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Kinetic Studies and Operating Strategies for an Industrial Selective Hydrogenation Process Wei Wu,*,† Yu-Lu Li,† Wen-Shing Chen,† and Chung-Chen Lai‡ Department of Chemical and Materials Engineering, National Yunlin UniVersity of Science and Technology, Douliou, Yunlin 64002, Taiwan, R.O.C, and Refining & Manufacturing Research Center, Chinese Petroleum Corporation, Chia-Yi 60036, Taiwan, R.O.C.
An industrial selective hydrogenation process for methylacetylene (MA) and propadiene (PD), consisting of two hydrogenation reactors, a separator drum, and a recycle design, is investigated. Based on the Langmuir-Hinshelwood/Hougen-Watson kinetic model and the on-site plant data, the system identification is accomplished by using nonlinear regression technique. Through dynamic simulation of each reactor, the effect of catalyst deactivation by green oil and the degree of vaporization are verified. To reduce the impurity of C3-cut stream and improve the catalyst deactivation, the feasible operating manner depends on the determination of H2/MAPD molar ratios at each reactor and recycle ratio. 1. Introduction In recent years, the extreme increased price in crude oil due to politics and overconsumption has induced a significant increase in the price of oil products such as propylene. For many naphtha-processing plants, propylene (C3-cut) was produced by fluid catalytic cracker (FCC) units. The C3-cut stream typically contains more than 90% propylene and up to 6% methylacetylene (MA) and propadiene (PD). To improve the yield as well as purity in the propylene stream, a MAPD converter is usually required. The MAPD removal mechanism is often performed in the gas-phase or liquid-phase selective hydrogenation process. The gas-phase selective hydrogenation process performed well in the early naphtha cracker process,1 but the use of the liquidphase selective hydrogenation process has gradually increased instead in current petrochemical processing plants due to operating cost, catalyst life, and competitiveness. By a wide survey on the relevant selective hydrogenation processes, the modeling and operating conditions of selective hydrogenation of acetylene in industrial fixed-bed reactors have been investigated by Szukiewicz et al.,2 Gobbo et al.,3 and Mostoufi et al.,4 the kinetic model of selective hydrogenation of pyrolysis gasoline has been introduced by Zhou et al.,5 and the kinetic models for a class of the gas-phase MAPD hydrogenation reactions have been validated via different experimental apparatus6-8 where the corrected kinetic model in terms of deactivating agent formation has been verified recently.9 In addition, the kinetic modeling and deactivation for the specific selective hydrogenation process has been investigated by Thakar et al.,10 and the kinetic modeling of an experiment-scale liquid-phase selective hydrogenation of MAPD was explored by Uygur et al.11 For industrial hydrogenation processes, the study for the MAPD removal in the liquid phase reactor process is quite rare, and the new strategy development is seldom seen in academic papers or public reports. In this article, an industrial MAPD converter shown in Figure 1 is considered. The kinetic model, operating strategy, and dynamic simulation are addressed sequentially. According to * To whom correspondence should be addressed. E-mail: weiwu@ yuntech.edu.tw. † National Yunlin University of Science and Technology. ‡ Chinese Petroleum Corporation.
the on-site sampling data from a naphtha-processing plant in Taiwan, the massive kinetic parameters are identified offline via the nonlinear regression technique. Through dynamic simulation of nonlinear regression model for each reactor, we found that the catalyst slowly deactivated due to the deactivating agent formed by oligomerization side reactions, and a little vapor appeared in one of reactors. In general, the operation for the high MAPD conversion can easily achieve the specification of process operation. When the low temperature operation is considered, the liquid phase process can effectively reduce the effect of catalyst deactivation due to green oil. In our approach, the feasible operating condition depends on the MAPD conversion, propylene (PP) selectivity, and operating temperature, which are affected by H2/MAPD molar ratios at each reactor and recycle ratio. 2. Process Description According to the structure of a MAPD converter shown in Figure 2, the C3-cut liquid stream mixed with hydrogen gas flow
Figure 1. The running on-site MAPD converter process.
10.1021/ie101183c 2011 American Chemical Society Published on Web 08/24/2010
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Figure 2. The running scheme of the MAPD converter process.
(i) The propylene formation reaction:
Table 1. Feedstock and Operating Conditions propylene (wt %) propadiene (wt %) methylacetylene (wt %) other components (wt %) temperature (K) pressure (bar) H2/MAPD recycle ratio (Rm) Tref 1 (K) Tref 2 (K)
85-93 0.5-2.5 0.8-3.5 1-13.4 294-330 2.4-2.6 1.5-2 1-5 320-350 300-325
is fed into the top of the first reactor (I). Since the MAPD selective hydrogenation reaction is exothermic, the outlet temperature from the bottom of the reactor is higher than the inlet temperature. Through two heat exchangers and a vapor-liquid separator drum, one of the outlet streams goes back to add in a fresh C3-cut stream, and another stream mixed with hydrogen gas flow is fed into the top of the second reactor (II). Notably, the liquid recycle stream may facilitate the selective hydrogenation reaction to carry out the liquid phase operation. In addition, the conventional ratio control structure is used to manipulate the amount of H2 flow and the recycle stream under the process specification. For this industrial reactor, the volume is about 60 m3; space velocity is between 500 and 1000 h-1. The inlet conditions for each reactor are listed in Table 1. Currently, the outlet concentration of MAPD at the first reactor is operated at 3000 ( 1000 ppm, and the outlet concentration of MAPD at the second reactor is close to 600 ppm. From the system engineering point of view, the improvement of product yield or reduction of the impurity of C3-cut stream is expected. In our approach, a kinetic study and reactor modeling will be addressed first. 2.1. Kinetic Model. It is assumed that the selective hydrogenation reactions including the propylene formation reaction and the formation of oligomers (green oil) are shown as follows.
rMA
rPP MA rPD 8 propylene 98 propane + H2 9 PD 98 H2
(1)
(ii) The oligomer formation reaction: rMA-C
rMA-C
6
9
2MA + H2 98 C6 + MA 98 C9 H2
H2
rPD-C
H2
H2
(2)
rPD-C
6
9
2PD + H2 98 C6 + PD 98 C9 Whereas the Langmuir-Hinshelwood/Hougen-Watson (LHHW) type models for catalytic hydrogenation reactions have been widely used,5,6,9,11,12 the following rate equations for hydrogenation and oligomerization are mainly based on the specific kinetic modeling:9 [MA][H2] 0 rMA ) kMAKMAKH2
A
[PD][H2] 0 rPD
) kPDKPDKH2
[PP] Kcat-PD
A
[PP][H2] 0 ) kPPKPPKH2 rPP
[PN] Kcat-PP
A 2 2 [MA] [H 2] 0 ) k rMA-C MA-C 6 6 B 2 2 [PD] [H 2] 0 ) k rPD-C PD-C6 6 B [MA][H2][C6] 0 rMA-C9 ) kPD-C9 B [PD][H2][C6] 0 rPD-C9 ) kPD-C9 B where
[PP] Kcat-MA
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A ) [1 + (KH2[H2])1/2 + KMA[MA] + KPD[PD] +
Table 2. Parameters for Modeling
KPP[PP] + KPN[PN]]3 and B ) [1 + (KH2[H2])1/2 + KMA[MA] + KPD[PD] + KC6[C6] + KC9[C9]]3 and where the brackets [ ] represents the concentration of each component. The reaction rate constant and equilibrium constant are formulated, respectively,
( ) ( )
-Ea,i , i ∈ Nk RT Eb,j Kj ) K0j exp , j ∈ NK RT
ki ) k0i exp
(4)
where Nk ) {MA, PD, PP, MA-C6, PD-C6, MA-C9, PD-C9} and NK ) {cat-MA, cat-PD, cat-PP, H2, MA, PD, PP, PN, C6, C9}. Notably, the bound of unknown parameters can be described by Ωk ) {ki0, Ea,i, Kj0, Eb,j, Rl|i ∈ Nk, j ∈ NK, l ∈ Nl}. The produced oligomers up to C12, called green oil (GO), may
Figure 3. Nonlinear regression for the first reactor.
porosity (ε) bulk density (Fb) solid density (Fs) specific heat of fluid (cpm) specific heat of solid (cps) ∆HMA ∆HPD ∆HPP ∆HMA-C6 ∆HPD-C6 ∆HMA-C9 ∆HPD-C9
0.2-0.5 650-1100 kg m-3 1100-1200 kg m-3 1.5-2.0 kJ kg-1 K-1 0.85-0.9 kJ kg-1 K-1 -164.86 ( 10 kJ mol-1 -171.55 ( 10 kJ mol-1 -124.15 ( 10 kJ mol-1 -110.16 ( 10 kJ mol-1 -120.35 ( 10 kJ mol-1 -70.56 ( 10 kJ mol-1 -95.79 ( 10 kJ mol-1
cover the active site of catalyst to reduce catalyst activation. Although the palladium catalyst deactivation can be observed from some experimental validation,13,14 oligomers on the catalyst surface are almost washed out by a liquid flow in the liquid phase operation. For the gas phase operation,9 the new rate equations need to account for the kinetic model of deactivation, where the rate of formation of the deactivating agent is written by rC ) rC0exp(-RCCC)
(5)
Notably, CC represents the amount of carbon per unit weight of catalyst and the initial rate of formation of the deactivating agent is described by
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∂Tµ ∂Tµ + εuzµFbcpm ) ∂t ∂z rl(-∆Hl)|µ, µ ∈ {I, II}
(11)
[εFbcpm + (1 - ε)Fscps]
∑
l∈Nl
rC ) kC6-C[C6][PP] 0
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3
(6) with the initial and boundary conditions
Thus, the rate equations for the main reaction in the presence of deactivation can be written as rl ) rl0Φl(CC),
l ∈ Nl
(7)
where the deactivation function Φl(CC) } exp(-RlCC), and Nl ){MA, PD, PP, MA-C6, PD-C6, MA-C9, PD-C9, C}. Moreover, the net rates of formation for each component are shown by RMA ) rMA + 2rMA-C6 + rMA-C9 RPD ) rPD + 2rPD-C6 + rPD-C9 RPP ) rMA + rPD - rPP RPN ) rPP RC6 ) rMA-C6 + rPD-C6 - rMA-C9 - rPD-C9 RC9 ) rMA-C9 + rPD-C9 RH2 ) rMA + rPD + rPP + 2rMA-C6 + 2rPD-C6 + rMA-C9 + rPD-C9 RC ) rC
(8) Remark 1. In our approach, most of rate equations depend on the operating temperature and catalyst deactivation. Referring to the kinetic model of deactivation in Wang and Froment9 and assuming a gas-liquid two phase operation, we need to identify the simplified formulation for the main reaction in the presence of deactivation by eqs 6 and 7. The kinetics in the industrial MAPD converter is too complicated to be precisely identified due to strongly nonlinear LHHW models and massive kinetic parameters. Notably, the name of the commercial catalyst is not shown in this article due to some confidential reasons. 2.2. Reactor Model. To study the operating strategy of this MAPD converter, the dynamic modeling for two fixed-bed reactors should be established. For simplicity, some assumptions for the reactor model are given: the fluid is a plug-flow type; there are no radial and axial dispersion; the catalytic phase is ignored; the reactor is adiabatic; there is no pressure drop. On the basis of the above hypothesis, the mass balance of each composition at each reactor is written as ∂Cµs ∂Cµs + εuµz ) ε ∂t ∂z µ Rs (β, Cµs , Tµ), s ∈ NC, µ ∈ {I, II}, β ∈ Ωk
µ Csµ ) Cs,0 ,
at z ) 0 all t,
Csµ
)
µ Cs,f ,
s ∈ NC, µ ∈ {I, II} s ∈ NC, µ ∈ {I, II} (10)
is molar where, NC ){MA, PD, PP, PN, C6, C9, H2, C}. concentration of component s at the prescribed reactor, ε is the bed porosity, and uµz is the bulk velocity of the prescribed reactor. Notably, a cluster of undetermined parameters β represents the previous kinetic parameters. Cµs,f represents the feed concentration of each reactor, and the plant feedstock Cµs,0 is treated as initials. The energy balance of each reactor is shown by Csµ
Tµ ) Tµ,0, Tµ ) Tµ,f,
µ ∈ {I, II} µ ∈ {I, II}
(12)
where Tµ represents the temperature of each reactor and Tµ,f represents the feed temperature of each reactor. Thermodynamic and physical parameters, for example, the specific heat of bulk fluid cpm, bulk density Fb, solid density Fs, specific heat of particle cps, and heat of reaction of reactant ∆Hl, are assumed to be constant if the variation of each reactor temperature is very small. In Tables 1 and 2, the feedstock and operating conditions for each reactor and system parameters are shown with different ranges. Remark 2. By eqs 9-12, the dynamic model for each reactor is a typically distributed parameter system. Since coefficients β are undetermined, they can be treated as adjustable parameters while the system identification approach is utilized. Regarding the on-site plant data, the outlet compositions and temperature for each reactor are collected over 10 months. The pattern of sampling data for each reactor during a prescribed time is depicted in Figures 3 and 4, respectively. Notably, main compositions and temperature at each reactor is normalized and defined by the following symbols Nθ )
[θ] - [θ]min , [θ]max - [θ]min
θ ) MA, PD, PP, PN, GC, C Tµ
@Tµ )
Tµref
,
µ ) I, II
(13) 3. Regression and Validation Since the catalyst deactivation is slower than other reactions in each reactor, the original dynamic reactor system is reduced as a pseudo-steady-state model:
{
j sµ dC j sµ, T j µ) ) Rsµ(β˜ , C dz j dTµ rl(-∆Hl)|µ ) εuzµFbcpm dz l∈N εuzµ
(9)
with the initial and boundary conditions at t ) 0 all z,
Αt t ) 0 all z, Αt z ) 0 all t,
s ∈ N˜C, µ ∈ {I, II}, β˜ ∈ Ω
∑
k
l
dCCµ ε ) RCµ (β˜ , CCµ , dt
∫ Cj z
0
µ PP
dτ)
(14)
where N˜C ) {MA, PD, PP, PN, C6, C9, H2} and β˜ represent a series of adjustable parameters. The minimization algorithm for following objective functions is used to determine the parameter β˜ , Nt
Nt
min Jµ ) ˜ β
∑∑
˜C j)1 s∈N
µ µ 2 j s,j (C - Cs,j ) +
∑ (Tj
u,j
- Tµj )2,
j)1
µ ∈ {I, II}
(15)
µ where Cs,j and Tµj represent the plant data for components and temperature in each reactor at prescribed time point j, respectively. The minimization algorithm is solved by the nonlinear regression, for example, the fourth-order Runge-Kutta method
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Figure 4. Nonlinear regression for the second reactor.
with the Levenberg-Marquardt algorithm which can be executed by using the system identification toolbox inside Matlab. Remark 3. Equation 14 can be treated as nonlinear regression model by tuning the parameter β˜ such that each objective function in eq 15 can be minimized. In our approach, the plant data with 30 samplings, that is, Nt ) 30, are distributed during the time period from 0 to near 3500 h. With the trial and error manner on the selection of undetermined parameters, the performances of nonlinear regression are shown in Figures 3 and 4, respectively. Moreover, the validation is employed by 50 new samplings, which are distributed during the time period from 3500 to near 8000 h, for the same nonlinear regression models. Using the definition of the mean square error (MSE), the identification performances of the pseudo-steady-state model in regard to main components of each reactor are depicted in Figures 5 panels a and b, respectively. Obviously, both nonlinear regression models can ensure the appropriate degree of accuracy. Remark 4. By the collected plant data, the product of PP increases in the second reactor but the corresponding concentration of GO decreases, and the temperatures of each reactor, @TI and @TII, are close to constant. In general, C3-cut stream exists in the liquid phase below 325 K. If the operating temperature is higher than 325 K, then the vapor-liquid two phase operation
may appear in the reactor. The regression model can predict the temperature profile for each reactor and estimate the amount of vapor while TI,TII > 325 K. When the degree of vaporization (DOV) is normalized by @Pµ )
moles of vapor total moles of input
|
I, II
(16)
Figure 6 depicts that the gas (vapor) phase exists in the first reactor and the typically liquid phase operation takes place in the second reactor. For the pseudo-steady-state model with deactivating agent, the concentration of green oil in regard to the effect of catalyst deactivation in each reactor is shown in Figure 7 panels a and b, respectively. Obviously, the catalyst deactivation in the liquid phase operation is very low. Consequently, the pseudo steady-state model is successfully identified owing to the small MSE for the model validation. In addition, the reactor model claims the effect of catalytic deactivation and degree of vaporization for each reactor. 4. Process Operation Since the amount of added hydrogen and the recycle stream can be manipulated according to the control configuration of
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Figure 5. System identification by regression and verification: (a) MSE for the first reactor; (b) MSE for the second reactor.
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Figure 7. The effect of catalyst deactivation for green oil: (a) the first reactor; (b) the second reactor.
With the aid of the coolant device, the reference temperature of the recycle stream Tref is close to the room temperature. In general, the outlet concentration of MAPD should satisfy the specification of process operation, that is, I I 2000 ppm e CMA , CPD e 4000 ppm II II , CPD e 600 ppm CMA
and the process should be kept in the liquid phase operation in order to effectively reduce the effect of catalyst deactivation due to green oil. In other words, the proper operating decision could depend on the MAPD conversion (XMAPD), the propylene selectivity (SPP), and the operating temperatures of each reactor.
Figure 6. Degree of vaporization in each reactor.
the MAPD converter, the feed concentrations of each reactor are affected by molar ratios of H2 to MAPD at each reactor; that is, H2/MAPD|I and H2/MAPD|II, and the recycle ratio Rm, which represents the recycle stream to the inlet stream of the second reactor. In addition, the recycle stream may change the temperature of each reactor. Moreover, the overall mass and energy balances for each reactor are shown by
{
{
RsI(β˜ , CsI, TI)
Fr I (Cˆ - CsI) ) Vr s,f Rm + 1 , s ∈ NC,β˜ ∈ Ωk Fr I II II ˜ II (C - Cs ) ) Rs (β, Cs , TII) Vr s
|
Fr 1 (Tˆ - TI) ) r (-∆Hl) Vr I,f Fbcpm(Rm + 1) l∈N l I
∑
l
|
(17)
Fr 1 (T - TII) ) r (-∆Hl) Vr I Fbcpm l∈N l II
∑
l
(18)
Since the recycle stream is equal to RmFr, where Fr is the volumetric flow rate of C3-cut stream, the mixed feed concentraI I ) Cs,f /(Rm + 1) and the mixed feed tion in the first reactor Cˆs,f temperature in the first reactor TˆI,f ) (Rm + 1)/(RmTref + TI,f).
input moles of MAPD - output moles of MAPD input moles of MAPD output moles of PP - input moles of PP ) input moles of MAPD - output moles of MAPD (19)
XMAPD ) SPP
Moreover, four operating indices for XMAPD, SPP, @TI, and @TII in regard to three input manipulations, H2/MAPD|µ)I, H2/ MAPD|µ)II, and Rm, are evaluated by simulations. Assumed that Rm ) 0.2 is given. Figure 8a shows that the high MAPD conversion is achieved by XMAPD|I,II > 0.98, while H2/MAPD|I,II > 2.5, but the corresponding propylene sensitivity SPP|I,II < 0.6 is shown in Figure 8b. Obviously, the too high H2/MAPD|I,II cannot improve the product yield. Figure 8c shows that the high H2/MAPD|I can increase the temperature of the first reactor, but the corresponding temperature of the second reactor becomes low. In addition, H2/MAPD|II only affects the temperature of the second reactor. To keep the liquid phase operation in the second reactor, the operating manner by high H2/MAPD|I and low H2/MAPD|II can ensure the low temperature of the second reactor, but the temperature of the first reactor tends to lift due to the increase of H2/ MAPD|I.
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Figure 9. Four operating indices are affected by different Rm.
5. Conclusions
Figure 8. Four operating indices are affected by a different H2/MAPD ratio at each reactor.
Assumed that H2/MAPD|I ) 1.5 is given. The large Rm (>2), shown in Figure 9a, can obviously increase the product selectivity SPP|I (>0.55), but the corresponding XMAPD|I decreases a little between 0.986 and 0.989. In Figure 9b, @TI < @TII, while Rm > 0.5. Obviously, the low temperature recycle stream can be used to effectively reduce the temperature of the first reactor. To sum up, the large Rm can be used to reduce the temperature of the first reactor even though the high H2/MAPD|I can improve the MAPD conversion of the first reactor and meanwhile its reactor temperature raises. To satisfy the operating constraints in eq 18 and keep the liquid phase operation, the feasible operating manner need depend on high H2/MAPD|I, low H2/ MAPD|II and large Rm. Referring to the previous control configuration in Figure 2, three control actuators can enforce adjustments in the C3-cut stream, H2 flow, and recycle stream. Moreover, the feasible operating condition is verified by the dynamic simulation while H2/MAPD|I > 2.5, H2/MAPD|II < 1.5, and Rm > 2. Obviously, the outlet concentration of MAPD in the second reactor, shown in Figure 10a, can satisfy the upper limit, but the MAPD conversion, shown in Figure 10b, can almost achieve 99%.
The kinetic modeling, operating strategy, and dynamic simulation with respect to an industrial MAPD converter with its control configuration has been addressed. The merits of our works are summarized: (1) The kinetics based on the LHHW model is identified by nonlinear regression technique. (2) The dynamic modeling of nonisothermal hydrogenation reactor is established. (3) The effect of catalyst deactivation by green oil and the degree of vaporization for each reactor are verified. (4) The effect of catalyst deactivation is quite small due to the almost liquid-phase operation. (5) The feasible operating condition is determined according to the MAPD conversion and the operating temperatures for each reactor. (6) By adjusting H2/ MAPD molar ratios at each reactor and recycle ratio, the outlet concentration of MAPD can satisfy the operating constraints of each reactor. In our approach, the feasible operating condition aims to enhance the MAPD conversion, but the propylene selectivity or product yield cannot be improved due to the conflicting operating manner. In fact, the desired operating manner not only depends on the high MAPD conversion and low operating temperature, but also the propylene selectivity should be effectively improved in the meanwhile. Apparently, the development of optimal operating policies needs to be investigated in the future. Acknowledgment The authors would like to thank the Chinese Petroleum Corporation of the Republic of China for financially supporting this research under Contract No. EEA9715001.
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) rate of each reaction without deactivation, kmol/(kg cat. s) rl ) rate of each reaction with deactivation, kmol/(kg cat. s) Rm ) recycle ratio Rsµ ) net rates of formation for component s at the prescribed reactor, kmol/(kg cat. s) SPP ) propylene selectivity Tµ ) the temperature at prescribed reactor, K Tµ,f ) the feed temperature at prescribed reactor, K Tµ,0 ) the initial temperature at prescribed reactor, K uzµ ) bulk velocity at prescribed reactor, m/h Vr ) reactor volume, m3 XMAPD ) MAPD conversion rl0
Greek Symbols ε ) bed porosity Fb ) bulk density, kg/m3 Fs ) solid density, kg/m3 ∆Hl ) heat of each reaction, kJ/kmol Rl ) deactivation constant for each component Φl ) deactivation function for each component
Literature Cited
Figure 10. The dynamic responses of the second reactor with prescribed operating condition H2/MAPD, Rm.
Nomenclature DOV ) degree of vaporization MA ) methylacetylene PD ) propadiene PP ) propylene PN ) propane GO ) green oil CC ) amount of carbon per unit weight of catalyst, kg C/kg cat. Csµ ) molar concentration of reactant s at prescribed reactor, kmol/m3 µ Cs,f ) the feed concentration at prescribed reactor, kmol/m3 µ Cs0 ) the plant feedstock, kmol/m3 cpm ) specific heat of bulk fluid, kJ/(kg · K) cps ) specific heat of particle, kJ/(kg · K) Eai ) activation energy of component i, kJ/kmol Eb,j ) enthalpy of adsorption of species j, kJ/kmol Fr ) volumetric flow rate, m3/h ki ) reaction rate constant, kmol/(kg cat. s) ki0 ) preexponential factor for each reaction, kmol/(kg cat. s) Kj ) equilibrium constant, m3/kmol Kj0 ) preexponential factor for equilibrium constant, m3/kmol r0C ) rate of formation of the deactivating agent without deactivation, kmol C/(kg cat. s) rC ) rate of formation of the deactivating agent with deactivation, kmol C/(kg cat. s)
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ReceiVed for reView January 21, 2010 ReVised manuscript receiVed July 8, 2010 Accepted July 19, 2010 IE101183C
