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Show constant, while C O yields increase, (3) Cham hydrocarbons are converted initially mto CH4 and CJHJ then CH4 disappears, and (4) H C N and H2 yields increase throughout secondary pyrolysis To suoport C F D applications, the model is being used to dentify the parameter values in the simple devolatilization -ate expressions used in C F D simulations that mimic the ^ - A S H C H A I N predictions, as descnbed previously (Niksa and Cor. 1997) Our virtual coal laboratory concept also ennances traditional combustion engmeenng approaches. One application, m whicn FLASHCHAIN-based yields and mtrogen -eiease 'or coal lame conditions are used as -egression variables to predict NOx and LOI emissions from 'ull-scaie utility boilers, is descnbed in another paper at this conference Additional applications of this approach are developing steadily, mdudmg the following: A fuel suppliers software package to manage all aspects of coal transport, handling, combustion and emissions for coal-burning utilities had previously rened pn tab testing to charactenze coal quality impacts. They recently replaced two of the five most expensive tests with a virtual coal laboratory, and are currently -epiacmg a third test. Results of a laboratory test could reliably forecast which ccais would form intolerable deposits within the supply tubes to utility burners A FLASHCHAIN-based -egression of this database has a 9 4 % correlation coefficient and the correlation accurately identified all the most troublesome coals to date . Similarly lab tests successfully characterized the autoignition propensities of Indonesian coals but were expensive and siow. This database could also be correlated within useful quantitative tolerances with FLASHCHAIN-based regression vanables. A virtual coal laboratory was used to tram a neural nerwork furnace control system to regulate the combustion of vanous coals to meet regulations on NOx and unburned carbon emissions. ACKNOWLEDGEMENTS The expansion of FLASHCHAIN for applications with petroleum coke and biomass was sponsored in part by the Eiectnc Power Research institute and administered by Mr. J. Stailmgs of EPRI's Energy Conversion Division. REFERENCES Brumsma. 0 S. L . Geertsma. R. S., Bank, P.. Moulijn, J. A. (1988) Fuel. Vol 67, p.334. Cai H (1995) Ph D thesis, Dept. of Chemical Engr. and Chemical Technoi., impenal College, Univ. of London. -hen J C Castagnoli, C . N.ksa, S. (1992). Energy Fuels. Vol 6, p 264 Y Chen, S Charpenay, A. Jensen, M A. Wojtowicz. and M A. Seno (1998), Twenty-Seventh int Symp on Combust. Combustion institute. Pittsburgh pp 1 327- 1334 Cho. S., Marlow, D , Niksa. S. (1995). Combust Flame vol 10V p.399. Fraga A.-R . A F Gaines, et al (1991) Characterization pf biomass pyrolysis tars produced in the relative absence of extraparticle secondary reactions. Fuel, vol 70(7). pp 803-809. Hajaligol. M. R.. J. B. Howard, et al (1982). Product compositions and kinetics for rapid pyrolysis of ceiiuiose Ind. Eng. Chem Process Des. Dev . Vol. 21. PP 457- 465. Jensen. A., K. Dam-Johansen, et al (1998) TG-F.IR study of the influence of potassium chloride on wheat straw pyrolysis. Energy Fuels. Vol 12(5). pp 929-938 Manow D„ Niksa. S.. Kruger, C H (1992), Twenty-Founh int. Symp. on Combust. Combustion institute Pittsburgh, p.1251. Nelson, P. F, Tyler, R. (1986), Twenty-First int Symp en Combust Combustion institute. Pittsburgh, p 427 Nik-Azar, M., M. R. Hajaligol, et al. (1997) Mineral matter effects m rapid pyrolysis of beech wood Fuel Process Technoi.. Vol. 51, PP 7-17 Niksa. S (1995a), Combust. Flame. Vol 100. P 384 Niksa S (1995b). Energy Fuels. Vol. 9, p. 467 Niksa, S, Coal Combustion Modelling (1996). No 31 EA Perspectives Series. IEA Coal Research. London Niksa S and J J Cor (1997). Predicting coal quality impacts on near-burner coal flame phenomena Fourth int Conf. on Technoi. and Combust for a Clean Environ Lisbon, Portugal. Caiouste Guibenkian Foundation Niksa, S., Cho, S. (1998), Twenty-Seventh Int. Symp on Combust. Combustion Institute, Pittsburgh, pp 2905- 2913. Nunn T. R., J. B. Howard, et al. (1985) Product compositions and kinetics in the rapid pyrolysis of m iled wood lignin. ind. Engr Chem. Process Des arc Develop.. Vol 24, pp. 844-852. Pindona, R. V„ J.-Y. Um, et al. (1997). Structural charactenzation of biomass pyrolysis tars/ons 'rom eucalyptus wood waste: effect of H2 pressure ana sample configuration. Fuel. Vol. 76(11). PP 1013-1023 Raveendran, K.. A. Ganesh, et al. (1995) influence^ pf mineral matter on biomass pyrolysis charactenstics rue/ Vol 74(12), pp. 1812-1822. Xu W C. Tomita. A. (1989), Fuel. Vol. 66. p. 627 |