An Integrated Experimental and Modeling Approach for Methanol-to

Subscriber access provided by UNIV OF LOUISIANA

Kinetics, Catalysis, and Reaction Engineering

An Integrated Experimental and Modeling Approach for Methanol-to-Propylene Conversion over Mn-Modified Desilicated HZSM-5 Catalyst in a Fluidized Bed Reactor Fatemeh Yahyazadeh Saravi, and Majid Taghizadeh Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b01132 • Publication Date (Web): 07 May 2019 Downloaded from http://pubs.acs.org on May 7, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

An Integrated Experimental and Modeling Approach for Methanol-to-Propylene 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, 4714871167, Babol, Iran

ABSTRACT: In this study the catalytic conversion of methanol to propylene (MTP) over Mnmodified desilicated HZSM-5 catalyst was evaluated in an experimental fluidized bed reactor at reaction temperature range of 450–540 °C and inlet gas velocity of 1.5–3 times greater than minimum fluidization velocity. Although both the change of temperature and inlet gas velocity affect the methanol conversion, the results showed that the former significantly influence the product yields, while the later does not have such 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 hybrid genetic algorithm, the kinetic model was coupled with two-phase structure hydrodynamic model. This coupled model can accurately predict the methanol conversion and product yields in 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 ranges of velocity. By applying this condition, the methanol conversion of about 99 % was obtained. Keywords: Methanol to propylene process, Fluidized bed, Lumped kinetic model, Dynamic two phase model, Hybrid genetic algorithm 1. INTRODUCTION Propylene is one of the major basic petrochemical products and has attained an increasing global demand over the last 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 *

Corresponding author. E-mail: [email protected] (M. Taghizadeh).

1 ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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 non-oil source such as biomass, natural gas and any gasifiable carbon substance (like coal).1–5 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 a 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 SiCsupported 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 some metals doping on three dimensional (3-D) printed ZSM-5 monolith catalysts. Their results showed that the olefins selectivity was improved over Mg- and Zn-doped monolith catalysts. Various methods have been reported in the literatures 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 case of MTO/MTP process, a wide variety of heterogeneous catalytic reactors have been suggested in patents including fixed bed reactor, moving bed reactor, fluidized bed reactor, and series or parallel reactors. A commercial scale fluidized bed reactor have been successfully used in MTO process by UOP, DICP and SINOPEC companies, whereas MTP process with multistage fixed bed reactor have been commercialized by Lurgy company so far. This is despite the fact that, the fixed bed and fluidized bed processes are competitive in 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 MTP reaction on these beds is 2 ACS Paragon Plus Environment

Page 2 of 31

Page 3 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


really difficult.22 On 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

micro-size catalyst results in uniform distribution of

deactivated catalyst in the fluidized bed reactor and constant catalyst activity.23 Some investigations revealed that good yield towards olefins over ZSM-5 and SAPO-34 was obtained in 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, a lot of criteria such as reaction rate, stoichiometry, 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 fluidized bed reactor at different fluidization regimes such as K-L model,27 modified two-phase model28 and core-annulus model29 for the bubbling, turbulent and fast regime of fluidization, respectively. In case of 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 that thick overlapping clouds surround bubbles and the beds with large particle (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 different gas flow pattern and assumption. Werther and Hartge31 modeled an industrial fluidized bed with considering 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. Fluidized bed reactors are often modeled based on the two-phase concept. In case of simple two-phase model, two main assumptions are considered for bubbling fluidized bed. First, the reaction occurs in emulsion phase at minimum fluidization condition and the second, the bubbles are particle-free. While 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, emulsion phase is different from minimum fluidization one specially for beds containing Geldart B particles.34 Such real conditions are considered in dynamic two-phase (DTP) model.



Despite the complexity of hydrodynamics, difficult scale up and more initial capital cost of fluidized bed reactors compared to those of fixed bed ones, these types of the reactors are viable ones to study 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 design and optimization of such reactors. Various kinetic models proposed for 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 specific group that named “lump” which treat as pseudo-component. On contrary, every single reaction step is investigated separately in detailed kinetic model.37,38 Schoenfelder et al.26 proposed a seven lump kinetic model for 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 MTO reaction over phosphorous modified HZSM-5 catalyst in a small scale fluidized bed reactor and proposed kinetic model based on hydrocarbon pool mechanism. They also used the assumptions of simple two-phase model for hydrodynamic reactor modeling. Aguayo et al.36 investigated methanol to hydrocarbon (MTH) reaction on HZSM-5 catalyst (Si/Al = 15) at temperature range of 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 separated lumps. Menges et al.38 developed a six-lump kinetic model based on hydrocarbon pool mechanism and methylation-cracking for MTO reaction with co-feeding of light olefins over high silica ZSM5/AlPO4-extrudates. This model was extended by Jiang et al.39 for MTP reaction over HZSM-5 catalyst in the modeling of moving bed with recycling of byproducts. Park and Froment40,41 formulated detailed kinetic model using carbenium ion mechanism with more than 30 parameters for MTO reaction over high silica HZSM-5 catalyst. Froment42 proposed single-event kinetic model that 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 intermediates, finding the kinetic parameters of detailed model are very time consuming, complex with low accuracy and sometimes impossible.37,39 In addition, the integration of the detailed kinetic model 4 ACS Paragon Plus Environment

Page 4 of 31

Page 5 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


with the mathematical model leads to complexity of computation, in modeling a reactor. Therefore, the lump kinetic models are usually used for the design purposes. The kinetics of 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 early stage and still in the pilot scale.5 Consequently, obtaining the kinetic parameters of MTP reaction in the fluidized beds is of prime importance, especially due to presence of high exothermic reaction. In this study, methanol to propylene reaction was implemented in bubbling fluidized bed reactor over manganese modified desilicated HZSM-5 catalyst (Geldart B) at different reaction temperatures (450–540 °C) and inlet gas velocity ranges 1.5–3 times higher than minimum fluidization velocity. Furthermore, a modified lumped kinetic model was proposed based on hydrocarbon pool mechanism that integrated the olefin-based cycle of dual-cycle theory to describe reaction pathway. This model was consisting of 8 reactions involving 6 lumps. Twophase 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 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 objective function. 2. EXPERIMENTAL 2.1. Catalyst Preparation The Mn-modified desilicated HZSM-5 catalyst (denote as Mn-DZSM-5) was synthesized according to the procedure reported in our previous paper.21 Briefly, first the high silica NaZSM-5 catalyst was hydrothermally prepared with 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 ion-exchange treatment via 1 M ammonium nitrate solution (NH4NO3, 95 wt.%), drying and finally calcination. Then the HZSM-5 catalyst was desilicated by 0.2 M mixture of NaOH and TPAOH in a NaOH/TPAOH ratio of 1.5,43 under reflux in an oil bath at temperature of 65 °C for 30 minutes. After that, the slurry was cooled immediately, washed several times, dried, calcined and converted to acid form (H) by ion-exchange treatment, the desilicated catalyst obtained after re-drying and re-calcination 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 DZSM-5 sample was added to this solution while stirring 5 ACS Paragon Plus Environment


vigorously at ambient temperature for 1 day. The modified catalyst obtained after drying and calcination was denoted as Mn-DZSM-5, containing 2 wt.% of manganese. The physiochemical properties of the Mn-DZSM-5 catalyst are listed in Table S1. A detail 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 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. The fluidized bed reactor was placed inside a tube furnace (PTF 12/75/750, Lenton Ltd, UK) 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 co-axial thermocouple where located in the center of reaction zone. Before adjusting the furnace temperature, the catalyst sample was pretreated with 20 mL/min of high purity N2 flow at 550 °C for an hour. The N2 flow was controlled by Brooks mass flow controllers (MFC) with a maximum flow rate of 500 mL/min. Then a mixture of 50 wt.% of 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 enters to the reaction zone through a gas distributor plate (with holes’ diameter of 0.3 mm located 5 cm above the reactor base) and contacts with 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 of 0.5–1.4 mL/min to obtain the superficial gas velocities which are 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 gas phase was determined by an online gas chromatograph (GC-Varian 3800) equipped with FID detector and a 50-m HP-PONA


Page 6 of 31

Page 7 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


capillary column. The liquid phase was injected into the offline GC at the end of the experiments. Methanol conversion, selectivity and yield of products are calculated as follows:

Methanol conversion  Selectivity 

i o o N MeOH  ( N MeOH  2 N DME )  100 i N MeOH

aN Coa H b



i o o N MeOH  N MeOH  2 N DME

(1) (2)



Yield  Methanol conversion  selectivity

(3)

where N is the moles number, a is carbon number and superscript 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 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 following groups: light paraffins (C1–C4: methane, ethane, propane and butane), ethylene (C2=), propylene (C3=), 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.



Page 8 of 31

Table 1. Product yields of MTP reaction over Mn-DZSM-5 catalyst after 12 h of TOS. No.

Temperature (°C)

u0/umf

Product yields (%) C1–C4

C2=

C3=

C4=

C5+

MDOH

1

450

1.5

8.22

8.36

41.84

31.33

7.05

3.2

2

480

1.5

5.02

8.77

47.38

31.44

5.23

2.16

3

510

1.5

4.39

11.52

49.4

28.71

4.9

1.08

4

540

1.5

3.1

13.22

50.08

28.13

4.94

0.53

5

450

2

7.76

7.48

43.62

31.08

6.56

3.5

6

480

2

4.63

8.41

48.33

31.12

5.17

2.34

7

510

2

3.61

10.21

50.06

29.29

5.56

1.27

8

540

2

3.14

12.21

51.69

28.07

4.35

0.54

9

450

2.5

7.12

7.18

44.55

31

6.15

4

10

480

2.5

4.25

8.18

49.02

30.59

5.28

2.68

11

510

2.5

3.29

9.9

51

29.33

4.94

1.54

12

540

2.5

3.31

12

52.53

28.43

3.07

0.66

13

450

3

6.74

6.67

43.17

30.58

7.84

5

14

480

3

3.88

7.97

47.95

30.08

6.23

3.89

15

510

3

3.24

8.94

49.37

29.08

6.7

2.67

16

540

3

3.17

11.65

51.21

27.73

4.86

1.38

According to Table 1, the yield 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 yield of ethylene and propylene increases with the consumption of C5+. On the other hand, regardless of the effect of temperature, the changes in product yields over the studied ranges of the inlet gas velocity are really slight and this indicates that products’ distribution is not very sensitive to the studied inlet gas velocity, as reported in 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 8 ACS Paragon Plus Environment

Page 9 of 31

enhanced with the increasing of temperature from 450 °C to 540 °C in all the 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. 100

99

Conversion (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


98

97

96

u 0 = 1.5 umf u 0 = 2 umf u 0 = 2.5 umf u 0 = 3 umf

95

94 440

460

480

500

520

540

560

Temperature (°C)

Figure 1. The effect of temperature on methanol conversion at different inlet gas velocities (TOS = 12 h). Moreover, this Figure shows that the conversion rate declines by increasing the inlet gas velocity from 1.5 umf to 3 umf. 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 lowest value at the velocity 3 times higher than minimum fluidization velocity (3 umf) possibly due to the generation of large bubbles. 4. REACTOR MODELING 4.1. Kinetic Model A six-lumped kinetic model based on hydrocarbon pool mechanism integrated with olefin-based cycle of dual-cycle theory is developed for simulation of methanol to propylene reaction on the hierarchical Mn-DZSM-5 catalyst in the fluidized bed reactor. Six individual lumps considered 9 ACS Paragon Plus Environment


Page 10 of 31

for modeling include: C1–C4 (light paraffins), C2= (ethylene), C3= (propylene), C4= (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 a one reactant denoted “MDOH”. The proposed scheme for 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 release 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. 1

2

3

MDOH

C1–C4

C 2=

C 3= 6

4

C 4=

8

7 5

C 5+

Figure 2. The Proposed scheme for MTP reaction. According to the dual cycle theory, two different catalytic cycles were considered for methanol to hydrocarbons conversion over the HZSM-5 catalyst: olefin-based and aromaticbased cycles.46,47 The former is in favor of propylene production in which more propylene and higher olefins are produced via methylation/cracking pathway of the C3+ olefins, while, the later 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 10 ACS Paragon Plus Environment

Page 11 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


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 aromatic-based 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 are 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 methylation reactions 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 pre-exponential factor (k0) and the activation energy (E) at reference temperature (Tref = 768 K).54 In this regard, the reaction rate (rs) equations for sth reaction step can be rearranged as follows: rs  k 0 s  exp[

Es 1 1 (  )]  x MDOH R T 768

r6  k 06  exp[

E6 1 1 (  )]  xMDOH xC  3 R T 768

(5)

r7  k07  exp[

E7 1 1 (  )]  xC  4 R T 768

(6)

r8  k08  exp[

E8 1 1 (  )]  xC  5 R T 768

(7)

s  1, 2,..., 5

(4)

where x is the mass fraction and its subscripts represents 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 presents the number of reaction step. 11 ACS Paragon Plus Environment


Page 12 of 31

4.2. Hydrodynamic Model In this study, Kunii and Levenspiel (K-L) model30 for bubbling fluidized bed integrated with dynamic two-phase model (DTP) to simulate the hydrodynamic of the fluidized bed for MTP reactor. According to 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 bubble volume fraction δ (m3 bubbles/m3 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 neglected due to high gas velocity.  The catalyst activity assumed constant.  The ideal gas law assumed.  All the bubbles assumed spherical with the same diameter.  Fluidized bed operates isothermally (T ± 1 °C) (the catalyst bed temperature was controlled by the co-axial thermocouple to keep the isothermal condition.) The mass balance equations for the bubble and emulsion phases of fluidized bed can be expressed as follow: Bubble phase: * b

u

dbg,i dz

  K be ( bg,i  eg,i )  (1   b )  s (rb ,i )

(8)

Emulsion phase:

deg,i  ue  ( K be ( bg,i  eg,i )  (1   e )  s (re,i )) dz 1  

(9)

3 where  b,g i and  e,g i ( kg / mreactor ) are the mass densities of component i in bubble and emulsion

phases, respectively,  s ( kg / m3 ) is the solid (particle) density. ub* and ue (m/s) are the velocity rise of the bubble and emulsion gas velocities, respectively.  b and  e denote average bubble and emulsion voidage, respectively. Kbe (1/s), rb,i and re,i (kg/kgcat s) represent the bubble to emulsion


Page 13 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


mass transfer coefficient and reaction rate of component i in bubble and emulsion phases, respectively. It is assumed that the catalyst bed is divided into equal volumetric elements with circular cross section area of reactor, A, (Figure S2b). The volumetric elements are numbered sequentially from bottom to top of the reactor and 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 numerical technique of finite differences as given in Eqs. (10) and (11), respectively. Fb , n xb ,i , n  Fb , n xb ,i , n 1  K beVb , n (  bg, n xb ,i , n   eg, n xe ,i , n )  Vbn (1   b )  s (rb ,i )

Fe , n xe ,i , n  Fe , n xe ,i , n 1 

 1 

K beVe , n (  bg, n xb ,i , n   eg, n xe ,i , n )  Ven (1   e )  s (re ,i )

(10) (11)

Fb,n and Fe,n (kg/s) are the mass flow rate of gas in bubble and emulsion phases, respectively. These parameters can be calculated from following equations. Fb , n  bg, n Aub* b

(12)

Fe,n   eg,n Aue (1   ) e

(13)

Vb,n and Ve,n (m3) are the volume of bubble and emulsion phases in each volumetric element that can be estimated from following equations. Vbn  A

H  Az nf

Ven  A(1   )

(14)

H  A(1   )z nf

(15)

where the height of each element, z (m), is obtained from dividing the height of catalyst bed, H (m), to the number of elements, nf. The average mass density of gas in bubble/emulsion phase at each element is calculated from the ideal gas law.

bg,n 

p x nL b ,i ,n  R T i 1 MW i

 eg,n 

p

(16)

(17)

xe ,i ,n  R T i 1 MW i nL



Page 14 of 31

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 bubble and emulsion phases (water free basis). A common approach for the modeling of hydrodynamic condition in bubbling fluidized bed reactors is the use of semi-empirical relations instead of explicit solving momentum balance.56 The aforementioned hydrodynamic parameters are described as follow. Reynolds number at minimum fluidization condition, Remf, and subsequently minimum fluidization velocity umf can be calculated from the following equations.27 1.75



3 mf

Re 2mf 

umf 

150  (1   mf ) 3  mf

Re mf  Ar  0

(18)

Re mf  

(19)

 dp g

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

 mf  0.586  (

1 0.029  g 0.021 ( s ) )  Ar

(20)

According to DTP model, the condition of emulsion phase, which is different from minimum fluidization one, changes with 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.

 b  1  0.146 exp(

u0  umf 4.439

 e   mf  0.2  0.059 exp(

  1  (0.466  0.534 exp(

)

(21)

u0  umf 0.429 u 0  u mf 0.413

)

(22)

))

(23)

The coefficient of mass transfer between two phases, Kbe, for intermediate bubble bed used in this study can be approximated by the following equation.30 14 ACS Paragon Plus Environment

Page 15 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


K be  4.5

umf

(24)

db

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

db  0.21h0.8  (u0  umf )0.42 exp[0.25(u0  umf ) 2  0.1(u0  umf )]

(25)

If the bubble diameter be 0.6 times larger than the reactor diameter, then slugging occurs and the velocity of single bubble, ubr, in fluidized bed can be calculated by Eq. (26). For bubble with smaller diameter, ubr is often calculated from Eqs. (27) and (28). When 0.125 

db , the rise of d

bubbles will slow down due to wall effects. This effects are considered in Eq. (27).27 ubr  0.35  ( g  d ) 0.5

ubr  0.711  ( g  d ) 0.5  1.2 exp(1.49 ubr  0.711  ( g  d ) 0.5

0.6 

, db ) d

, 0.125  ,

0.125 

db d

(26)

db  0.6 d

(27)

db d

(28)

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 equations27

ub  1.6  ((u0  umf )  1.13  d b0.5 )  d b1.35  ubr

(29)

ub  u0  u mf  ubr

(30)

where u0 is the superficial velocity of inlet gas. The larger value between Eqs. (29) and (30) is selected as bubble velocity.30 For intermediate bubble beds,

u mf

 mf

 ub 

5u mf

 mf

, the rise of bubble

velocity, ub* , can be determined by the following equation.30 (31)

ub*  ub  3umf

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

u0    ub 1 

(32)



Page 16 of 31

5. RESULTS AND DISCUSSION Both the aforementioned hydrodynamic equations and the kinetic equations of the proposed model were coded in MATLAB (2017b, the MathWorks) and the kinetic parameters were estimated by hybrid genetic algorithm (HGA) in MATLAB optimization Toolbox. Although genetic algorithm (GA), a stochastic optimization method, is able to solve the problem with no need to 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 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 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 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. Ob  RMSE 

  nE

nL

j 1

i 1

2 ( yipre , j  yi , j )

(33)

nE  nL

Herein, yi,j (%) is the experimental carbon-based molar yield of lump i in experiment j and yipre ,j is it’s corresponding predicted value. nE and nL are the number 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 next population. The elite count set as default (0.05 times of population size) to create children at each new generation. The crossover fraction was selected 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 reference temperature via HGA optimization technique are presented in Table 2. The minimum value of the objective function was obtained about 0.95%.


Page 17 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. The values of estimated kinetic parameters. Kinetic parameters

Value

Kinetic parameters

Value

k01 (kg/kgcat h)

1.261 ± 0.053

E1 (kJ/mol)

10.014 ± 0.110

k02 (kg/kgcat h)

1.665 ± 0.015

E2 (kJ/mol)

76.401 ± 1.390

k03 (kg/kgcath)

9.318 ± 0.256

E3 (kJ/mol)

55.283 ± 0.821

k04 (kg/kgcat h)

5.572 ± 0.099

E4 (kJ/mol)

41.927 ± 0.940

k05 (kg/kgcat h)

0.399 ± 0.031

E5 (kJ/mol)

36.701 ± 2.681

k06 (kg/kgcat h)

1.389 ± 0.132

E6 (kJ/mol)

15.247 ± 0.720

k07 (kg/kgcat h)

2.266 ± 0.224

E7 (kJ/mol)

27.997 ± 0.301

k08 (kg/kgcat h)

0.201 ± 0.011

E8 (kJ/mol)

77.448 ± 4.577

The values of kinetic parameters suggest that the 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 constant of the 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 energy of the primary reactions of MDOH to light olefins (steps 2–4) increase with decrease of olefins’ 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 6–7). It suggests that by temperature increase,

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 that the model successfully predicted conversion and product yields at the end of reaction at different temperatures and inlet gas velocities.



100 90

Predicted yield/conversion (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 31

80 70 60 50 40

C₁-C₄ C₂⁼ C₅⁺ C₃⁼ C₄⁼ Conversion

30 20 10 0 0

10

20

30

40

50

60

70

80

90

100

Experimental yield/conversion (%)

Figure 3. Model predicted data versus experimental data. The model predicted profiles of the component yields along the bed are illustrated in Figures 4 and S3 and compared to the experimental data recorded at the reactor outlet. As can be seen from these figures, the model predicted profiles of components yield cover the experimental data at the end of the reactor well. In addition, these figures 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 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 complete methanol consumption in the first half of the reactor. These changes are clearly visible in Figure S3(a-d). 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.


Page 19 of 31

100

100 Model predictions C 1 -C 4

70 60

80

C =3

C =3 C =4 C +5

C =4

70

C +5 MDOH

MDOH

Model predictions C 1 -C 4

(b)

90

C =2

C =2

80

Yield (%)

Experimental data C 1 -C 4

Yield (%)

(a)

90

50 40

60


C =2

C =2

C =3 C =4 C +5

C =4

C =3 C +5 MDOH

MDOH

50 40

30

30

20

20

10

10 0

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0

1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

h/H

h/H 100

100 Model predictions C 1 -C 4

Experimental data C 1 -C 4 C =2

C =2

80 70 60

80

C =3 C =4 C +5

C =3 C =4 C +5

70

MDOH

MDOH

50 40

60

20

20

10

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

C =2

C =2

C =3 C =4 C +5

C =4

C =3 C +5 MDOH

MDOH

40 30

0


50

30

0

Model predictions C 1 -C 4

(d)

90

Yield (%)

(c)

90

Yield (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0

1

0

0.1

0.2

h/H

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

h/H

Figure 4. Predicted profiles of the product yields along the bed at u0 = 1.5 umf and (a) T = 450 °C, (b) T = 480 °C, (c) T = 510 °C and (d) T = 540 °C. 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, while the later depicts the results the alteration of u0/umf at different temperatures.



100

100 Model predictions Experimental data C 1 -C 4 C 1 -C 4

(a)

Yield/conversion (%)

80 70 60

C =2

C =2

C =3 C =4 C +5

C =3 C =4 C +5

Conversion

Conversion

90

50 40

70 60

20

20

10

10

460

470

480

490

500

510

520

530

540

0 440

550

450

460

470

C =4

C +5

C +5

Conversion

Conversion

480

490

500

510

520

530

540

550

100


70 60

C =2

C =2

C =3 C =4 C +5

C =3 C =4 C +5

Conversion

Conversion

90

50 40 30

70 60

10

480

490

500

510

520

530

540

550

C =3

Conversion

Conversion

C =4 C +5

30

10

470

C =2

C =3 C =4 C +5

40

20

460

C =2

50

20

450

Model predictions Experimental data C 1 -C 4 C 1 -C 4

(d)

80


(c)

80

0 440

C =3

C =4

Temperature (°C)

Temperature (°C)

90

C =2

C =3

40 30

450

C =2

50

30

0 440


(b)

80


90


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 31

0 440

450

460

470

480

490

500

510

520

530

540

550

Temperature (°C)

Temperature (°C)

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


Page 21 of 31

100

100

(a)


80 70 60 50

C =2

C =2

C =3

C =3

C =4

C =4

C +5

C +5

Conversion

Conversion

40 30


(b)

90


80


90

70 60

C =2

C =2

C =3

C =3

C =4

C =4

C +5

C +5

Conversion

Conversion

50 40 30

20

20

10

10 0

0 1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

1.4

3

1.6

1.8

2

2.2

2.4

2.6

2.8

3

u 0/umf

u 0/umf 100


80 70 60

C =2

C =2

C =3 C =4 C +5

C =3 C =4 C +5

Conversion

Conversion


(d)

90 80

50 40 30


(c)

90


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


70 60

C =2

C =3 C =4 C +5

C =3

Conversion

Conversion

C =4 C +5

50 40 30

20

20

10

10

0

C =2

0

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

1.4

1.6

u 0/umf

1.8

2

2.2

2.4

2.6

2.8

3

u 0/umf

Figure 6. A comparison between experimental (points) and predicted (lines) data for the evaluation of MDOH conversion and product yields with the change of u0/umf at temperatures of (a) 450 °C, (b) 480 °C, (c) 510 °C and (d) 540 °C. Figure 5(a-d) shows that the conversion increases with temperature, both experimentally and by simulation. However, at lower u0/umf, there was a slight deviation between the 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 6a-d 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 three-dimensional surface plot of simultaneous effect of operational factors (reaction temperature and u0/umf) on propylene yield of the MTP reaction in fluidized bed reactor using the model. Similarly, two-dimensional contours 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 maximum temperature studied (540 °C) over the whole studied ranges of velocity. The literature surveys over ZSM-5 in MTP reaction confirmed that the yield of propylene increases with increasing reaction temperature.22,36,38

u / 0 u

mf

500

2

480 1.5

460

51.1222

51.1222

51.1222 51.1222

50.5059

49.8895

520

49.2731

45.57 4

44

540

2.5

48.6568

40 3

2

48.0404

45

9

46

42

7

44

47.424

47

u 0/umf

48

46

2.5

13

49

48

46.807

50

50

46.19

52

(b)

44.9 585

51

43.1 44.3 43.7 0943 422 258 .109 44.9 4 58 5 43.7 25 8 45.5 44.34 749 44.9 442.3 585 2 42 2 45.5 749 46.1 44.9 913 585 46.1 913 45.5 46.80 749 77 46.8 46.1 077 913 47.42 4 47 46.8 .424 077 48.04 04 4 47.4 8.040 24 4 48.656 48.04 8 48 04 .6568 48.65 49.273 68 1 49 .2731 49.27 31 49.8895 49.889 5 49.88 95 50.5059 50.5059 50.505 9

3

52

(a)

Propylene yield (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 31

43

)

°C ure ( erat p m Te

42

1.5 450

460

470

480

490

500

510

520

530

540

Temperature (°C)

Figure 7. (a) Three-dimensional surface plot and (b) Contours plot of the propylene yield as a function of temperature and u0/umf. 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 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 3 umf.


Page 23 of 31

6.2 6 5.8

6

5.6 5.4

5.5

P/E ratio

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


5.2

5

5 4.8

4.5 3

4 460

2.5

480

Tem

500 pera ture (°C)

2 520

540

u 0/u mf

4.6 4.4 4.2

1.5

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

R

2

  ( y y 1   (y )

 F

nE j 1

nE

nL

j 1

i 1

pre i, j

nE

nL

j 1

i 1

i, j

)2

(34)

2

i, j

 (y )      ( y y ) nL

i 1 nE

j 1

2

i, j

nL

i 1

pre i, j

nE

nL

j 1

i 1

i, j

2



2 ( yipre nP , j  yi , j )

(nE  nP)

(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 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 2.75 umf, temperature of 495 °C and gas velocity of 1.75 umf and last at 525 °C and 2.25 umf). The other conditions including the amount of loaded catalyst and the 23 ACS Paragon Plus Environment


Page 24 of 31

feed concentration are the same. The results of experimental data and their comparison with the model predicted yield of light olefins as well as their corresponding relative errors (RE) in validation tests are summarized in Table 3. Table 3. A comparison between the experimental data and model predicted yield 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

2.75

7.501

7.836

4.466

46.112

44.857

2.722

30.561

30.284

0.906

495

1.25

9.710

9.744

0.35

48.792

49.327

1.096

30.141

30.526

1.277

525

2.25

11.053

11.331

2.515

51.213

50.926

0.56

28.788

28.817

0.101

The results showed that the model well predict the yield of products in temperature ranges of 450–540 °C and velocity of 1.5–3 umf. Therefore, the proposed integrated model in this study and the calculated values for kinetic parameters are reliable for the Mn-DZSM-5 catalyst. 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 temperature from 450 to 540 °C and inlet gas velocity from 1.5–3 times higher than minimum fluidization velocity. The results revealed that temperature greatly affected the yield of products, while the role of inlet gas velocity above minimum fluidization velocity (u>umf) was insignificant. Moreover, a 6-lump kinetic model integrated with dynamic two-phase model was proposed to predict the performance of MTP reaction in fluidized bed reactor. To minimize root mean square error (RMSE) as objective function, the kinetic parameters were derived from the experimental data using genetic algorithm and fmincon as a hybrid function. The results demonstrated that the model fit the experimental data well with RMSE of 0.95%. The results of model prediction revealed that the maximum propylene yield (52%) obtained at maximum temperature studied (540 °C) over the whole studied ranges of velocity. The results of the model validation indicated that the model was reliable in the studied conditions.


Page 25 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ACKNOWLEDGMENTS The authors wish to thank the Iranian Nanotechnology Initiative Council for the 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. REFERENCES (1)

Jiao, Y.; Jiang, C.; Yang, Z.; Liu, J.; Zhang, J. Synthesis of Highly Accessible ZSM-5 Coatings on SiC Foam Support for MTP Reaction. Microporous Mesoporous Mater. 2013, 181, 201–207.

(2)

Xiang, D.; Qian, Y.; Man, Y.; Yang, S. Techno-Economic Analysis of the Coal-to-Olefins Process in Comparison with the Oil-to-Olefins Process. Appl. Energy. 2014, 113, 639– 647.

(3)

Rigby, C. R.; Han, H.; Bhowmik, P. K.; Bahari, M.; Chang, A.; Harb, J. N.; Lewis, R. S.; Watt, G. D. Soluble Viologen Polymers as Carbohydrate Oxidation Catalysts for Alkaline Carbohydrate Fuel Cells. J. Electroanal. Chem. 2018, 823, 416–421.

(4)

Losch, P.; Boltz, M.; Louis, B.; Chavan, S.; Olsbye, U. Catalyst Optimization for Enhanced Propylene Formation in the Methanol-to-Olefins Reaction. Comptes Rendus Chim. 2015, 18 (3), 330–335.

(5)

Cheng, Y.; Wei, F.; Jin, Y. Multiphase Reactor Engineering for Clean and Low-Carbon Energy Applications, first ed.; John Wiley & Sons: Hoboken, 2017.

(6)

Tian, P.; Wei, Y.; Ye, M.; Liu, Z. Methanol to Olefins (MTO): From Fundamentals to Commercialization. ACS Catalysis. 2015, 5 (3), 1922–1938.

(7)

Akhoundzadeh, H.; Taghizadeh, M.; Pajaie, H. S. Synthesis of Highly Selective and Stable Mesoporous Ni–Ce/SAPO-34 Nanocatalyst for Methanol-to-Olefin Reaction: Role of Polar Aprotic N, N-Dimethylformamide Solvent. Particuology. 2018, 40, 113–122.

(8)

Sharifi Pajaie, H.; Taghizadeh, M. Methanol Conversion to Light Olefins over SurfactantModified Nanosized SAPO-34. React. Kinet. Mech. Catal. 2016, 118 (2), 701–717.

(9)

Chen, H.; Shang, W.; Yang, C.; Liu, B.; Dai, C.; Zhang, J.; Hao, Q.; Sun, M.; Ma, X. Epitaxial Growth of Layered-Bulky ZSM-5 Hybrid Catalysts for the Methanol-toPropylene Process. Ind. Eng. Chem. Res. 2019, 58 (4), 1580–1589.

(10)

Liao, Z.; Xu, T.; Jiang, Y.; Jiang, B.; Wang, J.; Yang, Y.; Jiao, Y.; Yang, Z.; Zhang, J. 25 ACS Paragon Plus Environment


Methanol to Propylene over Foam SiC-Supported ZSM-5 Catalyst: Performance of Multiple Reaction–Regeneration Cycles. Ind. Eng. Chem. Res. 2019, 58 (1), 27–33. (11)

Magzoub, F.; Li, X.; Al-Darwish, J.; Rezaei, F.; Rownaghi, A. A. 3D-Printed ZSM-5 Monoliths with Metal Dopants for Methanol Conversion in the Presence and Absence of Carbon Dioxide. Appl. Catal. B: Environ. 2019, 245, 486–495.

(12)

Li, X.; Rezaei, F.; Rownaghi, A. A. Methanol-to-Olefin Conversion on 3D-Printed ZSM-5 Monolith Catalysts: Effects of Metal Doping, Mesoporosity and Acid Strength. Microporous Mesoporous Mater. 2019, 276, 1–12.

(13)

Hwang, A.; Le, T. T.; Shi, Z.; Dai, H.; Rimer, J. D.; Bhan, A. Effects of Diffusional Constraints on Lifetime and Selectivity in Methanol-to-Olefins Catalysis on HSAPO-34. J. Catal. 2019, 369, 122–132.

(14)

Meng, F. L.; Wang, Z. L.; Zhong, H. X.; Wang, J.; Yan, J. M.; Zhang, X. B. Reactive Multifunctional Template-Induced Preparation of Fe-N-Doped Mesoporous Carbon Microspheres Towards Highly Efficient Electrocatalysts for Oxygen Reduction. Adv. Mater. 2016, 28 (36), 7948–7955.

(15)

Sun, C.; Du, J.; Liu, J.; Yang, Y.; Ren, N.; Shen, W.; Xu, H.; Tang, Y. A Facile Route to Synthesize Endurable Mesopore Containing ZSM-5 Catalyst for Methanol to Propylene Reaction. Chem. Commun. 2010, 46 (15), 2671–2673.

(16)

Rahmani, M.; Taghizadeh, M. Synthesis Optimization of Mesoporous ZSM-5 through Desilication-Reassembly in the Methanol-to-Propylene Reaction. React. Kinet. Mech. Catal. 2017, 122 (1), 409–432.

(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. Bi2O3 Modification of HZSM-5 for Methanol-to-Propylene Conversion: Evidence of OlefinBased Cycle. RSC Adv. 2017, 7 (27), 16602–16607.

(20)

Rostamizadeh, M.; Taeb, A. Highly Selective Me-ZSM-5 Catalyst for Methanol to 26 ACS Paragon Plus Environment

Page 26 of 31

Page 27 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


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. Turkish 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. 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 Methanolto-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 Fluidized-Bed 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, Second 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. 27 ACS Paragon Plus Environment


(32)

Page 28 of 31

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 SAPO-34 Catalyst: Parameter Estimation, Effect of Reaction Conditions and Lifetime Prediction. React. Kinet. Mech. Catal. 2017, 122 (2), 1245–1264.

(38)

Menges, M.; Kraushaar-Czarnetzki, B. Kinetics of Methanol to Olefins over AlPO4Bound ZSM-5 Extrudates in a Two-Stage Unit with Dimethyl Ether Pre-Reactor. Microporous Mesoporous Mater. 2012, 164, 172–181.

(39)

Jiang, B.; Feng, X.; Yan, L.; Jiang, Y.; Liao, Z.; Wang, J.; Yang, Y. Methanol to Propylene Process in a Moving Bed Reactor with Byproducts Recycling: Kinetic Study and Reactor Simulation. Ind. Eng. Chem. Res. 2014, 53 (12), 4623–4632.

(40)

Park, T. Y.; Froment, G. F. Kinetic Modeling of the Methanol to Olefins Process. 1. Model Formulation. Ind. Eng. Chem. Res. 2001, 40 (20), 4172–4186.

(41)

Park, T. Y.; Froment, G. F. Kinetic Modeling of the Methanol to Olefins Process. 2. Experimental Results, Model Discrimination, and Parameter Estimation. Ind. Eng. Chem. Res. 2001, 40 (20), 4187–4196.

(42)

Froment, G. F. Single Event Kinetic Modeling of Complex Catalytic Processes. Catal. Rev. 2005, 47 (1), 83–124.

(43)

Ahmadpour, J.; Taghizadeh, M. Selective Production of Propylene from Methanol over High-Silica

Mesoporous

ZSM-5

Zeolites 28

ACS Paragon Plus Environment

Treated

with

NaOH

and

Page 29 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


NaOH/Tetrapropylammonium Hydroxide. Comptes Rendus Chim. 2015, 18 (8), 834–847. (44)

Hu, S.; Shan, J.; Zhang, Q.; Wang, Y.; Liu, Y.; Gong, Y.; Wu, Z.; Dou, T. Selective Formation of Propylene from Methanol over High-Silica Nanosheets of MFI Zeolite. Appl. Catal. A Gen. 2012, 445, 215–220.

(45)

Dahl, I. M.; Kolboe, S. On the Reaction Mechanism for Propene Formation in the MTO Reaction over SAPO-34. Catal. Letters. 1993, 20 (3–4), 329–336.

(46)

Bjørgen, M.; Joensen, F.; Spangsberg Holm, M.; Olsbye, U.; Lillerud, K. P.; Svelle, S. Methanol to Gasoline over Zeolite H-ZSM-5: Improved Catalyst Performance by Treatment with NaOH. Appl. Catal. A-Gen. 2008, 345 (1), 43–50.

(47)

Svelle, S.; Olsbye, U.; Joensen, F.; Bjørgen, M. Conversion of Methanol to Alkenes over Medium- and Large-Pore Acidic Zeolites: Steric Manipulation of the Reaction Intermediates Governs the Ethene/Propene Product Selectivity. J. Phys. Chem. C. 2007, 111 (49), 17981–17984.

(48)

Wen, M.; Wang, X.; Han, L.; Ding, J.; Sun, Y.; Liu, Y.; Lu, Y. Monolithic MetalFiber@HZSM-5

Core-Shell

Catalysts

for

Methanol-to-Propylene.

Microporous

Mesoporous Mater. 2015, 206, 8–16. (49)

Zhang, H.; Ning, Z.; Shang, J.; Liu, H.; Han, S.; Qu, W.; Jiang, Y.; Guo, Y. A Durable and Highly Selective PbO/HZSM-5 Catalyst for Methanol to Propylene (MTP) Conversion. Microporous Mesoporous Mater. 2017, 248, 173–178.

(50)

Zhang, J. G.; Qian, W. Z.; Tang, X.-P.; Shen, K.; Wang, T.; Huang, X. F.; Wei F. Influence of Catalyst Acidity on Dealkylation, Isomerization and Alkylation in MTA Process. Acta Physico-Chimica Sin. 2013, 29 (6), 1281–1288.

(51)

Gayubo, A. G.; Alonso, A.; Valle, B.; Aguayo, A. T.; Bilbao, J. Kinetic Model for the Transformation of Bioethanol into Olefins over a HZSM-5 Zeolite Treated with Alkali. Ind. Eng. Chem. Res. 2010, 49 (21), 10836–10844.

(52)

Svelle, S.; Rønning, P. O.; Olsbye, U.; Kolboe, S. Kinetic Studies of Zeolite-Catalyzed Methylation Reactions. Part 2. Co-Reaction of [12C] Propene or [12C] n-Butene and [13C] Methanol. J. Catal. 2005, 234 (2), 385–400.

(53)

Svelle, S.; Rønning, P. O.; Kolboe, S. Kinetic Studies of Zeolite-Catalyzed Methylation Reactions: 1. Coreaction of [12C] Ethene and [13C] Methanol. J. Catal. 2004, 224 (1), 115–123. 29 ACS Paragon Plus Environment


(54)

Van Boekel, M. Statistical Aspects of Kinetic Modeling for Food Science Problems. J. Food. Sci. 1996, 61 (3), 477–486.

(55)

Chaouki, J.; Gonzalez, A.; Guy, C.; Klvana, D. Two-Phase Model for a Catalytic Turbulent Fluidized-Bed Reactor: Application to Ethylene Synthesis. Chem. Eng. Sci. 1999, 54 (13–14), 2039–2045.

(56)

Wenzel, M.; Rihko-Struckmann, L.; Sundmacher, K. Continuous Production of CO from CO2 by RWGS Chemical Looping in Fixed and Fluidized Bed Reactors. Chem. Eng. J. 2018, 336, 278–296.

(57)

Broadhurst, T. E.; Becker, H. A. Onset of Fluidization and Slugging in Beds of Uniform Particles. AIChE J. 1975, 21 (2), 238–247.

(58)

Cai, P.; Schiavetti, M.; De Michele, G.; Grazzini, G. C.; Miccio, M. Quantitative Estimation of Bubble Size in PFBC. Powder Technol. 1994, 80 (2), 99–109.


Page 30 of 31

Page 31 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Graphical Abstract 284x173mm (300 x 300 DPI)

ACS Paragon Plus Environment

An Integrated Experimental and Modeling Approach for Methanol-to

Recommend Documents