IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v294y2024ics0360544224006327.html
   My bibliography  Save this article

Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization

Author

Listed:
  • Xue, Wenyuan
  • Lu, Yichen
  • Wang, Zhi
  • Cao, Shengxian
  • Sui, Mengxuan
  • Yang, Yuan
  • Li, Jiyuan
  • Xie, Yubin

Abstract

The temperature field is a critical factor for ensuring the safe combustion and energy conservation in boilers. However, an effective method for reconstructing the temperature field near the water wall is still under exploration. In this paper, a method for online reconstruction of the temperature field distribution near the water wall in a 330 MW tangentially fired coal boiler is proposed, which progresses from a well-established model for the entire furnace. A two-branch fusion network for transfer learning (TBFN-TL) method is proposed, incorporating additional key parameters, heat flux, during the transfer process to enhance the effectiveness. A Bayesian hierarchical neural architecture search (BHNAS) method is proposed to optimize the configuration of the hidden layers in building neural networks. Compared with computational fluid dynamics (CFD) results, the mean absolute percentage error (MAPE) of the reconstruction results for the entire furnace model, traditional transfer learning methods, and the proposed TBFN-TL are 11.57%, 5.738%, and 2.052%, respectively, demonstrating a significant enhancement. The proposed BHNAS method extends the search optimization space, obtaining more excellent configurations for the hidden layer nodes. The proposed methods have significant implications for temperature field reconstruction, the field of transfer learning, and the optimization of hidden layer configurations.

Suggested Citation

  • Xue, Wenyuan & Lu, Yichen & Wang, Zhi & Cao, Shengxian & Sui, Mengxuan & Yang, Yuan & Li, Jiyuan & Xie, Yubin, 2024. "Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006327
    DOI: 10.1016/j.energy.2024.130860
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224006327
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130860?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. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    2. Echi, Souhir & Bouabidi, Abdallah & Driss, Zied & Abid, Mohamed Salah, 2019. "CFD simulation and optimization of industrial boiler," Energy, Elsevier, vol. 169(C), pages 105-114.
    3. Belošević, Srdjan & Tomanović, Ivan & Crnomarković, Nenad & Milićević, Aleksandar, 2019. "Full-scale CFD investigation of gas-particle flow, interactions and combustion in tangentially fired pulverized coal furnace," Energy, Elsevier, vol. 179(C), pages 1036-1053.
    4. 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).
    5. Yuan, Yue & Chen, Zhihua & Wang, Zhe & Sun, Yifu & Chen, Yixing, 2023. "Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings," Energy, Elsevier, vol. 270(C).
    6. Du, Shuyi & Wang, Meizhu & Yang, Jiaosheng & Zhao, Yang & Wang, Jiulong & Yue, Ming & Xie, Chiyu & Song, Hongqing, 2023. "An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning," Energy, Elsevier, vol. 282(C).
    7. Shi, Yan & Zhong, Wenqi & Chen, Xi & Yu, A.B. & Li, Jie, 2019. "Combustion optimization of ultra supercritical boiler based on artificial intelligence," Energy, Elsevier, vol. 170(C), pages 804-817.
    8. Yang, Haolin & Schell, Kristen R., 2021. "Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets," Applied Energy, Elsevier, vol. 299(C).
    Full references (including those not matched with items on IDEAS)

    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. Li, Xinli & Wang, Yingnan & Zhu, Yun & Yang, Guotian & Liu, He, 2021. "Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling," Energy, Elsevier, vol. 231(C).
    2. 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).
    3. Aminmahalati, Alireza & Fazlali, Alireza & Safikhani, Hamed, 2021. "Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm," Energy, Elsevier, vol. 221(C).
    4. Tang, Zhenhao & Sui, Mengxuan & Wang, Xu & Xue, Wenyuan & Yang, Yuan & Wang, Zhi & Ouyang, Tinghui, 2024. "Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction," Energy, Elsevier, vol. 299(C).
    5. 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).
    6. Faramarz Saghi & Mustafa Jahangoshai Rezaee, 2023. "Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 559-591, February.
    7. Jun Liu & Yuyan Zhou & Lihua Chen & Lichuan Wang, 2023. "Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    8. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    9. Zhou, Jian & Zhang, Wei, 2023. "Coal consumption prediction in thermal power units: A feature construction and selection method," Energy, Elsevier, vol. 273(C).
    10. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    11. Bo Zhu & Bichen Shang & Xiao Guo & Chao Wu & Xiaoqiang Chen & Lingling Zhao, 2022. "Study on Combustion Characteristics and NOx Formation in 600 MW Coal-Fired Boiler Based on Numerical Simulation," Energies, MDPI, vol. 16(1), pages 1-30, December.
    12. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    13. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    14. Yangrui Zhang & Peng Tao & Xiangming Wu & Chenguang Yang & Guang Han & Hui Zhou & Yinlong Hu, 2022. "Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power," Energies, MDPI, vol. 15(4), pages 1-13, February.
    15. Zhe Liu & Qun Lei & Dingwei Weng & Lifeng Yang & Xin Wang & Zhen Wang & Meng Fan & Jiulong Wang, 2023. "A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization," Energies, MDPI, vol. 16(23), pages 1-24, December.
    16. Piotr Jóźwiak & Jarosław Hercog & Aleksandra Kiedrzyńska & Krzysztof Badyda & Daniela Olevano, 2020. "Thermal Effects of Natural Gas and Syngas Co-Firing System on Heat Treatment Process in the Preheating Furnace," Energies, MDPI, vol. 13(7), pages 1-15, April.
    17. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    18. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    19. 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).
    20. Yee Van Fan & Zorka Novak Pintarič & Jiří Jaromír Klemeš, 2020. "Emerging Tools for Energy System Design Increasing Economic and Environmental Sustainability," Energies, MDPI, vol. 13(16), pages 1-25, August.

    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:294:y:2024:i:c:s0360544224006327. 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.