Energy Fuels 2010, 24, 1475–1478 Published on Web 02/11/2010
: DOI:10.1021/ef9014586
Hydrotreating Residue Deactivation Kinetics and Metal Deposition Lijing Jiang,*,†,‡ Yanbo Weng,‡ and Changhou Liu† †
School of Chemical Engineering, Dalian University of Technology, Dalian, 116012, China, and ‡Fushun Research Institute of Petroleum and Petrochemicals, SINOPEC, Fushun, 113001, China Received October 16, 2009. Revised Manuscript Received January 28, 2010
Introducing a fractional type of time-varying activity coefficient, the residue hydrotreating catalyst deactivation kinetic model was established. On the basis of the operating conditions and data collected from a commercial residue hydrotreating units, the kinetic parameters such as the reaction order, activation energy, reaction rate constant, and the activity coefficient were estimated, and the time-varying equations of the activity coefficient were obtained. By timely measuring the metal content of inlet and outlet flows of the catalytic bed, and through its material balance of each period, the amount of metal deposition accumulating on the catalyst was determined. The relationship between the metal deposition and the activity coefficient was analyzed and discussed. It was found that the amount of metal deposition was linearly related with the activity coefficient in the stable operation period, this feature indicated that the reaction rate was controlled by pore diffusion in this stage. Combining the empirical equations correlated to metal deposition mount and activity coefficient with real running time, the operation period of the commercial units could be predicted.
expressed as an exponential equation, as in eq 1
1. Introduction
dCi ¼ kCin dt
Residue from the hydrotreating process contains a large amount of impurities, such as resin, asphaltene, metal, and so on. A lot of coke and metal sulfides would be generated in the residue of the hydrotreating process. During the reaction, the coke and metal sulphides will continuously deposit on the catalyst surface, cover the active sites of catalyst, block catalyst pores, and result in catalyst deactivation. Among them, the metal ions react with H2S to form metal sulphides and deposit at the catalyst surface, which is the one of main causes for catalyst deactivation. Therefore, the study of metal deposition and its effect on the catalyst deactivation plays an important role and has a practical significance in catalyst development. There are many studies on the residue hydrotreating reaction and deactivation characteristics at home and abroad,1-3 but most of them were only conducted in the laboratory, and the results had difficulty in predicting commercial operations. In this paper, based on the operation data, the hydrotreating reaction and deactivation characteristics were studied.
For making up the difference between the actual operation and equation calculation, the activity coefficient R should be introduced into eq 1, which is expressed as eq 2. dCi ¼ kCin R dt
ð2Þ
where R is related to the running time θ of the catalyst. Wojchiechowski gave a comprehensive relationship between R and run-time θ;4 Among them, the exponential model proposed by Levenspiel,5 R ¼ R0 expð-kd θÞ
ð3Þ
and more common used model is recommended by Chen,6 1 R ¼ ð4Þ β θ 1þ θC where θ is the real running time in commercial units, days; θC is the catalyst lifetime, in days; and β is the deactivation exponential of catalyst. Using two types of models above to fit the operating data, the results shown that latter model was more suitable. In this paper, the deactivation eq 4 was used to establish residue hydrotreating deactivation kinetics. 2.2. Hydrotreating Commercial Units and Data Collection. The residue hydrotreating unit has a processing capacity of 2 Mt/y, and the feedstock is Middle East sour residue. The
2. Residue Hydrotreating Deactivation Kinetics 2.1. Choice of Deactivation Kinetics Model. In most cases, including HDM (hydrodemetallization), HDS (hydrodesulphurization), HDN (hydrodenitrogenation), or removal of CCR (Conradson carbon residue), research results of hydrotreating kinetics have shown that the reaction rate could be *To whom correspondence is addressed. E-mail: jianglijing@ fripp.com.cn. (1) Callejas, M. A; Martinez, M. T. Energy Fuels 1999, 13 (3), 629– 636. (2) Susana, T.; Maria, A.; Martinez, M. T. Ind. Eng. Chem. Res. 1998, 37 (1), 11–17. (3) Reyes, L.; Zerpa, C.; Krasuk, J. H. Stud. Surf. Sci. Catal. 1994, 88, 85. r 2010 American Chemical Society
ð1Þ
(4) Wojchiechowski, B. W. J. Chem. Eng. 1968, 46 (1), 48–52. (5) Levenspiel, O. J. Catal. 1972, 25 (2), 265–272. (6) Junwu, C. Catalytic Cracking Technology and Engineering; China Petrochemical Press: Beijing, 2001.
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Energy Fuels 2010, 24, 1475–1478
: DOI:10.1021/ef9014586
Jiang et al.
Table 1. Residue Hydrotreating Typical Plant Operation Data concentration in inlet flow serial number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
concentration in outlet flow
date
run time (day)
residence time(h)
reaction temperature(k)
Ni (μg/g)
V (μg/g)
S (m%)
N (μg/g)
CCR (m%)
Ni (μg/g)
V (μg/g)
S (m%)
N (μg/g)
CCR (m%)
05-10-3 05-10-31 05-12-12 06-1-9 06-2-20 06-5-15 06-7-10 06-7-24 06-8-21 06-9-18 06-10-16 06-10-30 06-11-13 06-12-4 06-12-18 07-1-1 07-6-11 07-6-18 07-6-25 07-7-16 05-10-17 05-11-28 06-4-3 06-6-5 06-11-20
3 31 73 101 143 227 283 297 325 353 381 395 409 430 444 458 619 626 633 654 17 59 185 248 416
5.63 5.44 5.47 5.42 5.17 5.00 4.99 5.00 5.08 5.01 5.02 5.00 5.00 5.02 7.13 5.05 5.36 5.13 5.18 5.42 5.22 6.16 4.99 4.98 5.00
613.8 628.6 632.8 633.9 635.9 636.4 640.2 640.1 640.6 643.1 644.8 643.3 644.3 644.3 641 646.5 670.2 671.9 674.2 674 626.5 630.1 636.6 637.3 644.5
17.30 18.90 13.90 15.00 18.00 22.50 28.80 26.80 18.00 15.40 11.00 21.60 22.30 12.30 15.90 22.00 24.30 23.00 20.10 15.70 18.2 12.2 12.2 12.5 17.9
26.90 24.10 44.80 51.20 47.40 55.00 91.40 82.80 33.50 27.70 28.80 67.90 72.00 23.70 26.10 39.90 49.20 42.10 29.40 25.70 32.1 15.3 15.3 23.7 41.6
2.64 2.27 3.44 3.64 2.36 2.62 2.66 2.63 2.22 2.24 2.92 3.03 2.89 2.56 2.09 1.95 2.41 2.57 2.54 2.28 2.45 2.02 2.02 2.68 2.56
2562 2597 2587 2781 2934 2700 3454 3598 2941 2875 2488 3902 3336 2754 2218 2275 3260 4341 3179 2468 2894 2661 2661 2943 2792
8.56 9.35 11.98 11.99 10.36 9.14 11.27 10.83 8.98 9.00 10.03 9.24 9.81 8.82 5.95 9.36 8.99 11.01 9.96 9.76 10.13 7.08 7.08 9.07 8.27
6.56 7.78 5.06 5.30 6.69 6.90 6.66 8.61 6.82 6.29 5.11 6.33 7.80 6.88 7.23 9.81 14.10 13.60 10.80 9.82 6.36 5.18 5.18 5.35 7.37
6.31 6.07 9.65 10.40 9.82 11.10 15.80 18.30 8.09 7.16 7.98 13.90 16.50 8.28 7.02 10.80 17.10 17.40 11.00 10.00 6.33 4.17 4.17 6.06 10.80
0.43 0.35 0.45 0.45 0.28 0.37 0.33 0.37 0.32 0.31 0.32 0.38 0.38 0.34 0.14 0.33 0.69 0.88 0.80 0.90 0.33 0.22 0.22 0.33 0.31
1215 1315 1387 1323 1470 1431 1703 1845 1191 1254 1054 1509 1447 1108 764 657 1771 2091 1720 1051 1325 1318 1318 1351 1327
4.59 4.73 5.25 5.31 5.55 4.40 5.01 4.95 4.84 3.72 4.97 4.08 4.14 4.91 2.51 3.81 5.93 7.07 6.45 6.20 5.09 3.85 3.85 4.78 4.32
Table 2. Residue Hydrotreating Deactivation Kinetic Parameters reaction
reaction order n
pre-exponential factor, k0
HDNi HDV HDS HDN HDCCR
1.81 1.17 1.08 1.01 1.41
1.336 2.663 385.935 6385.346 2037.297
activation energy, ΔE(J 3 mol-1) 19 991 14 366 36 427 56 553 54 676
catalyst lifetime (day)
5 5 8 9 8
532 637 583 622 568
Equation 5 is integrated into eq 6-1: When n = 1 2
unit contains 2 trains in parallel, and each train has 5 reactors in series. The loaded catalysts are FZC series catalysts. The running period was from October 3, 2005 to July 16, 2007. In this very period, 20 groups of operating data were collected for kinetic model fitting, which is shown in Table 1(data groups 1-20). 2.3. Model Establishment and Parameter Estimation. It is assumed that, in the process of hydrogenation reaction, kinetics of HDNi, HDV, HDS, HDN, and HDCCR (Hydrode-Conradson carbon residue) are complied with the exponential form of eq 1. For the convenience of calculation, the model could be simplified. Furthermore, it is also assumed that the reactions do not interfere with each other. So, the reaction rate can be expressed by eqs 2 and 4; On the basis of the above assumptions, and taking into consideration the temperature effect on the reaction, the reaction rate can be expressed as follow: dC -ΔE ¼ k0 exp Cn R dt RT
catalyst deactivation exponential, β
3
7 6 6 -k0 -ΔE 7 t7 exp Cout ¼ Cin exp6 β 7 6 RT 5 4 θ 1þ θC When n 6¼ 1 2
0
1
ð6-1Þ
3 -1=n -1
C 6 B 7 C -ΔE 7 6 B k0 C B 1 -n Cout ¼ 6 t7 C þ ðn -1Þ 7 6 in B β Cexp RT 5 4 @ θ A 1þ θC ð6-2Þ where Cin is the inlet concentration of an impurity and Cout is the outlet concentration of an impurity. Equation 6-1 includes five kinetic parameters: reaction order n, reaction pre-exponential factor k0, the reaction activation energy ΔE, deactivation exponential of catalyst of impurity removal β, and catalyst lifetime θC. Fitting with the operating data given in Table 1 with eqs 6-1 and 6-2, the five kinetic parameters above can be estimated. The fitting process is carried out by firstopt software. A set of kinetic
ð5Þ
where C is the impurity concentration, mg 3 g-1; t is the residence time of feedstock in the reactor, h; k0 is the preexponential factor; T is the reaction temperature, K; ΔE is the activation energy, J 3 mol -1; n is the kinetic order; and R is the catalyst activity coefficient. 1476
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: DOI:10.1021/ef9014586
Jiang et al.
Table 3. Residue Hydrotreating Deactivation Kinetics Model Cheching Results prediction of outlet concentration
prediction of removal ratio /%
relative error of removal ratio /%
serial number
Ni (μg/g)
V (μg/g)
S (m%)
N (μg/g)
CCR (m%)
Ni
V
S
N
CCR
Ni
V
S
N
CCR
21 22 23 24 25
6.60 6.04 5.41 5.53 7.43
7.86 7.25 4.37 6.25 10.61
0.39 0.28 0.29 0.37 0.34
1552 1591 1283 1409 1239
5.24 4.67 3.72 4.46 4.06
63.76 70.68 55.66 55.80 58.48
75.53 81.83 71.43 73.65 74.49
84.19 89.41 85.51 86.22 86.57
47.40 55.37 51.77 52.11 55.62
48.28 55.60 47.51 50.81 50.94
1.99 -0.19 3.27 2.45 0.59
5.92 -1.01 1.81 1.06 -0.61
2.70 -2.79 4.04 1.67 1.46
12.56 -1.94 -2.57 3.66 -6.00
2.97 -8.93 -4.15 -7.42 -6.66
parameters are obtained by regressing nonlinearly with Marquardt method (Levenberg-Marquardt) and general global optimization method. By substitution of the parameters into eqs 6-1 or 6-2, it can predict the impuritiy concentration of outlet flow and its relative errors. In order to make the model applicable to HDNi, HDV, HDS, HDN, and HDCCR simultaneously, all these operating data were taken on the same day. The fitting process optimized of outlet impurities concentration quadratic sum Σ(R1 - R2)2 as the smallest relative error objective function (where R1 is the actual outlet concentration of impurity and R2 is the calculated outlet concentration of impurity), and then chose the optimal kinetic parameters included above HDV, HDS, HDN, and HDCCR reaction kinetic parameters are listed in Table 2, respectively. 2.4. Model Results and Discussion. It can be seen from the kinetic parameters in Table 2 that all the residue hydrotreating reaction kinetics orders are between 1 and 2, which are well matched with literature reports of 1-2 and the commercial results. By comparing catalyst lifetime in each reaction, it can be found that HDNi catalyst deactivation is the fastest, while HDV catalyst deactivation is the slowest. Activation energy shows the difficulty in a reaction. The activation energies of HDN, HDS, and HDCCR are higher than that of HDM, so the HDM reaction only needs to overcome the lower energy barrier. Meanwhile, activation energy of HDNi is different from that of HDV, namely, activation energy of HDNi is higher than that of HDV. Beuther et al.1 thought that V was mainly located in the exterior of asphaltene micelles, whereas Ni was located in the interior of asphaltene micelles. Thus, the double inhibitions of stronger adsorption of the oxygen-contained ligand and effusion of Ni reaction made nickel removal more difficult, so the activation energy of HDNi is higher than that of HDV, which is consistent with the kinetics predictions. At the same time, activation energy also suggests the sensitivity of the reaction rate to the reaction temperature. By comparing individual activation energy of the reactions, it is indicated that reaction rates of HDN, HDS, and HDCCR are more sensitive to temperature, and temperature increase is more favorable to those reactions. However, the HDM reaction temperature should be lower than the other three reaction bed temperatures. 2.5. Model Checking Computations. Table 2 shows the outlet concentration and the removal ratio of the various impurities calculated by the kinetic model. Table 1 (data groups 21-25) shows the real commercial running results, which is used for checking the reliability of the kinetic model. The comparing results are shown in Tables 3 and 4. As shown in Table 4, the kinetic model of residue hydrotreating was used to predict the commercial residue hydrotreating process. All the average relative errors between
Table 4. Average Relative Error of Impurity Removal Rate of Verified Results of Kinetic Model HDNi
HDV
HDS
HDN
overall HDCCR average
average 1.70% 2.08% 2.53% 5.35% relative error
6.03%
3.54%
predicted and commercial results are lower than 5.0% except for HDN and HDCCR, which are slightly larger than 5.0%. The accuracy of the five impurity removal ratio meets the kinetics requirements, which are less than 10%,7 which proves that the deactivation kinetic model, as well as kinetic parameters, is correct and reliable. 3. Effect of Residue Hydrotreating Metal Deposition on the Catalyst Activity 3.1. Relationship between Metal Deposition and Running Time. The relationship between metal Ni and V deposition and running time could be expressed by the Voorhies empirical equation:8 CM ¼ Aθn ð7Þ where θ is the real running time of commercial units during data collection, days; and CM is the amount of metal deposition accumulating on the amount of catalyst, kg 3 kg-1. Fitting eq 7 with measured commercial results could get the empirical equation as following: CM ¼ ANi θnNi þ AV θnV ¼ 3:31 10 -4 θ0:8945 þ 6:39 10 -4 θ0:9619
ð8Þ
Apply this equation to calculate the amount of different running time. A and n are fitting parameters, depending on temperature and composition of feedstock. The metal concentration in inlet and outlet flows were measured at a series of time intervals, and the accumulated amount of metal deposition can be calculated by mass balance at the different time intervals. The results are shown in Table 5. The average relative error is 8.98%. 3.2. Correlations of Metal Deposition and Catalyst Activity Coefficient. The activity coefficients of metals Ni and V at different running time were calculated by the following deactivation kinetics model: 1
RNi ¼ 1þ
5 ;
θ 532
1
RV ¼ 1þ
θ 5 637
ð9Þ
The relationship between the amount of metal deposition at different running time and activity coefficient can be derived (7) Li, J.-w.; Li, Y.-x.; Qu, J.-h.; Chen, B.-h.; Li, C.-y. Oil J. 2005, 21, 69-75. (8) Voorhies, A. Ind.Eng.Chem. 1945, 37, 318.
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: DOI:10.1021/ef9014586
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Table 5. Comparison of the Amount of Metal Deposition of Operating Data and Fitting Data measured values CM (kg 3 kg-1)
serial No.
date
Ni
V
Ni þ V
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
05-10-3 05-10-31 05-12-12 06-1-9 06-2-20 06-5-15 06-7-10 06-7-24 06-8-21 06-9-18 06-10-16 06-10-30 06-11-13 06-12-4 06-12-18 07-1-1 07-6-11 07-6-18 07-6-25 07-7-16
0.0005 0.0061 0.0168 0.0175 0.0246 0.0407 0.0500 0.0538 0.0607 0.0648 0.0681 0.0713 0.0744 0.0766 0.0779 0.0804 0.1033 0.1043 0.1052 0.1075
0.0013 0.0136 0.0280 0.0443 0.0652 0.1038 0.1443 0.1578 0.1773 0.1866 0.1963 0.2076 0.2192 0.2256 0.2284 0.2345 0.3066 0.3091 0.3110 0.3190
0.0018 0.0197 0.0448 0.0618 0.0898 0.1445 0.1943 0.2116 0.2380 0.2514 0.2644 0.2789 0.2936 0.3022 0.3063 0.3149 0.4099 0.4133 0.4161 0.4265
calculated values CM (kg 3 kg-1) 0.0027 0.0246 0.0550 0.0748 0.1038 0.1605 0.1977 0.2069 0.2253 0.2436 0.2618 0.2709 0.2799 0.2935 0.3025 0.3115 0.4141 0.4185 0.4229 0.4361
relative error 0.5274 0.2440 0.2274 0.2089 0.1556 0.1109 0.0175 -0.0221 -0.0536 -0.0310 -0.0099 -0.0289 -0.0465 -0.0290 -0.0124 -0.0107 0.0102 0.0124 0.0162 0.0225
Figure 1. Relationship between the amount of metal deposition of different running time CM and activity coefficient RNi. Figure 2. Relationship between the amount of metal deposition of different running time CM and activity coefficient RV.
by associating eqs 8 and 9, which can be used to predict the extent of catalyst deactivation and running time of commercial units. Because for a given commercial unit the catalyst loading amount is fixed and its metal tolerance is determined, when the accumulated metal deposition in the catalyst reached the tolerance limits the commercial unit should be shut down. Researchers can collect the operating data in various commercial units on the early and middle running stages. These data could be used to fit the relevant parameters to predict the catalyst cycle length.9 For visual purposes, the relationship between the amount of metal deposition CM and activity coefficient RNi, Rv can be drawn as shown in Figures 1 and 2. It can be found from Figures 1 and 2 that metal deposition and activity coefficients are in a good linear relationship in the stable operation period, which shows that the reaction rate is controlled by pore diffusion.
4. Conclusions (1) Overall average relative error of the impurity removal ratio is 3.54% by calculation of residue hydrotreating deactivation kinetics model. Accuracy of the five impurities removal ratio is less than 10%, which can meet kinetics study requirements. It also proves that the deactivation kinetics model, as well as kinetic parameters, is correct and reliable. (2) Applying the equation to fit the amount of metal deposition at different running time of residue hydrotreating commercial units, the average relative error is 8.98%; the accuracy of which can meet kinetics requirements basically. (3) The relationship between the amount of metal deposition at different running time and activity coefficient can be derived through fitting residue hydrotreating deactivation parameter, which can predict the amount of metal deposition in commercial units. The extent of catalyst deactivation of different metal deposition and the running time of commercial units can be predicted.
(9) Li D. Hydrotreating Processing and Engineering; China Petrochemical Press: Beijing, 2004.
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