Article pubs.acs.org/EF
Kinetic Evaluation of Hydrodesulfurization and Hydrodenitrogenation Reactions via a Lumped Model Yiqian Yang,†,‡ Fei Dai,‡ Chunshan Li,*,‡,§ Shuguang Xiang,† Muhammad Yaseen,‡ and Suojiang Zhang*,‡ †
Hi-Tech Institute for Petroleum and Chemical Industry, Qingdao University of Science and Technology, Qingdao, Shandong 266042, People’s Republic of China ‡ Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex System, and National Key Laboratory of Clean and Efficient Coking Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China § School of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China ABSTRACT: Multi-lump kinetic models for model compounds representative of various sulfur (S) and nitrogen (N) compounds in coker gas oil (CGO) were developed to describe CGO hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) processes, respectively. Model parameters were obtained from fitting of operating data. Validation results revealed that models could predict contents of S and N in products accurately, and hydrogen consumption of HDS and HDN can be estimated. The effects of the temperature, hydrogen/oil ratio, and pressure on the reaction performance were also investigated. complex mixtures. Torrisi et al.11 believed that S compounds with different boiling points showed different reaction rates; normally, the reaction rate increased as its boiling point decreased. Tang et al.12 proposed power index kinetic models to describe the process of HDS and HDN. The S and N compounds in the feed were lumped according to the reactivity into three or four group cuts to obtain three- and four-lump kinetic models. Results showed that three- and four-lump models predicted the performance of HDS and HDN effectively with changing operational conditions and feedstock. Therefore, multi-lump kinetic models are suitable for HDS and HDN of CGO. Generally, the more lumps in a kinetic model, the larger the number of parameters needs to be estimated using more detailed experimental data.13 Thus, selecting a proper division method of S and N compounds is the key to establish the kinetic model accurately. As for sulfur compounds in CGO based on the analysis of CGO, a conclusion can be drawn that most of sulfur compounds exist in the state of benzothiophene (BT), dibenzothiophene (DBT), and benzonaphthothiophene (BNT). Bannatham et al.14 established a multi-lumped kinetic model to evaluate the process of HDS in a trickle-bed reactor. The sulfur compounds in petroleum oil were divided into several groups according to the boiling point to establish multilumped models for HDS. The predicted results were reliable and described the process of HDS accurately. When it comes to nitrogen compounds in CGO, most exist in the state of pyridine, acridine, quinoline, carbazole, and their derivatives. The multi-lumped models were divided into several groups according to their basic and reaction rates.
1. INTRODUCTION With the increasingly stringent environmental requirements, it is imperative to produce higher quality fuel. Hydrorefining is the most effective method of removing sulfur (S), nitrogen (N), and other impurities from petroleum oil.1 Kinetic research provides a significant guidance for parameter optimization of hydrogenation refining. Many reports are available on kinetic models for hydrodesulfurization (HDS) and hydrodenitrogenation (HDN),2−5 which are mainly divided into two types: the power index model and Langmuir−Hinshelwood (L−H) model. Nguyen et al.6 proposed a new L−H model including liquid−vapor equilibrium and confirmed the reaction scheme of HDN. Ferdous et al.7 used a single simplified L−H equation for HDS and HDN reactions. In their work, it was proposed that the effect of ammonia adsorption on the HDN and HDS reactions can be neglected, while N and H2S adsorption had enormous impact on HDN and HDS reactions. In general, the L−H model is not applicable to the complicated real system, attributed to the presence of many coefficients that are difficult to determine.8 On the contrary, the power index model for a real system is widely adopted. Power index model parameters can be obtained via the least squares fitting of the experimental data. A pseudo-first-order kinetic model for total nitrogen, basic nitrogen, and non-basic nitrogen was presented by Wei et al.9 They investigated the effects of different operating conditions on HDN conversion, which increased with the increase in the temperature, hydrogen/oil ratio, and pressure. Xiang and Wang10 established a macroreaction kinetic model for the HDS of coker gas oil (CGO) in a slurry reactor. As a result of the presence of large amounts of N and S compounds in the CGO,9 it is difficult to build specific kinetic models to describe the reaction process. Generally, the lump method is regarded as the most useful way to characterize the © 2017 American Chemical Society
Received: February 22, 2017 Revised: March 19, 2017 Published: April 11, 2017 5491
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels
where Ci represents the concentration of sulfur for lumps 1, 2, and 3 (μg/g), k0i is pre-exponential factor (h MPa−b), H2/oil refers to the H2/oil ratio in v/v, P is the operating pressure in MPa, Ea is apparent activation energy in J/mol, LSHV is the liquid hourly space velocity, (h−1), R is the gas constant (J mol−1K−1), T is the temperature in K, n represents the reaction order, and a and b are fit parameters. In eq 1, the effect of operation conditions on the S (N) conversion are considered in the kinetics
Studies of catalyst, reactor, and reaction of CGO hydrorefining have been widely reported.9,10,15,16 As for heavy oil hydrorefining and the kinetic model, our group has performed a lot of relevant studies.17−25 It was proven that the lumping method was a well-established approach for characterizing a complicated system. Results predicted by models were in good agreement with the experimental data. In addition, experimental data obtained from the plant were an objective reflection of the performance of CGO hydrorefining and provide sufficient data for estimating model parameters. In this study, model compounds were used to describe the constituents of S and N in CGO and develop power index kinetic models suitable for CGO hydrorefining. The effect of the temperature, pressure, liquid hourly space velocity (LHSV), and hydrogen/oil ratio on the reaction performance were also investigated and considered in this model. In addition, hydrogen consumption can be calculated using empirical equations.
1/(1 − n) ⎛ 1 ⎟⎞ Ci = ⎜C0i n − 1 + (1 − n)P b(H 2 /oil)a k 0e−(Ea / RT ) ⎝ LHSV ⎠
(2) where C0i refers to the concentration of S or N in the feed for lumps 1, 2, and 3 (μg/g). The three-lump kinetic model for hydrogenation of CGO is shown in eq 3. 1/(1 − n1) ⎛ 1 ⎞⎟ C total = ⎜C01n1− 1 + (1 − n1)P b1(H 2 /oil)a1k 01e−(Ea1/ RT ) ⎝ ⎠ LHSV 1/(1 − n2) ⎛ 1 ⎞⎟ + ⎜C02 n2 − 1 + (1 − n2)P b2(H 2 /oil)a2 k 02e−(Ea2 / RT ) ⎝ ⎠ LHSV
2. METHODOLOGY
1/(1 − n3) ⎛ 1 ⎞⎟ + ⎜C03n3− 1 + (1 − n3)P b3(H 2 /oil)a3 k 03e−(Ea3/ RT ) ⎝ LHSV ⎠
For modeling the HDS and HDN, S and N compounds in CGO were divided into 2 time three groups according to conversion of the hydrogenation reaction and reactivity and 2 time three-lump kinetic models were established, respectively. 2.1. Description of the HDS Model. HDS of CGO is a complicated process as a result of different types of S constituents, i.e., BT, DBT, BNT, and their derivatives, having different reaction rates and boiling ranges. Therefore, the feed in the model can be divided into BT, DBT, BNT, and their derivatives, which react to H2S, as shown in Figure 1, assuming no mutual reaction between S compounds.
(3) On the basis of the nonlinear regression and least squares method, the kinetic parameters were estimated, which could describe HDS and HDN reactions accurately. 2.4. Estimation of Chemical Hydrogen Consumption. The chemical hydrogen consumption of CGO hydrorefining is mainly derived from the processes of HDS and HDN. On the basis of the result of the three-lump kinetic model, the contents of sulfur and nitrogen in the product can be predicted under different operational conditions. Empirical equations were regarded as an optimal method to estimate chemical hydrogen consumption for CGO hydrorefining without detailed analytical data. Equations are shown as follows:
2.2. Description of the HDN Model. Similar to HDS, N compounds in the HDN reaction are lumped into three groups according to their reactivity. Pyridine, acridine, quinoline, and their derivatives, mostly consisting of basic nitrogen with higher conversion, are grouped into lump 1. Carbazole and its derivatives, having mostly non-basic nitrogen with low conversion, can be regarded as lump 2. The other nitrogen compounds with highest conversion are considered as lump 3. No reaction among individual N compounds was assumed for the model (Figure 2).
2.3. Kinetic Models. On the basis of the above lump division, the n-order kinetic model of each lump can be described as follows:
(
1 LHSV
)
HHDN = β(Nf − Np)
(5)
3. RESULTS AND DISCUSSION 3.1. Effect of the Reaction Temperature on HDN and HDS. Because the temperature is the most critical factor influencing HDS and HDN, the effect of the reaction temperature ranging from 500 to 680 K at a pressure of 10 MPa, LHSV of 1 h−1, and hydrogen/oil ratio of 500 was investigated. Results are shown in Figures 3 and 4. Conversion of S and N compounds increased with an increasing reaction temperature, which became stable above 660 K. Moreover, different types of S compounds showed significant variations for HDS. Lump 1 showed the highest conversion and improved rapidly with an increasing temperature. Conversion of lump 2 was low below 540 K. At a temperature over 540 K, catalytic activity improved rapidly, resulting in higher conversion. The trend of lump 3 was similar to that of lump 2, and the only difference was that conversion increased rapidly when the temperature reached 570 K. A similar effect of the temperature on HDN was observed. Conversions of lumps 2 and 3 were more than that of lump 1, while lump 1 gave a higher
Figure 2. Conversion of nitrogen compounds via HDN.
d
(4)
where HHDS and HHDN are hydrogen consumption in HDS and HDN reactions (Nm3/m3), respectively, Sf and Nf represent the contents of S and N in the feed, respectively, Sp and Np represent the contents of sulfur and nitrogen in the product, and α and β are relevant parameters; according to the relevant literature,26 α = 18−23 and β = 62.
Figure 1. Conversion of sulfur compounds via HDS.
dCi
HHDS = α(Sf − Sp)
= − k 0i(H 2 /oil)a P be(−Ea / RT )Ci n (1) 5492
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels Table 2. Kinetic Parameters of the HDS Reaction
Figure 4. Effect of the temperature on the conversion of total, BN, NBN, and other compounds.
conversion at a high temperature. Thus, the conversion of total nitrogen mainly depended upon the temperature. However, the increase of coking and metal deposition was promoted with a rising temperature, resulting in catalyst deactivation rapidly, which led to a decreased conversion. Thus, conversion and catalyst life should be considered when selecting the optimal reaction temperature. On the basis of Figures 3 and 4, HDS and HDN showed satisfied conversion in a temperature range of 630−660 K. 3.2. Kinetics of HDN and HDS. The contents of S and N compounds in the product of CGO hydrorefining are strongly affected by the temperature, pressure, hydrogen/oil ratio, and LHSV. Different plant data sets of various levels of operational conditions (Tables 3 and 4) were fitted with three-lump kinetic models. Kinetic parameters of three-lump models for CGO HDN and HDS were determined and shown in Tables 1 and 2. The comparison of experimental data and calculated results are shown in Figure 5. The predicted contents of S and N compounds in the product from the three-lump model were in good agreement with those obtained from experimental data. It appeared that the three-lump model established in the present Table 1. Kinetic Parameters of the HDN Reaction parameter
lump 1
lump 2
lump 3
0.30 1.70 1.09 1.56 × 105 5.70 × 109
0.11 1.14 1.54 1.25 × 105 3.35 × 107
0.14 1.25 1.13 1.23 × 105 9.57 × 107
lump 1
lump 2
lump 3
0.31 0.59 1.69 1.33 × 105 7.36 × 108
0.30 0.59 1.54 1.35 × 105 3.17 × 108
0.31 0.63 1.16 1.52 × 105 3.84 × 1010
work can predict the performance of HDS and HDN accurately at different operating conditions. The average absolute relative deviations (AARDs) of HDS and HDN were 0.34 and 1.71%, respectively. To validate the model, seven sets of experimental data at different operating conditions with the same feedstock were obtained and shown in Figure 6. The three-lump kinetic model also predicted the conversion of sulfur and nitrogen compounds accurately. The AARDs of HDS and HDN were 4.06 and 1.31%, respectively. These results proved that threelump models were suitable for CGO HDS and HDN and can accurately predict the concentration of S and N compounds in the product stream at fixed operating conditions. The same model can also be used to estimate S and N compounds in the CGO hydrorefining process at different operating conditions. Data for these investigations are shown in Figures 7−9, which indicate that S compounds were preferentially removed and the temperature and pressure were the most influential factors on the performance of HDS and HDN. Figure 7 shows that the concentration of S compounds decreased with increasing time. This decrease was rapid initially and, at 0.1 h onward, became linear. This trend was very much linear and gradual for HDN regarding the N compound decrease. In comparison of HDS and HDN, the effect of the temperature on HDN was more remarkable than that on HDS and nitrogen compounds are more difficult to be removed. Figure 8 shows the effect of the hydrogen/oil ratio on HDN and HDS. The contents of S and N in the product reduce with increasing the amount of hydrogen under the same operational conditions. However, little variation can be seen in the performance of HDS and HDN with the change in the hydrogen/oil ratio. It can be proven that the hydrogen/oil ratio was not a critical factor affecting the CGO HDS and HDN. The increase of the hydrogen/oil ratio is favorable to more hydrogen participate in the reaction, promoting the degree of hydrorefining. However, the residence time of reactant on the catalyst bed decreases as the hydrogen/oil ratio increases, leading toward reducing the reaction time and decreasing the degree of hydrorefining. Therefore, a proper hydrogen/oil ratio facilitates the process of hydrorefining. Figure 9 shows the effect of the pressure on the performance of HDS and HDN. It was observed that the pressure predominantly affected HDN compared to HDS, while in both the cases, an increase in the pressure resulted in a higher performance of both of the processes for CGO hydrorefining. This is because improving the pressure can increase the hydrogen partial pressure, which is beneficial with HDS and HDN. Besides, the increase of the hydrogen partial pressure promotes the hydrogenation saturation of aromatics, in which hydrogenation saturation of aromatics is the first step to remove nitrogen compounds, and finally accelerates the reaction rate of HDN.
Figure 3. Effect of the temperature on the conversion of total, BT, DBT, and BNT compounds [X is the conversion rate: X = (Cexp − Ccal)/Cexp].
a b n Ea (J/mol) k0 (mol1 − n Ln − 1 h−1)
parameter a b n Ea (J/mol) k0 (mol1 − n Ln − 1 h−1)
5493
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels Table 3. Operating Data for HDN of CGO product (μg/g)
feed (μg/g)
P (MPa)
T (K)
H2/oil (v/v)
LHSV (h−1)
lump 1
lump 2
lump 3
lump 1
lump 2
lump 3
10 10 10 10 10 10 10 10 10 10 6 8 10 12 14 10 10
638 643 648 653 658 643 643 643 643 643 643 643 643 643 643 643 643
500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 700 1000
1 1 1 1 1 0.5 0.7 1 1.2 1.5 1 1 1 1 1 1 1
569 484 397. 5 305 240 174 313 484 610 670 834 660 484 387 272 435 443
260 221 182 139 110 79 143 221 278 306 381 301 221 176 124 198 202
775 659 541 414 327 237 426 659 830 912 1135 898 659 526 370 591 603
927 927 927 927 927 927 927 927 927 927 966 966 927 927 966 927 927
1159 1159 1159 1159 1159 1159 1159 1159 1159 1159 1209 1209 1159 1159 1209 1159 1159
2069 2069 2069 2069 2069 2069 2069 2069 2069 2069 2158 2158 2069 2069 2158 2069 2069
Table 4. Operating Data for HDS of CGO product (μg/g) −1
feed (μg/g)
P (MPa)
T (K)
H2/oil (v/v)
LHSV (h )
lump 1
lump 2
lump 3
lump 1
lump 2
lump 3
10 10 10 10 10 10 10 10 10 10 10 6 8 10 14 10 10
633 638 643 648 653 658 643 643 643 643 643 643 643 643 643 643 643
500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 700
1 1 1 1 1 1 0.5 0.7 1 1.2 1.5 1 1 1 1 1 1
16 12 9 6 4 3 3 6 9 14 15 14 11 9 7 9 8
295 227 166 117 83 63 56 110 166 255 281 259 215 166 139 166 154
1109 851 622 440 310 238 211 412 622 959 1054 972 808 622 522 622 577
5696 5696 5696 5696 5696 5696 5696 5696 5696 5696 5696 5612 5612 5696 5612 5696 5696
2779 2779 2779 2779 2779 2779 2779 2779 2779 2779 2779 2738 2738 2779 2738 2779 2779
3055 3055 3055 3055 3055 3055 3055 3055 3055 3055 3055 3010 3010 3055 3010 3055 3055
Figure 5. Comparison of predicted and experimental data.
3.3. Estimation of Hydrogen Consumption. The lump model was also used to predict the amount of hydrogen consumption in the hydrorefining process. The total amount of
hydrogen consumption predicted was 0.66%, which was in good agreement with the experimental data from the plant. To illustrate the applicability of the empirical equations, the 5494
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels
Figure 6. Comparison of the predicted value from the calculation and seven sets of experimental data.
Figure 7. Effect of the temperature on CGO HDS and HDN (P, 10 MPa; LHSV, 1 h−1; and hydrogen/oil, 500).
Figure 8. Effect of the H2/oil ratio on CGO HDS and HDN (P, 10 MPa; LHSV, 1 h−1; and T, 643 K).
Figure 9. Effect of the pressure on CGO HDS and HDN (hydrogen/oil, 500; LHSV, 1 h−1; and T, 643).
5495
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels empirical equations were used for other experimental data, and the results were shown in Figure 10. Reference literature data
Figure 10. Comparison of the predicted value from the calculation and literature data.
were compared to the empirical equations for hydrogen consumption, and the AARD was 7.99%. The calculation showed good agreement with experimental data, proving that the empirical equations possess wide adaptation.
■
4. CONCLUSION On the basic of the experimental data of CGO HDS and HDN, which were recorded at the steady state, three-lump kinetic models were proposed, in which the sulfur and nitrogen compounds in the feed were grouped into three lumps according to the composition of CGO and reaction mechanism. Results showed that the three-lump model can effectively predict conversions of sulfur and nitrogen. The effect of operational conditions on HDS and HDN were discussed. It can be concluded that the temperature and pressure were major influencing factors on the performance of HDS and HDN. In combination with empirical equations for hydrogen consumption and prediction of the content of sulfur and nitrogen in the product using the three-lump kinetic model, the chemical hydrogen consumption can be obtained. The present model can help in the formulation of reaction modeling, mechanism, and product distribution in the CGO hydrorefining process on an industrial level.
■
b = fit parameter Ccal = calculated content (μg/g) Cexp = experimental content (μg/g) Ci = concentration of sulfur for lump i, where i = 1, 2, and 3 (μg/g) C0i = concentration of sulfur (nitrogen) in the feed for lumps 1, 2, and 3 (μg/g) Ea = apparent activation energy (kJ mol−1) HHDS = hydrogen consumption in the hydrodesulfurization reaction (Nm3/m3) HHDN = hydrogen consumption in the hydrodesulfurization reaction (Nm3/m3) k0i = pre-exponential factors (h−1 MPa−b) LSHV = liquid hourly space velocity (h−1) Nf = mass fraction of nitrogen in the feed Np = mass fraction of nitrogen in the product n = reaction order P = operation pressure (MPa) R = gas constant (8.314 J mol−1 K−1) Sf = mass fraction of sulfur in the feed Sp = mass fraction of sulfur in the product T = reaction temperature (K) α and β = relevant parameters
REFERENCES
(1) Martínez, J.; Ancheyta, J. Modeling the kinetics of parallel thermal and catalytic hydrotreating of heavy oil. Fuel 2014, 138, 27−36. (2) Ferdous, D.; Dalai, A. K.; Adjaye, J. Hydrodenitrogenation and Hydrodesulfurization of Heavy Gas Oil Using NiMo/Al2O3 Catalyst Containing Boron: Experimental and Kinetic Studies. Can. J. Chem. Eng. 2005, 83 (83), 855−864. (3) Vonortas, A.; Papayannakos, N. Kinetic Study of the Hydrodesulfurization of a Heavy Gasoil in the Presence of Free Fatty Acids Using a CoMo/γ-Al2O3 Catalyst. Ind. Eng. Chem. Res. 2014, 53 (23), 9646−9652. (4) Hensen, E. J. M.; Vissenberg, M. J.; de Beer, V. H. J.; van Veen, J. A. R.; van Santen, R. A. Kinetics and mechanism of thiophene hydrodesulfurization over carbon-supported transition metal sulfides. J. Catal. 1996, 163 (2), 429−435. (5) Ferreira, C.; Tayakout-Fayolle, M.; Guibard, I.; Lemos, F. Hydrodesulfurization and hydrodemetallization of different origin vacuum residues: New modeling approach. Fuel 2014, 129 (7), 267− 277. (6) Nguyen, M.-T.; Tayakout-Fayolle, M.; Pirngruber, G. D.; Chainet, F.; Geantet, C. Kinetic Modeling of Quinoline Hydrodenitrogenation over a NiMo(P)/Al2O3 Catalyst in a Batch Reactor. Ind. Eng. Chem. Res. 2015, 54 (38), 9278−9288. (7) Ferdous, D.; Dalai, A. K.; Adjaye, J. Hydrodenitrogenation and Hydrodesulfurization of Heavy Gas Oil Using NiMo/Al2O3 Catalyst Containing Boron: Experimental and Kinetic Studies. Ind. Eng. Chem. Res. 2006, 45, 544−552. (8) Whitehurst, D. D.; Isoda, T.; Mochida, I. Present State of the Art and Future Challenges in the Hydrodesulfurization of Polyaromatic Sulfur Compounds. Adv. Catal. 1998, 42, 345−471. (9) Wei, Q.; Wen, S. H.; Tao, X. J.; Zhang, T.; Zhou, Y. S.; Chung, K.; Xu, C. M. Hydrodenitrogenation of basic and non-basic nitrogencontaining compounds in coker gas oil. Fuel Process. Technol. 2015, 129, 76−84. (10) Xiang, H. D.; Wang, T. F. Kinetic study of hydrodesulfurization of coker gas oil in a slurry reactor. Front. Chem. Sci. Eng. 2013, 7 (2), 139−144. (11) Torrisi, S.; Remans, T.; Swain, J. The challenging chemistry of ultra-low-sulfur diesel. Process Technol. 2009, 1−4. (12) Tang, X.; Li, S. Y.; Yue, C. T.; He, J. L.; Hou, J. L. Lumping kinetics of hydrodesulfurization and hydrodenitrogenation of the
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. *E-mail:
[email protected]. ORCID
Chunshan Li: 0000-0003-2460-8697 Suojiang Zhang: 0000-0002-9397-954X Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS This project was supported by the National Science Fund for Excellent Young Scholars (21422607), the National Natural Science Funds (21576261), the National Key Research and Development Program (2016YFB0601303), and the Innovation Fund of PetroChina (2015D-5006-0406).
■
NOMENCLATURE a = fit parameter 5496
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
Article
Energy & Fuels middle distillate from Chinese shale oil. Oil Shale 2013, 30 (4), 517− 535. (13) Dai, F.; Gao, M. J.; Li, C. S.; Xiang, S. G.; Zhang, S. J. Detailed Description of Coal Tar Hydrogenation Process Using the Kinetic Lumping Approach. Energy Fuels 2011, 25 (11), 4878−4885. (14) Bannatham, P.; Teeraboonchaikul, S.; Patirupanon, T.; Arkardvipart, W.; Limtrakul, S.; Vatanatham, T.; Ramachandran, P. A. Kinetic Evaluation for Hydrodesulfurization via Lumped Model in a Trickle-Bed Reactor. Ind. Eng. Chem. Res. 2016, 55 (17), 4878−4886. (15) Yang, Y. Q.; Wang, H. Y.; Dai, F.; Xiang, S. G.; Li, C. S. Simplified catalyst lifetime prediction model for coal tar in hydrogenation process. Energy Fuels 2016, 30 (7), 6034−6038. (16) Wang, G.; Liu, Y.; Wang, X.; Xu, C.; Gao, J. Studies on the Catalytic Cracking Performance of Coker Gas Oil. Energy Fuels 2009, 23 (4), 1942−1949. (17) Dai, F.; Gong, M. M.; Li, C. S.; Li, Z. X.; Zhang, S. J. Molecular Kinetic Model of Coal Tar Hydrogenation Process Based on the Carbon Number-based Component Approach. Appl. Energy 2015, 137 (1), 265−272. (18) Wang, H. Y.; Dai, F.; Li, Z. X.; Li, C. S. Upgrading shale oil distillation to clean fuel by coupled hydrogenation and ring opening reaction of aromatics on W-Ni/γ-Al2O3 catalysts. Energy Fuels 2015, 29 (8), 4902−4910. (19) Dai, F.; Wang, H. Y.; Gong, M. M.; Li, C. S.; Muhammad, Y.; Li, Z. X. Modeling of kinetic-based catalyst grading for upgrading shale oil hydrogenation. Fuel 2016, 166 (15), 19−23. (20) Dai, F.; Wang, H. Y.; Gong, M. M.; Li, C. S.; Li, Z. X.; Zhang, S. J. Carbon-Number-Based Kinetics, Reactor Modeling, and Process Simulation for Coal Tar Hydrogenation. Energy Fuels 2015, 29, 7532− 7541. (21) Dai, F.; Muhammad, Y.; Gong, X.; Li, C. S.; Li, Z. X.; Zhang, S. J. Low-temperature and low-pressure fuel hydrodesulfurization by solid catalyst coupling with ionic liquids. Fuel 2014, 134, 74−80. (22) Wang, H. Y.; Jiao, T. T.; Li, Z. X.; Li, C. S.; Zhang, S. J.; Zhang, J. L. Study on palmoil hydrogenation for clean fuel over Ni-Mo-W/γAl2O3-ZSM-5 catalyst. Fuel Process. Technol. 2015, 139, 91−99. (23) Gaurav, A.; Ng, F. T. T.; Rempel, G. L. A New Green Process for Biodiesel Production from Waste Oils via Catalytic Distillation using a Solid Acid Catalyst-Modelling. Economic and Environmental Analysis. Green Energy & Environment 2016, 1 (1), 62−74. (24) Kan, T.; Wang, H.; Li, C.; Zhang, S. Liquid Fuels From Ethylene Tar by Two-stage Catalytic Hydroprocessing. Energy Sources, Part A 2015, 37 (21), 2317−2322. (25) Dai, F.; Gong, M. M.; Li, C. S.; Li, Z. X.; Zhang, S. J. New kinetic model of coal tar hydrogenation process via carbon number component approach. Appl. Energy 2015, 137 (C), 265−272. (26) Li, D. D. Hydrotreating Processes and Engineering; China Petrochemical Press: Beijing, China, 2004.
5497
DOI: 10.1021/acs.energyfuels.7b00496 Energy Fuels 2017, 31, 5491−5497
