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A kinetic approach to the flash pyrolysis of biomass in a fluidized bed reactor
A kinetic approach to the flash pyrolysis of biomass in a fluidized bed reactor M. C. Samolada
and I. A. Vasalos
Chemical Process Engineering Research Institute and Department of Chemical Engineering, Aristotelian University of Thessaloniki, PO Box 7517, 540 06 University City, Thessaloniki, Greece (Received 7 October 1990; revised 1 February 1991)
A reaction scheme consisting of three independent parallel reactions has been used to describe the primary reactions of fir wood pyrolysis in a fluidized bed reactor, in the temperature range 400-500°C. A simple first order kinetic model and a model assuming an ultimate yield of each product at infinite time (UYM) have been applied to the evolution of total volatiles and gases. The UYM has been proved to be adequate for the prediction of the experimental results. An activation energy of 56.48 kJ mol- ’ and 94.49 kJ mol _ ’ has been estimated for the evolution of total volatiles and gases respectively. Carbon monoxide and carbon dioxide are the primary gaseous products from fir wood pyrolysis. (Keywords: pyrolysis; kinetics; fluidized bed)
Wood is a chemically complex material consisting of cellulose, hemicellulose and lignin. A lot of experimental work has been done in order to study the kinetics of the
thermal decomposition of biomass (cellulose, hemicellulose, lignin and wood). Most of the research has been carried out on the kinetics of cellulose pyrolysis’-’ which is a polymer of D-glucose with various degrees of polymerization dependent on the biomass source8. Lignin is a macromolecule consisting of hydroxyphenylpropane units although its chemical structure has not been precisely established. More recent research work focused on the kinetics of lignin pyrolysis prepared by different experimental techniques from the original wood samples 9- I2 . As reported*, wood does not have a uniform molecular weight, while there is still not an empirical representation of the molecule of lignin and hemicellulose. Moreover, the existence of other elements such as nitrogen, sulphur and ash, in proportions dependent on the source of biomass, introduce further difficulties to the kinetic studies of wood. Other factors, very important to kinetic studies of biomass, are the type of experimental unit used and the applied experimental conditions. Different techniques have been used to study thermal decomposition reactions: thermogravimetric analysis (TGA) and/or differential thermal analysis (DTA)‘j.r3,14; screen heater’,15; tube furnacer’j; entrained flow reactor”; and fluidized bed reactors”--19. Fluidized bed reactors seem to be preferable as a laboratory technique when isothermal conditions are desired. The reasons given above account for the large variation of the estimated kinetic parameters (Table 1) and the kinetic equations used to describe the experimental results. Kinetic parameters have been estimated for the total decomposition, as well as for the production of both pseudoproducts (gases, liquids and solids) and chemical compounds. The main interest of this study is the discrimination the most appropriate kinetic model for the production 00162361/91/070883-07 i: 1991 Butterworth-Heinemann
Ltd.
of volatiles and gases from the thermal decomposition of Greek fir wood samples in a batch fluidized bed reactor system.
KINETICS The reaction scheme proposed by Shafizadech*’ assumes that the thermal decomposition of wood can be described by three parallel reactions for the production of three pseudoproducts, namely gases, liquids (tar) and solids. Moreover, secondary reactions of the liquid products can be considered at higher pyrolysis temperatures. This reaction scheme has been used with various kinetic models in order to estimate the kinetic parameters of the thermal decomposition of wood. The most commonly used kinetic models for biomass pyrolysis have been based strongly on the theory already developed for coal pyrolysis and are as follows. 1 Simple first or nth order kinetic models16: dc,/dt = k,c”w 2
(1)
Simple first or nth order kinetic models assuming that an ultimate yield (VT) of each product is achieved at infinite reaction time (UYM), dependent on the reaction temperature, or constant through a temperature range and treated as a parameter”’ 1,15s19.21 : d VJdt = ki( VT - Vi)”
3
(2)
More complicated equations are also used, where the simple models become inadequate, assuming that the production of the pyrolysis products is achieved by an infinite number of parallel reactions, characterized by the same pre-exponential factor and an activation energy following a distribution function f(E). The distribution functions considered are either the Gaussian distribution22.36, or a linear function of the solid conversionq,24 and the yield of volatiles is given
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M. C. Samolada
and I. A. Vasalos
Table 1 Kinetic parameters reported in the literature with first order kinetics
Biomass
Experimental system
Lignin
TGA
Temperature (“C)
k, (min-‘)
25-800
v = 2.64 x 10s
E (kJ mol-‘)
v* (wt%)
58.1+289.25X
Ref. 9
x =0.4 Milled wood
Screen heater
325-1130
Lignin
u=2.OOx 10’
61.67
84.35
g = 8874
40.20
36.54
10
Cellulose
Screen heater
30+1100
o= 1.19 x 1O’O
132.45
94.08
1
Sweet gum
Screen heater
325-1130
v=2.OOx lo6
68.75
92.97
15
41.01
300-400
g=45516 g = 8.58 x 10’
49.40
Tubular reactor
Hardwood Oak
u=1.04x10*
16
88.60 106.50
Wood
>300
t = 5.46 x lo6
62.80
35
Lignin
160-680
v= 1.69 x lo6
25.00
11
410-1890
v=2.64x lo5
30.41
Almond shells Fir wood
Fluidized bed
4oo-460
u=2.78 x 10’
99.58
Pyroprobe 100
46@-605
v=9.36 x lo5
74.58
Fluidized bed
400-500
v=8162.37 g= 1.43 x lo6
56.48
55.67
94.49
9.55
a g = gases; t = overall decomposition;
V)/V=
m exp( - k,t exp( - E/RT))f(E) dE s0
The main difference between models 1 and 2 is that the first assumes the complete decomposition of the solid material, while the second considers an ultimate extent of the decomposition, that is an ultimate yield of volatiles and gases. The third class of models is more complicated, using an increased number of parameters (three or four) and a pseudo-Arrhenius dependence of the reaction rate constant from temperature. The model with the simplest form and the minimum number of parameters to be estimated is always preferable. First order kinetics are considered in this study using the kinetic models described by Equations (1) and (2). Equation (1) can be written in integrated form using the initial conditions t = 0, G = L = S = V = 0 and W = 100, for each of the pyrolysis products for the total decomposition and the production of volatiles, as follows, using yields (wt% of the original wood) instead of concentrations: G = (k,/k) (1 -exp( - kt))
(4)
L = (k,/k) (1 - exp( - kt))
(5)
s=(k,/k)
(1 -exp(-kt))
(6)
v=(k,/k)
(1 -exp(-kt))
(7)
W=exp(-kt)
(8)
The kinetic rate constants for the total decomposition and the production of volatiles are defined by the following equations: k=k,+k,+k,
(9)
k,=k,+k,
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Dividing Equations (6) and (7) gives (S/V)= (k,/k,), a ratio independent from the reaction time”j, which for infinite reaction time becomes: (S/T/) = (loo-
(3)
884
This work
u = total volatiles
by the following equation: (v*-
19
v*)/v*,
and W=(lOO(V *-V))/V*(ll)
The combination of Equations (1) (expressed in a form suitable for volatiles) and (11) results in the following expression for the prediction of total volatiles: I’=V*(l-exp(-k,t/l/*))
(12)
Equations (4)-(8) indicate that the prediction of the kinetic parameters, referred to the production of each pyrolysis product, requires the simultaneous estimation of the reaction rate constants k. Since the simultaneous estimation of four kinetic parameters is not an easy task in non-linear regression, Equation (12) enables the prediction of the kinetic parameters for the evolution of total volatiles independently. The regression equations obtained for the prediction of total volatiles and gases according to the simple first order kinetic model are listed in Table 2 (Equations A or Al and B, respectively). Integrating Equation (2) for total gases and volatiles respectively, the regression Equations C and D (Table 2) result and are used for the estimation of the kinetic parameters according to the UYM. It must be pointed out here that because of the existing correlation between the kinetic parameters (pre-exponential factor k, and the activation energy E) according to the Arrhenius Equation (13), the transformation given by Equation (15) is recommended25, in order that the estimated values of the kinetic parameters be uncorrelated. k=k,exp(-E/RT)
(13)
k,* = k, exp( - E/R T,)
(14)
(15) k=kx exp{-(EIR)C(lIT)-(l/T,)l} Comparing the regression Equations A to D (Table 2) the following conclusions arise: (a) the two models are
Kinetics of flash pyrolysis: M. C. Samolada Table 2
Regression
equations
used for volatiles
and gases
~___
Compound
Regression
Volatiles
A:
1/= V*(l -exp(-k,,t
Al:
v=k,,exp(-E,/RT)(l-exp(-kt))/k (k=k,
Gases
and I. A. Vasalos
equation exp(-E,/RT)/V*))
Model
Parameters
I I
km 6 k,, E
exp(-E/RT))
C:
V=I/*(l-exp(-kk,,texp(-EJRT))
2
V*. k,,.
B:
G=k,,texp(-E,/RT)/k(l-exp(-kt))
1
k,,. E,
D:
G=G*(l-exp(-k,,texp(-E,/RT)))
2
G*. k,,,
exactly the same if V* and G* are considered to be constant at a specific range of pyrolysis temperatures; (b) in order that the two models be identical, k, and k, resulting from the second model should be equal to the overall reaction rate constant k given by model 1; and (c) V* = k,/k, G* = kg/k and E = E,= E,, with k,, k,, k, E, E, and E, coming from the first model. In order to select the most appropriate kinetic equation for the prediction of the yield of total volatiles and gases the regression Equations A to D have been applied independently to the experimental data, with the combinations of parameters shown in Table 2.
V
CK
E, E,
,
Nitrogen Air Nitrogen
Volatiles to TCD
EXPERIMENTAL A fluidized bed reactor system consisting of sand particles was used for biomass pyrolysis experiments. A schematic diagram of the experimental unit is shown in Figure 1. The reactor vessel was equipped with a porous plate distributor through which the fluidization gas, nitrogen, was introduced. The gas flow was established to achieve the desired mean gas residence time (7-11 s). The stainless steel reactor was heated to the desired temperature with a three-zone radiant furnace. A fixed amount of inert heat carrier (silica sand, mass 150 g) was introduced into the reactor. Pneumatically operated valves at the top and the bottom of the tube section were used to isolate the tube section from the fluid bed reactor system. The sample was contained in an easily friable glass container. After the desired temperature was achieved the pneumatically operated valves were activated and the wood sample (mass 2 g) was dropped inside the fluidized bed. The wood sample was intimately mixed with the inert heat carrier and the volatile material was readily released, while the temperature of the bed remained constant (temperature variation + SC) and was assumed to be isothermal. The volatile products were continuously withdrawn from the reactor with the fluidization gas (nitrogen). The mean heating rate of biomass particles was estimated to have an order of magnitude of 1000°C s-l (Ref. 26) under the experimental conditions used. A six-port gas sampling valve was installed at the reactor outlet and connected with a gas chromatograph equipped with a thermal conductivity detector for the determination of the amount of volatiles and gases during the course of the experiment at particular time intervals. The transport lines were electrically heated at a temperature as low as 3OO”C, in order to prevent condensation of the liquid products and to minimize their further decomposition. The liquid products were removed by passing the reactor effluent through a stainless steel coil placed inside a cold liquid bath, maintained at 0°C. The liquids were
Figure 1 Fluidized bed reactor system: I, purging nitrogen; 2, fluidization medium; 3, sample injection valve; 4, isolation valve for atmospheric air; 5, heating of the fluidizdtion gas; 6, heated reactor outlet; 7, position of the sample; 8, cold liquid bath (0°C). F, filter; V, valve; CK, check valve
recovered from the stainless steel coil and the attached glass wool filter by washing with a 1: 1 (v/v) mixture of methanol and dichloromethane. After removing the extraction solvent, the liquids were determined gravimetrically. The volume of the gases produced by pyrolysis was measured by subtracting the known volume of the inert gas from the measured amount of the total volume of gases at the same time. The amount of volatiles was estimated by adding the amount of liquids and gases. The char produced during the experiment could not be measured independently, because of its very small amount compared to that of the sand-glass mixture. Moreover the recovery of the experimental system was estimated to be 95-99 wt%, including the mass of the inert compounds of the system (sand and glass). The estimation of the weight of char by subtracting the weight of gases and liquids from the weight of the original sample proved to be a satisfactory approximation. A sample of the gaseous products obtained at the end of each experiment was analysed by a refinery gas analyser, equipped with four columns and two detectors (flame ionization detector, FID and thermal conductivity detector, TCD). An automatic gas sampling valve and programming utilities were employed for the complete analysis of the gas sample. The four columns used were: Chromosorb 106, Porapak N, Molecular Sieve 54 and Chromosorb 20% BEEA. A more detailed description of the experimental unit can be found elsewhere2’. Previous research28 has shown that the yield of the liquid products reaches a maximum value at temperatures greater than 500°C. As a first step for the kinetic study of fir wood pyrolysis, the experiments were carried out at temperatures in the range 400.-5OO”C, in order
FUEL,
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Kinetics
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M. C. Samolada
and I. A. Vasalos
that secondary decomposition reactions could be ignored. Fir wood was used as biomass sample, with particles of 300-425 pm a size confirmed to be adequate for the absence of heat transfer effects29-31. The system pressure was maintained constant at 0.5 bar during the experiment. The mean gas residence time was varied in the range 7-l 1 s. The chemical composition of fir wood samples and the proximate and ultimate analyses are listed in Table 3. The gases produced by fir wood pyrolysis consist of carbon monoxide and carbon dioxide, which represent almost the whole volume of the gases (94-99 ~01%). Both compounds give yields increasing with temperature and have been characterized as primary decomposition products22,28*30, resulting from dehydration and decarboxylation reactions 32. The low molecular weight hydrocarbons are absent in the pyrolysis gases at temperatures lower than 5OO”C, as the secondary reactions begin at higher temperatures 28. The yields of pyrolysis products at different experimental conditions are listed in Tub/e 4. Tracer response studies using methane as a tracer compound have been applied to the whole experimental system and the transport lines separately. The fractional tank extension model of Stokes and Nauman33 has been applied to the experimental residence time distribution curves and the main extent of mixing found to be concentrated in the transport lines, while the gas phase of the fluidized bed reactor can be adequately described by a plug flow reactor. Transport lines can be Table 3
Analysis of fir wood (Ahies Alba x Abies Cephalonica)
wt %
Component Proximate analysis Moisture Ash Volatile matter Fixed carbon Chemical analysis Extractives Lignin Holocellulose Elemental analysis C H N 0
1.0 1.2 78.7 13.1
Analytical procedure”
Run no. 113 115 114 109 108 130 131 105
AND DISCUSSION
The yield of total volatiles and gases has been measured at each pyrolysis temperature as a function of reaction time. The regression equations described in Table 2 have been independently applied to the experimental results. A non-linear regression routine of the International Mathematics and Statistics Library (IMSL) based on the Levenberg-Marquardt algorithm, has been used and the best fit parameters have been estimated. The values of the estimated parameters according to the kinetic models considered are listed in Table 5, as well as the respective standard error of the predictions. It is obvious that the kinetic models give estimations with about the same error. Moreover, the magnitude of the errors are accepted as they are lower than a representative value of the experimental error of 10%. The lower error of the lit is desired for the prediction of the yield of gases, because of their small values in the range investigated. If the criteria for the selection between two kinetic models are applied34, the second kinetic model should be selected as the more suitable. In the case of volatiles both models give comparable errors, even though the estimated parameters with the first model have quite small values for thermal decomposition parameters. Unacceptable estimations of the kinetic parameters result if an inadequate model is forced to the experimental results. The difference in the values between the activation energy Results of regression analysis and values of the estimated parameters
:
4.7 21.3 68.0
; g
57.3 6.0 0.5 36.2
h h h i
Regression equation (Table 2)
k, (min- ‘)
E (kJ mot-‘)
I/* (wt%)
Standard error of tit” (wt%)
A Al C B D
7932.86 16.82 8162.37 2.73 x lo5 I .43 x lo6
32.61 kO.89 19.86kO.27 56.48 iO.78 66.25 k 0.01 94.49 kO.77
Experimental 55.67 9.55
2.99 2.87 3.18 1.05 0.66
a Defined as [(y,mode,- V,,,,,,,)2/(n-p)]“2 where n is the number of the data points and p is the number of parameters estimated by the model
Experimental results Temperature (“C)
Mean gas residence time (s)
Yield (wt% of wood) Liquids
Char”
CO,
co
CH,
C,H,
4.5 5.3 6.6
1.6 1.9 2.3 3.5
_ _ _ _
_
3.5 2.0 3.5
0.2 _ _
-
7.4 7.0
49.0 53.1
44.4 39.6
6.0 7.3
423 430 433 456
7.0 9.0
50.8 55.0
39.4 32.6
9.9 11.1
57.4 58.5
467 473
10.8 8.1
58.6 59.1
31.5 33.3 29.9
9.0 12.4 11.1
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~____
Gases
402 419
a Determined by difference
886
RESULTS
Table 5
:
’ ASTM D 2016-74; b, ASTM D 1102-84; c, Ref. 8; d, determined by difference; e, ASTM D 1105-84; f, ASTM D 1106-84; g, determined by difference; h, CHN 800, Leco Co; i, determined by difference
Table 4
satisfactorily simulated by 1.44 continuous stirred tank reactors (CSTRs) in series. The residence time distribution curve is incorporated into the theoretical kinetic model in order that the kinetic parameters be adequately determined.
8.2 11.5 13.7
8.8 7.2 6.1 8.0 8.9
4.7
_
_ 0.1
Kinetics
al
-3’ 0.4
’
I 0.8
I
I 1.2
I
I 1.6
I
I 2.0
I
I
I
2.4
TX 1O-3 (k) 1
I
b
of flash pyrolysis:
n/l. C. Samolada
and I. A. Vasalos
consistency is due to the fact that the temperature range considered has been characterized to be the decomposition range of lignin. As the other components of wood are more reactive than lignin2’, its decomposition is probably the dominating step. The value of the predicted activation energy for the production of gases is quite consistent with the value reported by Thurner and MannI for oak wood at 300-400°C and by Font et a1.19 for almond shells at 400-460°C. Our results proved to be almost an extension of those obtained by Thurner and MannLh (Figure 2b). The predicted evolution profiles of volatiles and gases at different pyrolysis temperatures are given in Figures 3 and 4 respectively. Although the kinetic model selected is quite simple for the description of such a complicated process as pyrolysis, predictions are in good agreement with the experimental data collected. Some deviations from the experiment are within the range of the accepted experimental error. CONCLUSIONS
7
.c
The conclusions follows.
E c 3 *
from this work can be summarized
as
The reaction scheme proposed by Shalizadech” with three parallel reactions, can be used to describe the primary thermal decomposition of fir wood.
E.4
0.8
1.2
1.6
2.0
2.4
a
TX 1D3 (k) Figure 2 a, Arrhenius plots for the evolution of total volatiles from fir wood pyrolysis (this study) and several samples from the literature. 0, Lignin’“; 0. sweet gumr5; & lignin”; A, almond shells”; 0, wood35; n , fir wood (this study). b, Arrhenius plots for the evolution of total gases from fir wood pyrolysis (this study) and several samples from the literature. 0. Lignin”; fJ, sweet gumr5; &oak”; A, almond shells“‘: n , fir wood (this study)
predicted for the evolution of volatiles and gases indicates that the two models cannot be considered to be identical, according to the theory previously described. The values of the estimated kinetic parameters are in the range of those found in the literature as given in Table 4. Comparison of the separate values of the kinetic parameters with those found in the literature (Table I) is not the best method for the evaluation of the results according to Roberts3’, because it is the values of the reaction rate constant which are of interest here. Figure 2 shows the dependence of the reaction rate constant on temperature and compares some representative values from the literature for volatiles and gases, as given in Table 1. Our kinetic results for total volatiles are quite consistent with those in the literature (Table I), especially with those obtained by Font et a1.19, Roberts3’ and Nunn et al.” (Figure 2a). The very good agreement of the results probably occurs because of the similar temperature range and the same heating rates applied, even though the experimental unit and the kind of biomass were different. Moreover, the predicted value of the activation energy for the total evolution of volatiles is consistent with the value reported by Nunn et aI.” for the pyrolysis of milled wood lignin. That type of lignin is similar to that present in the wood structure. The
Time (min)
r”
60
c 0
0
-0
b
I 2
I 4
I
6
I 8
10
Time (min) Figure 3 Evolution profile of total volatiles obtained from tir wood pyrolysis; comparison between experiment (0) and model predictions ( ~ ). a, T=413 C; b. T=434”C
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Kinetics
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M. C. Samolada
and I. A. Vasalos
15
REFERENCES
a
1
122
9-
6-
3-
0
0
0
I
I
I
I
2
4
6
6
10
Time (min)
8 9 10
15
b 12-
11 12 13 14 15 16 17 18
Time (min) Figure 4
19 20
Evolution pyrolysis; comparison (- -j. a, T=440”C;
profile of total gases obtained from fir wood between experiment (0) and model predictions b, 475°C
Simple first order kinetics can be used to describe the evolution of total volatiles and gases produced by fir wood pyrolysis. 3 The UYM is more suitable than the simple first order kinetic model and gives acceptable values of the kinetic parameters for total volatiles and gases. 4 The estimated kinetic parameters according to the selected model are within the range of values reported in the literature. Because of their close agreement to the kinetic parameters of lignin pyrolysis at high heating rates, we speculate that the decomposition of lignin is the dominating step in the range of the experimental conditions studied. 5 The model predictions are good enough in a broad range of product yields (O-60 WC%), with a standard error of fit less than 5 wt%. 6 Carbon monoxide and carbon dioxide appear in pyrolysis gases, with their yield increasing with pyrolysis temperature.
2
21
22 23 24 25 26 21
28 29 30 31
32 33 34
ACKNOWLEDGEMENTS The authors acknowledge the financial support of the European Economic Community (DGXII, under Contract EN3B0052-GR) and the General Secretariat for Research and Technology of Greece.
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Hajaligol, M. R., Howard, J. B., Longwell, J. P. and Peters, J. A. Ind. Enq. Chem. Proc. Des. Dev. 1982, 21, 457 Hajaligol, M. R., Peters, W. A. and Howard, J. B. Proceedings of Soecialists’ Workshon on Fast Pvrolvsis of Biomass. 19-22 October 1980, Copper Mountain, Colo;ado, USA, p. 215 Stamm, A. lnd. Eng. Chem. 1956,48(3), 413 Kanury, A. M. and Blackshear, P. L. Cornbust. Sci. Technol. 1970, 1, 339 Van Krevelen, D. W. Fuel 1951 20, 253 Alves, S. S. and Figueiredo, J. L. J. Anal. Appl. Pyrol. 1989,15, 347 Simmons, G. M. and Lee, W. H. in ‘Fundamentals of Thermochemical Biomass Conversion’. (Eds R. P. Overend. T. A. Milne and L. K. Mudge), Elsevier Applied Science Publishers, London: 1985 Probstem, R.F. and Hicks, R. E. ‘Synthetic Fuels’, McGrawHill, New York, 1982, Ch. 1, p. 13 Avni, E. and Coughlin, R. W. Thermochimicn Acta, 1985,90,157 Nuun, T. R., Howard, J. B., Longwell, J. P. and Peters, W. A. Ind. Eng. Chem. Proc. Des. Dev. 1985, 24, 844 Chan, R. W.-C. and Krieger, B. B. J. Appl. Polym. Sci. 1981, 26, 1533 Iatridis, B. and Gavalas, G. R. Ind. Eng. Chem. Proc. Des. Dev. 1979, H(2), 127 Figueiredo, J. L., Valenzuela, C., Bernalte, A. and Enciran, J. M. Fuel 1989,68, 1012 Alves, S. S. and Figueiredo, J. L. J. Anal. Appl. Pyrol. 1988, 13, 123 Nuun, T. R., Howard, J. B., Longwell, J. P. and Peters, W. A. lnd. Eng. Chem. Proc. Des. Dev. 1985, 24, 836 Thurner, F. and Mann, U. lnd. Eng. Chem. Proc. Des. Dev. 1981, 20,482 Scott, D. S., Piskorz, J., Bergognou, M. A., Graham, R. and Overend, R. P. Ind. Eny. Chem. Res. 1988, 27, 8 Liden, A. G., Berruti, F. and Scott, D. S. Chem. Eng. Commun. 1988,65, 207 Font, R., Marcilla, A., Verdu, E. and Devesa, J. lnd. Eng. Chem. Res. 1990, 29, 1846 ShaCzadech, F. ‘Fast Pyrolysis of Biomass - Proc. Specialists’ Workshop’, Solar Energy Research Institute, Colorado, USA, 1980 Van den Aarsen, F. G., Beenackers, A. A. C. M. and Swaaij, W. P. M. in ‘Fundamentals of Thermochemical Biomass Conversion’, (Eds R. P. Overend, T. A. Mime and L. K. Mudge) Elsevier Applied Science Publishers, London, 1985 Boroson. M. L.. Howard. J. B.. Lonawell. J. P. and Peters. W. A. AIChE j. 1989; 35(l), 121 Serio, M. A., Peters, W. A. and Howard, J. B. Ind. Eng. Chem. Res. 1987, 26, 1831 Tran, D. Q. and Rai, C. Fuel 1978, 57,293 Himmelblau, D. M. ‘Process Analysis by Statistical Methods’, John Wiley, New York, 1970, Ch..6, p. 194 Funazukuri. T.. Hudeins. R. R. and Silveston. P. L. J. Anal. Apyl. Pyrol. 1986, 9, i39 Vasalos, 1. A., Stoikos, T., Samolada, M. and Achladas, G. ‘Production and Utilization of Synthetic Liquid Fuels, July 1986July 1989’, Final Report prepared for EEC/DGXII under Contract EN3B0052-GR, in press Samolada, M. C., Stoicos, T. and Vasalos, I. A. J. Anal. Appl. Pyrol. 1990, 18, 127 Pyle, D. L. and Zaror, C. A. Chem. Eng. Sci. 1984,39(l), 147 Simmons, G. M. and Gentry, M. J. Anal. Appl. Pyrol. 1986. 10,117 Pyle, D. L. and Zaror, C. A. ‘Thermochemical Processes of Biomass - First Evolved European Workshop’, Butterworths, London, 1984 Antal, M. J. Jr, ‘Advances in Solar Energy’, American Solar Energy Society, New York, 1982, Ch. 4, p. 175 Raman, R. ‘Chemical Process Computations’, Eisevier Applied Science Publishers, London, 1985, Ch. 6, p. 422 Bacon, D. W. and Downie, J. ‘Kinetics - Evaluation of Rate Data-III’, AIChE Series E, 1981, 3, 65 Roberts, A. F. Combust. Flame 1970, 14, 261 Gavalas, G. R. ‘Coal Pyrolysis’, Elsevier Scientilic Publishing, Amsterdam, 1982, Ch. 6, p. 112
NOMENCLATURE Concentration C
at time
t (g
cm - 3,
Kinetics of flash pyrolysis: M. C. Samolada E G G* k k, kX L R S T T,
Activation energy (kJ mol- ‘) Yield of total gases at time t (wt%) Ultimate yield of total gases (wt%) Reaction rate constant Pre-exponential factor (min-’ or s-l) Transformed pre-exponential factor (min-‘) Yield of total liquids at time t (wt%) Universal gas constant Yield of the solid product from pyrolysis at time t (WV!!) Time (min) Temperature (K) Average value of the absolute temperatures of the regression experiments
V V* vi
VT W
and I. A. Vasalos
Yield of total volatiles at time t (wt%) Ultimate yield of total volatiles (wt%) Yield of the pyrolysis product i at time t (wt%) Ultimate yield of pyrolysis product i at time t (wt%) Unreacted percentage of the original wood at time t (wt%)
Subscripts Total gases F Product i ; Total liquids S Solid product from pyrolysis V Total volatiles W Wood
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889
and I. A. Vasalos
Chemical Process Engineering Research Institute and Department of Chemical Engineering, Aristotelian University of Thessaloniki, PO Box 7517, 540 06 University City, Thessaloniki, Greece (Received 7 October 1990; revised 1 February 1991)
A reaction scheme consisting of three independent parallel reactions has been used to describe the primary reactions of fir wood pyrolysis in a fluidized bed reactor, in the temperature range 400-500°C. A simple first order kinetic model and a model assuming an ultimate yield of each product at infinite time (UYM) have been applied to the evolution of total volatiles and gases. The UYM has been proved to be adequate for the prediction of the experimental results. An activation energy of 56.48 kJ mol- ’ and 94.49 kJ mol _ ’ has been estimated for the evolution of total volatiles and gases respectively. Carbon monoxide and carbon dioxide are the primary gaseous products from fir wood pyrolysis. (Keywords: pyrolysis; kinetics; fluidized bed)
Wood is a chemically complex material consisting of cellulose, hemicellulose and lignin. A lot of experimental work has been done in order to study the kinetics of the
thermal decomposition of biomass (cellulose, hemicellulose, lignin and wood). Most of the research has been carried out on the kinetics of cellulose pyrolysis’-’ which is a polymer of D-glucose with various degrees of polymerization dependent on the biomass source8. Lignin is a macromolecule consisting of hydroxyphenylpropane units although its chemical structure has not been precisely established. More recent research work focused on the kinetics of lignin pyrolysis prepared by different experimental techniques from the original wood samples 9- I2 . As reported*, wood does not have a uniform molecular weight, while there is still not an empirical representation of the molecule of lignin and hemicellulose. Moreover, the existence of other elements such as nitrogen, sulphur and ash, in proportions dependent on the source of biomass, introduce further difficulties to the kinetic studies of wood. Other factors, very important to kinetic studies of biomass, are the type of experimental unit used and the applied experimental conditions. Different techniques have been used to study thermal decomposition reactions: thermogravimetric analysis (TGA) and/or differential thermal analysis (DTA)‘j.r3,14; screen heater’,15; tube furnacer’j; entrained flow reactor”; and fluidized bed reactors”--19. Fluidized bed reactors seem to be preferable as a laboratory technique when isothermal conditions are desired. The reasons given above account for the large variation of the estimated kinetic parameters (Table 1) and the kinetic equations used to describe the experimental results. Kinetic parameters have been estimated for the total decomposition, as well as for the production of both pseudoproducts (gases, liquids and solids) and chemical compounds. The main interest of this study is the discrimination the most appropriate kinetic model for the production 00162361/91/070883-07 i: 1991 Butterworth-Heinemann
Ltd.
of volatiles and gases from the thermal decomposition of Greek fir wood samples in a batch fluidized bed reactor system.
KINETICS The reaction scheme proposed by Shafizadech*’ assumes that the thermal decomposition of wood can be described by three parallel reactions for the production of three pseudoproducts, namely gases, liquids (tar) and solids. Moreover, secondary reactions of the liquid products can be considered at higher pyrolysis temperatures. This reaction scheme has been used with various kinetic models in order to estimate the kinetic parameters of the thermal decomposition of wood. The most commonly used kinetic models for biomass pyrolysis have been based strongly on the theory already developed for coal pyrolysis and are as follows. 1 Simple first or nth order kinetic models16: dc,/dt = k,c”w 2
(1)
Simple first or nth order kinetic models assuming that an ultimate yield (VT) of each product is achieved at infinite reaction time (UYM), dependent on the reaction temperature, or constant through a temperature range and treated as a parameter”’ 1,15s19.21 : d VJdt = ki( VT - Vi)”
3
(2)
More complicated equations are also used, where the simple models become inadequate, assuming that the production of the pyrolysis products is achieved by an infinite number of parallel reactions, characterized by the same pre-exponential factor and an activation energy following a distribution function f(E). The distribution functions considered are either the Gaussian distribution22.36, or a linear function of the solid conversionq,24 and the yield of volatiles is given
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1991,
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Kinetics
of flash pyrolysis:
M. C. Samolada
and I. A. Vasalos
Table 1 Kinetic parameters reported in the literature with first order kinetics
Biomass
Experimental system
Lignin
TGA
Temperature (“C)
k, (min-‘)
25-800
v = 2.64 x 10s
E (kJ mol-‘)
v* (wt%)
58.1+289.25X
Ref. 9
x
Screen heater
325-1130
Lignin
u=2.OOx 10’
61.67
84.35
g = 8874
40.20
36.54
10
Cellulose
Screen heater
30+1100
o= 1.19 x 1O’O
132.45
94.08
1
Sweet gum
Screen heater
325-1130
v=2.OOx lo6
68.75
92.97
15
41.01
300-400
g=45516 g = 8.58 x 10’
49.40
Tubular reactor
Hardwood Oak
u=1.04x10*
16
88.60 106.50
Wood
>300
t = 5.46 x lo6
62.80
35
Lignin
160-680
v= 1.69 x lo6
25.00
11
410-1890
v=2.64x lo5
30.41
Almond shells Fir wood
Fluidized bed
4oo-460
u=2.78 x 10’
99.58
Pyroprobe 100
46@-605
v=9.36 x lo5
74.58
Fluidized bed
400-500
v=8162.37 g= 1.43 x lo6
56.48
55.67
94.49
9.55
a g = gases; t = overall decomposition;
V)/V=
m exp( - k,t exp( - E/RT))f(E) dE s0
The main difference between models 1 and 2 is that the first assumes the complete decomposition of the solid material, while the second considers an ultimate extent of the decomposition, that is an ultimate yield of volatiles and gases. The third class of models is more complicated, using an increased number of parameters (three or four) and a pseudo-Arrhenius dependence of the reaction rate constant from temperature. The model with the simplest form and the minimum number of parameters to be estimated is always preferable. First order kinetics are considered in this study using the kinetic models described by Equations (1) and (2). Equation (1) can be written in integrated form using the initial conditions t = 0, G = L = S = V = 0 and W = 100, for each of the pyrolysis products for the total decomposition and the production of volatiles, as follows, using yields (wt% of the original wood) instead of concentrations: G = (k,/k) (1 -exp( - kt))
(4)
L = (k,/k) (1 - exp( - kt))
(5)
s=(k,/k)
(1 -exp(-kt))
(6)
v=(k,/k)
(1 -exp(-kt))
(7)
W=exp(-kt)
(8)
The kinetic rate constants for the total decomposition and the production of volatiles are defined by the following equations: k=k,+k,+k,
(9)
k,=k,+k,
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1991,
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Dividing Equations (6) and (7) gives (S/V)= (k,/k,), a ratio independent from the reaction time”j, which for infinite reaction time becomes: (S/T/) = (loo-
(3)
884
This work
u = total volatiles
by the following equation: (v*-
19
v*)/v*,
and W=(lOO(V *-V))/V*(ll)
The combination of Equations (1) (expressed in a form suitable for volatiles) and (11) results in the following expression for the prediction of total volatiles: I’=V*(l-exp(-k,t/l/*))
(12)
Equations (4)-(8) indicate that the prediction of the kinetic parameters, referred to the production of each pyrolysis product, requires the simultaneous estimation of the reaction rate constants k. Since the simultaneous estimation of four kinetic parameters is not an easy task in non-linear regression, Equation (12) enables the prediction of the kinetic parameters for the evolution of total volatiles independently. The regression equations obtained for the prediction of total volatiles and gases according to the simple first order kinetic model are listed in Table 2 (Equations A or Al and B, respectively). Integrating Equation (2) for total gases and volatiles respectively, the regression Equations C and D (Table 2) result and are used for the estimation of the kinetic parameters according to the UYM. It must be pointed out here that because of the existing correlation between the kinetic parameters (pre-exponential factor k, and the activation energy E) according to the Arrhenius Equation (13), the transformation given by Equation (15) is recommended25, in order that the estimated values of the kinetic parameters be uncorrelated. k=k,exp(-E/RT)
(13)
k,* = k, exp( - E/R T,)
(14)
(15) k=kx exp{-(EIR)C(lIT)-(l/T,)l} Comparing the regression Equations A to D (Table 2) the following conclusions arise: (a) the two models are
Kinetics of flash pyrolysis: M. C. Samolada Table 2
Regression
equations
used for volatiles
and gases
~___
Compound
Regression
Volatiles
A:
1/= V*(l -exp(-k,,t
Al:
v=k,,exp(-E,/RT)(l-exp(-kt))/k (k=k,
Gases
and I. A. Vasalos
equation exp(-E,/RT)/V*))
Model
Parameters
I I
km 6 k,, E
exp(-E/RT))
C:
V=I/*(l-exp(-kk,,texp(-EJRT))
2
V*. k,,.
B:
G=k,,texp(-E,/RT)/k(l-exp(-kt))
1
k,,. E,
D:
G=G*(l-exp(-k,,texp(-E,/RT)))
2
G*. k,,,
exactly the same if V* and G* are considered to be constant at a specific range of pyrolysis temperatures; (b) in order that the two models be identical, k, and k, resulting from the second model should be equal to the overall reaction rate constant k given by model 1; and (c) V* = k,/k, G* = kg/k and E = E,= E,, with k,, k,, k, E, E, and E, coming from the first model. In order to select the most appropriate kinetic equation for the prediction of the yield of total volatiles and gases the regression Equations A to D have been applied independently to the experimental data, with the combinations of parameters shown in Table 2.
V
CK
E, E,
,
Nitrogen Air Nitrogen
Volatiles to TCD
EXPERIMENTAL A fluidized bed reactor system consisting of sand particles was used for biomass pyrolysis experiments. A schematic diagram of the experimental unit is shown in Figure 1. The reactor vessel was equipped with a porous plate distributor through which the fluidization gas, nitrogen, was introduced. The gas flow was established to achieve the desired mean gas residence time (7-11 s). The stainless steel reactor was heated to the desired temperature with a three-zone radiant furnace. A fixed amount of inert heat carrier (silica sand, mass 150 g) was introduced into the reactor. Pneumatically operated valves at the top and the bottom of the tube section were used to isolate the tube section from the fluid bed reactor system. The sample was contained in an easily friable glass container. After the desired temperature was achieved the pneumatically operated valves were activated and the wood sample (mass 2 g) was dropped inside the fluidized bed. The wood sample was intimately mixed with the inert heat carrier and the volatile material was readily released, while the temperature of the bed remained constant (temperature variation + SC) and was assumed to be isothermal. The volatile products were continuously withdrawn from the reactor with the fluidization gas (nitrogen). The mean heating rate of biomass particles was estimated to have an order of magnitude of 1000°C s-l (Ref. 26) under the experimental conditions used. A six-port gas sampling valve was installed at the reactor outlet and connected with a gas chromatograph equipped with a thermal conductivity detector for the determination of the amount of volatiles and gases during the course of the experiment at particular time intervals. The transport lines were electrically heated at a temperature as low as 3OO”C, in order to prevent condensation of the liquid products and to minimize their further decomposition. The liquid products were removed by passing the reactor effluent through a stainless steel coil placed inside a cold liquid bath, maintained at 0°C. The liquids were
Figure 1 Fluidized bed reactor system: I, purging nitrogen; 2, fluidization medium; 3, sample injection valve; 4, isolation valve for atmospheric air; 5, heating of the fluidizdtion gas; 6, heated reactor outlet; 7, position of the sample; 8, cold liquid bath (0°C). F, filter; V, valve; CK, check valve
recovered from the stainless steel coil and the attached glass wool filter by washing with a 1: 1 (v/v) mixture of methanol and dichloromethane. After removing the extraction solvent, the liquids were determined gravimetrically. The volume of the gases produced by pyrolysis was measured by subtracting the known volume of the inert gas from the measured amount of the total volume of gases at the same time. The amount of volatiles was estimated by adding the amount of liquids and gases. The char produced during the experiment could not be measured independently, because of its very small amount compared to that of the sand-glass mixture. Moreover the recovery of the experimental system was estimated to be 95-99 wt%, including the mass of the inert compounds of the system (sand and glass). The estimation of the weight of char by subtracting the weight of gases and liquids from the weight of the original sample proved to be a satisfactory approximation. A sample of the gaseous products obtained at the end of each experiment was analysed by a refinery gas analyser, equipped with four columns and two detectors (flame ionization detector, FID and thermal conductivity detector, TCD). An automatic gas sampling valve and programming utilities were employed for the complete analysis of the gas sample. The four columns used were: Chromosorb 106, Porapak N, Molecular Sieve 54 and Chromosorb 20% BEEA. A more detailed description of the experimental unit can be found elsewhere2’. Previous research28 has shown that the yield of the liquid products reaches a maximum value at temperatures greater than 500°C. As a first step for the kinetic study of fir wood pyrolysis, the experiments were carried out at temperatures in the range 400.-5OO”C, in order
FUEL,
1991,
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Kinetics
of flash pyrolysis:
M. C. Samolada
and I. A. Vasalos
that secondary decomposition reactions could be ignored. Fir wood was used as biomass sample, with particles of 300-425 pm a size confirmed to be adequate for the absence of heat transfer effects29-31. The system pressure was maintained constant at 0.5 bar during the experiment. The mean gas residence time was varied in the range 7-l 1 s. The chemical composition of fir wood samples and the proximate and ultimate analyses are listed in Table 3. The gases produced by fir wood pyrolysis consist of carbon monoxide and carbon dioxide, which represent almost the whole volume of the gases (94-99 ~01%). Both compounds give yields increasing with temperature and have been characterized as primary decomposition products22,28*30, resulting from dehydration and decarboxylation reactions 32. The low molecular weight hydrocarbons are absent in the pyrolysis gases at temperatures lower than 5OO”C, as the secondary reactions begin at higher temperatures 28. The yields of pyrolysis products at different experimental conditions are listed in Tub/e 4. Tracer response studies using methane as a tracer compound have been applied to the whole experimental system and the transport lines separately. The fractional tank extension model of Stokes and Nauman33 has been applied to the experimental residence time distribution curves and the main extent of mixing found to be concentrated in the transport lines, while the gas phase of the fluidized bed reactor can be adequately described by a plug flow reactor. Transport lines can be Table 3
Analysis of fir wood (Ahies Alba x Abies Cephalonica)
wt %
Component Proximate analysis Moisture Ash Volatile matter Fixed carbon Chemical analysis Extractives Lignin Holocellulose Elemental analysis C H N 0
1.0 1.2 78.7 13.1
Analytical procedure”
Run no. 113 115 114 109 108 130 131 105
AND DISCUSSION
The yield of total volatiles and gases has been measured at each pyrolysis temperature as a function of reaction time. The regression equations described in Table 2 have been independently applied to the experimental results. A non-linear regression routine of the International Mathematics and Statistics Library (IMSL) based on the Levenberg-Marquardt algorithm, has been used and the best fit parameters have been estimated. The values of the estimated parameters according to the kinetic models considered are listed in Table 5, as well as the respective standard error of the predictions. It is obvious that the kinetic models give estimations with about the same error. Moreover, the magnitude of the errors are accepted as they are lower than a representative value of the experimental error of 10%. The lower error of the lit is desired for the prediction of the yield of gases, because of their small values in the range investigated. If the criteria for the selection between two kinetic models are applied34, the second kinetic model should be selected as the more suitable. In the case of volatiles both models give comparable errors, even though the estimated parameters with the first model have quite small values for thermal decomposition parameters. Unacceptable estimations of the kinetic parameters result if an inadequate model is forced to the experimental results. The difference in the values between the activation energy Results of regression analysis and values of the estimated parameters
:
4.7 21.3 68.0
; g
57.3 6.0 0.5 36.2
h h h i
Regression equation (Table 2)
k, (min- ‘)
E (kJ mot-‘)
I/* (wt%)
Standard error of tit” (wt%)
A Al C B D
7932.86 16.82 8162.37 2.73 x lo5 I .43 x lo6
32.61 kO.89 19.86kO.27 56.48 iO.78 66.25 k 0.01 94.49 kO.77
Experimental 55.67 9.55
2.99 2.87 3.18 1.05 0.66
a Defined as [(y,mode,- V,,,,,,,)2/(n-p)]“2 where n is the number of the data points and p is the number of parameters estimated by the model
Experimental results Temperature (“C)
Mean gas residence time (s)
Yield (wt% of wood) Liquids
Char”
CO,
co
CH,
C,H,
4.5 5.3 6.6
1.6 1.9 2.3 3.5
_ _ _ _
_
3.5 2.0 3.5
0.2 _ _
-
7.4 7.0
49.0 53.1
44.4 39.6
6.0 7.3
423 430 433 456
7.0 9.0
50.8 55.0
39.4 32.6
9.9 11.1
57.4 58.5
467 473
10.8 8.1
58.6 59.1
31.5 33.3 29.9
9.0 12.4 11.1
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27.2
~____
Gases
402 419
a Determined by difference
886
RESULTS
Table 5
:
’ ASTM D 2016-74; b, ASTM D 1102-84; c, Ref. 8; d, determined by difference; e, ASTM D 1105-84; f, ASTM D 1106-84; g, determined by difference; h, CHN 800, Leco Co; i, determined by difference
Table 4
satisfactorily simulated by 1.44 continuous stirred tank reactors (CSTRs) in series. The residence time distribution curve is incorporated into the theoretical kinetic model in order that the kinetic parameters be adequately determined.
8.2 11.5 13.7
8.8 7.2 6.1 8.0 8.9
4.7
_
_ 0.1
Kinetics
al
-3’ 0.4
’
I 0.8
I
I 1.2
I
I 1.6
I
I 2.0
I
I
I
2.4
TX 1O-3 (k) 1
I
b
of flash pyrolysis:
n/l. C. Samolada
and I. A. Vasalos
consistency is due to the fact that the temperature range considered has been characterized to be the decomposition range of lignin. As the other components of wood are more reactive than lignin2’, its decomposition is probably the dominating step. The value of the predicted activation energy for the production of gases is quite consistent with the value reported by Thurner and MannI for oak wood at 300-400°C and by Font et a1.19 for almond shells at 400-460°C. Our results proved to be almost an extension of those obtained by Thurner and MannLh (Figure 2b). The predicted evolution profiles of volatiles and gases at different pyrolysis temperatures are given in Figures 3 and 4 respectively. Although the kinetic model selected is quite simple for the description of such a complicated process as pyrolysis, predictions are in good agreement with the experimental data collected. Some deviations from the experiment are within the range of the accepted experimental error. CONCLUSIONS
7
.c
The conclusions follows.
E c 3 *
from this work can be summarized
as
The reaction scheme proposed by Shalizadech” with three parallel reactions, can be used to describe the primary thermal decomposition of fir wood.
E.4
0.8
1.2
1.6
2.0
2.4
a
TX 1D3 (k) Figure 2 a, Arrhenius plots for the evolution of total volatiles from fir wood pyrolysis (this study) and several samples from the literature. 0, Lignin’“; 0. sweet gumr5; & lignin”; A, almond shells”; 0, wood35; n , fir wood (this study). b, Arrhenius plots for the evolution of total gases from fir wood pyrolysis (this study) and several samples from the literature. 0. Lignin”; fJ, sweet gumr5; &oak”; A, almond shells“‘: n , fir wood (this study)
predicted for the evolution of volatiles and gases indicates that the two models cannot be considered to be identical, according to the theory previously described. The values of the estimated kinetic parameters are in the range of those found in the literature as given in Table 4. Comparison of the separate values of the kinetic parameters with those found in the literature (Table I) is not the best method for the evaluation of the results according to Roberts3’, because it is the values of the reaction rate constant which are of interest here. Figure 2 shows the dependence of the reaction rate constant on temperature and compares some representative values from the literature for volatiles and gases, as given in Table 1. Our kinetic results for total volatiles are quite consistent with those in the literature (Table I), especially with those obtained by Font et a1.19, Roberts3’ and Nunn et al.” (Figure 2a). The very good agreement of the results probably occurs because of the similar temperature range and the same heating rates applied, even though the experimental unit and the kind of biomass were different. Moreover, the predicted value of the activation energy for the total evolution of volatiles is consistent with the value reported by Nunn et aI.” for the pyrolysis of milled wood lignin. That type of lignin is similar to that present in the wood structure. The
Time (min)
r”
60
c 0
0
-0
b
I 2
I 4
I
6
I 8
10
Time (min) Figure 3 Evolution profile of total volatiles obtained from tir wood pyrolysis; comparison between experiment (0) and model predictions ( ~ ). a, T=413 C; b. T=434”C
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Kinetics
of flash pyrolysis:
M. C. Samolada
and I. A. Vasalos
15
REFERENCES
a
1
122
9-
6-
3-
0
0
0
I
I
I
I
2
4
6
6
10
Time (min)
8 9 10
15
b 12-
11 12 13 14 15 16 17 18
Time (min) Figure 4
19 20
Evolution pyrolysis; comparison (- -j. a, T=440”C;
profile of total gases obtained from fir wood between experiment (0) and model predictions b, 475°C
Simple first order kinetics can be used to describe the evolution of total volatiles and gases produced by fir wood pyrolysis. 3 The UYM is more suitable than the simple first order kinetic model and gives acceptable values of the kinetic parameters for total volatiles and gases. 4 The estimated kinetic parameters according to the selected model are within the range of values reported in the literature. Because of their close agreement to the kinetic parameters of lignin pyrolysis at high heating rates, we speculate that the decomposition of lignin is the dominating step in the range of the experimental conditions studied. 5 The model predictions are good enough in a broad range of product yields (O-60 WC%), with a standard error of fit less than 5 wt%. 6 Carbon monoxide and carbon dioxide appear in pyrolysis gases, with their yield increasing with pyrolysis temperature.
2
21
22 23 24 25 26 21
28 29 30 31
32 33 34
ACKNOWLEDGEMENTS The authors acknowledge the financial support of the European Economic Community (DGXII, under Contract EN3B0052-GR) and the General Secretariat for Research and Technology of Greece.
888
FUEL, 1991, Vol 70, July
35 36
Hajaligol, M. R., Howard, J. B., Longwell, J. P. and Peters, J. A. Ind. Enq. Chem. Proc. Des. Dev. 1982, 21, 457 Hajaligol, M. R., Peters, W. A. and Howard, J. B. Proceedings of Soecialists’ Workshon on Fast Pvrolvsis of Biomass. 19-22 October 1980, Copper Mountain, Colo;ado, USA, p. 215 Stamm, A. lnd. Eng. Chem. 1956,48(3), 413 Kanury, A. M. and Blackshear, P. L. Cornbust. Sci. Technol. 1970, 1, 339 Van Krevelen, D. W. Fuel 1951 20, 253 Alves, S. S. and Figueiredo, J. L. J. Anal. Appl. Pyrol. 1989,15, 347 Simmons, G. M. and Lee, W. H. in ‘Fundamentals of Thermochemical Biomass Conversion’. (Eds R. P. Overend. T. A. Milne and L. K. Mudge), Elsevier Applied Science Publishers, London: 1985 Probstem, R.F. and Hicks, R. E. ‘Synthetic Fuels’, McGrawHill, New York, 1982, Ch. 1, p. 13 Avni, E. and Coughlin, R. W. Thermochimicn Acta, 1985,90,157 Nuun, T. R., Howard, J. B., Longwell, J. P. and Peters, W. A. Ind. Eng. Chem. Proc. Des. Dev. 1985, 24, 844 Chan, R. W.-C. and Krieger, B. B. J. Appl. Polym. Sci. 1981, 26, 1533 Iatridis, B. and Gavalas, G. R. Ind. Eng. Chem. Proc. Des. Dev. 1979, H(2), 127 Figueiredo, J. L., Valenzuela, C., Bernalte, A. and Enciran, J. M. Fuel 1989,68, 1012 Alves, S. S. and Figueiredo, J. L. J. Anal. Appl. Pyrol. 1988, 13, 123 Nuun, T. R., Howard, J. B., Longwell, J. P. and Peters, W. A. lnd. Eng. Chem. Proc. Des. Dev. 1985, 24, 836 Thurner, F. and Mann, U. lnd. Eng. Chem. Proc. Des. Dev. 1981, 20,482 Scott, D. S., Piskorz, J., Bergognou, M. A., Graham, R. and Overend, R. P. Ind. Eny. Chem. Res. 1988, 27, 8 Liden, A. G., Berruti, F. and Scott, D. S. Chem. Eng. Commun. 1988,65, 207 Font, R., Marcilla, A., Verdu, E. and Devesa, J. lnd. Eng. Chem. Res. 1990, 29, 1846 ShaCzadech, F. ‘Fast Pyrolysis of Biomass - Proc. Specialists’ Workshop’, Solar Energy Research Institute, Colorado, USA, 1980 Van den Aarsen, F. G., Beenackers, A. A. C. M. and Swaaij, W. P. M. in ‘Fundamentals of Thermochemical Biomass Conversion’, (Eds R. P. Overend, T. A. Mime and L. K. Mudge) Elsevier Applied Science Publishers, London, 1985 Boroson. M. L.. Howard. J. B.. Lonawell. J. P. and Peters. W. A. AIChE j. 1989; 35(l), 121 Serio, M. A., Peters, W. A. and Howard, J. B. Ind. Eng. Chem. Res. 1987, 26, 1831 Tran, D. Q. and Rai, C. Fuel 1978, 57,293 Himmelblau, D. M. ‘Process Analysis by Statistical Methods’, John Wiley, New York, 1970, Ch..6, p. 194 Funazukuri. T.. Hudeins. R. R. and Silveston. P. L. J. Anal. Apyl. Pyrol. 1986, 9, i39 Vasalos, 1. A., Stoikos, T., Samolada, M. and Achladas, G. ‘Production and Utilization of Synthetic Liquid Fuels, July 1986July 1989’, Final Report prepared for EEC/DGXII under Contract EN3B0052-GR, in press Samolada, M. C., Stoicos, T. and Vasalos, I. A. J. Anal. Appl. Pyrol. 1990, 18, 127 Pyle, D. L. and Zaror, C. A. Chem. Eng. Sci. 1984,39(l), 147 Simmons, G. M. and Gentry, M. J. Anal. Appl. Pyrol. 1986. 10,117 Pyle, D. L. and Zaror, C. A. ‘Thermochemical Processes of Biomass - First Evolved European Workshop’, Butterworths, London, 1984 Antal, M. J. Jr, ‘Advances in Solar Energy’, American Solar Energy Society, New York, 1982, Ch. 4, p. 175 Raman, R. ‘Chemical Process Computations’, Eisevier Applied Science Publishers, London, 1985, Ch. 6, p. 422 Bacon, D. W. and Downie, J. ‘Kinetics - Evaluation of Rate Data-III’, AIChE Series E, 1981, 3, 65 Roberts, A. F. Combust. Flame 1970, 14, 261 Gavalas, G. R. ‘Coal Pyrolysis’, Elsevier Scientilic Publishing, Amsterdam, 1982, Ch. 6, p. 112
NOMENCLATURE Concentration C
at time
t (g
cm - 3,
Kinetics of flash pyrolysis: M. C. Samolada E G G* k k, kX L R S T T,
Activation energy (kJ mol- ‘) Yield of total gases at time t (wt%) Ultimate yield of total gases (wt%) Reaction rate constant Pre-exponential factor (min-’ or s-l) Transformed pre-exponential factor (min-‘) Yield of total liquids at time t (wt%) Universal gas constant Yield of the solid product from pyrolysis at time t (WV!!) Time (min) Temperature (K) Average value of the absolute temperatures of the regression experiments
V V* vi
VT W
and I. A. Vasalos
Yield of total volatiles at time t (wt%) Ultimate yield of total volatiles (wt%) Yield of the pyrolysis product i at time t (wt%) Ultimate yield of pyrolysis product i at time t (wt%) Unreacted percentage of the original wood at time t (wt%)
Subscripts Total gases F Product i ; Total liquids S Solid product from pyrolysis V Total volatiles W Wood
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