Ind. Eng. Chem. Process Des. Dev. 7004, 23,
027
827-833
Toor, H. L. AIChE J . 1984~1,10, 448, 460. Toor, H. L. AIChE J . 1984b, IO, 545. Weiiand, R. H.; Taylor, R. Chem. Eng. Educ. 1982, 16(4) 158. Wilke, C.R. Chem. €ng. Prog. 1950, 46(2) 95. Wilson, G. M. J . Am. Chem. Soc.1984, 86, 127. Young, G. C.; Weber, J. H. Ind. Eng. Chem. Rocess Des. Dev. 1972, 1 1 , 440. Zuiderweg, F. J.; Harmens, A. Chem. Eng. Sci. 1958, 9 , 89.
Medina, A. G.; Mccermott, C.; Ashton. N. Chem. Eng. Sci. 1979, 3 4 , 861. 7 , 360. Nagata, I. J . Chem. Eng. Data 1982~1, Nagata. I. J . Chem. Eng. Data 1982b, 7 , 367. Oldershaw. C. F. Ind. €ng. Chem. 1941, 13, 265. Sakata, M.; Yanagi. T. I . Chem. E. Symp. Ser. No. 56, 1979. 3.2121. Slattery, J. C. “Momentum, Energy and Mass Transfer in Continua”; McOrawHili: New York, 1972. Smith, E. D. “Design of Equilibrium Stage Processes”: McGraw-Hill: New York, 1963; Chapter 15 by Fair, J. R. Smith. L. W.; Taylor, R. Ind. Eng. Chem. Fundam. 1983, 22, 97. Taylor, R.; Smith, L. W. Chem. Eng. Commun. 1982, 1 4 , 361. Toor, H. L. AIChE J . 1957, 3 , 198.
Received for review June 2, 1983 Accepted November 28, 1983
Slmulation of Catalytic Reactions in Shale Oil Refining Tek Sutlkno’ Midwest Research Institute, Kansas City, Missouri 6 4 110
Stanely M. Walas Department of Chemical and Petroleum Engineering, University of Kansas, Lawrence, Kansas 66044
Process yield data from advanced catalytic reaction of Paraho shale oil have been simulated. The properties and the required refining severity of shale 011 are different from those of petroleum crudes. The simulation was made to evaluate if mathematical models developed for hydrotreating, hydrocracking, and fluid catalytic cracking of petroleum-based oils can be applied for simulation of these reaction processes used for shale oils. Preliminary results indicate that refining of shale oil by these processes can be adequately simulated by the models derived for petroleum-based oils. However, further process data at several reaction conditions of a variety of shale oils are needed to confirm the applicabilities of these models.
Introduction The use of shale oil as an alternate feedstocks for the production of transportation fuels has been the subject of several investigations (Frost and Cottingham, 1974; Lovell et al., 1981; Sullivan et al., 1978). It is generally concluded that surface-retorted shale oil such as Paraho shale oil can be refined to various specification-grade transportation fuels-diesel fuel, gasoline, and jet fuel. The properties of shale oil are, however, significantly different from petroleum crudes, and the refining of shale oil is more complex and severe than that of petroleum. Advanced catalytic reaction processes are required to convert shale oil to transportation fuels. Most of the published models for simulations of advanced catalytic reaction processes in oil refining are developed from and verified with experimental data on petroleum-based feedstocks. These models can be used for the development of designs, operations, and refining strategy development. Because of the specific properties of shale oil and the refining severity required, this paper evaluates the applicabilities of the readily developed models for simulation of catalytic reaction processes in shale oil refining. This applicability evaluation is necessary if any of the petroleum refining reaction models is to be used for designs, yield predictions, or optimization of the respective reaction process in shale oil refining. Process models for catalytic reactions in oil refining are generally complex and involve several parameters. The values of these parameters must be known if the model is used for simulation or yield prediction purposes. Al0196-4305/84/1123-0827$01.50/0
though several research investigations of shale oil refining have been conducted, these investigations are feasibility studies in nature, and experimental yield data from catalytic reaction processes of shale oil refining are very limited. A literature survey indicated that the pilot plant refining data reported by Sullivan et al. (1978) are the most comprehensive at present; three pilot refining schemes shown in Figures 1to 3 were used in this pilot plant study of shale oil refining. In light of their comprehensiveness, those pilot plant data are used as the bases for present simulation study. As shown in Figures 1to 3, the major catalytic reaction processes in shale oil refining are hydrotreating, hydrocracking, fluid catalytic cracking, and reforming. Only hydrotreating, hydrocracking, and fluid catalytic cracking data are simulated in this study; the reforming process is not included because shale oil-derived naphtha is reportedly identical with petroleum-based naphtha (Lovell et al., 1981; Sullivan et al., 1978). Hydrotreating, hydrocracking, and fluid catalytic cracking processes in shale oil refining perform different functions, and simulation of each of these processes requires specific mathematical framework. The mathematical models for simulations of these processes have been selected from the literature; these models and the results of simulations are described next in the order of hydrotreating, hydrocracking, and fluid catalytic cracking. A. Hydrotreating In refining shale oil to transportation fuels, the main purpose of hydrotreating is removal of nitrogen; therefore, 0 1984 American Chemical Society
828
Ind. Eng. Chem. Process Des. Dev., Vol. 23, No. 4, 1984 .C"
Vrhoie
1
Battamr
y
Jet Fuei
Table I. Hydrotreating of Whole Shale Oil at Several Product Nitrogen Levels inlet nitrogen concentration 2.18% (wt) catalyst: ICR 106, on stream time 1300-1800 h reactor temperature, average 767 OF total pressure 2200 psig hydrogen partial pressure 1850 psia total gas in 7000 SCF/bbl recycle gas 5000 SCF/bbl Outlet Nitrogen Concentrations at Various LHSV 640 30 1.3 outlet nitrogen concentration, ppm LHSV, l / h 0.6 0.3 0.2 24 48 48 time on stream, h
Recycle
1 , C i i s butanes and iighter; Used for H2 plant feed, gasoline blending, refinery f u e l . ond liquified petroleum gas (LPG). 2 . Some naphtha from hydrotreating units Qiso used for H2 plant feed. 3. Foul gor and water treoted t> recover NH3 ond ruifur.
Figure 1. Shale oil refining-hydrotreating (Sullivan et al., 1978).
and hydrocracking
Whole Dewatered
1 . C i i s butanes and lighter; Used for H2 piant f e e d , gosoline b l e n d i n g , refinery fuel, and i i q u i f i e d petroleum gas ( L P G ) . 2 . Foul gos and water treoted to recover NH3 and sulfur.
Figure 2. Shale oil refining-hydrotreating cracking (Sullivan et al., 1978). Whole Dewotered
and fluid catalytic
Gilbert, 19731, effective catalyst wetting model (Mears, 19741, and others. Recently, a phenomenological model developed for trickle-bed reactors has been reported by Crine et al. (1980). Because of the complexity of the trickle-bed reactor, some of the models are sophisticated and involve several multiphase transport phenomena and reaction mechanisms. Verifications of these models are difficult and require clear identifications of all parameters affecting the performance of the reactor. However, confusion among authors still exists with respect to the theoretical interpretation of the experimental data [e.g., Marangozis (1980) and Shah (1980)l. In the present simulation of a commercial hydroprocessing trickle-bed reactor, simple correlating models were used rather than sophisticated ones because experimental data obtained from real reactors often are not accurate enough to discriminate between the prediction of simple and more sophisticated models. Among the models which have been evaluated with respect to the predictive accuracy, the effective catalyst wetting model developed by Mears (1974) has been reported to correlate the experimental data well [Montagna et al. (1977) and Gianetto (1978)l. It has been adopted here for the simulation of shale oil hydrotreating. This model is a semiempirical one, but no reliable theoretical model is available. The model can be briefly described as follows
;'4 Hydro-
dC - --k i W W _
C'
qGq
treating
Goroi ine
4 Refinery Fuel
(1)
dx LHSV where C = concentration, mol/cm3, x = dimensionless distance, z / L , ki = first-order reaction rate constant, cm3 of liquid/(cm3 of reactor volume), h, 7 = catalyst effectiveness, Aeff = A,/A,, dimensionless, where A , = wetted area of catalyst and A, = total area of catalyst, and LHSV = liquid hourly space velocity, cm3/h cm3 of catalyst. For scale-up purposes
Aeff = 1 - exp(-1.36GaL0~05WeL0~2(~c/6L)0~75) (2) 1 . Ca i s butanes ond iighter;
2.
Used
for hydrogen plant feed, gasoline
b l e n d i n g , refinery f u e l , and l i q u i f i e d petroleum go' (LPG). Foul go' and woter treated to recovery NH3 ond sulfur.
Figure 3. Shale oil refining-delayed (Sullivan et al., 1978).
coking and hydrotreating
a process model capable of predicting the reduction level of nitrogen content is essential. In the pilot study of Sullivan et al. (1978))surface-retorted Paraho shale oil was hydrotreated in trickle bed reactors in which the liquid oil and the gaseous hydrogen flow downward through a fiied catalyst bed. For trickle-bed reactors used in the hydroprocessing of petroleum crudes, several process models developed for performance prediction have been published. Comprehensive reviews of these models are presented by Satterfield (1975), Gianetto et al. (1978))and Shah (1979). Included in this review are the hold-up model (Henry and
where GaL = d:gpL/pL2, dimensionless Galileo number, d, = diameter of catalyst particle, cm, g = acceleration by gravity, cm/s2, p L = liquid density, g/cm3, WL = liquid viscosity, g/(cm)(s), WeL = GL2dp/BLpL, dimensionless, Weber number, G L = liquid mass velocity, g/(cm2)(h),BL = liquid surface tension, g/s2, and 6c = critical value of surface tension for a given cracking, g/s2. Combining eq 1 and 2 and integrating, one obtains 1 n Ci -zCo
kill
LHSV
[l - ~ X ~ ( - K L ~ ~ ~ ( L H S (3) V)~~~J]
where K = f(pL, BL, 6c, pL, d,) and L = reactor length, cm. As shown in eq 3, three constants, (ki, 9,and K) must be evaluated before the level of conversion can be computed. Two sets of conversion data at different liquid hourly space velocities are enough to evaluate these con-
Ind. Eng. Chem. Process Des. Dev., Vol. 23, No. 4, 1984
0 Hydrotreating of Shale Oil at
767' F
0 Hydrotreating of PetroleumBared Charge Stock10/
10
100
i ,000
10,000
Calculated Unconverted N i t r o g e n , ppm
Figure 4. Parity plot of model predictions vs. experimentalresults.
stants since ki and 7 are lumped together. In the hydrotreating of crude shale oil, Sullivan et al. (1978) reported the nitrogen content reduction data at a fixed reaction temperature for three values of LHSV. These data are shown in Table I. The two lumped constantrs, KL and kiq, were evaluated analytically from the fmt two conversion data (0.6 and 0.3 LHSVs). The values of kill and KL0.4are solved to be 2.14 and 4.2, respectively, and eq 3 becomes 2.14
{I- e~p[-4.2(LHSV)~.~l}(4)
Note that the parameter K is lumped together with since the length of the pilot reactor was proprietary and K could not be evaluated separately. Equation 4 was used to predict the nitrogen removal level at 0.2 LHSV; the result is shown in Figure 4. It appears that the agreement between the experimental and predicted values is fairly good. Comparing the experimental and the predicted values of nitrogen removal in hydrotreating of petroleumbased charge stocks (Figure 4), it is clear that an effective catalyst wetting model can simulate hydrotreating of shale oil as well as it simulates hydrotreating of petroleum. However, more experimental data with known values of relevant parameters must be generated to evaluate the individual values of the constants.
B. Hydrocracking As shown in Figure 1, the heavy fraction (650 to 850 O F ) of the hydrotreated shale oil was refined further in an extinction recycle hydrocracking unit. Hydrocracking convertes heavy hydrocarbons into high grade jet fuels or gasoline, reformer charge stock, and light gases. Pilot shale oil refining studies of Sullivan et al. (1978) indicated that hydrocracking is the only process scheme capable of efficiently maximizing jet fuel production; in the process of Figure 1 the main purpose of the extinction recycle hydrocracker is to produce jet fuels from the heavy fraction of the hydrotreated shale oil. Hydrocracking of shale oil is conducted in a trickle-bed reactor; thus, hydrocracking and hydrotreating processes are hydrodynamically similar. However, the primary function of hydrotreating is to remove impurities in the feedstocks while that of hydrocracking is mainly to upgrade the feedstocks by lowering the boiling range. The performance of a hydrocracker is generally based on the true boiling point (TBP) of the product; therefore, a mathe-
829
matical model capable of predicting the boiling range of the product is essential. Several publications on the modeling of hydrocracking have appeared. A comprehensive review has been made by Choudhary and Saraf (1975). Qader and Hill (1969) showed experimentally that hydrocracking of gas oil follows fit-order kinetics. Qader et d. (1970) have reported first-order rate constants for light and heavy petroleum oils in the hydrocracking process. Although it is reasonable to expect that the hydrocracking of the 650 to 850 O F fraction of the hydrotreated shale oil will follow first-order kinetics, verification is desirable and could be checked with adequate hydrocracking test data of shale oil, but such experimental data are not available. Zhorov et al. (1971) developed several chemical process schemes to describe the hydrocracking conversion of petroleum feedstock into gas, naphtha, diesel fuels, and residue. These process schemes contain stoichiometric coefficients describing the distribution of hydrocracking products. At a given temperature, the stoichiometric coefficients are relatively constant over a range of residence times. However, for more severe hydrocracking, these coefficients vary significantly and have not been properly correlated. Stangeland (1974) has developed a kinetic model capable of predicting hydrocracker yields satisfactorily and is actually being used to simulate and optimize commercial hydrocracker yields. This model is used here for simulation of shale oil hydrocracking. Stangeland (1974) assumed that the feed and product streams contain a continuum of compounds that can be characterized solely by boiling point. These compounds are then segregated into fixed boiling ranges of 50 O F each. Each is characterized solely by the true boiling point (TBP) of the end of the range. The change in the amount of the ith compound is the result of its cracking plus the contribution from the cracking of all heavier compounds.
-dfi(t) - - -k,f,(t) + i-1 CPijkjfj(t) dt
j=l
(5)
where f i = ith component with f i as the heaviest component, ki = first-order rate constant, and Pij = fraction of light component f i formed by cracking a heavier component fr Rewrite eq 5 in a single matrix differential equation
$0) = -(I - P)KF(t)
(6)
where F ( t ) = vector containing the weight fractions of all components I = identity matrix, P = lower triangular product distribution matrix, and K = diagonal matrix having the ki)s on the diagonal. If all cracking rate constants are different, the solution to eq 6 is F(t) = DE(t) (7) or in the matrix form
The procedure to compute Dij, Pij, and ki is rather lengthy and complicated, but it is described in detail by Stangeland (1974). Primarily, the model has three adjustable coefficients: A, B, and C. Coefficient A relates to reaction rate constants kj of each individual component;
830
Ind. Eng. Chem. Process Des. Dev., Vol. 23, No. 4, 1984
Table 11. Pilot Plant Hydrocracking of 650-850 OF Fraction of Hydrotreated Shale Oil operating conditions catalyst ICR 106 785 O F reactor temperature, average total pressure 2350 psig 2129 psia hydrogen mean pressure 8700 SCF/bbl total gas in 7300 SCF/bbl recycle gas, SCF/bbl 1.0/h liquid hourly space velocity 0.5351 per pass conversion accum wt, % C,+ products" C4 group 2.26 C5 - 175 O F 7.70 19.90 175 to 315 O F 315 to 560 O F 53.50 a
All temperatures shown are true boiling point temperature. 0 Doto from Shale Gor Oil A Data f r o m
-Model
Petroleum Gar O i l %
Predictions
900
0
IC
20
30
40 Y e d
3f
50
60
Cq-
V i r ic
70
80
90
100
Figure 5. Comparison of measured and predicted yields for extinction recycle hydrocracking of shale and petroleum gas oils.
coefficient B relates to the product distribution function ( P J ;and coefficient C affects the product distribution and determines the yield of butane. A computer program has been developed here to use Stangeland's model for data simulation of shale oil hydrocracking. However, only one pilot run was made of hydrocracking of 650 to 850 O F fraction of hydrotreated shale oil, for which the operating conditions and product yields are given in Table 11. The data in this table were generated from a pilot hydrocracker operation in an extinction recycle mode. In this mode, the fresh feed and the recycle constitute the total feed to the reactor. The yield data in Table I1 were applied with the developed computer program to obtain the results shown in Figure 5. The TBP values were calculated from the computer program with a specific set of values for A , B , and C. As shown in Figure 5, this specific set gives reasonable agreement between the calculated and observed TBP values and was the best among several other sets which had been tried. While the values of A , B , and C could have been determined by a nonlinear regression or other optimization methods to best fit the experimental data, these methods were not used because experimental data are too limited in number for statistically meaningful evaluation of A , B, and C. As shown in Figure 5, the agreements between the experimental and the predicted values are fairly good for both shale and petroleum gas oils. The values of the three parameters of the model are A = 0.9, B = 0.75, and C = 0.01. For hydrocracking of feedstocks of petroleum source, the ranges of these parameters are known to be -1.0 to 1.0 for A , -2.0 to 1.0 for B, and
0.0, to 2.0 for C (Stangeland, 1974). The values of the parameters used here for hydrocracking of hydrotreated shale oil are well within the ranges of those used successfully to represent the hydrocracking yields of petroleum-based feedstocks.
C. Fluid Catalytic Cracking Catalytic cracking is a widely used process for converting heavy oils into more valuable gasoline and lighter products. Its role in the oil refining industry is described in a recent review by Venuto and Habib (1979). In the pilot plant study conducted by Sullivan et al. (1978), and 650 O F + fraction of the hydrotreated shale oil was catalytically cracked to produce gasoline. Several process models for prediction of gasoline yield have appeared, for example by Reif et al. (1961), Schneider et al. (1969), and others. A widely used model which is capable of predicting the conversion level and the gasoline yield was initially developed by Weekman (1968, 1969). This model was based on a three-lump cracking kinetic scheme which includes a catalyst decay function. The model has been shown to predict adequately the performances of various types of cracking reactors including fixed bed, moving bed, and fluidized bed types. It has been used as a mathematical framework to analyze many aspects of the catalytic cracking process, for example, the effect of molecular composition of feedstocks on cracking kinetics (Nace et al., 1971), the effect of nitrogen poisoning and recycle (Voltz et al., 1972),and the selectivity behavior of catalytic cracking in various types of reactors (Weekman and Nace, 1970). The model was also utilized by Paraskos et al. (1976) and Shah et al. (1977) to describe the performance of a transfer line catalytic cracking reactor with reasonable success. Elnashaie and Hennawi (1979) used the same concepts to develop an integrated mathematical model for parametric analysis of a steady state fluid catalytic cracking unit. Weekman (1979) has reported the use of this model in the optimization ofcommercial catalytic crackers. The three-lump model of Weekman has a key weakness, namely, that each time the charge stock is changed, a new set of rate constants and the catalyst decay function must be determined from laboratory experimental data. This is due to the fact that the model does not take detailed account of the molecular compositions of the charge stocks. Since variation of compositions of charge stock is common in refineries, the experimental efforts required to determine the constants in the model for each charge stock are quite expensive. In an effort to correct this drawback, Jacob et al. (1976) developed a ten-lump model which includes the cracking characteristic of the charge stocks. In the present simulation of shale oil catalytic cracking, the three-lump model is adopted since the avilable pilot plant data of shale oil catalytic cracking are not enough to allow evaluation of the constants of the ten-lump model. Details of the three-lump model are as follows. Weekman (1968) represented the cracking process by the simple reactions A,
ko
a1A2+ azA3
(94
k2
A2 ----* AS
(9b)
Al represents the gas oil charged, A2 represents the C5-410 O F gasoline product, and A, the butanes, dry gases, and coke. For an isothermal, vapor phase, plug flow reactor with negligible intraparticle diffusion, the cracking reaction can be described in terms of two coupled continuity equations given below.
lnd. Eng. Chem. Process Des. Dev., Vol. 23, No. 4, 1984 831 Table 111. Fluid Catalytic Cracking of 650 OF+ Fraction of Hvdrotreated Shale Oil operating conditionsa 1 2 run no. 3 975 reactor temperature, "F 930 930 7.71 weight hourly space velocity, h 7.85 7.86 7.10 catalyst oil weight ratio 6.71 4.39 0.921 severity 0.855 0.558 76.97 conversion, w t % 69.25 61.25 Product Yields, Weight Fraction 0.2717 coke, butane, and lighter 0.2038 0.1699 0.4981 gasoline (C5 - 430) 0.4887 0.4426 0.2303 gas oil remaining 0.3075 0.3875
y1 and y2 represent the instantaneous weight fractions of gas oil and gasoline, respectively. VI is the vapor velocity in ft/s. The rate terms, R1 and R2, can be expressed as Ribi,t) = & ~ ( t ) ~ i ~
(12)
R2b2,t) = k24WY2
(13)
4(t) is the catalyst decay function. Equations 12 and 13 can be normalized into dimensionless equations.
a Catalyst
Davison CBZ-1.
At the exit of a fluid bed reactor (x = l), with y1 Ix=o = 1, eq 21 can be solved as
By dividing eq 17 by eq 18, one gets where dY1
With the boundary condition of no initial gasoline (yl
p v = the vapor density, lb/ft3 at reactor conditions, VI =
= 1, y2 = 0), the solution of eq 23 is
the reactor volume, ft3, Fo = the oil charge rate, lb/s, t, = the vapor phase residence time, s, t, = the catalyst residence time (s) under oil exposure condition, 0 = t 'It, where t 'is the clock time, s, x = z / L , where z is the axial distance (ft) in the reactor and L is the total reador length, ft, p o = initial charge density at reactor conditions, lb/ft3 (vapor density if no reaction), ho= density of liquid charge at room temperature, lb/ft3, s = Fo/pLoVr,and kl = alko. p,,'s in eq 14 and 15 strictly should have been p:s; however, the model assumes the vapor density variation due to cracking reaction is absorbed in the second-order cracking reaction approximation. For steady-state operation the time derivatives of eq 14 and 15 are zero. Moreover, 4 can be adequately expressed as 4(t) =
where
(16)
is the decay velocity constant. Equations 14 and 15 can be written in eq 17 and 18 by transforming du = @/s dx CY
dyl/du = -K0I2
(17)
dy2/du = KlY12 - K2Y2
(18)
where KO= P & O / ~ L ~K1 , = P O Q . I ~ O / P L ' ,K2 = P O ~ ~ P L ~ The stretched time u is a function of the nature of the catalyst decay function and the catalyst residence time. With the assumptions of perfectly mixed catalyst flow and plug gas flow in the fluidized bed reactor, the average velocity constant (k)can be defined as
k
= k o ~0 m I ( 0 ) ~ d (BO )
(19)
The internal age distribution I(0) for a perfectly mixed vessel is e"; 4(0) in eq 19 is e-x0with X = at,. k can be solved as
With this expression for the average reaction velocity constant, eq 14 can be solved as
KO
.
A combination of eq 22,24, and 25 can be used to predict yield fractions from a fluid bed catalytic cracker. However, the constants in eq 22 and 24 must be evaluated first if they are used for yield prediction. Two conversion data points are necessary for evaluation of all constants in the models. A total of five runs was reported by Sullivan et al. (1978). Three of these runs were conducted with a charge stock of 1300 ppm nitrogen content. The other two runs were made with two charge stocks with nitrogen contents of 385 ppm and 870 ppm. These last two runs were made at the same conversion level and therefore could not be used for the evaluation of constants in the model. Moreover, conversion level in catalytic cracking process is dependent upon the inlet nitrogen content, and a different level of nitrogen content may require a different set of values for the kinetic constants. The present data simulation of shale oil catalytic cracking are based on the three runs with 1300 ppm nitrogen charge. Table I11 displays the operating data and the product yield fractions. The data from run no. 1and 2 were used to evaluate the constants of the three-lump model. Because some of the constants in the model cannot be solved explicitly and the integration must be made numerically, a computer program using a successive substitution method was developed to evaluate the constants. The values of the various con-
832 Ind. Eng. Chem. Process Des. Dev., Vol. 23, No. 4, 1984
-
I , 2 . 3 Run Numberr in Catalytic Crocking of Shale Bared Go5 O i l
0.81
E 0.71 5 0.61
0
Gar Oil
A
Gasoline
0
Gasoline Y i e l d Doto from Petroleum Eared Gar O i i w
u.
/
8
$$
i
2.00
f
0.3
i
0.2
= 205.4/hi = 77, l/hr =
35.9/hr
= 0,23/hr
00
0.1
0.2
0.3
0.4
0 5 0.6
0.7
0 8 0 9 1 00
Predicted Product Weight Fraction
Figure 6. Parity plot of model predictions vs. experimental resulta. stants evaluated are shown in Figure 6. With the constants evaluated from run no. 1and 2, the model was used to predict the yield fractions from run no. 3. The experimental and the calculated values are shown in Figure 6. It can be seen that the calculated conversion is lower than the experimental value. This is expected because the reactor temperature in run no. 3 is 45 O F higher than the reactor temperatures of the first two runs from which the constants were evaluated. The cracking reaction is endothermic and high temperatures should increase the rate constants. Compared to the calculated and experimental value agreements obtained from the three-lump model for petroleum-based charge stocks (Figure 6 ) )the agreement for shale catalytic cracking seems reasonable. However, more experimental data with better accuracy and consistency are definitely required to confirm the applicability of the three-lump model or even the ten-lump model for yield predition of shale oil catalytic cracking.
D. Conclusion While the selected models seem to simulate well the hydrotreating, hydrocracking, and fluid catalytic cracking process data of Paraho shale oil, the present applicability assessment of these models for shale oil refining must be at best regarded as preliminary in nature. Constants or coefficients in the selected models have been evaluated from the limited refining data of Paraho shale oil. New values of these constants or coefficients may be necessary to fit the refining data of other shale oils with chemical composition significantly different from Paraho shale oil. Moreover, the present simulation study has been limited to a single temperature of each individual reaction process. Thus, for simulation of similar reaction processes in refining of shale o& from other sources or at other operating conditions, further simulation studies with more well-designed experimental data are needed to confirm the applicabilities of the selected models. Nevertheless, the present work indicates that pilot data on hydrotreating, hydrocracking, and fluid catalytic cracking of Paraho shale oil can be closely simulated by available models.
Nomenclature A = a parameter in Stangeland hydrocracking model; dimension1ess A, = gas oil weight fraction A2 = gasoline weight fraction A3 = butane, coke and gases weight fraction a1,.a2= coefficients of catalytic cracking reaction aa defined In eq 9, lb of product/lb of feed A& = ratio of wetted area to the actual surface area of catalyst,
dimensionless E' = ( p , Vr)/(Fotc), dimensionless
B = a parameter in Stangeland's hydrocracking model, dimensionless C = a parameter in Stangeland's hydrocracking model, dimensionless, or concentration, mol/cm3 D = production distribution matrix defined in eq 7 D,, = coefficients of matrix D d = diameter of catalyst particle, cm = oil charge rate, lb/h F ( t ) = vector containing the weight fractions of all components, dimensionless f , = weight fraction of ith component in hydrocracking feed or hydrocrackage, dimensionless Ga = dimensionless Galileo number GL = liquid mass velocity, g/cm2 h g = acceleration by gravity, cm/s2 Z = identity matrix Z(8) = internal age distribution function of catalyst, dimensionless K = diagonal matrix having the ki's on the diagonal, or a constant defined in eq 3 KO = (poko)/pLo,gas oil rate constant, l / h K l = (Poalko)/PL k, = mean average reaction velocity constant of ith reaction ki = reaction rate constant of ith reaction in both hydrotreating and hydrocracking models; k, is the reaction velocity constant of ith reaction at 8 = 0 in the model for fluid catalytic cracking L = reactor length, cm LHSV = liquid hourly space velocity, l / h P,,= a distribution parameter of product from hydrocracking RIP2= kinetic rate functions as defined in eq 14 and 15 r1 = Kl/KO r2 = &/KO s = F o / p L o V,, liquid hourly space velocity based on liquid charge density and reactor volume, l / h t ' = clock time, s t, = catalyst residence time, s t, = vapor phase residence time, s U, = vapor velocity, ft/s V, = reactor volume, ft3 W e L = dimensionless Weber number x = dimensionless distance y 1 = instantaneous gas oil weight fraction y 2 = instantaneous gasoline weight fraction y 3 = instantaneous weight fraction of light gases and coke z = axial distance in the reactor, ft Greek Letters
I
a = decay velocity constant, 1 h
6 = liquid surface tension, g/s 6, = critical value of surface tension for a given packing, g/s2 e = instantaneous weight fraction converted 8 = t'/tc X = extent of catalyst decay group, at,
9 = catalyst effectiveness, dimensionless p L = liquid density, g/cm3 pLo =. liquid density at room temperature, lb/ft3 p o = initial charge density at reactor conditions, lb/ft3 p , = vapor density, lb/ft3 pL = liquid viscosity, g/cm s
$ ( t ) = catalyst decay function, dimensionless Literature Cited
Choudhary, N.; Saraf, D. N. Ind. Eng. Chem. Prod. D e s . D e v . 1975, 1 4 , 74. Crine, M.; Marchot, P.; L'Homme. G. A. Chem. Eng. Sei. 1980, 35. 51. Elnashaie, S. S. E.; El Hennawi, I . M. Chem. Eng. Sei. 1979, 3 4 , 1113. Frost, C. M.; Cottingham, P. L. U . S . Bur. Mines Rep. Invesf. 1974, B W I 7844. Ganetto, A.; &Mi, G.; Specchi, 0 . ; Sicardi, S. A I C M J . 1978, 24(6),1087. Henry, H. C.; Gilbert, J. B. Ind. Eng. Chem. Process D e s . D e v . 1973, 12, 328. Jacob, S. M.; Gross, B.; Voltz, S. E.; Weekman, V. W. Jr., AIChE J . 1978,
22. Lovell, P. F.; Fryback, M. G.; Reif, H. E.; Schwedock, J. P. Hydroc8rbon Process. May 1981. Marangozis, J. Ind. Eng. Chem. Process D e s . D e v . 1980, 19, 326. Mears, D.E. A&. Chem. Ser. 1974, No. 133, 218. Montagna, A. A.; Shah, Y. T.; Paraskos. J. A. Ind. Eng. Chem. Process Des. Dev. 1977, 16, 152.
Ind. Eng. Chem. Process Des. Dev. 1904, 23, 833-845 Nace, D. M.; V o k , S. E.; Weekman, V. W., Jr. Ind. Eng. Chem. Process Des. Dev. 1971. 70. 530. Paraskos, J. A.; S&h, Y.T.; McKinney, J. D.; Carr, N. L. Ind. Eng. Chem. Process Des. Dev. 1976, 75. 165. Qader, S. A.; Hill, G. R. Ind. Eng. Chem. Process Des. Dev. 1969, 8, 98. Qader, S. A.; Singh, S.; Wiser, W. H.; Hill, G. R. J. Inst. Pet. 1970, 56(550), 187. Reif, H. E.; Kress, R. F.; Smith, J. S. Pet. Refiner 1961. 40(5), 237. Salterfield. C. H. AIChE J. 1975, 27(2). Schnaider, G. J.; Mukhim, I. I.; Chueva, M. A.; Kogan, Y. S. Khim Tekhno/. Topi. hfasei. 1969, NO. 7 , OI. Shah. Y. T. “Gas-Liquid-Soli Reactor Design”; McGraw-Hill: New York, 1979. Shah, Y. T. Ind. Eng. Chem. ProcessDes. Dev. 1980, 79, 328. Shah, Y. T.; Huling, 0. P.; Paraskos, J. A.; McKinney, J. D. Ind. Eng. Chem. Process Des. Dev. 1977, 16, 89. Stangeland, 6. E. Ind. Eng. Chem. ProcessDes. Dev. 1974, 73, 74. Sullivan. R. F.; Stangeland, 6. E.; Rudy, C. E.; Green, D. C.; Frumkin, H. A.
.
833
“Refining and Upgrading of Synfuels from Coal and Oil Shales by Advanced Cataiytic Processes,” First Interim Report, U.S. Dept. of Energy, April 1978, UC-gOd, FE-2315-25. Venuto, P. 6.; Habiie, E. T., Jr. “Fluid Catalytic Cracking with Zeolite Catalysts”; Marcel Dekker: New York, 1979. Voltz, S. E.; Nace, D. M.; Jacob, S. M.; Weekman, V. W.. Jr. Ind. Eng. Chem . Process Des. Dev. 1972. 7 1 , 26 1. Weekman, V. W., Jr. Ind. Eng. Chem. Process Des. Dev. 1968, 7, 90. Weekman, V. W., Jr.; Nace, D. M. AIChE J. 1970, 76(3), 397. Weekman, V. W., Jr. Ind. Eng. Chem. Process Des. Dev. 1969, 8, 305. Weekman, V. W., Jr. AIChEMonog. Ser. 1979, 75(11). Zhorov, Yu. M.; Panchenkov, G. M.;Tatarintseva, G. M.; Kuzimin, S. T.; Zenkovski, S. M. Int. Chem. Eng. 1971, 77(2), 258.
Received for review June 13, 1983 Accepted January 16, 1984
Thermophysical Properties of Complex Systems: Applications of Multiproperty Analysis Michael R. Brul6” Kerr-McGee Corporation, Oklahoma City, Oklahoma 73 125
Kenneth E. Starllng University of Oklahoma, Norman, Oklahoma 730 79
The “therm-trans” method is presented for predicting characterization parameters (e.g., critical properties) required in multiparameter corresponding-states correlations. Multiproperty analysis of thermodynamic and transport data is used to extract the characterization parameters through simultaneous calculation of the property data by an equation of state and a viscosity correlation. The property data needed are similar to the inspection data obtained for pseudocomponent fractions resolved from standard TBPdistillation analyses. This new approach for determining correlation parameters altogether circumvents the need for critical properties per se. Application to complex petroleum and coal-liquid fractions is demonstrated.
Prediction of Thermophysical Properties A modified Benedict-Webb-Rubin (BWR) equation of state has been applied to predict the thermodynamic properties of the simpler hydrocarbons found in petroleum and coal-derived fluids (Brul6 et al., 1979, 1982). This three-parameter corresponding-states correlation has been used in conjunction with a conformal-solution model to describe the vapor/liquid-equilibrium (VLE) bahavior of model multicomponent mixtures representative of the actual systems found in fossil-fuels processing (Watanasiri et al., 1982; Brul6 et al., 1983; Brul6 and Corbett, 1984). Equation-of-state characterization parameters and idealgas thermodynamic properties have been estimated, by using empirical correlations, as functions of the average measurable properties of fractions such as normal boiling point and specific gravity (Brul6 et al., 1982). Many problems remain in predicting complex-system thermophysical properties. Correlations must be capable of predicting properties of high-molecular-weight, multifunctional organic compounds at mild-to-severe operating temperatures and pressures. Multipolar and associating effects exhibited by some coal fluids are attributable to 0196-430518411123-0833$0 1.5010
the presence of heteroatomic and attached functional groups such as -N-, -NH,and -OH. Such intermolecular behavior cannot be described accurately by conventional three-parameter corresponding-states correlations. Characterization of Complex Fluids The problem of characterization further compounds the problem of correlation. A standard characterization procedure must be available for resolving a complex fossil fluid, with hundreds of diverse distillable and nondistillable organic compounds, into a pseudocomponent mixture with 20 or so fractions representing the overall properties of the parent full-range fluid (Brul6 et al., 1981). Once a full-range fossil fluid is resolved into effective pseudocomponent fractions, characterization parameters that enable properties correlations to predict accurate thermophysical properties must be determined for each of the fractions. As complex fluids increase in molecular weight, the determination of characterization parameters at the normal boiling point becomes less feasible. This presents an impasse, especially for many of the pure model compounds used for correlation development, since only low-temper0 1984 American Chemical Society
