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Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach

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  • Li, Qingwei
  • Wu, Jiang
  • Wei, Hongqi

Abstract

Mercury is an important pollutant emitted from coal-fired power plants. Elemental mercury (Hg0) is harder to be removed than oxidized mercury (Hg2+) and particulate bound mercury (Hgp) in the flue gas at back-end of furnace. In this study, a method based on computational intelligence was proposed to enhance Hg0 removal efficiency. It was realized by improving the transformation efficiency of Hg0 into Hg2+ and Hgp and then removing them by air pollution control devices. First, relationships between Hg0 concentrations at the stack and variables like open values of secondary air, open values of over fire air, oxygen at the exit of economizer, load, coal qualities and so on were modeled with aid of tuned PCA-support vector machine. Then, manipulated variables and regulated variables were optimized by particle swarm optimization algorithm to enhance transformation efficiency of Hg0. A field thermal adjustment test was carried out on some 600 MW unit and the proposed method was applied to that unit and compared with ACO. Results showed that removal efficiencies were enhanced greatly in general. The increment of removal efficiency can reach up to 14.71%. Besides, optimal strategies can be found in few iterations, making it suitable for online applications.

Suggested Citation

  • Li, Qingwei & Wu, Jiang & Wei, Hongqi, 2018. "Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach," Energy, Elsevier, vol. 160(C), pages 753-762.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:753-762
    DOI: 10.1016/j.energy.2018.07.037
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    References listed on IDEAS

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    1. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    2. Li, Qingwei & Yao, Guihuan, 2017. "Improved coal combustion optimization model based on load balance and coal qualities," Energy, Elsevier, vol. 132(C), pages 204-212.
    3. Wang, Junchao & Fan, Weidong & Li, Yu & Xiao, Meng & Wang, Kang & Ren, Peng, 2012. "The effect of air staged combustion on NOx emissions in dried lignite combustion," Energy, Elsevier, vol. 37(1), pages 725-736.
    4. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    5. Chen, Zhichao & Wang, Zhenwang & Li, Zhengqi & Xie, Yiquan & Ti, Shuguang & Zhu, Qunyi, 2014. "Experimental investigation into pulverized-coal combustion performance and NO formation using sub-stoichiometric ratios," Energy, Elsevier, vol. 73(C), pages 844-855.
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    Cited by:

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    2. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
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    4. Chistyakov, A.V. & Nikolaev, S.A. & Zharova, P.A. & Tsodikov, M.V. & Manenti, F., 2019. "Linear α-alcohols production from supercritical ethanol over Cu/Al2O3 catalyst," Energy, Elsevier, vol. 166(C), pages 569-576.

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