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Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic Liquid Systems
21st European Symposium on Computer Aided Process Engineering – ESCAPE 21 E.N. Pistikopoulos, M.C. Georgiadis and A.C. Kokossis (Editors) © 2011 Elsevier B.V. All rights reserved.
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic Liquid Systems Andreas Peschela, Benjamin Hentschelb, Hannsjörg Freunda, Kai Sundmachera,b a
Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany b Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
Abstract In this work, we apply our recently proposed reactor design methodology based on elementary process functions to the hydroformylation of long chain linear alkenes in a biphasic ionic liquid system with Rh/TPPTS catalyst. A potential selectivity increase towards the linear aldehyde of 35% compared to a reference case from literature is identified and an approximation of the best reaction route is proposed. Keywords: Hydroformylation, Reactors, Optimization, Design, Process Intensification.
1. Introduction Hydroformylation of alkenes is one of the most important homogeneously catalyzed processes. Beside the selectivity the catalyst recovery is crucial for the economics of the hydroformylation process. Due to the limited solubility of long chain alkenes in water the Ruhrchemie-Rhône-Poulenc process cannot be applied. Therefore, several innovative reaction concepts which allow easy catalyst separation such as thermomorphic solvent systems (Behr et al. 2004), micellar solvent systems (Haumann et al. 2004), gas expanded liquids (Jin and Subramaniam 2004), supported ionic liquid systems (Riisager et al. 2003), and biphasic ionic liquid (IL) systems (Brasse et al. 2000) were developed. However, the rigorous model based reactor design for such systems is still missing. In this contribution, we apply our recently proposed reactor design methodology (Peschel et al. 2010) based on elementary process functions (Freund and Sundmacher 2008) to the hydroformylation of long chain linear alkenes in a biphasic ionic liquid system in order to determine the best reaction concept and to derive the best suited technical reactor.
2. Determination of the Optimal Reaction Concept 2.1. Methodological Approach The main idea of the reactor design approach is to balance a fluid element and optimize the fluxes into and out of this element over time (dynamic optimization) to obtain the best process route. In order to develop an optimal reactor based on this idea, a three step approach is pursued. On the first level, the optimal reaction conditions at each point along the reaction coordinate with respect to temperature, H2, CO, and alkene concentration are obtained by optimizing the heat and the component fluxes into the tracked fluid element. On the second level, the influence of limited mass and energy transport is investigated and suitable control variables, which can be varied by the reactor design, are determined. On the third level, a reactor design is proposed based on the profiles of the best control variables. Here, all technical restrictions are taken into account and the reactor model
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic 1247 Liquid Systems accounts for non-idealities, e.g. radial temperature and concentration profiles in a tubular reactor. In this contribution, we focus on the first level, where process intensification measures are screened and the potential of the intensified reaction concepts are identified by comparison to a reference case. 2.2. Model for the Hydroformylation of 1-Octene in an Ionic Liquid Biphasic System The hydroformylation reaction network as shown in Fig. 1 includes the formation of the desired product n-aldehyde and the formation of undesired iso-aldehyde, internal alkenes, alkane, high boiling condensation products and alcohols. + CO + H2
r1
r2
1-octene (C8)
n-nonanal (nC9)
+ CO + H2
+ H2
+ H2
high boilers n-nonanol CO
r3
iso-nonanal (isoC9)
+ H2
+ H2
H2
internal octenes (inC8)
+ CO + H2
octane
iso-nonanol
octane
high boilers
Catalyst
Fig. 1: Reaction network of the hydroformylation with influencing factors for the reaction rates
In addition to the reaction network shown in Fig. 1, the catalyst exists in several states (refer to Fig. 2), where each state has a different activity and selectivity. For the investigated system and the used Rh/TPPTS-ratio, however, only the reactions r1–r3 were observed. HRh(CO)L3 (inactive)
-L +L
HRh(CO)L2 (selective)
+CO -CO
HRh(CO)2L2 (inactive)
-L +L
HRh(CO)2L (unselective)
Fig. 2: Catalyst states (L=TPPTS)
The reaction takes place in the ionic liquid phase ([bmim][PF6]) and the organic (decane) phase supplies the long chain alkene while the gas phase supplies hydrogen and carbon monoxide. The balance equations for the pseudo-homogeneous system are given by Eq. 1 with stoichiometric coefficients according to the reaction network shown in Fig. 1. The fluxes jorg,i are related to the organic phase and combine the flux into the fluid element and the specific exchange area. The reaction rate laws are taken from Sharma et al. 2010. The model is extended by taking the temperature dependency of the reaction rates into account. The activation energy of reaction 1 is taken from Sharma et al. 2010, while the activation energy of the isomerization reaction is obtained by regression from Disser et al. 2005. The activation energy of the formation of isomeric aldehyde is assumed to be the same as for the n-aldehyde formation, which is based on the observations of Bernas et al. 2008 for short chain aldehydes. § Vorg ρ + K i IL ¨¨ V ρ org © IL
NR · dCorg ,i Vorg ji , org + ¦ν ij rj = ¸¸ VIL j =1 ¹ dt
i = C 8, inC 8, nC 9, isoC 9
(1)
The partition coefficients of octene and nonanal between both liquid phases Ki are taken to be mean values of the data presented by Sharma et al. 2010, since the partition coefficients do not change significantly with the composition and a temperature dependency was not investigated. In the temperature and pressure region of interest Henry’s law is considered to be satisfactory to describe the gas solubilities. The Henry parameters are fitted according to the experimental data given by Sharma et al. 2010. A partial pressure of 30 bar is taken as upper boundary for both H2 and CO. The volume
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ratio of both phases, which is obtained from Sharma et al. 2010, and the density of each phase are assumed to be constant. All required modeling data is given in Tab. 1. Tab. 1: Modeling Data Numeric Value ª kJ º EA,1 « ¬ mol »¼
ª § m 3 ·3 º k0,1 «¨ IL ¸ s » «¬© kmol ¹ »¼ ª kg º
107.9 40.65e
18
Numeric Value ª kJ º EA,2 « ¬ mol »¼
ª§ m 3 ·3 º IL k0,2 «¨ ¸ » «¬© kmol ⋅ s ¹ »¼ ª kg º
ρIL « 3 » ¬m ¼
1.32e3
ρorg « 3 » ¬m ¼
KnC9, KisoC9 [-]
1.61e-3
T min − T max [ K ]
−1 ª kmol º H CO «¬ m3 MPa »¼
-2
ª kg º Ccat,IL « 3 » ¬m ¼
4.69e -8.65e-5·T 6.97e-3
ª kJ º EA,3 « ¬ mol »¼
43.6 11.16e
Numeric Value
6
6.86e2 253–373
ª § m 3 ·3 º k0,3 «¨ IL ¸ s » «¬© kmol ¹ »¼ KC8, KinC8 [-] Vorg/VIL [-]
p − p [ MPa ] 1.08e +2.65e-6·T (i=CO, H2) ª kg º ª kg º Ci,in « 3 » (i=inC8, CC8,org,in « 3 » 0 ¬m ¼ ¬m ¼ nC9, isoC9) (ref. case, case 1) −1 ª kmol º H H2 «¬ m3 MPa »¼
-2
min i
max i
107.9 33.51e18 1.00e-2 1.5 0-3
0.973
2.3. Optimization Strategy In order to maximize the selectivity towards the linear aldehyde, the heat, H2, CO and 1octene fluxes are optimized in order to obtain the optimal reaction conditions at each time. As shown in Fig. 3, several combinations optimizing the profiles of a control variable (f(T)) and optimizing the influencing factor as an optimization parameter (DoF) are investigated in order to find the most suitable integration concept and to determine the potential of the intensified reaction concepts. In general, three different intensification methods can be distinguished. First, the composition of the gas phase components can be changed. Second, the composition of the liquid phase components can be influenced. Third, the energy level of the reaction phases, here in form of the temperature, can be manipulated. Of course, influencing the gas phase composition, the liquid phase composition and the temperature at the same time will yield the highest objective function value. However, this reaction concept might be difficult to realize in a technical reactor and hence the simpler combinations of the enhancement options are of interest, too. As reference case, the industrially used reactor, a bubble column, is chosen and optimized using an idealized model. The CO and H2 partial pressures are optimized as DoF, while the temperature and initial 1-octene concentration are obtained from Sharma et al. 2010. It is assumed that the mass and heat transfer is much faster than the reaction and hence the transport kinetics do not have to be considered. In case 1, the gas phase component fluxes and the heat flux are considered as optimization functions. Due to the major influence of temperature and CO partial pressure on the catalyst equilibrium and hence on the selectivity, the optimal heat and CO flux profiles are of great interest. In case 2, the optimal 1-octene feed profile is calculated in addition to the gas phase component fluxes and the heat flux. Here, all states which have an influence on the reaction rates and hence on the selectivity are controlled by the according fluxes. In order to keep the number of investigated cases low, other combinations of the enhancement options are not considered at this point.
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic 1249 Liquid Systems Objective: Maximize n-Aldehyde Selectivity Reference case: T, CC8,org,0 = fix pH2, pCO , τ = DoF
Intensified case 1: CC8,org,0 = fix, τ = DoF q, jCO, jH2 = f(t)
Intensified case 2: CC8,org,0, τ = DoF jC8,org, q, jCO, jH2 = f(t)
Fig. 3: Overview of investigated cases (Decision structure to obtain the best reaction concept)
1
C [kmol/m3org]
0.8
X=0.95 S=0.49
0.6
C8 inC8 nC9 isoC9
0.4
b)
0.2
c) 0.8
C [kmol/m3org]
0.6
1000 2000 3000 Residence time [s]
0.4
24
0.2
22
X=0.95 S=0.76
200 400 600 Residence time [s]
20 800
0.8
0.2
0.7
Reference case Case 1 Case 2 CSTR
0.6 0.5
1000 2000 3000 Residence time [s]
pCO
d) 0.9
C8 X=0.95 inC8 S=0.84 nC9 isoC9
0.4
0 0
0.6
30 C8 inC8 28 nC9 isoC9 26
0 0
4000
Selectivity [-]
0 0
1
0.8 C [kmol/m3org]
a)
pCO [bar]
2.4. Results The concentration profiles for a fixed conversion of 95% and the selectivity depending on the conversion are shown in Fig. 4. For all three cases, the temperature and H2 partial pressure are constant (and hence not shown) at the upper boundary. This leads to a constant H2 concentration in the IL, however, the H2 flux depends on the reaction rate and is not constant. In the reference case, the optimal CO partial pressure depends on the conversion. In case 1, a distinctive CO partial pressure profile exists; the CO partial pressure increases for decreasing 1-octene concentrations. In case 2, the octene concentration and CO partial pressure profiles are constant at their lower and upper value, respectively. Since all states influencing the reaction rates r1 and r2 are constant and the 1-octene concentration is at its lower boundary, this reaction concept can be realized in a completely back mixed system (CSTR). In order to validate this result, a CSTR model was optimized and it yields the same selectivities and operation conditions as in case 2 (refer to Fig. 4d).
4000
0.4 0
0.2
0.4 0.6 Conversion [-]
0.8
1
Fig. 4: Concentration profiles: a) reference case, b) case 1, c) case 2. d) S-X diagram for different cases.
In all investigated cases, the amount of provided 1-octene (either given by the initial 1octene concetration or by dosing additional 1-octene) is constant. The lower limit of the 1-octene concentration is defined by the 1-octene outlet concentration depending on the conversion. It turns out that the selectivity increases for increasing conversions (refer to Fig. 4d). This can be explained by the rising differential selectivity at lower 1-octene concentrations.
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3. Conclusion and Discussion The presented results indicate that the selectivity in the hydroformylation of long chain alkenes can be increased by 35% by applying the our new rigorous reactor design approach (Peschel et al. 2010). In order to obtain such a selectivity increase, constant 1octene, H2, CO, and temperature profiles are required. Based on these profiles a reactor concept is derived. Here, these profiles can be obtained using an ideally back mixed system. An optimization of a CSTR model is not necessary, since this case is already included. However, it should be noted that in case of consecutive reactions, the profiles might be qualitatively different, and a CSTR might not be an adequate approximation in case consecutive reactions are taken into account. The presented model exploits the temperature dependency of the hydroformylation reaction system, and different operation conditions as used by Sharma et al. 2010 are recommended. In case 1, it was shown that an optimal CO partial pressure profile exists for the hydroformylation process. The results for H2 and CO partial pressures indicate that the experiments were not performed in the optimal partial pressures ranges. The H2 partial pressure can be increased further until alkane or alcohol formation is observed. The CO partial pressure can be increased until CO blocks the catalyst according to the catalyst equilibrium (refer to Fig. 2) and hence reduces the reaction rate significantly. The high observed partial pressures of CO might be explained by the low CO solubility in the used IL compared to the CO solubility in common organic solvents.
4. Outlook In order to complete the reactor design task for the hydroformylation system, level 2 and level 3 of the applied design approach still need to be performed. On level 2, the mass and energy transport kinetics are included to determine the exchange areas, and consequently the type of equipment that is required. On level 3, an optimal technical reactor is derived which is optimized taking all technical constraints into account. In addition, the model will be extended by the consecutive reactions in order to check whether a back mixed reactor is still optimal for the complete reaction network.
5. Acknowledgements Financial support by the Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged (TRR 63).
References A. Behr, D. Obst, C. Schulte, 2004, Chem. Ing. Tech., 76, 904–910. A Bernas, P. Mäki-Arvela, J. Lehtonen, T. Salmi, D. Y. Murzin, 2008, Ind. Eng. Chem. Res., 47, 4317–4324. C. C. Brasse, U. Englert, A. Salzer, H. Waffenschmidt, P. Wasserscheid, 2000, Organometallics, 19, 3818–3823. C. Disser, C. Muennich, G. Luft, 2005, Appl. Catal. A - Gen, 296, 201–208. H. Freund, K. Sundmacher, 2008, Chem. Eng. Process., 47, 2051–2060. M. Haumann, H. Koch, P. Hugo, R. Schomäcker, 2002, Appl. Catal. A - Gen., 225, 239–249. H. Jin, B. Subramaniam, 2004, Chem. Eng. Sci., 59, 4887–4893. A. Peschel, H. Freund, K. Sundmacher, 2010, Ind. Eng. Chem. Res., 49, 10535–10548. A. Riisager, A. Eriksen, P. Wasserscheid, R. Fehrmann, 2003, Catal. Lett., 90, 149–153. A. Sharma, C. Julcour Lebigue, R. M. Deshpande, A. A. Kelkar, H. Delmas, 2010, Ind. Eng. Chem. Res., 49, 10698–10706.
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic Liquid Systems Andreas Peschela, Benjamin Hentschelb, Hannsjörg Freunda, Kai Sundmachera,b a
Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany b Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
Abstract In this work, we apply our recently proposed reactor design methodology based on elementary process functions to the hydroformylation of long chain linear alkenes in a biphasic ionic liquid system with Rh/TPPTS catalyst. A potential selectivity increase towards the linear aldehyde of 35% compared to a reference case from literature is identified and an approximation of the best reaction route is proposed. Keywords: Hydroformylation, Reactors, Optimization, Design, Process Intensification.
1. Introduction Hydroformylation of alkenes is one of the most important homogeneously catalyzed processes. Beside the selectivity the catalyst recovery is crucial for the economics of the hydroformylation process. Due to the limited solubility of long chain alkenes in water the Ruhrchemie-Rhône-Poulenc process cannot be applied. Therefore, several innovative reaction concepts which allow easy catalyst separation such as thermomorphic solvent systems (Behr et al. 2004), micellar solvent systems (Haumann et al. 2004), gas expanded liquids (Jin and Subramaniam 2004), supported ionic liquid systems (Riisager et al. 2003), and biphasic ionic liquid (IL) systems (Brasse et al. 2000) were developed. However, the rigorous model based reactor design for such systems is still missing. In this contribution, we apply our recently proposed reactor design methodology (Peschel et al. 2010) based on elementary process functions (Freund and Sundmacher 2008) to the hydroformylation of long chain linear alkenes in a biphasic ionic liquid system in order to determine the best reaction concept and to derive the best suited technical reactor.
2. Determination of the Optimal Reaction Concept 2.1. Methodological Approach The main idea of the reactor design approach is to balance a fluid element and optimize the fluxes into and out of this element over time (dynamic optimization) to obtain the best process route. In order to develop an optimal reactor based on this idea, a three step approach is pursued. On the first level, the optimal reaction conditions at each point along the reaction coordinate with respect to temperature, H2, CO, and alkene concentration are obtained by optimizing the heat and the component fluxes into the tracked fluid element. On the second level, the influence of limited mass and energy transport is investigated and suitable control variables, which can be varied by the reactor design, are determined. On the third level, a reactor design is proposed based on the profiles of the best control variables. Here, all technical restrictions are taken into account and the reactor model
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic 1247 Liquid Systems accounts for non-idealities, e.g. radial temperature and concentration profiles in a tubular reactor. In this contribution, we focus on the first level, where process intensification measures are screened and the potential of the intensified reaction concepts are identified by comparison to a reference case. 2.2. Model for the Hydroformylation of 1-Octene in an Ionic Liquid Biphasic System The hydroformylation reaction network as shown in Fig. 1 includes the formation of the desired product n-aldehyde and the formation of undesired iso-aldehyde, internal alkenes, alkane, high boiling condensation products and alcohols. + CO + H2
r1
r2
1-octene (C8)
n-nonanal (nC9)
+ CO + H2
+ H2
+ H2
high boilers n-nonanol CO
r3
iso-nonanal (isoC9)
+ H2
+ H2
H2
internal octenes (inC8)
+ CO + H2
octane
iso-nonanol
octane
high boilers
Catalyst
Fig. 1: Reaction network of the hydroformylation with influencing factors for the reaction rates
In addition to the reaction network shown in Fig. 1, the catalyst exists in several states (refer to Fig. 2), where each state has a different activity and selectivity. For the investigated system and the used Rh/TPPTS-ratio, however, only the reactions r1–r3 were observed. HRh(CO)L3 (inactive)
-L +L
HRh(CO)L2 (selective)
+CO -CO
HRh(CO)2L2 (inactive)
-L +L
HRh(CO)2L (unselective)
Fig. 2: Catalyst states (L=TPPTS)
The reaction takes place in the ionic liquid phase ([bmim][PF6]) and the organic (decane) phase supplies the long chain alkene while the gas phase supplies hydrogen and carbon monoxide. The balance equations for the pseudo-homogeneous system are given by Eq. 1 with stoichiometric coefficients according to the reaction network shown in Fig. 1. The fluxes jorg,i are related to the organic phase and combine the flux into the fluid element and the specific exchange area. The reaction rate laws are taken from Sharma et al. 2010. The model is extended by taking the temperature dependency of the reaction rates into account. The activation energy of reaction 1 is taken from Sharma et al. 2010, while the activation energy of the isomerization reaction is obtained by regression from Disser et al. 2005. The activation energy of the formation of isomeric aldehyde is assumed to be the same as for the n-aldehyde formation, which is based on the observations of Bernas et al. 2008 for short chain aldehydes. § Vorg ρ + K i IL ¨¨ V ρ org © IL
NR · dCorg ,i Vorg ji , org + ¦ν ij rj = ¸¸ VIL j =1 ¹ dt
i = C 8, inC 8, nC 9, isoC 9
(1)
The partition coefficients of octene and nonanal between both liquid phases Ki are taken to be mean values of the data presented by Sharma et al. 2010, since the partition coefficients do not change significantly with the composition and a temperature dependency was not investigated. In the temperature and pressure region of interest Henry’s law is considered to be satisfactory to describe the gas solubilities. The Henry parameters are fitted according to the experimental data given by Sharma et al. 2010. A partial pressure of 30 bar is taken as upper boundary for both H2 and CO. The volume
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A. Peschel et al.
ratio of both phases, which is obtained from Sharma et al. 2010, and the density of each phase are assumed to be constant. All required modeling data is given in Tab. 1. Tab. 1: Modeling Data Numeric Value ª kJ º EA,1 « ¬ mol »¼
ª § m 3 ·3 º k0,1 «¨ IL ¸ s » «¬© kmol ¹ »¼ ª kg º
107.9 40.65e
18
Numeric Value ª kJ º EA,2 « ¬ mol »¼
ª§ m 3 ·3 º IL k0,2 «¨ ¸ » «¬© kmol ⋅ s ¹ »¼ ª kg º
ρIL « 3 » ¬m ¼
1.32e3
ρorg « 3 » ¬m ¼
KnC9, KisoC9 [-]
1.61e-3
T min − T max [ K ]
−1 ª kmol º H CO «¬ m3 MPa »¼
-2
ª kg º Ccat,IL « 3 » ¬m ¼
4.69e -8.65e-5·T 6.97e-3
ª kJ º EA,3 « ¬ mol »¼
43.6 11.16e
Numeric Value
6
6.86e2 253–373
ª § m 3 ·3 º k0,3 «¨ IL ¸ s » «¬© kmol ¹ »¼ KC8, KinC8 [-] Vorg/VIL [-]
p − p [ MPa ] 1.08e +2.65e-6·T (i=CO, H2) ª kg º ª kg º Ci,in « 3 » (i=inC8, CC8,org,in « 3 » 0 ¬m ¼ ¬m ¼ nC9, isoC9) (ref. case, case 1) −1 ª kmol º H H2 «¬ m3 MPa »¼
-2
min i
max i
107.9 33.51e18 1.00e-2 1.5 0-3
0.973
2.3. Optimization Strategy In order to maximize the selectivity towards the linear aldehyde, the heat, H2, CO and 1octene fluxes are optimized in order to obtain the optimal reaction conditions at each time. As shown in Fig. 3, several combinations optimizing the profiles of a control variable (f(T)) and optimizing the influencing factor as an optimization parameter (DoF) are investigated in order to find the most suitable integration concept and to determine the potential of the intensified reaction concepts. In general, three different intensification methods can be distinguished. First, the composition of the gas phase components can be changed. Second, the composition of the liquid phase components can be influenced. Third, the energy level of the reaction phases, here in form of the temperature, can be manipulated. Of course, influencing the gas phase composition, the liquid phase composition and the temperature at the same time will yield the highest objective function value. However, this reaction concept might be difficult to realize in a technical reactor and hence the simpler combinations of the enhancement options are of interest, too. As reference case, the industrially used reactor, a bubble column, is chosen and optimized using an idealized model. The CO and H2 partial pressures are optimized as DoF, while the temperature and initial 1-octene concentration are obtained from Sharma et al. 2010. It is assumed that the mass and heat transfer is much faster than the reaction and hence the transport kinetics do not have to be considered. In case 1, the gas phase component fluxes and the heat flux are considered as optimization functions. Due to the major influence of temperature and CO partial pressure on the catalyst equilibrium and hence on the selectivity, the optimal heat and CO flux profiles are of great interest. In case 2, the optimal 1-octene feed profile is calculated in addition to the gas phase component fluxes and the heat flux. Here, all states which have an influence on the reaction rates and hence on the selectivity are controlled by the according fluxes. In order to keep the number of investigated cases low, other combinations of the enhancement options are not considered at this point.
Optimal Reactor Design for the Hydroformylation of Long Chain Alkenes in Biphasic 1249 Liquid Systems Objective: Maximize n-Aldehyde Selectivity Reference case: T, CC8,org,0 = fix pH2, pCO , τ = DoF
Intensified case 1: CC8,org,0 = fix, τ = DoF q, jCO, jH2 = f(t)
Intensified case 2: CC8,org,0, τ = DoF jC8,org, q, jCO, jH2 = f(t)
Fig. 3: Overview of investigated cases (Decision structure to obtain the best reaction concept)
1
C [kmol/m3org]
0.8
X=0.95 S=0.49
0.6
C8 inC8 nC9 isoC9
0.4
b)
0.2
c) 0.8
C [kmol/m3org]
0.6
1000 2000 3000 Residence time [s]
0.4
24
0.2
22
X=0.95 S=0.76
200 400 600 Residence time [s]
20 800
0.8
0.2
0.7
Reference case Case 1 Case 2 CSTR
0.6 0.5
1000 2000 3000 Residence time [s]
pCO
d) 0.9
C8 X=0.95 inC8 S=0.84 nC9 isoC9
0.4
0 0
0.6
30 C8 inC8 28 nC9 isoC9 26
0 0
4000
Selectivity [-]
0 0
1
0.8 C [kmol/m3org]
a)
pCO [bar]
2.4. Results The concentration profiles for a fixed conversion of 95% and the selectivity depending on the conversion are shown in Fig. 4. For all three cases, the temperature and H2 partial pressure are constant (and hence not shown) at the upper boundary. This leads to a constant H2 concentration in the IL, however, the H2 flux depends on the reaction rate and is not constant. In the reference case, the optimal CO partial pressure depends on the conversion. In case 1, a distinctive CO partial pressure profile exists; the CO partial pressure increases for decreasing 1-octene concentrations. In case 2, the octene concentration and CO partial pressure profiles are constant at their lower and upper value, respectively. Since all states influencing the reaction rates r1 and r2 are constant and the 1-octene concentration is at its lower boundary, this reaction concept can be realized in a completely back mixed system (CSTR). In order to validate this result, a CSTR model was optimized and it yields the same selectivities and operation conditions as in case 2 (refer to Fig. 4d).
4000
0.4 0
0.2
0.4 0.6 Conversion [-]
0.8
1
Fig. 4: Concentration profiles: a) reference case, b) case 1, c) case 2. d) S-X diagram for different cases.
In all investigated cases, the amount of provided 1-octene (either given by the initial 1octene concetration or by dosing additional 1-octene) is constant. The lower limit of the 1-octene concentration is defined by the 1-octene outlet concentration depending on the conversion. It turns out that the selectivity increases for increasing conversions (refer to Fig. 4d). This can be explained by the rising differential selectivity at lower 1-octene concentrations.
1250
A. Peschel et al.
3. Conclusion and Discussion The presented results indicate that the selectivity in the hydroformylation of long chain alkenes can be increased by 35% by applying the our new rigorous reactor design approach (Peschel et al. 2010). In order to obtain such a selectivity increase, constant 1octene, H2, CO, and temperature profiles are required. Based on these profiles a reactor concept is derived. Here, these profiles can be obtained using an ideally back mixed system. An optimization of a CSTR model is not necessary, since this case is already included. However, it should be noted that in case of consecutive reactions, the profiles might be qualitatively different, and a CSTR might not be an adequate approximation in case consecutive reactions are taken into account. The presented model exploits the temperature dependency of the hydroformylation reaction system, and different operation conditions as used by Sharma et al. 2010 are recommended. In case 1, it was shown that an optimal CO partial pressure profile exists for the hydroformylation process. The results for H2 and CO partial pressures indicate that the experiments were not performed in the optimal partial pressures ranges. The H2 partial pressure can be increased further until alkane or alcohol formation is observed. The CO partial pressure can be increased until CO blocks the catalyst according to the catalyst equilibrium (refer to Fig. 2) and hence reduces the reaction rate significantly. The high observed partial pressures of CO might be explained by the low CO solubility in the used IL compared to the CO solubility in common organic solvents.
4. Outlook In order to complete the reactor design task for the hydroformylation system, level 2 and level 3 of the applied design approach still need to be performed. On level 2, the mass and energy transport kinetics are included to determine the exchange areas, and consequently the type of equipment that is required. On level 3, an optimal technical reactor is derived which is optimized taking all technical constraints into account. In addition, the model will be extended by the consecutive reactions in order to check whether a back mixed reactor is still optimal for the complete reaction network.
5. Acknowledgements Financial support by the Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged (TRR 63).
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