Application of modified BP neural network in optimization of low NO<sub>x</sub> combustion for a pulverized coal boiler

LU Jun; ZHANG Guangcai; XU Dangqi; ZHOU Ping; ZHOU Fei

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2025-09-28

- Application of modified BP neural network in optimization of low NO_x combustion for a pulverized coal boiler
- Thermal Power Generation Vol. 44, Issue 4, Pages: 106-111(2015)
- 作者机构：
  
  西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183;TM621.2
- Published：2015
- 稿件说明：
移动端阅览
LU Jun, ZHANG Guangcai, XU Dangqi, et al. Application of modified BP neural network in optimization of low NO_x combustion for a pulverized coal boiler[J]. Thermal power generation, 2015, 44(4): 106-111.
DOI：

LU Jun, ZHANG Guangcai, XU Dangqi, et al. Application of modified BP neural network in optimization of low NO_x combustion for a pulverized coal boiler[J]. Thermal power generation, 2015, 44(4): 106-111. DOI：

摘要

基于燃煤锅炉低氮燃烧优化试验数据

建立了预测NOx排放浓度和锅炉效率的改进BP神经网络模型

通过样本学习

该网络可以精确描述锅炉运行参数与NOx排放浓度和锅炉效率之间的非线性映射关系。仿真结果表明

网络的NOx排放浓度和锅炉效率预测值的相对误差分别小于3.92%和7.6%

能够描述NOx排放浓度和锅炉效率随SOFA风门开度和运行氧量变化的规律

而且预测精度高、泛化能力强

可为燃煤锅炉低氮燃烧优化提供指导。

Abstract

On the basis of field test data during optimization of low NOxcombustion for a pulverized coal boiler

a modified BP neural network for prediction of NOxemission and boiler efficiency was established.After learning the training samples

the BP neural network can describe the non-linear relationship between the operation parameters and NOxemission and boiler efficiency.The simulation results show that the relative error of prediction values of NOxemission and boiler efficiency is less than 3.92% and 7.6%

respectively

indicating the neural network can describe the change rules of NOxemission and boiler efficiency against the SOFA air damper opening degree and oxygen content.This modified BP neural network has high prediction accuracy and stable generalization ability

which can provide guidance for optimization of low NOxcombustion for pulverized coal boilers.

关键词

Keywords

references

Views

203

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Combustion system model based prediction of boiler efficiency and NO_x emission

Low NO_x combustion transformation strategy for W-flame boilers

Low-NOx combustion retrofitting and optimal adjustment tests for a 130 t/h CFB boiler

Multi-objective combustion optimization based on data mining with full-scale working condition

Combustion optimization for CFB boiler based on least square support vector machine and modified fruit fly optimization algorithm

Related Author

HE Yongchao Hebei Huadian Shijiazhuang Yuhua Thermal Power Plant Co.

PAN Dong

WANG Chunchang

DAN Huijie

CUI Laijian

CHEN Jianjun

ZHOU Junhu

ZHU Zhanheng

Related Institution

Hebei Huadian Shijiazhuang Yuhua Thermal Power Co., Ltd

State Key Laboratory of Clean Energy Utilization, Zhejiang University

Zhejiang Pyneo Co.,Ltd.

School of Mechatronical Engineering and Automation, Tianjin Vocational Institute

Tianjin Guodian Jinneng Co-generation Co., Ltd.

AI问答

⁰

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

DOI：

摘要

Abstract

关键词

Keywords

references

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