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Kinetic Model for Low-Temperature Coal Tar Hydrorefining Menglong Niu,† Huaan Zheng,‡ Xiaohong Sun,† Shengjun Zhang,‡ Dong Li,† Jing Qiao,‡ and Wenhong Li*,† †

School of Chemical Engineering, Northwest University, Xi’an, Shaanxi 710069, People’s Republic of China Shaanxi Coal and Chemical Industry Group Co., Ltd., Xi’an, Shaanxi 710065, People’s Republic of China

‡

ABSTRACT: In this work, the hydrorefining of coal tar was carried out in a three-stage fixed-bed reactor to produce clear fuel. Three catalysts containing hydroprotecting (HP) catalyst, hydrodesulfurization (HDS) catalyst, and hydrodenitrification (HDN) catalyst were loaded into three reactors in sequence in the grading ratio of 0.23:0.3:0.47. Based on 1200 h of experimental data, a kinetic model for HDS, HDN, and the production of the desired products of coal tar hydrorefining was established. The model can not only more accurately predict the yield of the desired products and the sulfur and nitrogen contents of the hydrorefining products at different liquid hourly space velocities (LHSVs), pressures, and temperatures, but can also predict the changea in product properties occurring as a result decreasing catalyst activity after long periods of operation. describe coal tar hydrogenation. Yang et al.23 constructed a simplified catalyst deactivation model to evaluate the catalyst lifetime characteristics in the process of coal tar hydrogenation. Because coal tar contains a lot of heavy metals, sulfur, nitrogen, and other elements, the catalyst activity will attenuate with increasing operation time of a hydrogenation test device, and this will affect the properties of the products, as well as the yield of the desired products.24 In addition, the treatment of coal tar requires not only that different catalysts be used for grading in industrial applications but also that different temperatures be set according to the different catalysts in the reactor. The gradation of hydrotreating and hydrocracking catalysts is a widely accepted method in research on upgrading coal tar. However, because of the complexity of the hydrocracking reaction network, the chemical composition of coal tar has usually been divided into 8−16 lumps in previous studies on hydrocracking.11−13 Because of the three clear goals of the hydrotreating process, namely, desulfurization, denitrification, and upgrading of oil, the compounds in coal tar can easily be divided into lumps, thereby simplifying the research process during the study of kinetics. In this study, the kinetics of the hydrotreating of coal tar was studied on a three-tube tandem fixed-bed hydrotreating unit. The established kinetic model takes into account the decay of the catalyst activity and the effects of process conditions, such as temperature, pressure, and space velocity, on the product properties.

1. INTRODUCTION Coal tar is a byproduct of coal pyrolysis.1−3 In recent years, it has attracted the attention of many researchers as a substitute for crude oil.4−7 As the heavy fractions of coal tar is rich in asphaltenes, most researchers have focused on the light fractions of coal tar.3−6 Low-temperature coal tar has high contents of nitrogen- and sulfur-containing compounds, and its composition is complex. The nitrogen-containing compounds are mainly pyridine, pyrrole, and quinoline. These nitrides tend to be alkaline and are easily adsorbed on the acidic centers of cracking, reforming, and isomerization catalysts, thereby reducing the activities of these catalysts.8 Sulfur compounds, including thiol, thioether, and thiophene, produce SO2 in the combustion process, causing environmental pollution.9,10 Therefore, it is a common to carry out the hydrorefining of coal tar before further hydrocracking, hydroisomerization, and reforming. In studies of the hydrogenation process, many researchers have chosen to establish a kinetic model to simulate the complex chemical reactions. Regardless of the types of raw materials used in research on fuel production by hydrogenation, the chemical reactions involved in the hydrogenation process are complex and difficult study directly. Therefore, it is a widely accepted method to construct a kinetic model by dividing the material into several lumps according to their properties.11−14 For example, Mederos et al.15 constructed a dynamic heterogeneous one-dimensional model for oil fraction hydrogenation, wherein hydrodesulfurization (HDS), hydrodenitrification (HDN), hydrodealkylation (HDA), olefin hydrogenation, and mild hydrocracking were taken into consideration. Dai et al.16 establish a lumped kinetic model for HDS, HDN, and hydrocracking based on catalyst grading in the shale oil hydrogenation process. Verstraete et al.17 developed a kinetic model involving eight chemical lumps to model fixed-bed residue oil hydrotreating processes, in which the evolutions of various lumps along the reactor were precisely predicted and the yield distributions of the various lumps were predicted. In recent years, many researchers have used dynamic methods to study the hydrogenation of coal tar.18−21 For example, Dai et al.22 established a carbon-number-based kinetic model to © XXXX American Chemical Society

2. EXPERIMENTAL SECTION 2.1. Feedstock and Catalyst. The feedstock used in the experiments was low-temperature coal tar distillate whose boiling point was lower than 633 K and separated by vacuum distillation. The coal tar was first produced through brown coal pyrolysis at 823 K. The properties of the feedstock are listed in Table 1. Three commercially available catalysts that contained HP catalysts, HDS catalysts, and HDN catalysts were loaded into three reactors in sequence according to the grading ratio of 0.23:0.3:0.47 (grading ratio is the volume fraction of the Received: November 22, 2016 Revised: March 17, 2017 Published: March 21, 2017 A

DOI: 10.1021/acs.energyfuels.6b03109 Energy Fuels XXXX, XXX, XXX−XXX

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LHSVs of 0.2−0.4 h−1. Because the experimental device consisted of three reactors, a large number of experiments were needed to study the reaction temperature of each reactor. To simplify the experimental investigations, only the temperature of the HDN reactor was varied in the experiments. 2.3. Analysis of Feedstock and Product. Characterization of the products involved several measurements including sulfur and nitrogen, tail gas, and carbon and hydrogen elemental analyses. The boiling range of the feedstock was determined by distillation experiments (standard method GB/T6536). The sulfur contents in the feedstock and products were determined by the infrared absorption method after combustion in an induction furnace. The nitrogen contents in the feedstock and products were determined by chemical titration. Both of the analyses are done on the sulfur and nitrogen content meter (TSN-2000SN, Jiangsu Jiangfen Instrument Co., Ltd.). The tail gas was measured by gas chromatography (HP 6890A, Agilent). An Al2O3 capillary column (50 m × 0.32 mm × 0.25 μm, Agilent) and FID were used for cracked gas detection. The flow rate of carrier gas (helium, 99.999%) was 20 mL/min, and the split ratio was 1:7. The light hydrocarbon content was calculated by the subtraction method. The contents of carbon and hydrogen in feedstock were determined by elemental analyzer (VARIO EL III, Elemen-tar Analysensysteme GmbH).

Table 1. Properties of the Feedstock property density (293 K) elemental analysis C H N S atomic H/C ratio distillation range initial boiling point 10% 30% 50% 90%

units

value

g/mL

0.998

wt % wt % wt % wt %

86.36 8.41 0.78 0.64 1.17

K K K K K

470 492 516 555 636

Table 2. Properties of the Catalysts units grading scheme chemical specification MoO3 WO3 NiO P2O5 ZrO2 SiO2 Al2O3 physical specification surface area pore volume pore size

HP catalyst

HDS catalyst

HDN catalyst

% v/v

0.23

0.3

0.47

wt % wt % wt % wt % wt % wt % wt %

10.33 0 5.23 0 0 0 balance

0 25.34 5.59 0 1.23 0 balance

21.44 0 6.19 2.71 0 4.44 balance

m2/g cm3/g nm

102 0.32 14.87

236 0.58 8.40

186 0.51 10.33

3. MODELING APPROACH 3.1. Kinetic Models for HDS and HDN. All sulfur-counting compounds in the coal tar were grouped into one lump. Assuming that the reaction order of the low-temperature coal tar HDS process is nS, the lumped kinetic model for HDS was constructed as follows dWS = −kWSnS dt

(1)

where WS represent the mass fraction of sulfur in coal tar, k represents the HDS reaction rate constant, and t represents time. After integration of eq 1, the kinetic model can be constructed as

four types of catalysts in the reactor). All of the catalysts were produced by SINOPEC Fushun Research Institute of Petroleum and Petrochemicals. The catalyst grading ratio was calculated based on the optimum liquid hourly space velocity (LHSV) of the coal tar HDM, HDS, and HDN with the three catalysts. The properties of three types of catalysts used in these experiments are reported in Table 2. 2.2. Hydrogenation Refining Experiments. All of the experiments were carried out in a continuous reactor comprising three fixed beds (45-mm i.d. and 1180-mm length for each reactor). As shown in Figure 1, the HP catalyst, HDS catalyst, and HDN catalyst were loaded in the HP reactor, HDS reactor, and HDN reactor, respectively. The catalysts were presulfided with 3 wt % CS2 in hexane for 48 h before the reaction.3,25 Different experiments were carried out under a constant H2/oil ratio of 1600:1 and a constant HDS reactor temperature of 593 K, HDN reactor temperatures of 613−653 K, pressures of 11−15 MPa, and

⎧W 1 − nS − Winlet,S1 − nS (nS ≠ 1) ⎪ outlet,S ⎪ = (nS − 1)kt ⎨ ⎪ Winlet,S (nS = 1) = kt ⎪ ln ⎩ Woutlet,S

(2)

where Winlet,S and Woutlet,S represent the mass fractions of sulfur in the coal tar before and after the hydrorefining, respectively. Many researchers consider that the HDS reaction cannot be assumed to be simply a first-order reaction.26,27 Thus, the kinetic model was constructed based on the assumption nS ≠ 1.

Figure 1. Experimental device for coal tar hydrorefining. B


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k0,S3 represent the corresponding pre-exponential factors of HDS in the three reactors. Following the same steps, the kinetic model for HDN in coal tar hydrorefining can be established as

Given that the influence of temperature on a reaction rate can be expressed by the Arrhenius equation and that the influences of both air velocity and pressure on the reaction rate can be expressed as exponential functions, the kinetic model can be expanded as Woutlet,S1 − nS − Winlet,S1 − nS

Woutlet,N

⎛ −E ⎞ = (nS − 1)k 0,S exp⎜ a ⎟(LHSV)aS pH bS 2 ⎝ RT ⎠

⎧ ⎪ ⎡ ⎛ −Ea,N1 ⎞ ⎪ = ⎨(nN − 1)⎢y1k 0,N1 exp⎜ ⎟ ⎢⎣ ⎝ RT1 ⎠ ⎪ ⎪ ⎩

(3)

where k0,S represents the pre-exponential factor of the HDS reaction, Ea represents the apparent activation energy of the HDS reaction; aS denotes the pressure index for HDS, and bS denotes the LHSV index for HDS. With increasing running time of a device, metal and carbon deposition inevitably occurs on the surface of the catalyst. Therefore, the influence of the resulting decrease in catalytic activity on hydrodenitrogenation (HDN) must be taken into account. Given that the kinetic formulation of catalyst deactivation conforms to the time-varying formulation of the activity coefficient as28

⎛ −Ea,N3 ⎞⎤ ⎛ −Ea,N2 ⎞ + y2 k 0,N2 exp⎜ ⎟⎥ ⎟ + y3 k 0,N3 exp⎜ ⎝ RT2 ⎠ ⎝ RT3 ⎠⎥⎦

2

1+

1

a= 1+

βN

( ) t

tc,N

(7)

βS

( ) t

1

× (LHSV)aN pH bN

⎫1/(1 − nN) ⎪ ⎪ + Winlet,N1 − nN ⎬ ⎪ ⎪ ⎭

tc,S

where Winlet,N and Woutlet,N represent the mass fractions of nitrogen in coal tar before and after the hydrorefining, respectively; Ea,N1, Ea,N2, and Ea,N3 represent the reaction activation energies of HDN in the three reactors; k0,N1, k0,N2, and k0,N3 represent the corresponding pre-exponential factors of HDN in the three reactors; t represents the device running time; tc,N denotes the HDN catalyst half-life; aN denotes the pressure index for HDN; and bN denotes the LHSV index for HDN. 3.2. Kinetic Model for the Production of the Desired Products. Fuels such as naphtha and diesel are the desired products in the process of coal tar hydrorefining. However, the production of byproducts such as cracked gas and water is inevitable. Because the hydrogenation activity of the HP catalyst is low and the yield of the desired products of the HP reactor is very low, the HP reactor will be ignored when establishing the kinetic model for the production of desired products. Because it was assumed that there were no desired products and byproducts after the feedstock had passed through the HP reactor, the grading ratios of HDS and HDN were redefined as yS and yN, respectively. It was also assumed that the reactions forming the desired products and byproducts in the process of coal tar hydrorefining conform to ideal parallel reactions and are in conformity with the first-order reaction kinetic model.29 Therefore, a simplified three-lump kinetic model containing feedstock (A), desired products (B), and byproducts (C) was developed to describe the process of producing the desired products in the HDS reactor as follows

(4)

the kinetic model can be modified as Woutlet,S1 − nS − Winlet,S1 − nS ⎛ −Ea,S ⎞ = (nS − 1)k 0,S exp⎜ ⎟(LHSV)aS pH bS 2 ⎝ RT ⎠

1 1+

βS

( ) t

tc,S

(5)

where t represents the device running time, tc,S denotes the HDS catalyst half-life, and β is the deactivation exponential of the HDS catalyst. Hydrogenation catalysts, in addition to their main function, also have other effects. As an example, the HP catalyst has weak hydrogenation activity except for asphalt and metal removal. In addition, the HDS catalyst and HDN catalyst also have three types of functions: HDS, HDN, and production of fuel oil. Considering that the HDS of coal tar in the process of hydrorefining relies on the common impact of the HP, HDS, and HDN catalysts, the kinetic model for coal tar HDS can be presented in the form Woutlet,S ⎧ ⎪ ⎡ ⎛ −Ea,S1 ⎞ ⎪ = ⎨(nS − 1)⎢y1k 0,S1 exp⎜ ⎟ ⎢⎣ ⎝ RT1 ⎠ ⎪ ⎪ ⎩

CA,S = CA0 exp[−yS (k1,S + k 2,S)(LHSV)a ]

⎛ −Ea,S3 ⎞⎤ ⎛ −Ea,S2 ⎞ + y2 k 0,S2 exp⎜ ⎟⎥ ⎟ + y3 k 0,S3 exp⎜ ⎝ RT2 ⎠ ⎝ RT3 ⎠⎥⎦ 1

× (LHSV)aS pH bS 2

1+

βS

( ) t

tc,S

C B,S =

⎫1/(1 − nS) ⎪ ⎪ + Winlet,S1 − nS⎬ ⎪ ⎪ ⎭

k1,S k1,S + k 2,S

(8)

CA0{1 − exp[−yS (k1,S + k 2,S)(LHSV)a ]} (9)

CC,S =

k 2,S k1,S + k 2,S

CA0{1 − exp[−yS (k1,S + k 2,S)(LHSV)a ]}

(6)

(10)

where y1, y2, and y3 represent the catalyst grading ratios of the three catalysts; Ea,S1, Ea,S2, and Ea,S3 represent the corresponding reaction activation energies of HDS in the three reactors; and k0,S1, k0,S2, and

where CA0 represents the initial concentration of the feedstock; Ci,S represents the mass fraction of lump i in the HDS reactor (%); k1,S and k2,S represent reaction rates of desired products and C


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byproducts, respectively, in the HDS reactor; and a denotes the pressure index for HDS. The kinetic model for the production of desired products in the HDN reactor was analogously established as

and TS and TN represent the temperatures in the HDS and HDN reactors, respectively.

CA,N = CA0 exp[−yS (k1,S + k 2,S)(LHSV)a ] × exp[−yN (k1,N + k 2,N)(LHSV)a ]

C B,N =

k1,N k1,N + k 2,N

CC,N =

k 2,N

4. RESULTS AND DISCUSSION 4.1. Parameter Estimation. In a coal tar hydrorefining experiment lasting 1200 h, 27 sets of data were collected. The experimental results are reported in Table 3. Based on these data, the kinetic parameters were fitted using the Levenberg−Marquardt method. The calculated parameters are reported in Table 4.

(11)

CA0 exp[−yS (k1,S + k 2,S)(LHSV)a ]

× {1 − exp[−yN (k1,N + k 2,N)(LHSV)a ]}

(12)

Table 3. Experimental Data on Operating Conditions for Coal Tar Hydrorefining

a

k1,N + k 2,N

CA0 exp[−yS (k1,S + k 2,S)(LHSV) ]

× {1 − exp[−yN (k1,N + k 2,N)(LHSV)a ]}

(13)

where Ci,N represents the mass fraction of lump i in the HDN reactor (%) and k1,N and k2,N are the reaction rates of the desired products and byproducts, respectively, in the HDN reactor. The ultimate yield of the desired products was calculated by adding the values for the two catalysts to obtain an expression as a function of catalyst grading and operating conditions: Because both the HDS reactor and the HDN reactor form products, the ultimate yield of the desired products should be calculated by adding the yields of these two reactors. Because all of the reaction conditions, such as temperature, pressure, and catalyst device running time, will have an impact on the reactions, these parameters also need to be considered in the kinetic model for the production of the desired products. As already mentioned, the influence of temperature on a reaction rate can be described by the Arrhenius equation, and the effects of both the LHSV and pressure on the reaction rate can be expressed as exponential functions. In addition, given that the kinetic formulation of catalyst deactivation conforms to the time-varying expression in eq 4, the final kinetic model is given by CB =

PH2 b 1+

β

() t tc

⎛ k1,S ⎜⎜ CA0 ⎝ k1,S + k 2,S

× {1 − exp[−yS (k1,S + k 2,S)(LHSV)a ]} +

k1,N k1,N + k 2,N

CA0 exp[−yS (k1,S + k 2,S)(LHSV)a ]

⎞ × {1 − exp[−yN (k1,N + k 2,N)(LHSV)a ]}⎟⎟ ⎠

(18)

t (h)

P H2 (MPa)

LHSV (h−1)

THDS (K)

WS (μg·g−1)

WN (μg·g−1)

CB (wt %)

72 96 120 144 168 192 216 264 312 360 408 456 504 552 600 648 696 744 792 840 888 936 984 1032 1080 1128 1176

13 13 13 13 13 11 15 11 13 15 11 15 11 13 15 11 15 11 15 11 13 15 11 15 11 13 15

0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.2 0.2 0.4 0.4 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4

633 633 653 613 633 633 633 613 613 613 613 613 613 613 613 633 633 633 633 653 653 653 653 653 653 653 653

36.35 51.98 21.59 58.13 21.44 40.01 34.12 37.48 35.14 33.18 62.09 55.12 86.19 78.04 75.86 23.97 20.51 56.64 49.54 13.69 12.59 11.53 24.59 20.79 36.46 34.01 31.1

34.74 57.38 17.03 70.04 18.09 39.39 31.92 40.86 37.56 35.19 79.01 65.95 120.3 110.85 102.19 20.84 17.13 64.04 55.43 10.12 9.03 8.28 19.85 17 32.85 30.3 27.94

90.546 90.274 90.609 90.387 90.534 90.395 90.582 90.378 90.465 90.537 90.179 90.356 89.601 89.687 89.757 90.239 90.415 89.913 90.089 90.167 90.253 90.324 90.158 90.335 90.071 90.157 90.227

Table 4. Kinetic Parameters of Coal Tar Hydrorefining HDS

(14)

where k1,S, k2,S, k1,N, and k2,N can be expressed as follows ⎛ −Ea,S1 ⎞ k1,S = k 0,S1 exp⎜ ⎟ ⎝ RTS ⎠

(15)

⎛ −Ea,S2 ⎞ k 2,S = k 0,S2 exp⎜ ⎟ ⎝ RTS ⎠

(16)

⎛ −Ea,N1 ⎞ k1,N = k 0,N1 exp⎜ ⎟ ⎝ RTN ⎠

(17) D

production of desired products

HDN

parameter

value

parameter

value

parameter

value

k0,S1 k0,S2 k0,S3 Ea,S1 Ea,S2 Ea,S3 aS bS tc,S βS nS

48164 15491 10249 58651 52568 52864 −0.529 0.202 30341 1.391 1.23

k0,1N k0,2N k0,3N Ea,1N Ea,2N Ea,3N aN bN tc,N βN nN

57328 15301 15613 58814 55382 54159 −0.578 0.198 29943 1.239 1.27

k0,S1 k0,S2 k0,N1 k0,N2 Ea,S1 Ea,S2 Ea,N1 Ea,N2 a b tc β

22091 1099 42791 2149 43320 38819 54814 59371 −0.878 0.007 185943 1.039


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Energy & Fuels Table 5. Comparison of Data Predicted by the Kinetic Model and the Measure Values WS (μg·g−1) number 1 2 3 4 5

a

WN (μg·g−1)

CB (%)

measured

predicted

relative error (%)

measured

predicted

relative error (%)

measured

predicted

relative error (%)

37.77 36.08 52.56 21.79 22.93

37.51 35.04 53.15 22.50 22.11

−0.70 −2.89 1.13 3.28 −3.58

36.44 34.59 57.12 18.24 19.65

37.05 34.27 58.39 18.01 19.06

1.66 −0.91 2.22 −1.28 −2.99

90.065 90.185 89.934 90.119 90.055

90.095 90.175 89.812 90.145 90.079

0.01 −0.01 −0.02 0.03 0.03

Conditions of experiment 1: pressure, 13 MPa; LHSV, 0.3 h−1; HDN temperature, 633 K; running time, 1200 h. Conditions of experiment 2: pressure, 15 MPa; LHSV, 0.3 h−1; HDN temperature, 633 K; running time, 1224 h. Conditions of experiment 3: pressure, 13 MPa; LHSV, 0.4 h−1; HDN temperature, 633 K; running time, 1248. Conditions of experiment 4: pressure, 13 MPa; LHSV, 0.3 h−1; HDN temperature, 653 K; running time, 1272 h. Conditions of experiment 5: pressure, 13 MPa; LHSV, 0.2 h−1; HDN temperature, 633 K; running time, 1296 h. a

After the running time of the device reached 1176 h, the results of five experiments were collected to verify the product properties based on the above kinetic model; the verification results are reported in Table 5. Errors between the predicted results based on both the HDS and HDN models and the experimental results were less than 5%, and the errors between the predicted results for the yields of the desired products and the experimental results were less than 0.02%. This agreement indicates that the kinetic model developed in this work provides great predictions of the properties of the hydrogenated products of coal tar hydroprocessing under different processing conditions and operating times. 4.2. Model Application. The kinetic model proposed in this work can predict the product properties under varying processing conditions. The predicted trends in the product properties with varying reaction temperature of the denitrification stage (from 613 to 653 K) are shown in Figure 2. In this range of

Figure 3. Yields of the desired products and contents of sulfur and nitrogen in the desired products at different pressures.

the dissolution of hydrogen molecules into the oil phase. Therefore, increasing the reaction pressure has a positive effect on the removal of S and N from coal tar and, consequently, on coal tar upgrading. The trends in the product properties with varying LHSV (from 0.2 to 0.4 h−1) are shown in Figure 4. In the specified LHSV

Figure 2. Yields of the desired products and contents of sulfur and nitrogen in the desired products at different temperatures.

reaction temperatures, increasing the reaction temperature will significantly reduce the sulfur (S) and nitrogen (N) contents and augment the yield of desired products. For reaction temperatures of the HDN stage higher than 631 K, the S content will be lower than the N content, implying that increasing the reaction temperature has a better promotive effect on the HDN reaction than on the HDS reaction for coal tar. The trends in the product properties with varying reaction pressure (from 11 to 15 MPa) are shown in Figure 3. In the investigated range of reaction pressures, increasing the reaction pressure will significantly decrease the S and N contents and augment the yield of the desired products. It can be seen from the figure that the effects of pressure on coal tar HDS and HDN are almost synchronous: Because these two reactions require hydrogen, increasing the pressure has a positive effect on both of these reactions. An increase in pressure can, on one hand, improve the content of hydrogen in the reactor and, on the other hand, promote

Figure 4. Yields of the desired products and contents of sulfur and nitrogen in the desired products at different LHSVs.

range, decreasing the LHSV will significantly reduce the S and N contents and augment the yield of desired products to some extent. For lower LHSV values (less than about 0.3 h−1), reducing the LHSV has little effect on the yield of the desired products, with the S content higher than the N content. However, in the higher LHSV range, the reduction of the LHSV significantly affects the yield of the desired products, with the N content higher than the S content. This kinetic model is applicable to the prediction of the product properties under various conditions; however, the kinetic parameters, calculated based on experimental results obtained over 1200 h, do not provide a wide scope of application. E


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Energy & Fuels Hence, when product properties are predicted for products obtained under process parameters that differ substantially from the experimental conditions used in this study, the results from the kinetic model and the relevant parameters might not be entirely accurate, but such deviations can be regarded as an indication of the limited scope of the model. The most valuable application of the kinetic model could be predicting the changes in product properties under the long-term operation of a device. Because the data were collected during a stable period of catalyst activity, the kinetic model’s predictions for the product properties are susceptible to slight distortion after the catalyst activity has entered into the attenuation period for excessive long-term operation. Consider a set of reference conditions: a reaction temperature of the desulfurization stage of 593 K, a reaction temperature of the denitrification stage of 653 K, an air velocity of 0.3 h−1, and a reaction pressure of 13 MPa. The predicted results under these reference conditions, during two years of operation, showing the relationships between the running time, the S and N contents, and the product recovery rate, are shown in Figure 5. According to the

Wenhong Li: 0000-0002-0898-1881 Notes

The authors declare no competing financial interest.

■

Figure 5. Yields of the desired products and contents of sulfur and nitrogen in the desired products at different running times.

results predicted according to the developed kinetic model, there will be a 7 wt % decrease in the yield of the desired products and increases in the S and N contents of 18 and 17 wt %, respectively, after this catalyst system has been in use for two years.

5. CONCLUSIONS In this work, a kinetic model for HDS, HDN, and the production of the desired products of coal tar hydrorefining was developed. Based on 1200 h of experimental data, the kinetic parameters were fitted using the Levenberg−Marquardt method, and the kinetic model for the hydrodenitrogenation of coal tar was established. According to a comparison of the experimental results and the calculated results, the errors between the predicted results, based on both the HDS and HDN models, and the experimental results were less than 5%, and the errors between the predicted results for the yield of the desired products and the experimental results were less than 0.02%. The model can not only more accurately predict the yield of the desired products and the sulfur and nitrogen contents of the hydrorefining products at different liquid hourly space velocities, pressures, and temperatures, but can also predict the changes in product properties occurring as a result of decreasing catalyst activity after long periods of operation

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*Tel.: +86-13186102519. E-mail: [email protected]. ORCID

Dong Li: 0000-0002-4578-0595 F


Article

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