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New Approach to the Characterisation of Petroleum Mixtures

Used in the Modelling of Separation Processes

Egon Eckert1 and Tomáš Vaněk 2

Department of Chemical Engineering, Institute of Chemical Technology, Prague, Technická 5,

166 28 Prague 6, Czech Republic

Characterisation of complex mixtures is a common tool especially in oil

processing industry. Characterisation procedures result in experimentally gained

characterisation curves, but for the simulation of industrial processes the

definition of a substitute mixture is required. Traditionally, a system of

pseudocomponents is derived from the TBP (True Boiling Point) characterisation

curve, but there are a number of disadvantages, e.g. the physical properties of

pseudocomponents must be estimated by unreliable empirical methods. The new

approach to the characterisation of complex mixtures is based on representing the

original mixture by a system of real components. Such substitute mixture is fully

defined, it has a chemical character, and physical properties can be simply

retrieved from databases. Utilisation of a substitute mixture of real components in

the simulation of crude oil processing proved that the new approach could replace

the traditional one in normal boiling temperature ranges where real components

are available. Both approaches could be also easily combined.

Keywords: Refinery processes, Oil processing, Characterisation procedures, Complex mixtures,

Pseudocomponents

1 [email protected]; 2 [email protected]



1. Introduction

Mixtures containing an extremely large number of various components can be often

encountered in industrial chemical technologies, particularly in oil processing and refining. Briesen

and Marquardt (2003, 2004a, 2004b) analysed thoroughly the past, present and future trends in this

branch of industry and pointed out the need to increase the accuracy in the modelling of oil refining

processes. There are direct economical and environmental consequences of any progress in this

point and improved treatment of complex mixtures is probably the most promising direction. In

order to deal with such mixtures in modelling and simulation calculations, it is necessary to

simplify the problem by utilising a substitute mixture possessing reasonably lower number of

components or to use an alternative representation of the mixture. For this purpose the continuous

thermodynamics can be employed (see e.g. Rätzsch & Kehlen, 1983), or, more recently, the

wavelet-Galerkin discretization has been proposed (Briesen & Marquardt, 2003, 2004a, 2004b).

While continuous thermodynamics has received only a little attention in industrial practice, the

adaptation of wavelet-Galerkin discretization method for the modelling of unit operations, where

complex mixtures are processed, is intensively being studied. Its main advantage is the possibility

to tune the representation of the mixture by means of adaptive control of the problem discretization.

On the other hand, this approach uses a non-trivial mathematical background and also the standard

models of unit operations must be reformulated in order to incorporate the continuous

representations (distributions) of some model variables, e.g. of the vector of composition (Briesen& Marquardt, 2003, 2004a, 2004b). Moreover, physical properties dependent on the composition

(e.g. K-values) must be also converted to distribution-based functions. Such methods can be

employed only for physical processes as distillation or absorption, but not for processes with

chemical reactions. Since the last decade of 20 th century, methods for the "reconstruction" of the

chemical composition have been developed presuming that some other information was available



beside the usual distillation curve. It might be data from elemental analysis, gas chromatography,

mass spectrometry, 1H and 13C NMR analyses, etc., (e.g. Whitson & Brulé, 2000).

When using substitute mixtures in steady-state or dynamic simulation, the classical unit

operation models can be employed. In fact, in simulation programs the solution engine deals equally

with real components as well as with pseudocomponents if properly defined during the data input

phase. The number of components used for the substitute mixture is usually up to 102, which is

considerably lower than for original complex mixtures, i.e. 104 - 107 or more in case of crude oils. It

might be felt that in the age of powerful computing machinery the number of components in

chemical engineering calculations could be practically unlimited. Nevertheless, there are at least

two good reasons why to keep lower number of components. First, the dimension of unit operation

models is dependent on the number of components involved and especially equation-oriented

simulators could still run into troubles according to memory requirements and internal limits.

Second, it is very hard to analyse the results of simulation run when the number of components is

high, e.g. thousands or more, and no new information could be obtained. Probably, there would be

subsets of components behaving almost equally, the content of each being on the ppm level. It could

be noticed that also for well-defined mixtures in practical calculations it is often desired to decrease

the number of components or even pseudocomponents. The method called lumping is based on

representing groups of components with close boiling points and/or some other properties by a

selected single member component inheriting in the mixture the "weight" of the entire group (Riazi,

2005). Montel & Gouel (1984) suggested to optimise the lumping scheme for the substitution of agroup of known components in order to preserve the PVT behaviour.

The approach based on pseudocomponents had been developed quite a long time ago

(Edmister, 1955; Katz & Brown, 1933) and first was used for flash calculations on an early

computer (Hariu & Sage, 1965). As the main advantage it was appreciated that the characterisation

procedure was non-iterative. It is still widely accepted as a convenient method in the simulation of

separation equipment, but a number of problems arise. Above all:



programs as ASPEN Plus, HYSYS and PRO/II (ASPEN Plus and HYSYS are registered

trademarks of Aspen Technology and PRO/II is a trademark of SimSci-Esscor). Beside these

standard features, there are most recent attempts to implement other approaches to oil and gas

processing, e.g. available with HYSYS 3.2 onwards as HYSYS Upstream™ Option.

The usage of pseudocomponents has after all one important advantage: when defined and

equipped by a set of estimated physical (pseudo)properties, pseudocomponents could be in

simulation programs treated as any other real components (except in processes with chemical

reaction). This is also the main presumption for the alternative way how to establish a feasible

substitute mixture as suggested Ba et al. (2003). It has been shown that the substitute mixture

composed of suitable real components can be used in simulation calculations instead of an

alternative mixture of pseudocomponents with comparable results (Eckert & Vaněk, 2003). The

primary intention was to overcome the main disadvantages of pseudocomponents mentioned above,

namely the need to estimate their properties. It is possible to retrieve property data from the

database, which is used as the source of real components. At the same time, wide variety of

thermodynamic models and packages of compatible methods, typically within simulation programs,

can be used. There is no need to limit to equation-of-state (EOS) models but other types of models,

especially for the description of the phase equilibrium, can be employed, even those requiring some

interaction parameters. Moreover, the mixture of real components exhibits a chemical character,

which can be utilized when modelling complex reaction schemes. For example, Bělohlav et al.

(2005) recently used substitute mixtures of real components for the prediction of yields from pyrolysis reactors.

Despite the simplicity of the new approach, it has not been used before, e.g. no attempt to

employ real components for this purpose is mentioned even in the most current Riazi's monograph

(Riazi, 2005). The principles of the new approach makes it directly usable in simulation programs

with current content of their databases of physical properties and for current models of unit

operations.



Further in this text, we shall recapitulate the two approaches employing substitute mixtures in

order to explain the difference between the appropriate algorithms. The verification of the new

approach is then provided on a non-trivial simulation case - crude oil refining.

2. Characterisation of mixtures using pseudocomponents

Normally, two steps are needed to define a system of pseudocomponents for a complex

mixture. First, some standard characterisation curve must be experimentally obtained, preferably the

TBP (True Boiling Points) curve, which is directly used in the next step. The TBP curve represents

the dependence of temperature measured in the head of a laboratory batch column on mass or

volume fraction distilled. The column should have a high number of stages and perform at large

reflux ratio (API, 1992). There are other possibilities, e.g. the TBP curve can be substituted by its

more accurate chromatographic equivalent SIMDIST or by transformation of curves resulting from

some other characterisation procedures (ASTM D86, EFV - Equilibrium Flash Vapour) using

empirical correlations (API, 1992). The later case is not too reliable and can lead to a considerable

deviation of the calculated TBP curve from the experimental curve (Ba et al., 2003). In the second

step the range of boiling points of the TBP curve is cut in order to obtain non-overlapping

temperature intervals (T i , T i+1), for i=1,..., I , continuously covering the entire temperature range.

There are various possibilities how to choose these intervals - some recommendations were

published, for example, in Whitson & Brulé (2000). Usually, it is sufficient to use about 15 K for

normal boiling points up to 700 K, about 30 K within 700 and 950 K and about 50 K for higher boiling mixtures as it is done in the HYSYS simulation program when using automatic cutting

option (see Hyprotech, 1998). Each temperature interval represents one pseudocomponent with

normal boiling point given by the arithmetic or, more precisely, the integral mean temperature over

the corresponding interval of fraction distilled - see Figure 1. The relevant definitions of arithmetic

and integral mean temperatures are given by Eqns (1) and (2) respectively.



I ,...,i ,T T

T Li

Ri

i 12

)()( b b b =

+=

Φ Φ (1)

( ) I ..., ,i ,d T T

Ri

Li

Li

Ri

i 11

b b =

−

= ò Φ Φ Φ Φ

Φ

Φ (2)

As also illustrated by Figure 1, the interval of fraction distilled I ..., ,i Li

Ri 1),( =−Φ Φ determines at

the same time the relative contribution of each pseudocomponent to the mixture, which is used to

define its composition.

For the consequent usage of the substitute mixture of pseudocomponents it is necessary to

supply the same structure of physical properties as for real components. Usually, it is not difficult to

measure besides the TBP curve also the density and/or viscosity of individual fractions collected in

the laboratory column during the distillation test. Moreover, for each fraction the mean molecular

weight can be estimated. Therefore, if we use pseudocomponents to represent these fractions then

two to four data items are available for each pseudocomponent: the mean normal boiling point and

one to three other properties, which can be used to trigger a series of empirical estimation

procedures delivering the remaining set of properties - critical properties, acentric factor etc. (see

e.g. Riazi, 2005; Whitson & Brulé, 2000). Unfortunately, we should be aware of low reliability of

such methods as it was mentioned in the introductory part of this contribution.

It is relatively simple to measure global parameters of a complex mixture, i.e. bulk properties as

the density, molecular weight, refraction index or viscosity, and this is often delivered with other

measurements. Unfortunately, their direct use for the derivation of a substitute or model mixture is

known only for the density where it is possible to construct under certain conditions an

approximation of the characterisation curve from the Watson factor (Wauquier, 1995). When the

global density of the mixture is known and the Watson factor

S

)T .(K

/ b

W

3181= (3)



can be assumed to be constant then it is possible to get an approximation of the density

characterisation curve (Wauquier, 1995). This could be an alternative to density data measured for

each fraction during the characterisation experiment.

3. Characterisation of mixtures using real components

The main disadvantages of the approach based on pseudocomponents can be overcome if we

employ real components to form the substitute mixture. Of course, the selection of suitable real

components and the derivation of the substitute mixture must follow certain criteria and an

appropriate algorithm must be defined. We shall use two consecutive phases, i.e. a set of

components to be present in the substitute mixture must be first selected and then the composition

of their mixture will be adjusted. The prerequisites for the algorithm are as follows:

a. The TBP curve measured for the original complex mixture is available

( )Φ b b T T = (4)

where bT is the measured "True Boiling Point" temperature and Φ is the mass or volume fraction

distilled.

b. Some other characterisation curve is available, e.g. all or some of dependencies

( )Φ M M = (5)

( )Φ ρ ρ = (6)

( )Φ η η = (7)

where M is the molecular weight, ρ the liquid density (eventually specific or API gravity) and

η is the liquid viscosity. The liquid density curve can be also obtained approximately using the

Watson factor as mentioned above. Instead of using Eqns (5)-(7) directly, it is better to convert

them into "phase portraits" by eliminating the mass or volume fraction distilled and establishing

direct relation between these properties and the boiling point temperature:



)( bT M M = (8)

)( bT ρ ρ = (9)

)( bT η η = (10)

c. The overall temperature range is divided (cut) into a system of non-overlapping temperature

intervals continuously covering the range. There are several possible approaches similarly to the

definition of pseudocomponents - an equidistant grid, adaptive grid taking into account the shape

of the curve or a grid based on boiling points of homological series of alkane compounds - see,

e.g., Table 5.2 in Whitson & Brulé (2000).

d. A sufficiently large database of chemical components and their physical properties is available.

Detailed requirements will be discussed further.

3.1 Selection of components

The basic assumption is that a suitable database is available. The "quality" of such database can

be expressed not only by the reliability of data included but also by the extent of the database. Here

we take primarily into account the number of components but on its own this is not sufficient. It is

important to have satisfactory representation for important families of chemical compounds, in our

case especially hydrocarbons or detailed groups as e.g. parrafines or aromatics. At the same time,

the range of normal boiling points for these compounds must be wide and the normal boiling points

should cover the range uniformly and densely. This is, of course, an ideal situation, which is more

or less fulfilled by databases from various sources. In "open" literature we can find, for example,

the API database (API, 1992) with 478 hydrocarbons up to C30, normal boiling points up to 720

K and molecular weights up to 420 kg/kmol. Generally, normal boiling points of higher

hydrocarbons are very difficult to obtain as the thermal decomposition takes place (Kopsch, 1995).

Accordingly, data for higher hydrocarbons (C18+) in databases are frequently estimated or their

origin is ambiguous. Nevertheless, their long-term usage in many chemical engineering calculations



giving realistic results gives certain guaranty that also the substitute mixture of real components

selected from such databases can sufficiently model the original complex mixture.

The internal database of the HYSYS simulation program (Hyprotech, 1998), which we used

for test calculations, incorporates 521 hydrocarbons and covers approximately the same range of

normal boiling points and molecular weights as the API database. The source of data is not

explicitly specified and probably the same limitations can be expected as for API data.

Thermal decomposition is also one of the reasons, why the number of available hydrocarbon

compounds rapidly decreases with increasing normal boiling point as demonstrated by Figures 2

and 3. This situation leads to a poor chance to find a suitable real component to represent higher

boiling point temperature ranges. Unfortunately, the mixtures in oil processing contain a significant

portion of higher boiling compounds forming the so-called "heavy end". Some components can

exhibit normal boiling point higher than 1000 K and molecular weight exceeding 800 kg/kmol.

Normal boiling points and densities, as the minimum information needed, can be found, e.g., in

Beilstien database (MDL Information systems, 2005), for about 1850 hydrocarbons up to C42,

where only two components are available - 1-cyclohexyl-hexatriacontane a 1-phenyl-

hexatriacontane. Figures 2 and 3 show another interesting feature. With increasing temperatures the

range of molecular weights of components having approximately the same boiling point is getting

wider, see for example the region around 700 K in Figure 3.

There are a number of other databases, which could be potentially used for the same purpose,

e.g. DIPPR (Design Institute for Physical Properties at Brigham Young University, 2000) or thesuite of internal databases attached to the ASPEN Plus. The reasons why we did not employ these

databases are technical as the program providing the selection of components needs a self-standing

"flat" data file. Our approach, initially designed for exclusive usage of real components in the

substitute mixture, can be extended to combine real components for lower and moderate boiling

point temperatures with pseudocomponents for the heavy end. Nevertheless, this demonstrates the

potential of the new approach.



The procedure for the selection of real components for the substitute mixture can be made

flexible in order to exploit all information about the original mixture. As stated above, if the

chromatographic analysis of the mixture is available it could be also a basis for the formation of a

substitute mixture. Certain components can be clearly identified and directly added to the list of

components in the substitute mixture. If no chromatographic analysis is available but the TBP curve

is known, all components for the substitute mixtures can be selected according to the following

algorithm:

Step 1: For each primary temperature interval (prerequisite c.) a set of candidate components is

selected from the database. The criterion is that each component selected must have its normal

boiling point within the considered temperature interval. If needed, filtration conditions can be set,

e.g. reflecting the requirements to include only some families of components or, on contrary, to

exclude another components or families. For example, it is known that no olefin components are

present in crude oils (Petrov, 1987). On the other hand, at least one component should be available

for each interval. If not, then either a set of wider intervals must be used or the interval can be

represented by a pseudocomponent. The combination of real components and pseudocomponents is

necessary for higher boiling mixtures.

Step 2: Exactly one component is selected for each primary temperature interval from the set

of candidate components by comparing their physical properties. We know the normal boiling point

of each candidate component and from phase portraits (8)-(10) the "desired" values can be derivedand compared with values retrieved from the database. The simplest way how to combine the

deviations for different properties is to use a weighted sum of relative differences. The criterion for

the selection is then defined by

c

K

k c ,k ,

c ,k ,c ,k ,

k w min1 m

mr →

−

å=

ζ

ζ ζ (11)



where K is the number of measured properties and c ,k ,mζ , c ,k ,r ζ are the measured and from database

obtained (calculated or simply retrieved) values of property ζ respectively. The expression is

calculated for each candidate component, c = 1,...,C i and the component with the lowest value of

criterion (11) is chosen to represent the interval. There are degrees of freedom in the choice of

weight factors k w , which can reflect the precision of measurements or some other demands. It

should be noted that measured viscosity and density in fact represent properties of discrete mixtures

(fractions), which can be hardly derived from properties of contained pure components according to

non-trivial and unreliable mixing rules.

Sometimes no other curves than the TBP curve are available. Step 2 of the algorithm can be

then replaced by choosing a component with its normal boiling point being closest to the mean

temperature of the appropriate temperature interval. This is an emergency solution but it can be

expected that the selected real component would be close to a pseudocomponent defined

traditionally while all properties of the real component are directly available from the database.

The result of this phase of the algorithm is simply a list of real components together with their

normal boiling points and other properties. If it is desired for some reason to include other

components into the substitute mixture, it can be done now. Such an obvious reason is, for example,

the confirmed presence of a particular component or component type in the original mixture.

Especially polar compounds are in the focus since they strongly affect the phase equilibrium in

multiphase systems. A compound can be added to the substitute mixture with or without

information about its amount in the original mixture. Both possibilities are aided in the second

phase of the algorithm.

3.2 Determination of the composition of a substitute mixture

The problem is that the normal boiling point of each selected component can fall anywhere in

the primarily defined temperature interval (T i , T i+1), see Figure 4, and no longer it is a meantemperature of this interval. If a model of the experimental characterisation procedure were



available then we could abandon the principle of mean temperatures and this step would consist in a

repeated evaluation of the characterisation curve from the model for varying composition of the

mixture until a satisfactory match with the experimental characterisation curve is reached. This kind

of an optimisation can be easily employed, for example, for the EFV curve (Eckert, 1999).

When the TBP curve is available then the procedure suggested by Ba et al. (2003) can be used.

Figure 5 illustrates the principles of the procedure, which at first glance could be found similar to

the method used to derive the composition of a mixture of pseudocomponents. The presumption

that each component should represent an interval of fraction distilled, where its normal boiling point

is the mean temperature according to Eqns (1) or (2), is preserved. The difference is that we cannot

expect that the intervals could be generally chosen to cover the whole range without gaps and/or

overlapping. This is automatically fulfilled for pseudocomponents where the procedure starts from

the other end - the mean temperatures are calculated for temperature intervals initially chosen to

continuously cover the entire temperature range without overlapping. Nevertheless, in the case of

real components the idea is to distribute the intervals to make them cover the range as much as

possible with minimum gaps and overlapping. This is an optimisation task where the objective

function can be defined as follows:

( ) ( )å+

+=

−→−=

1

1

2

1 min I

LE i

L

i

R

i

LF Φ Φ Φ (12)

For each interval the starting and end fractions distilled are denoted by the superscript L (= Left)

and R (= Right). Generally, we can utilise the information about light-end components, which can

be usually identified in the original mixture together with their composition, and incorporate them

into the substitute mixture. The index of the last light-end component is therefore denoted by LE

and the corresponding fraction distilled by R

LE Φ . On the other end of the range, I +1 can be denoted

by HE (heavy-end), even if no heavy-end components were present and by definition

11 ==+

L

I

L

HE Φ Φ . The reason why only the elements of the vector { } L

I

L

LE

L Φ Φ Φ ,...,1+= are considered

as independent optimisation variables in (12) is that Eqns (1) and (2) can be treated as implicit



equations for I LE i R

i,..,1, +=Φ , i.e. ( ) L

i

R

i

R

i Φ Φ Φ = . Alternatively, the reversed relation

( ) R

i

L

i

L

i Φ Φ Φ = could have been used with R

iΦ as the set of optimisation variables. It is reasonable

to define bounds for the values of L

iΦ in order to avoid getting some unreal solutions and the

following set of constraints was considered:

I LE i

I LE i

i

L

ii

L

i

L

i

,...,1,

,...,1,

11

1

+=≤≤

+=≤

+−

+

Φ Φ Φ

Φ Φ (13)

Optimisation problem (12) together with constraints (13) can be solved using some standard

package, e.g. MINOS (Murtagh & Saunders, 1995) as in our case.

In order to obtain a consistent composition of the substitute mixture it is necessary to convert

the intervals ( ) R

i

L

i Φ Φ , resulting from the optimisation to mass or volume fractions. In ideal case,

the intervals would exactly cover the entire range, the value of the object function (12) would be

zero and the following condition would be fulfilled:

( ) R

LE

I

LE i

L

HE

L

i

R

i Φ Φ Φ Φ −=−å+= 1

(14)

Then the length of each interval can be directly taken as the appropriate mass or volume fraction.

Nevertheless, this is not usually true according to possible overlapping of the intervals and

existence of areas not covered with any interval. The simplest way how to get a consistent

composition derived from the lengths of intervals ( ) R

i

L

i Φ Φ , is to "normalise" the vector of mass or

volume fractions:

( ) ( ) ( ) I ,..., LE j , x

I

LE i

Li

Ri

R LE

L HE

L j

R j j 1

1

+=−−−= å+=

Φ Φ Φ Φ Φ Φ (15)

A similar procedure can be used when some of the real components were identified in the original

mixture and their relative amounts, i.e. mass or volume fractions, are known.

The second phase of the algorithm was implemented as self-standing program incorporating the

MINOS (Murtagh & Saunders, 1995) optimisation package.



4. Application of the method

In order to approve the suggested approach it was reasonable to simulate real industrial

processes employing substitute mixtures. Unfortunately, the data measured, for example, on

distillation columns and published in open literature are almost always for binary mixtures and

small-scale columns. Data from major distillation columns in crude oil processing are very rarely

available. Therefore, an alternative way is to compare at least the two different approaches to

substitute mixtures on the same simulation problem, once using a substitute mixture composed of

pseudocomponents and then a mixture of real components.

An important point is to define some methodology how to compare different substitute

mixtures. In a former contribution (Eckert & Vaněk, 2003) we used the concept of a "theoretical

TBP curve" as curve of a staircase shape reflecting the distribution of normal boiling points of

individual components from the mixture. Such curve we could get in a batch distillation column

with the number of stages approaching infinity and very high reflux ratio. In its discrete version the

theoretical TBP curve is composed of points with co-ordinates (å−=

=

+

1

1

2i j

j

i j / x x ; T bi) for i = 1,..., I ,

taking into account for simplicity the arithmetic mean values defined by Eqn. (1). Analogical

theoretical curves can be constructed for molecular weight and density, eventually API gravity or

some other properties.

Theoretical TBP curves were constructed for examples of separations in a distillation andabsorption columns (Eckert & Vaněk, 2003) and the match between results obtained by both

approaches was excellent, thus showing that substitute mixtures of real components can replace

mixtures of pseudocomponents with all the extra benefits, which this approach brings. For these

examples only the measured TBP curves were available as input information.

The example presented further is based on the "Refining Tutorial" case delivered with the

simulation program HYSYS.Plant version 2.1 (abrev. HYSYS) and at the same time this program



has been used for all calculations. The advantage is that HYSYS incorporates a relatively extensive

database of hydrocarbons and it also enables to define pseudocomponents in a built-in tool called

"Oil Environment".

Example (Hyprotech, 1998)

Crude oil is processed in a fractionation facility to produce naphtha, kerosene, diesel,

atmospheric gas oil and atmospheric residue products. The crude oil is characterised by laboratory

assay data in Tables 1.-3. The TBP curve is accompanied by the dependence of molecular weight on

the liquid volume fraction distilled and a separate assay on API gravity is also available. A light end

is considered and its composition is known. The main flowsheet for the process in consideration is

shown in Figure 6. Preheated crude is initially fed to a pre-flash drum to separate vapours from the

liquids, which are further heated in a furnace. The pre-flash vapours are re-combined again with the

hot crude from the furnace and the resulting stream is then fed to the atmospheric crude column for

fractionation. Detailed description and specifications are presented in the Appendix. The icon of the

distillation column in Figure 6 represents in fact a complex separation process, which can be in the

HYSYS program further represented by an embedded flowsheet, i.e. subflowsheet in HYSYS

terminology. The appropriate subflowsheet for our example is shown in Figure 7. The Peng-

Robinson property package was used in HYSYS for the calculation of physical properties. In the

"Oil Environment" the standard setting of estimation methods was used, i.e. Twu critical property

correlation for molecular weight (Twu, 1984), constant Watson factor method for specific gravityand Lee-Kesler method for critical properties, the acentric factor and ideal enthalpy (Kesler & Lee,

1976).

It was intended to solve the simulation case using both methods for the characterisation of the

process feed (Preheat Crude stream). Particularly, Table 4 globally summarises how the substitute

mixtures were assembled. Temperature ranges defined in the first column of the table were for

pseudocomponents divided uniformly to intervals of the same width. For real components this was



also done but, as we know from the description of the algorithm above, it serves only for the initial

selection of subsets of real components as candidates for representing these intervals. For both

substitute mixtures the same light-end (Table 1) was supposed. Unfortunately, higher temperature

ranges had to be covered by 10 pseudocomponents, as there were only few hydrocarbons in the

HYSYS database with boiling points exceeding 426.7 oC. Our program used for the selection of real

components, accepts input data in various forms and physical units and provides necessary

conversions. For our example the conversions of characterisation curves from tables 2 and 3 to

phase portraits expressed by Eqns (8) and (9) were done using a piece-wise linear interpolation. The

phase portrait for the API gravity has been extended to include the light end components. The

reason is that this curve bends at the boundary between light end and the remaining part of the

mixture, which affects interpolation or extrapolation operations needed in the algorithm. Then for

each temperature interval a set of candidate components was chosen from the HYSYS database

having normal boiling points within this interval. This primary selection was limited to hydrocarbon

compounds only but also compounds known to be missing in oil fractions (e.g. olefins,

cycloolefins) were excluded. Technically, this is enabled by registering the chemical family of

compounds and setting an "oil" flag on or off for each component in the database. The most suitable

component for each interval was found according to criterion (11) for K = 2, i.e. taking into account

the molecular weight and API gravity. The weight factors given to the contributions of both

properties were simply set to be one.

The resulting list of real components for the substitute mixture is presented in Table 5 and thecomparison of the measured and retrieved values of both considered properties are depicted in

Figures 8 and 9 as phase portraits. Table 5 contains also the information about the number of

candidate components for each primary temperature interval. It is apparent that their number rapidly

decreases when approaching higher temperatures. It is also interesting that starting from interval

number 16 almost all components selected belong to a monotone series of alkanes. Only

components number 18 (n-hexylbenzene) and 23 (n-decylbenzene) do not follow this fact and show



larger deviation from the phase portrait as documented by Figures 8 and 9. Another component

showing larger deviation in Figure 9 is n-butylcyclohexane in interval 15. In order to improve the

selection it could be possible, for example, to try larger temperature intervals or their different

distribution, but it would disable to compare the new approach with the traditional one. Therefore,

the actual result of selection was left unchanged and passed to the second phase of the algorithm.

After selecting a unique component for each primary temperature interval the actual intervals

( ) R

i

L

i Φ Φ , attached to each component were computed in the second phase of the algorithm and the

composition was derived using Eqn. (15). Table 6 contains the complete overview of resulting

substitute mixtures including the light and heavy ends. Pseudocomponents were generated in

HYSYS Oil Environment and their names chosen by HYSYS reflect the mean temperatures of

appropriate intervals (in deg F). For the new approach the interpretation according to HYSYS could

be that the four light-end components (originally 1.13 vol. %) together with the system of selected

real components would form an extended light-end (in our example it will comprise 32 components

and 63 vol. % of the mixture). The measured and theoretical TBP curves of the Preheat Crude are

compared in Figure 10. The comparison of experimental additional curves with properties of real

components selected for substitute mixture is depicted in Figures 11 and 12. In these Figures it can

be also observed that in the region of higher boiling temperatures program HYSYS is not so

successful in the choice of pseudocomponents and a considerable deviation between the

experimental and estimated values of the molecular weight as well as API gravity is apparent.

The simulation calculations of the entire crude oil fractionation process employing both

approaches to the characterisation of complex mixtures run without any problems. The excellent

match between both approaches can be demonstrated by Figure 13, where the comparison is

provided for all the products of fractionation, which have distinct mean boiling points. The plotted

theoretical TBP curves were recalculated in order to eliminate water. In fact, the TBP curves reflect

the match in the composition of product streams, as the most important parameter, after processing

of a single feed stream in a number of unit operations within a relatively complex technology with



several recycle streams. Also for other parameters of the product streams, as flowrates, temperatures

and enthalpies, the results of simulation proved very nice match. It can be noted that these results

were reached despite the fact that the database used is relatively small compared to other data

sources. Moreover, the content of some of the real components selected into the substitute mixture

resulting from the second phase of the algorithm is practically negligible (see Table 6 - components

number 6, 14, 18, 28, 30).

5. Conclusions

The results presented in this paper allow us to say that the new simple approach to the

characterisation of complex mixtures is fully acceptable even for simulation calculations of large-

scale and complex processes including various mass and heat transfer operations. It can replace the

traditional approach based on the definition of pseudocomponents for low and moderate normal

boiling points. Of course, there is an important assumption about the availability and sufficient

number of real components with normal boiling points in the considered temperature range in the

database. If necessary, we can always use a combined approach, as in the example above, adding

pseudocomponents for higher boiling temperature range where no real components are available in

the database. We can remark that the number of real components not only in the HYSYS but also in

other databases we have been dealing with is still extremely low compared to the possible number

of all hydrocarbons. It is an interesting fact that considering hydrocarbon molecules with 25 carbon

atoms, the number of possible configurations already exceeds 600 millions (Krambeck, 1991) andcrude oil may contain a huge number of distinct molecular species in the order 106 (Altgelt &

Boduszynski, 1994). Nevertheless, substitute mixtures allow to deal with complex mixtures in

modelling and simulation of technological processes without need to dispose with data for such

extent of existing compounds. Systematic addition of missing data and inclusion of new

components into databases is continuously provided by the vendors of the main databases of

physical properties (e.g. Beilstein) and certainly it brings better possibilities for the selection of real



components into a substitute mixture, but even current pallet of available compounds gives good

results. This was proved not only by the example presented above but also by some other (Ba et al,

2003; Bělohlav et al., 2005; Eckert & Vaněk, 2003, 2005).

There are many benefits when using real components for substitute mixtures instead of

pseudocomponents:

• The usage of substitute mixtures with real components can be extended from usual calculations

of separation processes to processes with chemical reactions (Bělohlav et al., 2005) as the

substitute mixture receives a chemical nature, even if only a "substitute" one.

• Empirical estimation methods for physical properties are generally not needed. The values

retrieved from database are more reliable and precise despite the fact that for higher boiling

compounds (approximately C20 and higher) they are predicted only. On the other hand, the

knowledge of the molecular structure allows us to use group contribution methods to estimate

various physical properties, e.g. the phase equilibrium behaviour of the substitute mixture (and

therefore of the original mixture as well).

• The selection of components can be affected by partial information about components positively

occurring in the mixture or about the overall character of the mixture. Particularly, we can

recognise in some mixtures components containing nitrogen or sulphur, but there is certain

problem with availability of data. For example, the HYSYS database contains about 50

sulphuric compounds the presence of which in oil could be expected. On the other hand, the

inclusion of such components into the resulting substitute mixture is very important when

modelling separation processes having strong impact on the environment (Eckert & Vaněk,

2005).

The new approach could be also very convenient for the implementation into standard

commercial simulation programs where the combination with current proprietary databases and

libraries of numerical methods, especially optimisation procedures for the second phase of the



algorithm, could be very profitable. There are also certain possible improvements in the

construction of the substitute mixture, e,g. the selection of components an/or the composition can be

optimised in order to get best match between experimental characterisation data and results of the

modelling of the appropriate characterisation procedure.

Acknowledgement

Authors appreciate the support of the fund MSM 6046137306.

List of symbols

C number of candidate components in the primary temperature interval

F objective function

I total number of real components

K number of measured properties

K W Watson factor

M molecular weight

S standard specific gravity

T temperature

x volume or mass fraction

w weight in criterion (11)

η viscosity

Φ volume or mass fraction distilled

ρ density

ζ symbol for property

Subscripts

b at boiling point



c index of a candidate component

HE index of the first heavy-end component

i index of a component or temperature interval

j index of a component

k index of a property in criterion (11)

LE index of the last light-end property

m measured value

r value calculated or retrieved from the database

Superscripts

L left edge of an interval

R right edge of an interval

mean value

Literature Cited

Altgelt K., & Boduszynski M. (1994). Composition and Analysis of Heavy Petroleum

Fractions. New York: Dekker.

American Petroleum Institute (API) (1992). Technical Data Book - Petroleum Refining.

5th ed. Washington: API.

Ba A., Eckert E., & Vaněk T. (2003). Procedures for the selection of real components to

characterize petroleum mixtures. Chem. Pap., 57, 53. (full text available at

http://www.vscht.cz/uchi/procesy/)

Bělohlav Z., Zámostný P., Herink T., Eckert E., & Vaněk T. (2005). A Novel Approach

for the Prediction of Hydrocarbon Thermal Cracking Products Yields from the

Substitute Feedstock Composition. Accepted for publication in Chem. Eng. Technol.

(full text available at http://www.vscht.cz/uchi/procesy/)

http://www.vscht.cz/uchi/procesy/






Briesen H., & Marquardt W. (2003). An adaptive multigrid method for steady-state

simulation of petroleum mixture separation processes. Ind. Eng. Chem. Res., 42,

2334.

Briesen H., & Marquardt W. (2004a). New approach to refinery process simulation with

adaptive composition representation. AIChE J., 50, 633.

Briesen H., & Marquardt W. (2004b). Adaptive multigrid solution strategy for the

dynamic simulation of petroleum mixture processes. Comp. & Chem. Engng., 50,

633.

Design Institute for Physical Properties (2000). DIPPRâ

801. Brigham Young

University, U.S.A. Available at http://dippr.byu.edu/product.asp.

Eckert E. (1999). Non-traditional Characterization of Petroleum Mixtures in Terms of

Selected Components. Collect. Czech. Chem. Commun., 64, 571. (full text available

at http://www.vscht.cz/uchi/procesy/)

Eckert E., & Vaněk T. (2003). Simulation of separation columns using substitute

mixtures. Proc. of the 30th Int.Conf. of SSCHE on CD-ROM. Tatranské Matliare,

Slovakia, May 26-30, 2003. (full text available at


Eckert E., & Vaněk T. (2005). Extended utilisation of the characterisation of petroleum

mixtures based on real components. Proc. of the 32th

Int.Conf. of SSCHE on CD-

ROM. Tatranské Matliare, Slovakia, May 23-27, 2005. (full text available at


Edmister W. (1955). Improved integral technique for petroleum distillation

calculations. Ind. Eng. Chem., 47(9), 1685.

Hariu, O. H., & Sage, R. C. (1969). Crude Split Figured by Computer. Hydrocarbon

Processing, 48(4), 143.

Hyprotech Ltd. (1998). HYSYS.Plant 2.1 Documentation. Calgary: Hyprotech.









Katz D., & Brown G. (1933). Vapor pressure and vaporization of petroleum fractions.

Ind. Eng. Chem., 25(12), 1373.

Kesler M.G. & Lee B.I. (1976). Improved prediction of enthalpy of fractions.

Hydrocarbon Processing, 55, 153.

Kopsch H. (1995). Thermal Methods in Petroleum Analysis. Weinheim: VCH.

Krambeck F. (1991). Continuous mixtures in fluid catalytic cracking and extensions, in

Sapre A. and Krambeck F. (Eds.), Chemical reactions in complex mixtures. Pages

42-59. New York: Van Nostrand Reinhold.

Lindqvist P., Markkanen V., & Happonen V.M. (1994). Simulation of a heavy residue

vacuum column. ASPENWORLD '94. November 6-9. Boston, Massachusetts, 1994.

MDL Information Systems (2005). Crossfire Beilstein Database. San Leandro, CA:

Elsevier MDL. Available at

http://www.mdl.com/products/knowledge/crossfire_beilstein/.

Montel, F., & Gouel P.L. (1984). A new Lumping Scheme of Analytical Data

for Compositional Studies. Presented at the 59th Annual Technical Conference and

Exhibition, paper SPE 13119. Houston, Sept. 16-19,1984.

Murtagh, B.A., & Saunders, M.A. (1995). MINOS 5.4 User's Guide. TR SOL 83-20R.

Department of Operations Research, Stanford University. Stanford CA.

Petrov A.A. (1987). Petroleum Hydrocarbons. Berlin, Heidelberg: Springer-Verlag.

Rätzsch M., & Kehlen H. (1983). Continuous thermodynamics of complex mixtures.Fluid Phase Equilibria,14, 225.

Riazi, M. R. (2005): Characterization and Properties of Petroleum Fractions. Barr

Harbor: ASTM.

Twu, C.H. (1984). An Internally Consistent Correlation for Predicting the Critical

Properties and Molecular Weights of Petroleum and Coal-Tar Liquids. Fluid Phase

Equilibria 16(2), 137.



Wauquier, J.-P. (ed.) (1995). Petroleum Refining, Vol. 1: Crude Oil. Petroleum

Products. Process Flowsheets. Paris: Éditions Technip.

Whitson, C. H., & Brulé, M. R. (2000). Phase Behavior . SPE Monograph Series.

Richardson: Society of Petroleum Engineers, Inc.



Tables

Table 1. Light end components and their liquid volume %.

No. Component Liq. vol. %

1 i-butane 0.19

2 n-butane 0.11

3 i-pentane 0.37

4 n-pentane 0.46

Table 2. TBP Distillation Assay.

Liq. vol. % Temperature, oCMolecular weight,

kg/kmol

0 26.7 68

10 123.9 119

20 176.1 150

30 221.1 182

40 275.0 225

50 335.0 282

60 399.4 350

70 490.6 456

80 590.6 585

90 691.7 713

98 765.6 838



Table 3. API Gravity Assay.

Liq. vol. % API gravity

13 63.28

33 54.86

57 45.91

74 38.21

91 26.01

Table 4. Characterisation of the process feed (Preheat Crude), basic temperature intervals.

Temperature

range, oC

Traditional method of characterisation New method of characterisation

37.8 - 426.7 28 pseudo-components, uniformly 28 real components

426.7 - 648.9 8 pseudo-components, uniformly 8 pseudo-components, uniformly

648.9 - 760 2 pseudo-components, uniformly 2 pseudo-components, uniformly



Table 5. Resulting selection of real components (without the known light-end).

No. Temp. interval [K] ComponentT b

[K]

M

[kg/kmol]

API

grav.

Number of

candidate

components in

interval

5 310.95 - 324.84 2,2-dimethylbutane 322.88 86.18 84.90 3

6 324.84 - 338.73 2-methylpentane 333.41 86.18 83.60 3

7 338.73 - 352.62 n-hexane 341.88 86.18 81.60 3

8 352.62 - 366.51 2-methylhexane 363.20 100.21 75.70 12

9 366.51 - 380.40 2,2-dimethylhexane 379.99 114.23 70.70 9

10 380.40 - 394.29 2-methylheptane 390.80 114.23 70.00 27

11 394.29 - 408.17 2,4-dimethylheptane 406.05 128.26 65.20 23

12 408.17 - 422.06 2,3-dimethylheptane 413.65 128.26 62.30 46

13 422.06 - 435.95 2,6-dimethyloctane 433.56 142.29 61.80 62

14 435.95 - 449.84 5-methylnonane 438.26 142.29 60.60 45

15 449.84 - 463.73 n-butylcyclohexane 454.13 140.27 44.70 25

16 463.73 - 477.62 n-undecane 469.04 156.31 58.60 12

17 477.62 - 491.51 n-dodecane 489.43 170.34 56.50 8

18 491.51 - 505.40 n-hexylbenzene 499.30 162.27 32.58 2

19 505.40 - 519.29 n-tridecane 508.58 184.37 54.60 8

20 519.29 - 533.18 n-tetradecane 526.66 198.38 53.60 6

21 533.18 - 547.07 n-pentadecane 543.77 212.41 51.80 14

22 547.07 - 560.96 n-hexadecane 559.94 226.43 50.60 7

23 560.96 - 574.85 n-decylbenzene 571.10 218.37 33.12 6



24 574.85 - 588.74 n-heptadecane 575.30 240.46 49.50 4

25 588.74 - 602.63 n-octadecane 589.86 254.48 48.60 4

26 602.62 - 616.51 n-nonadecane 603.80 268.51 47.80 8

27 616.51 - 630.40 n-uneicosane 629.65 296.56 46.29 5

28 630.40 - 644.29 n-dodecosane 641.76 310.59 45.74 3

29 644.29 - 658.18 n-tricosane 653.37 324.61 45.04 6

30 658.18 - 672.07 n-tetracosane 664.43 338.64 44.72 3

31 672.07 - 685.96 n-pentacosane 675.04 352.67 44.27 2

32 685.96 - 699.85 n-heptacosane 695.26 380.72 43.46 1



Table 6. Resulting substitute mixtures.

Substitute mixture of pseudo-

components

Substitute mixture of real

components and pseudo-components No.

Real component /

pseudo-component

Liq.vol. % Real component /

pseudo-component

Liq.vol. %

1 i-butane 0.19 i-butane 0.19

2 n-butane 0.11 n-butane 0.11

3 i-pentane 0.37 i-pentane 0.37

4 n-pentane 0.46 n-pentane 0.46

5 NBP_109 1.05 2,2-dimethylbutane 2.47

6 NBP_135 0.99 2-methylpentane 0.00

7 NBP_161 1.25 n-hexane 1.54

8 NBP_185 1.50 2-methylhexane 3.04

9 NBP_210 1.64 2,2-dimethylhexane 0.20

10 NBP_235 1.84 2-methylheptane 2.22

11 NBP_261 2.10 2,4-dimethylheptane 1.25

12 NBP_286 2.42 2,3-dimethylheptane 2.90

13 NBP_311 2.81 2,6-dimethyloctane 3.25

14 NBP_336 3.12 5-methylnonane 0.00

15 NBP_361 3.14 n-butylcyclohexane 5.17

16 NBP_386 3.14 n-undecane 2.64

17 NBP_411 3.08 n-dodecane 5.20

18 NBP_436 2.83 n-hexylbenzene 0.00

19 NBP_461 2.64 n-tridecane 3.23

20 NBP_486 2.55 n-tetradecane 3.67



21 NBP_511 2.47 n-pentadecane 2.45

22 NBP_536 2.41 n-hexadecane 3.30

23 NBP_561 2.36 n-decylbenzene 0.21

24 NBP_587 2.30 n-heptadecane 1.40

25 NBP_612 2.28 n-octadecane 2.65

26 NBP_637 2.31 n-nonadecane 2.77

27 NBP_662 2.30 n-uneicosane 4.54

28 NBP_687 2.22 n-dodecosane 0.00

29 NBP_712 2.09 n-tricosane 3.51

30 NBP_737 1.91 n-tetracosane 0.00

31 NBP_762 1.73 n-pentacosane 3.06

32 NBP_787 1.62 n-heptacosane 1.19

33 NBP_825 3.03 NBP_821 2.78

34 NBP_875 2.90 NBP_868 3.02

35 NBP_925 2.84 NBP_919 2.90

36 NBP_975 2.80 NBP_970 2.86

37 NBP_1025 2.76 NBP_1021 2.81

38 NBP_1075 2.73 NBP_1072 2.79

39 NBP_1125 2.71 NBP_1123 2.77

40 NBP_1175 2.73 NBP_1175 2.7841 NBP_1251 5.65 NBP_1251 5.65

42 NBP_1372 8.64 NBP_1372 8.64



Appendix: Detailed Specifications for the Example

All plant data used for the simulation of the oil refining example originated from the HYSYS.Plant

documentation (Hyprotech, 1998) and the overview is given by Tables A.1 - A.7.

Table A.1. Stream parameters for crude oil.

Stream Temperature, oC Pressure, kPa

Preheated Crude

(flowrate 662.4 m3/h)

232.2 517.1

Hot Crude 343.3 448.2

Tower Feed 338.8 448.2

Table A.2. Configuration of the atmospheric crude column.

Unit operation Theoretical Stages

Main tray section 29

Condenser (for the main

tray section, partial)

1

Kerosene side stripper 3

Reboiler for the kerosene

side stripper

1

Diesel side stripper 3

AGO side stripper 3

Total 40



Table A.3. Connectivity for the Atmospheric crude column.

Stream Description From unit operation,

stage

To unit operation, stage

TowerFeed hot crude fed to the

column

- main tray section, 28

BottomSteam steam fed to the bottom of

the column

- main tray section, 29

Naphtha naphtha product condenser -

WasteH2O waste water condenser -

OffGas overhead vapor product -

TrimDuty energy stream - trim duty - main tray section, 28

Residue crude atmospheric residue main tray section, 29 -

Kerosene kerosene product reboiler for the

kerosene SS

-

Diesel diesel product diesel SS, 3 -

AGO atmospheric gas oil

product

AGO SS, 3 -

KeroSS_Energy kerosene SS reboiler duty - reboiler for the kerosene

SS

KeroSS_Draw liquid draw stream main tray section, 9 kerosene SS, 1

KeroSS_Return vapor return stream kerosene SS, 1 main tray section, 8

DieselSS_Draw liquid draw stream main tray section, 17 diesel SS, 1

DieselSS_Return vapor return stream diesel SS, 1 main tray section, 16



AGOSS_Draw liquid draw stream main tray section, 22 AGO SS, 1

AGOSS_Return vapor return stream AGO SS, 1 main tray section, 21

DieselSteam steam fed to the bottom of

the diesel SS

- diesel SS, 3

AGOSteam steam fed to the bottom of

the AGO SS

- AGO SS, 3

PA_1_Draw draw for pump around 1 main tray section, 2 pump around cooler 1

PA_1_Return return for pump around 1 pump around cooler 1 main tray section, 1

PA_1_Q cooler 1 duty pump around cooler 1 -









Table A.4. Parameters of column internal material streams.

Stream Flowrate, m3/h

Naphtha 132.5

OffGas 0

Kerosene 86.12

Diesel 112.6

AGO 33.12

PA_1_Draw 331.2

PA_2_Draw 198.7

PA_2_Draw 198.7

Table A.5. Parameters of column steam supply.

Parameter Stream

Flowrate, kg/h Temperature, oC Pressure, kPa

BottomSteam 3402 190.6 1034.2

DieselSteam 1361 148.9 344.7

AGOSteam 1134 148.9 344.7



Table A.6. Column duty streams.

Stream Duty, kJ/h

KeroSS_Energy 7.913×106

PA_1_Q -5.803×107

PA_2_Q -3.693×107

PA_3_Q -3.693×107

Table A.7. Various column parameters.

Parameter Value

Condenser pressure, kPa 135.8

Condenser pressure drop, kPa 62.1

Bottom stage pressure, kPa 225.5

Kerosen SS boil up ratio 0.75

Overflash specification, %

(or tray net liquid flow from

stage 27)

3.5

(23.19 m3/h)



New Approach to the Characterization of Petroleum Mixtures

Used in the Modelling of Separation Processes

Egon Eckert and Tomáš Vaněk

Figures

Figure 1: Definition of a pseudo-component and of its content in the substitute mixture from theTBP curve.

T 1

T 2

T i

T i+1

T bi

i

T I +1

T b

0 1

Φ ΦΦ Φ



0 100 200 300 400 500 600 700 800

T b [K]

0

50

100

150

200

250

300

350

400

450

M [ k g / k m o l ]

Figure 2. Plot of molecular weights vs. normal boiling points of hydrocarbon components from theHYSYS.Plant database.



0 100 200 300 400 500 600 700 800

T b [K]

-50

0

50

100

150

200

250

300

350

A P I g r a v i t y

Figure 3. Plot of API gravities vs. normal boiling points of hydrocarbon components from theHYSYS.Plant database.



Figure 4. Situation after the selection of a real component in the ith primary temperature interval

(T i ,T i+1).

T 1

T 2

T i

T i+1T bi

T I +1

T b

0 1

Φ

R

iΦ L

iΦ iΦ



Figure 5. Construction of intervals of fraction distilled in the second phase of the algorithmattached to real components selected in the first phase.

T bi

T b

0 1

Φ

T bi-1

R

i 1−Φ L

i 1−Φ 1−iΦ R

iΦ L

iΦ iΦ



Figure 6. Simplified flowsheet for the crude oil processing example.



Figure 7. Subflowsheet for the atmospheric column.



300 350 400 450 500 550 600 650 700

T b [K]

50

100

150

200

250

300

350

400

M [ k g / k m o l ]

Figure 8. Phase portrait for the molecular weight ( ) compared with values retrieved from thedatabase for real components (£).



300 350 400 450 500 550 600 650 700

T b [K]

30

40

50

60

70

80

90

A P I g r a v i t y

Figure 9. Phase portrait for the API gravity ( ) compared with values retrieved from the database

for real components (£).



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Φ

200

300

400

500

600

700

800

900

1000

1100

T b

[ K ]

Figure 10. Measured TBP curve ( ● ) compared with theoretical TBP curve for the substitutemixture gained by the new approach (£).



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Φ

0

150

300

450

600

750

900

M [ k g / k m o l ]

Figure 11. Measured characterization curve for the molecular weight ( ● ) compared with the

theoretical curve for substitute mixture gained by the new approach (£

).



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Φ

0

20

40

60

80

100

120

140

A P I

g r a v i t y

Figure 12. Measured characterization curve for the API gravity ( ● ) compared with thetheoretical curve for substitute mixture gained by the new approach (£).



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Φ

200

300

400

500

600

700

800

900

1000

1100

T b

[ K ]

Figure 13. Comparison of theoretical TBP curves for fractionation products (bottom to top: Offgas, Naphtha, Kerosene, Diesel, AGO, Residue) employing the traditional (¢) and the new (£)

approaches to characterization.

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