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

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

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
- Thermal Power Generation Issue 6, (2016)
- 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：
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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％。

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