Prediction of combustion efficiency of chicken litter using an artificial neural network approach

Fuel

Volumn 86, Issue 5-6, 2007, Pages 877-886

  Zhu, Shijun ^a   Lee, S ^a   Hargrove, S K ^a   Chen, G ^a  

^a Center of Excellence for Tactical and Advanced Communications Technologies   (United States)

Author keywords

ANN training and prediction; Design of experiment; Response surface

Indexed keywords

ALGORITHMS; COMBUSTION; FLUIDIZED BED COMBUSTORS; LEAST SQUARES APPROXIMATIONS; MOISTURE;

ANN TRAINING; CHICKEN LITTERS; DESIGN OF EXPERIMENTS; RESPONSE SURFACE;

FEEDFORWARD NEURAL NETWORKS;

EID: 33845603667     PISSN: 00162361     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuel.2006.09.029     Document Type: Article

References (29)

1. Jawson M, Smith L, Wright R, Amerman D, Bucks D, Lindsay J, et al. Manure and byproduct utilization. National Program Annual Report: FY 2001, Agriculture Resource Service, United States Department of Agriculture; 2001.
2. Bose A. In: Proceedings of the animal waste utilization workshop. US-DOE Federal Energy Technology Center (FETC), Montgomery (VA); 1999. p. 8-9.
3. Lee S, Zhu S. Final Report for Maryland Department of Environment: testing of the animal waste disposal system using advanced fluidized bed combustion technology. Morgan State University; 2002.
4. Frey H.H., Peters B., Hunsinger H., and Vehlow J. Waste Manage 23 (2003) 689-701
5. Sami M., Annamalai K., and Wooldridge M. Prog Energy Combust Sci 27 (2001) 171-214
6. Zhu S., and Lee S. Waste Manage 25 (2005) 511-518
7. Juan D.J., Roux M.V., and Jimenez P. Thermochim Acta 394 (2002) 261-266
8. Lin C.H., Teng J.T., and Chyang C.S. Combust Flame 110 (1997) 163-172
9. Sweeten J.M., Annamalai K., Thien B., and McDonald L.A. Fuel 82 (2003) 1167-1182
10. Lai W.C., and Krieger-Brockett B. Combust Sci Technol 85 (1992) 133-149
11. Fausett L. Fundamental of neural networks-architectures, algorithms, and applications (1994), Prentice-Hall, Englewood Cliffs (NJ)
12. Dernuth H., and Beale M. Matlab neural network toolbox (1992), The MathWorks Inc p. 6.2-6.13
13. Zhou H., Qian X., Cen K., and Fan J. Fuel Process Technol 85 (2003) 113-124
14. Laurent S., and Fernandes M. Comput Chem Eng 28 (2004) 2407-2419
15. Yao H.M., Vuthaluru H.B., Tadé M.O., and Djukanovic D. Fuel 84 (2005) 1535-1542
16. Gomesa H.K., and Awruch A.M. Struct Safety 26 (2004) 49-67
17. Zhu Q., Jones J.M., Williams A., and Thomas K.M. Fuel 78 (1999) 1755-1762
18. Haykin S. Neural networks, a comprehensive foundation. 2nd ed. (1999), Prentice-Hall, Upper Saddle River (NJ)
19. Kosko B. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence (1992), Prentice-Hall, Englewood Ciffs (NJ)
20. Zhu S, The constrained optimization of biomass waste co-combustion process in the advanced fluidized bed combustor using applied statistical methods. Doctoral Dissertation, Morgan State University; 2005.
21. Yang Y.Y., Linkens D.A., and Talamants-Silva J. J Mater Process Technol 152 (2004) 304-315
22. Zhang Z., and Friedrich K. Compos Sci Technol 63 (2003) 1-16
23. MATLAB Version 6.5.0 Help Files. MathWorks Inc.; 2002.
24. Cen K., Ni M., Luo Z., and Yang J. The theory, design and operation of circulating fluidized bed combustion boiler (1997), Electric Power Press
25. Saxena S.C., and Jotshi C.K. Prog Energy Combust Sci 20 (1994) 281-324
26. Suksankraisorn K., Patumsawas S., and Fungrammasan B. Waste Manage 23 (2003) 433-439
27. Montgomery D.C. Design and analysis of experiments. 6th ed. (2005), Wiley, Hoboken (NJ)
28. Hocking R.R. Methods and applications of linear models: regression and analysis of variance. 2nd ed. (2003), Wiley-Interscience, Hoboken (NJ)
29. Nordin A., Lennart E., and Marcus O. Fuel 74 (1995) 128-135