基于BKA-Transformer-BiGRU的垃圾焚烧炉SNCR脱硝系统动态建模

赵征; 梁磊

doi:10.19805/j.cnki.jcspe.2026.250251

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基于BKA-Transformer-BiGRU的垃圾焚烧炉SNCR脱硝系统动态建模

绿色低碳 | 更新时间：2026-09-15

- 基于BKA-Transformer-BiGRU的垃圾焚烧炉SNCR脱硝系统动态建模
- Dynamic Modeling of SNCR Denitrification System for Waste Incinerators Based on BKA-Transformer-BiGRU
- 动力工程学报 2026年46卷第9期页码：180-191
- 作者机构：
  
  1. 华北电力大学自动化系,河北,保定,071003
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071003
- 作者简介：
  
  赵征(1978—),女,河北保定人,副教授,博士,研究方向为热工过程建模与控制,E-mail:zheng_zhao@ncepu.edu.cn。
- 基金信息：
  
  国家自然科学基金资助项目(52276007);深圳市科技计划资助项目（2021N041）;生活垃圾焚烧智能优化控制及污染物超低排放技术研发资助项目（KCXFZ20201221173402007）
- DOI：10.19805/j.cnki.jcspe.2026.250251
  中图分类号： TM619
- 纸质出版：2026
- 稿件说明：
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赵征, 梁磊. 基于BKA-Transformer-BiGRU的垃圾焚烧炉SNCR脱硝系统动态建模[J]. 动力工程学报, 2026, 46(9): 180-191.
赵征, 梁磊. 基于BKA-Transformer-BiGRU的垃圾焚烧炉SNCR脱硝系统动态建模[J]. 动力工程学报, 2026, 46(9): 180-191. DOI： 10.19805/j.cnki.jcspe.2026.250251.

DOI： 10.19805/j.cnki.jcspe.2026.250251

摘要

针对垃圾焚烧炉选择性非催化还原（SNCR）脱硝系统影响因素多、非线性强、响应滞后，导致NOx排放难以准确测量等问题，提出了一种融合Transformer与双向门控循环单元（BiGRU）的动态建模方法，并利用黑翅鸢算法（BKA）优化模型相关参数。首先通过机理分析识别脱硝的主要影响因素，初选特征变量；随后利用组合特征提取算法——MRX-SynFilter算法进一步选取相关性高的变量，去除冗余变量；然后利用数据趋势分析法结合最大信息系数对变量迟延时间进行估计；接着利用BKA搜寻最优变量阶次组合，完成数据动态重构，增强模型对系统时序特性的感知能力；最后基于Transformer-BiGRU网络构建SNCR脱硝系统动态模型，并通过BKA同步优化网络超参数。实验结果表明：变量筛选、迟延估计和阶次选择均会提升建模准确性，且BKA-Transformer-BiGRU模型在拟合精度与误差控制方面均优于其他对比模型，能够更有效地捕捉SNCR脱硝系统复杂的非线性动态特征。

Abstract

The selective non-catalytic reduction (SNCR) denitrification system in waste incinerators involves multiple influencing factors

strong nonlinearity

and response delays

posing challenges to the accurate measurement of NOx emissions. A dynamic modeling method integrating Transformer with bidirectional gated recurrent unit (BiGRU) was proposed

with model parameters optimized using the black-winged kite algorithm (BKA). First

mechanism analysis was conducted to identify primary denitrification factors for preliminary feature selection. Then

the combined feature extraction algorithm—MRX-SynFilter algorithm was employed to select highly correlated variables and remove redundant ones. Subsequently

variable delay times were estimated by data trend analysis combined with the maximal information coefficient (MIC). Next

the BKA was used to search for optimal variable order combinations to complete dynamic data reconstruction

enhancing the model's ability to capture temporal characteristics. Finally

the dynamic model was built on the Transformer-BiGRU network

with network hyperparameters simultaneously optimized by BKA. Experimental results demonstrate that variable selection

delay estimation

and order selection all improve modeling accuracy. The BKA-Transformer-BiGRU model outperforms other comparative models in fitting accuracy and error control

effectively capturing the complex nonlinear dynamic characteristics of the SNCR denitrification system.

关键词

Keywords

references

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