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Trickle-bed reactor modeling for middle-distillates hydrotreatment
® 1997 Elsevier Science B. V. All rights reserved. Hydrotreatment and hydrocracking of oil fractions G.F. Froment, B. Delmon and P. Grange, editors
443
Trickle-bed reactor modeling for middle-distillates hydrotreatment C.G. Dassori^, N. Femdndez^, R. Arteca^, A. Diaz'^ and S. Bxiitrago* ^Intevep S.A., Apdo.76343, Caracas 1070A, Venezuela ^Universidad Sim6n BoKvar, Apdo.89000, Caracas 1080A, Venezuela
A comprehensive approach is presented for trickle-bed reactor modeling, which covers proper characterization of reactive species, the discrimination of major reactions and the development of a reactor model within process simulators' architecture. The reduction of aromatics content in middle-distillates is presented as a case study. Inlet and outlet streams are represented in terms of pseudocomponents determined from mass spectrometry, boiUng point distribution, molecular weight and elemental analysis. A global optimization algorithm determines the mixture composition and distribution of pseudocomponents, discriminated as paraflfins, naphthenics, monoaromatics and poUaromatics of different carbon nimibers. Thermod3niamic and transport properties of these species are also predicted. This analysis is applied to a set of experimental data obtained from a laboratory reactor. Inlet and outlet streams described in terms of pseudocomponents are used to determine, the most important reactions during the process from raw data and using an optimization program. Kinetic parameters for selected reactions are determined using a trickle-bed reactor model that is viewed as a collection of repetitive cells. The computational program is developed within a commercial process simulator. 1. E«*RODUCTION The reduction in aromatics content of diesel fuels is strongly driven by new regulations. These processes are commonly carried out in fixed-bed reactors where hydrogen is contacted with a liquid hydrocarbon feed. Depending upon operating conditions, partial or complete vaporization of inlet Uquid stream can be achieved within the reaction vessel. This paper presents a reactor model that incorporates liquid vaporization through the use of a process simulator for the vapor-Hquid equilibriimi. Complex hydrocarbon feeds have to be characterized in terms of pseudospecies that retain chemical composition information in order to develop a model for hydrotreating reactors. Mass spectrometry, elemental analysis and molecular
444
weight are employed as raw data sources from which pseudospecies are bxiilt in this work. A bank of pure hydrocarbon compounds is used to match the analj^ical information. The resulting mixture fits raw analjrtical data. This large set of molecules is lumped into compoimd classes, each one divided into carbon number groups. Using this approach, a manageable group of limiped species is obtained, from which a reaction mechanism is determined. The reactor model is used to predict conversion of reactants and product distribution as a fimction of inlet conditions, temperature and pressure for non-ideal mixtures. The process simulator provides thermodynamic information through appropriate vapor-Uquid equiUbriimi models. Pressure drop, phase holdups and catalyst wetting are incorporated into the reactor model through correlations available in the literature[2,4]. 2. EXPERIMENTAL An isothermal fixed-bed reactor was used for obtaining experimental data. Its length was 65 cm and it had a 25mm internal diameter with three axial points where internal temperature was measured. Liquid and gas feeds were introduced from the top. Pure hydrogen was used as inlet gas stream. An infrarred jacket was used to heat up the bed. Effluents were separated into gas and Uquid phases at reactor outlet and analyzed as reported in Table 1. The following parameters were varied: space velocity, pressure, hydrogen/ hydrocarbon inlet ratio and temperature. Catalyst C-448(Criterion) was used for the experiments and was diluted 1:1.5 with SiC.
Table 1 Analytical Characterization Methods Analysis
Norm
Sulfur API Gravity Digital Densimetry Simulated Distillation Mass Spectrometry Molecular Weight (osmometry) C (atomic spectroscopy) H (atomic spectroscopy) Gas analysis
ASTM/D-1552 ASTM/D-1298 ASTM/D-2887 ASTM/D-2425 VPO/INTEVEP Combustion Combustion GC
445
3. REACTOR MODEL The fixed-bed reactor was modeled as a group of repetitive cells as shown in Figure 1. It is implied that gas-liquid equilibrixmi prevails along the bed. Kods and Ho[l] have made similar assxmiption when a purely liquid phase reaction was considered. Pressure drop across cells and liquid holdup, ei, are computed using correlations by Larachi et al.[2]. Once ei is computed at a given step, liquid CSTR volume is calcxolated by: CSTRiiq.volume = eiAV
(1)
AV = V T / n
(2)
where V^ is total reactor volume and n is the nimiber of cell units. A similar procedure is followed to compute CSTR gas volxmie. Both flash and CSTR imits are computed by using PRO/II® (release 4.01)[3] process simulator of Simulation Science Inc. Catalyst wetting fraction, f^ was computed using the correlation proposed by Mills and Dudukovic[4]: f - a L (3s-l)/2
(3)
In the present work a = 0.85 and s = 0.52; LQ, is the liqtiid mass superficial velocity.
i_J. FLASH
T7. CSTR [^ [S] CSTR FLASH CSTR
CSTR
Figure 1. Reactor model scheme
446 4. PSEUDOCOMPONENT EVALUATION A bank of pure molecniles was used to fit analytical data using a global optimization program that gives as a result their respective concentrations in the sample. There were used more than 500 different molecules belonging to three compoxmd classes: paraffins, naphthenes and aromatics. From the selected optimxim set, different pseudospecies groups were constructed: paraffins(Pi), naphthenes(Ni), monoaromatics (MAi) and poliaromatics(PAi) of different carbon numbers; index i indicates carbon number group. That is, i=l, carbon atoms from 1 to 6; i=2, carbon atoms from 7 to 12, etc. Each pseudocomponent has a complete thermodynamic characterization in terms of critical and transport properties that are computed using proper mixing rules for the composition set determined in the optimization program. 5. REACTIONS By analyzing lumps distribution at reactor outlet and comparing them to feed composition a set of reactions was selected for representing the reaction network. This selection was done from a group of sixty possible reactions among the pseudocomponents obtained previously. Based on a global optimization technique[5] the integral mass balance for pseudopecies in the laboratory reactor is used to discriminate the largest reaction rates imder the conditions considered in this work. The following reactions were finally selected:
N03 PA02 PA03 PA04 PA05
N02 + POl •
^ ^
•^ -M
^ '—^^ ^ • •
MAn9.
MA03
^
MAHJ.
^
IVLrlUrt
^
... ^^ ^
N02 N03 N04
MA05
Rate laws for each reaction were assumed to be first order in both hydrogen and hydrocarbon species for each phase. The following expressions were used: for the liquid phase rijL = ^ijKjWf^CH2^CiL
(4)
and for gas phase: rijV = ^ijKjW(l-f^)CH2^CiV
(5)
where ry is the reaction rate for i-th component in j-th reaction, Kj is the kinetic consteint in j - t h reaction, W is catalyst weight in cell, xy is stoichiometric coefficient for reactant i in j - t h reaction, f^ is the wetting factor, C H 2 is hydrogen concentration, Ci is hydrocarbon concentration. Superscripts L and V stand for liquid and gas phase.
447
6. RESULTS AND DISCUSSION Experimental data obtained in the laboratory were used to determine kinetic constants for the reactions described above. Figure 2 shows that 25 is an adequate niimber of cells for performing the computations because increasing this value to 150 has minor effects on mixture composition in terms of global limips. Pseudocomponents concentrations are sensitive to number of cells when this value is smaller than 25. This is the mmierical evidence that the chemical reaction and gas-liquid mass transfer are adequately considered as simultaneous events and that an adequate discretization is employed (number of cells). Figure 3 shows the effect of liquid hourly space velocity (LHSV) on poliaromatics conversion of group 5 (PA5, more than 24 carbon atoms). Figure 4 shows the natural trend of increasing conversion with increasing hydrogen inlet pressure according to reaction rates employed (eqns.4-5). Figure 5 shows the difference in predicted conversion of group 5 poliaromatics depending on the equation of state selected to use in the process simulator(PR=Peng-Robinson; SRKM=Modified Redlich-Kwong-Soave).
H150 cells • 25 cells PRESSURE (atm};68,04S LIQUID aOWRATE (KgA^): 2.4587E-02 GAS FLOWRATE(Kg/h);7,9977E-02
Figure 2. Product distribution in terms of compound families
0,3
0,4
0.5
0,6
0.7
Dimensionless Axial Position
Figure 3. Effect of LHSV. T=650K;P=68atm;FH2/Fc=500Nm^/m3
448
0.3
0.4
0.5
0,6
0.7
Dimensionless Axial Position Figure 4. Effect of Pressure. T=655K;LHSV=lh-^FH2/Fc=544NmW;SRKM
- T.657.15 K.P.68.(M5irtm.SRKM. | -T-657.15 K. P-68.045«tm. PR. - EXPERIMENTAL VALUE
0.3
0,4
0.5
0.6
J— 0.7
Dimensionless Axial Position
Figure 5. Effect of Equations of state. LHSV=1.64h-^;FH2/Fc=500Nm3/m3 General results obtained with the present model indicate that proper thermodjntiamic properties and compound class limiping can be effectively used when modeling hydrotreating reactors within process simulator's architecture. REFERENCES 1. G.R. Kocis and T.C. Ho, Chem.Eng.Res.Des., 64 (1986) 288. 2. F. Larachi, A. Laurent, N. Midoux and G. Wild, Chem.Eng.Sci., 46 (1991) 1233. 3. PRO/II is a registered mark of Simulation Sciences Inc. 4. P.L. Mills and M.P. Dudukovic, AIChE J., 27 (1981) 893. 5. S. Buitrago and C.G. Dassori, Intevep Internal Report (1996).
443
Trickle-bed reactor modeling for middle-distillates hydrotreatment C.G. Dassori^, N. Femdndez^, R. Arteca^, A. Diaz'^ and S. Bxiitrago* ^Intevep S.A., Apdo.76343, Caracas 1070A, Venezuela ^Universidad Sim6n BoKvar, Apdo.89000, Caracas 1080A, Venezuela
A comprehensive approach is presented for trickle-bed reactor modeling, which covers proper characterization of reactive species, the discrimination of major reactions and the development of a reactor model within process simulators' architecture. The reduction of aromatics content in middle-distillates is presented as a case study. Inlet and outlet streams are represented in terms of pseudocomponents determined from mass spectrometry, boiUng point distribution, molecular weight and elemental analysis. A global optimization algorithm determines the mixture composition and distribution of pseudocomponents, discriminated as paraflfins, naphthenics, monoaromatics and poUaromatics of different carbon nimibers. Thermod3niamic and transport properties of these species are also predicted. This analysis is applied to a set of experimental data obtained from a laboratory reactor. Inlet and outlet streams described in terms of pseudocomponents are used to determine, the most important reactions during the process from raw data and using an optimization program. Kinetic parameters for selected reactions are determined using a trickle-bed reactor model that is viewed as a collection of repetitive cells. The computational program is developed within a commercial process simulator. 1. E«*RODUCTION The reduction in aromatics content of diesel fuels is strongly driven by new regulations. These processes are commonly carried out in fixed-bed reactors where hydrogen is contacted with a liquid hydrocarbon feed. Depending upon operating conditions, partial or complete vaporization of inlet Uquid stream can be achieved within the reaction vessel. This paper presents a reactor model that incorporates liquid vaporization through the use of a process simulator for the vapor-Hquid equilibriimi. Complex hydrocarbon feeds have to be characterized in terms of pseudospecies that retain chemical composition information in order to develop a model for hydrotreating reactors. Mass spectrometry, elemental analysis and molecular
444
weight are employed as raw data sources from which pseudospecies are bxiilt in this work. A bank of pure hydrocarbon compounds is used to match the analj^ical information. The resulting mixture fits raw analjrtical data. This large set of molecules is lumped into compoimd classes, each one divided into carbon number groups. Using this approach, a manageable group of limiped species is obtained, from which a reaction mechanism is determined. The reactor model is used to predict conversion of reactants and product distribution as a fimction of inlet conditions, temperature and pressure for non-ideal mixtures. The process simulator provides thermodynamic information through appropriate vapor-Uquid equiUbriimi models. Pressure drop, phase holdups and catalyst wetting are incorporated into the reactor model through correlations available in the literature[2,4]. 2. EXPERIMENTAL An isothermal fixed-bed reactor was used for obtaining experimental data. Its length was 65 cm and it had a 25mm internal diameter with three axial points where internal temperature was measured. Liquid and gas feeds were introduced from the top. Pure hydrogen was used as inlet gas stream. An infrarred jacket was used to heat up the bed. Effluents were separated into gas and Uquid phases at reactor outlet and analyzed as reported in Table 1. The following parameters were varied: space velocity, pressure, hydrogen/ hydrocarbon inlet ratio and temperature. Catalyst C-448(Criterion) was used for the experiments and was diluted 1:1.5 with SiC.
Table 1 Analytical Characterization Methods Analysis
Norm
Sulfur API Gravity Digital Densimetry Simulated Distillation Mass Spectrometry Molecular Weight (osmometry) C (atomic spectroscopy) H (atomic spectroscopy) Gas analysis
ASTM/D-1552 ASTM/D-1298 ASTM/D-2887 ASTM/D-2425 VPO/INTEVEP Combustion Combustion GC
445
3. REACTOR MODEL The fixed-bed reactor was modeled as a group of repetitive cells as shown in Figure 1. It is implied that gas-liquid equilibrixmi prevails along the bed. Kods and Ho[l] have made similar assxmiption when a purely liquid phase reaction was considered. Pressure drop across cells and liquid holdup, ei, are computed using correlations by Larachi et al.[2]. Once ei is computed at a given step, liquid CSTR volume is calcxolated by: CSTRiiq.volume = eiAV
(1)
AV = V T / n
(2)
where V^ is total reactor volume and n is the nimiber of cell units. A similar procedure is followed to compute CSTR gas volxmie. Both flash and CSTR imits are computed by using PRO/II® (release 4.01)[3] process simulator of Simulation Science Inc. Catalyst wetting fraction, f^ was computed using the correlation proposed by Mills and Dudukovic[4]: f - a L (3s-l)/2
(3)
In the present work a = 0.85 and s = 0.52; LQ, is the liqtiid mass superficial velocity.
i_J. FLASH
T7. CSTR [^ [S] CSTR FLASH CSTR
CSTR
Figure 1. Reactor model scheme
446 4. PSEUDOCOMPONENT EVALUATION A bank of pure molecniles was used to fit analytical data using a global optimization program that gives as a result their respective concentrations in the sample. There were used more than 500 different molecules belonging to three compoxmd classes: paraffins, naphthenes and aromatics. From the selected optimxim set, different pseudospecies groups were constructed: paraffins(Pi), naphthenes(Ni), monoaromatics (MAi) and poliaromatics(PAi) of different carbon numbers; index i indicates carbon number group. That is, i=l, carbon atoms from 1 to 6; i=2, carbon atoms from 7 to 12, etc. Each pseudocomponent has a complete thermodynamic characterization in terms of critical and transport properties that are computed using proper mixing rules for the composition set determined in the optimization program. 5. REACTIONS By analyzing lumps distribution at reactor outlet and comparing them to feed composition a set of reactions was selected for representing the reaction network. This selection was done from a group of sixty possible reactions among the pseudocomponents obtained previously. Based on a global optimization technique[5] the integral mass balance for pseudopecies in the laboratory reactor is used to discriminate the largest reaction rates imder the conditions considered in this work. The following reactions were finally selected:
N03 PA02 PA03 PA04 PA05
N02 + POl •
^ ^
•^ -M
^ '—^^ ^ • •
MAn9.
MA03
^
MAHJ.
^
IVLrlUrt
^
... ^^ ^
N02 N03 N04
MA05
Rate laws for each reaction were assumed to be first order in both hydrogen and hydrocarbon species for each phase. The following expressions were used: for the liquid phase rijL = ^ijKjWf^CH2^CiL
(4)
and for gas phase: rijV = ^ijKjW(l-f^)CH2^CiV
(5)
where ry is the reaction rate for i-th component in j-th reaction, Kj is the kinetic consteint in j - t h reaction, W is catalyst weight in cell, xy is stoichiometric coefficient for reactant i in j - t h reaction, f^ is the wetting factor, C H 2 is hydrogen concentration, Ci is hydrocarbon concentration. Superscripts L and V stand for liquid and gas phase.
447
6. RESULTS AND DISCUSSION Experimental data obtained in the laboratory were used to determine kinetic constants for the reactions described above. Figure 2 shows that 25 is an adequate niimber of cells for performing the computations because increasing this value to 150 has minor effects on mixture composition in terms of global limips. Pseudocomponents concentrations are sensitive to number of cells when this value is smaller than 25. This is the mmierical evidence that the chemical reaction and gas-liquid mass transfer are adequately considered as simultaneous events and that an adequate discretization is employed (number of cells). Figure 3 shows the effect of liquid hourly space velocity (LHSV) on poliaromatics conversion of group 5 (PA5, more than 24 carbon atoms). Figure 4 shows the natural trend of increasing conversion with increasing hydrogen inlet pressure according to reaction rates employed (eqns.4-5). Figure 5 shows the difference in predicted conversion of group 5 poliaromatics depending on the equation of state selected to use in the process simulator(PR=Peng-Robinson; SRKM=Modified Redlich-Kwong-Soave).
H150 cells • 25 cells PRESSURE (atm};68,04S LIQUID aOWRATE (KgA^): 2.4587E-02 GAS FLOWRATE(Kg/h);7,9977E-02
Figure 2. Product distribution in terms of compound families
0,3
0,4
0.5
0,6
0.7
Dimensionless Axial Position
Figure 3. Effect of LHSV. T=650K;P=68atm;FH2/Fc=500Nm^/m3
448
0.3
0.4
0.5
0,6
0.7
Dimensionless Axial Position Figure 4. Effect of Pressure. T=655K;LHSV=lh-^FH2/Fc=544NmW;SRKM
- T.657.15 K.P.68.(M5irtm.SRKM. | -T-657.15 K. P-68.045«tm. PR. - EXPERIMENTAL VALUE
0.3
0,4
0.5
0.6
J— 0.7
Dimensionless Axial Position
Figure 5. Effect of Equations of state. LHSV=1.64h-^;FH2/Fc=500Nm3/m3 General results obtained with the present model indicate that proper thermodjntiamic properties and compound class limiping can be effectively used when modeling hydrotreating reactors within process simulator's architecture. REFERENCES 1. G.R. Kocis and T.C. Ho, Chem.Eng.Res.Des., 64 (1986) 288. 2. F. Larachi, A. Laurent, N. Midoux and G. Wild, Chem.Eng.Sci., 46 (1991) 1233. 3. PRO/II is a registered mark of Simulation Sciences Inc. 4. P.L. Mills and M.P. Dudukovic, AIChE J., 27 (1981) 893. 5. S. Buitrago and C.G. Dassori, Intevep Internal Report (1996).