ARTICLE pubs.acs.org/EF
Whole Crude Oil Hydrotreating from Small-Scale Laboratory Pilot Plant to Large-Scale trickle-Bed Reactor: Analysis of Operational Issues through Modeling Aysar T. Jarullah,† Iqbal M. Mujtaba,†,* and Alastair S. Wood† †
School of Engineering, Design and Technology, University of Bradford, Bradford BD7 1DP, U.K. ABSTRACT: Catalytic hydrotreating (HDT) is a mature process technology practiced in the petroleum refining industries and used to treat the oil fractions from a crude oil distillation (CDU) unit for the removal of contaminants such as sulfur, nitrogen, etc. Hydrotreating of the whole crude oil before it goes to a CDU unit is a new technology and is regarded as one of the more difficult tasks that have not been reported widely in the public domain. Our recent study using a laboratory small-scale pilot plant shows significant improvement in middle distillate yields and quality of crude oil in terms of contaminants present. Recently, we also determined best kinetic parameters for several hydrotreating reactions using experimental data from the small-scale trickle-bed reactor (TBR), using parameter estimation techniques. With these parameters, we were able to develop a process model of TBR that was validated using the experimental data from the pilot plant. In this study, we scale up the pilot-scale TBR where the throughput of crude oil changes from 45 106 m3/h to 66.243 m3/h (10 000 bpd), the reactor height increases from 65 cm to 1061 cm, and the diameter changes from 2 cm to 399 cm. While isothermal conditions could be easily maintained in the small-scale TBR (an isothermal steady state model mentioned above was sufficient for the reactor), it is not the case for a large-scale reactor. Hence, a detailed model with energy balance was considered for the analysis of the large-scale reactor, and the temperature control issues are discussed. The ratio of reactor length to reactor diameter (LR/DR) is chosen in such a way so that radial variation could be neglected and it was obtained using an optimization technique. All the main simultaneously occurring hydrotreating reactions are considered. These reactions are hydrodesulfurization (HDS), hydrodenitrogentaion (HDN), hydrodeasphaltenization (HDAs), and hydrodemetallization (HDM), which includes hydrodevanadization (HDV), hydrodenickelation (HDNi). In addition, the chemical reactions responsible for converting part of the crude oil to middle distillate are also considered. The gPROMS modeling software is used for modeling and simulation of the scaled-up TBR process.
1. INTRODUCTION Hydrotreating of whole crude oil is a new challenge and new technology that has not been reported in the literature. Conventionally, all hydrotreatment operations are conducted on each oil fraction (such as gasoline, kerosene, and light and heavy gas oils) followed by CDU separately and not on the whole crude oil.1 In the petroleum refining industry, HDT reactions (such as hydrodesulfurization (HDS), hydrodenitrogenation (HDN), hydrodeasphaltenization (HDAs), and hyhdrodemetallization (HDM)) are carried out in three phase (solidliquidgas) trickle-bed reactors (TBRs).26 Jarullah et al.1,47 considered, for the first time, hydrotreating of the whole crude oil. Jarullah et al.4 studied a kinetic parameters estimation and simulation of trickle-bed reactor for HDS of crude oil. Jarullah et al.5 presented a kinetic model development and simulation of simultaneous HDN and HDM (includes HDV and HDNi) of crude oil in a TBR. HDAs of crude oil with kinetic model development and process simulation was investigated by Jarullah.6 As a result of full crude oil hydrotreating, significant improvement in middle distillate yields and fuel quality is observed by Jarullah et al.1 The kinetic models for converting a portion of crude oil to middle distillate yields have also been reported by Jarullah et al.7 For all the above-mentioned HDT reactions, detailed pilotplant experiments were carried out in a small-scale laboratory TBR at different operating conditions. The pilot-plant r 2011 American Chemical Society
trickle-bed reactor was operated isothermally by an independent temperature control of five zone electric furnaces.1 It is clear from the literature review that there is no study on the whole crude oil hydrotreating using large-scale TBR in the public domain. Therefore, the focus of this paper is to look at thescale up issues of an industrial TBR, on the basis of our previous works and through the use of mathematical modeling. In this study, we attempted to develop a model for an industrial tricklebed reactor used for whole crude oil hydrotreating (including chemical reactions for HDS, HDN, HDV, HDNi, HDAs, and crude oil conversion to middle distillate yields). The mass balance and reaction rate equations are taken from earlier works,47 and in addition, we now add energy balance equations. The optimal operation of an industrial hydrotreating unit is investigated to evaluate the viability of a large-scale process of the crude oil hydrotreating process. The dimensions of the industrial trickle-bed reactor are taken into consideration (in terms of the ratio of optimal length to diameter of the reactor). As a result of the exothermic behavior of industrial trickle-bed reactor, the control of the reaction temperature (which is regarded as an important factor in hydrotreating units, having a big impact on the conversion of process reactions) is the main issue in such Received: September 16, 2011 Revised: November 11, 2011 Published: November 14, 2011 629
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operations. Therefore, a hydrogen quenching process is used to control the temperature, which is achieved by introducing a part of the cold hydrogen stream into the catalytic beds. Many simulation processes, in terms of quench positions and quench rate fractions, are investigated here on the basis of the total annual cost. Modeling, simulation, and optimization tasks are carried out using gPROMS modeling software.
Table 1. Mass Balances and Reaction Rate Equations for HDT Reactions mass balance eqs gas cmpds (H2, H2S) in dPiG RT L PG ¼ k aL i CLi gas phase ug i dz hi
!
" # ! gas cmpds (H2, H2S) in dCLi 1 L PiG L S L S ¼ k aL Ci ki aS ðCi Ci Þ liquid phase ul i dz hi
2. SMALL-SCALE TBR: EXPERIMENTAL WORK Although the experimental set up for the small-scale process is previously described,1,47 we briefly summarize it here for convenience. All experiments were carried out in a continuous flow pilot-plant trickle-bed reactor (TBR). The small-scale pilot-plant reactor (with an inside diameter of 2 cm and length of 65 cm) is operated in isothermal mode by independent temperature control of five zone electric furnaces that provided an isothermal temperature along the active reactor section. A commercial cobaltmolybdenum on alumina (CoMo/γ-Al2O3) catalyst was employed for all experiments (0.67 g/cm3 bulk density, 180 m2/g surface area, 0.5 cm3/g pore volume, 1.8 mm mean pore diameter, 4 mm mean particle length). The fresh catalyst (90 cm3) was charged to the hydrotreating reactor and in situ activated by a solution of 0.6 vol % of CS2 in commercial gas oil. Iraqi crude oil was used as a feedstock for the hydrotreating process and has the following specifications: 2.0 wt % sulfur, 1.2 wt % asphaltene, 0.1 wt % nitrogen, 26.5 ppm vanadium, and 17 ppm nickel. The experimental studies were conducted under the following operating conditions: 335400 °C reaction temperature, 410 MPa Pressure, 0.51.5 h1 liquid hourly space velocity (LHSV), and constant H2/oil ratio at 250 L/L. For oil distillate yields, a laboratory distillation unit at atmospheric and vacuum pressure was used for feedstock and hydrotreated product composition. The feed and products compositions were defined as follows: gases, N (IBP-150 °C), HK (150230 °C), LGO (230350 °C), and RCR (350+ °C). Further details of the experimental pilot plant used in this study, the reactor, the equipment and procedure, the experimental runs, the catalyst loading, and the catalyst presulfiding can be found in Jarullah et al.1
liquid cmpds (S, N, Asph, V, and Ni) in
dCLi 1 ¼ kSi aS ðCLi CSi Þ uL dz
liquid phase H2 in solid phase
kSH2 aS ðCLH2 CSH2 Þ ¼ FB
∑ ηj rj
(j = HDS, HDN, HDAs, HDV, and HDNi) H2S in solid phase
kSH2 S aS ðCLH2 S CSH2 S Þ ¼ FB ηHDS rHDS
S, N, Asph, V, and Ni in kSi aS ðCLi CSi Þ ¼ FB ηj rj solid phase (j = HDS, HDN, HDAs, HDV, and HDNi) chemical reaction rates HDS
rHDS ¼ KHDS
HDN, HDAs, HDV, and HDNi HDS, HDN, HDAs, HDV, and HDNi H2S
ðCSsul Þn ðCSH2 Þm ð1 þ KH2 S CSH2 S Þ2
rj ¼ Kj ðCSi Þnj ðCSH2 Þmj EA j Kj ¼ A0j exp RT 2761 KH2 S ¼ 41769 3 8411 exp RT
Table 2. Mass Balances and Reaction Rate Equations for Increasing Oil Distillate Yields
3. MODELING INDUSTRIAL TBR A reliable mathematical model of industrial hydrotreating reactors is essential for determining optimal design and operation of such reactors. A common route for developing kinetic models for the HDT reactions is to conduct experiments in a pilot-plant-scale reactor using the same catalyst, same operating conditions, and same feedstock. An unavoidable difference in process between the large-scale reactors and the small-scale reactors is the catalyst bed temperature profile.8 Current economic considerations require that the smallest possible laboratory units be utilized for scale up.9 A pilot-plant reactor operates isothermally, while industrial units often operate nonisothermally. This means that an equation transposing the heat balance should be included in the large-scale reactor model.10 Also, care must be taken in terms of the choice of reactor dimensions (length and diameter) while using an isothermal model (developed for small-scale reactors) for large-scale reactors. The mass and heat balances are developed with the following assumptions: • steady-state operation of the reactor; • catalyst deactivation not considered in the models; • one-dimensional heterogeneous model; • complete wetting of catalyst; • no radial concentrations gradients; • constant pressure operation of the reactor.
mass balance eqs product comp (i = RCR, LGO, HK, naphtha, gases)
dyi dyi ¼ ¼ ri dτ dð1=LHSVÞ
chemical reaction rates RCR
rR ¼ ðk1 þ k2 þ k3 þ k4 ÞynR1
LGO
2 rLGO ¼ k1 ynR1 ðk5 þ k6 þ k7 ÞynLGO
HK
3 2 ðk8 þ k9 ÞynHK rHK ¼ k2 ynR1 þ k5 ynLGO
naphtha
n2 n3 n4 rN ¼ k3 yn1 R þ k6 yLGO þ k8 yHK k10 yN
gases
n2 n3 n4 rG ¼ k4 yn1 R þ k7 yLGO þ k9 yHK þ k10 yN
3.1. Model Equations. The main mass balance and reaction rate equations of the HDT reactor models are shown in Table 1. The model considers the gas compounds (H2, H2S) and organic compounds (S, N, Asph, V, and Ni). The hydrodesulfurization (HDS) reaction is modeled by the LangmuirHinshelwood model, which takes into consideration the H2S inhibiting impact. HDN, HDAs, HDV, and HDNi reactions are described by power 630
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law kinetic models, and the main mass balance and the reaction rate expressions used to evaluate the kinetic models for increasing the productivity of distillate fractions during crude oil HDT are summarized in Table 2. The differential heat balance equation used for the industrial trickle-bed reactor that has taken into account the non-isothermal behavior along the catalyst bed length can be written as follows:1116 dT ¼ dz
∑
εl ½ð ΔHR Þðrj ÞFB ηj G ug FG Cp εg þ uL FL CLp εl
Table 3. Differences in Length and Diameter between PilotPlant and Industrial TBRs11
ð1Þ
ð2Þ
The heat capacity of the liquid oil is determined by the following eq:8,17,18 ! 0:415 L ffi þ 0:0009ðT 288:15Þ Cp ¼ 4:1868 pffiffiffiffiffiffiffiffi ð3Þ F15:6 L The main gas products that have been taken into account during the hydrotreating process are CH4, H2S, and NH3. Therefore, the specific heat capacity of the gas mixture (which involves mainly reactants (H2) and products) can be evaluated using the following expression:18 H2 S CH4 NH3 2 CGp ¼ CH p x 1 þ Cp x 2 þ Cp x 3 þ Cp x 4
industrial reactor
length
0.52.0 m
10 25 m
diameter
0.54.0 cm
14 m
Table 3. The gasliquid velocities in pilot-plant reactors are much lower than those in industrial reactors, and lower liquid velocity significantly influences the overall performance and heat and mass rates, owing to reduced contact efficiencies and increased dispersion.22,23 An economic study should be made of the cost of the reactor at different length to diameter ratios. At large diameters and high pressures, the reactor wall must be thicker and hence more expensive. On the contrary, reducing the diameter of the reactor is possible, and it will improve the isothermal behavior; however, at the same time, side effects can be noticed with hydrodynamics, such as radial concentration gradient effects (the model assumed that there is no radial concentration gradient due to small reactor diameter in the pilot-plant). Consequently, an intermediate value for the diameter should be considered.24,25 Although the most common ratio of LR/DR ranges approximately between 2 to 311,13,14,26,27 for different oil feedstocks (not whole crude oil), it is necessary to find the optimal ratio of LR/DR while ensuring the radial concentration gradient is as low as possible, in addition to the economic considerations. Bischoff and Levenspiel, as published in Mederos et al.,10 have presented an important criterion to neglect radial dispersion effect in packed beds based on the ratio of the bed length (LR) to reactor diameter (DR) as follows:
Here, the gas density (FG) includes all reacting gases and the specific heat capacity of gas (CG p ) involves all gases (reactants and products). The gas phase fraction (εg) can be estimated on the basis of the bed void fraction and the liquid phase fraction as follows:10,12 εg ¼ ε εl
pilot-plant reactor
ð4Þ
LR u l DR > 0:04 DR εl DLr
The heat capacities of CH4, H2S, NH3, and H2 are estimated using correlations presented by Smith et al.19 The density of the reacting gases (which is hydrogen) as a function of reaction pressure and temperature is calculated using the following equation:20
ð7Þ
The liquid fraction (εl) is calculated by the following equation:13,28 !1=3 GL dS 1=3 dS 3 gF2L εl ¼ 9:9 ð8Þ μL μ2L
PMwH2 ð5Þ ZT The compressibility factor of hydrogen (Z) as a function of process condition can be estimated with the following correlation:21 1:9155P Z¼1 þ ð6Þ T FG ¼ ð12:03 103 Þ
The radial mass dispersion coefficient (DLr ) is needed, which can be estimated from the Peclet number (Pe):14,29 DLr ¼
dS ul Peεl
ð9Þ
The Peclet number can be determined from correlations presented in the literature, depending upon the mode of operation and the type of reactor (pilot plant or commercial reactor). For cocurrent operation with a commercial unit, the Peclet number is calculated by the SaterLevenspiel correlation, as reported in Mederos and Ancheyta14 as follows:
We should note that the model includes correlations for calculating gas and liquid properties (such as oil density, oil viscosity, gasliquid mass transfer coefficient, liquidsolid mass transfer coefficient, molecular diffusivity, molar volume, critical specific volume of liquid, Henry coefficients, and solubility of H2 and H2S) in addition to the characteristics of the catalyst bed (such as surface area, bed void fraction, and effectiveness factor) at process conditions, using information presented in the literature and reported within our previous studies.46 3.2. Choice of Reactor Dimension—Optimal Ratio of LR/DR. Knowledge of the catalyst bed dimensions is an important issue for an industrial reactor, because laboratory scale reactors need to match the space velocity of commercial processes. Generally, the aspect ratio LR/DR (LR is the length of the bed and DR is the diameter of the reactor) of a commercial reactor is quite different from that of pilot-plant reactors, as shown in
Pe ¼ 7:58 103 Re0:703 L ReL ¼
dS GL μL
ð10Þ ð11Þ
As mentioned earlier, by increasing the diameter and decreasing the length of the reactor, the capital cost of the reactor will increase. Thus, the capital cost of the reactor as a function of diameter and length is needed. The capital cost of the reactor (CR, $) is calculated using the following equation (assuming that 631
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the reactor is filled with the catalyst):30 M&S CR ð$Þ ¼ L0:802 ð2:18 þ FC Þ 101:9D1:066 R R 280 FC ¼ Fm Fp
ð12Þ ð13Þ
M&S denotes the Marshall and Swift index for cost escalation (M&S = 1468.631), and FC, Fm, and Fp are dimensionless factors that are functions of the construction material and operating pressure; typically, Fm = 3.67 and Fp = 3.93.30 3.2.1. Optimization Problem Formulation. With due attention to eq 7, A¼
LR DR
B ¼ 0:04
ð14Þ u l DR εl DLr
C ¼ AB
ð15Þ ð16Þ
C must be >0. To know the critical point where A = B, the lower limit of C is assumed to be zero. The optimization problem can be stated as follows:
Figure 1. Hydrotreating reactor: quench zone.51
the feedstock, the catalyst, the reaction temperature, the hydrogen pressure, and the liquid hourly space velocity, determine the length of the reactor (LR) and the diameter of the reactor (DR) so as to the reactor capital cost (CR) minimize subject to process constraints and linear bounds on all decision variables (mentioned above). Given
Because of the exothermic nature of the hydrotreating reactions, the average reactor temperature increases and this enhances the hydrotreating reaction rates.36,37 However, the reactor temperature has a limit dictated by mechanical constraints or by hydrocracking of the feed that decreases the yield of the desired product. In other words, in hydrocracking operations, increasing the temperature can cause an adverse influence upon the product distribution, owing to the strong impact of this operation variable upon hydrocracking selectivity. Also, at high temperatures, hot spots can be formed that result in enhanced coke formation and catalyst deactivation.38,39 Such an impact decreases the catalyst cycle life, and hence, more frequent shut downs of the units are required, affecting the global profitability of the operation. Therefore, temperature control in tricklebed reactors becomes one of the most significant issues to extend the catalyst cycle life and keep the products quality at desired levels.40 Commonly, quenching has been the conventional route to control the temperature in most of the industrial reactors operation.41 In hydrotreating, this is achieved by introducing a part of the hydrogen stream among the catalytic beds (at a certain length of the reactor), so-called “quenching” or sometimes “cold shot cooling”.42,43 Because a lot of equipment for hydrogen and a large quantity of quench gas are required, the same composition of the feed gas (which is hydrogen) as that of the base case is employed for the quench gas.18 Quenching with hydrogen takes place in some part of the reactor length and has several functions,18,37,4447 mainly (a) control of reaction temperature, (b) flow distribution enhancement in the reactor bed and delivery of the reactants to the next bed, (c) reduction of radial maldistribution with radial mixing, (d) replenishment of hydrogen, which has been consumed, and (e) a decrease in H2S and NH3 in the reactor that reduces the inhibition influence upon hydrotreating reactions, which always improves product quality. In a quench section (Figure 1), hot feedstock fluids from the previous bed are combined with relatively cold hydrogenrich quench gas before the mixture passes into the next bed. The quench deck consists of the following major parts: quench tube, liquid collector, redistributor, a gasliquid mixing zone, and
Mathematically, the optimization problem can be written as follows: Min LR, DR s.t
CR f(x(z), u(z), v) = 0 Cg0 LLR e LR e LU R DLR e DR e DU R
(model equation, equality constraint) (inequality constraints) (inequality constraints) (inequality constraints)
f(x(z), u(z), v) = 0 denotes the process model discussed in section 3.1, where x(z) refers to the set of all algebraic variables, u(z) denotes the control variables, and v represents the design variables (constant parameters). The function f is assumed to be continuously differentiable with respect to all its arguments.32,33 The optimization problem is posed as a Nonlinear Programming (NLP) problem and is solved using a Successive Quadratic Programming (SQP) method within gPROMS software. 3.3. Quench Process. The process of a large-scale (industrial) hydrotreating reactor is considered to be very close to adiabatic behavior because heat losses from the reactor are generally negligible in comparison with the heat generated by the hydrotreating reactions.34 The reaction temperature is an important operating condition that has an enormous influence on the conversion degree of the reactants and on the catalyst cycle life, particularly when hydrotreating heavy oil feedstock, and hence upon the overall economics of the process.35 632
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Table 4. Kinetic Parameters of Hydrotreating Reactions A0 (mol/cm3)1n
the final distributor. Quench tubes bring cold hydrogen quench gas inside the reactor (some are very simple, just a tube with many holes in series). In the liquid collector and redistributor, liquids are forced to flow down two angled slides into a canal. The benefit of using these slides is to give the liquids some angular momentum, while the canal gives them time to mix. In the gasliquid mixing zone, a tray (usually a bubble-cap tray) provides good contact between gases and liquids from the redistribution zone. Finally, a fine spray of fluids is sent down to the catalyst bed below via the final distributor.46 3.3.1. Model for the Quench Zone. The quench zone is represented as a mixer of the quench stream and the effluent from the first catalytic bed, as shown in Figure 2. The following expressions describe the quench zone mass balances. ð17Þ
Gas :q þ gout ¼ gin
ð18Þ
Liquid : lout ¼ lin
ð19Þ
eq 17 describes the global mass balance between the liquid and gas stream leaving the first catalyst bed (lout + gout) and the resulting liquid and gas streams entering the next catalyst bed (lin + gin), after mixing with the quench fluid (q). eqs 18 and 19 represent the gas and liquid mass balances, respectively. To determine the necessary quench fluid rate to cool the effluent to a certain temperature, the following energy balance equation with temperature dependent heat capacities is used to simulate the cooled mixture temperature (Tin) at constant quench rate or the quench rate for a fixed mixture temperature: Z T in Tout
lout CLp dT þ
q ¼ yg F
Z T in Tout
gout CGp dT þ
Z T in TQ
qCQp dT ¼ 0
n
m
EA (J/mol)
HDS
1.147
0.4709
50264.10
2026.23
HDN
1.672
0.3555
71775.5
2.85 107
HDAs
1.452
0.3068
HDV
1.251
0.6337
46181.6
126566
HDNi
1.688
0.5667
37678.3
1.045 108
104481
2.56134 108
bed, which can be assumed to be equal to the feed flow rates (lF and gF).16,18,48 3.3.2. Case Study. The kinetic model equations for HDS, HDN, HDAs, HDV, and HDNi reactions, in addition to middle distillate yields presented in previous work,47 together with the heat balance equation, are used for the quenching process. At a temperature above 410 °C, thermal cracking of hydrocarbons becomes more significant, leading to catalyst deactivation, and so, the maximum allowed temperature limit at the end of each bed (Tout) is set to 6 °C above the inlet temperature (400 °C). Once such a limit is reached, an appropriate quantity of hydrogen quench is injected to reduce the temperature to the inlet value.46 Also, the temperature of the hydrogen quench fluid is kept fixed at 70 °C.18 We note that the heat released by the HDV, HDNi, and HDAs, and hydrocracking is negligible in comparison with that of HDS and HDN4951 and the values of heat of reactions are12 251 kJ/kmol and 64.85 kJ/kmol for HDS and HDN, respectively. The following cases are considered: Case A: Base Hydrotreater. As mentioned earlier, hydrotreating processes generate copious amounts of heat during industrial HDT, which is reflected in a sharp temperature rise along the catalyst bed length. Thus, this case is identical to the crude oil hydrotreater with the absence of a quench stream. This case is studied as a reference to analyze the influence of quenching process on the reactor behavior. Case B: Hydrogen Quenching. Part of the hydrogen stream is used for quenching the hydrotreating reactions in addition to enhancing the composition of the gas phase. Because this technique is usually utilized for the hydrotreating operations, the present analysis will provide a better understanding of these systems. For the hydrogen quenching system, the best quench position and quench flow rate (described by the mass flow rate fraction of hydrogen quench of the main hydrogen feed) using many simulations on the reaction system is investigated with constraints on temperature. The target of this case is to keep the temperature between 400 °C (the base reactor temperature) and 406 °C (the maximum allowed temperature at the end of each bed (Tout)) with minimum total annual cost. Increasing the number of quenches leads to an increase in the capital cost of the reactor as well as the compressor. Also, increasing the quench flow rate increases the compressor capacity, and as a result, the compression cost will increase. The total annual cost (TAC) can be described as follows:
Figure 2. Model representation of quench zone.24
Global : q þ lout þ gout ¼ lin þ gin
(cm3/g s) (mol/cm3)m
reaction
ð20Þ
TAC ð$=yrÞ ¼ annualized capital cost ð$=yrÞ þ operating cost ð$=yrÞ
ð22Þ
capital cost ð$Þ ¼ reactor cost ð$Þ þ compressor cost ð$Þ
ð23Þ
ð21Þ
To solve the energy balance, it is required to know the liquid and gas mass flow rates at the exit of the first catalyst 633
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Table 5. Kinetic Parameters for Increasing of Oil Distillate Yields Temp reaction
kinetic constant
order (nj)
(wt %)1n h1
335 °C
n1 = 1.994
k1 k2
0.00822 0.00034
k3
0.0
0.01883
0.03754
82.77
k4
0.00261
0.00895
0.02327
114.54
k5
0.0
0.02161
0.10319
187.55
k6
0.0
0.00162
0.00595
155.55
k7
0.0
0.00010
0.00044
171.65
k8 k9
0.0 0.0
0.01344 0.0
0.03268 0.0
106.52
EA 370 °C
400 °C
(kJ/mol)
0.03724 0.00253
0.12000 0.01185
140.38 185.14
RCR
LGO n2 = 1.300
Figure 3. Reactor temperature profiles for base hydrotreater along bed length.
Table 7. Quench Positions and Rate Fractions for Two Beds with Cost Results
HK n3 = 1.114
reactor
Naphtha k10
n4 = 1.000
0.0
0.0
0.0
quench
quench rate
temp
TAC
position (z/L)
fraction (%)
(°C)
($/yr)
first bed
TF
400.0
Tout 404.8 quench zone 1
Table 6. Optimal Results of LR/DR Ratio decision variable type
0.1
second bed values
LR/DR
2.66
LR (cm)
1061.16
DR (cm)
398.80
CR ($)
2 371 882.80
first bed quench zone 1 second bed
To calculate the annualized capital cost (ACC) from capital cost (CC), the following equation is used:19
hp ηise
hp ¼ ðð3:03 105 Þ=γÞPin Q inððPout =Pin Þγ 1Þ γ¼
ððCp H2 =Cv H2 Þ 1Þ ðCp H2 =Cv H2 Þ
Cv H 2 ¼ C p H 2 R
400.0
TP
411.3
TF
400.0
760741.60
0.2
y1 = 18.45 (%) Tin TF
400.0 400.0
TP
408.2
TF
400.0
783393.90
Tout 410.0 quench zone 1 second bed
ð24Þ
n is number of years, and i is the fractional interest per year; n = 10 years; i = 5%.19 Reactor cost is determined by eqs 12 and 13, and the compressor cost (Ccomp) is estimated from the following correlations:30 M&S Ccomp ð$Þ ¼ ð517:5ÞðbhpÞ0:82 ð2:11 þ Fd Þ ð25Þ 280 bhp ¼
400.0
TF
Tout 407.9
first bed
ið1 þ iÞn ACC ¼ CC ð1 þ iÞn 1
y1 = 11.10 (%) Tin
0.3
y1 = 23.40 (%) Tin
400.0
TF
400.0
TP
406.1
798624.50
The operating cost (OP), which includes the compression cost, is estimated by using the following equation, which is based upon a motor efficiency of 90%52 and an average power price of 0.062 $/kWh:16 compression cost ð$=yrÞ bhp ðhpÞ 1 kW 0:062$ 24 h 340 day ¼ 0:9 1:341 hp kWh 1 day 1 yr ð30Þ
ð26Þ
4. RESULTS AND DISCUSSION The industrial trickle-bed reactor in this work has a processing capacity of 10 000 bbl/day (66.243 m3/h). The reactor is assumed to operate for 340 days/yr. In an earlier work,1 it was found that the hydrotreating process (from experimental results) shows maximum conversion of sulfur, nitrogen, vanadium, nickel, and asphaltene at a reaction temperature of 400 °C, liquid hourly space velocity (LHSV) of 0.5 h1, and hydrogen pressure 10 MPa. Also, the benefit of whole crude oil hydrotreating was an increased of middle distillate yields, owing to conversion of
ð27Þ ð28Þ ð29Þ
The isentropic efficiency (ηise) is assumed to be 80%, and Fd = 1 for a centrifugal motor compressor.30,52 634
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Figure 4. Reactor configuration with three catalyst beds and two quench zones.
Table 8. Different Simulation Results for Three Beds with Cost Results case
Z1 (cm)
y1 (%)
Tin1 (°C)
Z2 (cm)
y1 (%)
Tin2 (°C)
Z3 (cm)
TP (°C)
TAC ($/yr)
case 1 case 2
141.96 141.96
4.59 4.59
404 404
74.67 74.67
4.64 9.29
404 402
844.52 844.52
412.1 410.1
787568.9 801919.3
case 3
141.96
4.59
404
74.67
13.97
400
844.52
408.1
816360.7
case 4
141.96
9.24
402
180.39
4.65
404
738.80
410.1
801919.4
case 5
141.96
9.24
402
180.39
9.29
402
738.80
408.1
816226.6
case 6
141.96
9.24
402
180.39
13.97
400
738.80
406.0
830626.3
case 7
141.96
13.93
400
338.18
4.59
404
581.01
408.1
816214.1
case 8
141.96
13.93
400
338.18
9.24
402
581.01
406.0
830478.8
case 9
141.96
13.93
400
338.18
13.93
400
581.01
404.2
844837.6
4.2. Ratio of LR/DR. The optimization results of the decision variables for LR/DR ratio showed that the critical point where LR/DR equals the right side of eq 7 can be achieved at LR/DR of 2.534. On the other hand, below this ratio the effect of radial dispersion will appear and above this ratio there will be no impact of radial dispersion upon the process. Therefore, to ensure safe operation and to avoid any side effects of radial dispersion, 5% is added on LR/DR ratio. The optimal results with the final dimensions of the reactor are shown in Table 6. 4.3. Quench Results. The trickle-bed reactor model described previously is used to simulate the performance of an industrial crude oil hydrotreater with the proposed quenching schemes. Figure 3 shows the reactor temperature profiles for case A (without quench). As can be seen from this figure, the base case presents a reactor temperature increase as high as 16 °C, reaching almost 416 °C along the catalyst bed length. Operating at such high temperatures will increase the thermal cracking of hydrocarbons and consequently cause more deposits of carbon on the catalyst that will close the active sites of the utilized catalyst, leading to rapid deactivation and reduction of the cycle life of the catalyst. Therefore, it is necessary to control the reactor temperature with certain temperature range in the beds, which is discussed with case B. For case B, the simulation results at different quench positions (as a relative reactor length, z/L), quench rate fraction, temperature profiles using one quenchtwo beds, and total annualized cost are listed in Table 7. As can be seen from this table, with the first simulation at a quench position of 0.1 of the total bed length (which means that the hydrogen quench should be injected at this position), the best quench rate fraction to return the temperature to the base hydrotreater temperature (i.e., 400 °C) is 11.10% of the main hydrogen feed
high molecular weight compounds (that found in heavy cuts, particularly asphaltenes) into light compounds, which was observed at these conditions. Crude oil hydrotreating reactions reinforces hydrogenation of the cracked compounds that lead to increased in the H/C ratio of the products and, as a result, reduces the coke formation and catalyst deactivation. Therefore, in this study, reactor temperature (400 °C), LHSV (0.5 h1), and pressure (10 MPa) are used at z = 0. These conditions are used as a typical operating condition for the commercial trickle-bed reactor. The reactor volume can be calculated from the liquid hourly space velocity (LHSV) as follows:46 total volumetric f eed f low rate to the reactor LHSV ðh1 Þ ¼
m3 h
!
total catalyst volume ðm3 Þ
ð31Þ 4.1. Kinetic Parameters Estimation. The optimal set of kinetic parameters for crude oil hydrotreating reactions (including HDS, HDN, HDAs, HDV, HDNi, and middle distillate yields) have been estimated by minimizing the sum of the squared error between experimental and calculated data using a Successive Quadratic Programming (SQP) method within gPROMS software. Such kinetic parameters were validated by pilot-plant experiments, and these kinetic parameters (summarized in Tables 4 and 5) have been developed accurately with an average absolute error less than 5% among all results for all reactions, which can be confidently used for reactor design, operation, and control. Further details about kinetic models can be found in Jarullah et al.47 Note, in the process model developed and used in this work, kinetic parameters could be a source of uncertainty. However, sensitivity analysis of these parameters has been presented in detail in Jarullah et al.6 635
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Figure 5. Temperature profiles for cases 6, 8, and 9 with two quenchesthree beds.
at this position, and the temperature at the end of the first bed is 404.79 °C (i.e., below the maximum allowed temperature). After quench zone 1, the temperature of the mixture was returned to the base hydrotreater temperature, which is then the flow inlet temperature to the next bed. For the second bed, it is clearly observed that the temperature at the end of the second bed is around 411.35 °C (i.e., above the maximum allowed temperature), which means that the temperature of the second bed needs to be controlled. For a quench position at 0.2 of the total bed length, the temperature at the end of the first and second beds is higher than
406 °C. While the quench position at 0.3 of the total bed length showed that the temperature at the end of the first bed is higher than 406 °C in spite of the temperature at the end of the second bed being almost 406 °C. From the results presented in Table 7, it is clearly observed that extra quench need to be injected. On the other hand, the catalyst bed length should be divided into more than two beds. Thus, resimulation of processes with three beds and two quenches are studied. The reactor configuration with three catalyst beds and two quenches is illustrated in Figure 4. 636
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Figure 6. Conversion results for case 6 and 8. Figure 8. Impurities removal profiles along the industrial bed length.
Figure 9. Hydrogen profiles with quenching along the industrial bed length.
Figure 7. Industrial TBR configuration with feedstocks and products properties.
The aim of this process is to obtain the best annual cost at the best length of each bed while keeping the temperature between 400 and 406 °C along each bed length. A number of simulations based on different Tin1 and Tin2 have been carried out. Three values of Tin1 and Tin2 (such as 404, 402, and 400 °C) are considered. The simulation results for two quenchthree beds are shown in Table 8. In each case, the first step is to calculate the first bed length (Z1) that gives 406 °C at the end of the bed. In the
Figure 10. Hydrogen sulfide profiles with quenching along the industrial bed length.
second step, the first quench rate fraction (y1) is estimated to reduce the temperature in quench zone 1 to Tin1 (for example, 404 °C). Each temperature value of Tin1 (regarded as the initial temperature to the next bed) is used to determine the length of the 637
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due to increase in the reactor diameter. As a result, the surface area of the catalyst particles increased and consequently the compounds transported on the surface of the catalyst where the reactions happen (more diffusion of these compounds) will increase. Therefore, the chemical reaction rates increased, which leads to higher conversion. It has also been found that the middle distillate yields increased during simulation process of industrial trickle-bed reactor employed for crude oil hydrotreating and gives yields slightly higher than those obtained by pilot-plant performance of these fractions (naphtha, heavy kerosene, and light gas oil, in addition to gases and reduced crude residue). This behavior is quite clear at high reaction temperature. As discussed previously, the kinetic model used for all hydrotreating reaction, including middle distillate yields models (which are its reaction rate constants described by the Arrhenius equation), is mainly a function of reaction temperature. Concentration profiles of hydrogen partial pressure along the industrial bed length are illustrated in Figure 9. Hydrogen partial pressure decreases along the catalyst bed reactor as a result of hydrogen consumption. A similar observation was reported previously.46 After the quench zones, H2 partial pressures increased above the value before or without quenching. This jump in hydrogen partial pressure is produced by hydrogen quenching due to temperature reduction. Solubility of hydrogen is directly proportional to temperature; hence, when the temperature decreases, the dissolved hydrogen is transferred to the gas phase, and consequently, its partial pressure will increase. The partial pressure of hydrogen sulfide has the opposite phenomena, as shown in Figure 10. It is decreased along the reactor bed due to sulfur removal. At the quenching positions, the values of H2S partial pressure are reduced below that before quenching (seen in Figure 10 inset) because the temperature has the contrary effect on H2S solubility in comparison to that of hydrogen. Figure 11 shows the gas velocity profiles (ug), the latter relative to that at the entrance of the reactor at reactor temperature 400 °C, pressure 10 MPa, and LHSV 0.5 h1. The gas velocity profile gives an indication of the effect of the quench system upon the process. Differently from the laboratory scale, superficial gas velocity is influenced by the temperature rise in addition to the hydrogen consumption. Temperature rise produces expansion of the gas leading to increase in a gas velocity. Such an impact copes with the loss in gas volume by hydrogen consumption. In general, the profile of gas velocity is similar to that of hydrogen partial pressure presented in Figure 7. Gas velocity decreases gradually as a result of hydrogen consumption, which is more intense at high temperatures. Also, there is a jump in gas velocity among catalyst beds due to hydrogen quenching that increases of much greater magnitude.
Figure 11. Gas velocity profiles with and without hydrogen quenching.
second bed length (Z2) that gives 406 °C at the end of the bed is calculated. Then, the second quench rate fraction (y2) is calculated in quench zone 2 to reduce the temperature to Tin2 (such as 404 °C). Finally, the product temperature at the end of the third bed (TP) for each case is evaluated after estimating the remaining part of the bed length (which is Z3). At each case, the total annual cost is determined. It is noted from Table 8 that the constraint on temperature can be achieved by using cases 6, 8, and 9 with temperatures between 400 and 406 °C in each bed, which are shown in Figure 5 along the bed length for each case. The total annual cost (TAC) for case 9 is higher than those of cases 6 and 8; thus, case 9 is not taken into account. Although the TAC of case 8 is slightly lower than case 6, the comparison of the results of impurities removal is necessary (shown in Figure 6). As can be seen from this figure, the conversion of the impurities utilizing case 8 is the best compared with the conversions obtained via case 6. In other words, the quench rate fraction (y1 and y2) of the main hydrogen feed should be injected at positions Z1 and Z2, respectively, using case 8 to keep the allowed temperature limit under control. Therefore, case 8 is selected to describe the behavior of industrial trickle-bed reactor for crude oil hydrotreating. 4.4. Simulation of an Industrial HDT Reactor. The reactor model is applied to the prediction of the expected performance of the large-scale (industrial) trickle-bed reactor used for crude oil hydrotreating. The industrial TBR configuration that involves all oil feed and products content with quenching is shown in Figure 7. The impurities removal profiles of sulfur, nitrogen, asphaltene, vanadium, and nickel along the industrial bed length are presented in Figure 8. Because the large-scale (industrial) reactor operates non-isothermally, the industrial process usually is observed to give smaller impurities contents than the pilotplant results.13 It has also been noted that the industrial reactor achieves a higher conversion of sulfur, nitrogen, asphaltene, vanadium, and nickel than those obtained at pilotplant reactor.6 This gain in conversions is mainly attributed to the higher reactor temperature that increases the reaction rates of these reactions (all the kinetics parameters used for describing hydrotreating reactions are effected by the reaction temperature). Also, the bed void fraction of the catalyst, which depends mainly on the reactor diameter, is increased in the industrial trickle-bed reactor
5. CONCLUSIONS The mathematical model developed earlier and validated against an isothermally operated steady state hydrotreating reactor is further refined in this work to allow the investigation of the behavior of a large-scale hydrotreating reactor. The scale up was facilitated by incorporating energy balance (to represent nonisothermal operation), optimal reactor dimension (to maintain similar hydrodynamic condition as in the pilot-plant reactor), and a quench system model equations (to add temperature control of the large-scale reactor). It can be concluded that the decrease in LR/DR ratio leads to increase the hydrodynamic 638
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FC, Fm, Fp, Fd = dimensionless factors GL = liquid mass velocity, g/cm2 s g = gravitational constant, cm/s2 gi = gas mass flow rate of the i stream, g/s hi = Henry’s coefficient of i compound, MPa cm3/mol HK = heavy kerosene hp = compressor horsepower (hp) ki = kinetic constants, (wt %)1n h1 Kj = reaction rate constant for j reaction, (mol/cm3)1n (cm3/g s) (mol/cm3)m L Ki = gasliquid mass transfer coefficient for i compound, cm/s KSi = liquidsolid mass transfer coefficient for i compound, cm/s KH2S = adsorption equilibrium constant of H2S, cm3/mol LR = length of reactor bed, cm LHSV = liquid hourly space velocity, h1 LGO = light gas oil li = liquid mass flow rate of the i stream, g/s mj = order of reaction of hydrogen in reaction j M&S = Marshall and Swift index for cost escalation nj = order of reaction of i compound in reaction j MwH2 = molecular weight of H2, kg/kg mol P = reactor total pressure, bar Pe = Peclet number Pin = inlet pressure, lb/ft3 Pout = outlet pressure, lb/ft3 PG i = partial pressure of i compound, MPa q = quench mass flow rate, g/s Q = quench Qin = volumetric flow rate at compressor section, ft3/min ri = chemical reaction rate of i reaction, wt % h1 rj = chemical reaction rate of j reaction per unit mass of the catalyst, mol/g s1 R = universal gas constant, J/mol K ReL = Reynold number RCR = reduced crude residue Ti = temperature of the i stream, K TAC = total annual cost, $/yr ug = velocity of the gas, cm/s uL = velocity of the liquid, cm/s xi = mass fraction of i compound yi = weight fraction of compound i (wt %) y1,2 = mass flow rate fraction of quench fluid z = axial position along the catalyst bed, cm Z = compressibility factor
effects with decreases in the capital cost of the reactor. The optimal ratio of LR/DR was obtained at 2.661 to get safe operation and to prevent any side impacts. The kinetic models of hydrotreating reactions (including chemical reactions for HDS, HDN, HDV, HDNi, HDAs, and crude oil conversion to middle distillate yields) developed using pilot-plant experiments are assumed to represent the hydrotreating reactions of the largescale reactor (as a result of similar temperature and hydrodynamic conditions of the small- and large-scale reactors). Analysis of the large-scale (industrial) trickle-bed reactor with the optimal quench process has been studied using the process model for the purpose of the evaluating the viability of large-scale operation of the whole crude oil hydrotreating process. The model allowed us to describe the temperature and impurities removal profiles of an industrial reactor. The optimal quench system (the number of quenches, the quench position in the catalyst bed, and the quench flow rate) is determined through several simulations where the objective function was to minimize the total annual cost while controlling temperature within a given limit. It is found that the number of quenches during crude oil hydrotreating should be more than one in order to control the temperature inside the reactor and keep it below the maximum allowed temperature. In this work, the design of the reactor and the control of temperature are considered sequentially, which could have been done in an integrated fashion. The concentration profiles of H2, H2S, S, N, Asph, V, and Ni along the industrial bed length have also been investigated. It has been found that the hydrogen partial pressure increased after each quench position with opposite phenomena of H2S. With scaling up process, it can be concluded that the conversions of sulfur, nitrogen, asphaltene, vanadium, and nickel, in addition to middle distillate yields at industrial reactor, are higher than those obtained at pilot-plant reactor as a result of the non-isothermal mode of operation. Finally, we note that product specification of S, N, Asph, V and Ni could also been specified up front and the design of reactor and control of temperature could have been done in an integrated fashion but has not been the focus of this study.
’ AUTHOR INFORMATION Corresponding Author
*Fax: +44 (0)1274 235700. E-mail:
[email protected].
’ NOMENCLATURE ACC = annualized capital cost, $/yr aL = gasliquid interfacial area, cm1 aS = liquidsolid interfacial area, cm1 bhp = brake horsepower A0j = pre-exponential factor for reaction j, (mol/cm3)1n (cm3/g s) (mol/cm3)m CC = capital cost, $ Ccomp = compressor cost, $ CLi = concentration of i compound in liquid phase, mol/cm3 CSi = concentration of i compound in solid phase, mol/cm3 CR = capital cost of the reactor, $ Cp = specific heat capacity, J/g K Cv = specific heat capacity at constant volume, J/g K DR = reactor diameter, cm DLr = radial mass dispersion coefficient, cm2/s ds = equivalent diameter of spherical catalyst particle, cm EAj = activation energy for j process, J/mol
Greek Letters
ΔHR = heat of reaction, J/mol FB = bulk density of the catalyst particles, g/cm3 F15.6 = density of oil at 15.6 °C, g/cm3 L FG = gas density, g/cm3 FL = oil density, g/cm3 ε = void fraction of the catalyst bed εg = gas phase fraction εl = liquid phase fraction ηj = effectiveness factor of j reaction ηise = isentropic efficiency μL = liquid viscosity, g/cm s γ = specific heat ratio τ = residence time (h) Superscripts
G = gas phase 639
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L = liquid phase or gasliquid interface S = solid phase or liquidsolid interface i = compound (H2, H2S, S, Asph, N, V or Ni)
(14) Mederos, F. S.; Ancheyta, J. Mathematical Modeling and Simulation of Hydrotreating Reactors: Cocurrent versus Countercurrent Operations. Appl. Catal., A 2007, 332, 8–21. (15) Alvarez, A.; Ancheyta, J. Modeling Residue Hydroprocessing in a Multi-Fixed-Bed Reactor System. Appl. Catal., A 2008, 351, 148–158. (16) Alvarez, A.; Ancheyta, J.; Mu~ noz, J. A. D. Modeling, Simulation, and Analysis of Heavy Oil Hydroprocessing in Fixed-Bed Reactors Employing Liquid Quench Streams. Appl. Catal., A 2009, 361, 1–12. (17) Perry, R. H.; Green, D. W. Perry’s Chemical Engineers’ Handbook; McGraw-Hill: New York, 1999. (18) Alvarez, A.; Ancheyta, J. Simulation and Analysis of Different Quenching Alternatives for an Industrial Vacuum Gas Oil Hydrotreater. Chem. Eng. Sci. 2008, 63, 662–673. (19) Smith, J. M.; Van Ness, H. C.; Abbott, M. M. Introduction to Chemical Engineering Thermodynamics; McGraw-Hill: New York, 1996. (20) Wauquier, J. P. Crude Oil Petroleum Products Process Flowsheets; Institute Francais du Petrole: Paris, 1995. (21) Chen, H.; Zheng, J.; Xu, P.; Li, L.; Liu, Y.; Bie, H. Study on RealGas Equations of High Pressure Hydrogen. Int. J. Hydrogen Energy 2010, 35, 3100–3104. (22) Gunjal, P. R.; Ranade, V. V. Modeling of Laboratory and Commercial Scale Hydro-Processing Reactors Using CFD. Chem. Eng. Sci. 2007, 62, 5512–5526. (23) Al-Dahhan, M. H.; Dudukovic, M. P. Catalyst Wetting Bed Dilution for Improving Catalyst in Laboratory Trickle-Bed Reactors. AIChE J. 1996, 42, 2594–2606. (24) Lamine, A. S.; Colli, M. T.; Wild, G. Hydrodynamics and Heat Transfer in Packed Bed with Cocurrent Upflow. Chem. Eng. Sci. 1992, 47, 3493–3500. (25) Mary, G.; Chaouki, J.; Luck, F. Trickle-Bed Laboratory Reactors for Kinetic Studies. Int. J. Chem. React. Eng. 2009, 7, 1–68. (26) Ancheyta, J.; Marroquín, G.; Angeles, M. J.; Macías, M. J.; Pitault, I.; Forissier, M.; Morales, R. D. Some Experimental Observations of Mass Transfer Limitations in a Trickle-Bed Hydrotreating Pilot Reactor. Energy Fuels 2002, 16, 1059–1067. (27) Mederos, F. S.; Rodríguez, M. A.; Ancheyta, J.; Arce, E. Dynamic Modelling and Simulation of Catalytic Hydrotreating Reactors. Energy Fuels 2006, 20, 936–945. (28) Cotta, R. M.; Wolf-Maciel, M. R.; Filho, R. M. A Cape of HDT Industrial Reactor for Middle Distillates. Comput. Chem. Eng. 2000, 24, 1731–1735. (29) Gunn, D. J. Axial and Radial Dispersion in Fixed Beds. Chem. Eng. Sci. 1987, 42, 363–373. (30) Douglas, J. M. Conceptual Design Chemical Processes; McGrawHill: New York, 1988. (31) Chemical Engineering, Economic Indicators, www.che.com Accessed January 16, 2010. (32) Morrison, K. R. Optimal Control of Processes Described by Systems of Differential-Algebraic Equations, PhD Thesis; University of London: UK, 1984. (33) Ekpo, E. E.; Mujtaba, I. M. Performance Analysis of Three Controllers for the Polymerization of Styrene in a Batch Reactor. Chem. Prod. Process Model. 2007, 2, 1–29. (34) Shah, Y. T.; Paraskos, J. A. Criteria for Axial Dispersion Effects in Adiabatic trickle-Bed Hydroprocessing Reactors. Chem. Eng. Sci. 1975, 30, 1169–1176. (35) Alvarez, A.; Ancheyta, J.; Mu~ noz, J. A. D. Comparison of Quench Systems in Commercial Fixed-Bed Hydroprocessing Reactors. Energy Fuels 2007, 21, 1133–1144. (36) Song, Ch. An Overview of New Approaches to Deep Desulfurization for Ultra-Clean Gasoline, Diesel Fuel, and Jet Fuel. Catal. Today 2003, 86, 211–263. (37) Bharvani, R. R.; Henderson, R. S. Revamp Your Hydrotreater for Deep Desulfurization. Hydrocarbon Process. 2002, 81, 61–64. (38) Hanika, J.; Sporka, K.; Ruzicka, V.; Pistek, R. Dynamic Behavior of an Adiabatic trickle-Bed Reactor. Chem. Eng. Sci. 1977, 32, 525–528.
Subscripts
0 = at the first reactor length Comp = compressor f = at the end of reactor F = feed g = gas G = gas G = gases i = compound (H2, H2S, S, Asph, N, V, Ni, RCR, LGO, HK, naphtha or gases) ise = isentropic J = reaction (HDS, HDN HDAs, HDV or HDNi) l = liquid L = liquid R = reactor r = radial s = spherical
’ REFERENCES (1) Jarullah, A. T.; Mujtaba, I. M.; Wood, A. S. Improvement of the Middle Distillate Yields during Crude Oil Hydrotreatment in a trickleBed Reactor. Energy Fuels 2011, 25, 773–781. (2) Areff, H. A. The Effect of Operating Conditions on Vacuum Gas Oil Hydrotreating on Sulfur and Aromatics Content, MSc Thesis; University of Tikrit: Iraq, 2001. (3) Macías, M. J.; Ancheyta, J. Simulation of an Isothermal Hydrodesulfurization Small Reactor with Different Catalyst Particle Shapes. Catal. Today 2004, 98, 243–252. (4) Jarullah, A. T.; Mujtaba, I. M.; Wood, A. S. Kinetic Parameter Estimation and Simulation of trickle-Bed Reactor for Hydrodesulfurization of Crude Oil. Chem. Eng. Sci. 2011, 66, 859–871. (5) Jarullah, A. T.; Mujtaba, I. M.; Wood, A. S. Kinetic Model Development and Simulation of Simultaneous Hydrodenitrogenation and Hydrodemetallization of Crude Oil in trickle-Bed Reactor. Fuel 2011, 90, 2165–2181. (6) Jarullah, A. T. Kinetic Modelling Simulation and Optimal Operation of trickle-Bed Reactor for Hydrotreating of Crude Oil, PhD Thesis; University of Bradford: UK, 2011. (7) Jarullah, A. T.; Mujtaba, I. M.; Wood, A. S. Enhancement of Productivity of Distillate Fractions by Crude Oil Hydrotreatment: Development of Kinetic Model for the Hydrotreating Process. Comput.-Aided Chem. Eng. 2011, 29, 261–265. (8) Stefanidis, G. D.; Bellos, G. D.; Papayannakos, N. G. An Improved Weighted Average Reactor Temperature Estimation for Simulation of Adiabatic Industrial Hydrotreaters. Fuel Process. Technol. 2005, 86, 1761–1775. (9) Sie, S. T. Scale Effects in Laboratory and Pilot-Plant Reactors for Trickle-Flow Processes. Rev. Inst. Fr. Pet. 1991, 46, 501–515. (10) Mederos, F. S.; Ancheyta, J.; Chen, J. Review on Criteria to Ensure Ideal Behaviors in Trickle-Bed Reactors. Appl. Catal., A 2009, 355, 1–19. (11) Bhaskar, M.; Valavarasu, G.; Sairam, B.; Balaraman, K. S.; Balu, K. Three-Phase Reactor Model to Simulate the Performance of PilotPlant and Industrial Trickle-Bed Reactors Sustaining Hydrotreating Reactions. Ind. Eng. Chem. Res. 2004, 43, 6654–6669. (12) Tarhan, O. M. Catalytic Reactor Design; McGraw-Hill: New York, 1983. (13) Rodriguez, M. A.; Ancheyta, J. Modeling of Hydrodesulfurization (HDS), Hydrodenitrogenation (HDN), and the Hydrogenation of Aromatics (HDA) in a Vacuum Gas Oil Hydrotreater. Energy Fuels 2004, 18, 789–794. 640
dx.doi.org/10.1021/ef201406r |Energy Fuels 2012, 26, 629–641
Energy & Fuels
ARTICLE
(39) Furimsky, E.; Massoth, F. E. Deactivation of Hydroprocessing Catalysts. Catal. Today 1999, 52, 381–495. (40) Mu~noz, J. A. D.; Alvarez, A.; Ancheyta, J.; Rodríguez, M. A.; Marroquín, G. Process Heat Integration of a Heavy Crude Hydrotreatment Plant. Catal. Today 2005, 109, 214–218. (41) Arpornwichanop, A.; Kittisupakorn, P.; Mujtaba, I. M. Dynamic Modeling of Catalytic Hydrogenation of Pyrolysis Gasoline in TrickleBed Reactor. Comput.-Aided Chem. Eng. 2002, 10, 421–426. (42) Satterfield, C. N. Trickle-Bed Reactors. AIChE J. 1975, 21, 209–228. (43) Furimsky, E. Selection of Catalysts and Reactors for Hydroprocessing. Appl. Catal., A 1998, 171, 177–206. (44) Van Hasselt, B. W.; Lebens, P. J. M.; Calis, H. P. A.; Kapteijn, F.; Sie, S. T.; Moulijn, J. A.; van den Bleek, C. M. A Numerical Comparison of Alternative Three-Phase Reactors with a Conventional Trickle-Bed Reactor. The Advantages of Countercurrent Flow for Hydrodesulfurization. Chem. Eng. Sci. 1999, 54, 4791–4799. (45) Hsu, C. S.; Robinson, P. R. Practical Advances in Petroleum Processing; Springer: New York, 2006; Vol. 1. (46) Ancheyta, J.; Speight, J. G. Hydroprocessing of Heavy Oils and Residua; CRC Press: Boca Raton, FL, 2007. (47) Alvarez, A.; Ancheyta, J. Effect of Liquid Quenching on Hydroprocessing of Heavy Crude Oils in a Fixed-Bed Reactor System. Ind. Eng. Chem. Res. 2009, 48, 1228–1236. (48) Murali, C.; Voolapalli, R. K.; Ravichander, N.; Gokak, D. T.; Choudary, N. V. trickle-Bed Reactor Model to Simulate the Performance of Commercial Diesel Hydrotreating Unit. Fuel 2007, 86, 1176–1184. (49) Kam, E. K. Y.; Al-Shamali, M.; Juraidan, M.; Qabazard, H. A Hydroprocessing Multicatalyst Deactivation and Reactor Performance Model-Pilot-Plant Life Test Applications. Energy Fuels 2005, 19, 753–764. (50) Juraidan, M.; Al-Shamali, M.; Qabazard, H.; Kam, E. K. Y. A Refined Hydroprocessing Multicatalyst Deactivation and Reactor Performance Model-Pilot-Plant Accelerated Test Applications. Energy Fuels 2006, 20, 1354–1364. (51) Marafi, A.; Maruyama, F.; Stanislaus, A.; Kam, E. Multicatalyst System Testing for Upgrading Residual Oils. Ind. Eng. Chem. Res. 2008, 47, 724-741. (52) Bouton, G. R.; Luyben, W. L. Optimum Economic Design and Control of a Gas Permeation Membrane Coupled with the Hydrotreating (HAD) Process. Ind. Eng. Chem. Res. 2008, 47, 1221–1237.
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