Catalyst Grading Optimization and Kinetic Simulation of the Shale Oil

Mar 22, 2017 - clean fuels is an extremely important approach for ensuring energy security.1−3 As a primary alternative energy, shale oil has receiv...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/EF

Catalyst Grading Optimization and Kinetic Simulation of the Shale Oil Hydrotreating Process Hongyan Wang,† Fei Dai,† Yiqian Yang,† Zengxi Li,‡ Chunshan Li,*,† and Suojiang Zhang*,† †

Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, 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 ‡ College of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China ABSTRACT: The shale oil hydrogenation experiments were conducted on a fixed-bed reactor with grading of a hydrogenating protective (HP) catalyst, hydrofining (HF) catalyst, and hydrocracking (HC) catalyst. The effects of the reaction temperature and liquid hourly space velocity (LHSV) on product distribution of shale oil hydrotreating were investigated. Three kinds of lumping kinetic models for hydrodearomatization (HDA), hydrodesulfurization (HDS), and hydrodenitrogenation (HDN) were first established and applied in this process. The predicted reactive features and optimized operating conditions for HDS, HDN, and HDA agreed well with experimental results at different catalyst grading ratios, with a relative error of less than 3.8%. In accordance with operating conditions, the model can also be applied in catalyst grading scale calculation, which enhances the theory of shale oil hydro-upgrading application.

1. INTRODUCTION

describing the hydrodearomatization (HDA), HDS, and HDN reactions of shale oil. In the field of crude oil hydrogenation, the lumping kinetic models were widely used for designing the corresponding reactors and catalysts, simulating reactions, and optimizing operation conditions.18−21 Batch-wise hydrogenation kinetics of shale oil have been calculated by Johannes et al.22 The mathematical model which deduces coefficients and constants was used for quantitative evaluation of catalysts and feedstock for hydrogenation. Tang et al.23 investigated HDS and HDN of the middle fraction from Chinese shale oil via lumping kinetics. The result showed that three- and four-lump kinetic models were optimal models, of which the predicted data were consistent with the experimental data in a wide range of operating conditions. In this study, grading catalytic hydrotreating of shale oil fractions was studied and the lumping kinetic models were described. The main reactions, which occurred in the hydrotreating process, HDS, HDN, and HDA, were taken into consideration in this model. Kinetic parameters were determined according to experimental data obtained in hydrotreating of shale oil via a bench-scale reactor with grading catalysts. The developed model was built to predict the kinetic behavior of grading catalysts in a wide range of reaction conditions, which enhances the theory of shale oil hydro-upgrading application.

The utilization of unconventional energy resources for producing clean fuels is an extremely important approach for ensuring energy security.1−3 As a primary alternative energy, shale oil has received extensive attention worldwide. Shale oil reserve in the world was up to 475 billion tons and 5.4 times equivalent to the natural oil, which would amount to a huge resource.4,5 Therefore, it is essential to develop a suitable technology for upgrading shale oil. The catalytic hydrotreatment of shale oil fractions has received a lot of attention as one of the effective approaches to produce clean fuels. In the last few decades, extensive studies about the hydrotreating process of shale oil have been reported in the literature.6,7 The study on the hydro-upgrading of raw Kukersite oil can be traced back to the 1930s.8 Luik et al.9−14 have conducted studies on hydrotreating the diesel fraction, light mazut, heavy mazut, and shale oil, such as total shale oil, dephenolated shale oil, and its phenols of Estonian shale oil. The effects of the reaction temperature, hydrogen pressure, and residence time on the yield and composition distribution of products under catalytic hydrogenation were investigated in autoclaves. Landau et al.15,16 have developed a novel catalyst system for Israeli shale oil to reduce the concentrations of sulfur and nitrogen in the hydrogenated oil, in which the conversion of hydrodesulfurization (HDS) was higher than 99% and that of hydrodenitrogenation (HDN) varied over the range of 74.3− 99.9%. Upgrading rectification residuum fraction of Estonian shale oil has been studied.17 Three types of commercial catalysts were served as hydroconversion catalysts, which can be used for hydropurification, hydrocracking, and universal purposes. The yield of the fraction below 360 °C was up to 82.7%. However, only a few papers have been published dealing with the product distribution under grading catalysts and kinetic models © XXXX American Chemical Society

2. EXPERIMENTAL SECTION 2.1. Feedstock. The feedstock used for all experiments is shale oil distillate with a boiling point below 360 °C from the pyrolysis process (Huadian, China). Corresponding physicochemical property data are summarized in Table 1. Received: November 24, 2016 Revised: March 21, 2017 Published: March 22, 2017 A

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

2.2. Catalyst Preparation and Grading. The catalyst used for experiments was Ni, Mo, and W supported on γ-Al2O3 (Wish Chemicals Co., Ltd.) prepared through the ultrasonic impregnation method. The support was dried in an oven at 120 °C for 2 h to remove the surface water. Then, the support was impregnated in an aqueous solution with required molar ratios of nickel nitrate [Ni(NO3)2·6H2O], ammonium heptamolybdate [(NH4)6Mo7O24·4H2O], and ammonium metatungstate hydrate [(NH4)6W7O24·6H2O] at 60 °C for 6 h, accompanied by a 50 kHz ultrasonic vibration. After that, the impregnated catalyst was dried at 120 °C for 12 h, then heated to 300 °C at a rate of 5 °C/min, and held for 1 h under an air atmosphere. Then, it was calcined at 550 °C for another 6 h. The textural properties of the catalysts were analyzed by N2 physisorption in a Quanta Chrome Instrument NOVA 2000 equipment using the Brunauer−Emmett−Teller (BET) method. Period to analysis, all the samples were degassed at 300 °C for 6 h with nitrogen. A nitrogen sorption isotherm and corresponding pore size distribution profile of catalyst samples were presented in Figure 1. Temperature-programmed desorption of ammonia (NH3-TPD) was carried out on an Autochem II 2920 chemisorption analyzer (Micromeritics, Norcross, GA). The properties of catalyst samples were listed in Table 2. The hydrocracking (HC) catalyst has maximal specific surface area, while the hydrogenating protective (HP) catalyst has the largest pore volume and mean pore diameter, which could prevent microporous blockage during the

hydrogenation process. The hydrofining (HF) catalyst has a roughly equivalent size of pore volume and mean pore diameter with the HC catalyst. The total acid amount compiled in the following order: HF > HC > HP. The function optimization of a single catalyst has been discussed in our previous research.5,24,25 The results show that Mo−Ni and W−Ni catalysts supported by γ-Al2O3 have high HDS and HDN activities, while W−Ni catalysts exhibited better HDA and selective ring-opening performance with 15 wt % W loading. To reduce the contents of sulfur, nitrogen, and aromatics in the feedstock, to improve its stability and, ultimately, obtain the high-quality fuel oil, Mo−Ni and W−Ni catalysts were graded. The spherical catalyst, previously crushed and sieved, was loaded in the reactor with a total volume of 30 mL from top to bottom, and the catalyst grading scheme is given in Table 3. In the present study, a downflow fixed bed was used and catalyst loading followed these principles: the pore size decreased and hydrogenation activity increased along the direction of the reactant.26,27 2.3. Experimental Procedure. The fixed-bed reactor (110 × 15 × 26 mm) is a continuous bench-scale device operated in down-flow mode and heated by a three-zone electric furnace, as displayed in Figure 2. A thermowell with an external diameter of 6.35 mm was placed at the center of the reactor. Catalysts were crushed, sieved, and loaded into the reactor with a bulk density between 0.95 and 1.05 g/mL in the constant temperature stage (about 200 mm). The catalyst was resulfided according to a previous report prior to the hydrotreating experiments.24,25 After presulfiding, the experiments were carried out at a constant pressure of 8 MPa and a H2/oil volume ratio of 1000 (v/v), varying the reactor temperature from 340 to 380 °C and liquid hourly space velocity (LHSV) from 0.4 to 1.2 h−1. The products were separated via a water cooler and gas−liquid separator and finally collected by a fluid reservoir. The liquid product was sampled during the steady-state operation period. 2.4. Analytical Methods. Feedstock and specific hydrotreatment product samples were analyzed through the following techniques. The detailed distribution of chemical components was provided by gas chromatography−mass spectrometry (GC−MS) analysis. The samples were collected on a HP-5MS capillary column (30 m × 0.25 mm × 0.25 μm) with a syloxane stationary phase and recorded using GC−MS (Agilent 6890N/5975B), equipped with a flame ionization detector (FID). C, H, and O elements were determined by an elemental analyzer (Elementar VARIO ELIII, Germany). The S and N contents were measured by a fluorescence SN analytical instrument (KY3000SN, Jiangyan, China) following the ASTM D5453 method. The GC simulation distillation method was used to obtain the distillation range of feedstock and liquid product samples (ASTM D86 standard). Density and viscosity were performed on DMA 5000 (Anton Paar) according to the ASTM D4052 and ASTM D7042 methods, respectively.

Figure 1. Nitrogen sorption isotherm and corresponding pore size distribution profile (inset) of grading catalysts.

3. LUMPING KINETIC MODEL 3.1. Kinetic Models of HDS and HDN. The description of complex mixtures by lumping a huge number of chemical compounds into smaller groups of pseudo-components has been widely employed by researchers to establish simple kinetic equations.28 In comparison to fossil fuels, the most striking feature of shale oil is the high S and N contents, which accounted for 0.75 and 1.98%, respectively. It is necessary to select a reasonable division of sulfur and nitrogen compounds for establishing kinetic models that could perfectly describe HDS and HDN in the hydrotreatment of shale oil. In the present work, S or N compounds are divided into two groups according to reactivity and rate of hydrogenation reaction. Aliphatic and non-heterocyclic aromatic sulfur and nitrogen compounds were classified as the high-reactivity group, and other heterocyclic compounds were grouped into the lowreactivity group.29,30

Table 1. Properties of Feedstock characteristic density (g/cm3) viscosity (mPa s) elemental analysis C (wt %) H (wt %) O (wt %) N (wt %) S (wt %) H/C molar ratio distillation range (°C) IBP 10% 50% 90% 95% composition (%) paraffinic carbons m-aromatics p-aromatics

feedstock 0.8892 16.1 80.3 11.1 5.86 1.98 0.76 1.66 80 179 293 358 360 65.8 20.6 13.6

B

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels Table 2. Physical−Chemical Properties of Catalysts composition (wt %) catalyst

NiO

MoO3

HP HF HC

5 5 5

10 15

WO3

SBET (m2/g)

pore volume (cm3/g)

mean pore diameter (nm)

total acid amount (μmol/g)

15

157.6 165.2 190.5

0.74 0.39 0.36

19.4 8.72 8.14

724.7 1058.0 893.6

The two-lump kinetic equations are expressed as follows:

Table 3. Catalyst Grading Scheme

C = (ahC h0(1 − nh) + (nh − 1)Ah e−Ea,h / RT /LHSV)−1/(nh − 1)

loading density (g/cm3) number

grading scheme (volume ratio)

HP

HF

HC

G1 G2 G3

HP/HF/HC = 1:1:1 HP/HF/HC = 1:3:2 HP/HF/HC = 1:2:3

0.32 0.33 0.33

0.53 0.55 0.50

0.57 0.56 0.59

+ (alC l0(1 − nl) + (nl − 1)Al e−Ea,l / RT /LHSV)−1/(nl − 1) (2)

ah + al = 1,

(3)

where ah and al are the share of sulfur or nitrogen compounds of high- and low-reactivity lumps in the overall concentration of sulfur or nitrogen, respectively, and Ch0 and Cl0 represent the initial concentrations of sulfur or nitrogen in high- and lowreactivity lumps, respectively. 3.2. Kinetic Models of HDA. Generally, the lumping division for complex raw oil feeds is performed on the basis of their composition and structure.31 The hydrogenation of olefins to saturated hydrocarbon is easy to implement; in addition, hydrocracking of aliphatic hydrocarbons to light hydrocarbons and gases is mild, which is not discussed in the present study. In this study, the feed is lumped into paraffinic carbons (paraffin), monocyclic aromatic hydrocarbons (m-aromatics), and polycyclic aromatic hydrocarbons (p-aromatics) to predict the path and product distribution of the shale oil hydrogenation reaction adequately. The hydrotreating of aromatics, primarily naphthalene compounds, played an important role in the component distribution of products, which directly impact the performance of gasoline and diesel fractions. Therefore, the products obtained from shale oil hydrogenation are lumped into five groups, considering that the primarily desired products from aromatic hydrocarbons of shale oil hydrogenation are naphthenic hydrocarbons. Aromatic hydrogenation products can be considered as separate lumps: cycloalkanes (CA), bicyclic alkanes (BCA), phenyl cycloalkanes (PCA), and alkylbenzenes (AB). In addition, alkanes and other low-carbon pyrolysis gases, which accompanied coal tar hydrogenation, can be considered as one lump. On the basis of the above-mentioned division, the simplified reaction network among lumps is presented in Figure 4. Abbreviations: (1) cycloalkanes (CA), cycloalkanes with C5− C6 and alkyl cycloalkanes with C7−C11; (2) bicyclic alkanes

Figure 2. Experimental setup.

On the basis of the above division, the simplified reaction networks of lumps are shown in Figure 3. The kinetic models for HDS and HDN are established as follows:

Figure 3. Two-lump reaction network for HDS and HDN.

To simplify the model, the HDS/HDN of each lump was assumed as follows: (1) The deactivation of catalyst should be neglected. (2) The steams in the reactor are in accordance with an ideal trickle-bed reactor model. (3) The apparent reaction rate constants in the kinetic model can be expressed by the Arrhenius equation, ignoring the influence of chemical equilibrium. (4) The hydrogenation pathway can be described via a direct cleavage of the C−S or C−N bond without other reactions between the heteroatom-containing compounds. On the basis of the above assumptions, the corresponding reaction rate equations can be described as follows:28 C = (C0(1 − n) + (n − 1)A e−Ea / RT /LHSV)1/(n − 1)

(ah , al > 0)

(1)

where C represents the concentration of S or N, C0 is the initial concentration, and Ea, R, T, n, and 1/LHSV are the apparent activation energy, gas constant, temperature, reaction order, and residence time, respectively.

Figure 4. Eight-lump reaction network for shale oil hydrogenation. C

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

was the sum of the squares of the difference between the experimental and the calculated values. The parameter estimation problem was formulated mathematically using the following nonlinear optimization program described in eq 12

(BCA), decalins, methyldecalins, octahydro-1H-indene, and methyloctahydro-1H-indene; (3) phenyl cycloalkanes (PCA), alkyl tetrahydronaphthalenes with C11−C14 and alkyl-1Hindanes with C10−C12; (4) alkylbenzenes (AB), alkylbenzenes with C7−C11; and (5) alkanes, alkanes with C4−C27; gas, alkanes with less than 4 carbon atoms. For each reaction, a kinetic expression (ri) was formulated as a function of the lump concentration (Ci) and kinetic constants (ki). The reaction among the eight lumps is a highly coupled, complicated, parallel-order response. To simplify the model, a practical calculating process should have the following assumptions: (1) The deactivation of catalyst should be neglected. (2) The reactions are far from chemical equilibrium; therefore, they are considered pseudo-first-order reactions. (3) The reaction is conducted under the condition that the internal and external effects of diffusion have been eliminated. Therefore, the reaction is controlled by kinetics. On the basis of the above assumptions, the reaction rate equations of the proposed model are expressed as follows:30 r1 =

r2 =

r3 =

r4 =

dC1 d

1 ( LHSV )

dC 2 d

1 ( LHSV )

dC 3 d

1 ( LHSV )

dC4 d

1 ( LHSV )

n

min F(t ) =

i

Cexp i

n

∑ f (t )2 = ∑ [(Ciexp) − (Cical)]2 i=1

(12)

Ccal i

where and refer to the experimental value at a given operating condition and the corresponding value calculated using eqs 4−11, respectively.

4. RESULTS AND DISCUSSION 4.1. HDS and HDN Activities and Kinetics. 4.1.1. Effect of Reaction Conditions on HDS and HDN. The total contents of S

= −(k1 + k 2 + k 3 + k4 + k5)C1 (4)

= −k6C2 (5)

= −(k 7 + k 8 + k 9 + k10 + k11)C3 (6) Figure 5. Sulfur content of the hydrogenation product under different grading catalysts.

= k1C1 + k 7C3 + k16C7 − (k12 + k13)C4 (7)

r5 =

dC5 d

1 ( LHSV )

= k 2C1 + k 8C3 + k17C7 − (k14 + k15)C5 (8)

r6 =

dC6 d

1 ( LHSV )

= k 3C1 + k6C2 + k 9C3 + k12C4 + k14C5

+ k18C7 + k 20C8

r7 =

dC 7 d

1 ( LHSV )

C7 r8 =

(9)

= k4C1 + k10C3 − (k16 + k17 + k18 + k19) Figure 6. Nitrogen content of the hydrogenation product under different grading catalysts.

(10)

dC8 d

(

1 LHSV

− k 20C8

)

and N in hydrogenation products under different LHSVs and temperatures were presented in Figures 5 and 6, respectively. The results showed that high temperature and low LHSV benefited HDS and HDN reactions. HDS and HDN reaction activities were obviously influenced by LHSV under a low reaction temperature. However, this influence decreased with the reaction temperature up to 380 °C. In addition, the trend of HDS and HDN activities influenced by the LHSV and temperature is consistent with different catalyst grading ratios. The activity of HDS was highest under G2, as shown in Figure 5. The activity of HDS decreased obviously with the reaction temperature decreasing. There no obvious advantage of HDN under different catalyst grading ratios, as displayed in Figure 6. G2 showed a slim

= k5C1 + k11C3 + k13C4 + k15C5 + k19C7 (11)

where Ci represents the concentration of each lump (i = 1−8), ki refers to the kinetic constant for the reaction of lump to lump (i = 1−8), and 1/LHSV is the residence time. A program using MATLAB language and based on the nonlinear least squares method was used to estimate the kinetic parameters in the proposed lump model.31 The numerical solutions for eqs 4−11 were obtained using a fourth-order Runge−Kutta method. The objective function to be minimized D

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 7. S/N concentrations predicted by the two-lump model at 360 °C, 8 MPa, and hydrogen/oil of 1000 (v/v).

Figure 8. S/N concentrations predicted by the two-lump model at different reaction temperatures, 8 MPa, and hydrogen/oil of 1000 (v/v).

Table 4. Predicted Results of HDS/HDN at Different Grading Ratios CS (ppm)

CN (ppm)

graded catalyst

predicted

experimental

relative error (%)

predicted

experimental

relative error (%)

G1 G2 G3

136.3 80.6 57.4

131.4 78.8 59.3

3.8 2.3 3.3

114.5 61.5 128.9

112.1 60.8 125.9

2.2 1.1 2.4

Figure 10. Predicted CA distribution under different LHSVs and temperatures.

Figure 9. Product distribution under different LHSVs and temperatures (G2).

lumps are rapidly removed with an increasing residence time. The change of the S content in high-reactivity lump is negligible when the residence time is shorter than 1.0 h. The total N content is almost removed at the residence time of 1.0 h. However, the residence time for deep desulfurization is needed longer than 1.5 h, indicating that denitrification is the precondition of deep desulfurization in accordance with the previous research.32,33

advantage of HDN reactivity as the reaction temperature reached 360 °C. The catalyst grading ratio of G2 was illustrated to predict the sulfur concentrations of products in each lump and optimize the operation conditions. S and N concentrations of products predicted in each lump during hydrogenation were plotted Figure 7. The S and N compounds in high- and low-reactivity E

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Table 5. Reaction Rate Constants for G2 at Different Temperatures, 0.8 h−1, 8 MPa, and Hydrogen/Oil of 1000 (v/ v) k

Figure 11. Predicted BCA distribution under different LHSVs and temperatures.

number

A (°C/h)

E (kJ/mol)

340 °C

360 °C

380 °C

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1.16 × 103 7.09 × 1011 1.32 × 103 7.48 × 102 1.03 × 102 2.99 × 104 1.67 × 103 4.86 × 107 3.22 × 102 7.17 × 102 4.60 × 104 2.88 × 107 1.27 4.36 × 108 5.07 × 1011 3.39 0.42 7.29 × 102 1.88 6.72 × 103

35.27 171.97 48.33 32.00 27.09 37.87 36.94 124.42 38.25 31.79 59.11 134.83 10.03 148.74 156.05 23.59 6.29 76.66 16.63 91.67

1.2054 0.0012 0.1052 1.4704 0.4807 18.4893 1.2525 0.001 0.1856 1.4645 0.3794 0.00008 0.1805 0.0001 0.0233 0.0318 0.1197 0.0002 0.0706 0.0001

1.2757 0.0082 0.1252 1.5605 0.6633 20.7394 1.3439 0.004 0.2056 1.5669 0.766 0.0003 0.1843 0.0002 0.0833 0.0420 0.1290 0.0004 0.0831 0.0002

1.8478 0.0093 0.1885 2.1661 0.6634 29.2148 1.9587 0.0044 0.2947 2.1514 0.7663 0.0004 0.2038 0.0006 0.1509 0.0421 0.1290 0.0005 0.0861 0.0003

Figure 12. Predicted PCA distribution under different LHSVs and temperatures.

Figure 14. Product distribution under different graded catalyst scales.

4.1.2. Effect of Catalyst Grading Ratios on HDS and HDN. The kinetic model developed in this work was also applied to simulate the performance of shale oil HDS and HDN at the different catalyst grading ratios. Table 4 presents the data for validating the kinetic model under three types of catalyst grading and optimized operating conditions as follows: reaction temperature, 360 °C; pressure, 8 MPa; LHSV, 0.8 h−1; and H2/oil, 1000 (v/v). The catalyst grading ratio of 1:3:2 (G2) shows the better comprehensive hydrogenation performance. The relative error between the predicted and experimental values is less than 3.8% under catalyst grading ratios of 1:1:1, 1:3:2, and 1:2:3, indicating that the kinetic model could significantly predict the effect of HDS and HDN. 4.2. HDA Reactivity and Kinetics. During the hydrogenation process of shale oil to fuel oil, the aromatic hydrogenation product distribution strongly influenced the yield and performance of gasoline and diesel fractions. Previous studies showed that naphthenic compounds played a pivotal role in the cetane number of hydrogenation products. Selective hydrogenation of aromatics under different LHSVs, temper-

Figure 13. Predicted product distribution with the residence time under different reaction temperatures.

Figure 8 showed the effect of the reaction temperature actting on the amount of residual S and N. It indicates that S is removed significantly when the temperature reaches 360 °C. If the residence time exceeds 0.5 h, the S conversion increases slightly. The reaction rate constant for HDN showed similar trends under different reaction temperatures. Considering the various factors, the optimal operating conditions for HDS and HDN are 360 °C, 0.8 h−1, and hydrogen/oil of 1000 (v/v), which agree well with the experimental data shown in Figures 5 and 6. F

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Table 6. Kinetic Parameters of Shale Oil Hydrotreating Catalyzed under Different Graded Catalysts (360 °C, 0.8 h−1, 8 MPa, and Hydrogen/Oil of 1000, v/v) 4

a

16 (PCA → AB)

10

17 (PCA → BCA)

graded catalyst

Ea

k

E

k

E

k

E

k

G1 G2 G3

32.52 32.00 31.83

1.5210 1.5605 1.5850

33.26 31.79 31.02

1.4654 1.5669 1.6016

30.21 23.59 20.14

0.0414 0.0420 0.1056

10.23 6.29 9.89

0.1087 0.1290 0.3825

E is in units of kJ/mol.

The parameters of kinetic models for HDA are given in Table 5. The values of E1, E4, E7, and E10 are 35.27, 32.00, 36.94, and 31.79 kJ/mol, respectively, indicating that m-aromatics and paromatics more easily convert to PCA via hydrogenation than AB through the hydrogenation ring-opening reaction. The values of E16 and E17 are 23.59 and 6.29 kJ/mol, respectively. It means that PCA is more inclined to transform into BCA other than AB by the ring-opening reaction. In addition, almost no alkane + gas produced from AB, BCA, PCA, and CA, owing to the higher activation energy; E12, E14, E18, and E20 are 134.83, 148.74, 76.66, and 91.67 kJ/mol, respectively. CA was probably the final product of aromatic transformations, which is in accordance with the experimental data. 4.2.2. Effect of the Grading Catalyst on the Product Distribution of HDA. The product distribution under different grading catalyst scales with an optimized reaction temperature of 360 °C and LHSV of 0.8 h−1 was illustrated in Figure 14. The product distribution, especially products conversed from aromatic hydrogenation, showed significant differences with the catalyst grading scale changing. CA and BCA showed the highest proportion in product distribution at G2, constituting 14.6 and 3.62%, respectively, which indicates the highest hydrogenation activity. The low content of PCA could be ascribed to the high hydrogenation activity of the second aromatic ring, accounting for more PCA converting to BCA and afterward CA. For G1, a high selectivity of PCA and low distribution of BCA and CA displayed a low hydrogenation activity of graded catalysts. In comparison to G1, the proportion of AB was 13.0% under G3. More low-saturation compounds persisting in the product means that more PCA selectively converts to AB by the ringopening reaction rather than BCA through hydrogenation saturation. Kinetic parameters of shale oil hydrotreating catalyzed under different catalyst grading ratios are listed in Table 6. E of aromatic hydrocarbons produced by the PCA reaction (E4 and E10) decreased as the proportion of HC increased in graded catalysts, indicating that the hydrogenation activity increased as G1 < G2 < G3. However, E16,G2 > E16,G3, and E17,G2 < E17,G3, as shown in Table 6, suggesting that PCA was inclined to produce more AB than that of BCA under graded catalyst G3, which reduced the selectivity of BCA and CA. It sufficiently explained the reason for AB making up a larger proportion in hydrogenation products, as presented in Figure 13.

atures, and catalyst grading ratios was focused in the present study. 4.2.1. Effect of the Reaction Conditions on Product Composition. The product distribution under grading catalysts was exhibited in Figure 9 on the basis of G2. As shown in Figure 9, the product distribution law under different reaction temperatures and LHSVs is similar. Saturated hydrocarbon contents in hydrotreatment products decreased slightly with an increasing temperature and a decreasing LHSV, which is attributed to the fact that a small portion was converted to gas via the cracking reaction. A lower LHSV means a longer retention time for chemical reactions and a higher light fraction product yield. In this study, the CA content increased obviously as the LHSV decreased from 1.2 to 0.4 h−1 and the temperature increased from 340 to 380 °C. The BCA content presented the same change rule, but this change rule is not obvious with the temperature increasing. In addition, much more monocyclic compounds (CA + AB) were obtained from bicyclic compounds (BCA + PCA + AN) at a low LHSV. Nevertheless, the lowest content of AB at 0.4 h−1 suggested that CA was probably the final product of PCA hydrogenation. The results showed the hydrogenation activity of aromatic saturation higher than ring opening. To discuss the influence of the temperature and LHSV on hydrogenation product distribution in detail, a simulated calculation was used in the presented study. The content curves of CA, BCA, and PCA varying with the residence time (the reciprocal of LHSV) under different temperatures were illustrated in Figures 10−12. The results showed that the CA content was positively correlated with the temperature and residence time. The content of BCA was influenced by the temperature slightly, as displayed in Figure 9. It is worth noting that, when the residence time is more than 1.25 h (LHSV less than 0.8 h−1), there is a rise in the reaction temperature, leading to a reduction of the BCA content. It means that more BCA is converted to CA through the hydrogenation ring-opening reaction. As presented in Figure 9, the PCA content increased at first and then decreased with the residence time increasing. It indicated that a low LHSV generated profit for the yield of monocyclic compounds (CA and AB) from bicyclic compounds (BCA and PCA), which is in accordance with the experimental data, as shown in Figure 9. In the present study, hydrogenation products are mono- and dicycloalkanes, such as CA, BCA, and PCA.5 The distribution of hydrogenated products (CA + BCA + PCA) under different residence times and temperatures was demonstrated in Figure 13. The content of CA + BCA + PCA increased as the temperature increased, with the residence time less than 1.25 h (LHSV = 0.8 h−1). The influence of the reaction temperature on the hydrogenated product distribution almost disappeared, with the residence time of more than 1.25 h. Considering the saturation and yield of the liquid product, the optimized reaction temperature was at 360 °C in this study.

5. CONCLUSION A lumping kinetic model of HDS, HDN, and HDA reactions correlated with the catalyst grading ratio was developed to predict the effect of hydrogenation and optimize the operating conditions of the Huadian shale oil hydrogenation process. The predicted reactive features and optimized operating conditions for HDS and HDN are found to agree fairly well with the G

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels experimental results, with a relative error of less than 3.8% at different catalyst grading ratios. Aromatic hydrocarbon hydrogenation activity and product distribution were affected by the catalyst grading ratio obviously. The preferable catalyst grading ratio from the model calculation was 1:3:2, which provides a feasible reference for maximizing the desired products with ultralow sulfur and nitrogen. The result showed that the lumping kinetic model can be applied to simulate and optimize the HDS, HDN, and HDA reactions and catalyst grading of shale oil hydrotreatment.

■

■

0 = initial value

REFERENCES

(1) Song, W. N.; Dong, Y. L.; Xue, L. M.; Ding, H. X.; Li, Z.; Zhou, G. J. Oil Shale 2012, 29, 334−343. (2) Ma, Y.; Li, S. Y. Fuel Process. Technol. 2012, 100, 11−15. (3) Bellussi, G.; Rispoli, G.; Molinari, D.; Landoni, A.; Pollesel, P.; Panariti, N.; Millini, R.; Montanari, E. Catal. Sci. Technol. 2013, 3, 176− 182. (4) Niu, M.; Wang, S.; Han, X.; Jiang, X. Appl. Energy 2013, 111, 234− 239. (5) Wang, H. Y.; Dai, F.; Li, Z. X.; Li, C. S. Energy Fuels 2015, 29, 4902−4910. (6) Benyamna, A.; Bennouna, C.; Moreau, C.; Geneste, P. Fuel 1991, 70, 845−848. (7) Yu, H.; Li, S. Y.; Jin, G. Z. Oil Shale 2010, 27, 126−134. (8) Kogerman, P.; Kopvillem, J. J. Inst. Pet. Technol. 1932, 18, 833−845. (9) Luik, H.; Lindaru, E.; Vink, N.; Maripuu, L. Oil Shale 1999, 16, 141−148. (10) Luik, H.; Vink, N.; Lindaru, E.; Maripuu, L. Oil Shale 1999, 16, 249−256. (11) Luik, H.; Maripuu, L.; Vink, N.; Lindaru, E. Oil Shale 1999, 16, 331−336. (12) Luik, H.; Vink, N.; Lindaru, E.; Maripuu, L. Oil Shale 1999, 16, 337−342. (13) Luik, H.; Vink, N.; Lindaru, E.; Maripuu, L. Oil Shale 2000, 17, 25−30. (14) Luik, H.; Luik, L.; Krasulina, J.; Riisalu, H. Proceedings of the 32nd Oil Shale Symposium; Golden, CO, Oct 15−17, 2012. (15) Landau, M. V.; Herskowitz, M.; Givoni, D.; Laichter, S.; Yitzhaki, D. Fuel 1996, 75, 858−866. (16) Landau, M. V.; Herskowitz, M.; Givoni, D.; Laichter, S.; Yitzhaki, D. Fuel 1998, 77, 3−13. (17) Luik, H.; Luik, L.; Johannes, I.; Tiikma, L.; Vink, N.; Palu, V.; Bitjukov, M.; Tamvelius, H.; Krasulina, J.; Kruusement, K.; Nechaev, I. Fuel Process. Technol. 2014, 124, 115−122. (18) Mederos, F. S.; Elizalde, I.; Ancheyta, J. Catal. Rev.: Sci. Eng. 2009, 51, 485−607. (19) Sertić-Bionda, K.; Gomzi, Z.; Šarić, T. Chem. Eng. J. 2005, 106, 105−110. (20) Xing, X. Q.; Zhao, G. Y.; Cui, J. Z. Sci. China: Chem. 2012, 55, 1542−1547. (21) Zhao, W. X.; Chen, D. Z.; Hu, S. X. Comput. Chem. 2002, 26, 141−148. (22) Johannes, I.; Luik, H.; Kruusement, K. Fuel Process. Technol. 2006, 87, 711−716. (23) Tang, X.; Li, S. Y.; Yue, C. T.; He, J. L.; Hou, J. L. Oil Shale 2013, 30, 517−535. (24) Zhang, K. Y.; Zhang, A. H.; Yan, J. Petrochem. Technol. Appl. 2008, 26, 297−300. (25) Gong, Y. B.; Geng, X. G. Contemp. Chem. Ind. 2009, 38, 586−592. (26) Wang, H. Y.; Jiao, T. T.; Li, Z. X.; Li, C. S.; Zhang, S. J.; Zhang, J. L. Fuel Process. Technol. 2015, 139, 91−99. (27) Wang, H. Y.; Cao, Y. M.; Li, D.; Muhammad, U.; Li, C. S.; Li, Z. X.; Zhang, S. J. J. Renewable Sustainable Energy 2013, 5, 053114. (28) Dai, F.; Gao, M. J.; Li, C. S.; Xiang, S. G.; Zhang, S. J. Energy Fuels 2011, 25, 4878−4885. (29) Landau, M. V. Catal. Today 1997, 36, 393−429. (30) Dai, F.; Wang, H. Y.; Gong, M. M.; Li, C. S.; Li, Z. X.; Zhang, S. J. Energy Fuels 2015, 29, 7532−7541. (31) Dai, F.; Gong, M. M.; Li, C. S.; Li, Z. X.; Zhang, S. J. Appl. Energy 2015, 137, 265−272. (32) Yang, H.; Chen, J. W.; Fairbridge, C.; Briker, Y.; Zhu, Y. J.; Ring, Z. Fuel Process. Technol. 2004, 85, 1415−1429. (33) García-Gutiérrez, J. L.; Laredo, G. C.; Fuentes, G. A.; GarcíaGutiérrez, P.; Jiménez-Cruz, F. Fuel 2014, 138, 98−103.

AUTHOR INFORMATION

Corresponding Authors

*Telephone: 0086-10-82544800. E-mail: [email protected]. *Telephone: 0086-10-82544875. 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 work was supported by the National Basic Research Program of China (973 Program, 2014CB744306), the National Science Fund for Excellent Young Scholars (21422607), the General Program of National Natural Science Foundation of China (21576261), the Joint Funds of the National Natural Science Foundation of China (PRIKY15013 and 2015D-50060406).

■

NOMENCLATURE r = reaction rate (h−1) C = concentration of heteroatom in shale oil (ppm) k = apparent reaction rate constant (h−1) n = reaction order ah = share of sulfur or nitrogen compounds of high-reactivity lump al = share of sulfur or nitrogen compounds of low-reactivity lump Ch0 = initial concentration of sulfur or nitrogen in highreactivity lump Cl0 = initial concentration of sulfur or nitrogen in lowreactivity lump Ci = concentration in each lump of HDA (i = 1−8) ki = kinetic constant for the reaction of lump to lump (i = 1−8) Cexp i = calculated value at a given operating condition Ccal i = experimental value at a given operating condition

Abbreviations

HDS = hydrodesulfurization HDN = hydrodenitrification HDA = hydrodearomatization HP = hydroprotecting HF = hydrofining HC = hydrocracking LHSV = liquid hourly space velocity Subscripts

S = sulfur compound N = nitrogen compound h = high-reactivity lump l = low-reactivity lump i = lump component i H

DOI: 10.1021/acs.energyfuels.6b02720 Energy Fuels XXXX, XXX, XXX−XXX