Modelling the pyrolysis of tar sands in fluidized bed reactors

Modelling the pyrolysis fluidized bed reactors Milind D. Deo, John V. Fletcher, Alexa G. Oblad Department of Fuels Engineering, (Received 3 April 7997)

Dowon

University

of tar sands in

Shun,

of Utah,

Francis

Salt Lake City,

V. Hanson UT 84112,

and

USA

The state of Utah has vast tar sand resources. Surface thermal recovery processes, such as pyrolysis, have been investigated as possibilities in the sequence of processes for the surface mining of the tar sands and the subsequent recovery and upgrading of bitumen. Fluidized beds have been used extensively to effect the pyrolysis. In this paper, the hydrodynamics of laboratory scale fluidized beds and a mathematical fluidized bed model have been examined. Using newly developed correlations for minimum fluidization velocities, the hydrodynamics of the previously used laboratory reactors were evaluated. These hydrodynamic parameters helped demarcate slugging and bubbling beds. A kinetic model, which invoked a three-step reaction network and a simple two-phase bubbling bed model were used to generate product distribution curves. Reaction kinetics for the proposed reaction network and hydrodynamics from the two-phase theory of fluidized beds were integrated in the bubbling bed model. A uniform reaction model for the conversion of ‘solids’ was assumed. The product distributions calculated using these models were compared with those observed experimentally. Both the kinetic and the fluidized bed models qualitatively predicted the trends in product distributions with respect to temperature. The kinetic model also predicted trends with regard to residence times. Through the use of these simple models, a framework for the application of more complicated models was established. (Keywords: tar sand; kinetics; pyrolysis)

Depleting petroleum resources and increasing energy demands will eventually require better exploitation of unconventional petroleum resources such as oil shale and tar sands. Tar sands are a mixture of sand and bitumen. The organic constituent, bitumen, cannot be recovered by ordinary petroleum production methods due to its high viscosity and the lack of reservoir energy in most deposits. The world’s known tar sand reservoirs contain more than 2100 billion barrels of bitumen in place, with about 50 billion barrels of bitumen in place in the United States (1 barrel = 42.4 US gallons). Of the lower 48 states in the United States, the state of Utah has about 4&60% of the tar sand resource’. This represents a potentially significant domestic energy resource, compared with the crude oil reserves of the continental United States. Information regarding the Utah tar sand and oil deposits is available in reports by the Utah Geological and Mineralogical Survey2. Tar sand bitumen may be recovered by two basic methods: in situ thermal recovery or surface mining followed by recovery processing. The study of pyrolysis is essential to fully understand thermal processing of the resource, via either in situ or surface recovery methods. Pyrolysis of tar sands for the simultaneous recovery and upgrading of bitumen has been investigated extensively3. Fluidized beds and rotary kilns have been used for the pyrolysis process. On the laboratory scale, different sizes of fluidized beds have been employed. Certain distinct trends, with respect to pyrolysis temperatures, residence Presented Lexington,

at ‘Eastern KY. USA

Oil Shale

00162361/91/11127146 (’ 1991 Butterworth-Heinemann

Symposium’,

Ltd.

68

November 1990,

times and product yields were observed in these experiments. Fluid bed models for non-catalytic reactions have been

discussed in several books and papers4,‘. Despite a great deal of work in this area, general models for non-catalytic fluidized bed reactors do not exist. In general, the non-catalytic fuidized bed models tend to be more complex than the models for catalytic conversions. The fluidized bed models for tar sand pyrolysis are even more complicated due to the complex nature of the feed material, There has been considerable research effort on the kinetics of the pyrolysis reactions for the Athabasca bitumen6*7. In addition, Barbour and co-workers’ have studied the kinetics of pyrolysis for the Asphalt Ridge tar sands. The studies on the hydrodynamic aspects of fluidized beds involving group B particles (sands) have been too numerous to cite here. Developing a satisfactory fluidized bed model requires an integration of the pyrolysis kinetics with the hydrodynamic aspects. In this work, the experimental data of several researchers is first brought into perspective. The trends observed are identified and hydrodynamic regimes of operation are evaluated. In the subsequent sections, a kinetic model and a two-phase fluidized bed model are developed and examined. EXPERIMENTAL

PERSPECTIVE

The fluidized bed pyrolysis of Utah tar sands has been investigated by Venkatesan’, Jayakar”, Wang’ ‘, Smart 12, Dorius13, Sungi and Shun”. The operating parameters used in a few of these studies are listed in

FUEL, 1991, Vol 70, November

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Table 1 Parameters from selected studies on the pyrolsis of Utah tar sands d, Olm)

Reference

Bed diameter (m)

Loading (kg)

TK)

Gas flow (1h-’ @ STP)

Regime

Venkatesan’

225

0.035

0.68

113

142

Slugging

SungI

202

0.1016

6.0

118

1075

Slugging

This work (Fletcher)

209

0.1524

6.0

800

2000

Bubbling

l

I

I

1

648

RaInbow I (Ha 20n/nlN

PR

673

698

I

723

I

748

REACTOR

I

I

I

773

I

823

798

I

848

TEMPERATURE.

873

I

J

898

K

Figure 1 Effect of temperature on liquid yields: experimental data

Table 1. The process variables investigated’-I5 included reactor temperature, sand retention time and fluidization gas velocity. The product distributions consisted of Cc bitumen-derived hydrocarbon liquid, C,-C4 hydrocarbon gas and carbonaceous residue. Trends observed in the prior studies’ with respect to pyrolysis temperature and sand retention time are presented in Figures 1 and 2. The pertinent observations from these plots are that the liquid yield exhibited a maximum with respect to reactor temperature, and that the liquid yield decreased with increased sand retention time.

developed the following correlations: For round sands, with s,r < 0.48: Ar

Rem‘=

.

Ara1480

1400+5JAr’ Ar Re,,,r= ---; 1400

Ar < 1480

For moderately sharp sands, with E,,,~< 0.55: Remf=

Ar 117o+JAr

HYDRODYNAMIC

REGIMES

A fluidized bed can operate in either the bubbling, slugging or turbulent hydrodynamic regimes, depending on the diameter of the reactor, the fluidizing gas velocity, the solids loading and a few other parameters. Certain simple guidelines such as the ratio of the fluidization velocity to the minimum fluidization velocity or the ratio of the diameter of the bubbles to the diameter of the reactor can be used to identify the hydrodynamic regime in the fluidized bed. Numerous correlations are available for the prediction of minimum fluidization velocities for group B sand particles. Fletcher et a1.16 reexamined the two-constant, Ergun-type correlations and after inspection and analysis of a number of sets of published data and data specific to spent sands formed during the pyrolysis of tar sands,

1272

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1991,

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70, November

(1)

(2)

For very angular sands and coked sands: Re,,,r=

Ar 790+7&&r

(3)

After a detailed study, Fletcher et ~1.‘~ concluded that the best procedure for estimating U,, at elevated temperatures was to evaluate UmI at room temperature (if no experimental data was available) and extrapolate it to higher temperatures by using the following equation: u mf 7+.27=k

(4)

This procedure was used to predict the minimum fluidization velocities with nitrogen from the particle diameter and temperature. For gas-solid reactions in fluidized beds, the solids usually reach a high degree of

Modelling

90 I.-

I

k

component 1 f

k

component 2 4 heavy oil 5

w Tar

Sand

Triangle

l PR Spr’ng

Rainbow

A Whiterocks A Whiterocks

O,0

I CT= 798K)13

(T=823K)“1’3 (T=

798KJ9

lSunnys’de

(T=

773KJ9

0 Sunnyride

(T=

723

20

15

K)”

(T=853K)““3

fJSunnyr/de

SAND Figure 2

( T= 798

25

RETENTION

K)’

30

TIME.8,

35

40

min

Effect of reaction time on liquid yields: experimental data

conversion; hence, it is conventional practice to choose the properties of converted solids for hydrodynamic calculations5. For the fluidized bed parameters given in Tuble 1, hydrodynamic characteristics were evaluated, using the two-phase theory of fluidized bedsr7. The bubble diameters were calculated using the bubble growth model of Darton et a1.18: db = 0.54(ci - U,,)O.4(2 + 4J([email protected]

(5)

The classification of the fluidization regime as bubbling or slugging was based on the condition that the bubble diameter at 0.4 x H,, for a bubbling bed has to be less than a third of the reactor diameterr7. Additional calculations indicated that all of the previously reported studies on the fluidized bed pyrolysis of Utah tar sands’-” were conducted in beds operating in the slugging regime. A bubbling regime was predicted for the design values specified by Fletcher (Table 1). Subsequent model calculations use the hydrodynamic characteristics given by these design values.

A heavy oil + B carbonaceous residue C heavy oil + D gas

(6)

E light oil +F gas

In this preliminary analysis, all the reactions are assumed to be first order and component 1 and component 2 could be interpreted as asphaltenes and maltenes respectively, though the distinction, to a certain extent, is arbitrary. The product components, heavy liquid, light liquid, gas and carbonaceous residue could also be assigned logical properties based on detailed product characterization. It is recognized that several variations of the above scheme could be considered including the possibility of autocatalytic reactions as proposed by Allred” for oil shale pyrolysis. By definition, all the products of these reactions, with the exception of the carbonaceous residue, were assigned to the vapour phase at the temperatures of interest. The reactions were written as mass balances (rather than mole reactions). The choice of the reaction constants was based on kinetic parameters used for a variety of reaction sequences, but was again, to a certain degree, arbitrary. The kinetic parameters and the ‘stoichiometric’ coefficients used have been tabulated in Table 2. Using this kinetic model, the rates of formation of each of the components were derived and the expressions integrated analytically. Product distributions with respect to temperature and time were computed from the integrated expressions. For example, the expression for the dimensionless concentration of light liquid is given by:

u,,=s-[(*-e-“3’)-0a,(l

-e-I)]

3 CEf2 +

1-

[(I

_epuJr)-~(l

_e-o2r)]

(7)

(03b2)

Here, the dimensionless concentration is defined as the mass of the relevant component per unit volume of the system normalized by the initial mass of the bitumen per unit system volume. This is equivalent to the mass fraction of the component concerned. Product distributions as functions of temperature and reaction time, as predicted by the kinetic model, are presented in Figures 3 and 4. Qualitatively, the trends observed in these plots are similar to those in Figures I and 2. There is a maximum in the total liquid concentration as a function of temperature and also a maximum with respect to reaction time. Total liquid Table 2

KINETIC

of tar sands: M. D. Deo et al.

reaction network was considered:

I

I

I

I

pyrolysis

Kinetic parameters and stoichiometric coefficients

MODEL

Several reaction networks involving lumped components such as asphaltenes, maltenes, heavy oils, distillables, carbonaceous residue etc. have been used to explain the experimental results obtained for the thermal cracking of Athabasca bitumen. Recognizing that consecutive reactions are necessary to produce some of the trends observed in Figures I and 2, the following simplified

A=0.5, B=O.5, C=O.7, 0=0.3, E=0.57, F=0.43

FUEL, 1991, Vol 70, November

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1 0.9 0.8 .6

0.7 -

5 3 6

Oh-

i

0.5

ij

;;I-

0.2 0.1

&O

750

700

800

850

9ca

Temperature, K

Figure 3 Effect of temperature on product model. -, Total liquid; p-p1 gas; time = 24 min

distributions: ., coke.

kinetic Reaction

1

I

0 0

0.2

0.4

0.6

1

0.8

1.2

1.4

1.6

1.8

2

Time, lus.

Figure 4 Effect of reaction time on product distributions: kinetic model. --.Totalliquid;---.gas; .,coke.Temperature =773 K

refers to the total of light and heavy liquid products in Fiyures 3 and 4. The experimental trends with respect to gas and carbonaceous residue are not presented in Fiyures I and 2. In general, as the total liquid concentration increased, the gas concentration decreased and vice versa. The carbonaceous residue concentration was observed to be essentially constant and independent of the process operating variables; however, the product distributions were dependent upon the source of the feed sand9-15. The maximum in the liquid yields observed in Figures 3 and 4 is due to the competing nature of the reactions that generate the heavy liquid component and the cracking reaction that produces the light liquid and the gas components. Thus the inherent chemistry of the pyrolysis process can be utilized to explain trends observed experimentally. FLUIDIZED

BED MODEL

A homogeneous model could be used in single phase fluidized bed models. Appropriate pyrolysis kinetics for tar sands would have to be incorporated into the rate equations. However, to study the interaction of hydrodynamics and kinetics in a fluidized bed, it is

1274

FUEL, 1991,

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necessary to develop a two-phase model. In this work, a two-phase bubbling bed model has been developed. The two phases in a bubbling bed are the bubble phase and the dense phase. For the purposes of this study, the hydrodynamic parameters were assumed constant and were calculated according to the principles of two-phase theory. The amount of solids in the bubble phase was neglected and the voidage of the dense phase was assumed equal to the minimum fluidization voidage. A bed with the design dimensions given by Fletcher (Tub/e 1) was used in this case. The set of parameters, including the bed expansion, the bubble phase voidage, the minimum fluidization velocity, etc., are given in Tuble 3. The two-phase bubbling bed reactor model described by Grace’ was used to describe the reactor. The bed was assumed to be isothermal. In this paper, the bitumen impregnated sandstone entering the fluidized bed is referred to as ‘solid’, recognizing the limitation of this nomenclature. As in the kinetic model, the bitumen was assumed to consist of component 1 and component 2. It was also assumed that the first two reactions of the reaction scheme (6) take place at the solid surface, leaving unconverted bitumen and a carbonaceous residue on the solid phase while the produced hydrocarbons (heavy oil, light oil and gases) are transferred to the vapour phase. It was further assumed that this ‘decomposition’ is effected through uniform first order reactions. Thus, the conversions of component 1 and component 2 are given by first order kinetics. The solids in the reactor are assumed to be completely mixed and their exit age distribution function is the same as that of solids in a continuous stirred tank reactor:

The average conversions of components 1 and 2 are then given by: CC x1 = (9) x,f(t)dr s0

(10)

Jo

The rate of production of products due to the heterogeneous reactions are calculated based on these average conversions. The conversions are then given by: 1

X,=l-

Table 3

(11)

l+k,t Parameters

used in the bubbling

=0.13 EnIf = 0.46 Gdg = 0.4 &jr =0.47

Cb

,;

1;:;

Yb H,, H, m, U,, U

=O.l =0.25 m =0.29 m =0.72x 1O-3 kg s-l =3.49 x lo-* m s-l =8.17x lo-’ m s-l =3.0x lo-’ m s-l = 6zdd, m-l

k, ab

bed model

Modeling

pyrolysis

of tar sands:

M. D. Deo et

al.

(19) _

k,whHt

u 0.6 -

(21)

0.5 -

The rate of production of heavy liquid due to the heterogeneous reactions is calculated from: P=mv’[(f,ax,)+If2Cw2)l

“I@@ 7.50

800

Figure 5 Etfect of temperature model. -, Total liquid; time = I5 min

850

900

on product ---, gas:

950

loo0

1050

1100

distributions: fluidized bed ., coke. Residence

(12)

The uniform reaction model for the solids does not allow consideration of particles of different sizes. This limitation could easily be rectified by consideration of a shrinking core decomposition model for solids conversion. Movement of the thermal front into the particle or the diffusion of gases out of the particle could be controlling mechanisms. In such models, the time for complete reaction of the particle is determined by its size and is finite. Particle size changes due to reaction could also be incorporated into these models. The model being considered here is pseudo-steady state. The average conversions for solids are computed according to the above procedure and steady state material balances are written for the gaseous components. The material balance for the heavy liquid component in the bubble phase is given by: (13) In the dense phase, it is given by: kquhEb(Wd-wb)-EdsP+EdgkjWd =o

(14)

These two equations were solved analytically to obtain an expression for the heavy liquid concentration in the bubble phase as a function of the height of the bed. Using such an expression, the exit concentration of the heavy liquid component can be calculated:

(22)

The exit concentration of the heavy liquid could be determined from these equations. Other concentrations (light liquid and gases) are calculated using the process stoichiometry. One of the features of this model is that the concentration of heavy liquid in the bubble phase increases monotonically with P, the rate of production of heavy liquid due to the heterogeneous reaction; however, P increases monotonically with residence time. Thus, the maximum in heavy liquid concentration (and the total liquid production) observed for the kinetic model would not occur in this model. This suggests that if a pseudo-steady state model is chosen to describe the bubbling bed reactor, then thermal decomposition reactions must be included as part of the reaction scheme on the solid phase to predict correct trends with respect to residence time. Hence, for the two-phase bubbling bed model, only trends with regard to temperature were compared to experiments. The product distributions for three residence times, as functions of temperature, are plotted in Figures 5, 6 and 7. In these three figures, the dimensionless yield is plotted versus temperature. The dimensionless yield is defined as the mass rate of production of the relevant component from the reactor normalized by the mass rate of introduction of raw bitumen into the reactor. The same reaction rate constants and stoichiometric coefficients as for the kinetic model were used. The other parameters used in the calculations are presented in Table 3. It is seen that the maximum temperature with respect to liquid yield has increased for this model, as compared with the kinetic model. Again, delinking the heavy liquid

0.::

1

0.7c

\v$?

(1 _@_‘I) (‘1

(15)

9

z

0.6

where c’1=m+r, _ “2

-

m

1 -t rz

(16)

Phm

1 + r2

(17)

In these equations, rl, r2, m and ph are given by the following equations:

0.1

-I

1

700

750

800

850

900 Tempcmtwe,

Figure 6 Effect of temperature on product model. -. Total liquid: ---. gas; time = 24 min

950

1000

1050

1100

K

distributions: fluidized bed ” ” ‘, coke. Residence

FUEL, 1991, Vol 70, November

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Modelling

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1

Sung, S. H. PhD Dis.wr/urion, University of Utah, 1988 Shun. D. PhD Dissw/urion, University of Utah, 1990 Fletcher, J. V., Deo, M. D. and Hanson. F. V. Powder Techno/. in press Clift. R. in ‘Gas Fluidization Technology’ (Ed. D. Geldart), J. Wiley. London, 1986 Darton, R. C. Trms. Inst. Chm. Eng. 1979, 57. 134 Allred. V. D. C/rent. Eng. Prog. 1966, 62.

14 15 16 17

3

s

18 19

0.6

NOMENCLATURE

A,B,C,D,E,F Stoichiometric ab

Ar L

&I

750

800

850

900

950

1MM

1050

I llcil

c2 ‘k

Temperature. K

Figure 7 Effect of temperature on product model. -, Total liquid; p-P, gas; time = 30 min

cl

distributions: Auidized bed . ., coke. Residence

h2 .f(o

H mf Ht

production and cracking reactions causes this shift. Also, it may not be appropriate to use the same rate constants as for the simple kinetic model.

k 4,

k,, k,

k, SUMMARY

m

The task of modelling complicated gas-solid reactions such as the pyrolysis of tar sands in complex configurations such as fluidized beds and rotary kilns is a difficult task. Choice of an appropriate model is made difficult because, only gross experimental measurements are available and these could be matched by dozens of models. In this work, product distributions have been calculated using a kinetic model and a simple two-phase bubbling bed model. Both models predict the product distribution trends with respect to temperature, observed experimentally, though these were obtained in slugging beds. The kinetic model also explains the trends with respect to residence times. Through these simple models, a framework is established for the use of more complicated reaction networks for the pyrolysis process, more realistic solids conversion mechanisms such as shrinking core models and more complicated variations of hydrodynamic parameters.

mf P Pb r13 r2

R

Rem r t t T u UII u mf V wb Wbe

REFERENCES 1

2 3

4 5 6 1 8

9 IO II 12 13

1276

wd

Oblad. A. G., Bunger. J. W., Hanson, F. V., Miller. J.D.. Ritzma. H. R. and Seader. J. D. Ann. Ret.. Etwrp 1987,12,283 Ritzma. H. R. ‘Oil Impregnated Rock Deposits of Utah’, Utah Geological and Mineral Survey, Map 47, 1979 Hanson, F. V. and Oblad, A.-O. in ‘Proc. Fourth UNITAR!: UNDP International Conference on Heavv Crude and Tar Sands’, Vol. 5, 1989, p, 421 Kunii, D. and Levenspiel. 0. ‘Fluidization Engineering’. Butterworth-Heinemann,,Boston, 1991 Grace, J. R. in ‘Gas Flutdtzation Technology’ (Ed. D. Geldart), J. Wiley, London, 1986 Phillips, C. R., Haidar, N. I. and Poon, Y. C. Fuel 1985,64,678 Kbseoglu, R. c). Fuel 1987, 66, 741 Barbour, R. V., Dorrence, S. M., Vollmer, T. L. and Harris, J. D. in ‘Proc. 172nd National Mtg. Am. Chem. SOC.‘, San Francisco, Vol. 21, 1976, p. 279 Venkatesan, V. P/ID Di.c.rw/crt&n, University of Utah, 1979 Jayakar. K. M. PhD Dixwr/urion. University of Utah, 1979 Wang, J. MS T/wsi.s. University of Utah. 1983 Smart, L. M. MS T/rr.sls University of Utah, 1984 Dorius, J. C. P/ID Di,s.wrttr/iorl. University of Utah. 1985

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1991,

Vol 70, November

Xl Xl x2 22

Yb 2 ab % %rs “bIf fJ2 c3 e

coefficients Interphase area per unit bed volume (m-l) Archimedes number Coefficient defined in Equation (16) Coefficient defined in Equation (17) Bubble diameter (m) Average particle diameter (pm) Fractions of components 1 and 2 in feed Exit age distribution function of the solids Height at minimum fluidization (m) Total expanded bed height (m) Constant used in high temperature U,, correlation Reaction rate constants (time- ’ ) Interphase mass transfer coefficient (m s-l) Coefficient defined in Equation (20) Mass flow rate of bitumen (kg s- ‘) Rate of production of heavy liquid (kg s-l mm3) Coefficient defined in Equation (2 1) Coefficients defined in Equations (18) and (19) Universal gas constant Reynolds number at minimum fluidization Time (s) Average residence time (s) Temperature (K) Superficial gas velocity (m s-l) Dimensionless concentration of light liquid Minimum fluidizatin velocity (m s-‘) Total volume of the bed (m3) Concentration of heavy liquid in the bubble phase (kg m-“) Exit concentration of heavy liquid (kg mm3) Concentration of heavy liquid in the dense phase (kg m 3, Instantaneous conversion ofcomponent 1 Average conversion of component 1 Instantaneous conversion of component 2 Average conversion of component 2 Percentage by weight of bitumen in the tar sand Axial distance in the bed (m)

Volume fraction Volume fraction phase Volume fraction phase Void fraction at k,lk, k,lk 1 k,t

of the bubble phase of the gas in the dense of the solids in the dense minimum

fluidization