAg MR. Higher the temperature, higher the hydrogen permeated through the PdeAg membrane with a consequent higher methanol and CO consumption in the PdeAg MR than in the TR to produce hydrogen. In the silica MR, the further CO consumption through WGS reaction carried out in the permeate side is responsible for the lowest CO selectivity values compared to the other reactors. Nevertheless, raising the reaction temperature CO selectivity increases in all the reactors because WGS reaction is unfavored at higher temperature due to its exothermic nature. Higher methanol conversion and lower CO selectivity in silica MR provides higher hydrogen yield than that of the other reactors, Fig. 10. In the silica MR, the simulated hydrogen yield varied from ~74 to ~89% by increasing the reaction temperature from 493 to 573 K. The difference between hydrogen yields among the TR and MRs is in the range of 6–13% over the set temperatures of the simulations.

3.2.2. Evaluation of reaction temperature The influence of reaction temperature on methanol conversion in all the reactors considered in this work is illustrated in Fig. 8. The endothermic characteristic of MSR reaction determines that an increase of temperature involves an enhancement of methanol conversion in all the reactors. However, being silica membrane more hydrogen permeable than the PdeAg membrane, a temperature grow emphasizes the hydrogen permeating flux in the silica membrane, resulting in a higher shift effect on MSR reaction and, consequently, determining a higher methanol conversion than in the PdeAg MR. This opposite behavior with respect to the results shown in Fig. 4 is due to the GHSV effect, which is the same in the simulations of both PdeAg and silica MRs of Fig. 8, while in the PdeAg MR of Fig. 4 it is lower (1800 h−1) than that used in the silica MR (6000 h−1), globally determining worse performance in terms of methanol conversion and yield for the former. The TR shows lower methanol conversions than the MRs owing to the absence of the shift effect on MSR reaction, being not present in this reactor any hydrogen removal from the reaction side. CO selectivity in the PdeAg MR was lower than in the TR in the whole temperature range of the simulations of this work, Fig. 9. This may be once again attributed to the shift effect on the global mechanism of MSR reaction, which includes also the WGS reaction, due to the selective hydrogen removal from the reaction side toward the

4. Conclusion This modeling work dealt with MSR reaction carried out in two MRs adopting a fully hydrogen perm-selective PdeAg membrane (50 μm thick and with 5 cm of active length) and a not fully hydrogen permselective silica membrane (4 mm of thickness and 5 cm of active length), respectively, and in a TR as well. A CFD model was used to simulate the performance of the various reactors in terms of methanol conversion, hydrogen yield and CO selectivity, by varying operating parameters such as pressure and temperature, and keeping constant GHSV and feed molar ratio. After model validation, the simulations 6

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Fig. 10. Hydrogen yield vs reaction temperature during MSR reaction in Silica MR, PdeAg MR and TR at p = 1.5 bar, SF = 6, GHSV = 6000 h−1 and feed molar ratio = 3/1.

evidenced how the silica MR performances were superior to those of the PdeAg MR and TR, even though globally the MRs resulted to be a better choice than the TR. The silica membrane resulted to be more hydrogen permeable than the PdeAg membrane and this emphasized the shift effect on MSR reaction, allowing higher methanol conversion and hydrogen yield. Meanwhile, the allocation of a WGS catalyst in the permeate side of silica MR made possible to convert the CO permeated through the silica membrane into further hydrogen, lowering as much as possible CO selectivity. Furthermore, the CFD model allowed to find the optimal conditions to operate the MRs and their best performance as well, playing the role of a valid strategy to scale-up the MRs, offering different options in terms of membrane choice and related performance, pointing out how the silica MR represented a viable and economical solution for producing hydrogen with respect to the well-established Pd-based MRs technology, starting from the utilization of methanol as renewable feedstock. Abbreviations ANN CFD GHSV HAZOP MR MSR SF TR WGS

Artificial Neural Network Computational Fluid Dynamics Gas Hourly Space Velocity Hazard and Operability Membrane Reactor Methanol Steam Reforming Sweep Factor Traditional Reactor Water-Gas Shift Reaction

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