New Approach to the Characterisation of Petroleum Mixtures
Used in the Modelling of Separation Processes
Egon Eckert1 and Tomá� Vaněk2
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. 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 20th 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 a
group 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:
• The chemical character of components forming the mixture could also play a role in chemical
reactions occurring in the studied processes. For pseudocomponents we can't define any
chemical character.
• A pseudocomponent is primarily defined only by its (pseudo-) boiling point and by some
additional parameters, mostly by specific gravity, molecular weight or viscosity. All other
physical properties, e.g. needed for simulation calculations, must be estimated. Unfortunately,
the reliability of common estimation methods (listed for example by Whitson & Brulé, 2000)
for critical properties, the acentric factor, etc. is rather low according to the results of testing
published for example by Twu (1984), Lindqvist et al. (1994), Riazi (2005).
• For pseudocomponents we cannot use group contribution methods (e.g. UNIFAC) requiring
information about the molecular structure of compounds in order to estimate some parameters
(e.g. binary interaction parameters for vapour - liquid equilibrium).
• The information about the type of the mixture, e.g. if paraffinic or aromatic compounds are
prevailing, or about the type of some of its important components (e.g. polar compounds)
couldn't be easily utilised.
• Arbitrary combinations of pseudocomponents and compounds identified in the original mixture
are not supported in commercial simulation programs. That is, it is not possible to place a real
component into the middle of the temperature range used for the definition of
pseudocomponents without knowing its content.
When modelling distillation and absorption processes for complex hydrocarbon mixtures
within simulation programs, the original mixture is traditionally substituted by a mixture
comprising two different groups of components. For the light end, real components usually up to C5
are employed if they are present and if their content can be determined. For the remaining part, a
system of pseudocomponents is derived from certain global characterisations of the mixture based
on its distillation behaviour. This approach is implemented in recent major commercial simulation
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 (Ti, Ti+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,TTTLi
Ri
i 12
)()( bbb =
+=
ΦΦ(1)
( ) I...,,i,dTTRi
Li
Li
Ri
i 11bb =
−= � ΦΦ
ΦΦ
Φ
Φ(2)
As also illustrated by Figure 1, the interval of fraction distilled I...,,iLi
Ri 1),( =−ΦΦ 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
( )Φbb TT = (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
( )ΦMM = (5)
( )Φρρ = (6)
( )Φηη = (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:
)( bTMM = (8)
)( bTρρ = (9)
)( bTηη = (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 the
suite 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 derived
and 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,kw min
1 m
mr →−
�=
ζζζ
(11)
where K is the number of measured properties and c,k,mζ , c,k,rζ 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,...,Ci 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 kw , 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 (Ti, Ti+1), see Figure 4, and no longer it is a mean
temperature 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:
( ) ( )�+
+=− →−=
1
1
21 min
I
LEi
Li
Ri
LF ΦΦΦ (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 RLEΦ . 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 == +LI
LHE ΦΦ . The reason why only the elements of the vector { }L
ILLE
L ΦΦΦ ,...,1+= are considered
as independent optimisation variables in (12) is that Eqns (1) and (2) can be treated as implicit
equations for ILEiRi ,..,1, +=Φ , i.e. ( )L
iR
iR
i ΦΦΦ = . Alternatively, the reversed relation
( )Ri
Li
Li ΦΦΦ = could have been used with R
iΦ as the set of optimisation variables. It is reasonable
to define bounds for the values of LiΦ in order to avoid getting some unreal solutions and the
following set of constraints was considered:
ILEiILEi
iLii
Li
Li
,...,1,
,...,1,
11
1
+=≤≤
+=≤
+−
+
ΦΦΦΦΦ (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 ( )Ri
Li ΦΦ , 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:
( ) RLE
I
LEi
LHE
Li
Ri ΦΦΦΦ −=−�
+= 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 ( )Ri
Li ΦΦ , is to "normalise" the vector of mass or
volume fractions:
( ) ( ) ( ) I,...,LEj,xI
LEi
Li
Ri
RLE
LHE
Lj
Rjj 1
1
+=−−−= �+=
ΦΦΦΦΦΦ (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 (�−=
=
+1
1
2ij
jij /xx ; Tbi) 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 and
absorption 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 gravity
and 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 the
comparison 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
( )Ri
Li ΦΦ , 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) and
crude 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
KW 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
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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.
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Briesen H., & Marquardt W. (2003). An adaptive multigrid method for steady-state
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Briesen H., & Marquardt W. (2004a). New approach to refinery process simulation with
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Briesen H., & Marquardt W. (2004b). Adaptive multigrid solution strategy for the
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Eckert E. (1999). Non-traditional Characterization of Petroleum Mixtures in Terms of
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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
http://www.vscht.cz/uchi/procesy/)
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
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Kesler M.G. & Lee B.I. (1976). Improved prediction of enthalpy of fractions.
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Products. Process Flowsheets. Paris: Éditions Technip.
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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 characterisationNew 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] ComponentTb
[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-componentsNo.
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 2.78
41 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 -
PA_2_Draw draw for pump around 2 main tray section, 17 pump around cooler 2
PA_2_Return return for pump around 2 pump around cooler 2 main tray section, 16
PA_2_Q cooler 2 duty pump around cooler 2 -
PA_3_Draw draw for pump around 3 main tray section, 22 pump around cooler 3
PA_3_Return return for pump around 3 pump around cooler 3 main tray section, 21
PA_3_Q cooler 3 duty pump around cooler 3 -
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.
ParameterStream
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 MixturesUsed in the Modelling of Separation Processes
Egon Eckert and Tomá� Vaněk
Figures
Figure 1: Definition of a pseudo-component and of its content in the substitute mixture from theTBP curve.
T1
T2
Ti
Ti+1Tbi
xi
TI+1
Tb
0 1 ΦΦΦΦ
0 100 200 300 400 500 600 700 800Tb [K]
0
50
100
150
200
250
300
350
400
450M
[kg/
kmol
]
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 800Tb [K]
-50
0
50
100
150
200
250
300
350AP
I gra
vity
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(Ti,Ti+1).
T1
T2
Ti
Ti+1Tbi
TI+1
Tb
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.
Tbi
Tb
0 1 Φ
Tbi-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 700Tb [K]
50
100
150
200
250
300
350
400M
[kg/
kmol
]
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 700Tb [K]
30
40
50
60
70
80
90AP
I gra
vity
Figure 9. Phase portrait for the API gravity ( ) compared with values retrieved from the databasefor 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
1100T 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
900M
[kg/
kmol
]
Figure 11. Measured characterization curve for the molecular weight ( ● ) 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 1.0Φ
0
20
40
60
80
100
120
140
API g
ravi
ty
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
1100T 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.
Top Related