基于稳态特征提取和CWLS-SVM的W火焰锅炉NOx排放量预估

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基于稳态特征提取和CWLS-SVM的W火焰锅炉NOx排放量预估

更新时间：2025-10-29

- 基于稳态特征提取和CWLS-SVM的W火焰锅炉NOx排放量预估
- Issue 4, Pages: 116-124(2025)
- 作者机构：
  
  华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773
- Published：2025
- 稿件说明：
移动端阅览
[J]. 2025, (4): 116-124.
DOI：

[J]. 2025, (4): 116-124. DOI：

摘要

燃煤电厂排放的氮氧化物是环境污染的主要来源之一，燃烧优化可以有效降低锅炉NOx排放量，NOx排放量预测模型作为燃烧优化的基础，受到了人们广泛的关注。针对火电厂W火焰锅炉，提出了一种基于稳态特征提取的模型样本集构造方法，在此基础上，提出一种组合加权最小二乘支持向量机(CWLS-SVM)建立NOx排放量预测模型。首先通过机理分析确定模型输入变量，基于滑动窗口对海量历史运行数据进行稳态特征搜索，以组合相似度判断法进一步筛选特征，构造模型样本集；然后，针对实际生产中LS-SVM对异常值和噪声干扰敏感、不同输入变量对结果的差异性影响等问题，采用基于局部异常因子的经验风险项加权和基于最大信息系数的特征变量加权的方法对LS-SVM进行了改进；最后进行了多种仿真对比实验。结果表明，CWLS-SVM相比于LS-SVM与其他神经网络模型，具有更强的鲁棒性和泛化能力，对实现锅炉燃烧优化具有重要意义。

Abstract

NOx emission from coal-fired power plants is one of the main sources of environmental pollution. Combustion optimization is an effective method to reduce NOx emission from boilers. As the basis of combustion optimization

NOx emission prediction model has attracted wide attention. For W flame boilers in thermal power plants

we propose a model sample set construction method based on steady-state feature extraction. Furthermore

we propose a combined weighted least squares support vector machine(CWLS-SVM) to establish a NOx emission prediction model. Firstly

we determine the input variables of the model through mechanism analysis. Based on sliding windows

we conduct the steady-state feature search on the massive historical operation data

with the features further refined using a combined similarity judgment method to construct the model sample set. Then

aiming at the the problems such as the sensitivity of LS-SVM to outlier and noise interference in actual production

as well as the differential impact of different input variables on the results

we enhance the LS-SVM by employing the empirical risk weighted method based on local anomaly factors and the characteristic variable weighted method based on the maximum information coefficient. Finally

we conduct various simulation and comparison experiments. The results show that CWLS-SVM exhibits superior robustness and generalization ability compared to LS-SVM and other neural network models

marking its significance for boiler combustion optimization.

关键词

Keywords

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