Characterization of Crude Oil by Real Component Surrogates

Article pubs.acs.org/EF

Characterization of Crude Oil by Real Component Surrogates Anton M. Reiter,† Thomas Wallek,*,† Philipp Mair-Zelenka,† Matthaü s Siebenhofer,† and Peter Reinberger‡ †

Institute of Chemical Engineering and Environmental Technology, NAWI Graz, Graz University of Technology, 8010 Graz, Austria OMV Refining & Marketing GmbH, 1020 Vienna, Austria

‡

S Supporting Information *

ABSTRACT: For characterization of crude oil and its primary fractions, the generation of substitute mixtures (surrogates) containing only real chemical components is a promising approach. The abandonment of pseudo-components, except for the utmost high-boiling fractions, allows for rigorous application of standard thermodynamic models (e.g., activity coefficients and equations of states), increasing reliability of phase-equilibrium calculations and predictive capabilities using process simulators. In this paper, an improved algorithm for characterization of petroleum fractions with real components is developed and applied to characterization of crude oil and its products through generation of substitute mixtures. The capabilities of emulating the separation behavior of crude oil are verified through a comprehensive analysis of a simulation conducted with real components by comparison to real plant data of an operating crude oil distillation unit (CDU). Additionally, a simulation based on the traditional pseudo-component approach is used for comparison.

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MOTIVATION

INTRODUCTION Pioneering activities in the generation of substitute mixtures for petroleum fractions have been reported by Hecht et al.5 This research team focused on the characterization of diesel fuel with real components exclusively from the homologous series of alkanes, alkylbenzenes, or alkylcyclohexanes by fitting only the true boiling point (TBP) curve.5 Another attempt to facilitate real chemical components was conducted by Jabr et al.,6 characterizing naphtha and simulating a debutanizer column. On the basis of TBP curves and specific gravities of boiling range cuts, different feed properties were calculated. These properties were necessary because, for each real component used in the surrogate, one data point is required. As a result, the feed was characterized by 19 real chemical components, yielding fairly good simulation results. Eckert et al. published several papers dealing with the application of real chemical components.7−13 The application of the algorithm was demonstrated in the fields of crude oil processing,9,10 gasoline fractionation,9 modeling of gasoline blends,8 and modeling of pyrolysis and cracking of atmospheric gas oil.11,12 Their algorithm represents a two-step approach. In the first step, the real components are selected, and in the second step, the composition is derived. For component selection, the TBP curve is split into several temperature intervals analogously to the pseudo-component approach. For each interval, the component that best describes the interval is selected. In addition to TBP curve and liquid density, other properties, such as viscosity, can be considered. The composition is derived from an optimization algorithm, which shifts position and fraction of the intervals along the boiling curve and other property curves. However, this optimization can result in gaps or interferences of the cuts, which is a non-

Crude oil is a hydrocarbon mixture containing thousands of individual components ranging from light gases to very heavy, high-boiling components.1 This mixture of a vast number of components with unknown chemical composition has to be processed in the refineries. Because of the increased need for efficiency, a much deeper understanding of the chemical specificity of refinery streams will be necessary for optimization.2 A molecular-based characterization of the refinery streams can help to achieve this task.3 A state-of-the-art approach for crude oil characterization is the pseudo-component approach, which is readily available in commercial simulation programs. Pseudo-components are generated on the basis of measured bulk properties, and all further calculations are based on these artificial components. Within the generation of pseudo-components, especially the estimation of their critical data and the acentric factor is arguable and no single commonly accepted method has been established thus far.3 Another approach to characterize complex hydrocarbon mixtures is the use of real chemical components instead of pseudo-components. One advantage of using real components is the applicability of rigorous thermodynamic models instead of mainly empirical correlations for property estimation, which might be prone to errors.4 This allows also for the modeling of fractions and mixtures with non-traditional components, e.g., bio-based products, which are not captured in the original pseudo-component approach. Furthermore, such an approach enables use of reaction kinetics or inclusion of key components within the simulation.3 Moreover, it is possible to define key components within simulations, where calculations can rely on measured pure component data and interaction parameters. Additionally, the real component approach allows for experimental validation of predictions and applied models. © 2014 American Chemical Society

Received: June 25, 2014 Published: July 23, 2014 5565

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These individual parts can be related to the fractions obtained by crude oil analysis. The light end fraction is referred to as xLE, and the following higher boiling fractions are numbered in the following:

physical solution requiring a normalization step. In one of the applications shown,10 this algorithm is used to characterize crude oil and applied to further process simulations of a crude oil distillation unit (CDU), as reported in the “documentation” of the simulation program Aspen Plus 11.1. Albahri14,15 proposed an algorithm to facilitate the use of real components, which he applied to characterization of naphtha and simulation of naphtha splitters. This so-called molecularly explicit characterization model (MECM) yields the composition of a set of pre-selected components for the characterized fraction. With the distillation curve (ASTM D86 or TBP), the paraffin−naphthene−aromatic (PNA) content, and the Reid vapor pressure, several fraction properties are calculated with different correlations. All of these properties can then be used in an optimization step to calculate the composition of a mixture that exhibits these properties. Because a mathematical fit of the TBP curve is used, an unlimited number of boiling points can be calculated.14 Hence, the number of possible components in a substitute mixture is not limited any more by the number of different properties calculated.6 In this paper, a combination of the MECM by Albahri14 and the characterization algorithm by Ba et al.8 as well as Eckert and Vaněk13 is developed and applied to characterize crude oil and its primary fractions by substitute mixtures. With the exception of fractions containing the outmost high-boiling components, the surrogates consist of real components instead of pseudocomponents. Different from the proposal by Ba et al.8 and Eckert and Vaněk,13 the presented algorithm integrates the characterization approach for undefined mixtures of wide boiling range, as reported by Riazi and Daubert.16 The latter algorithm gives access to independent characterization of individual fractions obtained from TBP distillation. Although this approach still does not represent the true composition of hydrocarbon mixtures, it is a first step toward improvement of molecular information in petroleum process simulations. Because of the availability of comprehensive pure component databases, this approach can be implemented right away in standard simulation packages, such as ASPEN Plus, Aspen HYSYS, Petro-SIM, or PRO/II. Applicability of the surrogates is verified by comparison to a simulation based on the pseudo-component approach as well as comparison to operation data records from crude oil distillation.

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∫0

1

Θ(x) dx = u3

∫l =u 3

2

un = 1

∫l =u n

2

1

Θ(x) dx+ LE

Θ(x) dx

n−1

(2)

This partition of the whole crude oil allows for direct use of the analysis data of the individual fractions, and each of these fractions can be characterized independently. Furthermore, equations for the calculation of the normal boiling point and the densities of the fractions can be derived by this method as shown. The same approach of splitting the crude oil into several fractions can be used to split a fraction into several components. Using an algebraic equation for the TBP curve, TBP(x), and focusing on only a share of fraction i, the normal boiling point for a component j, which should cover the range (xi,uj−xi,lj) of the TBP curve can be calculated as the integral mean value of this interval. x

∫x i ,uj TBP(x) dx Tb(x

i , uj − xi , l j)

i ,lj

=

xi , uj − xi , l j

(3)

This exact calculation is frequently approximated by expressions requiring less mathematical effort, such as, for example, proposed by Ba et al.8 However, with both methods, the normal boiling point for fractions or components along the TBP curve can be calculated, resulting in a stepwise TBP curve approximation, as depicted by Albahri.14 The physical properties of the fraction are calculated by discretization of eq 1 for a finite number of m components. For the whole crude oil, this results in m

Θ=

∑ ΘjΔxj (4)

j=1

as deduced by others. If only one fraction i with its mass fraction Δwi is characterized, with Θ representing the specific volume of the component, vj = 1/ρj, and Δxi denoting the mass fraction of the component wj, the following ideal mixing rule for liquid mass density is obtained: 14

ρi =

Δwi m

∑j=1

wj ρj

(5)

Hence, it is shown that the properties of a real component surrogate are linked to the properties of a petroleum fraction through eqs 3 and 5. On the basis of this link, the algorithm generates a substitute mixture, which is able to mimic both the liquid densities of the fractions and the boiling behavior of the crude oil. For this purpose, a numerical optimization routine has to be solved, with the following objective function:17 ⎛ Tb(w − w ) − Tbj ⎞2 ⎛ρ − ρ ⎞2 i , uj i , l j i analyzed ⎜ ⎟ ⎜ ⎟ → min + Fρ⎜ ∑⎜ ⎟ ⎟ Tbj ⎝ ρanalyzed ⎠ j=1 ⎝ ⎠ m

f=

(6) The general design and structure of the objective function proposed for the individual fractions is similar to the one used by Albahri14 for naphtha samples. It is based on the least-squares method, where the first part accounts for the fitting of the TBP curve and the second part accounts for the fitting of the liquid density of the fraction. The whole function is based on the mass fractions of the components requiring an algebraic equation for the TBP curve. The integral mean value for the normal boiling point Tb(wi,uj−wi,lj) ∀ j = 1, ..., m components contained in

1

Θ(x) dx

Θ(x) dx+... +

u2

∫l =u =x

= ΘLE + Θ2 + Θ3 + ... + Θn

The presented characterization algorithm is based on standardized experimental data usually determined for crude oil. It is designed as a two-step approach. First, the possible real components are selected, and then the composition of the substitute mixture is calculated. The minimum input data for crude oil are the yield and the liquid density of the individual fractions obtained during TBP distillation. As an option, additional use of the light-end analysis increases characterization quality. Riazi and Daubert16 propose a characterization method for undefined mixtures of wide boiling range by dividing the fraction along the TBP curve into an infinite number of components. On the basis of this assumption, an arbitrary physical property Θ can be described as a function of the degree of vaporization x on the TBP curve by

∫0

Θ(x) dx+

1

IMPROVED CHARACTERIZATION ALGORITHM

Θ=

u1= x LE

∫l =0

(1)

The expression Θ(x) represents the pure component property considered. Fundamental mathematics allow for the splitting of the integral covering the whole boiling range into a sequence of n portions, with the lower boundary, li, and upper boundary, ui, for fraction i. 5566

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fraction i can be calculated then. The components fully cover the TBP curve, with each of them holding their mass fraction (wi,uj−wi,lj). In the first part of the objective function, a least-squares approach for TBP curve fitting is employed. In the nominator, the difference between the calculated value according to eq 3 and the pure component normal boiling point Tbj is determined, and in the denominator, Tbj is used to generate a dimensionless value. The absolute maximum difference in the nominator should not exceed the standard fit error of the algebraic TBP curve. In the nominator of the second part, the difference between the experimental fraction density ρanalyzed and the calculated liquid density ρi is determined and divided by the analyzed value. The density difference in this term should be lower than the reproducibility of liquid density determination according to ASTM D129818 for opaque liquids. The constant Fρ is intended for weighting the relative contributions of the TBP term and the density term. For high values of Fρ, the density of the fraction is fitted very well, while for values close to zero, good adjustment of the TBP curve is achieved at the expense of density precision. Within the algorithm proposed, this weighting factor is adjusted automatically to comply with both criteria mentioned. If the density criterion is not met, the factor is either increased or decreased by 10%. If no solution can be found, it is necessary to extend one of the criteria, whereas it is recommended to increase the criterion regarding the fit of the TBP curve. Another factor that highly influences the quality of the surrogate is the selection of real components. All hydrocarbons, except olefinic components, were selected from the pure component database of the simulation software Petro-SIM Express v4.0 SP1 (Build 561). Olefinic components were excluded because it is assumed that they are not contained in crude oil.19 With these limitations, about 400 pure components with boiling points up to 500 °C are available.

challenging fractions is the lowest boiling fraction, because this fraction contains components with a fixed amount of mass. Therefore, these components have to be explicitly considered in the component selection and optimization. The selection of the components of fraction 1 of lot R1 is illustrated in Figure 2.

Figure 2. Component selection for the lightest fraction of R1 with the analysis data (green line) together with the database components (red crosses), light ends (brown circles), and selection frame (orange dashed frame).

Strict requirement for selecting a component is that its normal boiling point is within the boiling range of the corresponding fraction, on both fraction limits extended with the standard fit error of the algebraic equation of the TBP curve. The density range is used to limit the number of possible components, with the selected components uniformly distributed over the whole range. On the basis of this component selection, the optimization algorithm is applied. Components yielding a mass share of less than 10−6 are assumed to be not significant and, therefore, are neglected. The crude oil fraction, which can be characterized with real components (RCs), and the according number of RCs together with the number of pseudo-components (PCs) needed for the high boiling part are given in the upper part of Table 1. The substitute mixtures generated for fractions 1−4

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APPLICATION OF THE CHARACTERIZATION ALGORITHM TO THE FEED STREAMS OF THE CDU The algorithm is applied to the CDU feed of three different lots of crude oil (R1, R2, and R3) and one naphtha stream (gas HDS). From the mentioned crude oils, the mass fraction of light ends and, from TBP analysis, the liquid-phase fraction density for the corresponding temperature range plus distributive amount evaporated were specified. On the basis of these data, an algebraic fit of the TBP curve was computed. For crude oil R1, the TBP analysis and the pure components are depicted in Figure 1, with the liquid-phase densities at a

Table 1. Characterization Details for Crude Oils R1, R2, and R3 crude oil fraction characterized with RCs in mass percentage number of RCs used number of PCs used absolute density deviation for fraction 5 in kg/m3 absolute maximum temperature deviation for fraction 5 in °C

R1

R2

R3

66.04

56.39

49.79

92 14 0.68

105 13 0.51

107 15 0.25

4.2

4.9

7.9

of all three crude oils represent the liquid density within ±0.1 kg/m3. With regard to fraction 5, it is necessary to slightly increase the temperature criterion, to allow for density characterization within the desired limits. Deviation of the calculated density and temperature from experimental data is shown in the lower part of Table 1. Although the temperature criterion is increased, the maximum absolute temperature deviations are less than twice the standard error of the fitted algebraic TBP curves. The feed gas HDS is characterized analogously to the lowest boiling crude oil fraction. It exhibits a liquid-phase density of 731.0 kg/m3 and the light end composition. The optimization results in a density weighting factor of 20 000 and an increased temperature criterion. The resulting 55 component surrogate is

Figure 1. Analysis data of crude oil 1 (R1) and available real components for characterization.

temperature of 15 °C plotted versus the normal boiling points. For a lack of real components of >410 °C, only fractions 1−5 can be characterized. For the remaining fractions, pseudocomponents must be used. The pseudo-components are generated with the process simulation software Petro-SIM Express v4.0 SP1 (Build 561) using the same analysis data as applied in real component characterization. Among the most 5567

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kg/m3. The pseudo-component characterization yields a liquidphase density of 869.4 kg/m3, and the characterization using real components results in a liquid-phase density of 880.9 kg/ m3, which corresponds to a deviation of 1.2%. This difference can be explained by the use of different models for density calculations. In the characterization model, an ideal mixing rule for the liquid density is used (eq 5), and within the simulation software, the liquid density is calculated rigorously by a corresponding state equation developed by Hankinson and Thomson,20 including the Chueh and Prausnitz correction factor for compressed fluids.21 Simulation of the CDU. The flowsheet of the CDU is shown in Figure 4. At first, the desalting of the crude oil blend is simulated through saturation with water. After heating and compression, the desalted crude oil is fed to the preflash unit with nine stages for removing light gas and naphtha fractions. Additionally, the head system of this column is connected to the fractionation unit, resulting in an additional reflux in the preflash column, as seen in Figure 4. After the pressure and temperature of the flashed crude oil are adjusted, this stream enters the fractionation unit, comprising of 41 trays, with the simulation model shown in Figure 5 in detail. This column includes three pump-arounds and four side strippers to withdraw and adjust the quality of the products kerosene, light gas oil (LGO), heavy gas oil (HGO), spindle oil, and atmospheric residue. In the head system of the preflash and fractionation units, the gas and naphtha fractions are handled. Parts of the condensed and dewatered gas and naphtha are used as liquid reflux in the preflash unit. The remaining main fractions of gas and naphtha are withdrawn in the head system and processed elsewhere in the refinery. This simulation model is used with identical specifications for both simulations with the real component approach and the pseudo-component approach. Comparison of Simulation Results to Plant Data. One of the most important features for a successful process simulation is the accuracy of the balance. Plant data based on online measurement during a test run are given in terms of volume flow rates for the main products. A balance on volumetric basis for all hydrocarbon streams passing the battery limits together with the data for the plant feed, the crude oil blend, is shown in Table 2. The simulation is set up based on these data, and values marked by the table footnote a indicate that these data were used as a specification for the simulation. The residue as the heaviest product stream is characterized similarly by both simulations, yielding a deviation in the order of 10%. The volume flow rate of “gasoline preflash” is described better with the simulations using real components. For the remaining two gasoline streams, no experimental data are available but similar results are obtained by both simulations. The liquid densities of the main products of the fractionation unit, obtained by sampling during the test run, are compiled in Table 3. Both simulations generate similar density values for the product streams. The products HGO and LGO are somewhat better described by the simulation based on pseudocomponents, whereas the liquid density of kerosene is described slightly better in the simulation based on the real component characterization. Both simulations result in acceptable values for liquid-phase density. A crucial point in the real component simulation is the distribution of the real components and pseudo-components because different models for density calculation are used for the component groups. This distribution for the real component

able to emulate the whole stream with a density deviation of less than 0.1 kg/m3 and a maximum absolute temperature deviation of 2.5 °C. The experimental data, algebraic fits of the TBP curve, the density range, and weighting factors for all characterized fractions are listed in the Supporting Information. Additionally, the final composition of the substitute mixtures for R1, R2, R3, and gas HDS as well as the property data of the pseudocomponents are included. The application of the algorithm shows that hydrocarbon fractions with a final boiling point of less than 410 °C can be described well with the algorithm. Automatic adjustment of the weighting factor between the TBP curve and liquid density usually works very well. Although it is necessary to increase the temperature criterion slightly, acceptable deviations are obtained, indicating reliable real component characterization.

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VALIDATION OF THE SURROGATE MIXTURE BY SIMULATION OF A CDU For the validation of the surrogate mixture, real plant data of a CDU are compared to the simulation results obtained with the pseudo-component approach and the crude oil characterization with real components. The main feed of the CDU is a blend of the three crude oils. Additionally, a naphtha stream is fed to the preflash unit of the CDU, as illustrated in the flowsheet of the simulation in Figure 4. The simulation is based on 159 real components and 42 pseudo-components. The validation of the feed characterization with real components includes the evaluation of the generated crude oil blend and the simulation results obtained for the main products, focusing on liquid-phase density and boiling curves. Characterization of the Main Feed of the CDU. The main feed is a blend of the three different crude oils, which were characterized independently, as shown earlier. This blend results in a mixture of 44.7% R1, 36.4% R2, and 18.9% R3 on a volumetric basis. For this feed, the experimental TBP curve, obtained by high-temperature simulated distillation in the temperature range of 36−750 °C and the liquid-phase density were specified. This TBP curve data together with the calculations based on real components and pseudo-components are displayed in Figure 3.

Figure 3. TBP curves of the crude oil blend.

In the range of 5−50 mass %, an absolute average deviation of 17.5 °C for the pseudo-component characterization and 20.1 °C for the real component characterization referring to the experimental data are achieved. At evaporation rates higher than 50 mass %, the TBP curves of both characterization approaches merge and differ by more than 30 °C from the experimental data. The second evaluated property for this stream is the liquid mass density, with an experimentally determined value of 870.4 5568

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Figure 4. Simulation model of the CDU.

Table 2. Experimental and Calculated Data with Real Components (RCs) and Pseudo-components (PCs) of Main Feed Streams (Upper Part) and Main Product Streams (Lower Part) of CDU on a Volumetric Basis volume flow rates in m3/h R1 R2 R3 gas HDS residue spindle oil HGO LGO kerosene preflash gasoline main gasoline to gasoline unit a

plant data

RC

PC

482.7a 393.2a 204.1a 26.2a 442.4 27.0a 48.0a 239.8a 142.5a 131.5 na na

482.7 393.2 204.1 26.2 399.3 27.0 48.0 239.8 142.6 132.2 110.2 20.5

482.7 393.2 204.1 26.2 402.9 27.0 48.0 239.8 142.5 117.9 112.7 18.3

Specified within the simulation.

Table 3. Deviations of Liquid-Phase Density for the Main Product Streams liquid density in kg/m3 residue spindle oil HGO LGO kerosene

Figure 5. Simulation model of the fractionation unit.

deviations in percentage

plant data

RC

PC

994.1 906.7 903.0 853.1 793.4

−0.21 2.37 −2.21 1.73 −0.71

−0.26 2.30 0.52 −0.02 −1.11

by simulated distillation, are analyzed in Figure 7. The absolute average deviation (AAD) obtained for kerosene is 6.9 °C for the real component characterization and 12.3 °C for pseudocomponents in the range of 5−95% vaporized. This remarkable difference between the AADs results from the significantly better TBP curve representation by the real component approach in the range of 10−70% vaporized. At degrees of vaporization higher than 70%, both simulations considerably

simulation is addressed in Figure 6. The residue contains more than 90% of all pseudo-components. Spindle oil is composed of 78.3% pseudo-components, and HGO contains 34.7% pseudocomponents. All of the other lighter streams, such as LGO, kerosene, and naphtha products, solely contain real components. To evaluate the capabilities of the real component approach, the TBP curves of the products LGO and kerosene, obtained 5569

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components, which is of primary interest for identifying nextgeneration fuels.

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ASSOCIATED CONTENT

S Supporting Information *

TBP analysis data together with the fraction densities as well as the light end analysis; algebraic fit for the TBP curves; for the fourth feed stream, the light end analysis and TBP curve together with the algebraic fit; density ranges for the selection of the components for all fractions characterized with real components together with the final weighting factors during optimization; and calculated compositions in terms of real components together with the physical property data of the applied pseudo-components for the high boiling part. This material is available free of charge via the Internet at http:// pubs.acs.org.

Figure 6. Distribution of products and composition in terms of real and pseudo-components.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +43-0-316-873-7966. Fax: +43-0-316-873-7469. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge support from NAWI Graz and thank OMV Refining & Marketing GmbH for providing financial support and plant data for scientific evaluation.

Figure 7. TBP curves of main products kerosene and LGO.

underestimate the temperature. The TBP curve of LGO is welldescribed by both simulations with an AAD of 7.6 °C for the real component characterization and 7.1 °C for pseudocomponents in the range of 5−95% vaporized.

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REFERENCES

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SUMMARY In this paper, an improved characterization approach for crude oil and its fractions based on real chemical components was presented and applied to specification of crude oil and a naphtha fraction. It was shown that each fraction of a crude oil can be characterized independently in a two-step approach. First, real components are selected, and second, the composition of the surrogate is deduced. Beyond reported approaches,7−11,13,14 this method allows for direct use of experimental data and component selection is only based on the boiling range of the fraction along with liquid bulk density. Opposite to the pseudo-component approach, which uses artificial constituents, the presented algorithm particularly uses real components to represent the given properties of the fraction. Provided that real components cover the whole boiling range, the algorithm allows for complete abandonment of pseudo-components. In the case of crude oil, this holds for primary fractions, such as LGO, kerosene, or naphtha, which can be fully described with real components. One advantage of real component surrogates is the applicability of rigorous thermodynamic models in fuel simulations, comprising equilibrium calculations, physical property data prediction, and consideration of chemical reaction kinetics. Furthermore, properties of real component surrogates can easily be validated by the experiment. In particular, this suggests application of the algorithm in the area of fuel combustion simulation and construction of design fuels. For the latter, the algorithm allows for prediction of fuel property changes because of the admixture of, e.g., biogenic 5570

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(20) Hankinson, R. W.; Thomson, G. H. AIChE J. 1979, 25, 653− 663. (21) KBC Advanced Technologies plc. Petro-SIM Express v4.0 SP1 (Build 561) User Guide; KBC Advanced Technologies plc: Walton on Thames, U.K., 2011.

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