Pure carbon-number components to characterize the hydrocarbon mixture for kinetic modeling of hydrogenation process

Fuel 202 (2017) 287–295

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Full Length Article

Pure carbon-number components to characterize the hydrocarbon mixture for kinetic modeling of hydrogenation process Fei Dai a, Yiqian Yang c, Hongyan Wang a, Chunshan Li a,⇑, Zengxi Li b, Suojiang Zhang a,⇑ a Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, The National Key Laboratory of Clean and Efficient Coking Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, PR China b School of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, PR China c Hi-Tech Institute for Petroleum, Chemical Industry, Qingdao University of Science, Technology, Qingdao, Shandong 266042, China

h i g h l i g h t s
A universal real component-based characterization approach was proposed.
The compositions of pure components in complex mixture were estimated.
Established the rigorous reaction kinetic modeling of hydrogenation process.
The chemical hydrogen consumption were predicted effectively.

a r t i c l e

i n f o

Article history: Received 10 September 2016 Received in revised form 2 January 2017 Accepted 7 March 2017

Keywords: Hydrogenation process Kinetic modeling Pure component Characterization

a b s t r a c t Hydrogenation is an important processing technology for upgrading inferior oil. Kinetic modeling for hydrogenation process continues to be a challenging task because of the complex compounds and reactions involved. Therefore, a systematic carbon-number components-based substitution approach was proposed in this work as representatives of real feedstock (e.g. residual oil, vacuum gas oil, coal tar, etc). The primary advantage of the approach lies in direct availability of chemical character and physical property data. The detailed molecular compositions of components were also determined in the optimization algorithm by correlating the bulk experimental properties of original mixture. On this basis, the detailed kinetic modeling of hydrogenation process based on the real components reaction pathway could be constructed. The approach was verified using a set of 64 pure components to characterize the coal tar feedstock and used to simulate the reaction modeling of coal tar hydrogenation process. Results revealed that the hydrogenation product distribution and the chemical hydrogen consumption were predicted effectively. This work provides a significant guidance for the design and optimization of hydrogenation process. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Hydrogenation technology has become one of the important chemical upgrading processes in refining industry from economic, technical and environmental point of view. The flexibility and versatility of the technology has made it greatly attractive to convert various inferior oils (residual oil, crude oil, vacuum gas oil, and coal tar, etc) into favorable, clean and lighter fuel products, such as gasoline, kerosene, and diesel [1]. To further improve hydrogenation process efficiency and product quality, great efforts are currently focused on the development of more accurate and reliable ⇑ Corresponding authors. E-mail addresses: [email protected] (C. Li), [email protected] (S. Zhang). http://dx.doi.org/10.1016/j.fuel.2017.03.010 0016-2361/Ó 2017 Elsevier Ltd. All rights reserved.

kinetic modeling for achieving better design, simulation and optimization of hydrogenation process. However, it is a tremendous formidable task because of complexity of feedstock stream with an extremely large number of components [2]. Therefore, effective characterization of hydrocarbon mixture has become a basis for kinetic modeling of hydrogenation process. Extensive studies on feedstock mixture characterization have been widely reported in opened literature [3–7]. The conventional approach is mainly based on the pseudo-component characterization method [8,9], in which the hydrocarbon mixture is divided into smaller groups of boiling point cuts by combining the bulk properties with distillation data of original mixture, such as density, API gravity and True boiling point (TBP) curve, etc. This method is still widely accepted as a convenient approach

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Nomenclature Tb x K N CP CO CN CA kPloy-A k-Ploy-A kDi-A k-Di-A kMono-A k-Mono-A kPloy-N kDi-N kMono-N kO kP

TBP temperature volume or mass fraction number of properties number of real components mass fraction of Paraffin mass fraction of Olefin mass fraction of Naphthene mass fraction of Aromatic hydrocarbons positive reaction rate constant of Ployaromatic hydrogenation reverse reaction rate constant of Ployaromatic hydrogenation positive reaction rate constant of Diaromatic hydrogenation reverse reaction rate constant of Diaromatic hydrogenation positive reaction rate constant of Monoaromatic hydrogenation reverse reaction rate constant of Monoaromatic hydrogenation rate constant of Ploynaphthene ring-opening reaction rate constant of Dinaphthene ring-opening reaction rate constant of Mononaphthene ring-opening reaction rate constant of Olefin saturation rate constant of Paraffin hydrocracking

and largely used in commercial software, including ASPEN-PLUS, PRO II, and HYSYS and so on. And kinetic models based on the pseudo-component characterization, which is also called lumping modeling, has been successfully developed for hydrogenation process. However, the method cannot capture the molecular information of mixture (i.e. the structure and chemical formula is not defined for a pseudo-component), which limits its further development [10]. In addition, a Molecular Type Homologous Series (MTHS) approach (as shown in Fig. 1) was originally proposed by Peng [11] to characterize the mixture stream on the molecular level, where rows refer to the carbon number groups with different sizes and the columns represent the different molecular classes, i.e. paraffin, olefin, naphthenic and aromatic hydrocarbons, etc. Yan et al. [12] extended the MTHS into a heavy oil system and developed a molecular model for the thermal cracking process, the model accurately predicted the product yield distribution. Dai et al. [13] currently used the characterization approach in their kinetic modeling of coal tar hydrogenation process, results showed the predictions of model in good agreement with plant data. Moreover, the MTHS approach presented a widespread applicability in characterizing various type of hydrocarbon mixtures by adjusting the molecular type and carbon number size, and this method has

nP iP nO iO N5 N6 2N 3N 1A 1A1N 2A 2A1N 3A 4A S N

C0 C1

Columns represent homologous series

…

C10 C11 …

C23 C24

Rows represent carbon number group

…

Mass or molar fraction of molecule or isomer lump (carbon number component)

Fig. 1. Molecular-type homologous series matrix.

kHDS kHDN

rate constant of Hydrodesulfurization rate constant of Hydrodenitrogenation

Greek symbols q liquid density xk weight factors f properties values Abbreviations MW molecular weight PONA paraffin, olefin, naphthenic and aromatic hydrocarbons HDS hydrodesulfurization HDN hydrodenitrogenation TBP true boiling point MTHS molecular type homologous series LHSV liquid hourly space velocity Subscripts i ith real component m measured value r retrieved value Superscripts n1,n2,n3,m1 pressure index

contributed to the molecular kinetic modeling of hydrogenation process. However, the properties data of each component in MTHS that cannot be directly available have to obtained from the empirical estimation method [14], whose reliability is doubtful. Another approach based on Substitute Mixture of Real Component (SMRCs) was firstly invented by Ba et al. [15] to characterize the complex feedstock. The substitute mixture was established by suitable pure components collected from the reliable database. Obviously, the main advantage of this method lies in direct availability of physical properties and explicit chemical character, which overcomes the deficiency of methods as mentioned above. Accordingly, the approach have been approved in widely practical applications, e.g. simulation of crude oil separation process [16], characterization of blending of petroleum mixtures [17], predicting of hydrocarbon thermal cracking product yield [18], etc. However, no any information on kinetic modeling of hydrogenation process based on real component characterization approach was reported. The present work highlights the development of real components-based characterization approach to substitute the various type of complex hydrocarbon mixture. The approach aims to establish the detailed kinetic modeling of hydrogenation process for deeply understanding of the hydrogenation reaction behavior, designing and optimizing the required process.

2. Methodology The utilization of substitute mixture represented by the infinite real component has been proved as a simple and effective approach for characterizing original mixture, which will be better suited for the various separation simulation and chemical engineering calculation. The method is governed by the principle that the experimentally measured bulk properties of a original mixture [19], such as density, API gravity, PONA contents, etc. should be close to that calculated by the infinite component. Once both global and real components properties are obtained, the corresponding composition of components could be estimated by using

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optimization algorithm. Obviously, the approach contains two consecutive contents i.e. selection of suitable component to substitute the original mixture and determination of composition. 2.1. Selection of real carbon number components Before selecting components, the prerequisite is to obtain the TBP curve or density curve of the oil mixture though experimental measurement. The liquid density curve will be further converted into ‘‘phase portraits” to eliminate the volume or mass fraction distilled for constructing the correlations between the density and boiling point [20]:

T b ¼ T b ðxÞ

ð1Þ

q ¼ qðxÞ

ð2Þ

q ¼ qðT b Þ

ð3Þ

where Tb represents the TBP temperature, q refers to the liquid density, x is the volume or mass fraction distilled. Later, the entire temperature range in the TBP curve is divided into a series of reasonable intervals with non-overlapping continuous temperatures as shown in Fig. 2, each interval is viewed as a pseudo-component, the boiling point and density of each interval are also estimated by mean boiling point in the same way of pseudo-component approach. To substitute these pseudo-components using real components, several criterions for choosing appropriate components should be followed. On the one hand, the normal boiling point of all candidate components have to be located into the designated intervals and its related properties should be as equal to the corresponding measured values as possible. The extent of component considered for a mixture stream is primarily subjected to the initial and end point of TBP curve. On the other hand, all the components could be easily retrieved from the existing property databank of NIST, API-TDB, and AIChE-DIPPR, as well as commercial software, such as Aspen plus, HYSIS, and PRO II, which can guarantee the credibility of property data. As for high-boiling point hydrocarbon with some missing properties, the group contribution method proposed by Ahmad et al. [7] can be employed in the present work to estimate the missing properties. Moreover, a particular refinement for selection of component is carried out by adopting the MTHS approach to ensure all the components in complex mixture to cover the different carbon number size and molecular classes [13]. The detailed molecular categories attached to real components are listed in Table 1. It is

worthy noted that the heteroatom compounds are contained in mixture, e.g. sulfur and nitrogen compound, which are treated as a pseudo-component in this text because of negligible amount of content. In order to capture one suitable component as several candidate components in the same hydrocarbon category fall in the same temperature interval, the optimization function for component determination can be formulated as follows [10]: K X jfr;k;c  fm;k;c j min xk fm;k;c k¼1

ð4Þ

where subscripts m and r represent the measured and retrieved data, respectively, c is the ith candidate component, K refers to the number of properties, xk is the weight factor and f represents the properties values. 2.2. Composition determination of components This section primarily emphasis on determining the composition of real carbon number components in substitute mixture. For pseudo-components, its composition is to be easily obtained since the temperature intervals representing the pseudocomponents could be mapped onto the intervals of xLi , xRi on the x-axis of TBP curve, due to these intervals entirely covers the range of volume or mass fractions distilled and also non-overlapping continuously, thus it is reasonable to take the width of these intervals as the corresponding component volume or mass fraction directly. On this basis, the boiling points of pseudo-components can be estimated using arithmetic mean values:

    T b xRi þ T b xLi T bi ¼ ; i ¼ 1; . . . ; N 2

ð5Þ

However, some slight difference is occurred for the substitute mixture of carbon number components. It is obviously irregular intervals of normal boiling points on TBP curve for real components, and it is inevitable to exist some grid overlapping among the different components, therefore, an optimization function was used to minimize the gaps by formulated as follows [18]: N   X  R 2 minf xL ¼ xi1  xLi

ð6Þ

i¼1

where xLi and xRi represent the left and right ends of the interval of the fraction to the ith component. N refers to the number of components.

Table 1 Categories of hydrocarbons. ID

Category

ID

Category

nP iP O

N-Paraffin Isoparaffins Olefin

A3 A4 AN

N1

One-ring Naphthenic compound Two-ring Naphthenic compound Three-ring Naphthenic compound Four-ring Naphthenic compound One-ring Aromatic compound Two-ring Aromatic compound

AN2

Three-ring Aromatic compound Four-ring Aromatic compound 1-Arom-1-Naphthene compound 1-Arom-2-Naphthene compound 1-Arom-3-Naphthene compound 2-Arom-1-Naphthene compound 2-Arom-2-Naphthene compound 1-Arom-1-Naphth-1-Arom compound Number of carbon atoms

N2 N3 N4 A1 A2 Fig. 2. Simulation of a true boiling point (TBP) curve using the real component.

AN3 A2N A2N2 ANA CN

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Then the length of each interval with respect to real components is directly treated as the appropriate volume or mass fraction and the compositions are finally normalized. The corresponding calculation formulation is listed as follows:

  xRj  xLj xj ¼ PN   ; j ¼ 1; . . . ; N R L i¼1 xi  xi

ð7Þ

In addition, the composition of the substitute mixture can be further optimized by interrelating the component properties and other experimentally bulk properties of original mixture such as density, MW, PONA structural contents etc. Since the properties of all the components can be directly retrieved from the databank, thus the density and MW of the mixture can be estimated based on the mixing rules:

q¼

N X xm;i =qi

!1 ð8Þ

i¼1

MW ¼

N X xm;i MW i

ð9Þ

i¼1

where q and qi represent the liquid density of complex mixture and pure components, respectively, while MW and MW i are molecular weight of the mixture and components, respectively. Besides, Paraffin, Olefin, Naphthenic and Aromatic (PONA) structural distribution that are not difficult to obtain by the experimental analysis is important indicators for characterizing industrial stream. Based on the molecular type of real components described in Table 1, the structural contents can be accumulated respectively:

CP ¼

I X xm;i C P;i

ð10Þ

i¼1

CO ¼

I X xm;i C O;i

ð11Þ

i¼1

CN ¼

I X xm;i C N;i

ð12Þ

i¼1

CA ¼

I X xm;i C A;i

ð13Þ

i¼1

where C P , C O , C N and C A are the mass fraction of PONA structural contents in original mixture, respectively. Theoretically, the more bulk properties we can get make it a better match between the properties of original and substitute mixture. However, it is not impractical in experimental measurement. Ultimately, the composition of all components selected can be determined by the Levenberg-Marquardt’s optimization algorithm in the MATLAB program platform. 2.3. Reaction kinetic modeling of hydrogenation process Oil hydrogenation process involves large number of complex reactions, the reaction kinetic model enables a substantial understanding of the hydrogenation reaction behavior, particularly to simulate, design and optimize the hydrogenation process. Existing kinetic modeling of hydrogenation process are mainly based on lumping kinetic [21–24] and molecular kinetic approach [25–28]. Both method exhibit a considerable power to predict product yield distribution or product property, and molecular kinetic modeling can fundamentally reflect the hydrogenation mechanism, but its

practical applications in modeling and simulation of hydrogenation have not been identified because of the complexity and numerous involved parameters. While new real component characterization approach indicates a feasible direction in detailed kinetic modeling of hydrogenation process. Generally, the hydrogenation reaction kinetic can vary substantially depending on the sources of feed, the operating condition and catalysts type, etc. Yet the reaction mechanism to hydrogenation is nearly identical. The primary reactions occurred in hydrogenation process including: Saturation of aromatic hydrogenation, Ring opening, Saturation of olefins, Paraffin hydrocracking, Hydrodesulfurization (HDS) and Hydrodenitrogenation (HDN), etc. [29] Therefore, the entire hydrogenation reaction network can be constructed once exactly obtain the real components of complex mixture. However, the large number of component used for substituting original oil inevitably generate a complicated reaction network, which resulting in a enormous obstacle for kinetic modeling of hydrogenation process. Much research demonstrates that the components with similar structure present a similar kinetic reaction characteristic [30,31]. As a example of the aromatic hydrogenation reaction, all the aromatic compounds with two benzene-ring can be converted into one-ring aromatic compound by hydrogenation reaction in the same way of kinetic model, thus the total aromatic compounds in the complex mixture is conventionally divided into three groups [30]: tri-aromatics, diaromatics and mono-aromatics, which greatly simplify the kinetic modeling work. Likely, to establish the reaction modeling of hydrogenation process based on real component characterization approach, several reaction criterions for simplify model parameters should be considered in this text: (1) According to the molecular classes described in Table 1, all the aromatic compounds in substitute mixture are classified into three groups: poly-aromatic, di-aromatics and monoaromatics, each aromatic group to hydrogenation holds the same reaction kinetic model. (2) Similarly, three naphthenic groups containing ploynaphthene, di-naphthene and mono-naphthene was also utilized to represent all naphthene compounds of the real components. Each naphthene class was supposed to the same reaction kinetic features in ring opening reaction. (3) All sulfur-containing and nitrogen-containing compounds in the mixture are considered as one lump and presented the unified kinetic characteristics in HDS and HDN reaction, respectively. According to above assumptions, detailed hydrogenation reaction network occurred by all the components in substitute mixture is constructed in Table 2, in which the corresponding hydrogenation kinetic model for any type of reaction is also listed by referring to previous research work [29,32]. The kinetic model parameters are estimated by means of fitting experimental hydrogenation data, where the rate constant can be expressed by Arrhenius equation function [22]. Ultimately, the kinetic modeling of hydrogenation process is built to predict the hydrogenation behavior, especially for prediction of the reaction hydrogen consumption.

3. Experimental section The hydrogenation experiment was conducted in a two-stage fixed bed reactor system. Each stage reactor has an internal diameter of 2.3 cm and a length of 80 cm. Coal tar with distillate under 360 °C was selected in this work as a complex mixture to test real component-based characterization approach,

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F. Dai et al. / Fuel 202 (2017) 287–295 Table 2 Reaction kinetic of oil hydrogenation. Hydrogenation Reaction

Reaction equations

Aromatic hydrogenation (HDA)

Kinetic kployA

Polyaromatic þ H2 ¢ diaromatic

r A1 ¼ kPloyA C rec P n1 H2  kPloyA C pro

kployA

kDiA

Diaromatic þ 2H2 ¢ monoaromatic

r A2 ¼ kDiA C rec P n2 H2  kDiA C pro

kDiA

Monoaromatic þ 3H2 Ring opening

kMonoA

¢

kMonoA

naphthene

kployN

Polynaphthene þ H2 ! dinaphthene kDIN

Dinaphthene þ H2 ! mononaphthene kMonoN

Mononaphthene þ H2 ! paraffin Saturation of Olefin

R  CH ¼ CH  R0 þ H2 ! R  CH2  CH2  R0

Paraffin hydrocracking

Cn H2nþ2 þ H2 ! Ci H2iþ2 þ CðniÞ H2ðniÞþ2

kO

kP

R  S þ H2 ! R þ H2 S

Hydrodenitrogenation

R  N þ 2H2 ! R  H þ NH3

kHDS

kHDN

4. Results and discussion 4.1. Application and validation of the approach Real components-based characterization approach was applied in this section to substitute the coal tar system for verifying the validity and accuracy of the model. Coal tar feedstock is a complex mixture consisting of various hydrocarbons (e.g. Paraffins, Olefins,

3

Density (g/cm ) Elemental analysis (wt.%) C H N S O H/C molar ratio Paraffins (wt%) Naphthalenes (wt%) Aromatics (wt%)

r N3 ¼ kMonoN C rec P H2 r o ¼ ko C rec P H2 r P ¼ kP C rec P H2

m2 r HDN ¼ kHDN C nN N P H2

Naphthene, Aromatic and Heteroatom compounds etc). The main products resulting from coal tar hydrogenation contains cracked gas and liquid fraction. The detailed analytical data shows that cracking gas primary consists of hydrogen sulfide, ammonia, methane, ethane and other low-carbon pyrolysis gases with carbon number less than 5C, while liquid product basically covers the hydrocarbons from 5C to 21C with corresponding distillation temperature ranges 20–360 °C. To characterize the coal tar feedstock and hydrogenation products, the range of normal boiling points to all the component selected should be within 360 °C, and the carbon number of components will cover 1-21C from GC–MS results. Moreover, the components with same carbon number (>5C) contain paraffin, naphthenic and aromatic compound. It should be pointed out that olefin is disregarded in the coal tar system as result of small quantity of compounds detected by GC–MS. The analysis finding that the component with carbon number over 19 in feedstock primarily exist in the form of paraffin. Based on above principle, a set of 64 pure components is finally chosen to characterize the coal tar feedstock and their properties are presented in Table 5. In addition, the composition of all components in substitute mixture were generated from the optimization algorithm proposed in Section 2.2, the results are also shown in Table 5. Fig. 3 indicates the comparison of experimentally measured TBP curve with that simulated from carbon number components, it can be observed that predicted results demonstrate an excellent match with the measured curve with an average deviation less than 1%, while the slight deviation is mainly attributed to the optimization of other properties (i.e. density, PNA contents, etc) except for boiling point. Nevertheless, the result indicates that the real componentsbased characterization approach could substitute coal tar feedstock well.

4.2. Application of kinetic model

Table 3 Main properties of coal tar feed. Properties

r N1 ¼ kPloyN C rec P H2 r N2 ¼ kDiN C rec P H2

m1 r HDS ¼ kHDS C ns S P H2

Hydrodesulfurization

its measured bulk properties were detailed listed in Table 3. Besides, a laboratory synthesized Mo-W-Ni/c-Al2O3 catalyst was utilized in this experiment and its properties were summarized in Table 4, these catalysts exhibited good performance in the hydrogenation tests. The detailed experimental setup and procedure on hydrogenation process have been described in previous work [33,34]. To investigate reaction kinetic of coal tar hydrogenation process, a series of hydrogenation tests were performed at fixed H2/ oil of 1600, varying reaction temperature of 340, 360 and 380 °C; pressure of 6, 8 and 12 MPa; liquid hourly space velocity (LHSV) of 0.4, 0.6, 0.8 and 1. The products samples were collected subsequently once at every 4 h under steady-state operation and were detailed characterized using some conventional techniques such as analysis of distillation range, density measurement, PNA analysis, etc. At the same time, Gas chromatography–mass spectroscopy (GC–MS) was used to analyze the detailed carbon number molecular compositions of product. Subsequently, the liquid product was further distilled into three cuts based on the boiling range: the gasoline (20–180 °C) and diesel (180–360 °C), some asphalt (>360 °C), each sub-products were further characterized by same analytical equipment. The resulting experimental data provides a basis for modeling the reaction kinetics of coal tar hydrogenation.

r A3 ¼ kMonoA C rec P n3 H2  kMonoA C pro

Values

Properties

Values

0.986

Molecular weight(MW) API Distillation range/°C IBP 10% 30% 50% 70% 90% 95% FBP

245.4 12.1

83.4 10.2 1.68 0.94 3.65 1.48 18.27 22.26 59.47

115 168.7 226.1 248.9 268.5 315.8 350.6 361.6

After the composition of real component in coal tar feed was accurately determined, the coal tar hydrogenation process can be simulated based on the kinetic model developed in Section 2.3, in which the parts of kinetic parameters estimated from experimental data are listed in Table 6. To verify the accuracy of the model, a comparison between experimental measured product TBP curves with that simulated by real component in product obtained at temperature 360 °C; 6 MPa; H2/Oil, 1600; LHSV, 0.6 h1 was performed as shown in Fig. 4. Result reveals that the measured curves considerably agree with expected outcome with an average error less than 3%. Under the same conditions, the mea-

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Table 4 Catalyst characterization. Catalysts

Compositions (wt.%) Mo

Ni

W

Mo-Ni/c-Al2O3 W-Ni/c-Al2O3

15 –

5 5

– 15

SBET (m2/g)

Pore volume (cm3/g)

Pore diameter (nm)

162.3 197.4

0.59 0.46

9.4 7.8

Table 5 Pure component selected for reactor modeling of coal tar hydrogenation. ID

Component

Tb (°C)

M (g/mol)

q at 20 °C

xi

ID

Tb (°C)

M (g/mol)

q at 20 °C

xi

N-heptylbenzene Benzylcyclohexane n-tetradecane N-nonylcyclopentane N-octylbenzene 1,1-dicyclohexylethane N-pentadecane trans-anti-transPerhydrophenanthrene 1,2,7-trimethylnaphthalene N-nonylcyclohexane Naphthalene,1,2,3,4tetrahydro-1,1,2,6tetramethyl N-hexadecane 1-N-butylnaphthalene N-heptadecane Naphthalene, 2-(1methylbutyl)Perhydropyrene

246.1 249.82 253.58 262.29 263.96 269.39 270.69 271.24

176.30 174.29 198.39 196.38 190.33 194.36 212.42 192.34

859.52 932.18 762.71 812.51 858.23 898.68 772.1 956.51

0.0262 0.0696 0.0578 0.0185 0.0723 0.0027 0.0026 0.0171

278.71 281.16 282.82

170.25 210.40 188.312

1031.27 820.77 944.02

0.0510 0.0000 0.0498

286.86 290.22 302.15 304.05

226.45 184.28 240.47 198.31

777.3 980.79 781.7 968.57

0.0016 0.0266 0.0000 0.0121

306.3

218.38

989.89

0.0147

307.39

222.41

875.89

0.0000

310.97 313.05

210.32 182.27

1034.99 1095.24

0.0282 0.0214

316.71

254.49

785.6

0.0000

Component

(kg/m3)

(kg/m3)

H2 H2S NH3 C1 C2 C3 C4 C5

Hydrogen Hydrogen sulfide Ammonia Methane Ethane Propane N-Butane N-pentane

252.76 60.35 33.43 161.49 88.59 42.04 11.72 27.84

2.02 34.08 17.03 16.04 30.07 44.09 58.12 72.15

0.0899 1.19 0.771 0.5548 1.04 1.56 2.05 627.42

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

C13A C13AN C14P C14N1 C14A1 C14N2 C15P C14N3

C6P C6N C6A

2,3-dimethylbutane Methylcyclopentane Benzene

60.26 71.81 80.09

86.18 84.16 78.11

657.41 751.74 882.63

0.0000 0.0000 0.0000

C13A2 C15N C14AN

C7P C7N C7A C8P

2,3-dimethylpentane Methylcyclohexane Toluene 2,3-dimethylhexane

89.78 100.93 110.63 115.61

100.20 98.19 92.14 114.23

699.01 772.08 873.86 715.83

0.0000 0.0000 0.0000 0.0000

C16P C14A2 C17P C15A

C8N

Cyclohexane, 1,4dimethyl 2,6-dimethylheptane

119.36

112.22

766.62

0.0078

C16N4

124.09

128.26

718.53

0.0004

C16N2

138.36 156.75

106.17 126.24

865.99 795.79

0.0543 0.0106

C16A2 C14A2 N

165.18

120.19

884.66

0.0283

C18P

174.15 187.31 195.93 198.223

142.28 138.25 156.31 154.29

735.68 874.99 742.51 830.76

0.0111 0.0523 0.0001 0.0171

C17A C18N1 C18A1 C19P

Undecan-6-ylbenzene N-tridecylcyclopentane N-dodecylbenzene N-nonadecane

319.27 325.45 327.61 329.9

232.41 252.48 246.44 268.53

948.28 792.6 853.76 789.2

0.0085 0.0032 0.0105 0.0043

207.62

132.21

974.32

0.0388

C18N3

336.95

248.452

944.23

0.0088

210.5

148.25

885.17

0.0003

C18N2

1,10 :20 ,100 -Tercyclohexane (6CI,7CI,8CI,9CI) 6-cyclohexylhexylcyclohexane

339.75

250.47

860.04

0.0000

C12P C12N C12A C12AN C13P

Para Xylene 1-trans-3,5trimethylcyclohexane 2-methyl-3ethylbenzene 2,5-dimethyloctane Trans-decaline N-undecane Tetrpentylcyclohexane 1,2,3,4,tetrahydronaphthalene 1,2-Dimethyl-3Propylbenzene N-dodecane N-heptylcyclopentane N-hexylbenzene 2-ethyltetralin N-tridecane

1,10 -butane-1,4diyldicyclohexane 1-tert-butyl-3-phenylbenzene 1,2,3,4tetrahydrophenanthrene N-octadecane

216.32 224.15 226.11 231.46 235.43

170.34 168.32 162.27 160.25 184.37

750.82 805.1 860.64 943.04 758.81

0.0425 0.0130 0.0145 0.0483 0.0345

C20P C18AN2 C18A2 C21P C18A3

343.78 344.14 350.02 356.50 364.13

282.54 242.40 240.39 296.58 232.33

790.67 1002.85 939.25 794.30 1123.48

0.0179 0.0234 0.0001 0.0099 0.0024

C13N

N-heptylcyclohexane

244.5

182.35

815.47

0.0516

C18A2N

N-eicosane P-dicyclohexylbenzene 2-octylnaphthalene N-heneicosane Naphthalene, 2-methyl-1(2-methylphenyl)Anthracene, 9,10-diethyl9,10-dihydro-

368.56

236.36

1017.61

0.0004

C9P C8A C9N C9A C10P C10N C11P C11N C10A C11A

sured bulk properties of hydrogenation product were also compared to calculated values as presented in Table 7. The result with good agreement indicates that the kinetic model is effective in predicting the hydrogenation reaction behavior, which in turn further proves the accuracy of real component-based characterization approach. The kinetic model presents a substantial superiority to trace the conversion behavior of each real component in hydrogenation process, which is significant important in controlling the quality of product in industry plant. For example, Fig. 5 shows the simulated yield distribution of components C14P, C14N2, C14N3, C14A1 and C14A2 with residence time in hydrogenation process. The change of yield was observed to exhibit a rapidly decrease for the component C14A2, but slowly for C14A1, this finding means that the

polycyclic aromatic compound is much easier to hydrogenation than mono-aromatic with stable structure in saturation reaction. Besides, the yield of component C14N3 presents a gradual increase in the initial stage and then slightly decrease or even unchanged, while the component C14N2 maintains a continuous greatly increase with the residence time, these complicated trends are ascribed to the fact that C14N3 and C14N2 can not only be generated from aromatic compound by the aromatic hydrogenation reaction, but also can be converted into other component by ring opening reaction. For C14P, its yield decrease gradually because of hydrocracking reaction occurred, this finding is consistent with experimental outcome. In summary, developed kinetic model provides a feasible guidance for deeply understanding of hydrogenation reaction behavior.

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F. Dai et al. / Fuel 202 (2017) 287–295 Table 7 Main properties of hydrogenation product. Property 3

Density (g/cm ) Paraffins (wt%) Naphthalenes (wt%) Aromatics (wt%) ASTM D86 Distillation °C IBP 5% 10% 30% 50% 70% 90% FBP

Experimental

Simulated

Error (%)

0.8522 19.35 53.76 26.89

0.8436 20.08 52.38 27.54

1.009153 3.77261 2.566964 2.41726

26.5 77.8 99.7 189.2 239.6 260.2 301.6 357.8

27.52 73.45 90.34 186.56 238.74 263.18 306.8 356.24

3.849 5.59126 9.388164 1.395349 0.358932 1.14527 1.72414 0.435998

Fig. 3. Comparison of coal tar TBP with that simulated from carbon number components.

Table 6 The parts of reaction kinetic parameters estimated. Reaction rate constants/h1

Pre-exponential factor (k0)/ (h1 MPa)

Activation energy (Ea)/(J/mol)

kDi-A -kDi-A kMono-A -kMono-A kPloy-N kDi-N kMono-N kP kHDS kHDN

1.09  105 2.05  105 6.01  107 7.09  108 2.15  106 9.35  105 9.65  105 1.56  105 4.95  106 1.79  107

64540 78385 105930 125584 89428 90837 95842 80836 81248 90250

Fig. 5. Simulated yield distribution of carbon number components in hydrogenation process.

Fig. 4. Comparison of product TBP with that simulated from real components products.

In addition, the optimization of hydrogenation operating condition can also be implemented by the proposed kinetic model for minimizing resource utilization and reducing the number of further experiments. For instance, the influence of reaction temperature and pressure to the sulfur conversion during the hydrogenation process are illustrated in Figs. 6 and 7. Fig. 6 indicates that the sulfur compound are rapidly removed by the hydrogenation with increasing residence time, whereas when reaction temperature reach to 360 °C, the sulfur conversion

Fig. 6. Comparison between experimental data (points) and measured sulfur concentration (lines) with the residence time (pressure, 8 MPa; LHSV, 0.6 h1; and H2/oil, 1600).

increase significantly. Fig. 7 implies that the effect of pressure is much lower than that of temperature, yet change in pressure from 6 to 8 MPa exhibits the fastest decrease in sulfur concentration. This finding shows a similar tend to the coal tar hydrogenation experiment. In addition, the comparison between experimental

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F. Dai et al. / Fuel 202 (2017) 287–295

Fig. 7. Comparison between experimental data (points) and measured sulfur concentration (lines) with the residence time (Temperature, 360 °C; LHSV, 0.6 h1; and H2/oil, 1600).

data and measured sulfur concentration with the residence time at different operating conditions were also performed as shown in Figs. 6 and 7, the prediction of sulfur concentration shows considerable good agreement with the experimental values, and this finding further indicates that the kinetic model developed could significantly predict product distribution. By comprehensive considering the various factors, the optimal reaction conditions for HDS were selected as 360 °C and 8 MPa. The hydrogen consumption is commonly viewed as a significant economic indicator for evaluating the coal tar hydrogenation process. Another advantage for the kinetic model lies in its validity in predicting the hydrogen consumption. Figs. 8 and 9 show the effect of different reaction temperature and pressure on the chemical hydrogenation consumption at fixing operating conditions, respectively. It is observed that hydrogen consumption in both figures exhibit a gradual increase with rising of temperature and pressure, but in contrast to pressure, the effect of temperature is more noticeable since the rate of hydrogenation reaction and hydrogenation extent are dominated mainly through temperature [21]. On the basis of the kinetic model, the chemical hydrogen con-

Fig. 9. Effect of the pressure on the chemical hydrogenation consumption.

sumption predicted at temperature 360 °C and pressure 8 MPa accounted for around 3.1% of feed rate. This result is in accordance with practical coal tar hydrogenation process. 5. Conclusions A systematic real component-based characterization approach was developed in this work to characterize the complex hydrocarbon mixture. The approach presented a considerable capacity in gaining the well-defined chemical character of the substitute mixture, which enables the utilization of developed method to analysis of complex reaction mechanism, especially to the hydrogenation process. The wide adaptability of method was successfully verified by characterizing the coal tar feedstock using a set of 64 pure components covering from the carbon number 1C to 21C. Result shows that the approach could substitute coal tar system well. The detailed compositions of the components were also determined from the bulk experimental properties such as density, MW, TBP curve and PONA contents by Levenberg-Marquardt’s optimization algorithm. Accordingly, detailed kinetic model of coal tar hydrogenation process based on the real component reaction pathway was developed, in which the kinetic parameters were obtained in two-stage fixed beds filled with laboratory-made catalysts at various operating conditions. The model effectively simulated the yield distribution of each component and predicted the chemical hydrogen consumption in hydrogenation process. The new approach exhibits a high flexibility in characterizing various complex feedstock and provides a significant guidance for detailed kinetic modeling of hydrogenation process. Acknowledgements The authors wish to acknowledge the National Science Fund for Excellent Young Scholars (21422607), National Natural Science Foundation of China (21576261), and the CAS/SAFEA International Partnership Program for Creative Research Teams. References

Fig. 8. Effect of the reaction temperature on the chemical hydrogenation consumption.

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