Modeling and Experimental Verification of Physical and Chemical

90

Ind. Eng. Chem. Res. 1996, 35, 90-98

Modeling and Experimental Verification of Physical and Chemical Processes during Pyrolysis of a Refuse-Derived Fuel Valerio Cozzani,† Cristiano Nicolella,‡ Mauro Rovatti,‡ and Leonardo Tognotti*,† Dipartimento di Ingegneria Chimica, Chimica Industriale e Scienza dei Materiali, Universita´ degli Studi di Pisa, via Diotisalvi n. 2, 56126 Pisa, Italy, and Istituto di Scienze e Tecnologie dell’Ingegneria Chimica, Universita´ degli Studi di Genova, via Opera Pia n. 15, 16145 Genova, Italy

A model for refuse-derived fuel (RDF) conventional pyrolysis in a fixed-bed reactor is presented. The model investigates the influence of the heat- and mass-transfer processes on the pyrolysis product yields. Solid degradation reactions have been modeled by assuming that the interactions between the main RDF components during pyrolysis are negligible and that the RDF pyrolysis behavior may be considered as the sum of the separate behaviors of “primary reacting species”. The model accounts for conductive and convective heat transfer within the solid matrix and secondary tar-cracking reactions, as well as for variability in physical properties and in the void fraction of the pyrolyzing material. Quite good agreement was found between model results and experimental data obtained for conventional pyrolysis of a RDF in a laboratory-scale fixedbed reactor. The model is able to predict the temperature transients, the rate of gas generation, and the product final yields during conventional pyrolysis of RDF. Introduction Disposal and energy recovery from municipal solid wastes (MSW) through economically viable technologies is of worldwide importance. Among the conventional means of disposal, thermal treatments of solid wastes may offer several benefits, providing a captive energy source, reducing the quantity of waste material to be deposited in landfills, and reducing pollutant-generating problems (Buekens and Shoeters, 1986). Furthermore, the fundamental knowledge of the phenomena that occur during the thermal decomposition of wastederived materials is also important since pyrolysis is an important stage for other thermochemical processes, such as gasification and incineration. The use of refuse-derived fuels (RDF) as starting materials for pyrolysis and gasification processes presents several advantages over the use of municipal or other solid wastes. RDF are produced by selecting the combustible fraction of MSW by mechanical sorting and processing. This results in a relatively constant composition and good transportation and storage possibilities, since putrescible components are eliminated (Muhlen et al., 1989). The implementation of pyrolysis and gasification processes depends on the reliable design of large-scale units, in which the pyrolysis reactor plays an important role. For this reason, the understanding of pyrolysis chemistry and physics is of great importance. No specific fundamental model for RDF pyrolysis is present in the literature. Nevertheless, the physical and chemical problems are similar to those found in modeling biomass thermal degradation, since cellulose and lignin are the main components of both biomass and RDF (Rampling and Hickey, 1988). Starting from the study of Bamford et al. (1946), considerable work has been done on the development of reliable models of biomass pyrolysis. Most of the models presented consider the solid as a single homogeneous species, include * Author to whom correspondence should be sent: telephone, (39)-50-511111; fax, (39)-50-511266; e-mail, tognotti@ ccii.unipi.it. † Universita ´ degli Studi di Pisa. ‡ Universita ´ degli Studi di Genova.

0888-5885/96/2635-0090$12.00/0

lumping of the reaction products into three main categories (char, tar, and gas), and consider single-step reaction kinetics. Obtaining predictions of product yields on a wider range of operating conditions required the introduction of more complex kinetic models, based on single-component multireaction schemes (Panton and Rittman, 1972; Chan et al., 1985). A further improvement has been the inclusion of secondary tar-cracking reactions in the kinetic scheme (Curtis and Miller, 1988; Hastouglu and Berruti, 1989; Di Blasi, 1993a). Different assumptions have been used in the description of the heat- and mass-transfer phenomena coupled to the thermal degradation of the solid matrix. Several models considered conductive heat transfer, a pseudosteady-state gas phase, and no pressure gradients within the solid (Kung, 1972; Panton and Rittman, 1972; Pyle and Zaror, 1984; Capart et al., 1985; Villermaux et al., 1986; Wichman and Atreya, 1987; Curtis and Miller, 1988; Koufopanos et al., 1991). A few models attempted to include convective heat transfer within the solid due to the gas-phase flow of the volatiles generated by the pyrolysis process (Chan et al., 1985; Alves and Figueiredo, 1989; Bilbao et al., 1993), while the radiative contribution to heat transfer was found to be of limited importance in the range of temperatures of interest for the pyrolysis process (Curtis and Miller, 1988). In some models the pseudo-steady-state assumption has been removed and the non-steady-state convective flow of volatiles toward a solid surface has been accounted for by the Darcy law (Kansa et al., 1977; Di Blasi, 1993a) or the Dusty gas equation (Hastaouglu and Berruti, 1989). A survey of the main results of these works is not within the scope of this paper. Details may be found in an exaustive review (Di Blasi, 1993b). The experimental work on RDF pyrolysis has pointed out that the product yields and temperature range of pyrolysis reactions are qualitatively similar for RDF and biomass pyrolysis processes (Evans and Milne, 1988; Mallya and Helt, 1988; Lai and Krieger-Brockett, 1992). However, RDF is a more complex substrate, yielding products that are not present in biomass pyrolysis, mainly due to the presence of plastic components. © 1996 American Chemical Society

Ind. Eng. Chem. Res., Vol. 35, No. 1, 1996 91

Furthermore, it is subject to much wider variability in composition. This paper presents a model for RDF conventional pyrolysis in a fixed-bed reactor. The model investigates the influence of the heat- and mass-transfer processes on the pyrolysis product yields. Solid degradation reactions have been modeled by a kinetic scheme that allows one to account for RDF composition by the weighted sum of the contributions of single components. Several features of previous models for biomass pyrolysis have been included for the description of the heatand mass-transfer phenomena, although some aspects that still are not clear have been investigated, i.e., the importance of the volatiles’ convective flow in the thermal balance. The model accounts for conductive and convective heat transfer within the solid matrix and secondary tar-cracking reactions, as well as for variability in physical properties and the void fraction of the pyrolyzing material. The effective thermal conductivity of the solid has been considered as a function of the void fraction and of the thermal conductivities of single components. Model results are compared to experimental data obtained for conventional pyrolysis of a RDF in a laboratory-scale fixed-bed reactor.

Table 1. Kinetic Model Equations

Experimental Section A. Materials. Experimental runs were performed on a commercial pelletized d-RDF. Details on samples preparation and on RDF ultimate, proximate, and marketing analyses are reported elsewhere (Cozzani et al., 1995a). B. Experimental Techniques. A fixed-bed reactor method was used for pyrolysis runs on RDF. The experimental system was fully described elsewhere (Cozzani et al., 1995b), so only the essential features are given herein. The reactor consisted of an inner tube mounted coanularly within the refractory of a tubular furnace. Milled RDF was supported in a cylindrical wire mesh basket, with a diameter of 35 mm and a length of 190 mm. The reactor was fluxed by a 1 Nl/ min helium stream. The temperatures of the gas stream and at two positions (center and wall) within the fixed bed were recorded by means of thermocouples. Characteristic bed heating rates were 0.5-1.5 °C/s. Due to its geometry, this system also allows gas-phase reactions to occur, since a hot tubular reactor section is present after the fixed bed. The product fractions recovered from RDF pyrolysis are operationally defined as follows: char, the solid fraction that remained in the basket at the end of the run; tar, the condensable volatile material that is recovered from the cold traps and other piping system surfaces following the reactor; gas, what is not condensed in the trap system. In order to evaluate the bed heat-transfer properties, experimental runs were performed using well-characterized beds of glass beads and char obtained from pyrolysis of RDF at 900 °C. The temperature profiles within the bed were measured during the heating transients following the insertion of the bed in the reactor, under the same experimental conditions used for pyrolysis runs on RDF. Model A. Kinetic Modeling. 1. Kinetic Model for the Reactions of Solid Degradation. Two different approaches have been used in the literature for the

modeling of the thermal degradation kinetics of complex solid fuels. The first considers the fuel as a single homogeneous species that undergoes degradation according to a semiglobal kinetic scheme. The second approach considers the fuel as a composite material: the overall pyrolysis process is the result of the different contributions of the various components. This latter approach makes it easier to account for variability in composition and has been used herein. The kinetic model used to describe the RDF pyrolysis process has been discussed extensively elsewhere (Cozzani et al., 1995a), so only the main features are given in the following. The pyrolysis rate of RDF has been considered as the weighted sum of the rates of the main fuel components, the “primary reacting species”: cellulose, lignin, hemicellulose, and poly(ethylene). Each of the species contributes to the formation of this sum to an extent proportional to its contribution to the composition of the virgin material. Cellulose, hemicellulose, and lignin were assumed to decompose through two competitive reactions to volatiles (tar and gases) and to char:

substrate f volatiles

(r1)

substrate f char

(r2)

Poly(ethylene) pyrolysis was described only by reaction r1. The rate of RDF pyrolysis is obtained as a weighted sum of the rate equations corresponding to these kinetic schemes. Kinetic model equations are summarized in Table 1. Table 2 reports the kinetic parameters used in eqs 1 and 2, and Table 3 shows the values of coefficients wi,0 used in eq 6. 2. Kinetic Model for the Homogeneous TarCracking Reactions. High molecular weight volatiles (tars) produced from primary solid degradation reac-

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Ind. Eng. Chem. Res., Vol. 35, No. 1, 1996

Table 2. Kinetic Parameters for the Pyrolysis Reactions of RDF “Primary Reacting Species” (Cozzani et al., 1995a) reaction 1

reaction 2

preexponential factor (s-1) activation energy (kJ/mol) reaction order heat of reaction (J/g) preexponential factor (s-1) activation energy (kJ/mol) reaction order heat of reaction (J/g)

cellulose

lignin

hemicellulose

poly(ethylene)

9.4 × 1015 216.6 1.5 120 3.1 × 1013 196.0 1.5 120

8.6 × 108 137.1 1.5 120 4.4 × 107 122.1 1.5 120

1.1 × 1014 174.1 1.5 120 2.5 × 1013 172.0 1.5 120

1.1 × 1015 248.0 0.3 650

Table 3. Initial Weight Fractions of RDF Components (Cozzani et al., 1995a) primary reacting species

weight fraction in RDF

cellulose lignin hemicellulose poly(ethylene) inerts

0.525 0.138 0.014 0.200 0.123

tions may undergo secondary decomposition reactions to produce light gases. The following kinetic scheme is used herein to model the secondary reactions: Figure 1. Differential control volume.

tar f gas

(r3)

The kinetic rate equation for the evaluation of tar conversion is reported in Table 1 (eqs 7 and 8). The values of ultimate tar conversion and the kinetic parameters used in eq 7 were estimated by experimental data fitting (Cozzani et al., 1995b). This approach was previously used by several authors (Boroson et al., 1989; Howard, 1981). B. Model EquationssEnergy and Mass Balances. 1. Modeling of Chemical and Physical Processes. It has been generally assessed that during the pyrolysis process of biomass and wood-derived materials the characteristic times of chemical reactions and of heat transfer are comparable [see Di Blasi (1993b) and references cited therein]. This leads to the necessity of accounting for both phenomena in the development of a mathematical model of the process. The pyrolysis reactor described in the Experimental Section has been modeled as an one-dimensional cylindrical porous bed of randomly oriented RDF particles where only radial gradients are present. These simplifications derive from the length/diameter ratio of the bed that is far higher than 5, so that end effects may be neglected (Alves and Figueiredo, 1989), and from the grossly homogeneous characteristics of the bed, since the RDF particles are small with respect to the reactor dimensions. The reacting medium has been modeled as a solid matrix where the void volume is initially filled by an inert gas. During pyrolysis, the volatile products formed within the solid flow outward, toward the surface of the bed, and are swept off by the purge-gas (He) flow. Medium properties inside the bed (thermal conductivity, apparent density, heat capacity, and void fraction) change with conversion, while residual char remains as the solid skeleton. The main assumptions in the formulation of present model are as follows: (1) Solid spatial dimensions are considered constant during pyrolysis. (2) The contribution of diffusion to mass transfer within the bed is negligible. (3) The contribution of radiation to heat transfer within the bed is negligible. (4) No pressure gradients are considered within the solid. (5) A pseudosteady-state is assumed for the flow of volatiles generated by pyrolysis reactions, and volatiles are considered to be in local thermal equilibrium with the solid matrix.

Assumption 1 has been widely used in the modeling of biomass pyrolysis. In the case of RDF, it is based on the experimental results obtained for cylindrical beds of RDF (Cozzani et al., 1995b) and slabs of cellulosic fibers (Mok and Antal, 1983; Curtis and Miller, 1988) undergoing pyrolysis, where no relevant shrinkage phenomena were observed. During the pyrolysis process, mass transfer by convection has been found to be much faster than that by diffusion within a solid bed (Chan et al., 1985); thus, assumption 2 seems to be justified. Radiative heat transfer plays a minor role in the pyrolysis process of slabs of cellulosic fibers, even when extreme values of radiative heat-transfer parameters are considered, as shown by Curtis and Miller (1988). Furthermore, the temperatures at which experiments have been carried out (500-900 °C) support this hypothesis. Thus, the radiative term in the heat balance equation within the solid bed may be neglected (assumption 3). The validity of assumptions 4 and 5 will be discussed later. The model equations based on the preceding assumptions are written in the following, considering the differential control volume schematized in Figure 1. 2. Mass Balance Equations. Mass balances for solid-phase species, tar, and gas are given by eqs 1-8 reported in Table 1. From assumption 1, the solid apparent density may be written as

ρs ) ρs,0W

(9)

where Fs,0 is the initial value of apparent density (Fs,0 ) 0.45 g/cm3) and W is the residual solid weight fraction. The rate of mass generation per unit volume at radius r may be written as

∂W mg(r) ) -ρs,0 ∂t

(10)

Thus, considering the cylindrical symmetry and assumptions 4 and 5, the mass balance equation has the following expression:

∂W ∂Φ ) -ρs,0 ∂r ∂t

(11)

where Φ(r) is the volatile flux toward bed surface at


radius r:

r dr ∫0rρs,0∂W ∂t

1 r

Φ(r) ) -

(12)

3. Heat Balance Equation. The convective heat flux per unit surface at radius r associated with the mass flux of volatiles has the following expression:

r dr ∫0rρs,0∂W ∂t

1 qc(r) ) CgΦ(T - T0) ) - Cg(T - T0) r

(13)

where Cg is the average specific heat of the volatiles. Thus, the heat balance equation has the following expression:

∂ ∂t

(ρsCsT) )

[

Cg

1

∂ξi

1 ∂

( ) ∂T

∑i wi,0ρi(-∆Hri) ∂t + Ks r ∂r r ∂r ∂W

]

1 ∂

+ ∂W

r r dr (rT) + Cg(T - T0)ρs,0 ρ ∫ 0 s,0 r ∂t r ∂r ∂t

(14)

where Fi is the density of component i, Cs is the specific heat of the solid, Ks is the effective thermal conductivity of the solid, and ∆Hri is the global heat of reaction for component i. Boundary and initial conditions are discussed in the following. It is assumed that the heat transfer from the surrounding gas and the furnace walls to the cylindrical bed is due to a combination of conduction, convection, and radiation. The bulk temperature of the gas surrounding the bed is assumed to be constant and equal to that of the furnace wall. Thus, at the solid surface (r ) Rb):

Ks

|

∂T ∂r

r)Rb

) h(Tf - T) + σE(Tf4 - T4)

(15)

where h is the convective heat-transfer coefficient (see the following), σ is the Stefan-Boltzmann constant, and E is the emissivity of the walls of the fixed-bed reactor (E ) 0.9). Furthermore, by assuming cylindrical symmetry with respect to the central axis of the bed:

|

∂T ∂r

r)0

)0

(16)

The initial conditions follow from the assumption that the bed is at ambient temperature when it is inserted into the hot zone of the furnace:

T(0,r) ) T0

0 e r e Rb

Figure 2. Experimental (symbols) and predicted (lines) temperature-time profiles within a cylindrical bed of glass beads at a furnace temperature of 500 °C.

(17)

4. Modeling of the Homogeneous Section. The final conversion of tar to light gases was obtained by eq 7. By assuming plug-flow behavior, mean residence times in the hot section were evaluated on the basis of the available reactor volume and the measured outlet gas flow rates. Tar-cracking reactions may take place within both the fixed bed and the hot tubular section that follows the bed. Nevertheless, the estimated residence times within the bed were negligible with respect to residence times in the tubular section of the furnace, which are on the order of 5-10 s. Hitherto, in the integration of eq 7 residence times within the bed were neglected. The final degrees of conversion of tar have been calculated by integrating eq 7 for the resi-

-dence time of the volatiles in the tubular section, assuming plug-flow behavior, i.e., without considering the distribution of axial velocity profiles. Hence, the residence time distribution is only due to the axial length of the bed. C. Physical Properties and Model Parameters. 1. External Heat-Transfer Coefficient. The external heat-transfer coefficient of eq 15 has been evaluated experimentally (see the Experimental Section). The temperature-time curves resulting from experimental runs on a bed of glass spheres have been fitted using eqs 14-17, where the terms due to chemical reaction and volatile flow were neglected. The heat-transfer coefficient has been assumed as the model parameter. Figure 2 reports a comparison between experimental data and model simulations. The best-fit value obtained for the heat-transfer coefficient is h ) 2.5 W/m2K. The heat-transfer coefficient can also be estimated from correlations for annular ducts (Perry and Green, 1984). The calculated values of the heat-transfer coefficient range between 1.5 and 3 W/m2K, which is in good accordance with the experimental results. 2. Effective Heat Conductivity of the Solid Bed. The evaluation of the radial effective conductivity of the solid bed, Ks, needs to account for the variability of the bed void fraction with conversion. No specific reliable models of effective thermal conductivity as a function of void fraction are available in the literature for beds of biomass particles. Since Ks is a crucial parameter for the evaluation of heat-transfer and temperature profiles within the bed, care must be taken in choosing a suitable correlation for the prediction of this parameter. Relevant work has been carried out for the theoretical prediction of effective heat-transfer parameters in packed beds [see Froment and Bishoff (1990) and references cited therein]. The results obtained by Kunii and Smith (1961) and by Littman and Sliva (1970) show that the contribution of “stagnant” radial heat conductivities in solid beds can be treated independently on fluid flow. Among the models present in the literature for the prediction of effective solid heat conductivities (see Dixon and Cresswell, 1979; Froment and Bishoff, 1990), the approach of Zehner and Schlu¨nder (1972) was followed for the prediction of Ks:

94


Ks )

[

( ) ( ) ( )

]

λg 1B x 2λg 1 - λs λs B-1 B+1 ln λgB 2 λgB λg 2 λgB 11- B 1λs λs λs (18) λs and λg are the solid and the gas conductivities, respectively, and

B)ψ

(1 - )

10/9

Ps )

∑i

cellulose lignin hemicellulose poly(ethylene) inerts char volatiles

density Fi (g/cm3)

specific heat Ci (J/kg K)

thermal conductivity λi (W/m K)

650a 650a 650a 250b 1500c 350a

1112 + 4.85Ta 1112 + 4.85Ta 1112 + 4.85Ta 2100b 1500c 1003 + 2.09Ta 1100d

0.13 + (3 × 10-4)Ta 0.13 + (3 × 10-4)Ta 0.13 + (3 × 10-4)Ta 0.42b 0.3c 0.08 - (1 × 10-4)Ta (4.2 × 10-2) + (3.8 × 10-4)T - (7 × 10-8)T2 c

a Koufopanos et al. (1989). b Thompson (1987). c Perry and Green (1984). d Di Blasi (1993a).

(19)

where ψ is a shape factor. This model was proposed for packed beds of spherical particles or Raschig rings. Its validity for application to a more complex medium of fibrous particles had to be checked. Furthermore, the value of factor ψ for fibrous particles had to be estimated. To achieve these goals, the temperature-time curves resulting from experimental runs on a RDF char have been fitted using eqs 14-17, again neglecting the terms due to chemical reaction and volatile flow. Weight loss and volatiles generation, if present, were negligible. The ZehnerSchlu¨nder model was used for the evaluation of solid effective conductivity, while shape factor ψ has been left as the fitting parameter; the values for physical properties of the char are reported in Table 4. Figure 3 reports a comparison between experimental data and model simulations. The best-fit value obtained for the form factor is ψ ) 3. The quite good agreement between experimental and model-predicted temperature-time curves justifies the use of the Zehner-Schlu¨nder model even for fibrous particles, if a proper value of ψ is used. In a previous paper (Cozzani et al., 1995b), it was shown, by means of SEM microphotography, that the char obtained from RDF pyrolysis retained the initial shape of virgin RDF and that the fibrous structure of RDF particles is not substantially altered by the pyrolysis process. This allows one to maintain a constant value of ψ throughout the process of RDF pyrolysis. 3. True Density, Specific Heat, and Thermal Conductivity of Volatiles and Solid. The values of the physical properties of the generated volatiles used in eqs 14-17, λg and Cg, are average values taken from the literature. The values used for the physical properties of the solid (λs, FT, and Cs) were estimated as the weighted sum of the values corresponding to the unreacted RDF components and char:

∑i (wipi + cipc)

Table 4. Values of Physical Properties Assumed for RDF Components

(20)

(wi + ci)

where Ps is the overall physical property of the solid and pi and pc are the values for the unreacted components and the char, respectively. All of the values used for the physical properties of the volatiles, the unreacted components of the solid, and the char are reported in Table 4.

Figure 3. Experimental (symbols) and predicted (lines) temperature-time profiles within a cylindrical bed of RDF char at a furnace temperature of 700 °C.

4. Void Fraction of the Bed. The following relation has been used to calculate the void fraction:

ρT,0 ρT

) 1 - W(1 - 0)

(21)

where FT,0 is the initial solid true density (FT,0 ) 0.68 g/cm3) and 0 is bed initial void fraction, determined experimentally by mercury intrusion porosimetry (0 ) 0.67). 5. Heats of Reaction. The energetics of the pyrolysis reactions of biomass and RDF are still uncertain: endothermic as well as exothermic data have been reported in the literature (Roberts, 1971; Kanury, 1972; Rampling and Hickey, 1988). This ambiguity is believed to be due to the contemporary presence of endothermic primary reactions and exothermic secondary reactions (Koufopanos et al., 1991; Di Blasi, 1993b). At low temperatures and short residence times of volatiles, only primary endothermic reactions occur, while at high temperatures and high residence times of volatiles, secondary exothermic reactions are also present. Differential scanning calorimetry (DSC) data obtained for the RDF used herein show that for small particles and slow heating rates (10 °C/min) the overall process is endothermic. A single averaged value has been obtained for cellulose, lignin, and hemicellulose degradation from the DSC data. Table 2 reports the heats of reaction used in the model. The endothermic effect of the moisture release has been accounted for by considering that when a volume element of the solid reaches the temperature 100 °C, the heat flow that reaches this element is spent for moisture evaporation until this has been completed. D. Model Integration. The nonlinear partial derivative equations of the model, along with the initial and boundary conditions, constitute a parabolic initial


Figure 4. Experimental (symbols) and predicted (lines) temperature-time profiles within the fixed bed during RDF pyrolysis: open symbols, center; closed symbols, wall; (1) model predictions considering the convective heat transfer due to volatiles flow; (2) model predictions neglecting the convective term. (a) Furnace temperature: 500 °C. (b) Furnace temperature: 900 °C.

value problem. The solution of the equations has been obtained through the finite difference representation of Crank-Nicholson (Smith, 1985). Results and Discussion The results of the model simulations have been compared to the experimental data in order to verify the model reliability as far as temperature profiles, final products, and rates of product generation are concerned. Figure 4 shows the experimental and predicted temperature-time curves at two different radial positions in the fixed bed (center and surface). In the figure, the results of model simulations are reported both including (1) and neglecting (2) the heat-transfer terms due to the flow of generated volatiles (the last two terms on the right-hand side of eq 14, respectively). The differences in temperature values between the experimental data and the model simulations that include the heat loss due to volatiles (1) are probably due to assumptions 4 and 5 used to describe the flow of generated volatiles (i.e., to assume an instantaneous release of all of the volatiles generated and to consider volatiles in thermal equilibrium with the solid matrix). Discording results are reported in the literature about the validity of these assumptions. Curtis and Miller (1988) in their pyrolysis model of slabs of cellulosic fibers found that the estimated residence time of volatiles in the solid is low compared with the time necessary for significant changes in temperature to take place. Di Blasi (1993a) accounted for the convective mass transfer in the pyrolysis of wood slabs by the Darcy law and found considerable pressure gradients and relevant residence times of the generated volatiles within the solid matrix. Bilbao et al. (1993), modeling the pyrolysis of spherical wood particles, found results similar to those reported in Figure 4. Moreover, they also found that neglecting the heat convection terms due to the volatiles flux in the heat balance leads to a more accurate simulation of temperature profiles. This can also be observed in the results reported in Figure 4. The pressure gradients and mass-transfer resistances probably have an important role in the flow of the generated volatiles within the solid, especially for the high molecular weight volatiles, which have larger density and viscosity. As a matter of fact, the assumptions that the generated

Figure 5. Experimental (symbols) and predicted (lines) temperature-time profiles for different furnace temperatures in the center of the fixed bed during RDF pyrolysis.

volatiles are in thermal equilibrium with the solid and that they instantaneously leave the fixed bed lead to overestimation of the heat loss. On the other hand, the presence of anisotropies and preferential paths for the flow of volatiles in the fixed bed makes it difficult to define a simple model for the volatile flow pattern. For these reasons, in the following the heat convection terms due to the generated volatiles have not been included in the heat balance, and the model simulations have been performed without considering the last two terms on the right-hand side of eq 14. Figure 5 shows a comparison between simulated and experimental temperature-time curves in the center of the fixed-bed reactor at various furnace temperatures. The simulations agree fairly well with the experimental data over a wide range of furnace temperatures. This implies that the model used for the calculation of the thermal conductivity and its variations with temperature and conversion is adequate. Figure 6 reports the model predictions for the temperature and the solid residual weight fraction W as a function of radial position at two different furnace temperatures. Figure 6a shows the presence of two

96


Figure 7. Final char, tar, and gas yields from RDF pyrolysis as a function of furnace temperature: symbols, experimental; lines, model.

phenomena enhance the possibility of interactions between RDF components. In particular, Figures 7 and 8 show that the basic assumptions of this study (to consider that the interactions between the main RDF components during pyrolysis are negligible, and the RDF pyrolysis behavior may be considered as the sum of the separate behaviors of each “primary reacting species”) give acceptable results even when large samples are considered. This confirms that possible interactions between RDF components have a limited influence on RDF global behavior during pyrolysis.

Figure 6. Temperature and solid residual weight fraction predicted by the model as a function of radial position at different times. Parameter: time (minutes) from sample insertion. (a) Furnace temperature: 900 °C. (b) Furnace temperature: 500 °C.

distinct reaction waves due to the different pyrolysis temperatures of biomass and plastics. The higher heating rate enhances the temperature gradient within the bed, which, in turn, produces two distinct fronts of thermal degradation. This is not the case of Figure 6b, where the lower furnace temperature and heating rate reduce temperature differences in the bed, so that pyrolysis is spread over the whole sample. Figure 7 shows a comparison between the experimental and predicted product yields obtained from RDF pyrolysis as a function of furnace temperature for a residence time of 60 min. The values predicted by the model are within the uncertainty of experimental data. However, it should be noted that gas production is slightly overestimated (and tar yield is underestimated). Figure 8 shows the results for two different furnace temperatures. Comparison is made with the experimental data for gas evolution and with the final yields of char and tar. The quite good agreement between experimental data and model predictions may be explained by the adequacy of the kinetic scheme used for the description of degradation reactions. The kinetic parameters used for primary reactions are based on data from thermogravimetric analysis (TGA) obtained on small samples where limited, if any, heat- and masstransfer limitations were present. However, the results show that the kinetic model predictions also seem to be reliable for samples in which heat- and mass-transfer

Conclusions The simulation of the pyrolysis process of refusederived fuels requires one to account for the heterogeneity and the variability in the composition of the starting materials. The approach used herein, based on the sum of the different contributions of the various components, seems to be a viable tool for the quantitative description of the overall pyrolysis behavior of such complex substrates. Previous results obtained in the modeling of heat and mass transfer during the biomass pyrolysis process may also be applied to the simulation of the pyrolysis of refuse-derived materials, although the physical properties of the solid should be calculated by accounting for the individual contributions of single components. When convective heat transfer in the solid due to the generated volatiles is considered, the temperatures predicted are lower than the experimental data. As a matter of fact, modeling mass transfer as a pseudosteady-state process seems to overestimate the flow rate of volatiles toward the bed surface. Nevertheless, the quite good agreement between experimental data and the results of model calculations shows that the model presented herein is able to reliably predict the temperature transients, the rate of gas generation, and the final product yields during conventional pyrolysis of RDF. In particular, the model assumed for describing thermal conductivity and its variations with temperature and conversion seems to be adequate. The kinetic scheme used herein, derived from TGA data, seems to give reliable results even when more complex reaction environments are considered, where heat-transfer phenomena play a relevant role.


Figure 8. Char, tar, and gas yields as a function of time. (a) Furnace temperature: 600 °C. (b) Furnace temperature: 800 °C.

Nomenclature

Literature Cited (s-1)

A ) frequency factor C ) specific heat (J/g K) ci ) weight fraction of char from component i per unit mass of solid E ) emissivity Ea ) activation energy (kJ/mol) fi ) normalized weight fraction of char from component i per unit mass of solid h ) external heat-transfer coefficient (W/cm2 K) Ks ) effective thermal conductivity of the solid bed (W/cm K) mg ) rate of mass generation per unit volume (g/cm3 s) n ) reaction order qc ) convective heat flux per unit surface (W/cm2) R ) gas constant (8.31 × 10-3 kJ/mol) Rb ) fixed-bed radius (cm) r ) radial coordinate (cm) T ) temperature (K) t ) time (s) W ) solid residual weight fraction wi ) weight fraction of unconverted component i per unit mass of solid wi,0 ) initial weight fraction of component i ζ ) degree of conversion F ) density (g/cm3) Fs ) solid apparent density (g/cm3) FT ) solid true density (g/cm3) Φ ) volatiles flux per unit surface (g/cm2 s) ∆Hri ) global heat of reaction for component i (J/g) σ ) Stefan-Boltzmann constant (5.67 × 10-12 W/cm2 K4) λ ) thermal conductivity (W/cm K) ) void fraction ψ ) shape factor Subscripts f ) furnace g ) volatiles i ) component i s ) solid 0 ) ambient value

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Received for review February 2, 1995 Revised manuscript received July 24, 1995 Accepted August 8, 1995X IE9500984

X Abstract published in Advance ACS Abstracts, November 15, 1995.

Modeling and Experimental Verification of Physical and Chemical

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