A NOx emission concentration prediction method for CFB unit based on one-dimensional semi-empirical model corrected by GRU network

Fang Wang, Suxia Ma, Yuanyuan Niu and Zhongyuan Liu

Energy, 2025, vol. 330, issue C

Abstract: The deep peak shaving of thermal power units has raised higher requirements for ultra-low emission of the units. Accurately monitoring NOx emission concentration in CFB units is a prerequisite for NOx emission control. Mechanism-based NOx emission concentration prediction methods are explanatory, but may have large prediction errors due to their inability to simulate the thermal inertia of boilers well sometimes; Machine learning based methods have high prediction accuracy, but poor interpretability due to lack of physical significance. A fusion model for predicting NOx emission concentration in CFB units is proposed in this paper. First, a one-dimensional semi-empirical model is constructed to simulate hydrodynamics, combustion, and NOx generation in the furnace and predict initial values of NOx emission concentration. Considering that CFB units often operate in off-design conditions to meet peak shaving requirements and the empirical values of some parameters are no longer applicable, a parameter optimization method is introduced to make the mechanism model more realistic. Then, a gated recurrent unit (GRU) neural network is introduced as an error correction model to fine-tune the initial NOx emission concentration. Taking two CFB units as research objects, the effectiveness of the fusion model is demonstrated in both steady state and dynamic process. The results show that the proposed model is superior to the single mechanism model, GRU model and other neural networks. The combination of mechanism and machine learning methods enables the fusion model to have both high prediction accuracy and physical significance.

Keywords: CFB boiler; NOx emission concentration prediction; One-dimensional semi-empirical model; Gated recurrent unit (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:330:y:2025:i:c:s0360544225026039

DOI: 10.1016/j.energy.2025.136961

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