燃煤机组SCR脱硝入口NO<sub>x</sub>质量浓度的多模型动态预测及协同部署

卢得勇; 韦智友; 刘宇博; 李海强; 丁得龙; 沈波; 李来

doi:10.19666/j.rlfd.202507085

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燃煤机组SCR脱硝入口NO_x质量浓度的多模型动态预测及协同部署

更新时间：2026-06-12

- 燃煤机组SCR脱硝入口NO_x质量浓度的多模型动态预测及协同部署
- Thermal Power Generation Vol. 55, Issue 4, Pages: 156-165(2026)
- 作者机构：
  
  1. 浙江浙能兰溪发电有限责任公司
  2. 浙江浙能科技环保集团股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202507085
  CLC： TM621;X773
- Published：2026
- 稿件说明：
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[1]卢得勇,韦智友,刘宇博,等.燃煤机组SCR脱硝入口NO_x质量浓度的多模型动态预测及协同部署[J].热力发电,2026,55(04):156-165.
[1]卢得勇,韦智友,刘宇博,等.燃煤机组SCR脱硝入口NO_x质量浓度的多模型动态预测及协同部署[J].热力发电,2026,55(04):156-165. DOI： 10.19666/j.rlfd.202507085.

DOI：10.19666/j.rlfd.202507085.

摘要

为满足燃煤机组选择性催化还原(selective catalytic reduction

SCR)脱硝系统对氮氧化物(NO

)质量浓度预测的高需求，设计并提出一种兼具高精度和低延迟的软测量及部署方案。基于某660 MW燃煤机组11万余组高维运行数据，在统一平台下系统对比了深度学习(deep learning

DL)与XGBoost(extreme gradient boosting)2种模型，采用时序交叉验证结合网格搜索优化模型超参数，并从预测精度、计算效率及可解释性三方面进行综合评估。在此基础上，提出“边缘–嵌入式”协同部署策略，将DL模型部署于边缘服务器以获取高精度预测结果，XGBoost模型嵌入分散控制系统(distributed control system

DCS)保障实时性。结果表明：DL模型在动态响应与预测精度上表现更优，其测试集均方根误差较XGBoost模型降低约10%，并在工况剧烈波动时DL模型能保持稳定性；变量重要性分析显示，烟气含氧量、燃烧器壁温和总风量是影响NO

生成的主要因素。所提协同部署架构理论上可实现毫秒级推理，并具备离线容错能力。

Abstract

To meet the high requirements of selective catalytic reduction(SCR) systems in coal-fired power plants for accurate and low-latency prediction of nitrogen oxides(NO

) mass concentrations

this study designs and proposes a soft sensing and deployment framework that balances high accuracy and real-time performance. Using more than 110 000 sets of high-dimensional operational data from a 660 MW coal-fired unit

a systematic comparison of deep learning(DL) and XGBoost models was conducted on a unified platform. Time series crossvalidation combined with grid search was employed to optimize hyperparameters

and model performance was comprehensively evaluate

d in terms of predictive accuracy

computational efficiency

and interpretability via local interpretable model-agnostic explanations. On this basis

an “edge-embedded” collaborative deployment strategy was proposed

in which the DL model is deployed on edge servers to deliver high-accuracy predictions

while the XGBoost model is embedded into the distributed control system(DCS) to ensure real-time responsiveness. The results show that the DL model outperforms XGBoost in dynamic response and predictive accuracy

achieving root mean square errors approximately 10% lower than that of the XGBoost

and maintaining stability under highly fluctuating conditions. Variable importance analysis highlights flue gas oxygen content

burner wall temperature

and total air volume as the dominant factors affecting NO

formation. The proposed collaborative architecture can theoretically achieve millisecond-level inference and provide offline fault tolerance

offering a practical pathway for intelligent ammonia injection control and combustion optimization.

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燃煤机组SCR脱硝入口NOx质量浓度的多模型动态预测及协同部署

DOI：10.19666/j.rlfd.202507085

摘要

Abstract

关键词

Keywords

references

燃煤机组SCR脱硝入口NO_x质量浓度的多模型动态预测及协同部署