Issue
Korean Journal of Chemical Engineering,
Vol.28, No.12, 2336-2343, 2011
Principles of optimization of combustion by radiant energy signal and its application in a 660MWe down- and coal-fired boiler
For the optimization of combustion in utility coal-fired boilers, a simple analytic model was set up to relate the radiant energy signal (RES) with the combustion rate (heat release rate) based on the heat transfer equation inside a boiler furnace. It was pointed out that as the air flow rate into the furnace changes, the highest RES corresponds to the highest efficiency, making RES a sensitive quantity for optimization of combustion in boilers. Experiments carried out in a 660 MW down- and coal-fired utility boiler confirmed the characteristics of RES as an indicator of combustion rate inside the furnace and its ability to reflect the boiler thermal efficiency varied with the air flow rate. The utilization of RES in the optimization of combustion can generally improve the boiler thermal efficiency at different unit loads, and the efficiency was raised about 1.0% especially at the rated and lower unit loads. It should be stated that except the lower unit load, the NOx emission from the boiler after optimization of combustion by RES would increase due to the limitation in supply of adequate air flow rate into the boiler, and some new combustion technologies are now available to solve the contradiction.
[References]
  1. Fan JR, Zha XD, Cen KF, Fuel, 80(3), 373, 2001
  2. Fang QY, Wang HJ, Yao B, Zeng HC, Zhou HC, J. China Univ. Min. Technol., 17, 566, 2007
  3. Fan JR, Liang XH, Xu QS, Zhang XY, Cen KF, Energy, 22(8), 847, 1997
  4. Tanetsakunvatana V, Kuprianov VI, Fuel Process. Technol., 88(2), 199, 2007
  5. Ren F, Li ZQ, Chen ZC, Xu ZX, Yang GH, Energy Fuels., 24, 1592, 2010
  6. Burdett NA, J. Inst. Energy., LX, 103, 1987
  7. Li ZQ, Fan SB, Su W, Chen ZC, Qin YK, Energy Fuels., 24, 3884, 2010
  8. Fang QY, Wang HJ, Zhou HC, Lei L, Duan XL, Energy Fuels., 24, 4857, 2010
  9. Wang HJ, Huang ZF, Wang DD, Luo ZX, Sun YP, Fang QY, Lou C, Zhou HC, Meas. Sci. Technol., 29, 114006, 2009
  10. Winkin PJ, Garcia MJ, Antonio J, Proc. Am. Power Conf., 52, 1166, 1997
  11. Bertucco L, Fichera A, Nunnari G, 6th IEEE Int. Workshop Cell. Neural Networks Appl., 455, 2000
  12. Huang YP, Yan Y, Lu G, Reed A, Meas. Sci. Technol., 10, 726, 1999
  13. Shimoda M, Sugano A, IEEE Trans. Energy Conv., 5, 640, 1990
  14. Yan Y, Lu G, Colechin M, Fuel., 81, 647, 2002
  15. Cheung KY, Zhang Y, Meas. Sci. Technol., 17, 3221, 2006
  16. Zhou HC, Lou C, Cheng Q, Jiang ZW, He J, Huang BY, Pei ZL, Lu CX, Proc. Combust. Inst., 30, 1699, 2005
  17. Luo X, Zhou HC, IEEE Trans. Instrum. Meas., 56, 1877, 2007
  18. Zhou HC, Zhang SS, Huang YL, Zheng CG, Dev. Chem. Eng. Mine. Proc., 8, 311, 2000
  19. Huang BY, Luo ZX, Zhou HC, Fuel Process. Technol., 91(6), 660, 2010
  20. Zhou H, Qian XP, Cen KF, Fan JR, Fuel Proc. Technol., 85, 113, 2003
  21. Zhang Y, Ding Y, Wu Z, Kong L, Chou T, Korean J. Chem. Eng., 24(6), 1118, 2007
  22. Kalogirou SA, Prog. Energy Combust. Sci., 29, 515, 2003
  23. Kuprianov VI, Renew. Sust. Energy Rev., 9, 474, 2005
  24. Sun DP, Fang QY, Wang HJ, Zhou HC, Asia-Pac. J.Chem. Eng., 3, 432, 2008
  25. Bahadori A, Vuthaluru HB, Fuel Process. Technol., 91(10), 1198, 2010