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Application of LES-CFD for predicting pulverized-coal working conditions after installation of NOx control system

Author

Listed:
  • Adamczyk, Wojciech P.
  • Isaac, Benjamin
  • Parra-Alvarez, John
  • Smith, Sean T.
  • Harris, Derek
  • Thornock, Jeremy N.
  • Zhou, Minmin
  • Smith, Philip J.
  • Żmuda, Robert

Abstract

The upcoming environmental requirements outlined at the 2017 European Commission Integrated Pollution Prevention and Control (IPPC) are becoming more and more strict in comparison to the existing standards – IED 2010/75/EU. Questions arise about changes in boiler operating conditions after the adoption of the regulations to fulfill specified emission limits. Of initial concern is the question of which technology to pursue in order to reach the specified level of NOx emissions at 150 mg/Nm3@6%O2. By introducing an air staging technique the NOx limit can be partially achieved; however these changes affect the boiler operation in a detrimental way. In order to investigate impact of de-NOx installation on the temperature and heat flux distribution, numerical techniques can be used. In order to determine the impact of the changes in the boiler operation, this paper presents the application of an advanced, multiphase open-source code Arches, developed at the University of Utah for modeling pulverized-coal combustion using Large-Eddy Simulation (LES). Numerical simulations showed the effect on the boiler's working parameters e.g., heat-transfer rate, temperature distribution, species concentration, and NOx emission before and after proposed modifications.

Suggested Citation

  • Adamczyk, Wojciech P. & Isaac, Benjamin & Parra-Alvarez, John & Smith, Sean T. & Harris, Derek & Thornock, Jeremy N. & Zhou, Minmin & Smith, Philip J. & Żmuda, Robert, 2018. "Application of LES-CFD for predicting pulverized-coal working conditions after installation of NOx control system," Energy, Elsevier, vol. 160(C), pages 693-709.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:693-709
    DOI: 10.1016/j.energy.2018.07.031
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    Cited by:

    1. 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).
    2. Lopez-Ruiz, G. & Alava, I. & Urresti, I. & Blanco, J.M. & Naud, B., 2021. "Experimental and numerical study of NOx formation in a domestic H2/air coaxial burner at low Reynolds number," Energy, Elsevier, vol. 221(C).
    3. Michalina Kurkus-Gruszecka & Piotr Krawczyk & Janusz Lewandowski, 2021. "Numerical Analysis on the Flue Gas Temperature Maintenance System of a Solid Fuel-Fired Boiler Operating at Minimum Loads," Energies, MDPI, vol. 14(15), pages 1-14, July.
    4. Wu, Haiqian & Kuang, Min & Wang, Jialin & Zhao, Xiaojuan & Yang, Guohua & Ti, Shuguang & Ding, Jieyi, 2020. "Lower-arch location effect on the flow field, coal combustion, and NOx formation characteristics in a cascade-arch, down-fired furnace," Applied Energy, Elsevier, vol. 268(C).
    5. Fang Liu & Li Yang & Jie Cheng & Xin Wu & Wenbin Quan & Kozo Saito, 2019. "Low Temperature deNOx Catalytic Activity with C 2 H 4 as a Reductant Using Mixed Metal Fe-Mn Oxides Supported on Activated Carbon," Energies, MDPI, vol. 12(22), pages 1-14, November.
    6. Xiao, Guolin & Gao, Xiaori & Lu, Wei & Liu, Xiaodong & Asghar, Aamer Bilal & Jiang, Liu & Jing, Wenlin, 2023. "A physically based air proportioning methodology for optimized combustion in gas-fired boilers considering both heat release and NOx emissions," Applied Energy, Elsevier, vol. 350(C).
    7. Chen, Xi & Zhong, Wenqi & Li, Tianyu, 2023. "Fast prediction of temperature and chemical species distributions in pulverized coal boiler using POD reduced-order modeling for CFD," Energy, Elsevier, vol. 276(C).
    8. Darbandi, Masoud & Fatin, Ali & Bordbar, Hadi, 2020. "Numerical study on NOx reduction in a large-scale heavy fuel oil-fired boiler using suitable burner adjustments," Energy, Elsevier, vol. 199(C).
    9. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T., 2022. "Atikokan Digital Twin: Machine learning in a biomass energy system," Applied Energy, Elsevier, vol. 310(C).
    10. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T. & Díaz-Ibarra, Oscar H., 2023. "Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system," Applied Energy, Elsevier, vol. 334(C).

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