Ind. Eng. Chem. Res. 1993,32,2915-2929
2915
Single Particle Refuse-Derived Fuel Devolatilization: Experimental Measurements of Reaction Products Wei-Chuan Lai and Barbara Krieger-Brockett’ Department of Chemical Engineering, University of Washington, Benson Hall BF-10, Seattle, Washington 98195 We present experimentally measured devolatilization product yields from single particles of refusederived fuel (RDF), a more uniform, transportable municipal solid waste. Disposal costs and environmental concerns have stimulated interest in thermochemical conversion of this material to chemicals and fuels. The composition, reaction conditions, and particle properties were systematically varied over the range found in practice to develop quantitative measures that rank the process controllables’ influence on altering the product slate. Specialized regression methods and experimental designs enhanced the accuracy in view of the feed heterogeneity and offer a general method t o extract real effects from experimental and sample “noise”. The results have been verified successfully using actual commercial RDF and fabricated compositions that surpass those normally found in municipal waste to anticipate the influence of trends in recycling. The results show that the reaction conditions have a greater influence on altering fuel utilization and the relative yields of char, condensibles, and gases than does the composition over the range found in MSW and RDF. Introduction and Scope Of the approximately 150 million tons per year of US. municipal solid waste (MSW) or an average of 3-4 lb waste per day per person, approximately 10% was recovered through recycling, 4 % was processed for energy, and 86 % was landfilled in 1985 (Franklin Associates, Ltd, 1986). Since then, increasing disposal costs to more than $30 billion today and rising by 17% per year (Van Voorst, 1992) as well as landfill closings have stimulated interest in the waste as a potentially large source of energy and chemicals. The raw MSW is highly variable but generally contains at least paper, yard and food waste, plastics, and incombustibles such as metal and glass. The organic fraction of MSW has a heating value (dry) of about 5800 to 6600 Btu’s per pound, approximately the BTU content of lignite coal (Campell, 1981;Shepherd, 1981). Each ton of MSW has the energy equivalent of a barrel of oil (Daugherty et al., 1986)and has an elemental composition between cellulose and wood owing to the large proportion of paper present. Thus, thermochemical treatment to either directly produce energy (by incineration) or fuel precursors to be upgraded (by pyrolysis, e.g., gasification or liquefaction) has become attractive in dealing with the waste problem (Penner et al., 1988). However, the heterogeneity of MSW remains an impediment to its use and to the study of its reaction behavior. Pretreatment of MSW to provide somewhat uniform moisture content and composition appears to increase reaction stability and minimize pollutants from combustors (Kilgroe et al., 1990). This has been one rationale behind commercially available densified refuse-derived fuel (d-RDF) (Ishii et al., 1987; Levie, 1988). It is made by removing some recyclables and noncombustibles from MSW and then drying and compressing the remaining organic polymers and inorganic substances into pellets of relatively constant density and size ( 1-2 cm diameter, 1-3 cm long cylinders). An inorganic binder is used to stabilize the pellets mechanically and biologically (Daugherty et al., 1986). This results in a more uniform, transportable fuel with a higher volatile fraction and heating value than untreated MSW. Because of its N
* To whom correspondence should be addressed. 0888-588519312632-2915$O4.OO/O
improved fuel value, d-RDF potentially can be transported to central large-scale sites having advanced reactors as well as extensive instrumentation to control conditions and minimize pollutants. Thus d-RDF is seen as an alternative to the proliferation of small, inefficient, polluting incinerators located in each community. However, optimal conditions for d-RDF thermal conversion and efficient plant size are unknown and economic assessments must be made to determine the likely effects of recycling incentives on reactivity and effective feed pretreatments (Penner et al., 1988). Our research method employs densified refuse-derived fuel (d-RDF) as a model system for studying the thermal conversion behavior of MSW. Both substances have been characterized by the National Bureau of Standards as to source, seasonal, and geographic characteristics (Evans et al., 1985). Several commercial facilities currently make d-RDF, and RDF-5 has been described by the Standard classifications for RDF developed by ASTM Committee E-38 on Resource Recovery (Sommerlad, 1981). Available composition data are reviewed in Lai (1991) where published MSW compositions from different sources are presented and used to quantify the maximum heterogeneity expected. Thus, the range and variation of constituents in d-RDF as a model MSW feed are known. Furthermore, the chemical composition and properties of d-RDF are reasonably similar from particle to particle. Therefore, realistic but well-defined single d-RDF particles can be studied to bracket the effects of heterogeneity in particle size, moisture, ash content, and composition. By studying single particle deuolatilization we focus on the solid phase internal processes such as spatial temperature history that control release of products. Additionally, the resulting single particle data can be used to guide the design, simulation, and optimization of a wide variety of reactor configurations because the investigated samples cover a realistic range of feedstock variations and the experiments are performed under practical industrial process conditions. For MSW or d-RDF, the optimum properties of the pretreated waste and most favorable reaction conditions have yet to be quantified despite some pilot- and large-scale, reactor-specific studies (McGrath et al., 1991). In those studies, gas temperatures were measured but solid-phase temperatures and processes, 0 1993 American Chemical Society
2916 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993
especially within large particles, were inaccessible. Our single particle methods not only provide otherwise inaccessible data but provide fundamental characterization of the thermal transformation of solid d-RDF, an essential step in the improvement of d-RDF and MSW pyrolysis, combustion, and minimization of pollutants. The goals of our research are to provide both quantitative laboratory data as well as a mathematical model of refusederived fuel devolatilization (Lai, 1991) for eventual use in a furnace or reactor simulation. Our data and model allow us to understand the fundamental mechanisms and rate-determining steps of solid-phase volatile8 release in the actual-size particle of d-RDF in order to appraise process and reactor design strategies. The specific scope of this paper is to present the measures that quantify the extent to which d-RDF particle size, initial particle moisture, and external heating rate alter, especially in a multiplicative way, the devolatilization product slate. To a lesser extent in this paper (seeLai and Krieger-Brockett, 1992;Lai et al., 1993a),the influence of MSW composition on devolatilizationwill be discussed. The ultimate product yields reported here and important determinants of them guided the development of the mathematical model which predicts detailed reaction rates and transport processes. It is the subject of another paper (Lai and KriegerBrockett, 1993b).
Previous Work Unlike coal and wood, the devolatilization of MSW has received much less attention due to its complexity. The large particles suffer from nonisothermal temperature distributions owing to the low thermal conductivity of MSW. Although MSW has been size-reduced to “RDF fluff“, heat transfer to this material is very poor (Milne, 1989). Some of the few previous studies on powdered, highly refined MSW and its constituents are mass spectrometric studies of MSW pyrolysis (Evans et al., 1985), experiments on basic mechanisms of pyrolysis (Mallya and Helt, 1988),plastics thermal decomposition (Agrawal and Helt, 1986; Khalturinskii, 1987; Hodgkin et al., 1982; Iidaet al., 1975;Ishii et al., 1987;Ishihara et al., 1990),and newspaper decomposition (McClusky, 1983; Agrawal et al., 1984). However, what is needed for furnace or incinerator simulations (Sommerlad, 1981)is quantitative reaction rate expressions that relate RDF composition and reaction conditions to the devolatilizationproducta formed. Simplified expressions do exist for small particles of coal and cellulose/lignin (Bradbury, et al., 1979; Nunn, et al., 1985), and the small particle rate and yield data has been used to simulate what happens to realistically sized wood particles (Chan, et al., 1985) and coal (Massaquoi and Riggs, 1983). The only known study usingrealistic particle sizes is that of Levie (1988) who measured the heat of reaction and rate of external heat transfer to RDF particles during devolatilization, but not product distribution. Numerous devices for thermochemical treatment of MSW have been studied, i.e., mass-burn incinerators (Kilgroe et al., 1990), gasifiers, cyclone reactors (Diebold and Power, 1988). One goal is to reduce the volume of the MSW significantly (about 70-95%). In the latter two devices,an objective is to produce valuable fuel precursors which can be upgraded by catalytic processes or which have reduced chlorine-containing contaminants (Levie, 1988; Hickman et al., 1984). In general, during thermal treatment the organic material is decomposed into three major products, gases, o i l h r or condensibles, and char. This is true for both incineration and pyrolysis, since devolatilization is the first step in each. Tsang (1990) has
emphasized that it is during pyrolytic conditions (in the low-oxygen, low-temperature regions of a furnace or converter) that the troublesome products of incomplete combustion such as high molecular wsight tarsare formed. Previous work on wood particles (Chan et al., 1985,1988; Murty Kanury and Blackshear, 1967) has identified that relative amounts of devolatilization products depend simultaneously on many factors including feedstock composition, particle density, moisture and ash content, and process conditions (heating rate/history, final temperature, and composition of diluent gas). It is not known to what quantitatiue extent each of these influence the MSW devolatilization products, however, and this paper will present measures of the process and composition variables’ influence.
Experimental Aspects Experimental Apparatus,Procedure,and Analysis System. A brief description of the single particle reactor (depicted in Chan et al., 19881, analysis system, and methodology is given below, and a more complete description is given in Lai (1991). A funnel-shaped glass reactor holds a single, fabricated d-RDF pellet inside a second removable tube at the intersection of the “funnel” and the “neck”. The double glass wall and intervening air space provide radial insulation and ensure one-dimensional axial heating of the cylindrical sample. During the fixed duration pyrolysis run, the pellet front face is heated radiatively with an Oriel xenon arc lamp whose beam is barely attenuated by passing through the large diameter, fused silica window on the reactor “funnel” fitted with a baffle. The incident radiation has been absolutely calibrated and, by spatial mapping, determined to be uniform across the pellet face. The fixed heating time is chosen short enough to examine only vigorous devolatilization (not “cooking”or gasification of the char) for all conditions. In a design sense, the fixed time is analogous to the transit time of a pellet in a reactor. Oxygen is excluded since we wish to study pyrolytic conditions preceding gas-phase combustion or other reactions. The d-RDF pellets are of variable thickness in the direction of heating and a constant 1.07 cm diameter. Intraparticle temperatures are continuously measured during pyrolysis by chromel-alumel thermocouples inserted near the pellet center line a t distances from the front surface of 2,4, and 6 mm for l-cm pellets; 2,6, and 12 mm for 2-cm pellets; and 2, 6, and 9 mm for 1.5-cm pellets. When comparing behavior of different length pellets, these distances provide both direct comparisons at the same distance, as well as comparisons of temperatures at the equivalent dimensionless distance. The surface temperature is measured somewhat less accurately using an infrared optical pyrometer mounted off-axis from the arc lamp beam. An experiment consists of heating the particle at constant applied heat flux for a fixed time. The outflowing volatile products are quenched by helium carrier gas and swept from the pellet surface to the analysis system. The baffle directs the carrier gas flow;the negligible backmixing of these gases and the short transit time to analysis have been quantified using tracer experiments. The volatile mixture passes through a dry icelacetone cold trap (at -40 “C) to condense tars and water, collectively called “condensibles”. Downstream of the trap, a small portion of the noncondensable gas flows through automated Valco sampling valves into 15 calibrated loops. The samples are taken more frequent early in the experiment and provide a semicontinuous hietory of the gas evolution rate
Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 2917 Table I. MSW Composition, Weight % (analyses from 10 studies)
MSW fraction 1. paper 2. yard waste 3. food waste 4.wood 5.plastics 6.rubber & leather 7.textiles subtotal 8. metal 9.glass 10.misc inorganics total moisture content
average 40.1 13.7 11.5 3.1 4.9 1.9 2.1 77.3 9.5 9.9 3.3 100.0 25.6
standard deviation 6.6 5.1 4.7 1.1 3.4 0.9 0.8 2.5 1.7 2.6 2.6
3.1 (dry basis)
and its composition as presented in Lai and KriegerBrockett (1992). The gas samples’ composition is quantified after the experiment using a Perkin-Elmer Sigma 2 gas chromatograph (GC) employing authentic samples for compound identification and external standards for quantitation. A GC analysis of the batch tar sample is done using an internal standard to determine water and the hydrocarbon composition. The fraction of the RDF sample that reacted and its char yield are determined gravimetrically after the experiment. The char is easily discernible from the unreacted portion of the particle and is separated using a scalpel. Consistent with one-dimensional heating, the interface between the char and unreacted d-RDF is flat across the pellet diameter. Char surface area is measured by dynamic gas adsorption and char composition by Fourier transform infrared spectroscopy. Although only time-integrated overallproduct yields are given here, Lai (1991) described the chemical composition and analysis methods in detail. Other papers present compositions within the gross product fractions (Lai and Krieger-Brockett, 1992) and comparisons to a mathematical model (Lai and Krieger-Brockett, 1993b). Sample Preparation. Since the composition of MSW varies significantly and the variability is of interest to us, a base case is taken to be the average of 10 published MSW compositions compiled in Lai (1991). The standard deviation is taken to measure likely variation in composition and both are shown in Table I. We can see that approximately 80% of MSW is combustible and consists of paper products, wood, garden waste, and organic consumer wastes. A major fraction (40%)of combustible MSW is paper or newsprint. Yard and food waste are similar to paper in that they are comprised of lignin and cellulose but are higher in moisture. Metal and glass contribute to noncombustible MSW. Daugherty et al. (1986)have examined by a laboratory screening procedure more than 100 binders to identify those making stable densified refuse-derived fuel. While they have found six effectivebinding materials such as lime, calcium hydroxide, and carbon black, our study uses powdered calcium hydroxide (Ca(0H)Z) as binder. The manipulated pellet compositions of laboratoryfabricated d-RDF pellets are chosen to explore a rational composition range within about one standard deviation of the average in Table I. While only 4 lumped composition classes of the 10 tabulated in Table I are differentiated and manipulated in our study, the 4 groups cover the major distinct categories and represent a compromise to limit the already large number of experiments required to investigate the independent variables. The actual composition range studied (Table 11)surpasses that published by EPA (Franklin Associates, Ltd, 1986) since we wish to
anticipate the composition changes owing to trends in recycling, Le., lower glass, metal, and higher plastics than the published average and range. Four components chosen to emulate the more numerous classifications of Table I are paper (newsprint), plastics {equal amounts of highdensity polyethylene (HDPE), low-density polyethylene (LDPE),and polyvinyl chloride (PVC)),metal (aluminum) and glass (equal amounts), and binder (Ca(0H)Z). All the components are cut to small pieces, dried in an oven at 90°C for about 48 h, and “well-mixed”before pelletization. Samples are compressed in a cylindrical mold to known densities with particular weight fractions of the components dictated by an experimental mixture design (Cornell, 1990). Finished pellets are kept in the air to reach equilibrium moisture content (about 5 % ). Additional moisture is added quantitatively using the procedure described in Lai (1991). Four types of pellets were used for pyrolysis experiments: laboratory-fabricated d-RDF, actual commercial d-RDF, “pure” newsprint, and wood pellets. The latter three pellets were used for comparison purposes. Commercial pellets were obtained from Lundel plant in Thief River Falls,Minnesota. The pellets’ densities ranged from 900 to 1030 kg/m3 and their moisture contents were about 5 % as received. Since the commercial pellets had larger diameters (1.91 cm) than our reactor (about 1.1 cm), reworked commercial pellets were used. They were made from the Thief River Falls pellets cut into smaller pieces, well mixed, and recompressed to the same density but smaller diameter. Newsprint pellets were made of newsprint with or without binder. Wood pellets were made from uniform sections of cut lodgepole pine wood provided by Weyerhauser Co. (CorvallisMill). The wood was lathed and sanded to fit tightly into the reactor diameter, and then the three studied lengths (thicknesses in the direction of heating) were cut . Experimental Design: Strategy for Choices of Experimental Conditions. We wish to measure unambiguously the change in reaction conditions or constituents of d-RDF that are needed to alter particular volatile products. Coefficients or “slopes” in equations fit to devolatilizationyield data measure these effects. However, for coefficients to truly measure the influence of a single variable, the independent variables must be orthogonal to one another (Draper and Smith, 1981). In this study three d-RDF particle sizes are studied with a view to examine a near-constant intraparticle temperature distribution at each particle size. However, for example at the 1.5-cm particle size, if one constituent is reduced in mass fraction, another must be increased to retain the intended sample size and intraparticle temperature distribution. This makes the composition variables correlated or collinear, i.e., the mixture mass fractions sum to one and are not orthogonal. When variables are collinear, resultant slopes in fit equations are artificially inflated (or deflated) by the influence of the covariant. Special mixture experimental designs (Cornell, 1990) are used to address this difficulty as explained in Lai (1991). To model mixture effects on pyrolysis yields, simplex-lattice (Scheffe, 1958) or simplex-centroid (Scheffe,l963) designs offer good prediction of yields, but the slopes or independent variable coefficients are not directly interpretable. Instead, for a mixture of q components we have chosen to work with a system consisting of (4-1) mathematically independent mixture-related variables, “MRVs” (Cornell, 19901, analogous to transformed mass fractions. They orthogonally span the range of interest and can be “untransformed” to indicate the actual mixtures to be studied. This approach
2918 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 Table 11. Ranges of Independent Variables Studied in the Devolatilization Experiments scaled conditions -1 symbol process variables heat flux moisture content particle thickness composition variables paper plastics metal/glass binder
8
12.6 x 104 5.0% 1.0
MC
L
0.57 0.00 0.05 0.00
x1
xz
x3 x4
- 16.8 x 10‘
scaled heat flux QC
scaled moisture content scaled particle thickness
=
4.2 x 104
MC - 17.5 MCc = 12.5 - 1.5 L, = L 0.5
(scaled variables have subscript c, e.g., Xb)
is convenient for model identification and interpretation of parameter estimates. It is also easy to apply standard statistical experimental designs as well as design optimality criteria. The effects of four mixture components and three reaction conditions on the devolatilization product slates are of interest. Designated “process” variables because they are unconstrained in contrast to composition variables, the three reaction conditions are applied heating rate (heat flux), initial moisture content, and particle size (for this one-dimensional pyrolysis, thickness in the direction of heating). These three largely determine the temperature inside the particle. The applied heat fluxes are chosen to cover those measured in industrial converters and to produce similar surface temperatures in our experiments to those found in practice. A discussion of the chosen ranges appears in Chan et al. (19881, Chan et al. (1985), and Chan (1983). The range encompasses that expected for converters having slow heating (carbonization) or intense heating (combustion). Particle size and moisture ranges are determined from measurements made on actual d-RDF and from the 10 reported surveys. Table I1 displays the actual experimental domain studied which spans the range of industrial importance. All independent variables are “scaled” to a range from -1 to +1 for several reasons. First, for each scaled independent variable, two unit changes span the range of practical interest. Second, the relative importance of each scaled variable is directly measured by a regression coefficient or “slope” and not obscured by the units in which a particular variable is measured. Third, in the event of multiplicative or higher order terms, a coefficient of a scaled term directly indicates its relative importance. In Table I1 the compositions under scaled condition 0 estimate average d-RDF composition in light of Table I. The adjacent columns of Table I1 show the expected extremes for d-RDF. The equations at the end of Table I1 are used for scaling and the reported regression results are in terms of scaled independent variables. Because the experiments were lengthy and we wished to distinguish real effects from sample and experimental noise, the principles of fractional factorial design (Box et
0 (average) unscaled conditions 16.8 x 104 17.5% 1.5 0.76 0.10 0.10 0.04
+I
comments 21.0 x 104 30.0% 2.0 0.95 0.20 0.15 0.08
W/m2 dry basis cm weight fraction weight fraction weight fraction weight fraction
scaled component 1
X,- 0.76 X,,= 0.19
scaled component 2
X, = 0.10
scaled component 3
X, = 0.05 X,- 0.04 X, = 0.04
scaled component 4
x,- 0.10
x3- 0.10
al., 1978) were used to develop the experiments to be performed. Thus, four mass fractions were mathematically transformed to three independent “MRVs” using a transformation matrix that is easily derived (Lai, 1991;Cornell, 1990;Thompson and Myers, 1968). To systematically and efficiently investigate the effects of the six independent variables requiring at least 26 (= 64) experiments, a staged fractional factorial design at two levels was applied as explained in Box et al. (1978). For the first stage of the designed experiments, a 2- resolution I11 design (eight experiments) was used and is illustrated in Table 111. It shows the design in terms of MRVs (w1,wp, and w3) at the top and in terms of the actual mass fractions (XI, 22, x 3 , and x 4 ) below them. The variables w4, wg, and W 6 are three process variables (also denoted by 21, Z Z , 23) and all six variables in all eight experiments are orthogonal. After the analysis of the experimental data from this first stage was completed, important variables which caused large changes in pyrolysis behavior were identified. However, because of the highly fractional ( V g ) factorial design, the results from the first eight experiments could only be fit to an additive, highly confounded model; yet, we were interested in identifying nonlinear pyrolysis behavior or interactions among the independent variables. Therefore, to uncover nonlinear effects and to clear the main effects of all two factor interactions, a second optimum stage of eight experiments was run. This was done by “foldingover”, that is, the added experiments had signs opposite to those in the original design. It has been shown by Box and Draper (1959) that the average mean square error of the predicted dependent variables can be minimized using this folding-over design. The third stage of 16 additional experiments was used to further resolve the two-factor confounding patterns and to investigate the experimental errors by adding replicates. The three stages of experimental conditions are summarized in Table IV. The experimental run number appears in column 1,columns 2-5 show the design in terms of scaled original composition variables (xl0 xpc, xsC,and x4J and columns 6-8 show the corresponding MRVs (w1, wp, and w3). The last three columns give the scaled conditions of the three process variables, w4, w5, w6 which are heat flux (Q),initial particle
Ind. Eng. Chem. Res., Vol. 32, No. 11,1993 2919 Table 111. First-Stage of the Experimental Mixture Design. (see notes below) mixture related variables
process variables
actual compositions
x1
xz
X4 symbol -0.5774 -0.5774 -0.5774 1.OOO 1.OOO 1.OOO 0.8500 0.0597 0.0731 0.0173 A 0.5774 -0.5774 -0.5774 -1.OOO -1.OOO 1.OOO 0.7478 0.1619 0.0731 0.0173 B -0.5774 0.5774 -0.5774 -1.OOO 1.OOO -1.OOO 0.8060 0.0475 0.1293 0.0173 C 0.5774 0.5774 -0.5774 1.OOO -1.OOO -1.OOO 0.7038 0.1497 0.1293 0.0173 D -0.5774 -0.5774 0.5774 1.OOO -1.OOO -1.000 0.8162 0.0503 0.0707 0.0627 E 0.5774 -0.5774 0.5774 -1.OOO 1.OOO -1.OOO 0.7140 0.1525 0.0707 0.0627 F -0.5774 0.5774 0.5774 -1.OOO -1.OOO 1.OOO 0.7722 0.0381 0.1269 0.0627 G 0.5774 0.5774 0.5774 1.OOO 1.000 1.OOO 0.6700 0.1403 0.1269 0.0627 H a (1)w1, w2, and w3 are mathematically independent MRVs. w1 through wg are an orthogonal set. They have an approximate interpretation as follows. w1: like plastics, w2: like metal and glass, w3: like binder, They are derived from XI, x2, x3, and x4 which are assigned as follows. XI: newsprint fraction, X Z : plastics fraction (equal amount of HDPE, LDPE, and PVC) x3: metal/glass fraction (equal amount of metal and glass), x4: binder fraction (Calcium hydroxide), 21: applied heat flux (scaled level), 22: moisture content (scaled level), 23: pellet thickness (scaled level). (2)The center, or base case operating condition is (XI,x 2 , x3, x4) = (0.76,0.10,0.10,0.04).(3)Region of study (spans range of practical interest): 0.57 I X I I 0.95;0.00 I 2 2 I 0.20;0.05 I x3 I 0.15;0.00 I x4 I 0.08;12.6 I 21 I 21.0 W/cmz; 5.0% I 22 I 30.0% dry basis; 1.00 I 23 I 2.00 cm. (4)Confounding pattern: Ll = 1 + 24 + 35 + 346 + 256;L2 = 2 + 14 + 36 + 345 + 156;L3 = 3 + 15 + 26 + 245 + 146;L4 = 4 + 12 56 + 235 + 136;L5 = 5 + 13 + 46 + 234 + 126;L6 = 6 + 23 + 45 + 134 + 125. L1 is the coefficient of variable w1 upon regression of data to a model equation. It actually is the sum of the effects of w1 (a variable-like plastics content), the product of w2 and w4 (the combined action of metal/glass and heat flux), the product of w3 and w5 (the combined action of binder and moisture content), the product of wg, w4, and wg (the combined action of binder, heat flux, and pellet thickness), and the product of w2, w5, and wg (the combined action of metal/glass, moisture content, and pellet thickness) (Box et al., 1978). expt no. 13 6 7 11 8 15 12 9
w1
w2
w3
w4 (21)
w5 (z2)
w6 (23)
x 3
+
Table IV. Summary of the Three-Stage Experimental Design for Laboratory-Fabricated d-RDF scaled variable mixture related variables
compositions
process variables
exptno.
xle
XZe
x9e
x4.2
w1
w2
W3
13 6 7 11 8 15 12 9
0.474 -0.064 0.242 -0.296 0.296 -0.242 0.064 -0.474
-0.403 0.619 -0.525 0.497 -0.497 0.525 -0.619 0.403
-0.538 -0.538 0.586 0.586 -0.586 -0.586 0.538 0.538
-0.568 -0.568 -0.568 -0.568 0.568 0.568 0.568 0.568
-0.5774 0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774 0.5774
-0.5774 -0.5774 0.5774 0.5774 -0.5774 -0.5774 0.5774 0.5774
-0.5774 -0.5774 -0.5774 -0.5774 0.5774 0.5774 0.5774 0.5774
1 -1 -1 1 1 -1 -1 1
1 -1 1 -1 -1 1 -1 1
1 1 -1 -1 -1 -1 1 1
16 17 18 19 20 21 22 23
-0.474 0.064 -0.242 0.296 -0.296 0.242 -0.064 0.474
0.403 -0.619 0.525 -0.497 0.497 -0.525 0.619 -0.403
0.538 0.538 -0.586 -0.586 0.586 0.586 -0.538 -0.538
0.568 0.568 0.568 0.568 -0.568 -0.568 -0.568 -0.568
0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774
0.5774 0.5774 -0.5774 -0.5774 0.5774 0.5774 -0.5774 -0.5774
0.5774 0.5774 0.5774 0.5774 -0.5774 -0.5774 -0.5774 -0.5774
-1 1 1 -1 -1 1 1 -1
-1 1 -1 1 1 -1 1 -1
-1 -1 1 1 1 1 -1 -1
31 32 33 34 35" 36" 38" 51 52 53 54 55 56 57 58 59 4 24 25 10
0.064 0.474 -0.296 -0.242 0.474 0.064 -0.296 0.296 -0.296 0.064 -0.242 0.296 0.474 -0.064 -0.474 0.242 0.474
-0.619 -0.403 0.497 0.525 -0.403 -0.619 0.497 -0.497 0.497 -0.619 0.525 -0.497 -0.403 0.619 0.403 -0.525 -0,403
0.538 -0.538 0.586 -0.586 -0.538 0.538 0.586 -0.586 0.586 0.538 -0.586 -0.586 -0.538 -0.538 0.538 0.586 -0.538
0.568 -0.568 -0.568 0.568 -0.568 0.568 -0.568 0.568 -0.568 0.568 0.568 0.568 -0.568 -0.568 0.568 -0.568 -0.568
0.OOO 0.OOO 0.OOO
0.OOO
1 -1 1 -1 1 -1 -1 1 1 1 1 1 -1 1 1 -1 1 0
1 1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 0
1 1 1 1 -1 -1 -1 1 1 -1 -1 -1 1 1 1 -1 1
0.OOO 0.OOO 0.OOO
0.5774 -0.5774 0.5774 -0.5774 -0.5774 0.5774 0.5774 -0.5774 0.5774 0.5774 -0.5774 -0.5774 -0.5774 -0.5774 0.5774 0.5774 -0.5774 0
0.5774 -0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774 0.5774 0.5774 0.5774 -0.5774 -0.5774 0.5774 -0.5774 -0.5774
0.OOO 0.OOO 0.OOO
-0.5774 -0.5774 0.5774 0.5774 -0.5774 -0,5774 0.5774 -0.5774 0.5774 -0.5774 0.5774 -0.5774 -0,5774 0.5774 0.5774 -0,5774 -0.5774 0
0
0
0
w4(Q)
WS(MC)
w6(L)
stage 1
stage 2
stage 3
averages of conditions a
0.02
-0.01
-0.05
O.Oo0
0.OOO
0.02
0
0 -0.02
0 0
-0.05
0 0 0
0.02
0 0.22
0 -0.16
0 0 0.22
Experiments 35, 36,38,and 59 are for verification purposes only.
moisture content (MC), and particle thickness in the direction of heating (L)respectively. The averages a t the bottom of Table IV are only slightly different from 0. It
is desirable that the averages are 0 because under that condition, the constant terms in a regression equation are identically equal to results at the "average" experimental
2920 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 Table V. Key to the Calculation of Gross Devolatilization Product Yields weight charl gasl con1 conbl reacted char2 gas2 con2 conb2
= WCh/Wlw X 100% = W,JWlW X 100% = Weon/wOuX 100% = 100% - charl - gasl = Wl,JW, X 100% = W c h /Wc X 100% =WgJwcX1OO% . = Wm$ W, X 100% = reacted - char2 - gas2
% % % % % % % % %
W,
g
W,
g
initial dry weight of d-RDF pellet initial combustible weighta = (XI + X2)Wp Wf final unreacted weight W,t the weight of the portion cut from reacted particle W c h the measured apparent char weight after sieving W8isvd the residual weight after sieving = WWt- W b W,, the total gas weight from GC analysis Wmerp the condensate weight in the cold trap and reactor Wm&d the weight of water initially added to the pellet W,,, the condensible yield = Wmrp - Wmeadd Wyield
W1w a
Wcon
Wchar Wgaa Wp - Wt - Wsieved
g g
g g g g g g g g
Definitions are consistent with notations in Tables II-IV for Xi.
conditions. Numerous additional experiments were performed for verification purposes but they were not necessarily part of this experimental design set.
Results Time-integrated overall yields of gross d-RDF devolatilization products such as gas, char, and condensibles are the subject of this paper. The products are reported as a weight percentage of that portion of the pellet that reacted, denoted with the suffix 1. This reporting reveals the relative product distribution which is useful for separation system design and for identifying selectivity. Products are also reported as weight percent of initial combustible materials, denoted by the suffix 2. This basis reveals the absolute yield of each product and is useful for assessing feedstock utilization and reactor sizing. Both methods use dry and ash-free bases (d.a.0, also adopted by other researchers. However, both the added moisture as well as the equilibrium moisture content have been subtracted to form the dry weight basis. Table V presents the notation and calculations used in reporting the pyrolysis product yields. Table VI presents the d-RDF devolatilizationyields after the 12-minheating. The experimental run numbers appear to the left (corresponding to conditions in Table IV), and the ultimate product yields appear to the right reported on the two bases. The mass balance for most experiments is usually better than 90-98%. Most of the discrepancy is due to a small amount of volatiles that condense on the walls during the experiments. Thus, an additional category of overall yield is reported, the condensible fraction “by balance”,that is, the condensible product that would result if the lack of mass balance closure were reported as condensibles. It is denoted in both calculation methods by “b” in the product fraction name as detailed in Table V. However, the condensibles and condensibles-bybalance yields include those volatiles that condense at -40 “C, and therefore water formed by dehydration reactions is still included in the reported condensibles fraction. This water amounts to about 10-15% by weight of the tars for wood. It is found that the discrepancies between measured and calculated condensible yields are amplified by the heating rate and initial moisture content.
In general, the more volatile material is produced, the larger the discrepancy. For comparison to the work of others, we report char on an ash-free moisture-free (d.a.0 basis. However, because by intention not all of the particle reacts in our fixed pyrolysis time, the determination of d.a.f. char requires an assumption. We must assume that noncombustibles are uniformly distributed in the particle, and a proportional amount of the noncombustibles is in the unreacted portion and the remainder is in the char. We designate this calculated char yield. However, because we measure char surface area, a nondestructive means of identifying carbonaceous char distinct from other solid residue such as ash is also necessary. The measured char yield is operationally defined as that residue that passes a 40mesh sieve. Thus, measured char slightly overestimates char because it may contain very small pieces of glass and binder. For this reason it is designated apparent char in the results and we emphasize it is a measured not calculated quantity. A calculated char yield based on the assumption of uniform distribution of noncombustibles results in the same trends but slightly different magnitudes than those reported here. In Table VI we see that over the chosen range of conditions found in practice and consistent with our goal of studying active devolatilization, about 15-90 % of the particle reacts in the fixed heating time of 12 min. Thus we have met our goal of studying no cases of char “cooking”,but retain some unreacted particle at the end of heating. This also means that our resulta are not directly comparable to the MSW or d-RDF pyrolysis data of others unless they also concentrated only on active devolatilization. The attention we pay to uncertainty in the measurements and our ability to extract real effects from experimental noise deserves further comment. It is accomplished by hidden replication (Box et al., 1978) in the experimental design and an adequate number of degrees of freedom in the tests. The pooled estimate to the error reported in Table VI is developed based on five sets of identical replicates run at different conditions to sample the range of reproducibility in our data. It is proper to interpret this error as the combination of experimental error (the slight imprecision in reaction conditions, instrument reproducibility, etc.) and sampling error (the imprecision in fabricating a presumed identical d-RDF pellet). Thus, experimental and sampling errors lead to the standard deviations in the replicate measurements given in the bottom row of Table VI. For example, 11 replicates show a reproducible fraction-of-the-pellet-thatreacts to within f6.9 or 7 % over the entire range studied. While this is a gravimetric measurement and quite accurately determined, the ability to make an exact replica d-RDF pellet is less so and may account for the entire variation in the fraction reacted. The mass balance error is truly an experimental or measurement error only, that is, we presumably know the initial mass of a particular pellet very accurately. However, in developing the mass balances we add the experimental errors in measuring all the product fractions, and they cumulatively reflect in the lack of mass balance closure. In Table VI, if we subtract the condensibles (measured) from the condensibles by balance (calculated), we see that the discrepancy ranges from -8 to 32 % with an average absolute value of 8 9% and standard deviation of f6%. This is nearly the same as the pooled error estimates determined from replicates, and thus the lack of mass balance closure has been properly treated within this framework. The combination of experimental and sampling error is the most realistic
Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 2921 Table VI. Summary of Gross Devolatilization Yields ( % ) weight percent of initial combustibles apparentchar2 gas2 con2 conb2
exptno.
reacted
13 6 7 11 8 15 12 9
39.5 21.5 34.0 85.0 89.5 35.5 30.5 38.7
2.8 2.2 2.4 14.1 19.0 2.2 2.8 4.7
7.3 2.4 2.4 16.1 16.4 3.6 2.8 4.8
16 17 18 19 20 21 22 23
44.6 70.1 47.4 20.1 19.9 48.6 67.5 42.4
8.2 9.8 7.5 4.8 1.0 5.6 8.9 4.5
31 32 33 34 51 52 53 54 55 56 57 58 4 24 25 10
39.4 15.6 36.7 19.4 47.7 47.9 75.0 70.5 78.2 13.3 36.7 42.5 39.0 54.0 41.7 37.1 6.9
8.2 1.4 6.3 2.2 9.3 5.0 13.7 15.1 16.3 1.3 4.5 8.2 3.2 10.7 9.6 4.2 2.2
weight percent of total reacted apparentcharl gas1 con1 conbl
stage 1 17.6 14.9 36.7 39.9 19.4 20.6 23.7
29.4 16.9 29.3 54.8 54.1 29.7 24.9 29.1
7.2 10.4 7.0 16.6 21.2 6.2 9.3 12.3
18.5 11.2 7.0 18.9 18.3 10.2 9.1 12.5
44.5 56.0 43.7 43.2 59.1 54.5 67.6 61.2
74.3 78.4 86.0 64.5 60.5 83.7 81.7 75.3
5.9 12.6 6.9 3.0 1.8 8.4 7.6 4.3
31.3 15.3 24.9 15.5 12.2 26.7 30.9 29.3
30.5 47.7 33.0 12.3 17.1 34.6 51.0 33.6
18.3 14.0 15.8 24.0 5.0 11.5 13.2 10.6
13.2 18.0 14.6 14.9 8.9 17.3 11.2 10.2
70.2 21.8 52.6 77.5 61.5 55.0 45.8 69.2
68.5 68.1 69.7 61.2 86.1 71.2 75.7 79.2
9.2 1.2 5.9 3.6 7.8 8.7 13.0 10.7 12.3 0.6 4.6 5.9 5.3 5.5 4.5 4.4 1.6
14.3 7.4 19.9 13.7 26.0 24.7 44.3 37.3 34.0 9.6 20.4 16.5 25.6 26.2 20.3 15.5 4.5
22.1 13.1 24.6 13.7 30.6 34.2 48.2 44.7 49.7 11.4 27.7 28.5 30.4 37.8 27.6 28.6 4.5
20.8 8.8 17.1 11.0 19.5 10.5 18.3 21.5 20.8 9.6 12.2 19.3 8.2 19.9 23.0 11.2 4.0
23.3 7.6 15.9 18.7 16.4 18.2 17.4 15.1 15.7 4.4 12.5 13.8 13.7 11.0 10.7 11.9 2.0
36.2 47.3 54.0 70.3 54.5 51.6 59.1 52.9 43.5 72.5 55.5 38.7 65.8 48.6 48.6 41.8 9.8
56.0 83.6 67.0 70.3 64.0 71.3 64.3 63.4 63.5 86.0 75.3 66.9 78.1 69.2 66.3 76.9 3.7
12.1
stage 2
stage 3
UpOoldO
a Notes: (1) up~oldis the pooled standard deviation based on five seta of experimental replicates, Le., (4, 13), (8,55), (32,56), (33,52), and (10, 24, 25); it has six degrees of freedom, (df = 6).
estimate for this type of study, and the indicated degrees of freedom are sufficient for the tests that reveal the reliability of the regression parameters and equations (Box et al., 1978; Box and Draper, 1987; Draper and Smith, 1981). One extra significant figure is reported in Table VI to improve accuracy of subsequent calculations. Quantitative Regression Analysis of the Results. The reaction products in Table VI are quantitatively analyzed by specialized regression techniques because of the slight collinearity due to staging the experimental mixture design and the desirability of detecting small but real nonadditive changes to pyrolysis behavior not detectable in simple comparative experiments. Least squares parameter estimation is highly susceptible to multicollinearity (Gunst and Mason, 1980; Draper and Smith, 1981); thus two other regression techniques, principal component regression and ridge regression, are employed. The coefficients as well as their sensitivity to experimental error, the accuracy of the model prediction, and one example demonstrating the use of the regression coefficients are presented here; additional information is in Lai (1991). Mechanistic or semiempirical functions are not easily chosen for fitting to the reaction product data. Temperature is the key determinant of pyrolysis product slate and yet it is a dependent variable having local and temporal variations within the large, low thermal conductivity d-RDF particle. With combinations of independent variables such as heat flux, moisture content, and particle
thickness simultaneously altering the temperature field, an empirical polynomial fit to the reaction product data is justified for initial ranking of the important controllables. The factored polynomial consistent with the three-stage two-level experimental design is presented in eq 1. This notation and type of polynomial is further explained in Cornel1 (1990). Equation 1 contains cross terms or interactions between the process variables and composition variables. 4
4
2
3
4
where y is the particular reaction product yield, is the mean yield at the average composition and average process conditions for the ranges chosen, and the & , m o r n are coefficients multiplying both the scaled composition variables, x i o and scaled process variables, Zm or n. The p’s are the measures we seek which quantify how much we can manipulate the product yields by changing the independent variables. The three-fold interaction of an d-RDF constituent with two process variables (a cubic nonlinear term) to produce a greatly enhanced devolatilization outcome is not consistent with early experimental data and our fundamental modeling work (Lai,1991; Lai and Krieger-Brockett, 1993b). In this case, a reduced model eq 2 is possible.
2922 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 Table VII. Regression Coefficients from PCR for Scaled Variables (yields reported as weight percent on two different bases, see Table V) overall devolatilization product fraction or dependent variables weight percent of initial combustibles model term
reacted(%) 44.61 -1.45 2.09 0.84 13.85
m
-2.52 -0.43 -4.09 3.32 -0.07 2.43 -12.22 -1.38 -1.43 1.17 -0.32 -3.93 1.05
apparent char2(%) 6.39
con2(%)
-03 -0.05 2.15 2.73 0.32 -0.33 0.23 -1.38
-0.13
0.14 0.28 -2.09 -0.23 0.11 0.02 -0.45 -1.05 0.91 -
-0.56 0.22 4.05 0 x -1.31 -2.21 -4.24 4.98 -1.57 -0.05 -4.51 0.32 -1.12
1.64 -0.28 0.18 3.24 -
conb2(%) 32.03 -1.27 0.60 -0.55 7.99 -
gas2(%)
6.10 -0ij
0.63 0.10
1.21
-2.34 -1.68 -1.22
3.79
-1.05 1.20 -8.34 1.95
-Ox 0.54 0.55 -1.75
-0.12
* -1.09
-1.10 0.42 0.37 -1.82
m 0.24 0.98 -0.55 -1.05
0.63
CO(%) 1.62
C02(%)
4.08 -Ox m 0.13 0.45 -0.22 0.82
0.39
m 0.05
1.99 -1.48
-0.14
a
-0.31 0.04 0.03
-0.48 -0.07 0.14 0.24 -0.02
-0.27 -0.00
0.26 -0.67
-0.70 0.39 0.30 -1.22
Ti3 0.09 0.69
-0.50 -0.71
0.60
weight percent of total reacted apparent charl(%) g a d ( % ) conbl(%) 12.90 73.75 13.34 -1.28 2.39 -0.89 0.12 0.15 -0.21 0.79 -5.15 4.41 2.72 -4.52 1.81 -4.82 4.46 0.75 1.23 -1.66 0.46 -3.56 0.28 4.10 -1.81 -0.90 2.57 -3.73 -2.30 6.30 1.15 0.64 -1.96 0.07 1.09 -1.03 -0.07 0.73 -0.67 1.25 0.33 -2.18 1.56 0.12 -1.99 -4.25 2.68 1.07 0 x 1.58 -0.82 1.23 -0.48 -0.69 0.86 -2.20 1.15
0.96 98 % 5.69 16 23.34 (W5) 0.48
0.91 0.91 0.95 0.94 0.95 0.93 0.90 0.63 0.85 95 % 96 % 94 % 98 % 97 % 89% 97 % 95 % 71 % 1.94 3.89 3.69 1.54 1.17 1.94 0.34 4.74 4.50 13 15 14 17 17 17 17 14 17 10.59 13.18 23.09 12.17 10.55 15.20 7.76 2.09 4.70 :17,14) (W6) (13,18) (W7) (17,14) (17,14) (17J4) (14,17) (17,14) 0.66 0.59 0.50 1.01 0.97 0.90 0.86 1.39 2.14 Felt. df (IO$) (12,6) (11,6) (8,6) (8,6) (8,6) (11,6) (46) 0 Q = heat flux, MC = moisture content, and L = particle thickness. Notes: (1)Those Coefficientsthat exceed the appropriate critical values o f t ( v 0.95) and t ( v , 0.975) are underlined and boldfaced, respectively (where t 2 ( v , 1 - a/2) = F (1, Y, 1 - a),F (1, Y, 1- a) is in F-variable with 1and v degrees of freedom under 100 (1- a)% confidence level). (2) R2 (the multiple correlation coefficient) measures the "proportion of total variation about the response mean explained by the regression". R2 cannot achieve 1.0 in w e repeat runs exist (Draper and Smith, 1981). There exists a maximum attainable R2, R2,. Thus R2/R2, represents the percentage of the variation that is explainable and has been explained by the model. (3) 8.e.r. (the standard error of the residuals for the model prediction) is a measure of prediction uncertainty. (4) Fht. (internal F statistic) tests the adequacy of the model (large when the model is adequate) and Fed (external F statistic) testa if no lack of fit is exhibited by the model (small when there is no lack of fit). The degrees of freedom associated with the F statistics are given below them.
where the Pi, Po,rn, &,m, and Po,rnn measure how much we can manipulate the products by changing the d-RDF composition, by changing the process conditions, by simultaneously changing the composition and reaction conditions, by simultaneously changing two reaction conditions at a time, respectively. To determine if the reduced model was better than the full model, several criteria were used. The first was to perform a full-versus-reduced model F-test, Fr-f=
(SSE,,,,,
- SSEfd)/(df,- dff) SSE,/ dff
(3)
where SSEreducedand SSEfd were the sum of squared residuals from the reduced and full models, respectively, and df, and dff were the residual degrees of freedom from the reduced and full models, respectively. Second, the fits were also compared by examining the models' F and R2statistics, prediction mean squared errors, as well as by a lack of fit test. Equation 2 was deemed satisfactory and details and their interpretation can be found in Draper and Smith (1981) and Cornel1(1990) and as applied to our case in Lai (1991).
Collinearity and Biased Estimates. The variables in Table IV exhibit multicollinearity (nonorthogonality) and thus potentially produce unreliable parameter estimates or ,6 coefficients in eq 2. Population multicollinearity is caused by the definition of the predictor (independent) variables. For example, a mixture system always has the constraint regarding the composition variables, n
E X i = 1.0
(4)
c=l
and thus the mixture variables show population multicollinearity. Sample multicollinearity is not inherent to the particular definition of a variable but is due to poorly chosen samples. One example is the data from a fractional factorial design. The multicollinearity due to samples or experiments in a fractional factorial design is described by the confoundingpatterns shown at the bottom of Table 111. Least squares parameter estimates are falsified by collinearity (Gunst and Mason, 1980)showingsign changes, distorted magnitudes, and large variances. More reliable coefficients result from biased regression techniques such as principal component regression (PCR) (or so-called incomplete principal component, IPC) and ridge regression. Several techniques for detecting collinearities are proposed by Gunst and Mason (1980)and Mason et al. (1989). Variance inflation factors (VIF) measure the stability of parameter estimates in a fitted model or how much the
Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 2923 uncertainty of the estimated coefficients is amplified by the lack of orthogonality in the design. The VIF for the ith estimated coefficient in the particular model is VIFi = 1/(1-R:) (5) where Ri2 is the multiple correlation coefficient of the ith predictor variable, X i , regressed on the remaining independent variables, that is, all the other terms in the model. Marquardt (1970) has indicated that if any of the VIF’s is greater than 10, then the corresponding least squares coefficient is poorly estimated and other estimation procedures should be used. In other words, the ith predictor variable is collinear with other predictors if Ri2 > 0.90 or equivalently if VIF > 10. The second technique examines the eigenvalues resulting from the correlation matrix of all the predictor variables. It is suggested that any eigenvalue less than 0.05 implies a collinearity among the predictor variables. Since collinearities in our data set exist, biased regression techniques are applied to find interpretable coefficients. Table VI1 presents the coefficients when principal component regression (PCR) is applied to the data in Table VI using eq 2. All the principal components are not used since using all corresponds to ordinary least squares estimation. While there is no universal selection rule for the number of PC’s, we use 13-17 PC’s (out of 18) which removes most of the collinearity and accounts for 96-99 % of the variance. This selection gives very reasonable mean square error (MSE),F-, and R2 statistics. It is important to note the regression coefficients refer to scaled original d-RDF constituents, i.e., Xic’s not MRV’s (consistent with eq 2). The coefficients of scaled original d-RDF constituents are recovered from the principal component regression coefficients by applying the transformation formula between the two. The recovery transformation was tested successfullyon several synthetic data sets where we knew a priori what had to be recovered. In Table VII, entries are underlined when t-values are greater than those of a 90 % confidence level and entries are also boldfaced when t-values exceed those of a 95% confidence level. The t-value is the ratio of a coefficient to its standard error. These coefficients have been compared to those resulting from ridge regression. The comparison reveals that although these two biased regression methods give slightly different parameter estimates, the coefficients which are large and statistically significant are substantially the same for both approaches and this data set. While extra significant figures are included in Table VII, the interpretation given in the discussion is made with due weight given to the uncertainty in the parameter estimates. However, calculation using a greater number of digits proves to be more reliable. On a different but frequently used basis, the d-RDF gross devolatilization product fractions can be reported as weight percent of the substrate that reacted. The
5% reacted = 44.61
% reacted = 44.61
findings reported on this basis (using suffix 1) have relevance for the design of a separation scheme for the reaction products, and one can more readily see how to optimize a certain product fraction such as gas or condensibles. The right side of Table VI1 presents these coefficients calculated using principal components regression. The same convention, underlined and boldfaced, is followed. Accuracy of the Prediction. The prediction accuracy of the regression is quite good. This is demonstrated by the measures at the bottom of Table VI1 and illustrated using an example. Percent reacted (first column Table VII) or how much of an d-RDF particle reacts in the fixed heating period is predicted from eq 6 which contains scaled variables. The multiple correlation coefficient, R2 = 0.96, means that 96 % of the total variation in the first column has been explained by the equation. For our experiments, R2 cannot achieve 1.0 because of repeat runs (Draper and Smith, 1981); rather there is a maximum R2 attainable, Rzmax~ due to the replicates. Thus R2/R2, = 0.98 implies that 98% of the variation that is explainable has been explained by the model. The internal F statistic, Fint., is 23.34 with 16 and 15 degrees of freedom and is used to test the model adequacy. The value 23.34,which is muchlarger than F(16,15,0.95) = 2.35 (the value from F-distribution table with 16 and 15 degrees of freedom using a 95% confidence level), indicates that the model is adequate. The value of the lack-of-fitF-test (Draper and Smith, 1981; Khuri and Cornell, 1987) with 8 and 5 degrees of freedom is Fext.= 0.48, which is small compared with F(9,6,0.95) = 4.10, the value from F-distribution table. In other words, no lack of fit is exhibited by the model. An example demonstrating the use of the unscaled and scaled variables with Table VI1 for predicting the product yields is useful. Suppose a 2-cm wet (initial moisture content 30% dry basis) d-RDF pellet has a composition of (xl,x2, x3, x4) = (0.85, 0.0597, 0.0731, 0.0173) which is 85% paper, 6 % plastics, 7% metal and glass, and 1.7% binder or inorganic salts. In a reactor or process, it is heated by a constant 21.0 X W/m2(5cal/cm2/s)applied heat flux at the surface, Le., let’s take experiment 13 in Tables 111,IV, and VI. By using the scaling functions, we get (XI,, XZ,, XS,, x k ) = (0.474, -0.403, -0.538, -0.568), and (Q, MC,L) = (+l, +1,+l).Substituting these values into the complete eq 6 we get eq 7, which gives predicted 5% reacted = 37.9 f 5.7 % compared to a measured % reacted = 39.5 f 6.9% (shown in Table VI). In a similar fashion, predicted values of other product yields can be obtained by substituting the reaction conditions and composition into the model equation. The good accuracy of allpredicted devolatilization yields is depicted in Figures 1 and 2 where experimental observations are plotted against predictions from eq 2 (eq 2 simply allows more degrees of freedom for the statistical tests). The line corresponding to exact agreement is drawn
- 1.45~2, -+ 2.09X3,
+0 . 8 4 ~ ~
+ 13.852, - O.94X2,Z1 - 2.52X3,Z1 - 0 . 4 3 ~ ~ ~ - 4.092, + 3 . 3 2 ~- 0~ .~ 0~7~~+ 2~ . 4~ 3~~ ~~2 , - 12.222, - 1 . 3 8 ~ ~ ~ -2 ,1.43X3,Z3 + 1.17XkZ3 - 0.322,Z2 - 3.932,2, + 1.05Z,Z3 - 1.45(-0.403) + 2.09(-0.538) + 0.84(-0.568)
+ 13.85(1)
- 0.94(-0.403)(1)
- 4.09(1) + 3.32(-0.403)(1) - 12.22(1) - 1.38(-0.403)(1) - 0.32(1)(1) - 3.93(1)(1)
- 2.52(-0.538)(1) - 0.07(-0.538)(1) - 1.43(-0.538)(1)
+ 1.05(1)(1)
~ ~
- 0.43(-0.568)(1)
+ 2.43(-0.568)(1)
+ 1.17(-0.568)(1)
(7)
2924 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993
satisfactory prediction ability even for conditions slightly
-cE
loo 80
a
60
-
40
-
U
.-*u
beyond the region of study. I Actual commercial d-RDF pellets were devolatilized to
U
E a
0
20
60
40
80
100
Measured values Figure 1. Predicted versua measured values of percent reacted for entire experimental design. 2o
compare their behavior to that of the fabricated pellets used to develop the regression parameters. Among others, four experiments were performed using a 2a1m fractional factorial design, Table IX, to investigate how process conditions altered the pyrolysis behavior. The results on fabricated d-RDF in Table VI must be grouped according to common process conditions and then averaged and compared with the actual commercial d-RDF pyrolysis product yields in Table X. The top part of the table displays the comparison at four matching reaction conditions, and the differences are in general within experimental error. The comparison implies two things. First, the reaction conditions cause similar yields from either actual commercial d-RDF pellets or fabricated research d-RDF pellets, despite the fact that the compositions, including the binder used, might be quite different. Second, compared with changing the process variables, changing composition over the range usually found in practice results in relatively smaller changes in the product slates. This is consistent with the small coefficients for composition variables found in Table VII. Note that by intentional construction the mean yields from these four real d-RDF samples as reported at the bottom of Table X represent the yields of actual commercial d-RDF pellets when they are pyrolyzed under the average process conditions, i.e., 1.5-cmwet pellets (MC = 17.5 % ,dry basis) under an applied heat flux of 16.8 X lo+' W/m2. When we comparethese means for four commercial sampleswith the means of 32 fabricated d-RDF samples at comparable average conditions in Table VI, we can find that they are in remarkable agreement as shown a t the bottom of Table X. The close magnitudes mean that when both actual and fabricated d-RDF with average composition are heated as in industrial converters (our chosen average process conditions), product slates will be very similar. This corroborates that our choice of base case and "average" d-RDF composition is close to that of actual commercial d-RDF.
I
0
5
10
15
20
Measured values Figure 2. Predicted versus measured values of char yield, char2, for entire experimental design.
as a diagonal. The replicates (marked with X's) are connected with horizontal lines, since equal values are predicted but the measurements differ. Figure 1 shows the most adequate fit to the data, percent reacted from experiments, judged by the highest internal F statistic and Rz; Figure 2 represents the least adequate model, char 2, expressed as a fraction of initial combustibles, asjudged by the same criterion. Thus, all the remaining models fit to the data have accuracy in between that represented in the two figures. The equations thus predict the pyrolysis results to better than experimental errors. A quantitative measure of the prediction accuracy is found in Table VI1 for each product. It is marked s.e.r.and can be interpreted as the uncertainty in the prediction. The empirical equations using Table VI1 can be directly applied to the design, simulation, and optimization of a wide variety of industrial thermochemicalprocesses for d-RDF as long as the feedstock and process variables are within the range of the variables studied here. The parameters in Table VI1 can also be used to predict devolatilization products slightly beyond the experimental domain. Deliberate tests of the fitted equations were performed and Table VI11 presents the comparison between experimental and predicted product yields for a "purennewsprint pellet devolatilized under three different process conditions. The new experiments were not used to develop the parameter estimates. Newsprint has the composition (XI, X Z , xg, x4) = (1.0, 0.0, 0.0, 0.0). The discrepancy between the predicted and experimentally measured data is within both the experimental error (the pooled standard deviation, ~ . ~ and l the ~ ) model prediction uncertainty (s.e.rJ as shown by the similarity of the two bottom rows of the table. Similar tests using fabricated pellets of other extreme compositions appear in Lai (1991). Thus the use of biased regression methods was quite beneficial, and these empirical models show
Discussion Interpretation of How the Process Variables Alter Devolatilization.The parameter estimates, the 8)s, are the measures we seek which quantify how much we can reliably(i.e., statistically significant above the 90% confidence level) manipulatethe d-RDF devolatilizationyields by changing the independent variables. Because the experimental design was constructed using scaled and centered variables, the row of constant terms at the top of Table VI1 reflects the product yields for auerage conditions and auerage d-RDF composition because these are the scaled "On conditions. The fact that the precise average conditions (Table IV, bottom row) are near zero, not exactly zero, bears only on the constant terms and is insignificant. For these average conditions (Table 11)about 457'% of the combustible fraction of the particle reacts in 12 min of oxygen-free heating, with about 6 % of the combustibles forming char, 32 % forming condensibles by balance, and -6% forming gas. These sum to the reacted combustibles, -45% of those in the particle. If expressed on a relative basis (suffix 11, that is, percent of that which reacts, the product gas and char yields are again comparable, about 13% each, and 74% of the mass reacted went to condensibles, all totaling 100%. The rather low extent of reaction and rather high char and condensibles yield are consistent with the attenuated N
Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 2926 Table VIII. Extended Composition Range for Testing the d-RDF Devolatilization Model. 'Pure" Newsprint Composition of (XI,q,9, 1 4 ) = (1.0, 0.0, 0.0, 0.0) or (XI,,,xam x h 4 ,, =) (1.263, -1, -2, -1)a product yields expt no. 71 72 75 78 pooled
process variables Q, W/mZ MC, % L,cm 12.6 x io+' 7.5 1.0 20.1 x 10+4 7.5 1.0 16.0 X lo+' 17.5 1.0 16.0 X lo+' 17.5 1.0 (df = 6)
8.e.r.
reacted ( % ) expt pred 35.9 27.0 65.5 76.2 28.2 36.5 28.4 36.5 6.9 5.7
char2 (%) expt pred 2.9 4.3 8.1 12.8 2.2 4.0 2.1 4.0 2.2 1.9
expt 5.1 11.6 2.7 3.6 1.6
pred 3.4 12.4 5.2 5.2 1.5
con2 (% ) expt pred 19.3 17.0 33.3 39.0 11.2 18.3 17.0 18.3 4.5 3.9
conb2 (%) expt pred 27.9 21.1 45.8 48.0 23.3 28.4 22.7 28.4 4.5 3.7
a Notes: (1)u p o o lis ~ the pooled standard deviation based on five seta of experimental replicates, thus has six degrees of freedom. (2) 8.e.r. (the standard error of the residuals for the model prediction) is a measure of prediction uncertainty.
Table IX. Experimental Design for Actual Commercial d-RDFa run no. 41 42 43 44
21
(8)
+1 -1 +1 -1
22
(MC) +1 +1 -1 -1
23
(L)
+1 -1 -1 +1
Notes: (1)21: applied heat flux (scaledlevel);22: moisturecontent (scaled level); zg: pellet thickness (scaled level). (2) Region of study (spans range of practical interest): 3.0 5 z1 5 5.0 cal/cm2/s;5.0% 5 22 5 30.0% dry basis; 1.00 523 5 2.00 cm. (3) Confoundingpattern: L 1 = 1 23 = Q MC * L;L2 = 2 + 13 = MC + Q * L;L3 = 3 + 12 = L + Q * MC. L1 is the coefficientof variable 21 upon regression of data to a model equation. It actually is the sum of the effecta of 21 (variable Q)and the product of 2 2 and 23 (the combined action of MC and L) (Box et al., 1978).
+
+
heating conditions that exist inside a realistically sized or thermally thick particle. The d-RDF temperature profiles measured by Lai and Krieger-Brockett (1992) show that depending on the moisture and heating intensity, the temperature drop from the surface to 2 mm inside the particle can be more than 200 "C owing to the insulating effect of the char and noncombustibles behind the reaction front. Bradbury et al. (1979) and others have suggested that low temperature favors char formation from cellulose, and this is what we observe as well in the large RDF particles. By intention, each coefficient estimate in Table VI1 represents a "slope" or change in outcome when the manipulated variable (first column) increases by half the range found in practice. A row of product slate changes can be attributed to a single variable if we hold the other variables studied at the scaled "0"or average condition. We discuss them in order of decreasing importance and from two different viewpoints regarding the devolatilization, that is, feedstock utilization as represented by the products reported on the absolute basis (suffix 21, and product slate optimization as represented by yields reported on the relative basis (suffix 1). First in magnitude: the utilization of the combustibles in d-RDF or the percent reacted is found to increase by about 14% for a heating increase of 4.2 W/cm2. This is true over the ranges studied for the mean size (thickness)found for commercial d-RDF. The 14% increase is comprised of an additional -3% char and gas each and about 8% additional condensibles (by balance), and does not preserve the initial product distribution. Nor does this 14% increase in fraction reacted for increased heating hold for all particle sizes. This is evidenced by the magnitude of the cross term coefficient, -3.9% in the row marked Q*L; thus 18% more is reacted for increased heating of the thinnest particles or as low as 10% reacted is found for the thickest particles studied. Second in magnitude: particles thicker than the average by about 5 mm (or a 33 % increase over the average commercial pellet) exhibit a diminished fuel
-
-
utilization on an absolute basis of only about 12% at the mean heating rate. This is comprised of 2 % reduction in char and gas (again about equal parts) and the rest a reduction in condensibles. Third: with respect to enhancing the fuel utilization, drying or reducing initial moisture by half the range studied (12.5%) has substantially less effect than heating more intensely. Since we are discussing drying (decreased moisture), the sign of the change in product distribution is opposite to that in the table. Drying augments the amount reacted by only 4% with again about equal increases in both gas and char yields. These three phenomena are interpreted using the RDF temperature profiles (Lai, 1991; Lai and KriegerBrockett, 1992) which reveal that an approximately 120 "C higher temperature is found inside the particle for an increased heating intensity of 4.2 W/cm2 at the surface. The temperature increase is the greatest for an increase in heating rate, but the particle thickness increase also causes a larger temperature gradient. For moist particles, an early 100 "C temperature plateau remains until the moisture is evaporated locally, but the temperature rises quickly to almost the same level as in an initially dry pellet. Thus we see the ranking of the process controllables' effects on devolatilization is explained by their effects on the internal particle temperature distribution. The absolute amount of the combustible d-RDF that actively devolatilizes in the 12-min fixed heating period appears to be nearly unchanged by alterations in the mix of constituents in the pellet. This is evidenced by the estimates for the composition effects in the 2nd through the 4th rows that are not significant a t the 95% confidence level. By construction, all the coefficients for composition changes refer to that particular fraction replacing an equal weight fraction of paper, retaining each sample size to preserve the intraparticle temperature distribution. An additional 4 % binder enhances the amount of char formed by 2 % and an additional 10% plastic in the pellet decreases the amount of CO formed from the combustibles. Lai and Krieger-Brockett (1992) show gas compositions that demonstrate the hydrocarbon product fraction is enhanced while the CO/CO2 fraction is reduced reflecting the lower oxygen content of plastics relative to paper. This is true even though the overall gas yield is reduced when plastics are increased as a proportion of the RDF. Effects that are noteworthy but do not exceed the significance tests made for the coefficients follow. The amount reacted is slightly enhanced (-1%) by more binder or metal and glass (replacingthe paper in a constant-sizesample). This could be attributed to both better internal heat transfer and in the case of binder, by possible catalytic action. The amount of combustibled-RDF that devolatilizes is slightly decreased (- 1% ) by additional plastics replacing the paper, indicating the lower temperature reactivity of the cellulose in paper relative to the plastic material in RDF.
2926 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 Table X. Comparison of Gross Products from Fabricated and Commercial d-RDF Pellets. weight percent of initial combustibles weight percent of total reacted expt no. reacted char2 gas2 con2 conb2 char1 gas1 con1 conbl 19.8 27.1 61.4 9.4 12.9 29.2 18.8 47.6 8.9 41 70.9 12.1 17.0 51.9 27.8 39.2 4.7 6.7 20.3 av: 13,9,31,4 14.2 42.2 41.4 7.2 5.9 17.5 28.3 17.5 68.3 42 84.8 17.1 8.6 49.1 2.3 3.0 29.5 6.6 34.8 av: 7, 15 11.7 16.5 53.3 71.8 34.3 46.2 64.4 7.5 10.6 43 17.1 48.7 79.6 15.6 13.7 38.4 50.3 19.7 63.2 av: 11,8,53,54, 55 15.4 64.0 16.9 1.9 2.6 10.8 12.4 11.5 73.2 44 2.9 18.5 10.2 13.0 64.7 23.8 2.4 15.5 av: 6, 12,34 76.8 2.0 6.9 2.2 1.6 4.5 4.5 4.0 9.8 3.7 ~pmled(df = 6) 3.4 17.8 5.4 2.9 5.7 6.7 6.1 8.8 3.5 max. u df (1) (1) (2) (2) (2) (1) (1) (1) (2) comparisons: mean of four commerical d-RDF pellets mean of 32 fabricated d-RDF pellets *constant terms from regression, Table VI1
mass balance comments 88.11 same conditions 92.11 same conditions 89.21 same conditions
J J
98.6 7 same conditions
42.6
6.4
7.1
18.9
29.0
14.9
16.5
46.7
68.7
92.0
44.7
6.9
6.5
22.7
31.3
14.2
13.8
53.9
72.1
93.6
44.6
6.4
6.1
22.7
32.0
13.3
12.9
73.8
Notes: (1) upooldis the pooled standard deviation based on five sets of experimental replicates,i.e., (4,13), (8,55), (32,56), (33,52), and (10,24,25);thus it has six degrees of freedom. (2) Max. u is the maximum standard deviation among the five sets of experimental replicates. (3) *These values are from the regression constant terms in Table VI1 but not calculated from the data in Table VI, and they are somewhat different. The differences are due to the fact that the average conditions of the 32 experiments in Table IV are not 0.0 but close to 0.0. The average conditions from the entire design are given in the bottom row of Table IV.
The multiplicative or nonlinear effects of simultaneous changes in composition and any of the process variables are discussed in the third paragraph below. A comparison of trade-offs results when we examine the relative devolatilization product distribution which is pertinent to product slate optimization. Because of the scaling and centering of the independent variables in the regression analysis, all the process variable coefficients in the right half of Table VI1 estimate directly that relative change in product which occurs when the reaction conditions are increased from the average by 1 scaled unit. Thus we can see that an increase in heating rate of 4.2 W/cmzarriving at the particle surface in a reactor increases the relative char and gas produced by about 2 and 3 % , respectively, at the expense of condensibles which decrease by about 5%. The condensibles often contain hard-tooxidize tars which lead to products of incomplete combustion (PIC'S) (Tsang, 1990). For a particle size of 0.5 cm over the average, we see that the relative gas yield is unaffected, but char is reduced slightly (1% ) and condensibles by balance are enhanced slightly (1% ). Increased heating, reduced moisture, and increased ash above average conditions diminish the rather large condensibles yield from devolatilization of average composition d-RDF .Over the range of particle size found for commercial d-RDF, particle size reduction has little effect in reducing relative production of condensibles since the nominally 1-cmthick particles are already "thermally thick" (Lai, 1991). Recall that our experiment is designed to minimize gas-phase reactions occurringoutside the particle but cannot prevent, and indeed is focused on, those inside the particle. Our data suggest that diminished condensibles observed at higher heating rates are likely to be the result of cracking within the solid phase to produce gases and char. Judging from the increased condensibles that result from simultaneous increased heating and increased plastics or binder content (at the expense of paper), the volatiles from the newsprint appear to be more labile and cracked at lower temperatures than are volatiles from the plastics (Khalturinskii, 1987). It is useful to examine how devolatilization products change by drying the MSW or d-RDF. Reference is made to the row of estimates for the effect of moisture content
(MC) on the product slate at the mean conditions, and drying represents a sign opposite to that in the table. On an absolute basis, a 12.5% moisture reduction increases the early particle temperatures and increases, as expected, the extent of reaction by about 4 % . The drying results in an absolute increase in char, condensibles, and gas produced. However,of that increased amount which reacts, a drying step has the effect of reducing the relative condensibles yield while the char yield is enhanced by about 2% and the gas yield is enhanced by about 1%. This is again consistent with intraparticle cracking to produce gas and char at the expense of tars for higher temperatures occurring in either dry particles or under intense heating. Simple comparative experiments at 2 or 3 reaction conditions would not have uncovered which of the process variables alter the pyrolysis behavior in multiplicative rather than additive ways. Our chosen experimental design and studied range of conditions offered quantitative evidence that the process variables by themselves interact to alter the absolute fuel utilization as judged from the row of significant cross term coefficients, zlz3(Q*L), for the absolute basis (suffix 2) in Table VII. Thus, across the entire product slate we see that manipulating the heat flux causes different outcomes at different particle sizes. For example, the condensible fraction of volatiles is reduced by nearly 2 5% for a combination of small particles and low heating rate or a combination of large particles and high heating. Thus reactor sizing may need to be specialized for a given particle size, or particle size range may need to be tightened when the commercial material is made. However, the relative product distribution is altered only when composition and reaction conditions both deviate from the prevalent values. For example, the relative gas yield from RDF pellets is reduced by 3-596 when both the plastics content and the heating rate experienced by the particle are increased, or both the binder content and heating rate are increased, or both the plastics and the moisture content of the particle are increased. This implies that downstream separation of devolatilization products in noncombustion applications will be complicated by heterogeneity in the feedstockRDF composition and nonuniform reaction conditions through-
Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 2927
-
Dry:
RDF
.w
Wood
....a...
-....m..
-"I
+-...o...
Char
0
30
20
10
+....+. 40
Applied Heat Flux, W/cmA2
'""1
..4
b
1
V
L 6 0
a'
-RDF
Reacted Condensibles
V
I
0
.
8
10
.
I
20
.
I
30
.
Wet: Wood
._.. a...
....m..
,
40
Applied Heat Flux, W/cmAZ
Figure 3. Devolatilization producta from wood (open symbols) and laboratory fabricated d-RDF (solid symbols) predicted from the correlations at common conditions.
out a particular reactor. Since the other interactions are significant only at lesser confidence levels, we can interpret the individual process variables as acting jointly (nonlinearly) only with composition variables to manipulate the relative product distribution. It is enlightening to compare the devolatilization behavior of wood to that of d-RDF. Densified refuse-derived fuel resembles wood in many ways but differs principally in terms of its high inorganics and plastics content. Figure 3 presents our predicted product slates from both d-RDF and wood pyrolyzed in the same apparatus for an equal range of heating intensities found in industrial reactors for both feedstocks. The chosen approximate comparison is for a common initial particle moisture of 5 9% (Figure 3a) and 17.5% (Figure 3b) and particle size of 1cm although the wood particle is less dense. The error bars are the standard error of prediction, s.e.r., and the comparison of the wet fuels is less reliable than the comparison of the dry ones. The experiments on wood were conducted using a three-level experimental design and thus the apparent curvature in the trend lines is real and is discussed in Chan et al. (1988). Wood appears to be a more reactive substrate than d-RDF judging from the greater magnitude and slope of the fraction reacted curve. However, the char and gas yields are higher from wood than from d-RDF for both wet and dry particles. This is consistent with the previously suggested greater reactivity of the tars formed from cellulose. This could also be a result of the lignin fraction in wood which has been shown to be char-forming (Shafizadeh and McGinnis, 1971;Nunn et a1.1985; Chan, 1983). Lignin is less abundant in d-RDF than in wood. Since increased binder increases char yield judging from the coefficients in Table VII, the high level of inorganics or binder in d-RDF cannot be the reason for its reduced char yield relative to wood. A previous paper discusses the increased condensibles yield from d-RDF as most likely being attributable to the plastics content therein (Lai and Krieger-Brockett, 1992).
Summary and Conclusions Experimentally measured gross product yields have been presented that allow one to assess the likely changes in product slate when d-RDF is heated under pyrolytic conditions normally found in practice. Special consideration was given to detecting real changes in product slate in spite of the inherent heterogeneity of the d-RDF feedstock. Parameters quantifying how much we can manipulate the product yields by changing the independent variables were developed using specialized regression methods and are presented in Table VII. The regressions have been tested with both real commercial RDF pellets and with compositions somewhat outside the range that was used to generate the parameter estimates. It appears that over the prevalent range of each, d-RDF composition is less influential in changing the devolatilization product slate than reaction conditions. This is a useful finding in that reactor design can proceed for the typical d-RDF without major adjustments in conditions if recycling incentives change. The empirical correlations for product formation presented here are suited for use in MSWIdRDF reactor or furnace simulations and represent an improvement over global expressions for particle weight loss that are usually used. Those expressions are not able to predict product distribution nor its variation owing to local temperature or heating rates, varying particle sizes, or differing d-RDF compositions. The active devolatilization period of d-RDF conversion provides a somewhat lower gas and char yield (d.a.f.) but a higher fraction of condensibles than the active devolatilization of wood. The plastics in d-RDF are likely to be the source of the increased tar yield. Increasing by 4.2 W/cm2 the heat flux arriving at the surface of these large particles, increases the organics utilized by about 10 and 20% for 1-and 2-cm thick particles, respectively. Particle size reduction of about 33 % at the nominal 1.5cm thickness appears to provide about 12% additional fuel utilization in a fixed time, normal intensity heating, and less than -1% change in all relative product yields. The small particle size required for this low conductivity material to be thermally thin or have a uniform temperature distribution is probably the reason for such a small sensitivity to particle size over the conditions studied. Drying by about 12-13% the often wet MSW causes only a small (-4 ?6 ) additional conversion of the d-RDF at the typical compositions and particle sizes found. This applies for the mid-range heating intensities typically used in industrial reactors. Composition changes alone at the average reaction conditions alter product slate less than a few percent except for the influence of binder which may have some catalytic role. However, the relative product yields are sensitive to a combined change in devolatilization conditions and composition. For example the relative production of condensibles can be enhanced by 6 % while reducing the gas by about 4 % and reducing the char by about 2% when the d-RDF is both wet and high in plastics content compared to typical RDF. While these percentages may seem small when regarded in light of the reported errors, because of the large number of experiments and the attention to statistical enhancement of real effects in the presence of noise, we can regard these changes to be real, that is, exceeding tests of significance despite the small size of the effect. Acknowledgment This work was supported by the National Renewable Energy Lab (formerly Solar Energy Research Institute), U.S.Department of Energy, Contract XK-7-07224-01.The
2928 Ind. Eng. Chem. Res., Vol. 32, No. 11, 1993 helpful comments and suggestions of the NREL staff and the encouragement given b y M.K.B. are much appreciated. Both Ferhan Kayihan of Weyerhauser Corp. and James Diebold of NREL provided us with samples and their cooperationis gratefully acknowledged. One of us (W.C.L.) also wishes to acknowledge partial fellowship support from the National Science Foundation, Grant No. CPE-8400655, for the work on wood devolatilization. The helpful comments of the anonymousreviewers are also appreciated and aided in clarifying the manuscript.
Nomenclature d.a.f. = dry, ash free df = degrees of freedom d-RDF = densified refuse-derived fuel HDPE = high density polyethylene LDPE = low density polyethylene MRV = mixture-related variable, a transformation to remove composition variable correlation MSW = municipal solid waste PVC = poly(viny1 chloride) LI, I = 1 - 6 = a regression coefficient representing a linear combination of confounded variable effects (see Table 111) L = length, or particle thickness in the direction of heating MC = initial moisture content, weight on a d r y basis Q = applied heat flux (constant) at the particle surface PC = principal component PCR = principal component regression s.e.r. = standard error of t h e residuals SSE = sum of squared residuals VIF = variance inflation factor wi, i = 1 , 2 , 3 , 4 , 5 , 6 = w refers to orthogonalscaled composition mixture-related variables and process variables where i = 1 for a MRV like plastics, 2 = for a MRV like metal/glass, 3 = for a MRV like binder and i = 4 for applied heat flux (Q),5 = moisture content (MC), 6 = pellet thickness (L) x i , i = 1 , 2 , 3 , 4 = x refers t o actual mass fractions in d-RDF pellet, where i = 1 for paper, 2 = plastics, 3= metal/glass, and 4 = binder xic, i = 1, 2, 3, 4 = scaled actual mass fractions for d-RDF pellet (see Table 11) zi, i = 1 , 2 , 3 = z refers to orthogonal scaled process variables where i = 1 for applied heat flux (Q), 2 = moisture content (MC), 3 = pellet thickness (L) Greek L e t t e r s a = square root of variance or standard deviation @ = mean response at average d-RDF composition and average process conditions pi = coefficient for the i t h variable Subscripts 0 = mean response or effect at average composition c = scaled variable, especially composition variable (see Table 11) int. = internal value of the statistic (based on residuals) ext. = external value of the statistic (based on independent estimates) i = denotes which composition variable m = denotes which process variable n = as a second index denotes which additional process variable in a quadratic term pooled = weighted average or pooled value for standard deviations as defined in most statistics texts (e.g., Box et al., 1978)
Literature Cited Agrawal, R. K.; Helt, J. E. Applications of Thermogravimetry in Energy from Solid Waste Research. 15th Annual North American Thermal Analysis Society Conference,Cincinnati, OH, September 1986.
Agrawal, R. K.; Gandhi, F.; McCluskey,R. J. Low-Pressure Pyrolysis of Newsprint: Product Formation. J.Anal. Appl. Pyrolysis. 1984, 6, 325-338. Box, G. E. P.; Draper, N. R. A Basis for the Selection of a Response Surface Design. J. Am. Stat. Assoc. 1959,54,622-654. Box, G. E. P.;Draper, N.R. Empirical Model-building and Response Surfaces;John Wiley & Sons: New York, 1987. Box, G. E. P.; Hunter, W. G.; Hunter, J. S. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building; John Wiley & Sons: New York, 1978. Bradbury, A. W. G.; Sakai, Y.; Shafiiadeh, F. A kinetic model for pyrolysis of cellulose. J. Appl. Polym. Sci. 1979,23, 3271-3280. Campbell, J. A. Waste Fuel Densification: Review of the Technology and Application. Proceedings of the International Conferenceon Prepared Fuels and Resource Recovery Technology; Argonne National Laboratory, ANL/CNSV-TM-60, Argonne, IL, 1981. Chan, W. C.R.Ph.D.Thesis. PhysicatandChemicalProcesseeDuring Pyrolysis of a Single Wood Particle; University of Washington, Department of Chemical Engineering, 1983. Chan, W.C.R.;Kelbon,M.;Krieger,B.B.ModelingandExperimental Verification of Physical and Chemical Process during Pyrolysis of a Large Biomass Particle. Fuel 1985,64,15061513. Chan, W. C. R.; Kelbon, M.; Krieger-Brockett, B. Single-Particle BiomassPyrolysis: Correlationsof Reaction Products with Process Conditions. Znd. Eng. Chem. Res. 1988,27, 2261-2275. Cornell, John A. Experiments with Mixtures (Designs,Models, and the Analysis of Mixture Data),2nd Ed., John Wiley & Sons: New York, 1990. Daugherty, K. E.; Ohlsson, 0.; Safa, A.; Venables, B. J. Densified Refuse Derived Fuel as a Power Source. Proc. Am. Power Conf. 1986,45,930-935. Diebold, J. P.; Power, A. Engineeringhpects of the Vortex Pyrolysis Reactor to Produce Primary Pyrolysis Oil Vapors for Use in Resins and Adhesives.Research in ThermochemicalBiomass Conversion; Bridgwater, A. V., Kuester, J. L., Eds.; Eleevier Applied Science: New York, 1988; pp 609-628. Draper, N. R.; Smith, H. Applied Regression Analysis; John Wiley & Sons: New York, 1981. Evans, R. J.; Soltys, M. N.; Milne, T. A. Mechanisms of the Pyrolysis of Municipal Solid Waste: Atlas of the Primcrry Pyrolysis Products of Municipal Solid Waste and Selected Constituents ( A Comparison of Spectra Obtained by Molecular-Beam Mass Spectrometry); SERI/SP-234-2597, Solar Energy Research Institute: Golden, CO; Nov 1985. Franklin Associates, Ltd. Prairie Village, KS. Characterization of Municipal Solid Waste in the United State. 198oto2000. Prepared for the U. S. Environmental Protection Agency. The data are in "Statistical Abstract of the United States 1987, 107th ed." published by US.Department of Commerce, 1986. Gunst, R.F.; Mason, R.L. Regression Analysis and its Application: A Data-Oriented Approach. MarcelDekker, Inc.: New York, 1980. Hickman, H. L.; et al. Thermal Conuersion S y s t e m for Municipal Solid Waste; Noyes Publications: Park Ridge, NJ, 1984. Hodgkin, J. H.; Galbraith, M. N.; Chong, Y. K. Combustion Products from Burning Polyethylene. J.Macromol. Sci., Chem. 1982,A17 (l), 35-44. Iida, T.; Nakanishi, M.; Goto, K. Investigations on Poly(viny1 Chloride). 11. Pyrolysis of Chlorinated Polybutadienes as Model Compoundsfor Poly(viny1Chloride).J.Polym. Sci.: Polym. Chem. Ed. 1975,13, 1381-1392. Ishihara, Y.; Nanbu, H.; Ikemura, T.; Takesue, T. Catalytic Decomposition of PolyethyleneUsing a Tubular Flow Reactor System. Fuel 1990, 69, 97&984. Ishii, Y.; Ishii, N.; IIda, Y. A System for Preparation and Production of RDF (Refuse Derived Fuel) from a Source-Separated Fraction of MSW (Municipal Solid Waste) in Narashino. Conseru.Recycl. 1987,10, 229-236. Khalturinskii, N. A. High-Temperature Pyrolysis of Polymers. J. Thermal Anal. 1987,32,1675-1682. Khuri, A. I.; Cornell, J. A, Response Surfaces: Designs and Analyses: Marcel Dekker, Inc.: New York, 1987. Kilgroe,J. D.; Nelson,L. P.; Schindler,P. J.;Lanier, W. S. Combustion Control of Organic Emissions from Municipal Waste Combustors. Combust. Sci. Technol., 1990, 74, 223-244. Lai, W.4. Reaction Engineering of Heterogeneous Feeds: Municipal Solid Waste as a Model. Ph.D. Dissertation. Department of Chemical Engineering, University of Washington, 1991.
Ind. Eng. Chem. Res.,Vol. 32, No. 11, 1993 2929 Lai, W.4.; Krieger-Brockett, B. Volatiles Release Rates and Temperatures During the large Particle Refuse-derived Fuel - Municipal Solid Waste Devolatilization. Combust. Sci. Technol. 1992, 85 ( l d ) , 133. Lai, W.-C.; Rodriguez, I.; Krieger-Brockett, B. Composition Effects on the DevolatilizationBehavior of Biomass and Municipal Solid Waste. Advances in Thermochemical Biomass Conversion; Bridgwater, A. V., Ed., Elsevier Applied Science: New York, 1993, in press. Lai, W.-C.; Krieger-Brockett, B. 1993, in preparation. Levie, B. E. PbD. Thesis. Pyrolysis of Refuse Derived Fuel Pellets. Department of Chemical Engineering, University of Colorado, 1988. Mallya, N.; Helt, J. E. Effects of Feedstock Componentson Municipal Solid Waste Pyrolysis. Research in Thermochemical Biomass Conversion;Bridgwater,A. V., Kuester, J.L., E&.; Elsevier Applied Science: New York, 1988, pp 113-126. Marquardt, D. W. Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation. Technometrics 1970, 12 (3), 591-612. Mason,R. L.; Gunst, R. F.; Hew, J. L. Statistical Design and Analysis of Experiments with Applications to Engineering and Science; John Wiley & Sons: New York, 1989. Massaquoi, J. G. M.; Riggs, J. B. One-Dimensional Model of the Physico-Chemical Processes Occurring Inside a Burning Coal Surface. AZChE J 1983 29 (6), 975. McClusky, R. Pyrolysis of Newsprint. Presented at American Chemical Society National Meeting, Washington, D.C., August 1983. McGrath, T. P.; Seeker, W. R.; Chen, S. L.; Linz, D. G. Pilot Scale Studies on PCDD/PCDF Formation and Control in Municipal Waste Combustion Systems. Presented at the Second International Congress on Toxic Combustion By-products: Formation and Control, Mar 26-20, 1991, Salt Lake City, Utah. Milne, T. A., unpublished studies, Solar Energy Research Inst., Golden, CO, 1989. Murty Kanury, A.; Blackshear, P. L., Jr. Pyrolysis Effects in the Transfer of Heat and Mass in Thermally Decomposing Organic
Solids. EleuenthSymposium (International)on Combustion,The Combustion Institute; Pittsburgh, PA, 1967; pp 517-523. Nunn, T. R.; Howard, J. B.; Longwell, J. P.; Peters, W. A. Product Compositionsand Kinetics in the Rapid Pyrolysisof Milled Wood Lignin. Znd. Eng. Chem. Process Des. Dev. 1985,24,844-852. Penner, S. S.; Chang, D. P. Y.; Goulard, R.; Lester, T. Waste Incineration andEnergy Recovery. Energy 1988,13(12),845-851. Scheffe, H. Experiments with Mixtures. J. R.Stat. SOC.,B 1958,20, 344-360. Scheffe, H. The Simplex-Centroid Design for Experiments with Mixtures. J. R. Stat. SOC.,B 1963, 25,235-263. Shafhadeh, F.; McGinnis,G. D. ChemicalCompositionand Thermal Analysis of Cottonwood. Carbohydr. Res. 1971,16, 273-277. Shepherd, K. R. RDF Fuels in a Waste-To-Energy System - The Teledyne National Experience. Proceedings of the International Conferenceon Prepared Fuekr and Resource Recovery Technology, Argonne National Laboratory, ANL/CNSV-TMSO,Argonne, IL, 1981. Sommerlad, R. E. ConsiderationsAffecting Dedicated Boiler Design for Refuse-Derived Fuels (RDF).Proceedings of thelnternational Conference onPreparedFuels andResource Recovery Technology, Argonne National Laboratory, ANL/CNSV-TM-60,Argonne, IL, 1981. Thompson, W. 0.; Myers, R. H. Response Surface Designs for Experiments with Mixtures. Technometrics 1968, 10 (4), 739756. Tsang, W. Mechanisms for the Formation and Destruction of Chlorinated Organic Products of Incomplete Combustion. Combust. Sci. Technol. 1990, 74, 99-116. Van Voorst, B. The Recycling Bottleneck. Time 1992, Sept 14,5254. Received for review January 7, 1993 Accepted August 3, 1993'
Abstract published in Advance ACS Abstracts, October 1, 1993. @
