Hydrodesulfurization Kinetics of Middle Distillates: A Four-Lumping

Dec 1, 2016 - The objective of the present study is to develop a better hydrodesulfurization (HDS) kinetic model on the basis of a fundamental and com...
0 downloads 7 Views 2MB Size
Download PDF
Article pubs.acs.org/EF

Hydrodesulfurization Kinetics of Middle Distillates: A Four-Lumping Model with Consideration of Nitrogen and Aromatics Inhibitions Hamza Albazzaz, Abdulazim MJ Marafi, Xiaoliang Ma,* and Thameem Ansari Petroleum Research Center, Kuwait Institute for Scientific Research, P.O. Box 24885, Safat 13109, Kuwait ABSTRACT: The objective of the present study is to develop a better hydrodesulfurization (HDS) kinetic model on the basis of a fundamental and comprehensive understanding of the different HDS reactivities of various sulfur compounds in middle distillates and the inhibition effects of the coexisting nitrogen and aromatic compounds. Five middle distillates blended from straight run gas oil, heavy gas oil, and coker gas oil were hydrodesulfurized over a commercial catalyst at 300 °C, 4.0 MPa H2 pressure, a H2/oil volume ratio of 200, and different liquid hourly space velocities. Identification and quantification of sulfur compounds in the feedstocks and their hydrodesulfurized products were conducted by using GC-PFPD to classify the sulfur compounds into four groups according to their molecular structures and reactivities. It was found that the HDS of each sulfur group follows pseudo-first-order kinetics. Correlation of the obtained first-order rate constants with both the nitrogen concentration and the feedstock density indicates that the effects of both on the rate constants can be expressed by a linear function of them. A four-lumping first-order-kinetics model including effects of the nitrogen concentration and density was developed, which can predict the total sulfur concentration in the HDS product from different middle distillates and is useful to understand how the composition and properties of the middle distillates influence their HDS reactivity and kinetics.

1. INTRODUCTION Hydrodesulfurization (HDS) has been one of the most important processes in the petroleum-refining industry for more than a half-century.1,2 HDS kinetics for middle distillates is crucial in the design and development of HDS catalysts and processes and in the prediction of the HDS performance in refineries. The continuous growth in demand of ultra-low-sulfur diesel has motivated the increasing research interests in developing more accurate and comprehensive kinetic models to reveal the HDS behavior of various real middle distillates and to predict the sulfur concentration of their products in HDS processes.3,4 Many kinetic models have been developed for HDS processes of middle distillates in a fixed-bed reactor.2,4,5 These models can be classified into two categories: learning models (such as artificial neural network) and predictive models. The latter can be further subclassified as deterministic models and stochastic models.2,6 The deterministic models include a continuous fixed-bed model, which can also be separated further into the pseudohomogeneous and heterogeneous models. The prevailing models for predicting HDS performance of the real middle distillates are deterministic models. Among them, the pseudohomogeneous models are the most popular. One of them is the power law model (PL model), which is expressed by a power of the total sulfur concentration: r=

dCS = −kappCSn dt

real feedstock. However, this model does not take into account the distribution of the sulfur compounds with different reactivities and the inhibition of other coexisting compounds, such as nitrogen compounds and aromatics. The reaction order in the model may also change with the changes of the sulfur compounds’ distribution in the feedstock and the HDS conversion degree. Thus, the practicality of this model is limited. In deep HDS of middle distillates, the inhibitions of some coexisting compounds, such as nitrogen compounds, H2S, and aromatics, become significant. In order to consider the inhibition effects of these coexisting compounds, the Langmuir−Hinshelwood model (LH model) is usually applied, which can be expressed as shown below r=

∑ KjCj)

(2)

where Cj is the concentration of inhibitor j and Kj is the inhibition coefficient of j. j can be nitrogen compounds, aromatics, and/or H2S. Since many parameters have to be determined in the LH model, it is quite challenge for practical applications.7 It seems that the reasonable way to improve HDS modeling for real feedstock is to divide the reactant mixture into two (active and refractory) or more lumps according to their reactivities, although the most common modeling is based on a single lump among the different approaches in the HDS kinetics of petroleum fractions during the last decades.2,4,8 Nowadays, much attention has been paid to using the multilump model (ML model) for HDS kinetics, as the single

(1)

where CS is the concentration of total sulfur in petroleum fractions, t is the pseudo-residence time (for a fixed-bed reactor, t = 1/LHSV), kapp is the apparent rate constant, and n is the reaction order within a range from 1 to 2. Since it is simple, the PL model is used widely in HDS kinetic studies, especially for a © XXXX American Chemical Society

dCS = −(kappCSn)/(1 + dt

Received: October 6, 2016 Revised: December 1, 2016 Published: December 1, 2016 A

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels lump is not valid for deep HDS due to no consideration of the distribution of various sulfur compounds with quite different HDS reactivities in different middle distillates.9−13 In the ML model, the kinetic behavior of the real middle distillates with a huge member of sulfur compounds can be expressed by lumping them into a couple of groups (m) of the sulfur components that present similar HDS reactivity, and whole reaction kinetics can be described by the sum of m parallel firstorder reactions, as shown below m

r = r1 + r2 + r3 + ··· + rm =

They found that among all these models, the best approach in terms of minimal differences between predicted and experimental data was the two-lumping first-order model. More recently, Tang et al. compared the two-lump, threelump, and four-lump first-order models for HDS kinetics of a middle distillate from shale oil.13 They used the nonlinear regression and Levenberg−Marquardt methods to estimate all the parameters, including the shares of lumps, and found that the best model for HDS is the three-lump model with the shares of 45.3, 48.8, and 5.6%, respectively. However, both Rodriguez et al. and Tang et al. determined the shares of the sulfur compound lumps by using an iterative procedure on the basis of the HDS experimental results.8,13 It limits the practical application of the developed model, as different feedstocks may have quite different shares of the lumps. Also, these ML models did not consider effects of the coexisting nitrogen-containing compounds and aromatics on the HDS kinetics. In the present study, the objective is to develop a ML model on the basis of a fundamental and comprehensive understanding of the HDS reactivities of various sulfur compounds in middle distillates and the inhibition effects of the coexisting nitrogen-containing compounds and aromatics on HDS kinetics. Five middle distillates blended by straight run gas oil, heavy gas oil, and coker gas oil from Kuwait refineries were used as feedstocks for HDS over a commercial catalyst in a fixed-bed reactor at the desired conditions. Identification and quantification of sulfur compounds in the feedstocks and the hydrodesulfurized products were conducted by using GCPFPD to separate the sulfur compounds into four groups according to their molecular structures and reactivities. A new HDS kinetic model, a four-lumping first-order model with consideration of effects of both coexisting nitrogen-containing compounds and aromatics, was developed, which is expected to predict more accurately the HDS kinetic behavior of various middle distillates and their blends.

m

∑ kiCSi = ∑ kiCSioe−k t i

i=1

i=1

(3)

where m is the number of lumping groups, ki is the rate constant for group i, CSi is the sulfur concentration of group i, and CSio is the initial sulfur concentration of group i In 1992, Kwak et al. proposed that HDS and hydrodenitrogenation (HDN) of a bitumen-derived liquid can be modeled by two parallel first-order reactions.9 CS = CS1oe−k1t + CS2oe−k 2t = CS,o[θ e−k1t + (1 − θ )e−k 2t ] (4)

where the parameter θ is the facile fraction of the organic sulfur that is more reactive, and 1 − θ is the refractory fraction, which is less reactive. In 1994, in a study of HDS reactivities of various sulfur compounds in a middle distillate, Ma et al. found that (1) there are two major types of the sulfur compounds, alkylbenzothiophenes (BTs) and alkyldibenzothiophenes (DBTs), in the middle distillate; (2) BTs show much higher reactivity than DBTs due to their different molecular structures and reaction mechanism; and (3) the steric hindrance of the alkyl groups in DBTs determines their HDS reactivities. On the basis of these findings, they classified the sulfur compounds in the middle distillate into four groups according to their molecular structures and HDS reactivities.10 The first group is dominantly BTs; the second, DBTs without any alkyl substituent at the 4and 6-positions; the third, DBTs with one alkyl substituent at either the 4- or 6-position; and the fourth, DBTs with alkyl substituents at both the 4- and 6-positions. The kinetic behavior of individual sulfur compounds in the same group is similar and follows the pseudo-first-order reactions. Consequently, the total sulfur concentration in the HDS product can be predicted by a sum of the four parallel pseudo-first-order reactions,10,14,15 as shown below

2. KINETIC MODELING According to the previous study of HDS reactivity of various sulfur compounds in gas oil,10 the sulfur compounds can be classified into four groups on the basis of their molecular structures and HDS reactivities. If the kinetics of each group follows the first-order reaction, the total reaction rate can be expressed by a sum of the reaction rates of the four groups r = k1CS1 + k 2CS2 + k 3CS3 + k4CS4

and the total sulfur concentration of the HDS product in a fixed-bed reactor can be estimated by eq 7

4

CS =

∑ CSi = CS1oe

−k1t

+ CS2oe

−k 2t

CS3oe

−k 3t

CS4oe

(6)

−k4t

CS,total = CS1oe−k1/LHSV + CS2oe−k 2 /LHSV + CS3oe−k3/LHSV

i=1

(5)

+ CS4oe−k4 /LHSV

where CSi is the sulfur concentration of group i. CSio is the initial sulfur concentration of group i, which can be determined by the detailed identification and quantification of the sulfur compounds by using GC-PFPD, GC-SCD, or GC-AED. k1, k2, k3, and k4 are the pseudo-first-order rate constants for the corresponding groups. Recently, Rodriguez et al. compared the five kinetic models for simulating the HDS of vacuum gas oil in a trickle-bed bench-scale reactor, including (1) the PL model, (2) the PL model with consideration of the liquid holdup in the packed bed reactor, (3) two-lumping first-order model (one of the ML models), (4) the PL model with consideration of the reaction carried out on the catalyst surface, and (5) the LH model.8

(7)

where LHSV is the liquid hourly space velocity. In examining the effects of 13 properties of middle distillates on HDS rate constant, Ho found that the sulfur steric hindrance, organonitrogen poisoning, and feed saturation are the three most important independent factors.16 As is wellknown, there are many nitrogen-containing compounds coexisting in the middle distillates. The nitrogen concentration can be from a couple of dozens to more than a thousand parts per million by weight (ppmw), depending on their sources.17 These coexisting nitrogen-containing compounds have been found to have strong inhibition to deep HDS.18−20 Many previous studies have shown that the inhibition of the nitrogen B

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels kapp, i = αiC Nf + βi D + γi

compounds can be expressed on the basis of the Langmuir isotherm21,22 kiCSi n dCSi /dt = − 1 + KNC N

where αi and βi are the coefficient of nitrogen concentration in the feedstock and the coefficient of the feedstock density, respectively, for group i. γi is a constant term for group i. Here, αi, βi, and γi can be determined by the linear regression analysis on the basis of the experimental data. Thus, the total sulfur concentration in the HDS product at different space velocity can be estimated by the following equation:

(8)

where n is the reaction order for sulfur, KN is the inhibition coefficient of nitrogen compounds, and CN is the nitrogen concentration of the coexisting nitrogen compounds. If assuming (1) the reaction is first-order for sulfur, (2) the nitrogen species are not changed significantly or converted but the formed nitrogen compounds show an inhibition similar to that of the initial nitrogen compounds, and (3) the term KNCN is much less than 1, the equation can be simplified to

4

CS =

(12)

3. EXPERIMENTAL SECTION 3.1. Middle Distillates. Five middle distillates with densities ranging from 0.8474 to 0.8620 g/mL, a sulfur concentration from 1.214 to 1.514 wt %, and a nitrogen concentration from 98 to 342 ppmw were used in this study. These middle distillates were prepared by mixing straight run gas oil (SRGO), heavy gas oil, and coker gas oil at different ratios. The composition and properties of these five middle distillates are shown in Table 1.

(9)

where CNf is the concentration of nitrogen in the feedstock. It indicates that the apparent reaction rate constants kapp,i can be described approximately by a linear function of the nitrogen content in the feedstock. The inhibition effect of the coexisting aromatic compounds on HDS has also been reported in the literature.16 If also considering the inhibition effect of the coexisting aromatic compounds through the competitive adsorption, an equation for the inhibition by the aromatics can also be obtained, like that by nitrogen compounds (eq 9): dCSi /dt = −

∑ CSio exp[ − (αiC Nf + βi D + γi)/LHSV] i=1

kiCSi n dCSi /dt = − ≈ −ki(1 − KNC Nf )CSi 1 + KNC Nf = kapp, iCSi

(11)

Table 1. Composition and Properties of the Five Middle Distillates density at 15 °C, g/mL total sulfur, wt % total nitrogen, ppmw

kiCSi n ≈ −ki(1 − KACAf )CSi = kapp, iCSi 1 + KACAf

A

D

C

E

B

0.8535 1.514 124

0.8492 1.422 98

0.862 1.415 342

0.8474 1.27 155

0.8500 1.214 237

3.2. HDS Experiment. The HDS of the middle distillates was run in a pilot unit fixed-bed reactor with a reactor length of 870 mm, a reactor internal diameter of 18.84 mm, and a thermocouple tube at the center with an outside diameter of 6.35 mm. The catalyst bed size was 60 mL. A commercial CoMo catalyst, which also contains phosphorus, with a cloverleaf shape, surface area of ∼100 m2/g, particle diameter of ∼1.2 mm, and packing density of 1.07 g/cm3 was used in this study. The catalyst was presulfurized in situ by using a feed containing 3 wt % dimethyl disulfide in a SRGO at 4.0 MPa H2 pressure, LHSV of 1.5 h−1, and H2/oil volume ratio of 200 under a temperature program. The HDS was conducted at 4.0 MPa H2 pressure, H2/oil volume ratio of 200, and LHSV of 0.5, 1.0, 1.3, 1.5, and 2.0 h−1, respectively. The HDS reaction temperature was set at 300 °C in order to get the kinetics data for the highly reactive sulfur group. The feedstock and fresh H2 were mixed and then sent to the top of the reactor to flow down through the catalyst bed. After the HDS reaction was run at 300 °C for 96 h, the kinetics investigation was stared under different LHSV values. When the HDS reaction at the desired LHSV became stable for 24 h, the HDS liquid product sample was collected from the bottom of the gas−liquid separator. No significant deactivation of the catalyst was found within the kinetics investigation under such conditions. The collected liquid sample was bubbled by N2 gas for 15 min to remove the H2S dissolved in the sample before sending for the sulfur analysis. 3.3. Analysis of Feedstocks and Products. The identification and quantification of sulfur compounds in various middle distillates and their products were conducted by using a Hewlett-Packard gas chromatograph configured with a pulsed flame photometric detector (GC-PFPD) (O.I. Analytical 5380) and a XTI-5 capillary column (length 30 m, i.d. 0.25 mm). The GC-PFPD analysis conditions were set at a split ratio of 100:1 and an injector and detector temperature of 300 and 240 °C, respectively. A temperature program for GC-PFPD analysis was used with the initial column temperature at 120 °C for 2 min, the first ramp rate of 5 °C/min from 120 to 180 °C, the second ramp rate of 10 °C/min from 180 to 300 °C, and then remaining at 300 °C for 5 min. The identification of the major sulfur compounds was performed by using some standard samples, such as DBT, 4methyldibenzothiophene (4-MDBT), and 4,6-dimethyldibenzothio-

(10)

where CAf is the concentration of aromatics in the feedstock, and KA is the inhibition coefficient of aromatics. An accurate measurement of the concentration of aromatics in the feedstock is more difficult than that of the density of the feedstock. According to the data reported by Ho for 13 different middle distillates,16 it was found that the correlation between the total aromatics and the density (D) is approximately linear with an R2 value of 0.8919 (see Figure 1), indicating that the CAf value can be expressed approximately by a linear function of the density of the middle distillates. Thus, the apparent first-order rate constant (kapp,i) for each group in eq 7 can be expressed approximately by a linear function of the nitrogen concentration and the density of the feedstock

Figure 1. Correlation between the total concentration of aromatics and the density for middle distillates. C

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels phene (4,6-DMDBT) and comparing the relative retention times of the peaks with those from the literature.10,23 Considering that the peak area in the GC-PFPD chromatograph is proportional to the concentration of the corresponding sulfur compounds, the quantification of the major sulfur compounds and sulfur groups was conducted by a normalization method, where the concentration of the sulfur compound, or the sulfur group, was calculated by the equation shown below

CSi = CS

Ai A total

4.2. Classification of Sulfur Groups. According to the previous study10 and the results shown in section 4.1, it is reasonable to separate the sulfur compounds into four groups on the basis of their molecular structures. Recently, GC-PFPD, GC-SCD, and GC-AED techniques have been used widely in identification and quantification of the sulfur compounds in the middle distillates.24 However, there are more than hundreds of sulfur compounds in petroleum middle distillates, and it is still a great challenge to identify and quantify all these sulfur compounds by these techniques, especially to determine the positions of alkyl substituents in DBTs. In order to solve this problem, the present study simplified the classification of the sulfur compounds in the middle distillates into four groups on the basis of their identified structure and the retention time of the sulfur compounds in the GC-PFPD chromatograph, as shown in Figure 3. The GC-PFPD chromatograph was first separated into three regions according to the retention time: region 1 with a retention time range from 4.0 to 19.6 min, region 2 from 19.6 to 20.8 min, and region 3 from 20.8 to 28.0 min. Group 1 contains all the sulfur compounds in region 1, except DBT, 4-MDBT 2/3-MDBT, and 1-MDBT, which represents the majority of the benzothiophene-type compounds. Group 2 includes DBT, 2/3-MDBT, and 1-MDBT, as shown in Figure 3. Group 2 represents the major dibenzothiophenes without any alkyl substituent at the 4and/or 6-positions. Group 3 includes 4-MDBT and all sulfur compounds in region 2, except 4,6-DMDBT, which represents the major dibenzothiophenes with only one alkyl substituent at the 4- or 6-position. Group 4 includes 4,6-DMDBT and all sulfur compounds in region 3. Group 4 represents the most refractory sulfur compounds. The determined concentration for each sulfur group in the five middle distillates is listed in Table 2. The results indicate that the distribution of the sulfur compounds is very different in various feedstocks. Among them, the middle distillate C contains the highest concentration (3995 ppmw) of the group 4, while the middle distillate D contains the highest concentration of the group 1. Different amounts and distributions of sulfur groups in various middle distillates will determine their different HDS kinetic behaviors. 4.3. Kinetics for Each Group. In order to get the kinetics data for each group in different feedstocks, the sulfur concentrations of each group in various feedstocks and their HDS products were determined on the basis of GC-PFPD analysis. The sulfur concentration of each group as a function of 1/LHSV for the five middle distillates is shown in Figure 4. It is clear that all four groups of sulfur compounds in the five middle distillates follow first-order kinetics. The first-order rate constants for each group were obtained by least-squares regression with R2 values higher than 0.9600, and the obtained values are listed in Table 3. Comparing the rate constants of the groups in feedstock D with their representative sulfur compounds in the group, as shown in Figure 2, the rate constant values are similar. For example, the rate constant is 1.895, 0.998, and 0.666 h−1, respectively for groups 2, 3 and 4, while the rate constant of DBT, 4-MDBT, and 4.6-DMDBT is 1.943, 0.798, and 0.516 h−1, respectively. The rate constant for groups 3 and 4 is slightly higher than that of 4-MDBT and 4.6DMDBT, as group 3 contains some sulfur compounds (such as BTs and DBTs) other than the alkyl DBTs with only one alkyl substituent at the 4- or 6-position, and group 4 contains some sulfur compounds (such as BTs and DBTs) other than the alkyl DBTs with two alkyl substituents at the 4- and 6-positions, respectively.

(13)

where CS is the total sulfur concentration of the sample, Ai is the peak area corresponding to sulfur compound i or group i, and Atotal is the total area of the peaks in the GC-PFPD chromatograph. The density of the middle distillates was measured by using method ASTM D4052. The total sulfur and nitrogen concentrations were measured by using methods ASTM D-5453 and ASTM D-4629, respectively.

4. RESULTS AND DISCUSSION 4.1. Kinetics for Individual Sulfur Compounds in Middle Distillates and Their Reactivities. As reported in the previous study,10 the HDS kinetics of the individual sulfur compounds in a middle distillate follows a first-order reaction. In order to confirm it further, the sulfur concentration of some representative sulfur compounds in middle distillate D as a function of 1/LHSV in HDS was examined, and the result is shown in Figure 2. It clearly indicates that all examined

Figure 2. Pseudo-first-order plots of some representative sulfur compounds in middle distillate D.

individual sulfur compounds, including 2,3,7-trimethylbenzothiophene (2,3,7-TMBT), DBT, 1-methyldibenzothiophene (1MDBT), 2- or 3-methyldibenzothiophene (2/3-MDBT), 4MDBT, 4,6-DMDBT, 2 or 3,6-dimethyldibenzothiophene (2/ 3,6-DMDBT), 2,4,6-trimethyldibenzothiophene (2,4,6TMDBT), follow the first-order reaction. The obtained rate constant decreases in the order of 2,3,7-TMBT (4.706 h−1) > DBT (1.949 h−1) ≈ 1-MDBT (1.750 h−1) ≈ 2/3-MDBT (1.700 h−1) > 4-MDBT (0.798 h−1) ≈ 2/3,6-DMDBT (0.751 h−1) > 4,6-DMDBT (0.518 h−1) ≈ 2,4,6-TMDBT (0.663 h−1), which are well in agreement with the previous classification of the sulfur compounds according their molecular structures and reactivities. The results confirm that the skeleton structure of sulfur compounds and the position of alkyl groups in them are crucial in determining their HDS reactivity. D

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 3. GC-PFPD of the representative sulfur compounds in typical feedstock and product as well as the classification of four groups.

Table 2. Determined Concentration of Each Sulfur Group in the Five Feedstocks A group 1 group 2 group 3 group 4 total sulfur

D

C

E

B

ppmw

%

ppmw

%

ppmw

%

ppmw

%

ppmw

%

7756 1183 2528 3673 15140

51 8 17 24

7790 1080 2204 3146 14220

55 8 15 22

6520 1075 2560 3995 14150

46 8 18 28

7336 914 1829 2641 12720

58 7 14 21

6849 851 1777 2664 12140

56 7 15 22

The first-order rate constants for the four sulfur groups as a function of the density of feedstocks are shown in Figure 6. It indicates that the feedstock density has also a negative and approximately linear effect on the rate constants, in agreement with the indication of eq 11. This effect on kapp,i increases in the order of kapp,4 < kapp,3 < kapp,2 < kapp,1, similar to that of the nitrogen concentration. Further investigation is also required to understand why the effect of the density is more significant for the reactive sulfur group than for the refractory sulfur group. Multiple linear regressions of the rate constants with both the nitrogen concentration and the density of the feedstocks for the four sulfur groups were conducted. The obtained parameters for eq 11 with the multiple R values are listed in Table 4. All multiple R values for the four groups are higher than 0.9200, indicating the excellent linear correlation of the rate constants with both the nitrogen concentration and density. The experimentally measured rate constants versus the model values predicted by eq 11 are shown in Figure 7. The rootmean-square deviation (RMSD) is 0.3352, 0.1171, 0.0479, and 0.0073 for kapp,1, kapp,2, kapp,3, and kapp,4, respectively, indicating a satisfactory result from the correlation. The obtained coefficients (αi, βi and γi) make it possible to estimate the HDS rate constants for the four groups in various petroleum middle distillates on the basis of their nitrogen concentration and the density. The values for αi and βi also inflect the sensitivity of the changes in the nitrogen concentration and the feedstock density to the change of the rate constant, which is in agreement with those found from Figures 5 and 6. 4.5. Comparison of the Results from Modeling and Experiment. According to the kapp,i values obtained from eq 11, the initial sulfur concentration for each group, and the

When comparing the rate constant for different sulfur groups, the rate constant value decreases in the order of kapp,1> kapp,2 > kapp,3 > kapp,4. For example, kapp,1 is about 10 times higher than kapp,4. That is why almost no sulfur compound belonging to group 1 existed in the HDS product at a LHSV of 0.5 h−1. Only the kinetic behavior of the sulfur compounds in groups 3 and 4 plays a decisive role in the deep HDS of the middle distillates. 4.4. Effects of Nitrogen and Aromatics Concentrations. As shown in Table 3, the rate constants for the same sulfur group in different middle distillates are quite different. This indicates that other compositions and properties, such as the nitrogen concentration and the density of the feedstocks, should also have a strong effect on the rate constants, as discussed in section 2. The first-order rate constants for the four sulfur groups as a function of nitrogen concentration in the feedstocks are shown in Figure 5. The results indicate that the nitrogen concentration has a negative and approximately linear effect on the rate constants, as indicated by eq 11. Interestingly, it is found that the effect of the nitrogen concentration on kapp,i increases in the order of kapp,4 < kapp,3 < kapp,2 < kapp,1, implying that the effect of the coexisting nitrogen compounds on HDS is more significant for the reactive sulfur groups than that for the refractory sulfur groups. This result appears to conflict with the observation that the refractory sulfur compounds, such as 4,6DMDBT, are hydrodesulfurized dominantly through the hydrogenation pathway, on which the coexisting nitrogen compounds are found to show a stronger effect than on the hydrogenolysis pathway.25 More investigations are necessary for clarifying it. E

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 4. Pseudo-first-order plots of each group in five middle distillates and the change of total sulfur with 1/LHSV.

experimentally measured ones are shown in Figure 8. An excellent correlation was obtained with an R2 value of 0.9911. The result implies that the four-lumping first-order model with consideration of the effect of the nitrogen concentration and the density of feedstock can successfully describe the HDS kinetic behavior of the various middle distillates at the present conditions. A further investigation is necessary to confirm whether the proposed model is suitable for the deep HDS that is conducted under more severe conditions.

Table 3. Measured First-Order Rate Constants for Four Sulfur Groups in Five Middle Distillates density at 15 °C, g/mL total N, ppmw rate constant, h−1 kapp,1 kapp,2 kapp,3 kapp,4

A

D

C

E

B

0.8535

0.8492

0.8620

0.8474

0.8500

124

98

306

141

209

7.361 1.843 0.844 0.651

7.523 1.895 0.998 0.667

4.648 1.339 0.706 0.588

8.209 2.231 0.959 0.676

6.873 1.826 0.808 0.631

5. CONCLUSIONS The present study further confirms that the individual sulfur compounds in petroleum middle distillates can be classified into four groups on the basis of their HDS reactivities. A simplified classification of the sulfur compounds into the four groups was conducted according to the molecular structures

nitrogen concentration and the density of the middle distillates, the total sulfur concentrations in the HDS products were estimated by using eq 12. The modeling-predicted sulfur concentrations in the HDS products in comparison with the F

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 7. Experimental and model-predicted rate constants for each sulfur group. Figure 5. Rate constants as a function of the nitrogen concentration in feedstock.

Figure 8. Comparison of the experimental and lumping-modelpredicted total sulfur concentrations in HDS products.

effects of both the nitrogen concentration and the density of feedstock was developed for HDS of various petroleum middle distillates. The developed model is able to predict the total sulfur concentration in the HDS product and is useful to understand how the composition and properties of the middle distillates influence their HDS kinetics behaviors. Further studies are necessary to include the HDS kinetics under more severe conditions used to produce ultra-low-sulfur diesel.

Figure 6. Rate constants as a function of the feedstock density.

Table 4. Obtained Parameters for eq 11 with the Multiple R Values and RMSD rate constant

α

β

γ

multiple R

RMSD

kapp,1 kapp,2 kapp,3 kapp,4

−137.758 −43.558 −9.948 −2.805

−0.007706 −0.000996 −0.000410 −0.000216

125.96 39.19 9.40 3.08

0.9948 0.9668 0.9221 0.9713

0.3352 0.1171 0.0479 0.0073

■

AUTHOR INFORMATION

Corresponding Author

*Tel: +965 24956920. E-mail: [email protected]. ORCID

Xiaoliang Ma: 0000-0003-0450-0662

and the retention times of the sulfur compounds in GC-PFPD chromatographs. It was found that the sulfur compounds in each group show the similar HDS reactivity, and each sulfur group follows pseudo-first-order kinetics. Both the coexisting nitrogen compounds and aromatic compounds in middle distillates have a significant effect on the rate constants, which can be expressed by a linear function of the nitrogen concentration and the density. Such effects on the rate constant, kapp,i, increase in the order of kapp,4 < kapp,3 < kapp,2 < kapp,1. A four-lumping first-order kinetics model that includes

Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors acknowledge the Kuwait Petroleum Corp. (KPC) and the Kuwait Institute for Scientific Research (KISR) for financing this research through PF045C project. Acknowledgement is also extended to the Kuwait National Petroleum Co. (KNPC) for the in-kind contribution and technical support. G

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

■

(7) Boahene, P. E.; Soni, K. K.; Dalai, A. K.; Adjaye, J. Catal. Today 2013, 207, 101−111. (8) Rodríguez, M. A.; Elizalde, I.; Ancheyta, J. Fuel 2012, 100, 91−99. (9) Kwak, S.; Longstaff, D. C.; Deo, M. D.; Hanson, F. V. In Eastern Oil Shale Symposium; Institute for Mining & Minerals Research, University of Kentucky; 1992; pp 208−15. (10) Ma, X. L.; Sakanishi, K.; Mochida, I. Ind. Eng. Chem. Res. 1994, 33, 218−222. (11) Mejdell, T.; Myrstad, R.; Morud, J.; Rosvoll, J. S.; Steiner, P.; Blckkan, E. A. In Studies in Surface Science and Catalysis 133; Fromen, G. F., Wapgh, K. C., Eds.; Elsevier Science B.V., 2001. (12) Dai, F.; Gao, M. J.; Li, C. S.; Xiang, S. G.; Zhang, S. J. Energy Fuels 2011, 25, 4878−4885. (13) Tang, X.; Li, S. Y.; Yue, C. T.; He, J. L.; Hou, J. L. Oil Shale 2013, 30, 517−535. (14) Song, C. S.; Ma, X. L. Appl. Catal., B 2003, 41, 207−238. (15) Song, C. S.; Ma, X. L. in: Hsu, C. S.; Robinson, P. R. Practical Advances in Petroleum Processing; Springer, 2006; volume 1, pp 317− 372. (16) Ho, T. C. Appl. Catal., A 2003, 244, 115−128. (17) Li, N.; Ma, X. L.; Zha, Q. F.; Song, C. S. Energy Fuels 2010, 24, 5539−5547. (18) Zeuthen, P.; Knudsen, K. G.; Whitehurst, D. D. Catal. Today 2001, 65, 307−314. (19) Turaga, U. T.; Ma, X. L.; Song, C. S. Catal. Today 2003, 86, 265−275. (20) Ho, T. C. Catal. Today 2004, 98, 3−18. (21) Ho, T. C.; Markley, G. E. Appl. Catal., A 2004, 267, 245−265. (22) Girgis, M. J.; Gates, B. C. Ind. Eng. Chem. Res. 1991, 30, 2021− 2058. (23) Ma, X. L.; Sun, L.; Song, C. S. Catal. Today 2002, 77, 107−116. (24) Barman, B. N.; Cebolla, V. L.; Membrado, L. Crit. Rev. Anal. Chem. 2000, 30, 75−120. (25) Rabarihoela-Rakotovao, V.; Diehl, F.; Brunet, S. Catal. Lett. 2009, 129, 50−60.

NOMENCLATURE Ai = peak area corresponding to sulfur compound i or group i Atotal = areas of the total peaks in the GC-PFPD chromatograph Cj = concentration of inhibitor j CS = total sulfur concentration of the sample CAf = concentration of aromatics in the feedstock CN = nitrogen concentration CNf = nitrogen concentration in the feedstock CSi = sulfur concentration of group i CSio = initial sulfur concentration of group i D = density of feedstock kapp = apparent rate constant kapp,i = apparent first-order rate constant for group i ki = rate constant for group i KA = inhibition coefficient of aromatics KN = inhibition coefficient of nitrogen compounds Kj = inhibition coefficient of j n = reaction order for sulfur m = number of lumping groups r = reaction rate t = reaction time αi = coefficient of nitrogen concentration in the feedstock for group i βi = coefficient of the feedstock density for group i γi = constant term for group i θ = facile fraction of the organic sulfur

Abbreviations

BT = benzothiophene DBT = dibenzothiophene GC-PFPD = gas chromatography with pulsed flame photometric detector GC-SCD = gas chromatography with sulfur chemiluminescence detector GC-AED = gas chromatography with atomic emission detector HDS = hydrodesulfurization LHSV = liquid hourly space velocity LH model = Langmuir−Hinshelwood model ML model = multilump model PL model = power-law model RMSD = root-mean-square deviation SRGO = straight run gas oil 1-MDBT = 1-methyldibenzothiophene 2/3-MDBT = 2- or 3-methyldibenzothiophene 2/3,6-DMDBT = 2,6- or 3,6-dimethyldibenzothiophene 2,3,7-TMBT = 2,3,7-trimethylbenzothiophene 2,4,6-TMDBT = 2,4,6-trimethyldibenzothiophene 4-MDBT = 4-methyldibenzothiophene 4,6-DMDBT = 4,6-dimethyldibenzothiophene

■

REFERENCES

(1) Topsoe, H.; Clausen, B. S.; Massoth, F. E. Hydrotreating Catalysis: Science and Technology; Springer: Berlin, 1996. (2) Mederos, F. S.; Elizalde, I.; Ancheyta, J. Catal. Rev.: Sci. Eng. 2009, 51, 485−607. (3) Song, C. S.; Ma, X. L. Appl. Catal., B 2003, 41, 207−238. (4) Stanislaus, A.; Marafi, A.; Rana, M. S. Catal. Today 2010, 153, 1− 68. (5) Te, M.; Fairbridge, C.; Ring, Z. 2003, 21, 157−181. (6) Shinnar, R. An Evaluation of Present Status and Research Needs. ASC Symp. Ser. 1978, 72, 1. H

DOI: 10.1021/acs.energyfuels.6b02581 Energy Fuels XXXX, XXX, XXX−XXX