Energy & Fuels 1996, 10, 915-920
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Modeling Simulated Distillation: A Tool for the Evaluation of Hydroconverted Petroleum Residues R. Bacaud*,† and L. Rouleau Institut de Recherches sur la Catalyse, C.N.R.S., 2, Avenue Albert Einstein, 69626 Villeurbanne Cedex, France
B. Bacaud Ecole Nationale Super´ ieure de Techniques Avancee´ s, 32, Boulevard Victor, 75015 Paris, France Received November 17, 1995X
Simulated distillation, commonly applied to the characterization of complex products issued from the conversion of heavy petroleum fractions, provides information concerning the yield of distinct classes of distillable products. A simple modeling of simulated distillation data has been applied to several distillates obtained by hydroconversion of a vacuum residue. This model gives access to characteristic parameters which allows a description of this complex mixture in terms of two distinguishable components. The lighter one is produced through two simultaneous mechanisms, involving the rupture of substituted carbon positions and a symmetrical cleavage of the resulting molecules. The remaining component is representative of the initial feed and is preserved in a slightly modified state up to high level of conversion.
Introduction The progessive introduction, in petroleum refining processes, of nonconventional feeds originated from heavy crude is a long-term tendency in connection with the development of new resources. The main characteristic of these crudes is the high content of asphalt and residue. Thus, a straight vacuum distillation of these crude oils generates large amounts of low value byproducts. The market for heating fuels, which is the main outlet of nondistillable residues, is being progressively reduced for ambient considerations and, in parallel, the demand for light products like transport fuels is increasing. Thus, the refining of petroleum appears as a process which consists essentially in satisfying a fluctuating demand, with little flexibility concerning the input. This conflicting evolution of supply and demand implies that petroleum residues must be converted and that processes for this transformation have to be developed. There are two broad categories of conversion processes: those performing an improvement of the hydrogen-to-carbon ratio through carbon rejection (coking or deasphalting) and, at the opposite, those increasing the hydrogen content of the feed (hydrocracking). Obviously, the latter performs a better valorization of the fossil carbonaceous matter contained in residues than does coking. Hydrocracking involves a complex set of reaction pathways whose ultimate goal is to cause a decrease of the mean molecular weight of the feed and may also involve a reduction of heteroatoms content. The control of such reactions relies essentially upon an adequate balance between the depth of conversion and the cost of hydrogen incorporation.1 In order to achieve † X
E-mail:
[email protected]. Abstract published in Advance ACS Abstracts, May 1, 1996.
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a proper control of process conditions, an adequate and fast-responding technique for products evaluation is necessary. Analytical procedures for characterizing processed products rely upon a compromise between extensive, time-consuming determinations and the necessity of a rapid response. They must provide information concerning the yield of distillate (which is the desired product) and its properties. Simulated distillation by gas chromatography (SimDis GC) is able to provide this essential information. As described in ASTM method D2887, SimDis GC is based upon the use of packed columns.2 The multiple drawbacks of this technology induced the introduction of capillary-based methods, which produce more precise results than ASTM method D2887.3 Capillary columns are presently applicable to a wide range of boiling points, since stationary phases withstanding temperature up to 440 °C allow the elution of hydrocarbons containing up to 120 carbon atoms.4 The yield of any given fraction of products can be deduced from SimDis GC data. The objective of the present work is to illustrate that more valuable information can be deduced from simulated distillation data through the development of a simple mathematical model. For this purpose, hydroconversion of a vacuum residue has been performed in the presence of various classes of dispersed disposable catalysts employed at low catalyst-to-feed ratio. The resulting hydroconverted raw products were characterized by simulated distillation. Applying curve fitting techniques, we calculated the parameters of a math(1) Rouleau, L.; Bacaud, R.; Breysse, M. Prepr.sAm. Chem. Soc., Div. Pet. Chem. 1994, 39 (3), 403-407. (2) Drews, A. W. In Manual on Hydrocarbon Analysis, 4th ed.; American Society for Testing of Materials: Philadelphia, 1989; ASTM D2887. (3) Workman, D. S.; Noel, F.; Watt, M. R. J. Chromatogr. Sci. 1993, 31, 95-99. (4) de Zeeuw, J. Chrompack News 1990, 17, 3.
© 1996 American Chemical Society
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ematical model of cumulative SimDis GC curves. The priority in choosing a representative model was not only obtaining a convenient fit between calculated and experimental data but also the generation of meaningful parameters which could be correlated with the usual conversion (yield of distillate) parameter and which produced some insight into the mechanism of hydroconversion. Experimental Section
Bacaud et al. Table 1. Calculated Parameters of Modeled SimDis GC Data of a Vacuum Residue, Obtained with Various Functions amplitude
(5) Rouleau, L.; Bacaud, R.; Breysse, M.; Dufour, J. Appl. Catal. A: Gen. 1993, 104, 137-147.
width
model
A
σ
X
σ
W
σ
cumulative Gaussian logistic dose sigmoidal exp SimDis GC values
88.38
0.897
55.72
0.206
10.56
0.233
95.43 88.87 88.9
1.061 1.116 naa
56.64 55.81 57
0.224 0.258 na
7.86 6.32 na
0.157 0.186 na
a
1. Materials. The charge for hydroconversion experiments was a butane-deasphalted oil (DAO) obtained from a 510+ °C vacuum residue. Its analytical characteristics were H/C atomic ratio ) 1.64; S ) 0.85 wt %; Ni + V ) 5 ppm; viscosity ) 60 cSt at 373 K; specific gravity ) 1.058 g·cm-3. Hydroconversion was performed either in thermal conditions or in the presence of catalysts. The catalysts of this study were used as dispersed, disposable solids. Three different kinds of solids have been used: nickel-carbon plasma-prepared catalyst,5 molybdenum naphthenate, and alumina-supported Ni-Mo catalyst (Shell 424). The catalyst-to-feed ratio was in the range 0-500 ppm, expressed as metal (Ni or Mo + Ni). 2. Equipment and Procedure for Hydroconversion. Experiments were carried out in a 250 cm3 autoclave equipped with a magnetically driven impeller (Autoclave Burton Corblin, Paris, France), a cooling coil, and a pressure transducer. The reactor was heated by induction (6 kW high-frequency generator, Celes, Lauterbourg, France). This heating system confers to this type of high-pressure vessel outstanding thermal performance: heating rate up to 1.5 K·s-1, isothermal stability better than (0.5 K. Hot DAO (100 g) was poured into the autoclave at 373 K. This temperature is required owing to the high viscosity of the charge at a lower temperature. The desired amount of catalyst was added. After the autoclave had been purged and cooled, hydrogen at a pressure of 14 MPa was introduced and the pressure was accurately measured. The temperature was then raised to 373 K and kept at this value for 10 min in order to start mixing and to enable the speed of the impeller to stabilize at 600 rpm. Afterwards, the heating program was started. The heating rate was 0.5 K·s-1 and the final temperature (713 K) was maintained for 60 min (catalytic conversion) or a variable time, 40-60 min in thermal conversion. During a run, pressure and temperature were continuously monitored. After the desired residence time, the reactor was cooled down and the final pressure was measured. Gases were recovered by heating the autoclave at 373 K in order to strip the liquid products; a trap maintained at 273 K and positioned at the output of the reactor condensed volatile products (mainly C5 to C7 hydrocarbons). This liquid fraction was collected together with the total liquid content of the autoclave. 3. Simulated Distillation by Gas Chromatography. A sample of the liquid products was diluted with carbon disulfide at the concentration of 0.4 wt %. A 1 µL portion of this solution was injected into a HT5 column (6 m × 0.53 mm) equipped with a 1.25 m precolumn. This column can be heated up to 700 K and allows the elution of hydrocarbons up to C120. The gas chromatograph (HP 5890), equipped with on-column injector and flame ionization detector, was programmed from 303 to 673 K at 10 K·min-1. The temperature of the injector was maintained at 3 K above the column temperature. Calibration of Retention Time Scale. Retention time (RT) can be converted to a scale of equivalent number of carbon atoms, x, contained in a series of linear paraffins which are eluted in a given range of RT. Low x values (C12 to C40) were calibrated with pure paraffins. A mixture of linear, even
center
na ) not applicable.
polyethylene (Polywax 655, Luzzato & Figlio, Paris, France) was used as calibration standard in the range C24 to C100. Quantitation. The detector signal was integrated every 0.1 min. On completion of the temperature program, the signal corresponding to a blank run performed in the same condition was subtracted. If the sample was totally eluted during the analysis, the total area of the detector signal was normalized to 100% and the amount of material eluted in a given range of temperature (or C atoms) was simply deduced from its contribution to the total area. In this procedure it is assumed that the molar response of the detector is constant over the whole elution range of the sample, i.e., that the mean molecular structure of the eluted compounds is constant. When solutions of unconverted DAO were analyzed, elution was limited to 92% of the injected material; in that case an internal standard (dodecane) was added to the sample. From the material balance of eluted material, the yield of 510- °C distillate was determined and defined as conversion according to the following expression:
conversion ) (wt % 510- °C in products wt % 510- °C in feed)/wt % 510+ °C in feed A commercial software package (Peakfit, Jandel Scientific) was used for curve fitting.
Results 1. Deconvolution of Simulated Distillation Curves. 1.1. Fitting SimDis GC Data of Feed. The distillation, or simulated distillation, of any cut of a petroleum-based material, consists in performing an inventory of a population of hydrocarbons belonging to a finite number of classes of boiling interval. If the process of classification is started before the initial boiling point (bp) and is pursued up to the total evolution/elution of the sample, thus covering the complete range of bp distribution of the sample, a plot of the cumulative amount of eluted material (y) as a function of bp is obtained. Alternatively, this quantity may be plotted as a function of the number of carbon atoms (x) of a linear paraffin possessing the same bp. This presentation was preferred since it provides more significant information about the structure of the feed and the transformation it suffers during hydroconversion. Furthermore, the calibration of RT scale with a series of paraffins avoids the conversion of carbon atoms number scale to bp scale, which is affected by some uncertainty for heavy paraffins. The resulting cumulative curve may be described by several functions, characterized by three parameters: (1) amplitude, A, which express the total eluting amount, normalized to 100%; (2) center of the distribution, X; and (3) width of the distribution, W.
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Energy & Fuels, Vol. 10, No. 4, 1996 917
Figure 1. SimDis GC of vacuum residue. Model: cumulative normal probability function.
Figure 2. SimDis GC of vacuum residue. Model: logistic dose response function.
The following functions were applied to the modeling of the SimDis GC curves of the vacuum residue used as the feed for hydroconversion experiments:
logistic dose response:
y)
A 1+
sigmoidal function: y )
A 1 + exp-z
(Xx )
W
with z )
x-X W
cumulative (integral form) of the normal probability (Gaussian) function, defined as: A z y ) 1 + erf 2 x2
[
( )]
where erf(z) is the error function:
erf(z) )
∫0zexp(1 - t2) dt
2 xΠ
Applying curve fitting technique, the parameters of these three functions were determined. The resulting values and the corresponding standard deviations, σ, are collected in Table 1. The amplitude parameter possesses the same physical significance as the total amount of eluted material during SimDis GC; and similarly, the center of distribution is comparable with the value of the x-axis at 50% elution. Considering the respective values of these experimental parameters as deduced from SimDis GC curves, the models seem satisfactory. The comparative graphs of calculated and experimental data are presented in Figures 1-3. In each graph, a plot of the difference between experimental and calculated values of y (residuals) is included. A random distribution and low values of the residuals are indicative of an adequate fit. From the point of view of fitting quality, no decisive distinction can be made between the Gaussian and logistic dose models. Both exhibit low residuals which do not exceed 2%, with the exception of a systematic deviation near the final part of the distribution, which may be attributed to the uncertainty resulting from incomplete elution of DAO. The results obtained by applying the sigmoidal function are less satisfactory (Figure 3) than the preceding models. Consequently, this function has been excluded since it does not properly reflect experimental data. The deci-
Figure 3. SimDis GC of vacuum residue. Model: sigmoidal function.
sion between the Gaussian and logistic dose models must consider the physical meaning of the equations. The logistic dose function is effective for describing transitions between two extreme normalized values, but its mathematical expression cannot be related with physical considerations. In contrast, a Gaussian model can be related with the composition of petroleum fractions, namely a vacuum residue or any product issued from hydroconversion. Such a complex mixture, which is composed of thousands of chemical entities, can be viewed as the sum of a large number of independent entities or microfractions. Each one of these fractions is characterized by a boiling point and a distillation interval. The observed boiling point, considered as a variable V possessing a variance σ2, can be regarded as the sum of a large number of independent random variables Y:
V ) Y1 + ... + Yn The common variance of the Yi terms is σ2/n. Let us make the change
Vi ) Yixn The expression of V becomes
V)
Vi + ... + Vn xn
The Vi terms are independent, identically distributed with a variance σ2. Thus, the limit of the expression of V, when n f ∞ is
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Energy & Fuels, Vol. 10, No. 4, 1996
1 σx2π
∫-∞V e-y /2σ 2
2
dy
which is the expression of the Gaussian distribution. This function is thus representative of random phenomena whose physical nature is microscopic, when they are observed at a macroscopic scale.6 This is the case of distillation, during which a physical property, namely the individual boiling point of the entities contained in a petroleum fraction, is the basis for a collective, macroscopic classification. Thus, Gaussian distribution effectively models the boiling point distribution of petroleum fractions. In contrast, any function such as the logistic dose, which conveniently fits simulated distillation data, cannot be related with the composition of distillated fractions. 1.2. Fitting SimDis GC Data of Hydroconverted Products. The existence of a possible relation between the parameters of a given feed (denoted f) and those of the products (denoted p) resulting from hydroconversion can be analyzed if the impact of a carbon-carbon bond rupture upon the previously defined parameters is considered. A centered C-C bond rupture will generate a Ci/2 molecule from an initial feed containing Ci molecules. If the lower and upper boundaries of the distribution function of the feed are defined as m and n carbon atoms, respectively, the position of the center of the distribution, X, is expressed as
Xf )
m+n 2
If a relative width parameter, W′, defined as the ratio of the width W to the center X of the distribution function is introduced, its expression as a function of m and n becomes
W′f )
Wf n-m )2 Xf m+n
A symmetrical C-C bond rupture will generate a new distribution of the products characterized by the parameters Xp and W′p:
Xp )
Xf m + n ) 2 4
W′p )
Bacaud et al.
2n - m m+n
Thus, in the case of a symmetrical C-C bond rupture
W′p ) W′f The effect of the reaction is a decrease in the mean number of carbon atoms by a factor 2, but the relative width of the distribution is unaffected. Let us now consider an asymmetrical bond breaking affecting a molecule at a distance S, expressed as a number of carbon atoms, from the center of the mean paraffinic chain. The expression of the center of distribution is now (6) Bre´maud, P. Introduction aux Probabilite´ s; Springer-Verlag: Berlin, 1988.
Figure 4. SimDis GC of hydroconverted residue. Model: onecomponent cumulative normal probability function.
Xp
m n + S) + ( - S) ( m+n 2 2 ) ) 2
4
which remain unchanged. The expression of the relative width W′p becomes:
W′p
n m + S) - ( - S) ( S 2 2 ) W′ + 4 ) m+n 4
f
Xf
Thus an asymmetrical C-C bond rupture causes an increase of the relative width of the distribution of the products. From the preceding expression, the symmetry factor S is obtained:
S)
Xf (W′p - W′f) 4
According to this definition, zero value of S implies that the considered product is issued from a symmetrical C-C bond rupture of a substrate. In case of nonselective bond breakage, the value of S increases. The results obtained when the normal probability function is applied to SimDis GC data of hydroconverted products reveals large bias and deviation between the model and experimental data (Figure 4). In order to produce a convenient fit, it is necessary to consider the SimDis GC curve as the sum of two contributions, each one being modeled by the normal distribution function (Figure 5). These two components, respectively noted H (heavy) and L (light), are described by the parameters amplitude AH, AL; center of distribution XH, XL; width of distribution WH, WL; symmetry factor SH, SL. This two-component model has been applied to the SimDis GC data of a large number of products obtained by hydroconversion of the previously characterized feed in varying conditions. For each product, the previously defined parameters were calculated for the light and heavy components, respectively, as well as the conversion expressed as the yield of fraction boiling below 510 °C. The existence of correlation between each determined parameter was investigated, as well as the correlation of these parameters with conversion, as deduced from SimDis GC. 2. Correlation of the Distribution Parameters with Conversion. The distribution parameters have been determined for a large number of experimental
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Energy & Fuels, Vol. 10, No. 4, 1996 919
Figure 7. Variation of the ratio of the center of distribution of heavy-to-light components, XH/XL, as a function of the conversion of vacuum residue. Figure 5. SimDis GC of hydroconverted residue. Model: twocomponent cumulative normal probability function.
Figure 8. Variation of the relative width, W′, of distribution as a function of the conversion of vacuum residue. Figure 6. Variation of the center of distribution, X, as a function of the conversion of vacuum residue.
SimDis GC data of hydroconverted products. These products resulted either from thermal conversion at variable reaction time or from catalytic conversion performed with distinct catalysts added in variable amount. The center of distribution of both components, XH and XL, is plotted against conversion in Figure 6. In the lowconversion region (up to 70%), the mean number of carbon atoms of the heavy constituent is about 50. This value must be compared with the initial value of X of the feed, which is 56. Thus, the mean number of carbon atoms of the feed is slightly affected by conversion, up to a relatively high level of distillate production. As conversion increases, XH decreases, but its value remains in the range C35 to C40. The XL parameter, concerning the light component, apparently follows a parallel evolution. However, the ratio XH/XL is not constant; its evolution with conversion is reported in Figure 7. Starting from about 2 at low conversion, it increases linearly at higher conversion. The evolution of XH with conversion is consistent with a mechanism involving, in the first stage, the elimination of short alkyl chains at substituted carbon atoms positions. Such a process generates low molecular weight fragments (gases) and has little effect upon the initial mean structure of the feed. Simultaneously, another mechanism must be invoked in order to explain the production of large amounts of distillate possessing XL values around 25. Considering that, at low conversion, the ratio XH/XL is about 2, the formation of this distillate fraction would be explained by a single symmetrical splitting of the initial heavy component.
Figure 9. Correlation of symmetry factor, S, with conversion of vacuum residue.
The initial rupture of short alkyl chains is an asymmetrical process that would consequently increase the width of distribution of the generated light fraction, without affecting the characteristics of the heavy products. The plot of the relative width W′ vs conversion is reported in Figure 8. It appears that the value of this parameter for the light fraction is effectively higher than the initial value W′f of the feed (0.2). Up to 70% conversion, the value of W′L remains constant and is approximately 3 times W′H. In parallel, W′H is constant and is identical to the initial relative width of distribution of the feed (0.2). Thus, in this considered range of conversion, a significant fraction of the initial feed is preserved in a slightly modified state. The symmetry factor, S, is plotted against conversion in Figure 9. Its value, for the heavy constituent, remains near zero up to 70% conversion; consequently this fraction of the distillate does not contain molecules
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Figure 10. Correlation of amplitude, A, with conversion of vacuum residue.
issued from randomly distributed C-C bond ruptures. In the same range of conversion, the symmetry factor associated with the light component is high. This reflects the impact of the elimination of alkyl chains upon the distribution of the low-bp components in the distillate and is compatible with the proposed mechanism for the initial stage of conversion. As conversion proceeds above 70%, the distinction between light and heavy component becomes less definite and XH values are within the range of XL observed at lower conversion. The relative widths of both components tend to equalize, as well as the symmetry factor, and the ratio of XH to XL increases. These observations indicate that at higher conversion, multiple bond ruptures are taking an increasingly important part in the formation of lighter fractions. The amplitude parameter, A, of the modeled SimDis GC curves of converted products is related with the relative proportion of the light and heavy components in the products. This relation is illustrated in Figure 10 where AH and AL are plotted vs conversion. In the considered range, the decrease of the relative contribution of the heavy component to the distillate is linear. But a linear extrapolation would indicate that, even at 100% conversion, a significant proportion of distillate exists as a heavy component. The existence of two distinct populations possessing different characteristics is illustrated by Figures 11 and 12 where the relative width of distribution and the symmetry factor are respectively plotted against the center of distribution. The light component, characterized by a mean number of carbon atoms inferior to 30, exhibits a relative width of distribution proportional to the center of distribution. In contrast, the relative width parameter of the heavy component decreases as the center of distribution increases. In the medium region of X, the distinction between these two populations disappears. Concerning the symmetry factor, the plot represented in Figure 12 confirms that the medium part of the distillate population is generated through a highly asymmetrical mechanism of bond ruptures and that the heaviest part of the products retains the initial structure of the feed.
Bacaud et al.
Figure 11. Correlation of relative width of distribution, W′, with center of distribution.
Figure 12. Correlation of symmetry factor, S, with center of distribution.
Conclusion Simulated distillation is a classical and very convenient tool for the fast evaluation of the yield of distillate produced in hydroconversion processes. Considering the distribution of distillated material vs boiling point as a Gaussian function, a simple model of SimDis GC data allows a description of distillate in terms of center and width of the distribution function. The application of the normal distribution function to hydroconverted products reveals the presence of two components, the light one resulting from an extensive cracking of the feed and the heavy one representative of the remaining fraction of the initial reactant. The impact of the mechanism of bond breaking upon these parameters and the introduction of a symmetry factor allows the mechanism of the progressive transformation of a heavy feed into light distillate to be described. The purpose of the present approach is to develop a simple tool for evaluation of hydroconversion. It will be further applied to the investigation of the impact of catalysts upon the mechanism of hydroconversion, through the search for correlation between catalyst addition and the parameters of modeled simulated distillation. EF950234H
