NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network

YU Tingfang; LIU Ran

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network

更新时间：2025-09-28

- NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network
- Thermal Power Generation Issue 7, (2016)
- 作者机构：
  
  南昌大学机电工程学院,江西,南昌,330031
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK223.23
- Published Online：15 August 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
YU Tingfang, LIU Ran. NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network[J]. Thermal power generation, 2016, (7).
DOI：

YU Tingfang, LIU Ran. NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network[J]. Thermal power generation, 2016, (7). DOI：

摘要

基于某超超临界660 MW机组燃煤锅炉现场热态实验数据

利用MATLAB智能工具箱

分别采用径向基(RBF)神经网络和BP神经网络对该锅炉NOx排放特性进行建模

采用交替梯度算法对RBF神经网络预测模型进行输出层权值及RBF函数的中心与标准偏差值优化

对BP神经网络采用动量法进行改进。2种模型的仿真和预测结果对比分析表明:参数优化后的RBF神经网络预测模型预测结果的最大误差为3.0％

平均误差为1.75％；改进后的BP神经网络预测模型预测结果最大误差为6.6％

平均误差为4.5％；2种建模方法均具有较好的准确性和泛化能力

其中RBF神经网络模型的计算速度快，拟合和泛化能力更强。

Abstract

关键词

Keywords

references

Views

204

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of non-symmetrical high-speed closing-to-wall air system to a Russian-made 530 MW unit coal fired boiler

STUDY ON REAL - TIME DATA - FORECASTING MODEL BASED ON MODIFIED BP NEURAL NETWORK FOR THERMAL POWER PLANTS

Application of neural network algorithm in steam turbine's exhaust enthalpy estimation

Cold-state test study on an HT-NR3 swirl burner

Application of modified BP neural network in optimization of low NO_x combustion for a pulverized coal boiler

Related Author

LIANG Xuebin

SUN Jun

MENG Yi

DU Zhihua

ZHANG Jianhui

LIU Yingwen

陆王琳

李蔚

Related Institution

Tianjin Guohua Panshan Power Generation Co.,Ltd.

Xi'an Thermal Power Research Institute Co.,Ltd.

Zhejiang University

State Key Laboratory of New Energy, North China Electric Power University

Xi'an Thermal Power Research Institute Co., Ltd

AI问答

⁰