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Show 100 Bagasse, 0 1 M P a , 30 s hold 80 i. 60 OJ _ _ 40 ._ - L 2 I ^ 100O ih_ Sllver Bircn' ° 1 M P a' 30 s h(Jld 80 5 60 _ OJ 40 > to h-o 20 o 3 • ' / , // /,'/• 1 i • 1 K/s o Total I Tar Total & Tar 10'K/S. • Total O Tar Total 4 Tar i . i . 400 500 600 700 800 900 Temperature, C Figure 5. Evaluation of bio-FC with the weight loss and tar yields from devolatilization of bagasse (top) and silver birch (bottom) for 1 and 1000 K/s at atmospheric pressure reported by Fraga et al. (1991). predicted PVM to the reported value. The specific adjustments were selected to exhibit the s a m e qualitative tendencies for higher degrees of mineral catalysis as seen in Nik-Azar's dataset (1997), in which the cation loadings in beech were varied systematically. This procedure shifts gas 'ormation to lower temperatures, while leaving the temperature range for tar release unchanged. The ultimate tar yields are suppressed by ash catalysis, whereas the weight loss is reduced by a m u c h smaller factor. The entire prediction scheme for biomass is evaluated in Fig 5 for atmospheric devolatilization, and in Fig. 6 for pressunzed applications. In these cases our full procedure was applied without parameter adjustments and the predicted tar yields, in particular, are accurate indications of the quantitative accuracy of bio-FC at this point in its development. The only sample-specific input were the proximate and ultimate analyses of the whole biomass samples. In Fig. 5, the evaluations demonstrate that bio-FC is able to reproduce the m u c h smaller yield enhancements for rapid heating with biomass, compared to coal devolatilization behavior. Moreover, the model predicts weight loss and tar yields from both bagasse and silver birch within useful quantitative tolerances for all temperatures above 500 °C At lower temperatures, the model 3* 2 > re i - o _ Ho 80- 60- 40- 20- 0- • o\^^ 0 Eucalyptus, 10 K/s • 450 C. 300 s • - • lotaij - - - 1 ---1 0 2 4 6 8 Pressure, MPa Figure 6. Evaluation of the predicted weight loss and tar yields from eucalyptus waste for devolatilization at 10 DC's to 450 °C for 300 s at various pressures, reported by Pindoria etal. (1997). underpredicts observed yields from both biomass forms which is probably an indication that the ultimate baseline values for the reaction rate parameters for both components have not yet been specified. in Fig. 6 the predictions are evaluated with data for slow pyrolysis at 450 °C for pressures from 0.1 to 7 MPa. Bio-FC correctly predicts that tar release is suppressed at elevated pressures, and that the reduction in weight loss is partially compensated for by higher gas yields. Both predictions at 0.1 M P a are within experimental uncertainty, as are weight loss levels for all pressures. But the predicted tar yields exceed the measured values at intermediate pressures DISCUSSION Rapid fuel devolatilization generates the gaseous fuels that ignite and stabilize suspension fired flames, and volatiles account for as much as half the total heat release in full-scale utility boilers plus most of the noxious gases Utility operators currently face a daunting selection of opportunity fuels, including coal, biomass, petroleum cokes, and wastes Test burns and drop-tube testing are established means to characterize just about any solid fossil fuel that can be pulverized. But physical tests are expensive and time consuming because they involve specialized personnel This paper demonstrates that it is currently possible to predict the devolatilization behavior of coals, petroleum cokes, and various forms of biomass within useful quantitative tolerances. The only sample-specific input requirements are the proximate and ultimate analyses Provided that an accurate thermal history can be assigned for the process under consideration, FLASHCHAIN'" and bio-FC can predict the complete distnbution of devolatilization products for any of these fuels at any operating conditions. Each simulation takes less than 10 s on m o d e m personal microcomputers. This paper also introduces a phenomenoiogicai mechanism that transforms predicted primary product distributions into the fuel mixtures that actually burn m pulverized fuel flames. The proposed mechanism for secondary pyrolysis transforms the primary products into soot, CH4, C2H2, H C N . H2S. H 20. H2, C O and C 0 2 it depicts all the major tendencies among the levels of aromatic hydrocarbons, oxygenated gas species, and H2, as follows (1) The sum of the yields of tars, oils, andvsoot remains nearly mvanant; (2) The yields of C 0 2 and H 2 0 stay |