IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v160y2018icp753-762.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218313331
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.07.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Shahaboddin Shamshirband & Masoud Hadipoor & Alireza Baghban & Amir Mosavi & Jozsef Bukor & Annamária R. Várkonyi-Kóczy, 2019. "Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    3. Kong, Biao & Wang, Enyuan & Lu, Wei & Li, Zenghua, 2019. "Application of electromagnetic radiation detection in high-temperature anomalous areas experiencing coalfield fires," Energy, Elsevier, vol. 189(C).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    2. Xie, Peiran & Gao, Mingming & Zhang, Hongfu & Niu, Yuguang & Wang, Xiaowen, 2020. "Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network," Energy, Elsevier, vol. 190(C).
    3. 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.
    4. 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).
    5. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(C).
    6. Hashimoto, Nozomu & Watanabe, Hiroaki & Kurose, Ryoichi & Shirai, Hiromi, 2017. "Effect of different fuel NO models on the prediction of NO formation/reduction characteristics in a pulverized coal combustion field," Energy, Elsevier, vol. 118(C), pages 47-59.
    7. Chuanpeng Zhu & Pu Huang & Yiguo Li, 2022. "Closed-Loop Combustion Optimization Based on Dynamic and Adaptive Models with Application to a Coal-Fired Boiler," Energies, MDPI, vol. 15(14), pages 1-16, July.
    8. 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.
    9. Ioannis E. Kosmadakis & Costas Elmasides, 2021. "A Sizing Method for PV–Battery–Generator Systems for Off-Grid Applications Based on the LCOE," Energies, MDPI, vol. 14(7), pages 1-29, April.
    10. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    11. Wei, Zhongbao & Lim, Tuti Mariana & Skyllas-Kazacos, Maria & Wai, Nyunt & Tseng, King Jet, 2016. "Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery," Applied Energy, Elsevier, vol. 172(C), pages 169-179.
    12. Chen, Zhichao & Qiao, Yanyu & Guan, Shuo & Wang, Zhenwang & Zheng, Yu & Zeng, Lingyan & Li, Zhengqi, 2022. "Effect of inner and outer secondary air ratios on ignition, C and N conversion process of pulverized coal in swirl burner under sub-stoichiometric ratio," Energy, Elsevier, vol. 239(PD).
    13. Ti, Shuguang & Kuang, Min & Wang, Haopeng & Xu, Guangyin & Niu, Cong & Liu, Yannan & Wang, Zhenfeng, 2020. "Experimental combustion characteristics and NOx emissions at 50% of the full load for a 600-MWe utility boiler: Effects of the coal feed rate for various mills," Energy, Elsevier, vol. 196(C).
    14. Liu, Ming & Yan, JunJie & Chong, DaoTong & Liu, JiPing & Wang, JinShi, 2013. "Thermodynamic analysis of pre-drying methods for pre-dried lignite-fired power plant," Energy, Elsevier, vol. 49(C), pages 107-118.
    15. Li, Zixiang & Qiao, Xinqi & Miao, Zhengqing, 2021. "A novel burner arrangement scheme with annularly combined multiple airflows for wall-tangentially fired pulverized coal boiler," Energy, Elsevier, vol. 222(C).
    16. Yu, Haoyang & Gao, Mingming & Zhang, Hongfu & Yue, Guangxi & Zhang, Zhen, 2023. "Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit," Energy, Elsevier, vol. 281(C).
    17. Dios, M. & Souto, J.A. & Casares, J.J., 2013. "Experimental development of CO2, SO2 and NOx emission factors for mixed lignite and subbituminous coal-fired power plant," Energy, Elsevier, vol. 53(C), pages 40-51.
    18. Yeo, In-Ae & Yee, Jurng-Jae, 2014. "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artifi," Applied Energy, Elsevier, vol. 119(C), pages 99-117.
    19. Liu, Guangkui & Chen, Zhichao & Li, Zhengqi & Zong, Qiudong & Zhang, Hao, 2014. "Effect of the arch-supplied over-fire air ratio on gas/solid flow characteristics of a down-fired boiler," Energy, Elsevier, vol. 70(C), pages 95-109.
    20. Hyuk Choi & Ju-Hong Lee & Ji-Hoon Yu & Un-Chul Moon & Mi-Jong Kim & Kwang Y. Lee, 2023. "One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants," Energies, MDPI, vol. 16(21), pages 1-18, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:160:y:2018:i:c:p:753-762. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.