Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine

ZHONG Yafei; WEI Hongqi; YAO Weigang; CHEN Jingwei

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Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine

更新时间：2025-09-28

- Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine
- Thermal Power Generation Issue 12, (2015)
- 作者机构：
  
  东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK229.6
- Published Online：15 January 2016，
  
  Published：2015
- 稿件说明：
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ZHONG Yafei, WEI Hongqi, YAO Weigang, et al. Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine[J]. Thermal power generation, 2015, (12).
DOI：

ZHONG Yafei, WEI Hongqi, YAO Weigang, et al. Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine[J]. Thermal power generation, 2015, (12). DOI：

摘要

排烟热损失是机组锅炉热损失中最大的一项

建立锅炉排烟温度模型对锅炉运行有重要意义。为此

以某660 MW机组锅炉燃烧调整试验数据为基础

采用支持向量机建立锅炉排烟温度模型

并利用改进的混沌粒子群算法对支持向量机的相关参数进行优化

使模型的准确性显著提高。与遗传算法相比

粒子群算法无交叉和变异

算法简单

泛化能力强

有很好的实用性。基于粒子群算法和支持向量机的排烟温度模型能够很好地预测排烟温度的变化

可为锅炉的运行提供指导。

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