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Fast prediction of temperature and chemical species distributions in pulverized coal boiler using POD reduced-order modeling for CFD

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  • Chen, Xi
  • Zhong, Wenqi
  • Li, Tianyu

Abstract

This study aims to develop a fast prediction method of 3D temperature and chemical species distributions in pulverized coal boilers for real-time combustion monitoring and optimization. Firstly, 585 CFD simulations of a 330 MW tangentially fired pulverized coal boiler were conducted, covering different operating parameter combinations, including coal types, wind scheme, excess air coefficient, boiler load, and et al. Then, the temperature and chemical species data in each cell from the simulations were collected into a snapshot matrix. Next, the proper orthogonal decomposition (POD) method was used to extract the POD modes and POD coefficients from the snapshot matrix so that the temperature and chemical species data among the 585 simulations can be expressed as a weighted sum of the POD modes and the corresponding POD coefficients. Finally, the relationship between the POD coefficients and the related operating parameter combinations was fitted using data-driven methods, which realizes the fast temperature and chemical species distribution prediction under arbitrary operating parameter combinations. The results indicate that the proposed fast prediction method can obtain the boiler's three-dimensional temperature and chemical species distributions within 180.7 s, which is only 1/936 of the time consumption of CFD simulation (169141.2 s). The root relative squared error (RRSE) of the predicted temperature field, O2, CO, CO2, and SO2 distributions are below 2%, 1.79%, 1.61%, 2.11%, and 1.79%, respectively, which shows the great potential of this method for boiler combustion monitoring and digital twin modeling.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223010575
    DOI: 10.1016/j.energy.2023.127663
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    References listed on IDEAS

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    1. Wang, Tianyi & Zhong, Wenqi & Chen, Xi & Zhou, Guanwen & Guo, Xiaogang & Yu, Li & Yang, Zhengze, 2025. "Three multi-fidelity data hybrid-driven strategies for multi-objective combustion optimization in coal-fired boilers using POD and XGBoost," Energy, Elsevier, vol. 336(C).
    2. 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).
    3. Kang, Yanjie & Yang, Jun & Zhou, Yuan & Yuan, Yuan & Qin, Sulin & Li, Yong & Yang, Yupeng, 2026. "A hard-constrained physics-informed neural network for localized digital twin modeling," Energy, Elsevier, vol. 347(C).
    4. 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).
    5. Wang, Tianyi & Zhong, Wenqi & Chen, Xi & Zhou, Guanwen & Shi, Jianliang & Zhang, Baihua, 2025. "A digital twin of coal-fired boiler for physical fields prediction using flow-field-informed POD-XGBoost reduced-order modeling," Energy, Elsevier, vol. 329(C).
    6. Wang, Hean & Zhong, Wenqi & Liu, Xuejiao & Chen, Xi & Zhou, Guanwen, 2024. "Study on the carbon evolution laws and carbon accounting methods of coal-fired boilers under deep peaking," Energy, Elsevier, vol. 308(C).
    7. Chen, Haoyu & Chen, Xi & Zhou, Guanwen & Zheng, Linghong & Xu, Ming & Yu, Li & Zhang, Hong, 2025. "Carbon emission accounting method for coal-fired power units of different coal types under peak shaving conditions," Energy, Elsevier, vol. 320(C).

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