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Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers

  • Presented at the 7th Korea-China Clean Energy Technology Symposium
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Abstract

A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NO x emissions of a utility boiler. At the lower level of HGS, a nonlinear static model is used to schedule the static parameters of local linear dynamic models (LDMs), such as static gains and static operating conditions. According to upper level scheduling variables, a multi-model method is used to calculate the predictive output based on lower-level LDMs. Both static and dynamic experiments are carried out at a 360 MW pulverized coal-fired boiler. Based on these data, a nonlinear static model using artificial neural network (ANN) and a series of linear dynamic models are obtained. Then, the performance of the HGS model is compared to the common multi-model in predicting NO x emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NO x emissions in the dynamic process.

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References

  1. Y. Zhang, Y. J. Ding, Z. S. Wu, L. Kong and T. Chou, Korean J. Chem. Eng., 24, 1118 (2007).

    Article  Google Scholar 

  2. S. P. Visona and B. R Stanmore, Chem. Eng. Sci., 53, 2013 (1998).

    Article  CAS  Google Scholar 

  3. C. F. M. Coimbra, J. L. T. Azevedo and M.G. Carvalho, Fuel, 73, 1128 (1994).

    Article  CAS  Google Scholar 

  4. H. Zhou, K. F. Cen and J. R. Fan, Energy, 29, 167 (2004).

    Article  CAS  Google Scholar 

  5. H. Zhou, K. F. Cen and J. B. Mao, Fuel, 80, 2163 (2001).

    Article  Google Scholar 

  6. A. J. Christopher and L. Roger, Proceedings of int’l joint power generation conference, Baltimore (1998).

  7. R. C. Booth and W. B. Roland, Proceedings of dynamic modeling control applications for industry workshop, Vancouver (1998).

  8. A. Laungphairojana, Boiler operation optimization for air pollution control, Vanderbilt Univ. Press (2003).

  9. S. Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, Control Systems Magazine, 20, 53 (2000).

    Article  Google Scholar 

  10. W. J. Rugh and J. S. Shamma. Automatica, 36, 1401 (2000).

    Article  Google Scholar 

  11. K. J. Astrom and B. Wittenamrk, Adaptive control, Beijing, Science Press (2003).

  12. P. C. Chen and J. S. Shamma, Journal of Process Control, 14, 263 (2004).

    Article  CAS  Google Scholar 

  13. Z.Y. Huang, D. H. Li, X. Z. Jiang and L. M. Sun, Proceedings of the CSEE, 23, 191 (2003).

    Google Scholar 

  14. V. Petridis and A. Kehagias, Automatica, 34, 469 (1998).

    Article  Google Scholar 

  15. L. Chen and K. S. Narendra, Automatica, 37, 1245 (2001).

    Article  Google Scholar 

  16. Y. Fu and T. Chai, Automatica, 43, 1101 (2007).

    Article  Google Scholar 

  17. J. R. Wilson and S. S. Jeff, Automatica, 36, 1401 (2000).

    Article  Google Scholar 

  18. F. C. Eduardo and A. O. Vilma, Automatica, 38, 1247 (2002).

    Article  Google Scholar 

  19. D. A. Lawrence, Automatica, 37, 1041 (2001).

    Article  Google Scholar 

  20. J. D. Francis, S. K. Harpreet and S. S. James, Chem. Eng. Sci., 53, 2675 (1998).

    Article  Google Scholar 

  21. H. Al-Duwaish and W. Naeem, Proceedings of the 2001 IEEE international conference on control applications, Mexico City (2001).

  22. A. T. Johjns and D. F. Warne, Thermal power plant simulation and control, London, The Institution of Electrical Engineers (2003).

    Google Scholar 

  23. L. L. Fang and Z.Y. Gao, Proceedings of the CSEE, 23, 211 (2003).

    Google Scholar 

  24. D. H. Chung, J.B. Yang, D. S. Noh and W. B. Kim, Korean J. Chem. Eng., 16, 489 (1999).

    Article  CAS  Google Scholar 

  25. S. A. Kalogirou, Prog. Energy Combust. Sci., 29, 515 (2003).

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Thermal Engineering, Tsinghua University, Beijing, 100-084, China

    Liang Kong, Yanjun Ding, Lichuan Yuan & Zhansong Wu

  2. School of Energy and Power Engineering, Dalian University of Technology, Dalian, 116-024, China

    Yi Zhang

Authors
  1. Liang Kong
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  2. Yanjun Ding
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  3. Yi Zhang
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  4. Lichuan Yuan
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  5. Zhansong Wu
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Correspondence to Yanjun Ding.

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Kong, L., Ding, Y., Zhang, Y. et al. Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers. Korean J. Chem. Eng. 26, 534–541 (2009). https://doi.org/10.1007/s11814-009-0091-0

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  • DOI: https://doi.org/10.1007/s11814-009-0091-0

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