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Kinetic modeling of pyrolysis of scrap tires
J. Anal. Appl. Pyrolysis 84 (2009) 157–164
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Journal of Analytical and Applied Pyrolysis journal homepage: www.elsevier.com/locate/jaap
Kinetic modeling of pyrolysis of scrap tires Golshan Mazloom, Fatola Farhadi *, Farhad Khorasheh Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 September 2008 Accepted 23 January 2009 Available online 4 February 2009
The disposal of used tires is a major environmental problem. With increasing interest on recovery of wastes, pyrolysis is considered as an alternative process for recovering some of the value in scrap tires. An accurate kinetic model is required to predict product yields during thermal or catalytic pyrolysis of scrap tires. Pyrolysis products contain a variety of hydrocarbons over a wide boiling range. A common approach for kinetic modeling of such complex systems is lumping where each lump is defined by a boiling point range. Available experimental data for thermal and catalytic pyrolysis of scrap tires from the literature were used to evaluate two types of lumping models; discrete and continuous lumping models. The lumps were described in terms of the boiling point distribution of the reactant mixture. In the discrete model, the conversion of heavier to lighter lumps was described in terms of series and parallel first order reactions. In the continuous model, the normalized boiling point was used to describe the reactant mixture as a continuous mixture. An optimization procedure was implemented for estimation of the model parameters using experimental data reported in the literature. Model predictions with indicated that although the discrete model could reasonably predict the yields of different cuts in the products, predictions of the continuous model were very good, especially in thermal pyrolysis. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Pyrolysis Scrap tires Kinetic modeling Continuous lumping Discrete lumping
1. Introduction Disposal of scrap tires is an environmental threat. It is estimated that 2.5 million tons per year are generated in the European Union, 2.5 million tons in North America and 1 million tons in Japan [1,2]. Landfills which were extensively used before are now banned in many countries. With increasing interest on recovery of wastes, alternative processes for tire recycling have been considered with the goal of recovering some of the value in the scrap tires. One of these processes is pyrolysis (heating to moderate temperatures in an oxygen-free atmosphere) where the volatile organics present in scrap tires are decomposed to gases and liquids which could be used as fuels or as a source for chemicals including benzene, toluene, xylene and limonene. The inorganic components, mainly steel and non-volatile carbon black, remain as a solid residue which is a suitable raw material for production of carbon black or active carbon. Process conditions can be optimized to maximize the yield of char, oil or hydrocarbon gases [3–8]. Several authors have studied the kinetics of tire pyrolysis by means of techniques based on thermal gravimetry (TG/DTG) [8– 10]. Most of these studies have only considered the kinetics of rubber decomposition (devolatilization) without establishing the
* Corresponding author. Tel.: +98 2166165423; fax: +98 2166022853. E-mail addresses: [email protected], [email protected] (F. Farhadi). 0165-2370/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2009.01.006
kinetic model for the formation of secondary products [11]. For the purposes of reactor design and process optimization, it is necessary to develop kinetic models that can accurately predict the product distributions under pyrolysis conditions. For feedstocks such as scrap tires containing complex hydrocarbon structures, due to the presence of a great variety of components, the development of such kinetic models is a challenging task. Because of great variety of structures in such mixtures, compound by compound identification and quantification is very difficult, if not impossible. An alternative approach is to consider the mixture in terms of selected lumps that can be specified in terms of such properties as boiling range, molecular weight ranges, carbon numbers, solubility class fractions and other structural characteristics. Olazar et al. [11] have applied discrete lumping schemes for kinetic modeling of complex reactions for pyrolysis of waste tires involving series and parallel reactions based on carbon numbers. Continuous lumping [12–17] is an alternative approach that can be considered for kinetic modeling of thermal and catalytic processing of complex feedstocks. In this work, experimental data reported in the literature [18,19] were used to develop appropriate kinetic models for thermal and catalytic pyrolysis of scrap tires using discrete and continuous lumping models. In the discrete lumping model, the boiling point distribution was used to describe the reactant and product mixtures in terms of selected boiling cuts (lumps) where conversion of heavier to lighter lumps was described in terms of
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158
Nomenclature A Ai a0 a1 B C Ci
boiling point range between 524 8C+ exponential factor, h1 model parameter model parameter boiling point range between 343 and 524 8C boiling point range between 177 and 343 8C mass concentration in g/cm3 of volume of reactor in thermal pyrolysis and g/cm3 of volume of stream in catalytic pyrolysis at time t, i = A, B, C, D, E, Coke C(k,t) mass concentration of the component with reactivity of k at time t, g/cm3 C1,2 concentration of components with reactivity between k1 and k2, g/cm3 D boiling point range between IBP-177 8C D(K) species type distribution function d diameter, mm E hydrocarbon gases activation energy, J/mol Ei h time, h ki rate constant, h1 kmax rate constant for the component with the highest TBP, model parameter model parameter kNmin L length, mm M number of experimental runs N total number of components P(k,K) yield distribution function t time T temperature, 8C TBP true boiling point TBPH, TBPL the highest and the lowest boiling point of the components in the mixture, respectively t residence time of reactor u normalized boiling point a model parameter d model parameter b model parameter
series and parallel reactions. The normalized boiling point was used to describe the reactant mixture as a continuous mixture. The aim of this study was to test the ability of the proposed models for prediction of the yields of various boiling fractions in the products. 2. Materials and methods Two sets of experimental data from the literature were used to evaluate the proposed kinetic models. The first set of data was acquired from thermal pyrolysis of scrap passenger car as well as truck tires. Thermal pyrolysis experiments were carried out under nitrogen atmosphere at 550 and 800 8C in a stainless steel reactor at atmospheric pressure using a semibatch operation where typically 130 g of scrap tire was loaded and held for 1 h at the desired temperature. Details of the experimental setup and procedures are described elsewhere [19]. The second set of data was from a study on catalytic pyrolysis of scrap passenger car tires [18] where experiments were carried out in a stainless steel reactor containing up to 200 g of tire samples held for 1 h 500 8C. A continuous purge of nitrogen was introduced
Table 1 Properties of scrap tires used for pyrolysis. Approximate analysis wt.%
Volatile content Fixed carbon Ash content Moisture content
Thermal pyrolysis (Ref. [19])
Catalytic pyrolysis Ref. [18]
Passenger car tire
Truck tire
Passenger car tire
58.2 21.3 18.9 1.6
66.1 27.5 5 1.4
62.2 29.4 7.1 1.3
Table 2 Distillation analysis of scrap tires used in thermal pyrolysis (Ref. [19]). Temperature, 8C
Cumulative wt.%
343 360 377 392 411 419.5 445 479 521.5
Passenger car tire
Truck tire
IBP (initial boiling point) 5.56 11.20 16.91 22.65 25.53 34.15 45.47 59.05
IBP 6.59 13.25 19.92 26.57 29.88 39.66 52.21 66.68
to the reactor to sweep the evolved gases through the reactor. The pyrolysis gases exiting the first reactor were passed to a secondary catalytic reactor containing about 100 g of zeolite. Operating temperatures of the catalytic reactor were 430, 500, 530 and 600 8C with nitrogen flow providing a gas residence time of approximately 30 s. Details of the equipments, sample preparation, and experimental procedures are given elsewhere [18]. The properties and distillation analysis of the tires used in the above studies are presented in Tables 1–4. Distillation cuts are Table 3 Distillation analysis of scrap tires used in catalytic pyrolysis Ref. [18]. Temperature, 8C
Cumulative wt.% Passenger car tire
343 360 377 394 402.5 419.5 436.5 453.5 470.5
IBP 7.21 14.17 20.90 24.16 30.52 36.63 42.49 48.11
Table 4 Distillation cuts of scrap tires. Distillation cuts wt.%
524 8C+ (cut A) 343–524 8C (cut B)
Thermal pyrolysis
Catalytic pyrolysis
Passenger car tire
Truck tire
Passenger car tire
40.2 59.8
32.5 67.5
36.5 63.5
Table 5 Boiling ranges of defined boiling cuts. Distillation cut
Boiling range
A B C D
524 8C+ 343–524 8C 177–343 8C IBP-177 8C
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Table 6 Overall product distribution from pyrolysis of scrap tires. wt.%
Thermal pyrolysis (Ref. [18]) Passenger car tire
Gas Oil Solid Coke
Catalytic pyrolysis (Ref. [17]) Truck tire
Passenger Car Tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
7.4 50.6 42 –
7.8 50.7 41.5 –
7.6 58.6 33.8 –
8.8 58 33.2 –
16.25 38.75 38.10 6.90
18.75 36.25 38.10 6.90
20 35 38.10 6.90
21.25 32.50 38.10 8.15
Table 7 Distillation analyses of products from pyrolysis of scrap tires. Cut, wt.%
Thermal pyrolysis (Ref. [18]) Passenger car tire
A B C D E
Catalytic pyrolysis Ref. [17] Truck tire
Passenger car tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
25.14 21.12 21.62 24.72 7.40
21.62 23.37 23.32 23.87 7.82
18.08 18.90 25.86 29.56 7.60
17.38 18.03 27.56 28.23 8.80
0 8.13 29.69 32.11 30.07
0 5.99 26.94 32.11 34.95
0 6.02 26.01 30.41 37.56
0 6.91 27.32 23.69 42.08
defined according to Table 5. Three kinds of materials were produced from pyrolysis including solids, liquids, and gas. The product distributions from the above studies are summarized in Tables 6 and 7 where cut E consists of the light hydrocarbon gases. Distillation cuts defined in Table 5 were used to describe the feed according to Table 4. The experimental product distributions given in Table 6 were also used along with the definition of each distillation cut to describe the pyrolysis products according to Table 7. Product distributions given in Table 7 were subsequently used in an optimization algorithm for determination of kinetic parameters.
The following equations give the concentration of each cut with reaction time: C A ¼ C Ai ek1 t
(2)
CB ¼
k1 C Ai k1 t ðe ekt Þ þ C Bi ekt k k1
(3)
CC ¼
k2 C Ai k1 k2 C Ai k2 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(4)
CD ¼
k3 C Ai k1 k3 C Ai k3 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(5)
CE ¼
k4 C Ai k1 k4 C Ai k4 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(6)
3. Kinetic modeling The kinetics of scrap tire pyrolysis was described in terms of discrete and continuous lumping models. In the discrete model, each lump was considered as a pseudo component. The main advantage of discrete models is that they result in a simple reaction network and require minimum computational effort. In the discrete model, the conversion of heavier lumps to lighter lumps was considered in terms of series and parallel reactions. In continuous models the reacting mixture is considered as a continuous lump where different properties such as boiling point or molecular weight can be used to describe the distribution of the components within the lump. Details of the kinetic models for discrete and continuous lumping models are given below. 3.1. Discrete lumping Thermal pyrolysis data [19] were obtained from a semibatch reactor where a flow of nitrogen would continuously remove the gaseous products from the reactor. In the development of the discrete kinetic model it was assumed that the components in the lighter cuts C, D, and E resulting from pyrolysis of the heavier fractions do not further decompose as they are continuously removed from the reactor. The following first order reactions were considered: k1
A!B k2 B!C k3 B!D k4 B!E
(1)
k ¼ k2 þ k3 þ k4
(7)
where CJi and CJ are the mass concentration of lump J in the feed and products, respectively, and t is the reaction time. Catalytic pyrolysis data [18] were obtained from two reactors connected in series. The first reactor containing no catalyst is similar to the reactor for thermal pyrolysis operating on a semibatch mode with continuous flow of nitrogen removing the gaseous pyrolysis products. The material leaving the first reactor would then flow over a bed of catalyst in the second reactor. The approach taken was to consider the reaction network given by Eq. (1) to account for thermal pyrolysis in the first reactor. The lighter cuts leaving the first reactor would subsequently decompose according to the following reactions: k1
k2
k3
B!C!D!E k4 B!Coke k5 C!Coke
(8)
It was assumed that the observed coke formation in the catalytic reactor was due to parallel reactions of the heavier cuts B and C entering the catalytic reactor. The governing equations for this
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with reactivity of K, and D(K) is the species type distribution function given by:
reactor based on plug flow assumption are given by: C B ðt Þ ¼
C B ðt Dt Þ 1 þ ðk1 þ k4 ÞDt
(9)
Na a1 DðkÞ ¼ a k kmax
(19)
C C ðt Þ ¼
k1 C B ðt ÞDt þ C C ðt Dt Þ 1 þ ðk2 þ k5 ÞDt
(10)
where N is the total number of components in the mixture. The proposed form of the P(k,K) function [12] is as follows:
C D ðt Þ ¼
k2 C C ðt ÞDt þ C D ðt Dt Þ 1 þ k3 Dt
(11)
Pðk; KÞ ¼
C E ðt Þ ¼ k3 Dt C D ðt Þ þ C E ðt Dt Þ
(12)
(21)
C Coke ðt Þ ¼ k4 Dt C B ðt Þ þ k5 Dt C C ðt Þ þ C Coke ðt Dt Þ
(13)
( ) 0:5 2 A ¼ exp a1 1k B¼d K
(22)
where t is the residence time in the reactor. All the rate constants were expressed in terms of an Arrhenius expression: ki ¼ Ai eEi =RT
(14)
The optimum values of the Arrehnius parameters, for thermal and catalytic pyrolysis, were obtained by an optimization algorithm in which the objective function, Eq. (15) was formulated as the sum of the difference between predicted and experimental concentration of each lump in the products for all experiments. The optimization algorithm was a simple direct search algorithm by MATLAB. objective function ¼
M X 4 X ðC i;experimental C i;model Þ2
(15)
j¼1 i¼1
S0 ¼
Z
K
0
" # 2 a 1 fðk=KÞ 0 0:5g pffiffiffiffiffiffiffi exp AþB a1 S0 2p
" # 2 ! a 1 fðk=KÞ 0 0:5g pffiffiffiffiffiffiffi exp A þ B DðkÞ dk a1 2p
(20)
(23)
where a0, a1, and d are addition model parameters. The concentration of components with reactivity between k1 and k2, C1,2, is obtained by the following equation: C 1;2 ¼
Z
k2
CðkÞDðkÞ dk
(24)
k1
where Ci,experimental and Ci,predicted are the experimental and predicted concentration of lump i in the products, respectively, and M is the number of experimental runs.
Implementing the above model for thermal pyrolysis in a batch reactor would result in the following expression:
3.2. Continuous lumping
Cðk; tÞ Cðk; t dtÞ ¼ kCðk; tÞ dt Z kmax þ Pðk; KÞKCðK; tÞDðKÞ dK
The continuous lumping model used in this study was that proposed by Laxminarasimhan et al. [12] which is briefly described below. In this model the hydrocarbon components are represented by a single continuous mixture in terms of their true boiling point, TBP. The TBP curve is converted into a distribution function with the weight percent of any component as a function of the normalized boiling point, u, which is defined as:
u¼
TBP TBPL TBPH TBPL
Cðkmax ; tÞ ¼
k 1=a ¼u kmax
(17)
where kmax, which represents the rate constant for the component with the highest TBP, along with a are model parameters. The mass balance for the component with reactivity of k is represented by: dCðk; tÞ ¼ kCðk; tÞ þ dt
where C(k,t) is the concentration of the component with reactivity of k at time t. The overall reaction time is divided in to 10 equal time steps, dt. After each time step, Eq. (25) is first solved for the heaviest component, component N, with corresponding reactivity, kmax, which is only converted to lighter components during pyrolysis reactions:
(16)
where TBPH and TBPL represent the highest and the lowest boiling point of the components in the mixture, respectively. The proposed relationship [12] between the first order rate constants, k, and u was of the following form:
Z
kmax
Pðk; KÞKCðK; tÞDðKÞ dK
(18)
k
where C(k,t) is the concentration of the component with reactivity of k at time t, P(k,K) is a yield distribution function for formation of the component with reactivity of k from cracking of component
(25)
k
Cðkmax ; t dtÞ 1 þ kmax dt
(26)
The calculation of the concentration of other components would then proceed from component N 1 down. Trapezoidal rule was used for numerical integration and the value of N was chosen as 100 (i.e. 100 divisions on the u axis). Because the reactor is semibatch and the products are withdrawn continuously, after each time step dt, cuts C, D, and E are removed and their concentration in the remaining mixture is set to zero for the next time step. At the end of the reaction, the amounts of cuts C, D, and E removed from the reactor during previous time steps, are summed to give the overall yield of each of these lighter cuts. The continuous model was extended for evaluation of experimental data from catalytic pyrolysis where an additional parallel reaction was included to account for coke formation as follows:
k
hydrocarbon compounds!lower molecular weight hydrocarbon compounds kCoke
hydrocarbon compounds! Coke
(27)
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161
Furthermore, the following simple relationship was used to express the rate constant for coke formation reactions, kCoke, as a linear function of the normalized boiling point: kCoke ¼ bu þ kNmin
(28)
where b and kNmin are adjustable model parameters. Mass balance for cut i with corresponding reactivity k is given by: dCðk; tÞ ¼ ðk þ kCoke ÞCðk; tÞ þ dt
Z
kmax
Pðk; KÞKCðK; tÞDðKÞ dK
(29)
k
Implementing the catalytic pyrolysis in the plug flow design equation would result in the following expression: Cðk; t Þ Cðk; t dt Þ
dt
¼ ðk þ kCoke ÞCðk; t Þ Z kmax Pðk; KÞKCðK; tÞDðKÞdK þ
Fig. 1. Experimental versus predicted wt.% of cut A for the discrete model in thermal pyrolysis.
(30)
k
4. Results and discussion
where t is residence time of reactor. In a similar manner to that described for thermal pyrolysis, solution of Eq. (30) proceeds from the heaviest component with reactivity kmax, Eq. (31), down to the lightest component. Cðkmax ; t Þ ¼
Cðkmax ; t dt Þ 1 þ ðkmax þ kCoke Þdt
(31)
A direct search optimization method was used for the estimation of the model parameters where the objective function is given by: objective function ¼
5 X ðC i;experimental C i;model Þ2
The optimized Arrhenius parameters for the discrete model involving series and parallel reactions are presented in Table 8 for both thermal and catalytic pyrolysis. As indicated by Tables 6 and 7, the amount of hydrocarbon gases and lighter fractions produced from catalytic pyrolysis were significantly higher than those from thermal pyrolysis. The optimized parameters of the discrete models were used to predict the weight percent of each boiling cut in the products
(32)
i¼1
The optimizations algorithm for estimation of the discrete and continuous model parameters consist of minimizing the objective functions as given by Eqs. (15) or (32). A pattern search tool program is used to minimize of the objective functions. Details of the algorithm are as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Set iteration counter, i = 1. Assign initial value for parameters. Evaluate the product composition. Evaluate objective function called OF. Call pattern search tool a simulation of MATLAB to select other value for parameters. i = i + 1. Evaluate the product composition. Evaluate objective function called OFN. If OFN < OF, store parameter values as optimum and set OF = OFN. Reduce the search region. Go to step 5. Stop.
Fig. 2. Experimental versus predicted wt.% of cut B for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Table 8 Optimized Arrhenius parameters for thermal and catalytic rate constants for discrete model. Thermal pyrolysis Ai, h k1 k2 k3 k4 k5
1
1.21 8.57 101 7.16 101 3.08 101 –
Catalytic pyrolysis Ei, J/mol
Ai, h1
Ei, J/mol
5.90 103 2.00 103 0.00 2.91 103 –
30.60 63.60 116.40 307.80 76.80
5.59 105 6.48 103 3.48 103 1.24 104 4.64 103
Fig. 3. Experimental versus predicted wt.% of cut C for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
G. Mazloom et al. / J. Anal. Appl. Pyrolysis 84 (2009) 157–164
162
Fig. 4. Experimental versus predicted wt.% of cut D for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 5. Experimental versus predicted wt.% of cut E for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
from both thermal and catalytic pyrolysis. The predicted versus experimental weight percent of each cut in the products are presented in Figs. 1–6 which indicate that the proposed discrete models for both thermal and catalytic pyrolysis can reasonably predict the yields of various boiling cuts in the products. It should be noted that the catalytic pyrolysis was carried out in two reactors. The first reactor was operated thermally (without any catalyst) and the evolved gases from the first reactor were subsequently processed under catalytic pyrolysis conditions. The amount of solid reported in Table 6 is the residue in the first reactor and the feed to the second reactor did not contain any of the heavy cut A. Furthermore, the kinetic model for thermal pyrolysis was applied for the analysis of the first reactor.
Fig. 6. Experimental versus predicted value for coke yield for discrete model for catalytic pyrolysis.
Fig. 7. Normalized boiling point curve of the feed and predicted curves for products in catalytic; —: Pyrolysis feed. – –: predicted TBP, T = 600 8C. – - -: Predicted TBP, T = 500 8C. *: Experimental values T = 500 8C. ^: Experimental values T = 600 8C.
The optimized model parameters for the continuous model are presented in Table 9 for each of the experiments reported. The optimized parameters from each experiment were used to predict the normalized TBP curve of the products. In the construction of the TBP curve for the feed, TBPL was taken as 150 8C that is close to the normal boiling point of methane, and TBPH, in the thermal and catalytic pyrolysis were taken as 700 8C and 524 8C, respectively. Furthermore, the distillation data reported in Tables 2 and 3 were used along with data extrapolation to the maximum boiling point for the construction of the TBP curve of the feed. The normalized TBP curve for the feed and products from catalytic pyrolysis are presented in Fig. 7. The solid curve represents the TBP curve of the feed and the dashed lines
Table 9 Optimized parameters for the continuous model. Thermal pyrolysis
Catalytic pyrolysis
Passenger car tire
a0 a1
a d kmax kNmin
b
Truck tire
Passenger car tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
2.13 102 1.22 101 2.20 102 1.35 1.11 – –
2.09 102 1.41 101 2.10 102 8.37 101 1.02 – –
2.05 102 9.95 102 2.00 102 7.40 101 1.36 – –
2.39 102 1.27 101 2.28 102 8.84 101 1.44 – –
1786 0.47 4.66 2.75 104 0.92 9.77 107 9.77 107
25.45 1.29 102 22.97 1.90 102 0.75 6.86 102 6.86 102
23.96 0.01 23.97 2.34 104 0.75 7.44 102 6.62 102
234.18 0.20 1.70 7.81 104 2.11 0.12 6.90 102
G. Mazloom et al. / J. Anal. Appl. Pyrolysis 84 (2009) 157–164
Fig. 8. Experimental versus predicted wt.% of cut A for the continuous model in thermal pyrolysis.
163
Fig. 11. Experimental versus predicted wt.% of cut D for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 9. Experimental versus predicted wt.% of cut B for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 12. Experimental versus predicted wt.% of cut E for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
represent the curves for the products and are shifted to the left as lighter compounds are produced during the course of the reaction. The predicted normalized TBP curve of the products in the continuous model were used to determine the weight percent of each boiling cut in the products that are compared with experimental values in Figs. 8–13. Coke yields are expressed as concentration (g/cm3) in the product stream. With the exception of the yields of coke, agreements between experimental and predicted product yields were quite satisfactory. Experimental
coke yields (Table 6) were somewhat questionable as they were nearly independent of reaction temperatures. It should be pointed out that the predicted values for each experiment were obtained from the optimized parameters set for that specific experiment (Table 9). As indicated in Table 9, the optimized parameters were consistent from run to run for the case of thermal pyrolysis. For catalytic pyrolysis, however, there were variations in parameter estimates from one experiment to the other which could be related to the uncertainties in the reported experimental data.
Fig. 10. Experimental versus predicted wt.% of cut C for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 13. Experimental versus predicted coke yields for the continuous model in catalytic pyrolysis.
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5. Conclusions From the experimental data reported in the literature, discrete and continuous lumping models were developed to predict the yields of different boiling point cuts in products of thermal and catalytic pyrolysis of scrap tires. The continuous model had only 5 adjustable parameters. For catalytic experiments where coke was also produced in small amounts, an additional 2 parameters were introduced to account for coke formation. Results indicated that although the discrete models could reasonably predict the weight percent of each cut in the products, the continuous model was superior. References [1] P.T. Williams, A.J. Brindle, J. Anal. Appl. Pyrolysis 67 (2003) 143–164. [2] M.F. Laresgoiti, B.M. Caballero, I. de Marco, A. Torres, M.A. Cabrero, M.J. Chomon, J. Anal. Appl. Pyrolysis 71 (2004) 917–934. [3] P.T. Williams, A.J. Brindle, Fuel 82 (2003) 1023–1031.
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Journal of Analytical and Applied Pyrolysis journal homepage: www.elsevier.com/locate/jaap
Kinetic modeling of pyrolysis of scrap tires Golshan Mazloom, Fatola Farhadi *, Farhad Khorasheh Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 September 2008 Accepted 23 January 2009 Available online 4 February 2009
The disposal of used tires is a major environmental problem. With increasing interest on recovery of wastes, pyrolysis is considered as an alternative process for recovering some of the value in scrap tires. An accurate kinetic model is required to predict product yields during thermal or catalytic pyrolysis of scrap tires. Pyrolysis products contain a variety of hydrocarbons over a wide boiling range. A common approach for kinetic modeling of such complex systems is lumping where each lump is defined by a boiling point range. Available experimental data for thermal and catalytic pyrolysis of scrap tires from the literature were used to evaluate two types of lumping models; discrete and continuous lumping models. The lumps were described in terms of the boiling point distribution of the reactant mixture. In the discrete model, the conversion of heavier to lighter lumps was described in terms of series and parallel first order reactions. In the continuous model, the normalized boiling point was used to describe the reactant mixture as a continuous mixture. An optimization procedure was implemented for estimation of the model parameters using experimental data reported in the literature. Model predictions with indicated that although the discrete model could reasonably predict the yields of different cuts in the products, predictions of the continuous model were very good, especially in thermal pyrolysis. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Pyrolysis Scrap tires Kinetic modeling Continuous lumping Discrete lumping
1. Introduction Disposal of scrap tires is an environmental threat. It is estimated that 2.5 million tons per year are generated in the European Union, 2.5 million tons in North America and 1 million tons in Japan [1,2]. Landfills which were extensively used before are now banned in many countries. With increasing interest on recovery of wastes, alternative processes for tire recycling have been considered with the goal of recovering some of the value in the scrap tires. One of these processes is pyrolysis (heating to moderate temperatures in an oxygen-free atmosphere) where the volatile organics present in scrap tires are decomposed to gases and liquids which could be used as fuels or as a source for chemicals including benzene, toluene, xylene and limonene. The inorganic components, mainly steel and non-volatile carbon black, remain as a solid residue which is a suitable raw material for production of carbon black or active carbon. Process conditions can be optimized to maximize the yield of char, oil or hydrocarbon gases [3–8]. Several authors have studied the kinetics of tire pyrolysis by means of techniques based on thermal gravimetry (TG/DTG) [8– 10]. Most of these studies have only considered the kinetics of rubber decomposition (devolatilization) without establishing the
* Corresponding author. Tel.: +98 2166165423; fax: +98 2166022853. E-mail addresses: [email protected], [email protected] (F. Farhadi). 0165-2370/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2009.01.006
kinetic model for the formation of secondary products [11]. For the purposes of reactor design and process optimization, it is necessary to develop kinetic models that can accurately predict the product distributions under pyrolysis conditions. For feedstocks such as scrap tires containing complex hydrocarbon structures, due to the presence of a great variety of components, the development of such kinetic models is a challenging task. Because of great variety of structures in such mixtures, compound by compound identification and quantification is very difficult, if not impossible. An alternative approach is to consider the mixture in terms of selected lumps that can be specified in terms of such properties as boiling range, molecular weight ranges, carbon numbers, solubility class fractions and other structural characteristics. Olazar et al. [11] have applied discrete lumping schemes for kinetic modeling of complex reactions for pyrolysis of waste tires involving series and parallel reactions based on carbon numbers. Continuous lumping [12–17] is an alternative approach that can be considered for kinetic modeling of thermal and catalytic processing of complex feedstocks. In this work, experimental data reported in the literature [18,19] were used to develop appropriate kinetic models for thermal and catalytic pyrolysis of scrap tires using discrete and continuous lumping models. In the discrete lumping model, the boiling point distribution was used to describe the reactant and product mixtures in terms of selected boiling cuts (lumps) where conversion of heavier to lighter lumps was described in terms of
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Nomenclature A Ai a0 a1 B C Ci
boiling point range between 524 8C+ exponential factor, h1 model parameter model parameter boiling point range between 343 and 524 8C boiling point range between 177 and 343 8C mass concentration in g/cm3 of volume of reactor in thermal pyrolysis and g/cm3 of volume of stream in catalytic pyrolysis at time t, i = A, B, C, D, E, Coke C(k,t) mass concentration of the component with reactivity of k at time t, g/cm3 C1,2 concentration of components with reactivity between k1 and k2, g/cm3 D boiling point range between IBP-177 8C D(K) species type distribution function d diameter, mm E hydrocarbon gases activation energy, J/mol Ei h time, h ki rate constant, h1 kmax rate constant for the component with the highest TBP, model parameter model parameter kNmin L length, mm M number of experimental runs N total number of components P(k,K) yield distribution function t time T temperature, 8C TBP true boiling point TBPH, TBPL the highest and the lowest boiling point of the components in the mixture, respectively t residence time of reactor u normalized boiling point a model parameter d model parameter b model parameter
series and parallel reactions. The normalized boiling point was used to describe the reactant mixture as a continuous mixture. The aim of this study was to test the ability of the proposed models for prediction of the yields of various boiling fractions in the products. 2. Materials and methods Two sets of experimental data from the literature were used to evaluate the proposed kinetic models. The first set of data was acquired from thermal pyrolysis of scrap passenger car as well as truck tires. Thermal pyrolysis experiments were carried out under nitrogen atmosphere at 550 and 800 8C in a stainless steel reactor at atmospheric pressure using a semibatch operation where typically 130 g of scrap tire was loaded and held for 1 h at the desired temperature. Details of the experimental setup and procedures are described elsewhere [19]. The second set of data was from a study on catalytic pyrolysis of scrap passenger car tires [18] where experiments were carried out in a stainless steel reactor containing up to 200 g of tire samples held for 1 h 500 8C. A continuous purge of nitrogen was introduced
Table 1 Properties of scrap tires used for pyrolysis. Approximate analysis wt.%
Volatile content Fixed carbon Ash content Moisture content
Thermal pyrolysis (Ref. [19])
Catalytic pyrolysis Ref. [18]
Passenger car tire
Truck tire
Passenger car tire
58.2 21.3 18.9 1.6
66.1 27.5 5 1.4
62.2 29.4 7.1 1.3
Table 2 Distillation analysis of scrap tires used in thermal pyrolysis (Ref. [19]). Temperature, 8C
Cumulative wt.%
343 360 377 392 411 419.5 445 479 521.5
Passenger car tire
Truck tire
IBP (initial boiling point) 5.56 11.20 16.91 22.65 25.53 34.15 45.47 59.05
IBP 6.59 13.25 19.92 26.57 29.88 39.66 52.21 66.68
to the reactor to sweep the evolved gases through the reactor. The pyrolysis gases exiting the first reactor were passed to a secondary catalytic reactor containing about 100 g of zeolite. Operating temperatures of the catalytic reactor were 430, 500, 530 and 600 8C with nitrogen flow providing a gas residence time of approximately 30 s. Details of the equipments, sample preparation, and experimental procedures are given elsewhere [18]. The properties and distillation analysis of the tires used in the above studies are presented in Tables 1–4. Distillation cuts are Table 3 Distillation analysis of scrap tires used in catalytic pyrolysis Ref. [18]. Temperature, 8C
Cumulative wt.% Passenger car tire
343 360 377 394 402.5 419.5 436.5 453.5 470.5
IBP 7.21 14.17 20.90 24.16 30.52 36.63 42.49 48.11
Table 4 Distillation cuts of scrap tires. Distillation cuts wt.%
524 8C+ (cut A) 343–524 8C (cut B)
Thermal pyrolysis
Catalytic pyrolysis
Passenger car tire
Truck tire
Passenger car tire
40.2 59.8
32.5 67.5
36.5 63.5
Table 5 Boiling ranges of defined boiling cuts. Distillation cut
Boiling range
A B C D
524 8C+ 343–524 8C 177–343 8C IBP-177 8C
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Table 6 Overall product distribution from pyrolysis of scrap tires. wt.%
Thermal pyrolysis (Ref. [18]) Passenger car tire
Gas Oil Solid Coke
Catalytic pyrolysis (Ref. [17]) Truck tire
Passenger Car Tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
7.4 50.6 42 –
7.8 50.7 41.5 –
7.6 58.6 33.8 –
8.8 58 33.2 –
16.25 38.75 38.10 6.90
18.75 36.25 38.10 6.90
20 35 38.10 6.90
21.25 32.50 38.10 8.15
Table 7 Distillation analyses of products from pyrolysis of scrap tires. Cut, wt.%
Thermal pyrolysis (Ref. [18]) Passenger car tire
A B C D E
Catalytic pyrolysis Ref. [17] Truck tire
Passenger car tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
25.14 21.12 21.62 24.72 7.40
21.62 23.37 23.32 23.87 7.82
18.08 18.90 25.86 29.56 7.60
17.38 18.03 27.56 28.23 8.80
0 8.13 29.69 32.11 30.07
0 5.99 26.94 32.11 34.95
0 6.02 26.01 30.41 37.56
0 6.91 27.32 23.69 42.08
defined according to Table 5. Three kinds of materials were produced from pyrolysis including solids, liquids, and gas. The product distributions from the above studies are summarized in Tables 6 and 7 where cut E consists of the light hydrocarbon gases. Distillation cuts defined in Table 5 were used to describe the feed according to Table 4. The experimental product distributions given in Table 6 were also used along with the definition of each distillation cut to describe the pyrolysis products according to Table 7. Product distributions given in Table 7 were subsequently used in an optimization algorithm for determination of kinetic parameters.
The following equations give the concentration of each cut with reaction time: C A ¼ C Ai ek1 t
(2)
CB ¼
k1 C Ai k1 t ðe ekt Þ þ C Bi ekt k k1
(3)
CC ¼
k2 C Ai k1 k2 C Ai k2 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(4)
CD ¼
k3 C Ai k1 k3 C Ai k3 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(5)
CE ¼
k4 C Ai k1 k4 C Ai k4 C Bi ð1 ekt Þ þ ð1 ekt Þ ð1 ek1 t Þ k k1 kðk k1 Þ k
(6)
3. Kinetic modeling The kinetics of scrap tire pyrolysis was described in terms of discrete and continuous lumping models. In the discrete model, each lump was considered as a pseudo component. The main advantage of discrete models is that they result in a simple reaction network and require minimum computational effort. In the discrete model, the conversion of heavier lumps to lighter lumps was considered in terms of series and parallel reactions. In continuous models the reacting mixture is considered as a continuous lump where different properties such as boiling point or molecular weight can be used to describe the distribution of the components within the lump. Details of the kinetic models for discrete and continuous lumping models are given below. 3.1. Discrete lumping Thermal pyrolysis data [19] were obtained from a semibatch reactor where a flow of nitrogen would continuously remove the gaseous products from the reactor. In the development of the discrete kinetic model it was assumed that the components in the lighter cuts C, D, and E resulting from pyrolysis of the heavier fractions do not further decompose as they are continuously removed from the reactor. The following first order reactions were considered: k1
A!B k2 B!C k3 B!D k4 B!E
(1)
k ¼ k2 þ k3 þ k4
(7)
where CJi and CJ are the mass concentration of lump J in the feed and products, respectively, and t is the reaction time. Catalytic pyrolysis data [18] were obtained from two reactors connected in series. The first reactor containing no catalyst is similar to the reactor for thermal pyrolysis operating on a semibatch mode with continuous flow of nitrogen removing the gaseous pyrolysis products. The material leaving the first reactor would then flow over a bed of catalyst in the second reactor. The approach taken was to consider the reaction network given by Eq. (1) to account for thermal pyrolysis in the first reactor. The lighter cuts leaving the first reactor would subsequently decompose according to the following reactions: k1
k2
k3
B!C!D!E k4 B!Coke k5 C!Coke
(8)
It was assumed that the observed coke formation in the catalytic reactor was due to parallel reactions of the heavier cuts B and C entering the catalytic reactor. The governing equations for this
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with reactivity of K, and D(K) is the species type distribution function given by:
reactor based on plug flow assumption are given by: C B ðt Þ ¼
C B ðt Dt Þ 1 þ ðk1 þ k4 ÞDt
(9)
Na a1 DðkÞ ¼ a k kmax
(19)
C C ðt Þ ¼
k1 C B ðt ÞDt þ C C ðt Dt Þ 1 þ ðk2 þ k5 ÞDt
(10)
where N is the total number of components in the mixture. The proposed form of the P(k,K) function [12] is as follows:
C D ðt Þ ¼
k2 C C ðt ÞDt þ C D ðt Dt Þ 1 þ k3 Dt
(11)
Pðk; KÞ ¼
C E ðt Þ ¼ k3 Dt C D ðt Þ þ C E ðt Dt Þ
(12)
(21)
C Coke ðt Þ ¼ k4 Dt C B ðt Þ þ k5 Dt C C ðt Þ þ C Coke ðt Dt Þ
(13)
( ) 0:5 2 A ¼ exp a1 1k B¼d K
(22)
where t is the residence time in the reactor. All the rate constants were expressed in terms of an Arrhenius expression: ki ¼ Ai eEi =RT
(14)
The optimum values of the Arrehnius parameters, for thermal and catalytic pyrolysis, were obtained by an optimization algorithm in which the objective function, Eq. (15) was formulated as the sum of the difference between predicted and experimental concentration of each lump in the products for all experiments. The optimization algorithm was a simple direct search algorithm by MATLAB. objective function ¼
M X 4 X ðC i;experimental C i;model Þ2
(15)
j¼1 i¼1
S0 ¼
Z
K
0
" # 2 a 1 fðk=KÞ 0 0:5g pffiffiffiffiffiffiffi exp AþB a1 S0 2p
" # 2 ! a 1 fðk=KÞ 0 0:5g pffiffiffiffiffiffiffi exp A þ B DðkÞ dk a1 2p
(20)
(23)
where a0, a1, and d are addition model parameters. The concentration of components with reactivity between k1 and k2, C1,2, is obtained by the following equation: C 1;2 ¼
Z
k2
CðkÞDðkÞ dk
(24)
k1
where Ci,experimental and Ci,predicted are the experimental and predicted concentration of lump i in the products, respectively, and M is the number of experimental runs.
Implementing the above model for thermal pyrolysis in a batch reactor would result in the following expression:
3.2. Continuous lumping
Cðk; tÞ Cðk; t dtÞ ¼ kCðk; tÞ dt Z kmax þ Pðk; KÞKCðK; tÞDðKÞ dK
The continuous lumping model used in this study was that proposed by Laxminarasimhan et al. [12] which is briefly described below. In this model the hydrocarbon components are represented by a single continuous mixture in terms of their true boiling point, TBP. The TBP curve is converted into a distribution function with the weight percent of any component as a function of the normalized boiling point, u, which is defined as:
u¼
TBP TBPL TBPH TBPL
Cðkmax ; tÞ ¼
k 1=a ¼u kmax
(17)
where kmax, which represents the rate constant for the component with the highest TBP, along with a are model parameters. The mass balance for the component with reactivity of k is represented by: dCðk; tÞ ¼ kCðk; tÞ þ dt
where C(k,t) is the concentration of the component with reactivity of k at time t. The overall reaction time is divided in to 10 equal time steps, dt. After each time step, Eq. (25) is first solved for the heaviest component, component N, with corresponding reactivity, kmax, which is only converted to lighter components during pyrolysis reactions:
(16)
where TBPH and TBPL represent the highest and the lowest boiling point of the components in the mixture, respectively. The proposed relationship [12] between the first order rate constants, k, and u was of the following form:
Z
kmax
Pðk; KÞKCðK; tÞDðKÞ dK
(18)
k
where C(k,t) is the concentration of the component with reactivity of k at time t, P(k,K) is a yield distribution function for formation of the component with reactivity of k from cracking of component
(25)
k
Cðkmax ; t dtÞ 1 þ kmax dt
(26)
The calculation of the concentration of other components would then proceed from component N 1 down. Trapezoidal rule was used for numerical integration and the value of N was chosen as 100 (i.e. 100 divisions on the u axis). Because the reactor is semibatch and the products are withdrawn continuously, after each time step dt, cuts C, D, and E are removed and their concentration in the remaining mixture is set to zero for the next time step. At the end of the reaction, the amounts of cuts C, D, and E removed from the reactor during previous time steps, are summed to give the overall yield of each of these lighter cuts. The continuous model was extended for evaluation of experimental data from catalytic pyrolysis where an additional parallel reaction was included to account for coke formation as follows:
k
hydrocarbon compounds!lower molecular weight hydrocarbon compounds kCoke
hydrocarbon compounds! Coke
(27)
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Furthermore, the following simple relationship was used to express the rate constant for coke formation reactions, kCoke, as a linear function of the normalized boiling point: kCoke ¼ bu þ kNmin
(28)
where b and kNmin are adjustable model parameters. Mass balance for cut i with corresponding reactivity k is given by: dCðk; tÞ ¼ ðk þ kCoke ÞCðk; tÞ þ dt
Z
kmax
Pðk; KÞKCðK; tÞDðKÞ dK
(29)
k
Implementing the catalytic pyrolysis in the plug flow design equation would result in the following expression: Cðk; t Þ Cðk; t dt Þ
dt
¼ ðk þ kCoke ÞCðk; t Þ Z kmax Pðk; KÞKCðK; tÞDðKÞdK þ
Fig. 1. Experimental versus predicted wt.% of cut A for the discrete model in thermal pyrolysis.
(30)
k
4. Results and discussion
where t is residence time of reactor. In a similar manner to that described for thermal pyrolysis, solution of Eq. (30) proceeds from the heaviest component with reactivity kmax, Eq. (31), down to the lightest component. Cðkmax ; t Þ ¼
Cðkmax ; t dt Þ 1 þ ðkmax þ kCoke Þdt
(31)
A direct search optimization method was used for the estimation of the model parameters where the objective function is given by: objective function ¼
5 X ðC i;experimental C i;model Þ2
The optimized Arrhenius parameters for the discrete model involving series and parallel reactions are presented in Table 8 for both thermal and catalytic pyrolysis. As indicated by Tables 6 and 7, the amount of hydrocarbon gases and lighter fractions produced from catalytic pyrolysis were significantly higher than those from thermal pyrolysis. The optimized parameters of the discrete models were used to predict the weight percent of each boiling cut in the products
(32)
i¼1
The optimizations algorithm for estimation of the discrete and continuous model parameters consist of minimizing the objective functions as given by Eqs. (15) or (32). A pattern search tool program is used to minimize of the objective functions. Details of the algorithm are as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Set iteration counter, i = 1. Assign initial value for parameters. Evaluate the product composition. Evaluate objective function called OF. Call pattern search tool a simulation of MATLAB to select other value for parameters. i = i + 1. Evaluate the product composition. Evaluate objective function called OFN. If OFN < OF, store parameter values as optimum and set OF = OFN. Reduce the search region. Go to step 5. Stop.
Fig. 2. Experimental versus predicted wt.% of cut B for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Table 8 Optimized Arrhenius parameters for thermal and catalytic rate constants for discrete model. Thermal pyrolysis Ai, h k1 k2 k3 k4 k5
1
1.21 8.57 101 7.16 101 3.08 101 –
Catalytic pyrolysis Ei, J/mol
Ai, h1
Ei, J/mol
5.90 103 2.00 103 0.00 2.91 103 –
30.60 63.60 116.40 307.80 76.80
5.59 105 6.48 103 3.48 103 1.24 104 4.64 103
Fig. 3. Experimental versus predicted wt.% of cut C for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
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Fig. 4. Experimental versus predicted wt.% of cut D for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 5. Experimental versus predicted wt.% of cut E for the discrete model. *: thermal pyrolysis, 4: catalytic pyrolysis.
from both thermal and catalytic pyrolysis. The predicted versus experimental weight percent of each cut in the products are presented in Figs. 1–6 which indicate that the proposed discrete models for both thermal and catalytic pyrolysis can reasonably predict the yields of various boiling cuts in the products. It should be noted that the catalytic pyrolysis was carried out in two reactors. The first reactor was operated thermally (without any catalyst) and the evolved gases from the first reactor were subsequently processed under catalytic pyrolysis conditions. The amount of solid reported in Table 6 is the residue in the first reactor and the feed to the second reactor did not contain any of the heavy cut A. Furthermore, the kinetic model for thermal pyrolysis was applied for the analysis of the first reactor.
Fig. 6. Experimental versus predicted value for coke yield for discrete model for catalytic pyrolysis.
Fig. 7. Normalized boiling point curve of the feed and predicted curves for products in catalytic; —: Pyrolysis feed. – –: predicted TBP, T = 600 8C. – - -: Predicted TBP, T = 500 8C. *: Experimental values T = 500 8C. ^: Experimental values T = 600 8C.
The optimized model parameters for the continuous model are presented in Table 9 for each of the experiments reported. The optimized parameters from each experiment were used to predict the normalized TBP curve of the products. In the construction of the TBP curve for the feed, TBPL was taken as 150 8C that is close to the normal boiling point of methane, and TBPH, in the thermal and catalytic pyrolysis were taken as 700 8C and 524 8C, respectively. Furthermore, the distillation data reported in Tables 2 and 3 were used along with data extrapolation to the maximum boiling point for the construction of the TBP curve of the feed. The normalized TBP curve for the feed and products from catalytic pyrolysis are presented in Fig. 7. The solid curve represents the TBP curve of the feed and the dashed lines
Table 9 Optimized parameters for the continuous model. Thermal pyrolysis
Catalytic pyrolysis
Passenger car tire
a0 a1
a d kmax kNmin
b
Truck tire
Passenger car tire
T = 550 8C
T = 800 8C
T = 550 8C
T = 800 8C
T = 430 8C
T = 500 8C
T = 530 8C
T = 600 8C
2.13 102 1.22 101 2.20 102 1.35 1.11 – –
2.09 102 1.41 101 2.10 102 8.37 101 1.02 – –
2.05 102 9.95 102 2.00 102 7.40 101 1.36 – –
2.39 102 1.27 101 2.28 102 8.84 101 1.44 – –
1786 0.47 4.66 2.75 104 0.92 9.77 107 9.77 107
25.45 1.29 102 22.97 1.90 102 0.75 6.86 102 6.86 102
23.96 0.01 23.97 2.34 104 0.75 7.44 102 6.62 102
234.18 0.20 1.70 7.81 104 2.11 0.12 6.90 102
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Fig. 8. Experimental versus predicted wt.% of cut A for the continuous model in thermal pyrolysis.
163
Fig. 11. Experimental versus predicted wt.% of cut D for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 9. Experimental versus predicted wt.% of cut B for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 12. Experimental versus predicted wt.% of cut E for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
represent the curves for the products and are shifted to the left as lighter compounds are produced during the course of the reaction. The predicted normalized TBP curve of the products in the continuous model were used to determine the weight percent of each boiling cut in the products that are compared with experimental values in Figs. 8–13. Coke yields are expressed as concentration (g/cm3) in the product stream. With the exception of the yields of coke, agreements between experimental and predicted product yields were quite satisfactory. Experimental
coke yields (Table 6) were somewhat questionable as they were nearly independent of reaction temperatures. It should be pointed out that the predicted values for each experiment were obtained from the optimized parameters set for that specific experiment (Table 9). As indicated in Table 9, the optimized parameters were consistent from run to run for the case of thermal pyrolysis. For catalytic pyrolysis, however, there were variations in parameter estimates from one experiment to the other which could be related to the uncertainties in the reported experimental data.
Fig. 10. Experimental versus predicted wt.% of cut C for the continuous model. *: thermal pyrolysis, 4: catalytic pyrolysis.
Fig. 13. Experimental versus predicted coke yields for the continuous model in catalytic pyrolysis.
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5. Conclusions From the experimental data reported in the literature, discrete and continuous lumping models were developed to predict the yields of different boiling point cuts in products of thermal and catalytic pyrolysis of scrap tires. The continuous model had only 5 adjustable parameters. For catalytic experiments where coke was also produced in small amounts, an additional 2 parameters were introduced to account for coke formation. Results indicated that although the discrete models could reasonably predict the weight percent of each cut in the products, the continuous model was superior. References [1] P.T. Williams, A.J. Brindle, J. Anal. Appl. Pyrolysis 67 (2003) 143–164. [2] M.F. Laresgoiti, B.M. Caballero, I. de Marco, A. Torres, M.A. Cabrero, M.J. Chomon, J. Anal. Appl. Pyrolysis 71 (2004) 917–934. [3] P.T. Williams, A.J. Brindle, Fuel 82 (2003) 1023–1031.
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