Integrated Experimental and Modeling Approach for Methanol-to

Article Cite This: Ind. Eng. Chem. Res. 2019, 58, 9017−9029

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Integrated Experimental and Modeling Approach for Methanol-toPropylene Conversion over Mn-Modified Desilicated HZSM‑5 Catalyst in a Fluidized Bed Reactor Fatemeh Yahyazadeh Saravi and Majid Taghizadeh* Chemical Engineering Department, Babol Noshirvani University of Technology, P.O. Box 484, 47148-71167, Babol, Iran

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ABSTRACT: In this study the catalytic conversion of methanol to propylene (MTP) over Mn-modified desilicated HZSM-5 catalyst was evaluated in an experimental fluidized bed reactor in the reaction temperature range 450−540 °C and inlet gas velocity of 1.5−3 times greater than the minimum fluidization velocity. Although both the change of temperature and the inlet gas velocity affect the methanol conversion, the results showed that the former significantly influences the product yields, while the latter does not have such an effect (over the studied ranges). Additionally, a six-lumped kinetic model was developed based on the integration of hydrocarbon pool and olefin-based cycle mechanisms to describe the reaction pathway. To estimate the kinetic parameters from the experimental data using a hybrid genetic algorithm, the kinetic model was coupled with a two-phase structure hydrodynamic model. This coupled model can accurately predict the methanol conversion and product yields in a fluidized bed, where the root-mean-square error (RMSE) is equal to 0.95%. The results of modeling indicate that the maximum propylene yield (52%) can be obtained at maximum temperature studied (540 °C) over the whole studied range of velocity. By applying this condition, a methanol conversion of about 99% was obtained.

1. INTRODUCTION

The MTP process is often performed by an acidic zeolite catalyst, specifically HZSM-5, which is the one with much higher propylene yield and more prolonged lifespan compared to that of SAPO-34 catalyst in an MTO process (methanol to olefins with similar yields of ethylene and propylene).6−8 Very recently, extensive research has been conducted on improving the performance of catalysts in MTO/MTP reactions.9−13 Liao et al.10 applied the foam SiC-supported ZSM-5 catalyst in the MTP fixed bed reactor, due to its excellent performance in terms of thermal conductivity and mechanical strength. Li et al.11 studied the effect of doping of some metals on three-

Propylene is one of the major basic petrochemical products and has attained an increasing global demand over the past decade. This light olefin is usually produced from oil-based methods such as fluidized catalytic cracking (FCC) and steam cracking processes. However, due to the depletion of oil reservoirs and the rise in global prices of petroleum, the production of light olefins from non-oil sources has attracted considerable attention in both industry and research communities. The technique of methanol to propylene conversion (MTP) is the process in which methanol is converted into olefin with much higher propylene yield over ethylene, where the methanol is supplied by a non-oil source such as biomass, natural gas, and any gasifiable carbon substance (such as coal).1−5 © 2019 American Chemical Society

Received: Revised: Accepted: Published: 9017

February 28, 2019 April 28, 2019 May 7, 2019 May 7, 2019 DOI: 10.1021/acs.iecr.9b01132 Ind. Eng. Chem. Res. 2019, 58, 9017−9029

Article

Industrial & Engineering Chemistry Research

Fluidized bed reactors are often modeled based on the twophase concept. In the case of a simple two-phase model, two main assumptions are considered for bubbling fluidized bed. First, the reaction occurs in an emulsion phase at minimum fluidization condition, and second, the bubbles are particlefree. In a real fluidized bed the presence of particles cannot be ignored in the bubbles32,33 and the reaction occurs in both phases. On the other hand, the emulsion phase is different from the minimum fluidization one especially for beds containing Geldart B particles.34 Such real conditions are considered in the dynamic two-phase (DTP) model. Despite the complexity of hydrodynamics, difficult scale-up, and greater initial capital cost of fluidized bed reactors compared to those of fixed bed ones, these types of the reactors are viable ones to study the kinetics of high exothermic reactions, where the temperature gradient and especially hot spots lead to product degradation and disruption in the fixed bed reactors.22,35,36 Besides hydrodynamic modeling of the fluidized beds, the kinetic modeling is essential for the design and optimization of such reactors. Various kinetic models proposed for the MTP process can be classified into two general categories in terms of lump and detailed models. The former is a traditional approach to simplify the complexity of the product pattern of MTP reactions. In this model, the reactants and products are classified into a specific group named “lump” which treat them as pseudocomponents. On the contrary, every single reaction step is investigated separately in a detailed kinetic model.37,38 Schoenfelder et al.26 proposed a seven-lump kinetic model for the MTO reaction on high alumina HZSM-5 catalyst in a fixed bed reactor and determined the kinetic parameters for the reactions. Also, Kaarsholm et al.22 investigated the MTO reaction over phosphorus modified HZSM-5 catalyst in a small scale fluidized bed reactor and proposed a kinetic model based on the hydrocarbon pool mechanism. They also used the assumptions of the simple two-phase model for hydrodynamic reactor modeling. Aguayo et al.36 investigated the methanol to hydrocarbon (MTH) reaction on HZSM-5 catalyst (Si/Al = 15) in the temperature range 400−550 °C in a fixed bed reactor and proposed a lumped kinetic model in which light olefins (C2=, C3=, C4=) are not considered as separate lumps. Menges et al.38 developed a six-lump kinetic model based on the hydrocarbon pool mechanism and methylation cracking for the MTO reaction with cofeeding of light olefins over high silica ZSM-5/ AlPO4 extrudates. This model was extended by Jiang et al.39 for the MTP reaction over HZSM-5 catalyst in the modeling of a moving bed with recycling of byproducts. Park and Froment40,41 formulated a detailed kinetic model using a carbenium ion mechanism with more than 30 parameters for MTO reaction over high silica HZSM-5 catalyst. Froment42 proposed a single-event kinetic model where full details of the reaction pathways are considered in complex catalytic processes such as MTO and catalytic cracking of vacuum gas oil. Generally, due to the lack of experimental information on every individual intermediate, finding the kinetic parameters of a detailed model are very time-consuming, complex with low accuracy, and sometimes impossible.37,39 In addition, the integration of the detailed kinetic model with the mathematical model leads to complexity of computation, in modeling a reactor. Therefore, the lump kinetic models are usually used for design purposes.

dimensional (3-D) printed ZSM-5 monolith catalysts. Their results showed that the olefin selectivity was improved over Mg- and Zn-doped monolith catalysts. Various methods have been reported in the literature to improve the catalytic activity and performance of HZSM-5, comprising generation of mesoporosity in microporous HZSM-514−17 (to overcome diffusion limitation and postpone the catalyst deactivation via desilication) and alteration in zeolite acidity18−20 (to increase propylene selectivity via doping with some metals). Our former study showed that the synergetic modification by controlled desilication and Mn impregnation over high-silica HZSM-5 catalyst plays a positive role in propylene selectivity and catalyst activity.21 In the case of the MTO/MTP process, a wide variety of heterogeneous catalytic reactors have been suggested in patents including the fixed bed reactor, moving bed reactor, fluidized bed reactor, and series or parallel reactors. A commercial scale fluidized bed reactor has been successfully used in the MTO process by UOP, DICP, and SINOPEC companies, whereas the MTP process with a multistage fixed bed reactor has been commercialized by the Lurgy company so far. This is despite the fact that the fixed bed and fluidized bed processes are competitive in the MTP process.5 Since several parameters such as the probability of radial and axial gradients of temperature, gas phase composition, and coke deposition on the catalyst affect the performance of fixed bed reactors, the interpretation of the experimental data obtained by the MTP reaction on these beds is really difficult.22 On the contrary, proper contact of catalyst with gas feed in fluidized bed reactors provides improved mass and heat transfer and results in isothermal operating condition, which is an important factor for controlling the heat released by highly exothermic reactions like MTP.5,22 Furthermore, the free movement of microsize catalyst results in uniform distribution of deactivated catalyst in the fluidized bed reactor and constant catalyst activity.23 Some investigations revealed that good yield toward olefins over ZSM-5 and SAPO-34 was obtained in a fluidized bed reactor for conversion of methanol, despite back-mixing that may lead to reduced olefin yield.22,24−26 In order to predict the behavior of a chemical fluidized bed reactor, many criteria such as reaction rate, stoichiometry, and mass and heat transfer, as well as the complex hydrodynamics and solid−gas contact need to be considered. Several hydrodynamic models were reported in the literature for a fluidized bed reactor at different fluidization regimes such as the Kunii−Levenspiel (K−L) model,27 modified two-phase model,28 and core−annulus model29 for the bubbling, turbulent, and fast regimes of fluidization, respectively. In case of the bubbling bed regime, Kunii and Levenspiel30 have proposed the K−L model for fine particles (Geldart A) in which rising bubbles are surrounded by thin clouds. They also extended the model to intermediate particle (Geldart B) beds where thick overlapping clouds surround bubbles and beds with large particles (Geldart D) containing cloudless bubbles. The aforementioned beds are called fast bubble, intermediate bubble, and slow bubble beds, respectively, and each one is governed by a different gas flow pattern and assumption. Werther and Hartge31 modeled an industrial fluidized bed with consideration of the influence of catalyst attrition and solid recovery efficiency in a cyclone. For reactor modeling, they assumed that the fluidized bed is divided into two bubble and emulsion regions with mass transfer between them. 9018

DOI: 10.1021/acs.iecr.9b01132 Ind. Eng. Chem. Res. 2019, 58, 9017−9029

Article


The fluidized bed reactor was placed inside a tube furnace (PTF 12/75/750, Lenton Ltd., U.K.) and operated under isothermal conditions. For each run, 4 g of meshed catalyst (450 μm) was loaded in the reactor. The reaction temperature was monitored with a K-type coaxial thermocouple located in the center of the reaction zone. Before adjusting the furnace temperature, the catalyst sample was pretreated with 20 mL/ min high purity N2 flow at 550 °C for 1 h. The N2 flow was controlled by Brooks mass flow controllers (MFCs) with a maximum flow rate of 500 mL/min. Then a mixture of 50 wt % methanol in water was pumped with an HPLC pump (Knauer Smartline 1000, Germany) into a preheater kept at 150 °C to evaporate. This vapor then entered the reaction zone through a gas distributor plate (with a hole diameter of 0.3 mm located 5 cm above the reactor base) and contacted the catalyst particle. The MTP experiments were assessed at temperatures of 450, 480, 510, and 540 °C, and the feed flow rates were adjusted in the range 0.5−1.4 mL/min to obtain the superficial gas velocities which were 1.5, 2, 2.5, and 3 times higher than the minimum fluidization velocity. After cooling to the temperature of 7 °C in the refrigerator, gaseous products of the reaction were separated from liquid products. The molar composition of the gas phase was determined by an online gas chromatograph (GC; Varian 3800) equipped with a flame ionization detector and a 50 m HP-PONA capillary column. The liquid phase was injected into the offline GC at the end of the experiments. The methanol conversion, selectivity, and yield of products are calculated as follows:

The kinetics of the methanol to propylene reaction have often been studied in fixed bed reactors, and the methanol to propylene conversion in a fluidized bed (FMTP) is in an early stage and still in the pilot scale.5 Consequently, obtaining the kinetic parameters of the MTP reaction in fluidized beds is of prime importance, especially due to the presence of high exothermic reaction. In this study, the methanol to propylene reaction was implemented in a bubbling fluidized bed reactor over manganese modified desilicated HZSM-5 catalyst (Geldart B) at different reaction temperatures (450−540 °C) and inlet gas velocity range 1.5−3 times higher than the minimum fluidization velocity. Furthermore, a modified lumped kinetic model was proposed based on the hydrocarbon pool mechanism that integrated the olefin-based cycle of dualcycle theory to describe reaction pathway. This model consisted of eight reactions involving six lumps. A two-phase structure hydrodynamic model for intermediate bubble bed was coupled with the kinetic model to investigate the performance of the reactor. A hybrid function based on a genetic algorithm and fmincon was developed to drive the kinetic parameters from the experimental data and minimize the root-mean-square error (RMSE) of the product yields as an objective function.

2. EXPERIMENTAL SECTION 2.1. Catalyst Preparation. The Mn-modified desilicated HZSM-5 catalyst (denoted as Mn−DZSM-5) was synthesized according to the procedure reported in our previous paper.21 Briefly, first the high silica Na−ZSM-5 catalyst was hydrothermally prepared with a gel molar composition of 20 SiO2:0.05 Al2O3:1 TPAOH:1.5 Na2O:200 H2O.16 This catalyst was converted to the H-form ZSM-5 (HZSM-5) by four times of ion-exchange treatment via 1 M ammonium nitrate solution (NH4NO3, 95 wt %), drying, and finally calcination. Then the HZSM-5 catalyst was desilicated by a 0.2 M mixture of NaOH and TPAOH in a NaOH/TPAOH ratio of 1.5,43 under reflux in an oil bath at a temperature of 65 °C for 30 min. After that, the slurry was cooled immediately, washed several times, dried, calcined, and converted to the acid form (H) by ion-exchange treatment; the desilicated catalyst obtained after redrying and recalcination was denoted as DZSM-5. Finally, a certain amount of Mn(NO3)2·4H2O (as promoter source) was dissolved in a known amount of distilled water and 1 g of DZSM-5 sample was added to this solution while stirring vigorously at ambient temperature for 1 day. The modified catalyst obtained after drying and calcination was denoted as Mn−DZSM-5, containing 2 wt % manganese. The physicochemical properties of the Mn−DZSM-5 catalyst are listed in Table S1. A detailed description of the preparation procedure and the characterization of the Mn−DZSM-5 catalyst has been reported elsewhere.21 The catalyst was pressed, crushed, and then sieved by 35−40 mesh particles (about 450 μm) for catalytic tests. 2.2. Reactor Test. The MTP reaction over Mn−DZSM-5 catalyst was conducted in a fluidized-bed stainless steel reactor connected to the catalytic evaluation setup under atmospheric pressure at 450−540 °C. A schematic flow diagram of the laboratory scale setup is illustrated in Figure S1. The total length of the fluidized-bed reactor was 25 cm with a 2 cm inner diameter of reaction zone and a 3 cm inner diameter of disengagement zone to minimize particle entrainment.

methanol conversion o o Ni ) − (NMeOH + 2NDME = MeOH × 100 i NMeOH selectivity =

i − NMeOH

aNCoaHb o (NMeOH

o + 2NDME )

yield = methanol conversion × selectivity

(1)

(2) (3)

where N is the number of moles, a is the carbon number, and superscripts “i” and “o” refer to the components at the inlet and outlet of reactor, respectively.

3. EXPERIMENTAL RESULTS The conversion of methanol over acidic zeolite catalyst ZSM-5 produces a wide range of hydrocarbons, from C1 to C10. It is worth mentioning that the presence of mesopores and their relevant great diffusivity of the desilicated ZSM-5 catalyst (here Mn−DZSM-5) have a significant influence on the efficient distribution of the products in the MTP reaction. In fact, the presence of mesopores facilitates the removal of the intermediate products, especially propylene and butylene, from the acidic sites of the catalyst and thus the possibility of their secondary reactions and conversions to heavier olefins and other heavy hydrocarbons reduce on the acid sites.17,44 Also, as the Mn modification on desilicated ZSM-5 catalyst (Mn−DZSM-5) alters the zeolite acidity, the propylene selectivity greatly increases. Apart from the catalyst modification, the operating conditions also have a significant effect on the products’ distribution. In this study, the products of MTP reaction over Mn−DZSM-5 catalyst are categorized into the following groups: light paraffins (C1−C4; methane, ethane, propane, and butane), ethylene (C2=), propylene (C3=), 9019


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Industrial & Engineering Chemistry Research Table 1. Product Yields of MTP Reaction over Mn−DZSM-5 Catalyst after 12 h of TOS product yields (%) no.

temp (°C)

u0/umf

C1−C4

C2=

C3=

C4=

C5+

MDOH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

450 480 510 540 450 480 510 540 450 480 510 540 450 480 510 540

1.5 1.5 1.5 1.5 2 2 2 2 2.5 2.5 2.5 2.5 3 3 3 3

8.22 5.02 4.39 3.1 7.76 4.63 3.61 3.14 7.12 4.25 3.29 3.31 6.74 3.88 3.24 3.17

8.36 8.77 11.52 13.22 7.48 8.41 10.21 12.21 7.18 8.18 9.9 12 6.67 7.97 8.94 11.65

41.84 47.38 49.4 50.08 43.62 48.33 50.06 51.69 44.55 49.02 51 52.53 43.17 47.95 49.37 51.21

31.33 31.44 28.71 28.13 31.08 31.12 29.29 28.07 31 30.59 29.33 28.43 30.58 30.08 29.08 27.73

7.05 5.23 4.9 4.94 6.56 5.17 5.56 4.35 6.15 5.28 4.94 3.07 7.84 6.23 6.7 4.86

3.2 2.16 1.08 0.53 3.5 2.34 1.27 0.54 4 2.68 1.54 0.66 5 3.89 2.67 1.38

butylenes (C4=), C5+ hydrocarbons, and methanol/DME (MDOH). The experimental data obtained after 12 h of time on stream (TOS) are summarized in Table 1. The reported values are the average of three GC separate runs to minimize the experimental error. According to Table 1, the yields of light paraffins (C1−C4) and heavy hydrocarbons (C5+) decrease with the temperature increment. Furthermore, the yields of ethylene and propylene increase, whereas the yield of butylenes slightly decreases by the temperature increment. Since the cracking reaction is endothermic, the literature survey on ZSM-5 catalyst revealed that the rate of the cracking reactions of heavy hydrocarbons (C5+) to light olefins increases at a temperature higher than 450 °C.36 Therefore, it seems that by temperature increment the yields of ethylene and propylene increase with the consumption of C5+. On the other hand, regardless of the effect of temperature, the changes in product yields over the studied range of the inlet gas velocity are really slight and this indicates that product distribution is not very sensitive to the studied inlet gas velocity, as reported in the literature.22 Figure 1 illustrates the effect of reaction temperature on MDOH conversion at different inlet gas velocities, after 12 h of time on stream (TOS). As can be seen, the MDOH conversion was enhanced with the increase of temperature from 450 to 540 °C at all studied velocities due to increment of the methanol consumption rate at higher temperatures. Indeed, the increase of reaction temperature enhances the collision frequency between the reactants and the active sites of zeolite and hence the methanol conversion improves. Moreover, Figure 1 shows that the conversion rate declines by increasing the inlet gas velocity from 1.5umf to 3umf. This behavior can be attributed to the reduction of contact time between the catalyst and feed gas at higher inlet gas velocity in the fluidized bed. Additionally, the conversion declined to the lowest value at the velocity 3 times higher than the minimum fluidization velocity (3umf) possibly due to the generation of large bubbles.

Figure 1. Effect of temperature on methanol conversion at different inlet gas velocities (TOS = 12 h).

cycle of dual-cycle theory is developed for simulation of the methanol to propylene reaction on the hierarchical Mn− DZSM-5 catalyst in the fluidized bed reactor. Six individual lumps considered for modeling include the following: C1−C4 (light paraffins), C2 = (ethylene), C 3= (propylene), C 4 = (butylenes), C5+ (C5 and higher hydrocarbons), and MDOH (methanol/dimethyl ether). Due to fast equilibrium reaction between methanol and DME, it is assumed that these two species behave as one reactant denoted “MDOH”. The proposed scheme for the MTP reaction is presented in Figure 2. According to this scheme, the primary products are formed during parallel reactions of MDOH based on the “hydrocarbon pool mechanism”.45 Since the formation of paraffins releases a lot of heat that should be considered in the reactor design,39 light paraffins (C1−C4) were also considered as a separate lump in the kinetic model. According to the dual-cycle theory, two different catalytic cycles were considered for methanol to hydrocarbon conversion over the HZSM-5 catalyst: olefin-based and aromatic-based cycles.46,47 The former is in favor of propylene

4. REACTOR MODELING 4.1. Kinetic Model. A six-lump kinetic model based on the hydrocarbon pool mechanism integrated with an olefin-based 9020


ÄÅ É ÅÅ E i 1 1 yzÑÑÑÑ zzÑÑxMDOH rs = k 0s expÅÅÅ− s jjj − ÅÅÇ R k T 768 {ÑÑÖ


Article

∀ s = 1, 2, ..., 5

ÄÅ É ÅÅ E i 1 1 yzÑÑÑÑ zzÑÑxMDOHxC3= r6 = k 06 expÅÅÅ− 6 jjj − ÅÅÇ R k T 768 {ÑÑÖ É ÅÄÅ E i 1 1 zyÑÑÑÑ Å zzÑÑxC4= r7 = k 07 expÅÅÅ− 7 jjj − ÅÅÇ R k T 768 {ÑÑÖ É ÅÄÅ E i 1 1 yzÑÑÑÑ Å zzÑÑx C5+ r8 = k 08 expÅÅÅ− 8 jjj − ÅÅÇ R k T 768 {ÑÑÖ

(4)

(5)

(6)

(7)

where x is the mass fraction and its subscripts represent the components. The pre-exponential factor (k0s) and activation energy (Es) are the model parameters at the reference temperature of 768 K that must be estimated from experimental data. The subscript s in r, k0, and E represents the number of reaction step. 4.2. Hydrodynamic Model. In this study, the Kunii and Levenspiel (K−L) model30 for bubbling fluidized bed was integrated with the dynamic two-phase model (DTP) to simulate the hydrodynamics of the fluidized bed for the MTP reactor. According to the K−L model for a bed with Geldart B particles (intermediate bubble bed), two side-by-side regions (phases) including bubble and emulsion ones were assumed, as illustrated in Figure S2. The extent of these phases was determined by the bubble volume fraction δ (m3 of bubbles/ m3 of bed). Moreover, based on the DTP model and considering the presence of catalyst in the bubbles,33,55 the chemical reaction occurs in both phases. The following assumptions are also considered in this study: • One-dimensional model is considered. • Gas products transfer between the bubble and emulsion phases. • The diffusion is neglected due to high gas velocity. • The catalyst activity is assumed constant. • The ideal gas law is assumed. • All the bubbles are assumed spherical with the same diameter. • The fluidized bed operates isothermally (T ± 1 °C). (The catalyst bed temperature was controlled by the coaxial thermocouple to keep the isothermal condition.) The mass balance equations for the bubble and emulsion phases of fluidized bed can be expressed as follows: bubble phase:

Figure 2. Proposed scheme for MTP reaction.

production in which more propylene and higher olefins are produced via a methylation/cracking pathway of the C3+ olefins, while the latter facilitates the production of ethylene via methylation/dealkylation of aromatic intermediates. The literature survey revealed that the olefin-based cycle was dominated for HZSM-5 catalyst and that the ethylene production via methylation/dealkylation of aromatic intermediates could be ignored.39,48 In addition, according to our previous study, 21 the synergetic modification by Mn impregnation and moderate desilication reduced both the density and strength of the strong acid sites. Since the low amount of total acid sites (especially the amount and strength of strong acid sites) inhibits the undesirable reaction of aromatization, cyclization, and hydrogen transfer,18,49−51 the formation of aromatic intermediates (in the aromatic-based cycle) from heavy olefins (included in the C5+ lump) in the olefin-based cycle are reduced and, consequently, the aromaticbased cycle is limited. As a result, in this study only the olefin-based cycle is considered and evaluated. In this approach, the methylation reactions of C3= to C4= and further to C5+ hydrocarbons accompanied by the cracking of C5+ to propylene are considered as secondary reactions, while the cracking reaction of C5+ to ethylene is ignored.39 Moreover, due to the low reactivity of ethylene to methanol relative to that of propylene and butylenes, the methylation of ethylene to propylene is ignored.39,52,53 Since the methylation reactions depend on the concentrations of both MDOH and methylate species, the concentration of both components should be considered in the methylation reaction rate of the kinetic model. However, since other reactions such as oligomerization (independent of MDOH concentration) along with methylation are carried out in the reaction of C4= to C5+, the concentration of methanol is neglected in this reaction.39 The reactions rates of the kinetic model are considered to be of elementary and first order for each reactant. The Arrhenius equation can be reformulated to deduce the correlation between the pre-exponential factor (k0) and the activation energy (E) at the reference temperature (Tref = 768 K).54 In this regard, the reaction rate (rs) equations for the sth reaction step can be rearranged as follows:

ub*

dρbg, i dz

= −Kbe(ρb,g i − ρe,gi ) + (1 − εb)ρs (rb, i)

(8)

emulsion phase: ue

dρe,gi dz

=

δ (Kbe(ρb,g i − ρe,gi ) + (1 − εe)ρs (re, i)) 1−δ (9)

where ρgb,i and ρge,i (kg/mreactor3) are the mass densities of component i in the bubble and emulsion phases, respectively, ρs (kg/m3) is the solid (particle) density. ub* and ue (m/s) are the velocity rise of the bubble and emulsion gas velocities, respectively. εb and εedenote average bubble and emulsion voidage, respectively. Kbe (1/s), rb,i, and re,i (kg/kgcat s) represent the bubble to emulsion mass transfer coefficient and 9021


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Industrial & Engineering Chemistry Research reaction rates of component i in bubble and emulsion phases, respectively. It is assumed that the catalyst bed is divided into equal volumetric elements with a circular cross section area of the reactor, A (Figure S2b). The volumetric elements are numbered sequentially from the bottom to the top of the reactor and are denoted by symbol n (n = 1, 2, ..., nf). The partial differential equations for both bubble and emulsion phases are written for all components (lumps) in each volumetric element and discretized using the numerical technique of finite differences as given in eqs 10 and 11, respectively.

150(1 − εmf ) 1.75 Remf 3 + Remf − Ar = 0 εmf 3 εmf 3 umf =

Fe, nxe, i , n = Fe, nxe, i , n − 1 +

(10)

(11)

Fb,n and Fe,n (kg/s) are the mass flow rates of gas in the bubble and emulsion phases, respectively. These parameters can be calculated from the following equations. Fb, n =

ρb,g n Aub*δεb

Fe, n = ρe,gn Aue(1 − δ)εe

i u − umf yz zz εe = εmf + 0.2 − 0.059 expjjj− 0 0.429 { k

(12) (13)

Ve, n = A(1 − δ)

H = A(1 − δ)Δz nf

(14)

(15)

db = 0.21h0.8(u0 − umf )0.42 exp[−0.25(u0 − umf )2 − 0.1(u0 − umf )]

(25)

If the bubble diameter is 0.6 times larger than the reactor diameter, then slugging occurs and the velocity of a single bubble, ubr, in the fluidized bed can be calculated by eq 26. For a bubble with a smaller diameter, ubr is often calculated from d eqs 27 and 28. When 0.125 < db , the rise of bubbles will slow down due to wall effects. These effects are considered in eq 27.27

i

p n xe, i , n ∑i =L 1 MW RT i

(22)

The bubble diameter at height h above the distributor plate can be given as follows:58

where the height of each element, Δz (m), is obtained from dividing the height of the catalyst bed, H (m), by the number of elements, nf. The average mass density of gas in the bubble/ emulsion phase at each element is calculated from the ideal gas law. p ρb,g n = n x b,i ,n L ∑i = 1 MW RT (16) ρe,gn =

(21)

i i u − umf zyyzz zzz δ = 1 − jjjj0.466 + 0.534 expjjj− 0 0.413 {z{ (23) k k The coefficient of mass transfer between two phases, Kbe, for the intermediate bubble bed used in this study can be approximated by the following equation.30 u Kbe = 4.5 mf db (24)

Vb,n and Ve,n (m ) are the volumes of the bubble and emulsion phases in each volumetric element that can be estimated from the following equations. H = Aδ Δz nf

0.021

i u − umf zy zz εb = 1 − 0.146 expjjj− 0 4.439 { k

3

Vb, n = Aδ

0.029i

g jj ρ yzz jj s zz (20) kρ { According to the DTP model, the condition of the emulsion phase, which is different from the minimum fluidization one, changes with the inlet gas velocity. In addition, the amount of solid particles in the bubble phase is a function of the inlet gas velocity. Consequently, the following equations were proposed by Cui et al.34 to estimate hydrodynamic parameters of the bubble volume fraction, δ, and the average voidage of the bubble and emulsion phases, εb and εe, respectively, in bed with Geldart B particles.

i1y εmf = 0.586jjj zzz k Ar {

δ KbeVe, n(ρb,g n xb, i , n − ρe,gn xe, i , n) 1−δ

+ Ven(1 − εe)ρs (re, i)

(19)

where Ar is the Archimedes number and μ denotes the gas dynamic viscosity. The void fraction at the point of minimum fluidization, εmf, can be determined by the following correlation.57

Fb, nxb, i , n = Fb, nxb, i , n − 1 − KbeVb, n(ρb,g n xb, i , n − ρe,gn xe, i , n) + Vbn(1 − εb)ρs (rb, i)

Remf μ ρg dp

(18)

(17)

Herein, MW represents the molecular weight. The mass fraction of component i in each element (xi,n) is the average value of that component in both the bubble and emulsion phases (water-free basis). A common approach for the modeling of hydrodynamic conditions in bubbling fluidized bed reactors is the use of semiempirical relations instead of explicit solving of the momentum balance.56 The aforementioned hydrodynamic parameters are described as follows. The Reynolds number at the minimum fluidization condition, Remf, and subsequently the minimum fluidization velocity umf can be calculated from the following equations.27

ubr = 0.35(gd)0.5 ,

0.6

db d

(26)

(27)

(28)


Article

Industrial & Engineering Chemistry Research where g is the gravitational acceleration. The bubble velocity in bed with Geldart B particles when the bed diameter is less than 1 m (d ≤ 1 m) can be expressed by the following equations:27 ub = 1.6((u0 − umf ) + 1.13db 0.5)db1.35 + ubr

(29)

ub = u0 − umf + ubr

(30)

genetic algorithm (GA), a stochastic optimization method, is able to solve the problem with no need for an initial point, it is very slow. In contrast, deterministic methods such as fmincon are highly sensitive to the initial point. Since there are too many local optimums in the estimation of the kinetic parameters, these methods trap at the local minimum. However, these methods make it possible to quickly estimate the true final values, provided that the appropriate initial point is available. Therefore, in this study these two methods are integrated to determine the kinetic parameters of the MTP reaction. In this regard, the initial point of fmincon is determined by the final response of GA and the minimum value of the objective function obtained by GA is improved by applying the fmincon function as a local search technique. The GA integrated constrained nonlinear optimization (fmincon) simultaneously optimized the kinetic parameters, k0 and E, by minimizing the root-mean-square errors (RMSE) as the objective function, where in the HGA the k0 parameter must be greater than 0.

where u0 is the superficial velocity of the inlet gas. The larger value between eqs 29 and 30 is selected as the bubble velocity.30 For intermediate bubble beds umf 5u < ub < mf εmf εmf The rise of the bubble velocity, u*b , can be determined by the following equation.30 ub* = ub + 3umf

(31)

Additionally, the emulsion phase velocity along the bed is almost constant and can be represented as follows:34 ue =

n

u0 − δub 1−δ

Obj = RMSE =

(32)

5. RESULTS AND DISCUSSION Both the aforementioned hydrodynamic equations and the kinetic equations of the proposed model were coded in

kinetic param (kg/kgcat h) (kg/kgcat h) (kg/kgcath) (kg/kgcat h) (kg/kgcat h) (kg/kgcat h) (kg/kgcat h) (kg/kgcat h)

value 1.261 1.665 9.318 5.572 0.399 1.389 2.266 0.201

± ± ± ± ± ± ± ±

0.053 0.015 0.256 0.099 0.031 0.132 0.224 0.011

kinetic param E1 E2 E3 E4 E5 E6 E7 E8

(kJ/mol) (kJ/mol) (kJ/mol) (kJ/mol) (kJ/mol) (kJ/mol) (kJ/mol) (kJ/mol)

value 10.014 76.401 55.283 41.927 36.701 15.247 27.997 77.448

± ± ± ± ± ± ± ±

nEnL

(33)

Herein, yi,j (%) is the experimental carbon-based molar yield of lump i in experiment j and ypre i,j is its corresponding predicted value. nE and nL are the numbers of experiments and lumps, respectively. The GA parameters applied in the optimization procedure include the initial population size of 200. The “stochastic uniform” was chosen as the parents’ selection function to generate the next population. The elite count was set as default (0.05 times the population size) to create children at each new generation. The crossover fraction was selected as 0.85. The other parameters of the GA were set on their default values. The estimated values of kinetic parameters with their 95% confidence intervals for the MTP reaction at the reference temperature via the HGA optimization technique are presented in Table 2. The minimum value of the objective function was obtained as about 0.95%. The values of kinetic parameters suggest that propylene is mainly produced from MDOH and a little amount is produced from the cracking of heavy hydrocarbons (C5+). At the reference temperature, the reaction rate constants of propylene production from MDOH and C5+ are 9.318 and 0.201 kg/kgcat h, respectively. As can be seen in Table 2, the activation energies of the primary reactions of MDOH to light olefins (steps 2−4) increase with decrease of olefin carbon number. This behavior is in agreement with experimental results in which the temperature increase is desirable for higher yields of the lower olefins. In secondary reactions, the activation energy of the cracking reaction (step 8) is higher than those of methylation reactions (steps 6 and 7). It suggests that, by temperature increase, the acceleration rate of the cracking reaction is higher than those of methylation/oligomerization reactions. In order to investigate the relationship between kinetic parameters (k0 and E), their correlation coefficient was calculated. The obtained value (0.11) suggests that there is no significant correlation between these parameters. The methanol conversion and product yield parity plots of the model predicted data versus the experimental data are presented in Figure 3. An insignificant difference between the experimental results and the simulation results demonstrated

Table 2. Values of Estimated Kinetic Parameters k01 k02 k03 k04 k05 k06 k07 k08

n

∑ j =E 1 ∑i =L 1 (yipre −yi , j )2 ,j

0.110 1.390 0.821 0.940 2.681 0.720 0.301 4.577

Figure 3. Model predicted data versus experimental data.

MATLAB (release 2017b, The MathWorks), and the kinetic parameters were estimated by the hybrid genetic algorithm (HGA) in the MATLAB optimization Toolbox. Although 9023


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Figure 4. Predicted profiles of product yields along the bed at u0 = 1.5umf and (a) T = 450 °C, (b) T = 480 °C, (c) T = 510 °C, and (d) T = 540 °C.

complete methanol consumption in the first half of the reactor. These changes are clearly visible in Figure S3. Figure S3a shows that approximately 80% of MDOH is consumed in the first half of the reactor, according to the model predictions. This may be due to insufficient residence time between the feed and catalyst at a high gas velocity. The model prediction of Figure S3 shows that the decrease in conversion due to the increased gas velocity along the bed at lower temperatures is compensated at higher temperatures. Figures 5 and 6 illustrate the model predicted conversion of MDOH and the product yields and their comparisons with the experimental data; the former represents the results with the change of temperature at different u0/umf values, while the latter depicts the results the alteration of u0/umf at different temperatures. Figure 5 shows that the conversion increases with temperature, both experimentally and by simulation. However, at lower u0/umf, there was a slight deviation between the

that the model successfully predicted conversion and product yields at the end of reaction at different temperatures and inlet gas velocities. The model predicted profiles of the component yields along the bed are illustrated in Figure 4 and Figure S3 and compared to the experimental data recorded at the reactor outlet. As can be seen from these figures, the model predicted profiles of component yields cover the experimental data at the end of the reactor well. In addition, Figure 4 and Figure S3 show that a large amount of MDOH is consumed and converted to products at the initial height of the bed according to model prediction. The model also predicts the effect of increasing temperature and decreasing gas feed velocity on the enhancement of methanol consumption along the bed. For example, at u0/umf = 1.5 and T = 450 (Figure 4a), about 95% of MDOH is consumed in the first half of the reactor (up to h/H = 0.5), while at higher temperatures the model predicts almost 9024


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Figure 5. Comparison between experimental (points) and model predicted (lines) data for the evaluation of MDOH conversion and product yields with the change of temperature at u0/umf values of (a) 1.5, (b) 2, (c) 2.5, and (d) 3.

reaction confirmed that the yield of propylene increases with increasing reaction temperature.22,36,38 Figure 8 shows a three-dimensional surface plot of the effect of temperature and u0/umf on the ratio of propylene to ethylene yield (P/E). The results show that the ratio of P/E increases with the temperature reduction and u0/umf increment. The P/E ratio of the optimum condition in which the maximum propylene yield is achieved is about 4.2−4.4, while the maximum P/E ratio (about 6.2) is obtained at a temperature of 450 °C and at a velocity of 3umf. 5.1. Model Validation. Two main statistical parameters in terms of R2 and F were calculated to verify the model by eqs 34 and 35, respectively.

experimental results and the simulation results at low temperatures. Simulation product yield trends show a good pattern with the experimental ones other than propylene yield, which shows a slight deviation at low temperatures and values. For the effect of u0/umf on conversion and yields at some chosen temperatures, Figure 6 shows that the simulation results perfectly fit the experimental results, especially at higher temperatures, and that u0/umf only has slight changes on the methanol conversion and the product yields. Figure 7a presents a three-dimensional surface plot of the simultaneous effects of operational factors (reaction temperature and u0/umf) on propylene yield of the MTP reaction in a fluidized bed reactor using the model. Similarly, a twodimensional contour curve of these effects is illustrated in Figure 7b to obtain more detailed information. As can be seen, the optimum operating conditions in which the propylene yield is maximized (about 52%) are at the maximum temperature studied (540 °C) over the whole studied range of velocity. The literature surveys over ZSM-5 in the MTP

n

2

R =1−

9025

n

∑ j =E 1 ∑i =L 1 (yipre −yi , j )2 ,j n

n

∑ j =E 1 ∑i =L 1 (yi , j )2

(34) DOI: 10.1021/acs.iecr.9b01132 Ind. Eng. Chem. Res. 2019, 58, 9017−9029

Article


Figure 6. Comparison between experimental (points) and predicted (lines) data for evaluation of MDOH conversion and product yields with change of u0/umf at temperatures of (a) 450, (b) 480, (c) 510, and (d) 540 °C.

(∑ F=

nE n ∑i =L 1 (yi , j )2 j=1 n

n

n

catalyst and the feed concentration are the same. The results of experimental data and their comparison with the model predicted yields of light olefins as well as their corresponding relative errors (RE) in validation tests are summarized in Table 3. The results showed that the model well predicts the yields of products in the temperature range 450−540 °C and velocities of 1.5umf−3umf. Therefore, the proposed integrated model in this study and the calculated values for kinetic parameters are reliable for the Mn−DZSM-5 catalyst.

)

− ∑ j =E 1 ∑i =L 1 (yipre −yi , j )2 /nP ,j

n

∑ j =E 1 ∑i =L 1 (yipre −yi , j )2 /(nE − nP) ,j (35)

where nP is the dimension of the model parameters. When R2 is larger than 0.9 (close to 1) and F is greatly larger than its critical value (Fα(nP, nE − nP)), the model is more reliable in prediction of the experimental data. The critical F value (α = 5%) obtained less than 4 from the F-test table. The calculated results of R2 (0.998) and F (1102.73) indicate that the model identification parameters meet the expected requirements well. Subsequently, in order to evaluate the proposed model, three different experiments were performed in the fluidized bed reactor at different operating conditions (reaction temperature of 465 °C and gas velocity of 2.75umf, temperature of 495 °C and gas velocity of 1.75umf, and last at 525 °C and 2.25umf). The other conditions including the amount of loaded

6. CONCLUSIONS The catalytic conversion of methanol to propylene (MTP) over manganese modified desilicated HZSM-5 catalyst (type Geldart B) was evaluated in a fluidized bed reactor at reaction temperatures from 450 to 540 °C and inlet gas velocities from 1.5 to 3 times higher than the minimum fluidization velocity. The results revealed that temperature greatly affected the yield 9026


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Figure 7. (a) Three-dimensional surface plot and (b) contour plot of propylene yield as a function of temperature and u0/umf.

The results demonstrated that the model fit the experimental data well with an RMSE of 0.95%. The results of model prediction revealed that the maximum propylene yield (52%) was obtained at the maximum temperature studied (540 °C) over the whole studied range of velocity. The results of the model validation indicated that the model was reliable in the studied conditions.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.9b01132. Physicochemical properties of Mn−DZSM-5 sample; schematic flow diagram of the laboratory scale setup; illustration of MTP bubbling fluidized bed reactor with Geldart B particles and schematic representation of dynamic two-phase model in this reactor; predicted profiles of product yields along the bed at u0/umf = 3 and T = 450, 480, 510, and 540 °C (PDF)

Figure 8. Three-dimensional surface plot of P/E ratio as a function of temperature and u0/umf.

■

of products, while the role of inlet gas velocity above the minimum fluidization velocity (u > umf) was insignificant. Moreover, a six-lump kinetic model integrated with a dynamic two-phase model was proposed to predict the performance of the MTP reaction in the fluidized bed reactor. To minimize the root-mean-square error (RMSE) as the objective function, the kinetic parameters were derived from the experimental data using a genetic algorithm and fmincon as a hybrid function.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Majid Taghizadeh: 0000-0002-6575-7341 Notes

The authors declare no competing financial interest.

Table 3. Comparison between the Experimental Data and Model Predicted Yields of Light Olefins as Well as Their Corresponding Relative Errors in Validation Tests for MTP Reaction in Fluidized Bed Reactor validation test conditions

ethylene yield (%)

propylene yield (%)

butylenes yield (%)

T (°C)

u/umf

actual

predicted

RE (%)

actual

predicted

RE (%)

actual

predicted

RE (%)

465 495 525

2.75 1.25 2.25

7.501 9.710 11.053

7.836 9.744 11.331

4.466 0.35 2.515

46.112 48.792 51.213

44.857 49.327 50.926

2.722 1.096 0.56

30.561 30.141 28.788

30.284 30.526 28.817

0.906 1.277 0.101

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(17) Ahmadpour, J.; Taghizadeh, M. Catalytic Conversion of Methanol to Propylene over High-Silica Mesoporous ZSM-5 Zeolites Prepared by Different Combinations of Mesogenous Templates. J. Nat. Gas Sci. Eng. 2015, 23, 184−194. (18) Liu, J.; Zhang, C.; Shen, Z.; Hua, W.; Tang, Y.; Shen, W.; Yue, Y.; Xu, H. Methanol to Propylene: Effect of Phosphorus on a High Silica HZSM-5 Catalyst. Catal. Commun. 2009, 10 (11), 1506−1509. (19) Zhang, H.; Ning, Z.; Liu, H.; Shang, J.; Han, S.; Jiang, D.; Jiang, Y.; Guo, Y. Bi2O3Modification of HZSM-5 for Methanol-toPropylene Conversion: Evidence of Olefin-Based Cycle. RSC Adv. 2017, 7 (27), 16602−16607. (20) Rostamizadeh, M.; Taeb, A. Highly Selective Me-ZSM-5 Catalyst for Methanol to Propylene (MTP). J. Ind. Eng. Chem. 2015, 27, 297−306. (21) Yahyazadeh Saravi, F.; Taghizadeh, M. Synergetic Effect of Mn, Ce, Ba, and B Modification and Moderate Desilication of Nanostructured HZSM-5 Catalyst on Conversion of Methanol to Propylene. Turk. J. Chem. 2018, 42 (6), 1640−1662. (22) Kaarsholm, M.; Rafii, B.; Joensen, F.; Cenni, R.; Chaouki, J.; Patience, G. S. Kinetic Modeling of Methanol-to-Olefin Reaction over ZSM-5 in Fluid Bed. Ind. Eng. Chem. Res. 2010, 49, 29−38. (23) Guo, W.; Xiao, W.; Luo, M. Comparison among Monolithic and Randomly Packed Reactors for the Methanol-to-Propylene Process. Chem. Eng. J. 2012, 207, 734−745. (24) Gayubo, A. G.; Aguayo, A. T.; Alonso, A.; Bilbao, J. Kinetic Modeling of the Methanol-to-Olefins Process on a Silicoaluminophosphate (SAPO-18) Catalyst by Considering Deactivation and the Formation of Individual Olefins. Ind. Eng. Chem. Res. 2007, 46 (7), 1981−1989. (25) Chen, J. Q.; Bozzano, A.; Glover, B.; Fuglerud, T.; Kvisle, S. Recent Advancements in Ethylene and Propylene Production Using the UOP/Hydro MTO Process. Catal. Today 2005, 106 (1−4), 103− 107. (26) Schoenfelder, H.; Hinderer, J.; Werther, J.; Keil, F. J. Methanol to Olefins-Prediction of the Performance of a Circulating FluidizedBed Reactor on the Basis of Kinetic Experiments in a Fixed-Bed Reactor. Chem. Eng. Sci. 1994, 49 (24), 5377−5390. (27) Kunii, D.; Levenspiel, O. Fluidization Engineering, 2nd ed.; Acrivos, A., Bailey, J. E., Morari, M., Nauman, E. B., Pearson, J. R. A., Prud’homme, R. K., Eds.; Butterworth-Heinemann: Boston, 1991. (28) Jafari, R.; Sotudeh-Gharebagh, R.; Mostoufi, N. Performance of the Wide-Ranging Models for Fluidized Bed Reactors. Adv. Powder Technol. 2004, 15 (5), 533−548. (29) Grace, J. R.; Lim, K. S. Reactor Modeling for High-Velocity Fluidized Beds. In Circulating Fluidized Beds; Grace, J. R., Avidan, A. A., Knowlton, T. M., Eds.; Springer: Dordrecht, 1997; pp 504−524. (30) Kunii, D.; Levenspiel, O. Fluidized Reactor Models. I: For Bubbling Beds of Fine, Intermediate, and Large Particles. II: For the Lean Phase: Freeboard and Fast Fluidization. Ind. Eng. Chem. Res. 1990, 29, 1226−1234. (31) Werther, J.; Hartge, E. Modeling of Industrial Fluidized-Bed Reactors. Ind. Eng. Chem. Res. 2004, 43, 5593−5604. (32) Gilbertson, M. A.; Yates, J. G. The Motion of Particles near a Bubble in a Gas-Fluidized Bed. J. Fluid Mech. 1996, 323, 377−385. (33) Aoyagi, M.; Kunii, D. Importance of Dispersed Solids in Bubbles for Exothermic Reactions in Fluidized Beds. Chem. Eng. Commun. 1974, 1 (4), 191−197. (34) Cui, H.; Mostoufi, N.; Chaouki, J. Characterization of Dynamic Gas-Solid Distribution in Fluidized Beds. Chem. Eng. J. 2000, 79 (2), 133−143. (35) Kaarsholm, M.; Joensen, F.; Cenni, R.; Chaouki, J.; Patience, G. S. MEOH to DME in Bubbling Fluidized Bed: Experimental and Modelling. Can. J. Chem. Eng. 2011, 89 (2), 274−283. (36) Aguayo, A. T.; Mier, D.; Gayubo, A. G.; Gamero, M.; Bilbao, J. Kinetics of Methanol Transformation into Hydrocarbons on a HZSM-5 Zeolite Catalyst at High Temperature (400 - 550° C). Ind. Eng. Chem. Res. 2010, 49, 12371−12378. (37) Mousavi, S. H.; Fatemi, S.; Razavian, M. Kinetic Modeling of the Methanol to Olefins Process in the Presence of Hierarchical

ACKNOWLEDGMENTS The authors wish to thank the Iran National Science Foundation (INSF) for financial support of the project. The authors also acknowledge the funding support of Babol Noshirvani University of Technology through grant program No. BNUT/370152/97.

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