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Operating Efficiency Optimal Control Algorithms of Flue Gas Denitrification System in Thermal Power Plant

  • Junying RenEmail author
  • Yuchen Liu
Conference paper
  • 35 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1147)

Abstract

When the current method is used to optimize the denitrification system in thermal power plants, the control performance is not high and the error is large. Therefore, an optimization control algorithm for the operation efficiency of flue gas denitrification system in thermal power plants is proposed. The flue gas flow forecasting model is established to control the future dynamic behavior through the forecasting model. The optimal control variables are solved and revised by using the control method of multi-variable constrained interval to complete the optimal control of flue gas denitrification system operation in thermal power plants. The simulation results show that the control error of the proposed algorithm is low, which shows that the control performance of the algorithm is good.

Keywords

Denitration system Operating efficiency Optimized control 

References

  1. 1.
    Li, X., Xiong, S., Lin, B.: Attitude control algorithm under simultaneous thruster attitude and orbit control. J. Jilin Univ. (Sci. Ed.) 54(3), 603–608 (2016)Google Scholar
  2. 2.
    Tao, Y., Wang, W., Ding, D.: Shaanxi Deyuan Fugu power plant 2 * 600 MW unit flue gas denox control strategy. Autom. Instrum. 4, 120–122 (2015)Google Scholar
  3. 3.
    Yan, M., Zhao, K., Zhu, Y., et al.: Self-adaptive hybrid dynamic model of SCR flue gas denitration system. Chin. J. Sci. Instrum. 37(12), 2844–2850 (2016)Google Scholar
  4. 4.
    Wang, G., Yu, Z., Wang, S., et al.: Operation optimization of selective catalytic reduction system based on support vector machine. Chin. J. Environ. Eng. 9(10), 5011–5016 (2015)Google Scholar
  5. 5.
    Wang, L., Yong, L., Song, H., et al.: Performance optimization of urea hydrolysis SCR flue gas denitration system for TPP. Electric. Power Environ. Prot. 35(6), 19–21 (2019)Google Scholar
  6. 6.
    Song, H., Li, Y., Zhang, H., et al.: Study on a selective catalytic reduction system for NOx removal in a 200 MW coal-fired power plant. Power Syst. Eng. 32(5), 49–52 (2016)Google Scholar
  7. 7.
    Hu, Y., Bai, Y.: SCR flue gas denitration technology and its application. Energy Conserv. Technol. 25(2), 152–156, 181 (2007)Google Scholar
  8. 8.
    Zhang, Q., Gao, L., Guo, J.: Preparation of nanosized TiO2 powders from hydrolysis of TiCl4. J. Inorg. Mater. 15(1), 21–25 (2000)Google Scholar
  9. 9.
    Fan, H., Zhong, Z., Jin, B., et al.: Experimental study of contribution of metal oxide Moo3 (Wo3) and V2o5 on the catalysts for the selective catalytic reduction of nitric oxide by ammonia. Environ. Chem. 26(4), 439–443 (2007)Google Scholar
  10. 10.
    Hu, D., Wang, H,, Guo, T., et al.: Research and development of mitigating technology of SO3 in flue gas from coal power plants. Sci. Technol. Eng. 15(35), 92–99, 105 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Metallurgical and Material Engineering DepartmentInner Mongolia Technical College of Mechanics and ElectricsHohhotChina
  2. 2.Institute of Chemical EngineeringInner Mongolia University of TechnologyHohhotChina

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