Carbon-Number-Based Kinetics, Reactor Modeling, and Process

Sep 29, 2015 - E-mail: [email protected]., *Telephone: 0086-10-82627080. ... A new carbon-number-based kinetic model containing 18 hydrocarbon ...
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Carbon-Number-Based Kinetics, Reactor Modeling, and Process Simulation for Coal Tar Hydrogenation Fei Dai,†,‡ Hongyan Wang,† Maoming Gong,† Chunshan Li,*,‡ Zengxi Li,† and Suojiang Zhang*,‡ †

College of Chemistry and Chemical Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100049, 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 S Supporting Information *

ABSTRACT: A new carbon-number-based kinetic model containing 18 hydrocarbon groups was developed in this work to describe coal tar hydrogenation, and the kinetic parameters were determined by means of fitting the experimental data, obtained in a two-stage fixed-bed reactor hydrogenation experiment, under various operating conditions. Model validation revealed that experimental data considerably agreed with expected outcomes. On this basis, a non-isothermal reactor model based on mass balance and energy balance as well as the proposed reaction kinetic model were constructed to further investigate the behavior of the hydrogenation fixed-bed unit, and the reactor model applied in a bench-scale plant of coal tar hydrogenation accurately simulated and predicted the yield distribution of carbon number products and the temperature profile along with the reactor. In addition, the entire process simulation for coal tar hydrogenation was developed using Aspen Plus. The simulation provided a significant guide for the optimization and design of industrial-scale hydrogenation.

1. INTRODUCTION More comprehensive environmental regulations and product quality specifications and gradually declining crude oil deposits highlight the importance of a processing technology in the petroleum refining industry.1 Hydrogenation technology is an indispensable chemical upgrading method for converting the heavy and low-quality feedstock into more favorable, clean, and lighter fuel products, such as gasoline, kerosene, and diesel. Hydrogenation is a promising technique because of the high clean product yield and versatile application for various feedstocks,2 such as residual oil, crude oil, vacuum gas oil, and coal tar. Coal tar, which is a kind of dark brown inferior oil, is a byproduct from coal carbonization or gasification, and its outputs in China had reached more than 18 million tons in 2012.3 Direct combustion of most coal tar that is used as fuel results in serious environmental pollution and resource wastage.4−7 Coal tar hydrogenation currently receives more attention to address environmental concerns because of its capacity to convert low-quality coal tar into invaluable clean fuels, such as gasoline and diesel. Extensive studies on catalysts, reaction mechanisms, and kinetic modeling for coal tar hydrogenation have been reported in the literature.8−13 However, minor research attention has been focused on modeling and simulation of a hydrogenation reactor. The reactor model that is based on a mass−energy balance and reaction kinetic model enables a substantial understanding of the hydrogenation reactor behavior, and this model also aims to facilitate hydrogen consumption prediction, reactor design and simulation, and operation condition optimization. An essential element for developing the hydrogenation reactor model is the kinetic modeling. The kinetic modeling in hydrogenation is an formidable work because of the © 2015 American Chemical Society

considerable quantity of chemicals in the feedstocks and the complex reactions involved,14 and various papers on distinct modeling approaches to hydrogenation have been widely published. Lumping kinetic modeling15−21 and detailed molecular kinetic modeling22−29 are the two main approaches to hydrogenation. Lumping modeling is primarily based on the discrete, structure, and continuous lumping, and this model involves a complex reaction mixture that is divided into various, smaller pseudo-components according to their properties, such as boiling point, molecular weight, carbon number distribution, and structural species. Past efforts had gradually improved the lumping model in terms of product yield distribution prediction and operating condition optimization. This model has also been used for hydrogenation unit modeling of various types of feedstocks. For examples, Veratraete et al.30 developed a kinetic model based on eight chemical lumps for modeling fixed-bed residue hydrotreating processes and precisely predicted the evolution of various lumps along the reactor. Yield distribution and atomic composition of each lump were also predicted. In another study, Elizalde et al.1 constructed a heterogeneous onedimensional (1D) model of the trickle reactor for oil fraction hydrogenation, wherein a three-lumped model that involves light gases, naphtha, and gas oil simulated hydrocracking reactions. Moreover, Hasan et al.31 used a method of continuous lumping in a hydrocracking reactor model that shows the association between the steady-state model prediction and the plant data. Li et al.2 recently reported that a lumping kinetic model, which is similar to Stangeland’s model, was used for simulating an industrial hydrocracking Received: July 1, 2015 Revised: September 27, 2015 Published: September 29, 2015 7532

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Energy & Fuels reactor. Several other related hydrogenation reactor models for different feedstocks based on the lumping kinetic approach have also been proposed and achieved good results.32−41 However, few studies have focused on the reactor modeling and simulation processes for the coal tar hydrogenation. Even though molecular kinetic modeling presents a considerable capacity to predict molecular component distribution and numerous product properties, its practical applications in modeling and simulation of hydrogenation have not been identified because of the complexity and numerous involved parameters.31 The present study therefore focuses on the development of a novel lumping approach in modeling the coal−tar hydrogenation reaction kinetics that is based on carbon number characterization. In addition, a non-isothermal model of the fixed-bed reactor was constructed to depict the behavior of the hydrogenation reactor. The coal tar hydrogenation simulation was also performed in the Aspen Plus platform. The present study aims to improve the design and optimize the hydrogenation process by predicting the carbon number of the products and temperature distribution along the reactor.

Table 2. Catalyst Characterization composition (wt %)

catalyst

Mo

Ni

W

SBET (m2/g)

Mo−Ni/γ-Al2O3 W−Ni/γ-Al2O3

15

5 5

15

162.3 197.4

pore volume (cm3/g)

pore diameter (nm)

0.59 0.46

9.4 7.8

(50−200 °C), diesel (200−400 °C), and some asphalt (>400 °C). The cracking gas was analyzed using the online gas chromatography equipment, and the liquid fractions were characterized by some conventional techniques (i.e., analysis of the distillation range, density, and elemental analyses of C, H, S, and N). In addition, gas chromatography−mass spectroscopy (GC−MS) analysis provided a detailed carbon number composition distribution of liquid products. The products were identified through Agilent 6890N/5975B GC−MS equipped with a flame ionization detector (FID) and a HP-5MS capillary column (30 m × 0.25 mm × 0.25 μm). The chromatographic peaks were identified by comparison to the National Institute of Standards and Technology (NIST) library. Assuming that the response factor of each homologous compound was the same, the concentrations of compounds in the products increased linearly with the peak area percentage. For example, Figures 1 and 2 present the

2. EXPERIMENTAL SECTION The hydrogenation experiment of coal tar was conducted in a benchscale plant equipped with a two-stage fixed-bed reactor unit. Each stage reactor has an internal diameter of 2.3 cm and a length of 80 cm, and the detailed experimental setup and procedure can be found in the previous studies.42 The feedstock selected for this study is a middletemperature coal tar with distillate under 360 °C, and its properties are summarized in Table 1. The catalyst was facilitated by a combination

Table 1. Main Properties of the Coal Tar Feed property

value

density (g/cm3) viscosity (mPa s) elemental analysis (wt %) C H O N S H/C molar ratio distillation range (°C) IBP 10% 50% 90% 95%

0.939 16.2 80.4 11.0 5.85 1.98 0.75 1.65

Figure 1. GC−MS analysis of the gasoline product. GC−MS analysis of the gasoline and diesel fractions, respectively, under the following conditions: T, 360 °C; P, 8 MPa; LHSV, 0.6 h−1; and H2/oil, 1600. The corresponding compound compositions were presented in the Supporting Information. Table 3 shows the complete carbon number composition distribution among hydrogenation

80.2 179.1 293.5 358.6 360.8

of laboratory synthesized hydrofining catalyst Mo−Ni/γ-Al2O3 (15 wt % Mo and 5 wt % Ni loadings) and hydrocracking catalyst W−Ni/γAl2O3 (15 wt % W and 5 wt % Ni loadings). The characterization of the catalysts is shown in Table 2, and these catalysts had been prestabilized for more than 24 h under the required operating conditions. The series of hydrogenation tests was performed under various operating conditions to obtain the data that will characterize the performance of coal tar hydrogenation, and the range of operating conditions was listed as follows: temperature (T), 360−390 °C, pressure (P), 6−12 MPa; liquid hourly space velocity (LHSV), 0.4−1 h −1 ; and H 2 /oil, 1000−1800. The products were obtained subsequently once every 4 h for a total of 16 runs. The cracking gas with liquid fraction was the main product of hydrogenation. The liquid component of the product was further distilled into three subcomponents based on boiling range: gasoline

Figure 2. GC−MS analysis of the diesel product. 7533

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Table 3. Detailed Composition Distribution of Products Obtained at Various Temperatures and LHSVs (Keeping P = 8 MPa and H2/Oil = 1600) 360 °C CN C0−4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20+

0.4 h

−1

3.44 0.16 1.15 2.84 4.43 6.21 5.89 6.27 7.06 11.94 15.19 10.73 7.75 5.34 3.05 1.97 1.96

0.6 h

−1

3.13 0.14 1.1 2.72 4.26 6 5.76 6.23 7.1 12.06 15.34 10.84 7.83 5.39 3.08 1.99 2.04

0.8 h

370 °C −1

2.79 0.13 1.01 2.51 3.93 5.55 5.35 5.82 6.66 11.33 14.41 10.18 7.36 5.06 2.9 1.87 1.86

−1

1h

2.45 0.12 0.92 2.27 3.55 5.02 4.86 5.32 6.11 10.4 13.23 9.35 6.75 4.65 2.66 1.71 1.75

0.4 h

−1

3.68 0.15 1.17 2.9 4.54 6.35 6.01 6.36 7.14 12.08 15.36 10.85 7.84 5.4 3.09 1.99 2.02

0.6 h

−1

3.38 0.15 1.13 2.8 4.39 6.16 5.89 6.33 7.18 12.18 15.49 10.95 7.91 5.44 3.12 2.01 1.96

0.8 h

380 °C −1

3.09 0.14 1.06 2.62 4.1 5.78 5.56 6.05 6.91 11.75 14.95 10.56 7.63 5.25 3.01 1.94 1.93

−1

1h

2.8 0.13 0.98 2.42 3.78 5.34 5.15 5.61 6.42 10.93 13.9 9.82 7.1 4.88 2.8 1.8 1.79

0.4 h

−1

0.6 h

4 0.16 1.2 2.97 4.65 6.49 6.11 6.44 7.2 12.16 15.47 10.93 7.9 5.43 3.11 2.01 1.95

−1

3.84 0.15 1.16 2.87 4.49 6.31 6.01 6.43 7.27 12.32 15.66 11.07 8.04 5.5 3.15 2.03 2.02

0.8 h

390 °C −1

3.59 0.14 1.09 2.69 4.21 5.94 5.72 6.22 7.11 12.09 15.38 10.86 7.85 5.4 3.09 1.99 1.98

1h

−1

3.21 0.12 0.96 2.38 3.72 5.29 5.18 5.75 6.68 11.41 14.51 10.25 7.41 5.1 2.92 1.88 1.87

0.4 h

−1

4.39 0.16 1.24 3.07 4.8 6.68 6.27 6.55 7.28 12.28 15.62 11.03 7.97 5.49 3.14 2.02 2.02

0.6 h

−1

4.19 0.15 1.19 2.95 4.62 6.48 6.15 6.54 7.36 12.46 15.85 11.19 8.09 5.57 3.19 2.05 2.05

0.8 h−1

1 h−1

3.94 0.15 1.13 2.79 4.37 6.15 5.9 6.38 7.27 12.35 15.71 11.1 8.02 5.52 3.16 2.04 2.01

3.56 0.14 1.06 2.61 4.09 5.77 5.57 6.07 6.95 11.82 15.04 10.62 7.68 5.28 3.02 1.95 1.94

Figure 3. Carbon number distribution for the hydrogenation products. products under varying operating conditions, in which the amount of components are expressed as the mass percentage yield and the noncarbon cracking gas formed in hydrogenation processes, such as ammonia and sulfur dioxide, is represented by C0. The analysis of data in Table 3 provides a basis for modeling the reaction kinetics of coal tar hydrogenation based on the carbon number approach.

9−12 C compounds are present in gasoline and diesel. A distribution function method proposed by Wu et al.43 was adopted in this work to deal with different liquid fractions having similar hydrocarbons and to determine the carbon number distribution in the products. Table 4 shows the

3. MODELING OF COAL TAR HYDROGENATION 3.1. Feed and Product Characterization. Coal tar feedstock is a complex mixture of many hydrocarbons, such as paraffins, olefins, naphthenes, aromatics, and some heteroatom compounds, and the feed usually contains compounds classified according to carbon number. The carbon number is dependent upon the nature of the feed introduced. A considerable number of reactions occurred during the coal tar hydrogenation, and the reaction products were cracking gas and the liquid component that includes gasoline, diesel, and some asphalt. Detailed component analysis shows that the cracking gas was composed of methane, ethane, ethylene, propane, and other hydrocarbons with less than five carbons. In addition, 0.2% of the products include ammonia and sulfur dioxide. The carbon number of hydrocarbon classes is 5−12 in gasoline, 9− 23 in diesel, and >23 in asphalt. Figure 3 summarizes the carbon number distribution of the coal tar feedstock and all of the products, and it is worth noting that similar carbon compounds may be present in different liquid fractions because of the cross of the distillation frequency;32 for example, some

Table 4. Distribution Parameters of 9-12 C Hydrocarbons in Diesel and Gasoline CN

gasoline (%)

diesel (%)

C9 C10 C11 C12

87.4 62.3 28.8 6.8

12.6 37.7 71.2 93.2

distribution parameters of 9−12 C hydrocarbons in gasoline and diesel. In addition, analysis results showed that the yields of target products (i.e., gasoline and diesel) reached up to 96% at optimal conditions; however, the cracking gas and asphalt yields are considerably low, and these findings suggest that the kinetic reaction of cracking gas and asphalt is far less prevalent than that of gasoline and diesel. A carbon number method that extended to the coal tar system was proposed in this section for the kinetic modeling of coal tar hydrogenation, and the method is governed by a principle that the products are divided into N carbon number 7534

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Energy & Fuels Table 5. Predicted Properties of Carbon Number Lumps CN

normal BP (°C)

API gravity (deg)

specific gravity (g/cm3)

MW (g/mol)

critical temperature (°C)

critical pressure (MPa)

C0−4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20+

−66.5 37.3 74.7 101.6 122.6 152.8 174.6 194.3 214.7 234.2 252.3 269.6 285.2 301.2 315.8 329.5 343.1

190.05 78.07 58.05 57.1 56.14 47.44 46.02 45.09 42.54 39.99 37.91 36.05 34.88 33.12 32.03 31.16 30.11

0.440 0.675 0.746 0.750 0.754 0.791 0.797 0.80151 0.813 0.825 0.835 0.845 0.850 0.859 0.865 0.870 0.876

37 71.1 85.2 99.2 113.2 127.2 141.3 155.3 169.3 183.4 197.4 211.4 225.44 239.5 253.5 267.5 281.5

53.8 211.4 263.9 289.1 308.5 344.6 364.9 382.5 402.7 422.1 439.8 456.4 470.5 485.9 499.0 511.0 523.3

36.4 38.4 37.2 31.7 28.3 26.7 24.2 22.2 20.8 19.6 18.6 17.7 16.9 16.2 15.5 14.9 14.4

Figure 4. Carbon number reaction network for coal tar hydrogenation.

groups and each is considered as a lump. To simplify the modeling, the cracking gas with less than 5 C was taken as a lump C0−4 and the compounds with a carbon number from 19 to 23 in the diesel were grouped into a lump C19 as a result of the small quantity of compounds detected by GC−MS, whereas the asphalt was viewed as another lump C20+. Ultimately, a total of 18 carbon number groups were used to characterize the products obtained from the coal tar hydrogenation, and these groups represent the composition of products without neglecting the main information. Table 5 lists all of the lumps of the coal tar hydrogenation system. In addition, lump properties are the basis for reactor modeling and hydrogenation simulation. The present study employed a mixing rule proposed by Dai et al.44 to determine the boiling point and molecular weight of each lump, and other properties, such as critical temperature and critical pressure, were estimated using Aspen Plus from values of fundamental properties. All of the predicted properties of carbon number lumps are presented in Table 5. 3.2. Kinetic Model. A reaction kinetic model of coal tar hydrogenation was developed and served as a guide for predicting the carbon number product distribution and operating condition optimization. The hydrogenation reactions are extremely complex because of the highly coupled and parallel order reaction; thus, several assumptions were considered to simplify the model: (1) The cracking reactions in the coal tar hydrogenation converted heavier hydrocarbons into any lighter lumps and not vice versa; for example, the ith lump can generate (i − 1)th, (i − 2)th, ..., (i − k)th lump, where i = k + 1. A detailed reaction network between the coal

tar feedstock and carbon number products was constructed, as shown in Figure 4. (2) Among the hydrogenation reactions, the first-order reaction is taken to characterize the hydrogenation process because excess hydrogen exists.44 (3) Catalyst deactivation was neglected in this present work because of the very few coke deposited on the hydrogenation catalysts.21 According to the above-mentioned assumptions, the kinetic model of coal tar hydrogenation based on the carbon number reaction network can be expressed as follows: r1 =

r2 =

r3 =

dC1 d

1 ( LHSV )

dC 2 d

1 ( LHSV )

dC 3 d

1 ( LHSV )

= −(k1 + k 2 + ... + k17)C1 (1)

= k1C1 − (k18 + k19 + ... + k 33)C2 (2)

= k 2C1 + k18C2 − (k 34 + k 35 + ... + k48)

C3 r16 =

(3)

dC16 d

1 ( LHSV )

= k15C1 + k 31C2 + k46C3 + ... + k148C15

− (k151 + k152)C16 7535

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Figure 5. Simulated flow sheet for the coal tar hydrogenation process.

r17 =

dC17 d

(

1 LHSV

)

− k153C17 r18 =

(5)

dC18 d

estimated in this study and listed in the Supporting Information. 3.3. Reactor Model. Modeling of hydrogenation reactors is significant in predicting major issues in the hydrogenation plant that include product yield, reactor temperature distribution, and hydrogen consumption. Hence, a non-isothermal model of a two-stage fixed-bed unit reactor was constructed to investigate the behavior of the hydrogenation reactor. The fixed-bed unit consists of a two-bed hydrofining (HF) reactor and a four-bed hydrocracking (HC) reactor. Quench hydrogen was added on the intervals among the beds to control the temperature rise from the exothermic reaction in the reactor, and this is also to create a hydrogen-rich environment for hydrogenation reactions. The main reactions in the HF reactor include hydrodesulfurization, hydrodenitrogenation, and mild hydrocracking. In the HC reactor, the liquid mixture from HF is further decomposed into lighter compounds with a carbon number from C1 to C23+, such as asphalt, diesel, gasoline, and cracking gas. Several assumptions were made with the consideration of the negligible diffusional resistance, plug flow, steady-state, and adiabatic operation conditions to simplify the reactor modeling. Moreover, the hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) reactions in HF are incorporated into a light-end product cracking reaction instead of being considered separately. Consequently, the reactor model can be represented on the basis of mass balance, energy balance, and reaction kinetic model. The material and energy balance equations based on the reaction kinetic model described in section 3.2 are shown below

= k16C1 + k 32C2 + k47C3 + ... + k149C16

1 ( LHSV )

= k17C1 + k 33C2 + k48C3 + ... + k152C16

+ k153C17

(6)

In eqs 1−6, ri represents the reaction rate of the ith carbon number lump, Ci is the mass fraction of ith carbon number lump, k refers to the first-order reaction rate constant, and 1/ LHSV represents the reaction residence time (h). To accurately predict the distribution behavior of all carbon number products, 18 mathematical equations containing 153 reaction rate constants were constructed. In addition, the rate of hydrogenation reactions was dependent upon hydrogen concentrations, yet it is reasonable to express the reaction rate without hydrogen partial pressure in eqs 1−6 because hydrogen was present in excess during the entire hydrogenation.33 The kinetic constants can be expressed using the Arrhenius equation, which is expressed as a function of the reaction temperature. As shown below ki = ki0e−Eai / RT

(7)

where ki0 is the pre-exponential factor of each rate constant, Ea represents the reaction activation energies, and R is the ideal gas constant. n

min F(t ) =

2

∑ f (t ) = ∑ i

u0

n i=1

[(Ciexp)

−

(Cical)]2

dCi = Lri dz N+1

(8)

L

The rate parameters in the kinetic model were estimated using MATLAB based on the nonlinear least-squares optimization method. The differential solution for eqs 1−6 was implemented by a four-order Runge−Kutta algorithm. The parameter estimation was transformed into an optimization calculation of the objective function with the sum of the squares of the difference between the expected and experimental values (as shown in eq 8). On the basis of the experimental data in Table 3, all of the rate parameters, including ki0 and Eai, were

−ΔHi =

(9)

N

∑ GiCpi i

i = 1, 2, 3, ..., N

dT + T0 ∑ ( −ΔHi)Giri = 0 dz j

∑ products

Hpi −

∑ reactants

(10)

Hri (11)

where u0 is the flow velocity of feed in the reactor (m/h), z represents the dimensionless length of the reactor, L is the reactor length (m), Ω is the cross-sectional area of the reactor (m2), i refers to the ith carbon number lump, N + 1 represents 7536

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Energy & Fuels the hydrogen component, Gi is the flow rate of the ith carbon number lump (kg/h), Cpi is the heat capacity of the ith carbon number lump (kJ kg−1 K−1), T0 is the dimensionless inlet temperature of the feed, and −ΔHi represents the reaction heat produced by the ith carbon number lump in the hydrogenation reaction, whose value is estimated using the difference in the enthalpy of the product (Hpi) and reactant (Hri) at the reaction conditions; Hpi and Hri (i.e., the enthalpy of the ith carbon number lump) were calculated by Aspen Plus based on the Edmister method. Because each bed was replenished by quench hydrogen for cooling the reaction mixture and controlling the reaction temperature, the inlet temperature of each bed for the reaction mixture were calculated using the following equation that is similar to that by Zhou et al.:32

temperature and high-pressure separator (LHPS), resulting in pure hydrogen in RICH-H1. The remainder of hydrogen from S2 was further purified by the high-temperature and lowpressure separator (HLPS) and SEP in series. Subsequently, hydrogen from RICH-H2 is added to the RICH-H1, and this gas mixture was compressed and recycled back to the reaction section along with makeup hydrogen for quenching between the beds and in part for mixing with the feed. The bottom liquid products S5 from SEP and S7 from LHPS enter the lowtemperature and low-pressure separator (LLPS) to gain further the purified cracking gas C0−4 component (GSA1) that contains methane, ethane, ethylene, propane, etc. While the bottom products S4 from HLPS and S6 from LLPS are sent to the downstream separation system consisting of a stripper [where remaining cracking gas (GSA2) and some light gasoline (L-GASO1) were recovered from the top] and a fractional column. The main products from the column include light gasoline (L-GASO2), heavy gasoline (H-GASO1), diesel, and asphalt.

n

Tin, h =

∑i = 1 Gout, i , h − 1CpiTout, h − 1 + G H2hCpH2TH2 n

∑i = 1 Gout, i , h − 1Cpi + G H2hCpH2

h = 2, 3, ..., 6

(12)

4. RESULTS AND DISCUSSION 4.1. Validation and Application of the Kinetic Model. This section discusses the validation of the reaction kinetic model proposed above. Several groups of experimental data with detailed carbon number analysis were gathered to make a comparison to the calculations by the model. Figure 6 exhibits

where the subscript h represents the hth reactor bed, Tin,h is the inlet temperature in the hth reactor bed, Tout,h − 1 refers to the out temperature in the h − 1th reactor bed, and Gout,i,h represents the outlet flow rate of the ith carbon number lump in the h − 1th reactor bed. To simulate an actual coal tar hydrogenation unit in a benchscale plant, various sets of the feed flow rate, unit operation details, and amount of hydrogen makeup in each bed should be provided. The model of the hydrogenation reactor presented above is complex and nonlinear, and in this work, the programming of the Runge−Kutta method was coded in MATLAB for solving simultaneous differential equations for the kinetic model, mass balance, and energy balance. 3.4. Process Simulation. After validation of the developed reactor model to produce the hydrogenation data, the entire coal tar hydrogenation process with a bench-scale plant was simulated using Aspen Plus for the guidance of the process optimization and industrial enlargement design. Figure 5 shows the simulated flow sheet of the coal tar hydrogenation in the Aspen Plus platform. The simulation process consists of two main sections: the reaction section and the separation section. The reaction section in Aspen Plus started by generating a series of pseudocomponents based on the carbon number characterization method that is identical to section 3.1 in this work. The complete properties of these preudo-components were obtained using the property estimation module in Aspen Plus, and resulting detailed properties were listed in Table 5. Subsequently, six plug flow reactors representing six beds were used to simulate the two-stage fixed-bed reactors of coal tar hydrogenation. On the basis of the carbon number reaction network in Figure 4, all of the hydrogenation reactions were added to the reaction module and then the developed kinetic model of coal tar hydrogenation was coded in Fortran language as a subroutine to simulate hydrogenation reaction behavior. After the operating condition and unit configuration data were set, the reaction simulation calculation was performed. Subsequently, the gas−liquid mixture from the HC was placed in a separation section for the purification and recovery of required products. Because excess hydrogen exists in the gas phase, the mixture was allowed to pass through a hightemperature and high-pressure separator (HHPS) and a low-

Figure 6. Calculated and experimental carbon number product yields (T, 380 °C; P, 8 MPa; LHSV, 0.4 h−1; and H2/oil, 1600).

the comparison of the calculated and experimental carbon number product yields obtained under indicated conditions (T, 380 °C; P, 8 MPa; LHSV, 0.4 h−1; and H2/oil, 1600). Results show that the developed model predicts the product yields precisely with the average relative error of only 1.88%. In addition, on the basis of the characterization method of the product described in section 3.1, the carbon number product yields predicted by the model constitute the different fractional products, including cracking gas, gasoline, and diesel. To validate the accuracy of the method, the predicted hydrogenation product yields were compared to the measured value in this section. A comparison was given in Figure 7 to show the discrepancy between the measured and predicted hydrogenation product yields with the residence time under fixed operating conditions. Furthermore, the predicted and measured diesel yields obtained at various operating conditions were also compared, as shown in Figure 8. From Figures 7 and 8, the 7537

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profiles and product distribution along the reactor were simulated, and the results are presented in Figures 9 and 10. Table 6. Actual Operating Conditions of the Hydrogenation Unit

Figure 7. Comparison between experimental data (points) and measured product distribution (lines) with the residence time (T, 360 °C; P, 8 MPa; LHSV, 0.6 h−1; and H2/oil, 1600).

condition

value

condition

value

flow rate of feed (mL/min) reactor internal diameter (cm) first bed length (cm)

2 2.3

Tin (K) Pin (MPa)

633 8

36.8

29.4

second bed length (cm) third bed length (cm) fourth bed length (cm) fifth bed length (cm) sixth bed length (cm)

43.2 19.2 19.2 19.2 22.4

H2 in the reactor inlet (NL/h) second quench H2 (NL/h) third quench H2 (NL/h) fourth quench H2 (NL/h) fifth quench H2 (NL/h) 6th quench H2 (NL/h)

36.6 48.13 27.2 25.1 25.6

Figure 9. Simulated temperature distribution along the axial position of the reactor (T, 360 °C; P, 8 MPa; LHSV, 0.4 h−1; and H2/oil, 1600).

Figure 8. Comparison between experimental data (points) and predicted variation in the yield of diesel (lines) with the residence time at different temperatures (T, 360 °C; P, 8 MPa; LHSV, 0.6 h−1; and H2/oil, 1600).

predictions of the products show considerable good agreement with the experimental values, and this finding further indicates that the reaction kinetic model developed for coal tar hydrogenation could significantly predict carbon number product distribution. In addition, the kinetic model can also be used to optimize the hydrogenation operating conditions for reducing the number of further experiments and minimizing resource use. For example, Figure 8 further reveals the effect of the different reaction temperatures on the yield distribution of the products at keeping other conditions constant. The diesel yield showed a great increase as the temperature changed from 360 to 380 °C. However, when the temperature was over 380 °C, the yield increased slightly, attributed to the secondary cracking of products, and this shows a trend similar to the coal tar hydrogenation experiment.21 In light of the simulated results, 380 °C was selected as an optimal temperature for coal tar hydrogenation. 4.2. Reactor Simulation Performance. After the reaction kinetic model was validated, the formulated non-isothermal reactor model was applied to predict the behavior of the hydrogenation reactor. On the basis of the actual hydrogenation unit configuration data, the flow rate and the operation conditions are described in Table 6. The temperature

Figure 10. Simulated yield variation of carbon number products along the axial position of the reactor (T, 360 °C; P, 8 MPa; LHSV, 0.4 h−1; and H2/oil, 1600).

Figure 9 shows the temperature profile simulated along the fixed-bed reactor. As an adiabatic reactor, the overall temperature rise of around 30 °C is attributed to the highly exothermic character of hydrogenation reaction.33 Generally, the temperature dominates the hydrogenation reaction, and the extortionate temperature usually influences the product quality and causes the potential safety hazard. To maintain the optimal reaction temperature in hydrogenation, quench hydrogen is adopted to replenish between the beds. The jagging shape in the temperature profile in Figure 9 is caused by hydrogen quenching used in each reactor bed. A comparison to an 7538

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the reactor length, and this finding is consistent with the industrial process. On the basis of the carbon number components, the simulated yields of factional products (cracking gas, gasoline, and diesel) generated at the outlet bed were compared to measured data with average deviations of 4.1, 4.6, and 2.9%, as shown in Table 7. In summary, the application of the developed reactor model in a bench-scale unit of coal tar hydrogenation with the process capacity of 2 mL/min and hydrogen consumption of 0.19 N m3/h shows that it can predict and simulate the performance of the fixed-bed reactor precisely. This model can serve as a significant guide for the simulation and design of coal tar hydrogenation in a industrial scale. 4.3. Validation and Analysis of Process Simulation. Under the same operating conditions and hydrogenation unit configuration described in Table 6, the whole coal tar hydrogenation process was simulated in the Aspen Plus platform and the simulated results of the reaction section on the product yield and temperature at the outlet of the reactor were compared to the measured value presented in Table 7. The process simulation in Aspen Plus is greatly effective in simulating the actual hydrogenation case, which, in turn, testifies the reliability of the simulation method. In addition, the reaction simulation section is relatively difficult throughout in the entire hydrogenation, while the separation simulation based on gas−liquid equilibrium in Aspen Plus is progressive; thus, it is relatively convenient to purify and recover the required products by a series of separators, such as HHPS, HLPS, LHPS, stripper, and fractional column. Table 8 listed the simulated carbon number percentage yields for different product streams, and results show that the streams L-GASO1, L-GASO2, and HGASO with more than 95% compositions are mainly carbon number products covering from C5 to C12 and the diesel stream basically contains the components with carbon numbers 9−19, which matches the composition of experimental products. Even though there are some C5−C8 in streams GAS1 and GAS2 and C16−C19 in stream ASPHALT, it is acceptable from the

average deviation of 0.86% between the measured and predicted outlet temperatures of the reactor, presented in Table 7, indicates the prediction accuracy of the reactor model. Table 7. Comparison between the Predicted and Measured Values Obtained at the Reactor Outlet cracking gas (wt %) gasoline (wt %) diesel (wt %) temperature (°C)

measured

predicteda

predictedb

3.15 19.7 71.6 385.2

3.28 20.6 69.5 388.5

3.34 21.2 70.8 390.4

a

Predicted value by the reactor model. bPredicted value by process simulation.

The temperature rise distribution simulated by the reactor model is vital in providing a feasible guidance for predicting the hydrogen consumption in industrial application. The yield variation of carbon number products along the axial reactor position in hydrogenation is presented in Figure 10. The yields for most of the carbon number components exhibit a great increase in the initial third beds and then slightly increase or even unchange. This is attributed to the high concentration of the raw material and fast cracking reaction in the initial stage. At the onset of the reaction, the concentration of the raw material is gradually decreased and carbon number products occurred with the secondary cracking, which resulted in a slow product yield increase. In addition, several carbon number products, such as C20+, C19, and C18, reached a maximum yield rapidly in the second beds and eventually decreasing upon further reactions, and this result may be ascribed to the fact that heavier hydrocarbons adsorbed on catalysts react faster than lighter hydrocarbons.32 Meanwhile, the cracking rate of these components were faster than generation as a result of the concentration decrease of the raw material in subsequent beds. Moreover, the yield of cracking gas C0−4 maintained a continuous rapid increase along

Table 8. Simulated Carbon Number Percentage Yields for Product Streams cracking gas

gasoline

diesel

asphalt

CN

GAS1

GAS2

L-GASO1

L-GASO2

H-GASO

DIESEL

ASPHALT

H2 C0−4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20+ total

8.797 × 10−5 0.021162 0.00047 0.00166 0.001425 0.000706 0.00031 8.325 × 10−5 2.582 × 10−5 8.338 × 10−6 3.98 × 10−6 1.325 × 10−6 2.407 × 10−7 4.504 × 10−8 1.117 × 10−8 2.298 × 10−9 3.751 × 10−10 9.567 × 10−11 0.0259

5.621 × 10−6 0.00695 0.00035 0.00163 0.0017 0.00097 0.000464 0.00011 1.406 × 10−5 1.327 × 10−7 8.334 × 10−10 3.198 × 10−12 7.039 × 10−15 1.82 × 10−17 6.867 × 10−20 2.486 × 10−22 6.7 × 10−25 2.232 × 10−27 0.0122

1.289 × 10−8 0.001537 0.000489 0.005307 0.012763 0.016817 0.01782 0.010076 0.00277 5.729 × 10−5 7.899 × 10−7 6.894 × 10−9 3.492 × 10−11 2.052 × 10−13 1.416 × 10−15 9.233 × 10−18 5.437 × 10−20 4.137 × 10−22 0.0676

4.605 × 10−10 0.000409 0.000193 0.00281 0.010449 0.02198 0.034024 0.007593 0.000359 2.902 × 10−7 7.0006 × 10−11 1.119 × 10−14 1.124 × 10−18 1.481 × 10−22 2.159 × 10−26 3.348 × 10−30 4.545 × 10−34 6.28 × 10−38 0.0778 0.0231

3.277 × 10−27 1.022 × 10−12 5.024 × 10−10 1.332 × 10−7 7.93 × 10−6 0.000215 0.005431 0.035323 0.036191 0.0005613 2.406 × 10−6 6.918 × 10−9 1.184 × 10−11 2.423 × 10−14 4.753 × 10−17 9.053 × 10−20 1.70 × 10−22 3.766 × 10−25 0.0777

3.540 × 10−21 2.576 × 10−9 6.471 × 10−8 3.803 × 10−6 5.562 × 10−5 0.000376 0.001914 0.005701 0.025689 0.073511 0.126465 0.159911 0.112252 0.078836 0.052208 0.027394 0.015221 0.01355 0.693 0.693

3.243 × 10−31 1.295 × 10−17 1.636 × 10−15 2.101 × 10−13 7.608 × 10−12 1.40 × 10−10 2.238 × 10−9 2.631 × 10−8 2.988 × 10−7 2.868 × 10−6 3.472 × 10−5 0.000263 0.000835 0.001883 0.002073 0.00324 0.004296 0.010214 0.0288 0.0288

0.0381

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engineering perspective as a result of the low yields of GAS and ASPHALT in the hydrogenation process. The aforementioned results proved the feasibility and effectiveness of the developed process simulation for simulating the hydrogenation reaction and product separation. However, the process optimization for coal tar hydrogenation (tower number, feed stage, reflux ratio, heat load, etc.) needs to be carried out further in future work because of its complexity.

5. CONCLUSION On the basis of the detailed analysis of products from a hydrogenation fixed-bed reactor filled with a Mo−W−Ni/γAl2O3 catalyst, a carbon-number-based kinetic model containing 18 carbon number groups and 153 kinetic constants was developed to describe the coal tar hydrogenation. The kinetic parameters were determined by fitting the 16 group experimental data. A comparison between the measured and predicted values of the model showed a considerable agreement, with an average relative error of less than 5%, and the validated results indicate that the model is more suitable for the coal tar hydrogenation. Subsequently, the reactor model of the fixed bed based on the developed reaction kinetics was built. The internal mass balance and energy balance within the phases in the reactor were taken into account in the form of an ordinary differential equation. The reactor model was applied in simulating an actual bench-scale unit of coal tar hydrogenation with the process capacity of 2 mL/min, and the predictions of the behavior of the reactor, including the yield of the products and temperature distribution along the reactor, are shown to be consistent with the experimental data, with average relative errors of less than 4.6 and 1.4%, respectively. In addition, a whole process simulation for coal tar hydrogenation has also been performed successfully using Aspen Plus. This work provides a significant guide for the optimization and design of coal tar hydrogenation.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.5b01476. GC−MS analysis of the gasoline in Figure 1, GC−MS analysis of the deisel in Figure 2, and obtained model parameters (XLSX)

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AUTHOR INFORMATION

Corresponding Authors

*Telephone: 0086-10-8254480. E-mail: [email protected]. *Telephone: 0086-10-82627080. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the National Program on Key Basic Research Project (2012CB214905) and the National Natural Science Foundation of China (21276267 and 51104140).

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REFERENCES

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