Independent component analysis-based fuel type identification for coal-fired power plants

Combustion Science and Technology

Volumn 184, Issue 3, 2012, Pages 277-292

  Tan, Cheng ^a   Xu, Lijun ^a   Li, Xiaomin ^a   Yan, Yong ^b  

^a BEIHANG UNIVERSITY   (China)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Feature extraction; Flame feature; Fuel type; Independent component analysis (ICA); Support vector machine (SVM)

Indexed keywords

COAL-FIRED POWER PLANT; FEATURE VECTORS; FLAME FEATURE; FLAME MONITOR; FLAME OSCILLATIONS; FUEL TYPES; INDEPENDENT COMPONENTS; SUPPORT VECTOR MACHINE TECHNIQUES; SVM MODEL;

COAL; FEATURE EXTRACTION; FOSSIL FUEL POWER PLANTS; FUELS; IMAGE RETRIEVAL; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

INDEPENDENT COMPONENT ANALYSIS;

EID: 84860805519     PISSN: 00102202     EISSN: 1563521X     Source Type: Journal    
DOI: 10.1080/00102202.2011.635613     Document Type: Article

References (37)

1. Andrés, J.M., and Bona, M.T. 2006. ASTM clustering for improving coal analysis by near-infrared spectroscopy. Talanta, 70, 711-719. (Pubitemid 44444970)
2. Barroso, J., Ballester, J., Ferrer, L.M., and Jiménez, S. 2006. Study of coal ash deposition in an entrained flow reactor: Influence of coal type, blend composition and operating conditions. Fuel Process. Technol., 87, 737-752.
3. Belbot, M., Vourvopoulos, G., and Paschal, J. 2001. A commercial on-line coal analyzer using pulsed neutrons. Presented at the Sixteenth International Conference on Application of Accelerators in Research and Industry, November 1-5, 2000, Denton, TX.
4. Bonfanti, L., De Michele, G., Riccardi, J., and Lopez-Doriga, E. 1994. Influence of coal type and operating conditions on the formation of incomplete combustion products: Pilot plant experiments. Combust. Sci. Technol., 101, 505-525.
5. Cao, L.J., Chua, K.S., Chong, W.K., Lee, H.P., and Gu, Q.M. 2003. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55, 321-336. (Pubitemid 37336682)
6. Chang, C., and Lin, C. 2001. LIBSVM: A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html
7. Chen, J., Hsu, T., Chen, C., and Cheng, Y. 2010. Monitoring combustion systems usingHMM probabilistic reasoning in dynamic flame images. Appl. Energy, 87, 2169-2179.
8. Déniz, O., Castrillón, M., and Hernández, M. 2003. Face recognition using independent component analysis and support vector machines. Pattern Recognit. Lett., 24, 2153-2157. (Pubitemid 36810737)
9. Fang, J., Zeng, T., Yang, L.I.S., Oye, K.A., Sarofim, A.F., and Beér, J.M. 1999. Coal utilization in industrial boilers in China-a prospect for mitigating CO2 emissions. Appl. Energy, 63, 35-52. (Pubitemid 29348215)
10. Fodor, I. 2002. A survey of dimension reduction techniques. Lawrence Livermore Nat'l Laboratory, Center for Applied Scientific Computing, Livermore, CA.
11. Gävert, H., Hurri, J., Särelä, J., and Hyvärinen, A. 2005. The FastICA Package For MATLAB. http://www.cis.hut.fi/projects/ica/ fastica/index.shtml
12. Hampartsoumian, E., Folayan, O.O., Nimmo, W., and Gibbs, B.M. 2003. Optimisation of NOx reduction in advanced coal reburning systems and the effect of coal type. Fuel, 82, 373-384. (Pubitemid 35459549)
13. Haykin, S. 2001. Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, Inc., Tshingua University Press, Beijing, China.
14. Hsu, C.W., Chang, C.C., and Lin, C.J. 2003. A practical guide to support vector classification. Available at: http://www.csie.ntu.edu.tw/~cjlin
15. Huang, Y., McMullan, J.T., and Williams, B.C. 2000. Influences of coal type on the performance of a pressurised fluidised bed combustion power plant. Fuel, 79, 1595-1601.
16. Huggins, F.E. 2002. Overview of analytical methods for inorganic constituents in coal. Int. J. Coal Geol., 50, 169-214.
17. Hyvärinen, A. 1999. Survey on independent component analysis. Neural Computing Surveys, 2, 94-128.
18. Hyvärinen, A., Karhunen, J., and Oja, E. 2001. Independent Component Analysis, Wiley-Interscience, New York.
19. Jones, A.R. 1988. Flame failure detection and modern boilers. J. Phys. E: Sci. Instrum., 21, 921-928.
20. Kambara, S., Takarada, T., Toyoshima, M., and Kato, K. 1995. Relation between functional forms of coal nitrogen and NOx emissions from pulverized coal combustion. Fuel, 74, 1247-1253.
21. Kambara, S., Takarada, T., Yamamoto, Y., and Kato, K. 1993. Relation between functional forms of coal nitrogen and formation of nitrogen oxide (NOx) precursors during rapid pyrolysis. Energy Fuels, 7, 1013-1020.
22. Kleiber, L., Fink, H., Niessner, R., and Panne, U. 2002. Strategies for the analysis of coal by laser ablation inductively coupled plasma mass spectroscopy. Anal. Bioanal. Chem., 374, 109-114. (Pubitemid 35013635)
23. Kwak, N., Choi, C., and Choi, J. 2001. Feature extraction using ICA. In Proceedings of the International Conference on Artificial Neural Networks, Vienna, Austria; Springer-Verlag, London.
24. Li, R.F., and Wang, X.Z. 2002. Dimension reduction of process dynamic trends using independent component analysis. Comput. Chem. Eng., 26, 467-473. (Pubitemid 34246502)
25. Lu, G., Yan, Y., Colechin, M., and Hill, R. 2006. Monitoring of oscillatory characteristics of pulverized coal flames through image processing and spectral analysis. IEEE Trans. Instrum. Meas., 55, 226-231. (Pubitemid 43203211)
26. Naha, S., Briones, A.M., and Aggarwal, S.K. 2004. Effect of fuel blends on pollutant emissions in flames. Combust. Sci. Technol., 177, 183-220.
27. Odgaard, P.F., and Trangbaek, K. 2006. Comparison of Methods for Oscillation Detection-Case Study on a Coal-Fired Power Plant, Elsevier, Oxford, UK.
28. Qiu, J., Li, F., Zeng, H., Yao, B., and Ma, Y. 2000. Determination of optimum blending ratio during coal blends combustion. Combust. Sci. Technol., 157, 167-184. (Pubitemid 32031333)
29. Sbarbaro, D., Farias, O., and Zawadsky, A. 2003. Real-time monitoring and characterization of flames by principal-component analysis. Combust. Flame, 132, 591-595. (Pubitemid 36344131)
30. Shigeo, A. 2005. Support Vector Machines for Pattern Classification, Springer-Verlag, London
31. Tan, C., Xu, L., and Cao, Z. 2009. On-line fuel identification using optical sensing and support vector machines technique. Proceedings of the International Instrumentation and Measurement Technology Conference, Singapore, IEEE, pp. 1144-1147.
32. Vapnik, V.N. 1999. An overview of statistical learning theory. IEEE Trans. Neural Networks, 10, 988-999.
33. Widodo, A., and Yang, B. 2007. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst. Appl., 33, 241-250. (Pubitemid 44959934)
34. Xu, L., Tan, C., and Li, X. 2010. On-line identification of fuel type using joint probability density arbiter and support vector machine techniques. Proceedings of the International Instrumentation and Measurement Technology Conference, Austin, TX, IEEE, 127-130.
35. Xu, L., and Yan, Y. 2006. A new flame monitor with triple photovoltaic cells. IEEE Trans. Instrum. Meas., 55, 1416-1421. (Pubitemid 44120935)
36. Xu, L., Yan, Y., Cornwell, S., and Riley, G. 2004. On-line fuel identification using digital signal processing and fuzzy inference techniques. IEEE Trans. Instrum. Meas., 53, 1316-1320.
37. Xu, L., Yan, Y., Cornwell, S., and Riley, G. 2005. Online fuel tracking by combining principal component analysis and neural network techniques. IEEE Trans. Instrum. Meas., 54, 1640-1645. (Pubitemid 41100708)