IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9076-d989339.html
   My bibliography  Save this article

Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods

Author

Listed:
  • Gavirineni Naveen Kumar

    (School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, India)

  • Edison Gundabattini

    (Department of Thermal and Energy Engineering, School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, India)

Abstract

One of the main energy sources utilized to produce power is coal. Due to the lack of combustion enhancement, the main issue with coal-based power plants is that they produce significant amount of pollutants. The major problem of slagging formation within the boiler; it sticks to the water tube walls, superheater, and reheater. Slagging might decrease the heat transferred from the combustion area to the water or steam inside the tubes, increasing the amount of coal and extra air. The abrupt fall of slag on the tube surface into the water-filled seal-trough at the bottom of the furnace might occasionally cause boiler explosions. In order to maximize heat transmission to the water and steam tubes by reducing or eliminating slag formation on the tube surface, the work presented here proposes an appropriate computational fluid dynamics (CFD) technique with a genetic algorithm (GA) integrated with conventional supercritical power plant operation. Coal usage and surplus air demand are both decreased concurrently. By controlling the velocity and temperatures of primary air and secondary air, the devised technique could optimize the flue gas temperature within the furnace to prevent ash from melting and clinging to the water and steam tube surfaces. Heat transmission in the furnace increased from 5945.876 W/m 2 to 87,513.9 W/m 2 as a result of the regulated slag accumulation. In addition to reducing CO 2 emissions by 8.55 tonnes per hour and saving close to nine tonnes of coal per hour, the boiler’s efficiency increased from 82.397% to 85.104%.

Suggested Citation

  • Gavirineni Naveen Kumar & Edison Gundabattini, 2022. "Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods," Energies, MDPI, vol. 15(23), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9076-:d:989339
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9076/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/9076/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tritippayanon, Rattapong & Piemjaiswang, Ratchanon & Piumsomboon, Pornpote & Chalermsinsuwan, Benjapon, 2019. "Computational fluid dynamics of sulfur dioxide and carbon dioxide capture using mixed feeding of calcium carbonate/calcium oxide in an industrial scale circulating fluidized bed boiler," Applied Energy, Elsevier, vol. 250(C), pages 493-502.
    2. Liu, Xingrang & Bansal, R.C., 2014. "Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant," Applied Energy, Elsevier, vol. 130(C), pages 658-669.
    3. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.
    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. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Murilo Eduardo Casteroba Bento, 2023. "Wide-Area Measurement-Based Two-Level Control Design to Tolerate Permanent Communication Failures," Energies, MDPI, vol. 16(15), pages 1-15, July.

    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. Qianchao Wang & Hongcan Xu & Lei Pan & Li Sun, 2020. "Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    2. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Long, Rui & Li, Baode & Liu, Zhichun & Liu, Wei, 2015. "Multi-objective optimization of a continuous thermally regenerative electrochemical cycle for waste heat recovery," Energy, Elsevier, vol. 93(P1), pages 1022-1029.
    4. 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).
    5. 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).
    6. Zeng, Guang & Xu, Mingchen & Tu, Yaojie & Li, Zhenwei & Cai, Yongtie & Zheng, Zhimin & Tay, Kunlin & Yang, Wenming, 2020. "Influences of initial coal concentration on ignition behaviors of low-NOx bias combustion technology," Applied Energy, Elsevier, vol. 278(C).
    7. Chandrakant Nikam, Keval & Jathar, Laxmikant & Shelare, Sagar Dnyaneshwar & Shahapurkar, Kiran & Dambhare, Sunil & Soudagar, Manzoore Elahi M. & Mubarak, Nabisab Mujawar & Ahamad, Tansir & Kalam, M.A., 2023. "Parametric analysis and optimization of 660 MW supercritical power plant," Energy, Elsevier, vol. 280(C).
    8. Sang-Mok Lee & So-Won Choi & Eul-Bum Lee, 2023. "Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)," Energies, MDPI, vol. 16(19), pages 1-33, September.
    9. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Identification and isolability of multiple gross errors in measured data for power plants," Energy, Elsevier, vol. 114(C), pages 177-187.
    10. Tang, Wei & Feng, Huijun & Chen, Lingen & Xie, Zhuojun & Shi, Junchao, 2021. "Constructal design for a boiler economizer," Energy, Elsevier, vol. 223(C).
    11. 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).
    12. Yin, Chungen, 2015. "On gas and particle radiation in pulverized fuel combustion furnaces," Applied Energy, Elsevier, vol. 157(C), pages 554-561.
    13. Hong-Wei Shi & Hai-Peng Wang, 2023. "Research on Full Premixed Combustion and Emission Characteristics of Non-Electric Gas Boiler," Energies, MDPI, vol. 16(21), pages 1-28, November.
    14. Xu, Wentao & Huang, Yaji & Song, Siheng & Yue, Junfeng & Chen, Bo & Liu, Yuqing & Zou, Yiran, 2023. "A new on-line combustion optimization approach for ultra-supercritical coal-fired boiler to improve boiler efficiency, reduce NOx emission and enhance operating safety," Energy, Elsevier, vol. 282(C).
    15. Wu, X.D. & Xia, X.H. & Chen, G.Q. & Wu, X.F. & Chen, B., 2016. "Embodied energy analysis for coal-based power generation system-highlighting the role of indirect energy cost," Applied Energy, Elsevier, vol. 184(C), pages 936-950.
    16. Chang, Hsuan & Hsu, Jian-An & Chang, Cheng-Liang & Ho, Chii-Dong & Cheng, Tung-Wen, 2017. "Simulation study of transfer characteristics for spacer-filled membrane distillation desalination modules," Applied Energy, Elsevier, vol. 185(P2), pages 2045-2057.
    17. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    18. Wang, Yuelan & Ma, Zengyi & Shen, Yueliang & Tang, Yijun & Ni, Mingjiang & Chi, Yong & Yan, Jianhua & Cen, Kefa, 2016. "A power-saving control strategy for reducing the total pressure applied by the primary air fan of a coal-fired power plant," Applied Energy, Elsevier, vol. 175(C), pages 380-388.
    19. Böhler, Lukas & Krail, Jürgen & Görtler, Gregor & Kozek, Martin, 2020. "Fuzzy model predictive control for small-scale biomass combustion furnaces," Applied Energy, Elsevier, vol. 276(C).
    20. Mollo, Malebo & Kolesnikov, Andrei & Makgato, Seshibe, 2022. "Simultaneous reduction of NOx emission and SOx emission aided by improved efficiency of a Once-Through Benson Type Coal Boiler," Energy, Elsevier, vol. 248(C).

    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:gam:jeners:v:15:y:2022:i:23:p:9076-:d:989339. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.