Time-Resolved Product Analysis of Dimethylether ... - ACS Publications

Subscriber access provided by UNIV OF WISCONSIN - MADISON

Article

Time-resolved product analysis of dimethyletherto-olefins conversion on SAPO-34 Marius Kirchmann, Christoph Hauber, and Alfred Haas ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.9b00765 • Publication Date (Web): 13 May 2019 Downloaded from http://pubs.acs.org on May 13, 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 37 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

ACS Catalysis

Time-Resolved Product Analysis of Dimethylether-to-Olens Conversion on SAPO-34 Alfred Haas, Christoph Hauber, and Marius Kirchmann

∗

hte GmbH, the high throughput experimentation company, 69123 Heidelberg, Germany

E-mail: [email protected]

Phone: +49 (0)6221 7497297

Abstract The evolution and distribution of products during the reaction of dimethylether-toolens on SAPO-34 was studied in a xed-bed reactor at dierent severities ranging from 0.220 h−1 . Thereby, the timescale of deactivation at low severities smaller 1 h−1 is slow enough for online GC to give detailed information on changes of the product distribution and the reaction network. However, at higher and commercially relevant severities > 1 h−1 the timescale of deactivation becomes too fast to be adequately followed by online GC. In order to follow the rapidly changing reaction products, FTIR spectroscopy in the gas-phase was applied in measurement intervals down to 5 s. Multivariate analysis of the FTIR spectra and correlation to the online product analysis by GC was used for in-situ training of chemometrical models for all major products between C1C5. These models were used for the prediction of product selectivities from the FTIR spectra in high time resolution giving valuable insights into the product formation during induction, steady state and deactivation period of the catalyst. The results indicate, that the product distribution on SAPO-34 depends on the TOS deactivation and coke content.

1

ACS Paragon Plus Environment

ACS Catalysis 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

Partially burning-o the coke on catalyst further demonstrated that the remaining coke on catalyst is linked to the product distribution and that a certain selectivation by coke is required to yield the maximum olens selectivity. Finally, the temperature and severity were varied in a short design of experiments (DoE) showing the inuence of these parameters on product distribution and conversion capacity. Keywords: methanol-to-olens, IR spectroscopy, chemometrics, deactivation, coke formation, zeolites

2


Page 2 of 37

Page 3 of 37 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

ACS Catalysis

Introduction The conversion of methanol and dimethylether to hydrocarbons (MTH) has attracted signicant attention in the past decades since it provides ecient routes for the production of basic petrochemicals and fuels from exible carbon feedstocks such as natural gas, coal or biomass. 1 The MTH reaction is catalyzed by various zeolites which microenvironment with regard to topology and Brønsted acidity plays a signicant role on the product distribution and catalyst lifetime. 1 For methanol-to-olens (MTO), the silica-alumino phosphate SAPO-34 with a chabazite (CHA) framework and small 8-ring pore windows is very selective towards the formation of lower olens and governed by product shape selectivity. 2 Linear reaction products can exit, while larger molecules such as branched and aromatic products are retained within the pores and eventually forming hydrocarbon residues restricting the pores and leading to relatively fast deactivation. 3 The changes in product distribution during catalyst lifetime can be assigned to induction, steady-state and deactivation period. Since the induction period happens very fast and is therefore dicult to follow, it is not well studied and the underlying mechanism for the formation of the initial hydrocarbon pool is still under debate, while there is growing evidence for a direct mechanism of the rst C-C bond formation during the initial stage of the reaction. 4,5 In contrast to the induction period, the mechanism for the following steady-state period is widely accepted as dual-cycle hydrocarbon pool mechanism in which aromatic and olenic compounds within the pore system act as reaction intermediates and autocatalytic species while being subsequently methylated, dealkylated and cracked to form the nal products. 6 Steady-state operation is well studied and there are a number of techniques that can follow evolution of hydrocarbon pool species and link it to activity and selectivity. Since selectivity strongly depends on the nature and amount of hydrocarbon residues, a time dependent product distribution is observed. 7 These hydrocarbon residues or coke eventually lling and restricting the zeolite pores are the main reason for catalyst deactivation, while 3


ACS Catalysis 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 4 of 37

it is not well established during the deactivation period, how coke aects the signicantly changing selectivity patterns in the nal stage. High selectivity of SAPO-34 towards lower olens was recently leading to its successful commercialization in a uidized-bed reactor-regenerator conguration with continuous regeneration. 8 Since the catalyst age distribution in a uidized-bed reactor is close to uniform in space, only the variation of the product distribution with time needs to be taken into account. 9 In a xed-bed plug ow reactor, the time dependence of the product distribution is additionally inuenced by the evolution of a spatial age distribution of the catalyst, leading to an axial coke deactivation pattern. This spatiotemporal deactivation by coking is depicted as "cigar-burn". 10 Once the inlet zone during the induction period has build up a sucient concentration of hydrocarbon pool species for the autocatalytic reaction to start, an active reaction band moves down the catalyst bed leaving deactivated catalyst behind. It was suggested to further divide these reaction zones into deactivation, methanol conversion and olen conversion zones, 11 which has been supported by visual analyses of partially deactivated catalyst. 12 In one approach to model catalyst deactivation for the MTH reaction in ZSM-5, catalyst deactivation and the changing product distribution is described as a reduction of the eective amount of catalyst or contact time in the reactor with time. 13 This is based on the assumption that deactivation is non-selective, meaning the product distribution at a given conversion is the same on a fresh catalyst as on a partially deactivated catalyst and that coke formation of products is negligible. The model was further adapted to the autocatalytic nature of the MTH reation, taking into account that a more complex relation between applied contact time

τ0 and the observations such as methanol conversion capacity and the product distribution exist. 14,15 In order to investigate these rapidly changing regimes of catalytic conversion, a sucient time resolution of the products is important. Multiple spectroscopic approaches such as UVVis, NMR, XRD, Raman and combinations thereof were applied as 4


in-situ

and

in-operando

Page 5 of 37 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

ACS Catalysis

techniques to gain additional insights into the mechanism, the hydrocarbon pool, the species present in the operating catalyst and coke species. 1618 While spectroscopic techniques deliver details in a high time resolution within seconds on the catalyst surface, relatively slow online gas chromatography (GC) has been the preferred tool to determine the evolving product distribution of the MTH reaction. An approach to analyse the rapidly changing product composition by GC in a higher resolution was developed by Schulz using an ampoulle technique to catch euent samples in a high time resolution and carry out a time-decoupled analysis by GC. 19 More recently, GC analysis of the products was supplemented by continuous mass spectrometry and could follow especially the fast induction period in a high time resolution, providing evidence for the direct mechanism by C-C coupling. 5 In this publication, we focus on the detailed product analysis during induction, steadystate and deactivation in a xed-bed in high time resolution. In order to follow the rapidly changing product distribution also at higher and commercially relevant severities, FTIR spectroscopy in the gas-phase was applied in measurement intervals down to 5 s. Though spectroscopic methods such as FTIR can measure product spectra within seconds, strongly overlaying absorptions of the complex hydrocarbon mixture with similar vibration frequencies render a simple interpretation and quantication

via

single compound calibration di-

cult and requires an interpretation by multivariate analysis and chemometric models. These models were trained in-situ by running online GC and FTIR next to each other at low severities < 1 h−1 , proting from the fact that the conversion capacity and lifetime for SAPO-34 increases with an increase in applied contact time τ0 while the obtained range of selectivities remains similar. By using this approach, the lifetime of SAPO-34 was extended to hours, while sucient data points for a range of time dependent product distributions were obtained by detailed multicolumn GC analysis, correlated to the corresponding FTIR spectra at the same time-on-stream (TOS) and used to build chemometric models for all major product in the C1C5 range. Based on these models, product selectivities could be predicted from the FTIR spectra in high time resolution also for higher severities and very short contact times 5


ACS Catalysis 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

while catalyst lifetime decreased to minutes and the changing product distribution could not be followed adequately by GC. As a result of this novel investigatory approach, insights into the changing product distribution during induction, steady-state and deactivation period could be obtained in an unprecedented resolution. Multiple severities from 0.220 h−1 in the range of factor 100 were applied and the inuence of severity or contact time on selectivity, lifetime and conversion capacity was investigated. The dependence of the coke level on the product composition was further investigated by burning-o the coke partially and analyzing the product distribution when bringing these samples back on stream. These experiments led to an improved understanding of the importance of product shape selectivity for controlling the product distribution of SAPO-34 by either following the product distribution in high time resolution while coke is being build up or providing a certain coke level before starting the reaction. In order to explore new conditions to optimize light olen yield, the inuence of temperature on the product distribution was investigated for multiple severities within a short design of experiments (DoE) and evaluated by regression analysis.

6


Page 6 of 37

Page 7 of 37

Results and discussion Continuous dosage In order to follow the product distribution of the reaction of dimethylether-to-olens (DTO) on SAPO-34 by multicolumn online GC, the reaction was carried out at a low severity of 0.2 h−1 in a xed-bed reactor. Since methanol conversion capacity and lifetime increases with increasing contact time τ0 , 14,15 the lifetime of SAPO-34 under these conditions was increased to 250 min time-on-stream (TOS) before the DME conversion dropped below 90%. The reactor was operated at 400◦ C and a DME partial pressure of 1 bar (50 vol% DME in nitrogen) at a total reactor pressure of 2 bara. The results are shown in Figure 1a and the large data points represent the results obtained from the online GC analysis with a measurement interval of 12 min. It is evident, that the online GC analysis can follow the changes in product distribution under low severity conditions quite well.

Yield (C%)

80 a) ●

60

●

● ●

40

●

20

● ● ● ● ● ●

0

rel. conc. (%)

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

ACS Catalysis

● ● ● ● ● ● ●

● ● ● ● ● ●

●

●

●

●

●

●

●

●

●

● ● ●

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ●

● ● ● ● ● ●

●

●

● ● ●

● ● ● ● ● ●

● ● ●

● ●

● ●

● ●

● ●

● ● ● ● ●

● ● ● ● ●

● ● ● ● ●

● ● ● ●

● ● ●

●

● ● ● ● ●

● ● ●

b)

● ● ● ● ● ● ● ● ● ● ●

Ethene Propene C4 Olefins Methane Ethane Propane Butane Methanol Dimethylether Sum Olefins Sum C5+ CO CO2 H2O H2 conc

0

100

200

300

TOS (min)

Figure 1: a) Product distribution at WHSV = 0.2 h−1 and 400◦ C versus TOS. The big symbols show the GC data, whereas the smaller symbols show the predicted data from the FTIR spectra.b) not quantied values for H2O, CO and CO2 by FTIR and hydrogen by GC TCD analysis. By means of providing an analytical method to follow the changes in product distribution in a higher time resolution, a trace heated FTIR gas cell was set up parallel to the online GC to analyze the same product stream with a measurement interval of 30s and the obtained 7


ACS Catalysis

Absorbance

a)

3750

3250

2750

2250

1750

1250

750

250

Absorbance

b)

300 200 0

Absorbance

3750

3250

2750

2250

1750

1250

750

100

TOS (min)

250

c)

45

TOS (min)

0

3750

3250

2750

2250

1750

1250

750

Wavenumber cm−1

250

Figure 2: FTIR spectra collected during deactivation of the catalyst at WHSV = 0.2 h−1 and 400◦ C. a) overlayed spectra from 0-331 min TOS. b) Staggered view from 0-331 min TOS with initially every 4th spectrum / 60s, at TOS > 45 min every 20th spectrum (300 s) shown. c) staggered view with focus on the initial 0-45 min TOS with every 4th spectrum (60 s) shown. Ethene

Propene

40

30

30

20

20

10 0

Predicted yield (C%)

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 8 of 37

10 0

10

20

30

Methane

0

0

10

30

40

1

2

3

4

0

5

10

0

20

25

75

5.0

50

2.5

25

40

60

Dimethylether

100

7.5

0.0

0

20

0 0

Methanol

5

15

20

Butane

10

10

40

1 0

5

60

2

10

0

Propane

3

5

C4 Olefins

Ethane

4

12.5 10.0 7.5 5.0 2.5

20

25 20 15 10 5 0

0 0.0

2.5

5.0

Measured yield (C%)

7.5

0

25

50

75

Figure 3: Measured vs. predicted values based on a PLS model including 15 principal components. 8


Page 9 of 37 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

ACS Catalysis

spectra are shown in Figure 2. In the supporting information in Figure S1 and S2, the IR spectra of selected expected products for the DTO reaction over a small pore 8-ring zeolite such as SAPO-34 were extracted and visualized from the NIST database. 20 It is evident, that too many overlaying absorptions exist to allow for a simple interpretation

via

calibration

with single compounds thereby necessitating the evaluation by multivariate analysis and chemometric models. These partial least squares (PLS) regression models were trained by correlating the FTIR spectra with the quantied product distributions from detailled online GC analysis obtained at the same TOS and multiple lower severities from 0.20.4 h−1 . As a result, individual models for the yield of each major product, product lump or educt in the C1C5 range were obtained and used to predict the yields in all IR spectra not overlapping with the online GC analysis and therefore unknown product distribution. The accuracy of predition is shown in Figure 3 with good correlation between measured online GC data and predicted product yields from the IR spectra. As a result, the product distribution in Figure 1a was complemented by the smaller data points representing the predicted product yields from the IR spectra, increasing the time resolution signicantly and providing the base for measuring at higher severities and expectedly faster deactivation. It should be noted, that signicantly dierent product spectra or dierent catalysts can require individual models for each catalyst. Since these can be obtained under low severity for each catalyst, this will only add complexity to the evaluation and data processing and not to the way of testing. In Figure 1a, the reaction starts at 100% DME conversion. It is surprising that DME and partial conversion thereof is not observed and the induction period cannot be clearly distinguished. This might be attributed to the utilized bed length. Even though partial conversion could have occurred in the rst part of the catalyst bed during induction period, the lower parts of the bed would have converted it until 100% conversion is reached. Future experiments with very short beds lengths or signicantly higher WHSVs utilizing the full bed length could help to obtain data in the partial conversion range. As shown in experiments with partial coke burn below, initial partial conversion can be detected when it occurs. Initial 9


Page 10 of 37

TOS 0 min

5 4 3 2 1 0

TOS 30 min TOS 60 min TOS 90 min TOS 120 min TOS 150 min TOS 180 min TOS 210 min

401.2

401.0

400.8

400.6

400.4

400.2

400.0

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

Catalyst bed height (cm)

ACS Catalysis

Te m p e ra tu re (°C )

Figure 4: Temperature proles collected during deactivation of the catalyst at WHSV = 0.2 h−1 and 400◦ C. The position of the catalyst bed is shown schematically. methane is low and increases slowly with increasing TOS by possibly dehydrogenation of retained carbon species or dehydrogenation / decomposition of methanol and DME. 19 The low concentration of ethane among the parans indicates low availability or reactivity of ethene for hydrogen transfer. In contrast to this, high yields of propane and butane indicate high initial hydrogen transfer activity of propene and butenes and could potentially result from aromatics build-up during hydrocarbon pool formation. During steady-state, the initial high hydrogen transfer activity to parans decreases and olens become the predominant products. Ethene, propene and the C2 / C3 ratio increases to a maximum until DME and methanol break through, while a maximum of butenes peaks at earlier TOS preceeded by the maximum of pentenes (see Figure S3, S4 and S5 in the supporting information for the detailed C4/C5 isomer distribution and olenicities). Figure S5 shows, that the olenicities increase sequentially for C5, C4 and C3 while the C2 olenicity seems to follow a dierent pathway. This supports recent results from the literature, that the accumulation of retained aromatics or coke species reduce the cage volume for products and reactants and thus aect product selectivities. 11 At the same time, secondary reactions of olens in the olens conversion zone yielding

10


Page 11 of 37

WHSV 0.2

WHSV 0.3

WHSV 0.4

WHSV 0.8

WHSV 1

100

Yield (C%)

80

60

●

●● ● ● ● ● ● ● ● ● ●

●

●● ●

● ●

● ●

●

●

● ●

●

●

●

●

●

●

●

●

40

0

●

● ●

●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●●●●● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ●● ●●●● ●●● ●● ●● ● ●● ●● ● ●● ●● ●● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●●●●● ● ●●●●●●●●● ●●●●●●●●●●

0

100

200

● ●

● ●

●

●

●

●

●

●

●

●

●

●

●

20

● ●

●● ● ● ● ● ●● ● ● ● ●● ●●

300 0

● ● ● ●

● ● ●

●

●

●

●●● ● ● ● ● ●

●

●

●

●

● ● ● ●● ● ●●● ● ● ●● ●●● ●●●● ● ●●●●

100 150 2000

50

● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

●

100

●

●

● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ●

● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●●●● ●● ● ●●●● ●● ●●

50

● ●

●

●

●

● ●

● ● ● ●

0

● ● ●

20

● ●

●

●

●

● ●

●

●

● ●

● ● ● ● ●

40

● ● ● ● ●

60

● ● ● ●

● ● ●

80 0

●

● ●

● ●

●

●

● ● ● ●

●

Ethene Propene C4 Olefins Methane Ethane Propane Butane Methanol Dimethylether Sum Olefins Sum C5+

● ● ● ● ●

10 20 30 40 50

TOS (min)

Figure 5: Product distribution at lower severities versus TOS ranging from 0.21 h−1 at 400◦ C with horizontal split by severity. The big symbols show the GC data, whereas the smaller symbols show the predicted data from the FTIR spectra. The solid line indicates a local regression (LOESS) for the FTIR data.

WHSV 2

WHSV 3.4

WHSV 8

WHSV 16

WHSV 20

100

●

80

●

Yield (C%)

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

ACS Catalysis

●

60

● ●

●

40

20

● ●

0

● ● ● ●

0

5

10 15 20 25 0

●

●

●

●

●

● ●

● ● ●

● ● ● ●

5

10

15

0

2

4

6 0

1

2

●

● ● ● ● ● ● ● ● ● ● ●


● ● ● ● ●

3 0

1

2

TOS (min)

Figure 6: Product distribution for dierent severities versus TOS from 220 h−1 at 400◦ C with horizontal split by severity. The big symbols show the GC data, whereas the smaller symbols show the predicted data from the FTIR spectra. The solid line indicates a local regression (LOESS) for the FTIR data.

11


ACS Catalysis 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

in the formation of parans and C4+ products during the induction period are greatly reduced since diusion of olens into occupied cages is restricted. It was suggested that these changes are also caused by a shift from the initially more dominant olenic cycle at lower turnover numbers (TON) to the aromatic cycle at higher TON when retained aromatics are accumulating inside the small pore chabazite cages. 21 In addition to the spacial restriction by build-up of retained aromatics inside the zeolite pores, the active reaction zone ("burning cigar") moves down the catalyst bed and the olens have less chances to further react. The eective contact time decreases and products are more and more yielded under kinetic control until the active reaction zone reaches the lower end of the catalyst bed while DME and methanol break through and the catalyst deactivates. The strong inuence of coke content on the product distribution for SAPO-34 was followed in the literature by UV-Vis and build up of polyaromatics was linked with product distribution. 17 Additionally, commercial operation is carried out at a certain amount of coke on catalyst in a uidized-bed to enhance the olen selectivity. 8 This is in contrast to ZSM-5, which product distribution is predominantly independent of coke content. 18,22 In order to look into more details, specic absorptions for additional compounds were extracted from the FTIR spectra. It can be seen in Figure 2, that a strong absorption occurs at around 2280 cm−1 when the rst products reach die FTIR cell during the induction period. This absorption can be assigned to the stretching mode of CO2 and indicates that CO2 is initially formed. In addition, the specic absorptions of CO at 2100 cm−1 and water at around 1700 cm−1 (one of the rotation sidebands with low likelyhood of having underlying absorptions of other compounds) were selected. The CO, CO2 and water concentrations are plotted in Figure 1b relative to the normalized product yields from online GC analysis. Water increases initially and shows a constant concentration as long as the catalysts operates at 100% conversion and drops when partial conversion and the deactivation period is reached. The delay in initial increase could indicate, that water is absorbed by the formerly calcined zeolite before saturation is reached. It should be noted that the experimental setup includes 12


Page 12 of 37

Page 13 of 37 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

ACS Catalysis

additional volume downstream of the reactor and deviation from ideal plug ow behaviour due to backmixing is present. In addition, the IR gas cell requires a certain ush time depending on the space velocity until a representative concentration is reached. CO is slowly increasing with TOS, whereas CO2 shows high initial values which are then reduced and increase again with TOS. Furthermore, the concentration of free hydrogen from online GC TCD analysis is shown, 23 which seems to stay constant initially and is not aected by the high initial hydrogen transfer activity. It is evident, that the slopes of CO, CO2, CH4 and H2 increase slightly at approximately half the lifetime and much stronger during deactivation of the catalyst when DME is breaking through. None of the reported acetate or formiate species could be observed though overlapping water absorption bands would most likely obscur any of these compounds which might be present in trace amounts as detected by others with mass spectrometry (MS). 5 Nevertheless, the high initial CO2 values could indicate the presence of these oxygenates during the induction period as nal decomposition product. As shown in Figure S1 and S2 in the supporting information, all stronger absorptions of oxygen containing species (formaldehyde, acetaldehyde, methyl acetate, acetic acid) exibit absorptions in the region of the broad water absorption between 12502000 cm−1 or other hydrocarbon products. Therefore, a more sensitive method than FTIR in the presence of water might be required to identify species involved into the the rst C-C bond formation. Figure 4 shows the corresponding temperature proles measured every 30 min by moving a thermocouple along the catalyst bed. It is evident that there is an initial hot-spot located at the beginning of the catalyst bed which rst stays at the same spatial position while decreasing and then moving down the catalyst bed. Even though the maximum temperature increase of 1◦ C is expectedly low at low severity, it indicates the predominant presence of exothermic reactions (i.e. initial adsorption, hydrogen transfer reactions, aromatization and formation of hydrocarbon pool) at initial TOS. With increasing TOS, the exothermicity decreases and movement down the catalyst bed can be related to the axial deactivation and 13


ACS Catalysis

the "burning cigar model" reported. 10,24 WHSV 0.4

WHSV 1

WHSV 2

WHSV 8

WHSV 20

100

80

Yield (C%)

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 14 of 37


60

40

20

0 0

25

50

75 100 0

25

50

75 100 0

25

50

75 100 0

25

50

75 100 0

25

50

75 100

Conversion (%)

Figure 7: Product distribution for dierent severities versus dimethylether and methanol conversion from 0.420 h−1 at 400◦ C with horizontal split by severity based on FTIR. Since the reaction of dimethylether to olens is commercially performed at signicantly higher severities, 8 the question arises whether the results of catalytic measurements at low severity can be related to higher and more realistic severities in commercial operation. Therefore, the severity was stepwise increased in order to investigate the inuence of severity on the product distribution, lifetime and methanol conversion capacity of the catalyst. In Figure 5, the severity was increased from 0.2 h−1 to 1 h−1 from the left to the right. It is visible, that the lifetime of the catalyst decreased from 250 min to 40 min. This brings the online GC analysis with its 12 min measurement interval to the limit of profoundly resolving the changes in product distribution and rendering the exact determination of the maxima of olen yields and the breakthrough of DME dicult. By supplementing the online GC analysis with the predicted product yields from the IR spectra, the missing data points could be provided in a 5s measurement interval. Further experiments with increasing severity from 2 h−1 to 20 h−1 are shown in Figure 6. It can be seen that at a severity of 20 h−1 , the lifetime is reduced to 50s and online GC analysis delivered only a single snapshot of the product yields whereas all other data points are based on the prediction by FTIR. While propene increases 14


Page 15 of 37

Ethene

20 10 0 0

25

50

Propene

50 40 30 20 10 0

30

75

100

C4 Olefins

20 10 0 0

25

Methane

Yield (C%)

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

ACS Catalysis

50

75

100

4 2 0 50

75

100

20 0 0

25

50

75

100

0

25

50

0

25

75

100

50

75

100

75

100

Dimethylether

10.0 7.5 5.0 2.5 0.0

0

100

Propane

Methanol

5

75

40

Butane

10

50

60

4 3 2 1 0

6

25

25

Ethane

8

0

0

WHSV 0.2 0.3 0.4 0.8 1 2 3.4 8 16 20

60 40 20 0 0

25

50

75

100

0

25

50

Conversion (%)

Figure 8: Yield versus dimethylether and methanol conversion from 0.220 h−1 at 400◦ C for various compounds based on FTIR. consistently for all WHSVs towards the breakthrough of dimethylether, dierent behaviour is observed for ethene which increase is higher up to a WHSV = 1 and conrms an increase in ethene-to-propene ratio attributed to product shape selectivity. 7 For higher WHSVs, the increase of ethene becomes less and results in a decrease in ethene-to-propene ratio during TOS. This behaviour at higher WHSVs is in contrast to the literature and indicates dierences for catalyst selectivation, deactivation and reaction zone distribution possibly due to higher linear velocities and broader utilization of the catalyst bed. It is evident, that the propene maximum is clearly linked to the breakthrough of dimethylether, while this is true for ethene only at lower WHSVs. At higher WHSVs, no clear maximum for ethene can be observed. Butenes yields shows a distinct maximum with almost no inuence of WHSV and declines almost linearly during TOS until the catalyst deactivates. The maximum of C5+ yield peaks even earlier than butenes and shows a stronger decline during TOS until deactivation. In Figure 7 and 8, the product yields are plotted

versus

conversion and indicate changes

of the product distribution during deactivation period when the catalyst entered the partial conversion range and the active reaction zone reached the end of the catalyst bed. This is 15


ACS Catalysis 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

additionally shown in the supporting information in Figure S6 and S7 for the selectivites. Especially the product yields of ethene, propene, butenes and C5 follow a linear approach towards the origin at low conversion and thereby indicate their formation as primary products according to the method developed by Wojciechowski. 25 A dierent picture is seen for methane, which yield seems to be independent of the conversion at low severities and decreasing with increasing severity. Since methane can also be a decomposition product of methanol or dimethylether, the longer residence time at lower severity could favor its formation together with formerly seen higher CO2 values during deactivation period. Ethane seems to follow a linear approach towards the origin with an upward curvature as primary and secondary stable product, while propane and butane are negligible at lower conversions and are mostly present during steady-state period at 100% conversion as secondary stable products. As expected, methanol is seen during partial conversion in equilibrium with dimethylether. Both ethene and propene show a downward curvature towards the highest conversion as primary unstable products, while butenes and C5 show an upward curvature towards the highest conversion as primary and secondary stable products. In contrast to full conversion, the ethene-to-propene ratio is increasing for all WHSVs with increasing deactivation and decreasing conversion and conrms the suggested product shape selectivity of SAPO-34. 7 The relationship between severity (for better comparison with reported literature values calculated as methanol equivalents) and extracted maximum product yields is visualized in Figure 9a and shown in Table 1, indicating that propene and total olen yield show a maximum at a methanol equivalent severity of 3-4.9 h−1 , whereas butenes show almost no dependency and ethene yield decreases with increasing severity. In the literature, similar behaviour with C2-C4 olen yields exhibiting a maximum is reported at dierent reaction conditions at a severity of 2.6-3.6 h−1 in the presence of water. 26 More experiments between severities of 510 h−1 would have been benecial for a more accurate determination of the maximum. It should be noted that temperature proles were not recorded at higher severities 16


Page 16 of 37

Page 17 of 37 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

ACS Catalysis

due to the speed of reaction and hot-spots were not followed by thermocouples. Therefore, the real temperature in the catalyst bed at these high WHSVs was most likely higher than 400◦ C and increases with increasing WHSV. In addition to the WHSV, this could have an inuence on the selectivities. 24 In contrast to the horizontal split by severity in Figure 6, which compares the yield of the products with the same y-axis scale, Figure S9 in the supporting information pictures the same data with a vertical split by severity in order to make the dependence of catalyst lifetime more visible. In all cases the maximum of olens yield is correlated with the breakthrough of DME. In Figure 10a, the correlation of severity and the breakthrough time is shown for DME conversion dropping below 95% (dark blue points), 90% (blue points) and 50% (grey points), respectively. The tting function at lower severities can be roughly described by a 1/severity function and reaches 50 s at a severity of 20 h−1 . In Figure 10b, the breakthrough time is plotted vs. contact time and a linear correlation between lifetime and contact time is evident. 14,15 While deactivation seems to be a function of catalyst mass, the kinetics of olen formation in a xed-bed with band-aging is dicult to determine when measured at the end of the catalyst bed. If there is active catalyst below the active reaction zone, olens can react further and only when the reaction band reaches the lower part of the catalyst bed, products under kinetic control can be detected with the limitation, that this part of the bed is not a fresh catalyst and has been exposed to reaction products before. Looking at the amount of cumulated DME converted on the catalyst (calculated as methanol equivalents) in Figure 11, we observe an increase in conversion capacity at lower severities and longer contact times whereas the conversion capacity decreases and reaches stable values of 0.4 g/g at higher severities > 8 h−1 and shorter contact times. This tendency of increasing conversion capacity with increasing contact time was found for other small pore zeolites in literature. 15 The values of 0.8 g/g at lower severities are close to the values observed for SAPO-34 when pure methanol feed was used without water. 27 In this paper, the eect of water was not investigated and it could be of interest for future experiments to measure the 17


ACS Catalysis

product distribution in high time resolution by varying the feed between methanol, DME and water co-feed. It was shown in the literature, that water inhibits the conversion and coking of olens and can extend catalyst lifetime. 27,28

80

80

60

Olefins_max Ethene_max Propene_max Butenes_max

40

Yield [C%]

b) 100

Yield [C%]

a) 100

20

Olefins_max Ethene_max Propene_max Butenes_max

40

0 10

20

WHSV �gMeOH eq. gcat h

−1

30

�

0

Figure 9: Maxima of product yields a) 300

100

1

2

3

4

τ0 �gcat h mol−1�

5

a) severity and b) contact time τ0 .

b) 300

10 9 8 7 6 5 4 3 2 1 0

200

versus

< 50% conv. < 90% conv. < 95% conv.

10 12 14 16 18 20 22 24 26 28 30

DME breakthrough �min�

0

DME breakthrough (min)

60

20

0

0

200 < 50% conv. < 90% conv. < 95% conv.

100

0 0

10

20

30

0

WHSV ( gMeOH eq. gcat h−1)

1

2

3


4

5

Figure 10: Breakthrough time of DME conversion dropping below 95%, 90% and 50% versus a) severity and b) contact time τ0 .

1.0 ccoz_50 ccoz_90 ccoz_95

0.5

0.0 0

10

20

WHSV �gDME gcat h−1�

30

MeOH converted �gMeOH eq. gcat�

b) 1.5

a) 1.5 MeOH converted �gMeOH eq. gcat�

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 37

1.0 ccoz_50 ccoz_90 ccoz_95

0.5

0.0 0

1

2

3


4

5

Figure 11: Cumulated DME converted on catalyst (as methanol equivalents) versus a) severity and b) contact time τ0 .

18


Page 19 of 37 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

ACS Catalysis

Table 1: Breakthrough time, conversion capacity and maximum selectivities vs. WHSV and contact time. WHSVWHSV DME MeOH h-1 h-1 0.2 0.3 0.3 0.4 0.4 0.6 0.8 1.1 1.0 1.4 2.0 2.9 3.4 4.9 8.0 11.4 12.0 17.2 16.0 22.9 20.0 28.6

Contact time s 4.87 3.25 2.44 1.22 0.97 0.49 0.29 0.12 0.08 0.06 0.05

TOS 90 min 278.3 174.1 113.2 66.0 41.3 17.1 11.0 3.5 2.3 1.8 1.2

ccoz 90 g/g 0.93 0.87 0.75 0.88 0.69 0.57 0.62 0.46 0.45 0.47 0.40

TOS 50 min 295.3 190.4 127.4 75.2 48.1 22.4 16.0 5.5 3.8 2.8 2.2

ccoz 50 g/g 0.98 0.95 0.85 1.00 0.80 0.75 0.91 0.73 0.75 0.74 0.74

19

Olens max C% 81.6 84.3 84.5 88.1 90.9 93.0 94.4 89.4 87.3 84.0 81.4


Ethene max C% 33.4 34.6 33.4 33.4 33.9 30.4 31.2 23.9 22.5 20.7 22.5

Propene max C% 37.4 39.7 40.8 43.2 44.5 47.7 48.0 46.2 44.0 42.4 40.5

Butenes max C% 25.0 25.2 23.9 23.8 25.8 24.9 23.9 24.5 23.5 23.3 23.1

ACS Catalysis

Partial coke burns The deactication caused by hydrocarbon residues is a reversible process and the catalytic activity can be recovered by oxidative regeneration at 300-600◦ C. Full coke burns at 500◦ C in air were successfully applied in the experiments above in order to regenerate the catalysts between each run. However, SAPO-34 is operated commercially in a uidized-bed reactorregenerator conguration. 8 It was found, that leaving around 8 wt% of coke on the catalyst favors the selectivity to ethene and propene while the regenerator is operating in partial-burn instead of full-burn mode, thereby delivering a coke selectivated catalyst to the reactor. This is in line with the literature, that selectivity strongly depends on the nature and amount of hydrocarbon residues. 7 Therefore, it was of interest to burn-o only part of the coke deposits and use a fast analytical method in order to detect, whether the hydrocarbon pool is still active or requires another induction period. Moreover, it was of interest which selectivities

20 min

15 min

10 min

5 min

can be obtained by the selectivated catalyst when exposing it to DME again.

Rate CO + CO2 [g(C)/min]

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 37

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95 100 105 110 115

TOS (min)

Figure 12: CO + CO2 rate detected during full-burn of coke deposits in air at 500◦ C. As indicated by dashed lines, the burn was stopped for the partial regeneration after 5, 10, 15 or 20 min. In Figure 12 the summarized CO and CO2 rates during regeneration are shown. It can be seen that the full regeneration required around 110 min until CO and CO2 levels dropped down to the base line. The total amount of coke can be obtained by integration of the CO

20


Page 21 of 37

Full Burn 1

Partial Burn 1

Partial Burn 2

Partial Burn 3

Partial Burn 4

100 ●

80

●

● ● ● ● ● ● ● ● ● ● ●

●

Yield (C%)

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

ACS Catalysis

60 ● ●

●

40

● ●

20

●

● ●

● ● ● ●

0 0

10

● ● ●

●

●

● ● ●

20

30

0

10

● ● ● ● ● ●

● ● ●

20

30

0

10


20

30

0

10

20

30

0

10

20

30

TOS (min)

Figure 13: Product distribution at WHSV = 1.6 h−1 at 400◦ C with horizontal split by completeness of coke burn. The big symbols show the GC data, whereas the smaller symbols show the predicted data from the FTIR spectra. The solid line indicates a local regression (LOESS) for the FTIR data. and CO2 rates over time and yielded a coke content of 3 wt% coke on catalyst. For the partial regeneration, the coke burn was stopped after 5, 10, 15 or 20 min accounting for 49, 59, 67 and 72% of coke burned, respectively. Details on the coke burn are shown in Table S1 in the supporting information. These partially regenerated catalysts were then exposed to DME at a WHSV of 1.6 h−1 and the eect on the initial selectivities and the product patterns can be seen in Figure 13 with a horizontal split by completeness of regeneration. It is evident, that the initial selectivity varies with the amount of coke deposits left on the catalyst and starts with increased olenic and decreased hydrogen transfer products when moving from full-burn to partial burn 3. For all samples, the catalysts started at 100% conversion and shows a similar development in product changes towards high olen yields until deactivation while the maximum C2-C4 olen yield of around 90 C% was reached shortly before. It would have been of interest to investigate the range of coke deposits left on the catalyst between partial burn 3 and 4 in more detail and whether the olens maximum could have been obtained as

21


ACS Catalysis

initial selectivity. This would be the ideal case for a catalyst coming from the regenerator and delivering selectivities close to the olens maximum in the reactor. For partial burn 4, the amount of coke left on the catalyst was obviously too high for the catalyst to start at full conversion and partial conversion with 75 C% olens yield was achieved. It seems the partially burned retained hydrocarbon species within the pores are still in an operable state, while it is dicult to assess whether a short induction period is present to generate active hydrocarbon pool species and could be cleared by detailed analysis of hydrocarbons retained. In order to assess the lifetime of the selectivated catalyst compared to the fresh catalyst, Figure S10 in the supporting information displays a vertical split of the product composition. As expected, the samples with higher coke deposits left on the catalyst show a signicantly reduced lifetime. Full Burn

Partial Burn 1

Partial Burn 2

Partial Burn 3

100

80

Yield (C%)

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 37

● ● ● ● ● ● ● ● ● ● ●

60

40

20


0 0

1

2

0

1

2

0

1

2

0

1

2

TOS (min)

Figure 14: Product distribution at WHSV = 20 h−1 at 400◦ C with horizontal split by completeness of coke burn. The big symbols show the GC data, whereas the smaller symbols show the predicted data from the FTIR spectra. The solid line indicates a local regression (LOESS) for the FTIR data. In Figure 14, the same experiment with partial coke burns was repeated at a WHSV of 20 h−1 . While the total amount of coke was in the same range of around 3 wt% coke on catalyst, the coke burn was stopped at 5, 7.5 and 10 min. It is evident that partial burn 2 22


Page 23 of 37 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

ACS Catalysis

is close to the point when the catalyst reached partial conversion and the olens maximum of 80 C% is obtained as initial selectivity. These results are in line with suggested product shape selectivity for SAPO-34 and support the idea, that a certain amount of coke enhances olen selectivity and that a partial coke burn can deliver a catalyst with high olen yields at initial TOS.

23


ACS Catalysis

Variation of parameter space b)

c)

88

14

16

2.5

2.5 3

80

76

74

18

WHSV (h-1 )

5

72

20

14

16

42

Propene yield (C%)

44

45

35

30 25

4.5

5.5

6.5

12

14

18

20

12

WHSV (h-1 )

14

16

18

46

47 46

45 43

WHSV (h-1 )

20

20

47

20

6

18 16 WHSV (h-1 )

43

3.5

4

82

78

4

3.5

400 12

40

3

380

84

86

450

T (°C)

45

5.5

420

420

90

20 18 16 14 WHSV (h-1 )

460

460

T (°C)

T (°C)

94

12

7

5

4.5

94

12

6.5

450 T (°C) 400

20

40 38 36 500

T (°C)

6

500

42

420

500

500

12

92

92

18 16 14 WHSV (h-1 )

44

500

Ethene yield (C%)

450 T (°C) 400

46

380

14

12

20 18 16 WHSV (h-1 )

500

20

500

450 T (°C) 400

3

30

460

500

4

T (°C)

75

5

420

80

6

40

380

85

460

d)

7

90

Methane yield (C%)

Total olefins yield (C%)

a)

380

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 24 of 37

39

40

12

14

16

44

42 38

18

41

37

20

WHSV (h-1 )

Figure 15: Regression planes and contour plots for a) total C2-C4 olens, b) methane, c) ethene and d) propene yield. The regressions are based on linear models including linear predictors (WHSV and T), interaction terms (WHSV:T) and quadratic terms (T2 ). All former experiments have been carried out at 400◦ C by varying the WHSV. In order to look at the inuence of temperature at dierent WHSVs on the product distribution and lifetime of SAPO-34, a small design of experiments (DoE) was set up covering the parameter space in the range of temperature between 375-475◦ C at 6 levels and WHSVs between 12-20 h-1 at 3 levels. Besides the motivation to investigate the T and WHSV dependence of the product distribution, the suitability of the fast analytical method and experimental setup for fast parameter screening was tested. The experiments were carried out in a way, that 4 reactors in the high throughput system were loaded with the same catalyst. For statistical signicance and reproducibility, each experimental set of conditions was performed on 2 reactors. The condition at 400◦ C and WHSV = 12 h−1 was measured as reference condition and was repeated multiple times with reproducible results. The reactors were brought on stream sequentially, the changes in product distribution until deactivation were measured and the catalysts were fully regenerated by in-situ coke burn-o with air at 500◦ C, while the amount of coke was determined by integration of CO and CO2 rates. All 18 experiments of 24


Page 25 of 37

the DoE were carried out within 4 days and the detailed product distribution is shown in Figure S11 in the supporting information, with a good overlay of predicted FTIR yields and the single data points obtained by GC. It can be concluded that the chemometric models trained at a temperature of 400◦ C deliver reliable predictions also at dierent temperatures. Since chemometric models are limited with regard to extrapolation and only predict formerly trained parameter ranges reliably, catalysts and zeolite topologies delivering signicantly

Coke yield feed (wt%)

0.4 500 450

T (°C)

400 12

18 16 14 WHSV (h-1 )

20

10 5 500 450

T (°C)

15

460

460

3.5

T (°C)

T (°C)

1

420

0.9

7

0.8

14

10

8

3 2 1 500 450

20

T (°C)

400

4

3

9

6

4

14

12

13

12

5

420

460

20 18 16 WHSV (h-1 )

3.5

2.5

2

1

1.5

3

0.7

0.5

0.6

380

1

14

0.5

16

WHSV (h-1 )

18

20

12

14

16

0.4

18

3

0.3

20

4

12

WHSV (h-1 )

380

2 1.5

380

T (°C) 420

2.5

12

400

11

3

18 16 14 WHSV (h-1 )

12

500

500

12

20

0.6

4

500

400

18 16 14 WHSV (h-1)

0.8

460

450

T (°C)

d)

T (°C)

2.0 1.5 1.0 0.5 500

c) 15

420

2.5

b) 1.0

Coke on catalyst (wt%)

3.0

500

Breakthrough time (min)

a) 3.5

Cumulated MeOH eq. (g/g)

dierent product distributions will most likely require individual models.

380

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

ACS Catalysis

14

16

WHSV (h-1 )

18

20

1

12

14

16

18

20

WHSV (h-1 )

Figure 16: Regression planes and contour plots for a) breakthrough time (until conversion drops below 90%), b) cumulated methanol equivalents (until conversion drops below 90%, c) coke on catalyst and d) coke yield referred to fresh feed.The regressions are based on linear models including linear predictors (WHSV and T), interaction terms (WHSV:T) and quadratic terms (T2 ). The maxima of C2-C4 olens, methane, ethene and propene yield obtained during TOS are plotted including tted regression planes vs. temperature and WHSV in Figure 15. It should be noted that temperature proles were not recorded due to the speed of reaction and hot-spots were not followed by thermocouples. Therefore, the real temperature in the catalyst bed at these high WHSVs was most likely higher than the reported values. 24 It can be seen in Figure 15a that the maximum of total C2-C4 olens of 94 C% (excluding coke, CO, CO2 and H2 in the normalized product distribution) is obtained in a temperature range 25


ACS Catalysis 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

of 440-470◦ C, which corresponds well with the temperature of the commercial process of 460◦ C. 8 For the total olens yield, temperature plays a signicant role and WHSV seems to be less relevant at higher temperatures, while having more inuence at lower temperatures. Regarding methane yield in Figure 15b, there seems to be minimum between 420-460◦ C with WHSV having less inuence. Ethene yield in Figure 15c increases almost linearly with temperature while propene in Figure 15d has a clear maximum of 47 C% between 430-460◦ C. In Figure 16, the inuence of temperature and WHSV on parameters regarding deactivation are shown. In Figure 16a, the breakthrough time until conversion drops < 90% increases with decreasing WHSV and as we have seen before in Figure 10. For cumulated methanol equivalents or conversion capacity in Figure 16b, only an increase with temperature is observed. Regarding coke on catalyst in Figure 16c, a strong increase vs. temperature is seen while no dependance on WHSV exists. There is a minimum at 400◦ C while values increase slightly at lower temperature at 375◦ C possibly due to the formation of oligomers and decreased cracking activity. When the coke is referred to the fresh feed in Figure 16d, between 1-3 C% of DME is converted to coke. While coke on catalyst seems to be almost independent of WHSV, relating a similar amount of coke on catalyst to a higher amount of processed fresh feed results in a lower percentage of fresh feed converted to coke at higher WHSVs.

Conclusions In this contribution, the changing product distribution in the conversion of dimethylether over SAPO-34 was measured in a xed-bed reactor within a broad parameter space. High data density was achieved on the one side by increasing methanol conversion capacity and breakthrough time by decreasing severity and analyzing the product distribution by detailed multicolumn GC analysis. On the other side, higher and commercially relevant severities with faster timescales of deactivation were analyzed by fast FTIR spectroscopy combined with chemometrical interpretation of the spectra. This novel investigatory approach pro26


Page 26 of 37

Page 27 of 37 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

ACS Catalysis

vides a new perspective on the changing product distribution during induction, steady-state and deactivation period while being followed for a range of severities in measurement intervals down to seconds. Thereby, the inuence of WHSV and contact time on product distribution, lifetime and conversion capacity was shown. Since full conversion at initial TOS was observed even at high WHSVs, future experiments with this methodology at lower conversion levels might give additional insights into the induction period. It was found that interfering water absorptions in the FTIR limited the identication of species indicative of the rst C-C coupling. Nevertheless, initial CO2 could indicate decomposition products of oxygenates during induction period while high initial hydrogen transfer activity to parans could indicate aromatics build-up during hydrocarbon pool formation. During the following steady-state period, detailed selectivities provide information on the development of ethene, propene, butene and C5 yields and conrm product shape selectivity of SAPO-34. For the deactivation period, the product yields were followed at partial conversion enabling the determination of primary and secondary stable or unstable products. By burning-o coke partially, the eect of selectivation by coke was studied and the initial selectivies after regeneration indicate, that the maximum olen yield can be obtained directly when bringing the catalyst back on stream while leaving the right amount of coke on the catalyst during regeneration and adds further evidence to the concept of product shape selectivity. The fast analytical method was also suitable to rapidly screen through a short DoE, thereby revealing the optimum conditions with regard to temperature and WHSV to obtain maximum olens yields, low methane and coke, increased lifetime and methanol conversion capacity. It was shown in the literature that results generated in a xed-bed reactor can be related to the performance in uidized-bed reactors and therefore support the scale-up to commercial operation. 8,29

27


ACS Catalysis 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 28 of 37

Experimental Catalyst The catalyst SAPO-34 was obtained as commercial sample from Zeolyst. Due to this, no additional information and characterization results are available. The material was shaped into the particle size fraction of 315-500 µm by pressing, crushing and sieving to minimize pressure drop in the xed-bed reactor. In order to dilute the catalyst, quartz split in the same particle size fraction was added to 1.85 g catalyst per reactor in the weight ratio of 3:1 (diluent:catalyst). The samples were pretreated

in-situ

in the reactors in owing nitrogen

at 500◦ C for 5 hours before the temperature was reduced to 400◦ C reaction temperature.

Catalytic tests Catalytic experiments were carried out in a 16-fold parallel xed-bed reactor unit and the simplied process scheme is shown in the supporting information in Figure S8. The unit has a modular design and the feed section pictured on the upper left provides consistent dosage of dimethylether (DME 3.0) and evaporates it pulse-free in a stream of dry nitrogen (from liquid N2/evaporator system). On the upper right, the purge section with multiple mass ow controllers can supply additional gases such as nitrogen for keeping unused reactors under inert or air for burning-o coke. Both gas ows are distributed into 16 single gas streams and it can be chosen between feed or purge ow for each individual reactor. This oers exibility for activation and regeneration procedures or for keeping reactors under inert conditions and bringing them sequentially on stream with a zero time switch from purge to feed. These selected gas streams from the feed or purge section enter the reactors (15 mm ID, 15 cm isothermal zone length) placed in individually controllable heaters. Information on the temperature prole for each reactor is provided by movable inner 3-point thermocouples which are placed concentric in the reactor. In the downstream section below the reactors, products are diluted by a pressure control system that keeps the pressure in each reactor 28


Page 29 of 37 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

ACS Catalysis

exactly the same. The euent of one selected channel is directed via a heated line to online gas chromatographic analysis for measurement of the hydrocarbon composition utilizing an Agilent 7890 equipped with ame ionization detector (FID) and thermal conductivity detector (TCD). In parallel to the GC, the analysis ow was also directed to a gas cell for measurement of the infrared spectra. This gas cell was manufactured by SICK with a optical path length of 75 cm, a volume of 500 ml and was heated to 180◦ C. Infrared spectra were recorded with a Bruker Matrix-M FTIR running with the OPUS software package. The DTO reaction was carried out at a total pressure of 2-5 bar and a methanol concentration of 50 vol% in nitrogen. The WHSV was varied from 0.2 to 20 h−1 by changing the total ow and the reactors were brought on stream sequentially. In order to regenerate the catalysts, the reactors were heated to 500◦ C and switched from nitrogen to dry air sequentially. During all experiments, the hot gas ow from the unit was measured and corrected by the molar heat capacities of the euent compounds, thereby correcting the ow deviation from pure nitrogen. Product rates were obtained by multiplication of the hot gas ow with the product concentrations from the GC analysis. The mass balance was evaluated by relating educt in-rates vs. product out-rates and was >95% for the GC measurements. Ignition of the carbonaceous deposits was monitored for each reactor / zeolite by measuring on-line CO and CO2 generated by the combustion with an ABB continuous process analyzer based on absorption in the infrared region in addition to the Bruker FTIR. If indicated, cumulated DME converted on zeolite is expressed as equivalents to MeOH converted.

FTIR analysis and chemometrics The IR spectra were recorded between 7000 and 400 cm−1 . As pre-processing steps, a baseline correction followed by a min-max normalization step was carried out. For further data evaluation, the wavenumber regions between 1040-1244 cm−1 and 2700-3100 cm−1 were 29


ACS Catalysis 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

excluded due to excessive absorptions reaching the detector limit. This is due to the downstream concept of the high throughput unit which operates at dierent dilution ratios to keep the reactor pressure constant. Therefore, low dilution is added at high WHSVs and the products are more concentrated while dilution is high at low WHSVs and products are more diluted. A compromise was made to cover the variation in WHSV by a factor 100 and to have enough sensitivity at low WHSVs at which training of models was performed while high WHSVs exceeded the detector limit. This is acceptable since the absorptions of the C-H stretching modes in the region between 2700-3100 cm−1 are less distinctive compared to more specic C-H bending and C-C and C=C stretching modes at lower wavenumbers to predict yields for lower hydrocarbons such as propane. The absorptions between 10401244 cm−1 are specic for DME and parts of this vibration modes below the absorption limit were still included and complemented by other DME specic absorptions. Absorptions at wavenumbers ranging from 3100-7000 cm−1 were not taken into account. In order to perform a partial least squares (PLS) regression, absorptions of these selected wavenumbers were correlated to the product yields of individual compounds (i.e. methane, ethane, ethene, propane, propene, methanol, dimethylether, butane, C4 olens, sum C5+ ). The latter were obtained from the GC analysis at low severitis (0.2-0.4 h−1 ) when the timescale of deactivation was slow enough to be followed by GC and FTIR. The relevant Eigenvectors (i.e. principal components) with signicant contribution to the variance were determined for each of the individual compounds resulting in multiple separate PLS models. The rst 15 PLS vectors resulting in minimal errors (root mean square error of predicion, RMSEP) were used. By using these PLS models, spectra containing unknown product yields were evaluated. For data processing, R and more specically the pls package was used. 30 It should be noted that reliable predictions can only be made in within yields ranges, in which the model has been trained. Extrapolation outside this ranges can potentially deliver reliable predictions, but correctness can´t be proven since experimental data is missing.

30


Page 30 of 37

Page 31 of 37 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

ACS Catalysis

Acknowledgement We would like to thank Andreas Sundermann (hte) for his introduction and support with regard to chemometrics. We also thank Robert Neubauer (hte) and Thomas Karle (hte) for analytical support concerning the multicolumn GC and FTIR analysis.

Supporting Information Available A listing of the contents of each le supplied as Supporting Information should be included. For instructions on what should be included in the Supporting Information as well as how to prepare this material for publications, refer to the journal's Instructions for Authors. The following les are available free of charge. Supporting information

References (1) (a) Stöcker, M. Methanol-to-Hydrocarbons: Catalytic Materials and their Behavior. Microporous Mesoporous Mater.

1999, 29, 3 48; (b) Olsbye, U.; Svelle, S.; Bjørgen, M.;

Beato, P.; Janssens, T. V. W.; Joensen, F.; Bordiga, S.; Lillerud, K. P. Conversion of Methanol to Hydrocarbons: How Zeolite Cavity and Pore Size Controls Product Selectivity. Angew. Chem., Int. Ed. 2012, 51, 58105831; (c) Yarulina, I.; Chowdhury, A. D.; Meirer, F.; Weckhuysen, B. M.; Gascon, J. Recent Trends and Fundamental Insights in the Methanol-to-Hydrocarbons Process.

Nat. Catal.

2018, 1, 398411.

(2) Hereijgers, B. P. C.; Bleken, F.; Nilsen, M. H.; Svelle, S.; Lillerud, K.-P.; Bjørgen, M.; Weckhuysen, B. M.; Olsbye, U. Product Shape Selectivity Dominates the Methanol-toOlens (MTO) Reaction over H-SAPO-34 Catalysts.

31


J. Catal.

2009, 264, 77 87.

ACS Catalysis 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 32 of 37

(3) Wilson, S.; Barger, P. The Characteristics of SAPO-34 which Inuence the Conversion of Methanol to Light Olens.

Microporous Mesoporous Mater.

1999, 29, 117 126.

(4) (a) Wang, W.; Buchholz, A.; Seiler, M.; Hunger, M. Evidence for an Initiation of the Methanol-to-Olen Process by Reactive Surface Methoxy Groups on Acidic Zeolite Catalysts.

J. Am. Chem. Soc.

2003,

, 1526015267; (b) Yamazaki, H.; Shima, H.;

125

Imai, H.; Yokoi, T.; Tatsumi, T.; Kondo, J. N. Direct Production of Propene from Methoxy Species and Dimethyl Ether over H-ZSM-5.

J. Phys. Chem. C

2012,

,

116

2409124097; (c) Li, J.; Wei, Z.; Chen, Y.; Jing, B.; He, Y.; Dong, M.; Jiao, H.; Li, X.; Qin, Z.; Wang, J.; Fan, W. A Route to Form Initial Hydrocarbon Pool Species in Methanol Conversion to Olens over Zeolites.

J. Catal.

2014, 317, 277 283.

(5) (a) Liu, Y.; Müller, S.; Berger, D.; Jelic, J.; Reuter, K.; Tonigold, M.; SanchezSanchez, M.; Lercher, J. A. Formation Mechanism of the First Carbon-Carbon Bond and the First Olen in the Methanol Conversion into Hydrocarbons. Int. Ed.

2016,

Angew. Chem.,

, 57235726; (b) Chowdhury, A. D.; Houben, K.; Whiting, G. T.;

55

Mokhtar, M.; Asiri, A. M.; Al-Thabaiti, S. A.; Basahel, S. N.; Baldus, M.; Weckhuysen, B. M. Initial Carbon-Carbon Bond Formation During the Early Stages of the Methanol-to-Olen Process Proven by Zeolite-Trapped Acetate and Methyl Acetate.

Angew. Chem., Int. Ed.

2016, 55, 1584015845; (c) Wu, X.; Xu, S.; Zhang, W.;

Huang, J.; Li, J.; Yu, B.; Wei, Y.; Liu, Z. Direct Mechanism of the First CarbonCarbon Bond Formation in the Methanol-to-Hydrocarbons Process. Int. Ed.

Angew. Chem.,

2017, 56, 90399043.

(6) (a) Dessau, R. On the H-ZSM-5 Catalyzed Formation of Ethylene from Methanol or Higher Olens.

J. Catal.

1986,

, 111 116; (b) Dahl, I. M.; Kolboe, S. On the

99

Reaction Mechanism for Propene Formation in the MTO Reaction over SAPO-34. Catal. Lett.

1993,

, 329336; (c) Svelle, S.; Joensen, F.; Nerlov, J.; Olsbye, U.;

20

Lillerud, K.-P.; Kolboe, S.; Bjørgen, M. Conversion of Methanol into Hydrocarbons over 32


Page 33 of 37 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

ACS Catalysis

Zeolite H-ZSM-5: Ethene Formation is Mechanistically Separated from the Formation of Higher Alkenes.

J. Am. Chem. Soc.

2006, 128, 1477014771; (d) Ilias, S.; Bhan, A.

Mechanism of the Catalytic Conversion of Methanol to Hydrocarbons.

ACS Catal.

2013, 3, 1831. (7) Qi, G.; Xie, Z.; Yang, W.; Zhong, S.; Liu, H.; Zhang, C.; Chen, Q. Behaviors of Coke Deposition on SAPO-34 Catalyst During Methanol Conversion to Light Olens. Process. Technol.

Fuel

2007, 88, 437 441.

(8) Tian, P.; Wei, Y.; Ye, M.; Liu, Z. Methanol to Olens (MTO): From Fundamentals to Commercialization.

ACS Catal.

2015, 5, 19221938.

(9) Wei, Y.; Yuan, C.; Li, J.; Xu, S.; Zhou, Y.; Chen, J.; Wang, Q.; Xu, L.; Qi, Y.; Zhang, Q.; Liu, Z. Coke Formation and Carbon Atom Economy of Methanol-to-Olens Reaction.

ChemSusChem

2012, 5, 906912.

(10) Haw, J.; Marcus, D. Well-Dened (Supra)Molecular Structures in Zeolite Methanol-toOlen Catalysis.

Top. Catal.

2005, 34, 4148.

(11) Luo, M.; Zang, H.; Hu, B.; Wang, B.; Mao, G. Evolution of Conned Species and their Eects on Catalyst Deactivation and Olen Selectivity in SAPO-34 Catalyzed MTO Process.

RSC Adv.

2016, 6, 17651.

(12) Schulz, H. Coking of Zeolites During Methanol Conversion: Basic Reactions of the MTO- MTP- And MTG Processes.

Catal. Today

2010, 154, 183 194.

(13) Janssens, T. V. A New Approach to the Modeling of Deactivation in the Conversion of Methanol on Zeolite Catalysts.

J. Catal.

2009, 264, 130 137.

(14) Janssens, T. V.; Svelle, S.; Olsbye, U. Kinetic Modeling of Deactivation Proles in the Methanol-to-Hydrocarbons (MTH) Reaction: A Combined Autocatalytic-Hydrocarbon Pool Approach.

J. Catal.

2013, 308, 122 130. 33


ACS Catalysis 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 34 of 37

(15) Rojo-Gama, D.; Etemadi, S.; Kirby, E.; Lillerud, K. P.; Beato, P.; Svelle, S.; Olsbye, U. Time- And Space-Resolved Study of the Methanol to Hydrocarbons (MTH) Reaction Inuence of Zeolite Topology on Axial Deactivation Patterns.

Faraday Discuss.

2017,

, 421446.

197

(16) (a) Bentrup, U. Combining in Situ Characterization Methods in One Set-Up: Looking with more Eyes into the Intricate Chemistry of the Synthesis and Working of Heterogeneous Catalysts.

Chem. Soc. Rev.

2010,

, 47184730; (b) Wang, W.; Jiang, Y.;

39

Hunger, M. Mechanistic Investigations of the Methanol-to-Olen (MTO) Process on Acidic Zeolite Catalysts by in Situ Solid-State NMR Spectroscopy. Catal.

Today

2006,

, 102 114; (c) Dai, W.; Wu, G.; Li, L.; Guan, N.; Hunger, M. Mechanisms of

113

the Deactivation of SAPO-34 Materials with Dierent Crystal Sizes Applied as MTO Catalysts.

ACS Catal.

2013, 3, 588596; (d) Vimont, A.; Thibault-Starzyk, F.; Da-

turi, M. Analysing and Understanding the Active Site by IR Spectroscopy. Soc. Rev.

2010,

Chem.

, 49284950; (e) Qian, Q.; Vogt, C.; Mokhtar, M.; Asiri, A. M.;

39

Al-thabaiti, S.; Basahel, S.; Ruiz-Martínez, J.; Weckhuysen, B. Combined Operando UV/Vis/IR Spectroscopy Reveals the Role of Methoxy and Aromatic Species During the Methanol-to-Olens Reaction over H-SAPO-34.

ChemCatChem

2014, 3396

3408; (f) Müller, S.; Liu, Y.; Kirchberger, F. M.; Tonigold, M.; Sanchez-Sanchez, M.; Lercher, J. A. Hydrogen Transfer Pathways During Zeolite Catalyzed Methanol Conversion to Hydrocarbons.

J. Am. Chem. Soc.

2016,

, 1599416003; (g) Borod-

138

ina, E.; Sharbini Harun Kamaluddin, H.; Meirer, F.; Mokhtar, M.; Asiri, A. M.; AlThabaiti, S. A.; Basahel, S. N.; Ruiz-Martínez, J.; Weckhuysen, B. M. Inuence of the Reaction Temperature on the Nature of the Active and Deactivating Species During Methanol-to-Olens Conversion over H-SAPO-34.

ACS Catal.

2017, 7, 52685281; (h)

Wragg, D. S.; Johnsen, R. E.; Balasundaram, M.; Norby, P.; Fjellvåg, H.; Grønvold, A.; Fuglerud, T.; Hazovic, J.; Vistad, Ø. B.; Akporiaye, D. SAPO-34 Methanol-to-Olen Catalysts under Working Conditions: A Combined in Situ Powder X-Ray Diraction 34


Page 35 of 37 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

ACS Catalysis

Mass Spectrometry and Raman Study.

J. Catal.

2009,

, 290 296; (i) Goetze, J.;

268

Yarulina, I.; Gascon, J.; Kapteijn, F.; Weckhuysen, B. M. Revealing Lattice Expansion of Small-Pore Zeolite Catalysts During the Methanol-to-Olens Process Using Combined Operando X-Ray Diraction and UV-Vis Spectroscopy.

ACS Catal.

2018, 8,

20602070. (17) Goetze, J.; Meirer, F.; Yarulina, I.; Gascon, J.; Kapteijn, F.; Ruiz-Martínez, J.; Weckhuysen, B. M. Insights into the Activity and Deactivation of the Methanol-to-Olens Process over Dierent Small-Pore Zeolites as Studied with Operando UV-Vis Spectroscopy.

ACS Catal.

2017, 7, 40334046.

(18) Mores, D.; Stavitski, E.; Kox, M. H.; Kornatowski, J.; Olsbye, U.; Weckhuysen, B. M. Space- And Time-Resolved in-Situ Spectroscopy on the Coke Formation in Molecular Sieves: Methanol-to-Olen Conversion over H-ZSM-5 and H-SAPO-34. J.

Chem. - Eur.

2008, 14, 1132011327.

(19) Schulz, H. About the Mechanism of Methanol Conversion on Zeolites. Catal. Lett. 2018, , 12631280.

148

(20) Linstrom, P. J., Mallard, W. G., Eds. NIST ence Database Number 69

Chemistry WebBook NIST Standard Refer-

; National Institute of Standards and Technology: Gaithers-

burg MD, 20899, 2005. (21) Hwang, A.; Prieto-Centurion, D.; Bhan, A. Isotopic Tracer Studies of Methanol-toOlens Conversion over HSAPO-34: The Role of the Olens-Based Catalytic Cycle. Catal.

J.

2016, 337, 52 56.

(22) (a) Bleken, F. L.; Janssens, T. V.; Svelle, S.; Olsbye, U. Product Yield in Methanol Conversion over ZSM-5 is Predominantly Independent of Coke Content. Mesoporous Mater.

Microporous

2012, 164, 190 198; (b) Li, J.; Wei, Y.; Liu, G.; Qi, Y.; Tian, P.;

Li, B.; He, Y.; Liu, Z. Comparative Study of MTO Conversion over SAPO-34, H-ZSM-5 35


ACS Catalysis 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 36 of 37

and H-ZSM-22: Correlating Catalytic Performance and Reaction Mechanism to Zeolite Topology.

Catal. Today

2011, 171, 221 228.

(23) Gao, Y.; Cao, Y.; Chen, S.-L.; Wang, Y.; Zhu, R.; Sun, W.; Zhang, Q.; Fan, Y. Dierences in Product Distribution Measured with Flame Ionization Detector Gas Chromatography and Thermal Conductivity Detector Gas Chromatography During the Dimethyl Ether-to-Olens and Methanol-to-Olens Processes.

Energy Fuels

2017,

, 1326613272.

31

(24) Yarulina, I.; Kapteijn, F.; Gascon, J. The Importance of Heat Eects in the Methanol to Hydrocarbons Reaction over ZSM-5: On the Role of Mesoporosity on Catalyst Performance.

Catal. Sci. Technol.

2016, 6, 53205325.

(25) Wojciechowski, B.; Ko, A.-N. On Determining the Mechanism and Kinetics of Reactions on Decaying Catalysts.

Prog. React. Kinet.

1983, 4, 201262.

(26) Wu, X.; Abraha, M. G.; Anthony, R. G. Methanol Conversion on SAPO-34: Reaction Condition for Fixed-Bed Reactor.

Appl. Catal., A

2004, 260, 63 69.

(27) Wu, X.; Anthony, R. Eect of Feed Composition on Methanol Conversion to Light Olens over SAPO-34.

Appl. Catal., A

2001, 218, 241 250.

(28) De Wispelaere, K.; Wondergem, C. S.; Ensing, B.; Hemelsoet, K.; Meijer, E. J.; Weckhuysen, B. M.; Van Speybroeck, V.; Ruiz-Martínez, J. Insight into the Eect of Water on the Methanol-to-Olens Conversion in H-SAPO-34 from Molecular Simulations and in Situ Microspectroscopy.

ACS Catal.

2016, 6, 19912002.

(29) Yuan, X.; Li, H.; Ye, M.; Liu, Z. Comparative Study of MTO Kinetics over SAPO-34 Catalyst in Fixed and Fluidized Bed Reactors.

Chem. Eng. J.

2017, 329, 35 44.

(30) (a) R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2017; (b) Mevik, B.-H.; Wehrens, R.; 36


Page 37 of 37 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

ACS Catalysis

Liland, K. H. Pls: Partial Least Squares and Principal Component Regression. 2016; R package version 2.6-0.

Graphical TOC Entry

37


Time-Resolved Product Analysis of Dimethylether ... - ACS Publications

Recommend Documents