1. 1. 2. 3. 4. © Predictive Modeling Of Mercury Speciation In Combustion Flue Gases Using GMDH-Based Abductive Networks Abdel-Aal, RE ELSEVIER SCIENCE BV, FUEL PROCESSING TECHNOLOGY; pp: 483-491; Vol: 88 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modem data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input-output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance. (c) 2006 Elsevier B.V. All rights reserved. References: *ABT CORP, 1990, AIM US MAN ABDELAAL RE, 1994, ENERGY, V19, P739 ABDELAAL RE, 1995, WEATHER FORECAST, V10, P310 ABDELAAL RE, 2004, ENG APPL ARTIF INTEL, V17, P543, DOI Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

2. 5. 6. 7. 8. 9. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 44. 45. 46. © 10.1016/j.engappai.2004.04.002 ABDELAAL RE, 2004, METHOD INFORM MED, V43, P192 ABDELAAL RE, 2005, COMPUT METH PROG BIO, V80, P141, DOI 10.1016/j.cmpb.2005.08.001 ABDELAAL RE, 2005, ELECTR POW SYST RES, V74, P83, DOI 10. 43. 10.1016/j.epsr.2004.09.007 BARRON AR, 1984, SELF ORG METHODS MOD, P87 CANAKCI M, 2006, APPL ENERG, V83, P594, DOI 10.1016/j.apenergy.2005.05.003 CHARYTONIUK W, 2000, P INT C EL UT DER RE, P554 CHI Y, 2005, J ENVIRON SCI-CHINA, V17, P699 DASILVA APA, 2001, IEEE POW TECHN C POR FARLOW SJ, 1984, SELF ORG METHODS MOD, P1 FERNANDES AM, 2004, PATTERN RECOGN, V37, P2039, DOI 10.1016/j.patcog.2004.04.002 FORTUNA L, 2003, CONTROL ENG PRACT, V11, P1491, DOI 10.1016/S0967-0661(03)00079-0 GALBREATH KC, 2005, FUEL PROCESS TECHNOL, V86, P429, DOI 10.1016/j.fuproc.2004.03.003 HIPPERT HS, 2000, P IEEE INT JOINT C N, P414 JENSEN RR, 2004, FUEL PROCESS TECHNOL, V85, P451, DOI 10.1016/j.fuproc.2003.11.020 JIMENEZ D, 1998, IEEE WORLD C COMPUTA, V1, P753 KELLIE S, 2005, ENERG FUEL, V19, P800, DOI 10.1021/ef049769 KOMPANYZAREH M, 2002, ANAL CHIM ACTA, V469, P303 KRIJNSEN HC, 2001, FUEL, V80, P1001 LEE SJ, 2006, ATMOS ENVIRON, V40, P2215, DOI 10.1016/j.atmosenv.2005.12.013 LEWIS HW, 2001, P 2001 IEEE MOUNT WO, P23 LIN CJ, 2006, ATMOS ENVIRON, V40, P2911, DOI 10.1016/j.atmosenv.2006.01.009 MATSUI T, 2001, P IEEE POW ENG SOC W, P405 MONTGOMERY GJ, 1990, P SPIE C APPL ART NE, P56 NISHITANI T, 2000, FUEL PROCESSING TECH, V63, P197 RAIMUNDO IM, 2003, SENSOR ACTUAT B-CHEM, V90, P189, DOI 10.1016/S0925-4005(03)00027-3 TASSADDUQ I, 2002, RENEW ENERG, V25, P545 TSIROS IX, 2003, J ENVIRON SCI HEAL A, V38, P2495, DOI 10.1081/ESE-120024441 XU MH, 2003, COMBUST FLAME, V132, P208 ZHOU H, 2005, INT J ENERG RES, V29, P499, DOI 10.1002/er.1070 ZHOU ZH, 2002, ARTIF INTELL MED, V24, P25 For pre-prints please write to: radwan@kfupm.edu.sa Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa