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Modeling Simulated Distillation. Influence of Catalyst Addition upon Model Parameters R. Bacaud* and L. Rouleau Institut de Recherches sur la Catalyse, CNRS, 2 Avenue Albert Einstein, 69626 Villeurbanne Cedex, France
B. Bacaud Ecole Nationale Supe´ rieure de Techniques Avance´ es, 32 Boulevard Victor, 75015 Paris, France Received April 12, 1996X
Simulated distillation data of distillates produced by hydroconversion of a vacuum residue have been described as a two-component cumulative normal distribution function. The influence of catalyst addition upon calculated parameters of the model indicates that distillate fraction is produced through a consecutive mechanism. A modified three-component model has then been applied. It reveals that catalyst limits the extent of the first bond cleavage step and reduces the yield of light distillate. Furthermore, the distribution of hydrogen consumption is strongly affected by catalyst addition, which favors hydrogen incorporation in distillate.
Introduction Simulated distillation is a widely disseminated and fast technique that provides the quantitative distribution of distillate fractions in petroleum-derived products. This information is essential for a rapid evaluation of products generated by conversion processes affecting the distribution of boiling points, as does hydrocracking for example. When performed with capillary columns, the range of application extends to fractions containing up to 120 carbon atoms. In a previous paper,1 we proposed a modeling of simulated distillation (simdist) data relying upon the simple assumption that the boiling points of processed feeds and products are conveniently described by a normal (Gaussian) distribution function. Attempts to fit experimental data corresponding to a hydroconverted residue with a normal distribution function revealed that convenient fits could not be obtained, unless it was assumed that simdist curves were the sum of the contribution of two distinct populations: a light component issued from extensive hydrocracking and a heavy one possessing the characteristic parameters of the feed. Numerical values of the calculated parameters indicated that a significant fraction of distillate remained as slightly converted feed, even in those samples corresponding to high conversion level (high yield of distillate). This two-component model was applied to a large number of simdist curves of hydroconverted products. The derived parameters could be correlated with conversion, and an interpretation of the variation of the calculated model parameters revealed some insight into the mechanism of hydroconversion. The development of this model was based upon widely varying conditions of hydroconversion and aimed at establishing validity and internal consistency. The X Abstract published in Advance ACS Abstracts, October 15, 1996. (1) Bacaud, R.; Rouleau, L.; Bacaud, B. Energy Fuels 1996, 10, 915920.
present paper focuses on the impact of various amounts of catalyst upon the calculated model parameters and the existence of a relation with hydrogen transfer balance in hydroconversion. Experimental Section Hydroconversion of a butane-deasphalted oil (DAO) obtained from a 510+ °C vacuum residue was carried out in an autoclave at 713 K for 1 h, under 14 MPa of hydrogen pressure. The catalyst was a nickel-carbon plasma-prepared catalyst used as dispersed, disposable solid. To reflect realistic concentrations, the catalyst-to-feed ratio was maintained at low level and was varied between 50 and 800 ppm, expressed as nickel concentration in the charge. The properties of the feed, processing conditions, products workup, and calibration of simulated distillation are described in reference 2. After a run, the volume of recovered gases was metered and analyzed by gas chromatography to establish a material balance of hydrogen consumption. The distribution of hydrogen utilization, between gas production and liquid incorporation, was defined as total H ) consumption of gaseous hydrogen (mmol of H2/100 g of feed), gas H ) hydrogen consumed for gases formation, and liquid H ) total H - gas H. Simulated distillation data allowed the determination of conversion, defined as the weight percent of 510- °C fraction produced by hydroconversion. Simdist data, expressed as weight percent of distillate vs the number of carbon atoms (equivalent to a boiling point scale), were described as the sum of two normal cumulative functions, using the fitting technique described in ref 1. A cumulative normal distribution function is described by means of three parameters: amplitude A, center X, and width W. The two-component model distinguishes a light and a heavy component, respectively, designated L (light) and H (heavy). Thus, the following nomenclature is defined: amplitude, AH and AL; center of distribution, XH and XL; and width of distribution, WH and WL. (2) Rouleau, L.; Bacaud, R.; Breysse, M.; Dufour, J. Appl. Catal., A 1993, 104, 149-159.
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Figure 1. Variation of conversion as a function of catalyst content.
Figure 3. Variation of XH-to-XL ratio as a function of catalyst content, two-component model.
Figure 2. Mean number of carbon atoms, X, vs catalyst content, two-component model.
Figure 4. Relative width of distribution, W′, vs catalyst content, two-component model.
Two additional derived parameters were introduced: the relative width, W′, defined as the ratio of the width to the center of the distribution, and a symmetry factor, S, which is related with the symmetry of C-C bond rupture (eq 1), defined as
catalyst loading (Figure 3) indicates that high values of this ratio are observed in thermal conversion (no catalyst), while it decreases to approximately 2 for products obtained in the presence of high catalyst loading. An XH-to-XL ratio >2 implies the light component contains products generated through secondary bond ruptures. The relative width of distribution of the heavy component, W′H, of products obtained in catalytic conversion is identical to the corresponding parameter of the feed (0.2 in Figure 4); similarly, a null value of the symmetry factor SH concerning this heavy component is observed (Figure 5). These two parameters are increased when conversion is performed in thermal conditions. These observations are consistent with a consecutive bond rupture mechanism, which might be described as follows: The feed, possessing a mean composition C56, first suffers the elimination of short alkyl chains. This process yields a slightly modified fraction (X ) 48) and low molecular weight fragments in the form of gas. In a second step, a symmetrical splitting of this heavy component generates a light fraction, which has a mean number of carbon atoms approximately half the initial value. The radical species generated by this bond rupture may alternatively stabilize or propagate. If a stabilization pathway is effective, the process will stop; this situation prevails when conversion is performed in the presence of catalysts which maintain a convenient level of active hydrogen availability. Otherwise, propagation of radical species takes place and results in multiple bond cleavages. This nonselective process
S ) (Xfeed/4)(W′product - W′feed) Null value of S indicates that the corresponding products are issued from a symmetrical bond cleavage. Nonselective bond ruptures cause the value of S to increase.
Results Application of the Two-Component Model. A first obvious consequence of catalyst addition upon hydroconversion consists in a decrease of conversion, as illustrated in Figure 1. Conversion, expressed as the yield of 510 °C distillate, reflects the global efficiency of the process, and, as such, a limitation of this parameter appears as a counterproductive effect. The evolution of the center of distribution, X, determined by applying the two-component model, against catalyst addition is described in Figure 2. XH, representative of the mean number of carbon atoms in the heavy component, levels off at ca. 48, as catalyst content is increased. This value must be compared to the corresponding parameter of the feed (56) and indicates that this component has a similar composition. Concerning the light component, the evolution pattern of XL with increasing catalyst addition is apparently similar. However, a plot of the XH-to-XL ratio against
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Figure 5. Symmetry factor, S, vs catalyst content, twocomponent model.
Figure 7. Mean number of carbon atoms, X, vs catalyst content, three-component model.
Figure 6. Comparison of residuals, two- and three-component models.
Figure 8. Relative width of distribution, W′, vs catalyst content, three-component model.
randomly affects hydrocarbon chains and must therefore cause an increase of the relative width of distribution of the resulting light products, and the corresponding symmetry factor must be higher. In contrast, the light fraction obtained during catalytic conversion would exhibit lower dispersion of distribution and lower values of the symmetry factor. The values of these parameters, in Figures 4 and 5, respectively, contradict this hypothesis. This discrepancy between the model parameters and mechanistic description can arise from the fact that the twocomponent model does not consider the products issued from consecutive, secondary bond cleavages, which yield a lighter component. If this interpretation is consistent, a three-component model would effectively fit experimental data and would generate significant values for the calculated parameters of the normal distribution function. Three-Component Model. To compare the validity of both models, the residuals (difference between experimental and calculated simulated distillation curves) were determined and are presented in Figure 6. Residuals are effectively lower and randomly distributed when three components are considered in the cumulative normal distribution function. The calculated parameters A, X, W′, and S are indexed 1, 2, 3, the lower index being attributed to the lightest component. The variation of the mean number of carbon atoms, X, of the three fractions, as a function of
catalyst loading, is plotted in Figure 7. In contrast with the preceding values calculated by application of the two-component model, the mean composition of the lighter fractions 1 and 2 is nearly constant (respectively, 10 and 20) and is not affected by the presence of increasing amounts of catalyst. The heavier fraction, 3, representative of slightly converted feed, suffers deeper conversion in thermal conditions, and its corresponding mean X3 value augments with increasing catalyst loading. The relative width of distribution, W′, is plotted against catalyst concentration in Figure 8. The heaviest fraction, 3, is associated with the highest values of W′, which are lower for the subsequent lighter fractions. For the three considered components, this parameter decreases as catalyst content increases. The highest values of W′ concern the lightest fraction, 1, and the value decreases with the mean number of carbon atoms of the considered fractions. If a consecutive mechanism is assumed and is schematically described as
feed f fraction 3 f fraction 2 f fraction 1 then the expression of the symmetry factor must be modified as
S ) (Xi+1/4)(W′i - W′i+1) where i is the fraction index. In this scheme, S reflects the position of attack in a hydrocarbon chain; a null value implies the cleavage position produces a splitting, without affecting the
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Figure 9. Symmetry factor, S, vs catalyst content, threecomponent model.
Figure 10. Amplitude of generated fractions, A, as a function of total conversion, three-component model.
relative width of the normal distribution of the considered fraction. It appears from the data plotted in Figure 9 that catalyst only affects the first step of the proposed mechanism, since significant differences of S values between catalytic and noncatalytic conversion are exclusively observed for fraction 3. This primary step is more likely to concern substituted carbon atoms positions, which are more labile, than long alkyl chains. The result is an elimination of short substituents and the generation of a relatively heavy fraction 3. This is an asymmetrical process, and it effectively reflects upon the corresponding value of the symmetry factor, S3, of this fraction. The consecutive steps generating the lighter components are not affected by catalyst in terms of symmetry of bond rupture. The values of symmetry factor corresponding to fractions 2 and 1 are around zero (negative values are meaningless). This implies that a selective, symmetrical bond cleavage of fraction 3 is responsible for the production of fraction 2. The same holds true for the generationof fraction 1 through hydrogenolysis of fraction 2. A plot of the relative quantitative contribution, A, of each fraction vs total conversion (Figure 10) indicates that the production of distillate is associated with a drastic decrease of fraction 3, a corresponding increase of the lightest fraction 1, and a relative stability of fraction 2. This tendency confirms the role of intermediate attributed to this fraction in the proposed consecutive scheme.
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Figure 11. Amplitude of generated fractions, A, vs catalyst content, three-component model.
Figure 12. Variation of hydrogen consumption vs catalyst addition.
The difference between catalytic and thermal conversion is essentially quantitative, as evidenced by the relative amplitude, A, of the three components plotted vs catalyst concentration in Figure 11. As catalyst concentration is increased, the relative contribution of the heavier fraction 3 is higher and the lighter fractions are produced in minor amounts. Considering that the goal of hydroconversion is the production of a maximum amount of distillate possessing optimum properties, catalyst appears as counterproductive from a quantitative point of view. However, another important aspect of process economy is the distribution of hydrogen utilization. The experimental determination distinguishes hydrogen consumption associated with gas formation (gas H) and hydrogen incorporation in the liquid products (liquid H). Total hydrogen consumption is practically independent of catalyst concentration (Figure 12). In contrast, hydrogen incorporation in liquid products changes from negative values in thermal conversion (products are hydrogen deficient as compared to the feed) to positive values in the presence of as low as 200 ppm of active phase. This term includes two distinct classes of hydrogen consumption: one is related with consecutive bond cleavages; the other consists in the hydrogenation of unsaturated species. Since the yields and X value of the three components are affected by catalyst, the amount of hydrogen involved in hydrogenolysis reactions for the production of light fractions must be different. It may be determined if it is considered that the number of bond ruptures involved in the production of a fraction possessing a mean
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fraction 3 is necessarily associated with hydrogen depletion. On the other hand, at higher catalyst concentration, an enrichment in hydrogen has no consequence upon the distribution of the respective fractions. Conclusion
Figure 13. Amplitude of generated fractions (three-component model) vs neat hydrogen incorporation.
number of carbon atoms Xi through bond cleavage of a Xi+1 fraction is
(Xi+1/Xi) - 1 Assuming a mean formula CH1.66 , corresponding to the elemental composition of the feed, the number of moles of the corresponding entities in 100 g of feed is
100/13.66Xi Taking into account the relative amount of fraction i (Ai) with respect to the feed, the number of moles hydrogen involved in this transformation is
Hi ) [(Xi+1/Xi) - 1](Ai/13.66Xi) expressed as moles of H2 for 100 g of feed, for one step generating a fraction i from a fraction i+1. Due to the variations of H/C ratio in the distinct fractions, this expression is not rigorous; the results are marginally affected by this approximation. This calculated amount of hydrogen is subtracted from the previously determined liquid H, giving a corrected hydrogen incorporation that reflects an actual enrichment of the products (presumably corresponding to reduced aromaticity). Considering the plot of corrected hydrogen incorporation vs catalyst loading, positive values of hydrogen are only attainable in the presence of high catalyst concentration. The consequence of hydrogen balance upon conversion is highlighted in Figure 13, where the respective contribution of the three components is plotted against hydrogen incorporation. A reduction of the heaviest
Simulated distillation of raw products obtained by catalytic or noncatalytic conversion of a vacuum residue has been performed. The parameters calculated through fitting technique of experimental data are consistent with a two-component cumulative normal (Gaussian) distribution function. The heavy component reflects slightly converted residue, and the light one is representative of distillate fraction. The respective values of the center of distribution of the fractions suggest they are produced through a consecutive bond cleavage mechanism and that a third fraction could be considered. A modified three-component model has thus been applied to simulated distillation data. The calculated parameters confirm that the initial step of hydroconversion consists in an attack at substituted carbon atom positions and that it results in the generation of a heavy fraction, which consecutively splits into lighter components. The impact of catalyst addition consists in limiting the extent of multiple bond cleavages of the feed and, as a consequence, reduces the yield of distillate. Another aspect of catalyst influence concerns hydrogen utilization. Although total hydrogen consumption is not largely modified, a reduced share of hydrogen involved in gas production and hydrogenolysis reactions implies that the products obtained in the presence of catalyst are hydrogen rich, as compared to the feed. Thus, catalysts influence both qualitative and quantitative aspects of hydroconversion. Hydrogen enrichment of products, which improves properties, is only possible at the expense of distillate yield. The use of catalysts at low concentration allows a fine modulation of process economy through an adequate balance between distillate production and hydrogen consumption. Finally, the simple modeling of simulated distillation data provides a precise description of hydroconverted products, which may prove useful for process control since the calculated parameters are directly related with operational parameters, such as conversion and distillate yield. EF960055F
