NO<sub>x</sub> emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network

YU Tingfang; LIU Ran

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

Thermal Power Generation Vol. 45, Issue 8, Pages: 94-98(2016)

更新时间：2025-09-28

- NO_x emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network
- 作者机构：
  
  南昌大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183;TM621.2
- Published：2016
- 稿件说明：
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YU Tingfang, LIU Ran. NO_x emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network[J]. Thermal Power Generation, 2016, 45(8): 94-98.
DOI：

YU Tingfang, LIU Ran. NO_x emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network[J]. Thermal Power Generation, 2016, 45(8): 94-98. 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

Combing with the hot state test data of an ultra supercritical 660 MW coal-fired boiler

the radial basis function(RBF)neural network and BP neural network regression algorithms were used to establish models for NOxemissions from this boiler

by using the intelligent MATLAB toolbox.Moreover

optimization was performed for the RBF neural network prediction model of output layer weights and the center of RBF function and standard deviation

and the momentum method was adopted to improve the problems existing in the BP neural network.The results show that

the maximum error of the RBF prediction model after parameter optimization simulation was 3.0% and the average error was 1.75%.The maximum error of the improved BP network prediction model was 6.6%and the average error was 4.5%.Both the above two modeling methods have well accuracy and generalization ability.The RBF neural network model is obviously superior to the BP neural network model in terms of computing speed

fitting ability and generalization ability.

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Related Institution

太原科技大学先进控制与工业智能山西省重点实验室

山西华光发电有限责任公司

Beijing Jiaotong University

Beijing Guodian Huabei Electric Power Engineering Co., Ltd

School of Energy and Power Engineering, Xi'an Jiaotong University

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

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摘要

Abstract

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references

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