Kinetic Modeling of the Thermal Cracking of a Brazilian Vacuum

Apr 30, 2015 - Petrobras Research Center (CENPES), 21941-915 Rio de Janeiro, RJ, Brazil. ‡ Escola de Química, Universidade Federal do Rio de Janeir...
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Kinetic Modeling of the Thermal Cracking of a Brazilian Vacuum Residue Bruno M. Souza,† Leonardo Travalloni,*,‡ and Mônica A. P. da Silva‡ †

Petrobras Research Center (CENPES), 21941-915 Rio de Janeiro, RJ, Brazil Escola de Química, Universidade Federal do Rio de Janeiro, C.P. 68542, 21949-900 Rio de Janeiro, RJ, Brazil



ABSTRACT: The thermal cracking of a vacuum residue from a Brazilian refinery was studied in a continuous pilot plant. Reaction runs were carried out at 440−520 °C, 446−1825 kPa, and space times of 23−69 s. Reaction products were lumped into gas, naphtha, light gas oil (LGO), and heavy gas oil (HGO). The increase of the reaction pressure increased the residue conversion and the product yields. A power law kinetic modeling of the experimental data was performed, comprising first-order irreversible reactions. Two reaction schemes were evaluated: the first scheme consisted of four parallel reactions (one for each product lump), and the second scheme included the consecutive conversion of HGO in LGO. Kinetic parameters were estimated taking into account the experimental error. In the parallel-reaction scheme, almost all estimated parameters were statistically significant (except for the small gas formation at the highest pressure). The LGO and HGO formation reactions presented lower activation energies and higher rate constants. Inclusion of the consecutive reaction led to parameters without statistical significance for all reaction conditions. Furthermore, empirical correlations from the literature were fitted to the product yield data as a function of the process severity. The severity index was modified to include the effect of pressure explicitly, providing a slight improvement of the correlations.

1. INTRODUCTION The production of increasingly heavy oils has generated increasing amounts of residual petroleum fractions whose use is rather limited.1 This increase in residual fractions has intensified the use of processes for converting residues in lighter oil fractions, which have a much higher demand. To attend these changes, significant investment in refining conversion processes has become inevitable to make use of these heavy crude oils. Residue upgradation processes can be classified into two groups: carbon rejection and hydrogen addition.2 The hydrocracking process is an example of hydrogen addition, while the carbon rejection processes include thermal processes, such as visbreaking and delayed coking, which are responsible for over 60% of the processed residue volume.3 The severity of delayed coking is higher compared to visbreaking. This process is found in two different settings: furnace cracking, a high temperature and low residence time route, and soaking cracking, a low temperature and high residence time route.4 Several factors may support the choice by thermal processes, including its simple configuration and low cost compared to catalytic processes; therefore, thermal conversion reactions have been the subject of many studies. Thermal cracking reactions of residues occur through a freeradical chain mechanism, including free-radical formation via scission of C−C, C−H, and C−heteroatom bonds, hydrogen abstraction, β-scission, and dehydrogenation reactions.5 According to Gray and McCaffrey,6 the chain mechanism is responsible for reducing the overall activation energy of the process. In the composition of vacuum residues, C−S bonds are more susceptible to cracking in the range of 350−400 °C, while the cracking of C−C bonds is more favorable above 400 °C.7 Because the sulfur present in heavier oil fractions has a lower © 2015 American Chemical Society

dissociation energy, it plays a role as an initiator of free-radical chain reactions.8 Other properties of residual fractions, such as the carbon residue and the contents of saturates, aromatics, asphaltenes, and metals, may also influence the activation energy of the thermal cracking process, which can vary in the range of 58−326 kJ/mol.3 Vacuum residues are viscous semi-solids, containing a solid phase (asphaltenes, soluble in toluene) dispersed in a liquid phase (maltenes, soluble in toluene and pentane).6 Thermal cracking reactions of residues undergo an initial period in which there is only formation of lower molecular weight molecules. The lighter products vaporize, reducing the amount of maltenes and increasing the concentration of asphaltenes in the emulsion. When the concentration of asphaltenes reaches a critical limit (coke-induction period), a new phase is generated (mesophase) and formation of coke (insoluble in toluene) begins. For paraffins, two main reactions take place: homolytic scission of C−C bonds and linking to aromatic and naphthenic rings.9 Asphaltenes react predominantly through dealkylation, condensation, and precipitation; condensation reactions are responsible for the coke formation.1 Global kinetic parameters can be obtained for a thermal cracking process with a basis on the overall residue conversion.10,11 However, a more detailed kinetic study of this process is a complex task, because of the rather large number of components in the residue, in the order of 105− 106,12 and the great diversity of reactions involved. Therefore, it is indispensable to reduce the observation universe by lumping the major compounds present in the feed and reaction Received: February 23, 2015 Revised: April 28, 2015 Published: April 30, 2015 3024

DOI: 10.1021/acs.energyfuels.5b00412 Energy Fuels 2015, 29, 3024−3031

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Figure 1. Schematic diagram of the thermal cracking pilot plant: (1) feedstock tank, (2) scale, (3) pump, (4) lead bath furnace, (5) coil, (6) electrical trace heater, (7) quench, (8) stripper, and (9) trap.

products5 based on either the carbon numbers or the boiling points of the comprised hydrocarbons. Most of the lumped kinetic models reported in the literature can be classified into parallel or parallel consecutive reaction models.3,13 In the parallel reaction models, all product lumps are formed only from the residue directly, while in the parallel consecutive reaction models, at least one pyrolysis product is further cracked to a lighter lump. More complex models can include pseudo-lumps to represent reactive intermediates.14 The model reactions are generally assumed irreversible and first-order,3,15 although it is known that reaction rates deviate from first-order behavior after the induction period.16,17 Del Bianco et al.18 obtained a good fitting of residue thermal cracking data with a simple parallel consecutive reaction model based on three product lumps: distillate, coke, and a reactive intermediate to coke formation. Krishna et al.11 reported that consecutive cracking reactions of the heaviest products occurred for residue conversions above 7%. Kataria et al.3 developed a five-lump model for the visbreaking of vacuum residues and asphalts, comprising the complete set of parallel consecutive reactions and verified that the feeds underwent mainly parallel reactions, with only one significant consecutive reaction, namely, the cracking of the heaviest product lump. AlHumaidan et al.19 reported that the Eureka process for thermal cracking of vacuum residues is also dominated by parallel reactions. Other literature works have addressed the kinetic behavior of several reactions related to the upgradation of residues.20−23 The aim of this work is the kinetic modeling of the thermal cracking of a Brazilian refinery vacuum residue based on experimental data from a continuous furnace cracking pilot plant. Reaction runs were carried out in different temperatures, pressures, and space times. Reactions that take place in the visbreaking process and in the furnace of delayed coking units were addressed in this study. Irreversible first-order kinetic models were evaluated, comprising a parallel and a parallel consecutive reaction scheme. Also, empirical correlations from the literature were fitted to the product yield data as a function

of the process severity, taking into account the effect of the reaction pressure in a simplified way.

2. EXPERIMENTAL SECTION Reaction runs were carried out in a continuous thermal cracking pilot plant (Figure 1) with a capacity of 3 kg/h, maximum temperature of 570 °C, and maximum pressure of 2500 kPa. The plant has a coil-type furnace jacketed with a lead bath, which is heated to the reaction temperature by an electrical trace heater. The coil is 3.8 mm in internal diameter and 1.6 m in length. The residue is pumped to the furnace from a feedstock tank placed on a scale that measures the flow rate. The furnace effluent is quenched in an air cooler; the products are separated in a stripper with level control; and the gas product follows to another vessel, where the heavier components are condensed. The gas volume is measured by a wet gas meter. A nitrogen makeup is required in the stripper to favor the liquid−gas separation and the pressure control. The plant was fed with a refinery vacuum residue from Marlim crude oil, collected at different times. All residue samples were characterized as for their density (ASTM D70), viscosity (ASTM D4402), sulfur content (ASTM D2622), nitrogen content (UOP 384), asphaltene content (ASTM D6560), Ramsbotton carbon residue (ASTM D524), boiling point distribution (high-temperature gas chromatography; ASTM D7169), and contents of saturated hydrocarbons, aromatics, resins, and asphaltenes through the SARA analysis.24 The composition of gas samples was analyzed by gas chromatography. Mass balance errors were within 2%. A total of 52 reaction runs were carried out at 440−520 °C, 446− 1825 kPa, and space times of 23−69 s (on the basis of the volumetric flow rate of the feed at 20 °C). Although the visbreaking reactions occur in a narrower temperature range, higher temperatures were used to enable reactions that occur in the furnace of delayed coking units.4 Reaction products were classified into four lumps: gas (components up to C4), naphtha (boiling up to 150 °C), light gas oil (LGO, boiling at 150−400 °C), and heavy gas oil (HGO, boiling at 400−525 °C), besides the residue itself (boiling above 525 °C). The yield of each lump was defined as the ratio between the lump mass produced and the residue mass fed to the plant (corrected for the presence of a small amount of HGO in the feed itself). The selectivity of each lump was defined as the ratio between its yield and the residue conversion, i.e., the ratio between the lump mass produced and the residue mass converted in the process. 3025

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Figure 2. (A) Parallel reaction scheme and (B) parallel consecutive reaction scheme for the residue thermal cracking.

⎛ ⎛ T ⎞⎞ kj = exp⎜aj + bj⎜1 − R ⎟⎟ ⎝ ⎝ T ⎠⎠

3. KINETIC MODELING A power law kinetic modeling of the experimental data was performed, comprising first-order irreversible reactions. Foremost, a single rate expression was fitted to the global conversion of the residue in products. Next, two reaction schemes were evaluated (Figure 2): the first scheme consisted of four parallel reactions, one for each product lump, and the second scheme included the most important consecutive reaction, the conversion of the heaviest product (HGO) in the immediately lighter lump (LGO). According to Kataria et al.,3 the gas, naphtha, and LGO lumps are relatively stable; therefore, their secondary cracking were neglected in this work. The coil was modeled as a plug-flow reactor, and the mass balance equations for the lumps in the parallel reaction scheme were −

dwR = (k1 + k 2 + k 3 + k4)wR dτ

where T is the reaction temperature, TR is a reference temperature (480 °C, the mean temperature in the evaluated range), and aj and bj are the fitting parameters, related to the apparent activation energy (Ej) and the frequency factor (k0j) by Ej = RTR bj

k 0j = exp(aj + bj)

(2)

dw N = k 2wR dτ

(3)

dwLGO = k 3wR dτ

(4)

dwHGO = k4wR dτ

(5)

(6)

dwHGO = k4wR − k5wHGO dτ

(7)

(10)

4. EMPIRICAL CORRELATIONS Besides the power law kinetic modeling, empirical correlations from the literature3 were adapted to this work and fitted to the experimental data for all reaction conditions at once. Each correlation relates the yield of lump i (Yi) to the severity index of the process (SI)

while in the parallel consecutive reaction scheme, the last two equations were replaced by dwLGO = k 3wR + k5wHGO dτ

(9)

where R is the ideal gas constant. The kinetic parameters were estimated for each experimental pressure separately through a hybrid numerical routine, combining a heuristic optimization method (particle swarm) with a deterministic method (Gauss− Newton) for the minimization of the weighted least-squares objective function.25,26 The differential balance equations were numerically integrated with Dassl routine.27 The experimental error of the measured mass fraction of each lump was estimated from replicas obtained in some of the reaction conditions; for these conditions, an average relative variance was calculated and extrapolated for the other conditions. The confidence interval of each parameter was obtained for a confidence level of 95%.

(1)

dwG = k1wR dτ

(8)

YR = c exp(dSI)

(11)

YHGO = eSI f

(12)

YLGO = gSIh

(13)

where c, d, e, f, g, and h are fitting parameters, estimated through the procedure described in section 3. In the original correlations developed by Kataria et al.,3 parameters c, d, e, and g were explicit functions of some properties of the residue being cracked. In this work, however, these functions were omitted, because the feed of the thermal cracking plant was essentially the same in all experiments. Therefore, the values estimated in this work for c, d, e, and g are specific for the employed residue. In the work of Kataria et al.,3 the severity index was defined as

where wi is the mass fraction of lump i, kj is the rate constant of reaction j, and τ is the space time, calculated on the basis of the volumetric flow rate of the residue in the reaction conditions. The residue density at the reaction conditions was estimated with the PETROX process simulator of Petrobras, which indicated that it remains in the liquid phase for the evaluated ranges of the temperature and pressure. The Arrhenius equation for the reaction rate constants was reparameterized in a form that reduces the parametric correlation25 3026

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Energy & Fuels ⎛ E ⎛1 1 ⎞⎞ SI = τ exp⎜⎜ − R ⎜ − ⎟⎟⎟ TR ⎠⎠ ⎝ R ⎝T

(14)

where TR = 480 °C, τ is the residence time of the reaction mixture in the furnace, and ER is the reference activation energy, whose adopted value was 110 kJ/mol, the mean activation energy estimated in this work for the global residue conversion, in accordance with the literature.28,29 Because the effect of the pressure on thermal cracking reactions is seldom addressed in the literature, the previous severity index does not include the reaction pressure explicitly, but, this variable indirectly affects the severity index through the residence time.30 A rigorous calculation of the residence time demands a complex simulation of two-phase flow in a reacting system.31 In this work, however, such complexity was avoided and the space time defined in section 3 was used instead of the residence time. As a consequence, the effect of the reaction pressure on SI was neglected.To make up for this limitation, the severity index was modified (SI*) by the inclusion of a pressure factor based on the formulation of Van Camp et al.29 n ⎛ E ⎛1 1 ⎞⎞⎛ P ⎞ SI* = τ exp⎜⎜ − R ⎜ − ⎟⎟⎟⎜ ⎟ TR ⎠⎠⎝ PR ⎠ ⎝ R ⎝T

Figure 3. Boiling point distribution of the residue. IBP is the initial boiling point, and FBP is the final boiling point (obtained for vaporized amounts of 75−85%).

(15)

where P is the reaction pressure, PR is the reference pressure (1136 kPa, the mean pressure in the evaluated range), and n is a fitting parameter. The empirical correlations, eqs 11−13, were fitted to the experimental data of this work using both definitions of severity index, eqs 14 and 15.

Figure 4. Residue conversion as a function of the temperature and pressure (for space time of 46 s).

5. RESULTS AND DISCUSSION Table 1 presents properties of the vacuum residue fed to the thermal cracking pilot plant, and Figure 3 shows its boiling Table 1. Properties of the Vacuum Residue property density 20/4 °C density (deg API) viscosity at 120 °C (Pa s) sulfur content (wt %) nitrogen content (wt %) asphaltene content (wt %) Ramsbotton carbon residue (wt %) saturate content (wt %) aromatic content (wt %) resin content (wt %) asphaltene content (wt %) HGO content (wt %) residue content (wt %)

method ASTM D70 ASTM D4402 ASTM D1552 UOP 384 ASTM D6560 ASTM D524 SARA

ASTM D7169

value 1.02 7 2.6 1.0 1.03 8 19 4 41 32 24 6 94

± ± ± ± ± ± ± ± ± ± ± ± ±

0.02 3 0.6 0.2 0.08 1 2 1 4 7 8 2 2

Figure 5. Product yields as a function of the temperature (for 618 kPa and space time of 46 s).

point distribution. This residue is characterized by a relatively low sulfur content. There is a significant difference in the contents of asphaltenes measured by the ASTM D6560 and the SARA methods. While the ASTM D6560 method is specific for the quantification of asphaltenes, the SARA analysis is usually less accurate with respect to these components because it does not provide an efficient separation between large polycyclic aromatics and polar compounds,24 resulting in overestimated asphaltene contents. Figure 4 presents the effects of the temperature and pressure on the residue conversion, and Figures 5 and 6 present the effects of these reaction conditions on the product distribution

Figure 6. Product yields as a function of the pressure (for 520 °C and space time of 46 s).

of the thermal cracking process. In Figure 6, the yields of gas and naphtha were added because of their low formations. 3027

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may be due to the combination of these lumps or experimental difficulties in the measurement of their low produced amounts, especially at high pressures. Table 2 presents the estimated parameters of the singlereaction model and the parallel reaction model (Figure 2) for each experimental pressure, including the corresponding rate constants at 480 °C. The parameters were statistically significant for almost all experimental conditions, except for the gas formation at the highest pressure, because of a very low gas yield in this condition. In general, the formations of gas and naphtha presented significantly higher activation energies and lower rate constants, indicating that these are slow reactions. Indeed, the rate constants for the formations of these lumps were at least 1 order of magnitude lower than those for LGO and HGO. Singh et al.33 also reported a high activation energy for the naphtha formation from a similar residue. Moreover, the parameters for the gas and naphtha formations presented the broader confidence intervals because of the low yields of these lighter lumps, so that the activation energies for these reactions cannot be distinguished. The activation energies for the formations of LGO and HGO were lower compared to the other lumps, which indicates a predominance of C−C bond breaking reactions of saturates and naphthenic aromatics over dealkylation reactions of asphaltenes and polar aromatics.3 Besides, these activation energies were close to each other and within the range usually found in the literature, of 100−280 kJ/mol.3,33 The same is true for the activation energy of the global residue conversion, because LGO and HGO are the main products of the thermal cracking process. However, the global activation energies are near the bottom of the literature range, which can be related to compositional differences between the residues used in different works; in particular, the high asphaltene content in the residue of this work is highlighted, because side chains appended to the asphaltene centers reduce the global activation energy of the process.11,16 Moreover, the low estimated activation energies may be due to a non-chemical effect of the temperature, namely, the variation of the phase distribution

Increasing either the temperature or the pressure leads to higher residue conversions and product yields, because of the increase in the process severity. Also, the pressure increase enhances the condensed fraction of the reaction mixture, rising its residence time and favoring the thermal cracking reactions.32 With respect to the product distribution, little effect of the reaction temperature was observed but the increase in pressure slightly shifted the process toward the LGO formation, suggesting an increase in the consecutive cracking of HGO at the highest pressures. Figure 7 shows the relationship between the yields of the product lumps and the residue conversion for all experimental

Figure 7. Relationship between the yields of the product lumps and the residue conversion.

conditions. Fairly linear profiles were observed for the formations of HGO and LGO. The slopes of these straight lines are the selectivities of the respective lumps, which are very close for HGO and LGO. Moreover, the linearity of these profiles indicates that the selectivities of HGO and LGO are independent of the reaction conditions. This behavior may have resulted from the low number of lumps in which the reaction products were classified, so that variations in product distribution may have been disguised through compensations within a same lump. For gas and naphtha, on the other hand, the profile in Figure 7 was poorly fitted by a straight line, which

Table 2. Estimated Kinetic Parameters of the Single-Reaction and Parallel Reaction Models pressure (kPa)

reaction

446

global 1 (gas) 2 (naphtha) 3 (LGO) 4 (HGO) global 1 (gas) 2 (naphtha) 3 (LGO) 4 (HGO) global 1 (gas) 2 (naphtha) 3 (LGO) 4 (HGO) global 1 (gas) 2 (naphtha) 3 (LGO) 4 (HGO)

618

1136

1825

a 2.3 −1.3 −3 1.7 1.5 2.57 −1.5 −2.7 1.9 1.74 2.82 −2 −3 1.8 2.2 2.73 −5 −3 2.1 2.0

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

b 0.1 0.8 1 0.1 0.1 0.08 0.3 0.4 0.1 0.08 0.06 1 1 0.1 0.1 0.08 20 1 0.1 0.2

19 27 61 15 22 18 35 48 19 20 16 55 71 10 17 17 86 63 19 15 3028

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

2 18 46 3 3 3 8 11 4 2 1 35 26 4 4 2 449 34 5 6

E (kJ/mol)

k480 °C (h−1)

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

13 110 290 17 17 16 49 69 22 13 7 216 165 24 24 12

9.93 0.28 0.03 5.22 4.63 13.05 0.22 0.07 6.45 5.71 16.82 0.14 0.06 6.03 8.94 15.34

396 ± 211 117 ± 34 96 ± 35

0.06 8.10 7.15

120 170 383 96 138 114 221 302 119 122 98 343 442 64 106 109

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Figure 8. Fitting of the parallel reaction model for (A) 446 kPa, (B) 618 kPa, (C) 1136 kPa, and (D) 1825 kPa. The insets are zoom views of the product mass fractions.

Table 3. Estimated Parameters of the Empirical Correlations, for Yi in wt % and τ in h eq 11

eq 12

eq 13

SI

c

d

e

f

g

h

n

eq 14 eq 15

99.1 ± 0.6 99.0 ± 0.6

−0.0034 ± 0.0002 −0.0038 ± 0.0002

0.4 ± 0.2 0.4 ± 0.2

0.7 ± 0.1 0.7 ± 0.1

0.2 ± 0.1 0.3 ± 0.1

0.9 ± 0.1 0.9 ± 0.1

− 0.28 ± 0.04

inside the reactor, which was not controlled.32 As the temperature is increased, reaction rates increase (chemical effect), but the vaporized fraction of the reaction mixture also increases (physical effect), reducing the mean residence time. This limits to some extent the overall increase in residue conversion, reducing the apparent activation energies. The rate constant of the gas formation decreased with the pressure increase, while for the LGO and HGO formations, the rate constants increased. This is due to the increase in the condensed fraction of the reaction mixture at higher pressures, which favors the cracking reactions, as discussed before, but leads to lower amounts of gas in the reactor effluent. Figure 8 shows the fitting of the parallel reaction model to the experimental data for each reaction pressure. Good fittings were obtained for all experimental conditions, and similar fittings were obtained for the single-reaction model (not shown). A higher dispersion of predicted values was observed for 1136 kPa, probably because of the increase in the condensed fraction of the reaction mixture at higher pressures, which favors consecutive reactions not taken into account in this model. On the other hand, for the highest pressure (1825 kPa), the predicted values was less disperse, but the number of experimental points acquired at this reaction pressure was the lowest compared to the other conditions. In the parallel consecutive reaction scheme (Figure 2), the parameters related to the conversion of HGO in LGO were not statistically significant for the evaluated experimental conditions. This indicates that there is no need to consider such reaction to correlate the data within the experimental error nor other consecutive reactions, which are less relevant, especially

the secondary formations of gas and naphtha, whose yields were small and subject to higher relative errors. According to the literature,3,32,34 the thermal cracking of HGO has a slower rate compared to the residue cracking reactions. Table 3 shows the parameters estimated for the empirical correlations adapted from Kataria et al.,3 eqs 11−13, using the original definition of the severity index, eq 14. All parameters were statistically significant for the yields of residue, HGO, and LGO, but correlations for gas and naphtha could not be determined, because of the low yields of these lumps. In comparison of the exponents f and h, it can be noted that the severity index has a higher influence on the LGO yield than on the HGO yield, which was also observed by Kataria et al.3 Figure 9 shows the fitting of these correlations to the experimental data. The best fit was obtained for the residue yield, because of the fact that the process severity affects the residue conversion directly. As for the LGO and HGO yields, the lower correlation coefficients can be ascribed to a small occurrence of secondary cracking reactions of these lumps.3,35 Anyway, a considerable dispersion of predicted values was observed for all three lumps. For this reason, the fitting of the correlations was repeated on the basis of the severity index modified by the pressure term, eq 15, and the results are also shown in Table 3. For the empirical correlations, eqs 11−13, based on the modified severity index, eq 15, the parameters were very close to the values obtained with the original severity index, eq 14. Figure 10 shows the fitting of the correlations based on the modified severity index. Inclusion of the pressure effect 3029

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Figure 9. Fitting of the empirical correlations, eqs 11−13, based on the original severity index, eq 14, for the yields of (A) residue, (B) HGO, and (C) LGO.

Figure 10. Fitting of the empirical correlations, eqs 11−13, based on the modified severity index, eq 15, for the yields of (A) residue, (B) HGO, and (C) LGO.

provided a slight improvement of the data fittings compared to those in Figure 9.

Empirical correlations from the literature were also fitted to the experimental data, relating the process severity to the yields of the major lumps present in the reactor effluent. The effect of the pressure on the severity index was neglected, but reasonable fittings were obtained. The inclusion of the pressure effect through an explicit term in the severity index resulted in a slight improvement in the performance of these correlations.

6. CONCLUSION The main product lumps of the vacuum residue thermal cracking were LGO and HGO for all reaction conditions. The increase of the pressure increased the residue conversion and the product yields, slightly favoring the LGO formation and suggesting that the consecutive cracking of HGO is more relevant at the highest evaluated pressures. Fairly linear relationships were observed between the residue conversion and the yields of LGO and HGO. In the first-order kinetic modeling of the experimental data, the parallel reaction model provided better fittings for the lower pressures. This is probably because high pressures lead to high condensed fractions of the reaction mixture, increasing the residence time and favoring consecutive reactions not considered in this model. The estimated activation energies for the formations of LGO and HGO were close to the lowest literature values, which can be due to the high asphaltene content in the residue of this work and the uncontrolled phase distribution inside the reactor. The inclusion of a consecutive reaction in the model, the conversion of HGO in LGO, resulted in parameters with no statistical significance, suggesting that consecutive reactions were of little relevance compared to the primary cracking of the residue, for most of the experimental conditions.



AUTHOR INFORMATION

Corresponding Author

*Telephone: +55-21-3938-7606. Fax: +55-21-3938-7567. Email: [email protected]. Notes

The authors declare no competing financial interest.



REFERENCES

(1) Joshi, J. B.; Pandit, A. B.; Kataria, K. L.; Kulkarni, R. P.; Sawarkar, A. N.; Tandon, D.; Ram, Y.; Kumar, M. M. Ind. Eng. Chem. Res. 2008, 47, 8960−8988. (2) Furimsky, E. Stud. Surf. Sci. Catal. 2007, 169, 1−387. (3) Kataria, K. L.; Kulkarni, R. P.; Pandit, A. B.; Joshi, J. B.; Kumar, M. Ind. Eng. Chem. Res. 2004, 43, 1373−1387. (4) Feintuch, H. M.; Negin, K. M.; Van Tine, F. M. In Handbook of Petroleum Refining Processes; Meyers, R. A., Ed.; McGraw-Hill: New York, 2004; pp 12.33−12.102. (5) Bozzano, G.; Dente, M. Comput.-Aided Chem. Eng. 2005, 20, 529−534.

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DOI: 10.1021/acs.energyfuels.5b00412 Energy Fuels 2015, 29, 3024−3031

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Energy & Fuels (6) Gray, R. M.; McCaffrey, W. C. Energy Fuels 2002, 16, 756−766. (7) Alvarez, E.; Marroquín, G.; Trejo, F.; Centeno, G.; Ancheyta, J.; Díaz, J. A. I. Fuel 2011, 90, 3602−3607. (8) Rahmani, S.; McCaffrey, W.; Gray, M. R. Energy Fuels 2002, 16, 148−154. (9) Voge, H. H.; Good, G. M. J. Am. Chem. Soc. 1949, 71, 593−597. (10) Al-Soufi, H. H.; Savaya, Z. F.; Mohammed, H. K.; Al-Azawi, I. A. Fuel 1988, 67, 1714−1715. (11) Krishna, R.; Kuchhal, Y. K.; Sarna, G. S.; Singh, I. D. Fuel 1988, 67, 379−383. (12) Rahimi, P. M.; Gentzis, T. In Practical Advances in Petroleum Processing; Hsu, C. S., Robinson, P. R., Eds.; Springer: Berlin, Germany, 2006; Vol. 2, pp 149−186. (13) Sadighi, S.; Mohaddecy, S. R. S. J. Pet. Gas Eng. 2013, 4, 81−90. (14) Yang, J.; Chen, D.; Shen, G.; Xiao, J.; Yang, C. Energy Fuels 2012, 26, 3626−3633. (15) Wiehe, I. A. Ind. Eng. Chem. Res. 1993, 32, 2447−2454. (16) Benito, A. M.; Martínez, M. T.; Fernández, I.; Miranda, J. L. Fuel 1995, 74, 922−927. (17) Jia, N.; Moore, R. G.; Mehta, S. A.; Ursenbach, M. G. Fuel 2009, 88, 1376−1382. (18) Del Bianco, A.; Panariti, N.; Anelli, M.; Beltrame, P. L.; Carniti, P. Fuel 1993, 72, 75−80. (19) AlHumaidan, F.; Lababidi, H. M. S.; Al-Rabiah, H. Fuel 2013, 103, 923−931. (20) Ramírez, S.; Ancheyta, J.; Centeno, G.; Marroquín, G. Fuel 2011, 90, 3571−3576. (21) Ramírez, S.; Martínez, J.; Ancheyta, J. Fuel 2013, 110, 83−88. (22) Martínez, J.; Ancheyta, J. Fuel 2014, 138, 27−36. (23) Trejo, F.; Ancheyta, J.; Noyola, A.; Sámano, V. Pet. Sci. Technol. 2014, 32, 2592−2598. (24) Radke, M.; Willsch, H.; Welte, D. H. Anal. Chem. 1980, 52, 406−411. (25) Schwaab, M.; Pinto, J. C. Chem. Eng. Sci. 2008, 63, 4631−4635. (26) Schwaab, M.; Biscaia, E. C., Jr.; Monteiro, J. L.; Pinto, J. C. Chem. Eng. Sci. 2008, 63, 1542−1552. (27) Petzold, L. R. A description of DASSL: A differential/algebraic system solver. IMACS Transactions on Scientific Computation; IMACS: New Brunswick, NJ, 1983; pp 65−68. (28) Davis, H. G.; Farrel, T. J. Ind. Eng. Chem. Process Des. Dev. 1973, 12, 171−181. (29) Van Camp, C. E.; Van Damme, P. S.; Willems, P. J.; Clymans, P. J.; Froment, G. F. Ind. Eng. Chem. Process Des. Dev. 1985, 24, 561−570. (30) Varfolomeev, D. F.; Stekhun, A. I.; Fedotov, V. E. Chem. Technol. Fuels Oils 1982, 18, 621−624. (31) Mateus, F. A. D.; Gualdrón, J. A. C. CT&F, Cienc., Tecnol. Futuro 2010, 4, 89−100. (32) Takatsuka, T.; Kajiyama, R.; Hashimoto, H. J. Chem. Eng. Jpn. 1989, 22, 304−310. (33) Singh, J.; Kumar, M. M.; Saxena, A. K.; Kumar, S. Chem. Eng. J. 2005, 108, 239−248. (34) Yasar, M.; Trauth, D. M.; Klein, M. T. Energy Fuels 2001, 15, 504−509. (35) Xiao, J.; Wang, L.; Chen, Q.; Wand, D. Pet. Sci. Technol. 2002, 20, 605−612.

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DOI: 10.1021/acs.energyfuels.5b00412 Energy Fuels 2015, 29, 3024−3031