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ENHANCED METHOD FOR PREDICTING THE
PROPERTIES OF PETROLEUM FRACTIONS
Tareq A. Albahri
Chemical Engineering Dept. - Kuwait UniversityP.O.Box 5969 - Safat 13060, Kuwait
IntroductionThe properties of petroleum and its fractions are usually
determined experimentally in the laboratory. Several methods areavailable in the literature to predict these properties for petroleumfuels from their bulk properties such as the boiling point and thespecific gravity for example. Although accurate enough, these
methods are not suitable for incorporation into the molecularlyexplicit models for simulating the kinetics and dynamics ofpetroleum refining processes.
In previous work1 we have developed a molecularly explicit
characterization model (MECM) that allows for the simulation of themolecular composition of petroleum fractions using a pre-selected setof pure components. What is lacking, however, is the ability topredict the properties of the various streams as the molecular
composition changes during processing by physical separation orchemical reaction. This work focuses on the development of such aproperty estimation method from the molecular composition of
complex, multicomponent mixtures such as petroleum.
Technical Development
In our previous work on the simulation of light petroleum
fractions1 we have found that not all the properties of the petroleumfuel are required to be optimized against those from the purecomponents. In fact only the ASTM D86 Distillation, the PNAcontent and the RVP were sufficient to provide a feasible solution.
All the other properties calculated form the bulk properties of thepetroleum fraction and those from the pure components in them were
almost alike. This lead us to believe that the properties of a petroleumfraction can be estimated from the above three properties alone.
The concept of the proposed model is that the global propertiesof a petroleum fraction such as the boiling point, the vapor pressure
and the paraffins, naphthenes, and aromatics content must be equal tothose calculated from the pure components contained in that
petroleum fraction. When both bulk and pure component propertiesare available, the composition of the petroleum fraction may be
predicted using optimization algorithms as simplified in Figure 1.The predicted composition of a limited set of pure components may
then be used to predict the other properties of the petroleum fuelusing appropriate mixing rules.
MECMModel
RVP
ASTM D86 orTBP Distillation
Detailedcompositionof 68predefinedmolecules
C3
C11PNA
MixingRules
APIMWRIH/CViscositySurface TensionOther properties
Figure 1. Simplified schematic representation of the proposedmodel.
Experimental values of the RVP and PNA are always desirableas inputs. However, when these are not available they may be
predicted using methods available in the literature2,3 making theASTM D86 distillation or the true boiling point (TBP) the minimum
model input required.
The internally calculated properties are the molecular weight,the Reid vapor pressure (RVP), the true vapor pressure at 100F, the
specific (API) gravity, the cubic average boiling point (CABP), themean average boiling point (MeABP), the volumetric average boilingpoint (VABP), the weight average boiling point (WABP), the molar
average boiling point (MABP), the Watson characterization factor(Kw), the refractive index, the carbon to hydrogen ratio (C/H), thekinematic viscosity at 100 and 210F, the surface tension, the aniline
point, the true and pseudo critical temperatures and pressures, thecritical compressibility factor, the acentric factor, the freezing point,the heat of vaporization at the normal boiling point, the net heat ofcombustion at 77F, the isobaric liquid heat capacity at 60F, the
isobaric vapor heat capacity at 60F, the liquid thermal conductivityat 77 F, and the paraffins, naphthenes, and aromatics content. Theseproperties are calculated for the petroleum fraction using well
established methods in the literature or were developed specificallyfor this project1.
The same properties are calculated from the pure component
composition using the appropriate mixing rules from the literature.When the pure component properties are not available in databases
they were estimated using group contribution methods available inthe literature or were developed specifically for this project1.
The difference between the values obtained from the twodifferent methods for the true boiling point and the PNA content areminimized in the objective function the purpose of which is tocalculate the values of xi which is the mole fraction of the pure
components in the petroleum fraction. This is shown in Equation 1where both PNA and Tb of the pure components are a function of x i.The composition of the light ends was determined using the RVP
which is converted to the true vapor pressure at 100 F and then usingsimple bubble point calculations.
The First line in the objective function represents the sum oferrors in the boiling points of the pure components and thecorresponding value on the true boiling point (TBP) curve.The purecomponent concentrations are determined by minimizing the
following modified objective function,
( )
( )
1
1
2
2
( ) 100
( ) 100
n
j j j j
oS b b W b
PNA PNA W PNA
T T T=
+
=
(1)
wherejis the index number of the molecule and nis the total numberof molecules. PNAi and PNA'i refer respectively to the actual and
predicted paraffin, naphthene, and aromatic content of the petroleumfraction. Tbj and T'bi refer respectively to the boiling point of the pure
component jand the corresponding value on TBP curve. W1 and Woare weighting factors and S is the objective function to be minimized.
An optimization algorithm based on the least square method wasused to minimize the objective function while calculating the
concentration of the pure components. The nonlinear regressionalgorithm minimizes the sum of the difference between the fuels bulk
properties and those estimated from pure components. Using theMicrosoft Excel Solver tool and the global optimization algorithm,convergence was achieved in less than one minute for all cases on aPentium IV-1.7 GHz PC.
Discussion
The model was tested to predict the properties of 30 petroleumnaphtha samples ranging in API from 35 to 91, IBP from 62 to 267
F and FBP from 152 to 312 F. Some of these results are shown inTable 1 and Figures 2 to 4. The MECM model proves to be apowerful tool for simulating the properties of petroleum fuels.
Prepr. Pap.-Am. Chem. Soc., Div. Fuel Chem. 2004, 49(2), 925
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This work demonstrates that the complex nature of petroleumfuels may be modeled by a limited set of representative pure
components using non-linear-regression optimization models.Considering the difficulty and limitations in predicting the propertiesof petroleum fuels in the currently used pseudo component
techniques, the proposed method can be an effective alternative. Theclear advantage of the model is its ability to compliment themolecularly explicit models for petroleum refining processing.
20
40
60
80
100
120
20 40 60 80 100 120
API gravity determined experimentally
APIGravitycalcu
latedfrompurecomponents R
2= 0.99
Figure 2. Bar plot for the predicted API gravity from purecomponents for 30 petroleum naphtha samples versus that calculated
from global properties using published methods.
50
60
70
80
90
100
110
120
130
50 70 90 110 130
Molecular weight determined from global properties
Molecularweightcalculatedfrompurecomponents
R2
= 0.99
Figure 3. Bar plot for the predicted molecular weight from pure
components for 30 petroleum naphtha samples versus that calculatedfrom global properties using published methods.
10
12
14
16
18
20
22
24
26
28
30
10 15 20 25 30
Surface tension calculated from global properties
(dynes/cm)
Surfacetensioncalculatedfromp
urec
omponents.
(dynes/cm)
R2
= 0.99
Figure 4. Bar plot for the predicted surface tension from purecomponents for 30 petroleum naphtha samples versus that calculated
from global properties using published methods.
Table 1. Error analysis for some of the properties investigated
No. Property Av. % error Corr. Coef.
1. API gravity 2.67 0.995
2. Cubic average boiling point 1.34 0.9953. Mean average boiling point 0.99 0.995
4. Volume average boiling point 1.34 0.9955. Molar average boiling point 0.83 0.995
6. Mass average boiling point 1.07 0.9967. Watson characterization factor 0.80 0.970
8. Molecular weight 2.06 0.9909. Refractive index 0.21 0.99310. Hydrogen content 2.57 0.96511. Viscosity at 210 F 4.04 0.960
12. Viscosity at 100 F 5.41 0.97213. Surface Tension 2.67 0.995
14. Aniline Point 4.27 0.83015. Critical temperature 0.93 0.99116. Pseudocritical temperature 0.80 0.98917. Pseudocritical pressure 2.22 0.890
18. Heat of vaporization 2.07 0.94819. Heat of combustion 0.80 -
20. Freezing 5.38 -
21. Acentric factor 3.13 0.94622. Critical compressibility factor 0.25 0.83223. Flash point 5.16 0.924
AcknowledgmentThis work was supported by Kuwait University, Research Grant
No. EC04/01.
References[1] T. A. Albahri, Am. Chem. Soc., Div. fuel chem. prep., 2004, 49(1), 327-
328.
[2] M. R. Riazi & T. E. Daubert, Ind. Eng. Chem. Process Des. Dev., 1980, 19
(2), 289-294.
[3] Jenkins, G. I. and white, M. M.. J. Inst. Petrol., 1969, 55 (543), 153.
Prepr. Pap.-Am. Chem. Soc., Div. Fuel Chem. 2004, 49(2), 926