Application of Adaboost integrated BP neural network in SO<sub>2</sub> concentration detection in power plant

YAN Yue; YAN Shi; YANG Yong-bin; JIANG Yun

doi:10.13873/J.1000-9787(2016)09-0148-04

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Application of Adaboost integrated BP neural network in SO₂ concentration detection in power plant

更新时间：2025-09-11

- Application of Adaboost integrated BP neural network in SO₂ concentration detection in power plant
- Vol. 35, Issue 9, Pages: 148-151(2016)
- 作者机构：
  
  1. 重庆工商大学检测控制集成系统重庆市市级工程实验室
  2. 重庆川仪分析仪器有限公司
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)09-0148-04
  CLC： TM621;TP183
- Published：2016
- 稿件说明：
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[1]严玥,严实,杨永斌,江赟.Adaboost集成BP神经网络在火电厂SO_2浓度检测中的应用[J].传感器与微系统,2016,35(09):148-151.

YAN Yue, YAN Shi, YANG Yong-bin, et al. Application of Adaboost integrated BP neural network in SO₂ concentration detection in power plant[J]. 2016, 35(9): 148-151.
[1]严玥,严实,杨永斌,江赟.Adaboost集成BP神经网络在火电厂SO_2浓度检测中的应用[J].传感器与微系统,2016,35(09):148-151. DOI： 10.13873/J.1000-9787(2016)09-0148-04.

YAN Yue, YAN Shi, YANG Yong-bin, et al. Application of Adaboost integrated BP neural network in SO₂ concentration detection in power plant[J]. 2016, 35(9): 148-151. DOI： 10.13873/J.1000-9787(2016)09-0148-04.

摘要

针对火电厂在线SO2浓度检测中

检测精度受到温度、压力（大气压及烟气压力）、燃煤质量、水分含量、电子器件噪声、光学镜片老化、气体吸收峰值交叉干扰等多种因素的干扰

很难以单一方法进行改进这一问题。以国内某中型火电厂2015年实际生产数据为依据

设计预处理装置完成气体的前期处理

以尽可能达到分析仪分析要求（温度、流量、含水量等）

减少可预见干扰

采用Adaboost算法集成BP神经网络进行优化

降低其他因素对检测结果的干扰

仿真测试分析

证明了该方法的有效性。

Abstract

Aiming at problem of online SO2 concentration detection in power plant

detection precision may be interfered by all kinds of factors

such as temperature

pressure（which includes atmosphere and smoke pressure）

coal mass

moisture content

noise of electric devices

optical lens aging

interference at spectral of gas absorption

it is difficult to improve by a single way.According to real production data of a large thermal power plant in 2015

a kind of gas pretreatment device is developed in order to meet the requirements of analyzer and reduce predictable interference.Adopt Adaboost algorithm to optimize integrated BP neural network in order to avoid unpredictable interference.By simulation

test and analysis

it is verified that this method is effective.

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references

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Application of Adaboost integrated BP neural network in SO2 concentration detection in power plant

DOI：10.13873/J.1000-9787(2016)09-0148-04

摘要

Abstract

关键词

Keywords

references

Application of Adaboost integrated BP neural network in SO₂ concentration detection in power plant