Continuous Age Distribution Method for Catalytic Cracking 2

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Kinetics, Catalysis, and Reaction Engineering

Continuous Age Distribution Method for Catalytic Cracking 2. Understanding non-idealities David Matheson Stockwell Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b02193 • Publication Date (Web): 12 Sep 2018 Downloaded from http://pubs.acs.org on September 21, 2018

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Industrial & Engineering Chemistry Research

Continuous Age Distribution Method

for Catalytic Cracking 2. Understanding non-idealities David M. Stockwell* BASF Corporation 25 Middlesex-Essex Turnpike Iselin, NJ 08830, USA [email protected]

+1-732-205-7035

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

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ABSTRACT In a previous paper we introduced a novel steam-deactivation method, which could, in principle, reproduce many features of refinery FCC equilibrium catalysts. However, non-ideal zeolite decay kinetics had prevented full realization of the potential of the method. Further analysis now shows those kinetic deviations were due to a ubiquitous 10-20% of metastable framework Si in zeolite Y, which is already familiar via the zeolite ultra-stabilization process.

Unexpectedly high

framework Si collapse rates have also been found above 870 C, but these may not be relevant to the refinery. The effects of these non-idealities can be appropriately excluded by collapsing metastable Si in a mild presteaming at 732 C, and/or by elongating catalyst deactivation time in the method. Variations of the method are then used to demonstrate that the 0-5th age percentile of refinery equilibrium catalyst does substantially contribute to coke and hydride transfer, and that presteaming can be used to systematically effect changes in zeolite mesoporosity, which in turn have a striking linear effect on cracking yields.

Keywords: FCC; zeolite; steam deactivation; deactivation kinetics; age distribution

1

Introduction The fluidized catalytic cracking (FCC) process is often the principle molecular weight reduction

process in the petroleum refinery, and a highly profitable unit operation.1 More than four-hundred fifty of these units exist around the world,2 producing about 45% of the global supply of gasoline,1 and consuming perhaps 700 000 metric tons per year of cracking catalyst. Substantially improved results in the operation of a 100 000 barrel per day FCC unit can yield profitability improvements on the order of $10 000 000 per year. Because of the critical role these units play in the petroleum sector, many refiners and catalyst manufacturers conduct catalyst screening and optimization tests, 2 ACS Paragon Plus Environment

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the purpose being to choose the most profitable catalyst, or to bring new catalysts into the market. It has long been acknowledged,3-6 however, that one of the weakest links in the selection or optimization processes has been the proper laboratory-based deactivation of the catalyst, prior to the catalytic evaluation. Despite the importance of the outcomes, and after decades of use, current laboratory deactivation processes remain essentially empirical in nature, and sometimes unchanged since inception. If fundamentally sound, alternative methods could be developed, the resulting catalytic evaluations would, hypothetically, be more predictive of refinery results. Immediately upon contact with gas oil or residual feeds in the refinery, FCC catalysts become fouled with coke, and within seconds, require regeneration by burning off the coke in air. The stoichiometric ratio of the coke is C1H1, so heat and steam are produced during fluid bed combustion. The catalyst is then deactivated by hydrolysis reactions with steam at the elevated temperatures. In a previous contribution,7 we explained how the continuous addition of fresh catalyst to the FCC unit leads to an exponential residence time distribution of catalyst ages, and how this age distribution could, in principle, be reproduced by the novel steam-deactivation procedure described in that paper. This new procedure involved continuously feeding fresh catalyst to a laboratory steaming reactor, while slowly reducing the reactor temperature according to a logarithmic temperature profile.7 That profile had been derived for the first order decay of the tetrahedral silica (SiT) portion of the zeolite, the zeolite being the most active, and therefore the most important ingredient in the catalyst. Although fundamental kinetic and reactor models had been used, and first order SiT decay was confirmed by Pine8 as well by fitting7 published equilibrium catalyst density separation data,9-14 we nevertheless found substantial deviations from ideal first order SiT decay.7 The deviations were evident both by the classical Arrhenius method, and by implementing the new continuous age distribution method (CADM) experimentally.7 The



reasons for the discrepancy between the refinery and laboratory kinetics were not understood. These deviations were significant enough, however, to render the method only semi-quantitative, leaving more rigorous reproductions of refinery catalyst properties out of reach. The purpose of this paper is to resolve this discrepancy, and to understand the non-idealities in the kinetics of steam-induced zeolite SiT collapse, so that the full potential of the method might later be realized. The literature on this subject is surprisingly sparse. Pine8 determined the activation energies for the overall collapse of zeolite Y in the presence of La, Na and V, and because his zeolites were presteamed to low tetrahedral aluminum (AlO4-, or AlT) concentrations (~ 1%), AlT itself could not contribute much to the measured rate of zeolite collapse. Pine’s results can then safely be attributed mainly to SiT. 8This same approximation could be applied to other zeolites having high framework Si/Al ratios, suggesting that kinetic studies of crystallinity loss in other zeolites could be informative. However, general literature searches on the kinetics of zeolite collapse, as well as a citation search on Pine,8 yield just a few papers of potential relevance.15-22 Among these, some work16-18 modeled catalyst activity, not zeolite properties, while others explored kinetic synergy between Na and V,15, 22 but did not evaluate activation energies for zeolite or SiT collapse, or non-idealities in these kinetics. The lack of literature on the kinetics of hydrothermal SiT collapse is doubtless due in part to the fact that SiT is not catalytically active. Instead, it forms a host framework for the anionic AlO4- tetrahedra that then lead to active sites. AlT collapse (dealumination) is thus studied much more extensively,23-32 sometimes including kinetic studies of dealumination.24, 25, 28, 30 Ong et al, for example, find dealumination of one AlT site in HZSM-5 to be a first order reaction,28 but their measurements were made at very mild steaming conditions, representative of only the very freshest catalyst in an FCC unit. Guisnet et al initially reported for NH4NaY an effective second order dependence on AlT,24 but this was later


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revealed to be comprised of two first order regimes: an initial faster one due to proton-exchanged AlT, and a later, slower one due to AlT back-exchanged with non-framework aluminum (NFA or EFAl). These occurrences of first order kinetics in AlT concentration are analogous to the first order decay of SiT,7, 8 but they unfortunately provide no information on the non-ideal kinetics of SiT collapse for zeolite Y FCC catalysts. While the dealumination reactions and kinetics may be complex, the impact of Al T on zeolite crystallinity can be simplified as follows. Refinery catalyst density separation results have already revealed that dealumination is much faster than SiT collapse.7, 9, 10 More specifically, about 90% or more of the refinery equilibrium catalyst mixture contains zeolite Y whose framework AlT is equilibrated to a low value, typically less than about 6% AlT.7 Since the framework percentage of AlT is nearly constant, any substantial change in zeolite crystallinity must be due to Si T collapse. To make the evaluation of SiT decay more quantitative, one can easily determine the framework aluminum content, before and after steam deactivation, by correlation33 with the unit cell size (UCS). The small contribution of AlT to the measured zeolite crystallinity or micropore volume can then be subtracted from the total,7 leaving a more accurate measure of the fraction of SiT retained during steam-deactivation. We used this technique earlier7 to improve the reliability of our kinetic analysis, and will do so again here. It is appropriate to mention that standard laboratory FCC catalyst deactivations most often involve batch-wise steaming for 4-24 hours in 1 bar of steam between about 700 and 800 C,6, 34 with the exact steaming conditions being determined empirically, often long ago. In consideration of the refinery age distribution, sometimes 5% of fresh catalyst is added35, 36 to the steamed catalyst before cracking tests. In a few cases, blending together progressively more severe steamdeactivations is recommended, so as to form a segmented age distribution.37-40 The goal of CADM



development has been to provide a continuous range of catalyst ages whose zeolite crystallinities match those within the refinery age distribution, as well as for the refinery E-cat overall, for a given set of refinery operating conditions. For those not already familiar with FCC, further general background can be found in any number of review articles4, 5, 41, 42 or books.1, 43 In this paper, we will resolve the SiT collapse kinetics into two or three kinetic regimes, and show how these affect zeolite retention results obtained in the laboratory more than in the refinery. Nearly ideal zeolite crystallinity profiles will be obtained within age distribution, or to changes in refinery catalyst makeup rates, under conditions that are described. Catalytic results are then provided to illustrate the roles of the different age fractions in determining the overall E-cat activity and selectivity. Although very important, outside the scope of this paper are optimization of CADM to match E-cats, predicting E-cat properties from refinery operating conditions, as well as the effects of contaminant metals, matrix, and zeolites other than Y. Work on some of these factors is ongoing, and we hope to report on these later.

2

Methods In an earlier paper,7 we took the hypothesis that refinery zeolite collapse kinetics were separable

into tetrahedral framework aluminum (AlO4-, AlT) and silicon (SiO4, SiT) contributions. The separability hypothesis is common in the study of differential equations,44 and made reasonable in the present case because, as noted above, that E-cat dealumination is much faster than SiT collapse.7, 9, 10 After subtracting AlT, the earlier-derived expression for the fractional retention of zeolite framework SiT (eq 67) explained7 the full range of refinery and pilot plant data available in the literature.9-14 Refineries, however, sometimes employ mixtures of catalysts, which can include two different zeolites. Some of our earlier7 laboratory results (Figures 3, 5 and 67) also appear to be consistent with a zeolite mixture hypothesis. 6 ACS Paragon Plus Environment

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2.1

SiT decay for a mixture of two-zeolites

The equation 3 provided earlier7 describes ideal first order decay for SiT during hydrothermal treatment.33, 45 If two ideal but kinetically distinct zeolites designated 1 and 2 are present, two of the earlier equations 37 can be summed to describe their combined decay, where Z represents the micropore area associated with SiT, and x1 represents the fraction of zeolite that is initially type 1. 𝑍 = 𝑍1 + 𝑍2 = 𝑍0 [𝑥1 𝑒 −𝑘1 𝑡 + (1 − 𝑥1 )𝑒 −𝑘2 𝑡 ]

(1)

The net rate of decay of the two zeolites is then 1 𝑑𝑍 𝑍0 𝑑𝑡

= [−𝑘1 𝑥1 𝑒 −𝑘1 𝑡 − 𝑘2 (1 − 𝑥1 )𝑒 −𝑘2 𝑡 ].

(2)

At a single temperature, the net rate contains a faster and a slower decay component, forming a three-parameter model. If the temperature varies and the activation energies differ, this becomes a five-parameter model. Importantly then, the temperature response of the effective rate constant for the net rate is then dependent on the two different activation energies. Because the two decay rates differ, the relative amounts of type 1 and type 2 will vary, so that the effective rate ‘constant’ varies with time. So, while the Arrhenius calculations can be made, the result will be timedependent and non-linear. If the individual kinetics and initial x1 are known or hypothesized, however, the effective rate constant can be calculated. A five-parameter optimization can thus be conducted to fit this model for two-zeolite mixtures to experimental data. Similarly, if two zeolites are present, two of the earlier equations 67 can be weighted by x1 and summed to describe the SiT retention, Z(R), as a function of the weight-fraction in the refinery age distribution, R. Further details on the method of AlT subtraction, or the calculation of SiT decay rate constant or activation energy, can be found in Section S1.1 of the Supporting Information.



2.2

Experimental methods

The experimental methods are mainly the same as before,7 except that many samples were first pre-steamed8 at mild conditions prior to a more severe conventional or CADM steamdeactivations. Pre-steamings (PS) were conducted at 732 C for 17 hours in 100% steam. Both types of steamings were done in conventional, quartz fluid-bed reactors (ASTM 4463) at just above minimum fluidization velocity. For CADM, 3-4 LPM of nitrogen was used to transport FCC catalyst from a small screw feeder to the steaming reactor, with tangential entry to maximize catalyst retention.7

The catalyst eductor described before7 was modified for gravity assist by

inserting the horizontal screw feeder helix through the side arm of a ¼ inch Swagelock T-fitting and into the center bore, thus allowing the catalyst leaving the feeder to fall downwards with the flowing carrier gas. CADM begins with an empty reactor at a very high temperature, but the reactor temperature is then reduced while catalyst is injected at a constant rate.7 The temperaturetime profile has been calculated and verified7 to provide a zeolite crystallinity-weight fraction profile reasonably close to actual refinery equilibrium catalyst. A plot of typical temperature-time profiles will be found in the Results below. For experimental convenience, interlocked backpressure gauges were added to the transport nitrogen lines, before the water bubbler and after the screw feeder, so that injection pressures could be monitored, and the feeder shut down in the event of a plug in the transport system. Further details were as described before.7 For ACE® fixed fluidized bed cracking tests,46 nine-gram catalyst blends were formulated with inerts as before,7 but now the cracks for a given study were run in fully randomized order to insulate against occasional systematic errors. The same 24.29 API, 0.36 wt% Conradson carbon residue, 11.92 UOP KW gasoil was used as before;7 further properties have been reported elsewhere (Table 5, Feed A).47 ACE results are reported in graphical form, including both raw data and bestfit regression curves. The best fit gasoline curves were determined by mass balance on the other 8 ACS Paragon Plus Environment

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regressed yields, so that the sum of the regressed yields is always 100%. Linear regressions were run using empirical transformations of the data. Plots were constructed by inverting these transformations back to the wt% on feed domain. Conversion is defined as 100% - LCO – Bottoms, with simulated distillation cut points of 215 C and 360 C.

3

Results

3.1

Understanding the earlier failure of Arrhenius calibration

As noted above, an initial attempt7 to obtain SiT decay kinetics by the Arrhenius method45 did not provide the expected linear results, indicating that the assumption of ideal first order decay was incorrect. More specifically, the effective rate constants measured at low temperature (Figure 3 in CADM 17) were unexpectedly high, implying a lower activation energy at low temperature than at high temperature. Equivalently, the zeolite surface areas produced earlier7 at lower aging temperatures (Figures 5 and 67) were below expectations for ideal first order decay, or higher than the first order model at the most severe deactivation conditions. These observations are consistent with either a shift in mechanisms, or a heterogeneous mixture of microporous materials. If we hypothesize the existence of two kinetically distinct but crystallographically equivalent zeolites Y, their combined kinetic behavior is described by eqs 1 and 2. In fact, this model had already been applied to obtain the smooth curves shown in Figure 3 of CADM 1.7 The optimal kinetic parameters we had obtained earlier are now provided in Table 1, which imply that these catalysts contain 10-20% of a temperature-insensitive zeolite that has a lifetime, Si, of only a few hours in the steamer. The balance of the material has a much greater lifetime near regenerator temperatures (Figure 1), but owing to their high activation energies, the lifetime of the more stable material becomes comparable to the metastable material at temperatures approaching 900 C.



Table 1. Kinetic parameters from a two-zeolite model applied to Arrhenius data7 of Figure 3 in CADM 1.7 Catalyst E Catalyst F Catalyst G Ea1, kJ/m

92

28

2

ko1, h-1

1.90 x 104

8.17 x 101

1.00 x 100

1,a h

3.21

0.33

1.26

wt% of Z1 19%

12%

8%

Ea2, kJ/m

452

457

584

ko2, h-1

3.83 x 1030 4.96 x 1020 1.31 x 1026

2,a h

794

wt% of Z2 81% a

1198

16,372

88%

92%

Lifetimes at 732 C in 100% steam.

5.00 4.00 3.00

Stable

2.00 Log(Si/days)

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

1.00 0.00 -1.00 -2.00

Metastable

-3.00 600

700

800

900

1000

Temperature, C

Figure 1. SiT lifetimes (Si = 1/k1) as a function of temperature for stable and metastable portions of cat. E ( ), cat. F ( - - - ), and cat. G ( …... ). While the fits obtained earlier (Figure 3 in CADM 17) were excellent, this is not unexpected for a five-parameter model, and perhaps not very significant. In contrast, however, Pine8 had obtained 10 ACS Paragon Plus Environment

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linear Arrhenius plots after presteaming for 16 h at 760 C. Equivalently, Figure 1 implies that we should be able to isolate the hypothetical stable zeolite by steaming for several hours closer to regenerator temperatures. Since the purpose of CADM is to prepare the full age distribution, including the front end, mild presteaming conditions are preferred. Kinetic analysis of a broad range of commercial catalyst technologies indicated that presteaming 17 h at 732 C in 1 bar of steam should be more than sufficient to eliminate all observed cases of metastable zeolite. We then tested the metastable zeolite hypothesis by carrying out that presteaming and remeasuring the Arrhenius response of the pretreated materials. The results with and without presteaming are shown in Figure 2. 10.000

10.000

1.000

-1

0.100

kSi(T), hr

-1

1.000

kSi(T), hr

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


0.010

0.010

0.001

0.001

0.000 0.0009

0.100

0.0010

0.000 0.0009

0.0011

0.0010

0.0011

1/T, Kelvin-1 1/T, Kelvin-1

Figure 2. Arrhenius plots for SiT decay in cat. E (), cat. F (●), cat. G (□), cat. H (), cat. J (), cat. K (▲), cat. L (○), cat. M (), with (left) and without (right) presteaming 17 h at 732 C. Refinery FCCU decay rates () grossed up to 1 bar steam are also shown. Figure 2 shows that, in agreement with Pine,8 an extended presteaming results in ideal first order SiT decay. The R-squared values in all cases are 0.95 or higher. The catalysts shown vary between



1 and 3 wt% REO, originate from at least four different manufacturing processes and include one experimental sample. One sample also contained phosphorus. Pine had already shown that the activation energy for zeolite Y is independent of the presence of Na, V or RE, obtaining an average value of about 326 kJ/m.8 After presteaming, our activation energies vary between 303 and 389 kJ/m, with an average value of 352 kJ/m. Our results are consistent with Pine’s values, but significantly lower than the zeolites type 2 in Table 1, or the 490 or 473 kJ/m we obtained earlier from eq 117 and CADM Ea hypothesis testing.7 The reason for this discrepancy will be addressed further below. On the right side of Figure 2 are the Arrhenius plots and two-zeolite model fittings for the same group of catalysts without presteaming. It is evident that the presteaming procedure has eliminated the source of the high collapse rate at low temperature, and that all catalysts originally contained metastable SiT. Also shown in Figure 2 are the rates of collapse of SiT we estimated for six refineries. In these cases, we used the earlier eq 107 and the fraction of metastable zeolite to estimate the rate constants at regenerator conditions. We then estimated the refinery steam partial pressure and extrapolated the refinery rate constant to 1 bar of steam, assuming first order dependence on steam partial pressure. The estimated rates are in reasonable agreement with the presteamed kinetics, but inconsistent with kinetics obtained without presteaming. A final assessment of the reasonableness of the presteamed vs not presteamed kinetics can be made by estimating the SiT retention within the refinery age distribution. For this we use a weighted sum of two of the CADM 1 equations 6,7 and this is shown for the kinetics of Table 1 in Figure 3. To construct the plot, we have adopted a regenerator steam pressure and temperature of 0.25 bar and 693 C, a catalyst turnover time, R, of 80 days, and first order dependence on steam. We find that 10-20% of the original SiT is quickly lost in the newest portion of the age distribution,


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but most or all of the second type of zeolite is predicted to be retained in the refinery. This latter result does not represent well density separation data from refineries.9-14 On the other hand, if we adopt the presteamed kinetic parameters for cat. E as the second zeolite, and then optimize the remaining three parameters to best fit the non-presteamed data for cat. E, we obtain the solid curve shown in Figure 3. The overall SiT retention in this case is then predicted to be 55%, in reasonable agreement with refinery experience and published density separation results.

100% 90% 80% 70% SiTSA retained, %

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


60% 50% 40% 30% 20% 10% 0% 0%

25%

50%

75%

100%

Percentile in refinery age distribution

Figure 3. SiT retention profile within the refinery age distribution from eq 6,7 shown for NPS cat. E (….), cat. F (-  -  -), and cat. G (- - -) of Table 1; or for cat. E PS (  ). Overall, the agreement is encouraging and suggests that CADM based on kinetic models from presteamed catalysts should be predictive of refinery performance. Still, it is important to see if the presteaming method will improve our ability to reproduce refinery effects by CADM.

3.2

Confirming reproduction of refinery effects

Prior elimination of the metastable zeolite would tend, at least directionally, to reduce the nonidealities observed earlier (Figures 5 and 6 in CADM 1).7 13 ACS Paragon Plus Environment

Accordingly, we presteamed (PS)


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cat. E for 17 h at 732 C and then, using the kinetics measured for cat. E PS and a typical refinery Si = 73 d, made full CADM age distributions using refinery turnover times R between 10 and 120 days (Table 2). The zeolite micropore surface area (ZSA) results were close to the Targets calculated for first order decay after 6 h of addition, but these continued to increase over time for the larger values of R, where the ZSA targets were lower (eq 67) and steaming temperatures higher (eq 97). Our earlier Figure 47 indicates that if the ZSA rises with time then the Ea hypothesis used in the ramp is too low. Indeed, the 317 kJ/m value measured for cat. E is below the PS average from Figure 2. If we then re-optimize the kinetic parameters for the CADM ramp to the data of Table 2 using the earlier eq 11,7 we obtain a new calibration of ko = 9.19 x 1019 h-1 and Ea = 442 kJ/m. This Ea is much higher than that of Pine (326 kJ/m) or Figure 2 presteamed (352 kJ/m), but closer to our earlier results7 of 490 or 473 kJ/m. Although Figures 2 and 3 indicate this new calibration is unlikely to be predictive of refinery kinetics, we nevertheless used it to repeat the experiment of Table 2. These results now show (Figure 4) that the ZSA for a given R were constant with time, and that they agree rather well with expectations for ideal first order decay, as determined from the earlier eq 5.7 Table 2. Zeolite surface areas, in m2/g, or UCS, in Å, of cat. E PS after 6 or 21 h of addition.a R, days

a

0.0b

10

30

60

100

120

6 hours

182

155

107

85

67

21 hours

186

168

130

111

97

Targetc

212

184

148

114

87

78

UCS at 21 h

24.36

24.34

24.34

24.32

24.31

24.32

Ea = 317 KJ/m, ko = 1.43x1014 h-1 for cat. E; Si = 73 days, L = 21 h, and Tmax=927 C. b Zero days indicates properties after presteaming at 732 C for 17 h. c Refinery ZSA targets calculated from eq 57 and Si = 73 days.


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Matching the SiT shape in R implies that we may be closer to the goal of first principles emulation of refinery E-cat behavior, but the shape of SiT within the age distribution should also be evaluated.7 This is done by operating the catalyst feeder only during the specific time periods, corresponding to chosen weight fractions of the age distribution. The 10-20 wt% refinery age segment, for example then, is made by operating the feeder only between the last 2.1 and 4.2 h of a 21 h CADM deactivation. The results can then be compared to theory for first order decay (eq 67) and published experimental data.7, 9, 11 100% 90% 80% SiT retained after presteaming

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% 50% 40% 30% 20% 10% 0% 0.0

50.0

100.0

150.0

R, days

Figure 4. SiT retention for Cat. E PS by CADM, vs refinery turnover time, after 6 h (▲) or 21 h () of addition; and first order decay model (- - -) from eq 5.7 Ea = 442 kJ/m, ko = 9.19 x 1019 h1

, Si = 73 d, L = 21 h, and Tmax = 927 C. To obtain the results in Figure 5, we first fitted the earlier eq 67 to the density separation data of

Palmer and Cornelius’ cat A,9 and of Zhao and Cheng11 (full symbols), obtaining R / Si values of 3.57 and 0.409, respectively. Since their data were incomplete, we had assumed values of MSA and zeolite retention, as reported before,7 the choice of which values effectively erased any



potential unreported effects of metastable zeolite, if present. Presteaming before CADM will have had an equivalent effect. While these steps allow the fittings of Figure 5 to appear more ideal than the prediction of Figure 3, the agreement is independently quite good. Also shown are the SiT retentions for 10 wt% CADM age segments of cat. E and cat. N (open symbols), both of which were presteamed and otherwise unrelated to the published data.9, 11 While the agreements at R/Si = 0.409 are reasonably good, the deviations from the targeted properties becomes systematically worse for the more severe deactivations defined by the larger values of R/Si. These deactivations are run at systematically higher temperatures (eq 97), suggesting a problem with the CADM calibration may exist at high temperatures. 120%

120%

100%

100%

SiTSA maintained, %

SiTSA maintained, %

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

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80%

60%

40%

20%

80%

60%

40%

20%

0%

0% 0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

Percentile in age distribution, R

Percentile in age distribution, R

Figure 5. Fitting of the eq 67 model for E-cat SiT retention to density separation data (11, ●9), and comparison of CADM results obtained (◊, □, ○) on cat. N (left) or cat E (right) using those as aging targets. Model curves for R/Si = 0.409 ( ; 11, ◊), 1.00 ( - - -; □), and 3.57 ( ; ●9, ○). Other conditions same as Figure 4.


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3.3

Understanding deviations and calibration

To varying degrees, Table 2 and Figures 4 and 5 all indicate a residual deviation in SiT retention for more severe, higher temperature deactivations. According to our earlier Figure 4, 7 CADM ZSA rising with time mainly at higher temperatures indicates that the Ea,H value used to create the CADM ramp is too low. Equivalently, Figure 6 shows how the kinetic parameters encountered so far affects the CADM temperature ramp determined from eq 9,7 and helps to illustrate the role of Ea,H in CADM. Each ramp has given a similar ZSA after about one day of steaming, but the higher Ea,H ramps have cooler temperatures initially and hotter temperatures later than the 317 kJ/m ramp obtained by the presteamed Arrhenius method of Figure 2. Thus, increasing the ramp Ea,H will increase initial surface areas and reduce final surface areas, so long as there is a compensating change in ko,H. Increasing Ea,H will thus improve obedience to first order decay in Table 2 and Figure 5, but curiously, the improvement is only needed in the higher temperature region.

950

900 Addition Temperature, 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


850

800

750

700 0%

25%

50%

75%

100%

Wt% of total catalyst added

Figure 6. Temperature ramps for Ea,H and ko,H of 490 kJ/m and 2.79 x 1022 h-1 (….., from CADM 17), or 317 and 1.43 x 1014 (- - -, Figure 2 and Table 2), or 442 kJ/m and 9.2 x 1019 h-1 ( , Figure 4), when R = 36 d, Si = 49 d, L = 21 h, and Tmax = 927 C. 17 ACS Paragon Plus Environment


The linkage between CADM recalibration, the ramp Ea,H and SiT kinetic measurements can perhaps best be illustrated through the Arrhenius plot of Figure 7, which compares directly the kinetics obtained by CADM and Arrhenius measurements. Here we see that while the slope of the presteamed Arrhenius data, -Ea/R (solid line), differs from the CADM recalibration (442 kJ/m, dashed line) from Figure 4, the kinetics agree in the high temperature range. It is now clear that the seemingly minor deviation of the Arrhenius data at the highest temperatures may in fact represent the onset of a new kinetic regime, and that SiT retention in catalyst added at the earliest times during CADM are apparently affected by this deviation. As addition time increases the temperature drops and this initial catalyst is diluted with more fresh catalyst, reducing the contribution of high temperature material to the overall measured properties. The actual SiT decay kinetics will also begin to follow the Arrhenius data more closely, which are more likely to agree with the kinetics in the refinery. To get catalyst most representative of the refinery then, one should employ longer CADM addition times (L), which lowers the average steaming temperature and dilutes the contribution of catalyst initially added to the steamer at the highest temperatures.


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10.000

-1

1.000

0.100

kSi(T), hr

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


0.010

0.001

0.000 0.0009

0.0010

0.0011

1/T, Kelvin-1

Figure 7. SiT decay kinetics at 1 bar steam from cat. E PS lab steamings (,  ; from Figure 2), from refinery catalysts () when first order in steam pressure (from Figure 2), and from the CADM recalibration of Figure 4 to 442 kJ/m ( - - - ). While Figure 7 apparently establishes the existence of a high temperature kinetic deviation after presteaming, Figure 2 affirmed the existence of a metastable portion in fresh cracking catalyst, the behavior of which is illustrated by Figures 1 and 3. It is of interest then to see how deeply their effects penetrate into the CADM age distribution, and how long of a catalyst addition time is needed to render them correctly. These questions can be approached as follows. Our earlier eq 117 describes the evolution of the cumulative ZSA of the blend in the CADM steamer as a function of laboratory kinetics, refinery turnover time, and the ramp hypothesis. Since eq 117 describes only idealized zeolite behavior, we constructed a three-zeolite model consisting of the weighted average of three equations 11, each following the same temperature ramp but independent SiT decay kinetics. Again, for sole purpose of illustration, for the first zeolite, Z1, we adopted the Z1 model for cat. E metastable zeolite in Table 1. The remaining 81% of the fresh



zeolite was divided into two parts, Z2 being assigned the Arrhenius Ea2,T of 317 kJ/m used for the ramp of Table 2, and Z3 being assigned the CADM recalibration kinetics of Ea3,T = 442 kJ/m and ko3,T = 9.19 x 1019 h-1 (Figure 4). The lone free parameter of the proportion of Z2 vs Z3 was then regressed, as if to fit a blend of the two lines in Figure 7 to the experimental data in Figure 7. The result is 19% of Z1, 70% of Z2, and 11% of Z3. For the CADM ramp we note that the slopes of all of the presteamed catalysts in Figure 2 and of Pine8 seem equivalent, so we chose the average Ea,H of all of our materials, 352 kJ/m, to fit the Arrhenius data of cat. E. The quality of this fit (not shown) is essentially equivalent to the free regression line in Figure 7, but this time with k o,H = 7.52 x 1015 h-1. The results for all three zeolites, without presteaming, are shown in Figure 8.

60%

50% Fraction of fresh SiT retained

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

40%

30%

20%

10%

0% 0%

25%

50%

75%

100%

Fraction of total lab catalyst added to steamer

Figure 8. SiT retention of Z1 (92 kJ/m,  ), Z2 (317 kJ/m, - - -), Z3 (442 kJ/m,    ), Z2 + Z3 (      ), and Z1 + Z2 + Z3 (  ) according to eq 117 for L = 168 h and ramp Ea,H = 352 kJ/m for cat. E. At the end of 168 h, the predicted fractions of the original NPS zeolite retained are 1% as Z1, 32% as Z2 and 8% of Z3, for 41% overall zeolite retention. Z1 is short-lived, declining to 2% 20 ACS Paragon Plus Environment

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within 10 h. Z3 also equilibrates fairly quickly, projected to be at 6% within about 4 h. Z2 on the other hand requires more than a day to reach 34% retention, falling another 2% by seven days because the measured Ea of 317 kJ/m differs from the ramp hypothesis of 352 kJ/m. If instead we use a ramp Ea equal to the Ea2, Z2 will be constant with time. In summary then, if secondary zeolite materials or kinetics exist that have Ea substantially different than the ramp Ea, their influence can sometimes be reduced simply by steaming for an extended time. The ramp Ea,H then should focus on flattening the ZSA at longer addition times, ignoring earlier deviations from the target ZSA. This is a departure from our earlier method. These calculations of course assume that presteaming had not materially changed the kinetics of the remaining zeolite (Z2 and Z3), and that they are distinct materials. It is also important to note that Figure 8 does not yet quantitatively reproduce the trends of the earlier Figure 4,7 or in Table 2, but this is not its purpose. Instead, we simply employ the existing kinetic parameters to aid in our understanding of their impact on CADM results. At this point then, we conclude that the deviations from ideal behavior encountered earlier and above can now be understood, at least qualitatively, in terms of two or three kinetic regimes or as mixtures of materials. Their combined ZSA drifts over time because only one of them can be matched by the ramp Ea. The results suggest a revised test method for FCC catalyst deactivation, where one presteams fresh catalyst 17 h then deactivates by CADM using a ramp Ea,H of about 340 kJ/m. We presume that this method, designated CADM 2, will produce results more predictive of or consistent with the refinery than the prior CADM 1 procedure.7 The activity and selectivity consequences for these adjustments may of course be significant, and so their impacts will be assessed below.



3.4

Comparison of three deactivation methods

We found earlier7 that the presence of an age distribution or changes in the CADM parameters can have a dramatic effect on catalyst properties, selectivity, and sometimes performance ranking. To determine any performance effects due to the present changes in CADM parameters, as well as to illustrate the utility of the revised method, we next made a randomized ACE fixed fluidized bed comparison of a high pore volume (HPV) catalyst E against a low pore volume (LPV) catalyst N, doing so by the original CADM 1 method (from Figure 57), the present CADM 2 method (from Figures 4, 5), and by 816 C steaming for 6 h. The rare earth (3 wt%), steamed unit cell size (24.32 Å), surface area, and chemical compositions of cat. E and cat. N are otherwise equivalent, so that any performance differences can be attributed to their different pore volumes. The SiT retentions as a function of age fractions were already shown above in Figure 5. Other properties are discussed further below. Figure 9 provides the catalytic results.


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85

51

83

50

81 49

Gasoline, wt%

Conversion, wt%

79 77 75 73

48

47

46

71 45 69 44

67 65

43 2.0

4.0

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2.5

3.5

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5.5

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0.88 16 0.86 14 Bottoms, wt%

0.84 C3H6 / (C3H6 + C3H8)

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


0.82 0.80 0.78 0.76

12

10

8

0.74 6 0.72 0.70

4 65

70

75

80

85

2.5

Conversion, wt%

3.5

4.5

5.5 Coke, wt%

Figure 9. ACE conversion, gasoline and bottoms vs coke, and C3 olefinicity for HPV cat. E (open symbols) and LPV cat. N (full symbols) after CADM 1 (○, ●, ), CADM 2 (□, ,  ), or 816 C steaming for 6 h (, ▲, - - - -). Curves from best fit regressions of the data.



Page 24 of 44

For convenience, Figure 9 displays the high pore volume catalyst as open symbols and categorizes the deactivation methods by symbol shape and regression line format.

From the

results, it is clear that the CADM 1 protocol gave the largest differentiation between the HPV and LPV catalysts. The smaller performance differences obtained by CADM 2 and 816 C steaming are best illustrated by comparing the deactivation-specific formatted regression curves, where, perhaps surprisingly, we find that the continuous curves denoting the new CADM 2 protocol gave the smallest performance differentiation. Since the foregoing suggests the CADM 2 protocol is most closely related to the refinery deactivation kinetics, one might interpret the results as indicating that CADM 1 and 816 C steaming artificially inflate the performance differentiation. However, other ACE comparisons have shown that 816 C steamings sometimes under-predict analogous performance differences found on E-cats. We also found7 that CADM 1 deactivation more closely replicated E-cat performance than a single 816 C steaming, especially in C3 olefinicity or hydride transfer. So, although an E-cat was not run in this test, the CADM 1 ACE results for cat. E can be taken as more correct than the 816 C steaming. As a result, the similar catalytic performance of cat. E by the CADM 2 and 816 C deactivations indicates that an important negative performance effect is missing. We shall see momentarily that the missing factor is the relatively poorer selectivity of the freshest catalyst in front end of the age distribution.

3.5

Effect of 0-5% front end on selectivity

The fresh cat. E has a UCS of 24.61 Å, but after presteaming this has already been reduced to 24.36 Å. This difference in the UCS of the raw materials fed during the two CADM procedures of Figure 9 could potentially account for the observed differences in C3 olefinicity and other selectivities, since the CADM blends always contain a small amount of essentially fresh catalyst. Similarly, the front end of the refinery age distribution may have a significant performance effect, 24 ACS Paragon Plus Environment

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but it has, before now, been difficult to accurately assess this. Some important questions35, 36 are whether the front end is significantly different in specific activity or selectivity, and if it is different, does it have any measurable impact after dilution with the rest of the catalyst in the latter part of the age distribution. We addressed the first question by running randomized ACE on the 0-5 wt% fraction of cat. E vs the 0-100 wt% full age distribution range, and the second question by also running the 5-100 wt% range produced by CADM. Presteamed CADM seems most realistic in the back end, but we also tested the back end not presteamed (NPS) for completeness. The 0-5% front end was not presteamed. Figure 10 shows that including the NPS 0-5% age fraction as 5% of the blend () does materially degrade selectivity. Coke is increased by about 13%, whether or not the balance of the material had been presteamed. Including the PS 0-5% fraction (0-100% PS case) gave essentially the same selectivity results as PS 5-100%, so that presteaming renders the impact of the front end unmeasurable. While the abscissa of Figure 10 has been scaled to make these differences apparent, this leaves only two data points visible for cracking on the NPS 0-5% fraction alone. Five more points (Figure S1, Supporting Information) confirm that this fraction does contribute a disproportionate share of coke, dry gas, bottoms and hydride transfer.

This explains the

performance differences in Figure 10 after dilution of the 0-5% NPS segment into the older fractions. When the PS and NPS results of Figure 10 are plotted together, they reaffirm the method-related differences first seen in Figure 9. But now that we see that the NPS 0-5% age fraction does have measurably different selectivity after appropriate dilution, the question arises whether the other age fractions, perhaps the oldest fractions, also materially affect the performance of the overall age distribution.



51

30

49

25

47 Bottoms, wt%

Gasoline, wt%

20 45

43

15

10 41 5

39

37

0 1.0

3.0

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Coke, wt%

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7.0

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7.0

Coke, wt%

51 30 49 25 47 20 45

Bottoms, wt%

Gasoline, wt%

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 26 of 44

43

41

15

10

39

5

37 1.0

3.0

5.0

7.0

0 1.0

Coke, wt%

Figure 10.

3.0 Coke, wt%

ACE gasoline and bottoms vs coke for cat. E CADM age fractions 0-100% (○, ●,

), 5-100% (□, , - - - ) and 0-5% (▲, ; 24.45 Å), with (open symbols) and without (full symbols) presteaming. Also shown: 0-5% NPS + 5-100% PS (, -  -  - ). Curves from best fit regressions of the data.


Page 27 of 44

3.6

Properties and Selectivity of latter age segments

To determine how activity, selectivity and physical properties might vary within the rest of the age distribution, we further evaluated the age segments first presented in Figure 5. The LPV cat. N and HPV cat. E were separately evaluated by randomized ACE C/O studies, but several months apart. Because the different age fractions do vary systematically in surface area (Figure 5), we should expect the activity of each fraction to vary at least proportionally. For ACE then, each age fraction was diluted down with inert steamed kaolin microspheres to give an overlapping range of surface areas, and by plotting against the ACE blend surface area applied per gram of oil (BET 48 x C/O), Figure 11 shows, in effect, the specific activity of the age fractions. The samples being derived from Figure 5, it is important to recall that each of these age segments was presteamed, even the 0-5% range.

85

85

80

80

75

75 Conversion, wt%

Conversion, wt%

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

65

70

65

60

60

55

55

50

50 0

500 m2

1,000

1,500

0

500

1,000

1,500

m2 catalyst / g of oil

catalyst / g of oil

Figure 11. ACE conversion vs (BET x C/O) for 0-10% (●), 10-20% (▲), 30-40% (), 50-60%, (○), 70-80% (), 90-100% (□), and 0-100% (, ) fractions, for LPV cat. N (left) and HPV cat. E (right). Regression curve is also shown for the full 0-100% blend. 27 ACS Paragon Plus Environment


As seen in Figure 11, the age fractions have remarkably similar activities per unit surface area. Despite the annealing effect of the presteaming, the younger fractions (full symbols) still have incrementally higher conversion per unit surface area than the older fractions (open symbols) or the full blend (*, regression curve), and this might be explained by systemically changing zeolite/matrix ratio or framework aluminum content (UCS33). To test this, Figure 12 plots the specific activity (conversion regressed to 750 m2 /g oil) vs age fraction and UCS. For comparison, the solid symbols denote the full 0-100% range. As expected, older fractions do have lower conversion at fixed surface area, and these lower conversions are associated with the lower UCS found in the back end of the age distribution. To see whether the UCS variation fully explains the change in specific activity, we also include ACE conversion regressed to 600 m2/g oil for the earlier rare earth ladder (Figure 7 in CADM 17). Although this reference curve was generated much earlier from full 0-100% distributions, this qualitative comparison indicates that the effect of age on specific activity can be understood in terms of steamed UCS. The confounded zeolite/matrix ratio may also be important, but we did not assess this. The plot against UCS also suggests an activity limitation for the LPV catalyst, as would be expected if diffusion is at least partly limiting.49 The two sets of data were obtained months apart, however, so this conclusion is less secure.


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80

81

80

79

79 78

Conversion, wt%

Conversion, wt%

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


77

78

77

76 76 75

75

74 0

20

40

60

80

100

74 24.26

24.28

Percentile in the age distribution

24.30

24.32

24.34

24.36

24.38

24.40

Steamed Unit Cell size, A

Figure 12. Conversion regressed to 750 m2/g oil for 10 wt% segments of HPV cat. E (○) and LPV cat. N (), vs percentile in the CADM age distribution (left) or age fraction UCS (right). Solid symbols for full 0-100% blend. Reference curve (--x--) interpolated at 600 m2/g oil for R = 36 days (reproduced from Figure 7 of CADM 17). Figure 13 provides the yields obtained during these ACE runs, and they show comparatively small but significant variation between the age fractions for both the LPV (left) and HPV (right) catalysts. It is clear that the 90-100% fraction (□) gave lower conversions and poorer selectivity for both LPV and HPV. The surface areas of these fractions were too low to be able to fully overlap with the other blends and so were left out of Figure 12. And while the higher conversions from the younger fractions complicates the comparison, especially for the LPV catalyst it does appear that the younger fractions generate lower gasoline, or higher bottoms and coke than older fractions or the 0-100% full distribution. The yields for the HPV catalyst on the other hand are more closely gathered around the 0-100% full blend regression curve, especially for the hydride transfer index. Degradation


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Coke, wt%

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19 15 Bottoms, wt%

Bottoms, wt%

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13 11 9

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1.20 i-C4H8 / i-C4H10

i-C4H8 / i-C4H10

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

Gasoline, wt%


1.00 0.80 0.60 0.40

1.00 0.80 0.60 0.40

0.20

0.20

0.00 55

65 75 Conversion, wt%

85

0.00 55

65

75

85

Conversion, wt%

Figure 13. ACE gasoline or bottoms vs coke, and hydride transfer vs conversion from different age fractions of LPV cat. N (left) and HPV cat. E (right). Tests and symbols same as Figure 11. 30 ACS Paragon Plus Environment

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in selectivity of the front end of the age distribution is anticipated by theory49 in the event of a mass transfer limitation, and these impacts are reduced by the otherwise chemically similar HPV catalyst. The sensitivity of the LPV cat. N selectivity to age (Figure 13) can be understood from the changes in mercury pore size distribution shown in Figure 14.

Here we see that the youngest

fractions exhibit limited mesoporosity above 100 Å radius and no macroporosity near 1000 Å radius. As cat. N ages and zeolite collapses, zeolite micropore volume is liberated and the mesopores widen and increase in volume. This mesoporosity trend, confirmed by measurements on density-separated E-cat,50 results in improving selectivity from the front end to the 50-80 wt% range.

The fresh front end of the HPV cat. E on the other hand already has substantial

macroporosity (Figure 14, right), restoring balance between diffusion and reaction for the youngest catalyst, resulting in front end selectivity largely indistinguishable from the full blend (Figure 13). While these mercury porosimetry results explain the main differences in performance between LPV cat. N and HPV cat. E, the performance differences shown in Figure 13 are minor compared to those obtained with and without presteaming in Figure 9. Figure 10 had then shown the dramatic effect of PS vs NPS on the 0-5% front end. Since presteaming is the key enabler for the corrections obtained in Figures 2, 4, 5 and 7, we thought it important to evaluate more generally the effect of presteaming on the properties and performance of the full age distribution.


0.50

0.50

0.45

0.45

0.40

0.40

0.35

0.35 dV/d(LOG(R))

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

dV/d(LOG(R))


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Page 32 of 44

0.00 10

100

1,000

10,000

10

Pore radius, A

100

1,000

10,000

Pore radius, A

Figure 14. Mercury pore size distributions for 0-10% (), 10-20% (- - -), 30-40% ( ̶ ̶ ̶ ), 5060%, (-  -  - ), 70-80% (  ), 90-100% (    ), and 0-100% (●) fractions, for LPV cat. N (left) and HPV cat. E (right).

3.7

Effect of presteaming time on porosity and selectivity

Presteaming time was first varied between zero and 48 h on cat. E, and these products were next deactivated by the earlier 473 kJ/m calibration (Table 1 in CADM 17). We then found ACE selectivities varied systematically with the presteaming time, and that the catalytic results appeared to correlate with changes in the mercury pore size distribution. Figure 15 provides these PSD, which shows a systematic increase in 200-600 Å diameter mesoporosity that can again be attributed to mesoporosity changes in the zeolite component. Remarkably, the effect of varying presteaming time is visible even after the higher temperature CADM deactivation. Figure 16 then plots the major ACE yields against that 200-600 Å mesoporosity, with the changes expressed as deltas from the NPS control, all evaluated at a constant 4.18 wt% coke. The result is a striking


Page 33 of 44

linear improvement in all the major yield categories with increasing 200-600 Å pore volume. Coke selectivity is reduced by up to 22%. 0.35

0.30

0.25 dV/d(LOG(R))

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


0.20

0.15

0.10

0.05

0.00 10

100

1,000

10,000

Pore radius, A

Figure 15. Mercury PSD for cat. E after PS for 0 (), 1 (- - -), 3 (-  -  ), 9 (  ), 17 (    ), or 48 h (), and then CADM.



2.5 2.0 1.5 1.0 Yield difference, wt%

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 44

0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 0.08

0.09

0.10

0.11

0.12

0.13

Pore volume (200-600 A), mL/g

Figure 16. Effect of PS CADM mesoporosity on ACE bottoms (●), LCO (○), gasoline (), LPG (), and dry gas () yield deltas vs NPS control when regressed to 4.18 wt% coke.

4

Discussion

4.1

Kinetic nonidealities

The kinetic analysis has identified two or three regimes in FCC catalysts that may or may not represent physically distinct materials. Metastability in zeolite Y had been reported much earlier,51 and it seems likely that our observations correspond to the completion of the ultra-stabilization51, 52

process to form USY,23,52 where SiT is lost more rapidly during dealumination, due to the

replacement of Al-O-Si bonds with more reactive H-O-Si silanol nests.

While catalyst

manufacturers are careful to reduce fresh framework Al content, Figures 2, and 3 or Palmer and Cornelius9 suggest that it is most often completed in the regenerator, with a pre-steamed UCS (Table 2) or 0-2% CADM front end7 as low as 24.36 Å (AlT = 8%).33 Indeed, the regenerator can supply appropriately mild steam calcination residence times orders of magnitude longer than a 34 ACS Paragon Plus Environment

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manufacturer, so much so that manufactured USY itself might be much more important in laboratory testing than in the refinery, from a zeolite collapse perspective. A second contribution to metastability may be the defect zones that exist between the nanocrystalline domains within industrial zeolite crystals.53 The incremental instability of SiT and AlT at defect sites and along grain boundaries was found sufficient to enable their selective leaching with ammonium fluoride solutions, revealing by TEM and electron tomography an intrinsic mosaic structure reportedly ubiquitous in zeolites.53 Examples so far include ZSM-5, zeolite Y, mordenite and ferrierite.53 While our linking these defects to metastability in steaming is speculative, the results seem self-consistent. Crystal fracturing and nanocrystalline Y has already been reported in the oldest fractions of FCC equilibrium catalysts10 or after accelerated laboratory steam-aging,54 but their morphologies differ, and their formation was not linked by those authors to defect zones formed during zeolite synthesis. As for the high temperature deviation represented by Z3, it seems kinetically unrelated to refinery (Figure 7), but still important in the laboratory when aging is greatly accelerated with very high temperatures (Table 2, Figs. 5 and 6). Long steaming times are one choice (eq 9,7 Figure 8) to diminish the role of Z3, and these were additionally found7 to lower coke (CADM I). Shorter CADM addition times might still give reasonable results with the Arrhenius Ea for the ramp if the initial high temperature deactivation rates are moderated by empirically adjusting Tmax7 until experimental surface areas using presteamed catalyst are constant with time.7 One might later use NPS catalyst in such a method to enable the effects of the fresh front end, assuming of course that presteaming does not affect the true kinetics of Z2 and Z3. In this case one would ignore initially high ZSA values due to the contributions of Z1 (Figure 8).



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While the projections in Figure 8 are self-consistent with the kinetics and existing data, we have not further optimized or verified the projections experimentally. It seems clear overall, however, that excessive acceleration of zeolite deactivation degrades the accuracy of the CADM, or any other steam deactivation, since nitrogen mesoporosity for longer CADM times agrees more closely with E-cat,7 and because literature density separations do follow first order decay (eq 6),7 even in the presence of high levels of vanadium.

4.2

Effect of deactivation methods on catalysis

As noted in our earlier paper, there has been speculation in the literature on the effects of various age fractions,35-38 steaming conditions6 or other factors4 on selectivities or catalyst rankings. Some of these questions are now readily addressed by CADM. From the catalytic data, we find that either fresh catalyst UCS or some other aspect of the 0-5% front end does affect hydride transfer and coke, even after appropriate dilution in older catalyst (Figure 10). After presteaming 17 h however, the impact of that 0-5% PS front end becomes invisible. Clearly the pretreatment and the percentile range one uses will affect the results obtained, but CADM provides the first rational method we are aware of to assess this effect, which is important due to its impact on propylene, butylenes and coke selectivity in the refinery. Perhaps surprisingly, the latter age segments were comparatively invariant in selectivity (Figure 13, HPV). This is perhaps because the ZSA/MSA ratios for this catalyst varies little with age, as well as its macroporosity. ACE selectivity could be expected to vary more substantially for low ZSA/MSA catalysts.

The performance of both

catalysts N and E improved with increasing mesoporosity (Fig 13 from Figure 14, or Fig 16 from Figure 15, respectively). Although the steamed UCS was varying in the former case (Figure 12), the large selectivity effect in the latter case can only be attributed to changes in zeolite texture. A similar effect was found earlier.7 Many such reports of favorable selectivity changes due to 36 ACS Paragon Plus Environment

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mesoporosity can be found in the literature, for a variety of reactions.55-59 But the deactivation methods employed before the catalytic testing can be quite arbitrary. The question that remains is how one should best assess the effect of induced fresh catalyst mesoporosity, in an extended aging environment like FCC, where substantial mesoporosity with very large diameters are often generated due to months of aging at comparatively mild conditions. The CADM methodology is well suited to addressing this problem, although very long addition times may be required to generate the most predictive results.

4.3

Future work

Earlier we found that CADM 1 with a two day addition time would give selectivity very close to E-cat, the coke selectivity on a one-day NPS CADM 1 protocol being too high.7 Considering Figures 9 and 10 as well, the best CADM 2 protocol is likely to include something like NPS 0-5% blended with PS 5-100% age fractions, but work to validate that against the corresponding E-cats is not yet completed. We hope to report on this work later. Perhaps of greatest interest will be to see if CADM 2 can reproduce E-cat samples in the laboratory, using parameters based solely on regenerator conditions. This will only be possible if the laboratory and refinery Si T kinetics are congruent, as appears to be the case for the presteamed catalysts of Figure 2. If metals-free gas oil CADM is successful, ultimately method development will turn towards contaminant metals, where excessive dehydrogenation activity has been an ongoing problem. With some exceptions, most earlier method development has focused on oxidation-reduction cycling,60,61 with special measures sometimes being taken to additionally minimize metals activity.62, 63 Since the activity of Ni diminishes when the FCC support material is presteamed severely, temperature-programmed cyclic red-ox methods with appropriately higher loading of Ni on the oldest fractions can help close the dehydrogenation activity gap between E-cat and laboratory preparations. In principle, 37 ACS Paragon Plus Environment


CADM provides the correct temperature-time profile that can correctly reproduce zeolite crystallinity and activity, even if it is applied step-wise during repeated crack and burn cycles. As a final note, the commonality of at least quasi-ideal first order kinetics by Pine,8 density separation results,7 and Figures 2, 4 and 5 using unrelated catalysts suggests that CADM may have more general utility.

Indeed, calibration for two more molecular sieves has already been

successful. It would be especially useful if predictive, pseudo-equilibrium samples can be prepared of materials that have not been used before commercially. This would allow for facile performance and economic screening of multiple zeolite frameworks, improving insight into the behavior of novel materials.

5

Conclusions Kinetic analysis of the SiT framework collapse in Y zeolite has revealed the existence of two or

three kinetic regimes, potentially limiting the application of the continuous age distribution method (CADM) reported earlier. However, Z1 is a metastable form that can be eliminated with a mild presteaming, leaving behind a nominally ideal Z2 with an activation energy for collapse of about 350 kJ/m. The first order decay of Z2 breaks down, however, at the very highest temperatures needed to fully collapse the zeolite representing the oldest fractions of refinery catalysts. The influence of these nonidealities can be minimized by using longer, lower temperature deactivations. Two or more days of CADM aging may be required for best results. CADM has been used to prepare, in the laboratory, narrow FCC catalyst age segments that mimic those obtained by density separations of refinery equilibrium catalysts. Characterization of these segments shows that zeolite crystallinity decreases and mesoporosity increases, just as in the refinery, while catalytic results show that mesoporosity increases from mild pre-steamings also systematically improve coke selectivity. The 0-5% front end of the age distribution, which should 38 ACS Paragon Plus Environment

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not be presteamed, individually yields high coke and hydride transfer, which impact is still measurable even after appropriate dilution into the full age distribution.

6

Notation

C/O

Catalyst/oil weight ratio, g/g

k1, kR

First order rate constant for SiT decay, 1/h

ko, Ea

Arrhenius pre-exponential (1/time), and activation energy, kJ/m

L

Time the laboratory steaming is ended, h

R

Universal gas constant, J/m-K

SiTSA

Contribution of tetrahedral Si (SiT) to overall zeolite surface area, m2/g

τSi

Lifetime of tetrahedral Si (SiT) in the refinery = 1/kR, days

τR

Turnover time of FCC catalyst inventory, days

ωR or ωL

Percentile in the refinery or laboratory age distribution, wt%

Z

Simplified notation for SiTSA, m2/g Subscripts and superscripts o

Fresh catalyst property or initial value

H

Hypothetical or inaccurate kinetic parameter for SiT

R

Value in the refinery regenerator

T

True or accurate kinetic parameter for SiT decay 39 ACS Paragon Plus Environment


7

Supporting Information Section S1: Rate constant and AlT subtraction calculations. Section S1: Figure 10 re-plotted

with coke extended out to 16 wt%.

8

Acknowledgements This work was funded entirely by BASF Corporation. The author is grateful to Ms. Michelle

Scamporino for the experimentation, and to BASF, both for supporting this work and granting permission to publish it.

9

References

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Continuous Age Distribution Method for Catalytic Cracking 2

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