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

ZHANG Wenguang; ZHANG Yue; SUN Yazhou; GAO Mingming; LI Baogui

English

您当前的位置：

首页 >

文章列表页 >

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

Thermal Power Generation Issue 6, (2016)

更新时间：2025-09-28

- Combustion optimization for CFB boiler based on least square support vector machine and modified fruit fly optimization algorithm
- 作者机构：
  
  1. 华北电力大学新能源电力系统国家重点实验室,北京,102206
  2. 中国人民解放军93469部队,河北,石家庄,050071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK223
- Published Online：15 July 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
ZHANG Wenguang, ZHANG Yue, SUN Yazhou, et al. Combustion optimization for CFB boiler based on least square support vector machine and modified fruit fly optimization algorithm[J]. Thermal Power Generation, 2016, (6).
DOI：

ZHANG Wenguang, ZHANG Yue, SUN Yazhou, et al. Combustion optimization for CFB boiler based on least square support vector machine and modified fruit fly optimization algorithm[J]. Thermal Power Generation, 2016, (6). DOI：

摘要

针对电厂循环流化床(CFB)锅炉降低污染物排放和提高锅炉燃烧效率的问题

本文首先应用最小二乘支持向量机(LS-SVM)建立了锅炉效率、NOx和SO2排放特性的软测量模型

并对比了LS-SVM和BP神经网络模型的性能；然后基于LS-SVM建立的模型

提出了3种优化策略

采用改进果蝇优化算法(MFOA)在一定范围内对CFB锅炉运行工况的可调参数进行优化。结果表明:LS-SVM模型与BP神经网络模型相比

训练时间较短

预测精度较高

泛化能力较强；CFB锅炉效率最多提高了0.61％

NOx和SO2排放质量浓度最多降低了7.88％和18.13％。

Abstract

关键词

Keywords

references

Views

383

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

Synergistic removal technologies for flue gas pollutants and their engineering applications in coal-fired units

Application of ultra-low emission technology in a 600 MW coal-fired unit

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

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

Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA

Related Author

WU Baohui

LI Shuaiying

NIU Guoping

CHANG Changhui

LI Gang

CHENG Xinxing

LYU Chenfeng

LI Shuaiying

Related Institution

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

Huaneng Tongchuan Zhaojin Power Plant

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

School of Mechatronical Engineering and Automation, Tianjin Vocational Institute

Tianjin Guodian Jinneng Co-generation Co., Ltd.

AI问答

⁰