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Staged supply of fuel and air to the combustion chamber to reduce emissions of harmful substances

Author

Listed:
  • Bolegenova, Saltanat
  • Askarova, Аliya
  • Georgiev, Aleksandar
  • Nugymanova, Aizhan
  • Maximov, Valeriy
  • Bolegenova, Symbat
  • Adil'bayev, Nurken

Abstract

The paper presents the results of numerical experiments on the implementation of Over Fire Air (OFA) technology at a coal burning thermal power plant (TPP) in order to reduce emissions of harmful substances into the atmosphere. To implement the OFA technology, various options for supplying additional air through injectors in the upper part of the combustion chamber have been studied. For the first time, various heights (h = 8 m, 9 m, 10 m, 11 m, 12 m) of the location of OFA injectors in the combustion chamber at the kazakh TPP were studied. For the first time, different volumes of additional air supply through the injectors are simulated when OFA is 0% - this is the base case (traditional combustion) and when OFA is 5%, 10%, 15%, 18%, 20%, 25% and 30% of the total volume air required for complete combustion of the fuel. It is shown that at the optimal location height of OFA injectors (h = 9 m), an increase in the volume of additional air to 18% leads to a decrease in the concentrations of carbon monoxide CO by about 36%, and nitrogen dioxide NO2 by 25% compared with the base case (OFA = 0%).

Suggested Citation

  • Bolegenova, Saltanat & Askarova, Аliya & Georgiev, Aleksandar & Nugymanova, Aizhan & Maximov, Valeriy & Bolegenova, Symbat & Adil'bayev, Nurken, 2024. "Staged supply of fuel and air to the combustion chamber to reduce emissions of harmful substances," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003943
    DOI: 10.1016/j.energy.2024.130622
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    References listed on IDEAS

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    1. Qiao, Yanyu & Li, Song & Jing, Xinjing & Chen, Zhichao & Fan, Subo & Li, Zhengqi, 2022. "Combustion and NOx formation characteristics from a 330 MWe retrofitted anthracite-fired utility boiler with swirl burner under deeply-staged-combustion," Energy, Elsevier, vol. 258(C).
    2. Li, Zixiang & Miao, Zhengqing & Zhou, Yan & Wen, Shurong & Li, Jiangtao, 2018. "Influence of increased primary air ratio on boiler performance in a 660 MW brown coal boiler," Energy, Elsevier, vol. 152(C), pages 804-817.
    3. Bolegenova, Saltanat & Askarova, Аliya & Georgiev, Aleksandar & Nugymanova, Aizhan & Maximov, Valeriy & Bolegenova, Symbat & Mamedov, Bolat, 2023. "The use of plasma technologies to optimize fuel combustion processes and reduce emissions of harmful substances," Energy, Elsevier, vol. 277(C).
    4. Aliya Askarova & Montserrat Zamorano & Jaime Martín-Pascual & Aizhan Nugymanova & Saltanat Bolegenova, 2022. "A Review of the Energy Potential of Residual Biomass for Coincineration in Kazakhstan," Energies, MDPI, vol. 15(17), pages 1-15, September.
    5. Ti, Shuguang & Chen, Zhichao & Li, Zhengqi & Xie, Yiquan & Shao, Yunlin & Zong, Qiudong & Zhang, Qinghua & Zhang, Hao & Zeng, Lingyan & Zhu, Qunyi, 2014. "Influence of different swirl vane angles of over fire air on flow and combustion characteristics and NOx emissions in a 600 MWe utility boiler," Energy, Elsevier, vol. 74(C), pages 775-787.
    6. Gu, Mingyan & Wang, Mingming & Chen, Xue & Wang, Jimin & Lin, Yuyu & Chu, Huaqiang, 2019. "Numerical study on the effect of separated over-fire air ratio on combustion characteristics and NOx emission in a 1000 MW supercritical CO2 boiler," Energy, Elsevier, vol. 175(C), pages 593-603.
    7. Zhou, Hao & Cen, Kefa & Fan, Jianren, 2004. "Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks," Energy, Elsevier, vol. 29(1), pages 167-183.
    8. Steele, Amanda Harker & Sharma, Smriti & Pena Cabra, Ivonne & Clahane, Luke & Iyengar, Arun, 2023. "A tool for measuring the system cost of replacement energy," Energy, Elsevier, vol. 275(C).
    9. Jiang, Yu & Lee, Byoung-Hwa & Oh, Dong-Hun & Jeon, Chung-Hwan, 2022. "Influence of various air-staging on combustion and NOX emission characteristics in a tangentially fired boiler under the 50% load condition," Energy, Elsevier, vol. 244(PB).
    10. 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.
    11. Askarova, Aliya & Georgiev, Aleksandar & Bolegenova, Saltanat & Beketayeva, Meruyert & Maximov, Valeriyu & Bolegenova, Symbat, 2022. "Computational modeling of pollutants in furnaces of pulverized coal boilers of the republic of Kazakhstan," Energy, Elsevier, vol. 258(C).
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