Furnace temperature three-dimensional distribution model based on the ReconBoost algorithm

Zhenhao Tang, Yuan Yang, Hongrui Dong, Jiaqi Wu, Mengxuan Sui, Shengxian Cao and Yizhou Chen

Energy, 2025, vol. 335, issue C

Abstract: Rapid three-dimensional (3D) temperature distribution prediction during the boiler combustion process enables operators to monitor the temperature distribution within the furnace in real-time. This study proposes a reconstruction-boost (ReconBoost) algorithm to predict the 3D temperature distribution. First, computational fluid dynamics (CFD) numerical simulations of the boiler are conducted to obtain temperature distribution data under 60 operational conditions. Subsequently, the simulation results are combined with on-site operational parameters as data samples. Data fusion, classification, and central condition identification are applied to construct a modeling dataset. Finally, a data-driven model based on the ReconBoost algorithm is developed to establish the relationship between the 3D temperature distribution and the operational parameters. The experimental results demonstrate that the ReconBoost algorithm can effectively predict the 3D temperature distribution with the mean absolute percentage error (MAPE) consistently below 14 % and R2 above 0.8. The proposed algorithm offers strong potential for efficient combustion state monitoring in boiler systems.

Keywords: Numerical simulation; Data-driven model; Temperature distribution prediction; Data fusion; Reconstruction-boost algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225036308

DOI: 10.1016/j.energy.2025.137988

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