feddstock characterization

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is undefined. It should be also noted that the UOP K

index considers only feedstock physical properties

that correlates with the paraffinicity of feedstock.

However, it is known that the residue fractions areusually more naphthenic and aromatic in nature, and

the reactivity of heavy feedstock, either thermal or

catalytic, is strongly dependent on the feedstock

chemistry. Hence, there is a tremendous incentive to

develop a feedstock characteristic index for heavy

crudes, in order to optimize the refinery processes.

The need for this is even more critical to China, which

has limited process options and imports much of their

petroleum feedstock from a variety of sources.

Due to the complexity of residue species, there is

no standard analytical tool or database that predicts

the characterization of heavy crudes. As a result, an

ambiguous black oil concept is commonly used to

describe heavy crudes. A number of methods are

available for fractionating heavy petroleum feedstock.

Conventional vacuum distillation is capable of top-

ping distillates up to 524 jC. The use of high-vacuum,

short path distillation has allowed the cut point to be

extended to 700 jC. For non-distillable residua,

sequential extraction fractionation is used to prepare

subfractions based on solubility of residua in various

solvents. Alternatively, the residua can be separated

by gel permeation chromatography. While these meth-ods are capable of separating the non-distillable re-

sidua, they are tedious and produce only small

amounts of sample. These sample volumes are ade-

quate for only limited characterization studies.

Recently, Yang and Wang (1999) and Shi et al.

(1999) described the use of a new tool, supercritical

fluid extraction and fractionation (SFEF), to prepare

narrow, deep cuts of residua. The characterization of

these narrow cuts was used to develop a feedstock

characteristic index for Chinese heavy crudes and

residua, KH:

KH 10 H=CM0:1236n q

2

where Mn is the number average molecular weight, q

is the density at 20 jC (g/ml) and H/C is the atomic

hydrogen-to-carbon ratio, which accounts for the

feedstock chemistry effect. The new KH correlates

adequately with various Chinese feedstocks, but it

shows variability for feedstocks originated from other

sources.

This paper is an extension of the work of Wang and

his co-workers by incorporating various benchmark

petroleum residua from Middle East and Athabasca

bitumen pitch. A generalized feedstock characteristicindex for petroleum residua from various sources is

developed. Critical properties of residue fractions are

also derived.

2. Experimental

A wide variety of vacuum residua were considered:

Daqing and Shengli crudes from China, Saudi light

and medium crudes, Iranian light and heavy crudes,

Oman crude and Athabasca bitumen. The details

regarding the preparation of narrow-cuts of petroleum

residua using the SFEF have been described elsewhere

(Wang et al., 1993; Chung et al., 1997). In summary,

about 1 kg of residue was charged to the SFEF unit

with n-pentane as supercritical solvent. The extraction

and fractionation section of the SFEF unit was main-

tained at above 200 jC (critical temperature of n-

pentane). The pressure of the SFEF unit was initially

set at 4 MPa and was increased to 12 MPa at 1 MPa/h.

About 50 g of narrow-cut samples were collected

subsequently. The SFEF end-cut is the last fraction

of residue, which is not extractable with pentane evenunder the most severe supercritical conditions.

The narrow-cuts of each residue were subjected to

various analyses. The elemental analysis was carried

out on a Perkin-Elmer CHNS/O Analyzer 2400 (Per-

kin-Elmer, USA). Knauer vapour pressure osmometer

(Knauer Instruments, Germany) was used to deter-

mine the number average molecular weight. VT500

viscometor (HAAKE, German) was for viscosity

measurement. Saturates, aromatics, resins and asphal-

tenes (SARA) analyses were performed according to

the procedures described by Liang (1995) using n-heptane as solvent. Saturate, aromatic and resin frac-

tions were collected from chromatographic separation

of maltenes in an alumina column. The average

boiling points of narrow-cut samples from the front-

fraction of residua were determined using high tem-

perature simulation distillation. Together with boiling

points of various distillate narrow-cuts, a two-param-

eter boiling point correlation for heavy feedstock was

derived by multi-variable, non-linear regressions (Xu,

1994).

S. Zhao et al. / Journal of Petroleum Science and Engineering 41 (2004) 233242234


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3. Results and discussion

3.1. SFEF narrow-cut characterization

The quality of heavy petroleum feedstock is com-

monly defined by the amount of contaminants such as

sulfur, nitrogen and metals, and the nature of hydro-

carbon constituents it contains. This information is

crucial to the downstream refineries to route the heavy

feedstock to the appropriate processing units and prod-

uct optimization. The key bulk properties of residua are

shown in Table 1. The data show a wide variation of

properties for residua from various sources.

Daqing residue is relatively paraffinic and has the

largest amount of saturates, no asphaltenes and low

sulfur and metal concentrations. Generally, heavy

feedstock with these characteristics is considered

premium feedstock, which can be easily processed.

On the other hand, Athabasca residue is highly

aromatic and has large amounts of asphaltenes, sulfur

and metals. This type of feedstock requires complex

and expensive processes to remove the contaminants

prior to the refining steps. The contaminants are

known to poison or deactivate catalysts that are

crucial and used in converting large hydrocarbon

molecules into useful products in refinery processes.

The properties of Middle East residua vary betweenthose of Daqing and Athabasca. The properties of

Shengli residue from China are similar to those of

Middle East residua, except for higher nitrogen and

resins contents.

Table 2 compares the properties of SFEF end-cuts

from various residua. The results show that most of thecontaminants in residua are concentrated in the end-cut

fractions. The hydrocarbon constituents of residue

end-cuts enrich with more aromatic species (low H/C

ratio) such as resins and asphaltenes and have a high

coke-forming propensity as indicated by high Con-

radson carbon residue (CCR) values. In most residue

end-cuts, the concentrations of contaminants are so

high that they are typically processed in cokers in

which most of the contaminants are partitioned and

removed as part of product cokes.

A major advantage of the SFEF technology is that

it can be used to prepare sufficient quantity of narrow,

deep cuts of residua for characterization. Unlike the

bulk properties shown in Table 1, this allows the

distribution of key species to be determined, indicat-

ing the variation of residue properties as the fraction

becomes heavier. These are important feedstock data

which allow the refiners to increase yield by cutting

deeper into the bottom of residua to produce more

high-value feedstock. The distributions of sulfur,

CCR and metals in various residua are shown in Figs.

1 3, respectively, indicating that these species are

concentrated in the heavier fractions and are unevenlydistributed. The refiners can fractionate the residua

accordingly to meet the feedstock process criteria for

Table 1

Properties of residua from various sources

Daqing Shengli Saudi light Saudi medium Iranian light Iranian heavy Oman Athabasca

bitumen

Density (g/cm3)

at 20 jC

0.9392 0.9724 1.0045 1.0258 1.0057 1.0222 0.9637 1.0596

Viscosity (mPa s)

at 70 jC

5852 9722 2392

Mn (VPO) 1051 967 804 1046 1052 909 979 1191

H/C (atomic) 1.79 1.58 1.47 1.510 1.49 1.44 1.60 1.34

S (wt.%) 0.145 3.01 3.99 4.79 2.92 3.11 1.68 5.29

N (wt.%) 0.44 0.95 0.45 0.53 0.93 0.62 0.45 0.66

Ni (ppm) 7.6 55.7 23.0 36.7 66.37 89.96 18.0 160

V (ppm) 0.066 3.3 60.6 147.4 245.7 205.8 21.8 422

CCR (wt.%) 8.2 16.0 19.9 20.05 19.2 22.1 13.8 27.13

Aromaticity, fA 0.139 0.248 0.317 0.255 0.298 0.354 0.193 0.342

Sat. (wt.%) 41.9 16.1 16.5 15.85 18.46 12.60 26.3 6.26

Ar (wt.%) 32.7 30.6 49.5 40.04 44.83 46.63 40.6 32.97

Re (wt.%) 25.4 51.1 26.8 33.7 30.58 29.92 31.2 29.37

Asp (wt.%) 0.0 2.2 7.3 9.3 6.12 10.84 2.0 31.4

S. Zhao et al. / Journal of Petroleum Science and Engineering 41 (2004) 233242 235


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a given process unit such as the critical CCR and

metals contents for fluid catalytic cracking (FCC)

unit. Despite similar trend in sulfur, CCR and metals

distributions in these residua, the individual data set

for each residue is quite discrete. This is probably

related to the distribution of residue hydrocarbon

constituents. Fig. 4a and b compares the distribution

of saturates, aromatics and resins in Daqing and Iran

heavy residua. Saturates are concentrated in the front

end of the residua, decreasing as the fractions become

heavier. The amount of resins increases as the frac-tions become heavier. There are, however, obvious

differences between Daqing and Iran heavy residua.

Iran heavy residue has more aromatics, resins and

asphaltenes than Daqing residue, as shown in Table 1.

In addition, as shown in Fig. 4a and b, the amount of

aromatics in Daqing residue increases as the fractions

become heavier, whereas the Iran heavy resid ue

fraction is relatively constant with a maximum at

the middle fraction.

3.2. Feedstock characteristic index

Feedstock characteristic index is used to define the

nature of feedstock hydrocarbon constituents and

correlate it to its reactivity. In developing a general-ized feedstock characteristic index for heavy petro-

leum feedstock, the work of Wang and his co-workers

(Yang and Wang, 1999; Shi et al., 1999) was extended

by incorporating and substituting additional properties

Fig. 1. Distributions of sulfur in various residua. Fig. 2. Distributions of CCR in various residua.

Table 2

Properties of SFEF end-cuts from various residua

Daqing ShengliL Saudilight Saudi middle Iran light Iran heavy Oman Pitch VTB

Yield (%) 12.2 28.2 19.8 33.4 19.5 23.01 12.9 34.2Density (g/cm3)

(20 jC)

0.9654 1.0002 1.0533 1.034 1.060 1.062 1.0024 1.0857

CCR (wt.%) 40.3 54.6 31.3 49.9 52.4 47.9 48.9

Mn (VPO method) 2458 5515 2952 2882 3964 4254 5681 4185

H/C (atomic) 1.38 1.16 1.276 1.19 1.15 1.39 1.22

N (wt.%) 0.98 1.09 1.28 1.76 1.18 1.11 1.05

S (wt.%) 0.30 5.14 5.94 6.5 4.3 5.33 3.24 6.51

Ni (ppm) 111.6 122.3 88.6 96.1 25.6 258 105 339

V (ppm) 1.18 8.75 299 547 449 536 103 877

Sat. (wt.%) 0.2 0.0 0.2 0.58 0.46 0.31 0.1 0.0

Ar (wt.%) 7.9 17.6 11.1 41.9 16.6 11.11 7.0 2.19

Re (wt.%) 92.1 48.7 34.9 34.10 33.9 22.79 78.4 9.38

Asp (wt.%) 0.3 33.7 53.9 23.1 49.0 65.78 14.5 88.03



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that could be easily determined, and revising the

functional form of Eq. (2). After extensive data

regression analysis, a new feedstock characteristic

index, KR, is proposed, consisting of H/C, molecular

weight (Mn) and viscosity (g):

KR 10 H=C

2

M0:1236n g0:1305 3The narrow-cut characterization data, H/C, Mn and g,

for various residua that used in deriving KR in Eq. (3),

are shown in Figs. 57, respectively.

The characteristics of feedstock hydrocarbon con-

stituents were used to validate the chemical signifi-

cance of KR index. Figs. 8 11 show the goodness of

fit of CCR, saturates, aromatics and resins contents as

a function of KR, respectively. In general, the data in

Figs. 811 are strongly correlated with KR. The CCR,

Fig. 4. (a) Distributions of saturates, aromatics and resins in Daqing

residue. (b) Distributions of saturates, aromatics and resins in Iran

heavy residue.

Fig. 3. Distributions of metals in various residua: (a) Ni and (b) V.

Fig. 5. Hydrogen-to-carbon (H/C) atomic ratios of various residue

narrow-cuts.



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saturates, aromatics and resins contents can be

expressed as a function of KR as follows:

CCR wt:% 65:685=KR 5:810 r2 0:909 4Saturates wt:% 10:885KR 28:391 r2 0:922

5Aromatics wt:% 92:464 7:575KR r2 0:867for KR > 4 6Resins wt:% 225:504=K1:528R r2 0:700for KR > 4 7The lack of fit data for aromatics and resins at

KR below 4 is likely due to separation difficulty

encountered in analytical procedure, in which

heavy aromatic fractions are known to overlap with

resins.

The linear relationship of saturates versus KRand the inverse relationship between CCR, aro-

matics and resins versus KR are consistent with

feedstock reactivity. Feedstock with high saturates

content is easier to process; conversely, that with

high CCR, aromatics or resins contents is difficultto process. Based on the previous feedstock reac-

tivity studies (Long, 1990; Zhao, 1992; Chen,1992;

Wang, 1994), the processability of various heavy

Fig. 7. Viscosities of various residue narrow-cuts.

Fig. 8. CCR as a function of KR index.

Fig. 6. Molecular weights of various residue narrow-cuts.

Fig. 9. Saturate content as a function of KR index.



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feedstock can be classified according to KR in three

categories:

3.3. Critical properties

Critical properties are parameters that are common-

ly used by process simulators in the unit operation

design. The methodology used to derive the critical

properties of heavy petroleum fractions was similar to

that reported elsewhere (Kesler and Lee, 1976; Riazi

and Daubert, 1980; Twu, 1984; Brule et al., 1982;

Maslanik et al., 1981; Lin and Chao, 1984). A set of

correlations for alkane system up to C24 were

obtained:

Tob 111:6361 0:1369646ftb 0:92635220 102f2tb=1 0:44162923 101ftb 0:73097615 103f2tb 8

Toc =To

b 1:70711 0:207576019 101ftc 0:98431965 103f2tc=1 0:23293035 101ftc 0:16839807 103f2tc 9

Poc 4:8721 0:24434610 102fpc 0:43087800 104f2pc=

1

0:16088038

101fpc 0:20717865 102f2pc 10

SGo 0:62621 0:66878721 101fsg 0:59212241 102f2sg=1 0:062791972 101fsg 0:42682682 102f2sg 11

Voc Toc 0:187921021051 0:89331074 101fvc

0:43087800 103f2vc=1 0:20935548 102fvc 12

qoc Mon=Voc 13

where T, P and V are temperature in K, pressure in

MPa and volume in cm3/mol, respectively. The su-

perscript o and subscript c denote the ideal and critical

states, respectively. The parameterf used in Eqs. (8)

(13) are defined as follows:

ftb Mon 16:04262=3 14

ftc Mon 16:04262=3 15

Fig. 11. Resin content as a function of KR index.

1st category KR>6 adaptable to processing

2nd category 4


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fpc Mon 30:06942=3 16

fsg

Mon

72:1486

2=3

17

fvc Mon 16:0426 18

Properties of other hydrocarbons or heavy petro-

leum fractions can be correlated as perturbations of

those of n-alkanes with same boiling point tempera-

ture as follows (Zhang et al., 1998):

Tc Toc 17985:731 27924:736=SG 10655:703=SG2DSGT 121794:13 82527:92=SG 68835:678=SG2DSG2T 248433:99 405746:33=SG 68835:678=SG2DSG3T 19

Pc Tc=VcPoc Voc =Toc 0:11893861 165:35472=Tc55837:029=T2c DTCP 0:78087709 103 1:0397575=Tc 307:23148=T2c DTC2P 20

Vc Mn=qc 21

qc qoc 0:89687121 104T1:1908482b DSG0:5158375d22

lnMn lnMon 1:7454987 0:21527818 104=Tc 0:63682878 106=T2c DTCM 0:52774857 0:65871831 103=Tc 0:19785982 106=T2c DTC2M 0:036689969 0:45890527 102=Tc 0:13987772 105=T2c DTC2M 23

where the parameters used in Eqs. (19) (23) are

defined as follows:

DSGT SG SGo=SG 24

DSGd ASG SGoA=SG 25

Fig. 12. Boling point curves of various residua.

Fig. 13. Critical temperatures of various residue narrow-cuts.

Fig. 14. Critical pressures of various residue narrow-cuts.



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DTCP Tc Toc 26

DTCM ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ATc

Toc Ap 27

The most adequate correlation for the average boiling

point Tb of various petroleum residue fractions7 in Eq.

(22) is:

Tb 79:23M0:3709n q0:1326 28

Figs. 1214 show the values of Tb, Tc and Pc of

various residue narrow-cuts, respectively. The models

proposed in this work have been compared with

various thermodynamic models for hydrocarbon sys-

tems in another paper(Zhang et al., 1998) as shown inTable 3, using a number of database most available

(Marsh, 1992; Danner and Daubert, 1992; Lin et al.,

1980a,b,c). Absolute average deviation, as defined in

Eq. (29), is used as a criterion to determine the

goodness of fit of data.

AAD %

XNdata

i1ycali yexpi =yexpi

Ndata

100 29

The results in Table 3 show that the correlationsderived in this work gave more accurate predictions

than other models. This is likely due to the use of

residue narrow-cut data in deriving the correlations,

which are not available before.

4. Conclusions

(1) SFEF is an important tool to prepare narrow-cuts

from a variety of petroleum vacuum residua.

(2) Characterization data of SFEF narrow-cuts shows

the uneven distribution of key contaminants,

indicating increased concentration as the fraction

becomes heavier.

(3) A generalized feedstock characteristic index KHwas developed, which correlates well with the

feedstock hydrocarbon constituents and can be

used to assess the feedstock reactivity and

processability.

(4) Narrow-cut data were used to develop critical

properties of residue fractions.

References

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Table 3

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data points

(refs. ae) Thiswork

Kesler andLee (1976)

Riazi andDaubert

(1980)

Twu(1984)

Brule et al.(1982)

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Lin andChao (1984)

Jalowka andDaubert (1986)

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Vc 309 1.90 7.11 7.11 4.46 17.3 9.10

Mn 318 2.78 5.11 5.11 4.21 11.3 8.42

Note: (a) Marsh (1992), (b) Danner and Daubert (1992), (ce) Lin et al. (1980a,b,c).



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