Simplified Catalyst Lifetime Prediction Model for Coal Tar in the

Jun 24, 2016 - Simplified Catalyst Lifetime Prediction Model for Coal Tar in the. Hydrogenation Process. Yiqian Yang,. †. Hongyan Wang,. ‡. Fei Da...
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Simplified Catalyst Lifetime Prediction Model for Coal Tar in the Hydrogenation Process Yiqian Yang,† Hongyan Wang,‡ Fei Dai,‡ Shuguang Xiang,*,† and Chunshan Li*,‡ †

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, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China ABSTRACT: A simplified catalyst deactivation model was proposed in this work to evaluate the catalyst life character. The model mainly considered the effect of coking deposition on the catalyst deactivation. A three-lumped kinetic model based on the catalyst deactivation model was established to describe the coal tar hydrogenation process, and model parameters were determined by means of fitting the experimental hydrogenation data. Validation results revealed that the model could predict the overall trend of catalyst deactivation effectively. This work serves as a significant guidance for the design and optimization of a hydrogenation catalyst.

1. INTRODUCTION In view of growing concern on the petroleum depletion crisis and rising fuel price, major efforts are being dedicated to the development of various alternative energy sources to ensure energy security. China has been the biggest coal producer in the world, in which around 3.6 billion tonnes of coal were produced in 2012, accounting for over 47% of the world’s total coal output. Coal tar is a byproduct of coal carbonization or gasification. Coal tar hydrogenation is one of the effective technologies for converting coal tar into high-value clean fuel, such as gasoline and diesel. However, one main issue existing in coal tar hydrogenation is the catalyst deactivation, resulting from metals and coking deposition. Catalyst deactivation that occurred by various mechanisms is a complex process both chemical and physical in nature. Reasons of catalyst deactivation can be roughly divided into three categories: poisoning, coking, and sintering.1 On the basis of various mechanisms of catalyst deactivation, many researchers developed relevant deactivation models suitable for heavy oil hydrogenation. These models can be loosely grouped into three categories. The first is the coking deactivation model. Froment2,3 has proposed a relation equation between catalyst activity and coking deposition. Beekman and Froment4 and Dumez and Froment5 used a pore-plugging model to explain the catalyst deactivation, which considers the structure and size of the catalyst. To simplify the deactivation model, Wojchiechowski has developed a regression correlation equation between the catalyst activity coefficient and time on stream (TOS)6 by postulating that the process of deactivation is uniform. Another model is based on metal deposition. Tamm et al.7 has mentioned that the pore mouth plugging model could describe partial surface poisoning by metal deposition. Rajagopalan and Luss8 have analyzed in detail deactivation results from pore mouth plugging and proposed a relevant mathematical expression. The third is the model9 that describes catalyst deactivation caused by coking and metal deposition © XXXX American Chemical Society

∂=

1 − (xMOC(z , t ))γj (1 + a jt )nj

(1)

where the first term in eq 1 is used to describe the effect of coking deposition on catalyst activity, which can be expressed as a function of the running time t. As the start-of-run period is terminated, the coke content reaches equilibrium and then keeps constant. Although it is very expensive and timeconsuming to collect a large amount of experimental measurements, this is a unique deactivation model because of considering the coking and metal deposition at the same time. Whichever kind of reason lead to catalyst deactivation, a typical deactivation pattern for a variety of catalysts can be described as three periods: start of run (SOR), middle of run (MOR), and end of run (EOR). Each of the periods has a different catalytic deactivation mechanism. In the SOR, catalyst activity exhibits a great decrease. A large amount of coking deposition appears during this period, while the content of metal sulfide deposition is usually less than 1%, which is supported by the literature.10 The asphaltene and other substances that are easy to form coke will adsorb on the catalyst surface and then form the coke precursor. Catalyst activity will decay with the increase of coking deposition. Chang10 proposed a deactivation model to describe the trend of initial catalyst activity. The model was represented as follows: ∂ = t −n

(2)

where ∂ is the catalyst activity coefficient, t represents the TOS, and n refers to the deactivation index. A regression correlation equation between the catalyst activity coefficient and TOS was used in the deactivation model, indicating the effect of coking deposition on the catalyst activity. Received: March 19, 2016 Revised: May 30, 2016

A

DOI: 10.1021/acs.energyfuels.6b00625 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Subsequently, the catalyst activity will slightly decrease at the MOR period. The irreversible metal deposition, particularly vanadium and nickel, dominates catalyst deactivation. Vanadium sulfide and nickel sulfide are deposited on the surface of the catalyst particles and in the pores of the catalyst. The main reason for declining catalyst activity is that depositions of metal sulfide plug pore mouths. Hence, most of the catalyst deactivation models at the MOR are related to depositions of vanadium and nickel. As a result of deposition of metal sulfides on the catalyst being inhomogeneous, Newson11 has proposed a pore-plugging model to predict catalyst life by the effectiveness factor. Chang10 proposed a simplified model shown as follows:

( ∂=1− (V

WV ρV Q V cat

η( t ) =

∫0

L

+

−

WNi ρNi Q Ni

WC ρC

Table 2. Property Analysis of Coal Tar

(3)

C r dy C0 r0

(4)

where α is the activity coefficient at time t, WV is the weight of vanadium deposition, WNi is the weight of nickel deposition, WC is the weight of carbon deposition, QV represents the distribution of vanadium on catalyst particles, QNi represents the distribution of nickel on catalyst particles, ρV is the density of vanadium sulfide, ρNi is the density of nickel sulfide, ρC is the density of coking deposition, Vcat is the pore volume of the fresh catalyst, η(t) is the effectiveness factor, C/C0 is the dimensionless concentration, and r/r0 is the dimensionless distance. After that, the catalyst activity declines rapidly again at the EOR period, which is attributed to the fact that a large amount of deposition (such as coke, metal sulfide, and other impurities) blocks pores of the catalyst. At present, there is not an accurate deactivation model to describe the process of catalyst activity decay at the EOR. However, very few papers focused on the catalyst deactivation model for the coal tar hydrogenation process. As for coal tar hydrogenation upgrading, our group has performed a lot of relevant studies.12−19 The objective of this study is to develop a simplified deactivation model suitable for coal tar hydrogenation. Experimental data are used to predict the trend of catalyst deactivation.

MoO3 (%)

WO3 (%)

NiO (%)

hydrofining hydrocracking

178 165

0.48 0.35

17.46 24.30

30.96

5.16 3.62

(5)

where α is the catalyst activity coefficient and b is a parameter that considers the effect of metal deposition on the catalyst activity. When it comes to coal tar hydrogenation, compositions of feedstock and products are extremely complex and plenty of reactions occur during hydrogenation. As for coal tar hydrogenation, the gasoline and diesel are regarded as the primarily desired products from coal tar hydrogenation, whereas coke and cracking gas are considered as byproducts, in which the total yield is less than 5% at optimal conditions. In addition, catalyst activity usually determines the content of cracking gas and coke in the product. When the catalyst presents high activity, more gas and less coke will be produced from the coal tar hydrogenation. On the contrary, more coke and less gas will be obtained under the catalyst with low activity. However, neither case is a desirable outcome in this work. Thus, it is reasonable to consider gas and coke as one lump to evaluate the catalyst activity. Moreover, the division method was also supported by Dai et al.20 The simplified kinetic model of the three-lump containing coal tar, desired product, and gas + coke was developed to simulate coal tar hydrogenation, as shown in Figure 1. The reaction rate of the proposed model can be expressed by first-order kinetic equations as follows:

Table 1. Properties of the Catalyst pore volume (mL g−1)

1.04 83.49 8.27 0.31 1.10 0.68 0.05

∂ = bt −n

The experimental setup was carried out in a two-stage fixed-bed hydrogenation reactor, whose detailed description of the hydrogenation experimental procedure and setup was given elsewhere.19 The catalyst that consists of Ni, Mo, and W with alumina and kaolin as carriers was used. The properties of the catalyst are shown in Table 1. Two different coal tars with distillate less than 360 °C were used for hydrogenation. Their main properties are shown in Table 2. To consider the effect of catalyst deactivation on the liquid product yield, the reaction conditions were as follows: hydrogen pressure of 12 MPa,

BET surface area (m2 g−1)

coal tar B

1.206 88.6 7.25 0.42 0.96 0.03 0.02

3. METHODOLOGY The catalyst deactivation models for the heavy oil hydrogenation were mainly established on the basis of the coking and metal deposition. Metals generated in the hydrogenation process were deposited on the surface catalyst and resulted in permanent irreversible deactivation at TOS. The deposition tends to increase with time, which reduces the pore diameter and, finally, decays the catalyst activity. Unlike the heavy oil, coal tar contains higher aromatic content and much less metal content. The effect of metal deposition on catalyst deactivation can be weakened. According to this situation, a simplified deactivation model for coal tar hydrogenation was proposed in this section, which was expressed as follows:

2. EXPERIMENTAL SECTION

catalyst

coal tar A

liquid hourly space velocity (LHSV) of 0.4 h−1, reaction temperatures of 360, 370, 380, and 390 °C, and hydrogen/oil ratio of 1600. Feed and products were analyzed according to the following methods: density on DMA 5000 (Anton Paar, Austria), C and H elemental analyses determined by Elementar VARIO ELIII (Germany), N and S determined by KY-3000SN (Jiangsu Jiangyan KEYUAN Electronic Instrument Co., Ltd.; standards, ASTM D5453 and D4629), and nickel and vanadium contents determined by atomic absorption using ASTM D5863. The Brunauer−Emmett−Teller (BET) surface area and pore volume were determined using the nitrogen adsorption isotherm method (Quanta Chrome Instrument NOVA 2000).

)

)η(t)

property density at 20 °C (g mL−1) C (wt %) H (wt %) S (wt %) N (wt %) Ni (μg g−1) V (μg g−1)

B

DOI: 10.1021/acs.energyfuels.6b00625 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

reaction conditions are not changed. The only reason to explain this phenomena is that catalyst activity is decaying. The more serious catalyst activity decays, the less liquid product is obtained. Thus, the deactivation model can be established using regression of data of the catalyst activity coefficient with different times. The parameter b equals 1.173, and n equals −0.038. Other kinetic parameters are shown in Tables 3 and 4. Table 3. Reaction Rate Constants at Different Temperatures

Figure 1. Three-lump kinetic model for coal tar hydrogenation.

dC1 = −(k1 + k 2)C1∂ dt

(6)

dC 2 = k1C1∂ dt

(7)

dC 3 = k 2C1∂ dt

(8)

k1∂ C1 (1 − e−(k1+ k 2)t ) k1 + k 2 0

⎛ E ⎞ ki = k 0i exp⎜ − ai ⎟ ⎝ RT ⎠

reaction rate constant, k2 (h−1)

633.15 643.15 653.15 663.15

1.4546 1.6634 1.8859 2.1264

0.0298 0.039 0.0506 0.0577

i = 1, 2

reaction rate constant, k (h−1)

pre-exponential factor, k0 (h−1)

activation energy, Ea (J/mol)

k1 k2

6273.657 43282

44044 74451

The yield of liquid products can be predicted using eq 9 when the deactivation model and kinetic parameters are known, and the predictions of the yield of liquid products are shown in Figure 2. By comparison of experimental measurements to predicted results, they show a good agreement with each other. The average absolute relative deviation (ARD) for the yield of liquid products is 0.6%. To verify the reliability of the deactivation model, the deactivation model is applied to another experimental data with coal tar B. Figure 3 shows a

(9)

(10)

According to eq 9, the expression of the catalyst activity coefficient can be obtained. ∂=

reaction rate constant, k1 (h−1)

Table 4. Pre-exponential Factor and Activation Energy

where ki is the reaction rate constant of component i, C is the yield of component i, and ∂ is the catalyst activity. The yield of liquid product can be expressed as C2 =

temperature, T (K)

(k 1 + k 2 )C 2 k1C10(1 − e−(k1+ k 2)t )

(11)

4. RESULTS AND DISCUSSION Figure 2 shows results of coal tar A hydrogenation. The yield of the liquid product, which includes gasoline and diesel, declines with the increase of TOS, while the feedstock, catalyst, and

Figure 3. Comparison of the predicted yield of the liquid product and the measured yield of the liquid product.

comparison between experimental data and predicted values. The ARD is 2.44%. Although catalyst activity is influenced by increasing the amount of metals (nickel + vanadium), the model is still able to describe the trend of catalyst deactivation. In addition, the deactivation model also shows excellence predication results for coal tar hydrodenitrogenation. The relevant experimental data were obtained from the literature.21 Prediction results was shown in Figure 4. The ARD is 3.7%.

Figure 2. Comparison of the predicted yield of the liquid product and the measured yield of the liquid product. C

DOI: 10.1021/acs.energyfuels.6b00625 Energy Fuels XXXX, XXX, XXX−XXX

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ACKNOWLEDGMENTS The authors acknowledge the National Basic Research Program of China (973 Program, 2014CB744306), the National Science Fund for Excellent Young Scholars (21422607), and the National Natural Science Foundation of China (21276267 and 21576261).

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Figure 4. Comparison of the predicted denitrification rate and the measured denitrification rate.

In conclusion, the deactivation model is reasonable and can accurately describe the deactivation process of coal tar hydrogenation. Thus, this deactivation model can be used for predicting the coal tar hydrogenation, and the results are shown in Figure 5. The predicted deactivation curve is basic, consistent with the trend of experimental measurements.

Greek Symbols

■

α = catalyst activity coefficient γj = fitting parameter of eq 1 ρC = density of coking deposition (kg/m3) ρNi = density of nickel sulfide (kg/m3) ρV = density of vanadium sulfide (kg/m3)

REFERENCES

(1) Chen, J. W. Catalysis Cracking Technology and Engineering, 2nd ed.; China Petrochemical Press: Beijing, China, 2005. (2) Froment, G. F.; Bischoff, K. B. Chemical Reactor Analysis and Design, 2nd ed.; John Wiley & Sons: New York, 1990; Vol. 59, p A29. (3) Froment, G. F. A quantitative approach of catalyst deactivation by coke formation. Stud. Surf. Sci. Catal. 1980, 6, 1−19. (4) Beeckman, J. W.; Froment, G. F. Catalyst deactivation by site coverage and pore blockage: Finite rate of growth of the carbonaceous deposit. Chem. Eng. Sci. 1980, 35, 805−815. (5) Dumez, F. J.; Froment, G. F. Dehydrogenation of 1-butene into butadiene. Kinetics, catalyst coking, and reactor design. Ind. Eng. Chem. Process Des. Dev. 1976, 15, 291−301. (6) Wojchiechowski, B. W. A theoretical treatment of catalyst decay. Chem. Eng. J. 1968, 46, 48−52. (7) Tamm, P. W.; Harnsberger, H. F.; Bridge, A. G. Effects of feed metals on catalyst aging in hydroprocessing residuum. Ind. Eng. Chem. Process Des. Dev. 1981, 20, 262−273. (8) Rajagopalan, K.; Luss, D. Influence of catalyst pore size on demetallation rate. Ind. Eng. Chem. Process Des. Dev. 1979, 18, 459− 465. (9) Centeno, G.; Ancheyta, J.; Alvarez, A.; Marroquín, G.; Alonso, F.; Castillo, A. Effect of different heavy feedstocks on the deactivation of a commercial hydrotreating catalyst. Fuel 2012, 100, 73−79. (10) Chang, J. Study on reaction kinetics and catalyst deactivation model of residue oil hydrotreating. Ph.D. Thesis, SINOPEC Research Institute of Petroleum Processing, Beijing, China, 1997. (11) Newson, E. Catalyst deactivation due to pore-plugging by reaction products. Ind. Eng. Chem. Process Des. Dev. 1975, 14, 27−33. (12) 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, 19−23. (13) Wang, H. Y.; Dai, F.; Li, Z. X.; Li, C. S. Upgrading shale oil distillation to clean fuel by coupled hydrogenation and ring opening

Figure 5. Predict yield of the liquid product.

5. CONCLUSION In this paper, a simplified catalyst deactivation model was used for description of catalyst deactivation for coal tar hydrogenation and the ARD for the yield of liquid products is 0.6%. According to the establishment of the three-lump kinetic model, its parameters can be regressed. This deactivation model can be used for predicting the trend of catalyst deactivation in coal tar hydrogenation for 8000 h.

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NOMENCLATURE aj = fitting parameter of eq 1 b = parameter of eq 5 Ci = yield of component i (%) C/C0 = dimensionless concentration k = reaction rate constant L = average equivalent length of catalyst (m) n = deactivation index nj = fitting parameter of eq 1 QV = distribution of vanadium on catalyst particles QNi = distribution of nickel on catalyst particles r/r0 = dimensionless distance t = time on stream (h) WC = weight of carbon deposition (%) WNi = weight of nickel deposition (%) WV = weight of vanadium deposition (%) y = distance coordinate along the length of a pore (m)

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. Notes

The authors declare no competing financial interest. D

DOI: 10.1021/acs.energyfuels.6b00625 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels reaction of aromatics on W−Ni/γ-Al2O3 catalysts. Energy Fuels 2015, 29, 4902−4910. (14) Kan, T.; Wang, H. Y.; He, H. X.; Li, C. S.; Zhang, S. J. Experimental study on two-stage catalytic hydroprocessing of middletemperature coal tar to clean liquid fuels. Fuel 2011, 90, 3404−3409. (15) 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, 4902−4910. (16) Wang, H. Y.; Jiao, T. T.; Li, Z. X.; Li, C. S.; Zhang, S. J.; Zhang, J. L. Study on palm oil hydrogenation for clean fuel over Ni−Mo−W/ γ-Al2O3−ZSM-5 catalyst. Fuel Process. Technol. 2015, 139, 91−99. (17) 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. (18) 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, 4878−4885. (19) Kan, T.; Wang, H. Y.; He, H. X.; Li, C. S.; Zhang, S. J. Experimental study on two-stage catalytic hydroprocessing of middletemperature coal tar to clean liquid fuels. Fuel 2011, 90, 3404−3409. (20) 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, 19−23. (21) Sun, Z. H.; Li, D.; Li, W. H.; Li, Z.; Lei, Y. C. Kinetics of coal tar hydrodenitrogenation. Acta Pet. Sin., Pet. Process. Sect. 2013, 29 (6), 1035−1039.

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DOI: 10.1021/acs.energyfuels.6b00625 Energy Fuels XXXX, XXX, XXX−XXX