PCA-SVR model based NO<sub>x</sub> emissions prediction for coal-fired boilers

ZHONG Yonglu; LI Haishan; LIU Fasheng; TAN Peng; ZHANG Shangzhi; ZHANG Cheng

English

您当前的位置：

首页 >

文章列表页 >

PCA-SVR model based NO_x emissions prediction for coal-fired boilers

Thermal Power Generation Vol. 44, Issue 1, Pages: 87-90(2015)

更新时间：2025-09-28

- PCA-SVR model based NO_x emissions prediction for coal-fired boilers
- 作者机构：
  
  1. 国网江西省电力科学研究院
  2. 华中科技大学煤燃烧国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773;TK229.6
- Published：2015
- 稿件说明：
移动端阅览
ZHONG Yonglu, LI Haishan, LIU Fasheng, et al. PCA-SVR model based NO_x emissions prediction for coal-fired boilers[J]. Thermal Power Generation, 2015, 44(1): 87-90.
DOI：

ZHONG Yonglu, LI Haishan, LIU Fasheng, et al. PCA-SVR model based NO_x emissions prediction for coal-fired boilers[J]. Thermal Power Generation, 2015, 44(1): 87-90. DOI：

摘要

以某超超临界700 MW机组锅炉为对象

建立了基于主成分分析(PCA)和支持向量回归(SVR)的氮氧化物(NOx)排放预测模型(PCA-SVR模型)。运用PCA方法对包含有2 000条锅炉运行记录的数据集进行分析

降低数据集维数

提取有效信息(主成分);以得到的主成分为输入变量

锅炉NOx排放值为输出变量

利用SVR建立NOx预测模型。与传统SVR模型相比

PCA-SVR模型的计算时间更短

并且能获得较高的NOx排放预测精度

其预测NOx排放浓度与实际排放浓度相比平均误差在1%以内。

Abstract

Taking a 700 MW ultra-supercritical boiler as the object

a nitric oxides(NOx)emissions model was established

by combining principal component analysis(PCA)and support vector regression(SVR).The PCA was employed to analyze 2 000 operational records of the boiler

which reduced the dimension of the input vectors and kept the most information of the data simultaneously.The SVR was used to build the NOxemissions prediction model by taking the principal components as inputs and NOxemissions as output.Compared with the conventional SVR model

the PCA-SVR model required less computational cost and had a higher predictive accuracy.According to the test

the average relative error between the measured value and the result predicted by the PCA-SVR model was below 1%.Furthermore

the computational response was also very fast.

关键词

Keywords

references

Views

285

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network

Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine

Fault diagnosis model for steam turbine vibration based on feature reduction theory

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

Multi-objective combustion optimization for a 300MW unit using support vector machine theory combining with genetic algorithm

Related Author

吴磊

顾华

姚一鸣

张军

苏军

陈依

CUI Shiwen

TIE Zhixin

Related Institution

School of Information Science and Technology, Zhejiang Sci-Tech University

Focused Photonics (Hangzhou) Inc.

School of Energy, Power and Mechanical Engineering, North China Electric Power University

Huadian Power International Corporation Limited

Huadian Electric Power Research Institute

AI问答

⁰

PCA-SVR model based NOx emissions prediction for coal-fired boilers

DOI：

摘要

Abstract

关键词

Keywords

references

PCA-SVR model based NO_x emissions prediction for coal-fired boilers