Chinese J . Chem. Eng., 14(5) 584-591 (2006)

Modeling, Simulation and Optimization of a Whole Industrial Cata- lytic Naphtha Reforming Process on Aspen Plus Platform*

HOU Weifeng(fEEq), SU Hongye(%%;'$)**, HU Yongyou(H &%) and CHU Jian(##) National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China

Abstract A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat- form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti- mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuanK' averagely, which is close to the calculated result. Keywords catalytic reforming, kinetic model, Aspen plus, computer simulation, process optimization

1 INTRODUCTION Catalytic naphtha reforming is a very important

process for producing high octane gasoline, aromatic feedstock and hydrogen in petroleum-refining and petrochemical industries"]. To design new plants and optimize the existing ones, an appropriate mathema- tical model for simulatin the industrial catalytic re- forming process is needed %,31 .

The naphtha used as catalytic reforming feed- stock is very complex usually consisting of about three hundred hydrocarbons with carbon number ranging from one to twelve, and each of them under- goes various reactions. Thus a detailed kinetic model considering all the components and reactions is too complex. By this reason, attempts have been made to model naphtha by considering groups of components (i.e. kinetic lumps) taking part in reforming reactions. Accordingly, various lumping kinetic models to rep- resent catalytic reforming reactions have been re- ported in the literature, which have different levels of sophi~tication'~-'~'.

In a previous study, we presented a simple lump- ing kinetic model for catalytic reforming with 17 lumps involving only 17 reaction^"^]. However, this model did not subdivide 8-carbon aromatics into their four isomeric compounds, i.e. PX (para-xylene), MX (meta-xylene), OX (ortho-xylene), and EB (ethyl-benzene). When the catalytic reforming process is for producing BTX (benzene, toluene, xylene), the subdivision of 8-carbon aromatics is necessary. In ad- dition, this model supposes that the rate coefficients of hydrocracking reactions of the same paraffin lump are almost equal, which is not consistent with the experi- ment r e s ~ l t s " ~ " ~ ~ . In this article, the 17-lump kinetic

model is extended in order to consider these deficien- cies.

On the other hand, most of the studies were based on their own programs merely for simulating catalytic reforming reaction units, so that the whole reforming process composed of many unit operations could not be simulated. Aspen plus system is one of the standard software for flowsheet simulation in the process industries. It is supported by strong databases, complete sets of modules, and flexible simulation tools. However, some complex chemical reactions, such as catalytic reforming reactions, can not be simulated appropriately on Aspen plus platform by using its built-in modules. Therefore, the purpose of this article is to develop a modified kinetic model for catalytic reforming reactions as a self-defined model and then connect it with Aspen plus as a user module. This technique utilizes the strong databases, comp- lete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the self-defined model. In this way, a whole com- mercial continuous catalytic reforming process is simulated on Aspen plus platform.

Finally, the process is optimized based on the model of the whole reforming process and the opti- mization results are tested in an actual industrial cata- lytic reforming process.

2 18-LUMP KINETIC MODEL The proposed kinetic model is an extension of

the reported 17-lump model[141. Fig.1 displays the re- forming reaction network used for kinetic modeling.

The characteristics and the assumptions made in this work are listed below.

~~~~

Received 2005-08-18, accepted 2006-01-19. * Supported by the National Natural Science Foundation of China (No.60421002).

** To whom correspondence should be addressed. E-mail: hysu@iipc.zju.edu.cn

Modeling, Simulation and Optimi2ation of a Whole Industrial Catalytic Naphtha Reforming procesS on Aspen Plus Plalfonn 585

Figure 1 Reaction scheme for naphtha reforming

(1 ) Because of the wide variation in thermody- namic and kinetic behavior, paraffins, naphthenes, and aromatics (P, N, A) within a carbon number fraction, in c6 to C9 range, are treated separately for the lump- ing scheme.

(2) All paraffins or naphthenes in each carbon number group are not divided but lumped together.

(3) The Cg aromatics are subdivided in detail. For the 8-carbon aromatics, the isomerization reactions between PX, MX, and OX occur so rapidly so as to approach the thermodynamic equilibrium under nor- mal reforming conditions, while isomerization be- tween EB and xylene isomers occurs so slowly that this reaction is ign~red"~'. Hence, in this work the 8-carbon aromatics are subdivided to two lumps, EB and the xylene isomers, and the xylene distribution is also calculated by chemical equilibrium.

(4) Under the reaction conditions, hydrocracking of aromatics and naphthenes to lower carbon number paraffins is almost negligible.

( 5 ) All the hydrocracking reactions of paraffins are subdivided in detail.

On the basis of the above discussion, the chemi- cal reactions included in the proposed kinetic model are presented in Table 1.This model has 18 lumps (not including H2) and 31 reactions. Except for isomeriza- tion, all the main reactions, such as dehydrogenation of naphthenes to aromatics, dehydrocyclization of paraffks to naphthenes, hydrocracking of paraffins,

Table 1 Reactions of the 17-lump kinetic model and the proposed 18-lump one Reactions 17-Lump model This work

dehydrogenation N; +A; N; + A;, i = 6,7,9 + i=6,7,8,9+ N8 +PX+OX+MX

N8 + EB

dehydrocyclization

hy drodealkylation

hy dro-cracking

P; + N; i=6,7,8,9+ A; + A;-1

A,+ + A7

i=7,8,9+

P; + N;

i=6,7,8,9+ A7 $ A6 PX +OX + MX + A, EB + A7 A,+ +PX+OX+MX -49, + EB

A,+ + A7 4

Ps + P; + Ps-i P5+-CP; 1

i=l 2

Pg 3- C P i + P 4 4 f 7 ;=I 1

i=1,2

i=1,2,3

P7 -+ P; + P7-i

i=1,2,3

Pg 3 P; + Pg-;

i=l,2,3,4

P9+ --j P; + P9-i 1 8 4 ;=I

p9+ + - c p ;

i=1,2,3,4

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586 Chinese J. Ch. E. (Vol. 14, NOS)

and hydrodealkylation of aromatics, are included in this model. And the 17-lump kinetic model is also given in Table 1 for comparison.

All the rates of reactions shown in the second col- umn of Table 1 are of nonlinear pseudomonomolecular form. Corresponding rate equations are presented below.

rJ =dYN /dt=k,.(YNz -YA,/Kep,),j=l-5 (1)

r, =dY,/dt=k,.(Y, -YN, l K , ) , j = 6 - 9 (2)

Dehydrogenation

Dehydroc yclization

Hydrodealkylation

Hydrocracking r, = dY,, /dt = k, . Y,, , j = 10 - 15

r, = dYp, /dt = k, .Yp,, j = 16-31

(3)

(4) In Eqs.( 1)-(4), the subscript j represents the re-

actions in sequence in the second column of Table 1; P,, N, and A, represent paraffin, naphthene and aro- matics of the corresponding reaction. The reaction equilibrium constant KepJ can be calculated by the fol- lowing thermodynamic relation,

( 5 ) All the rate coefficients obey the well-known

Kc, = exp (-AG, / R T ) , j = 1 - 9

k, = k o , .exp(-E,/RT).P,' 8 .@ Arrhenius' law,

0 < @ 6 1 , j 4 - 3 1 (6) where @ is the catalyst deactivation function, which varies between 0 and 1, and is used to multiply the

rates of the main reforming reactions calculated with- out deactivation"*'.

Under the normal reformer operating conditions, radial and axial dispersion effects were found to be

For radial flow reactor, the global mate- rial and heat balance equations are given by Eqs.(7) and (8) respectively,

dY/dR = 2xR. H I(LHSV .V,). K , .Y (7) dT I dR = 2xR. H I(LHSV .V,).

j= l

where Y is the vector of molar flow rates including 18 lumps and H2. Eq.(7) is solved by a mixed numerical algorithm of fourth order Runge-Kutta and Gear and Eq.(8) is solved by modified Euler method. The ther- mochemical properties of each lump, such as heat of formation, free energy of formation and specific heat coefficient of ideal gas, etc., are computed by taking an arithmetic average of the properties of the corre- sponding pure chemical components constituting the lump.

3 PLATFROM 3.1 Flowsheet of a whole reforming process

Figure 2 shows the industrial flowsheet of a whole continuous catalytic reforming process to pro- duce aromatics and hydrogen. The naphtha feedstock is mixed with recycle gas and then heated to required

MODEL DEVELOPMENT ON ASPEN PLUS

nit

to LPG system Figure 2 Industrial flowsheet of a whole continuous catalytic reforming process

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Modeling, Simulation and Optimization of a Whole Industrial catalytic Naphtha Reforming Process on Aspen Pius platform 587

reaction temperature by heat exchanger E-1 and heater B-1. Since the major reactions in the reactors are en- dothermic, catalytic reformers are designed with mul- tiple reactors and with heaters between the reactors to maintain reaction temperature at operable levels. Ac- cordingly, the reactant passes in the sequence of heat- ing in heaters B-1 to B-4 and reacting in reactors R-1 to R-4. The effluent from the last reactor R-4 is cooled by heat exchanger E-1 and coolers E-2 to E-3 and then enters the product separator F-1. The majority of flashed vapor, containing 80% to 90% (by mol) hy- drogen, circulates to join the naphtha feedstock as recycle gas. All that mentioned above is the reaction unit of the reforming process, as presented within the thick line of Fig.2.

Excess hydrogen from separator F-1 passes to compressor G-1 and cooler E-4, and enters separator F-2. After through compressor G-2 and pump G-3 respectively, the liquid from separator F-1 is mixed with the vapor from separator F-2. The mixture, com- prised mostly of the desired reformate product but also containing light gases, passes through coolers E-5 to E-6, heat exchangers E-7 to E-10, ammonia cooler E-11 in sequence, and then enters separator F-3. The flashed vapor, containing over 90% hydrogen, passes heat exchangers E-10 and E-8 and is obtained as hy- drogen product. The flashed liquid from F-3 is mixed with the liquid from separator F-2 after through heat exchangers E-9 and E-7. The mixture is then fed into distillation column D- 1 after passing heat exchanger E-12. The distillate is separated into two streams. One is vapor and is sent to the fuel gas system, the other is liquid and is sent to the LPG (liquefied petroleum gas) system. The reformate product, having removed the light gases, is acquired as the bottom from D-1 and fed into distillation column D-2. The mixture of xy- lene, ethyl-benzene, and heavy aromatics is obtained as the bottom product of D-2 and sent to the xylene unit. The distillate containing benzene, toluene, and heavy paraffins is sent to the aromatics extraction unit.

3.2 Design of reforming reaction user module on Aspen plus platform

The modified 18-lump kinetic model is devel- oped as a user module to simulate the reforming reac- tion unit. The steps of design of the user module are described as follows:

(1) The reactor model including the 18-lump ki- netic model is developed as a subroutine in FORTRAN language.

(2) The subroutine is compiled into an object file or a shared library by an appropriate FORTRAN com- piler using given command.

(3) The object file or shared library is copied to the working directory and distributed to Aspen plus users.

(4) A USER module is selected on the Aspen plus

user interface. The name of the subroutine is then written in the page layout of the block input identifi- cations as the name of the user’s model. The subrou- tine will be loaded automatically when the user mod- ule is simulated.

(5) The input parameters, calculation results and important inner information (such as temperature and reformate composition profiles within the four reac- tors) are output in the report file by calling Aspen plus common blocks.

According to these steps, the proposed kinetic model is loaded in the USER module and connected with Aspen plus.

3.3 Modeling of the whole reforming process on Aspen plus platform

For the recycle gas contains 80% to 90% hydro- gen, a little light gas and negligible heavy paraffins, the convergence of all lumps is difficult and unneces- sary. In this work, separator F-1 and the circulation shown within the thick line of Fig. 2 are included in the USER module mentioned in section 3.2 and only the convergence of hydrogen is executed within the user subroutine. The effluent of this module is then defined as the outlet charge of reactor R-4 when the convergence is met. Another USER module only re- peats the flash computation of F- 1.

Except for the reaction unit, comprised of four reactors R-1 to R-4, four heaters B-1 to B-4, and the product separator F-1, is simulated by the above two USER modules, and the other equipment is all repre- sented by the built-in modules of Aspen plus system. The heat exchangers are represented by HEATX mod- ules, the heaters and coolers by HEATER modules, the separators by FLASH2 modules, the pumps by PUMP modules, the compressors by COMPR modules, and the distillation column systems by RADFRAC modules.

The 18 lumps (i.e. pseudocomponents) are ap- plied for all the modules to represent about three hun- dred hydrocarbons flowing through the catalytic re- forming process. The property methods specially used for petroleum systems are selected to compute the thermochemical properties of the reforming system. The GRAYSON method is applied for the reforming reaction unit and separators F-1 to F-3, the CHOA-SEA method for the distillation column sys- tem D-1, and the BKlO method for the distillation column system D-2.

4 RESULTS AND DISCUSSION After the model of the whole reforming process

is developed on Aspen plus platform, the estimation of related parameters is a very important step to simulate the industrial continuous catalytic reforming process. To decrease the difficulty in estimating the kinetic parameters, i.e. the rate constants k in Eq.(l), we take

Chinese J. Ch. E. 14(5) 584 (2006)

588

the value of E and I9 in Eq.(6) from the literature"" and only estimate ko embodying the estimation differ- ence of parameters E, I9 and un-modeling kinetics. The operating and assay data of the industrial process for several months, which are firstly reconciled by mate- rial balance, are used to estimate ko by Marquardt op- timization algorithm. The stage efficiencies of distilla- tion columns D-1 and D-2 are amended by actual stage temperature values.

The developed second simulation software on Aspen plus platform is applied to the commercial con-

Chinese J. Ch. E. (Vol. 14, No.5)

tinuous catalytic reforming process as presented in Fig.2. The simulation results are listed in Table 2. Ta- ble 2 shows that the calculated compositions of the recycle gas stream, the hydrogen product stream, the bottom stream of D-1, and the distillate and bottom streams of D-2 agree well with the operating data. The calculated values of several important operating points, such as liquid yield, aromatics yield, total drop of re- action temperature, catalyst coking content, distillate and bottom temperatures of distillation column, sensi- tive stage temperature, are also close to the operating

Table 2 Comparison between calculated and actual results of chief operation points

Bottoms of D-1 (S3) Distillate of D-2 (S6) Bottoms of D-2 (S7) Recycle gas (SI) Net gas (S2) Item

Calc. Actual Calc. Actual Calc. Actual Calc. Actual Calc. Actual

flowrate, k g C ' 37.82 37.82 16.31 16.31 21.51 21.51 11.69 11.65

compositions

PI (methane)

P2(ethane)

P3(prop=)

p4

ps

p7

P8 p9+

N7

N8 N9 NA

A6(benzene)

A7( toluene) PX

MX

ox EB

'49,

p6

N6

0.00 0.00 0.00 0.00 0.39 6.97 6.54 0.70 0.00 0.34 0.19 0.04 0.00 15.17 7.36 20.75 5.44 11.73 '6.37 3.21 29.97

0.00

0.00 0.00 0.00 0.74 7.33 5.96 0.96 0.00 0.36 0.29 0.03 0.00 15.67 7.18

20.39 5.18 11.17 6.53 3.35 30.53

(by mass) (by volume)

0.00 0.00 0.00 0.00 0.90 16.16 15.16 1.08 0.00 0.79 0.44 0.03 0.00 34.56 17.06 47.75 0.13 0.27 0.14 0.09 0.00

35.96 16.65 46.99 0.08 0.17 0.09 0.06 0.00

0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.41 0.00 0.00 0.00

0.05 0.00 0.46 0.00

0.27 9.46 20.42 11.10 5.58 52.71

0.28 0.00

0.21 9.05 19.51

11.41 5.85 53.69

3.97 3.73 3.13 2.21 0.13 0.04 0.01 0.00

0.00 0.02 0.00

0.00 0.00

0.01 0.01 0.00 0.00 0.00 0.00 0.00

2.95 2.80 2.94 2.06 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00 0.00 0.00 0.00 0.00

0.00

3.49 3.42

(by volume)

3.98 2.03 1.02 0.59 0.18 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00

0.01 0.01 0.00 0.00 0.00 0.00 0.00

3.41 1.87 1.06 0.39 0.03 0.00

0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 H2 0.00 0.00 0.00 0.00 86.74 88.49 92.13 93.24

Operating points Calculated value Actual value

liquid yield, % (by mass) 77.98 77.97

aromatics yield, % (by mass) 66.91 66.50

total drop of reaction temperature, "C 256.97 252.57

catalyst coking content, % (by mass) 5.28 5.6

distillate temperature of D-1, "C 87.67 91.64

bottom temperature of D- 1, "C 219.13 221.29

sensitive stage temperature of D-1, "C 121.84 121.00

distillate temperature of D-2, "C 109.88 107.03

bottom temperature of D-2, 'C 180.91 176.13

sensitive stage temperature of D-1, "C 118.39 116.78

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Modeling, Simulation and Optimization of a Whole Industrial Catalytic Naphtha Refonning procesS on Aspen Plus Platform 589

data. It can be concluded that the modified 18-lump kinetic model and the selected property methods are applicable to the flowsheet simulation of the industrial continuous catalytic reforming process.

Based on the model of the whole reforming proc- ess, the optimization can be realized on Aspen plus platform to improve the process performance and maximize the process profitability. After plentiful sensi- tivity analyses of the process variables, the four reactor inlet temperatures and the reaction pressure are consid- ered as key variables to influence the process perform- ance such as aromatics yield (AY) and energy cost. As the reaction pressure can not be changed obviously due to the given process technology, the four reactor inlet temperatures are then selected as decision variables to maximize the process profit. The optimization problem with process constraints is described as follows,

i a

s.t. Equations of the process model listed in sec- tion 3

Tk d Tk d T y k = 1,2,3,4

C K L d CK d CKu HCL < HC d HCu ECL d EC d ECu (9)

where Yprducti and Y f d i are the molar flow rates of product and feed of each lumps; EC is the energy cost of heaters B-1 to B-4, a=24kg.GJP', pi and pfuel represent the price of each lump and fuel. The process profit f means the return after all operating and mate- rial expenses have been met.

Tk ( k l , --, 4) are the four reactor inlet tempera- tures in sequence. In the sensitivity analyses based on the above process model, it is found that the aromatics yield decreases slowly when the first or second reactor inlet temperature increases over 520"C, and it in-

creases rapidly when the fourth reactor inlet tempera- ture closes to 525°C. Thus the upper and lower limits of the four reactor inlet temperatures (?--F) are further compressed within 5 18-523"C, 5 18-523 "C , 5 15-525 "C and 523-528 "C respectively. Process constraints are mainly catalyst coking content (CK), molar ratio of hydrogen to oil ( H C ) and EC. Their upper limits mainly lie on the loads of regeneration system, compressors and heaters of the catalytic re- forming process.

The SQP (sequential quadratic programming) method is adopted to solve this NLP (non-linear pro- gramming) problem. This method is widely used in industrial process (such as chemical engineering, elec- tric power and metall~rgy)"~'. Because the four deci- sion variables are limited within very narrow ranges, the SQP algorithm is proved to be an effective method and can converge rapidly. The CPU time required for solving the NLP problem on an IBM PC with Pentium 41.7GHz, 256M DDR memory and an ASPEN PLUS 11.1 version software is only about 3 e 1 2 O s depend- ing on the initial values of input variants. Table 3 gives the optimization results of key operation condi- tions to maximize the process profit under specific naphtha feedstock.

As shown in Table 3, the optimized reactor inlet temperatures can bring a potential increase of process profit with 700Yuan.h-', which is mainly gained from the increase of aromatics yield with 0.3% (by mass). Furthermore, the optimized process variables are also tested in the actual industrial continuous catalytic re- forming unit for about two months and the testing re- sults are presented in Table 4. The aromatics yield and process profit increase about 0.49% and 1000Yuan.h-' averagely, which are close to the calculated value listed in Table 3. Therefore, the optimization results obtained by process simulation and optimization are appropriate. The process profit acquired by only changing the four reactor inlet temperatures a little will reach about 8 X lo6 Yuan annually.

Table 3 Calculated optimization results for key process variables

Process variable Constraint value Current value Optimal value Increase

Tl, "C 5188T16523

T2, "C 5 188T28S23

T3. "C 5156T36525

T4, 'C 523 6 T4 8 528

HC, mol.mol-' 64.0

CK, % (by mass) 6 6 . 5

EC, G J 6 ' G275

522.1

521.3

517.5

523.9

3.29

5.28

263.2

521.0 - 1 . 1

520.1 - 1.2

517.5 0

525.7 1.8

3.28 -0.01

5.30 0.02

263.4 0.2

AY, % (by mass) - 66.91 67.2 1 0.30

profitx YuanK' - 2.68 2.75 0.07

Note: Naphtha feed=171793kg.h-'; LHSV=l.O2h-'; latent aromatics=51.9%; catalyst type: F't-Sn/A1203; catalyst distribution, % (by mass):Rl:R2 :R3 :R4=10:15:25:50

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590 Chinese J. Ch. E. (Vol. 14, NOS)

Table 4 Actual testing results of the optimization project

T I , “C T2, “C T3, “C T4. “C LHSV, h- ’ latent aromatics, % (by mass) HC, mol.mo1-’ CK, % (by mass) EC, G J 6 ’ AY, % (by mass)

521.9 521.3 517.6 523.7 1.02 53.2 3.57 5.96 266.0 67.36

Averaged values of 20

- 521.3 520.3 517.8

525.8 1.02 53.1 3.58 6.01 266.4 67.85

.0.6 -1.0 0.2 2.1 0

-0.1 0.01 0.05 0.4 0.49

ProfitX YuanK’ 2.79 2.89 0.10

5 CONCLUSIONS This article proposes a new 18-lump kinetic

model for naphtha catalytic reforming reactions and the model is then developed as a user module on As- pen plus platform. Using the technique of user’s model, a whole industrial continuous catalytic refor- ming process is simulated. When the calculated data are in fair agreement with operating data, the devel- oped second simulation software is used for monitor- ing process performance, troubleshooting, diagnosing faults, optimizing process and process control. In this article, the process is optimized and the optimization results are tested in the actual industrial unit. The testing results show that the process simulation and optimization is appropriate.

NOMENCLATURE AY a CK

E EC AG H HC AH Kep Kr k ko LHSV M P, N, A

PX, MX, OX, EB

CP

Ph

Pfuel

Pi R r T

aromatics yield, % (by mass) cost of fuel oil per energy unit, kg-GJ-’ catalyst coke content, % (by mass) specific heat, Id.kmol-l.K-’ activation energy, W.rnol-’ energy cost of four heaters, GJ.h-’ free energy of reaction, ~d.kmol-’ height of the catalyst bed, m molar ratio of hydrogen to oil heat of reaction, Idknol-’ reaction equilibrium constant matrix for reaction rate coefficients reaction rate coefficient, h-’ frequency factor, s-’.MPa-’ liquid hourly space velocity, h-’ molecular weight, kgkmol-‘ paraffins, naphthenes and aromatics partial pressure of hydrogen, MPa para-xylene, meta-xylene, ortho-xylene and ethyl-benzene price of fuel oil, Yuan.kg-’ price of each lump, Yuamkg-’ radius of catalyst bed, m (or gas constant) reaction rate, kmol.h-’ temperature, K

t reaction time, h V C catalyst volume, m3 Y, Y

e pressure exponent L, U

i j reaction number k reactor number

molar flow rate, kmo1.h-l Superscripts

Subscripts upper and lower limits of variables

carbon atom number or lump number

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