Detailed Kinetic Model for the Hydro-desulfurization of FCC Naphtha

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Energy Fuels 2009, 23, 5743–5759 Published on Web 11/03/2009

: DOI:10.1021/ef900632v

Detailed Kinetic Model for the Hydro-desulfurization of FCC Naphtha Prasenjeet Ghosh,* Arthur T. Andrews, Richard J. Quann, and Thomas R. Halbert ExxonMobil Research and Engineering, 1545, Route 22 East, Annandale, New Jersey 08801 Received June 24, 2009. Revised Manuscript Received October 10, 2009

A detailed kinetic model has been developed that describes the selective hydro-desulfurization chemistry of FCC naphtha at minimal olefin saturation. The FCC naphtha is represented by 348 molecular lumps measured by different gas-chromatographic methods. The reaction chemistry is specified in terms of reaction rules using the structure-oriented lumping (SOL) framework. A total of 15 reaction rules, which describe 444 individual reaction steps, have been used to model the governing chemistry. Relative reactivity relationships among different molecular lumps within the same rule have been derived using model feeds or established results from the literature. The kinetic parameters have been estimated from a comprehensive experimental data set comprising of both pilot plant and commercial refinery data spanning a wide range of process conditions and feed compositions. The kinetic model quantitatively predicts the product composition, product properties, extent of hydro-desulfurization, olefin saturation, and the associated octane loss. The modeling framework is generic to naphtha hydroprocessing technologies, and its specific application to the Selective CAtalytic Naphtha hydrofining (SCANfining) process, a proprietary technology of ExxonMobil, is demonstrated.

atmospheric residue or vacuum distillates, which contain significant amounts of sulfur molecules. A significant portion of this sulfur is retained in the FCC products. The important sulfur molecules in FCC naphtha are the thiophene, its light alkyl derivatives (e.g., methyl and ethyl thiophenes), tetrahydrothiophenes, and benzothiophenes,6,7 which have to be desulfurized from the gasoline to meet environmental regulations. Numerous technologies, both catalytic and noncatalytic, exist in the literature to achieve deep desulfurization of FCC naphtha.1 Technologies based on the hydro-desulfurization (HDS) of naphtha are among the most prevalent. HDS reactions remove the sulfur in naphtha as H2S using hydrogen. Although conventional HDS is quite effective in converting most sulfur-containing molecules, it will also saturate olefins in FCC naphtha, which constitute around 20-40 wt % of the naphtha composition and have high octane numbers. Loss of product octane can have severe economic consequences. Therefore, the challenge is to selectively hydro-desulfurize the naphtha stream, eliminating a maximum amount of sulfur with minimum olefin saturation and minimal octane loss. The selective hydro-desulfurization process called SCANfining (Selective CAtalytic Naphtha hydrofining) jointly commercialized by ExxonMobil and Akzo Nobel, offers a viable approach to selectively remove sulfur with low olefin saturation of FCC naphthas. SCANfining uses a unique CoMo/ Al2O3 catalyst (RT-225) with low metals content and high dispersion to achieve high HDS/olefin saturation selectivity.3,8,9 The highly selective RT-225 catalyst in tandem with

1. Introduction With the introduction of more stringent environmental regulations on gasoline fuels, and particularly those acting to drastically reduce sulfur emissions, desulfurization of gasoline has become an important area of process research. The Environmental Protection Agency (EPA) in the United States and the governments of numerous countries have now adopted regulations that mandate gasoline fuels to have 10 ppm or less elemental sulfur by 2009.1-3 Commercial gasoline is blended from different refinery process streams including reformates, fluid catalytic cracked (FCC) naphtha, C5/C6 isomerates, light straight run naphtha (LSR), alkylates, and refinery butane, each of which contribute a certain amount to the sulfur content of gasoline. Tables 1 and 2 report the relative amounts (by volume) of the different process streams in commercial gasoline and their relative contribution of sulfur to the gasoline pool.4 The FCC naphtha, which represents the largest component (45-60%) in gasoline is also the greatest source of sulfur in it, contributing 90-99% of the sulfur, depending on whether the feed to the FCC unit was hydrotreated or not.4,5 Reformates, which are the second largest component in gasoline (20-30%) contribute little or no sulfur. Reformer feeds are usually naphtha distillation cuts from the crude tower and therefore lower boiling than most of the sulfur-containing hydrocarbons in the crude. In addition, hydrotreating removes nearly all of the sulfur present prior to processing over the noble metal reforming catalyst. FCC feeds, by contrast, are usually

(6) Albro, T. G.; Dreifuss, P.; Wormsbecher, R. F. J. High Res. Chromatogr 1993, 16, 13. (7) Yin, C.; Zhu, G.; Xia, D Am. Chem. Soc. Prepr. Div. Pet. Chem. 2002, 47 (4), 391. (8) Riley, K. L., Kaufman., J. L., Zaczepinski, S., Desai, P. H. Mayo, S. W., Akzo Nobel Catal. Symp. 1998, 3, 1 (9) Halbert, T. R., Stuntz, G. F., Brignac, G. B., Greeley, J. P., Ellis, E. S., Davis, T. J., Kamienski, D. P., Mayo, S., Akzo Nobel Catalyst Symposium on SCANfining: A Commercially Proven Technology for Low Sulfur Gasoline, Noordwijkaan Zee, The Netherlands, 2001.

*To whom correspondence should be addressed. E-mail: prasenjeet. [email protected]. (1) Song, C. Catal. Today 2003, 86, 211. (2) Song, C.; Ma, X. Appl. Catal., B 2003, 41, 27. (3) Kaufmann, T. G.; Kaldor, A.; Stuntz, G. F.; Kerby, M. C.; Ansell, L. L. Catal. Today 2000, 62, 77. (4) ExxonMobil Technology Website, http://internet-xom.na.xom. com/refiningtechnologies/index.html. (5) Upson, L. L.; Schnaith, M. W. Oil Gas. J. 1997, 95 (49), 47. r 2009 American Chemical Society

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Table 1. Typical Composition of Commercial Gasoline Blending Components when FCC Feed is Not Hydrotreated gasoline blending component

sulfur (ppm)

typical % of gasoline pool

% contribution of sulfur

full range FCC gasoline light straight run gasoline butanes alkylate reformate C5/C6 isomerate

2000 150 10 3 1 3

45 3 5 10 32 5

99 430 °F). However, these compounds are more difficult to desulfurize than the lighter thiophenic compounds. Consequently, control of the end-point of an FCC gasoline feeding to the SCANFiner is critical in meeting the sulfur specifications of the product. Tetrahydrothiophene also contributes a small amount to the total sulfur (∼3-5%). Aliphatic sulfur in the form of mercaptans, sulfides, and the disulfides constitute the remaining 10-15% of the total sulfur in the naphtha. Interestingly, thiophenes and methyl thiophenes, which constitute the major fraction of sulfur molecules in FCC naphtha, are rarely present in the FCC feedstocks. Many postulates have been put forward to explain their origin, but two widely accepted postulates are (a) the cracking of heavier sulfur molecules like dibenzothiophenes and polyalkylated thiophenes to thiophenes and (b) addition of H2S to olefins and diolefins followed by cyclization to tetrahydrothiophenes to their eventual dehydrogenation to thiophenes. Many excellent and comprehensive reviews on the origin and type of sulfur molecules in FCC naphtha are available in the literature,11-14 and the reader is directed to these references for more details. A detailed understanding of the reaction chemistry of these sulfur molecules and its quantification lies at the crux of the efforts to selectively eliminate them from FCC naphtha. Most of the published work in the literature, although extensive, deals with either the catalysis of HDS (without mention of olefin saturation) over Co-Mo and Ni-Mo catalysts or model compound studies elucidating specific reaction pathways.15,16 Kinetics studies are often rudimentary and limited

Figure 1. Process flow diagram for SCANFining process adapted from refs 3 and 9.

optimized process conditions in SCANfining provides much lower olefin saturation for a given amount of desulfurization, for example 20% olefin saturation at around 90% HDS on a typical FCC feed.9,10 In contrast, a conventional hydrotreating catalyst would typically allow 80% olefin saturation at the same % HDS. Since the extent of olefin saturation dictates the extent of octane loss, SCANfining offers minimal octane loss at the same level of %HDS. Figure 1 shows the schematic of the SCANfining process. The naphtha feed is first pretreated with recycle hydrogen in a pretreat reactor for diolefin saturation, since reactive diolefins, if present, can create fouling problems downstream. The effluent of the pretreater is then passed to the HDS reactor filled with the RT-225 catalyst, where it is selectively desulfurized with a minimum of olefin saturation. The operating conditions (T = 250-360 °C, P = 200-450 psig and LHSV = 2-8 h-1) are such that the reactions occur completely in the vapor phase. The desulfurized naphtha is then cooled and separated from H2S and recycle gas in the separator. Remaining H2S and the light endgases are removed from the desulfurized naphtha in the final product stripper. Recycle gas is stripped of H2S in the amine scrubber and circulated back to the reactor. Additional details of this process have been reported elsewhere.3,9,10 The feed to the SCANfiner is FCC naphtha, whose main sulfur molecules are mercaptans, sulfides, disulfides,

(11) Brunet, S.; Mey, D.; Perot, G.; Bouchy, C.; Diehl, F App. Catal., A 2005, 278, 143. (12) Leflaive, P; Lemberton, J. L.; Perot, G.; Mirgain, C.; Carriat, J. Y.; Colin, J. M. App. Catal., A 2002, 227, 201. (13) Yin, C.; Zhu, G.; Xia, D Am. Chem. Soc. Prepr. Div. Pet. Chem. 2002, 47 (4), 398. (14) Hatanaka, S.; Yamada, M.; Sadakane, O. Ind. Eng. Chem. Res. 1997, 36 (5), 1519. (15) Topsoe, H.; Clausen, B. S.; Massoth, F. E. Catalysis: Science and Technology; Springer Pub.: 1996; Vol 11. (16) Li, X.; Wang, A; Egorova, M.; Prins, R. J. Catal. 2007, 250, 283.

(10) Greeley, J. P.; Zaczepinski, S.; Halbert, T. R.; Brignac, G. B.; Gentry, A. R.; Kraus, R. L.; Mayo, S. Am. Chem. Soc., Prepr. Div. Pet. Chem. 2000, 45, 357.

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to single and simple components, where the rate of HDS is described by empirical rate-laws. Such simplistic models are restrictive in their predictive ability and provide limited value when applied to scenarios with varying real feeds, catalysts, and operating conditions. In contrast, predictions based on detailed fundamental composition-based kinetic models would be more reliable and robust in such scenarios. Froment and co-workers17-21 have developed a number of kinetic models for hydrotreating reactions that are extremely noteworthy in this area. Their early modeling work focused on describing the kinetics of model compounds such as thiophenes and benzothiophenes, which was later extended to describe reaction networks for real feedstocks. There has been considerable advancement in analytical technology since these early papers by Froment and co-workers that now enable us to obtain detailed compositional information of naphtha streams than what was possible earlier. Together with the availability of cheap computational power, it is now possible to develop much more detailed kinetic models of such complex processes than was possible earlier. More recently, microkinetic modeling approaches have been employed to provide mechanistic insights into naphtha hydrotreating.22,23 For a brief review of the various modeling approaches to the kinetics of hydro-desulfurization prior to this work, the readers are directed to the review by Te et al.24 In this paper, we present a kinetic model for the reaction chemistry of the SCANfining process. Since SCANfining chemistry is a specific example of the much broader class of hydroprocessing chemistry, we consider the development of a general kinetic network for hydroprocessing of naphtha. In doing so, the kinetic model will not only be applicable to SCANfining, but also to other similar hydroprocessing technologies in the naphtha range. Our approach to modeling follows the methods of structure-oriented lumping (SOL) published previously.25,26 Detailed hydrocarbon and sulfur selective gas-chromatographic methods are used to identify the critical molecular species in FCC naphtha. The reaction chemistry is expressed in terms of reaction rules and applied to the set of molecules to produce a detailed reaction network. Reactivity relationships with molecule structure for different reaction rules are developed and the necessary kinetic parameters are determined based on a comprehensive data set spanning a range of FCC naphthas and process operating conditions.

Figure 2. Various homologous series used to represent FCC Naphtha. Each homologous series is depicted by showing a representative molecule of that series.

as: (1) Identify the set of homologous series which accurately characterize and represent FCC naphtha. (2) Define the important reaction chemistries as a set of reaction rules. (3) Apply the reaction rules to the homologous series to generate the kinetic network. (4) Assemble the resulting set of governing equations and solve for product concentrations and properties. These steps are described in more detail in the next section. 2.1. Molecular Characterization and Representation of FCC Naphtha. The first step in the development of the kinetic model is the molecular characterization and representation of the FCC naphtha stream. FCC naphtha usually contains hydrocarbon molecules from C4 to C14 with nominal boiling points below 430 °F. Even within this rather small range, thousands of molecular structures and astronomical numbers of individual isomers exist. Analytical methods cannot provide a complete identification of these individual molecules,28 however sufficient information can be obtained to organize these molecules into molecular groups and homologous series.26,29 Figure 2 shows the different homologous series used in this work to represent naphtha streams. A total of 41 different homologous series (excluding light gases, e.g., H2S, NH3, CO2, H2O, etc.) have been employed that include naphthenes, aromatics, naphtheno-aromatics, paraffins, olefins, and various sulfur- and nitrogen-containing molecules. The naphthenes, aromatics, and naphtheno-aromatic

2. Development of the Kinetic Model The development of a complex kinetic model using the methods of SOL requires a systematic sequence of steps,25-27 which may be summarized for the selective HDS of naphtha (17) Van Parijis, I. A.; Froment, G. F. Ind. Eng. Chem. Prod. Res. Dev. 1986, 25, 431. (18) Van Parijis, I. A.; Hosten, L. H.; Froment, G. F. Ind. Eng. Chem. Prod. Res. Dev. 1986, 25, 437. (19) Delmon, B.; Froment, G. F. Catal. Rev. Sci. Eng. 1996, 38, 69. (20) Froment, G. F.; Depauw, G. A.; Vanrysselberghe, V Ind. Eng. Chem. Res. 1994, 33, 2975. (21) Vanrysselberge, V.; Froment, G. F. Ind. Eng. Chem. Res. 1998, 37, 4231. (22) Daudin, A.; Lamic, A. F.; Perot, G.; Brunet, S.; Raybaud, P.; Bouchy, C. Catal. Today 2008, 130, 221. (23) Toulhoat, P.; Raybaud, J. J. Catal. 2003, 216, 63. (24) Te, M.; Fairbridge, C.; Ring, Z. Pet. Sci. Technol. 2003, 21 (1,2), 157. (25) Quann, R. J.; Jaffe, S. B. Ind. Eng. Chem. Res. 1992, 31 (11), 2483. (26) Quann, R. J.; Jaffe, S. B. Chem. Eng. Sci. 1996, 51 (10), 1615. (27) Christensen, G; Apelian, M. R.; Hickey, K. J.; Jaffe, S. B. Chem. Eng. Sci. 1999, 54, 2753.

(28) Sullivan, R. F.; Boduszynski, M. M.; Fetzer, J. C. Energy Fuels 1989, 3, 603. (29) Quann, R,J. Environ. Heal. Perspec. 1998, 106 (6), 1441.

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N1); a methylene -CH2- group (R); biphenyl bridge (AA); hydrogen deficiency (H); heteroatom structures containing S, N, and O; and the degree of branching (br) and ring substitutions (me). Each increment has a specific C, H, S, N, and O atomic stoichiometry and thus molecular weight. The term increment is used because most cannot exist independently and must occur as an incremental part of a molecule. Figure 5 shows the representation of a few molecules in the naphtha range using the 22-element SOL vector. For example, benzene, which has only one aromatic ring has A6 = 1, while the rest of the increments are 0. Methyl-propyl naphthalene with two aromatic rings and an alkyl chain is constructed with a A6 = 1 and a A4 = 1 increment, accounting for the rings, and a R = 4 and br = 1 increment accounting for the 4 carbon-atom alkyl chain with a single branch. The hydrogen increment, IH = 1 indicates the paraffinic nature of the alkyl side chain. 2-Methyl-1-butene, with five carbon atoms and an olefinic bond is represented by R = 5 and IH = 0 increment, where IH = 0 indicates the presence of olefinic bond in the molecule. Heteroatoms like thiophene, with one five-membered ring and a sulfur atom, is represented by N5 = 1, NS = 1, and IH = -2 increments. The details of the SOL representation syntax with many more examples have been published previously and readers are advised to refer to these publications for details.25,26 Thus, suffice it to say that any molecule in the naphtha range, and in general in petroleum, can be represented using SOL increments. Note that with the current representation, structural isomers (e.g., 2,3,4-trimethylpentane and 2,2,4trimethylpentane) will be indistinguishable. Although it is possible to distinguish them using a more detailed SOL representation if needed, such a distinction was not included in this paper. 2.3. Reaction Chemistry. The chemistry of naphtha hydrodesulfurization has been studied extensively over the last

homologous series include different one-ring, two-ring, and biphenyl structures. The paraffin and olefin homologous series include both the linear (normal) and branched chain (one branch and multibranch, i.e., g 2) molecules. Within the isoparaffin and iso-olefin series, the information about the position of the branch is ignored at present although this can be added subsequently if needed. The rationale is such that the octane number of the naphtha stream (the primary property of interest here) depends primarily on the number of branches and to a lesser degree on the position of the branch. The olefin series also includes the cyclo-olefins. The sulfur homologous series include different linear and branched mercaptans, and ring sulfur molecules like the thiophene, tetrahydrothiophene, benzothiophene, and dihydrobenzothiophene series. (Although FCC naphtha contains some sulfide molecules (RSR) as well, they are lumped as mercaptans (RSH) in the model.) The nitrogen series include the anilines, pyrroles, pyridines, indoles, and the napthenopyridine molecules. For each of the homologous series, different alkylated molecules are included up to a maximum of 14 carbon atoms. Figure 3 shows such alkylated molecules for two homologous series, the monomethyl iso-olefin and the thiophene series. Combining all such alkylated molecules for the 41 homologous series and the light gases results in a total of 348 molecules, which characterize the composition of the naphtha stream. 2.2. SOL Representation. Each of the 348 molecules included in the model is represented using the syntax outlined in the SOL approach.25,26,29 Since the details have been published earlier, only the salient features of this representation are described here. The basic concept of SOL is that any hydrocarbon molecule (including heteroatoms) can be constructed from a set of different structural features or increments. A structural increment is a specific combination (or geometric configuration) of C, H, S, N, and O atoms, used to construct different molecules. The canonical set of 22 increments is shown in Figure 4. The set consists of three aromatic increments (A6, A4, A2); six naphthenic increments (N6 to

Figure 5. SOL representation of a few molecules: (a) benzene, (b) methyl-propyl-naphthalene, (c) 2-methyl 1-butene, and (d) thiophene.

Figure 3. Different alkylated molecules of the monomethyl isoolefin and thiophene homologous series.

Figure 4. Structural Increments in SOL Representation.

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three decades, and a number of excellent reviews detailing the current understanding of various reaction mechanisms, individual mechanistic steps, reaction rates, and thermodynamics are available in the literature.1,9,11,30 A review of all the relevant mechanistic steps, including different intermediates, catalytic sites on which they occur, the nature, number and distribution of sites, and so on is beyond the scope of this paper. Instead, we review here the most important reaction pathways relevant to naphtha hydro-desulfurization and list those which are included in the kinetic model. Each reaction pathway is described as a reaction rule, where each rule brings a certain characteristic change in the structure of the involved molecular classes. Since many different molecular classes can follow the same reaction pathway, each reaction rule may be applicable to more than one molecular class. In addition, the same reaction rule will be applicable to the entire homologous series of each molecular class. Each reaction rule is presented in the text with an example reaction along with the set of molecular classes that participate in the reaction rule. Although the emphasis of this work is to highlight the most significant reactions relevant to SCANfining, other reaction rules pertinent to naphtha hydroprocessing chemistry are also included in the model for the sake of completeness. These other reaction rules may occur in SCANfining, albeit very slowly and to a limited degree. In a broad sense, the two principal classes of reactions in SCANfining are hydro-desulfurization and olefin saturation reactions. Within these general classes, separate reaction rules govern the chemistry of different sulfur molecules and olefins, depending on the specific molecular structural change dictated by the reaction. For the hydro-desulfurization reaction class, two types of sulfur removal reactions are considered. The first involves removal of sulfur from a ring structure, either aromatic or naphthenic (e.g., thiophene, tetrahydrothiophene, and benzothiophene desulfurization) while the second involves removal of sulfur from an aliphatic chain (e.g., mercaptan hydro-desulfurization). For saturation reaction class, three types of reaction rules are considered: (a) reactions that convert thiophene and benzothiophene to tetrahydrothiophene and dihydrobenzothiophenes, (b) reactions that convert mono-olefin (linear or cyclic) to either a paraffin or a naphthene, and (c) reactions that convert diolefins to the corresponding mono-olefins. These rules are described in more detail below. Ring Sulfur Hydro-Desulfurization. Ring sulfur can exist either in the naphthenic or the aromatic ring of the molecule. Two reaction rules describe this chemistry. The first rule, labeled as “napthenic S HDS” removes sulfur from a naphthenic ring structure, for example, tetrahydrothiophene, dihydrobenzothiophene etc., while the second rule, labeled as “aromatic S HDS” removes sulfur from an aromatic ring structure, for example, thiophene and benzothiophene. Example reactions are shown along with the molecular classes to which they apply for both reaction rules. As indicated previously, reaction rules apply to each member of the homologous series of these molecular classes. Figure 6 shows the application of the aromatic S HDS reaction rule to all the alkylated members of the thiophene homologous series. The reaction rules suggest a direct hydrogenolysis pathway for desulfurization of thiophene,

Figure 6. Application of thearomatic S HDS reaction rule to the thiophene homologous series. The diolefins formed will quickly saturate to monoolefins.

although there exists an additional pathway via hydrogenation of the aromatic ring to tetrahydrothiophene that will be discussed later.

Aliphatic Sulfur Desulfurization. Aliphatic sulfur primarily exists in the form of mercaptans and to a lesser extent in the form of sulfides and disulfides. Since the reactivity of mercaptans and sulfides are similar, we do not explicitly differentiate sulfides from mercaptans in our model, but lump them into mercaptans. The following reaction rule has been considered in the model to describe this chemistry:

The desulfurization of mercaptan is a reversible reaction, where the olefin formed can react with H2S reverting back to

(30) Girgis, M. J.; Gates, B. C. Ind. Eng. Chem. Res. 1991, 30, 2021.

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captans, in accordance with eq 1. Therefore the hydrodesulfurization temperature needs to be chosen carefully to balance mercaptan desulfurization, catalyst deactivation, and paraffin-olefin equilibrium. In contrast, increasing pressure increases mercaptan formation since the desulfurization reactions lead to molar increase. Thiophene and Benzothiophene Saturation.

Closely associated with the desulfurization reactions are the saturation reactions, which may be critical intermediate steps. For instance, the desulfurization reactions for thiophenes and benzothiophenes to olefins may proceed through a saturation step intermediate where the thiophene and benzothiophene are first converted to tetrahydrothiophene and 1,3dihydrobenzo[b]thiophene respectively before eventually getting desulfurized to the olefin as per the reaction rule “Naphthenic S HDS”. The literature is conflicted with respect to the importance of formation of saturated intermediates prior to desulfurization. Amberg and co-workers33-36 studied the HDS of thiophene over a number of catalysts, including a commercial CoMo/Al2O3 catalyst, and concluded that the sole route of thiophene HDS is the direct C-S bond cleavage to form 1,3-butadiene, thereby suggesting that tetrahydrothiophene is not an intermediate product. Kolboe37 studied the relative rates of desulfurization of thiophene, tetrahydrothiophene, and n-butanethiol over unsupported MoS2 and CoMo/Al2O3 catalyst and concluded that thiophene and tetrahydrothiophene HDS occurs by two C-S bond cleavages through a β-elimination process. Thus, thiophene should form diacetylene and tetrahydrothiophene would form butadiene. This means that no hydrogenation of thiophene is required prior to its desulfurization and the desulfurization products of thiophene and tetrahydrothiophene should be different. However, they found that both thiophene and tetrahydrothiophene gave qualitatively the same (if not identical) product distributions. In contrast, several authors38,39 reported that both butadiene and butenes were the primary products in thiophene HDS. The presence of butenes is an indirect evidence of the formation of tetrahydrothiophene intermediate (see rule Napthenic S HDS), although it could have also formed from the saturation of the butadiene. Givens and Venuto40 studied the HDS of benzothiophenes over CoMo/Al2O3 catalysts and observed that hydrogenation-dehydrogenation equilibrium (between benzothiophene and tetrahydrobenzothiophene) was established at a rate higher than the rate of desulfurization,

Figure 7. Variation of log10 Keq as a function of inverse T (K-1) for three different kind of mercaptan-olefin equilibrium reactions.

the mercaptan.31,32 It is this recombination reaction and the ensuing equilibrium that makes the removal of sulfur, especially under conditions of deep HDS from naphtha, a major challenge. Although the forward reaction dominates for the most part, the reverse reaction becomes critical at deep HDS levels in the presence of significant olefin concentrations. Assuming, the reaction is at chemical equilibrium we can relate the equilibrium molar concentrations of olefin [Rd] and mercaptan [RSH] as ½Rd½H2 S ð1Þ ½RSH ¼ K eq where Keq is the equilibrium constant. This implies that the amount of mercaptans at equilibrium would be proportional to the molar concentrations of H2S and olefins. In fact, for high desulfurization extent where the partial pressure of H2S may be assumed constant, eq 1 suggests that the amount of mercaptans should vary linearly with the olefin content of the hydro-desulfurized gasoline at a given temperature and pressure. This is indeed observed experimentally. The equilibrium constant Keq increases with temperature. Calculation of Keq based on Stull et. al.57 as a function of inverse temperature for a few representative olefinmercaptan equilibria are shown in Figure 7. Three different olefin-mercaptan equilibrium reactions are considered. Pentanethiol-pentene represents equilibrium between a normal olefin and a mercaptan, 2me2butanethiol-2me2butene represents equilibrium between an iso-olefin and a mercaptan while cyclopentanethiol-cyclopentene represents equilibrium between a cyclo-olefin and a mercaptan. For all three cases, increasing temperature favors mercaptan desulfurization and decreases its equilibrium concentration. From this point of view, higher temperatures are favorable for HDS reactions. However, increasing temperature causes catalyst deactivation and coking, which results in reduced desulfurization reactions. In addition, increasing temperature (for instance >650 °F) also favors olefin formation via paraffin dehydrogenation reactions (reverse of olefin saturation), which in turn produces higher equilibrium mer-

(33) Owens, P. J.; Amberg, C. H. Adv. Chem. Ser. 1961, 33, 182. (34) Owens, P. J.; Amberg, C. H. Can. J. Chem. 1962, 40, 941. (35) Owens, P. J.; Amberg, C. H. Can. J. Chem. 1962, 40, 947. (36) Desikan, P.; Amber, C. H. Can. J. Chem. 1963, 40, 1966. (37) Kolboe, S. Can. J. Chem. 1955, 47, 749. (38) Kwart, H.; Schuit, G. C. A.; Gates, B. C. J. Catal. 1980, 61, 128. (39) Moser, W. R.; Rossetti, G. A.; Gleaves, J. T.; Ebner, J. R. J. Catal. 1991, 127, 190. (40) Givens, E. N.; Venuto, P. B. Prepr. Am. Chem. Soc. Div. Pet. Chem. 1970, 15 (4), A183.

(31) Satchell, D. P.; Crynes, B. L. Oil Gas J. 1975, 48, 23. (32) Hatanaka, S.; Morita, E.; Shimada, K. J. Jpn. Petrol. Inst. 2007, 50, 179.

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implying a hydrogenated intermediate formation prior to desulfurization. Similar results were obtained for the HDS of 2,3,7-trimethylbenzo[b]thiophene. Further support for a hydrogenated intermediate argument may be provided by considering the difficulty in a direct cleavage of the strong aromatic C-S bond.41,42 Hydrogenation of one or both CdC bond weakens the C-S bond, thus facilitating the scission of the C-S bond.43,44 These results do suggest the existence of intermediate saturation reactions of thiophene and benzothiophene to tetrahydrothiophene and 1,3-dihydrobenzo[b]thiophene, respectively, prior to their desulfurization. However it is unclear if this is the dominant pathway over hydrogenolysis. Consequently, both reaction pathways have been included in our kinetic model. Olefin Saturation. Olefin saturation reactions are important reactions in naphtha hydro-desulfurization as they are responsible for the octane loss during HDS. Depending on reaction conditions and the nature of the hydrotreating catalyst, olefins can undergo a multitude of reactions. However, selective hydro-desulfurization catalysts (including the SCANfining catalyst) are either neutral or weakly acidic. Furthermore, these acidic sites are generally poisoned quickly by adsorption of nitrogen species in catalytic naphtha. As a result, saturation reactions dominate, with skeletal isomerization reactions contributing to a negligible extent. Linear and branched mono-olefins saturate to the corresponding linear and branched paraffins, while cycloolefins saturate to cycloparaffins. The diolefins (both linear and cyclo-) saturate to the corresponding monoolefins. Meerbott and Hinds45 studied the isomerization of olefins and noticed that n-olefins tend to saturate faster than the corresponding i-olefins at the same carbon number, which is in agreement with what has been historically observed on metals.46 Experimental studies in our laboratory investigating the selectivity of HDS over olefin saturation on a real naphtha feed over various Co-Mo/Al2O3 catalysts also reach the same conclusion that, regardless of the molecular weight of the olefin, the n-olefins saturated faster than the corresponding i-olefins.

Figure 8. Relative rates of olefin saturation, arranged by molecular weight, as a function of time on a real FCC feed. Here C(t) is the olefin concentration at any time t. A single first order rate-constant can be extracted from this data, suggesting that olefins saturation rate is independent of the molecular weight.

Figure 9. Isomerization reactions for C5 olefins over the SCANfining catalyst. The solid arrows indicate the rapid establishment of equilibrium, while the dotted arrows indicate that the reaction does not occur (or occurs insignificantly) under SCANfining conditions although it is mechanistically possible on a hydrotreating catalyst.

as a function of space-time is plotted. A single first-order rate-constant, independent of molecular weight, can be employed to explain the time depletion data for different olefins, suggesting that the saturation rate of olefins is independent of molecular weight. Olefins can also undergo isomerization reactions over hydrotreating catalysts, depending on the reaction conditions and catalyst acidity. Two types of isomerization reactions are potentially possible: double bond isomerization, which moves the position of the double bond within the molecule (e.g., 1-pentene to 2-pentene) and skeletal isomerization, which causes a skeletal rearrangement of the molecular structure (e.g., 1-pentene to 2-methyl-1-butene). Figure 9 shows potential isomerization reactions for C5 olefins. Our experimental analysis of the product C5 olefin distribution on the SCANfining catalysts indicates that it promotes rapid double bond isomerization to yield an equilibrium distribution of internal and terminal (external) olefins (shown by the solid arrows in Figure 9), with negligible skeletal isomerization. This is not surprising since skeletal isomerization requires an acidic catalyst and the Co-Mo/Al2O3 catalyst is weakly acidic. The equilibrium ratio of internal to terminal olefins decreases with temperature, as shown in Figure 10, favoring

The effect of olefin molecular weight on the relative rates of olefin saturation is shown in Figure 8 over a Co-Mo/ Al2O3 catalyst. The rate of disappearance of different olefins (41) Markel, E. J.; Schrader, G. L.; Sauer, N. N.; Angelici, R. J. J. Catal. 1989, 116, 11. (42) Kraus, J.; Zdrazil, M., React. Kinet. Catal. Lett. 1977, 6, 475. (43) Angelici, R. J. Bull. Soc. Chim. Belg. 1995, 104, 265. (44) Schulz, H.; Schon, M.; Rahman, N.Catalytic Hydrogenation; Cerveny, L. Ed.; Elsevier: Amsterdam, 1986; p 201. (45) Meerbott, W. K.; Hinds, G. P. Ind. Eng. Chem. Res. 1955, 47 (4), 749. (46) Kraus, M. Adv. Catal. 1980, 29, 151.

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Figure 10. Variation of equilibrium ratio of internal to terminal C5 olefins as a function of temperature.

more internal olefins at lower temperature. Further, it is believed that the internal olefins tend to saturate at a rate slightly slower than terminal olefins.11,47,48 Usually, the internal olefins have higher octane numbers than their corresponding terminal olefins.49,50 Thus, in principle, rapid isomerization to internal olefins at lower temperatures will not only reduce the susceptibility to saturation, but will also increase the octane number. Indeed, small octane number increases in the range of 0.1-0.3 numbers are observed. The increase is small because the equilibrium ratio of internal to terminal olefins changes only marginally in the operating temperature range for SCANfining and, in general, naphtha hydroprocecssing. However, as the double bond migration reactions are always equilibrated under conditions of interest, we do not explicitly distinguish between internal and terminal olefins in our model development. Other Relevant Chemistry of Hydrotreating. The above reaction rules capture most of the reaction chemistry relevant to SCANfining. Since we intend to develop a generic kinetic model for naphtha hydro-desulfurization, of which SCANfining is a special case, additional reaction pathways and chemistries are also considered in the model and are shown in Figure 11. These include various aromatic saturation reactions, ring-opening reactions, cracking reactions, and hydro-denitrogenation reactions. The figure depicts each of the reaction rules with an example reaction and the set of molecular classes that belong to the reaction rule. For the particular case of SCANfining, most of the reaction rateconstants for these additional pathways are small, but are included for completeness in the general case. 2.4. Reaction Rate Constants. Application of the reaction rules discussed above to the 348 molecules considered in this model results in 444 different chemical reactions. Each of these reactions is associated with a rate-constant that needs to be specified to quantitatively solve this system of equations. We have structured the information on rate-constants in a three-tier hierarchy. At the first tier, each reaction rule is associated with a base rate constant that dictates the base

Figure 11. Other additional hydrotreating reactions in naphtha considered in the model. Note that these reactions are of minor importance in SCANfining, but included for sake of completeness.

rate with which all the different molecular classes affected by that rule react. For example, the aromatic S HDS rule affects the molecular classes, thiophenes, and benzothiophenes, and therefore these molecular classes have the same base rateconstant in the first tier. However, experimentally, we observe benzothiophene desulfurization rates to be 3-5 times faster than thiophene over CoMo/Al2O3 catalyst under SCANfining process conditions, where the reactions occur completely in vapor-phase. A similar reactivity trend was earlier observed by Van Parijis et.al. for experiments in vapor phase at 70-440 psig.17,18 These differences in relative rateconstants due to differences in molecular classes are reflected in the second tier of the rate-constants. Specifically, the base rate-constant is multiplied by a structure-reactivity function that accounts for the different relative rates for the different

(47) Nocca, J. L.; Cosyns, Q.; Debuisschert, B.; Didillon, B. Proceedings of NPRA Annual Meeting, San Antonio, TX, March 2000. (48) Badawi, M.; Vivier, L.; Perot, G Prepr. Pap. Am. Chem. Soc., Div. Pet. Chem. 2005, 50 (4), 418. (49) American Society for Testing and Materials (ASTM), Special Technical Publication No. 225, Knocking Characteristics of Pure Hydrocarbon; 1958. (50) The American Petroleum Institute. API Technical Data Book on Petroleum Refining; API: Washington DC, 1986.

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molecular classes affected by the same reaction rule. Thus, the benzothiophene HDS rate-constant is higher than the thiophene HDS rate-constant in the model. Finally, at the third tier, the difference in relative rate-constants between molecules within the same molecular class is accounted for by another set of structure-reactivity functions. These differences arise due to differences in molecular weight, length of the alkyl chain, steric hindrance, and degree of branching. For instance, the difference in rate-constants between thiophene, methyl thiophene, and ethyl-thiophene is accounted for in the third tier. Summarizing this three-tiered representation, the rateconstant for a reaction involving species i reacting through rule r may be expressed as:

rate-constants. For each molecule in the reaction network, an adsorption is defined that is site specific. Further, the adsorption constants for all the molecules are structured in a three-tier hierarchy analogous to the three-tier hierarchy for kinetic rate constants described previously.

φψri k r, i ¼ k base r

where VFeed is the total volumetric flow rate of the feed (m3/h), Fcat is the density of the catalyst (kgcat/m3), ci is the molar concentration of species i (kmol/m3), and A and z are the cross-sectional area (m2) and the length direction (m) of the reactor. Ri,r,s is the reaction rate of species i reacting by rule r on site s (kmol/s 3 kgcat) Each reaction rate term has a Langmuir-Hinshelwood (LH) type structure as shown in eq 4 k r, i K s, i pi pnH2 !m ð4Þ Rr, i, s ¼ P pH2 S 1 þ j K s, j pj þ K H2 S pH2

3. Model Formulation For a system of N components, we can write N steady-state species balance equations. These equations have the following form ! XX V Feed d ðci Þ ¼ Ri, r, s i ¼ 1; :::; N ð3Þ Fcat A dz r s

ð2Þ

is the base rate-constant for rule r, φ is the where kbase r structure-reactivity function that differentiates between the relative rates of different molecular classes participating in rule r (e.g., thiophene versus benzothiophene), and ψri accounts for the difference in relative rates of species i compared to other members of its homologous series for the reaction rule r (e.g., thiophene vs methyl thiophene). These reactivity relationships are based on both published literature as well as on many model compound studies in our laboratories. In cases where such information is unavailable, we have assumed ψri = 1, implying that molecules belonging to the same homologous series have the same rate constant k for a particular reaction. In addition to the reaction rate constants, each reaction rule occurs on a specific site on the catalyst surface that needs to be specified as well. The number, structure, and type of sites on a hydrotreating catalyst is a subject of much research and debate. It is generally recognized that there are at least two kinds of distinct active sites, one for hydrogenation and one for hydro-desulfurization, although there are arguments also suggesting the two sites are interconvertible with the extent of interconversion depending on the partial pressures of H2 and H2S.51,52 We have considered the following sites on the catalyst surface. The first site, which we call the saturation site accounts for all the saturation reactions, which includes the reaction rules: (a) thiophene saturation, (b) olefin saturation, (c) A6 saturation, (d) A4 saturation, and (e) HDN reactions. The second site, which we call the desulfurization site accounts for all the desulfurization reactions and includes the reaction rules: (a) naphthenic S HDS, (b) aromatic S HDS, and (c) aliphatic S HDS. The third site, which we call the acidic site accounts for the isomerization and cracking reactions and includes the reaction rules: (a) aromatic ring-opening, (b) N6/N5 ring-opening, (c) N4 ring-opening, (d) aromatic/naphthene alkyl crack, (e) aromatic/naphthene biphenyl bridge crack, (e) paraffin cracking, and (f) isomerization. Note, however, that the first two sites (namely saturation and desulfurization) are the only relevant sites for SCANfining. Consequently our discussion in the rest of the paper would only focus on them. The third site (i.e., acid site) is of very little importance in SCANfining but is important for hydroprocessing chemistry in general and is included in this model only for the sake of completeness. The adsorption constants are modeled similar to the reaction

where kr,i is the reaction rate constant for species i in rule r (and it includes the total number of active sites); Ks,i is the adsorption constant of species i on site s; pi, pH2, and pH2S are the partial pressures of species i, hydrogen, and H2S, respectively; n is the hydrogen order of the reaction; and m is an exponent dependent on the postulated rate-controlling step in the reaction mechanism. The partial pressures of species are related to their molar concentrations as pi = ciRT. The temperature dependence in the reaction rate and adsorption constants is built in the standard Arhennius form. The P summation in the denominator term jKs,jpj runs over all the molecular species, except H2S, which is modeled differently. The inhibition due to H2S is expressed as the ratio of the partial pressures of H2S and H2. This is because H2S behaves differently than other molecular species in the catalytic chemistry. In addition to acting as an inhibitor, much like any other molecular species that competes for the available catalytic sites, H2S also controls the creation and depletion of active sites (or sulfur vacancies) on the catalyst surface. Specifically, the creation and depletion of active sites is controlled by the equilibrium reaction MS þ H2 T M* þ H2S, where MS is the metal-sulfide (such as MoS2) and M* is the active site created as a result of the removal of sulfur from the metal sulfide by hydrogen. The dynamics of this reaction determines how many active sites are available, which in turn depends on the partial pressures of H2S and H2. Equation 4, as written in its general form, accounts for inhibition (competitive adsorption) from all the participating molecular species in the system. In principle, each molecule would have an adsorption constant for each site it adsorbs on. In our model, this would lead to 348 different adsorption constants for each site corresponding to each of the different molecules in the model. However, for the naphtha HDS kinetics of interest here, we have grouped the important adsorption terms into nine different molecular groups. These are: aromatics, aromatic sulfur (which includes both thiophenes and benzothiophenes), olefins, and light gases such as H2, H2S, NH3, CO, and CO2. Molecular groups such as

(51) Delmon, B. Catalysis in Petroleum Refining; Elsevier: Amsterdam, 1990; pp 1-40. (52) Li, Y. W.; Delmon, B. J. Mol. Catal. A: Chem. 1997, 127, 163.

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aromatics, aromatic sulfur, and olefins include many different molecules in their group, and as a starting point it is assumed that the inhibition constants for all the molecules within a molecular group are the same. This is the base inhibition constant for the group. Within each molecular group, the base inhibition constant is then multiplied by two structure-function relationships, one accounting for the structural differences between the molecules and the other accounting for the carbon number and molecular weight effect. Typical example of difference in adsorption constant due to structural differences would be change in adsorption constant due to number of rings (e.g., thiophene vs benzothiophene, which are both part of the aromatic sulfur molecular group, or benzene vs naphthalene, which are both part of the aromatic molecular group.) An example of difference in adsorption constant due to molecular weight and carbon number would be the change in adsorption constant due to the length of the alkyl chain in different members of the homologous series. Since the adsorption of a molecule on a particular site depends on the geometry of the molecule and the site and the molecule’s affinity for that site, the same molecule will have different effect on HDS and olefin saturation reactions as they occur on different sites. Among all the different inhibition terms, H2S, CO,53,54 and CO2 strongly inhibit HDS sites, but have a weak effect (if at all) on the olefin saturation sites. The inhibition effect of H2S is shown in Figure 12. Figure 12a shows the effect of increasing H2S concentration on the composition of product sulfur and olefins, while Figure 12b presents the same results in terms of the extent of HDS and olefin saturation. The figure indicates that increasing H2S concentration increases the amount of sulfur in the product (i.e., lowers the extent of desulfurization), but it has little to no effect on the extent of olefin saturation. Such selective poisoning further reinforces the idea of the existence of two separate sites on the catalyst, one for HDS and the other for olefin saturation. The inhibition effect of CO on the extent of HDS and olefin saturation is shown in Figure 13. The source of CO is the hydrogen stream in the refinery. Typically, the hydrogen in a refinery can come from a variety of sources such as (a) catalytic reforming, (b) steam reforming of light hydrocarbons, or (c) partial oxidation. Although the hydrogen from catalytic reforming is usually devoid of CO and CO2, hydrogen from steam reforming or partial oxidation of hydrocarbons can often contain trace levels of residual CO and CO2. Steam reforming converts light hydrocarbon feed into synthesis gas (mixture of H2, CO, CO2, CH4, and H2O) by reaction with steam over a nickel-based catalyst. Likewise, the production of hydrogen through partial oxidation consists of treating a hydrocarbon fraction at a high temperature with oxygen to produce synthesis gas. Most of the CO and CO2 is generally separated from the hydrogen stream either by methanation or adsorption, however residual amount of CO (often up to 100 ppm) is still dissolved in it. Figure 13 shows a steep decrease in the extent of HDS with increasing CO concentration indicative of the strong inhibition of CO on HDS sites. CO also inhibits olefin saturation sites, but not to the same extent as it does on HDS sites. Since only a few ppm of CO diminishes the

Figure 12. Effect of H2S inhibition on olefin saturation and HDS reactions. (a) Product S and product olefin as a function of H2S wt % in treat gas. (b) % OSAT and % HDS as a function of H2S wt % in treat gas.

Figure 13. Effect of CO inhibition on the extent of olefin saturation and desulfurization reactions. Feed: ICN, olefin (wt %) = 35, S = 1556 ppm, RSH = 32 ppm, T = 525 °F, P = 240 psig, LHSV = 4 h-1.

catalyst activity significantly, the inhibition constant for CO is around 4 orders of magnitude higher than any other inhibition constant. In contrast, the adsorption constants for H2 is lower compared to other organic compounds by at least 1-2 orders of magnitude, thus exhibiting very weak inhibition on both

(53) Ellis, E. S.; Halbert, T. R.; Brignac, G. B.; Greeley, J. P.; Demmin, R. A.; Lalain, T. A. Low CO for Increased Naphtha Desulfurization, US Patent 7,422,679. (54) Bournay, L.; Baudot, A. Process for the Selective Desulfurization of Olefinic Gasolines, Comprising a Hydrogen Purification Step; US Patent Application 20060076271A1.

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of the individual molecules and their weight percent in the mixture. Primary Properties are property correlations of individual molecular properties based on the molecular class/homologous series concepts and SOL molecular structure description. This category includes boiling point, specific gravity, refractive index, viscosity, and octane number. The molecular property for each molecular class is predicted based on a predictive function (derived from the pure-component data in the literature). The stream property is then calculated based on appropriately derived mixing rules. Derived Properties are calculated based on standard, published correlations for individual molecular properties that require direct or primary properties as inputs. This category includes vapor pressure, heat of vaporization, and critical properties (for VLE calculations). Group Contribution Methods from the literature have been incorporated for molecular heat capacity and enthalpy of formation (Benson groups) and for molecular liquid phase activity coefficients (UNIFAC groups). Thermodynamic equilibrium constants have been used from the literature when reported57 and estimated based on Benson group computations of enthalpy of formation and Gibbs free energy otherwise. The SOL description of a molecule is translated into the appropriate set of groups defined in each method. The molecular property is then calculated as the sum of the individual group properties. Since octane number is the primary property of interest in selective naphtha hydro-desulfurization, an octane predictive model based on molecular composition is described here. Octane number is a measure of the antiknock property of the fuel and is defined as the volume percentage of isooctane in a blend of n-heptane and iso-octane that produces the same knock intensity as the test fuel under standard test conditions in an ASTM internal combustion engine. Knock results from the premature combustion of the gasoline due to compression in the engine. As the fuel/air mixture is compressed in the internal combustion engine, certain molecules in gasoline tend to self-ignite even before they reach the ignition spark, thereby creating a resistive expansive motion in the compression stroke of the engine and hence the knock. Depending on the thermal stability of the molecule and the ensuing radicals, some molecules tend to combust sooner (and knock more) than others depending upon their molecular structure. The development of the octane model as a function of molecular composition has been previously reported,58 therefore only the important points of the model will be highlighted here. The model correlates the naphtha composition, represented by a total of 57 molecular lumps obtained from gas chromatography to the octane number using nonlinear mathematical transformation. The molecular lumps vary from individual molecules (e.g., n-butane, n-pentane) to a group of similar molecules (e.g., total monomethyl i-octanes). Table 5 summarizes the different molecular lumps considered in the octane model. The model is predicated on the postulate that the individual contribution of each molecular lump (defined by its blend number) toward the octane number of the naphtha fuel varies linearly with the octane number of the naphtha fuel it is part of. Interaction terms are augmented to account for known nonlinear blending interactions between paraffin-olefin and paraffin-naphthene hydrocarbon classes. The model

Table 4. Inhibition Constants for different molecules on HDS and Olefin Saturation Sites Ea (kcal/mol)

Ko

aromatics organic S organic N NH3 H2 CO CO2 olefins H2S

OSAT

HDS

OSAT

HDS

2500 5000 0 0 10 1 250 000 0 200 0

5000 5000 0 0 10 10 000 000 0 200 90

4 0 6 3 0 0 0 5 0

4 0 4 3 0 0 0 5 15

HDS and saturation sites. Inhibition from aromatics and aromatic sulfur is mild, consistent with the observations of La Vopa and Satterfield.55 Finally, olefins inhibit HDS sites only, as reported experimentally by Hatanaka et. al.56 The adsorption (inhibition) constant for all the different molecular groups along with their respective activation energies for adsorption is reported in Table 4. Equations 3 and 4 specify the species balance equations for all the N species, which are sufficient for the isothermal mode of reactor operation. However, for adiabatic mode of reactor operation, the energy balance equation needs to be specified as well, which is shown in eq 5 P dT V Feed i ð -ΔH i Þ dci P ¼ ð5Þ dz dz j Fj vj C pj where vi, Fi, and, Cpi are the volume flow rate (m3/h), density (kg/m3), and heat capacity (kJ/kg 3 K) of species i respectively. (-4H)j is the net heat of reaction for reaction j in kJ/kmol. Equations 3, 4, and 5 thus represent the complete mathematical description of the kinetic model. 4. Property Models The kinetic equations predict the product compositions and yields, but often the quantities of interest are not so much the product compositions, but product properties like the octane number, distillation curve, specific gravity, etc. that depend on composition. Property models are needed to convert the detailed compositional information into the desired properties of interest. For example, the distillation curve for a feedstock or product stream can be calculated directly from the component weight percents and their boiling points. Another example is the sulfur content of a stream, which can also be directly computed from the component weight percents and their sulfur stoichiometry. All properties;including physical, thermodynamic, or quality; are calculated from molecular composition information and individual molecular properties, either directly or indirectly. We have employed four different groups of methods for calculating these stream or mixture properties from composition: Direct Properties include C, H, S, N, O, Bromine number, and molecular weight. Bromine number is an indirect measure of the olefin content based on a bromine titration. Direct properties can be calculated from the stoichiometry (55) La Vopa, V.; Satterfield, C. N. J. Catal. 1988, 110, 375. (56) Hatanaka, S.; Yamada, M.; Sadakarne, O. Ind. Eng. Chem. Res. 1997, 36, 1519. (57) Stull, D. R.; Westrum, E. F.; Sinke, G. C. The Chemical Thermodynamics of Organic Compounds; Robert, E., Ed.; Kriger Publishing Company: 1987.

(58) Ghosh, P.; Hickey, K. J.; Jaffe, S. B. Ind. Eng. Chem. Res. 2005

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Table 5. Molecular Lumps Considered in the Present Octane Model paraffins

naphthenes

aromatics

olefins/cycloolefins

n-butane isobutane n-pentane isopentane n-hexane C6 mono-methyls 22-dm-butane 23-dm-butane n-heptane C7 mono-methyls C7 di-methyls 223-tm-butane n-octane C8 mono-methyls C8-dimethyls C8-trimethyls n-nonane c9 mono-methyls c9 dimethyls c9 trimethyls n-decane C10 monomethyls C10 dimethyls C10 trimethyls n-undecane C11 monomethyl C11 dimethyls C11 trimethyls n-dodecane C12 monomethyl C12 dimethyls C12 trimethyls

cyclopentane cyclohexane m-cyclopentane C7 naphthenes C8 naphthenes C9 naphthenes

benzene toluene C8 aromatics C9 aromatics C10 aromatics C11 aromatics C12 aromatics

N-butenes N-pentenes iso-pentenes cyclopentene N-hexenes iso-hexenes total C6 cyclic olefins total C7= total C8=

equation is shown in eq 6, P P vi β ONi þ I P P vi βi ONi P P ON ¼ P PONA i PONA vi βi þ I P ð P vi βi P vi Þ

oxygenates MTBE TAME ethanol

ð6Þ

where vi and ONi are the volume fraction and pure component octane number of molecular lump i and βi is an adjustable model parameter quantifying the blend contribution of lump i. The subscript PONA (representing paraffins, olefins, naphthenes, and aromatics) on the first summation indicates that the sum runs over all the molecular lumps in the model. However, the subscript P in the second summation indicates that the summation runs over only the paraffinic lumps that includes both normal and iso paraffins. The nonlinear blending behavior between different molecular lumps is described by the interaction term Ip, which captures blending interactions between paraffin-naphthene and paraffin-olefin lumps. It is defined as 0 1 ðaÞ ðaÞ k v þ k v N O PN PO A ð7Þ IP ¼ @ ðbÞ ðbÞ 1 þ k PN vN þ k PO vO of where vN and vO represent the total volume concentration P = v and v = naphthenes and olefins in the fuel (i.e., v N N O P (a) (b) (a) (b) O v) and kPN, kPN, kPO, and kPO are the adjustable interaction parameters of the model. The predictive capabilities of this model for different naphtha fuels have been reported previously.58 Here we report the predictive capabilities of this model specific to different SCANfining feeds and products. Figure 14 shows the model predictions for the RON and MON for a number of different SCANfining feeds and products. Each point in this figure is either a feed to the SCANfiner or a hydrotreated product from the SCANfiner. The feeds include varying grades of FCC naphtha, ranging from full range naphtha (FRN) to narrow boiling point cuts such as

Figure 14. Comparison of octane model predictions for RON and MON for different SCANfining feeds and products. The solid line indicates the parity line, while the dotted lines indicate the (1 octane number error lines.

light cat-naphtha (LCN), intermediate cat-naphtha (ICN), and heavy cat-naphtha (HCN) to their blends (e.g., LCN mixed with 5754

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HCN). The products include hydrotreated products under different severity conditions. Across this broad spectrum of feed and product compositions, the model predictions are good within (1 octane number, which is within the repeatability and reproducibility of the octane measurement itself.

ICN-HCN, etc. Each feed was run at many different process conditions to obtain wide range of products from low to high conversion. The feeds and the products were analyzed for detailed composition using a combination of hydrocarbon- and sulfur-specific gas chromatographic-mass spectroscopic methods (GC-MS). The molecular compositions measured by the GC-MS were reconciled with the bulk property measurements such as total sulfur, total nitrogen, mercaptan sulfur, bromine number, etc. to ensure data from the different analytical methods were consistent. This reconciled data set was then used to regress the different rateconstants and structure-reactivity relationships that best described the complete data set. Since the experimental data set was collected over several months, over which the catalyst can deactivate significantly, catalyst deactivation was explicitly accounted for in the model when regressing the rate-constants. A deactivation curve was calculated based on running a reference feed in the pilot plant under a standard set of conditions at different points during the pilot plant data collection process. We have assumed that the rate of deactivation of both sites (HDS and olefin saturation) to be the same. 5.2. Model Predictions. Against this set of experimental data, the reaction rate constants, adsorption constants, and the associated activation energies are calculated. The model predictions are presented below. The critical properties of interest in SCANfining are the product olefin (wt %), sulfur (wt %), mercaptans (RSH, wt %), and RON and MON, respectively. Figure 15 shows the model predictions for product bromine number (TLP Br), total sulfur (TLP S, ppm), mercaptan sulfur (TLP RSH, ppm), and octane (RON) for all the experimental points run in this program. Each point on this plot represents a pilot plant run of a naphtha feed at a given set of process conditions. Different naphtha feeds and a wide range of process conditions have been considered here. The points have been color-coded by the boiling of the feeds they originated from such as FRN, ICN, ICN/HCN, and LCN/ HCN. In each plot, the measurement accuracy of the test is also plotted to quantify the measure of uncertainty in the data set. Bromine number can be measured within (5 numbers, RSH within (5 ppm, and octane number within (1 numbers. The parity plots (panels a and b) show the comparison of TLP Br and sulfur, which are indicative of the extent of olefin saturation (% OSAT) and extent of desulfurization (% HDS). The parity plot (panel c) shows the predictions for mercaptan in the product indicative of the olefin-mercaptan equilibrium. Finally, the parity plot (panel d) shows the predictions for the octane number of the different products. Here the results for RON are displayed, though the predictions are similar for MON as well. The parity plots reveal encouraging quantitative agreement of the model predictions with the experimental data points, suggesting that the kinetic parameters and the structure-reactivity relationships have been robustly determined. “Selectivity” and “octane loss at a given olefin saturation” are the two most important properties of interest in the SCANfining process. Selectivity is defined as the extent of olefin hydrogenation (% OSAT) at a given extent of desulfurization (% HDS). Figure 16 shows few typical selectivity curves as measured for various naphtha feeds. The experimental data suggests that the catalyst RT-225 usually provides between 10 and 25% olefin saturation at 90% HDS, the

5. Results and Discussion Equations 3-5 represent the complete set of equations that must be solved to predict the composition of the naphtha stream as it is selectively hydro-desulfurized in the reactor. This results in a large set of coupled ordinary differential equations (ODEs), which are solved by a variable-order variable step-size ODE solver. However, the solution of the ODEs requires the specification of the individual kinetic rate constants, which have to be regressed from experimental data. The experimental program used for the determination of kinetic rate constants is described below. 5.1. Experimental Program. Experimental programs for kinetic studies can include a variety of feeds such as (a) simple feeds like model compounds to (b) feeds of intermediate complexity like synthetic gasoline, to (c) real feeds with all the molecules present in the naphtha range. Each of the feeds provides its own advantages and disadvantages. For example, numerous model compound studies using thiophene as the reactant feed and butane as the desulfurization product have been considered in the literature to quantify the desulfurization and hydrogenation activities. Such studies are helpful in qualitatively understanding the reaction pathway, and the nature of intermediate species, however their use in kinetic studies is often compromised by the consecutive nature of these reactions, which makes it difficult to extract reaction rates and rate-constants reliably. In addition, the absence of other molecular species in the reaction mixture makes such data less representative of real feed scenarios, especially when competitive adsorption and inhibition effects become important. Alternatively, one could either consider synthetic gasoline, which is a mixture of chosen olefin and sulfur species, as a potential feed or use a real FCC naphtha feed for kinetic studies and measure simultaneously the desulfurization and the hydrogenation activity. The former would give much cleaner information about various rate-constants, although not necessarily representative of the real situation. In contrast, the latter would be most appropriate for model building as it reliably replicates real commercial scenarios, although the information might not be always as clean for every reaction pathway. However, this shortcoming of real feeds can be overcome by spiking the real feed with certain model compounds (where information is clouded) that magnifies the desired pathway under study and can provide better kinetic data. We have used real feeds for our experimental program. A large database of experimental runs (mostly pilot plant and in a few instances of a commercial refinery) has been collected. The pilot plant reactor setup consists of a fixed bed reactor (1/2 in. diameter, 50 cm3 catalyst volume) operating in the upflow mode. The database includes 45 real feeds run at many different process conditions that include varying the temperature, total pressure, H2 partial pressure, and space velocity through the reactor. Feeds spiked with certain model compounds have been run in some cases to better understand specific pathways and inhibition effects. The feeds represent varying grades of FCC naphtha, that include FRN, HCN, ICN, LCN, and their blends such as 5755

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Figure 15. Comparison of model predictions against experimental data for different SCANFining products. Each point represents a product resulting from a naphtha feed run at a specified process condition in the SCANFining reactor. The following parities are shown: (a) olefin (wt %), (b) total liquid product (TLP) sulfur (ppm), (c) mercaptan sulfur in TLP (ppm), and (d) RON.

tive agreement with the experimental data points, suggesting that the model is able to capture selectivity effect as a function of feed composition and different process operating conditions. For a given selectivity and therefore a given % OSAT, the extent of octane loss determines the effectiveness of the SCANfining process. Figure 18 compares the model predictions against experimental data for the octane loss (ΔRON) as a function of the extent of olefin saturation for the same six feeds as in Figure 17. As before, the points here reflect a wide range of process conditions in space-velocity, temperature, pressure, and H2 partial pressure. The figure shows that the model quantitatively predicts the experimental data over a wide range of process conditions and feeds. Notice from the figure that different feeds follow different Δoctane-Δolefin curves since the octane loss depends not only on the extent of olefin saturation, but also on the type of olefins saturated. Since the kinetic model explicitly accounts for the different olefins and their relative rates, it is able to quantitatively capture these compositional variations in the feeds using a single set of kinetic parameters and reactivity relationships. The results presented so far (Figures 15-18) show encouraging results for the model predictions in the pilot plant data. Although acceptable, it would be desirable to further tighten the model predictions, especially for the predictions for TLP S and TLP Delta RON than what is demonstrated in Figure 15. It is difficult to say from these parity plots that whether this scatter is due to an error in the model structure or due to measurement errors, either direct or indirect. For instance, the scatter in TLP S may be due to error in TLP S measurement (direct error) or due to TLP Br measurement,

Figure 16. Selectivity curve (experimental data) for the SCANfining process for the different feeds in the pilot-plant study. The experimental data are color-coded by boiling point cuts of the feeds into LCN, ICN, HCN, and FRN. Each category represents multiple feed-product pairs.

exact number depending on the type of feed and process operating conditions. Figure 17 shows the selectivity predictions of the model for individual FCC naphtha feeds as function of HDS conversion. Six different FCC feeds are shown. The model comparison is shown specifically for data points in the deep HDS regime (>80% HDS), as it is the regime of commercial interest. The points reflected in this plot have varying spacevelocity and temperature and in some cases varying pressure to obtain deep HDS. The model predictions are in quantita5756

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Figure 17. Comparison of model predictions with measured data for selectivity for a variety of feeds. Open-circles are measured data-points, closed-circles are the corresponding predicted data points.

which controls TLP S through the olefin-mercaptan equilibrium (indirect error). A significant contributor to the prediction error in the model originates from the low-fidelity nature of some experimental data, particularly the bromine number measurement (which is known to have substantial repeatability errors, almost 10 numbers in some cases). A possible way to mitigate the error introduced in the model predictions due to measurement errors in the experiments is to run the model in a “tuning” mode, where some of the rate-constants in the model are adjusted to exactly match certain product properties, while other properties are predicted using these adjusted rate constants. Tuning is thus an indirect way (in general) to remove model prediction error due to error(s) in the experimental measurement. The underlying idea is that if certain experiments (or properties, e.g., Br number) were measured perfectly and the model was able to predict it perfectly, then running the model in the tuning mode can show how well the model predictions would be on all the other properties of interest (e.g., TLP S, RSH, etc.) that are not tuned. If the model is indeed correct, then it should show much tighter parity than before (i.e., when predicted without the tuning). These tunings come in the form of premultipliers to the base rate-constants. In this study, we used two tuning parameters, CF1 and CF2. CF1 is the tuning factor for olefin saturation reactions, that is, all olefin saturation rate-constants are premultiplied by CF1 such that the model predic-

tions “exactly” match the product bromine number. Similarly, CF2 is the tuning factor for HDS reactions, that is, all HDS reactions were multiplied by CF2 such that the model predictions “exactly” match the product non-mercaptan S. Given these tuning factors, the total S, mercaptan S, and RON were then purely predicted. Figure 19 shows the parity comparisons of the model predictions against experimental data, when the model was run in the tuning mode. Certainly, the parity predictions for TLP S, TLP RSH, and TLP Delta RON improve when the model is run in the tuning mode (contrast Figure 19 with Figure 15). This indicates that experimental measurement errors have a significant bearing on the performance of the model, and if such errors can be minimized (either by better measurements or using the model in the tuning mode), then the model predictions are significantly improved. It is important to emphasize that the model predictions may be compromised in the absence of good experimental data. This comment is especially relevant for commercial refinery data predictions where experimental data quality is often not as tight and in many cases incomplete. 5.3. Commercial Data Predictions. The kinetic model has also been extensively tested and validated against commercial refinery data. SCANfining units from a total of eight different refineries were chosen to test the model performance. 5757

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Figure 18. Comparison of model predictions with measured data for octane loss as a function of % OSAT for a variety of feeds. Delta RON = Feed RON - Product RON. Open-circles are measured data-points, closed-circles are the corresponding predicted data points.

Figure 19. Comparison of model predictions against experimental data for different SCANFining products. The model has been “tuned” to match TLP Br.

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Figure 20. Parity predictions for TLP bromine number for commercial refinery data from eight different refineries.

Since detailed feed GCs (both PIONA and sulfur) were unavailable for most of the refinery data, the refinery feed composition was estimated using a suitable reference feed (preferably a similar feed used in the pilot plant study) and adjusting the composition of the reference feed to match the measured bulk properties of the refinery feed. We call this data reconciliation process “synthesis” and the feed as being synthesized. API gravity, total sulfur, mercaptan S, bromine numbers, and distillation targets such as T10, T30, T50, T70, and T90 were used as bulk property targets. For distillation, T10 implies the temperature at which 10% of the material distills off. Figure 20 compares the parity plot for bromine number for different commercial refinery data from eight different refineries. The figure shows very close agreement of the measured bromine number with the predicted bromine number. Most of the data points are predicted with (5 numbers, which is well within the repeatability of the bromine number test. Similar parity plots as Figure 20 are obtained for both total S, mercaptan S, RON, and MON, indicating that the model is successful in predicting the overall commercial data. The model is therefore useful for a variety of applications in the refinery such as process monitoring, process optimization, catalyst selection, and catalyst management activities.

underlying reaction mechanisms is still lacking. We have presented here a detailed composition-based kinetic model and validated it on a wide range of FCC naphthas and process conditions. The model incorporates our best understanding of the reaction mechanisms relevant in the HDS of naphtha and is formulated using the SOL-methodology. It contains 348 molecular species and 444 chemical reactions. The model accurately predicts the selectivity, octane loss, and detailed product composition using a single set of kinetic parameters and reactivity relationships for a wide range of pilot-plant and commercial refinery data. List of Abbreviations ASTM = American Society for Testing and Materials FBP = final boiling point FCC = fluid catalytically cracked FRN = full range naphtha HCN = heavy cat naphtha HDS = hydrodesulfurization ICN = intermediate cat naphtha IBP = initial boiling point LCN = light cat naphtha MON = motor octane number OSAT = olefin saturation PIONA = parrafins, isoparaffins, olefins, naphthenes, aromatics RON = research octane number SOL = structure-oriented lumping TLP = total liquid product

6. Conclusions With the push toward increasingly stringent environmental regulations and the associated economic consequences of octane loss, the selective desulfurization of naphtha is a high priority in the petroleum industry. Although the chemistry of naphtha desulfurization has been extensively studied in the past two decades, a detailed kinetic model based on the

Acknowledgment. We would like to thank William Riedinger, John Greeley, Karlton Hickey, and April Ross for many useful discussions and critique during the development of the model.

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