Prediction model of NO<sub>x</sub> emission from coal-fired boiler based on multi-model clustering ensemble

ZHEN Chenggang; LIU Huaiyuan

doi:10.19666/j.rlfd.201808160

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Prediction model of NO_x emission from coal-fired boiler based on multi-model clustering ensemble

Thermal Power Generation Vol. 48, Issue 4, Pages: 33-40(2019)

更新时间：2025-09-28

- Prediction model of NO_x emission from coal-fired boiler based on multi-model clustering ensemble
- 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：10.19666/j.rlfd.201808160
  CLC： X511;TM621.2
- Published Online：01 April 2019，
  
  Published：2019
- 稿件说明：
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ZHEN Chenggang, LIU Huaiyuan. Prediction model of NO_x emission from coal-fired boiler based on multi-model clustering ensemble[J]. Thermal Power Generation, 2019, 48(4): 33-40.
DOI：

ZHEN Chenggang, LIU Huaiyuan. Prediction model of NO_x emission from coal-fired boiler based on multi-model clustering ensemble[J]. Thermal Power Generation, 2019, 48(4): 33-40. DOI： 10.19666/j.rlfd.201808160.

摘要

电站锅炉烟气NO_x排放量的预测控制对电站的经济效益和环境污染治理有重要影响。为了提高NO_x排放量预测模型的精度

本文提出了一种基于多模型聚类集成的锅炉烟气NO_x排放量建模方法。首先根据输出NO_x排放量的高低划分数据空间

通过基于相关性分析的变量权重和基于信息熵的分层聚类确定参与聚类的变量

然后利用提出的多模型聚类集成(VMSC)算法聚类得到各子空间的隶属度矩阵

最后采用融合隶属度的最小二乘法对各子空间的最小二乘支持向量机(LS-SVM)模型进行集成。仿真结果表明

通过集成模糊C均值聚类(FCM)和有监督的遗传算法-软模糊聚类(GA-SFCM)的VMSC算法提高了建模的精度

比单一模型的仿真性能更好。

Abstract

Predictive control of NO_x emission from flue gas of utility boilers has important influence on power plants' economic benefits and environmental pollution control. To improve the accuracy of the NO_x emission prediction model

a modeling method of boiler NO_x emission based on multi-model clustering ensemble was proposed. In this method

the data space is firstly divided according to the level of NO_x emission

and the variables that participate in clustering are determined by using the variable weight based on relevant analysis and hierarchical clustering utilized information entropy. Then

the proposed algorithm VMSC is used to obtain the new membership degree matrix of each subspace. Finally

the multiple least squares support vector machine(LS-SVM) model of each subspace is integrated by the least-squares method fused membership degree. The simulation results show that

the VMSC algorithm integrating the soft fuzzy C-means clustering(SFCM) with the genetic algorithm-soft fuzzy clustering(GA-SFCM) improves the accuracy of the clustering

and the simulation performance is better than the single model.

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Prediction model of NOx emission from coal-fired boiler based on multi-model clustering ensemble

DOI：10.19666/j.rlfd.201808160

摘要

Abstract

关键词

Keywords

references

Prediction model of NO_x emission from coal-fired boiler based on multi-model clustering ensemble