Kinetic modelling of thermal cracking of petroleum residues: A critique

Fuel Processing Technology 94 (2012) 131–144

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Review

Kinetic modelling of thermal cracking of petroleum residues: A critique Jasvinder Singh a,⁎, Surendra Kumar b, Madhukar O. Garg a a b

Indian Institute of Petroleum, Dehradun 248005, Uttarkhand, India Indian Institute of Technology Roorkee, Roorkee 247667, Uttarkhand, India

a r t i c l e

i n f o

Article history: Received 20 May 2011 Received in revised form 18 October 2011 Accepted 28 October 2011 Available online 1 December 2011 Keywords: Visbreaking Pyrolysis Residues Kinetics Modelling

a b s t r a c t Several models have been reported in the literature for thermal cracking of petroleum residues. Most of these models are either highly empirical or first principles based models requiring detailed analysis of the feedstock. The present paper is an attempt to put forward a critical appraisal of various published models. The authors have also generated the experimental data on a batch reactor for different feedstocks, and multi-lump parameter models (reported elsewhere) have been developed with the generated data. Three different models for prediction of experimental yields in terms of gas and distillate fractions (i.e. kerosene, LGO and VGO) have been critically evaluated in the light of experimental data. © 2011 Elsevier B.V. All rights reserved.

Contents 1.

Introduction . . . . . . . . . . . . . . . . . . 1.1. Processes for residue upgradation . . . . . 1.1.1. Hydrogen based processes . . . . 1.1.2. Non-hydrogen based processes . . 2. Kinetic modeling of thermal cracking . . . . . . . 2.1. Lumping schemes . . . . . . . . . . . . 2.2. Models with two to five lumps . . . . . . 2.3. Complex models with more than five lumps 3. Critical evaluation of three latest models . . . . . 3.1. Model evaluation . . . . . . . . . . . . . 4. Estimation of yields . . . . . . . . . . . . . . . 5. Four lump model . . . . . . . . . . . . . . . . 6. Further directions . . . . . . . . . . . . . . . . 7. Conclusions . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction In the present day scenario the availability of more heavy crude oils has resulted in the increased production of atmospheric and vacuum residues and simultaneous decrease in light and middle distillate fractions. In recent years an increased demand of light and middle

⁎ Corresponding author. Tel.: + 91 135 2525784; fax: + 91 135 2660202. E-mail address: [email protected] (J. Singh). 0378-3820/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2011.10.023

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131 132 132 132 132 133 133 139 140 140 141 141 142 143 144

distillates has resulted in the increased popularity of processes for upgradation of these heavy fractions to more useful lighter products. The presence of large amount heavy molecules as well as high metal contents of residual feedstocks renders these unsuitable for processing via catalytic routes. The high molecular weight compounds e.g. resin and asphaltenes contribute in coke formation and metals work as catalyst poison. In view of these factors, thermal cracking is the preferred route for upgradation of the crude oil residues (bottom of the barrel) as well as for other heavy oils e.g. shale oils, tar sands and heavy crude oils.

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J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

1.1. Processes for residue upgradation Hus [1] mentioned in his review paper that the cracking of a hydrocarbon compound could be represented by monomolecular reaction mechanism and observed that the velocity constant depends upon the following: • Type of hydrocarbon to be cracked — Cracking reactivity decreases in the following order: Normal Hydrocarbons, Iso-paraffins, Cycloparaffins, Aromatics, Aromatic naphthenes, and Poly-nuclear aromatics. • Molecular weight or boiling points of the hydrocarbons — higher the hydrocarbon, larger the molecule. • Temperature at which the reaction occurs. • Pressure at which cracking takes place. For the pressures commonly used in visbreaking process, the influence of the pressure may be neglected except for its effect on the residence time. The essential requirement of the residual upgradation is to improve its properties, especially H/C ratio. This can be achieved by adding hydrogen or by rejecting carbon. Thermal upgradation processes may be classified in following two categories namely hydrogen based processes and non-hydrogen based processes. 1.1.1. Hydrogen based processes These processes use hydrogen to upgrade hydrogen deficient heavy molecules under suitable processing conditions. The examples of hydrogen based thermal cracking processes are Hydrovisbreaking, Donor solvent visbreaking and DYNA cracking. The essential details about these processes are described in the following subsections. 1.1.1.1. Hydrovisbreaking. This is a thermal conversion process [2,3], operated in the presence of hydrogen. It is based on the concept that the solubility of hydrogen in oil increases with the increase in pressure and temperature and part of it takes part in reaction to yield products of higher stability. 1.1.1.2. Donor solvent visbreaking. This process is the outcome of further improvement of hydrovisbreaking process. In this process the addition of a hydrogen donor solvent ensures comparatively more conversion than the one obtained in the presence of hydrogen alone. This process seems to be ideal for conversion of heavy oils from conventional crude oils [4,5]. 1.1.1.3. DYNA cracking. It is a thermal hydrocracking process that converts a wide variety of heavy oil feedstocks into distillate products and high heating value fuel gas in the presence of internally generated Hydrogen [6]. The reactor in the process consists of three different sections, i.e. thermal hydrocracking, stripping, and coke gasification. Operating conditions can be varied to provide product slate flexibility. 1.1.2. Non-hydrogen based processes As the name implies, these processes facilitate hydrocarbon molecular transformation by heat and catalyst or heat transfer alone, in the absence of hydrogen or hydrogen donor solvents. The thermal conversion processes in this category are carbon rejection processes, which have the flexibility to operate with all type of heavy feedstocks for their upgradation into light distillates/ liquid products. In fact these processes have staged a comeback in the refinery industry to process heavy residues for the production of feedstocks for secondary conversion processes. Brief details of the important processes in this category are given below, while the kinetic modeling of visbreaking has been reviewed separately thereafter. Delayed coking and flexicoking [7] are the two commercially applied residue upgradation processes. In delayed coking, the coke

formed remains in the coke drum and is removed by hydraulic water jets, while vapour leaves the top of the coke drum. Depending upon the feedstock quality (mainly metals and sulfur contents) the green coke produced may either be used as fuel or after calcination as metallurgical grade coke in aluminum industry. Flexicoking is generally based on high sulfur feedstock, and is currently practiced with gasification process to obtain better yields than delayed coking; it drastically reduces the coke yield. Principal process disadvantages include the disposal of the drag coke to specialty markets. A modification of fluid cracking reactor technology is Asphalt Resid Treating (ART) [8,9]. It is used to demetallize and decarbonize heavy oils such as heavy crudes, atmospheric residues, tar sands and bitumens and like by contacting the feedstocks with ARTCAT (trade-mark) fluidizable contact material in a dilute phase riser environment. During the process, over 95% of the metals are removed, and products are essentially free of asphaltenes. Here an inert solid is circulated instead of FCC catalyst. Oil, metal and coke precursors deposit on the solid surface while a substantial proportion of the feed is vaporized and cracked. Visbreaking is another popular thermal conversion process widely used for residue upgradation [10–12]. It is a comparatively mild thermal cracking process mainly used to lower the viscosity and pour point of the residual oil, and has shown a revival worldwide[1] as a convenient and cheap tool for residue upgradation. This process makes an important contribution in reducing the amount of cutter stocks (i.e. straight run gas oil) used in adjusting the viscosity of residues for their conversion to fuel oils in order to meet standard specifications. The most recent problem for refiners is the development of process(es), which may reduce the production of heavier fuel oil and increase the production of lighter distillates [13]. In spite of the thermal cracking being quite an old process, the detailed kinetic modelling of this process initially did not get due attention in literature. The models reported in literature were either developed with pilot scale or industrial data [14,15] or based on detailed structural analysis [28] of the feedstock, normally not carried out in a refinery in routine. Most of these models did not present detailed analysis of in terms of distillate fraction. Some earlier attempts were made to develop the kinetic models based on a very simple description with a single first order reaction [1,12,17]. Later attempts were made using more comprehensive reaction schemes using up to sixteen pseudo-components [15]. Dente et al. [18] have critically examined the pyrolysis models for various hydrocarbon fractions in gas as well as liquid phase. They feel that further theoretical as well as experimental analysis will lead to still better understanding of detailed chemistry of pyrolysis and fouling processes. Recently some studies have been reported on non isothermal kinetics of heavy hydrocarbon pyrolysis [19–21]. This paper presents a critical evaluation of the available models for kinetic modeling of thermal cracking process. The models reported in literature have been reviewed in the light of various parameters e.g. no. of lumps, coke formation, nature of feedstock, and data generation method. Finally three most recent models have been evaluated with the lab data generated by authors and reported earlier [22].

2. Kinetic modeling of thermal cracking The available kinetic models can be classified into two major parts on the basis of their complexity. In the present analysis we have taken the models with one to five lumps as simple models, and the models with more than five lumps or models requiring more computational effort have been classified as complex models. A number of models available in literature have been reviewed and critically examined. Table 1 list some significant kinetics and modelling studies reported in the literature. A brief description of the reviewed models has been presented in following sub sections.

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

2.1. Lumping schemes A number of lumped parameter models have been proposed. Some studies are reported with one feed and one product lump [14,23,24] whereas number of lumps has been reported up to sixteen [15]. The criteria for lumping have been either based upon statistical information [16,25], characteristic information [26], or pseudocomponents [27–30]. The pseudo-component lumping schemes suggested by Del Bianco et al. [27] do not include distillate as separated lumps of gasoline and gas oil fractions, and therefore, it is not suitable for the design and optimization of a mild thermal cracking process to maximize distillates. The coke lump has been considered, which makes it suitable for describing more severe thermal cracking only. Molecular modeling of heavy oil pyrolysis has been reported by Yan et al. [31]. They feel that molecular modeling of petroleum processes may be more useful for producing refinery products requiring tighter specifications. Recently some authors have reported models with two gas lumps based on flue gas and LPG [32,33]. 2.2. Models with two to five lumps The simplest form of reported kinetic model is one reactant and one product lump [14,23,24]. Although all of these models describe cracking of residue as a first order reaction, the studies are different in terms of feedstocks, experimental setup, and experimental conditions. Di Carlo and Janice [14] have reported two lumps model based on data obtained on a continuous pilot plant using three different Atmospheric residue feedstocks. In addition to the kinetic parameters, the authors also discussed the stability of the cracked residue. Influence of the operating parameters has been discussed on the cracking behavior of three feedstocks. The reactivity of the studied feedstock has been discussed at low and high temperatures. The observed behavior has been analyzed on the basis of characteristics of the feedstocks as well as reaction mechanism of the cracking reactions. The paper provides a reasonably good estimate of the kinetic parameters. Krishna et al. [24] studied Aghajhari long residue on a continuous bench scale coil type reactor unit. They have also analyzed the data assuming similar two lump model. The temperatures and flow rates studied were in the range of 427–500 °C and 2.04–2.91 lit/h respectively at a pressure of 17 kg/cm 2(g). The reaction was found to observe first order kinetics in terms of the conversion of IBP-150 °C cut, at low conversion range (b 7%). Beyond 7% conversion the yield pattern showed marked deviation, indicating higher order kinetics. Such deviation on the order of reaction at severe conditions has also been later reported by Martinez et al. [34] in their study on decomposition of an asphaltenic residue. The additional study reported in Krishna et al. [24] model is effect of feedstock conversion on viscosity reduction, which is not studied by Di Carlo and Janice [14] and Al Soufi et al. [23]. Further to two-lump model are three lump models. Del Bianco et al. [27] presented the kinetic analysis of thermal cracking reaction taking three lumps, with one Intermediate I. They conducted lab scale studies on Belayam crude in a 30 ml capacity stainless steel autoclave at temperatures 410, 430, 450 and 470 °C for reaction times up to 120 min. The concentration of three pseudo components namely vacuum residue, distillate and coke were observed and data from the experiments have been used for defining a three lumps, three parameter model. Distillate conversion has been assumed to be a first order reaction whereas coke formation seems to be the consequence of secondary reactions, in particular asphaltenes. A study of structural changes of asphaltenes as a function of severity was also performed and the variation in average molecular parameters with conversion were correlated. The advantage of their study is inclusion of coke lump. The estimation of liquid product shall be more precise as compared to two lump models with no coke was accounted for separately.

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Martinez et al. [34] studied the kinetics of thermal decomposition of an asphaltenic residue, obtained from the synthetic crude by coal liquefaction. The kinetics of formation of oil + gas and coke (toluene insoluble) from the conversion of the asphaltenic fraction were determined. A three lump model is proposed, which considers parallel reaction for oil + gas and coke formation. Conversion data has been reported fitting second order kinetics throughout for asphaltene conversion and oil + gas and coke formation. The activation energies ranged from 63.14 to 86.13 kJ/mol, the highest being for asphaltene conversion. These values are comparatively less as compared to those reported by Del Bianco et al. [27]. They have also compared and found the obtained values are less than those reported in some earlier studies [35]. The highest asphaltene conversion (55.9%) was obtained at 475 °C. At short reaction times, the asphaltenic fraction converted to coke (~30%) was much less than that converted to oil (~70%) at the four temperatures used, whereas at long reaction times this trend was reversed, especially at 475 °C. Structural analysis shows that lower aromaticity and higher H/C and N/C ratios in the oil from products than from feed. The difference in activation energies obtained above, may be attributed to these structural characteristics. Yasar et al. [29] studied the effect of reaction environment on pathways and selectivity by experiments on resids and isolated asphaltenes from Arabian light and Arabian heavy feedstocks. The results were compared with previous experiments with Hondo and Maya feeds to determine the effect of these parameters. The experiments were conducted at temperatures of 400, 425 and 450 °C for holding times ranging from 20 to 180 min in a micro-batch reactor. Reaction products were recovered as gas, maltene, asphaltene and coke lumps. The goal of these experiments was to determine the effect of the variation among the four feeds and also to provide a comparison of asphaltene behavior when pyrolysed neat and in a resid environment. The comparison of relative kinetics and apparent activation energies yielded insight into the thermal reaction pathways, feedstock effects and asphaltene environment effect. At 400 and 425 °C, asphaltenes reacted predominately to coke. Isolated maltene pyrolysis indicated that the asphaltene and coke formed in series, i.e. Maltene → Asphaltene → Coke It was also concluded that resids with higher asphaltene content are more reactive than resids with low asphaltene content. The range of obtained activation energies are slightly higher than those reported by Martinez et al.[34], but in good agreement with those reported by Al Soufi et al. [23]. Recently, the authors have generated and published experimental data on a batch reactor (Singh et al., [36]) and also suggested a five lump model (Singh et al., [37]) and a four lump reduced model (Singh et al., [38]) for the prediction of the product yields. An important consideration in development of these models was to include the industrially important lumps, fixed on the basis of boiling ranges of the distillate fractions. It is believed that the inclusion of LGO, VGO and Kerosene fractions will improve the applicability of this model for estimation of operating conditions to maximize the yield of lower boiling distillate fractions. A five lump model has also been reported by Kataria et al., [39], taking similar lumps. A critical review of the reported models of less than five lumps reveals that these models have been developed around the experimental data obtained on feedstocks with wide range of asphaltene contents and isolated asphaltenes. The main purpose of most of these studies was to study the effect of nature of feed stock on the reaction kinetics. These models are simple to use and provide a reasonable estimate for preliminary evaluation of a feedstock in terms of obtained product. First order reaction kinetics has been assumed in most of these studies except few researchers (Krishna et al. [24] and Martinez et al. [34]) who have explained the kinetics of coke formation from maltene / oil fraction as more than first order. These

134

Table 1 Kinetic Models. S.N.

Authors

Feed Characteristics

Al-Soufi et al. (1988)

Atmospheric Residue from Iraqi crude Composition, wt % CCR 14.76 Asphaltene 9.50

2.

Castellanos et al. (1991)

Atmospheric Residue / Vacuum Residue (TBP available)

3.

Chakma and Islam (1990)

4.

Del Bianco et. al (1993a)

Bitumen feed stock. Composition, wt% Gas : 0.0 Coke: 0.0 Maltenes: 80.0 Asphaltenes: 15.3 Vacuum Residue (Belayam crude), Composition, wt% Asphaltene 18.6 Saturates 9.9 Aromatics 35.6 Resins 54,5

Continuous lab scale unit with 2 l /h feed capacity T = 435–480 °C Res. Time: Coil 67 s Soaker 297 s P = 7 bar Azcapotzalco Refinery (Mexico) coil visbreaker data taken. (Operating conditions not mentioned)

Proposed Model Atmospheric Residue

Conclusions / Remarks

→ Visb. Product k

E = 99.78 kJ/mol (Visbreaking) E = 252.6 – 378.9 kJ/mol (condensation and coking) • May be used for analysis of the effect of temperatue and residence time. • Successfully used for the design of new units and improvement of existing units.

All reactions are first order. Bitumen pre-heated at 400 °C is atomized • Continuity equation, Momentum equations (three dimensional) in cylindrical co-ordinate system, species mass balance, and k-ε as the droplets of the size 20–30 micron turbulence model constitutes model of a visbreaking vertical jet reactor. diameter in the form of a jet. • solved by using SIMPLE algorithm of Patankar (1981). Thermal cracking in a micro reactor, Temp Range: 410 – 470 °C; Residence time: up to 120 min. P = 10 kg/cm2

k1

E1 = 207.97 kJ/mol

D

E2 = 174.29 kJ/mol

VR

k2

E3 = 269.02 kJ/mol k3

I Temp., oC 410 430 450 470

• Validated the predicted and experimental viscosities. • May be modified to include thermal effects and chemical reaction. • Good agreements between calculated and experimental results. • Same model can be used to correlate variation of available model parameters of asphaltene conversion.

C

Rate constants (10 -2 min-1 ) k1 k2 1.19 0.91 3.36 2.17 8.95 4.93 22.58 10.71

k3 0.21 0.80 2.84 9.40

VR : Vacuum residue; D : Distillates; I : Intermediate Product for Coke formation; C : Coke, 5(a). Dente et al. (1993)

Used experimental data from Di Carlos et al. (1992) ; Al-Soufi et al. (1988) ; and Krishna et al. (1988)

5(b). Dente et al. (1995 and 1997)

Composition, wt% Asphaltenes 5–17 CCR 17–23 Sulphur 3.0–3.8

Coil Inlet Temp. 326 °C Coil Outlet Temp. 450 – 468 °C Res. Time: 24–26 min. (with soaker) 9 min (without soaker)

• Simplified property predictions equations formed by grouping similar class of compounds. A statistical function defined such as the fraction of pseudo component formed is given by fi = Aclass exp (− kclass ni), where the index kclass depends upon the class of component (paraffin, aromatic etc.) ; ni is number of C atoms, and Aclass is determined by re-normalisation on the basis of pertaining class. Same as above.

• Good agreements between calculated and experimental results.

• Compared predicted properties with their own results. • Good agreement.

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

1.

Process / Experimental Conditions

6.

7.

8.

10.

Atmospheric Residue: ROSPO DI MARE nC5 insol. 40.76 Saturates 8.67 Aromatics (N + P) 50.57 BELAYAM nC5 insol. 13.67 Saturates 25.75 Aromatics (N + P) 60.58 ES-SIDER nC5 insol. 4.15 Saturates 48.03 Aromatics (N + P) 47.82 (All compositions in wt. %) Filho and Vacuum residue feedstock, Sugaya (2001) characterised by correlations given by other authors. Krishna et al. Aghazari long residue (1988) CCR 7.9 n-C5 insol. 5.66 (All compositions in wt. %) Martinez et al. Asphaltenic Residue from Synthetic (1997) Crude. Composition, wt% Maltenes 19.6 Asphaltenes 65.5 Coke 15.0

Xiao et al. (2002)

Heavy Oil

Atm. Residue

Pilot Plant Experiments T = 480–530 °C P = 1 atm. Continuous flow bench scale reactor, flow rates 2.04 – 2.90 l /h T = 427–500 °C P = 17 bar Tubular SS Reactor Length = 40 cm ID = 1.3 cm T = 425–475 °C P = 1 atm.

A sixteen lump model proposed for predicting yields.

Lab Scale Microreactor T = 400–500 °C

11

Yasar et al. (2001)

Micro batch reactor T = 400 – 450 °C P = 5 atm.

Atmospheric Residue → Visb. Product k

Lumped parameter model proposed

• The feedstocks rich in asphaltenes, resins and polars show higher selectivity towards gas and lower selectivity towards gasoil as compared to light and paraffinic crudes. • Qualitatively correlated the difference between the aromatic carbon of asphaltes and aromatic carbon of maltenes in the feedstock with the conversion of residue.

• Global kinetic rate constants and their dependence on temperature obtained. E = 224.8 kJ/mol

• Both the parallel reactions from Asphaltene to oil + Gas and also to coke follow second order kinetics. • Estimated kinetic constants and activation energies.

A twelve lump model is proposed using temperature cuts as a lump with first order • Good agreement with experimental (conversion) data at low temperatures. kinetics. • E = 268 kJ/mol (cracking reaction) = 531 kJ/mol (condensation reaction)

A four lump model

• Values of kinetic constants and activation energies are estimated.

Reaction from Maltene to asphaltene is condensation reaction. (continued on next page)

135

Resids and asphaltenes from: Hondo Asphaltenes 23% Saturates 13% Aromatics 43% Resins 21% Maya Asphaltenes 12% Saturates 26% Aromatics 51% Resins 11% (All compositions in wt. %) Arabian Light Asphaltenes 6% Saturates 30% Aromatics 45% Resins 19% Arabian Heavy Asphaltenes 15% Saturates 30% Aromatics 44% Resins 16% (All compositions in wt. %)

k → Visb. product

Experimental Data on a Continuous Pilot Plant T = 460–470 °C;

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

9.

Di Carlo and Janis (1992)

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Table 1 (continued) Authors

Feed Characteristics

Process / Experimental Conditions

Proposed Model

Conclusions / Remarks

12.

Zhou et al. (1999)

Daqing Vacuum residue Saturates 41.47 Aromatics 23.30 Resins 35.23 Asphaltenes 0.01 Guanshu Vacuum residue Saturates 24.14 Aromatics 20.63 Resins 52.13 Asphaltenes 2.67 Liaohe Vacuum Residue Saturates 26.84 Aromatics 20.84 Resins 47.52 Asphaltenes 4.80 (All compositions in wt. %)

Lab scale reactor. T = 400–460 °C, P = 5 kg/cm2.

An eleven lumped model has been proposed with following lumps: S : Saturates; Al: Light aromatics; Ah: Heavy aromatics; Rl : Light resins; Rh: Heavy resins, B: Asphaltenes; G: Gas; L: Naphtha, V1 : Middle fraction1; V2: Middle fraction 2; C: Coke.

Values of kinetic constants and activation energies are estimated.

13.

Singh et al. (2005a)

(All compositions in wt. %)

Lab scale batch reactor of 400 ml capacity, T = 400 – 430 °C, P = 12 kg/cm2(g)

A five lump seven kinetic parameter model with following lumps

Values of kinetic constants are listed in Table 2(a)

North Gujarat Short Residue Saturates 10.30 Aromatics Naphthenic 58.71 Polar 29.20 Asphaltenes 1.85 Bombay High Short Residue Saturates 32.89 Aromatics Naphthenic 59.82 Polar 4.23 Asphaltenes 3.03 Mathura Refinery Visbreaker Feed Saturates 14.76 Aromatics Naphthenic 68.07 Polar 11.86 Asphaltenes 7.72 Haldia Refinery Asphalt Saturates 5.63 Aromatics Naphthenic 68.07 Polar 11.86 Asphaltenes10.15 (All compositions in wt. %)

F: Feedstock (500 °C +) G: Gas (C5-), GLN: Gasoline (IBP-150 °C), LGO: Light Gas Oil (150–350 °C), VGO: Vacuum Gas Oil (350–500 °C)

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

S.N.

14

Singh et al. (2005b)

Same as above

Same as above

A four lump four kinetic parameter mode l(VGO lump dropped from above)

Values of kinetic constants are listed in Table 2(d)

15

Kataria et al. (2004)

North Gujarat Short Residue

Lab scale batch reactor of 400 ml capacity, T = 400 – 430 °C,

A five lump seven kinetic parameter model with following lumps

Values of kinetic constants for four feedstocks are listed in Table 2(c). Values for other feedstocks have also been estimated by the authors.

P = 12 kg/cm2(g)

VR: Feedstock (500 °C +); Gas (C5-); Gasoline (IBP-150 °C); LGO: Light Gas Oil (150–350 °C); VGO: Vacuum Gas Oil (350–500 °C)

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

Saturates 10.30 Aromatics Naphthenic 58.71 Polar 29.20 Asphaltenes 1.85 Bombay High Short Residue Saturates 32.89 Aromatics Naphthenic 59.82 Polar 4.23 Asphaltenes 3.03 Arabian Mix Short Residue Saturates 15.00 Aromatics Naphthenic 66.60 Polar 6.74 Asphaltenes 6.50 Mathura Refinery Visbreaker Feed Saturates 14.76 Aromatics Naphthenic 68.07 Polar 11.86 Asphaltenes 7.72 Haldia Refinery Asphalt Saturates 5.63 Aromatics Naphthenic 68.07 Polar 11.86 Asphaltenes10.15 Arabian Mix Asphalt Saturates 5.12 Aromatics Naphthenic 66.05 Polar 17.81 Asphaltenes 9.61 (All compositions in wt. %)

(continued on next page)

137

138

S.N.

Authors

Feed Characteristics

Process / Experimental Conditions

Proposed Model

Conclusions / Remarks

16.

Mohaddecy and Sadighi (2011)

Mixture of VR and slop oil from vacuum tower,

Commercial soaker visbreaker.

A six lump, fourteen kinetic parameter model with following lumps

• Values of frequency factors and Activation energies have been determined for fifteen reactions • Reduced model has been presented with fourteen parameters

Sp. Gr. 1.006

Temperature:

S, wt% 3.19 Ni + Va 188 (ppm)

Inlet 325 °C Outlet 440 °C

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

Table 1 (continued)

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

authors also have observed first order kinetics for conversion to oil / maltene fraction. 2.3. Complex models with more than five lumps The models based on less than five pseudo lumps are quite useful for understanding the cracking behavior of the heavy feedstocks, but do not quantify effect of structural parameter or hydrocarbon type on the kinetic parameters. In an effort to overcome this shortfall, some authors have developed the kinetic models using more detailed structural analysis and lumping based on class of compound and boiling range of the constituent lumps. This sub section discusses few complex models developed using more detailed characteristics and having more than five lumps. A kinetic model has been proposed by Castellanos et al. [26] for the prediction of visbreaker yields. Their proposed model is based on the sets of feed and product hydrocarbon components characterized by their true boiling points, API gravity and molecular weight. According to this scheme, if n is the number of discrete pseudocomponents, and i equals the number of carbon atoms in the compound of given pseudo component; it follows that i can be any number between 1 and n, both inclusive. Any i-carbon atom component can have two degrees of saturation – Si and Oi. An S-component has a C/H ratio corresponding to the average degree of saturation of the feed components, and an O component has an equivalent structure, but with the unsaturation incorporated into the molecule in shape of two fewer hydrogen atoms than corresponding S component. Each Si component undergoes a first order reaction producing, through i-2 parallel reactions, the set of lighter S components from Si-2 to S1, with simultaneous production of the corresponding O components. For a system represented by n pseudo-components, there would be (n-2)(n-1)/2 such reactions. The main limitation of the proposed model is that it does not consider reactions of cracking, polymerization or condensation of O components to yield lighter diolefins or heavier hydrocarbons and coking reactions of certain asphaltenic fractions. Dente et al. [16,25] conducted a comprehensive study on the modelling of kinetics and reactor, following lumping methodology developed by them (Dente et al. [16]). They followed the approach of lumping real components into the pseudo-components. Initially they suggested a model containing 150 equivalent pseudo-components and 100 reactions. For classification of compounds of same class, a statistical distribution function was used, which is given below: f i ¼ Aclass expð−kclass :ni Þ

ð1Þ

where f is the fraction of a class of component, kclass depends on the class of component, viz. paraffins, olefins etc. Aclass is determined by re-normalization on the basis of the total amount of the pertaining class. Typical considerations adopted for kinetics into the model are as follows: • The chain initiation reactions and the β-Scission, i.e. radical decomposition of olefins and smaller radicals, is more or less strongly endothermic reaction and because of formation of two molecular bodies, have the following expression for kinetic constant.

kliq ¼ kgas exp −

# ΔSg;liq

R

! exp

# ΔHg;liq

RT

! ð2Þ

# # Where ΔSg, liq and ΔHg, liq are respectively the difference of the variation of entropy and enthalpy in forming the transition state from the reactants to be decomposed into the products.

139

• The Hydrogen abstraction and the substitution reactions in the liquid phase have practically the same constants of their equivalent into gas phase. • The “radicals’ recombination reactions” (chain terminations) are so exothermic to be limited by an oriented diffusion of the colliding radicals. The sterical factors for the re-combinations were considered practically to be the same as for the equivalent gas phase, and the liquid phase collision frequency is well established, so that the kinetic constants for the radicals’ recombinations rate could be deduced. The model was evaluated using data available in the literature for lab scale, pilot scale and commercial units [14,23,24,27] and the predicted results have been found in satisfactory agreement with the experimental data. Dente et al. [16] model therefore have two advantages over the one reported by Castellano et al. [26] First is the model overcomes the one major limitation of the latter [26] in respect of cracking, polymerization or condensation reactions to yield lighter di-olefins or heavier hydrocarbons and coking reactions. Secondly, its good agreement with the earlier data firms up the validity of its application and gives further strength to the model. The authors further extended their work to give a comprehensive model (Dente et al. [25]) to predict product amounts and their property predictions. The additive corrective contributions of the activation entropies and energies were applied to the previous ones. Chakma and Islam [40] reported modelling of visbreaking of bitumen in a jet reactor. In their experiment, bitumen preheated to about 400 °C was atomized into very fine droplets of size of 20–30 micron diameter through a nozzle at very high velocities. The heat input was through heating elements placed around the reactor wall. The effluents at the reactor exit are cooled down and separated. The flow and mass balance equations were formulated and solved by using SIMPLE algorithm given by Patankar [41]. Dawson et al. [42] has also obtained a patent on a viscosity reduction process, using this type of jet reactor. Being a theoretical model the accuracy of estimations would be solely dependent on the accuracy of numerical solution of the governing equations. Zhou et al. [28] presented a kinetic model for thermal conversion of vacuum residues. An eleven lump kinetic model was presented taking six feedstocks and five product lumps. The feedstock lumps were defined as vacuum residue (VR), Saturates(S), Light Aromatics (Al), Heavy Aromatics (Ah), Light Resins (Rl), Heavy resins (Rh) and Asphaltenes. The product Lumps were taken as Gas (G), Naphtha (L), Middle fraction-1 (V1), Middle fraction-2 (V2) and Coke (C). Parameters for the kinetic model were determined from the experimental data on thermal cracking of three different vacuum residues, namely Daqing, Guanshu and Liaohe vacuum residues. Filho and Sugaya [15] presented a model for the development of a computer-aided tool for the simulation of heavy oil thermal cracking process. A dual plug flow reactor (DPFR) representation was proposed for the light pyrolysis of petroleum distillate residue in a coil type reactor. The proposed reactor model (DPFR) consists of two parallel plug flows each for vapour and liquid, traveling at different speeds in a coil. Reaction is assumed to be rate-controlling process with equilibrium between the phases. Because of the pyrolysis reactions and pressure drop, vaporization takes place continuously along the coil so there is a decrease in the liquid holdup. The derived model uses a heuristic lumping approach based on the pilot plant data. The resulting pseudo kinetic scheme presents a certain feed independence within the range of stocks available for the study. An industrial case study was explored to provide insight into the problem of reconciling the kinetics of pyrolysis and carbonization for the upgradation of distillation residues. The kinetics of reaction has been modeled assuming first order kinetics and 16-lump scheme.

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J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

The kinetic parameters have been determined using pilot plant data available elsewhere [43]. The findings of the study indicate that the coil reactor modelling at low conversions can be performed with a single set of Arrhenius parameters within a limited feedstock range. A case study on delayed coker, where pyrolysis is performed in two sequential units in order to optimize the delicate balance between selectivity and coke drum cycle time, caused by the parallelism between radical decomposition and condensation reactions. The results suggest that the changes responsible for the difference takes place mainly in the downstream unit, in spite of the fact that substantial conversion can occur in the mainstream unit. Wang and Anthony [44] extended the study by Martinez et al. [34]. They found that the Martinez et al. [34] model could not explain the behavior of asphaltene cracking at higher temperatures. So they re-examined the data and gave the expressions for oil + gas yield as a function of asphaltene conversion or residence time; these described the data well. Their proposed approach is not dependent on the assumed reaction orders of the cracking. A six lump model has been proposed by Mohaddecy and Sadighi [33] for simulation of a soaker Visbreaking plant. They have characterized the visbroken product into five lumps as Gas (C1–C2), LPG (C3–C4), gasoline (IBP-180 °C), gas oil (180–320 °C), and fuel (320 °C +). The proposed kinetic model is based on fifteen reactions and thirty kinetic parameters, which is finally reduced to seven reaction pathways, as listed in Table 1. Thus fourteen kinetic parameters (frequency factors and activation energies for seven reactions) were found appropriate to simulate the performance of the reactor with reasonable accuracy. The advantage of this model as reported by authors is considering gaseous product as two separate lumps as gas and LPG. It would be useful for better economical evaluation of the process, as LPG production may be a value addition to the process. Nine sets of industrial data have been used for development of the model. The complex models are more accurate and sometimes help in further explanation of the process, as above for Wang and Anthony's model. But due to the requirement of a detailed analysis of feedstocks and products, these have limited applicability. Normally such detailed analysis is not always possible to obtain for industrial feedstocks in a refinery. Another problem with complicated models is comparatively large computational effort required for their implementation. Thus, a simple model requiring least characteristic information and simple analysis would be more useful for an industrial application.

Table 1. The subscripts of rate constants in the following discussion refer to the corresponding rate constants defined in the respective models. The estimated values for rate constants for these models have been listed in Tables 2a–2d. Table 2a lists the values of the rate constants of the five lump model of Singh et al. (Model-A) reproduced from their earlier publication [37]. Table 2b lists the estimated values of Model-B using DE technique. The values of this model reported in their publication [39] are listed in Table 2c. The reported kinetic parameters of Model-C have been listed in Table 2d. It seems clear from Tables 2a–2c that there is a significant variation in the three sets of values. The values reported by Kataria et al. [39] for Model-B are in reasonable agreement with the estimated values by DE algorithm. The variation in the estimated values can be attributed to the higher dependence of k values on temperature. Also it is evident from the estimated values of rate constants that the values for k7 in Model-B are very low in case of NGSR and MVBF feedstock. Also, the values estimated for this rate constant do not exhibit a regular trend with variation of temperature. It is thus indicated that this route of reaction may not be dominant for all the feedstocks. Singh et al. have established the reaction pathways using Delplot analysis [46] coupled with parameter estimation by DE algorithm. This approach is more rational for determination of reaction pathways. However the accuracy in the estimated yields of various fractions by Model-A does not indicate any improvement as compared to those estimated by Model-B. However, the possibility of absence of the conversion route VGO to Gas is well supported in the kinetic model (Model-A) presented by Singh et al. [38]. It is also reported by Steacie and Bywater [47] that at higher pressures, the C–C chain normally breaks from the middle. If it is taken to the consideration, the possibility of cracking of VGO fraction to Gas again seems to be very less.

Table 2a Estimated Rate Constants of Model-A. Feedstock

NGSR

3. Critical evaluation of three latest models The two models given by Singh et al. [37,38] and a five lump model reported by Kamal et al. [39] have been critically evaluated in the light of available data, and the analysis is being presented in following sections. In subsequent discussions the five-lump model reported by Singh et al. [37] has been referred as Model-A, the model reported by Kamal et al. [39] has been referred as Model-B and the four lump model reported by Singh et al. [38] has been referred as Model-C.

BHSR

MVBF

3.1. Model evaluation The kinetic parameters for the Model-B reported by Kataria et al. [38] were estimated using experimental data reported by Singh et al. [36], by differential evolution (DE) algorithm [45]. The estimated yields were compared with the obtained experimental data as well as values obtained by using Model-A. Since the lumps chosen for both the models are identical on boiling ranges, direct comparison of results could be possible. The lumps taken for both the models were – Gas, Gasoline (IBP – 150 °C), LGO (150–350 °C), and VGO (350–500 °C). The reaction pathways suggested by these models have been schematically shown in Figures at S. Nos. 13–15 in

HRA

Rate Constant min-1

Temperature 400 °C

410 °C

420 °C

430 °C

k1 k2 k3 k4 k5 k6 k8 k1 k2 k3 k4 k5 k6 k8 k1 k2 k3 k4 k5 k6 k8 k1 k2 k3 k4 k5 k6 k8

0.001236 0.000108 0.004628 0.009187 0.019956 0.010195 * 0.000498 0.000364 0.004328 0.011561 0.005431 0.007075 * 0.001060 0.000208 0.005431 0.005410 0.051968 0.019675 * -

0.001798 0.000252 0.007057 0.011023 0.038174 * 0.006477 0.001101 0.000719 0.004681 0.017333 0.043201 0.009923 * 0.002043 0.002054 0.011832 0.011959 0.081568 * * 0.002013 0.000910 0.009735 0.013170 * 0.004800 *

0.00289 0.001572 0.012457 0.015704 0.044900 * * 0.003226 0.001994 0.010592 0.020086 0.045147 * * 0.003363 0.003220 0.014652 0.013360 0.082126 * * 0.002778 0.000931 0.009885 0.017241 0.042068 * 0.015830

0.005629 0.003515 0.022350 0.033992 0.178453 * * 0.005295 0.003630 0.024238 0.020233 * * 0.009689 0.004584 0.003844 0.015224 0.016973 0.10025 * 0.038158 0.006671 0.001451 0.013439 0.032437 0.107765 * 0.018484

* These values are of the order of 10

–9

and, therefore, are not mentioned here.

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144 Table 2b Estimated Rate Constants of Model-B. Feedstock

NGSR

BHSR

MVBF

HRA

Table 2d Estimated Rate Constants of Model-C.

Rate Constant min-1

Temperature 400 °C

410 °C

420 °C

430 °C

k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7

0.001236 8.93E-05 0.004985 0.008855 1.84E-02 7.27E-03 7.87E-09 0.000499 0.000359 0.004945 0.020207 0.001969 0.001845 * 0.001059 2.38E-10 0.005702 0.013227 3.43E-02 0.016318 1.27E-09 -

0.001798 2.49E-04 0.007062 0.011023 1.91E-02 7.31E-03 2.69E-08 0.000231 5.68E-05 * 0.014904 5.27E-02 7.24E-03 5.75E-03 0.00012 2.06E-03 1.18E-02 0.007584 8.65E-08 1.28E-08 2.27E-02 0.001177 1.88E-04 0.006661 0.009563 1.63E-03 1.10E-02 5.79E-03

0.00289 1.57E-03 0.012457 0.015704 4.49E-02 * * 0.000108 1.41E-03 8.88E-03 0.020833 3.06E-02 5.68E-03 1.43E-02 0.003291 3.15E-03 1.30E-02 0.010576 3.15E-02 * * 0.002699 4.31E-04 0.009104 0.017238 3.84E-02 1.85E-02 3.23E-03

0.005513 3.44E-03 0.01785 0.031267 1.67E-01 * * 0.001675 2.55E-03 2.40E-02 0.016463 * 1.04E-02 2.06E-02 0.001202 4.09E-03 2.19E-02 0.01327 2.98E-07 4.07E-03 2.24E-02 0.003034 1.49E-03 0.017807 0.030309 5.49E-02 1.57E-02 3.81E-02

* These values are of the order of 10

–9

Feedstock

and, therefore, are not mentioned here.

4. Estimation of yields The yields of converted products were estimated using the three models. These results have been tabulated in Tables 3–6. It may be seen from these tables that the estimated yields from the two five Table 2c Rate Constants of Model-B reported by the authors in their publication [56]. Feedstock

NGSR

BHSR

MVBF

HRA

141

Rate Constant min-1

Temperature 400 °C

410 °C

420 °C

430 °C

k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7

0.00117 0.00035 0.00425 0.00565 0.00083 0.00084 1.74E-05 0.00044 0.00052 0.00343 0.00533 3.76E-08 3.76E-08 3.76E-08 0.00104 0.00098 0.00690 0.00356 0.00017 6.78E-05 0.00012 -

0.00168 0.00069 0.00810 0.00792 0.00099 0.00085 0.00012 0.00122 0.00112 0.00756 0.00588 0.00108 0.00010 0.00009 0.00205 0.00212 0.01182 0.00587 0.00044 0.00010 0.00021 0.00143 0.00130 0.00618 0.00816 0.00069 0.00107 0.00092

0.00277 0.00151 0.01356 0.01248 0.00133 0.00121 0.00151 0.00272 0.00261 0.01524 0.01362 0.00233 0.00059 0.00007 0.00299 0.00308 0.01476 0.00756 0.00062 0.00034 0.00158 0.00277 0.00145 0.01194 0.01218 0.00083 0.00129 0.00134

nd nd nd nd nd nd nd 0.00567 0.00402 0.02382 0.01218 0.00451 0.00145 0.00259 0.00487 0.00424 0.02070 0.01050 0.00172 0.00170 0.00214 0.00660 0.00654 0.01866 0.01932 0.00217 0.00351 0.00450

NGSR

BHSR

MVBF

HRA

Rate Constant min-1

Temperature 400 °C

410 °C

420 °C

430 °C

k1 k2 k3 k4 k1 k2 k3 k4 k1 k2 k3 k4 k1 k2 k3 k4

0.000428 0.000390 0.003832 0.002311 0.001205 0.000097 0.005494 0.010472 0.001021 0.000221 0.006968 0.017452 -

0.001015 0.000756 0.007551 0.004256 0.001672 0.000261 0.008181 0.008241 0.001975 0.002003 0.011534 * 0.001361 0.000208 0.006921 0.013528

0.002825 0.001961 0.012320 0.005150 0.002658 0.001446 0.013838 * 0.003102 0.003040 0.015452 * 0.002718 0.001106 0.010700 0.004425

0.005015 0.003630 0.023256 0.006569 0.005162 0.003223 0.026695 * 0.004347 0.004434 0.020661 * 0.005987 0.001721 0.020689 0.011251

* These values are of the order of 10

–9

and, therefore, are not mentioned here.

lump models do not vary much. Also, the amount of error of estimation is also of the same order of magnitude for the product lumps namely GAS, GLN and VGO. But in case of LGO fraction, the errors of estimation are higher with Kataria et al. model [39] than those estimated from Singh et al. [37] model. This may be due to the reason that in Model-B, the conversion of LGO to other lower boiling fractions has been omitted. In Model-A the conversion of LGO to gasoline seems to compensate for this error. It may be observed here that the additional route of conversion of VGO to GAS in Model-B does not significantly contribute in increase or decrease in error of estimation for VGO fraction. It is therefore again established that the conversion of VGO to Gas lump will not be significant, as indicated by Delplot analysis of Singh et al. [37]. It seems clear from these tables that in most of the points the values estimated by rate constants for Model-B reported by Kataria et al. [39] in their paper show slightly higher error as compared to those estimated using rate constants obtained by DE minimization. It may be due to wide variation in the rate constants with variation in temperature. Also, the parameters estimated by a gradient method may sometimes converge to local minima, whereas the parameters estimated using a random search method such as DE is most likely to yield global minima. 5. Four lump model A four lump kinetic model with four possible reaction pathways (Model-C) have been proposed by Singh et al. [38]. The reaction scheme has been depicted in Table 1 at S. No. 14. There are six possible reaction pathways, out of which only four were found to be logically existing on the basis of mathematical (parameter estimations), Chemical (Flash calculations), and Delplot analysis [46]. The results of the estimated values of the rate constants k1–k6 reveal that the rate constants k5 and k6 do not assume reasonable values. Their estimated values are of the order of magnitude of 10 –9 to 10 − 11. The Delplot analysis [46] carried out on the experimental data also indicates the absence of reactions indicated by these pathways. The flash calculations performed for the final reaction mixtures in ASPEN plus process flowsheet simulator using Chao-Sieder thermodynamic property set reveal that the gasoline fraction exists mostly in vapor phase (69–95%) at reaction temperatures, which rules out the reaction via path represented by k5. Pyrolysis of gasoline is very difficult in vapor phase and may not take place without a catalyst. The flash calculations also indicate the liquid fraction of LGO between 13 and 60%, at studied temperatures. In thermal cracking, the paraffin chain is more likely to break near the middle, as compared to near the

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J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

Table 3 Experimental and Predicted Yields by Five Lump Models (Feed: BHSR). GAS*

GLN*

LGO*

VGO*

T, °C

t, min

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

400 400 400 400 400 410 410 410 410 410 420 420 420 420 420 430 430 430 430 430

3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

0.1880 0.3100 0.3660 0.4080 0.6200 0.3190 0.4540 0.7310 1.1080 1.9400 0.5150 0.7840 1.4340 3.5660 3.7380 1.2090 2.1800 3.9230 6.7260 6.9750

0.1438 0.2769 0.4002 0.5143 0.6200 0.3073 0.5935 0.8599 1.1080 1.3390 0.9173 1.7407 2.4796 3.1428 3.7380 1.4865 2.7864 3.9230 4.9169 5.7861

0.1441 0.2773 0.4006 0.5146 0.6200 0.2677 0.5439 0.8250 1.1080 1.3903 0.5150 1.1881 1.9731 2.8330 3.7380 1.2090 2.5599 3.9982 5.4811 6.9750

0.1320 0.2602 0.3847 0.5056 0.6231 0.4185 0.7592 1.0896 1.4102 1.7213 0.8031 1.4385 2.0129 2.5322 3.0018 1.5957 2.9299 4.1227 5.1914 6.1511

0.1200 0.0420 0.2530 0.3800 0.8090 0.2460 0.3770 0.6080 1.1140 1.6850 0.5970 0.8690 1.4330 3.4670 3.1250 1.0910 2.0950 3.2750 5.1610 4.3690

0.1200 0.2612 0.4233 0.6060 0.8090 0.2174 0.4679 0.7638 1.1140 1.5244 0.5970 1.1996 1.8174 2.4576 3.1250 1.0910 2.1837 3.2750 4.3621 5.4429

0.1200 0.2629 0.4266 0.6093 0.8090 0.2460 0.5179 0.8091 1.1140 1.4278 0.5970 1.2218 1.8591 2.4966 3.1250 1.0910 2.1889 3.2750 4.3350 5.3586

0.1538 0.3032 0.4483 0.5892 0.7261 0.3601 0.6759 0.9826 1.2806 1.5704 0.7306 1.3613 1.9379 2.4655 2.9490 1.1352 2.0661 2.8941 3.6321 4.2912

2.1770 2.1770 3.2320 4.4110 6.5290 2.3020 2.8570 5.6190 8.5060 11.9400 3.5800 5.9290 9.9410 18.4510 18.3810 7.5700 12.1930 17.7590 25.2640 25.8110

1.3846 2.7297 4.0353 5.3016 6.5290 1.5945 3.7246 6.2381 9.0102 11.9400 3.3610 6.9915 10.7673 14.5898 18.3810 6.7804 12.6623 17.7590 22.1693 25.9797

1.4446 2.8147 4.1157 5.3523 6.5290 1.6846 3.7122 6.0075 8.5060 11.1531 3.5799 7.2808 11.0210 14.7372 18.3810 6.7279 12.6123 17.7590 22.2604 26.1975

1.0130 1.9968 2.9524 3.8805 4.7819 3.0912 5.0354 6.8957 8.6758 10.3794 5.2187 8.8580 12.1717 15.1912 17.9451 7.9201 13.2764 17.9976 22.1639 25.8449

9.4590 11.4790 12.2640 14.0040 15.5590 7.0760 8.5190 12.3600 11.7030 14.4300 9.1740 7.4120 9.5570 8.6440 16.5210 3.1980 7.5540 6.8380 7.2090 11.6640

5.7524 10.9862 15.7411 20.0542 23.9595 4.7706 8.5190 11.4164 13.6076 15.2149 5.3742 9.5168 12.6403 14.9244 16.5210 3.1980 5.9028 8.1791 10.0831 11.6640

5.7980 11.0948 15.9287 20.3354 24.3475 4.6852 8.5190 11.6198 14.0910 16.0228 5.5576 9.7578 12.8515 15.0478 16.5210 3.5093 6.1415 8.0610 9.4049 10.2869

1.5753 3.1054 4.5914 6.0348 7.4367 9.9125 11.2997 12.6058 13.8346 14.9899 11.6210 14.6727 17.3894 19.8042 21.9469 11.7621 14.0789 16.0020 17.5843 18.8720

*Legends: Exp. Experimental values. Pred-1 Values determined using Model-A. Pred-2 Values determined using Model-B, kinetic parameters estimated using DE technique. Pred-3 Values determined using Model-B model, using kinetic parameters reported by Kataria et al. [56].

ends [47] at higher pressures. One or both of these may be the reasons for cracking of LGO fraction only to Gasoline fraction and absence of reaction pathway along rate constant k6. Based on these analyses, a four lump model has been suggested by four rate constants, namely k1 to k4. The predictions by the developed model are well within acceptable range. The advantage of this model is that it requires boiling range data of only three product lumps against four in the previous model. The limitation of this model is that it is applicable for prediction of the results of cracking of long residue, due to feedstock lump being the fraction boiling above 350 °C.

The data used for parameter estimation for this model are same as in five lump models. The Model-C predicts nearly 70% data points within less than 10% error and 75% data points are predicted with less than 15% error [38]. The predictions with more than 25% error are reported to be only 15%. 6. Further directions An important shortfall of these three models is their limited dependence on the structural characteristics of the feedstock. The

Table 4 Experimental and Predicted Yields by Five Lump Models ( Feed : MVBF). GAS*

GLN*

LGO*

VGO*

T, °C

t, min

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

400 400 400 400 400 410 410 410 410 410 420 420 420 420 420 430 430 430 430 430

3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

0.4320 0.5990 0.8530 1.0860 1.4410 0.3830 0.7540 1.1150 2.1630 3.3080 1.0700 1.9050 2.4810 2.8570 3.9740 1.0560 2.0990 3.4370 5.4620 6.1500

0.3085 0.5990 0.8725 1.1301 1.3726 0.5937 1.1506 1.6730 2.1630 2.6226 0.9604 1.8298 2.6168 3.3291 3.9740 1.2921 2.4318 3.4370 4.3237 5.1057

0.3085 0.5990 0.8726 1.1303 1.3730 0.3830 0.8829 1.4816 2.1630 2.9128 0.9604 1.8298 2.6168 3.3291 3.9740 1.0308 2.2545 3.5600 4.8741 6.1500

0.3076 0.6043 0.8904 1.1664 1.4327 0.6307 1.2013 1.7463 2.2670 2.7647 0.8924 1.6723 2.3995 3.0778 3.7113 1.5011 2.6758 3.7380 4.7003 5.5736

0.0030 0.3380 0.6480 0.7830 1.8060 0.7340 0.9520 1.6820 2.0080 3.0790 1.0830 1.7110 2.5530 2.9250 3.8040 1.2770 2.2980 3.5660 4.5420 4.6730

0.1124 0.3380 0.6909 1.1791 1.8060 0.5969 1.1568 1.6820 2.1746 2.6367 0.9193 1.7515 2.5048 3.1867 3.8040 1.2770 2.4471 3.5303 4.5420 5.4943

0.0905 0.3380 0.7104 1.1808 1.7267 0.5974 1.1573 1.6820 2.1738 2.6347 0.9193 1.7515 2.5048 3.1867 3.8040 1.2770 2.4628 3.5514 4.5420 5.4375

0.2881 0.5658 0.8335 1.0916 1.3404 0.6156 1.1853 1.7239 2.2334 2.7154 0.9028 1.7496 2.5508 3.3099 4.0298 1.2200 2.2359 3.1531 3.9825 4.7340

2.0000 3.9340 5.5880 6.7590 11.0170 4.6620 5.7190 9.6890 11.5930 15.9560 6.4720 9.3740 12.6290 13.7610 17.4940 7.0570 11.9490 16.1160 20.8270 21.9330

1.8203 3.9340 6.2284 8.6132 11.0170 3.4385 6.6637 9.6890 12.5267 15.1883 4.5758 9.3740 14.2312 19.0302 23.6893 4.9836 10.4814 16.1160 21.6402 26.8985

1.8505 3.9340 6.1889 8.5640 11.0170 3.4411 6.6663 9.6890 12.5220 15.1771 4.5758 9.3740 14.2312 19.0302 23.6892 5.2456 10.7039 16.1160 21.3189 26.2160

2.0320 3.9898 5.8761 7.6937 9.4449 3.9172 7.0656 10.0369 12.8417 15.4900 4.8989 8.5875 11.9792 15.0982 17.9665 6.9990 11.7375 15.9534 19.7060 23.0477

4.6750 6.5070 6.2130 7.1530 11.7880 1.5720 1.7990 4.8210 7.9970 6.5750 3.0230 5.9310 5.9620 7.1750 7.7530 4.4530 9.4360 6.6020 8.3300 9.1130

3.5990 6.4683 8.7255 10.4704 11.7880 1.5720 3.0465 4.4296 5.7269 6.9438 3.0230 5.1033 6.4726 7.3100 7.7530 4.2431 6.8399 8.2939 8.9659 9.1130

3.5706 6.4305 8.6920 10.4506 11.7880 1.8554 3.4535 4.8210 5.9824 6.9597 3.0230 5.1033 6.4726 7.3100 7.7530 4.3434 6.9570 8.3853 9.0136 9.1130

1.0489 2.0580 3.0290 3.9631 4.8617 5.4947 6.8796 8.1380 9.2789 10.3106 6.7385 8.4913 10.0623 11.4670 12.7196 10.2712 12.4550 14.3346 15.9455 17.3192

*Legends: Exp. Experimental values. Pred-1 Values determined using Model-A. Pred-2 Values determined using Model-B, kinetic parameters estimated using DE technique. Pred-3 Values determined using Model-B model, using kinetic parameters reported by Kataria et al. [56].

J. Singh et al. / Fuel Processing Technology 94 (2012) 131–144

143

Table 5 Experimental and Predicted Yields by Five Lump Models ( Feed : NGSR). GAS*

GLN*

LGO*

VGO*

T, °C

t, min

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

400 400 400 400 400 410 410 410 410 410 420 420 420 420 420 430 430 430

3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9

0.3770 0.7090 0.8860 0.9850 1.7610 0.5540 0.8970 1.1640 1.9510 2.3280 1.1960 1.3520 2.3280 2.8700 3.3110 2.0280 2.7930 3.7460

0.3626 0.7090 1.0400 1.3564 1.6586 0.5234 1.0162 1.4801 1.9168 2.3280 0.8260 1.5750 2.2542 2.8700 3.4284 1.5178 2.7930 3.8643

0.3626 0.7090 1.0400 1.3563 1.6586 0.5235 1.0163 1.4802 1.9169 2.3280 0.8260 1.5750 2.2542 2.8700 3.4284 1.5178 2.7930 3.8643

0.3469 0.6821 1.0062 1.3194 1.6222 0.4908 0.9560 1.3971 1.8154 2.2120 0.8031 1.5523 2.2521 2.9067 3.5197 na na na

0.0540 0.1310 0.3060 0.2020 0.7970 0.1070 0.2020 0.5100 0.7460 1.0430 0.5130 0.7100 1.2810 1.5610 1.8440 1.1980 1.7440 2.3870

0.0540 0.1546 0.3060 0.5113 0.7726 0.1070 0.2663 0.4790 0.7460 1.0672 0.4493 0.8566 1.2260 1.5610 1.8647 0.9478 1.7440 2.4129

0.0540 0.1580 0.3060 0.4923 0.7120 0.1070 0.2728 0.4889 0.7479 1.0430 0.4493 0.8566 1.2260 1.5610 1.8647 0.9478 1.7440 2.4129

0.1046 0.2098 0.3156 0.4220 0.5289 0.2043 0.4036 0.5979 0.7875 0.9727 0.4401 0.8548 1.2460 1.6157 1.9656 na na na

1.6930 3.1300 4.4940 4.4630 8.8230 2.9140 4.3370 6.5150 8.9410 10.6940 5.7220 7.8720 11.9870 13.3320 15.3320 9.2610 14.4440 16.7300

1.4848 3.1300 4.8923 6.7345 8.6251 2.1429 4.3370 6.5570 8.7810 10.9902 3.8539 7.8770 11.9870 16.1175 20.2158 6.7958 15.1506 23.7747

1.5326 3.1300 4.7794 6.4695 8.1898 2.1464 4.3370 6.5563 8.7907 11.0281 3.8539 7.8770 11.9870 16.1175 20.2158 6.7966 15.1519 23.7763

1.2567 2.4752 3.6567 4.8026 5.9139 2.3677 4.6151 6.7487 8.7746 10.6985 3.8957 7.4669 10.7415 13.7452 16.5014 na na na

3.5170 4.7010 5.5400 5.8520 9.7870 4.3030 5.7520 6.6660 10.0060 7.7570 5.8910 7.4690 11.1120 11.5770 13.1890 8.1220 9.7320 10.6520

2.5448 4.7010 6.5158 8.0309 9.2834 3.0839 5.7520 8.0465 10.0060 11.6653 4.1943 7.4690 9.9764 11.8463 13.1890 6.7265 9.7320 10.6520

2.4988 4.7010 6.6336 8.3214 9.7870 3.0839 5.7520 8.0465 10.0060 11.6653 4.1943 7.4690 9.9764 11.8463 13.1890 6.7264 9.7320 10.6520

1.6609 3.2574 4.7917 6.2661 7.6825 2.3048 4.4723 6.5101 8.4251 10.2240 3.5568 6.7613 9.6447 12.2355 14.5596 na na na

*Legends: Exp. Experimental values. Pred-1 Values determined using Model-A. Pred-2 Values determined using Model-B, kinetic parameters estimated using DE technique. Pred-3 Values determined using Model-B model, using kinetic parameters reported by Kataria et al. [56].

experimental data used for the development of model only characterizes the feedstocks on the basis of their hydrocarbon type in terms of polar and non polar aromatics, paraffins and resins or asphaltenes. A detailed structural analysis of the feedstocks as well as cracked products, in terms of breakage of bonds and change in nature of hydrocarbon species would be an added advantage for the description of cracking behaviour in more detail. This will also be helpful in the development of a model based on structural changes during visbreaking. 7. Conclusions A critical analysis of kinetic models of thermal cracking of petroleum residues has been presented in the present paper in terms of their suitability for application, as well as error in their predictions. It is clearly indicated in the above discussion that both the five lump models, Model-A and Model-B are simple and relatively easy to be used for the prediction of the product yields with reasonable

accuracy. The model-B suggested by Kataria et al. [39] is evolved on the basis of certain assumptions which are concluded from the past experiences of the cracking behavior. The model developed by Singh et al. [37] has been developed on a strong theoretical basis (Delplot analysis), which has been established mathematically by Bhore et al. [46]. However the yields predicted by the earlier model are also in the acceptable range, and do not show much variation with respect to the values estimated by latter. But the model suggested by Singh et al. is more realistic and can be justified by stronger theoretical basis. The four lump model suggested by Singh et al. (Model-C) is basically a reduced parameter model derived from the five lump model and is supported by same theoretical basis. The overall advantage of all of these models is their simplicity, minimum characteristics information requirement, and less computational expense. However, these models can be made more useful if the variation in kinetic parameters can be estimates as a function of some simple structural characteristics e.g. hydrocarbon type, resin and asphaltene contents.

Table 6 Experimental and Predicted Yields by Five Lump Models ( Feed : HRA). GAS*

GLN*

LGO*

VGO*

T, °C

t, min

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

Exp

Pred-1

Pred-2

Pred 3

410 410 410 410 420 420 420 420 420 430 430 430 430

6 9 12 15 3 6 9 12 15 3 6 9 12

0.7630 1.1190 1.5530 2.0730 0.7980 1.1310 3.6640 2.7930 3.6800 1.2850 2.8820 4.7550 7.2150

0.5812 1.1190 1.6167 2.0774 0.7980 1.5292 2.1992 2.8132 3.3757 1.8475 3.4188 4.7550 5.8914

0.7630 1.1811 1.6189 2.0730 0.7980 1.5686 2.3075 3.0119 3.6800 1.2850 3.0791 5.1141 7.2150

0.8309 1.2251 1.6062 1.9747 0.8026 1.5537 2.2574 2.9173 3.5370 1.8725 3.5490 5.0555 6.4139

0.2830 0.4940 0.9450 1.2370 0.2550 0.7180 1.8540 1.4370 1.9760 0.5960 1.7240 2.1750 3.1830

0.2830 0.5848 0.9034 1.2370 0.2550 0.7180 1.3335 2.0570 2.8529 0.5960 1.3992 2.2869 3.1830

0.2830 0.5386 0.8591 1.2370 0.2550 0.7180 1.3335 2.0570 2.8528 0.5960 1.3913 2.2772 3.1830

0.7542 1.1145 1.4643 1.8040 0.4244 0.8277 1.2114 1.5770 1.9257 1.8477 3.4877 4.9478 6.2520

3.8190 5.3320 7.5230 8.8040 3.4180 5.8750 13.2000 11.4140 13.9370 5.8490 11.3490 15.5050 18.9250

2.7900 5.3320 7.6452 9.7472 2.7934 5.8750 9.1340 12.4831 15.8547 5.0428 11.3490 18.1046 24.8110

3.8190 5.6012 7.3033 8.9290 2.8864 6.0054 9.2585 12.5687 15.8764 5.5800 11.3490 17.0001 22.3491

3.5342 5.1773 6.7433 8.2358 3.4384 6.6051 9.5223 12.2101 14.6873 5.2076 9.7088 13.6029 16.9753

5.5230 7.3200 9.2470 9.4650 5.2570 7.9160 12.8840 12.1420 13.3210 7.6860 11.7240 13.3440 12.2240

3.8017 7.3200 10.5760 13.5892 4.5229 7.9160 10.3986 12.1510 13.3210 7.4689 11.5070 13.3440 13.8040

5.1507 7.3200 9.2470 10.9512 4.5226 7.9160 10.3989 12.1514 13.3210 7.1477 11.2639 13.3440 14.0844

4.6157 6.7241 8.7085 10.5752 3.4846 6.6493 9.5205 12.1225 14.4776 5.2901 9.6688 13.2705 16.2103

*Legends: Exp. Experimental values. Pred-1 Values determined using Model-A. Pred-2 Values determined using Model-B, kinetic parameters estimated using DE technique. Pred-3 Values determined using Model-B model, using kinetic parameters reported by Kataria et al. [56].

144

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