Molecular-Level Kinetic Model for C12 Continuous Catalytic Reforming

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Catalysis and Kinetics

A Molecular Level Kinetic Model for C12 Continuous Catalytic Reforming Xiang Zhou, Zhen Hou, Jieguang Wang, Wei Fang, Aizeng Ma, Jinbiao Guo, and Michael T Klein Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00950 • Publication Date (Web): 22 May 2018 Downloaded from http://pubs.acs.org on May 22, 2018

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A Molecular Level Kinetic Model for C12 Continuous Catalytic Reforming Xiang Zhou1, Zhen Hou2, Jieguang Wang1, Wei Fang1, Aizeng Ma1, Jinbiao Guo1, Michael T. Klein2,3 1

SINOPEC Research Institute of Petroleum Processing, Beijing 100083, China and 2

Energy Institute, University of Delaware, Newark, U.S.A. 3

Center for Refining and Petrochemicals, King Fahd University of Petroleum and Minerals, Dhahran, Saudi

Arabia

Abstract A detailed kinetic model for a CCR process was developed. The model included 447 naphtha molecules (C1C12) that underwent 1,469 reactions. Paraffin and naphthenic isomers up to C9 components were fully depicted, whereas aromatic isomers were fully described up to C10. Coking kinetics and the corresponding deactivation of the catalyst were integrated into the model. The steady state kinetic parameters were tuned using pilot plant data for a widely used industrial catalyst. To enable the use of commercial plant data, the energy balance and catalyst moving mechanism of typical CCR reactors were also formulated. The model was then used to simulate an industrial unit loaded with the same catalyst after deactivation calibration by adjusting a few deactivation parameters. The results showed that calculated PONA fractions, individual aromatic species and the temperature drops of each reactor were in good accord with industrial data.

Keywords: molecular level; kinetics modeling; CCR

1 ACS Paragon Plus Environment

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1. Introduction The upgrade of clean fuel specifications has accelerated drastically in China over the last few decades. For gasoil, the upper limits of olefin and sulfur contents have been reduced, which has resulted in the reduction of high octane number components and an increasing demand for hydrogen. As a conventional unit to support high grade gasoline production and hydrogen supply, continuous catalytic reforming (CCR) has become more and more important for domestic refineries. In newly built refineries, CCR units were required and were built with large processing capacities. Kinetic models are useful for catalyst evaluation, operation optimization and process control of refinery units. Because of significant progress of modern analytical techniques and the detailed catalysis studies of refining reactions, the species in naphtha and the chemical conversions between them can be illustrated at the molecular level. This led to the development of detailed kinetic models for naptha catalytic reforming, such as the classic models by Mobil (ref KINPTR) and Exxon (. Powerforming1 ). Powerforming described the molecular details of Semi-Regenerative Reforming (SRR) up to C7 and illustrated application cases in the industrial pilot plants. Froment and coworkers2,3 elegantly described the reforming kinetics and coke formation mechanism of C6 and C7 components and later extended to a mechanistic reforming model4 up to C10 components via the single event approach. The single event approach describes each elementary step of the reforming reactions and considers both molecules and intermediate species (e.g. ions). Although the feedstock of reforming, i.e. naphtha, is a light fraction of petroleum, the mechanistic model of naphtha complex with full isomeric details is still practical significant computational burden for industrial applications. As a result, a model with some degree of lumping the isomeric molecules is useful to satisfy the practical computational burden. A molecule-based reforming model at the pathways level was therefore developed by Klein’s Research Group (KRG)5. KRG used Bond Electron (BE) Matrix to represent molecules and created a reforming model using an automated model building Kinetic Model Toolkit (KMT)6,7,8,9,10containing79 components and 480 reactions to describe a SRR process up to C9, with isomeric details up to C8. The literature thus provides an extensive foundation for the construction of detailed reforming models. The model described herein sought to extend the isomeric detail and apply this chemistry to the CCR process. State of the art GC techniques enable the analysis of the isomeric details of naphtha up to C9-C10 and thus provide the initial data support to us to describe the details of heavier isomers (C9-C10) in naphtha reforming. Moreover, CCR is the most widely used reforming unit in refineries at the present time. Therefore, there is a high demand to develop a molecule-based CCR model with the details of heavier isomers. Although the carbon number of the conventional feed of CCR is up to 10, there still exists a few cases from our refining plants that process the range of naphtha feeds heavier than C10. In order to extend the usability of the model, it is necessary to allow the limit of the feed range of the model be a bit higher. Based on the GC analysis of our data, C12 is the appropriate upper bound of the carbon number.


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In what follows, we illustrat an approach to the development of a molecular-level detailed kinetic model of CCR up to C12 naphtha. The model explored full isomeric details of paraffinics and naphthenics up to C9 and aromatics up to C10.


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2. Model Development Both commercial and pilot plant data were used to support the model development. Usually, detailed data from a typical industrial plant is limited, but sufficiently detailed data can be obtained from a pilot plant. This motivated a two-step process for model development: the steady state main reaction model was built using pilot plant data, in which catalyst activity was treated as constant; the process and deactivation issues of CCR were calibrated using the commercial data. The pilot and commercial units both used the same industrial catalyst. In this section, the details of model building steps were addressed as follows: species and reaction network, kinetics and rate law of reforming, deactivation and coking kinetics, and CCR reactor model. 2.1.

Species and Reaction Network

The eight typical reforming reaction families were used: aromatization, dehydrocyclization (DHC), ring isomerization, paraffin isomerization, paraffin hydrocracking, aromatic methyl shift, aromatic sidechain cracking and aromatic dealkylation. A brief description of the above reaction families is shown in Figure 1.


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Figure1 the typical reaction families in CCR Differentiated by the carbenium ion details in the reactants and the products, the paraffin isomerization and paraffin hydrocracking reactions were further classified as Isomer-A (tertiary to tertiary), Isomer-B1 (tertiary to secondary), Isomer-B2 (secondary to tertiary) and Isomer-C (secondary to secondary), PPCracking-A (tertiary to tertiary), PPCracking -B1 (tertiary to secondary), PPCracking -B2 (secondary to tertiary) and PPCracking -C (secondary to secondary). Based on industrial experimental observations, the CCR catalyst has kinetically significant activity for hydrogenolysis reactions. A paraffin hydogenolysis reaction was therefor included in this model, and two main reaction families (aromatic side chain cracking and dealkylation) were further classified into four reaction family sub cases (acid/metal aromatic side chain cracking and acid/metal dealkylation).


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The number of isomers increases dramatically with increases in the carbon number. To balance the information content and the computational efficiency, the isomerization of C10-12 species was organized into a single lumped reaction family with fewer isomer details. In reforming process modeling, olefins are usually not considered at the pathways level due to the abundance of hydrogen. Moreover, only a very low level of olefins up to C8 were detected in the experimental data used to build the model. We therefore considered olefins up to C8 in this model. Olefin species usually are intermediate species to describe the reforming reactions in the mechanistic level. For example, a DHC reaction at the pathway level can be described as a set of mechanism steps shown in Figure 2.

Figure2 reaction steps of DHC at mechanistic level The rate control step of this scheme is ring closure/ring opening and thus the dehydrogenation/hydrogenation step is usually treated as a virtual equilibrium. So, we treated those olefin species as being in a de/hydrogenation equilibrium. Indane was reported in a very minor amount in the experimental data. The assumption paths of the formation of Indane could be the DHC of alkyl benzenes (e.g. A9) or the Diels Alder reaction via dehydrogenated cyclopentanes (e.g. cyclopentadiene). Considering our experimental observations that C9~C12 aromatics were rapidly broken down to A6~A8 species, we assume the path from the DHC of alkyl benzenes has a lower probability. Therefore, these species were modeled to form via a Diels Alder reaction path11. Using KMT’s INGen tool, 20 reaction families were applied to the model building seed (essentially the feed naphtha), which produced a total of 1469 reactions and 447 species including 65 olefin species The statistics of the reaction network and species are shown in Table 1 and Table 2. Table 1 The statistics of the reaction network for C12 CCR model Reaction Family Name

Index

# of Rxn

Dehydrogenation

1

63

Aromatization

2

52

RingIsomerization

3

316


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AromaticMethylShift

4

22

Dealkylation_Ctlby_Acid

5

28

Dealkylation_Ctlby_Metal

6

17

Hydrogenolysis(Metal Cracking)

7

36

ParaffinCyclization5

8

248

C11-C12 ParaffinIsomerization

9

38

C9- Paraffin PathAIsomerization

10

69

PathAPPCracking

11

14

C9- Paraffin PathB1Isomerization

12

47

PathB1PPCracking

13

31

C9- Paraffin PathB2Isomerization

14

68

PathB2PPCracking

15

44

C9- Paraffin PathCIsomerization

16

159

PathCPPCracking

17

141

SideChainCracking_Ctlby_Acid

18

21

SideChainCracking_Ctlby_Metal

19

54

Diels Alder

20

1

Sum

1469

Table 2 The statistics of the species for C12 CCR model C#

nPar

iPar

nMO

iMO

Nap5

Nap6

Aro

CycloOlefin

1

1

0

0

0

0

0

0

0

2

1

0

0

0

0

0

0

0

3

1

0

0

0

0

0

0

0

4

1

1

2

1

0

0

0

0

5

1

2

2

3

1

0

0

1

6

1

4

3

7

1

1

1

2

7

1

8

3

17

4

1

1

0

8

1

17

4

23

9

5

4

0

9

1

33

0

0

28

12

9

0

10

1

48

0

0

52

40

22

0

11

1

9

0

0

6

7

7

0

12

1

10

0

0

6

9

9

0


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2.2.

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Reforming Kinetics and Rate Laws

With the reaction network in hand, the next step is to determine the kinetics and rate law expressions. The challenge is that this CCR model comprises over 1400 reactions, each reaction having a pair of the kinetic rate parameters (the pre-exponential factor A and the active energy E). It is impractical to reconcile these rate constant parameters at the same time, and we thus employed a practical approximation to this problem through the use of Linear Freedom Energy Relationship (LFER) in order to reduce the complexity and number of model parameters. The Evans-Polanyi principle12 (Eq. 3) was thus used as a LFER formation in this model. Ei* = E 0* + α ⋅ ∆H rxn

(1)

As a result, the rate constants in a reaction family can be expressed as a function of the enthalpies of reaction, which are easily accessible since they are differences between heats of formation of stable compounds. ln

ki 1 α =− ∆ ( Ei* ) = − (∆H rxn ) k0 RT RT

(2)

E * + α ⋅ ∆H rxn lnk i = ln k 0 − ⋅ (∆H rxn ) = A0 − 0 RT RT

α

From Eq.2, the rate constant for reactions in the same family are a function of A0, E0 ,

α and ∆H rxn . The heats

of formation are the inherent thermodynamics properties of the species calculated based on the Benson method. For each reaction family, there are only three parameters (A0, E0 and

α ) required to be tuned.

As discussed above, the CCR model contained 20 reaction families. To simplify the model calculations, we used same kinetic parameters for the four reaction sub-families for paraffin hydrocracking and paraffin isomerization. The lumped C10-C12 paraffin isomerization used the same kinetic parameters as C9- paraffin isomerization. Moreover, the parameters of dealkylation and side chain cracking on acid and metal sites were considered to be the same. So only nine reaction families were needed to tune this CCR model. This led to a considerable reduction in tuned parameters and tuning complexity. The rate laws were formulated using the Langmuir-Hinshelwood-Hougen-Watson (LHHW) formalism. The general formula is shown as Eq. 3. r=

( Kineticgroup)( Drivingforcegroup)

(3)

( Adsorptiongroup ) n

In Eq. 3, the kinetic group employed the LFER notions described above and the driving force group was determined from the stoichiometry. The denominator of the Eq.3 represents the competitive adsorption for sites of the catalyst.

For instances, consider the following reaction involved with catalyst site [L].


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We used the following rate equation of LHHW (E.q. 4) for the surface limit case.

(4)

Where, Ki (i refer to A, B, C, and D) is the adsorption equilibrium constant for each reactants on L, K is the overall equilibrium constant of the reaction; and kSR is the rate constant of the surface reaction.

The commercial reforming catalyst is a dual site catalyst that contain acid and metal sites, which were differentiated in this model. We therefore used the structure-property correlation for the adsorption constants (Korre et al.)13 shown in Eq. 5 for both metal and acid sites. LnK ads,i = A +

B1 ⋅ N AR + B 2 ⋅ N NR + B3 ⋅ N SC RT

(5)

In Eq. 5, NAR and NNR are the number of aromatic rings and naphthenic rings, respectively, and NSC is the saturated carbons of the side chains or paraffins. Kads,i is adsorption constant, R is the gas constant, and T is the temperature. A, B1, B2 and B3 are the correlation parameters. As a summary, a total of 35 parameters, shown in the Table 3, were used to tune the main reaction model of C12 reforming. Table 3 The parameters used to tune the main reaction model of C12 Reforming LFER

# of parameters

Descriptions of Parameters

Aromatization

3

lgA0,E0,alpha

RingIsomerization

3

lgA0,E0,alpha

AromaticMethylShift

3

lgA0,E0,alpha

Hydrogenolysis

3

lgA0,E0,alpha

DHC

3

lgA0,E0,alpha

ParaffinIsomerization

3

lgA0,E0,alpha

ParaffinHydroCracking

3

lgA0,E0,alpha

Dealkylation/SideChainCracking Acid

3

lgA0,E0,alpha

Dealkylation/SideChainCracking Metal

3

lgA0,E0,alpha

Adsorption on acid site

4

A,B1,B2,B3

Adsorption on metal site

4

A,B1,B2,B3

Sum

35

The pilot plant data comprised 16 data sets with over 35 measurements (Global PIONA and detailed carbon number distributions of PIONA) per experimental set. The model tuning was therefore well supported by the data statistically. . The pilot plant reactor approximated an isothermal ideal plug flow reactor (PFR). The tuning was accomplished using KMT’s KME components. The detailed results are discussed below.


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Application of these tuning results to the commercial reactor required an account of coke formation and the associated deactivation of the catalyst. 2.3.

Coking and Deactivation

The deactivation of the CCR catalyst was described by two steps: 1) coke formation; and 2) the associated change of the catalyst’s activity. A popular formalism for modeling coke formation is to invoke the notion of a coke precursor. The fivemembered cycloalkanes (e.g. cyclopentane) are a sensible choice since they are unable to undergo the full dehydrogenation to an aromatic. Following this idea, coke formation was modeled over the metal sites, where five-membered naphthenic components were dehydrogenated to cylcoalkadienes (e.g., cyclopentadiene). Cylcoalkadienes were converted to coking precursors via Diels-Alder reaction11, 14 , 15 , 16 , 17 and later accumulated to form coke. To reduce the computational burden, we used a simplified lumped scheme to describe coke formation. The Diels-Alder and the dehydrogenation reactions were lumped into one reaction of the coking precursor formation as shown in Eq. 6, where CPi + CPj → CokeP + nH 2

(6)

CPi and CPj are lumped components formed by combing cylcoalkanes isomers with the same carbon number

and CokeP is the coking precursor. Since the aromatization of the cycloalkanes with higher carbon number is very fast, the carbon number of cycloalkanes considered in the coking formation reaction was limited to 8. As a result, 10 coke formation reactions were added into the CCR model by the combination of two lumped cycloalkanes with carbon number ranging from 5 to 8. The rate law of the coking reaction is shown in Eq. 7. Rate = k

y[CPi ] * y[CPj ] * Cat[L2] y[ H 2 ]n

(7)

E + αC # lnk = lnA − 0 RT

The rate law of the coking reaction was thus described as an empirical power law in terms of the concentration of cycloalkanes, hydrogen and metal active site L2, with n being a tunable parameter. In general, the coking rates decreased when the carbon number of reactant increased. The major contribution to coke formation is from cyclopentane because it cannot undergo aromatization after it is dehydrogenated to cyclopentadiene on the metal site. Other cycloalkanes as the reactants in the coking reaction can be isomerized and aromatized to aromatics in the main reforming reactions. In addition, the species with the higher carbon number has better performance in the aromatization and thus has lower possibility to be dehydrogenated and converted to the coke precursor. So, we used a simplified LFER expression to describe the kinetics. All coking reactions were treated as one reaction family and thus shared one lgA parameter. The activation energy (E) was expressed as a linear correlation in term of the average carbon number of reactants.


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The final coke formation chemistry was expressed as a lumped polymerization reaction from CokeP shown in Eq. 8. The rate law was expressed as an empirical power law with two parameters. mCokeP → Coke

(8)

rate = k * y[CokeP]m

As a result, a total of five parameters were used for the coke formation reactions. In this model, two variables CAT [L1] and CAT [L2] were used to represent the the acid and metal catalyst sites, respectively.

The deactivation of the each catalyst site modeled as an adsorption-like reaction as

illustrated in Eq. 9 CokeP + [ L1 ] → CokeP[L1 ]

(9)

CokeP + [ L2 ] → CokeP[L 2 ]

The coking effect for deactivation was the exponential form shown in Eq. 10. r1 = exp(-α 1CokeP)

(10)

r2 = exp(-α 2 CokeP)

The two parameters of Eq. 10 added to the five mentioned above to amount to seven parameters to express the kinetic of coke formation and the associated deactivation effect for both acid and metal catalysts. This limited number of parameters allowed the tuning work of the industrial CCR model O (10) data points including bulk PONA and temperature profile. To do this required the creation of CCR reactor simulation. 2.4.

CCR Reactor Model

The CCR reactor was modeled as the Radius-based Plug Flow Reactor (RFR). The flow of catalyst was from the top of the reactor to the bottom of the reactor following the axial direction. Meanwhile, the flow of the main reactants was from the outside of the reactor to the center of the reactor following the radial direction. Because the flowrate of catalyst is significantly slower than the flow rate of the main reactants, we invoked the “pancake” reactor model shown in Figure 3. This permitted the use of a time-decoupled model to avoid the numerical issues associated with solving PDE equations. As shown in Figure 3, the reactor was divided into a set of pancake sections following the axial direction. The height of each section i was corresponding to a contact time

τ i . The coke level and the activity of the catalyst in each section are constant. The main reactant

flow was divided into a set of sub-reactant at the reactor inlet proportional to the contact time pancake section i.


τ i in each

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Figure3 The pancake model of the CCR reactor. The kinetics of the reforming and deactivation reactions were written in a set of decoupled ODE’s, as shown in Eqs. 10 and 11. dFi dFi = = rA dVi 2πRLi dR Fi = f i ⋅ F Li = f i ⋅ L fi =

(10)

∆τ i

∑ ∆τ

i

rA is the main reforming reaction rate d[L] = - rc dτ i [L]τ i = [L]τ i -1 − rc ⋅ ∆τ i ∆τ i =

πR 2 L V = Qc Qc

(11)

Qc is the flow rate of catalyst rc is the rate of coking reaction in SAM model

Starting from the first section with τ 1 , the catalyst is in fresh status. The reactants in the first section follow the main reforming model shown in Eq. 12. Then the catalyst flow follows the SAM model of Eq. 13. As a result, the coking level and activity of catalyst are updated and transferred to the second section with reactant/product of the second section is estimated by Eq. 12 again.

τ 2 and the

Following this procedure,

reactants/products and the coke profile of all pancake sections are calculated recursively from the top to the end of the reactor.The results of each pancake section are combined together as the product of the reactor outlet.


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The commercial CCR reactors are adiabatic, and the associated energy balance is shown as Eq. 12.

T is reaction temperature. is the rate of the ith reaction.

(12)

is the change of enthalpy in the ith reaction. is the total molar flow rate. is the mixed heat capacity. is the reaction contact time. The thermodynamic properties (heat capacity, the change of reaction enthalpy, etc.) were provided by KMT’s PropGen based on the Benson method.


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3. Model Tuning of Steady State C12 Reforming 3.1 Data preparation and Initial Estimates As noted above, the CCR model tuning was divided into two steps: the tuning of steady-state main reaction model and the deactivation calibration for CCR reactor. To calibrate the kinetic parameters of the main reforming reactions, 16 sets of pilot plant data18 which represented different feedstocks (ranging from highly paraffinic to highly naphthenic) and different operation conditions (Temperature ranging from 470C to 510C, Pressure ranging from 3.45 atm to 6.91 atm) were used. Eight of the 16 datasets were selected to perform the model tuning and the other eight datasets were used to ascertain the predictive capability of the model. Model tuning amounted to minimizing an objective function that compared model and measured values of its termrs. The global PIA (Paraffinic, Isoparaffinic, and Aromatic) weight percent and C5 plus yield were considered first. In addition to the global properties, the carbon number (C5-C12) weight distributions of PINA (Paraffinic, Isoparaffinic, Naphthenic, Aromatic) components were added into the objective function. As the important aromatic products in the reforming process, three isomers of xylene were added into the objective function. In summary, 38 measurements per dataset were put into the objective function and a total of 304 measurements were used to calibrate 35 parameters based on selected eight datasets. 3.2 Results discussion The optimization of kinetic parameters employed a Simulated Annealing (SA)19 algorithm that was integrated into KME. The tuned parameters 20 are not allowed to be exposed by SINOPEC IP protection. The model predictions after tuning are shown in term of the parity plots of Figures 4 and 5. the former for the data used in the calibration and the latter for the data reserved for validation.


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Figure 4 The parity plot of the overall calibrated results for C12 main reaction model.

Figure 5 The parity plot of the overall predictive results for C12 main reaction model. As indicated in the plots, the overall results show that the model fit the experimental data well, which demonstrated that the model has reached the general tuning criteria of a reforming model. The results of detailed PINA carbon number distributions were also considered. Figure 6, for the calibrated results, shows the parity plot of the aromatics including the C6-C12 carbon number distribution and three isomers of xylene. The same information for the predictive results is shown in Figure 7.


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Figure 6 The parity plot of the aromatics calibrated results for C12 main reaction model .

Figure 7 The parity plot of the aromatics predictive results for C12 main reaction model. Aromatics are increasingly important products of reforming, and so the ability to provide a good prediction of their behavior is important in a reforming model. As indicated in the plots, the aromatics predictions fitted the experimental data well, including BTX. Ortho xylene and meta xylene were a bit underestimated for all datasets. The deviations of xylene isomers can be caused by several factors. The aromatic methyl reaction can directly affect the distributions of xylene isomers Moreover, the formation of xylenes is also indirectly affected by DHC, aromatic dealkylation/cracking because three isomers of xylenes can be derived from the products of


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those reactions from the components with the higher carbon number. Therefore, the simplifications of the isomeric details of C11-C12 may also affect the predictions of xylene isomers. Figures 8 and 10 show the parity plots of the iso paraffins and normal paraffins, including the C5-C12 carbon number distribution, for the calibrated results. As the comparison to test the model validity, the parity plot for the predictions of iso paraffins and normal paraffins are shown in Figure 9 and Figure 11.

Figure 8 The parity plot of the iso paraffins calibrated results for C12 main reaction model.

Figure 9 The parity plot of the iso paraffins predictive results for C12 main reaction model.


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Figure 10 The parity plot of the normal paraffins calibrated results for C12 main reaction model .

Figure 11 The parity plot of the normal paraffins predictive results for C12 main reaction model Based on the results shown in Figures 4-11, it is clear that the purely predictive results were similar to the results from calibration. Comparing to the conventional lumped model, this model can obtain a good agreement for the data in a wide range of feed compositions, operating condition (temperature, pressure, etc.) via only one set of tuning parameters. Therefore, this model obtained a stronger predictive capability that is less dependent of the feedstocks and operating conditions than the lumped model.


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4. CCR Reactor Model Calibration and Prediction Typical commercial plant data were selected for CCR reactor simulation. This plant has four CCR reactors. The inlet temperature is the same for all four reactors: 524℃. The inlet pressures of four reactors are 4.56 atm, 4.06 atm, 3.47 atm, and 3.35 atm, respectively. The PONA distribution of the feed in this CCR plant has significant differences with the feeds we used to build the steady state model from pilot plant data. The coking and deactivation of the CCR reactor need to be considered and calibrated. The temperature profile is one key factor for CCR and thus the temperature drops of four reactors were also considered in the objective function. To simplify the computational burden, we only considered the overall product results in the objective function: PINA (Paraffinic, Isoparaffinic, Naphthenic, and Aromatic) weight percent and C5 plus yield. So at total of nine measurements were put into the objective function. Only the kinetic parameters of the coke formation and deactivation were considered as adjustable factors. As a summary, there are nine measurements and seven parameters for this CCR model’s calibration. Figure 10 shows the parity plot for the overall calibrated results including C5 plus yield and the global weight percents of PIONA. Figure 12 shows the parity plot for temperature drop of the four reactors.

Figure 12 The parity plot of overall predictive results for C12 CCR commercial plant.


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Figure 13 The parity plot of temperature drops of four reactors for C12 CCR commercial plant. Inspection of Figures 12 and 13 reveals that the overall product results and the temperature profile among four reactors show a good agreement with the experimental data.. To validate the model’s predictive capability, the results of the C6-C12 carbon number distribution for PINA are shown as a parity plot in Figure 14.

Figure 14 The parity plot of the predictive carbon number distribution of PINA for C12 CCR commercial plant. As shown in Figure 14, the predictive results of the carbon number distribution for PINA have a good agreement with experimental data without calibration. This supports the validity of the kinetic parameters


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obtained from tuning the steady state model and suggests that the kinetic parameters obtained for the main reforming are intrinsic kinetic parameters of reforming catalyst and can be used to model commercial plants.


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5. Summary A molecule-based kinetic model was developed for continuous catalytic reforming.

A detailed reaction

network and molecular compositions was built using the KMT software tool INGen, based on the bondelectron matrix representation. The model consisted of 447 molecular species up to C12 and 1469 reactions with 20 reaction families. The isomers of C9 minus molecules in PONA classes were fully explored. In addition, the isomers of aromatics were fully discovered up to C10. LFER correlations were used to constrain the number of kinetic parameters to a practical level. Model tuning was divided into two steps. The first step was to obtain the intrinsic kinetic parameters of main reforming reactions from pilot data. Eight datasets (ranging from different feedstocks and reaction conditions) were selected to tune the model. Another eight datasets served as the predictive comparison. A total of 464 measurements were calibrated via 35 kinetic parameters in the model. From the parity plots of the model tuned results, it was demonstrated that the overall measurements were fit well. The model obtained a good prediction for aromatic, normal paraffinic and iso-paraffinic products. The predictive results were similar to the calibrated results, which shows this C12 CCR model has a strong simulation capability that is less dependent of the different feedstocks and reaction conditions. This model can support to describe species up to C12, so it provides an flexible extensibility to handle heavier naphtha feeds in selected refining cases. Due to the limitation of the computation and GC analysis, we simplify the species of C11-C12 to a lump scheme. It may lead some deviations of the predictions in some selected molecules. (e.g. xylene isomers). But it can be improved when we enhance our capability of GC analysis and computation.

The second step of model tuning was to obtain the kinetic parameters of coking formation and deactivation. Using a pancake methodology, a four-bed CCR commercial plant was effectively simulated. The kinetic parameters achieved from the first step were directly used in this calibration model, where seven parameters were used to calibrate the temperature profile and the overall PONA of CCR products. The model fit experimental data well. The predictive results of the carbon number distribution of PINA show a good agreement with experimental results and verify that the intrinsic parameters of catalyst obtained from the pilot plant data are applicable to industrial plant. With such a set of kinetic parameters of a reforming catalyst, the industrial CCR plant can be calibrated with limited deactivation parameters and the model can provide good predictive molecular product results (e.g. the carbon number distribution of PINA). Therefore, predictions and evaluations made by the model can be effectively used for CCR operations in order to meet the rigorous product specifications. In addition, the molecular level modeling approach provides more fundamental details to model the reforming process and enhances the extensibility of the model application.


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6. Acknowledgement Michael T. Klein acknowledges collaborations with and support of colleagues via the Saudi Aramco Chair Program at KFUMP and Saudi Aramco.


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6 Broadbelt LJ, Stark SM, Klein MT (1996) Comput Chem Eng 20(2):113–129 7 Joshi PV (1998) Molecular and mechanistic modeling of complex process chemistries. Ph.D Dissertation, University of Delaware 8 Gang H (2001) Integrated chemical engineering tools for the building, solution, and delivery of detailed kinetic models and their industrial applications. Doctoral Dissertation, University of Delaware 9 Bennett C (2010) User-controlled kinetic network generation with INGen. Doctoral Dissertation, Rutgers University 10 Hou Z (2011) , Software tools for molecule-based kinetic modeling of complex systems, Ph.D Dissertation, Rutgers University, New Jersey

11 Anderson, J. R., Y-F. Chang, and R. J. Western. "Retained and desorbed products from reaction of 1-hexene over H-ZSM5 zeolite: routes to coke precursors." Journal of Catalysis 118.2 (1989): 466-482. 12 M. G. Evans and M. Polanyi Trans. Faraday Soc., 1938,34, 11-24

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18 The Department of Reforming Process, Research Institute of Petroleum Processing (RIPP), SINOPEC 19 http://www.ingber.com/#ASA


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20 Hou,Z, Klein, M.T. “Detailed Kinetic Model of RIPP C12 Continuous Catalytic Reforming”, University of Delaware Energy Institute(UDEI). A technical project report for Research Institute of Petroleum Processing (RIPP), SINOPC. 2016


Molecular-Level Kinetic Model for C12 Continuous Catalytic Reforming

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