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Dynamic modeling and simulation of heavy paraffin dehydrogenation reactor for selective olefin production in linear alkyl benzene production plant
Applied Catalysis A: General 358 (2009) 1–6
Contents lists available at ScienceDirect
Applied Catalysis A: General journal homepage: www.elsevier.com/locate/apcata
Dynamic modeling and simulation of heavy paraffin dehydrogenation reactor for selective olefin production in linear alkyl benzene production plant G. Zahedi a,*, H. Yaqubi a, M. Ba-Shammakh b a b
Simulation and Artificial Intelligence Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran Chemical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 August 2008 Received in revised form 30 December 2008 Accepted 5 January 2009 Available online 11 February 2009
Modeling of a heterogeneous industrial fixed bed reactor for selective dehydrogenation of heavy paraffin with Pt–Sn–Al2O3 catalyst has been the subject of current study. Using mass balance, momentum balance for appropriate element of reactor and applying pressure drop, rate and deactivation equations, a detailed model of the reactor has been obtained. Mass balance equations have been used for five different components. In order to investigate reactor performance in time, the reactor model which is a set of partial differential equations (PDEs), ordinary differential equations and algebraic equations has been solved numerically. Variation of paraffins, olefins, dienes, aromatics and hydrogen mole percent as a function of time and reactor radius have been found by numerical solution of the model. Modeling results have been compared with an industrial reactor data at different operating times. The comparison successfully confirms validity of the proposed model. It was found that at radius of 0.75–0.68 m of catalytic bed, there is not sensible conversion of paraffin as surface reaction is rate determining. At entrance of the bed variation of diene was near zero. Hydrogen production was high at entrance of the catalytic bed. Hydrogen production decreases along the reactor bed because of olefin cracking. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Linear alkyl benzene Paraffin Dehydrogenation Fixed bed reactor Modeling
1. Introduction Paraffin dehydrogenation for olefins production has been used since the late 1930s. During World War II, catalytic dehydrogenation of butanes over a chromia-alumina catalyst was practiced for butanes’ production, which were then dimerized to octane and hydrogenated to yield high-octane aviation fuel. A different approach to catalytic dehydrogenation was first introduced in the mid-1960s for the supply of long-chain linear olefins for the production of biodegradable detergents [1,2]. Synthetic detergents, based on the use of branched alkyl benzene sulfonates, had been introduced in the 1940s [1,3]. In 1960s, it was found that branched dodecyl-benzene-based detergents do not biodegrade readily and are accumulating in the environment. The need for biodegradable detergents prompted the development of catalytic dehydrogenation of long-chain linear paraffins to linear olefins. This was the basis for the PacolTM process for production of linear olefins to manufacture biodegradable detergents. In 2006, there were more than 35 commercial Pt-catalyzed dehydrogenation units in operation for the manufactures of
* Corresponding author. Fax: +98 831 4274542. E-mail address: [email protected] (G. Zahedi). 0926-860X/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.apcata.2009.01.043
detergent alkylate. Linear alkyl benzene (LAB) is a major compound for production of biodegradable detergents. Bisetoon Petrochemical Complex (BPC) which is located in Kermanshah-Iran is the first industrial scale LAB plant build by IFP (French) company. This plant processes n-paraffin C10–C13 [4]. Paraffin dehydrogenation unit is the major unit at this plant and dehydrogenation reactor is the heart of dehydrogenation unit. The main reaction in dehydrogenation reactor is formation of mono-olefin (desirable product) from the corresponding feed paraffin. Other reactions include consecutive and side reactions. The catalyst rapidly deactivates due to fouling by heavy carbonaceous materials (Fig. 1) [4–12]. In order to optimize process strategies and to outline the most promising experimental approaches to increase the yield of the product mathematical modeling of the reactor is necessary. Recently, dynamic modeling of chemical reactors has gained more and more attention, to describe start-up and shut-down phenomena, transient operation as well as catalyst deactivation. Dynamic models are needed to optimize operation cycles of fixed beds, where the catalyst is subject to deactivation and the deactivation effects cannot be decoupled from intrinsic kinetics. No work on modeling of heavy paraffin dehydrogenation reactor has been carried out for IFC plant which was commercialized in BPC for first time.
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
2
Table 1 Feed specifications of dehydrogenation reactor [4].
Nomenclature catalyst activity ac gas concentration of component i (mol/m3) Ci density of catalyst loading dload diameter of catalyst particle (m) DP activation energy (J/mol) EA deactivation energy (J/mol) Ed f friction factor F molar flow rate (mol/s) reaction rate constant, i = 1–3 (mol Pa2/(h g cat)) ki reaction rate constant, i = 4 (mol Pa/(h g cat)) ki equilibrium constant for step i Ki KP, KO, KD, KA, KH adsorption equilibrium frequency factor (h1) kd0 L height of reactor (m) N degree of catalyst activity mole of component i (mol) Ni P pressure of feed (Pa) Pi partial pressure of component i (Pa) R radius of reactor (m) rate of reaction i (mol/(g cat s)) ri hydraulic radius (m) RH t time (s) T temperature (K) u velocity of feed (m/s) W weight of catalyst (kg) r density of feed (kg/m3) e porosity factor m viscosity (kg/(m s)) u = W/F (g cat h)/mol paraffin feed Subscripts P paraffin O olefin D diene A aromatic LP light paraffin H hydrogen
In the remaining sections of the paper, first BPC dehydrogenation reactor is described. Section 3 is devoted for auxiliary equations which will be necessary for solving process model. Process modeling has been described in Section 4. Numerical routines for solving the reactor model are described in Section 5. Finally, results and validation of the proposed model are given in Section 6. 2. Process description 2.1. Bisetoon petrochemical complex dehydrogenation unit Fig. 2 represents schematic of BPC dehydrogenation unit. The dehydrogenation unit is designed to produce mono-olefin, to feed hydrogen fluoride alkylation unit for LAB production [13]. As the dehydrogenation conversion is low, the non-reactant paraffins are
Temperature (K) Pressure (bar) Mass flow rate (kg/h) Molar flow rate (kmol/h)
Input
Output
738.15 2.7 55,698 2131
723.15 2.5 55,698 2161
Table 2 Dehydrogenation catalyst specification [4]. Type
DP-805
Supplier Catalyst + characteristics Loading method Shape Average diameter Sock loading Loaded quantity
Procatalyse Alumina impregnated with platinum and tin Sock Spherical 2.24 mm 420–450 kg/m3 3.33 m3/reactor
recycled from the alkylation unit. This process consists of a radialflow reactor (dehydrogenation reactor), selective hydrogenation and product recovery sections [13]. The unit is designed for processing of 61,394 kg/h of fresh feed, and 55,922 kg/h of recycled n-paraffins. LAB is the final product of whole complex (50,000 tons/year of LAB) [13]. 2.2. Dehydrogenation reactor IFPs catalytic dehydrogenation process typically makes use of radial flow adiabatic fixed-bed reactors with modified Pt-alumina catalysts. Feed flow consists of heavy paraffin and hydrogen (hydrogen/hydrocarbon = 6/1) [1,13]. Gas flow is distributed at top of the reactor by a diffuser between external cylinder and packed bed. Feed flow from external cylinder is directed to center of internal cylinder and hence there is not axial velocity inside the packed bed. Paraffin dehydrogenation occurs in annulus between radius equals to 0.75 m to radius equals to 0.4 m [13]. Fig. 3 depicts schematic of BPC dehydrogenation reactor. Longchain paraffins are both valuable and highly prone to cracking. Therefore, in order to maintain high selectivity and yield, it is necessary to operate at relatively mild conditions, typically below 500 8C, and at relatively lower conversions [13]. Feed specifications have been shown in Table 1. Table 2 represents catalyst characterizations. 3. Review of reaction kinetic 3.1. Kinetic model Literatures on kinetics of higher paraffin dehydrogenation are very limited. Studies by Krylova et al. were based on H2–D2 exchange and dehydrogenation of n-decane for Pt/Al2O3 and other promoted systems [14]. Stepwise dehydrogenation involving desorption of olefins and di-olefins as the rate determining step was proposed by Krylova et al. [14]. However no mechanistic models were proposed. Initially the rate expressions were derived for Pt–Li–W/Al2O3 catalyst and later extended to Pt–Sn/Al2O3 catalyst as well [14]. Chaudhuri et al. studied the kinetics of ndodecane dehydrogenation on promoted platinum catalyst and
Fig. 1. Catalyst deaactivation sequence.
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
3
Fig. 2. IFPs catalytic dehydrogenation process [13].
Partial pressure of component i can be expressed as below: Pi ¼ Z C i RT;
i ¼ P; O; D; A; H
(9)
Kinetic parameters have been obtained from Ref. [3] are described as following: 75 0:2 k1 ¼ 3:25 1015 exp (10) RT
Fig. 3. The catalytic dehydrogenation reactor of IFPs process with radial feed flow [13].
derived the reaction kinetics and reaction scheme using Langmuir– Hinshelwood–Hougen–Watson (LHHW) mechanistic model (with surface reaction as rate-determining step) [3]. In their study they have considered the following four equations as dominant reactions in dehydrogenation reactor: First reaction (r1): dehydrogenation, olefins formation: C12 H26 $ C12 H24 þ H2
(1)
Second reaction (r2): secondary dehydrogenation, diens formation: C12 H24 $ C12 H22 þ H2
29:5 0:0001 k1 ¼ 3:65 103 exp RT
(11)
81:9 0:021 k2 ¼ 8:60 1015 exp RT
(12)
38:8 0:22 k2 ¼ 1:28 103 exp RT
(13)
111:2 0:12 k3 ¼ 2:49 1018 exp RT
(14)
288:4 3:2 k4 ¼ 3:55 1013 exp RT
(15)
K P ¼ 2:78 102 exp
(3)
K O ¼ 2:78 exp
(4)
25:2 0:1 K H ¼ 3:04 102 exp RT
Third reaction (r3): aromatics formation: C12 H22 $ C12 H18 þ 2H2 Forth reaction (r4): olefins cracking: C12 H24 þ 2H2 $ < C11 Hydrocarbons
Reaction rates are expressed in terms of partial pressure of the components [3] as r1 ¼
r2 ¼
r3 ¼
k1 K P ð pP pO pH =K E1 Þ ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2 k2 K O ð pO pD pH =K E2 Þ ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2 k3 K D pD ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2
r 4 ¼ k 4 p0
25:4 0:061 RT
(2)
(5)
K D ¼ 7:6 exp
61:3 0:34 RT
78:3 0:48 RT
78:0 3:06 K A ¼ 8:2 102 exp RT
(16)
(17)
(18)
(19)
(20)
(6) 3.2. Catalyst activity (7) (8)
Mansourpour et al. studied catalyst deactivation in heavy paraffin dehydrogenation reactor [2]. ac is the activity of dehydrogenation catalyst which varies with time and gives the
4
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
Table 3 Catalyst deactivation parameters [2].
f ¼ 5
EA Ed N kd0
1.5 10 J/mol 3.9 104 J/mol 1.78 0.0019 h1
RH ¼
reactor unsteady state behavior. Kinetic parameters were obtained by utilizing a least square nonlinear curve fitting of industrial time–temperature trajectory data in 470–495 8C. They have used curve fitting toolbox in Matlab software and have been employed LSCURVEFIT command in this toolbox to obtain deactivation parameters based on nonlinear curve fitting. Considering industrial scale of the reactor the following deactivation rate was selected to use in current research [2]: t¼
ð1 eÞ2
1 ac ð1nEd =EA Þ kd0 ð1 n þ Ed =EA Þ
4. Mathematical modeling In order to derive macroscopic transport equations knowledge about fluid flow between the pellets is necessary. As the real fluid flows inside the packed bed, especially at the high flow rates, it is impossible to rigorously derive equations for the macroscopic quantities starting from known equations of change [15,16]. Therefore, instead of the averaging of fundamental equations and various simplifying assumptions, which are needed to make the problem mathematically tractable, we will first simplify the problem. In this case a physically acceptable transport model on a scale comparable to the size of the pellets and according to the concepts of the kinetic theory of gases will be built. This model can be considered as a microscopic model of dispersion process in a packed bed [16]. The model has been used based on the following assumptions: 1. Molecular diffusion is negligible compared to bulk flow [3]. 2. Thermal effects are negligible due to low conversion of the paraffin [4]. 3. Velocity change occurs in radial coordinate because of gas radial flow [1,4]. 4. Pressure drop occurs in radial coordinate [13]. 5. Surface reaction is the rate determining step [3].
Based on the pre-mentioned assumptions, the momentum balance for gas phase can be written as Bernoulli equation [16]: (22)
Where dhfs is [15]: u2 dr 2 RH
cross section available for flow e ¼ wetted perimeter a
a ¼ ð1 eÞ
6 DP
(24)
(25)
(26)
Pressure in Bernoulli equation is calculated using Peng– Robinson equation of state [17,18]. Continuity equation:
@r þ urr ¼ rðruÞ @t
(27)
@r r @u r ¼ u @r r @r
(28)
4.2. Mass balance Based on the assumptions, the differential equations for the molar balance of the gas phase are as below [16]:
@C P C P u u @C P C P @u ¼ þ þ dload ac ðr 1 Þ þ re e @r e @r @t
(29)
Eq. (6) can be rewritten for each reaction component: Molar balance for olefin:
@C O C O u u @C O C O @u ¼ þ þ dload ac ðr 1 r 2 r 4 Þ þ re @t e @r e @r
(30)
Molar balance for diene:
@C D C D u u @C D C D @u ¼ þ þ dload ac ðr 2 r 3 Þ þ re @t e @r e @r
(31)
Molar balance for aromatics:
@C A C A u u @C A C A @u ¼ þ þ dload ac ðr 3 Þ þ re @t e @r e @r
(32)
Molar balance for light paraffins:
@C LP C LP u u @C LP C LP @u ¼ þ þ dload ac ðr 4 Þ þ re @t e @r e @r
(33)
Molar balance for hydrogen:
@C H C H u u @C H C H @u ¼ þ þ dload ac ðr 1 þ r 2 þ 2r 3 2r 4 Þ þ re @t e @r e @r
(34)
CP, CO, CD, CA, CLP and CH are density of paraffin, olefin, diene, aromatic, light paraffin and hydrogen, respectively. dload is catalyst load density.
4.1. Momentum balance
dh fs ¼ f
75 ðD p ru=mÞ
Based on assumptions, Eq. (27) simplifies to Eq. (28) as (21)
Table 3 shows values for Eq. (21) constants. Chaudhuri’s kinetic model has been derived from n-dodecane dehydrogenation on a commercial catalyst based on promoted Pt– Sn/Al2O3. Catalyst deactivation has not been studied in Chaudhuri’s work [3]. In this paper rates of reaction have been obtained from Chaudhuri’s work and deactivation has been used as is from Mansourpour’s study [2].
2 dP u þ dh fs ¼ 0 þd r 2
e3
(23)
5. Numerical solution Eqs. (22) and (29)–(34) were solved simultaneously. The PVT calculation with equation of state has been carried out separately. The numerical method of line was implemented for solution of the equations [19]. The method of lines is a general technique for solving partial differential equations by typically using finite difference method for spatial derivatives and ordinary differential equations. All programmings were carried out in MATLAB 7.4 [20]. The 4th order Runge–Kutta approach was used to solve integrals [19,20].
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
5
Fig. 4. Superficial velocity profile for fixed bed reactor with radial feed flow. Fig. 7. Olefin molar percent.
Fig. 5. Pressure drop for fixed bed reactor with radial feed flow.
6. Results and discussion Fig. 8. 3D diene molar percent.
Fig. 4 shows variation of fluid superficial velocity across the reactor bed. Noting Fig. 5, reactor pressure drop is 0.23 bar. Industrial value for this case is 0.225 bar which is near to model anticipations and confirms model prediction. At start of process, reactor feed temperature is 738.15 K and at end of the run is 748.15 K. Because of coke formation on catalyst active sites, activity of catalyst decreases hence feed temperature should be increased. By increasing temperature, effects of catalyst deactivation can be abated, but dehydrogenation reaction leads to olefin (desirable product) dehydrogenation and coke formation. Hence there is limitation in temperature increasing at the end of the run. Results of simulation have been presented in Figs. 6–11. Fig. 6 illustrates variation of dynamic paraffin molar percent along the reactor. According to Fig. 6, paraffin molar percent increases with time. This is because of catalyst deactivation which decreases conversion and consequently amount of paraffin. At radius between 0.75 and 0.68 (catalytic bed), there is not sensible conversion of paraffin because of surface reaction as rate determining step [3,6].
Fig. 7 illustrates olefin molar percent along catalytic fixed bed. As it is obvious variation of paraffin percentage is initially low. Olefin percentage increases linearly along the catalytic bed while reaches a constant value because of secondary dehydrogenation reaction and other side reactions. Olefin production decreases in time because of catalyst deactivation. Fig. 8 illustrates dynamic dienes molar percent as a function of reactor bed. Diene generation rate is smaller than olefins due to presence of catalyst promoter (Sn). Variation of dienes percentage is near zero, at entrance of the bed. This is because of presence of dehydrogenation catalyst promoter and low percentage of olefin in the feed. Dienes percentage decreases with time as a result of coke formation. Diene concentration is near zero; hence by increasing diene concentration, the aromatics are generated by dehydrogenation of dienes. Fig. 9 illustrates aromatics molar percent along catalytic fixed bed. According to Fig. 9 aromatic molar percent decreases to r = 0.5 while experience a minimum in this point. For r > 0.5
Fig. 6. Paraffin molar percent as a function of time and reactor radius.
Fig. 9. Dynamic aromatics molar percent along the reactor bed.
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
6
Table 4 Comparison of simulation results and industrial data [13].
Start of run After 10 days After 16 days After 22 days After 28 days
%Paraffin (exp)
%Paraffin (sim)
%Olefin (exp)
%Olefin (sim)
%Hydrogen (exp)
%Hydrogen (sim)
11.99 12.09 12.18 12.22 12.29
11.77 11.87 11.94 11.99 12.03
1.22 1.09 1.02 0.98 0.92
1.35 1.29 1.25 1.21 1.19
81.57 81.55 81.54 81.53 81.52
81.59 81.58 81.58 81.57 81.57
7. Conclusions A dynamic industrial model of dehydrogenation reactor in LAB plant has been presented in this study. Momentum balance, mass balance and energy balance have been applies to an element of reactor to build reactor model. The kinetic of reactions were used as is from Ref. [3]. The proposed model has been solved numerically using method of line. Variations of paraffin, olefin, diene, hydrogen and aromatics with time and reactor radius have been investigated in this paper. The model has been validated with a typical plant industrial data. Noting acceptable accuracy of the proposed model, the model will be implemented for dynamic optimization of the plant as a future work [21]. Fig. 10. 3D representation of light paraffin’s molar percent.
References
Fig. 11. Hydrogen molar percent as a function of time and reactor radius.
aromatic molar percent increases but this rate of increase is not as sharp as 0.5 > r > 0. By cracking of the heavy paraffin’s, light paraffin’s are generated. Higher temperatures accelerate this reaction. Cock formation which causes catalyst deactivation is another accelerating factor of heavy paraffin cracking. Fig. 10 depicts light paraffin generation. Minimum hydrogen production occurs at r = 0.51. For r > 0.6 paraffin production reaches steady and constant value. Fig. 11 illustrates hydrogen molar percent along catalytic fixed bed. Concentration of hydrogen is a function of dehydrogenation rate and cracking rate of olefin to light paraffin. Hydrogen production is high at the entrance of the catalytic bed. Hydrogen generation decreases toward the end of the reactor because of olefin cracking. Hydrogen generation is affected by catalyst deactivation and decreases. Table 4 provides comparison of plant data and modeling results at different operating time. As it is obvious there is good agreement between model estimations and industrial data.
[1] M.M. Bhasin, J.H. McCain, B.V. Vora, T. Imai, P.R. Pujado´, Applied Catalysis A: General 221 (2001) 397–419. [2] Z. Mansourpour, S. Sahebdelfar, M. Saeedizad, 11th National Chemical Engineering Congress, Tarbiat Modarres University, Tehran, Iran, December, 2006. [3] G. Padmavathi, K.K. Chaudhuri, D. Rajeshwer, G.S. Rao, K.R. Krishnamurthyb, P.C. Trivedic, K.K. Hathic, N. Subramanyamc, Chemical Engineering Science 60 (2005) 4119–4129. [4] N. George, B.V. Kamath, A.G. Basrur, K.R. Krishnamurthy, Reaction Kinetics and Catalysis Letters 60 (2) (2005) 379–386. [5] D.T. Gokak, A.G. Basrur, D. Rajeswar, G.S. Rao, K.R. Krishnamurthy, Reaction Kinetics and Catalysis Letters 59 (2) (1996) 315–323. [6] N.A. Gaidai, S.L. Kiperman, Kinetics and Catalysis, vol. 42, no. 4, 2001, pp. 527–532 (Translated from Kinetika i Kataliz, vol. 42, no. 4, 2001, pp. 581–587), Original Russian Text Copyrightß 2001 by Gaidai, Kiperman. [7] M. Bowker, T. Aslam, M. Roebuck, M. Moser, Applied Catalysis A: General 257 (2004) 57–65. [8] K. Surla, H. Vleeming, D. Guillaume, P. Galtier, Chemical Engineering Science 59 (2004) 4773–4779. [9] I. Miracca, L. Piovesan, Catalysis Today 52 (1999) 259–269. [10] C. Audo, J.F. Lambert, M. Che, B. Didillon, Catalysis Today 65 (2001) 157–162. [11] K. Kumbilieva, N.A. Gaidai, N.V. Nekrasov, L. Petrov, A.L. Lapidus, Chemical Engineering Journal 120 (2006) 25–32. [12] F. Sandelin, P. Oinas, T. Salmia, J. Paloniemi, H. Haario, Chemical Engineering Science 61 (2006) 4528–4539. [13] Bistoon Petrochemical Complex, IFP Manual, 2007. [14] T.L. Krylova, N.V. Nekrasov, B.S. Gudkov, V.R. Gurevich, S.L. Kiperman, Kinet. Catal. (Engl. Transl.) vol/issue: 21:6; translated from Kinet. Katal.; 21, no. 6, 1482–1486 (November 1980). [15] M. Mohagheghi, Gh. Bakeri, M. Saeedizad, National Petrochemical Congress, Tehran, Iran, 2006. [16] R.B. Bird, W.E. Stewart, E.N. Lightfoot, Transport Phenomena, second edition, John Wily Press, New York, 2002. [17] J.O. Valderrama, Industrial and Engineering Chemistry Research 42 (2003) 1603– 1618. [18] R.H. Perry, D.W. Green, Perry’s Chemical Engineer’s Handbook, McGraw Hill Press, 1999. [19] S. McDonald, Finite Difference Approximation for Linear Stochastic Partial Differential Equations with Method of Lines, Social and Information Systems Laboratory, California Institute of Technology, 2006. [20] MATLAB, the Language of Technical Computing, Version 7.4.0.246 (R14), August 2, 2005, License Number: 161051, Copyright 1984–2007, the Math Works, Inc. [21] G. Zahedi, A. Elkamel, A. Lohi, Energy and Fuels 27 (2007) 2977–2983.
Contents lists available at ScienceDirect
Applied Catalysis A: General journal homepage: www.elsevier.com/locate/apcata
Dynamic modeling and simulation of heavy paraffin dehydrogenation reactor for selective olefin production in linear alkyl benzene production plant G. Zahedi a,*, H. Yaqubi a, M. Ba-Shammakh b a b
Simulation and Artificial Intelligence Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran Chemical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 August 2008 Received in revised form 30 December 2008 Accepted 5 January 2009 Available online 11 February 2009
Modeling of a heterogeneous industrial fixed bed reactor for selective dehydrogenation of heavy paraffin with Pt–Sn–Al2O3 catalyst has been the subject of current study. Using mass balance, momentum balance for appropriate element of reactor and applying pressure drop, rate and deactivation equations, a detailed model of the reactor has been obtained. Mass balance equations have been used for five different components. In order to investigate reactor performance in time, the reactor model which is a set of partial differential equations (PDEs), ordinary differential equations and algebraic equations has been solved numerically. Variation of paraffins, olefins, dienes, aromatics and hydrogen mole percent as a function of time and reactor radius have been found by numerical solution of the model. Modeling results have been compared with an industrial reactor data at different operating times. The comparison successfully confirms validity of the proposed model. It was found that at radius of 0.75–0.68 m of catalytic bed, there is not sensible conversion of paraffin as surface reaction is rate determining. At entrance of the bed variation of diene was near zero. Hydrogen production was high at entrance of the catalytic bed. Hydrogen production decreases along the reactor bed because of olefin cracking. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Linear alkyl benzene Paraffin Dehydrogenation Fixed bed reactor Modeling
1. Introduction Paraffin dehydrogenation for olefins production has been used since the late 1930s. During World War II, catalytic dehydrogenation of butanes over a chromia-alumina catalyst was practiced for butanes’ production, which were then dimerized to octane and hydrogenated to yield high-octane aviation fuel. A different approach to catalytic dehydrogenation was first introduced in the mid-1960s for the supply of long-chain linear olefins for the production of biodegradable detergents [1,2]. Synthetic detergents, based on the use of branched alkyl benzene sulfonates, had been introduced in the 1940s [1,3]. In 1960s, it was found that branched dodecyl-benzene-based detergents do not biodegrade readily and are accumulating in the environment. The need for biodegradable detergents prompted the development of catalytic dehydrogenation of long-chain linear paraffins to linear olefins. This was the basis for the PacolTM process for production of linear olefins to manufacture biodegradable detergents. In 2006, there were more than 35 commercial Pt-catalyzed dehydrogenation units in operation for the manufactures of
* Corresponding author. Fax: +98 831 4274542. E-mail address: [email protected] (G. Zahedi). 0926-860X/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.apcata.2009.01.043
detergent alkylate. Linear alkyl benzene (LAB) is a major compound for production of biodegradable detergents. Bisetoon Petrochemical Complex (BPC) which is located in Kermanshah-Iran is the first industrial scale LAB plant build by IFP (French) company. This plant processes n-paraffin C10–C13 [4]. Paraffin dehydrogenation unit is the major unit at this plant and dehydrogenation reactor is the heart of dehydrogenation unit. The main reaction in dehydrogenation reactor is formation of mono-olefin (desirable product) from the corresponding feed paraffin. Other reactions include consecutive and side reactions. The catalyst rapidly deactivates due to fouling by heavy carbonaceous materials (Fig. 1) [4–12]. In order to optimize process strategies and to outline the most promising experimental approaches to increase the yield of the product mathematical modeling of the reactor is necessary. Recently, dynamic modeling of chemical reactors has gained more and more attention, to describe start-up and shut-down phenomena, transient operation as well as catalyst deactivation. Dynamic models are needed to optimize operation cycles of fixed beds, where the catalyst is subject to deactivation and the deactivation effects cannot be decoupled from intrinsic kinetics. No work on modeling of heavy paraffin dehydrogenation reactor has been carried out for IFC plant which was commercialized in BPC for first time.
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
2
Table 1 Feed specifications of dehydrogenation reactor [4].
Nomenclature catalyst activity ac gas concentration of component i (mol/m3) Ci density of catalyst loading dload diameter of catalyst particle (m) DP activation energy (J/mol) EA deactivation energy (J/mol) Ed f friction factor F molar flow rate (mol/s) reaction rate constant, i = 1–3 (mol Pa2/(h g cat)) ki reaction rate constant, i = 4 (mol Pa/(h g cat)) ki equilibrium constant for step i Ki KP, KO, KD, KA, KH adsorption equilibrium frequency factor (h1) kd0 L height of reactor (m) N degree of catalyst activity mole of component i (mol) Ni P pressure of feed (Pa) Pi partial pressure of component i (Pa) R radius of reactor (m) rate of reaction i (mol/(g cat s)) ri hydraulic radius (m) RH t time (s) T temperature (K) u velocity of feed (m/s) W weight of catalyst (kg) r density of feed (kg/m3) e porosity factor m viscosity (kg/(m s)) u = W/F (g cat h)/mol paraffin feed Subscripts P paraffin O olefin D diene A aromatic LP light paraffin H hydrogen
In the remaining sections of the paper, first BPC dehydrogenation reactor is described. Section 3 is devoted for auxiliary equations which will be necessary for solving process model. Process modeling has been described in Section 4. Numerical routines for solving the reactor model are described in Section 5. Finally, results and validation of the proposed model are given in Section 6. 2. Process description 2.1. Bisetoon petrochemical complex dehydrogenation unit Fig. 2 represents schematic of BPC dehydrogenation unit. The dehydrogenation unit is designed to produce mono-olefin, to feed hydrogen fluoride alkylation unit for LAB production [13]. As the dehydrogenation conversion is low, the non-reactant paraffins are
Temperature (K) Pressure (bar) Mass flow rate (kg/h) Molar flow rate (kmol/h)
Input
Output
738.15 2.7 55,698 2131
723.15 2.5 55,698 2161
Table 2 Dehydrogenation catalyst specification [4]. Type
DP-805
Supplier Catalyst + characteristics Loading method Shape Average diameter Sock loading Loaded quantity
Procatalyse Alumina impregnated with platinum and tin Sock Spherical 2.24 mm 420–450 kg/m3 3.33 m3/reactor
recycled from the alkylation unit. This process consists of a radialflow reactor (dehydrogenation reactor), selective hydrogenation and product recovery sections [13]. The unit is designed for processing of 61,394 kg/h of fresh feed, and 55,922 kg/h of recycled n-paraffins. LAB is the final product of whole complex (50,000 tons/year of LAB) [13]. 2.2. Dehydrogenation reactor IFPs catalytic dehydrogenation process typically makes use of radial flow adiabatic fixed-bed reactors with modified Pt-alumina catalysts. Feed flow consists of heavy paraffin and hydrogen (hydrogen/hydrocarbon = 6/1) [1,13]. Gas flow is distributed at top of the reactor by a diffuser between external cylinder and packed bed. Feed flow from external cylinder is directed to center of internal cylinder and hence there is not axial velocity inside the packed bed. Paraffin dehydrogenation occurs in annulus between radius equals to 0.75 m to radius equals to 0.4 m [13]. Fig. 3 depicts schematic of BPC dehydrogenation reactor. Longchain paraffins are both valuable and highly prone to cracking. Therefore, in order to maintain high selectivity and yield, it is necessary to operate at relatively mild conditions, typically below 500 8C, and at relatively lower conversions [13]. Feed specifications have been shown in Table 1. Table 2 represents catalyst characterizations. 3. Review of reaction kinetic 3.1. Kinetic model Literatures on kinetics of higher paraffin dehydrogenation are very limited. Studies by Krylova et al. were based on H2–D2 exchange and dehydrogenation of n-decane for Pt/Al2O3 and other promoted systems [14]. Stepwise dehydrogenation involving desorption of olefins and di-olefins as the rate determining step was proposed by Krylova et al. [14]. However no mechanistic models were proposed. Initially the rate expressions were derived for Pt–Li–W/Al2O3 catalyst and later extended to Pt–Sn/Al2O3 catalyst as well [14]. Chaudhuri et al. studied the kinetics of ndodecane dehydrogenation on promoted platinum catalyst and
Fig. 1. Catalyst deaactivation sequence.
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3
Fig. 2. IFPs catalytic dehydrogenation process [13].
Partial pressure of component i can be expressed as below: Pi ¼ Z C i RT;
i ¼ P; O; D; A; H
(9)
Kinetic parameters have been obtained from Ref. [3] are described as following: 75 0:2 k1 ¼ 3:25 1015 exp (10) RT
Fig. 3. The catalytic dehydrogenation reactor of IFPs process with radial feed flow [13].
derived the reaction kinetics and reaction scheme using Langmuir– Hinshelwood–Hougen–Watson (LHHW) mechanistic model (with surface reaction as rate-determining step) [3]. In their study they have considered the following four equations as dominant reactions in dehydrogenation reactor: First reaction (r1): dehydrogenation, olefins formation: C12 H26 $ C12 H24 þ H2
(1)
Second reaction (r2): secondary dehydrogenation, diens formation: C12 H24 $ C12 H22 þ H2
29:5 0:0001 k1 ¼ 3:65 103 exp RT
(11)
81:9 0:021 k2 ¼ 8:60 1015 exp RT
(12)
38:8 0:22 k2 ¼ 1:28 103 exp RT
(13)
111:2 0:12 k3 ¼ 2:49 1018 exp RT
(14)
288:4 3:2 k4 ¼ 3:55 1013 exp RT
(15)
K P ¼ 2:78 102 exp
(3)
K O ¼ 2:78 exp
(4)
25:2 0:1 K H ¼ 3:04 102 exp RT
Third reaction (r3): aromatics formation: C12 H22 $ C12 H18 þ 2H2 Forth reaction (r4): olefins cracking: C12 H24 þ 2H2 $ < C11 Hydrocarbons
Reaction rates are expressed in terms of partial pressure of the components [3] as r1 ¼
r2 ¼
r3 ¼
k1 K P ð pP pO pH =K E1 Þ ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2 k2 K O ð pO pD pH =K E2 Þ ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2 k3 K D pD ð1 þ K P pP þ K O pO þ K D pD þ K A pA þ K H pH Þ2
r 4 ¼ k 4 p0
25:4 0:061 RT
(2)
(5)
K D ¼ 7:6 exp
61:3 0:34 RT
78:3 0:48 RT
78:0 3:06 K A ¼ 8:2 102 exp RT
(16)
(17)
(18)
(19)
(20)
(6) 3.2. Catalyst activity (7) (8)
Mansourpour et al. studied catalyst deactivation in heavy paraffin dehydrogenation reactor [2]. ac is the activity of dehydrogenation catalyst which varies with time and gives the
4
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Table 3 Catalyst deactivation parameters [2].
f ¼ 5
EA Ed N kd0
1.5 10 J/mol 3.9 104 J/mol 1.78 0.0019 h1
RH ¼
reactor unsteady state behavior. Kinetic parameters were obtained by utilizing a least square nonlinear curve fitting of industrial time–temperature trajectory data in 470–495 8C. They have used curve fitting toolbox in Matlab software and have been employed LSCURVEFIT command in this toolbox to obtain deactivation parameters based on nonlinear curve fitting. Considering industrial scale of the reactor the following deactivation rate was selected to use in current research [2]: t¼
ð1 eÞ2
1 ac ð1nEd =EA Þ kd0 ð1 n þ Ed =EA Þ
4. Mathematical modeling In order to derive macroscopic transport equations knowledge about fluid flow between the pellets is necessary. As the real fluid flows inside the packed bed, especially at the high flow rates, it is impossible to rigorously derive equations for the macroscopic quantities starting from known equations of change [15,16]. Therefore, instead of the averaging of fundamental equations and various simplifying assumptions, which are needed to make the problem mathematically tractable, we will first simplify the problem. In this case a physically acceptable transport model on a scale comparable to the size of the pellets and according to the concepts of the kinetic theory of gases will be built. This model can be considered as a microscopic model of dispersion process in a packed bed [16]. The model has been used based on the following assumptions: 1. Molecular diffusion is negligible compared to bulk flow [3]. 2. Thermal effects are negligible due to low conversion of the paraffin [4]. 3. Velocity change occurs in radial coordinate because of gas radial flow [1,4]. 4. Pressure drop occurs in radial coordinate [13]. 5. Surface reaction is the rate determining step [3].
Based on the pre-mentioned assumptions, the momentum balance for gas phase can be written as Bernoulli equation [16]: (22)
Where dhfs is [15]: u2 dr 2 RH
cross section available for flow e ¼ wetted perimeter a
a ¼ ð1 eÞ
6 DP
(24)
(25)
(26)
Pressure in Bernoulli equation is calculated using Peng– Robinson equation of state [17,18]. Continuity equation:
@r þ urr ¼ rðruÞ @t
(27)
@r r @u r ¼ u @r r @r
(28)
4.2. Mass balance Based on the assumptions, the differential equations for the molar balance of the gas phase are as below [16]:
@C P C P u u @C P C P @u ¼ þ þ dload ac ðr 1 Þ þ re e @r e @r @t
(29)
Eq. (6) can be rewritten for each reaction component: Molar balance for olefin:
@C O C O u u @C O C O @u ¼ þ þ dload ac ðr 1 r 2 r 4 Þ þ re @t e @r e @r
(30)
Molar balance for diene:
@C D C D u u @C D C D @u ¼ þ þ dload ac ðr 2 r 3 Þ þ re @t e @r e @r
(31)
Molar balance for aromatics:
@C A C A u u @C A C A @u ¼ þ þ dload ac ðr 3 Þ þ re @t e @r e @r
(32)
Molar balance for light paraffins:
@C LP C LP u u @C LP C LP @u ¼ þ þ dload ac ðr 4 Þ þ re @t e @r e @r
(33)
Molar balance for hydrogen:
@C H C H u u @C H C H @u ¼ þ þ dload ac ðr 1 þ r 2 þ 2r 3 2r 4 Þ þ re @t e @r e @r
(34)
CP, CO, CD, CA, CLP and CH are density of paraffin, olefin, diene, aromatic, light paraffin and hydrogen, respectively. dload is catalyst load density.
4.1. Momentum balance
dh fs ¼ f
75 ðD p ru=mÞ
Based on assumptions, Eq. (27) simplifies to Eq. (28) as (21)
Table 3 shows values for Eq. (21) constants. Chaudhuri’s kinetic model has been derived from n-dodecane dehydrogenation on a commercial catalyst based on promoted Pt– Sn/Al2O3. Catalyst deactivation has not been studied in Chaudhuri’s work [3]. In this paper rates of reaction have been obtained from Chaudhuri’s work and deactivation has been used as is from Mansourpour’s study [2].
2 dP u þ dh fs ¼ 0 þd r 2
e3
(23)
5. Numerical solution Eqs. (22) and (29)–(34) were solved simultaneously. The PVT calculation with equation of state has been carried out separately. The numerical method of line was implemented for solution of the equations [19]. The method of lines is a general technique for solving partial differential equations by typically using finite difference method for spatial derivatives and ordinary differential equations. All programmings were carried out in MATLAB 7.4 [20]. The 4th order Runge–Kutta approach was used to solve integrals [19,20].
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
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Fig. 4. Superficial velocity profile for fixed bed reactor with radial feed flow. Fig. 7. Olefin molar percent.
Fig. 5. Pressure drop for fixed bed reactor with radial feed flow.
6. Results and discussion Fig. 8. 3D diene molar percent.
Fig. 4 shows variation of fluid superficial velocity across the reactor bed. Noting Fig. 5, reactor pressure drop is 0.23 bar. Industrial value for this case is 0.225 bar which is near to model anticipations and confirms model prediction. At start of process, reactor feed temperature is 738.15 K and at end of the run is 748.15 K. Because of coke formation on catalyst active sites, activity of catalyst decreases hence feed temperature should be increased. By increasing temperature, effects of catalyst deactivation can be abated, but dehydrogenation reaction leads to olefin (desirable product) dehydrogenation and coke formation. Hence there is limitation in temperature increasing at the end of the run. Results of simulation have been presented in Figs. 6–11. Fig. 6 illustrates variation of dynamic paraffin molar percent along the reactor. According to Fig. 6, paraffin molar percent increases with time. This is because of catalyst deactivation which decreases conversion and consequently amount of paraffin. At radius between 0.75 and 0.68 (catalytic bed), there is not sensible conversion of paraffin because of surface reaction as rate determining step [3,6].
Fig. 7 illustrates olefin molar percent along catalytic fixed bed. As it is obvious variation of paraffin percentage is initially low. Olefin percentage increases linearly along the catalytic bed while reaches a constant value because of secondary dehydrogenation reaction and other side reactions. Olefin production decreases in time because of catalyst deactivation. Fig. 8 illustrates dynamic dienes molar percent as a function of reactor bed. Diene generation rate is smaller than olefins due to presence of catalyst promoter (Sn). Variation of dienes percentage is near zero, at entrance of the bed. This is because of presence of dehydrogenation catalyst promoter and low percentage of olefin in the feed. Dienes percentage decreases with time as a result of coke formation. Diene concentration is near zero; hence by increasing diene concentration, the aromatics are generated by dehydrogenation of dienes. Fig. 9 illustrates aromatics molar percent along catalytic fixed bed. According to Fig. 9 aromatic molar percent decreases to r = 0.5 while experience a minimum in this point. For r > 0.5
Fig. 6. Paraffin molar percent as a function of time and reactor radius.
Fig. 9. Dynamic aromatics molar percent along the reactor bed.
G. Zahedi et al. / Applied Catalysis A: General 358 (2009) 1–6
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Table 4 Comparison of simulation results and industrial data [13].
Start of run After 10 days After 16 days After 22 days After 28 days
%Paraffin (exp)
%Paraffin (sim)
%Olefin (exp)
%Olefin (sim)
%Hydrogen (exp)
%Hydrogen (sim)
11.99 12.09 12.18 12.22 12.29
11.77 11.87 11.94 11.99 12.03
1.22 1.09 1.02 0.98 0.92
1.35 1.29 1.25 1.21 1.19
81.57 81.55 81.54 81.53 81.52
81.59 81.58 81.58 81.57 81.57
7. Conclusions A dynamic industrial model of dehydrogenation reactor in LAB plant has been presented in this study. Momentum balance, mass balance and energy balance have been applies to an element of reactor to build reactor model. The kinetic of reactions were used as is from Ref. [3]. The proposed model has been solved numerically using method of line. Variations of paraffin, olefin, diene, hydrogen and aromatics with time and reactor radius have been investigated in this paper. The model has been validated with a typical plant industrial data. Noting acceptable accuracy of the proposed model, the model will be implemented for dynamic optimization of the plant as a future work [21]. Fig. 10. 3D representation of light paraffin’s molar percent.
References
Fig. 11. Hydrogen molar percent as a function of time and reactor radius.
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