Modeling of the Unburned Carbon in Fly Ash

Weiping  YAN; Jun  LI

doi:10.4236/epe.2009.12014

Weiping YAN, Jun LI

Abstract: Numerical simulation of the content of unburned carbon in fly ash on the 300MW tangentially pulverized coal fired boiler is performed by the numerical simulation software COALFIRE, which is based on international advanced TASCFLOW software platform. Firstly, take the result of calculation of number value as the sample, and then set up the support vector machine model of unburned carbon content on the boiler. The relative error between the predicted output and measured value is 0.00186%, which proves the modeling is good for the unburned carbon in fly ash predict.

Keywords: numerical simulation, unburned carbon content, support vector machine

Cite this paper: nullW. YAN and J. LI, "Modeling of the Unburned Carbon in Fly Ash," Energy and Power Engineering, Vol. 1 No. 2, 2009, pp. 90-93. doi: 10.4236/epe.2009.12014.

References

[1] M. H. Fan and R. C. Brown, “Precision and accuracy of photo acoustic measurements of unburned carbon in fly ash [J],” Fuel, Vol. 80, No. 11, pp. 1545–1554, 2001.

[2] K. Styszko-Grochowiak, J. Golas, H. Jankowski, et al., “Characterization of the coal fly ash for the purpose of improvement of industrial on-line measurement of un- burned carbon content [J],” Fuel, Vol. 83, No. 13, pp. 1847–1853, 2004.

[3] A. K. Ouazzane, J. L. Castagner, A. R. Jones, et a1., “Design of an optical instrument to measure the carbon content of fly ash [J],” Fuel, Vol. 81, No. 15, pp. 1907–1911, 2002.

[4] H. Zhou, H. B. Zhu, T. H. Zeng, et a1., “Artificial neural network modelling on the unburned carbon in fly ash from utility boilers [J],” Proceedings of the CSEE, Vol. 22, No. 6, pp. 96–100, 2002. (in Chinese)

[5] X. T. Fang and N. Y. Ye, “A system forpredicting the anburned carbon of the fly ash from utility boilers based on BP artificial neural netwoks [J],” Jouunal of Huazhong Uruversity of Science & Technology, Nature Science Edition, Vol. 31, No. 12, pp. 75–77, 2003. (in Chinese)

[6] M. Sebastia,I. F. dez Olmo, and Angel Irabien, “Neural network prediction of unconfined compressive strength of coal fly ash cement mixtures [J],” Cement and Concrete Research, Vol. 33, No. 8, pp. 137–145, 2003.

[7] G. Y. Zhang and J. Zhang, “Fuzzy SVM-based multilevel binary tree classifier for fault diagnosis of hydroturbine speed regulating system [J],” Proceedings of the CSEE, Vol. 25, No. 8, pp. 100–104, 2005. (in Chinese)

[8] Y. C. Li, T. J. Fang, and E. K. Yu, “Study of support vector machines for short-term load predicting [J],” Proceedings of the CSEE, Vol. 23, No. 6, pp. 55–59, 2003. (in Chinese)

[9] Y. Wang, Z. H. Zhou, and A. Y. Zhou, “The apply of machine study [M],” The Publish House of Tsinghua University, Beijing, pp. 1–27, 2006.

[10] A. Smola and B. Scholkopf, “A tutorialon support vector regression [R],” Royal Holoway College, London,1998.

[11] V. N. Vapnik, “The nature of statistical learning theory [M],” Springer, New York, 1999.

[12] G. Z. Li, M. Wang, H. J. Zeng (translate), N. Cristianini, J. Shawe-Taylor (write), “Introduction of support vector [M],” The Publish House of Electric Industry, Beijing, 2004.

[13] X. G. Zhang (translate), Vapnik (write), “Theory of statistic study [M],” The Publish House of Electric Industry, Beijing, 2004.

[14] X. G. Zhang, “Introduction to statistical learning theory and support vector machines [J],” ACTA Automatica Sinica, Vol. 26, No. 1, pp. 32–43, 2000.

[15] X. D. Wang and J. Q. Wang, “A survey on support vector machine traing and testing algorithms [J],” Computer Engineering and Applications, Vol. 40, No. 13, pp. 75–78, 2000.

[16] S. S. Keerthi and C. J. Lin, “A symptotic behaviors of support v machines with Gaussian kernel [J],” Neural Computation, pp. 1667–1689, 2003.

[17] C. L. Wang and H. Zhou, et a1., “Support vector machine modeling on the unburned carbon in fly ash [J],” Proceedings of the CSEE, Vol. 20, No. 25, pp. 72–76, 2005. (in Chinese)