Forecasting unburned carbon content in the fly ash from coal-fired utility boilers based on SVM

Ranshao Kexue Yu Jishu/Journal of Combustion Science and Technology

Volumn 12, Issue 4, 2006, Pages 312-317

  Cai, Jie Jin ^a   Ma, Xiao Qian ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Support vector machine; Unburned carbon content; Utility boiler

Indexed keywords

BACKPROPAGATION; CARBON; COAL ASH; FLY ASH; FORECASTING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

BP NETWORK MODEL; SUPPORT VECTOR MACHINE; UNBURNED CARBON CONTENT; UTILITY BOILER;

COAL FIRED BOILERS;

EID: 33750219415     PISSN: 10068740     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Ouazzane A K, Castagner J L, Jones A R, et al. Design of an optical instrument to measure the carbon con tent of fly ash[J]. Fuel, 2002, 81(15): 1907-1911.
2. Fang Xiangtao, Xuan Yimin, Feng Changqing, et al. Development of MCE-1 carbon-in-ash measuring system on-line non-extranctive[J]. Journal of Engineering Thermophysics, 2003, 24(3): 523-527 (in Chinese).
3. Liu Hong, Zhou Keyi. Recent research and development of unburned carbon in fly ash detector[J]. Boiler Technology, 2004, 35(2): 65-68 (in Chinese).
4. Zhou Hao, Zhu Hongbo, Zeng Tinghua, et al. Artificial neural network modelling on the unburned carbon in fly ash from utility boilers[J]. Proceedings of the CSEE, 2002, 22(6): 96-100 (in Chinese).
5. Fang Xiangtao, Ye Nianyu. A system for forecasting the unburned carbon of the fly ash from utility boilers based on BP artificial neural netwoks[J]. Journal Huazhong University of Science and Technology: Nature Science Edition, 2003, 31(12): 75-77 (in Chinese).
6. Marta Sebastia, Inaki Fernandez Olmo, Angel Irabien. Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures[J]. Cement and Concrete Research, 2003, 33(8): 1137-1146.
7. Lin Kuanming, Lin Chihen. A study on reduced support vector machines[J]. IEEE Transactions on Neural Networks, 2003, 14(6): 1449-1459.
8. Anguita D, Boni A, Ridella S. A digital architecture for support vector machines: Theory, algorithm, and FPGA implementation[J]. IEEE Transactions on Neural Networks, 2003, 14(5): 993-1009.
9. Wang Xihuai, Zhu Sifeng. Ship power load forecasting using support vector machine[J]. Proceedings of the CSEE, 2004, 24(10): 36-39 (in Chinese).
10. Li Yuancheng, Fang Tingjian, Yu Erkeng. Study of support vector machines for short-term load forecasting[J]. Proceedings of the CSEE, 2003, 23(6): 55-59 (in Chinese).
11. Zhu Sifeng, Wang Xihuai. A SVM approach to ship power load forecasting based on RBF kernel[C]. IEEE Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, China: 2004: 1824-1828.
12. Pai Pingfeng, Hong Weichang. Support vector machines with simulated annealing algorithms in electricity load forecasting[J]. Energy Conversion and Management, 2005, 46(17): 2669-2688.
13. Liu Han, Liu Ding, Liang Yanming, et al. Research on natural gas load forecasting based on least squares support vector machine[J]. IEEE Proceedings of the Third International Conference on Machine Learrling and Cybemetics, 2004(5): 26-29.
14. Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer, 1999.
15. Smola A, Scholkopf B. A tutorial on support vector regression[R]. London: Royal Holloway College, 1998.